From b8229412d5f40cd563bd07ee1494ccd99abbb8e3 Mon Sep 17 00:00:00 2001 From: Axel Nilsson Date: Fri, 4 Aug 2023 22:36:25 +0200 Subject: [PATCH] Working GARCH model. --- src2/autograd.ipynb | 39 ++++- src2/models/ar.py | 8 +- src2/models/garch.py | 139 +++++++++++---- src2/models/model.py | 2 +- src2/models/model_factory.py | 11 -- src2/run.ipynb | 35 ++-- src2/testing.ipynb | 317 +++++++++++++++++++++++++++-------- src2/utils/config.py | 2 +- src2/utils/exceptions.py | 1 - 9 files changed, 409 insertions(+), 145 deletions(-) delete mode 100644 src2/models/model_factory.py diff --git a/src2/autograd.ipynb b/src2/autograd.ipynb index bb5f887..b17e708 100644 --- a/src2/autograd.ipynb +++ b/src2/autograd.ipynb @@ -89,7 +89,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -98,6 +98,43 @@ " method=\"BFGS\",\n", " jac=True) # NB: we will compute the jacobian" ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + " fun: -72.71104166630812\n", + " hess_inv: array([[3.43587575e-06, 4.82986207e-12],\n", + " [4.82986207e-12, 9.07303824e-05]])\n", + " jac: array([2.63101469e-08, 3.41229494e-06])\n", + " message: 'Optimization terminated successfully.'\n", + " nfev: 21\n", + " nit: 9\n", + " njev: 21\n", + " status: 0\n", + " success: True\n", + " x: array([-0.02342842, 0.93132647])" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "res" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/src2/models/ar.py b/src2/models/ar.py index 6e02357..e105062 100644 --- a/src2/models/ar.py +++ b/src2/models/ar.py @@ -7,7 +7,7 @@ from models.model import Model from utils.config import MAXIMUM_AR_ORDER -from utils.exceptions import ParameterError, StationarityError +from utils.exceptions import StationarityError class AR(Model): @@ -32,7 +32,9 @@ class AR(Model): * Model signifiance can be ascertained through regression like F-test. REFERENCES: - * + * Time Series Analysis by Hamilton. + * PACF Statistical Cutoff: + http://sfb649.wiwi.hu-berlin.de/fedc_homepage/xplore/tutorials/xegbohtmlnode39.html """ def __init__(self, time_series: pd.Series): @@ -69,7 +71,7 @@ def _log_likelihood(self) -> torch.Tensor: variance = torch.square(self._sigma) pi = torch.tensor(torch.pi) - return - (self._number_of_observations - self._order) * (torch.log(pi) + torch.log(variance)) - 1 / (2 * variance) * torch.sum(squared_difference) + return - (self._number_of_observations - self._order) / 2 * (torch.log(pi) + torch.log(variance)) - 1 / (2 * variance) * torch.sum(squared_difference) def calibrate(self): diff --git a/src2/models/garch.py b/src2/models/garch.py index d6d7f36..773c794 100644 --- a/src2/models/garch.py +++ b/src2/models/garch.py @@ -4,47 +4,69 @@ import pandas as pd from scipy.optimize import minimize +from torch.distributions import Normal from models.model import Model from utils.config import INITIAL_VARIANCE_GARCH_OBSERVATIONS, INITIAL_GARCH_PARAMETERS +from utils.exceptions import ParameterError class GARCH(Model): def __init__(self, time_series: pd.Series): super().__init__(time_series) + @property + def parameters(self) -> tuple[float, 3]: + self._check_calibration() + + optimal_parameters = self._optimal_parameters + alpha = optimal_parameters[0] + beta = optimal_parameters[1] + long_run_variance = self._long_run_variance + + return alpha, beta, long_run_variance + + @property + def _number_of_parameters(self) -> int: + return len(self.parameters) + + @property + def _optimal_parameters(self) -> np.array: + return self._uncondition_parameters(parameters=torch.tensor(self._solution.x)).numpy() + + @property + def _inital_solution(self) -> np.array: + return np.array(self._precondition_parameters(parameters=torch.tensor(INITIAL_GARCH_PARAMETERS))) + + @property + def _log_likelihood(self) -> torch.tensor: + optimal_cost = torch.tensor(self._solution.fun) + pi = torch.tensor(torch.pi) + number_of_observations = torch.tensor(self._number_of_observations) + return -(1 / 2) * (number_of_observations * torch.log(2 * pi) + optimal_cost) + def calibrate(self): self._initiate_parameters() - self._inital_solution = np.array(self._precondition_parameters(parameters=torch.tensor(INITIAL_GARCH_PARAMETERS))) - solution = minimize(self._cost_function, - self._inital_solution, - method="BFGS", - jac=True) - self._solution = solution + self._solve_maximum_likelihood() + self._sanity_check() self._calibrated = True def transform_to_true(self, uniform_sample: torch.Tensor) -> torch.Tensor: - ... + self._check_calibration() def transform_to_uniform(self): - ... + self._check_calibration() + + parameters = torch.Tensor(self._solution.x) + variance = self._compute_variance(parameters=parameters) + volatility = torch.sqrt(variance) + returns = self._data + residuals = returns / volatility + + return Normal(0, 1).cdf(residuals) - def _compute_variance(self, parameters: torch.Tensor) -> torch.Tensor: - initial_variance = self._initial_variance - variance = np.zeros(self._number_of_observations) - mu_corr = torch.exp(-torch.exp(-parameters[0])) - mu_ewma = torch.exp(-torch.exp(-parameters[1])) - for i in range(self._number_of_observations): - if i == 0: - variance[i] = initial_variance - else: - variance[i] = self._long_run_variance + mu_corr * (mu_ewma * variance[i - 1] - + (1 - mu_ewma) * self._squared_returns[i - 1] - - self._long_run_variance - ) - return torch.tensor(variance) def _cost_function(self, parameters: np.array) -> tuple[float, 2]: """ @@ -57,11 +79,22 @@ def _cost_function(self, parameters: np.array) -> tuple[float, 2]: tuple(float, float): log loss value and the corresponding gradient. """ parameters = torch.tensor(parameters, requires_grad=True) - variance = self._compute_variance(parameters=parameters) - log_loss = torch.sum(torch.log(variance) + self._squared_returns / variance) - log_loss.backward() - print(log_loss, parameters.grad) + mu = torch.exp(-torch.exp(-parameters)) + mu_corr = mu[0] + mu_ewma = mu[1] + log_loss = torch.tensor(0.0) + for i in range(self._number_of_observations): + if i == 0: + variance = self._initial_variance + else: + variance = self._long_run_variance + mu_corr * (mu_ewma * variance + + (1 - mu_ewma) * self._squared_returns[i - 1].detach() + - self._long_run_variance + ) + log_loss = log_loss + torch.log(variance) + self._squared_returns[i].detach() / variance + + log_loss.backward() return log_loss.data.cpu().numpy(), parameters.grad.data.cpu().numpy() def _initiate_parameters(self): @@ -70,18 +103,54 @@ def _initiate_parameters(self): self._initial_variance = self._compute_inital_variance() self._long_run_variance = torch.square(torch.std(self._data)) + def _solve_maximum_likelihood(self): + solution = minimize(self._cost_function, + self._inital_solution, + method="L-BFGS-B", + jac=True) + self._solution = solution + def _compute_inital_variance(self) -> torch.Tensor: if self._number_of_observations > INITIAL_VARIANCE_GARCH_OBSERVATIONS: return torch.square(torch.std(self._data[:INITIAL_VARIANCE_GARCH_OBSERVATIONS])) return self._initial_squared_returns - @property - def _number_of_parameters(self): - return super()._number_of_parameters + def _compute_variance(self, parameters: torch.Tensor) -> torch.Tensor: + initial_variance = self._initial_variance + variance = torch.zeros(self._number_of_observations) + mu_corr = torch.exp(-torch.exp(-parameters[0])) + mu_ewma = torch.exp(-torch.exp(-parameters[1])) - @property - def _log_likelihood(self): - return super()._log_likelihood + for i in range(self._number_of_observations): + if i == 0: + variance[i] = initial_variance + else: + variance[i] = self._long_run_variance + mu_corr * (mu_ewma * variance[i - 1] + + (1 - mu_ewma) * self._squared_returns[i - 1] + - self._long_run_variance + ) + return variance + + def _sanity_check(self): + parameter_check = self._check_parameters() + solution_check = self._check_solution() + + if not parameter_check: + # log. + ... + + if not solution_check: + # log. + ... + + if not parameter_check or not solution_check: + raise ParameterError("Parameters could not be asceratined succesfully.") + + def _check_parameters(self) -> bool: + return self._solution.success + + def _check_solution(self) -> bool: + return np.sum(self._optimal_parameters) < 1 @staticmethod def _precondition_parameters(parameters: torch.Tensor) -> torch.Tensor: @@ -103,7 +172,7 @@ def _precondition_parameters(parameters: torch.Tensor) -> torch.Tensor: return torch.tensor([z_corr, z_ewma]) @staticmethod - def _uncondition_parameters(params: torch.Tensor) -> torch.Tensor: + def _uncondition_parameters(parameters: torch.Tensor) -> torch.Tensor: """ Unconditioning to obtain more original parameters from transformed parameters. @@ -113,8 +182,8 @@ def _uncondition_parameters(params: torch.Tensor) -> torch.Tensor: Returns: torch.Tensor: GARCH parameters. """ - mu_corr = torch.exp(-torch.exp(-params[0])) - mu_ewma = torch.exp(-torch.exp(-params[1])) + mu_corr = torch.exp(-torch.exp(-parameters[0])) + mu_ewma = torch.exp(-torch.exp(-parameters[1])) alpha = mu_corr * (1 - mu_ewma) beta = mu_corr * mu_ewma diff --git a/src2/models/model.py b/src2/models/model.py index 1aa55aa..1a3795e 100644 --- a/src2/models/model.py +++ b/src2/models/model.py @@ -80,10 +80,10 @@ def bic(self) -> torch.Tensor: log_likelihood = self._log_likelihood number_of_parameters = torch.tensor(self._number_of_parameters) number_of_observations = torch.tensor(self._number_of_observations) - pi = torch.tensor(torch.pi) return number_of_parameters * torch.log(number_of_observations) - 2 * log_likelihood + def _check_calibration(self): """ Checks if successful calibration has been made. diff --git a/src2/models/model_factory.py b/src2/models/model_factory.py deleted file mode 100644 index 13c1c01..0000000 --- a/src2/models/model_factory.py +++ /dev/null @@ -1,11 +0,0 @@ -# -*- coding: utf-8 -*- - -import torch -import pandas as pd - -from utils.instruments import RiskFactor - -class ModelFactory: - - def __init__(self, risk_factors: list[RiskFactor]): - self.risk_factors = risk_factors diff --git a/src2/run.ipynb b/src2/run.ipynb index 646a5b7..09acd13 100644 --- a/src2/run.ipynb +++ b/src2/run.ipynb @@ -2,22 +2,9 @@ "cells": [ { "cell_type": "code", - "execution_count": 22, + "execution_count": 1, "metadata": {}, - "outputs": [ - { - "ename": "ImportError", - "evalue": "cannot import name 'INITIAL_VARIANCE_GARCH_OBSERVATIONS' from 'utils.config' (/Users/axelnilsson/Desktop/Abacus/src2/utils/config.py)", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mImportError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m/var/folders/d7/rpl_88f12ln2z1s2h4m9ybjm0000gn/T/ipykernel_43843/1386310877.py\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mutils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata_handler\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mYahooDataHandler\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 10\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mmodels\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mar\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mAR\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mmodels\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgarch\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mGARCH\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 12\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/Desktop/Abacus/src2/models/garch.py\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mmodels\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mModel\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mutils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconfig\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mINITIAL_VARIANCE_GARCH_OBSERVATIONS\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mINITIAL_GARCH_PARAMETERS\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 10\u001b[0m \u001b[0;32mclass\u001b[0m \u001b[0mGARCH\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mModel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mImportError\u001b[0m: cannot import name 'INITIAL_VARIANCE_GARCH_OBSERVATIONS' from 'utils.config' (/Users/axelnilsson/Desktop/Abacus/src2/utils/config.py)" - ] - } - ], + "outputs": [], "source": [ "import torch\n", "import pandas as pd\n", @@ -36,14 +23,9 @@ "plt.style.use(['science', 'notebook', 'grid'])" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] - }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -52,7 +34,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -91,7 +73,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -100,6 +82,13 @@ " data = risk_factor.price_history.log_returns" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "code", "execution_count": 8, diff --git a/src2/testing.ipynb b/src2/testing.ipynb index bbc57be..4f00fca 100644 --- a/src2/testing.ipynb +++ b/src2/testing.ipynb @@ -75,7 +75,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "[-0.05507313832640648, 0.003895258530974388, 0.0003181590873282403, 0.022512581199407578]\n" + "[-0.05507330596446991, 0.0038955030031502247, 0.0003181590000167489, 0.022512583062052727]\n" ] } ], @@ -100,7 +100,7 @@ { "data": { "text/plain": [ - "tensor(-54072.6602)" + "tensor(-24765.1270)" ] }, "execution_count": 4, @@ -120,7 +120,7 @@ { "data": { "text/plain": [ - "tensor(-54046.9688)" + "tensor(-24739.4336)" ] }, "execution_count": 5, @@ -150,49 +150,58 @@ "cell_type": "code", "execution_count": 6, "metadata": {}, + "outputs": [], + "source": [ + "garch_model = GARCH(data)\n", + "garch_model.calibrate()" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, "outputs": [ { - "ename": "RuntimeError", - "evalue": "Can't call numpy() on Tensor that requires grad. Use tensor.detach().numpy() instead.", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m/var/folders/d7/rpl_88f12ln2z1s2h4m9ybjm0000gn/T/ipykernel_52369/1694265763.py\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mgarch_model\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mGARCH\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mgarch_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcalibrate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;32m~/Desktop/Abacus/src2/models/garch.py\u001b[0m in \u001b[0;36mcalibrate\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 17\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_initiate_parameters\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 18\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_inital_solution\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_precondition_parameters\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparameters\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mINITIAL_GARCH_PARAMETERS\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 19\u001b[0;31m solution = minimize(self._cost_function,\n\u001b[0m\u001b[1;32m 20\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_inital_solution\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 21\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"L-BFGS-B\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/Desktop/Abacus/venv/lib/python3.9/site-packages/scipy/optimize/_minimize.py\u001b[0m in \u001b[0;36mminimize\u001b[0;34m(fun, x0, args, method, jac, hess, hessp, bounds, constraints, tol, callback, options)\u001b[0m\n\u001b[1;32m 679\u001b[0m **options)\n\u001b[1;32m 680\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0mmeth\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'l-bfgs-b'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 681\u001b[0;31m res = _minimize_lbfgsb(fun, x0, args, jac, bounds,\n\u001b[0m\u001b[1;32m 682\u001b[0m callback=callback, **options)\n\u001b[1;32m 683\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0mmeth\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'tnc'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/Desktop/Abacus/venv/lib/python3.9/site-packages/scipy/optimize/_lbfgsb_py.py\u001b[0m in \u001b[0;36m_minimize_lbfgsb\u001b[0;34m(fun, x0, args, jac, bounds, disp, maxcor, ftol, gtol, eps, maxfun, maxiter, iprint, callback, maxls, finite_diff_rel_step, **unknown_options)\u001b[0m\n\u001b[1;32m 306\u001b[0m \u001b[0miprint\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdisp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 307\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 308\u001b[0;31m sf = _prepare_scalar_function(fun, x0, jac=jac, args=args, epsilon=eps,\n\u001b[0m\u001b[1;32m 309\u001b[0m \u001b[0mbounds\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnew_bounds\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 310\u001b[0m finite_diff_rel_step=finite_diff_rel_step)\n", - "\u001b[0;32m~/Desktop/Abacus/venv/lib/python3.9/site-packages/scipy/optimize/_optimize.py\u001b[0m in \u001b[0;36m_prepare_scalar_function\u001b[0;34m(fun, x0, jac, args, bounds, epsilon, finite_diff_rel_step, hess)\u001b[0m\n\u001b[1;32m 261\u001b[0m \u001b[0;31m# ScalarFunction caches. Reuse of fun(x) during grad\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 262\u001b[0m \u001b[0;31m# calculation reduces overall function evaluations.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 263\u001b[0;31m sf = ScalarFunction(fun, x0, args, grad, hess,\n\u001b[0m\u001b[1;32m 264\u001b[0m finite_diff_rel_step, bounds, epsilon=epsilon)\n\u001b[1;32m 265\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/Desktop/Abacus/venv/lib/python3.9/site-packages/scipy/optimize/_differentiable_functions.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, fun, x0, args, grad, hess, finite_diff_rel_step, finite_diff_bounds, epsilon)\u001b[0m\n\u001b[1;32m 156\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 157\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_update_fun_impl\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mupdate_fun\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 158\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_update_fun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 159\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 160\u001b[0m \u001b[0;31m# Gradient evaluation\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/Desktop/Abacus/venv/lib/python3.9/site-packages/scipy/optimize/_differentiable_functions.py\u001b[0m in \u001b[0;36m_update_fun\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 249\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_update_fun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 250\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf_updated\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 251\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_update_fun_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 252\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf_updated\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 253\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/Desktop/Abacus/venv/lib/python3.9/site-packages/scipy/optimize/_differentiable_functions.py\u001b[0m in \u001b[0;36mupdate_fun\u001b[0;34m()\u001b[0m\n\u001b[1;32m 153\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 154\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mupdate_fun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 155\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfun_wrapped\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 156\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 157\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_update_fun_impl\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mupdate_fun\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/Desktop/Abacus/venv/lib/python3.9/site-packages/scipy/optimize/_differentiable_functions.py\u001b[0m in \u001b[0;36mfun_wrapped\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m 135\u001b[0m \u001b[0;31m# Overwriting results in undefined behaviour because\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 136\u001b[0m \u001b[0;31m# fun(self.x) will change self.x, with the two no longer linked.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 137\u001b[0;31m \u001b[0mfx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 138\u001b[0m \u001b[0;31m# Make sure the function returns a true scalar\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 139\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misscalar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - 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"\u001b[0;32m~/Desktop/Abacus/venv/lib/python3.9/site-packages/torch/_tensor.py\u001b[0m in \u001b[0;36m__array__\u001b[0;34m(self, dtype)\u001b[0m\n\u001b[1;32m 757\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnumpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 758\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 759\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnumpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 760\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 761\u001b[0m \u001b[0;31m# Wrap Numpy array again in a suitable tensor when done, to support e.g.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mRuntimeError\u001b[0m: Can't call numpy() on Tensor that requires grad. Use tensor.detach().numpy() instead." - ] + "data": { + "text/plain": [ + "tensor(2.2510)" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "torch.sqrt(garch_model._long_run_variance) * 100" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor(0.0003)" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", 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" ] }, "metadata": { "image/png": { - "height": 358, - "width": 495 + "height": 352, + "width": 482 }, "needs_background": "light" }, @@ -200,19 +209,30 @@ } ], "source": [ - "garch_model = GARCH(data)\n", - "garch_model.calibrate()" + "garch_model._solution\n", + "vol = garch_model._compute_variance(parameters=torch.tensor(garch_model._solution.x))\n", + "plt.plot(vol*100)\n", + "torch.sqrt(garch_model._initial_variance) * 100" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([0.07986214, 0.90859563])" + " fun: -32102.430072461833\n", + " hess_inv: <2x2 LbfgsInvHessProduct with dtype=float64>\n", + " jac: array([ 4.00799529e-05, -3.92028649e-04])\n", + " message: 'CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH'\n", + " nfev: 10\n", + " nit: 9\n", + " njev: 10\n", + " status: 0\n", + " success: True\n", + " x: array([4.45567392, 2.47339131])" ] }, "execution_count": 8, @@ -221,29 +241,18 @@ } ], "source": [ - "parameters = garch_model._solution.x\n", - "parameters = garch_model._uncondition_parameters(parameters)\n", - "parameters" + "garch_model._solution" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - " fun: -32102.429028866623\n", - " hess_inv: <2x2 LbfgsInvHessProduct with dtype=float64>\n", - " jac: array([ 0.0007276 , -0.01818989])\n", - " message: 'CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH'\n", - " nfev: 30\n", - " nit: 8\n", - " njev: 10\n", - " status: 0\n", - " success: True\n", - " x: array([4.45594381, 2.47401689])" + "tensor(12386.5635)" ] }, "execution_count": 9, @@ -252,46 +261,216 @@ } ], "source": [ - "garch_model._solution" + "ar_model._log_likelihood" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, "outputs": [ { "data": { - "image/png": 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9Nkk2p1sQERERURoxySaiolAlyb2K8uKqxBvw3pNNRERERJQkgZJsIcQ+QoirhBDzhBCbhBCtQohlQogZQogvRN3I3HuWCSG+KYR4SAixUgjRLoRYL4R4TQhxiRBiV5/7200I8WshxFNCiMbc/tqFEO8LIZ4TQtQIIT5SjM+SBJMnT467CZRBqsTZLpnu9ZhlM5ZJB4xj0gHjmHTBWKawfC/hJYT4CYA/ABhm+tY+uX8ThRAPAviOlHJb6Bb2vefuAO4CcKzpWzvn/h0N4JdCiB9IKR/wsL8fArgewAjFt3fN/ZsA4CIhxDUAaiTHrhKFpkqc7YaLA/yTIyIiIqL08dWTnUtOb8ZAgj0fwHUAagA8AKA79/zpAB4QQgwO20AhxBgAT2IgwW4D8B8AlwH4I4Dlued3BHC3EOIUl/39GMBfMZBgbwZwB4Arc//uBLA1973Bufe5OuznSJrp06fH3QTSnCpFdlon28zrcHHGMumAcUw6YByTLhjLFJbnnmwhxL4AbjQ8dZGU8hrTNkcCeAx9PcEnAJiK8Anq9QAOyj1eBOBUKeUKw3teCODPAH6Mvs9zuxBiP1UvuhBiNPpuCuRNBzBVSrndtN1IAH8C8N3cUxcKIf4qpVwZ8rMQZVqPcp42C58RERERkT789GRfjr6eXQCYYU6wAUBKORfA2YanfpNLbAMRQhwI4Jzcl10AvmZMsHPv2QPgPAAv5Z7aGcAFNrs8BQM92O8B+LE5wc7tcxuAyQDy71UG4KQgn4GIBqgS6o3NHeptmWMTERERUQp5SrKFEDsAOCP3pURfwq0kpXwSwKu5L0eib+h4UN/CQBvvlVK+Y/OevQCuMDx1ls3+9jY8fi2XoCtJKbsx8DkAYCfX1qbIhAkT4m4CaU7VEW2XOKt6rb32ZDOWSQeMY9IB45h0wVimsLz2ZJ8EYGju8Xwp5WKX7e8xPP6K71YNOM3w+G6XbZ8CsCX3eO/c0HWzNsNjL0nzzobHWg0V58GD4tCjWMILAFZsaLE857Unm7FMOmAckw4Yx6QLxjKF5TXJ/pjh8Ysetp9leKxKdl0JIYYAONjr++Z6s192eV9juyYIIaoc3v8TAD6T+3ITgEccG5wytbW1cTeBMsgucVYl3157shnLpAPGMemAcUy6YCxTWF6T7EMMj9/1sP1Sw+M9coXE/DoQQHnu8RYp5Uaf73uI+ZtSyjcA1OW+rADwrBDiSiHER4UQI4UQo4QQhwkhrgbwdG6bZgCTpJRbzftLs+bm5ribQJrzMwRcVWHca082Y5l0wDgmHTCOSReMZQrLa3Xx3QyP17htLKXcLIRowUCRsV0B+F0z29d75qy2eb3RJAC3oW+O+UgAl+T+mbWhb4j6pVLKJR7fv4AQwvO2rKRMWWC7XJfief5NEBERpVt+pFp5ma9Vg4li4Sd3c+M1ya40PLZOnlRrxUCSXem0YcTvqXp9PynldiHE19A3z/xyAEfb7Gsp+oaor7b5fqRqamoAAGPHjsWUKVNQV1eHhoaG/u9PnToVjY2NmDlzZv9z1dXVqKqq6n8tAIwfPx6TJk3CjBkzsGTJwL2BmpoaNDQ0oK6uryN/zpw5WLx4McaNG1cwJKaqqgrV1dWYNm0ampqaAACVlZWYOnUq6uvrUV9f37/t5MmTARSuJThhwgRMmDABtbW1/XcBS/WZAGDixIn8TAn5TB0d+2JgMEqfpUuXAxgOs5tv+QsK6xICCxYuQm3tbNfPNGfOHNTU1PD3xM+U6s80duxY7T6Tjr8nfibnzzRnzhzU1dVp9ZkA/X5PpfpMb24bhWc37oJRIwbjii/vjfdeG5h5mfTPlL+2yMLviZ9p4DNFSXjpLRJCLAOwT+7Lz0kpn/XwmlUA9sh9eZyU0stcbuPrzwJwe+7LWVLK4z285nsA/pn78hkp5Yk2230BfQl2FfrmWz+MgaHm+wH4EoAdc18vBvAlKeVS834c2iEB9sRRto2bfDea27sLnjvuoF0wa+F6y7azLj8Fx132eMFzpx75Ydz9i89YtiUiIqJka+noxm7nDtQs3nOnEXjnhtMcXkGULvlebymlsvvb69gNY1XuwbZbFRpqeNxqu1WJ31MIcQn6iphVAbgTwJ5Syu9IKa/I/fsOgL1y3wOAAwA8JYQI0hufWMa7TETFoLrFZFNcXDmM3OtNKsYy6YBxTDpgHFPe9//6csHXqzZ6HZSaDIxlCstrkm2c/T/CdqtCxjGhQaoHRP6eQojPArgy9+UrAM6RUlr+6qWU2wGcg4F1svcG8EuPbUgF41AKolLpLULhM8Yy6YBxTDpgHFPeI294LaeUTIxlCstrkv2+4fHubhsLIUajMDFe56NNgd5TsZ3qPacaHt8opeyx21HuezcanjrDYxuIyIafJBucbkFEREREKeQ1yV5geLy/h+33MzxeI6X0W1kc6JsLnR9cOloIsZPP931H8f1PGh6/5WF/8w2PvXxuIspR5ch21cVV62R77ckmIiIiIkoSr0n2G4bHx3rY/jjD47nemzNAStmOwuTe8X2FEGUAPuXyvsZ51X4v4W1mk6bT1KlT3TciiphqqS4g3JxsxjLpgHFMOmAcky4YyxSW1yT7CQDtuceHCyHGu2z/NcPjB3y3asBDhsdnumz7OQBjco9XSinfUGyz0fD4MA/vb9ymycP2qdHY2Bh3EyiD/AwX93oXjLFMOmAckw4Yx6QLxjKF5SnJzhUCezD3pQBwqd22QogTMdCj3Gx4XRD/wUAP8plCiINs3rPM1KY7bPb3muHxebnXKQkhygGcb3jqedfWpohxvTiiYlD1RPupLm7X623GWCYdMI5JB4xj0gVjmcLy2pMNAJcB6Mo9/rYQ4kLzBkKIwzGwtjUAXCul3KzYboIQQub/2b2hlHKhYX+DAdwnhNjLtK9y9BUoyw9R3wjgeptd/svw+FgA/xRCDDdvJITYAX2J+ifyTQHwN7t2EpE3PTY92d09wXuyiYiIiIiSpMLrhlLKd4UQvwBwc+6p64QQ3wLwOPrWpD4cQDWAQbnvPw/7ZNePC9BXsOwAAAcBWCCEuB99hdHGADgNwL65bbvRtyzXVpvPUCeE+C+Ab+SeOgdAtRDiYQBLc8/tn/scYwwv/YOUck4En4Uo0+wLn6mW8GKaTURERETp4znJBgAp5S25IdbXARiKvsT6cMWmdQDOklJ2hG2glPIDIcRJAO5CX7I9HMC3FZtuBnCulPIRl12ehb7lvX6Kvp78DwH4js22XQBqAFztv+XJVl1dHXcTSHOqFNluCLhUbG03tNyMsUw6YByTDhjHpAvGMoXlK8kGACnlTUKIRwGcC+BUAHuiL+F+H31znu/wkOj6fc9VQohPo68HeiKAIwDsir453+8B+B+Av0sp37fdycC+ugD8XAhxC/qS6+PQ10s+Gn15wWYACwHUA/iXlHJNlJ8lKaqqquJuAmWQXe+0KqH22pPNWCYdMI5JB4xj0gVjmcLyMye7n5RymZTyN1LKw6WUY6SUw6SUH5FSftNLgi2lrJdSivw/j+/ZK6WcKaX8spRyTynlECnlTlLKo6SUV3hJsE37WyKlvERKebyUctfc/oZKKcdKKU+QUl6ua4INADU1NXE3gTLItidbkVB7HS3OWCYdMI5JB4xjArwvwZlkjGUKK1CSTUQUhG1PtirJZukzIiKi1Hn13Y3uGxFpjkk2ERWFKp+2W5UrzBJeRERElBwbtrUrn9ehh5vIKybZGTV+/Pi4m0AZZFddXN2T7Q1jmXTAOCYdMI4JAITNRNA05diMZQqLSXZGTZo0Ke4mUAbZJ9nW57ze8WYskw4Yx6QDxjEBgIA6y07T0pyMZQqLSXZGzZgxI+4mkOaUy3LZVhcPXviMsUw6YByTDhjHBABlNtlFmpJsxjKFxSQ7o5YsWRJ3EyiD7OZZq068Xk/GjGXSAeOYdMA4JsChJ1uxXGdSMZYpLCbZRFQydrXM1MPFi9sWIiIiip5dTzZXDaEsYZJNREWhrC5ucxtb1cOdpmFlRERE1Md+TnaJG0IUI8Fy+tETQkiASxVQtn3oe3ehs7swqa4cWoHm9m7LtjeecxR+fuvsgucO2WM0Xv39F4raRiIiIorWU/Mbccb19ZbnG6edicphg0rfIKIiELky+lJK5V0l9mRnVENDQ9xNoAzyN1zc200qxjLpgHFMOmAcEwCU2azhlaYRaoxlCotJdkbV1dXF3QTSnHK4uF11cWXhM2/vw1gmHTCOSQeMYwL0WCebsUxhVcTdACLSy0uL12Phmq3o6rHOv7ZdJ1u5hFeKzsZEREQEQI+ebKKwmGQTUWT+N2c1vvXnWbbftzvBqpJvFkghIiJKH7uebCbZlCUcLp5REydOjLsJpKEfTn/F8fu2PdmKE6/XnmzGMumAcUw6YBwT4LROdnqSbMYyhcUkO6PGjRsXdxNIQ6rK4UZ2eXOYdbIZy6QDxjHpgHFMgP062d096UmyGcsUFpPsjKqtrY27CUT9wvRkM5ZJB4xj0gHjmPqoe7I7FbVakoqxTGExySai2CkLn8XQDiIiIgqnzGZOdmd3epJsorCYZBNR7FTDxdM0d4uIiIj6CJvKZ90p6skmCotJdkZVVVXF3QSifqqh4V6rkDKWSQeMY9IB45gA++riaerJZixTWEyyM6q6ujruJhD1UyXUm1s6Pb2WsUw6YByTDhjHBNivk52mJJuxTGExyS4iIYTlX1JMmzYt7iYQ9VMNDd/e3u2p+BljmXTAOCYdMI4J0KMnm7FMRkFyOibZGdXU1BR3E4j62U2/fn7BOtfXMpZJB4xj0gHjmAC72uJAV4qSbMYyhVURdwN05nUJIqKss5t/vWLD9hK3hIiIiMKwu/rt6U1Pkk1kpMrp3Hqz2ZOdUZWVlXE3gahfj01XtpcC44xl0gHjmHTAOCYAsOtjStOiIYxlCotJdkZNnTo17iYQ9bPryfayjBdjmXTAOCYdMI7Jid0N9SRiLFNYTLIzqr6+Pu4mEPWzG0HmZRkvxjLpgHFMOmAcE2A/XTJNSTZjmcJikp1RPHhQktgl015OyIxl0gHjmHTAOCYnTLIpS5hkE1Hs7O56exkuTkRERMlhd+ZmQWDKEibZRBQ7255snpCJiIhSxe7UnaaebKKwmGRn1OTJk+NuAlE/u/WwvZyQGcukA8Yx6YBxTE7SlGQzliksJtlEFLsFa7Yqn+dwcSIionSxLXzG0WmUIUyyM2r69OlxN4HIlZfq4oxl0gHjmHTAOCYnaerJZixTWEyyiSix0nRCJiIiIvvCZxydRlnCJJuIiIiIiCLBwmdETLIza8KECXE3gciVl+lbjGXSAeOYdMA4JidepoAlBWOZwmKSnVE8eJAuGMukA8Yx6YBxTH1sCp+lqCebsUxhMcnOqNra2ribQOTKrkKpEWOZdMA4Jh0wjslJmuZkM5YpLCbZGdXc3Bx3E4hceTkdM5ZJB4xj0gHjmAA95mQzliksJtlERERERFRUXCebsoRJdkaNHTs27iYQufJyPmYskw4Yx6QDxjEBevRkM5YpLCbZGTVlypS4m0AUCcYy6YBxTDpgHBMASJvJXmmak81YprCYZGdUXV1d3E0gcuWl8BljmXTAOCYdMI7JSZqW8GIsU1hMsjOqoaEh7iYQufJyOmYskw4Yx6QDxjEBegwXZyxTWEyyiSgy5WUi7iYQERFRAqUpySYKi0k2EUWmTESbZKdoZBkRERFBj55sorCYZGfU1KlT424CaagshiMKY5l0wDgmHTCOCXAofJaiO+eMZQqLSXZGNTY2xt0E0lDkPdkeZmUzlkkHjGPSAeOYnKSpJ5uxTGExyc6omTNnxt0E0lAcw8UZy6QDxjHpgHFMgB7DxRnLFBaTbCKKTNSFz1I0soyIiIgcpGmdbKKwmGQTUWRYXJyIiCjbpM0d8jTNySYKi0l2RlVXV8fdBNKQiHi4uJfdMZZJB4xj0gHjmADYVlNJ03BxxjKFxSQ7o6qqquJuAmmoLOKubC93vRnLpAPGMemAcUxO0pRkM5YpLCbZGVVTUxN3E0hDURc+6+1134axTDpgHJMOGMcE6FH4jLFMYTHJJqLIRF34jPO3iIiI9JCmJJsoLCbZRBSZqAufsRIpERFRukibWdk8pVOWMMnOqPHjx8fdBNJQ5MPFPfRkM5ZJB4xj0gHjmAD74eK9XuaAJQRjmcKqiLsBOlNVWrZb1qDUJk2aFHcTSENxFD5jLJMOGMekA8YxOUnTcHHGMhkFWT2HPdkZNWPGjLibQBqKfri4+zaMZdIB45h0wDgmQI/CZ4xlCos92UWUlF5rlSVLlsTdBNJQ1MPFezz8DTGWSQeMY9IB45icpCjHZixTAVVO59a7zZ5sIopM5MPF03RGJiIiItvCZ2nqySYKi0k2EUUmjsJnRERElBw6DBcnCkskeUhzWgkhJJDs4eJExXDURY9g0dqtke3vW8ftg7+de0xk+yMiIqLieuSNNfjmn16wPH/KEeNwzy8nlL5BREWQHy4upVT2MLEnO6MaGhribgJpKPrCZ+6VzxjLpAPGMemAcUyA0xJe6el8YixTWEyyM6quri7uJpCGoh8u7r4NY5l0wDgmHTCOyUmKcmzGMoXGJJuIIlPOwmdERESZxsJnREyyiShCLHxGRESUbSx8RsQkO7MmTpwYdxNIQ2URH1G8nI8Zy6QDxjHpgHFMTtKUZDOWKSwm2Rk1bty4uJtAGoq6J9vLCZmxTDpgHJMOGMcEOBQ+S9HoNMYyhcUkO6Nqa2vjbgJpqCzqOdkeTsiMZdIB45h0wDgmJ2nqyWYsU1hMsokoMgIsfEZERJRt6nM3z+mUJUyyiSgydhVFg0rT0DIiIiJi4TMigEl2ZlVVVcXdBCJXXu56M5ZJB4xj0gHjmJyk6cY5Y5nCYpKdUdXV1XE3gciVl5vejGXSAeOYdMA4JsBusHi6erIZyxQWk+yMmjZtWtxNIA1FfZPay11vxjLpgHFMOmAck5M0JdmMZQqLSXZGNTU1xd0EIlfG4eLNbV2444VleO7t9wu2YSyTDhjHpAPGMQGAtLlBnqYkm7FMYVXE3QAiIjs9uRO1lBKfv/IpvLN6CwDgT+cche+fsH+MLSMiIiIVFj4jYk92ZlVWVsbdBNKQ3d3roHp7+/6vf2ddf4INAOffOrv/MWOZdMA4Jh0wjslJ1NcIxcRYprCYZGfU1KlT424Ckav8nOxl65ptt2Eskw4Yx6QDxjEB9st5pqknm7FMYTHJzqj6+vq4m0DkKp9klwn7bRjLpAPGMemAcUxO0pRkM5YpLCbZGcWDBxVD1KfP/hOysM+yGcukA8Yx6YBxTIDDnOwUDRdnLFNYTLKJKLHy87e2tHTG3BIiIiLywi6X7k1RTzZRWEyyiSg6EZ8/Gze34W9PLsZv754X7Y6JiIiopNI0XJwoLCbZGTV58uS4m0DkqmlzGy68s8FxG8Yy6YBxTDpgHBOgR+EzxjKFxSSbiIiIiIiKqjdFc7KJwmKSnVHTp0+PuwmkIbu718XEWCYdMI5JB4xjAhwKn6WoJ5uxTGExySYiIiIioqJi4TPKEibZREREREQUCbtUOk1LeBGFxSQ7oyZMmBB3E0hDcZw/GcukA8Yx6YBxTMDA8ptmaRouzlimsJhkZxQPHqQLxjLpgHFMOmAckxMm2ZQlTLIzqra2Nu4mkIbi6MlmLJMOGMekA8YxAfbXAlLa93InDWOZwmKSnVHNzc1xN4EoEoxl0gHjmHTAOCY3aVnGi7FMYVXE3QCdCSEsz6XlDh4RERERkV9OV7o9vRLl7OKjlFHldG4Y5hk1duzYuJtAGopjnWzGMumAcUw6YBwT4NyhlJZ52YxlCkuwZzV6QggJsNeasue4yx7DvBWbS/JezbdPKsn7EBERkXd3vLAMP/7Ha8rvNU0/EzsMHVTiFhFFL9+7LaVUdnOzJzuj6urq4m4CUSQYy6QDxjHpgHFMgHMR1JR0ZDOWKTQm2RnV0NAQdxNIQ3EM3mAskw4Yx6QDxjG5SctwccYyhcUkm4iIiIiIIuFc+Ky3ZO0gihOTbCJKJdY8ICIiSh6n83NvSnqyicJi4bMiSEPhs+bmZlRWVsbdDNLMsf/3GOavKk3hs623TkRZmWAskxYYx6QDxjEBwK31S3Hev15Xfu/dP38Fu40eVuIW+cdYJjcsfEZKjY2NcTeBKJT8vC7GMumAcUw6YBwT4FyfJS1zshnLFBaT7IyaOXNm3E0gDZVyneze3FmcsUw6YByTDhjH5CYtSTZjmcJikk1EqZSWEzUREVGWOBc+47mbsoFJNhGlUm+Cax4QERFlllPhM567KSOYZGdUdXV13E0gDZXy3Jm/G85YJh0wjkkHjGNyk5aebMYyhcUkO6OqqqribgJRKPnzNGOZdMA4Jh0wjglwHi6eliW8GMsUFpPsjKqpqYm7CUSh9PT2AmAskx4Yx6QDxjG5SUtPNmOZwmKSTUSRKeWpk9O6iIiIkkeHJbyIwmKSTUSpxBM1ERFR8kgWPiNikp1V48ePj7sJRKHk53UxlkkHjGPSAeOY3KTlBjljmcJikp1RkyZNirsJpCGnu9d//M5Rkb5X/kTNWCYdMI5JB4xjAvRYJ5uxTGExyc6oGTNmxN0Eyhghot1ffsgZY5l0wDgmHTCOyU1akmzGMoXFJDujlixZEncTSENOU60izrH7T9SMZdIB45h0wDgmQI852YxlCotJNhGVRsRd2Wk5URMREWWJ0+k5LetkE4XFJJuISqJYPdlERESUDi8tXh93E4hKQjgN6aBghBAScB4uQ6Sjoy56BIvWblV+76FffRanXfdcwXNCBF/vevbVX8SBHx4V7MVERERUFH99cjF+dWeD7febb2dRMUo/kRuhKaVU9iOxJzujGhrsD35ExfDZQ3bD+LEj+7/+3dePQHlZ8P7tfE82Y5l0wDgmHTCOSReMZQqLSXZG1dXVxd0EyhghBJ669PO4etLHcNtPjsUvvngQykLM087PyWYskw4Yx6QDxjEBeozkZCxTWBVBXiSE2AfADwB8AcCeAIYCaALwGoA7pZSPRtbCgfcsA/B1ABMBHAFgVwDbALwH4H8A/iGlXBdgvzsB+CaALwMYn9tvO4B1AJYCqAfwhJTyrdAfgkhzbifWHXcYgp+ecmD/11H0ZBMREVFyaJBjE4XmO8kWQvwEwB8ADDN9a5/cv4lCiAcBfEdKuS10C/vec3cAdwE41vStnXP/jgbwSyHED6SUD/jY708BXAFgtOlbQ3PPHQDgiwC+BGBCgKYTkYNQPdlMsomIiIgogXwl2UKIHwK42fDUfACPA2gFcDiA6tw+TwfwgBDiVCllZ5gGCiHGAHgSwEG5p9oA3A9gMYAxAE5DX3K/I4C7hRDVUsrHXfYpANwC4EeGp+cCeAl9PdhDAIwFsB+AT4Zpf1JNnDgx7iYQhevJzt0qZyyTDhjHpAPGMQGADrfAGcsUluckWwixL4AbDU9dJKW8xrTNkQAeQ9+Q6xMATAVwdcg2Xo+BBHsRgFOllCsM73khgD8D+DH6Ps/tQoj9XHrRf4uBBHsBgO9JKV9TbSiE2AF9PdpaGTduXNxNIA35HSIWIsfu78lmLJMOGMekA8Yx6YKxTGH5KXx2OYDBucczzAk2AEgp5wI42/DUb4QQo4M2TghxIIBzcl92AfiaMcHOvWcPgPPQ1wsN9A0fv8Bhn0cCuCT35WIAn7JLsHP73y6l1K7EYG1tbdxNIEJZWfDai/k52Yxl0gHjmHTAOCbAuT7LaUftUcKWBMdYprA8XeHmenPPyH0p0ZdwK0kpnwTwau7LkegbOh7UtzDQxnullO/YvGcv+uZW553lsM9LMdCDP1lKqV7Ul4h88ztELFRPtg7j0YiIiDTjdHre1tpVsnYQxclrN9JJ6CsGBgDzpZSLXba/x/D4K75bNeA0w+O7XbZ9CsCW3OO9cz3WBXIF1L6c+3KBlPKFEG0jopDCzMlm4TMiIqJ0Wb+tPe4mEJWE1yT7Y4bHL3rYfpbhsSXZ9UIIMQTAwV7fN9eb/bLL+34eQHnu8Qu59zlECPEXIcRSIUSbEGKzEGKeEOJ6IcRHgrQ9DaqqquJuAhHKwiTZueFojGXSAeOYdMA4JsC5Psu21lD1kEuGsUxheU2yDzE8ftfD9ksNj/cQQoz03qR+B2IgId4ipdzo830PUXz/GMPjd4UQv0BfVfEfAdgXA0t3HY6+om2LhRC287vTrLq6Ou4mkIbc1sk2Kw+xhFd+TjZjmXTAOCYdMI7JTXdKRqExliksr0n2bobHa9w2llJuBtBieGpXP40K8p45q21en7e/4fHpAG4AMAjAe+irUH4J+pYoW5nbZhCAPwghLvL4/gWEEJ7/ldq0adNK/p5EZlH0ZDOWSQeMY9IB45gA5xvuPSlJshnL2RRl7uZ1Ca9Kw+MW260KtQIYoXi9V0HfU/X6vDGGx8fl/v8TgF9JKfsrMQghpuaezy/zdYUQ4lEp5Zse2+FbTU0NAGDs2LGYMmUK6urq0NAwUNR86tSpaGxsxMyZM/ufq66uRlVVVf9rAWD8+PGYNGkSZsyYgSVLlhTsv6GhAXV1dQCA+vp6TJgwAePGjSuooFhVVYXq6mpMmzYNTU1NAIDKykpMnToV9fX1qK+v79928uTJAIDp06f3PzdhwgRMmDABtbW1aG5uLulnAvrWNeRniu8z2f2pH7XX8IJt859p65bNGFi0wJ8NGz9ATc101NfXo6mpib8nfqZUf6ampibtPpOOvyd+JufPVF9fj3Hjxmn1mQD9fk/F/kwLFiyAnW3NLaipqUn8Z6qrq0NTU5PWvyd+JutnipLwMrxTCLEMwD65Lz8npXzWw2tWAcjX6T9OSullLrfx9WcBuD335Swp5fEeXvM9AP/MffmMlPJE0/eXom9YeN4TUspTbPYlADwH4DO5p/4jpfy2x7ZLwP/Q2VKqqakpCC6iKBxxYR2WrWsueG7nkUPx8G9OwMG7j1Zs/z8sW7c90Hvddf7x+OLHdmcskxYYx6QDxjEBwB8fWYDL/jtP+b3Rwwdh9d/OLG2DAmAsk5t8b7aUUtmt7bUnu83w2Gu301DD41bbrUr7nm2mr6+y25GUUgohrsZAkv0FIYSQSc6cfaisDDK4gMif23/6aXzm4F2x4w5DlN8Ps052frg4Y5l0wDgmHTCOCXAufJaW4eKMZQrL6xWusWtqhO1WhYbbvN6rYryn8bl2FFYjV3kBQHfu8RgA2lQbnzp1atxNIC0VnjwP3WO0bYINhFwnO3eiZiyTDhjHpAPGMblJS+EzxjKF5TXJft/weHe3jYUQo1GYGK/z0aZA76nYTvWeTcbvSym7Fdv0k1K2AdhkeGonj+1IPON8B6JicasLEWqd7Nx5mrFMOmAckw4YxwToUfiMsUxheU2yjRUM9rfdasB+hsdrpJTbvDep32IAvbnHo4UQXhJc4/u+o/i+8Tmvf+XpOBr4xIMHJUFZqCW8+g4PjGXSAeOYdMA4JjfdPem4rGYsU1hek+w3DI+P9bD9cYbHc703Z4CUsh2Fyb3j+wohygB8yuV95xge7yaEcJyTLoQYCuBDhqfWO21PlHV+KxaE6snudd+GiIiISsvpUqBXykQXBiaKitck+wn0zWEGgMOFEONdtv+a4fEDvls14CHDY7dShJ/DwBJdK6WUbyi2eQpAvpTxUBQm5SrHY6A43Hop5QqX7YnIwG0dwTA92X9/5l0sfT/IIBkiIiIqFrccupdJNmWApyRbSrkdwIO5LwWAS+22FUKciIHktdnwuiD+g4Eh42cKIQ6yec8yU5vuUG2Xm2N9l+Gpi+3eOLeE10WGp8LcLEic/LpzRFFavt7fclxherJffXcDjv2/x3DqV88KvA+ipOAxmXTAOCYv0jBknLFMYflZP+cyAF25x98WQlxo3kAIcTgG1rYGgGullJsV200QQsj8P7s3lFIuNOxvMID7hBB7mfZVDuBGDAxR3wjgeofP8VsALbnHJwshbhBCDDLtczCAmwFMyD3VBuBah30SZV6Q4V8hOrIBAK2dPfjD48vD7YSIiIgi5Hw9kJbiZ0RheE6ypZTvAviF4anrhBDzhBDXCCEuE0LcB2A2gLG57z8P52TXqwvQVwQNAA4CsEAIcYcQ4v+EELW57/009/1uAOdIKbc6fI5GAN/HQA/5LwAsFkL8SQhxkRDizwCWAPhx/iUApkgp34vgsyTG9OnT424CaWZ7u7VYv9uJtDzEOtl5z7wdZPEComThMZl0wDgmL9KQZDOWKSzHwl9mUspbckOzr0PfnObDc//M6gCcJaXsCNtAKeUHQoiT0DfM+5PoWwv724pNNwM4V0r5iId9/jc3V/QvAHZE3/rXP1dsui23z7sDNp8oM8oUQ783bXc+BESQY6NHhuwOJyIiosi4DWxLy1rZRGH4SrIBQEp5kxDiUQDnAjgVwJ7oS7jfB/AagDu8JLo+33OVEOLTAL4BYCKAIwDsir453+8B+B+Av0sp37fdiXWf/xVCPAfgewC+DGAf9CXcW9HXO/4ogL9JKTfZ74WI8lTDxQ/ZfbTja8LMye5/XzDJJiIiSgq36WM9XB6EMsB3kg0AUsplAH6T+xfk9fWAvytjKWUvgJm5f5GQUq4HcE3uX6ZMmDAh7iZQBlQOG+T4/TDVxYl0wmMy6YBxTF70pKDwGWOZwopgsCalEQ8eFDXzjesRQ9zv4UXRk02kAx6TSQeMYwLcyp6lY042Y5nCYpKdUbW1tXE3gTTnpZOaPdlEfXhMJh0wjsmLNCTZjGUKi0l2RjU3N8fdBCJlsTSiLOIxmXTAOCZAj8JnjGUKi0k2EUUiyCmTOTYREZFeWPiMiEl2Zo0dO9Z9I6IQvOTPUayTTaQDHpNJB4xj8qI7BYXPGMsUFq9wM2rKlClxN4E043bnWoU92UR9eEwmHTCOCXAf2dYb4Hqh1BjLFBaT7Iyqq6uLuwmkOeGhqBmrixP14TGZdMA4Ji/SUPiMsUxhMcnOqIaGhribQMTq4kQ5PCaTDhjHBHgofJaC4eKMZQqLSTYRRSLI6C/2ZBMREemFhc+ImGQTUZF4WiebSTYREVGmpGEJL6KwmGRn1NSpU+NuAmkm2BJeTLKJAB6TSQ+MYwI8FD5LQZLNWKawmGRnVGNjY9xNIM15SZ/Zk03Uh8dk0gHjmLxIQ3VxxjKFxSQ7o2bOnBl3E4g4J5soh8dk0gHjmAD3Gi1pqC7OWKawmGQTUSS4TjYRERFJlwHjrHtGWcAkm4iKgutkExERkRmri1MWMMnOqOrq6ribQJoJMsWKhc+I+vCYTDpgHBPgfj2QgtHijGUKjUl2RlVVVcXdBCL2ZBPl8JhMOmAckxdpmJPNWKawmGRnVE1NTdxNIGJPNlEOj8mkA8YxAXoUPmMsU1hMsokoEm6FTlS4hBcREZFeXAufpWAJL6KwmGQTUVF4KXzGnmwiIqJs6U1BTzZRWEyyM2r8+PFxN4E0E+TGNOdkE/XhMZl0wDgmQI/h4oxlCqsi7gboTNWTF2Qt4WKYNGlS3E0gzXnppC7jbT4iADwmkx4Yx+RFGoaLM5bJyMvoTDNe4mbUjBkz4m4CEco5XJwIAI/JpAfGMQF69GQzliks9mQXUVJ6rVWWLFkSdxOIWPiMKIfHZNIB45gA98JnaUiyGctkpMrp3Hq32ZNNREXhJX3mnGwiIqJsScNwcaKwmGQTUSSCjNxgdXEiIiK96DBcnCgsJtkZVVNTE3cTSHNBikQQZRWPyaQDxjF5kYYcm7FMYTHJzqiGhoa4m0CEZ99+P+4mECUCj8mkA8YxAe4j29KwTjZjmcJikp1RdXV1cTeBNBNkilXT5tboG0KUQjwmkw4Yx+RFT29v3E1wxVimsJhkE1FReBktzhHlRERE2ZKCjmyi0JhkE1Ekgp0zmWUTERHphIXPiJhkZ9bEiRPjbgJpLur0eebPj8dnD9kt4r0SJQOPyaQDxjGpmJfrTEOSzVimsJhkZ9S4cePibgLF6IaHF+DwC/6Hb980C5u2d8TWDr/DxSvK2fNNeuIxmXTAOCYAkKaxbeYkOw3rZDOWKSwm2RlVW1sbdxMoJgvXbMFv756H5eu346HZq/GPZ96NZL9B1sl2MvHYj/Q/rigX+PSBu1hO1ES64DGZdMA4JsA6XLwihT3ZjGUKi0k2UcZc8+DbBV9fcd/8oryPl3Wynba59KuH4bSj9sCRe++If/3oWIweMRgV5TxkERERpYmlJzsFSTZRWBVxN4CISquzpzhLZwTpyHZKw3cbPQx3nndcwXPsySYiIko28/VAGoeLE4XFbqGMqqqqirsJpDkv863PmbCvr32ah5wR6YLHZNIB45hUysoK0400DBdnLFNYTLIzqrq6Ou4mEOHrn9zb9nuqJN1uuHjU88GJSo3HZNIB45gA98JnaUiyGcsUFpPsjJo2bVrcTSDNBDllDhvib8aK3XDxjq7iDIEnKhUek0kHjGNSSeNwccYyhcUkO6OampribgJpzsvAbr9zrO16stu6enzthyhpeEwmHTCOCdCjujhjmcJikk1EkQgyZNvvHGu7pLy9s9v3exMREVH0XAufcfAZZQCT7IyqrKyMuwmkOS9LePnuybbZvq2TPdmUbjwmkw4Yx6RinZOd/CybsUxhMcnOqKlTp8bdBCLHJFtd+IxJNumJx2TSAeOYAEXhM9NUrzTMyWYsU1hMsjOqvr4+7iZQTIq1CFagdbKFQJmXtb5yysts5mRzuDilHI/JpAPGMamkcbg4Y5nCYpKdUTx4ULF5TZ39DBm368luZ+EzSjkek0kHjGMC3Odkp6HwGWOZwmKSTZQxXuZKBxH0lOkrybbtyWaSTURElATm6wFLdfEUDBcnCotJNhEVhddc3q53WsUuIW/tYJJNRESURNbh4kyySX9MsjNq8uTJcTeBYrJpe0fcTShglzgLxYBz++HinJNN6cZjMumAcUyAdUnPshQOF2csU1hMsoky5sVF64uy3yDrZAOIpPBZd0/yT9hERERZlMY52URhMcnOqOnTp8fdBNKc17nfFeXeD0N2Pdk8YVPa8ZhMOmAcE+Be+CzoTflSYixTWEyyiSgSpSl8xiSbiIgoyayFzwrTDZ6zKQuYZBNlzAHjRpbkfYqxhJfdtr0puCtORESURRwuTlnEJDujJkyYEHcTKCZ+ktpSsOudVo02txtazhM2pR2PyaQDxjEB1uHgluriKbgxzlimsJhkZxQPHtnlp9CYH0HPmRwuTsRjMumBcUwqaawuzlimsJhkZ1RtbW3cTaCYlKon22subz75Oim36cnu7un1vA+iJOIxmXTAOCbAvfBZCnJsxjKFxiQ7o5qbm+NuAsWkWEl20GqhvqqLsyebNMVjMumAcUyAqvCZuSc7+TfGGcsUVkXcDdCZagmjNCxbQHor1nBxM69LeEVR+IxJNhERUTJZerJ5zqaU8XpNa8Se7IwaO3Zs3E2gmPgZnu1H4CW8bA5cqgOa3TrZaSiiQuSEx2TSAeOYAC+Fz0rZmmAYyxQWe7KLKMm91lOmTIm7CRSTks3J9ridXeKs3LaM1cVJTzwmkw4Yx6SSxiW8GMtkpMrp3Hq32ZOdUXV1dXE3gWJSquHiXvkrfMbh4qQnHpNJB4xjAlSFzwrTjTScsxnLFBaT7IxqaGiIuwkUk6QVPvO3hBd7sklPPCaTDhjH1Cf962QzliksJtlEGZO0JbzsEmcVFj4jIiJKF/O5u7uH52zSH5NsoowpXk92sNf56sm2GS7OEzYREVEymK8HBlcUphvdPclfwosoLCbZGTV16tS4m0AxUfUwF2c5jeiX8LIfLs4TNqUbj8mkA8YxqZiT7K4UJNmMZQqLSXZGNTY2xt0ESpA4h1v7qS5uVyQtDfO7iJzwmEw6YBwTYF3Sc5Apye7sTn6SzVimsJhkZ9TMmTPjbgLFRJWPRpFkB92Dn2rnnJNNuuIxmXTAOCbAWgh1cHn6erIZyxQWk2wiQk8ReoI9Fz4rtx6G7F7LJJuIiChdLMPFU9CTTRQWk2wiiqYnuwRLeDHJJiIiSjbz5cCgivKCr9PQk00UFpPsjKquro67CRQTVTJcjCRVeOzKZpJNxGMy6YFxTCrmnuw0zMlmLFNYTLIzqqqqKu4mUILEWZ27gkk2EY/JpAXGMQHWGi1prC7OWKawmGRnVE1NTdxNoASJYgmvUqyTbVckjUk2pR2PyaQDxjEB1hFz5tor3T3JP2czliksJtlEVJzh4h63Uy3LJWxebZePM8kmIiJKpkGmpTp5zqYsYJJNlDGqU1u862R7PwzZzfPmCZuIiCgZzCPbLD3ZMU5RIyoVJtkZNX78+LibQAlSnMJn3rbzM1zcDpNsSjsek0kHjGNSMdde6e6RgVckKRXGMoXFJDujJk2aFHcTKEHiTFLLvWbjsE/cmWRT2vGYTDpgHBNgHTFXJoSlpkpvwpNsxjKFxSQ7o2bMmBF3EygmqvNanOtkV5T7SbLthotz6BmlG4/JpAPGMQHW6wEhrOf6pBc/YyxTWEyyM2rJkiVxN4ESJGnrZNu91G6PUVRHJ4oTj8mkA8YxqQghrEPGE37eZixTWEyyiSiSYVtB96CqLm7HLvlO+smaiIgoK1RnZOsyXhyBRnpjkk2UMaph3XEu4VVR5v0wNKSiXPk852QTERElk4B11FrSh4sThcUkO6NqamribgIlSJwnOz/VxXf/0HAcMG6k5Xkm2ZR2PCaTDhjHBKhrv5jP9UmvpcJYprCYZGdUQ0ND3E2gBGnp6A69j6Ajzv0k2UIIzPz58Rg7ZljB80yyKe14TCYdMI4JsCt8Zh4unuzzNmOZwmKSnVF1dXVxN4ESpKWjK/J9el2ZS1Vd3Om1+48didt+8umC55hkU9rxmEw6YByTmrXwWdLP24xlCotJNlHGqE5r29ui6MkOdsJUvcztDre595vVxYmIiJJBXfgsXdXFicJikk1EaO0Mn2SbeV3C66N7jvG9b+vcLp6siYiIkkgIoLyM1cUpW5hkZ9TEiRPjbgIlSJxJ6tBB/g9DTLJJNzwmkw4YxwSoR6iZe7KTft5mLFNYTLIzaty4cXE3gWKiOvlFcbILugdzMRQvLEuBJLxKKZEbHpNJB4xjUlEXPkv2eZuxTGExyc6o2trauJtACRLnOtl+qovnpa2ACpEbHpNJB4xjAtQ1Wszn7aTPyWYsU1hMsokokjvKpVjCK6+MSTYREVEimc/IAsJa+CzhPdlEYTHJJsoYqRjYXZSebI+5c5Akm9XFiYiI0kEIoEykqyebKCwm2RlVVVUVdxMoQeI82QWbk134GvZkU9rxmEw6YBwTYDNc3DInO9nnbcYyhcUkO6Oqq6vjbgIlSCSFzwKOF4+iJ5tJNqUdj8mkA8YxAdbRZeVl1uHiPQkvWMpYprCYZBeREMLyLymmTZsWdxMoQZ5/5/3I9yk8lj4zF0PxwpJkB50QTpQQPCaTDhjHBADm+97lZQIVKRuBxlgmoyA5HZPsjGpqaoq7CRQTVT5av2Bd+P0GfB17sol4TCY9MI4JsJ6Ty4Sq8Fmyz9uMZQqrIu4G6Czo8FkiHXgduGGuFO6FZZ3shJ+siYiIssKcZJeXCet5O+HDxYmMVDmdW282e7IzqrKyMu4mEAEIWvjMXF2cJ2tKNx6TSQeMYwKAXqmak52uwmeMZQqLSXZGTZ06Ne4mUEyKNcCilOtkc7g46YbHZNIB45gA6zlZCGGpv5L08zZjmcJikp1R9fX1cTeBNOc16Y6k8FnCT9ZEbnhMJh0wjglQDxe39mQnewQaY5nCYpKdUTx4UNSWNG0t+Hr+qs2eXhdJTzbrH1DK8ZhMOmAcE2AzXNwyJzvZ523GMoXFJJsoY2TgOuDOfnfPm4FeF8WcbPZkExERJYN5newyYS1ymvSebKKwmGQTEYDw1fDXbmoL9LqyAOvHM8kmIiJKJutw8bLUFT4jCotJdkZNnjw57iZQwoRNVIMM+wZgWTvTC3NiLqX1zjlRmvCYTDpgHBPgbbh40m+OM5YpLCbZRAQg/F3loEl2kNcJYV1zM+knbCIioiwwn4/LhPWGeg+X3iTNMcnOqOnTp8fdBIqJ3ajw7pAnvMA92WXBDkNMskknPCaTDhjHBNhUFy9L13BxxjKFxSSbiADEN1w8qtexwjgREVH8zJcTQgiUl6erujhRWEyyiQhAuoaLq17HnmwiIqL49ZpGxql7sjlcnPTGJDujJkyYEHcTKCZ2Hb7mQiV+BU2Wzct6BH0/JtmUZjwmkw4YxwRYe7LLy4RiTnayz9mMZQqLSXZG8eBBZmHvKgdNsoMyVxjnXXFKMx6TSQeMYwIUhc8U1cWTfs5mLFNYTLIzqra2Nu4mUMLENVw8KPOam1zCi9KMx2TSAeOYAEXhMyGs62Qn/JzNWKawmGRnVHNzc9xNoJhIqE9sYQuHlTrJ5nDxZHvt3Q3457Pvomlza9xNSQUek0kHjGMC1Otkm6eGJb26OGOZwmKSTUQA4h0uvuuooaHfj0l2cjw1vxGfv/IpnH/rbBxzyWPY2toZd5OIiKhEPA0X5zrZpDkm2Rk1duzYuJtACRP2rvLXP7l34NdecsZhvl9jPmFzCa/kmDztlf4Ce5u2d+CvTy6Ot0EpwGMy6YBxTIDNOtmm4eI9Ce/JZixTWEyyM2rKlClxN4FiUqzq4ruOHlbw9YSDd/X8WnPVUS/MPdlJL6KSJRubOwq+fn3pxphakh48JpMOGMcEWGuklAlrT3bSR58xliksJtkZVVdXF3cTKGHC9mRLU5K+/9iRnl87qNz/oWj5+u0FX5sTO0oOFqVzx2My6YBxTID1pn2Zoic76cPFGcsUFpPsjGpoaIi7CZQwUfcECx+d01EUTfsbhyQnVtJ7LJKAx2TSAeOYAPVwcevos2SfFxjLFBaTbCICEO+cZvMd7iDue21VBC2hYmCOTUSUHebRS31zss2Fz3hiIL0xySbKGPOw7rzww8WDv7bUy3/FRUpp+/PXmd2ycUREpB/zTfsyYb2Z3sM6KqQ5JtkZNXXq1LibQAkT9ZBeAe+Jc5DCZ2lz5X3zMeqcmTjsgv/h7VWb425OSblNvevq7sXydc1o6eguTYMSiMdk0gHjmACb6uKmm+ldCU+yGcsUVuAkWwixjxDiKiHEPCHEJiFEqxBimRBihhDiC1E20vCeZUKIbwohHhJCrBRCtAsh1gshXhNCXCKE8F7O2P49ThVCSOO/KNqeNI2NjXE3gRKmJ8YiJEEKn6XJ6o0tuPahtyElsGJDCy65a27cTSoppxs4rR3dOOWqp3H4hXU45uJHsGLDdtttdcZjMumAcUyA9cZqmbDOyU56rQ7GMoUV6MpWCPETAG8DuAjA4QDGABgGYB8AEwE8IoR4QAjhvbyw+3vuDuAFADMBfBnAngCGANgZwNEArgSwQAjxlRDvMQrA9PCtTb6ZM2fG3QSKid1pLfRw8RCv1X24+ENzVhd8/ezb78fUknjYLQ/X0dWDXc+9u3+JrxUbWnDV/fNL2bTE4DGZdMA4Jiml5ZjfV/jMNFw84Uk2Y5nC8p1kCyF+COBm9CXVADAfwHUAagA8ACA/3u90AA8IIQaHbaQQYgyAJwEcm3uqDcB/AFwG4I8Aluee3xHA3UKIUwK+1Q0AdgfQGby1ROkU+XBxH3lzRVn4nuzhg8tD76NYzMPkssZuHvpjc9danpv50ooit4aIiIrFfLgXAhCChc8oe3xd2Qoh9gVwo+Gpi6SUh0spfy2l/J2U8gz09Sqvy33/BABRTGq4HsBBuceLABwspfy2lPIKKeUvAYwH8Jfc9ysA3O63F10IcTKA7+W+/H0EbSZKlTjXrCwPMCe7zJTFf+u4faJqTuSiqJ6eZna13n78j1dL2xAiIioq8w37/LnaMlw84XOyicLye+V3OYB8z/QMKeU15g2klHMBnG146jdCiNHBmgcIIQ4EcE7uyy4AX5NSrjC9Zw+A8wC8lHtqZwAX+HiPkQD+nvvyCQC3B21vWlRXV8fdBIqLTcITvrp48NcHmZP9m9MPLfh62OCKwO9fbKrh8AvWbCl9Q2JiN0qCHRkDeEwmHTCOSVX0DLCO6Er6cHHGMoXl+cpWCLEDgDNyX0r0JdxKUsonAeS7KEaib+h4UN/CQDvvlVK+Y/OevQCuMDx1lo/3qAWwB4AWAFOCNDJtqqqq4m4CJYx5Xcuw/A0X99+TXTlsUMHXcRZuc6Oqnn7cZY9j7nubYmhN6dnNyfYTI7rjMZl0wDgm1XxswDqiK+nDxRnLFJaf7qOTAAzNPZ4vpVzssv09hseBi5EBOM3w+G6XbZ8CsCX3eG8hxJFuOxdCfB7AD3JfXiylXOm7hSlUU1MTdxMoYWIdLh4gyU5TpVLVcPHO7l6cf+vrMbSm9OwGOTDHHsBjMumAcUx2w8Utc7JDjp4rNsYyheUnyf6Y4fGLHrafZXjsmuyqCCGGADjY6/vmerNf9vq+QohKDAwTfwV9Bd2IMin8cPHgrw0yZ9nc+53kE7ZdT/0bme/JZppNRKQT89E+f/oz11FJ8o1xoij4ubI9xPD4XQ/bLzU83iPgcl4HAsiXDN4ipdzo830Psd2qzx8A7IW+auLfzyXpRFqTNpOyI68u7qOfMlBPtmXoWXL/fLNe+MwutphjExHpxa4+i3W4eHLP2URR8FMpaDfD4zVuG0spNwshWgCMyD21K4BtPt7P93vmGBek3c1uIyHECQAm5768Ukq50GfbXPnppQlTNCqI8ePHl/T9KPnCzo8K8+pBFXr3ZOu+Drgb257sErcjyXhMJh0wjsksfy1sKXyW4HM2wFjOqihH2PlJsisNj1s8vqYVA0l2pdOGEb+n6vX9ckXc/om+a7y3AFiqpJdafu7H2LFjMWXKFNTV1aGhoaH/+1OnTkVjYyNmzpzZ/1x1dTWqqqoK5o2MHz8ekyZNwowZM7BkyZKC/Tc0NKCurq7/ucWLF2PcuHGora3tf66qqgrV1dWYNm0ampqaAACVlZWYOnUq6uvrUV9f37/t5Ml99yimT5/e/9yECRMwYcIE1NbWorm5ueSfaeLEifxMLp9p7do9MVBeYUB9/fP4zmf2DfyZVg0uPCG9+uqrqFla5+kzbe2qAFC4BFdNTY3jZ2retrVg+ztnLcdfzz0mkb+np596AsBwqOTfT6/YK4yFDRs29LfB+Jm2tI51/Jkk6zP1KdbvadKkSdp9Jh1/T/xM7p+prq5Ou8+k4++pWJ/pG9/+Loza29vR3NyMNatXFTzf3NLS396kfqaamhptf0/8TOrPFCXhtQdVCLEMA1fBn5NSPuvhNavQV7UbAI6TUnqZy218/VkYWE5rlpTyeA+v+R76EmgAeEZKeaJim78A+BGAHgCflFLONn1/bwDv5b+WUvq6rSGEkLnX+XlZSc2YMSPyYKJ0+MxvH1fOBb7h7I/j3BOD37m9+fFFuGjGG/1f/+TkA3DNt7xV59ze3oWxk+8peK75duf4vLV+Kc77V2HhMLfXxOXJNxvx1dp65feS2uYwKs+eUfD1frtVYu511uVQzNvl6fgzccNjMumAcUxbWjqxx4/u7f961PBBWPO3M/HO6i045pJH+58/6MOj8PrVX4yjiZ4wlslNvtfbLk/0M0azzfB4sO1WhYzdZa22W5XwPYUQEwD8MPfljeYEOyuMd56IgAiGi4e4qbTD0EH4yckH9H/9+4nutRK7utMznyvzw8UVsZWm318p8JhMOmAck92VwNDB5QVft3V2F78xITCWKSw/w8WbDY9H2G5VyDg+stl2qxK9pxBiBAaGiS8HcGmANhFpKe5Kn9d8qwpf/+TeGDKoHIfsMdp1e3PhM6Av0U9ixWrVOtlZYjcnm4iI9JY/+40cNqjg+a2tXZZtV2zYjntfXYlDdh+NU4/8cAlaR1Q8fpLs9w2Pd3fbWAgxGoWJ8Tof7xXoPRXbmd/zNxgY8j5ZShmkd50o1ezyne6e+HsWP7bPh7xvrPggXT29GFxRrtg4XuVlrC5uZlflnoiI0stuVNuo4dYk23hjvLmtC5++9LH+5PufP/wUvv6pvYvaVqJi8nPlt8DweH8P2+9neLxGSum3sjgALAaQv/IfLYTYyef7vmP63h6Gx08LIaTqHwzzsYG+OdaGf+f4/RBJZJzsTwTEW108qvfrSmi10gR2rpeUKrbYuV2Ix2TSAeOYzPJJ9OCK8oKpU71SoqunF4vWbsV767fjn8++W9C7/f2/vVzythoxliksP0n2G4bHx3rY/jjD47k+3qeflLIdhcm94/sKIcoAfCrs+2aBsfIeEaCeNxtGsYdtq5K0zoTO8816Qhn3VIQ04DGZdMA4Jqfz3SDTNK+f/vN1HHXRIzjsgv/h0v/OK27DfGIsU1h+hos/AaAdfYXFDhdCjJdSOlUF+Jrh8QNBGpfzEIBDc4/PzH1t53MAxuQer5RSvmH6vtfq5jsA+Krh69sMj5d63Eei1dXVoarKW+VnyoYkDBf3QzUkLW2fIStUa5hn/caDGY/JpAPGMTkZVC7QbpiKPfOl9+w3jhljmcLynGRLKbcLIR4E8E301TG4FMBZqm2FECdioEe5GcCDIdr4HwAXoa/X/UwhxO+llAsV71mGwkJmd5i3kVL+A8A/3N4wt4RXf5ItpTzHd6uJEspuLmz46uKhXu7//RTPsWJ1MvX0Wn8vnJNNRKQfp2N7haJgKZGu/Eb7ZQDy96C+LYS40LyBEOJwDKxtDQDXSik3K7abYJoHrZRLqPP7GwzgPiHEXqZ9lQO4EQND1DcCuN7jZyIiRD+kt9jzkFU92V3syU4k9mQTERGTbMoSP8PFIaV8VwjxCwA35566TgjxLQCPo29N6sMBVAPIlxB8HtEkuxcA+CSAAwAcBGCBEOJ+9BVGGwPgNAD75rbtBnCOlHJrBO+rrYkTJ8bdBEoYVSKUZKokjUl2Mqmri5MRj8mkA8YxmRnrs6RpOUvGMoXlK8kGACnlLbmh2dchNz8798+sDsBZUsqOcE0EpJQfCCFOAnAX+pLt4QC+rdh0M4BzpZSPhH1P3Y0bNy7uJlBM7HoQVUN6/e23tGnTV4/ZCxfeWViYJLk3CpLartLoDhlbWcBjMumAcUx+Cp8lGWOZwgoU7VLKm9BXjOxaAPMBbEFfUbQVAP4L4EtSyi9H2ZsspVwF4NMAJqEvgV8NoBPABwDmoG8o+8FSyvuiek+d1dbWxt0ESpjIh4ujuHesdx451PJcUquLZ516uHi2bzyY8ZhMOmAck5lx6tigFPVkM5YpLN892XlSymUAfpP7F+T19YC/q3ApZS+Ambl/RSOlXAGfbSNKu+T2Ats7cu8dMXfFpv6vOVw8GYRwn3PNHJuIKFvKy9LTk00UFqOdKGNsh4uHzHriyJnM87u2tnbG0Aoyq/BwIcUcm4hIf8az9KAKph2UHYz2jOLaf2QW9RrTxa4uDgBNm9sKvr758UXFf9MAstZr66W4DYeLF+IxmXTAOCanY3uahoszliksJtkZVV1dHXcTKGGiTrJLYc2m1oKvn5rfFFNLyKiizEuS7fS97CXgPCaTDhjHZFZQXTxFw8UZyxRWeqKdIjVt2rS4m0AJ0xVyTnYG8yKyUe4hyXaSxvoAYfGYTDpgHJPTtUCa1slmLFNY6Yl2ilRTE3v8skrazIaNumhYKYaLq8x86b143pj6lXu4kHJKo7O45BePyaQDxjGZFVQXr0jPcHHGMoXFJJuIAABdKVz+6rA9x1iemzztlRha4ixrvfzehovb/1DCjqogIqJ4OB290zRcnCgsRntGVVZWxt0EShivc7Ifn7cWNz66EI2m+dBxzKM979QDS/6eUdNx/rGX4eJOHzuNN3zC4jGZdMA4JrOC6uIpKnzGWKawAq+TTek2derUuJtAMbFLbjo9JDYzXlyOKdNfBQD86ZEFWPDH0zBssPowIkqw1PwRe+9Y9Pcotl4pUR7X2Poi8TLvzm7aApDOInxh8ZhMOmAck9ON4zTNyWYsU1jpiXaKVH19fdxNoITxMkQ3n2ADwMbmDsx8cWD+cxz9sYM1WHNTxyJfqp5sPz32UdcHSAMek0kHjGMyK6gunqIkm7FMYaUn2ilSPHiQWZDEZlHjVtvvlaJzNi0nbMde2179kmwV880Ep5w7Kz8TIx6TSQeMY3I6tvsdLt4b47mAsUxhpeMKlYgiY3fKCjJEtyzmYc5eCmwlXVtnd9xNiJyq19pPxfAszskmItJRQXVxnzfGs7jSBOmDSTYRAfA2J9uszJDkxlG/K+x6zEnw+LzGuJtQEtaebM7JJiLSjdOlwJgdBvvaF1eaoDRjkp1RkydPjrsJlDBBhos7LcdRik7u8hRVKrXzzuotcTchcqr8eXt7l+s2eVm8sOIxmXTAOCYz41n6wzuO8PXaOG+4MpYpLCbZRAQg2Mks7p7ktKy56ZRQHr6Xda1vHa3c2OJ52ywWPiMi0oHTKKXddxzua188F1CapeMKlSI3ffr0uJtAMbE7AQbpPSzMsUvf+1ihQU/2sMHlcTchcqpIeHvVFtdt8hY7FNTTFY/JpAPGMZkZq4v7HS7eE+OoJsYyhcUkm4gABJuT7aQU62SnpSfbSVbu1F9+75sA+m7y3P/aSvxw+iu22/7fXfNK1CoiIoqSc3Vxf+fsrJwfSU8VcTeAiJIhyMks7qWW7Hqyu7p7MSgla2jruE62arTE5pZOAMBLizfgO7e85Pj6Ddvai9IuIiIqLeNZ2u8UsyzW5yB9pOMqlCI3YcKEuJtAMbG7yxxkTrYxQUxSdfEPtneUuCXOnIt8ZetO/bNvN8XdhETiMZl0wDgmp0sBvze/4yx8xlimsJhkZxQPHmQWZG3iHoc1LEtRXVzYvEnUQ9+LSc+ebPvvPTp3bekakiI8JpMOGMdkZjxN+53i9dCc1RG3xjvGMoXFJDujamtr424CJUxngDvGPTEPF7eTpnWW09TWKPR6iJk9d/K3zIsOeEwmHTCOyam6+CCfxUp/d8+bYZsTGGOZwuKc7Ixqbm6OuwkU0rwVm7B5eycO3XM0BleUYdRwb1U77U5/3T0SUkrb3mG71/TvN47x4jbSNAQ77nntxeD0ibzMydt318roGpMSPCaTDhjHZGa8pqjwWfgsToxlCotJdhGpkpUkJSKUXv969l38/NbZ/V8PG1yOaZM/ia8cvWeo/Xb19GJwhfclpbqdhouHaol3FaIX3bLwxJ20HnbpkHZmrSd7k4f58mm6SUJERAOczr46LLtJ2eSnAyovPbeUKFJjx46NuwkUgjHBBoC2zh589y/OFZu98DuXOQnziYcrbhUmoV1e6Vg91elmYuPmNtfXr9rYEmVzUoHHZNIB45jMjKmJ3yW84sRYprDSE+0pJKW0/EuKKVOmxN0EipjX3lunOPSbZBvfM67o3mEH6/zdNPWEOhWP001+GS83qza24OoH3ipya5KFx2TSAeOYnC510zRcnLFMRkFyuvREO0Wqrq4u7iZQAvmtMO5cXbw0w8I62q1rKqdpCHaQqu5Jpzr1VA6tQGdXj+d9XPXAW9ja6i0p1wGPyaQDxjGZGS8F/BY+ixNjmcJikp1RDQ0NcTeBEshvT/asReuL1BLvurqsiVjSiok53fBMWluL5dA9x/h+zYr124vQkmTiMZl0wDgmpx6+cp9LeMWJsUxhpSfaichV2CkJfpfxajLMr41rNsTw8m7Lc6nqyU5RW71SxUJ3T6/vgnRZuQFBRKQr46i2QRX+047Obu8joIiShEk2kUZ6PWS6TptEOXS5RKPF8dkPbbA8l6bCZ2lqaxhbW7t831DQ8QYEEZHOHKuLe1jC0ezuV1YGbwxRjJhkZ9TUqVPjbgIVQVd3yJ7sFM4Pvv7iH1uec1paLGnS1OvulWpExYZt7b6T5qzcgAB4TCY9MI7JLGx18TdXbIquMT4wliksJtkZ1djYGHcTqAjC9vyFGZYVV/X8pqYmfOHIDxc8F/ZmQ9ScfjZZSSRbOrp9f9YsVV7nMZl0wDgmp/NdkHWy45qKxlimsJhkZ9TMmTPjbgIVgZck2+l8FaQn+39zViufL1UN0ZkzZ1qWBUlTcqbjkGhVjHX19Prutb+tflk0DUoBHpNJB4xjMjNOHRNCoNznkPG4RqYxliksJtlEGmntsBYB8yNIwnfOLS8BiG+dbMA6zytNvcNZKe4lJdDh8ybOPa+uxPb2riK1iIiIoubW8+x3yHhXis7nREZMsok08qn/ewzrt7a5b2gjSE+2XWJeqnWyAesQtKYtwX8GpdajY0+2zTVRW6f/m0DPvPV+yNYQEVF8Cs/PfoeM61i3hLKBSXZGVVdXx90EKoLNLZ245YnFjts4zZdKY+Gz6upqvLlyc8Fz055y/hmUmtN9eB2Hi9tp6/Q/599LxXwd8JhMOmAck9sR229P9kd22SF4Y0JgLFNYTLIzqqqqKu4mUJHc8PCCwK91u2Ps1DkdVy5UVVWFxY3bCp5bsaElnsYEoONwcbsbOe1dAZJsDX8+Kjwmkw4Yx2Rmvm7wOyd7+JCKCFvjHWOZwmKSnVE1NTVxN4ESyK0nu6LM+yGjVKPF0x7LUa5NnnTt7Mm2lfY4JgIYx+S+0sigCn+pR1wj7BjLFBaTbCLq53Yy83sHmtxp2ZNt83ygnuyMJNlERDoy12cxFyp1k6Ub0aQXJtlEGeOUs4RJsuNaJzsNnH40WSrq0tHl/7NqeA+CiEhbUVcXT2OtGCKASXZmjR8/Pu4mUAJFWYSrVH3eaY/l1g7/vbtJZ3eRFaQnO0ixtDRKexwTAYxjsjJfC1T4XsIrniSbsUxhMcnOqEmTJsXdBEogtzvGSeytnjRpEq74xhEFz+06amg8jQng1Xc3xN2EkgmSZM94cXkRWpI8PCaTDhjH5MbvEl5xDRdnLFNYTLIzasaMGXE3gWITfAkvpxQ7rvR7xowZOGb8zgXP7bnTiJhaQ4D9zZiOAEn2uhSteR4Gj8mkA8YxmZmLoKZluDhjmcJikp1RS5YsibsJlEBd3cGG5vb0Wk+C5mInxbJkyRIMNp20Wzq6S/LeXkmXWxCqn5+OgvRk7zQyPaMSwuAxmXTAOCa3EW9+e7I7YxouzlimsJhkE1E/9+HiwV5XbDsMG1Tw9eqN6VknGwC6upM3DL8Y5iz7wPdrhg+OZ41UIiIKz1Jd3O+c7IA3/4nixiSbKGMcq4u73DG265Ht6Op1rShaTPvtWlnwdXN7d6p6h+O6U19qLy5a7/s13Sn6PRIRZZ3bpUBFWTqGixOFxSQ7o2pqauJuAiVQ0AIjquHZJRotjpqaGpSVCQwZVHg4S1PvsG7rgEZZIC8rF1g8JpMOGMfBzV62Ed//60u48r75gepXJJX5UmDEUH+jk+I6BzCWKSwm2RnV0NAQdxMogYIOF//U/z2GTds7itAid/lYNhdTiWvZDxW3nDNJbY1ClLc3OgOsrZ1GPCaTDhjHwWxv78IXr34Gd7+yEtc+9Daur3sn7iYF5na+GzdmmK/9xZVkM5YpLCbZGVVXVxd3EyiBghSmAoBN2zvwr+eWRtwab/KxnOQk241uPdlR6sjIfDwek0kHjONg7nppBdo6B4511zz4doytiZZ5VFulqYaKm+6YzuWMZQqLSTZRxjjdZL7jBec1if2NAi7RePEcczGVuE7MQeg2JzvK+flZGS5ORNm1pbUz7iZExm260OAKn3OyNTs/UnYwySaiArOXbbT9nttSVHEaZFoWpKsnuW01Y0+2PZ3mJhIRqfhdOzpNzNXFzUtuuuGNVkorff+qydHEiRPjbgIl1EsBKkDHKR/L5ouUJJ2YMzcnO8Ku7I4E/R6Licdk0gHjOBi/vbtJ5nb0Hzyo3Nf+4roJzVimsPT5qyZfxo0bF3cTKCZuCZDTd/3kTqWqLp6PZfNw8TQlruzJtteZkZ5sHpNJB4zjYHTuyTbzPVw8pvMjY5nCys5fNRWora2NuwmUQnGuhW0nH8uDKjgnOymiDJOs9GTzmEw6YBwHYz5/pZn5OsF8vz0tw8UZyxSWPn/VRJRp5hN3mnqH03RDoNQ6u3sjHX5ORJQ05poiOvPbk52mUWlERkyyiTImTL7ip/BZqS8ZBlWYC58l58Ts9nNL0vzxKESdEyfpd0lEFDWt5mSbTgDmqWN+e+11Oz9SdujzV02+VFVVxd0ESqEkdijmY9k6JzuBjbVx9QNvszfbQUeX/j8bHpNJB4zjYDgn215cN1kZyxRWdv6qqUB1dXXcTaCAmja3um4TZnhtVIm0edmOYsnHsvkiJU3DxV99dwPufmVF3M2ITNTDuzu69S9+xmMy6YBxHIz5JrFOhGlc2+CKdFQXZyxTWPr+VSeAEMLyLymmTZsWdxMooJsfX+y6TXeIXtxrH3wr8GvjkI9lS5Kdsp7hKdNfjbsJidWZgZ5sHpNJB4zjYHTqyXa7+vD7WeMaLs5YJqMgOZ0+f9XkS1NTU9xNoID+/NhC122M1apfWrweDzes6R+O7HYCbO1U9xomtfhUPpbNhWOSlGQn9EdXNFF/3PmrNke8x+ThMZl0wDgOprzMesHe05ucc1gY5lwkLUt4MZYpLCbZRSSltPwjKoX8SenmxxfhlN8/jYk3voBv/umFkrah1AM30rxONjn77d3z4m4CEVHRqK4P01rwy+1S129PdlcPV5ig+AXJ6ZhkZ1RlZWXcTaAiyvdaXzTjjf7nnnizEUvf3+bp9aqDR1LPcflYNlcsvf35ZXE0hxB9rCxYszXaHSYQj8mkA8ZxdNKaZJuZb7i3dHT73kccN80ZyxQWk+yMmjp1atxNoCKyOzmvWL8dvb2FGdCnD9zFsl1PryLJjnwQcDTysbyttavg+cfnNcbRHKJAeEwmHTCOg1GdXdOaZLv18PUGuAsbx8+CsUxhMcnOqPr6+ribQEVkd9e3u1eivatwzvXwwdZKnx0RnNBKNVo8H8trNrlXXY+L12sKXYbEFeOGzJoPWjxV1k8rHpNJB4xje4sbt+KLVz+DCTWP48VF61y3b7epj5I25uriJxy6m+99xJFkM5YpLCbZGcWDh97ebVIPC+/ukegwJ9lDKizbmbcBkjtcPB/LQwel/3CmGkFAfQ7+5UM48PyH8I9n3o27KUXBYzLpgHFs74Lb5+CFhevQsHwTfvC3VwpGlanOr83tXdYnU8DtLLbD0EGO37/xnKOw88ihBc91xzBcnLFMYaX/qpSILK56QL0MV09vL9pNyyGNUJzwVHeNk5pk5w0Z5G/tzSSKYgRBEhQjVqTsG2Z47UNvR79zIqIiq18w0Hu9dlMrltjcDM/b1pbOJNvMTxHUpy79PL53wv6WCuRpHTpP2cYkmygB1m1pw9f/+Dwqz56BA89/EOu3toXa35xlHyif7+jqLRhKLoS6BziKE1qp14UfqkGSreuFxL67RldA5v0tbdoMqyei7DKOGFNNsWlOaZId9Pj83c/uh2P23xmAdZkvXc+NpDcm2Rk1efLkuJtABr+95008NnctgL473Pue9wC6inBS2W6q6jmkoly5PqdyuHhCC5/lY1mVZCclGfP6s+vq1mMOnvnH7nddVDc6DqvnMZl0wDj2zq1idkeXHoml1/vtxiHh5mW+4kiyGcsUFpNsogT4z6zlludeX7bR8pzXpHHHHYYonzcnz4MrypRJdhqHi1eUWz9HUpOxQ/cYrXxe17v1USfZuv6ciCg7jMcx1fk1jmWrohD0rGtMss3njLT+LCjbmGRn1PTp0+NuArlQDRWzW/rCfKf4G5/aS7mdOTkpE0CZ4jZzNMPFQ+/Ck3wsqz5HUk/Me+28A179/Rcsz2szJ9t0mWVewzwsHZNsHpNJB4xj79zOT52ajGwyVxe3090zcN5IwnBxxjKFxSSbKKB1W9rQWMRlo1RDxex6Zi/5ykcLvh5Urp6fbD5RCSFQphounju59/ZK3P78Mlz74FtYv7XdU7vj4rVHPikO2WM0xo8dWfBcMaYIJEHUPdlJvXlCRORVl0tPdpLPX06CjnrrNlzfmG/MpvVnQdlmXbuHiFxd8+Bb+P39fRW8f3vm4big+pDI30N1F9suya4cVlghvKtHfQfcPOe3rEyoe7JzCf61D73dX6n8n88tdW90jFRJdlKSMbuLjiGD9LyQsMzJLndPsvfaaQRWbmzxtH9dfk5ElF1dPc7ZqDY3XT2OautyGi6uy8+CMoU92Rk1YcKEuJuQWltbO/sTbACorXunKGs4tiuKj9kl2YMrCnuuO7t7C9bgHNinebi4sOkB7ntv41JgTZv9VTwvVW3xfCwrh4sn9MScb6r5QqJDk+GBZl56smeefzyO2HuMp/3pmGTzmEw6YBx7VzAnWzGTOa3Th4IWHC2Yk10e/5xsxjKFxZ7sjOLBIzjzsOnt7d14b/127G8a+huWKpGwq+OlGlrV3Wt9fXtXYXVxuznZaTq552NZ3ZOdzMJneeZh/bokj+ZrrEEV1ukLwwaXY/rkT+KFhevwxY/tjo/uOQb3X/BZ3PLEIuwwdBB+d8+btvvXZa6iEY/J2fTKkg14cdE6fP6wcThi7x3jbk5ojGPv3G7O63I+8HrD3diJUJGA6uKMZQqLPdkZVVtbG3cTtGLs8Y2Kahkt+55s69Aq1bZtndbh4mmby2yWj2XV3PKkJ2Pm4eJJ7XkPS9WTXXv2x3H60Xvihu8chc99dCwAYOeRQ1Fz5hG4oPoQjB4+yPKaPB1/TjwmZ8/rSzfilN8/jcvvnY/P/u4JLGnaFneTQmMce2fsndVqTraHbS421ZEBCo/rSSh8xlimsJhkZ1Rzc3PcTUgt1Qnk3ldXRv4+Hd29aG7rwpaWzv7nehS904B1aNVdL69QJtmzl31Q8PXWlk4octNIktNS9YbnY1n1OboT0pNtN3ouCRcSxWAe+qjqyTD3VJgNVqx7nqfLz8mIx+TsuWjGG/0rRnT3SFx619yYWxQe49g7tyTbXEMlrYRitNyUz4+3PGcsfGZdwqv0P4skxbKUEkuatmHDtmQXoKVCTLKJEupfz76LPX98L/Y9737c9vwyAFDOswbUPdyq595ZvaXg69bOHmVPtqqyuV/b261LkBVTWZn1cJbUZCz/Ex9iSiTNIw108cDsVZbnKlR3RQyGOiXZCSloRxTG60s3Fnz9wsJ1MbWE4uA2IietxzkvU7J33GGI5bmkLeGVJN//68uo+vXDOOyC/+HZt5vibg55xCQ7o8aOHRt3E1IraFEPv1ZsaEF3j0Rndy9++s/X0NsrbYeLtymGlquGm6uMHD7Y8tyds5b7a6xCFIm6F/lYVuVsSakubmeHIYVlMVo6SntjoljMfyKqPxnVzR0jp2JpOg4X5zGZSnXMLCbGsXcFPdmKMXK6JJaKjmwlYx0Zcx2POH4WSYnlRWu34p7caMnt7d2YPO2VmFtEXjHJzqgpU6bE3QTy6YPtHbZJdo8imdxsGGbu5FvH7WN57pUlG/w1TqFU86HzsZzGueUjhpqS7PZumy31U64YeWDklGQn/fcaBI/JlPSbgl4wjr1zK8zZmdKbLuaboKriqip77TSi/7GlungMx/ykxPLLi9cXfL1uK4eMpwWT7Iyqq6uLuwmpVaKObIv1W9uV1cV/+PnxyqTDa5I9cph9gakwStUrk49lVZJdjKXVglEHzYghhT/75gwl2RXlwXuyjfG+uaUT/35uKerfeT+ytsWBx+TscfsbSCPGsXcFS3ipCp8l5vzlT9OW1oKvdxk1VLndrT8+tuDrS844rP9xEoaLJyWWhw/hQlBpxSQ7oxoaGuJuAvnU3tWj7Mm+9ltVOGDcKMvz5vl+Tg4YF2z5sdOP2sP2e6Xqyc7H8smHf1jRhmRepOQLwQwfUjgkzusQ/7T59WmHWp5zSzAGORRGy/9ee3p78dmaJ/Czf7+O6mufxR0vLAvX0BjxmJw9QxRL26Ud49g7XZfw2tpaOO3pQ4r51wBwxif2xE3fOxpnHb8PHrzwswXLoJqXJY1jlEdSYplJdnoxySZKic7uXkt18X122QFlZQKfOXhXy/aXzPReqTboxZ6qeEleqecXnnz4OMtzSR9+ab5b36FBNVlVcb5PH7iL5bkK1+Hi9jGZrzT7xJuNWLZuoALsj//xmtdmEsXOabQG6U/X6uLmc4DdDVUhBM6ZsB/+8oNj+pdxzDPfZJ21qHDIdJYMH6zfzbis4BGeKAG8HETf39Jm6cnOrw1dViYwfmyw3mjAul6zV45Dekuc4JaVCUuinfSeAHMiqUPho27TjaBB5WWWXgkgXOGz/O914ZqtAVpIlAytndmZHkJWbjeBk37+stNrumPgdqxXefqtxoKvn3yz0WZL/ZlXIaH0YJKdUVOnTo27CdqxW17Li0oP86JvrV9qeQ/jycupV9lN0IO4U29jZ4mGPhtjeZjpZsW2ti7s89P7UXn2DFSePQML12wpSZvM7ObxDzEPiUvpRZWReW3y8jKhTJjdhoubf5dG+YtPnS4+eEzOlu6eXi1uqpkxjr0rTKL1qS5uvhTyWvjMaMX67YHeW0qJ/8xajh/+/VU83LAm0D7ykhLLqpsU5lGNYcx9bxNeXLS+ZCvnZAmT7IxqbMzuXcGw7A5E2zuC90p4yc+ffft9S0+28eAbZuihak1iLwdcp/dsL9EFpDGWze2555UV2LBtoBLn0Rc/WpI2uclfc+g4XNwcoxXlwlIpFnCvLu40Dy1/M2KQRoWjeEzOlrAJQFIxjr3TdU62+RwgAiTZPznlwEDv/cSbjfjh31/Ff2Ytx8QbX8CbKzYF2g+QnFhWXYlFdYPupscW4vjfPo5Tr3oaP/3X65HskwYwyc6omTNnxt0E7Wxr9VbNW8XrHURzMm5MsoMO+TbvJ8/LfOYkLLNkjOUKUzL37NvJrjg9eFD8a4FGrducZJcJjNtxuGU7tyGEXnqyQwweSRwek7NlUaOeUx0Yx965VhdP6fkgiuHi5jnaR+w9xtPrfvbvwkTxgjuCFy9LSiyrYqMjoti42FC75/bnl2FjM5cHixKTbCKf7K7rwxz0NjZ3eNrOMidbGJPs4ENnVTeaH5q92vV1zkl26Xtl01ZIyDxcPK0XVUbm3pny8jLsPNK6hItbfKhGV/S/1uEGEIe8URqoVoqgbHE73qf1fGCe1hZkuLi5GKvbmuJ5TZvbCr5erMXNLOtnL1ZRvMZNbe4bkWfpuiIlSrCgc5DXb/V2UDt6v50s83CMd4hHDx8c6P0B4CO77GB57nt/fdnxNR+qHOKY1EZ1p9WPpCbZdpcHluHiGizh5TSlwai5zXl6hdMSXk5z19N6YUrZcs2Db0e2r3ebtqFxU6v7hpQoxhuSqnNEfhWFtDH3ZLvMDFIyj8wLem7U4ZyqUqx6DnF0jugsmVekVHTV1dVxNyG17DrKgs5BvunxRZ622230MNvq4kC4wme//NIhvl8zdvQwx97zEw7dLXB7/DDGslMhtiTJ/9bM7dUhQbTMybZJsncZZe3dLnidh3WyVb3WcdzciQKPyQT4H4nxqzvn4GO/fhgfveB/uP+1lUVqlXeMY+/cemfTej6wFGgN0pMd0VSqMOeDpMSycipBkVZvSWvMJRWT7IyqqqqKuwnaCVq0auWGFm/77+px7CWsHGZfKMrNbqOHKZ93GuY1dHC5Y2/jz089KHB7/DDGstM83jy3YjOlZL5br8MJzvzzzSfLvz3z8P7nDt1jND72kR0d9zOowj72BpJs6/fS2nPBYzIB1ur8TjZsa8dfn1wCoO9v4ju3vFSsZnnGOPbu3lcHboqobq6k9XxgqS4eYE62eSqV1+O6+ZIlzLSMpMSyMskOeZ7b2tqJ1Rut155+jj/kjkl2RtXU1MTdhNSy62kIenHvdYhzR1ePY0GRYixntMNQ+8R9SEW5bdsPGDcSu9ok7lEzxrKXJDtJPZ06VhdXFT4DgKlfOhh3nnccas/+OB6/5ETXirNON3DyQ9rUVVfT+TPkMZkAf8eA5euai9iSYBjH/ix9fxsmT3tZeYMkrUs6OtWO8SroKK8op4wlJZal4kwX5jrm5cXr8dGp/8PBv3zI8j0OF49W8K4vIirQHvDi3imZMOro7nXsyTbf+fXivFOdl8kYPqQC29q6lN/73dcPx+oP1L3wuyuqSZeCU7GsvPbOHoxwWB4q75UlG/DKkg04+fBxOGSP0aHaZXdjRsfh4uae7HwvhhACpx21h+f9OCfZ+eri+gwXJwL65lru4DyTol/lsEHFbQwV3Vevr8dymzWh03osi6K6eNA52UMqyrVcf94szLXCr//TgM0t6tVwijUMPavYk00UkaAHdq93Xju7e2Cqexa6uvgXjtzd8ftON6CP2ncnDCpXv2eQk2oUvCTZXu7UvrJkA075/dP47d3zcPxvH8d7NhdBQeV7cc2/+znLPoj0feJgno/nNLfaiVMMOQ0XDzuMjihOfnqSVMf8tPZ+Ftt767djw7bkLU9kl2AD6b3pak6yg6yTba4u7rXmTVKLn4ahXt4t+Hlu3orNtt/j8SNa+kVjggghLP+SYvz48XE3IbVsl/AKeHHvdX3rjq5ex+riQZJst2TYrgbP/mNHoqxM2La9PGBiFYQxlod6GC7u5WT9y9tm918odHb34nf3zAvcPieqmwLmJDVtzMPFg95wGeRhDfa4Cp+9tHg9jr7oEXz8Nw/j+QXRrMXOY3J2OBU381NAU/WX1dyuHnlUKkmM4/NvfR2HXfA/HPrLh/DY3LVxN8ezYi3TVGzW4eL+91FRLgpu8vdK6ameSpTT5pISy1Guoe52fZHWGzulECSnY5KdUZMmTYq7CdoJenFv1xts1t7V45jABBkuPnq483BDu4vBgerY6ve0qyhdDMZY9jQn28PNkLdXbyn4+qXFG3y3y4t9d620PBd02kFSmEMmaCQ4LuGVu9hSRWcpfn7n3zobC9duxeLGbfjJP16LZG1uHpOzQzXNIc/PzVrVXra3Oy+NV2xJi+NVG1vwz2eXAgBaO3sweZrz0pTFEuQYkdaExzziLsiNViGEpTfbyzVWkOsgO0mJZeWc7AAjJ7u6e/G1G+qdt+Fw8UgxyS4iKaXlX1LMmDEj7iaklt2vsdg92Z3dvWjvLHyPYYMH5hYHOZHt/qERjt+3i9j8DTy7uc1e55lHwRjLXu5iB/k9hT3x2MWMqrc2rfPw8szHuaADeLzMyVYPFy/+z2/R2q39j1dubMEqRZVWv3hMzg5zEmLkJ7FSXVNsj7knO2lxPPe9wik4W1rj+fkEufxL6/xY6zrZwU4CQwPMyx4cYU920mLZKMgNmMfmrcVT85si329WBMnpmGRn1JIlS+JugnZKUV28tbOwl2K4oec2SAVPt8I5dseQ/DIPO49UV+ipKC9dT7Yxlod5KXwW4PcU9bJfxp+O+WeY9jnF5pARAfuynWKov7q4crh46X9+G5s7Qu+Dx+TsUPVM5YWN360xJZF5SYtjp2knSdfdI1M5fch8XA5ybQJYE2YvCWCUPdlJieWo5mRPe8r983BOdrTSe/Qhiond3Ss/c+mMPFcX7+pB/TuF8z+HGXqSizHl3+6z5nt2P2xTRTzoSTWsoYPdq4YHGWZVzLUjrct4pfskZxkuXoSe7PyFgHoJr9L//Hj3n/xw6sn2NVxc8QeQxOJecSrlqConTjdWnKRxWUenVVD8CLJWdppvqtiJqvaIl1GTPJdFS79oJIpJ0J5sryegLa1dmPnSioLnCu9yR5/YuhV5sxue/cLCdZG3xQtv62QH6Ml2uioOKciFRJKVYrh4S0e+J9v6vWL//FQXPFxblPxwmpPd0uF9TrVqL+u3Msk2Skq16aCzBdO4HJVluHjAc4B5iUtvc7KjGy6eFKrQCZIMe7nhVKopCrMWrsPnr3gKX//j81ixIdrVW5IkGUcfKrmampq4m5BatolnwAtt811fP4w922U+/5p/fdqhBV//7utHWLaxuzBwO8A3bm7z15gQjLHsJckOMly8qztcT7ZTL4Z5SFzaC49ENVx8xFD7UQmvvpsvRJeM4eJRXAjzmJwdTkn2fa+uDLXvdVtLd+xVSVocq5YQTFJ9HDdpvIFnvj4IOrLN3PPqZSqVl95ar9O/khbLRkGmlXmpWWM3XPzhhjWovuYZTL19Nprbwk1J6e7pxbdvehGvvrsBj81di4tnvBFqf0nGJDujGhoa4m6CdsxFybxyuuDyw28y85NTDiz4esrnrctV9Nr04CYpETTG8jBPw8X9/57cfkdbWjrx27vn4eKZb6BxU6vr/ozXHNae7OT8bIOIqif7xI+Odfz+trYum57s4v78VO8ZRUVzHpOzw+m+qnm0khNVsrhxW/j6AGGkIY7jOMZmqSd72brmgq93GzMs0H6CVBd3GrmwfmsbPvu7JzDmu3fh7JtftCyJapaUWFbOyQ5wDeZlVIfqXLZhWzu+9edZqF+wDtOffhfn3PIiXlmyIfDNqvkrN2PT9oHjVF3DmkD7SQMm2RlVV1cXdxO0U6x1C50Yi0MdvtcYz6/71AE7Y8yIwQXPjRhSYVm32W6eeZJO/MZY9raEl3vb/RZuO3fay7jh4QW46bFF+PZNs3ydfKxzstPXc2Fk6ckOmGQPGVSOI/a2j+ltrZ2xDN1WjUqI4j15TM6OYhazivv4kbQ4Vv2s41gmMeic7DQu6bilpbPg6z1cVjGxMzhIdXGH4eJ3vLAcc5b1VZt/4PVV+N8c5+QuKbEc1RJeXoaLq/Z7a/2ygo6GJ+c34aQrn8Jv/hOsB1o1uiTuVRGKhUk2kU92+VPQk2GY4eLGg9UeO43AxGM/4ul1dsO3zEmq3WdKUk+2UVTrZPstlvP4vMb+x7OXfYDFjds8v9Y8hKsUS1AVk3Wd7OC1Ai796uG239ve3m2zTnYcPdnp/p1RaV14xxzb7339k3t53o+66jBj0ahJMXUpliQ74Gk+jb9P83Bit1VM7Jh7ssNWF6+5582Cry9KyTBlVex0BbiZ5qUnW3V91N6prhPxlycXo83me04GKTox8jc/dMMkmygiQU+GYZLs/XYbWfD19Cmf9PQ6u2JrQz0kqUkW1ZzsiqCVWnJWbiws5OF0gWUZLp72nmxrlh2Y05Dx5nb1cPGW9i4sWru1pHMZ077sGpVOV3cv7np5he33B4Us3JTGpKyY/vLkIstzbQGndsUhjYUwm9sLE69Kh/oaTizrZHs4pquqi29t7VRsme4igcWqLq76GQ8fYv/72+awZOD7W9rw4qL1aDUVc9ymmNOt63ErWORT6k2cODHuJqSW/RJeAXuyQ8zJ/tWXDwn0Orue7KjWmLzsa/Y9kFEzxrKn4VAeDuaq4Ux+fOCybrIw/PyDrAWaZNbCZ8GVlQmMHTNM2Ru1tUU9XPzqB9/G1Q++jf3HjsRT//d5fKhySIgWWBVrTjaPydnQ5hIrYW/YPPD6Kt+vWbelDb+6swFNW9rw69MOxedc6iE4SVocNyzfZHnOrmeumALPyU7h+cBcn8bL0poq5nNje2ewn0XT5jbl0nZuI/KSEstRraIxqDzYSD+nJNsuPt9atRlfuOppbGntwvixIzHr8lP69/Pg7NWW7dM4LcIL9mRn1Lhx4+JugnYCFz4LcQ7de5cdAr3OrqPWS/VJL6r22TGS/XhhjGXhYQKwl5OTnyRblei1dniPBfONjdQn2ZZ1ssONChhhc4L/2b9fd5zl+G7TtqIMB1TOj4vgd8Zjcja4VTb2E0tRVcm+7O55uP/1VXhlyQacffOLgYaA5qUhjuOY3hF0TnYaq4ubR+cFHRlmPTe6/yyUo5s6uvHdv7zk+/2TEsuq2PEyZU9KifteXYm/PrkYW1s7PQ4Xt+7X7hzct736d3LpXXOxJdfLvaRpG/757Lv937v5cevokqDXz0nHJDujamtr425CatmdKuMYLu6l51ZVEK3b5j3Nhc+C8rr2dxT8xrKXO6aqOUN2VJXH/dxlNt/YsBvalhZRVRfPs5sCsPqDVtcCUjNfei/cmyso58FGcBeex+RscE2yfcRSVOXTZrw48Heyra0Lj7wRvNpvGuJ44dotcTfBsyQVGfXKuk52sJNAkHWyVd7f0oZ5Kzb7fl1SYjnoKhrXPfQ2zvnLS/jVnQ049aqnA1cXD5JkP/P2+wVf3/+a8wgbtxE+acUkm1JLSokN29oTM2cp6HCXMEmVlyrYquTf7gAdVU92KZNsv9yGcgP+Cp+pbpKYY8Gpw2kn03Dmpe8322yZDlGtk53nFJNxrHbLwmcURnePc9Rubw8/lDlsD/eshetDtyHJrrxvfsnfM/hw8WRc3/hhPicGvR4Isk626qywamNLoPdPMi+dOlfe/1b/47dWbcG8FdapE17263Sd6TU5dmtvHFM4SoFJNqVSd08vzrzheezz0/vxiYsfwfJ1pUtM7C5ggg7ruu35ZYHb4iUZVLWrq0fd1qh6ssMMgS+2W+vdf95+lvBSfVa3E4px7/vuWlnwvfT3ZBd+HbYn+5KvfNT2e2FGgUQpjRfCFA+7UUR5m1u8r3Ntl7iFnXLyr+eWhnp90q3+oLXk7xn0SLUhhcW5zD3ZQZNs65KiwYaLr1i/3fpkiqhXEfB/zlHNSzdT/YydDlleh3m7reudpmKEfjDJzqiqqqq4mxDKU/Ob8MSbfcsmLVu3Hdc8+JbLK4ovyEFC1au6987e15T0cvJSXXDZXYSZq3kGVcolvooRy37mZKsK1/kZ1TByeOHyJlH0ZMUr2uHiJxw6Ft86bh/l91pi+Fmp1ywNf4GQ9mMyeeN2bFywZqvnfUVdhDMKjGO1oKMLVqasF1ZKGdmNVvNqJ16usVQ/5RUbgiXZSYnlqOqAeAlB1bnMKXa9Xve6jUJg4TPSSnV1ddxNCOXvzywp+HrmSyviaYhBkLlTqgPaMB+VOL0UlVJVV7ZLsoMMFz/+oF0tz5UyyS5GLPtKshW3ef0kXSOGFCbZ5jVG0ybKdbKBvgrjfzv3GPzs1IMs35v29BLFK4or6Pw4N2k/JmdBb6/EG8s/wNpNwXtC3eZkA31F+8IIe9PHaQ6mm6TF8ciAazQnhXn5o6Qznw/LhAhc/HJYoJ5s6wH6g+32o0MaHf6WkxLLUdUB6fYwxFCVNKvqzgxs7y0+3W4KsCebtDJt2rS4mxBK2CWWwrA73AS5sFH1RIcdsn3bT44t+PrqSR+zbGPfk+3/va/9lvf9F0OQWHa70FUVPmuxudjxNCfbYbBg5bDCC9r8CI20inq4eF5S5vmrfpNRVABO+zFZd1JKfONPz+MzNU/giAvr8NT8YH+nbnOyAaDmnje9tcnm+bA1AnbcYXDg1yYtjj9/mHU5soM+PAoAsGDNFtz02EK8sfyDorcj6JzstPXwRTVUHACGmW72eFm1Q/VzdrpR8Z5DL3fSYtkoSE+2l+lVqjWsnWLXc0+265zsdMW5V0yyM6qpqSnuJoTipzBVqQQ5GaruEIZNJr788T1wyRkfxacP3AXXTPoYjj1gF8s2dgc887qUXhy6p7V6eSnnygaJZbfflbmqKQDMWrjO8tzW1k7s+aN7Lc+79WwaE88dhlp7WtI8L9t8QyGqJLsseX/y/aIofJb2Y7LuXlu6EY/P60us27t6cMb19YH246Une/5K9wJFgP3F7/b2cKNhwsxZTlocq35GFeUCS9/fhuN/+zgunjkXJ1z+JBqKnGgHPSMmpbCrV5ae7BDHbWtPtnuvqern7JScO1U+T0osq4ttBujJ9nCDb5vi2sO5J9tbO7oMN6I/vONw635SFudeJfiyhcienyWWomZ3vAlyMlQmoyE/WkV5GX5z+kfx2MUn4ienHKgcqhX1nOzqqt37Hw8ZVIbPfdTae5AkQYb3XnW/dd7/XTZLRPkbLm4dmumlQElSRT1cPC/oMjBRUw1HTNuFMPn39HzrBfcdL/gvWnntQ2+7bhP2ps0nLn401Ot1oio0197Vi6vuf6v/PNDTKzH19tmlbponaVu5wFJZPMRx2zwn21tPtvX3vcyhMG5Ua80Xl7WNnQHiwq3oIgBsa+u2/EycXuZ1uLjxmlMVEW0pmxbhFZPsjKqsrHTfKMGS2ZPt/6CnKshRiuUm7Ia3Bh2qfulXD8Phe43BrqOG4vqzPl7SeXDmWL7ym0e6vsYtKVKtvzxXsfzFBXc0KF/v5y5zUoZBR0X74eI+hyN6lfZjsu5UvcM//sdrni/Sl61rxvtb2lDX4L4GdYvHnmin9w4bk0GTj6TFsWrkQEdXD543jUxqWO5t9EBQQX+e6RsuXvh1mOP2cFN9mqDVxZ1cef98PGda0zkvKbGsrAPiMkVJNezbyyiaXiktxVejKHzWathO1TPOnmzSytSpU+NuQiixzsm2OeB09fSix+faVX9+bJHluVIUvop6neyDdh+NF684FUtvOgPnTNgvTNN8M8fyz049EB/f90OOr3GbzxR2uLs5iXc68ZuX8Op7fbp6L4wsw8Uj2m/QnmzVDZMwVHtbu7kt9H7TfkzWnd0xodXDReYFt8/BERfW4dCpD1m+N2q49Ybkdo8JslNkPzrXPZnPGzPCOgc7aHKXtDhW92T3lPxGfeB1slOWfES1Rjag6sku/Lu4c9ZyHH3RI/jmn17A+q19x2C/P+ZZC9fjy9c9iyfeXGv5XtJi2cgtLmbMWm55zmtCbE7QHZfw8hGf+XOx6ljKwmeklfr6+ribEMqgimSG7nqfa1o+Ntd6YD9491FRNceW3TrQUa2TXUrmWBZC4Lnfnuz4GrcTVOgk222dbEPCWKa4CElb74WRtSc7mjR719HDAr0u6jvkqptsmxyq13qV9mOy7uySBbeCPuu3tvVXwVfdPFPdPIpiBGuLh6G1A+9nfcOgSwkmLo5tpneUespZ0F9p2s4F5puaYY7/wwfbVxdfv7UNP/nHa1i4diseeWMNbnh4AYDgIwa+/9eXLc8lJZZVn8jtRvyFd1pH2Xkd2m2el+08YsZ7fK7PTYNTXV6x8BlpJSkHj6DCzPMJy+kQXlu3IPT+j1MsiRU1u7v4QXuy4xQklt0uXFTDmfz0pPq9MDpm/51DvT5JzCfkqP5Uv/mpvQO9rhRL4ETR25T2Y7LuVDfDAPff/cK13te99sspn/CTbIRdhtAoaXGsvKDv6kF5kispGqQt+YiyuvhQ03Bx4woftz2/vOC9bnliMYDgNzO2tlpHECYlllV/y2sCLCPoNSHe6qsn2/v5Nd9bre7J5pxsosT4x7PvWp77oDl8b1JYUazb63PEuSfmnmu7XkGvhc9Uy3alSZCe7C9/fHfFlsH2b2b+uadtiKCR+ScX1e2w4UMqsOdOI3y/LmiPnB1V7qLrUDcaMN3m2O72u3cbHRSmp88pkfbToafz8E3VDdOOrt6S13jIypxs87lrY4jrMktPtiEm/U7NSzO70JmzbKOv/XR5mJMNANtMNxycplzZHSdUh7V8bPQqfndpi3OvAiXZQoh9hBBXCSHmCSE2CSFahRDLhBAzhBBfiLqRufcsE0J8UwjxkBBipRCiXQixXgjxmhDiEiGEp+4/IUSFEOIEIcTvhRBPCSHW5PbVKoRYLYR4RAjxcyHE6GJ8Diqemx5fGHcTItHVE/3B5q8/OKbg6z98u0q5ndsF4Tc/tTcu/8YRmPL58ZG1LQ5uQ61UF51+hpCbi+C5vdI8giDNJ5xiDRcHgI/v4zzXXsVrESmvVL/Lnl6JrhKuDU+lZ3fMcLshNmywdfUAo2JVN/azV9WhLc03+ozsEgSnZYmitmFbu+e1z82i/j1IKXH1A2/h8Av+h+/95SVlgawwnrUpIhbEcNPKG8aEzm40XiqKhftkrnOS9393zcO6LW0471+v4Ud/fxWrIyqaa64L5PQjtRtp8aWPWTsl+pPsDN2odj76KwghfgLgDwDMXWH75P5NFEI8COA7UsptoVvY9567A7gLwLGmb+2c+3c0gF8KIX4gpXzAYT9TAFwFYEebTXbP/fsCgBohxM+klHeEbX8STZ48Oe4mRK62bgEu++rhtsP6olLsg7iXtQwB4Duf2dfzPr9y9J54c+VmvLhoHb70sd1xvM2QdKd1snceORR//+GnPL9nqQSJ5c0tzutQqy7A3t9iLW61y6ihynn4nS4XRuYINRd40arwWYR/jkGmM7REPFzcLilq7+oJVStCx2NyFrjdEHM7HamORap1ZFUczxQhh4sHrWWQtDi2+ylEXRDR9v2lxDf++DxmLwu2DnfUS3g1LP8AVz3Qtxzl8vXbceRHdsR5px4U2f6fXxBdkm0+LxqHFA+2OdZGedMqabFs9tLi9Th32it47p2+n/mitVvxXI1zPRovzMPFHedk2yTHquLE+WOl6niTtmkRXvm6IhBC/BDAzRhIsOcDuA5ADYAHAOT/Ak4H8IAQwlqy0ichxBgAT2IgwW4D8B8AlwH4I4B8Cb0dAdwthDjFYXdHYiDBlgAWAPgHgN/l9ncngPyNgdEAbhdCnBf2M1DpvLR4fdxNCO2MT+zlus0n9t8Jl3/jCM/7HDKoHFdP+hhmXX4qfn36R21vRDgNF9/a6pyYpsmaD5zv+KouwFRJ9qfG72x5DvDfE20eQXD/ayvxqf97FKdf9yxWGpZ56+2VmPbUYlx4xxwsKuJczzCs62RHZ0iAddyjTrLtpHn0AQXn9nv3MgLmqomFyw7utbP/aRFmfuIxyjnZSWPXYx22uKVXKza0BE6wgeiPK1feN7/g64tnzo10/0ftu1Nk+zIPFzf2dtom2ZG9e3I43TfIJ9gAMGf5B5Fcp5kLnzndkGr3MZc6XySSS3gpCCH2BXCj4amLpJSHSyl/LaX8nZTyDPT1KOcXHzwBQBT1768HkL/NtgjAwVLKb0spr5BS/hLAeAB/yX2/An2J8UiH/W1AX1K9r5TyECnluVLKmtz+zgKwFwDjOhvXCyEOiOBzJMr06dPjboJFb6/E/+asxn2vrrRdz69xUyuOv+xx231MvX1OsZrXr1jD+/KOPUCduOWdcOhuePrSk7DjDkMif2+nnkK3KrpxsYvl759gv5TYBy7VoL3OUbQ70fvtfTD/3B+cvRpvrdqCZ95+v2CY4Y2PLcQFdzTgb08twYlXPFmSol5+Wf48IuzKDlL9vhRzsoHwd+KTeEwmd1GsVHCM6Wad1wQ3SA+TivKiN2A8Jy2O7abuqpb2Koa1m8IN4Y36ZkeXx5FyQY0d420UhhfmqRYFw8UrrOeC7p7eUCMN//rkYtz/2sr+v6ukxLKfz5T/Ge23W/A1vs3DxV9essH+/WziU3VsYk+2s8sB5HumZ0gprzFvIKWcC+Bsw1O/CTO3WQhxIIBzcl92AfialHKF6T17AJwH4KXcUzsDuMBml/8EsHcuqX5PtYGUcguAM9HXSw/0feZkjxnRxEUz3sC3/jwL5/zlJZw77RXlNn98ZAHmrthku48tKettPcq0nvPIYYNc57AWcyhxGpfwsvOzUw/CoXuMxuCKMkuRmyC9T6rX2J38rOtkO58lnUYQ3Pvqyv7Hl/13Xv/jra1duO35ZY77jUOx1skGEjJc3KavxO1OvJQSj85dg4dmr85U0R7duR5LPFwhm4+7Xm/SBZkradmHTfvOuL4ejQEqGCeN3d9rqXqyw4q6J9vuxnBUzPH05Y/vEXhfw8zrZHd29+9/1DDr+vKNm9tCdYL86s4GfOeWl3B93TuB9xGlhuUf4Ld3z8OT8xs9vyY/x/7o/YKPKDDfoDNeg5i9vHgDHpq92vJzV/0W2vvnZFu/a/zd6sTTX5sQYgcAZ+S+lOhLuJWklE8CeDX35Uj0DR0P6lsYaOO9Ukpl5EspewFcYXjqLJvtZkspXc8aUsouAH81PPVJb82lMP7y5OL+x/e+utJyNw0A/vaUc/XuUtwNi/IwsMuowtIGvzrtUNfXdHQX7zPqlGTvs2slXvn9F/DBv75pqYbuFid2PTvmk4DdUES335H5PkrQpdOWvd8c6HXFZC18Ft2+g8Rn1L39QXuyf/OfN/CNP76Ab980Cz/6+2uRtonic96/Xsdjc9faft9t7u/EY/e2JtkRLGfT6nEfTte1n7/yqdDtiJvd/SzzUGSgOMsIhb2f1t0jbUf2BRGmboQX5nAKUyKnorysoMCZlAMj6lTnzNaO7kiuzy6/d777RkX23vrtOOF3T+KGhxfgvy+v8Py6H/29L/0Kk7Aaz5lebgh/+6ZZmLXQfapmZ+7moeoGl/F3qxOvf20nARiaezxfSrnYaWMA9xgef8V3qwacZnh8t8u2TwHYknu8txDiSIdtvTD2dPsvaZtwEyZMiLsJrra4FKdSSds8MnOS5mWIT2cRe7LTuE62l1j2W73brrq4eQkMu+tnvxdGQW9ubE/BcPEoq4sHic9SDUN75u0mx+8bbyLOfOk9S/KfhmMyWTVtbsPX//g8XrapB+LWYzq1+pDgPdkOu27zuCauXU8vAKza2OL7nJq0OLa7Eaq6oN+8PfqRcFFUMe+IMPkodk+2+aZS2OP/8CHm3mz73tAFa7ZE2hsaZyz/7p55gWLn9aUb0dHVg5kvrQj83sabTV3d3tpw9i0vFnytanp7V19Hhd3H0rHCuNe/NmM30Iu2Ww2YZXgcKNkVQgwBcLDX9831Zr8c9n0NPmx4bD8hIaWSdiJUCVIIobWzBy8tXl/U5XTcjnt+qpaaL8Dyw5q/dox98bNi9mSPGOJ7wYHYeYll87Azt4O53YXxZf+dV1BYxOkk+N767bbfMwuaZEe9PFUUijlc3Os67kb548i2ti5cMnMuzvvXa1ixwfvvxquG5f6KG23YVliVPg3H5Cw76MOjHL//03+9rnze7UL5gHGjLFWUvY6+cEoovJ4/3c5n/35uqaf95CUtju1+/tsVx85i3LSMIsmOsod9sM3SV1ExH//DLvZimZed+x2pfqz3vroSKzdEs4wV4D+Wu3t68fv75+OE3z2Bax98K9S0oDBLobnd8HVjHC7udW3tD0zroauOTR1dPY5/DzoWD/X613aI4fG7HrY3HpX3cClEZudAAPkzzxYppZdV143ve4jtVt583fB4lu1WKVVbWxt3E1wF7ZU+5fdP49Srny7ZEh1mfg4U5gNRWe6u70Vf+Sj23XUH5WuK2VtfqZjnlHReYnmnyqEFX7sNs7ZLsm95YjHOueWl/q+dYuzOWcv7H7tdZwUdQRB1Ua8oFLMnO8jNiOdz1Vd/edts/Pmxhbi1fhm+8ofnAvd42L1u7GjzqpbOZr5UWBYkDcfkLKsod47jd5vUK5Z6mftrjuvNLZ2hz19tnhN15+9feGeDr/dNXBzbfD5VQr094jWjgWiWCouyDot57emomfPKsMd/yw3y3PWP6jjc0tGNhRGturG1tdN3LD86dy2uefBtzF72Aa68/y08/VbwZDdMwvnD6a+6b+TAeOzwmmSb2c3JdjoeZrknezfD4zVuG0spNwMw3k5SL8ob4XvmrLZ5vS9CiJMA5Beb6wbwr4D78fyv1JqbkzeX0yzMQea1dzfi8Tft58mF43zS9HOgsOvJHj92JBqu/RI2/vMbltdEOXTMzCnJ/sUXD7b9Xpy8xPI+uxYOw9/c4lxd3CkBe/qtJnR254es2e+jzPHvuvB7bj20dkPPm5PYk13EOdlBbkbUL1iHA89/sGBe29L3mwNfjNmFht8bVNNM9SWc4vjp+Y24ZOZcvLBgne02VFxBcyWnzqwDxvX1P5gTCQCo97DesFOTPM/JjnjRo6RdW9j1nHUrqmwXY7m/KO71R9nDN2aH0CvrOoq+J9uUZOd+R6rf67wVm8O9mcGZNzzvO5Z//I/C5PbiGcGXRwvzY9scYKqlUYthqknTZuvSpUE1t3U5Hg+jqEURhShzN6+3tIxXqF7HYrQCyC/2GKSWfND3VL3eMyHEbuirQp53k5TSS+99KDU1NQCAsWPHYsqUKairq0NDw8Ad5KlTp6KxsREzZ87sf666uhpVVVX9rwWA8ePHY9KkSZgxYwaWLBm4iKupqUFDQwPq6uoAAPX19Vi8eDHGjRtXcLeuqqoK1dXVmDZtGpqa+u7CVVZWYurUqaivr0d9fX3/tpMn9xVdNy5zMGHCBEyYMAG1tbX9Byjvn2l8wc/kb9P/iUeGtRV8Jj+u+edDeH2nDZF/ptVtwwDYV8x8Z9ESPPvIff1fO/2e1jYWXkiVl4mC31Nu64JtNm/djrq6uqL8nr7/g3NtP1fjG0+gZuGDrp/JLfYAYOLEiZHFXn19PWpqahxjb1b9MwWfZcWGFnz64oewX/di7D281fKZWtv2xcBAGqvt7d14b/E8LFy0CIB6xMFbrz6DuqGrUF1djefqnwNQ2NNp/Ezzt42E033B62r/hM7WbTDHwrKVa/t/B1EfI4Bgv6f/PjwHwLj+7Tva2wveP8wxYuH2SgBjbX9OdtYqqiR//Yp7cNqujRhUJn3F3unf/I7yPdq7elw+U+HvbntLa//Ppbq6GgCUv6crbpmB63J10v782EK8ePkp6P7gvaL9PeWFO5Yn6xgRxWda9/5eAJyXTTR+1vxnuu2/zwDYXbl979a1/cdys9vql6Fs42LHz7S2fSiAPZX7buvs8fR7OvnULzp+JuPn8vJ7qq+vL9r5KUjsrVm7JwZKCjmb/q/b8cyIlkhjb3nrCBTOPvTvjzfehO9+9eRI/p7mvPwC+hbhGfDXv03Duvej+T01lu8B47nurbfeQk3NU4GPEZs2FO7vpr9Mw9ih7Vi0fQcYzzMAsMllaU4/XlmyAdvXzgZqajzH3tbWwmP8ynWbUVNTE+j31NnVBX8LQEVn87bt/b+TR9bvhr4a1u5qamr6P9PChdZro0effgHb3rgfwP7K1//937fjhkt/Fvv5KUrCy5A5IcQyAPvkvvyclPJZD69ZhYFM5DgppZe53MbXnwXg9tyXs6SUx3t4zfcwkCA/I6U80ed7DgPwHIBP5J6aB+AYKaWvv1whhASKv55yGNOmTcOUKVPibkaByrMLk+j7L5iAzx9WeBD9yE/uw8Zmb7+O7352P/z5u0dH1r68Fxetw6lXPWP7/bnXfQn77ebtoPSFq58uqMr4yG8+h+MPLhz4Yf65jBhSgff//nUUi/n98p645ER86oBdiva+QXmJ5a2tndj9h/dant9haAXeu/mrljmRu/7gv47rzL5zw2nYc6cR+FptPZ54U728xg1nfxznnth30v3PrOX44d8H7nJPPPYjmD5lYNGCu156z3bZOgBYfvMZ2HnkUOXvpvn2aE8KYT34+iqcdfPA4f7LH98D//nZcZHs+6n5jTjj+vpI9pX33c/uhyu/eST+/OhCvLJkA776iT3x3c/uZ3uX+v0tbdj/Zw9Ynp984v6oPfso5WuklBj5nZkFz40bMwyLbxyoC2oXx5+teQJzDPO9P7H/Tnj60pM8fTaKzlEXPYJFLqMfVH+LT7y5Fl+rfV65/VeO3hO3//TTAKzH3S8c+WH89xefcXy/V5ZswEk2FcCP3m8nPHOZe5y0d/Zg5x/813EbP8eYpF1bfPrSx/DmSm89nP/60adw5if3jvT9H5u7Fl//o/r379XzNSfjY/tEU3/3/+6aixsfXVjw3Hu3nGGZUhWU9Vy3N6ZP+VTg/Z161dN4cZH1GumeV1bge3992eGV4U09ogU1v7TveDAz/w3vPHIolt98hs3WznY79+6ijKzw4sAPj8Lsq/tuvh35qzos9biKifE48Y0/Po9HTasuTDx2b9SefRTGTbnH/FIAwOOXnIhjE3iN6SR/nSClVF4weL1NYhwv4HWsifEvNshiiyV9TyHEYAD3YSDBXgPgK34T7LQ4+PjT8Y0/Po9f39mgLACSBKohUn7mZBarqrDbvRM/72seLl7mYWxVXMUhzGtNJ4WXCzrVcEygr0d6UaP1wtltiF++krDXwkNut9vc4vp5h6GjxSjiFYb5s0Y5XHznkdFcCBr9+7mlOPJXdbj2obfxwsJ1+Pmtsx2HHdr9zp2miaiGNp50eOENRLs4nmMqqPbau17Kk1DUgt40d5qD6PSn4WUupOPxJ6bh4qo4XrBmC95ZvSXS9/HKT+Ex1YiXUr6/nS2t4Yb/5q3f2mZJsIFoq6pbj/8hq4sr1soGovm5uvnKGV8N9fowl0wxzCLtZyzuGrSIsOq309LhPCe7VCuBlJLXJNt4G2OE7VaFhtu83quSvacQYhD6lgg7NfdUE/p67Fd43UeabGvrwunXPYNH567FX55cjGsfejvuJimpCnyt8XESDFqwISw/VdHN81NUiez5Xzyo4OsLqsPW9AvGyw2AOBQOrVcbVF5mO0faPN95c0un642Mc6e9gu3tXY7JuFPhIb/rZF9ft8D2e3FdvNoxX/hHGTU7DC1O0Z71Wwsrff/6P/bFnuyu7ZwKEqrmoZn34yWOKT5Br+mdLhy7HQ4gh+01xnXfjnOyPS7hFTVzHP/hf2/jExc/imMueRRXP/BWydvjZ070pf+dV4T3VzfATxLlVqjTq2seVF/r/eOZ6GZERr2E14ihhbUu8p1Cpahr+9zzL4R6vZeih3aca7o422snrymT2rot7f3XRarr6D0+NNzynBdtnd2ON0e81pFIE69JtrEbRT25yEAIMRqFiXGQai2+3lOxnaf3zCXY/8XAmtzvA/islHKJ/avS7e6XV8B43v/TI9Y7m34tbtyKc255ET/+x6tYtyWaQgnmnqHpT/v7lRSrCneUPdmdpuW4Bikq2P7s1IOw/9i+4ef7jx2JySeq57MUW5iDfjEZ597YEUJY1tvMMyfUt9Z7W7LmP7OWO54w/NxsUf3eVVS/gqTNSnnGtPTIfI9DNb0wL+dSLGs+sC8DYvfjfuD11TbfUV9om4cCeoljik/QnrNVG+1jyXjsOe0o+zofQXjuyY74+GGO48vvnd//+KoHwi1rFITf6t5+ll709v7q53ff0Xui0hzRKhJ/t0mm//Lk4kj2D1iPj2HvzY8ZUTiQdVOu170U0zFnvx3u5kOYJHtQiPXMD3RZbtBNr5T9MXf8Qda61V7Ow3bV39mTrWbsRvFyhb+f4fEaKaV6bQtniwHkD0+jhRA7+Xzfd9w2NiTY+Ylx6wCcIKWM7oiTQB80t7tv5IOUEt/44wu477VVuOOF5Tj/ttmR7Nd8spt6+xxfr+8sYhVuJ36qi5svtFXrVO88ciheuuIUzLnmi3jx8lOwyyh/SwVFJalJtld2Q7LNv6+XDPO/nKzd1OZ4AefnhOF2EfWZ3IluB0V8lGLYnB+3P7+s4OvlEV60DgmwTnYQ7Q5L5thd3DmNnFH9jsJM07GrNk/FE/TPbP02+/Ot8RhxzP6Fxajs1spu3NSKZ99uwra2Lsc2eT0PeflYQYeMqpR6mR6/v7bL730z2ve3+SV1dPfiK0eri9aZlaLq8nURjWiMuifbXA09X9ysFD3Zy1vVRU29CnNuDrJcZZ5dnRg/8iPxPlRZWOzx9KP2UF6nAoXnJdVHf3nxBpcpLtlNst8wPD7Ww/bGKjeBathLKdtRmNw7vq8QogyAsbqC4/sahojnE+z16Euww3frJlzUx6aVG1uwbN3AcKaHG7yuuOasMeTSAcVKst3msPk5UJgvpOzWsBw2uAIHjBtV9DUuAWCkzVJECR0t7pndvOyW9m509/RiwZotOPWqpz2foHqldDzRGwunud11HzXcefmn/N3fHRS/m7jWg49DqXqyg46Csfs9q35FXofzfuqAnS3PRdWzRd4F7Tlzmu9q7Mk2zz3965PWkVvvrN6Coy9+BKdd9xw+ecmjBXMnzTwn2R4+V5QFmEp9Ie33+HjvqyujfX+7JLurB9dM+pinYeOqIpyd3T3413NLcWv90khuglxx3/xIevGj7skeMaTwnJePn1Kc9wYJ7z/Xpe9b+xLD9GS7LetZbHZz34/Zf2fb61Avf9tO566oag8kidff4hMA8rdjDxdCjHfaGMDXDI+tZVi9e8jw+EyXbT8HID+JaaWU8g27DXMJ9j0ATs89tQF9Cbb9xEeNRN3xVayOtLsN69oG8d56//OY1m9tw2/+04CTf/8Uau6ZZxnO7UV7l/cLkhbThXYpkmg3t3z/E8rnk1r4bOrUqZ62s0vQzrr5RYybcg8+cfGjBVVM3fT0Ssc71U69D+a7+6ohWUb5i9zhis8Q5GZS46ZWxyHRSTUsxN19P5zm5Dsd7/IXwx80d+DL1z6LyrNnYOKNLyh7nls6Cnuy7eL40D1GW55rbktmsUqdeTnNqRJWp7XsjTdzvFyg/u7eN7G1tW9/qza24JYnFtlu297V4ykR8XL+9pNkG+NY9fO4+oG3SnpjMO6RPvZJdi/G7Tgcfzv3GByxt/P8e9WoqB/87RX8/N+v47x/vW5Zn9nOoHLnS/4//C98b7alJkfInmzLOtmd3bj2wbfwk3++Fmq/Xhxz9Mddt3ln9Rb88rbZOPJXD1u+FybOB1dEd6472VRk04v/zHoPgPVGQXmZwAibqXfGKSp2n/y5t+2LuE5/Sr9Zup6SbCnldgAP5r4UAC6121YIcSIGepSbDa8L4j8YGDJ+phDiINVGuV5sY5vucGhfPsHOz8HOJ9iuw8t1UeFyoPVrsGLuSBQn0a6CoSf+97dig78koq5hNfY97wHc8sRivLx4A2rrFuCeV6x3td2a4udOvbnHLMwQoah87qPqdYiTWvissdFbz7NdTzYQrHel1yXJdloCzMztb/LOWcshpVQO0/Lby/T3p5fgoF88hIN/+RD+9Ei67iuWlQlUFqn4mZHTjQunP/9pTy3BorVbcfsLy/DcO30XEw83rMEVhnmpedtNd/Tt4lg1pzOpK0LozMs5SPU379TL2GH43r67VVq+f5tp6sVjpiVxjMs/qnipC+HlzOrnGON2PP77M+/iT4oK18US92wauznZHbkb+JM+vQ9mXX4qfnSSfd/Vw28Ujg7s6e3FA6+v6v/6rpdXeIpPt+k2d7yw3HUfbszNCHvZYD5v//XJJbjyfm8F9OymuP3gBG91bdZ94LxkX0tHNz5/xZO2c917XH4nG7a14/F5a7HBNKXkubffx0KX5QL9OGrfD+GAcd6Wlc27vq4vJVIl2Z56sm0+u1PH1SGKG8pp5yfbugxA/sz+bSHEheYNhBCHY2BtawC4VkppqXojhJgghJD5f3ZvmBu6nd/fYAD3CSH2Mu2rHMCNGBiivhHA9ar9OSTYySyvXSSqA22YIhKqhNpP0Scn+eFwz7zVFOj1foqs/Pzf1rnkxvUevfLTy9Rtal+FxwJYxWSXjCZ1TvbMmTPdN0L0Q417pbS9gAKiHxb54OzVyqkKdnM37fzy9jnolRJS9lXTjXK+ZSnEVZMgz+lY+du75+Goix7BZaYqxdMURRvNvze7OP7Hs9YLuG3syS45L/eNVX+LTvPnjaNdjt7PWnbmp/98DZff+ybG//wBTLzRf6VjL8cGT8PFDTeE1m9tw+xlG20vlmfOnIneXokn3lyLp23O27+9e57re0alFAWyHN/f5jaGuVkXfvlQy/zXvLWbWjFn2cDSfaqbgF6W9oyyd9SO+cZz2OsG881IP8psMpzRIwbjRJvOBKMF7zrfdPjvS+85Dn+2Gy5+76srUHn2DOzz0/tx5g3P44gL6/qnXLZ0dOObfwq3rrpZWZnAwbuPDvTaoEm23V+dUzzEVUepmDxfdUop3xVC/ALAzbmnrhNCfAvA4+hbk/pwANUA8hMonodNsuvTBQA+CeAAAAcBWCCEuB99hdHGoC9h3je3bTeAc6SUdreAbsFAgg0AjwI4RQhxiod2TA9YwC0V2jp7Ag9VVvXmtXV22xZHULE7EW7a3olRwwfjpsfth8U5aenowchh3u4lme8mBmW31ubW1k78311zsXJDC3526oE48bBxygNY3CrKyzC4osxywEtoju3Zhm3RVL3Pa+lwXo6iYOhUBNd5Z9/8onLosJ8ec5WtbZ3YqTK69acnffojmPHie/1fn3SY+8WMH6NHOM9fTwsvF48PzVZXLOdwcaslTduwasN2HH/wrkVJJrwka0vfb7as5e60TJexwJ5qKggA/OF/fT1KQWqdeCm+6G24eF+8zX1vE7587TPY0tqFI/feEc9cdpKyCvL5t83Gv5/ztkpDscU/XNzbdjuPHIrXr/oCXly0Hqs2tliWE/vlbXPwwuV9l6pdPerKzW43kktRONJa+Czc/sJMaxIQUKV7QvRNizvg/AcdX9/Z6/zzcltSVpVk9/T24ld3Fs5m3dbWhf++9B4uPuMwPNywOvQ53UwIYanS7sXqjS24tb5wNE1ZmSiowWTk5aZep8NNxyhrPySFr6xKSnlLbmj2dQCGoi+xPlyxaR2As6SUHWEbKKX8QAhxEoC70JdsDwfwbcWmmwGcK6V8xGF35vE43/HRlHsBaJFkq/7wWzq6AyfZqv21dfQA1tFvtuzOg/lE5VmHeRxOtrd12RbyMnIrdPT2qs343b1vYvjgCpzgcgd0S6v6AvjqB97qP2C9+u4GLL3pjEQm2UBfER5zkp2UtgW1bF20S7Pc8cJyfHyfD9l+f86yD9Dc1oVKRfwF/Umq/kzMc3v9inrZjErTuqafP8z/fDAnQwfFW7cgqkt2L2uCfvumWcrnw/Tu6OjxeWsx6cZZ6OrpRdU+O+LZy06OfHqLl2TphoffwT2/nFDwXIdDpXrjeacYCZCXGPMSz/l4++3d8/rPb3NXbMJdL7+Hs47ft2DbHonEJNiA/yrUUY8m8zN1bpdRw3DGJ/bCU/OtQ+7nrtjU/1g1+qjVwzXXkBDLQnllLXwW7uf59U/tjb8FnKtbVgZAcXoTAMbtOBzzr/8yDrvgf7av73JJst0+m+q6dv3WdmWHzt2vrMTFZxzmuLJFUALB6v2cdbP1/FNeJtBkU5S4sNirep/bHW4Qt2h4XvP9FyelvAnAoQCuBTAfwBb0FUVbgb7lsL4kpfyyQ2+yb1LKVQA+DWAS+hL41QA6AXwAYA76hrIfLKW8L6r31Jldkh2Usifb53BxuyFVbZ09eLcp+L2N7R4/l1MVSCklvvXnWXh8XiPuf30Vzr/1dcd9tdm85y1PDKwM19bZgzueX6aYv5SMRHaY4oCclLaZVVdXx/bec5Z/YPu9ts4eHH5hHZbb3PUNQnXB5ne4uFnUw9rNx4Oow8Zpbn0pRNUx1tHVi6/84bn+QjB+4jiqUTe6+OVts/treDQs3xTJEjZmXnqyVTeDX1psP2/aOMQ3bIEoFS9/257mmueOMfk6A3n3v7bKsm3L2E9ZnouT3+Hi3T0y8OoCKkF60t16pFVTELycB0oyXDzinuxDAg5zBuyvWfJ/azuPVA/Pzxs20rkgXZDPZld/JV/ZvRg3QsrKhK+RpXkNyzdZnisvE7Y/t/aC0XvquHcaXq9jT3ag36aUcpmU8jdSysOllGOklMOklB+RUn7TpSc5//p6KaXI//P4nr1Sypm5BH5PKeUQKeVOUsqjpJRXSClduzqllBOM7+vz3wov7UyDqC/U1T3Z0fyxtHV244aHgxdn2t7Wha7uXky9fTYOOv9BnPev15QnKKfTYOPmtoK1frsVQ7WMvPQeAMBvZhQOGSovE0W50ArCvJwMkNzCZ1VVVXE3wdaGbe244j5r0aug1D3Z4S4Io7ygBFSFb6KNG7sk+4efd1v0IhpRzvF8+q0mfP2Pz2Nra6cljp3qSaxOYWX4Ylr9QeGwzRcWrov8Pbx0SB57wC4FX7v9bXmZRxuGtznZ7vuxqwGgOtf9/eWNii3jE6QGq1NF+FK8/042c7PzVFPS8gnKzY8vwkenPoQzb6i33IxTFak1C1u0Nuqe7GGDyyO/UZvfn1vi2SOch1h7aZZ51IFdjYb8zRgvvyO/yoT9PGq/+qqLq0eHehnm7lS00+u1c5rEuxAbxUK9nEzw4FadpH33ZNsOF+8JVeSnpaMbj85dg+lPv4s1m1pxa/0y5VA2pxOL32HSXte/Dfs+xfRBs3WmR1J7smtqajxtd/WkjxW3ITbufXVlZEOMVQle2J7sTQ7r+AZh7ckuTZL9q9MOjfR9rnvobVSePQOVZ89Ag2HEQtQzPNu7enDb88sscTx/5Rbb12xv0+9iJEpR3zgCvN1cOXj3Ueju6cWCNVvwQXNHwQoZ6n1G1Tq1qEapPPi6tccaCDfVxE9R0jCC9CQHPYerbGnxf3zdf6zzuO+bFTVqWju6sXxdMy6a8QZWbGjB4/MacdNjhdt5mZIQtmht1HPghQjWCwv0rVCzyyhrvZH8Gcnt3PT+Rkvd5gKqegRm5p/nv1ymUtw5K3yFd7MyAXQFWI5WpbxM2FaM97KEl9OKCBwuTlpQ9TxH3ZP9+lJ/d7PtjsutHT2hhoc2t3fhyfmFFU5/efsc6/sHfgfr3dCgd+OSlGSr5pVXJKh9Qfz4pAPibkI/1bn9vFMPdH2d6u/Ezw0yVaLw+wei62kHVD0Zke7etkjbsMHluPZb0d1IMY5AmFDzRFGHaK/fat230/WfjsPqotRRhCq1Xs4RtzyxGGO+exc+cfGjOPJXdZizzH46SSl4Gi7u4ZPtuIO6ZzVMEh9lIuskSM4X9sal0f/dNdf3a8rtymLnzFtpTf5aOrox3bSKwR9NSzR6GS4e9rOHmd5nJ2gvbHePxD67WG9YGJNrp86DDtOc7Oa2Lpx/6+s49aqn8dDsVa7rjgOFQ6gB4JoH7Rcz6u2VeGp+sJV0nAghcGJEtVHKy8psb060eZiTbVc0DeiL4bhXA4gak+wMUq3dF6aQjurO5X9fWuFrH/ZzsrtDrR29va0by953nxPr1JPtdiFhPgEEvfBIUpKtMrgElUmLqaxM4PSj9oi7GbZ+evKBymV8jDYqRhj4uShSnb9eXrzB8+u9sPwtRdyTvdsY9RJeQgj8+OQDsfGf3yjK7/kvuZoKm7aHrudp0aa4Med0YanjsLooFaMn2+8w2s0tnfjZv53rd1w18cgwTXKliiszL9e0dr2bXvZvu88SxXCQntWobmKpRoRFYajiXOzlPODlGiNsUUVzNWq7Zdz8CNqTDQDtXdbPYzwlffUTe9q+tluWFYz8/NMjC/DPZ5fixUXr8Z1bXsI6xc1Rsy9d86znOHCq3xBGmQj3Myzcl8BZx++j/F5hku3/766nV2q3jFe6r5opkFL0ZA8bEk2BjdtfWBaqJ3t7RzdGeGiL0+Fgs8tFtXn+ctD56EmvGDykBEVTghg/3vtc3NqzP17EltjLr/fuZNyOw/HMZSc5zsdTJXjPL1inPKFt2t6Bs29+ER/79cP465N9CaKXXquwzO8Q9a2jUTarBeTfZ8igcox2Wa5k7nVf8v2+t9YvxW3PL8MJv3vS92vdtHb0WOLY6ecWVc0LXRVjrrM5rhf96XRMm3yM42veW++8osG3jlNfrEYlqqWA7OItTE922FoSXplvjqjqjZhF1ZPdtNl5iaegxo4ZbnmupaMbQ10+m5cbDlH24gPA4sbwPdth5hPPW2Ht9TceW932/ZcnBwrWXpdbTg/ou+79i6GYrZ2Fa7f2DxF3u1F3yUz/ox68sJuT/aWq3X3vq7xM4PSj1Dcmbnx0Ifb44T046qJH8M6aYLWvk34d7BeT7AyKurq4ao73a+9GM1x81sL1lnVH/dje3oURQ92X8HI6+G12mVNlPngtX7+9aHew41SKNTaDmDRpkudtdxk1DNtum1jE1qh5GU2RN+8P/qulq5Y4+euTi/HA66vwbtM2/OrOBix9f1ukc0BVy8gA1r+lqAvmqZZEAwp7J9xuzNlVeHWysbkDP/3na75f50V7V48ljp0uiEuVoMRl0dqteOatJtsYcxN1xXzAGtdDBpVh7GhrsuPVmcfsZTsMOyreCp+5HxSesVlCM9xw8VL1ZBd+7eV6IKqe7CDHGS9OPtw69Hf5uu22a63nNSvq2xz44VEFXydxKoqXGyN+GIc77zDU+Wd2ycy56OntVa7X7XWUxOX3vgkA6HCZF91YpJsyQqjPiedM2BeXfe0wfPFju2OszQgxs/IyYTsXfe2mVmxp7cKitVsDT68q1XGhVJJ51UxFFXV1cbvlUvwURXE6VoW5ENne1uXpLqhTD9/mFpeebEVP+eEX/g+vLIl2GG7cvMw/isOMGTN8be+3ENf+Y0f62l7F3LPm1IZRwwfjvVvO8LV/1R1w89yv82+dbXtR4Gco7Mbmdpx4xZPY8Xt34Rt/fN6SCBW7J3sHm4tk49w6tyVwyhNWxK+lo9sSx07HRN0uRIzqGlbjqIsewel/eA47fu8u1+OoKklsVFRfjpqAwAiXC3Q7Y0YMxuXfOMLy/GcO3jVkqwpFVV28bzvFKiKherJLE8Pmdjst15n30OzVkby3l/cKQjWP+Pq6d5Q92e+s3tL/+K1VWwq+d/Lh47CLqRMjiccWtx56OxedfigOGGc9fxt/fE7r2Od96ZpncdAvHgrUhrzunl7XNbCLdVOmzKZ43IghFbjwy4firvOPx1c/sZenfQ2KeB15syTe5AkjmVfNVFRR92TbFXJ4cZH3ZVScktwwhRCWr9/ubbi4w1u4VV9WXdBvbe1ynY+XZJNP3N/yXFKWFzNbssTai+vGT6/8GJehx174rZBvV9zLjrmKcV2D9SKxb1i5+vVOxUjM7nppRf9IlUfnrsU9r64o+H4SerLdej7KA64bWixtHd2WOHbuydbrQsTop/8sPG6edOVTBaOlnn27CZ/6v0dx0pVPYcGaLcqiccUYLm7+fZSVCYy0iUUn53/xIDz8m89h9w+NsHzvj985KnD7VDwl2R731dHVa0nu3KqnOylVMme+fvBSUyGqCs9OSfZ+uzlXEFdZt6UN19e9g7tefk/5/WGK+jVXP/iW7f7O/+JBlk6CqIfrHrG381rTXjhVpHbyg8/tj+9O2M9xm3tN5y+VFxeFnyu9cmMLOl2OS8Wqy1NWJpS1jYwjH7xeEwW94eGV0xJfacQkO4NmL7MO5X5+QfTriqoKrAUR5kS+bkubsifbfOJ1SuS3KiptGw2xGTqzaG2wOSlJ8JOT3Stdp5nbsLq8X592KHYb7W0YFQCMGq6+6FYN0yumW22WCbGbG75yg/PcUaOLTOu7/+LW2aYtTEt4ed6zN5U2vYfC8E7DXBLo8jKB8RGMUIiKqrCUczFGfxXl7dZmTSJVErQgN7+vp7cX5057BW+t2oJXlmzAr+9sUE7nCbPsox3zKULAfaipyhXfOBKH7aVOPPYfOxIH7z5K+b0gvNyM8XoTu7m9y1N9lN2GeBsmWqobReY/o3139ZbcRnETwO7vbuigctcbKhdUH2J57jM1T+B397xpOx3vf3OsN1edeuW3tXVZbja2dIT72zlsz8LY/lGMq3rsMmoYJn76I5bnjaMsTz7iwyVpy5aWTtebf15GXZ5+1B74yC47+HpvAfXNbmNSbzdCzCxfob5YfS6ck02pp7rz//yCdZFXZPUzvNjpPK/6ozPOHzlJMT/J+NoKxXIY5iFCTqO63O6sJbWHN4x9dq0sqHT99U96G0qUFl6L6X3z2L1x6VcP87zfSpsT1TaXGzVRMy9bl2dXCElVtdzOYNNNJfM+zX9LUfdk2/29GZ92W9O9rEyg9jvxFMFTaVPMsXY6Jq23me+2uaUTry/d2H/MWtK0DUf+6mGM+e5duPAO69KFSWOXlOR7kZet215w/qpfsE45Ymj91vbIl4Ix76+sTNiOqgjj4/s6rzDgh5fCZ15/TC0d3Z6Om8Jj33ipqoubf29f+Ji3pMrub8yPbsUf8WMXn4g513wREw7ZzfG1PznFmpyudZkGUe/QWaL6e+jplfiQqdDmhm3h6smMGlH4N7GHYsRGKammGxpv8Pzgc9ZRe8XQ3NbluryZl+XP/v3jY3HT94729d5250xjvaPvfGZfT/vK94jf8dPjfLXBq2b2ZFPa7TpK3TN37UP26/cF4We+ltNp2dwrsceHhuOhCz+LL398D0w8dm/c/L2j0TT9TBy5946W127v6FYORd9uulvr1Gt036srHduuYY4NALj7F5/Br087FL8983Dc/P1PxN0cWzU1Nb5f47Va6ajhg3Hgh0fhhrM/jsP2HINvu1QDHmnTk23uQY4rZOyqBPsp1DdquPPwefPFXNSfdV+bYZbGxNrthmF5mcDH9/kQfv6F/2/vvMOjqNY//j27m0ZISIeEFhJ6CyV0kA4C0kEBFRUFC5aroGIBsWHFelHh2uvvWrCgXhVUBLGgICKIVGkCSgtJIEAC8/tjdpLZ2TMzZ2Zns5vN+3meebI7c+bsZPedM+c9b2vh6LXZ5cTpMj85NnIXP1V61i8Wfveh4+h6+yfof88X6HbHpzhYeBJPfbqpPBTguaVb8OvOI45fu5Pc8y6/ZrtiVeR5DenFbP997CR+31uAnRa8NIzwq0wHe5ZsM/Q8Nexg1138tWt7om6Kb1K34hK+JVv77KxbTyxjcaVlF9f8g1P6GrsPKxQ74A2hXTTq2iQdPZtnoGG6uSUyLSHW0cR4vPGkf+tMP0+tAwUlAX1O2Rnfz3HCBdrIkGIHteFG1LMhUApLSvEvP68vX0Ri+D1uF3o1t5a7QVnovnFYy/J9g9pmIkt1j6cnxqJb03Tzvrw/58hO9XGZ4L1kBbJkE1UevRv5kY824u3vdlquB6qnZJpl5VZjZHXQutqOyK+PFvWS8Mb1vbDoyu7ITK6BmrFR+HLOICy5tZ9P2+KSUu5K/QnvA/6Pv45h0pMrcNmzq3Q/f9ch/6ySdtBaALVM6J7tyOc4RWpCDO4c2xYzh7cyTSQVStasWWP5HBF38UFtM8tXeqcOaIpV9w3Bs1O7Gk6C9UpHBcN9VYsyoTNywyzWU7It1H42+v+PnyrDZ+t8EyGaWZWtohdLrf6YxnWMXcHdLgbGGOaOz3Mk5j5QSk6f8ZNjs3F4yAPLfN4/8/kf2HdUniDvPHgcj3/yO175xrdm7Qs6YQThwuOf/M7dryiLVmSpyfXvo8vtn6LdzUvw2ort5ieYwLNkuzleUoHiZK4AOy7ZWclxGNW5gV+24eJTZdxKH0c0iUGPF4s9LysjOR3gr1zyyl/xKHJgsq+da3ksJo0SyScjylmOk0iNGA+S430VeaPQpp0Hi3Hjyz/hrrfXoUBnfqddWHAimZfWBT1Q1PdFZeXmKCopdWwuadU7TFnouPv8PLxxfS8snNYVb95wjl+7K/qZW/XVdaxH5te3dB0iOLG4FU6Qkl0NMbKQXP7cd7hvMd+aoMfwjvwbbZPNOnlatAqK3gAT5XGhjWYwLj6pp2SXoezMWXS67RMsWbMX3222nwmcQX+hQT0xM4t50bptEWIsWbLE8jkiddx5DyHA2AqepTOBs+KObZflGw/gwQ9+Q+61i3Xb6LmtW7k+XkblH7YexJz/rkOdqW/73a/B8PTg1RdWu8QNaW9s+VAmHR63Cw9e2MF0ASzYlJw+4yPH3/x+APM/5iucCj9uPYQdqoR1CzQ1Wxd85l/D1UrFh3BCkU/RkjlqzpyVcM3zgZde41myrdIgzdx1tmYALujaRYhASnhpQ1+KS0q5id6Oa5TRowUFpp8JAPPe10/I5SR+CesEfzgnEjBprboei8pRIPWh1UiS5Pc9KOF8CXG+n/Hi19vw8Zq93D5GPfwVnv9qKx77+Hfc+gZ/cVu7sOCEJbtJpnVrs5HXmfq3FQ0dC5Rg52UxGluU+5YxhhH59TGpZw5ieInQBOStsSqXSTC+OycWt8IJUrKrIWYuKY98tNFSf3qT6A17jgr3YTR38lOyDWbt2gdG0Um+u/jh4lNYyKktbJfZY/O4+9Wx32d5S8kq9GoPEs4jYsnmPYQAYxfR2rXs13QPlNGPLsf9i38ztF7t1bEeFeokROPBWywa/uBXulZIpy3ZgHn28GiPG78a1BtX52mY1DMH+xeNx22jWjt2fVY5UnwKz+zMwehHvsad//cLznvwK24SIy2LllWMYdocGDyF9LRJndZwZbfXAhSkikhCaJ8jdsT6kYs7mrbRy+sggnZsEkt85vteWazSLqYdP1XGdS3Xunc6HAofMNrrER2PnHBbPXM2MKuuU+EIuw4d9/8evJfCyysw8ckVSJj8JjbtLSjft/PgcWz/uyL04s1v+RnOA11Y4DGuq3lOGK1r/W2j2+i2Vd8XlZVTx+lY4zvH+P5/D12oP7YkmYR4KZhZ9e8c08Znoc2JnBTa+7Gyk8QGG5rVV0NE4j6sJI3Rc2sUSeJQ/nkGx7Q3ndFDMtrj9plsnjkrcZMKvfDlVszSZEm2DWO6LrQtb/qw/EFl9r0HMrkirBHICqzRaq9oSR+R53pubWsZREU4pJPMR3RCWXD8NDcG1ihrajDmMCLhCzm1E1D06iQsnzvY75i2DEm0x20pi7waM4VflJKzHiz7bT+e/HST8Dlq67XIhDyQusahRFlo1SotVgg0EZpdZU3NoLbmsaWBKFbasUlrZeahV9de+0xbuekfrN7mn9Vau5jupI595uxZFJWU+swxFv+4Cze8tBpL1+8zOLMCf0s2/3eb2MM3C7UTk31t4jOrbr45Gc7EC89bvF73e0iM01fAbnrVOFkiLwyqLMCFBR5KRms9ls4eiG1PjcZd4/MwpnMD/PHEKEPLrsh94TRmVWqsMqVfE3RpIidJHJFfHwPbZuq2zRBc/Od5qam5dZSvYm+Wn0WERI1hjEp4EVWa/vd8gU0CpaWsxJDq6Y5OxWRrS46ZhcFpJym8/+Udk2RmVtEbnA4WnkTn2z9FYUmpqRtMVopYrBjhy8SJEy2fY7ZiaxQ7Fx+jr0gH4uqpZcEVXR3rS0HvvhaN3bzr7XWWPzMolmwLsYodGqWgRd2Kskh6yc7MJhh68FzXQ4GIVcHpChKVhTIpDsSSfbossDJmWiVFEWsr4i3iOhvIxFU7rm3YU2AaIuCXqJApffnK08JlW7iLNJv3FUKSJKzfdRQ7/i5CcrJ4/KzRosmhopMYcM9SZF35DkY8/BVOnCrDM5//gUsWrMKLX2/DmEeX47fd5t5y2o/Q+720XnDh4C7Oi4G3w1urdhoo2frjhro+NG+Oxiv9GIzEZ4B+LpB7L2iHrk3SEeVxYebwVniFk7RPi3ZR+aqBTctfO1HXm4eV5KIK2oVfdeKy9MRYLL1zII69PBFvXN9L1/MOAJpliZWrNJoX8cqT6pUstcI1mnKxkeYuHr7ZjIigIJo5svhkmfDDXu9BWXyyDKfLzpiuQpqhHbRLy0wswnFRPgp+ZayMmVmhpzyjn1hNQdSlh/AlK8t65lGzOspGibOMktE44Rqn0KNZBp68rDNu4JQosoteTLZ6Zf+33Uexae8xDMzL8ksK9qKNxFmhsmRXfD7Dp7f3x0tfb0dqQgwu1lGKReuEagmXXAoiCXxOBahohgqldq/VpJxq9heUIFsgq7MeepbsGtEeoUWqKLdLyDU1kIkrzwo+ecG3ePvG3uXvz56V8Pgnv+OTtXvRu2UdTNLUEVausWac2D22/2gJrn9pNV5evh1uFxPylFMoLCnTTTz41Kd/4OcdhwHIJUbf+WEXbn3D1/vs1jfW4NPbBhh+htbN38UYGPP/PbX3fyS5iwOcnAJeUdSriKFQduYsPG4XN/zkmc8349mpXVFadhb3vPcrvvxtP7ZoPBitJnvTIzUhhquAiXgHtK6fhA17Csrf92qR4XP8gUkd0DQzEQUnSnF5v8ZoeM17AV+vFitGJ4WlswfC42JYtGwr6iTFYtqApj7HmVeWzRB1iTfy0uMtlifERnHvJSs0zfSda33x6z70vftzxEW7Mf/ifLSol2S/8zCALNnVjP1HxZRsK65SRs9UvQyUWqzcpAcKjLOSah9MwY7xYDC3gn3+q7lrGy92nDBn/vz5ls8xy9p60qCGa7yOMlYz1oMcwXIguwWzjA7vKFYOR5R9R/n3jpJ1/KsN+3HOXZ/h8ue+Q7c7PhVKnGRGMGLerLpopyXE4uYRrTClb2Pd3Ad2J7R2lXOr9GyeYXhc5PpFEp9JkoQXvtqKGa/+hF/+5Jf8+mHrQYx6+Ctc9swq09q9TlBcbsm2P0a2mfERbn7tZ9uKup4lWzQ5VZSgshGQJZsjA//75S+f5/CKTX9j7ju/4qfth/Hoko1+sf/KVdYU/L+2/12El5fL2dutKNiAfi6If46V+OV4uPMt//CulZv+8dunRpIkTsw5cOtI//wL2vvYiRjanQd9x3mri7BO1mH3y47vFWAzA4Gi2PJ+29dX7gAAfPrLXjzxySb8trvAr40T7uKAvlu7iKfU01M6l1vU42M8PhZhQL7GqQOa4uYRrRwtm6Zmv86z14i4KDfqpcbjngva4ZrBzR37LvWw+gx0uZhwiByPKX0b+8n4wcKT+Hn7Yazc9A/+9YpxybOqACnZ1YzSM2KWDEvu4gYPVtHVOyuPZrMVZu3DsjLKJzlRBqKJSdkhwjnMLKEnDJQRPQX9/okddEt4aflqwwGhdik1nfVu+Gn7Ye7+494J5S2vryn3HPnryAm88NXWgD+zqtSRt6ssO1nX2Ai3i+Gruwb57JOtCPLvJTIGGcXOKzz/5Vb86+WfsGjZVgyZtwwHNXH8ZWfO4oLHV+DLDQfw7g+7cP2LgWfuNmOJN9txIJZsQK4VvmqzsWKmh54lWzS/g+gEOSBLto4MHFGV6Lvz/37xOTb3nV993iv3q+j9sFFlIbSKXvKs/3zpP+6UnrH+2/PWZBhjSOUoUtr7uLgk8AVG7Xdt1arrpJKtvXUUfd9MSVKMFEbrW0b1n53y7oqO4t8/It3n56bh67sG45GLOmLFPeea1ik3im+2y1qdBUsjjFzAg4GhJVvni9Z6mlohMznO0JMikKo/4QIp2QQXa5ZsB5RsCxYKM9c8rfsZLxYmV9DiKAJj9h6GT1zaqfz1gDaZaK6KGyWCi5kltMRAxngK+tGXJmBK38aO19x0u1yO1nHWKkwKyj21eZ+vq596MWD9LvFqAWqCkW24qWCMmRXs/Ha/PjLcViy31kVOBBdj6JSb5lNuTJIqFoRELKoiSvZDH24of338VBnmL/GtNrHr0HEfpe2L9fsrpTTYmbNnLVtKeWgVHxF4zyfFQ0PUki2uZNu/36Oj3Nw4XnUs9a8m93GFu7jYMy2QhHJ6Zbw++Mk/s76dmHq9OGRt4kPA//8NNMyMl8nfan4KR93Fz2o9MeRrMatqUlSedFD/dy4wqE7hlPU1RifsUNRTqn2jFFw1qJnQ2HvP+e2sXJptPpnV3/C41QSt901oH8jlIM5AqdeTXdF8Lnp9moVJVtU8IgqkZBNcrCjZRoPv0WLrcShmmGWG1MZI8moAO13Kxs4E/fJ+TfDVXYPw9o29fWLmCGt07GheFkfLJX0aG054SgwG9hjOiroykXBayQaA56/q7nifWo6dKMUXnJAG9QPUjnIC2FfOjejdsrbPQtn9EwObXADWF8ruGNMGObUTdC1+j1/SibsfAC7pk2vps4AK91Vtmbg93tADEWVPZMLy9zHfhRhtNnneAlRhifPjvP9nlDlSwsvOpNBIjxT1ZBB1Fw9EsXK7GHdRTvHmEnnuWXUXN/L6EWHe4vV+csn7bD0vPCPvBr2yVbzYfK3bdKAJmHipaqx+V05asrOn+8YZiyqniuzoGVMKS0oNS2I6lfhsWIe63P2BJjTk0bpBsu1qE1Y4p2Vtw+PRFsu6qhO42cEovj0YHmmM+RvFtIgkag5nSMmuZigp/82w4mJtVKrLaIVTjZXFcLNYKa2SzZtUOZHURIExZnti1Ck3DUPa16Ua2QEwfLh+PWQ96iTFYd7E9khNiEF+TqrfcSNr0vYDRbrH6qU6nyG+t8mD2CnGzl/ut08dk/31RjEXdy3BSDzodrnw5ZxBeHBSB7x+XU9cd25z85NMsLpAMqSdPOnjxTT2aJaBK/o3Qf/WdbjnRtuw7vzsdfVvkOarIPxz7CQ+W/cX3hWomLDn8Alc+NRKrNsp7rqoVTYe+9i/Hvo+wVwfgXDsxOmAYrIVSm1MynnJsxTSBDNAa+uY6xFIDgM3Y9xnibIIss1g7Kr4fPmvaOKzQL0LHvhgA57+7A+ffXreIbyF0WKDRRO/OHrvEkLvlrXRRGXRvG1Ua8ezi/Nk1WqfwSzrKar7KgYXvcWMPYeOG3rzOGXJntKvCXe/g7lGfWhZL7iehSIealbHgpgoNzo3Fpvj6/HgpA7c/cGoEsIYM/Xc6TXnM/S7+3Nb2dnDAZrZBxE585/vFmpuH93GvBGsWbKNJlhHi8VuDCtJv0wt2QKJKwpLSpGf669c2aWykh8R/ixcuNDWedPPbY6dC8bi67mD/TwJnrxU3wppNKkUzaTfoVGK2EVCPy5LmyE1GARqpQomqQkxmH5uc4zs1MCRsdVO0heArxAo1ptZOuNtIItq2pjdg4UnMfW574TP/+jnPRj/2DcoE8zPsXV/IVre+AFyrl2MD1bv5pY/FFHwzTBzOy48UeqIu7gtt2ODMlBzxrYV6qMyFlLdLlbu2aBGqSpg5X83KlXoNHdr4sL1Yma5iqvBXOX+xb7u6IolmzGGL+4cgLnj8/Dc1K6YNaoNJ7t4KfYcOo6Xl2/D9r/NFyeOnyrD5n3Hyr0FeJJqNQlrMOcVojW7zdzFj504jWYGLthOxWTHx3hw7wXt/PaP7tzAkf61OFED2girNdNFiQ0wjlvPEKf3iB3anu9hIIKLMW7ohpafth/GS8utVzZxGjs6HSnZ1QzREiZm1uLTZWfw4ldbTUv6iMdkCzUDAPTVsQ4piJTUOXNWcsyazWA9doZwjv379wfcx8C2mbh5RCu0bZCMG4a2wND2+lm9r9fUWa5vw3p98TnW3YW1LJrWzfHs41qcyC5+uY4FItywanFRFGmecq5YrLo2SUdWsr/boahVk4d28vf1xgMo0CnNpseBghJLCcD2HD6Bg4UncfG/v+Ued8Kl75jJ/1BYYmzJfmxyvpA3wmnBxQU1Rpbs1g2Scdf4PNM+rMiXNiRAFLeLcePuFc80sYUVJet06Cq8NsoQL7VWWJ6YS8LqbYfwh1cWj5047ZehnEHlgZAQixnDW+HCXjlwcdzs1+w4gpY3fYjrXlyNdjcvwe97C3SvYe/h4+h2x6fIn/UJet/1OQpLSrmWX6tzDq113UlELZMi7uKZnDFOwakSXgCQlez/rDWriW0X0STBamrViMLdgvHcwbAMA0CsToI4UTrmpHIzrOtVJpra3/7z3co6g3YhrqpASnYQkctH+G6hRrQ8waFCYwv01Oe+xw0v/2Raw1e0hJcVZgxvZXhctG7tEUEruwhWLWlOxJASzuFxuzBnXB5W3TcE901ob2h16t2yNqYPbgZAdin76cHzfI6LeIs4MXmKi/YgMcir7U4o2U1sJPmqCiiTJJ5i9+c/xeWva9fiKNk2rJqKYp4U72vdsvsbHXEwX0Zew+SA+9h6QD/sCJCVcD1r2sQe2Zg6oKlpzV8AOG0jkY6RJRsAZg5vhaJXJ+HT2/QTGVmx6L1/c1+f9+O6NhRyX3W7XbhqYDO//coChogngPK/2UnoZ4ZZnOt7P+zC5c+uwmvfbBfuUzEIXLnoe/S/5wt0uu0TPPvFZhwo8FcKjBL/ZafXNFR8nudkPFdYtGxr+T2/YU8BFi3bwlVKrVqyg+kuXmJQplKN8v3qiU7hiVLD+Y/H5ZyawbN6BstD1MhDQo8Xr+6Bm85rad4QFYu0bRsEPnaq0SsxKgpjDB/P6ifcvmUAdazDwbvXCnZ0OlKyqxmiNe0WLttS/lqSJLyxcgdmvvozft5+CGfPSli8erdQPzzXwkDITI4zzQwuupDgpJJtlYZp4iv1hDEJCc5lihfB7XLhwQs7oujVSfhx3jA/JWvG8Jamli0nksHUiHFbToxilROCE7HqiOLGz8vmrLYY8hS/aLcLk3o2svR50wbIFgOtJduOZRbQV87tuBs64S7+2oodhscLS0qx5Gf/rNMA4PJO5EWUEjse51pLtt7csGfzDCy4vAuacbLfW/FeaNMgGT/OG4oxnRvgsr6N8ejF+XhuajfT89wuxq2nrsRklwkp2fI/l5Zgz5puhIsBs3Xc61dvO4RLn1mFt7/fhR2qRaqK6+L3WVxShgMFJXhr1c7yfbe8vsbPVdyMKI/LsHwar6yYgtZi/vjHv3PlzGpMtmhJSDuYeY4omLmLF5YYh3E4lfgMCNwV2gq1bHz3ysKKiKKtfC2BZgTXoq0Bz6sJb4aV2tcZNr1ugIrv4FoHcqqEK6RkVzNcLmYpHhQA/u+7nbjqPz9g4bItGHTfMuw+7B/zpcfholOG5V2+33IQYx79Glct+l6oP5GkNaIljwKp76fGzmqcg4u71Z4ZM2aE+hJ8iPa4MXN4K0NF221RAIZw4p5io9yW72WrnCoNLHNrmwZJzlxIGFI3RbbK8WLm1cnCeBMWj9slnB9D24+2vw855Y5EKOGMy5IkCZX50vInRymyyksmoUeFJ07r/q/lNX8FLNl2LLT+NYb5Yz5jDJN752LJrf6WoCiPtedEy3pJeOXannjqss5ITYhB+0Yp+G3+CIzqVB/ntsvCWzec43eO28XQMcd/TFA800TqjCtXmRQfjaQAanZz+2ZMN3yk/z1fGJ6rZzQqLCnFXs6c5H1BQ4AaM6X26PHTmPHqTzj/8W8Mwy303MWtZiyPiXJXitv+oLws3WNFJu7ix06cNpyXORl7XJlheZN1QrrmT87H9/cN4R5TxtSbzmuJS/vkokezDN1cH8oY0rd1He6zwG7OlVb1k3DHmDbITI7DoLaZuNJGxnEruQACSWynfAeKZ2AkQlP9asj8yfnISY1C87q1MLm3fmyoksDj1tfXlO8rPXMW975rLTaiw61LdPs//7HlWLp+P75YLxZXyyvHpSXYCSucwMnV3erO8uXLQ30JXIxcI63GY2kfwu2zU8AYw8QejYI+8Sg4ftp2HeS549s5ezEhYgwnuY46yZ3RRJjn0h/tcekmd9JDyRrr1PjG81L4eO1e2/0FOxzq2IlS7D1ygnvMynhqxxqm/d/MPo33mzuRZTk7vSZeu64X3rmpD7o29U9QtGVfITJqxaF1/SSf/TsPyosgIgvL6soi6ZxQh0Bg8E/cFyjFJ0sdSxBmdm1db/8Ei5Ztxf9++QvnP/ZNuTdIDc4YzEvmevUg68pEvdR4y+dY5f4J7XX/9wWfb0bC5Ddx7v3LuMcLS0qFPCScID831Wes5XltOAXPQwkApg1oitY6Lt6KC36tGtF4ekoXfHbHAHx+x0BuW/Xiw4j8+n7HJ/aw5umkZtaoNtjy5Gi8N7Ov7v9hhDYkCQhO7LvyHWQl1whajHqoISW7GpKfm4bRtTbipweGYZ5BbPDR4tMoKin1S162avNBnTP47Dl8gps0ZOWmfywn7BEhpaa9SWjjOrLbsYsxvHi1eG1iO2NDpA4ooSBslWwDNyqrngztslNwx5g2yEqOw5D2dctjNmOi3Nj42Eg8dCG/7IYT1L/6XbS/hb9QZsQTl3YytJCEI3rjYd/WdXwS+Ggz2hrFxvOsgaI1kxXyGiajVwu5lJtTSjZv4WTSkytt98ezjDuJkXuromQP62CeCNBOwkv/esvGvx9P6RKxIluhVpy/HBw9Li9CP3NFV5/93/z+NwDgF4HSbepY5vREsdArURhzrqSTgtU4ZyPM4ofVlVQKS0ox/rFvAPDrWfPWnK6xoWTbyWlh1QravG4t/PTAMHxwc188YVBZg0fhiVJ8pBPG4bRiFhftwaOT85EcH41GGTVxDyfbuFOYecXw8lDEceqF63lHqIcQnvdlKA0xbpcLj03O99lnNMewW6lH+RddLmY6bz+/W0NbnxFqSMmu5hhN2I4eP40JT6zw23+o6KTlz/l6Q0WN3bIzZ7Fo2RZMf+FHy/2IwBvoRPj0tv54f2YffHffEIzvli18nhJ/aWVQJCU78kk2yA1g5wE6a1QbbH5yNN6+sbdPcr/0xFhcM7g5dzXcKfQsiEZURW8NPatRWkIsHr04H7VrxaJjTgru1MSVGsWwNa/rn7BKsYKP7WJefmbp7IFYOntguVs6z8pgh+MOJLVTs/KPvwM63yhDMSDHFeslWFP0V5Gs3GZK2dodh/HO9zt92vnXWzaGF0LkdDk8XvI8pUQXT0G4/a21uOvtdZY+w+m47GAkOio6WSpcP90s4ajVuc2KTX/j5Okz3EU27aKKx81QP826VdpOXPantw2wHCubmVwD/dtkor5Fy3nBCf0EigPaZFrqS4RJPXOw+9lxWP/oCHTKDawmtBG8MT1Glbl7vkYJjYt2Y0g7/7AuPQ8B9fOR9xuH+vk5dUBT7H52HL6+axC2PjUaIzvpP6semNQBaQYJh1vXT+ImJFaPB2YlNKtqAlVSsgndEgs/bD2IFZv8J05GcZp6bpOz3lxb/vr1lTsw49Wf8ZfBxL12rVic47Xc2OGpyzpbPifK48KAtllopXG1M0Nxr7NSZ7cK6h+ERYxcD4PxAM0wcAvr09L+vWSEUfmyUE8S7JCks+jodsmxpNueHoPlc89FU80D38jqwVOylTF31qg2aFU/yVBWujZJ91k4dMqSXVRSik17CzD+seW48KmV2PF3ETdhlyhXPCteq5uHdkKula3CE6W6ZXVKvAsGIkrhydIz5aFQWj76eQ/63P05pjz7Hc6567PyBVSrlmwedupzW0VZOOHJ8dP/+8Nyf45bsr1/ldAHJyg4zo9/5nGdSYIlO67Z63YdQS2OQnbrG2t83qcKJmTVojcmmWEleZWaGjHWwil2HdTPx2BlThRu8JIoPjipY/nrLk3Ssfah89C5cRqGtK+Lj2f152Y/T4iN4no7qhVMXshXoPlQnCA5Phr5uWmmVQG6NknH74+PxJ5nx+GBSf4W76T4aDx/pX/iRvWYuPOgca4npz1gKouqedVEwEybNq38dR2duCujkhV6jO7SEO2y+daGYQ98iTNnz/pYtfVgjOH163tZ/nwF0eRngZ4DVJQp004cGtdJQJcm/MmE1cRXhD5qWQ4njJQhdxAsOjV0agRP6tkIdYMU1/fjvGEY35XvxlUVvTV0XftMbte+rer4vM+tXRFvzVvhV7LCN69bCz/cPxR7nxuPdfeIjXdOKdnHTpzG5AWr8Nm6ffjo5z249sUfA3LvLDhRGlDJN60i2yHH1wWxsKRUN6ZYSSiVJqgU7vibrxjc8vqa8uvYdqAIb3+/E4B1SzaPQOvXinDc+z2IlrE0w2jhLhAWXN7Fsb52HyrG1c//YNru6SmdTS3pduqTHz9ZxnUXf+9H38RrdhOAWbVkK1ns7ca+82pRG/HT9sO6x0Tq1ocrMVEuNM6o+C5Sasbg0j6+OYyaZCbiyzmD8PaNvXUXjlwuxl0oUT8feXLpZBhEZRAX7UFSfDR3Hh0X7UZ/jleDlf+xKi7aA6RkEwBy6/BLIP22u8ByX9Ful27ChhWb/sbd76wXKv/FmKz0Tunb2Ge/aKILqxNRt4v5Kb6iDylFydau4D1yUUddxZ107MjHyJIQjDwxIzv5u4vfMqIVHrukE9fS4gRx0W68cHV3fMfJtnqqEix3TqM3oTVbFLluSIvye50x4KGLKiwePOsqr5xTXLQbb2gWFid0z/Zr55QCtfvQCfzx17Hy9ys3/ROwC3ntqW/jne932kqCpi0DpB07j504jTPagtVelOzSou7NeqFKWu8qJZZZ++/YcXuO8QQ/M3KRqkRU++zAKw8EWnNXi/K1Na9byy85m10++GkP1u08atpOJOEdr1KAGcUnS4Wy2ttddLSS4d3FGBZcIS9gdMyxFyebY1Ii1Qp6C79VAcYY7h7TFG0aJKFtg2S8feM5tq2pvLmk2aJLkcVyb+ECL0whLtrDHTPVSRbNynjFBLlcabComldNBMyiRYvKX2vjCxVE45zUxES5DJMAaetJmjFrVOtyl6Motwt3n99O6Dyrq7i82EhRy7byLTXLqoX/3d4f1wxqhjeu74UBbbOw9zDfJb4qWvnCFbUshxNGE7bDQajR3rlxWrkcJ8dHY+nsgZg9Lg/xMR7HM/oCcnkuj9sFxhjacLKtWq0JGw7oxZWZeZ4kxkXhx3lDMX9yPj6/YyAG51XE5vG+e57b86JFi3Beh3rlbtNZyXHceqt23UC18GIpDx6znm9Dy5RnvzOtec1D+7xJjvf9LQpLSlGqY8lWknUZxQWqWb3tUPniqBFKG/862dbHbzulw8zQWtauUJXHapJpT1lSJzHSq3/MKxMmgvp769EseJmheQRPyS4T6tuuIS5DIMP7gsu74Is7B2L1A0PLjRxXD2qmGwpYWdiZQ4YTa794G9/dNxSr7huCLk3SbffDM/qYyUNhFbNkK/CymeuFIR1TPYPum9DOsN+hAkktwxFSsgl0bZKOqwdZr6XHI8rjMlVOeZlXtSjjT2ZyDfxw/1A8eWknLJ87WDiWy6qLFW8SLZpATV3ep2fz2njooo7lSai0sZsKTtaOJKoewfr5X7qmB36bPwLrHx2BrqpJgZ3kOWa8fp2xe7OIEhNuJMRFcccvEc+TzOQamDagKbo19Z2MWVHIXC6GxTP7YMP8EVj78HC0qJckfK5VjnAWev46aj3BHQ87SS39LNk1tZbs0vIYaS128mIs+Nw8RvnTX/7Cpr0FHEu2ef+3jGjl837WqNbC1ybKbaPalFuO0hJicP3QFuXH7IYVqJPL5etYQ3mLaiKofz/t7xtsogXc9af259fwNkKvJrYWu0nf+rQyzqeRUjMG47s1RLem6WiWVZH/oXGdRCy/a7DuedcPaaF7LMah0IbKyENQFdjIqa6jdX/u0Mh34Wpoe/8kalUBnpKtl7BSXTHCaCH71pGtkW2x5GW4QEo2AQB4+KJ8fDNXf0AWJcbjNlWERbKsHiysmAA2TK+JKf2aoK3OjcrD6gSDl6l1QFv/GJIhmoGvfqo8sdZjuo4LDFmyqzdD2wdnVZYxhuz0mn5KtZX74eNZ/YTaNUw3jvM+WBi4VTQU8CYJgcaDqUNoateKRftG+pZAxhgaptc0jGec3DtX95go/3Cs1qFMtqNVoLXxwIeLTulaspWEVXqKDG+8ffeHXULX1fn2T/3c30XG76kDmqJLkzREuV24rG/joFhus1JqYPW8Yfjwlr74cd5Qn4mo3YU19f+mF541fbCxa6ceh4oqnut2c6DYRcRd387iQcHx00IWW7tDiN7YfVnfxrhzTBusvOdcXYNAXnYKvr5rEBqkxSM2yo0bhrbAjcNa4sFJHXD3+XkG1+rM/EQvUWF1g5dLQisy872lyQCgX+s66N60cj09nCIjMdbn3o5yu3S9AHJq+yrOz03tym1n9LwMd6puwAQREH369PHbZ5ZBUISYKJftbJhqAh2crbrH8urWzjivFV5Zvh2FJaVwuxg+uqUferXIwPurd+OPv46hR/MMdGuaXl6Oh0dDnZIdZMh2Dp4shwsXn5PDdZ3lJcoJJiIxgwotBa2n2onYkPZ18b9f/ip/f1GvHOHPDCdSE2KA/b77Ai099MCkDoiP8eBw8SncPKIVd9XeihzPHZ+HV7/Zrnu8Rd1aiI1yC9VFDhe0luyasR4kxHrKk5qVnjmrW2JJXd+8cZ0EbDtQ5HOcpwRZKQmjdd0UEYc6SXFYNnuQ8GfYJTUhBv1a+y8IiyyspSbE4HCRr0eD+r7W87jiZcwXQV1LPRjeNUaIWmcfvbgjZr62xryhlyPFp3Td6tUEorh2b5aO7zYf9Nk3qG0Wzutovlibn5uGDfNHWPOocUjJblPfnsdDuODU3CI9MdZv0XnDngKf9/m5afjl4eE4WHgSTTITqqy3Y5THhXmTOuDGl3+CBAnzJnYo1y1emd4DlyxYJbdzu3DVQN+68Rf2ykFunQQMvHepz35e9vWqAinZ1RTe4MGz4Fjl5OkziPK40CQzEVv3Fwbcn108bhdqxnpQfFIskc8eTux0akIMfpw3FJ+t24f2jVLKE4mM6cLPpsyjts7CRVUdQMORcFayrx/Swk/JnjOOnwMhmFjJEiwa16qdtN1zfjts2H0Uew6fwHkd6wW1hmkwSeGU2gnUkp2aEIPHL+1k2MaKHKcnxuLnB4chf9Yn3ONLbu2HjFqxWPzjbuw+fByvLN+G7ToZtSsTSZLw1qo/sWz9fgzMy8KE7tnlcrRTUwrI5WLIqBWLopPFqvN9++vaJB2jO9fH4Lys8n33TWiPCU+sML0W3u+sx8Y9x3zeVwVPJBEltl5KDX8lW6OLxsd4uAnxerXIwMpN/9i+vsp2JRaNt64tEAOtZufBYsOFdoVAFup45b92/FPEaenMZ2dn1MRGjRJolQZp8RieXzXjaBWcmlvMOK+lTxlbPVITYhxLbBlKLuqVgwndsyFJvl6iozs3wJmzEtb+eQRjujTgVrPo2iQd157bHP/+TA7nSUuICaicb6ghd/Fqyvz58/328VymraLEWDxyUceAysE4QaaFUhRf/LqPu79eajyu6N/EdqZOAHj8Ev/JdTBKOFVXeLIcLjTK8I8julKzelsZdMpNQwOOV0W/1nX89olMyHhKRvO6tbDmofOw499j8Ob1varsQhJvklMZSpVVOW5SR98SWzspDowxjO3aEDcOa2k7hlaErGS+UnLfe+txqtQ3NGjFpr9x5aIf8M4PuzBt4ff49g9ZSVu747BfNQuPy4X0RGOF54Nb+uKawc19ZJbnkcW7D/85VmLYt5qLnl7p874qDN8iWanrc8YErazrJUZ9ekpgZbj08pUEC9Hs7ukWy3gpJfDMCGQ85C0IOZWdnYfaM8QOT17aCSvuPldo8SGccWpuoU4mqKDn5RgpeNwuP52CMYbx3bLxwKQOhovwc8a1xR1j2uCyvo3x+Z0Dq2yNbICU7GpLUZH4KqgVWtaT3cj6t8nEH0+MwtGXJjhSSsQO2kQSoUIbdwKQJdtJgiXLThAT5UZ/lSLbPjvFsezQVnC5GN6+sTe6N6uIjWqUUVN3Am3eH39/XLQH6YmxAbtXh5IUTkKmyqjRaVWO9caQiT2y/fYNaJvl39AhHlaVK1Pz0Icb8IrKpf3sWQnnPfiVT5vrXlwNAHiMU3XC7bVkG+HhfAc8j6z7J7bHf67s5rNv96Hjfu1EF1OqgnSLWLIbpHKUbM13qjzTteTWThBKYqpH1wCyNduhRozYtQarNnggMsNTsnu3DJ51r0/LOpg7Pk840ayaST0bYUq/JhFhkXVqbsErZfasTvwxIc8jZo1qg6cu61zpi3FOQ0o24Sjju2X7vPe4XZg6wHrGTiewEpcdzFVFnqtXVXA3JJzh5ek9cfOIVrh1ZGssEUwqFgxa1U/C53cMxL6F47Hi7nOx6t4hyM9JRSuVRUSxbGfqWCcVIll+efdrZSjZdnjpmu5++24Y6l/2a0L37KApNUZJ/Ga8+jMA4Pe9BWgz8yO/49v/liexH/7kbwmsGetBbRMlO5rjfcVbJOnbqg4G5fkuNOw6eNwnoVnZmbPCJYeqgvyLxGSLWLJ7t6zt4wUzTfU81wuHEqGyF5pzMsRKmgXyPxlRctp+DXreXMasrGAguFwMM4a3wpdzBuGOMW0sncsrO1jd4Xl16iUVJCILUrKrKZmZ/olSAuW+Ce25CZ3SEoKzMmxGvo47Cs99+/mr/SerTsFbhQ7TOXuVJBiy7CRJ8dGYMy4Pd45ta7usjpMkxEWhfaMUJMRFgTGGV6/tibFdGmBSz0ZYOE229i243NgVtCooGXYJlbu4HTke1zUbi2f2QW7tBFzQPRs7/j3GZ9FEISbKjfdm9sHL1/SwfX1zxrX18YRQiPK4sPGxkYbnXrJgFddybETjOgmmlmyex0R8jMcnr0BmchziYzxIjo/28SI5WXoGf6syrH+/xTexlPHnCjcNGbzFIi11kswz6btdLnxyW39cNbApZo9ti3kTO5QfCzSPS6BuyVYQVeoT46KQbVI5wQ7a2HcrhML7ScFK7oJrz23uU0asquPU3CKlZkx5eTjG5DloVfb2IsQhJbuacuWVV3L366XQN6NOUhyuGcSPNU1LDI3b0NguDbj7x3RpgKJXJ2HhtK4Y1DYT78/sE1TXtRTepJ20bMfQk2VCjKaZiXh5ek8snNatPJ51QBvjyUUkzw94mZO1JZyCgV05Htg2C+seGY7nr+puqPQkxkVhbNeGPuELZtw+ug1yMmpibJcGmDagKT6+tb/P8du8tZ8bpMXjX8P4dXeXbzyAP/46xj1mRGJctKHrrl44EGMMj16cj/gYD+Ki3bh/Qnswxryl0XyVp/W7jpa/3mIhUWdUFYgRFFGAeW14C0rZ6TXxyMX5uGVka58EYmaeBoDvdzW6s+8zedqAprbqh394S1/L51ghKwj5ZERKl+pRM4RKtmiptacu61ypiyaVgZNzi/mT87Hi7nOxet4wXGGjHjtRNQn/JwURFJYsWcLdP6lnI+5+I8Z1bYjlcwfrJk4TsWRn1Ir1sZLMthkrqiba48aqe4f47Hv9up7lK7OTeubgvZl9gxqvCMiWFXUSGo+bWc5gSuijJ8uEfcxW2SPZkp3PSXJYUmp/gixKZclxw3T/HBE8JnTPxm2j2+DXR0fg5ek9UatGNKI8Lmx6fCRuGdEKT17WGbNGVbiS6inEIx/+2vSzeLG9MVEuZBiMk0YLlWO7NsTuZ8fir+fG+4QwZWv+97Hzl+OFr7YCsGaxqwqJeGKj3YYW0JvOa6mjZIt/hpmnAQA8O7ULstPj0aVJmt9zPSbKjTvGtMX0wWLJIJtlJaLg5QnckmVqAg3/cqLSipMkxIa/kn1Z38YRZ511ckxmjKF9oxTb5e+Iqkn4PymIoLBmDb8OJGMMHk7NaADI4WRoBYCF07oaZhIXKQn00IUd8MHNffHMFV3w3xvPwc0jWpmeI0LbhsnYv2g8fpw3FAefvwAjO/Gt28Hm/27sjbhoNxgDbhjaIiKSgoQLerJMBEavFvoxY5HsiMEY8/GCSYj1VEryxsqSY71s4FpG63gC1UuNx+xxeZjSt7GPoquncJnFOX/z+wEkcsIoGDNOfGYWJx/tcfst/PIWGP718k9Yu+MwJv/7W8P+1FQVT6R0HS+yKX0bY+74PK4yecpCaS2zxeJRnerjgu6N8Nv8kVg2e5BubfJxXRuWL9zxvATmjGuLRVd2w1d3DTaNRX7zhl44J8CkYKJlDCsLkcWMYNHJRvKzSIHmFkSgUJ1swo+0hFgcKPAvb3L7mDa44rnvffbdP7G9aZmGmrHGYja5dy7GdmkIxhguPifX+gWbUDM2Ci3rJTnerxV6NMvA3ufGoeD4aUPrDEGEC/Mvzkfn2z/lHgvXRGBOcd+E9ig7K+GfYydx26g2wjV2qwKipQ3jORlxjcgwKbelxzXP/4h6KTV8njkJ3meGkXLhsZH4qX4q/3/vPfdzS/0cO3Ha8meHgrTEWG599KEd6oIxxlUm9x05Idy/3u8TE+VCTkYC7p0g5j6cn5uGpbMH4ts//sGANplIrBGF0Y98jW0HinDNoGa46byWfsr1nHF5uOfdX/36iotyB5yRONws2R1zUtAgLb48r8GYzpVnLBDJI3L9EH6oCEFUd0jJJvyYObwlZr7mv4J3QfdG6NuqDt789k/8U3gSF3TLRp6AhcfMhWhij0YR52bEI9rjJgWbqDK0qJeE/93eH69+swNvrfrT51hsdGQ/OuqlxuP163qF+jKCglnmeAWrLtF2rW27Dx33S4r27FQ5AZ9RTLadhR4rLuFG7D8qXmM7lOh9f0rNaN5z92DhSb99uv3r/Ob7F54Pj5tZeq53bpzmUzLql4eHG7a/tE8uV8lOTYjFhb1ycP/i33DSG+ZhNQzOzJKdlhCDQ5xEZgPbZmLp+v3cc+zEniu4XS68fWNvPPThBiTFR+OucXm2+3KK+ya0x5I1e5BbOwEzHfI8JIhII7JnSoQuM2bM0D120Tm5fkr26nlDAQAZteLwr2HOlmgQSZ5CEHoYyTIRGD2b10aPZhl+Svbxk6UhuqLIpbLkuHEdMSsfrzyWEU66tCrZnWvEeJAQ60HRSf/yR3aU7H4Wkr61bZCM9buPmjcMY/TcuY1+W57yqAdPiW9ZN1E3P4uTpCfGolaNKBw74TsW1UutgfTEWCy4vAse/mgD6qXUsJzjxcySrff/aWP+Fc5tl4Xpg5tbugYtreon4dVrewbUh12euaILrnn+R599E7pn44ahkW3BprkFESikZFdT9u3bh2bN+MlG4mM8+Ps/52Ptn0eQnR6Puik1ArY0d2+Wju8280uk6MVpEYQIRrJMBA5jDG4Xw5mzFbG1PKWHCIzKkuNGOrk1tERbtGQ76WKrzqacUSsWRSf9XZ71cocYYcWTqFlWIuaMb4tx87+x/DnhAq9EFwDD8IdYThI6PXg1pc9tWXnP84xacX5KtvK/nd89G+d3z7bVb5sGyYbHz++Wjee/3Irjp3zHQV5Sq7UPnVfl5zit6/t/H5EUQqMHzS2IQKHEZ9WUt956y/B4jRgPejbPQL3UeEdcuYd1qMfdf/WgpgH3TVRvzGSZCByjxIaEM1SmHD95aSfTNpVhjdSjpioePF0n1rvguL246N3PjhNqN7pLA2QJxq+HK3qLCjGq33bRld18jt0+uo22uX7/nIWVX9auFT4/UHiLCDEOyG1Obf2FqPgYD64a2NRPwQb4C01VIRO9GTxvwzgLizFVFZpbEIFS9e9+okpwzaBmuOeCdn77p/SleoEEEe4MaVfX5327bGNLDxHeTOnXBF/OGYQ3ru+Fj27px20jWrpHzSMXdQz00gAA8apkmbV1rLE/bT9sq+/k+Ghc3q+xabsa0R7d+HU7VvRQoPfdRauskGM6N8DVg5qiZb1amDm8lSWX+hqc5HjFZZXnIMlT8p2oYe52uTC5d0US1pGd6uPLOYPw4KQO+ObuwaiXGo9uTdP9zouLdvsYDlrXTyoPfajK8BYPrIaTEER1hNzFiUrB43bhxmEtcV6HerjwqZXYdqAINwxtQTUDCaIKcPE5OVj05RYo1ZjGdc0O6fUQgaMkmSo7wy/ZZKfM4IW9cnDz64GXvampqgtcLwheFHPHt8MLX20zbBPtcSFVJ1Ha/MnmngDhQBuOmy8An/8rJsqNhy/Kd+wzo1ziJcACpVmW//zBqfJqT17aCT2apaPsjIQJPbIR7XH7JGa7ZUQrjH50uc85mUlxuG9CezSuk4ijxadwef8mEZHUNcrjwiW9c/HKN9sByCXgIuH/IohgQ0p2NWX4cOPMncGiSWYiVj8wLCSfTUQmoZLl6kRedgreuuEcfLB6N7o2TcdlfcwtgYQ1QiXHeu6sZqUZeSTEReGf589HxhVvc4/3blkbBwpKcKT4tG4W60FtM33e10913hKYJGClz6mdAMYY8nNT8bPKan7bqNa4rI/zpSaDQf20eJzXsR4+XrPXZ78dLwU97r2gHWb/d135+2tHVt4CRG7thKD17XG7MKlnju7xfq195TQ9MRbN69ZCtMeNaQMiLwzuqcs6Y1BeFhgDhrXnh/9FGjS3IAKFlOxqSseOzrj1EUSoIVmuHIZ1qKebW4EInFDKcUrNGBwpFs8qbURctAeLZ/bBGI2VDwCW3NoPjDGcKj2DtMv/yz3/puG+5YDqp/GV7BH59QO6zszkON1SXNcPaVGeh+DeC9ph2sLvUXyyDNcNaYGbq1i5ojev74UGV7+LAm+CsEk9Gzlm7QWAKwc2xeHiU9iwuwAXn5OD4V0aOta3GVkpoSuJ6XIx/PHEKNzx1loUHD+NWaPbRHQyMJeLBXzPVTVobkEECgVVVFPmzp0b6ksgCEcgWSYigVDK8czhvmUZrwzQEjewbRZ3v+JiqqeMMAZ0bZLms6+BjpI9fXBgWX/1XMEzasXi/onty9/3bF4b6x8dgV3PjK1yCjYgf+fbnh6DJy/thAWXd8G/p3RxtP+4aA/uvaA93r+5L8Z0aVipcpyfk+Zjle+ikZ1gUzelBl6e3hMf3NIPXZv4x2gTVRuaWxCBQko2QRAEQVRjpvZvitb1kwDIrsSXOOAOfVEvfVdbALjpvJZ++764cyDcLt9pSZPMRG5N7ARVmS87DO/I98q4l5Og0+N2VekY1JgoN6b0a4LJvXNDmjXeaWKj3Xji0k6okxSHxnUS8MCkDqG+JIIgiHLIXZwgCIIgqjGx0W6suPtc/LLzCBpl1HSk5nWnxml4feUO3eMt6/knrUqI9Vec42M8iPG4cOL0GZ/9gWaRvmZwczy3dAuOekuBXdI7F5f0yUWn3Mq1hhKBMaZLQ4ypRBd1giAIUUjJDiK8lW9JSc8bYpo2jbzEHET1hGSZiARCLcdRHpdP9uRA6a8pBZWfm+rzvnGdRL9zeCWhAOA0JwN6oGW0kuKjsfqBYfhk7V60bZhMyrVDhFqOCcIpSJYJNXa8mVi4KH2RBGNM90ul75sgCIKoDtz6+ho888VmxES58OLVPXwSJx07cRr1rnrXp/2WJ0chM9m/ZNfY+cvxxa/7fPb98/z5iIsmOwFBEAQRfIyUbEmSuAcjJzgnDJEkyW8LF958881QXwJBOALJMhEJRKIcP3RRR6x/dAQ2PT7KLzNxrRrRyEyuyA6dGBelW5t79ti2Pu/7tqpDCnaYEolyTFRPSJYJNXZ0OlKyqylbtmwJ9SUQhCOQLBORQKTKsVGM9/zJ+UiI9cDtYrhrfJ5ube522SlYdGU3DMrLwpxxeVg8s08Qr5gIhEiVY6L6QbJMBAotBRMEQRAEUekM71gf/Z/OBKAfj60wsUcjTOzRqDIuiyAIgiAChpRsgiAIgiBCgplyTRAEQRBVEUp8FgSUxGf03RIEQRAEQRAEQUQWSjI0SnxG+LBmzZpQXwJBOALJMhEJkBwTkQDJMREpkCwTgUJKdjVlyZIlob4EgnAEkmUiEiA5JiIBkmMiUiBZJgKFlGyCIAiCIAiCIAiCcAhSsgmCIAiCIAiCIAjCISjxWRCoConPNm/ejGbNmoX6MggiYEiWiUiA5JiIBEiOiUiBZJkwgxKfEVyysrJCfQkE4Qgky0QkQHJMRAIkx0SkQLJMBAop2dWU+fPnh/oSCMIRSJaJSIDkmIgESI6JSIFkmQgUUrIJgiAIgiAIgiAIwiFIySYIgiAIgiAIgiAIhyAlu5rSsWPHUF8CQTgCyTIRCZAcE5EAyTERKZAsE4FC2cWDQFXILk4QBEEQBEEQBEFYh7KLE1wWLlwY6ksgCEcgWSYiAZJjIhIgOSYiBZJlIlBIya6m7N+/P9SXQBCOQLJMRAIkx0QkQHJMRAoky0SgkLt4EAh3d3GVe0OIr4QgAoNkmYgESI6JSIDkmIgUSJYJEchdnCAIgiAIgiAIgiAqCVKyCYIgCIIgCIIgCMIhSMkmCIIgCIIgCIIgCIcgJZsgCIIgCIIgCIIgHIKUbIIgCIIgCIIgCIJwCFKyKwHGWHkGOsKfcPt+wul6wulagPC7nnAi3L4buh59wulawpFw+n7C6VoAup6qRLh9N+F0PeF0LUD4XU84EW7fDV2POKRkEwRBEARBEARBEIRDkJJNEARBEARBEARBEA5BSjZBEARBEARBEARBOAQp2QRBEARBEARBEAThEEySpFBfQ8TBGKMvlSAIgiAIgiAIIoKRJImbeY0s2QRBEARBEARBEAThEGTJJgiCIAiCIAiCIAiHIEs2QRAEQRAEQRAEQTgEKdkEQRAEQRAEQRAE4RCkZBMEQRAEQRAEQRCEQ5CSTRAEQRAEQRAEQRAOQUo2QRAEQRAEQRAEQTgEKdkEQRAEQRAEQRAE4RCkZBMEQRAEQRAEQRCEQ5CSXU1gjOUwxuYxxtYxxo4wxk4wxrYzxt5kjA0N9fURVQPGWBpjbDBj7HbG2HuMsV2MMUm19bHRp4sxNoEx9qG3v5OMsX8YYz8yxu5gjNW20WdbxtiTjLGNjLFjjLFixtgWxth/GGM9bPSXyBj7F2NsBWNsP2PsFGNsL2PsC8bYFYyxaKt9EqGByXRjjM1mjH3KGNvpHQ9Pen/bZV75rmOxX5JjolJhjDVmjF3IGHucMfY1Y+wPxtghxlgpY6yAMfY7Y+w1xthIxpjbQr8xjLGpXrnY65WT/V65+RdjLNHGtfZkjD3vld9irzxvZIw9wRhra6O/Ot776kfvfXbSe9996L0PaX4bAXjHwNOaeUa24LmOj3eMsaFMnjdv9z43jjB5Xn0/YyzHRn80N49kJEmiLcI3ANMBnAAgGWzvA0gM9bXSFr4bgKtMZEgC0Mdin/UAfGvS52EAowX7YwDuBVBm0udCAFGCfZ4DYLdJf78BaBnq34g2099yBIC/BORYAlACYKZgvyTHtFX6BmCboCxLANYBaCPQZ2sAG0z62gWgp+A1RgP4j0l/pQDutvB/j/XeT0Z9rgBQN9S/EW32NwAeAGs5v222wLmOjncAkgB8aNLfcQBXWfj/aG4e4Rvz/tBEhMIYuwrAs6pd6wF8BvnGzgMwHPJABgBfARgiSdLpSr1IokrAGPsXgMc1u4sAxAKI8r7vK0nScsH+kgGsAtDCu6sEwGIAmwEkAxgJQFkZLgMwXJKkz0z6fBDArapd3wH4xnt+FwADISswAPA6gMmSwSDIGMsHsBxAvHfXAQDvef9mQ57sJXmP7QPQTZKk3UbXSIQOxtgsAA+odm2HLIO7IctfI8hjotrq/JgkSTMM+iQ5JkICY2wbgFzIC0drAeyArHyWAkgF0AFAbwCKFbsAQHdJkjbp9JcN4HsAdVTt3wOwE0AmgDGqY0UAekuS9IvB9TEAbwCY6N0lAfgCwGrI847eALqrTrlfkqQ7Tf7nYZCVHeV/2uF9fxRAM+81xnmPbQDQS5KkAqM+ifCEMTYbwD0ATkNerFFoJEnSToPzHB3vGGMxAD6HLK+APA4vAfCr9zPOBdBGdcpUSZKeN/nfaG5eHQi1lk9b8DbID99TqFgRm8Vp0x7ywKO0uS3U101beG4AroD84JoPYBLkCQ2DPAFT5KePhf5eUJ23CZrVaciTqAWqNv/AYEUXQC9V2zIAF3LaDABQrGo30aA/D4A/VG0XA4jTtEmFrGApbT4P9e9Em6HMzQJQ6JXh1jptYgE8B19rQl+DPkmOaQvJBmA0gAYmbZrC1zL9lUHbL1XtvgWQqjkeB9myprTZCMBt0N/FqrZFAAZw2lwEX4+Nbgb9JcHXgr1A+/mQlSi1vC8M9e9Em/UNstKqzF/naMbjbIPzHB/vAMxWtd0PoD2nzSxVm5Mm10hz82qyhfwCaAvijyuvICs36BsG7Qap2h0DkBTqa6et6mywoWQDaA7gjPec0wBa6bRzwdcN9x6DPtUPzfsN2k1TtfsTgEun3VRVu60Aaui0qwPgiKptv1D/JrTp/vZtAaQItv1U9Zu+p9OG5Ji2sN8AtFL9rmcB1Oa0Gahqc5jXxtuuhleOlLZTdNq5IbuVK+2mGlzfPFW7FQbt7le1W2kg862996OyUNUk1L8BbeIbZEX5Z1S4dEepfnczJdvR8Q5ACuQFIqXdQIPPflPV7lWDdjQ3ryYbJYaIUBhjNSG7TQHeSZ1eW0mSvgDwg/dtIoBRQb04ggAuREXixXclSdrIayRJ0lnIsakKF/PaMcZyUeF2eALAowaf/SKAvd7X2ZBjt3hMVr1+RJKkEzrXeABybCzvPCKMkCRpvSRJRwSbP6V63U2nDckxEfZ45fIf71sGWV60qH/vhZIk/a3Tl1Yu9eSkD4AG3td7IMurHo9AlncA6MVLbOV1PVffN/d67yveNW6A7B4MyMr+hQafTYQftwLoCHlB6HJJkkotnOv0eDcaQE3v6+8lSVpq8Nn3QJ5vA8BYxlgNbQOam1cvSMmOXAZBdnsEgPWSJG02af+O6vXo4FwSQZQzUvX6bZO2SyHHBgJANmOsvUl/SyVJOqrXmSRJZZBdHhX85J0xlo4KZUcC8K7JNarvn5GU2TYi+FP1OlWnDckxEfYwxqJQoSgAFQqtctwFOQZUwUyW30WFMtGLMZbGaaOW5cWSJJ3R68wr51+qdvHmIO0B1Pe+Pgpgmck10pymCsIYawXZPRwAnpQkabWFc4Mx3gmP8ZIk/QE5NAOQPT4Gc5rR3LwaQQ/QyKWD6vW3Au1Xql7zJn8E4QjeJCItVbsM5dNrrfhOtYsnn07Lex4qxsffBayf6yC7lAFy3GC2wDUQ4U1d1euD2oMkx0QV4lrIk35Atmj/rjmeA6CW93UR5CRMukiSdBhy/gFAli9eCS6nZVnd33d6Vmyd/lpSebrwx1tm7iXISc7+BGCYBI9DMMa7YMoxzc0jHFKyI5dWqtdbBdpvU72ub6cOJkEI0hyqbLeSJB0SOEctn604xwOR94D780741JZPXp9E1eJ81euVnOMkx0RYwuSa7SmMsb6MsZcBPKY6fAfHqqz+nbcLKLCAueypF6BCIcsHIcexAnKMbzOBayBCy80AOnlfT9Vz9TbA0fGOMVYLvoutlS7HoLl5lYaU7Miljur1Xt1WXrzuWsdVu2rrtSWIALEkm1726Jxvt091fyled8pA+tP2ybtGoorgdVmcotq1kNOM5JgIGxhjDzLGJMaYBDkZ32HIpX8u8TYpAXCJxC8t5KiceL08ki326fS9oW1HshzGMMZaAJjrffuiJElfGjTXw+nxTv2+WJKkYzDHUTmmuXnVhpTsyCVB9fq4bitf1KuGCbqtCCIwgiGbVvvUrpBr+6T7p5rCGIuHXHtaUVjfl/i130mOiarCfwE0lCTpVZ3jTsuJ9r1VWXbi3hDpkwgDVG7iMZDLVs2w2VUw5ThUY7xIn0SY4jFvQlRR4lSvRQvYn1S99suKSBAOYDiLcgAACQBJREFUEQzZtNrnSc37GpDLedjtT9sn3T9VEG/im9cAtPPu2gu5JAwPkmMinPgEgBKyEAUgA3JW/C4ALgDQhTE2Q5KkxZxznZaTOM17q7LsxL0h0icRHsyALKcAMF2SpAKb/QRTjkM1xov0SYQppGRHLiWq16IJP2JVr63GwhCEKMGQzRIA8Rb6jNW81/ZJ9081w1siaCEqMrgWAhjlTfLEg+SYCBskSVoJTu4AxlhnyPV7cwG8xxi7VpKkBZpmTstJieZ9NPwXhKz0p+2TZDlCYIw1A3C39+1inUUgUYIpx06O8U73SYQp5C4euRSpXsfrtvJFvUJWpNuKIAIjGLJptU/tarC2T7p/qh//BnCF93URgHMlSVpj0J7kmAh7vCWQ+qPCNfVxxlhjTTOn5UT73qosO3FviPRJhBCv59BLkJXIAgDTA+wymHIcqjFepE8iTCElO3I5oHpdz6wxYywJvjf8305fEEF4sSSbnHY82bTap7rNUUmSSgPsT9uO7p8qBGPsaQDXeN8WAxgiSdL3JqeRHBNVAkmSdkHOMwDIruSXapo4KieSJJ1CRU140T6dvjdE+iRCy2WQQxoAYIYkSQeMGgvgtIyo39f0ZhsPpD+A5ubVClKyIxd1HcwmAu3VK9t7JUkqdPh6CEJhMwClREwSYyxN4By1fG7kHA9E3gPuz7sin2PSJxGGeBXsa71vjwMYKknSKoFTSY6JqoS69nWe5phaTnK8cmBGuMtyGipqf5+BfL8S4UV91esXlOz4vE1z3p+qY3NV+x0d77yx4fus9Ing3hc0N69ikJIduaxVve4h0L6X6vUvDl8LQZQjSdJJ+D5oDOXT+yDsrtrFk0+n5X0dAOXB3oIxlsxpoyYPFVk/j8G39iYRpjDG/o0KBfsEgGHe2FZTSI6JKoY6B49bc2w7KmpKJwJoY9QRYywFFXWwzwL4ldPMaVlW99fNm0NBtL/fvdZ1IrJZB+fHu2DKMc3NIxxSsiOXz1GRaCSPMdbUpP041ev3g3NJBFHOh6rX403a9kdFzdVdkiSt5bRR9zfQ62LFxVsuZLRql5+8S5J0EMB33rcuAGNNrlF9/3woSdJZ3ZZEWMAYW4CKGEBFwf7GYjckx0RVoZPqtU99Xu/v/LFql5ksjwWgKLnfSpJ0iNNGLctjvPLKxSvnA1S7eHOQtai47hTI95MRNKcJf9YBeEVwU/Oeav86ZWeQxjvhMd47z1YWqEogz8O10Ny8OiFJEm0RugF4C/KqngTgNYN2A1TtCgEkh/raaas6G4CdKvnpI3hOC8gufBKAUwBa6LRzAVih6v9egz6/F2x3hardTgBunXZXqtptBhCn06425NI5Stv+of5NaDOVvwWq3+s4gL42+yE5pi3sNwCtvPKp/LbjOG0Gq44fApCh01ccgC2qtpfrtPMA2G3Wztv2PlW7lQbtHlC1+wYA02nXEnJ5JAlAGYCmof4NaAtsU/3uEoBsg3aOjncAUiHn6TAdFyHnPRCZc9PcvJpsIb8A2oL448rxHqdVN+nNnDZ5kGNOlDZ3hPq6aataG2wo2d7zXlKd9zuAhprjbgBPq9ocBFDLoL/eqralACZy2vSHnJ1TaXehQX9Rmsnku9oHNmSLykpVm6Wh/j1oM5U7RxRsVX8kx7RV+gZgNoA5AOqZtBsGYL9GRqN12n6tarcSQIrmeBxkK6LSZhMAj8FnX6JqWwigH6fNJMiKsNKuh0F/yZDrwCttn4JmcQlAQ+//qLT5T6h/K9oC31S/p5mS7fh4B+AuVdt9API4bW5RtTkFoJFBfzQ3ryYb8/6YRITCGJsOuTSNwq8APoPsHpkHYDjkQQmQV4YHSxS7ROjAGHuXs3sIKkpMrICsRKh5W5Kktzl9pQJYBaCZd9cJAIshrz4nAxgJubYrIE/CRkmS9InJ9T0CYKZq17eQ5foMgC4ABqHCzfEtyMqJ7iDorTH7ter/2w95knkAQDZkdzTFBfgAgG6SJO00ukYidDDGbgdwv2rXEsgyK8J/JUnaw+mT5JiodBhjTwC4AfIE/FfIbrN7IVvd4iD/rr3gm9jpEIABkiTxYqjBGMuB7EmR4d11FLKSsgtAHchykuk9Vgx5UVW3zJ03bvr/AJzv3SVBdpddDdnSfQ6AnqpTHpQk6TaT/3skZNlV3M+3Q3bpPQr5HhyDCjn/HUBPSZKOGvVJhD+a5GeNjMYnp8c7xlgsgKWokNVSAB9BTiYYD9kLJE91ylWSJC00+X9obl4dCLWWT1vwNwDXQY4PkQy2j2BgXaGNNknyW00W3eYa9NcAcgyV0flHAIwVvD4G2aWwzKTP56FjzeH02Qfy5NWov40AWof696HN9Ld82aYMSzDw0iA5pq2yNwCPWZTfpQAaC/TbFr6WYN62B8A5gtcZA+BFk/7KANxn4X8fD1+LNm9bBRMrP21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