estimating_rbc.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Estimating a Real Business Cycle DSGE Model by Maximum Likelihood in Python\n",
    "\n",
    "This notebook demonstrates how to setup, solve, and estimate a simple real business cycle model in Python. The model is very standard; the setup and notation here is a hybrid of Ruge-Murcia (2007) and DeJong and Dave (2011).\n",
    "\n",
    "Since we will be proceeding step-by-step, the code will match that progression by generating a series of child classes, so that we can add the functionality step-by-step. Of course, in practice a single class incorporating all the functionality would be all you would need."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from __future__ import division\n",
    "\n",
    "import numpy as np\n",
    "from scipy import optimize, signal\n",
    "import pandas as pd\n",
    "from pandas_datareader.data import DataReader\n",
    "import statsmodels.api as sm\n",
    "from statsmodels.tools.numdiff import approx_fprime, approx_fprime_cs\n",
    "from IPython.display import display\n",
    "import matplotlib.pyplot as plt\n",
    "# import seaborn as sn\n",
    "\n",
    "from numpy.testing import assert_allclose\n",
    "\n",
    "# Set some pretty-printing options\n",
    "np.set_printoptions(precision=3, suppress=True, linewidth=120)\n",
    "pd.set_option('float_format', lambda x: '%.3g' % x, )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Household problem\n",
    "\n",
    "$$\n",
    "\\max E_0 \\sum_{t=0}^\\infty \\beta^t u(c_t, l_t)\n",
    "$$\n",
    "\n",
    "subject to:\n",
    "\n",
    "- the budget constraint: $y_t = c_t + i_t$\n",
    "- the capital accumulation equation: $k_{t+1} = (1 - \\delta) k_t + i_t$\n",
    "- $1 = l_t + n_t$\n",
    "\n",
    "where households have the following production technology:\n",
    "\n",
    "$$y_t = z_t f(k_t, n_t)$$\n",
    "\n",
    "and where the (log of the) technology process follows an AR(1) process:\n",
    "\n",
    "$$\n",
    "\\log z_t = \\rho \\log z_{t-1} + \\varepsilon_t, \\qquad \\varepsilon_t \\sim N(0, \\sigma^2)\n",
    "$$\n",
    "\n",
    "## Functional forms\n",
    "\n",
    "We will assume additively separable utility in consumption and leisure, with:\n",
    "\n",
    "$$u(c_t, n_t) = \\log(c_t) + \\psi l_t $$\n",
    "\n",
    "where $\\psi$ is a scalar weight.\n",
    "\n",
    "The production function will be Cobb-Douglas:\n",
    "\n",
    "$$f(k_t, n_t) = k_t^\\alpha n_t^{1 - \\alpha}$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Optimal behavior\n",
    "\n",
    "Optimal household behavior is standard:\n",
    "\n",
    "$$\n",
    "\\begin{align}\n",
    "\\psi & = \\frac{1}{c_t} (1 - \\alpha) z_t \\left ( \\frac{k_t}{n_t} \\right )^{\\alpha} \\\\\n",
    "\\frac{1}{c_t} & = \\beta E_t \\left \\{ \\frac{1}{c_{t+1}} \\left [ \\alpha z_{t+1} \\left ( \\frac{k_{t+1}}{n_{t+1}} \\right )^{\\alpha - 1} + (1 - \\delta) \\right ] \\right \\} \\\\\n",
    "\\end{align}\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Nonlinear system\n",
    "\n",
    "Collecting all of the model equations, we have:\n",
    "\n",
    "$$\n",
    "\\begin{align}\n",
    "\\psi c_t & = (1 - \\alpha) z_t \\left ( \\frac{k_t}{n_t} \\right )^{\\alpha} & \\text{Static FOC} \\\\\n",
    "\\frac{1}{c_t} & = \\beta E_t \\left \\{ \\frac{1}{c_{t+1}} \\left [ \\alpha z_{t+1} \\left ( \\frac{k_{t+1}}{n_{t+1}} \\right )^{\\alpha - 1} + (1 - \\delta) \\right ] \\right \\} & \\text{Euler equation} \\\\\n",
    "y_t & = z_t k_t^\\alpha n_t^{1 - \\alpha} & \\text{Production function} \\\\\n",
    "y_t & = c_t + i_t & \\text{Aggregate resource constraint} \\\\\n",
    "k_{t+1} & = (1 - \\delta) k_t + i_t & \\text{Captial accumulation} \\\\\n",
    "1 & = l_t + n_t & \\text{Labor-leisure tradeoff} \\\\\n",
    "\\log z_t & = \\rho \\log z_{t-1} + \\varepsilon_t & \\text{Technology shock transition}\n",
    "\\end{align}\n",
    "$$\n",
    "\n",
    "### Variables / Parameters\n",
    "\n",
    "This system is in the following variables:\n",
    "\n",
    "$$\n",
    "\\begin{align}\n",
    "x_t = \\{ \\quad & &\\\\\n",
    "& y_t, & \\text{Output}\\\\\n",
    "& c_t, & \\text{Consumption}\\\\\n",
    "& i_t, & \\text{Investment}\\\\\n",
    "& n_t, & \\text{Labor}\\\\\n",
    "& l_t, & \\text{Leisure}\\\\\n",
    "& k_t, & \\text{Capital}\\\\\n",
    "& z_t  & \\text{Technology} \\\\\n",
    "\\} \\quad & &\n",
    "\\end{align}$$\n",
    "\n",
    "and depends on the following parameters:\n",
    "\n",
    "$$\n",
    "\\begin{align}\n",
    "\\{ \\quad & &\\\\\n",
    "& \\beta, & \\text{Discount rate}\\\\\n",
    "& \\psi, & \\text{Marginal disutility of labor}\\\\\n",
    "& \\delta, & \\text{Depreciation rate}\\\\\n",
    "& \\alpha, & \\text{Capital-share of output}\\\\\n",
    "& \\rho, & \\text{Technology shock persistence}\\\\\n",
    "& \\sigma^2 & \\text{Technology shock variance}\\\\\n",
    "\\} \\quad & &\n",
    "\\end{align}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save the names of the equations, variables, and parameters\n",
    "equation_names = [\n",
    "    'static FOC', 'euler equation', 'production',\n",
    "    'aggregate resource constraint', 'capital accumulation',\n",
    "    'labor-leisure', 'technology shock transition'\n",
    "]\n",
    "variable_names = [\n",
    "    'output', 'consumption', 'investment',\n",
    "    'labor', 'leisure', 'capital', 'technology'\n",
    "]\n",
    "parameter_names = [\n",
    "    'discount rate', 'marginal disutility of labor',\n",
    "    'depreciation rate', 'capital share',\n",
    "    'technology shock persistence',\n",
    "    'technology shock standard deviation',\n",
    "]\n",
    "\n",
    "# Save some symbolic forms for pretty-printing\n",
    "variable_symbols = [\n",
    "    r\"y\", r\"c\", r\"i\", r\"n\", r\"l\", r\"k\", r\"z\",\n",
    "]\n",
    "contemporaneous_variable_symbols = [\n",
    "    r\"$%s_t$\" % symbol for symbol in variable_symbols\n",
    "]\n",
    "lead_variable_symbols = [\n",
    "    r\"$%s_{t+1}$\" % symbol for symbol in variable_symbols\n",
    "]\n",
    "\n",
    "parameter_symbols = [\n",
    "    r\"$\\beta$\", r\"$\\psi$\", r\"$\\delta$\", r\"$\\alpha$\", r\"$\\rho$\", r\"$\\sigma^2$\"\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Numerical method\n",
    "\n",
    "Using these equations, we can numerically find the steady-state using a root-finding algorithm and can then log-linearize around the steady-state using a numerical gradient procedure. In particular, here we follow DeJong and Dave (2011) chapter 2 and 3.1.\n",
    "\n",
    "Write the generic non-linear system as:\n",
    "\n",
    "$$\n",
    "\\Gamma(E_t x_{t+1}, x_t, z_t) = 0\n",
    "$$\n",
    "\n",
    "in the absense of shocks, this can be rewritten as:\n",
    "\n",
    "$$\n",
    "\\Psi(x_{t+1}, x_t) = 0\n",
    "$$\n",
    "\n",
    "or as:\n",
    "\n",
    "$$\n",
    "\\Psi_1(x_{t+1}, x_t) = \\Psi_2(x_{t+1}, x_t)\n",
    "$$\n",
    "\n",
    "and finally in logs as\n",
    "\n",
    "$$\n",
    "\\log \\Psi_1(e^{\\log x_{t+1}}, e^{\\log x_t}) - \\log \\Psi_2(e^{\\log x_{t+1}}, e^{\\log x_t}) = 0\n",
    "$$\n",
    "\n",
    "First, we define a new class (`RBC1`) which holds the state of the RBC model (its dimensions, parameters, etc) and has methods for evaluating the log system. The `eval_logged` method evaluates the last equation, above.\n",
    "\n",
    "Notice that the order of variables and order of equations is as described above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "class RBC1(object):\n",
    "    def __init__(self, params=None):\n",
    "        # Model dimensions\n",
    "        self.k_params = 6\n",
    "        self.k_variables = 7\n",
    "        \n",
    "        # Initialize parameters\n",
    "        if params is not None:\n",
    "            self.update(params)\n",
    "    \n",
    "    def update(self, params):\n",
    "        # Save deep parameters\n",
    "        self.discount_rate = params[0]\n",
    "        self.disutility_labor = params[1]\n",
    "        self.depreciation_rate = params[2]\n",
    "        self.capital_share = params[3]\n",
    "        self.technology_shock_persistence = params[4]\n",
    "        self.technology_shock_std = params[5]\n",
    "        \n",
    "    def eval_logged(self, log_lead, log_contemporaneous):\n",
    "        (log_lead_output, log_lead_consumption, log_lead_investment,\n",
    "         log_lead_labor, log_lead_leisure, log_lead_capital,\n",
    "         log_lead_technology_shock) = log_lead\n",
    "        \n",
    "        (log_output, log_consumption, log_investment, log_labor,\n",
    "         log_leisure, log_capital, log_technology_shock) = log_contemporaneous\n",
    "        \n",
    "        return np.r_[\n",
    "            self.log_static_foc(\n",
    "                log_lead_consumption, log_lead_labor,\n",
    "                log_lead_capital, log_lead_technology_shock\n",
    "            ),\n",
    "            self.log_euler_equation(\n",
    "                log_lead_consumption, log_lead_labor,\n",
    "                log_lead_capital, log_lead_technology_shock,\n",
    "                log_consumption\n",
    "            ),\n",
    "            self.log_production(\n",
    "                log_lead_output, log_lead_labor, log_lead_capital,\n",
    "                log_lead_technology_shock\n",
    "            ),\n",
    "            self.log_aggregate_resource_constraint(\n",
    "                log_lead_output, log_lead_consumption,\n",
    "                log_lead_investment\n",
    "            ),\n",
    "            self.log_capital_accumulation(\n",
    "                log_lead_capital, log_investment, log_capital\n",
    "            ),\n",
    "            self.log_labor_leisure_constraint(\n",
    "                log_lead_labor, log_lead_leisure\n",
    "            ),\n",
    "            self.log_technology_shock_transition(\n",
    "                log_lead_technology_shock, log_technology_shock\n",
    "            )\n",
    "        ]\n",
    "    \n",
    "    def log_static_foc(self, log_lead_consumption, log_lead_labor,\n",
    "                       log_lead_capital, log_lead_technology_shock):\n",
    "        return (\n",
    "            np.log(self.disutility_labor) +\n",
    "            log_lead_consumption -\n",
    "            np.log(1 - self.capital_share) -\n",
    "            log_lead_technology_shock -\n",
    "            self.capital_share * (log_lead_capital - log_lead_labor)\n",
    "        )\n",
    "        \n",
    "    def log_euler_equation(self, log_lead_consumption, log_lead_labor,\n",
    "                           log_lead_capital, log_lead_technology_shock,\n",
    "                           log_consumption):\n",
    "        return (\n",
    "            -log_consumption -\n",
    "            np.log(self.discount_rate) +\n",
    "            log_lead_consumption -\n",
    "            np.log(\n",
    "                (self.capital_share *\n",
    "                 np.exp(log_lead_technology_shock) * \n",
    "                 np.exp((1 - self.capital_share) * log_lead_labor) /\n",
    "                 np.exp((1 - self.capital_share) * log_lead_capital)) +\n",
    "                (1 - self.depreciation_rate)\n",
    "            )\n",
    "        )\n",
    "        \n",
    "    def log_production(self, log_lead_output, log_lead_labor, log_lead_capital,\n",
    "                       log_lead_technology_shock):\n",
    "        return (\n",
    "            log_lead_output -\n",
    "            log_lead_technology_shock -\n",
    "            self.capital_share * log_lead_capital -\n",
    "            (1 - self.capital_share) * log_lead_labor\n",
    "        )\n",
    "        \n",
    "    def log_aggregate_resource_constraint(self, log_lead_output, log_lead_consumption,\n",
    "                                          log_lead_investment):\n",
    "        return (\n",
    "            log_lead_output -\n",
    "            np.log(np.exp(log_lead_consumption) + np.exp(log_lead_investment))\n",
    "        )\n",
    "    \n",
    "    def log_capital_accumulation(self, log_lead_capital, log_investment, log_capital):\n",
    "        return (\n",
    "            log_lead_capital -\n",
    "            np.log(np.exp(log_investment) + (1 - self.depreciation_rate) * np.exp(log_capital))\n",
    "        )\n",
    "    \n",
    "    def log_labor_leisure_constraint(self, log_lead_labor, log_lead_leisure):\n",
    "        return (\n",
    "            -np.log(np.exp(log_lead_labor) + np.exp(log_lead_leisure))\n",
    "        )\n",
    "    \n",
    "    def log_technology_shock_transition(self, log_lead_technology_shock, log_technology_shock):\n",
    "        return (\n",
    "            log_lead_technology_shock -\n",
    "            self.technology_shock_persistence * log_technology_shock\n",
    "        )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Later we will estimate (some of) the parameters; in the meantime we fix them at the values used to generate the datasets in Ruge-Murcia (2007)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>name</th>\n",
       "      <td>discount rate</td>\n",
       "      <td>marginal disutility of labor</td>\n",
       "      <td>depreciation rate</td>\n",
       "      <td>capital share</td>\n",
       "      <td>technology shock persistence</td>\n",
       "      <td>technology shock standard deviation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>value</th>\n",
       "      <td>0.95</td>\n",
       "      <td>3</td>\n",
       "      <td>0.025</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.04</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "                   0                             1                  2  \\\n",
       "name   discount rate  marginal disutility of labor  depreciation rate   \n",
       "value           0.95                             3              0.025   \n",
       "\n",
       "                   3                             4  \\\n",
       "name   capital share  technology shock persistence   \n",
       "value           0.36                          0.85   \n",
       "\n",
       "                                         5  \n",
       "name   technology shock standard deviation  \n",
       "value                                 0.04  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Setup fixed parameters\n",
    "parameters = pd.DataFrame({\n",
    "    'name': parameter_names,\n",
    "    'value': [0.95, 3, 0.025, 0.36, 0.85, 0.04]\n",
    "})\n",
    "parameters.T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Steady state"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Numeric calculation\n",
    "\n",
    "To numerically calculate steady-state, we apply a root-finding algorithm to the `eval_logged` method. In particular, we are finding values $\\bar x$ such that\n",
    "\n",
    "$$\\log \\Psi_1(e^{\\log \\bar x}, e^{\\log \\bar x}) - \\log \\Psi_2(e^{\\log \\bar x}, e^{\\log \\bar x}) = 0$$\n",
    "\n",
    "These will be confirmed analytically, below."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we create a derived class, `RBC2` which extends all of the functionality from above, but now includes methods for numerical calcualtion of the steady-state."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>output</th>\n",
       "      <th>consumption</th>\n",
       "      <th>investment</th>\n",
       "      <th>labor</th>\n",
       "      <th>leisure</th>\n",
       "      <th>capital</th>\n",
       "      <th>technology</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>value</th>\n",
       "      <td>0.572</td>\n",
       "      <td>0.506</td>\n",
       "      <td>0.0663</td>\n",
       "      <td>0.241</td>\n",
       "      <td>0.759</td>\n",
       "      <td>2.65</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "       output  consumption  investment  labor  leisure  capital  technology\n",
       "value   0.572        0.506      0.0663  0.241    0.759     2.65           1"
      ]
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   "source": [
    "class RBC2(RBC1):\n",
    "    def steady_state_numeric(self):\n",
    "        # Setup starting parameters\n",
    "        log_start_vars = [0.5] * self.k_variables  # very arbitrary\n",
    "\n",
    "        # Setup the function the evaluate\n",
    "        eval_logged = lambda log_vars: self.eval_logged(log_vars, log_vars)\n",
    "\n",
    "        # Apply the root-finding algorithm\n",
    "        result = optimize.root(eval_logged, log_start_vars)\n",
    "        \n",
    "        return np.exp(result.x)\n",
    "\n",
    "mod2 = RBC2(parameters['value'])\n",
    "\n",
    "steady_state = pd.DataFrame({\n",
    "    'value': mod2.steady_state_numeric()\n",
    "}, index=variable_names)\n",
    "\n",
    "steady_state.T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analytic evaluation\n",
    "\n",
    "In this case, we can analytically evaluate the steady-state:\n",
    "\n",
    "$$\n",
    "\\begin{align}\n",
    "\\frac{\\bar y}{\\bar n} & = \\eta \\\\\n",
    "\\frac{\\bar c}{\\bar n} & = \\eta - \\delta \\theta \\\\\n",
    "\\frac{\\bar i}{\\bar n} & = \\delta \\theta \\\\\n",
    "\\bar n & = \\frac{1}{\\left ( \\frac{1}{1 - \\alpha} \\right ) \\psi \\left [ 1 - \\delta \\theta^{1-\\alpha} \\right ]} \\\\\n",
    "\\bar l & = 1 - \\bar n \\\\\n",
    "\\frac{\\bar k}{\\bar n} & = \\theta\n",
    "\\end{align}\n",
    "$$\n",
    "\n",
    "where\n",
    "$$\n",
    "\\begin{align}\n",
    "\\theta & = \\left ( \\frac{\\alpha}{1/\\beta - (1 - \\delta)} \\right )^\\frac{1}{1-\\alpha} \\\\\n",
    "\\eta & = \\theta^\\alpha\n",
    "\\end{align}\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
       "      <th>output</th>\n",
       "      <th>consumption</th>\n",
       "      <th>investment</th>\n",
       "      <th>labor</th>\n",
       "      <th>leisure</th>\n",
       "      <th>capital</th>\n",
       "      <th>technology</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>numeric</th>\n",
       "      <td>0.572</td>\n",
       "      <td>0.506</td>\n",
       "      <td>0.0663</td>\n",
       "      <td>0.241</td>\n",
       "      <td>0.759</td>\n",
       "      <td>2.65</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>analytic</th>\n",
       "      <td>0.572</td>\n",
       "      <td>0.506</td>\n",
       "      <td>0.0663</td>\n",
       "      <td>0.241</td>\n",
       "      <td>0.759</td>\n",
       "      <td>2.65</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          output  consumption  investment  labor  leisure  capital  technology\n",
       "numeric    0.572        0.506      0.0663  0.241    0.759     2.65           1\n",
       "analytic   0.572        0.506      0.0663  0.241    0.759     2.65           1"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class RBC3(RBC2):\n",
    "    \n",
    "    def update(self, params):\n",
    "        # Update the deep parameters\n",
    "        super(RBC3, self).update(params)\n",
    "        \n",
    "        # And now also calculate some intermediate parameters\n",
    "        self.theta = (self.capital_share / (\n",
    "            1 / self.discount_rate -\n",
    "            (1 - self.depreciation_rate)\n",
    "        ))**(1 / (1 - self.capital_share))\n",
    "        \n",
    "        self.eta = self.theta**self.capital_share\n",
    "    \n",
    "    def steady_state_analytic(self):\n",
    "        steady_state = np.zeros(7)\n",
    "\n",
    "        # Labor (must be computed first)\n",
    "        numer = (1 - self.capital_share) / self.disutility_labor\n",
    "        denom = (1 - self.depreciation_rate * self.theta**(1 - self.capital_share))\n",
    "        steady_state[3] = numer / denom\n",
    "        # Output\n",
    "        steady_state[0] = self.eta * steady_state[3]\n",
    "        # Consumption\n",
    "        steady_state[1] = (1 - self.capital_share) * self.eta / self.disutility_labor\n",
    "        # Investment\n",
    "        steady_state[2] = self.depreciation_rate * self.theta * steady_state[3]\n",
    "        # Labor (computed already)\n",
    "        # Leisure\n",
    "        steady_state[4] = 1 - steady_state[3]\n",
    "        # Capital\n",
    "        steady_state[5] = self.theta * steady_state[3]\n",
    "        # Technology shock\n",
    "        steady_state[6] = 1\n",
    "        \n",
    "        return steady_state\n",
    "    \n",
    "mod3 = RBC3(parameters['value'])\n",
    "\n",
    "steady_state = pd.DataFrame({\n",
    "    'numeric': mod3.steady_state_numeric(),\n",
    "    'analytic': mod3.steady_state_analytic()\n",
    "}, index=variable_names)\n",
    "\n",
    "steady_state.T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Log-linearization\n",
    "\n",
    "The system we wrote down, above, was non-linear. In order to estimate it, we want to get it in a linear form:\n",
    "\n",
    "$$\n",
    "A E_t \\tilde x_{t+1} = B \\tilde x_{t} + C v_{t+1}\n",
    "$$\n",
    "\n",
    "where $v_{t+1}$ contains structural shocks (here, $z_t$ is included in the $x_t$ vector, and the only structural shock is $\\varepsilon_t$, the innovation to $z_t$).\n",
    "\n",
    "This can be achieved via log-linearization around the steady state. In this case, DeJong and Dave (2011) show that:\n",
    "\n",
    "$$\n",
    "A = \\left [ \\frac{\\partial \\log [ \\Psi_1 ]}{\\partial \\log(x_{t+1})} (\\bar x) - \\frac{\\partial \\log [ \\Psi_2 ]}{\\partial \\log(x_{t+1})} (\\bar x) \\right ] \\\\\n",
    "B = - \\left [ \\frac{\\partial \\log [ \\Psi_1 ]}{\\partial \\log(x_t)} (\\bar x) - \\frac{\\partial \\log [ \\Psi_2 ]}{\\partial \\log(x_t)} (\\bar x) \\right ] \\\\\n",
    "$$\n",
    "\n",
    "where $\\tilde x_t = \\log \\left ( \\frac{x_t}{\\bar x} \\right )$ expresses the variables in proportional deviation from steady-state form.\n",
    "\n",
    "The matrix $C$ can be constructed by observation. In this case, we have:\n",
    "\n",
    "$$\n",
    "C = \\begin{bmatrix} 0 & 0 & 0 & 0 & 0 & 0 & 1 \\end{bmatrix} \\qquad \\text{and} \\qquad v_{t+1} \\equiv \\varepsilon_t\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Numeric calculation\n",
    "\n",
    "Since the `eval_logged` method of our class evaluates $\\log \\Psi_1(e^{\\log x_{t+1}}, e^{\\log x_t}) - \\log \\Psi_2(e^{\\log x_{t+1}}, e^{\\log x_t})$, we can apply a numerical gradient procedure to it to get $A$, when we differentiate with respect to the lead variables, and $B$, when we differentiate with respect to the contemporaneous variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
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       "    <tr>\n",
       "      <th>static FOC</th>\n",
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       "      <th>euler equation</th>\n",
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       "      <th>production</th>\n",
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       "      <th>aggregate resource constraint</th>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <th>capital accumulation</th>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>labor-leisure</th>\n",
       "      <td>-0</td>\n",
       "      <td>-0</td>\n",
       "      <td>-0</td>\n",
       "      <td>-0.241</td>\n",
       "      <td>-0.759</td>\n",
       "      <td>-0</td>\n",
       "      <td>-0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>technology shock transition</th>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "                               $y_{t+1}$  $c_{t+1}$  $i_{t+1}$  $n_{t+1}$  \\\n",
       "static FOC                             0          1          0       0.36   \n",
       "euler equation                         0          1          0    -0.0472   \n",
       "production                             1          0          0      -0.64   \n",
       "aggregate resource constraint          1     -0.884     -0.116          0   \n",
       "capital accumulation                   0          0          0          0   \n",
       "labor-leisure                         -0         -0         -0     -0.241   \n",
       "technology shock transition            0          0          0          0   \n",
       "\n",
       "                               $l_{t+1}$  $k_{t+1}$  $z_{t+1}$  \n",
       "static FOC                             0      -0.36         -1  \n",
       "euler equation                         0     0.0472    -0.0737  \n",
       "production                             0      -0.36         -1  \n",
       "aggregate resource constraint          0          0          0  \n",
       "capital accumulation                   0          1          0  \n",
       "labor-leisure                     -0.759         -0         -0  \n",
       "technology shock transition            0          0          1  "
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       "                               $y_t$  $c_t$  $i_t$  $n_t$  $l_t$  $k_t$  $z_t$\n",
       "static FOC                        -0     -0     -0     -0     -0     -0     -0\n",
       "euler equation                    -0      1     -0     -0     -0     -0     -0\n",
       "production                        -0     -0     -0     -0     -0     -0     -0\n",
       "aggregate resource constraint     -0     -0     -0     -0     -0     -0     -0\n",
       "capital accumulation              -0     -0  0.025     -0     -0  0.975     -0\n",
       "labor-leisure                     -0     -0     -0     -0     -0     -0     -0\n",
       "technology shock transition       -0     -0     -0     -0     -0     -0   0.85"
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       "                               $\\varepsilon_t$\n",
       "static FOC                                   0\n",
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   "source": [
    "class RBC4(RBC3):\n",
    "    \n",
    "    def A_numeric(self):\n",
    "        log_steady_state = np.log(self.steady_state_analytic())\n",
    "\n",
    "        eval_logged_lead = lambda log_lead: self.eval_logged(log_lead, log_steady_state)\n",
    "        \n",
    "        return approx_fprime_cs(log_steady_state, eval_logged_lead)\n",
    "\n",
    "    def B_numeric(self):\n",
    "        log_steady_state = np.log(self.steady_state_analytic())\n",
    "        \n",
    "        eval_logged_contemporaneous = lambda log_contemp: self.eval_logged(log_steady_state, log_contemp)\n",
    "        \n",
    "        return -approx_fprime_cs(log_steady_state, eval_logged_contemporaneous)\n",
    "    \n",
    "    def C(self):\n",
    "        return np.r_[[0]*(self.k_variables-1), 1]\n",
    "\n",
    "mod4 = RBC4(parameters['value'])\n",
    "        \n",
    "display(pd.DataFrame(mod4.A_numeric(), index=equation_names, columns=lead_variable_symbols))\n",
    "display(pd.DataFrame(mod4.B_numeric(), index=equation_names, columns=contemporaneous_variable_symbols))\n",
    "display(pd.DataFrame(mod4.C(), index=equation_names, columns=[r'$\\varepsilon_t$']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analytic evaluation\n",
    "\n",
    "This system can be log-linearized directly, as well, yielding (see e.g. Ruge-Murcia (2007) Appendix A for these formulas, with slightly different notation):\n",
    "\n",
    "$$\n",
    "\\begin{align}\n",
    "\\tilde c_t & = \\tilde z_t + \\alpha \\tilde k_t - \\alpha \\tilde n_t \\\\\n",
    "-\\tilde c_t & = -E_t \\tilde c_{t+1} + \\zeta (\\alpha - 1) E_t \\tilde k_{t+1} + \\zeta (1 - \\alpha) E_t \\tilde n_{t+1} + \\zeta E_t \\tilde z_{t+1} \\\\\n",
    "\\tilde y_t & = \\tilde z_t + \\alpha \\tilde k_t + (1 - \\alpha) \\tilde n_t \\\\\n",
    "\\tilde y_t & = \\gamma \\tilde c_t + (1 - \\gamma) \\tilde i_t \\\\\n",
    "\\tilde k_{t+1} & = (1 - \\delta) \\tilde k_t + \\delta \\tilde i_t \\\\\n",
    "0 & = \\frac{\\bar l}{\\bar l + \\bar n} \\tilde l_{t+1} + \\frac{\\bar n}{\\bar l + \\bar n} \\tilde n_{t+1} \\\\\n",
    "\\tilde z_{t+1} & = \\rho \\tilde z_t + \\varepsilon_t\n",
    "\\end{align}\n",
    "$$\n",
    "\n",
    "where $\\zeta = \\alpha \\beta \\theta^{\\alpha - 1}$ and $\\gamma = 1 - \\delta \\theta^{1 - \\alpha}$ is the steady-state consumption-output ratio."
   ]
  },
  {
   "cell_type": "code",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.64</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.36</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>aggregate resource constraint</th>\n",
       "      <td>1</td>\n",
       "      <td>-0.884</td>\n",
       "      <td>-0.116</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>capital accumulation</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>labor-leisure</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.241</td>\n",
       "      <td>-0.759</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>technology shock transition</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               $y_{t+1}$  $c_{t+1}$  $i_{t+1}$  $n_{t+1}$  \\\n",
       "static FOC                             0          1          0       0.36   \n",
       "euler equation                         0          1          0    -0.0472   \n",
       "production                             1          0          0      -0.64   \n",
       "aggregate resource constraint          1     -0.884     -0.116          0   \n",
       "capital accumulation                   0          0          0          0   \n",
       "labor-leisure                          0          0          0     -0.241   \n",
       "technology shock transition            0          0          0          0   \n",
       "\n",
       "                               $l_{t+1}$  $k_{t+1}$  $z_{t+1}$  \n",
       "static FOC                             0      -0.36         -1  \n",
       "euler equation                         0     0.0472    -0.0737  \n",
       "production                             0      -0.36         -1  \n",
       "aggregate resource constraint          0          0          0  \n",
       "capital accumulation                   0          1          0  \n",
       "labor-leisure                     -0.759          0          0  \n",
       "technology shock transition            0          0          1  "
      ]
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>$y_{t+1}$</th>\n",
       "      <th>$c_{t+1}$</th>\n",
       "      <th>$i_{t+1}$</th>\n",
       "      <th>$n_{t+1}$</th>\n",
       "      <th>$l_{t+1}$</th>\n",
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       "      <th>$z_{t+1}$</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>static FOC</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>euler equation</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>production</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>aggregate resource constraint</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>capital accumulation</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.025</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.975</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>labor-leisure</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>technology shock transition</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.85</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
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       "                               $y_{t+1}$  $c_{t+1}$  $i_{t+1}$  $n_{t+1}$  \\\n",
       "static FOC                             0          0          0          0   \n",
       "euler equation                         0          1          0          0   \n",
       "production                             0          0          0          0   \n",
       "aggregate resource constraint          0          0          0          0   \n",
       "capital accumulation                   0          0      0.025          0   \n",
       "labor-leisure                          0          0          0          0   \n",
       "technology shock transition            0          0          0          0   \n",
       "\n",
       "                               $l_{t+1}$  $k_{t+1}$  $z_{t+1}$  \n",
       "static FOC                             0          0          0  \n",
       "euler equation                         0          0          0  \n",
       "production                             0          0          0  \n",
       "aggregate resource constraint          0          0          0  \n",
       "capital accumulation                   0      0.975          0  \n",
       "labor-leisure                          0          0          0  \n",
       "technology shock transition            0          0       0.85  "
      ]
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     "metadata": {},
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   ],
   "source": [
    "class RBC5(RBC4):\n",
    "    \n",
    "    def update(self, params):\n",
    "        super(RBC5, self).update(params)\n",
    "        \n",
    "        # Now calculate some more intermediate parameters\n",
    "        self.gamma = 1 - self.depreciation_rate * self.theta**(1 - self.capital_share)\n",
    "        self.zeta = self.capital_share * self.discount_rate * self.theta**(self.capital_share - 1)\n",
    "    \n",
    "    def A_analytic(self):\n",
    "        steady_state = self.steady_state_analytic()\n",
    "        \n",
    "        A = np.array([\n",
    "            [0, 1, 0, self.capital_share, 0, -self.capital_share, -1],\n",
    "            [0, 1, 0, self.zeta * (self.capital_share - 1), 0, self.zeta * (1 - self.capital_share), -self.zeta],\n",
    "            [1, 0, 0, (self.capital_share - 1), 0, -self.capital_share, -1],\n",
    "            [1, -self.gamma, (self.gamma - 1), 0, 0, 0, 0],\n",
    "            [0, 0, 0, 0, 0, 1, 0],\n",
    "            [0, 0, 0, -steady_state[3], -steady_state[4], 0, 0],\n",
    "            [0, 0, 0, 0, 0, 0, 1],\n",
    "        ])\n",
    "        \n",
    "        return A\n",
    "\n",
    "    def B_analytic(self):\n",
    "        \n",
    "        B = np.array([\n",
    "            [0, 0, 0, 0, 0, 0, 0],\n",
    "            [0, 1, 0, 0, 0, 0, 0],\n",
    "            [0, 0, 0, 0, 0, 0, 0],\n",
    "            [0, 0, 0, 0, 0, 0, 0],\n",
    "            [0, 0, self.depreciation_rate, 0, 0, 1 - self.depreciation_rate, 0],\n",
    "            [0, 0, 0, 0, 0, 0, 0],\n",
    "            [0, 0, 0, 0, 0, 0, self.technology_shock_persistence],\n",
    "        ])\n",
    "        \n",
    "        return B\n",
    "\n",
    "mod5 = RBC5(parameters['value'])\n",
    "\n",
    "display(pd.DataFrame(mod5.A_analytic(), index=equation_names, columns=lead_variable_symbols))\n",
    "assert(np.all(np.abs(mod5.A_numeric() - mod5.A_analytic()) < 1e-10))\n",
    "\n",
    "display(pd.DataFrame(mod5.B_analytic(), index=equation_names, columns=lead_variable_symbols))\n",
    "assert(np.all(np.abs(mod5.B_numeric() - mod5.B_analytic()) < 1e-10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## System reduction\n",
    "\n",
    "The system currently has 7 equations in 7 unknowns. This can be reduced into a system of 3 equations in 3 unknowns (consumption, capital, and the technology shock), by substituting out output, investment, labor, and leisure. Given a solution to the reduced system, the remaining four unknowns can be calculated. Also, we remove the technology shock transition from the state vector and include it instead in the stochastic shocks component $(v_{t+1})$.\n",
    "\n",
    "First, notice that (using the static first order condition, production function, aggregate resource constraint, and labor-leisure tradeoff equations, respectively):\n",
    "\n",
    "$$\n",
    "\\begin{align}\n",
    "\\tilde n_t & = - \\frac{1}{\\alpha} \\tilde c_t + \\frac{1}{\\alpha} \\tilde z_t + \\tilde k_t \\\\\n",
    "\\tilde y_t & = \\tilde z_t + \\alpha \\tilde k_t + (1 - \\alpha) \\left [ - \\frac{1}{\\alpha} \\tilde c_t + \\frac{1}{\\alpha} \\tilde z_t + \\tilde k_t \\right ] \\\\\n",
    "& = \\frac{1}{\\alpha} \\tilde z_t - \\frac{1 - \\alpha}{\\alpha} \\tilde c_t + \\tilde k_t \\\\\n",
    "\\tilde i_t & = \\frac{1}{1 - \\gamma} \\left [ \\frac{1}{\\alpha} \\tilde z_t - \\frac{1 - \\alpha}{\\alpha} \\tilde c_t + \\tilde k_t - \\gamma \\tilde c_t \\right ] \\\\\n",
    "& = \\frac{1}{1 - \\gamma} \\left [ \\frac{1}{\\alpha} \\tilde z_t - \\frac{1 - \\alpha + \\gamma \\alpha}{\\alpha} \\tilde c_t + \\tilde k_t \\right ] \\\\\n",
    "\\tilde l_t & = - \\frac{\\bar n}{1 - \\bar n} \\tilde n_t\n",
    "\\end{align}\n",
    "$$\n",
    "\n",
    "then we can plug these values into the remaining three equations (Euler, capital accumulation, and shock transition equations):\n",
    "\n",
    "$$\n",
    "\\begin{align}\n",
    "k_{t+1} & = (1 - \\delta) \\tilde k_t + \\delta \\frac{1}{1 - \\gamma} \\left [ \\frac{1}{\\alpha} \\tilde z_t - \\frac{1 - \\alpha + \\gamma \\alpha}{\\alpha} \\tilde c_t + \\tilde k_t \\right ] \\\\\n",
    "& = \\underbrace{\\left [ 1 + \\delta \\left ( \\frac{\\gamma}{1 - \\gamma} \\right ) \\right ]}_{B_{11}, F_{11}} \\tilde k_t + \\underbrace{\\frac{-\\delta (1 - \\alpha + \\gamma \\alpha)}{\\alpha (1 - \\gamma)}}_{B_{12}, F_{12}} \\tilde c_t + \\underbrace{\\frac{\\delta}{\\alpha (1 - \\gamma)}}_{C_1, G_{12}} \\tilde z_t\n",
    "\\end{align}\n",
    "$$\n",
    "\n",
    "and\n",
    "\n",
    "$$\n",
    "\\begin{align}\n",
    "E_t \\tilde c_{t+1} & = \\tilde c_t + \\zeta (\\alpha - 1) E_t k_{t+1} + \\zeta (1 - \\alpha) E_t \\left [ - \\frac{1}{\\alpha} \\tilde c_{t+1} + \\frac{1}{\\alpha} \\tilde z_{t+1} + \\tilde k_{t+1} \\right ] + \\zeta E_t \\tilde z_{t+1} \\\\\n",
    "& = \\tilde c_t + \\zeta (\\alpha - 1) E_t \\tilde k_{t+1} - \\zeta \\frac{(1 - \\alpha)}{\\alpha} E_t \\tilde c_{t+1} + \\zeta \\frac{(1 - \\alpha)}{\\alpha} E_t z_{t+1} + \\zeta (1 - \\alpha) E_t \\tilde k_{t+1} + \\zeta E_t \\tilde z_{t+1} \\\\\n",
    "& = \\underbrace{\\frac{\\alpha}{\\zeta + \\alpha (1 - \\zeta)}}_{B_{22}} \\tilde c_t + \\underbrace{\\frac{\\zeta \\rho}{\\zeta + \\alpha (1 - \\zeta )}}_{C_2} \\tilde z_t\n",
    "\\end{align}\n",
    "$$\n",
    "\n",
    "and\n",
    "\n",
    "$$\n",
    "\\begin{align}\n",
    "\\tilde z_{t+1} & = \\rho \\tilde z_t + \\varepsilon_t\n",
    "\\end{align}\n",
    "$$\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save the names of the equations, variables, and parameters\n",
    "# in the reduced model\n",
    "reduced_equation_names = [\n",
    "    'euler equation', 'capital accumulation'\n",
    "]\n",
    "reduced_variable_names = [\n",
    "    'consumption', 'capital'\n",
    "]\n",
    "reduced_parameter_names = parameter_names\n",
    "\n",
    "# Save some symbolic forms for pretty-printing\n",
    "reduced_variable_symbols = [\n",
    "    r\"c\", r\"k\"\n",
    "]\n",
    "reduced_contemporaneous_variable_symbols = [\n",
    "    r\"$%s_t$\" % symbol for symbol in reduced_variable_symbols\n",
    "]\n",
    "reduced_lead_variable_symbols = [\n",
    "    r\"$%s_{t+1}$\" % symbol for symbol in reduced_variable_symbols\n",
    "]\n",
    "\n",
    "reduced_parameter_symbols = parameter_symbols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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       "      <th>steady state</th>\n",
       "      <td>0.506</td>\n",
       "      <td>2.65</td>\n",
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       "              consumption  capital\n",
       "steady state        0.506     2.65"
      ]
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       "                      $c_{t+1}$  $k_{t+1}$\n",
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       "euler equation         1.19 -0.574\n",
       "capital accumulation      0  0.884"
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   "source": [
    "class ReducedRBC1(RBC5):\n",
    "    def __init__(self, params=None):\n",
    "        # Model dimensions\n",
    "        self.k_params = 6\n",
    "        self.k_variables = 2\n",
    "        \n",
    "        # Index for the reduced form variables\n",
    "        self.reduced_idx = [1, -2]\n",
    "        \n",
    "        # Initialize parameters\n",
    "        if params is not None:\n",
    "            self.update(params)\n",
    "\n",
    "    def steady_state_numeric(self):\n",
    "        return super(ReducedRBC1, self).steady_state_numeric()[self.reduced_idx]\n",
    "        \n",
    "    def steady_state_analytic(self):\n",
    "        return super(ReducedRBC1, self).steady_state_analytic()[self.reduced_idx]\n",
    "    \n",
    "    def A(self):\n",
    "        return np.eye(self.k_variables)\n",
    "    \n",
    "    def B(self):\n",
    "        B11 = 1 + self.depreciation_rate * (self.gamma / (1 - self.gamma))\n",
    "        B12 = (\n",
    "            -self.depreciation_rate *\n",
    "            (1 - self.capital_share + self.gamma * self.capital_share) /\n",
    "            (self.capital_share * (1 - self.gamma))\n",
    "        )\n",
    "        B21 = 0\n",
    "        B22 = self.capital_share / (self.zeta + self.capital_share*(1 - self.zeta))\n",
    "        \n",
    "        return np.array([[B11, B12],\n",
    "                         [B21, B22]])\n",
    "        \n",
    "    def C(self):\n",
    "        C1 = self.depreciation_rate / (self.capital_share * (1 - self.gamma))\n",
    "        C2 = (\n",
    "            self.zeta * self.technology_shock_persistence /\n",
    "            (self.zeta + self.capital_share*(1 - self.zeta))\n",
    "        )\n",
    "        return np.array([C1, C2])[:,np.newaxis]\n",
    "        \n",
    "# Setup the model\n",
    "reduced_mod1 = ReducedRBC1(parameters['value'])\n",
    "\n",
    "# Display the steady-states\n",
    "reduced_steady_state = pd.DataFrame({\n",
    "    'steady state': reduced_mod1.steady_state_analytic()\n",
    "}, index=reduced_variable_names)\n",
    "display(reduced_steady_state.T)\n",
    "\n",
    "# Test the model coefficients for accuracy\n",
    "# (results from \"Emsm\", downloaded from\n",
    "# https://sites.google.com/site/frugemurcia/home/replication-files\n",
    "# on 06/19/2015)\n",
    "assert_allclose(\n",
    "    reduced_mod1.B(),\n",
    "    np.array([[1.1906433, -0.57400910],\n",
    "              [0,          0.88408644]])\n",
    ")\n",
    "assert_allclose(\n",
    "    reduced_mod1.C(),\n",
    "    np.array([[0.59900910], [0.15394769]])\n",
    ")\n",
    "\n",
    "# Display the linear model coefficients\n",
    "display(pd.DataFrame(reduced_mod1.A(), index=reduced_equation_names, columns=reduced_lead_variable_symbols))\n",
    "display(pd.DataFrame(reduced_mod1.B(), index=reduced_equation_names, columns=reduced_contemporaneous_variable_symbols))\n",
    "display(pd.DataFrame(reduced_mod1.C(), index=reduced_equation_names, columns=[r'$z_t$']))\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Solving the model\n",
    "\n",
    "This model can be solved using the method of Blanchard-Kahn (1980). First we put it in the required form:\n",
    "\n",
    "$$\n",
    "\\begin{bmatrix}\n",
    "\\tilde k_{t+1} \\\\\n",
    "E_t \\tilde c_{t+1}\n",
    "\\end{bmatrix} = \\begin{bmatrix}\n",
    "B_{11} & B_{12} \\\\\n",
    "B_{21} & B_{22}\n",
    "\\end{bmatrix} \\begin{bmatrix}\n",
    "\\tilde k_{t} \\\\\n",
    "\\tilde c_{t} \\\\\n",
    "\\end{bmatrix} + \\begin{bmatrix}\n",
    "C_{1} \\\\\n",
    "C_{2}\n",
    "\\end{bmatrix} \\tilde z_t\n",
    "$$\n",
    "\n",
    "and the solution yields the following equilibrium dynamics:\n",
    "\n",
    "$$\n",
    "\\begin{bmatrix}\n",
    "\\tilde k_{t+1} \\\\\n",
    "\\tilde c_{t+1}\n",
    "\\end{bmatrix} = \\begin{bmatrix}\n",
    "F_{11} & F_{12} \\\\\n",
    "F_{21} & F_{22}\n",
    "\\end{bmatrix} \\begin{bmatrix}\n",
    "\\tilde k_{t} \\\\\n",
    "\\tilde c_{t}\n",
    "\\end{bmatrix} + \\begin{bmatrix}\n",
    "G_{11} & G_{12} \\\\\n",
    "G_{21} & G_{22}\n",
    "\\end{bmatrix} \\begin{bmatrix}\n",
    "\\tilde z_{t+1} \\\\\n",
    "\\tilde z_t\n",
    "\\end{bmatrix}\n",
    "$$\n",
    "\n",
    "in particular, we will find a policy function for the control variable, where the optimal choice for $\\tilde c_t$ depends only on the contemporaneous state variable $\\tilde k_t$ and exogenous shock $\\tilde z_t$. Then the policy expression can be substituted in to the reduced form capital accumulation equation, providing the evolution of the state variable in terms only of itself and the exogenous shock. The evolution of the exogenous shock is an $AR(1)$ as described above.\n",
    "\n",
    "$$\n",
    "\\begin{align}\n",
    "\\tilde c_t & = \\phi_{ck} \\tilde k_t + \\phi_{cz} \\tilde z_t \\\\\n",
    "\\tilde k_{t+1} & = \\underbrace{\\left [ F_{11} + F_{12} \\phi_{ck} \\right ]}_{T_{kk}} \\tilde k_t + \\underbrace{\\left [G_{12} + F_{12} \\phi_{cz} \\right ]}_{T_{kz}} \\tilde z_t \\\\\n",
    "\\tilde z_{t+1} & = \\rho \\tilde z_t + \\varepsilon_t \\\\\n",
    "\\end{align}\n",
    "$$\n",
    "\n",
    "where $F_{11}, F_{12},$ and $G_{12}$ are defined above (and we have $G_{11} = 0$), and $\\phi_{ck}, \\phi_{cz}$ are generated by the solution method. $F_{21}, F_{22}, G_{21}, G_{22}$ will not be required for estimation, but can be found by forwarding the equation for $c_t$ and plugging in for $k_{t+1}$."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Some solution details\n",
    "\n",
    "Write the Jordan decomposition of $\\mathbf{B}$ as:\n",
    "\n",
    "$$\\mathbf{B} = \\Lambda^{-1} J \\Lambda$$\n",
    "\n",
    "where\n",
    "\n",
    "$$\n",
    "\\Lambda = \\begin{bmatrix}\n",
    "\\Lambda_{11} & \\Lambda_{12} \\\\\n",
    "\\Lambda_{21} & \\Lambda_{22} \\\\\n",
    "\\end{bmatrix}\n",
    "\\qquad \n",
    "J = \\begin{bmatrix}\n",
    "J_1 & 0 \\\\\n",
    "0 & J_2 \\\\\n",
    "\\end{bmatrix}\n",
    "$$\n",
    "\n",
    "and we assume that the eigenvalues have been re-ordered so that the explosive ones are contained in $J_2$. Note that to solve the system using the Blanchard-Kahn method, we need to separate the variables into predetermined and non-predetermined. For a unique solution to exist, it must be that the number of non-predetermined variables is equal to the number of explosive eigenvalues (so that we have \"saddle-path\" stability).\n",
    "\n",
    "Here, $k_{t+1}$ is predetermined, and $c_{t+1}$ is non-predetermined, so we have a unique solution if there is one explosive eigenvalue and one non-explosive eigenvalue.\n",
    "\n",
    "Finally we can express the coefficients in the policy function above, as (see for example DeJong and Dave, section 4.3):\n",
    "\n",
    "$$\n",
    "\\begin{align}\n",
    "\\phi_{ck} & = - \\Lambda_{22}^{-1} \\Lambda_{21}\\\\\n",
    "\\phi_{cz} & = - \\Lambda_{22}^{-1} J_2^{-1} \\left (I - \\rho J_2^{-1} D_2 \\right )^{-1} \\\\\n",
    "\\end{align}\n",
    "$$\n",
    "\n",
    "where $D_2$ is the lower component of $D = \\Lambda \\mathbf{C}$. Here, $D_2 = \\Lambda_{21} C_1 + \\Lambda_{22} C_2$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def ordered_jordan_decomposition(matrix):\n",
    "    # Get eigenvalues and left eigenvectors of `matrix`\n",
    "    # Note that the left eigenvectors of `matrix`\n",
    "    # are the transpose of the right eigenvectors of the\n",
    "    # transpose of matrix, and that `matrix` and `matrix'`\n",
    "    # have the same eigenvalues\n",
    "    eigenvalues, right_eigenvectors = np.linalg.eig(matrix.transpose())\n",
    "    left_eigenvectors = right_eigenvectors.transpose()\n",
    "    \n",
    "    # Sort in increasing order\n",
    "    idx = np.argsort(eigenvalues)\n",
    "    \n",
    "    # Return eigenvector matrix, diagonal eigenvalue matrix\n",
    "    # Note that the left eigenvectors are in the rows now,\n",
    "    # not the columns\n",
    "    return np.diag(eigenvalues[idx]), left_eigenvectors[idx, :]\n",
    "\n",
    "def solve_blanchard_kahn(B, C, rho, k_predetermined):\n",
    "    # Perform the Jordan decomposition\n",
    "    # this yields J, Lambda\n",
    "    eigenvalues, left_eigenvectors = ordered_jordan_decomposition(B)\n",
    "    left_eigenvectors = left_eigenvectors\n",
    "\n",
    "    # Dimensions\n",
    "    k_variables = len(B)\n",
    "    k_nonpredetermined = k_variables - k_predetermined\n",
    "\n",
    "    k_stable = len(np.where(eigenvalues.diagonal() < 1)[0])\n",
    "    k_unstable = k_variables - k_stable\n",
    "\n",
    "    # Make sure we're saddle-path stable\n",
    "    if not k_unstable == k_nonpredetermined:\n",
    "        raise RuntimeError('Blanchard-Kahn condition not met. Unique solution does not exist')\n",
    "\n",
    "    # Decouple the system (this is D, above)\n",
    "    decoupled_C = np.dot(left_eigenvectors, C)\n",
    "\n",
    "    # Create partition indices\n",
    "    p1 = np.s_[:k_predetermined]\n",
    "    p2 = np.s_[k_predetermined:]\n",
    "\n",
    "    p11 = np.s_[:k_predetermined, :k_predetermined]\n",
    "    p12 = np.s_[:k_predetermined, k_predetermined:]\n",
    "    p21 = np.s_[k_predetermined:, :k_predetermined]\n",
    "    p22 = np.s_[k_predetermined:, k_predetermined:]\n",
    "\n",
    "    # Solve the explosive component (controls) in terms of the\n",
    "    # non-explosive component (states) and shocks\n",
    "    tmp = np.linalg.inv(left_eigenvectors[p22])\n",
    "    \n",
    "    # This is \\phi_{ck}, above\n",
    "    policy_state = - np.dot(tmp, left_eigenvectors[p21])\n",
    "    # This is \\phi_{cz}, above\n",
    "    policy_shock = -(\n",
    "        np.dot(tmp, 1. / eigenvalues[p22]).dot(\n",
    "            np.linalg.inv(\n",
    "                np.eye(k_nonpredetermined) -\n",
    "                rho / eigenvalues[p22]\n",
    "            )\n",
    "        ).dot(decoupled_C[p2])\n",
    "    )\n",
    "\n",
    "    # Solve for the non-explosive transition\n",
    "    # This is T_{kk}, above\n",
    "    transition_state = B[p11] + np.dot(B[p12], policy_state)\n",
    "    # This is T_{kz}, above\n",
    "    transition_shock = np.dot(B[p12], policy_shock) + C[p1]\n",
    "    \n",
    "    return policy_state, policy_shock, transition_state, transition_shock\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "class ReducedRBC2(ReducedRBC1):\n",
    "    def solve(self, params=None):\n",
    "        # Update the model parameters, if given\n",
    "        if params is not None:\n",
    "            self.update(params)\n",
    "        \n",
    "        # Solve the model\n",
    "        phi_ck, phi_cz, T_kk, T_kz = solve_blanchard_kahn(\n",
    "            self.B(), self.C(),\n",
    "            self.technology_shock_persistence, 1\n",
    "        )\n",
    "        \n",
    "        # Create the design matrix\n",
    "        inv_capital_share = 1. / self.capital_share\n",
    "        tmp1 = (1 - self.capital_share) * inv_capital_share\n",
    "        phi_yk = 1 - tmp1 * phi_ck\n",
    "        phi_yz = inv_capital_share - tmp1 * phi_cz\n",
    "        phi_nk = 1 - inv_capital_share * phi_ck\n",
    "        phi_nz = inv_capital_share * (1 - phi_cz)\n",
    "        design = np.r_[\n",
    "            phi_yk, phi_yz, phi_nk, phi_nz, phi_ck, phi_cz\n",
    "        ].reshape((3,2))\n",
    "        \n",
    "        # Create the transition matrix\n",
    "        transition = np.r_[\n",
    "            T_kk[0,0], T_kz[0,0], 0, self.technology_shock_persistence\n",
    "        ].reshape((2,2))\n",
    "        \n",
    "        return design, transition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Modulus of eigenvalues of B: [0.884 1.191]\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>$\\phi_{ck}$</th>\n",
       "      <th>$\\phi_{cz}$</th>\n",
       "      <th>$T_{kk}$</th>\n",
       "      <th>$T_{kz}$</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>solution</th>\n",
       "      <td>0.534</td>\n",
       "      <td>0.487</td>\n",
       "      <td>0.884</td>\n",
       "      <td>0.319</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          $\\phi_{ck}$  $\\phi_{cz}$  $T_{kk}$  $T_{kz}$\n",
       "solution        0.534        0.487     0.884     0.319"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "reduced_mod2 = ReducedRBC2(parameters['value'])\n",
    "\n",
    "# Check the Blanchard-Kahn conditions\n",
    "eigenvalues, left_eigenvectors = ordered_jordan_decomposition(reduced_mod2.B())\n",
    "print('Modulus of eigenvalues of B:', np.abs(eigenvalues.diagonal()))\n",
    "\n",
    "# Test the eigenvalues and eigenvectors\n",
    "# (results from \"Emsm\", downloaded from\n",
    "# https://sites.google.com/site/frugemurcia/home/replication-files\n",
    "# on 06/19/2015)\n",
    "assert_allclose(eigenvalues.diagonal(), [0.88408644, 1.1906433])\n",
    "assert_allclose(left_eigenvectors[0,:], [0, 1])\n",
    "# Note the second eigenvector returned by numpy\n",
    "# is scaled differently than the one returned\n",
    "# by Gauss, so we need to rescale it to compare.\n",
    "assert_allclose(\n",
    "    left_eigenvectors[1,:] / left_eigenvectors[1,1],\n",
    "    [-0.53406267, 1]\n",
    ")\n",
    "\n",
    "# Solve the model\n",
    "design, transition = reduced_mod2.solve()\n",
    "\n",
    "# Test the solved model coefficients for accuracy\n",
    "assert_allclose(design[2,:], [0.53406267, 0.48719795])\n",
    "\n",
    "# Display the results\n",
    "display(pd.DataFrame(\n",
    "    np.r_[design[2,:], transition[0,:]],\n",
    "    columns=['solution'],\n",
    "    index=[r'$\\phi_{ck}$', r'$\\phi_{cz}$', r'$T_{kk}$', r'$T_{kz}$']\n",
    ").T)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## State-space form\n",
    "\n",
    "Assuming that $\\xi_t = (\\tilde k_t, \\tilde z_t)'$ is unobservable and $s_t = (\\tilde y_t, \\tilde n_t, \\tilde c_t)'$ is observable, the above equations give use the following relationship:\n",
    "\n",
    "$$\n",
    "\\begin{align}\n",
    "\\begin{bmatrix}\n",
    "\\tilde y_t \\\\\n",
    "\\tilde n_t \\\\\n",
    "\\tilde c_t \\\\\n",
    "\\end{bmatrix} & = \\begin{bmatrix}\n",
    "\\phi_{yk} & \\phi_{yz} \\\\\n",
    "\\phi_{nk} & \\phi_{nz} \\\\\n",
    "\\phi_{ck} & \\phi_{cz} \\\\\n",
    "\\end{bmatrix}\n",
    "\\begin{bmatrix}\n",
    "\\tilde k_{t} \\\\\n",
    "\\tilde z_{t}\n",
    "\\end{bmatrix} \\\\\n",
    "& = \\begin{bmatrix}\n",
    "1 - \\frac{1-\\alpha}{\\alpha} \\phi_{ck} & \\frac{1}{\\alpha} - \\frac{1-\\alpha}{\\alpha} \\phi_{cz} \\\\\n",
    "1 - \\frac{1}{\\alpha} \\phi_{ck} & \\frac{1}{\\alpha} \\left [ 1 - \\phi_{cz} \\right ] \\\\\n",
    "\\phi_{ck} & \\phi_{cz} \\\\\n",
    "\\end{bmatrix}\n",
    "\\begin{bmatrix}\n",
    "\\tilde k_{t} \\\\\n",
    "\\tilde z_{t}\n",
    "\\end{bmatrix}\n",
    "\\end{align}\n",
    "$$\n",
    "\n",
    "The above is the observation equation. In order to overcome the stochastic singularity problem, we augment with measurement errors $u_t = (u_{yt}, u_{nt}, u_{ct})'$:\n",
    "\n",
    "$$\n",
    "\\begin{align}\n",
    "s_t & = \\mathbf{\\Phi} \\xi_t + u_t\n",
    "\\end{align}\n",
    "$$\n",
    "\n",
    "And the transition equation is:\n",
    "\n",
    "$$\n",
    "\\begin{align}\n",
    "\\begin{bmatrix}\n",
    "\\tilde k_{t+1} \\\\\n",
    "\\tilde z_{t+1} \\\\\n",
    "\\end{bmatrix} & = \\begin{bmatrix}\n",
    "T_{kk} & T_{kz}\\\\\n",
    "0 & \\rho \\\\\n",
    "\\end{bmatrix}\n",
    "\\begin{bmatrix}\n",
    "\\tilde k_{t} \\\\\n",
    "\\tilde z_{t}\n",
    "\\end{bmatrix} + \\begin{bmatrix}\n",
    "0 \\\\\n",
    "1\n",
    "\\end{bmatrix}\n",
    "\\varepsilon_t\n",
    "\\end{align}\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Python package `statsmodels` has support for maximum likelihood analysis of state space models."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calibration\n",
    "\n",
    "There are six parameters\n",
    "\n",
    "$$\\{ \\beta, \\psi, \\delta, \\alpha, \\rho, \\sigma^2 \\}$$\n",
    "\n",
    "and of those three will be calibrated: $\\{ \\psi = 3, \\delta = 0.025, \\alpha = 0.36 \\}$ (note that $\\psi$ only influences the steady-state labor supply and so does not enter into the state-space form parameters). The calibration is done because the model has difficulty estimating all of the parameters, see Ruge-Murcia (2007) for more details.\n",
    "\n",
    "The remaining three parameters, $\\{ \\beta, \\rho, \\sigma^2 \\}$, will be estimated by maximum likelhood."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data\n",
    "\n",
    "We will evaluate the model using two datasets. The first will be simulated, allowing us to explore the capacity of the model to recover the true parameters. The second will estimate the model on observed economic data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Simulated dataset\n",
    "\n",
    "As in Ruge-Murcia (2007), we can simulate the linear model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(12345)\n",
    "\n",
    "# Parameters\n",
    "T = 200   # number of periods to simulate\n",
    "T0 = 100  # number of initial periods to \"burn\"\n",
    "\n",
    "# We can use the exact random draws from \"Emsm\", downloaded from\n",
    "# https://sites.google.com/site/frugemurcia/home/replication-files\n",
    "# on 06/19/2015)\n",
    "rm2007_eps = [0.0089954547, 0.069601997, -0.0081704445, -0.036704078, -0.026966673, -0.013741121, 0.0089339760, -0.0056557030, -0.0073353523, 0.027214134, 0.0036223219, -0.033331014, 0.032539993, 0.044695276, 0.012599442, -0.020012497, -0.065070833, 0.024777248, -0.058297234, -0.072139533, 0.080062379, 0.023164655, -0.028318809, 0.023734384, -0.023575740, 0.058697373, -0.00080918191, 0.029482310, 0.059178715, -0.010752551, 0.049127695, 0.063137227, -0.015733529, 0.018006224, 0.051256459, -0.014467873, 0.042611930, -0.078176552, -0.0040812905, -0.0086694118, 0.016261678, 0.0055330257, 0.026286130, -0.0066732973, 0.019133914, 0.018442169, 0.0046151171, 0.0015229921, 0.047776839, -0.058401266, 0.014895019, -0.0070732464, -0.036637349, 0.018778403, 0.0030934044, -0.033385312, -0.0044036385, -0.0029289904, -0.029415234, -0.010308393, -0.023496361, -0.023784028, 0.045396730, -0.021532569, -0.086991302, 0.046579589, 0.015086674, 0.0054060766, 0.0094114004, 0.014372645, -0.060998265, -0.0047493261, -0.030991307, -0.022061370, -0.020225482, -0.013470628, -0.013967446, -0.021552474, -0.054801903, -0.0052111107, 0.0080784668, 0.042868645, -0.0015220824, -0.061354829, 0.053529145, -0.020002403, -0.00053686088, 0.085988265, 0.037919020, 0.023531373, 0.0046336046, 0.012880821, 0.0037651140, -0.059647623, -0.027420909, -0.063257854, -0.010324261, -0.025627797, -0.017646345, -0.00091871809, 0.0066086013, 0.0018793222, 0.019543168, -0.031823750, -0.0092249652, 0.013246704, 0.014181125, 0.047271352, 0.047259268, 0.010107337, -0.083925083, -0.036031657, -0.0022387325, -0.035090684, -0.022218572, -0.017554625, 0.033953597, 0.010744674, -0.010891498, -0.0035293110, -0.033522281, -0.072168448, -0.0042416089, -0.025190520, 0.11066349, 0.029308577, -0.018047271, 0.055748729, -0.0016904632, -0.035578602, -0.10830804, -0.013671301, -0.010389470, -0.012295055, 0.055696357, 0.020597878, 0.026447061, -0.054887926, -0.045563156, 0.060229793, 0.028380999, -0.0034341303, 0.038103203, 0.012224323, 0.016752740, -0.0065436404, -0.0010711498, -0.025486203, -0.055621838, 0.0096008728, -0.088779172, 0.092452909, 0.057714587, -0.0057425132, 0.023627700, -0.029821882, -0.012037717, -0.074682148, -0.062682990, -0.038800349, -0.094946077, 0.074545642, -0.00050272713, -0.0075839744, -0.037362343, 0.012332294, 0.10490393, 0.049997520, 0.033916235, -0.061734224, -0.015363425, 0.057711167, -0.051687840, 0.031219589, 0.041031894, 0.0051038726, -0.013144180, 0.054156433, -0.0090438895, 0.023331707, -0.0079434321, -0.0029084658, -0.0064262300, 0.044577448, 0.014816901, 0.043276307, -0.011412684, -0.0026201902, -0.021138420, -0.0020795206, -0.042017897, -0.028148295, 0.063945871, -0.049724502, -0.048571001, -0.061207381, 0.050007129, 0.0062884061, 0.057948665, -0.012780170, -0.020464058, 0.023577863, 0.030007840, -0.013682281, 0.044281158, 0.033864209, -0.016235593, 0.0052712906, 0.035426922, -0.084935662, -0.061241657, 0.038759520, 0.019838792, -0.038971482, -0.043112193, -0.10098203, 0.011744644, 0.014708720, 0.035224935, 0.0098378679, 0.031205446, 0.026015597, -0.048897576, -0.042539822, -0.036330332, -0.033689415, 0.029665808, 0.0086127051, 0.038663112, -0.064534479, -0.036174560, -0.034225451, -0.0084848888, -0.011724560, -0.037544322, -0.013054490, -0.062983798, 0.011448707, 0.0022791918, -0.054508196, 0.046134801, -0.063884585, 0.048918326, 0.018358644, -0.011278321, 0.021175611, -0.0069196463, -0.084987826, 0.016286265, -0.031783692, -0.041129528, -0.11686860, 0.0040626993, 0.057649830, 0.019174675, -0.010319778, 0.080549326, -0.058124228, -0.027757539, -0.0028474062, 0.012399938, -0.088780901, 0.077048657, 0.070548177, -0.023784957, 0.035935388, 0.064960358, 0.019987594, 0.062245578, 0.0014217956, 0.057173164, 0.043800495, -0.023484057, 0.021398628, -0.012723988, 0.012587101, -0.049855702, 0.070557277, -0.017640273, -0.031555592, -0.030900124, -0.028508626, -0.029129143, 0.0024196883, -0.026937200, -0.011642554, -0.045071194, -0.013049519, -0.021908382, 0.017900266, -0.019798107, -0.040774046, -0.027013698, 0.065691125, 0.0081570086, -0.012601818, 0.017918061, 0.017225503, 0.0021227212, 0.032141622, 0]\n",
    "# Or we can draw our own\n",
    "gen_eps = np.random.normal(0, reduced_mod1.technology_shock_std, size=(T+T0+1))\n",
    "\n",
    "eps = rm2007_eps\n",
    "\n",
    "# Create and solve the model\n",
    "reduced_mod2 = ReducedRBC2(parameters['value'])\n",
    "design, transition = reduced_mod2.solve()\n",
    "selection = np.array([0, 1])\n",
    "\n",
    "# Generate variables\n",
    "raw_observed = np.zeros((T+T0+1,3))\n",
    "raw_state = np.zeros((T+T0+2,2))\n",
    "\n",
    "for t in range(T+T0+1):\n",
    "    raw_observed[t] = np.dot(design, raw_state[t])\n",
    "    raw_state[t+1] = np.dot(transition, raw_state[t]) + selection * eps[t]\n",
    "\n",
    "# Test that our simulated series are the same as in \"Emsm\"\n",
    "# Note: Gauss uses ddof=1 for std dev calculation\n",
    "assert_allclose(np.mean(raw_state[1:-1,:], axis=0), [-0.0348286036, -0.0133121934])\n",
    "assert_allclose(np.std(raw_state[1:-1,:], axis=0, ddof=1), [0.122766006, 0.0742206044])\n",
    "assert_allclose(np.mean(raw_observed[1:,:], axis=0), [-0.027208998, -0.0021226675, -0.025086330])\n",
    "assert_allclose(np.std(raw_observed[1:,:], axis=0, ddof=1), [0.14527028, 0.089694148, 0.090115364])\n",
    "\n",
    "# Drop the first 100 observations\n",
    "sim_observed = raw_observed[T0+1:,:]\n",
    "sim_state = raw_state[T0+1:-1,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(13,4))\n",
    "\n",
    "ax.plot(sim_observed[:,0], label='Output')\n",
    "ax.plot(sim_observed[:,1], label='Labor')\n",
    "ax.plot(sim_observed[:,2], label='Consumption')\n",
    "\n",
    "ax.set_title('Simulated observed series')\n",
    "ax.xaxis.grid()\n",
    "ax.legend(loc='lower left')\n",
    "\n",
    "fig.tight_layout();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(13,4))\n",
    "\n",
    "ax.plot(sim_state[:,0], label='Capital')\n",
    "ax.plot(sim_state[:,1], label='Technology shock')\n",
    "\n",
    "ax.set_title('Simulated unobserved states')\n",
    "ax.xaxis.grid()\n",
    "ax.legend(loc='lower left')\n",
    "\n",
    "fig.tight_layout();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Observed economic data\n",
    "\n",
    "We use data on hours worked, consumption, and investment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get some data\n",
    "start='1984-01'\n",
    "end = '2015-01'\n",
    "labor = DataReader('HOANBS', 'fred', start=start, end=end)        # hours\n",
    "consumption = DataReader('PCECC96', 'fred', start=start, end=end) # billions of dollars\n",
    "investment = DataReader('GPDI', 'fred', start=start, end=end)     # billions of dollars\n",
    "population = DataReader('CNP16OV', 'fred', start=start, end=end)  # thousands of persons\n",
    "recessions = DataReader('USRECQ', 'fred', start=start, end=end)\n",
    "\n",
    "# Collect the raw values\n",
    "raw = pd.concat((labor, consumption, investment, population.resample('QS').mean()), axis=1)\n",
    "raw.columns = ['labor', 'consumption', 'investment', 'population']\n",
    "raw['output'] = raw['consumption'] + raw['investment']\n",
    "\n",
    "# Make the data consistent with the model\n",
    "y = np.log(raw.output * 10**(9-3) / raw.population)\n",
    "n = np.log(raw.labor * (1e3 * 40) / raw.population)\n",
    "c = np.log(raw.consumption * 10**(9-3) / raw.population)\n",
    "\n",
    "# Make the data stationary\n",
    "y = y.diff()[1:]\n",
    "n = n.diff()[1:]\n",
    "c = c.diff()[1:]\n",
    "\n",
    "# Construct the final dataset\n",
    "econ_observed = pd.concat((y, n, c), axis=1)\n",
    "econ_observed.columns = ['output','labor','consumption']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(13,4))\n",
    "\n",
    "dates = econ_observed.index._mpl_repr()\n",
    "\n",
    "ax.plot(dates, econ_observed.output, label='Output')\n",
    "ax.plot(dates, econ_observed.labor, label='Labor')\n",
    "ax.plot(dates, econ_observed.consumption, label='Consumption')\n",
    "\n",
    "rec = recessions.resample('QS').last().loc[econ_observed.index[0]:].iloc[:, 0].values\n",
    "ylim = ax.get_ylim()\n",
    "ax.fill_between(dates, ylim[0]+1e-5, ylim[1]-1e-5, rec, facecolor='k', alpha=0.1)\n",
    "\n",
    "ax.xaxis.grid()\n",
    "ax.legend(loc='lower left');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Estimation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "class EstimateRBC1(sm.tsa.statespace.MLEModel):\n",
    "    def __init__(self, output=None, labor=None, consumption=None,\n",
    "                 measurement_errors=True,\n",
    "                 disutility_labor=3, depreciation_rate=0.025,\n",
    "                 capital_share=0.36, **kwargs):\n",
    "\n",
    "        # Determine provided observed variables\n",
    "        self.output = output is not None\n",
    "        self.labor = labor is not None\n",
    "        self.consumption = consumption is not None\n",
    "        self.observed_mask = (\n",
    "            np.array([self.output, self.labor, self.consumption], dtype=bool)\n",
    "        )\n",
    "        \n",
    "        observed_variables = np.r_[['output', 'labor', 'consumption']]\n",
    "        self.observed_variables = observed_variables[self.observed_mask]\n",
    "        \n",
    "        self.measurement_errors = measurement_errors\n",
    "        \n",
    "        # Construct the full endogenous array\n",
    "        endog = []\n",
    "        if self.output:\n",
    "            endog.append(np.array(output))\n",
    "        if self.labor:\n",
    "            endog.append(np.array(labor))\n",
    "        if self.consumption:\n",
    "            endog.append(np.array(consumption))\n",
    "        endog = np.c_[endog].transpose()\n",
    "        \n",
    "        # Initialize the statespace model\n",
    "        super(EstimateRBC1, self).__init__(endog, k_states=2, k_posdef=1, **kwargs)\n",
    "        self.initialize_stationary()\n",
    "        self.data.ynames = self.observed_variables\n",
    "        \n",
    "        # Check for stochastic singularity\n",
    "        if self.k_endog > 1 and not measurement_errors:\n",
    "            raise ValueError('Stochastic singularity encountered')\n",
    "        \n",
    "        # Save the calibrated parameters\n",
    "        self.disutility_labor = disutility_labor\n",
    "        self.depreciation_rate = depreciation_rate\n",
    "        self.capital_share = capital_share\n",
    "        \n",
    "        # Create the structural model\n",
    "        self.structural = ReducedRBC2()\n",
    "        \n",
    "        # Setup fixed elements of the statespace matrices\n",
    "        self['selection', 1, 0] = 1\n",
    "        \n",
    "        idx = np.diag_indices(self.k_endog)\n",
    "        self._idx_obs_cov = ('obs_cov', idx[0], idx[1])\n",
    "        \n",
    "    @property\n",
    "    def start_params(self):\n",
    "        start_params = [0.99, 0.5, 0.01]\n",
    "        if self.measurement_errors:\n",
    "            start_meas_error = np.r_[[0.1]*3]\n",
    "            start_params += start_meas_error[self.observed_mask].tolist()\n",
    "        \n",
    "        return start_params\n",
    "\n",
    "    @property\n",
    "    def param_names(self):\n",
    "        param_names = ['beta', 'rho', 'sigma.vareps']\n",
    "        if self.measurement_errors:\n",
    "            meas_error_names = np.r_[['sigma2.y', 'sigma2.n', 'sigma2.c']]\n",
    "            param_names += meas_error_names[self.observed_mask].tolist()\n",
    "        \n",
    "        return param_names\n",
    "    \n",
    "    def transform_params(self, unconstrained):\n",
    "        constrained = np.zeros(unconstrained.shape, unconstrained.dtype)\n",
    "        \n",
    "        # Discount rate is between 0 and 1\n",
    "        constrained[0] = max(1 / (1 + np.exp(unconstrained[0])) - 1e-4, 1e-4)\n",
    "        # Technology shock persistence is between -1 and 1\n",
    "        constrained[1] = unconstrained[1] / (1 + np.abs(unconstrained[1]))\n",
    "        # Technology shock std. dev. is positive\n",
    "        constrained[2] = np.abs(unconstrained[2])\n",
    "        # Measurement error variances must be positive\n",
    "        if self.measurement_errors:\n",
    "            constrained[3:3+self.k_endog] = unconstrained[3:3+self.k_endog]**2\n",
    "        \n",
    "        return constrained\n",
    "    \n",
    "    def untransform_params(self, constrained):\n",
    "        unconstrained = np.zeros(constrained.shape, constrained.dtype)\n",
    "        \n",
    "        # Discount rate is between 0 and 1\n",
    "        unconstrained[0] = np.log((1 - constrained[0] + 1e-4) / (constrained[0] + 1e-4))\n",
    "        # Technology shock persistence is between -1 and 1\n",
    "        unconstrained[1] = constrained[1] / (1 + constrained[1])\n",
    "        # Technology shock std. dev. is positive\n",
    "        unconstrained[2] = constrained[2]\n",
    "        # Measurement error variances must be positive\n",
    "        if self.measurement_errors:\n",
    "            unconstrained[3:3+self.k_endog] = constrained[3:3+self.k_endog]**0.5\n",
    "        \n",
    "        return unconstrained\n",
    "    \n",
    "    def update(self, params, **kwargs):\n",
    "        params = super(EstimateRBC1, self).update(params, **kwargs)\n",
    "        \n",
    "        # Get the parameters of the structural model\n",
    "        # Note: we are calibrating three parameters\n",
    "        structural_params = np.r_[\n",
    "            params[0],\n",
    "            self.disutility_labor,\n",
    "            self.depreciation_rate,\n",
    "            self.capital_share,\n",
    "            params[1:3]\n",
    "        ]\n",
    "        \n",
    "        # Solve the model\n",
    "        design, transition = self.structural.solve(structural_params)\n",
    "        \n",
    "        # Update the statespace representation\n",
    "        self['design'] = design[self.observed_mask, :]\n",
    "        if self.measurement_errors:\n",
    "            self[self._idx_obs_cov] = params[3:3+self.k_endog]\n",
    "        self['transition'] = transition\n",
    "        self['state_cov', 0, 0] = self.structural.technology_shock_std**2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Estimation on simulated data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/fulton/miniconda3/envs/python3/lib/python3.6/site-packages/ipykernel_launcher.py:75: RuntimeWarning: overflow encountered in exp\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                  Statespace Model Results                                  \n",
      "============================================================================================\n",
      "Dep. Variable:     ['output' 'labor' 'consumption']   No. Observations:                  200\n",
      "Model:                                 EstimateRBC1   Log Likelihood                3160.668\n",
      "Date:                              Thu, 01 Nov 2018   AIC                          -6309.336\n",
      "Time:                                      20:36:40   BIC                          -6289.546\n",
      "Sample:                                           0   HQIC                         -6301.328\n",
      "                                              - 200                                         \n",
      "Covariance Type:                                opg                                         \n",
      "================================================================================\n",
      "                   coef    std err          z      P>|z|      [0.025      0.975]\n",
      "--------------------------------------------------------------------------------\n",
      "beta             0.9500   5.57e-06    1.7e+05      0.000       0.950       0.950\n",
      "rho              0.8499    8.3e-05   1.02e+04      0.000       0.850       0.850\n",
      "sigma.vareps     0.0440   1.23e-06   3.58e+04      0.000       0.044       0.044\n",
      "sigma2.y      5.362e-10   5.39e-09      0.099      0.921      -1e-08    1.11e-08\n",
      "sigma2.n      4.172e-05   8.27e-05      0.504      0.614      -0.000       0.000\n",
      "sigma2.c       8.71e-13   4.81e-09      0.000      1.000   -9.43e-09    9.43e-09\n",
      "======================================================================================\n",
      "Ljung-Box (Q):          40.66, 0.21, 79.19   Jarque-Bera (JB):   0.09, 318540.26, 0.08\n",
      "Prob(Q):                  0.44, 1.00, 0.00   Prob(JB):                0.95, 0.00, 0.96\n",
      "Heteroskedasticity (H):   0.86, 0.00, 0.66   Skew:                  -0.05, 13.96, 0.04\n",
      "Prob(H) (two-sided):      0.54, 0.00, 0.09   Kurtosis:              2.95, 196.51, 2.93\n",
      "======================================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
      "[2] Covariance matrix is singular or near-singular, with condition number 5.64e+18. Standard errors may be unstable.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/fulton/projects/statsmodels/statsmodels/base/model.py:508: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  \"Check mle_retvals\", ConvergenceWarning)\n"
     ]
    }
   ],
   "source": [
    "# Setup the statespace model\n",
    "sim_mod = EstimateRBC1(\n",
    "    output=sim_observed[:,0],\n",
    "    labor=sim_observed[:,1],\n",
    "    consumption=sim_observed[:,2],\n",
    "    measurement_errors=True\n",
    ")\n",
    "\n",
    "# sim_res = sim_mod.fit(maxiter=1000, information_matrix_type='oim')\n",
    "sim_res = sim_mod.fit(maxiter=1000)\n",
    "\n",
    "print(sim_res.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 936x504 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(2, 1, figsize=(13,7))\n",
    "\n",
    "# Filtered states confidence intervals\n",
    "states_cov = np.diagonal(sim_res.filtered_state_cov).T\n",
    "states_upper = sim_res.filtered_state + 1.96 * states_cov**0.5\n",
    "states_lower = sim_res.filtered_state - 1.96 * states_cov**0.5\n",
    "\n",
    "ax = axes[0]\n",
    "lines, = ax.plot(sim_res.filtered_state[0], label='Capital')\n",
    "ax.fill_between(states_lower[0], states_upper[0], color=lines.get_color(), alpha=0.2)\n",
    "\n",
    "lines, = ax.plot(sim_res.filtered_state[1], label='Technology shock')\n",
    "ax.fill_between(states_lower[1], states_upper[1], color=lines.get_color(), alpha=0.2)\n",
    "\n",
    "ax.set_xlim((0, 200))\n",
    "ax.hlines(0, 0, 200)\n",
    "\n",
    "ax.set_title('Filtered states (simulated data)')\n",
    "ax.legend(loc='lower left')\n",
    "ax.xaxis.grid()\n",
    "\n",
    "ax = axes[1]\n",
    "\n",
    "# One-step-ahead forecasts confidence intervals\n",
    "forecasts_cov = np.diagonal(sim_res.forecasts_error_cov).T\n",
    "forecasts_upper = sim_res.forecasts + 1.96 * forecasts_cov**0.5\n",
    "forecasts_lower = sim_res.forecasts - 1.96 * forecasts_cov**0.5\n",
    "\n",
    "for i in range(sim_mod.k_endog):\n",
    "    lines, = ax.plot(sim_res.forecasts[i], label=sim_mod.endog_names[i].title())\n",
    "    ax.fill_between(forecasts_lower[i], forecasts_upper[i], color=lines.get_color(), alpha=0.1)\n",
    "\n",
    "ax.set_xlim((0, 200))\n",
    "ax.hlines(0, 0, 200)\n",
    "\n",
    "ax.set_title('One-step-ahead forecasts (simulated data)')\n",
    "ax.legend(loc='lower left')\n",
    "ax.xaxis.grid()\n",
    "\n",
    "fig.tight_layout();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Estimation on observed data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                  Statespace Model Results                                  \n",
      "============================================================================================\n",
      "Dep. Variable:     ['output' 'labor' 'consumption']   No. Observations:                  124\n",
      "Model:                                 EstimateRBC1   Log Likelihood                1446.774\n",
      "Date:                              Thu, 01 Nov 2018   AIC                          -2881.547\n",
      "Time:                                      20:36:43   BIC                          -2864.625\n",
      "Sample:                                  04-01-1984   HQIC                         -2874.673\n",
      "                                       - 01-01-2015                                         \n",
      "Covariance Type:                                opg                                         \n",
      "================================================================================\n",
      "                   coef    std err          z      P>|z|      [0.025      0.975]\n",
      "--------------------------------------------------------------------------------\n",
      "beta             0.9850      0.010     96.667      0.000       0.965       1.005\n",
      "rho              0.9616      0.023     42.722      0.000       0.918       1.006\n",
      "sigma.vareps     0.0022      0.000      9.797      0.000       0.002       0.003\n",
      "sigma2.y       9.75e-06   2.58e-06      3.775      0.000    4.69e-06    1.48e-05\n",
      "sigma2.n      2.398e-05   4.66e-06      5.143      0.000    1.48e-05    3.31e-05\n",
      "sigma2.c      1.568e-05   2.16e-06      7.251      0.000    1.14e-05    1.99e-05\n",
      "=====================================================================================\n",
      "Ljung-Box (Q):          38.83, 120.95, 47.83   Jarque-Bera (JB):    16.90, 0.23, 3.25\n",
      "Prob(Q):                    0.52, 0.00, 0.18   Prob(JB):             0.00, 0.89, 0.20\n",
      "Heteroskedasticity (H):     1.68, 0.90, 0.53   Skew:                0.40, -0.11, 0.32\n",
      "Prob(H) (two-sided):        0.10, 0.74, 0.05   Kurtosis:             4.62, 3.01, 3.46\n",
      "=====================================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n"
     ]
    }
   ],
   "source": [
    "# Setup the statespace model\n",
    "econ_mod = EstimateRBC1(\n",
    "    output=econ_observed['output'],\n",
    "    labor=econ_observed['labor'],\n",
    "    consumption=econ_observed['consumption'],\n",
    "    measurement_errors=True,\n",
    "    dates=econ_observed.index\n",
    ")\n",
    "\n",
    "econ_res = econ_mod.fit(maxiter=1000, information_matrix_type='oim')\n",
    "\n",
    "print(econ_res.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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uh0TRhXH/nm3jOdb8vNeRiQNYA0TGYAwMFxucm67hWEJ3NkFfLkFHytX9r1JKqVuOBqdq2TzHumgM6lKFk/zQcGKszIvjFbZ0pdjYkSLlaUqwUurlK4wMlWZAqdZivNyk3AjaDXhCHEqChWBZghA3/omAyIXrlggdSRdLlu79tNvrnCMC+ZQ7vw3Fqs/YbAMBsgkXy4LF9tTZhENXxiPjOSRdC7nCayqllFKrRYNTdd2WOpnxHKHbSRBGhsHpOmenavTlEvR3pulIOnoSpJS65UWRoeaHVBo+4+UmM1Ufg8ESIe3Z9GQTq75NtiVkkw5ZHIwxtMKIYMEsYQt3vZPlJkMzdSwRHFvoycbTkOWSWl9AKaXU2tHgVN00tiUU0h7GGEq1gNHZaTYXUuzuzeJouplS6hYSRoZqK6BSD5ioNCnVfaJ29kjKtSmkr9zjudpEhISzdJCZcGxy7etBGDFd8Rku1cEIA50ptnSlNUhVSim16jQ4VTedSNyanzE2Y6UGlUbAgc15TfVVSq1bQRhRbYXM1n2mqk1KtWC+ZzThWORTNycYbQYh56frnJuucmaqxtmpKmenatT9MB5aYVskXJuEHQ+zsP0W9zeHeHBHNxvzyet6Tce2yNoWWRwiYxidbTBUrLOlM81AV+qKQa5SSim1kjQ4VatGROjKJKg0Ag6fmeZgf57OjLfWm6WUUkSRodyMK+NOVprMNnyMAbvdA9mZdpc9JCGMDC9NVCjVfWqtkLofUmsF1FohtVZIww9pBVFclTeIaAbx7UozYGy2wVw9JMcStnaluWMgTy7hxM8JI1rBhct4zfCxR07zsUdOs7UrzQM7unhwZze7+7LXFTxbIhRSHmFkGCrWGZypsb07w6ZCSue9VkopddNpcPoKcXqygh8a9m7IXX3hmyybjE+ynjxfZHdvhi1daR2HqpRadXMB6WS5yUipQRBFWAgpz6Yz5V3TfmmkVOfJc0WePD/DM4Mlaq3wsmUESHk2Sccm4c71gsa/O1IufR1JXr+3l+3dGbZ1p9mcT111apjB44dxNh/ksdNTPHZ6mr98YpC/ODJIV9pjcyE5XwzJmHZhJGNIuDb7NuY4uDnPbRtzi6bv2pbQmY6D1NNTVc7N1Ni3IUdfx/X1ziqllFLLocHpajERll8FVr9IxvMjs/z03x6lFUS8+1X9vP+BbWs+xYDnWHSlPU5OVCg3AvZsyGmrvFLqpptL110YkDqWRTZxbXOETldbHBsu8cxgiSfPzzA22wSgL5fgdbt7uGtLgb5ckrRnk/bsOCh17ZuSCrwxn+Sdd/fzzrv7KTd8Dp+d4RunpynWWvOvJwIICEK54fMXh8/z5+Y8tiXs6ctycHOeA/0d3NlfuGhfbFtCdyaBH0YcHS4xUG+xqzen86kqpZS6KTQ4XS1+jXTpJOQPgaxeEHZ6ssqHPn2MrozHwf48f/nEEE+eL/ITb9nHlq70qm3HYmxL6M0mma62+OqpSZKORdKzSbkOKTe+7loWS53L2ZaQ9q5+QmmMiceO1fw4Da7cIOHYJByLhKNTKCj1chW25w2ttQJKNZ+ZaouaH/doOpZFxrNxbPcqa4n3IePlJseGSxwdnuXYUInhUgOIiyHdOZDnO+/u556tnWzKJ9d0n5JLurxxXx9v3Nd3xeVqrYDnRmY5NjTL0eESf/3UEJ96YpCujMf77tvCW/ZvuKhwnWtb9GQSDBcbzDYCDmzSugFKKaVW3ooEpyLyNuA3ARv4A2PML1/yeAL4Y+BeYAr4bmPMGRHpBj4F3Ad83BjzwQXPuRf4OJACPgP8B7PUxJq3CLdVxGrNEiUKq/J6w8U6P/fQUZKOzS+88yAbOpLcv72L//WlF/mPn3yKH3zNDt5+cOOaB2dzFX3DyOAHhnqrRRBGhMYgCMjib/vcp6Ej5dKV9sin3PneiYYfUmkGTFWaTJRbhFEEQCuMeG549sIsgQIZzyGdiE+ygjDejjAyBO3fxszNRRiPm104R+FihHhqhkzCJptwSDj2/Byxa91jrdTLXTMIKVZbDJcalOo+EKfTurZFwrHpzlz9sGeMYaTU4NmhEkeHSxwdmmWyEveMZhMOBzZ38LaDGzmwOc/OnswtWX087Tkc2tbFoW1dADT8kKNDJT55ZJDf/vIp/uqJIb73ga28fk/vfAOgSNyLWmkEPH5mits3ddCT0zRfpZRSK+eGg1MRsYH/DbwFGAQeF5GHjDHPLVjsh4AZY8xuEXkf8CvAdwMN4GeAg+3LQr8DfAD4OnFw+jbgsze6vWvJCpu41TGaqxCcTlWa/MzfHiWIDL/87jvY0B4n9ODObvZtyPEbX3yR3/3nUxw+M82/f9MeOtNrW5hI2nPtXWtRSGMMzSDi/HSNsyae6t6xhKBdUSRh2xel69XaKWoLn++HhtlagLRT3uJAVPBswXLkstdbOIZrKU0/otIIGIrqF55LfIJcSLl0pj0ySYeUa2s6s1I3qBVEFGstRkp1ZmrxfKMZN260Wm7j23i5wZGzMxwdmuXoUInpWguAQsrlQH+e92zu58DmPFu706s6ZUwYGZpBiB+aixrG5vZXxsTL3GiabdK1ObS9i3u3dXLk7Ax/8thZ/p9/eIFPHRnk+x7YyoM7u+f/l3HdAIunh0rs6A7wIoOlab5KKaVWwEr0nN4PnDTGvAQgIn8GvBNYGJy+E/hQ+/qngI+IiBhjqsAjIrJ74QpFZBPQYYz5Wvv2HwPv4hYPTiM7gVMbpVnYCdbVU8muJAijJVvrZ+s+P/PQMcqNgA+/6yBbL0nf7cx4fOgdt/PpZ0b4o6+e5t/96RPcNVBge3eabe1CHBs6ktd0AmaMYbraYrBYZ0Mued1TGlwrESHp2hcV9LiWEzURwXPkGgLE5f9PUlweaYeRodwImKq2MMYQEacFdmU8CmmXbCIOWNe6N1up9cyYOF23XPcZKzeZrrYwBtKefc0B6VdPTvHIyUlOjJUB6ErHQyAO9ndwsD/PQCG19PqiEG/2DH5uAGMvv55AZOKMjLl5UhfeDkKD3870gLhBqyPlkvFsDIYo4qLsDoBK0yeIDI5lkfbsG8rQEBEObe/iVds6+eqpKf7PY2f5pc8+z94NWf7Tm/fR35kC4roBPZkE56ZrtGZL7OrNaZqvUkqpG7YSwWk/cH7B7UHggaWWMcYEIlICuoHJK6xz8JJ19i+2oIh8gLiHla1bt17rtq8uEcQYnMYMQfrK44GW0vBD/ucXTvD4mWl29mQ52J/njv4Obt+cJ5twqLUCPvR3xxgt1fnQOw4sWZ1XRHjHXZu5cyDP/3nsHC+Ol3nk5IW3I+labO1Ks7EjSSbhkJ27JJ35nsihmTrnZ2qcn45/L6xOuaUrzf3bu7h/Rxf7Nqxu8Yz1XKjDtoRMwiGz4D4/jJgqtxgu1sHEcw52Zz26Mx6ZhEPa02D1ZSUKwdKT+Gs1N9XKdLXJRLlJM4gDuJS7/IDUtOfwfOyl6YsC0p29GX7gwW08uKv7ysHoxSsjUTpFongq3r7CrvnXqPvx9DFCnDFx6W9LwLYsbEtwLMGyBFsE2xKSbhyMJt24qu/VGs5mTtm8elcPlUbAVLXJ2GyD2YaPJULSsZccfuDa1hX3lZYIr93dw6t3dvNPJ8b5w0dP82OffIp//6Y9vHZ3z/wy3ZkEL01GPHVuhl29GXo71nbMrVJKqVvbSgSnix2FLk14XM4y17W8MeajwEcBDh06tO7HpIZuCrcyeF3B6Uytxc9/+jlemqjwlv0bGCzW+fQzw/zNU0MIsKM3AwbOTFX5r2/fz50DV08f3tad4b++fT8QF8g4P13nzFR1fuL3k+MVKs2ASjOYn3tvoULaZWtnmjfu62NLV5r+Qopz01W+cXqav3lqiL98YpCOZDy26TW7u7lve5eeuFzCteOxqNn21zGMDMWaz/hsXHDFsS0GCil6cgkyCa1hdksyBuozMHMG/BpsffUrIkCdG0semgs9fdHcWG7aPYftHsMwMkQmbqwJIoMfRPhRFPckhhGNIMKC+d7BbMIFY5CwgZHLvxfGGKaqLV4cK/PieIWT7Uu5GQAXAtLX7O5hcyF1zX+bWx7ELZ+nld2IFM8ya/US2AlEhELaZVt3mnTCmQ86RVhwfWX3gbYl5NMu+bTLjp4M1VZIsdZiqtK86MApCw6t5UZAGEUY4uEPSddaNBvHtoQ379/AXQMFfuVzz/Mrn3ue43dt5l9/0/b5HtpswiGIIl6cqDJT89nemyFxrWM01C0n/k5HhO2TA836UUqthJU40x0Etiy4PQAML7HMoIg4QB6Yvso6B66yzlvKp54cpb8ubCuksOtTiF/DuMuvljs4U+NDf3eMYs3nv719P/fv6Abi3oQTY2WODpU4OlRiqFjnP755Lw+0H78Wac9h38Yc+zZe3ts61xtQacSBqh8aNuWTdKQuT0++e0uB77irn2oz4IlzM3zjzDSPn5nmSyfGObStkw++cTfd2dWfUudWYVtCNuFAOxANwohz0zVemqySSzoMdKbozHh68ncrCAOoTsD0S+BXwU1Bqw71ImSu/Tu63jX8kGozTlufrrRohmFcUKz9+Fzv4dx15m/HYdPceG/LEqy56+0ewGzi8n2N3ZwhNfkstb57iLwOwsjwzGCRr7w4yeGz08zU4oJIlsD27gyv3tXN7r4sdw0UrisgnRPOjiLjx6l6BaJ6RE6E7e402f7bySavbUqaZZurzHYVIjKf6TLQufQxZm6fXm2GTFebTFZaBI0Ag6Ej6V6WGtybS/A/3n0HH//qGR56epgTo2V+8m376GsXRHKseHqwUt3n6XNFdvdl6MpqsaT1JlrQWLTwIzU3hnlOEEX4QZxi3mxnAtT9+LofRvhhRGQu7kmwLYuerEd3NhEPUdE0b6XUdViJ4PRxYI+I7ACGgPcB33vJMg8B/wr4GvAe4EtXqrxrjBkRkbKIPAg8BvwA8L9WYFvXxFSlya996Qxpy+PX3xyxwbJw6hP47rZlPf+5kVk+/OnnsCzhl77zjotSdT3H4o7+PHf052/W5gPxCU/ac0h7Dsvt880kHF63p5fX7ekljAyfeXaET3ztDP/uT5/gh1+7kzft77tlW1mNiXuBmn5EMwhpBhHNIKIVRARRRD7l0p1JrMjB2bEtCu2CVQ0/5PnRMgbozSboL6TIp1wtRrLe+A2YHYKZs2BCSOQg0xs/ZgyUzi8rOI0iQyuMaIVxD2IQRjT8kEYQ/24FEUKcCppw42q0CdfCtSxcxyLl2jctzX2uGFm9FTJTazFRblJvT9Pi2RZJ1765KenG4BVPEhp44fgz/MNUL4+eLlKq+6Rcm/u2d7F/U47dfVl29Fx/T9783+mHGGOwWmU2lI6R6e4ll86Q9Cw8Kw+1cXD33nA9gUXVpmHqFAwcWlaAuhwL9+m9uQR7jaHhR8xUW7w0WSGIDIWUd9Hnx7UtfuR1O9m/qYPf+uKL/Mc/f4off8s+tixoa8wlXfww4thImc0Fn4HOtDakrQJj2vuKIMIPDU0/pNoKqPsRLT/OQmgF4XwGlLSTzAVpNxSZS3rZ2+sFrHYF+jgN3SLlOmQ8Lvtuh5FhpuozOttAiOs59GQTdGa8eK5f7VlVSi3DDQen7TGkHwQ+TzyVzB8aY46JyM8Dh40xDwEfA/5ERE4S95i+b+75InIG6AA8EXkX8NZ2pd9/y4WpZD7LLVwMqTub4Pfed4Af/JOn+cl/qvFrb8zQXRnCz2296onGoycn+bV/OEFvNsGHvuMAm/LX39q/lmwrHuN6aHsnv/nFF/nNL73II6cm+eAbd9Nzk3tRz8/UeKTdkxI1m2w4+zyd6biSZ2fao5BxsUQo1lrMtOdCLNZ9ZmotZus+zfbBfq612G8HCsvJIU+5Nt1Zj65MPIZ0cyHF9u4MO3oy9OUS13ygniv+ZExcWOnpwSKeY7GlM01vLnFRYSi1BqIIZodh8kR8O9kB1iW7WS8b96YGTXAS7acZqq2Ahh9RbQbzl3oQgpGLThttiU8QbSs+YTQGKs2AUr2dNmvM/NhGiFMuO1Iu+aRDql1wy14wFdKln0Fj4vTaaEHvSmRgptqi3gqptALK9YBqy4dWA8s0IdlJ2rNJe6uTdl5rBTx3epBnTtZ5eNQU2pu1AAAgAElEQVRhqm5I2hPct6Ob1+3p5d5tXUuP1TQRll8lcjOLzjm9MOg27f98LumyvTtNhxOQmTiG27EBLs18sZy4MaLvthX+Y6dh8AgQgV8H7+bMTy0ipDyblJeityPBaKnBmckqkTHkLwlSX7u7hx3dGX75c8f57393jG/dW+BdB7pw7XgZ17boTntMzrYYLzXp70qxoSOpQeoKavghtVbIbN1nqtKi0vLBLMxIiMczzwWVCTturLqZlaZtS+K6FFzI+hmfbTJUrMeVpi2hKx0fDzNJh8wy5ilXSr3yrMiZhDHmM8TTvSy872cXXG8A713iuduXuP8wl08vc8u6sz/Hz97b4r8fSfCT/9ziNx5s4LZKS855GhnDQ08N84ePnmbfxhw//S9uJ79ICu21sFoVIi97Q+u4UZvyKX7pO+/g759Z2Iu6gzfuu9CLGqcXxSdLc1NEFOv+fPBYrLUoNwK62sFefyHFxnzyojS0kVKdr7w4yVdenODMVA0BbtuYwwBnJqs8WWtdVMBpoYRj0Zn26Ey78ydUri3tsaEyP0bUdSyS7flLE45NwrFIOBaWJZTaJwzT1SZT1RZTlRbPDs3yTycm5l8n7dls686wvTvNzp4s+zZm2dqVWdbBemHqnh9GnJqocHKiwoZckv5Cio6Uoy3Uq60+A+PHoVmBdOflQemc9vvSKI1RSWxgotxkstIkjOKg0rYufM46l9nT4LF4IDY3VdJkuRkX3Fpsc7i4jSxqp/otTMettQKeGSwCgmdbuI5QSHkk6y/hVgapJ+8ltLuuup3XK4wMpyYqPHm+yJPnZnh+tEwYGRK2zaGNNm/Y5vKazjJOwaZZ6F6y0U/CJonpF3Dq40R2Ej83QJjqwbfTVJsBfhQhAh1Jlx29GTqSLpmEHY/FDH0YehYs6/LAFOKGiOJ5KGxduQByLjBN5uLPlV+7acHpQq5tsaUrrto+VKxxbqqGJUJHyp0Pbvo7U/zqe+7iD77yEp97boxjYzV++P4+BvJxg4u0lw+NYWi6zvBMnf7ONH0dCQ1Sr0PDD5ltxI2n09UWzSCa318kHIvO1PKrVK8Wx7boSF3YN4WRodIMmGyPhbZE6Mp49OUS80XAlHpFCX0w0XxDtYppdZVVdKAr4r+/Ls1PP1zjJ77u8X/nRnA3Xh6cPjtY5GOPnubURJVX7+zmx9+694YP5k55iGTxBWob7lvzANWSi3tRf+tLJ/mtL528pnUkHGu+Wme8znhMVH8hxWw94OREBYgD0h953Q5es6uH7myCweOHGdh/LxAf7Iv1+GAfGdMOSL2bOk6m1go4N13jzGSNM1NVzkxVefiFCT57dBSIe1r3bMiyb0OO2zZ2sG9j7qqNEq5t0Z1JYIyhWGsxNtsg7dls7UrTnU3oPKo3m9+Ix5SWBsHLQLZ3yUXrfkip7jNTMtRGj1HuS5KwbXIJ96b0ICx3qqSF8/cu1rNSs4SuzMUHT8uv4tRGCZMFUpNPU++9hzB58f5sblzjbCOg0ggoN3wqzaCd/r4wG6GdshxE1JoB1VZIrRVQbca/p6stqu3GpF29Gd59oJMHs2PcNtCF1+6tw3TilM8TOhmC3OXF3a1mieTUMcSEBOleosAnmDxFFJ4AL8fGjbvo3rCRbDqFY0l8wmAMRK04Yp94AVpVSC8RhIsFdrv3dMP+K/6/l6U6BUNH4qDXSUAwN1a558bXvUyeY7GjJ8umfIrB6RpnZ2p0prz5hsCka/PBb9nD3oLwiSMT/MIXB3n3wW7esic//zmyRSikPUJjGJyuMTRTY6AzTXdfqEHqFUSRodIKKFbjfXqtFRJhSNoOSXfxMdir6dI6FH0dybhWwhXY1oVUcogb4SuNOFgFyHg2GzqSFDIeuYQ2sKpXgPHjUB6N9/GpzviSyMYNoK/ggFWD01X2qo0OH3ptip97pM5/++IkH/quBulkXDRicKbGx796hsdOT9OTTfDjb9nL6/f23nAajlMbJznzPMZycCuDNLtWOO3sOs31oj78wgRjsw0M8bkgxAc+mBtv6VJIxfOAzl33HItKI2C4VGe4WGeoGP8eLjawLeH/+qbtvHZPz3yxjsUkXZuNrs3GjtUr2pH2HG7b2MFtGzvm75ub3uLEaJkTo2WeHy3zl08Mzo8N2t2X5Zv39PK6PT1XLCQlIuSSLjmgGYScGCsj42U25+Oe5VxybU9mXpZKQzDxfByYZHoW7bELI5htxNWXy40AyxKSToIup0jSC4jctT8AiQjXupdxy+cxloOxE4QeOGNP8azZy1NjPk8PxsXZyg1/0SrfS/Fsi3TCJuPFUyhlEg492Xje0QOb89y9pUA+aZMefRwjGYy9YKtFCJJdJIsnqLuZC4GyMTiVYbzpE9StFC0yRHUfW4Surj660h5pK8D2z8DYmUv/MRd2SiKQvso44WQ+Hk/cuTVuqLhe1UkYfvJCYArgpKA2Bey+4lNvhqRrs3tDjkLa5bmRWVzLJpu8cPpw9+YMO7sSfOLIBJ98ZopnRqr84H19dKcv7HMWBqnnpmvUT02xMZ9kUyF16wQiN3kaqIYfUmkGcTZFuUloDJYIGc+hK7Myp2tBGE/JVPdDrHb16LnftgiWBdVmyFSlyWQ1rvg8WWkxVY3nE56da2RqBPPz7ELcQLyrN8udAwXuHMhz+6aOq/aEWtKeWq0d1LaCiDNTNcKJKinPZltXmp5c4obm7VXEvXO2Hv/XnSiMh/hkuuPG0GYpvm0M8byCKejYHD+e6FixegO3Ag1OV0NjFu/zP0mH6cYvvJ37N7v89DfBzz9a4xf+7hg/9q0H+Ksnh/js0RESjs0PPLiN77h784q0KtuNGZKTRwmSBRAHtzpCq2MbxlkfY1ctEd6w7/rmfM0mHfYmc0vO5XqzWc0ikZe/4R2GiLApn2JTPjX/v2j4IacmKjw3PMtXT03xsUdP84ePnub2zR18895evmlXzxV7VOM0Y5swMozNNhmcqZNLOfHBPnvtY11vOdWpuAiR49281yiPwehRyHQtmsJbb4VM11qMzzYJTETSsckvOFk3gWDXJ+Oxj0vww4iJYpnpiVEmpZPpaouZBWOjq62AfMql0O7170y7878HOtN0Zm7O3y9+jag0zIutAk+fbvLkWMCz4w6N8CUE2NWX5cGd3XQkHXJJJ240SV6YL3kuVd5ZkCo/d5J8NU5tHMuvESwWKFo2oZcjOfkMM92vohYKqeJJTH2URqKLbCpBb9KZ7725cM7rgLcCjVQi4LjxlEEbDlzfOqqTMPQEpPJgL3j/bC9O811ugBRFUJuE7PXtXxfTk0tyX8Ll2HCJqWrzovllO5IOH/ymjTxypsz/9/QkP/eFQb77rm5esz130ftqi8Rj/dMek+UWw8UG2USc6dGVXceBSG06/r733bZi/9MgjKg25wqKNeazAxK2TS55STZF5GMFzfZY6cu/J80gZLzcZHy2yXi5wVj7d7EWZyuUG+1x7P7iw1muxJubezslbOnwyG3MkU26dCTjuc/TnsPZqSrPDJbmp5BzLGHfxhx39OfZv6mD2zbmrjom3XMsvPY+e24mghfGy+2hO6mr9syqRYQ+nP1a3MjVuT1uYLP1/7guNMtxUCpWfPGysPCQHbZg5jRMn4obF3L9kO2BRD4eXvIypp/Q1eBlsc9/lR7SjOx5OwCvGXD5L/c7/I9v1PmhPz6MJfCtBzbyvfdvna/MeqOs1iypyWcIEx3zFSSN2LiV4fkJ49X1sWsTpCaews/20+zcu/T4wuuUdG0ObI57i957aAtDM3UefnGCh1+c4Le/fIrf/edT3LO1k7fs38D9O7qWPKGzLZkPYuutkKNDs2zMJ9nTl110TsOXhdIwjDwFnTtWJr1yMY1ZGH3msrGlUQTlZsBoqU65GWC3K6IudhwJvRxe+Tx+bgvlZsjxkfJ8JsBIqcFIqc5Eubmg5zEer+w58ZQdnZn4BH+27nNuuk6x1rqoJwOgK+2xszfDzt4sO3sy7OrNsqHj6o0TfhjR9CMaQUjDD3lxJuLo0VEGZ2oMFusMTpYYr7oYagBs6bB4606XV/UY7uls4QzsvjnDB0yEV3qJMLH4ultBRN23sH3ITB9jIGWTzvp4m3biOfbqNDwn8nGl5sK2OD1rufxG3Go+fvzywBQuBCStatyjejWtchxMbX3gxnpxL5HybO7Z2smpiTKDM3UKqQvbKSK8bkcHt/Wm+IPHx/n4kQk+/0KRd9zeyX0D2YuC1LkxrBA3xh0fLSMSZ3r0ZOM5ndfVkITySJziPfwUdGyCnr3XnHYXRYaaHzJb85moNCnWWxjD/H6ie6531ERYQQ2rUcNqzuA0ilhBDUyEn9lANbeLk1MtnhuZ5fnRMifGykxXWxe9lmMJvbkEnWmP3myCHT0ZsglnvpEo5doYaM8v3J6PuH1Je057ShiP7kyCXNLB9qukR76On9tCs2vPZX/ba3f38P4H4uPM8ZFZnhkq8vRgiU8ePk9k4p7Vbd0ZbtuY4/ZNHezf1HHFooCeY9HtJAgjw2ipyfnpOoV2o1su6ej41OWqTkLYBNuGkWfihq38FujYGDfgqrXTKF25c8P2LgwjiQKYHYTimfico2dv3Kv6Mu1o0OB0NVgWwZ3vJ/uVX8Ztn4wCvGFHBiuc5iulPt5733a2di2/0IXVmsVY7pI9oJZfJTX+FKGTwiw4yQkTObzyIH5uAGOvfTrhrUiCBsnp44TJLpzaBJZfodFzx03tje7vTPE992/lffdt4fRklYdfnOTLJ8b55c89TyHl8i239fGW2zdccV7DlGeTdC3Gyw1qrYADm/MvvwN8eQzGjsYptsVz8QE41bmyrxE044O8m55PlfJDQ7HaYmS2TjM0JB3rij3bkTG8WBSOnA35+qNPcmKiPh+EZjybTYUU+zZ08C27DNsZoSfrUsjlyA4cWHI6BmPiYiMzNZ+pSpOz0zVemqjw0kSVJ87NzK/fEhZJ5YvnFW21g9JLg9zYSTzHYiCf4EAh4Ft3JNnaYXOgx6YnfSGIkECwJp6k3veqK/YKXw+nNoEV1AlSF3pNg8BQ8wMMkHZttnWm6UjnSQTl+CDu3bxCTUy+EFdn3vmGC/eJxCcV0y/Bpjuv/PwwiAtpzQ5CZTJ+bqqwdAqesPzgtFGCRjEeC927b5l/0PLYlrB3QweFlMfx0TK1VnBRr1hv1uU/v2EzTwxV+dvnpvnoY+N8+vgM33F7F/f2Zy7rIZ+rQj6X6THULt6VTTj05RLkU96FwlRrIQxgdiRujEKgMhFnZ2y4/aq9qMbA+GyDyWqTqUqLMDSIxH/zfBEjY+JgtFLGqY9jN2YQ4poKke3SJMFTxQ6eGgt4bnyaE8UZ/HbJhU35JHcO5BnoTLMhl6A3l2BDR5LOtLdy49ijgOTUc0RuGqc2RrOwa8kG2ZRn86ptnbxqW7zfrbUCToyWOT4yy/HRMl8+caHGwqZ8kkPbOjm0rYuD/flFGyMWNrDWWgFHh0oA5JJOPD417ZLxHJ1ObTHGxPuhRC5uSHHTcebF7Pm4Ry5ZiBtwNUhdG+WR5TccWk58bIA4UB07CpXxOJPDXR+ZkCtJg9NV4h98L84jv0r+9GeZvPMD8/e/cUB4ze05/I5lBqZRQKL0Em55ECNC5Gbxs5sJE52YdgVJCRokJ57G2B7GuSRVTeLWUqc6it+xvHlWbyUSNLAbRcJk4fK//UrPC1sXBfFLMobkzAmMCMZ2CVOdWK0K6dHHafTcScvLE7XHqM2d/K8kEYl7wXqzfP+D23jy3AxfeG6Mv316mL96cogDmzt46+0b+KZdPYsGniJCVzrBbN3n8Nlp7hwo0LHex6JWJy/eMS+lMh73mKbbabbJbNwLteWBlRsnFkUw9hxEPiZZoNEKmay0GK80MAYynkPKW/w990PD14cDHh0MODIaUGwaBJu9XT7vPbSFe7YU2NLuFRARiPx4bKWdxVgeTmOKGg0iWbw3bn7McdJla1eae7ZeCMqbQcjZqRqnJirzvbFzPSZRZAjbv+MK1HZ73lR7/npr4jR333kXPbkEqZkXcOo1wsTi3y/jpDDGkBp/ilrfPfP7pRsWhXilU/hOjkYzpBnGlXVTrsVAZ4qOlEtq4WfeublzPzNzFj79Y9CqwXu3xylzcxIdcZGLwpZ43JCJiOf5iOJLFMaPl0fi224qHld0tVbwudTejk1X377KWBw4lc7H23YTimv0dSRJJxy+Mj1OsebTkXLmA09LhEMDWV7Vn+HwYJWHnpvmd78+xkDe4x37O3nbBnNZkLowEIH4c3tmqoYxVZD298uNxyKnEzaeHVdL92zr5gau9RnAXJh6KN0ZN1It0osaRnGRn+lak7Fig2rT5/jQNAnHosOx4g5xYwAfq1HCqU/h1MeRKG5gMU6SMJlnog6PDQc8Nhzw1FiTRgiuBXs6Hd61y3BHocnu7dvI9W1fdEqklZQovYQV1giSXdj1KexmiTB19XmaIa6zcM/Wzvn9URgZzk5VOTY8yxPnZvj8sTH+7pkREo7FXQMFDm3v5P7tXYvWV1hYTKkZhJyZrBIaM99LvDGfoiN5i4xfXg2NYtyYtbBIn2VfaLBtluMgZ+CBtUkTDf14f5bbsPqvvdaCZvz/v54Cd5YT79sbJTj7Vei7HXIbX1a9qBqcrpZ0D7O999Jx/otM3f4D84FT6GVxK+fxc/1XPcBYzRLJ6eNI2Ix7DkSQoEFi5iRCROSk8TObcKujgCFa4qQwTHTgzZ7Fz26+KRPGh5FZ9bnLJKjjVobwyoNAhLFcGp37CdNX+eJHAW75PInZMzQ6byPIXjjpMyaeNzII45P3MDI45SFMcZRWqgep+xhASEAIzrnHaHXug45+EKEVRoQmnifSEpkv8mQApz1dyI2cVNmWcGh7F4e2dzFTbfGlE+N84dgov/6PL/LRh1/iDfv6+NYDG9jRc3kw05FyafghR87McPumDjbkV68o1DWZORsHmGJBvh+6doG7yLbOFZBJLUizddNxb9TsUDy9xw2KIkN99AVaE0PMWHlKE8X5z3rWc5c8tg+WQz53skHrzGO8K/pHinIQs+ld3LfJ4d4NFj1Sprr58u9ionQGCf359NjIcq+7oFnCsdm74frHZw9G5+jrSMbfs+owQfLKvZGRm46zNyaeot53zzVlFVw6z2rUvs3sMFGlQpTuoZB22JL2SLtCwl2Dw1i9CJ/7qThYdCM48nF484cuPC4CXgoGDy8oqiTtS7tX2m43uFxLYOGmoD599eXCAOqluKHGb8SBcOfNaYzMJhwODhQYnKlxfqZO1rMvqpdgiXD/liyHBjI8fr7CQ8dn+J2vj/FXzxV524FNvHl/35JDWebGzsOFaZHmsgOCKJov4mWIeyILqbhoXsp1SHrWylUDnh28vHfCScQnlpUJmsVRqpFLsdZkttpETIAl0O0Is6HF1uLjFz117tOAMUSOR+hm8I3Fc1Mhh0cCvjFS46Vi3DW6MSO8dafLA5sc7upzSDhz1alTOPWzhBMlGl37r6kx9lrYtcl4+EEqPpYaJ4lbHVl2cHrZ+qwLDazvuGszzSDk2cESh8/O8PiZab5xZprflVPct72Lb79zM3cN5BcNNhd+NsLIXDR+eVt3hq6M9/IdurJcxcErN0olcu0sgPE4uFlts8NxQcHk61+WvX9X1Kzc+DqS+TjAH30mbozsvW3x86NbkAanq2hq8xsojH+D3NBXmN32lvhOy0WiCpnhr9LK9hOmeojc7CUTD4a45XMkSqcJvQxR8kKPiHGShO2DkoQtvNnTGMsl8i45CTVQavgkHIukayMmwqlNEGQ33/DfZYyh4Udxap0Bz4rwjTV/GmaJzLdwr3SxC8uv4pbP41ZHMGK3Cz9ZSNgiNfkMfnYzzcIujDjUWiFhZJiptbBEcJtF0sUTRFGTqptDRp+lkqvRyg4AAmJIOTaphINrCYmoRq5yHmtgAMuKJw+fu1iWYEc9WI1RyHlxS7plY9on2EF04XczCKk2AyrNkErDp9nw5/9XTnvOOs+xrqlKc2fG47teNcC77+nn6PAsXzg2yheeG+Xvnx1h74Ysb719I6/f03vRNDlJ18a2hKPDJaqtAC8y6yc1ai4dafJkuwquFfeMlkegew/kBy70htam24HpIumQ6QJMnIB0zzXPDxlFhmoroFwPmKw2qU2NkJ56llayC88JlxxLCtAKDY8OBvzzyRlum/4i/875Altlgsi2eK17itP3v2e+p15qEU5jhiB9IT3QapZwy+cJUheCwMjrWPOCZt7sOYzlXLWFNoqgYZKYZhlz7nFK3XcROcn5z3l7RmMgnsJGFjTexGnHFq4lOI5F0rZxieipjpLe2k8qlYpfPmzBX30g/q694adWr9U4aMIXfjqunPuO34Szj8KT/y9MnYLuBWP5vWx8WUmWA0Ej3oYrnXS2ysQ9fRKnAM+cvvg7s8JsS9jWnaGQdnlhrEKj7l/Wg2WJ8MDWHPdtyXJksMpXBxt84mtn+D+PneXVu7r5tgMbOdi/eCACC6ZFWmJOXz+MmKn5F1V+9xyL7oxHdzZx/eMUg2bc+HVJAS4/NJQbPpMVh1I1xJImnm2TznYgVrshQgQzWyJIXd6Lb4xhsBxxZDDkyGiTp8YDGkH8+T/YY/MjdyV4YLPD1g5r8f+J2ATpbqzWLOnRb9Do2k+YXnoaq+sRD2F5jiBRwCCEgSE0SazZMWaTOwgtdz5TyJh4ireUe23p1wnHnm9k/dHX7+T8TJ0vnxjn88dGeez0NAOdKd5+cBNv2t+3ZEEl27p4/PJzI7PYljDQmWJDR/KqhZhelvx63CiVuUojQirfPkZ2r25F3yiMj/FIPD90797Ve+31oDqxMv9v2417UetFOPc12Hjn1d/zW8Ar8Bu7dqr5vTRzW8if+eyF4BQIk50QBXiV88jsGSInhZ/pJ0h1IyYiMX0c268QpDpBlj64GtsjXCI1dbbh05VxaQaGYs3HJUW2dJogveG6Tljm5jibq/yXT7ns786Rp0Jy5iTBxntoSoJmEFFtBpSbPrP1gFK9RTax/Mm2rWYRK2gseOH5H9iNKdz6OJHlxj05Cw7gxvbi/19lFFMco1TYT0dXLwnHZleXi0ydRBpDBNksoV0gMhHJ3Cb6g2GsXAavZxeeY18I1qIQBp+DznzcI7LoxjqQ6Y3HOooDvXsQERxbuLgB34UFbQdBGNEKI+qtkHIjoFhvMVv3idpzTzqWRdqzlxXYiwh39Oe5oz/PBxo7+acT43z+2Bgf+aeTfOyR03zbwY1834Pb5tfl2hY92QRnp2pY9Qq7erNrH6Ca9pySM2fiynRzPUupQjzWYvKF+GDWtz9+zwePQDJ3eQEZiN8T24mfs/nuK75s2GpQ80PKLYuJSpNS+z0QIE2d3toLRIUeUlfINpisRTx0ssWxU+f4rvCz/L7zMGm3yWzn7Yzs/mEiJ0n/136OzOg3qPS/Nn5dN4VbGboQnEYhyennCb3Mxb1qIu2CZkM087vmGzwgLn5iW3JT09ku9JpePobXmDjNrhnEvVmWCPm0QyLTQyKssEVOwcZ7sb3U/DjXuU21RBAMUp/GmjqFRRj/3ZYdX8SJU1+TQHrBd+/EZ+Ogay7wetX337S//aI/9OFfjVPh3vyh+DOYH4Bjfw1H/gje+uGVf80ogMc/Fhcxue3tgMSpelcKTuulC58d24WGHwdYNzl9Lp/yuHtLgbNTVUZnG+STLs4lLTiWCPdtyfIv7tvD+Zkanz86yhefH+crL07SX0jxpv193LOlk53/P3vnHR7ZWZ7932nTi0ZdK2l7X2+xd93besFgm27T8+EQiumEQICEkEAglBBSTYBgwAkkYFMM2NgUG/e+6+2976p3jWY09Zzzfn88M6ojabTVcHFfl1armTMzZ855y1Pu535qJtemTgfLKAQ/xyi6Oq5iYDhP51AGlATkqsMeKoNeQuUKLqUKmWpNw3EhmbXpS2boT+VRCnyWTkXQRzm9mAYyLtu6HLZ12WzttOlOyfydE9K5fr7FhnrJjganKA0oBdcTKQRjd5EP1pGLLhrJorpKmD9jWQjyuxgWKgFVTPa7hPt34eRd8goMzcZr6ng9Bj4sgr40ZrQCy9QxNA0F9A/n6E1kyWXygDie/kIQtBxomsbcygC3Xj6fN188lycP9/LArg6+9cRRvvfsca5bVstVi6tZ2RCZ0gEeW7/cOpDmRG+KuqiPBdXBPzyNhemQ6BKqbjmlApmEZFmrFpybcwNxzhxbHKnBE8Ju+gPJ+s0IpSTTeQaF6vBHIZeUa/lH5/SPmBU0jfj8G6nd9S28g4fJVozpV6ebOF6pqZMM6FG88UMAuKZ/nADIbJHJO3hMnbmVQUxdYzhn05vMEe9pJ9nXjq+ycZzj47hqhFon2T5XNrjCZqYV/qkIeFhQHSQasIRe47rQsg1yw5g9ezAb1xP0eqgstLNQShzjw90JeoczRH2eaR0uzc4Q6N4um+hYx1OB7SjyukneiqHrOroSQZex3zljO5h6hPoKWGYexuvTeFRzaIxvB9OFhubJC7fyQ/I4WBpUj1EkHDheXn1AsRfi4HGpNytjsTUL1N6AxyzU2QRHstHpvEM8LXSloXQeXdcIec2yHNWwz+LVaxt51Zo57O9M8MCuDu7Z1saBrgR/dcPyESqdrmlUh7zs785i6hrzq4Pnr2bHdYXmM9QqdTITz0M35R7YGWh7QZwWf8X0hrqvQrKuyR5UsJqs7RZ+HFJZh+FMjmzfccz+o4WxpmP4wtT6oyhvCGV48Q4eBtM3JQ1+f5/DPQeybG8Z5KPGj/mC+RDKMkg2XcPJRa8RAREA5ZD3VRE++bsR51RZAcx0H5qdRpl+rGQrKpci641hZ22cAsUVQFM+zJ7jDBm1WN4APkvHVZDNSw2mUuOIoxiaJvQ3a3aZ+FLwFPqaukonb7vkHRcrcZJsoAHdkNkdIgwAACAASURBVFYxc6KSpfBZY5VxfVIbM7ALmjZMvlfZhAQPhvtE3Va3RmszbRtURr5RcAyV2M7Ctu9D3QVCR9vyHahcCPOvnPmLjO1bOlts/R4cfggufteoCJI3DKvfKM5pz4EzKz7k2vDIl+DI78R4W36TnHc2MariWArJzvGGjzck7QhCtWc9w2wZOotrw8QCFke6h7Fdm4jfwijxuc2xAO+6eiFvu3weTx3u5Ve7O/neMyf43jMnCHtNLmiMsra5grVNURor/LNelwxdI+QzCRVMnbzj0hnP0jaQxgVCHoOqkCjaBrzGJBpw1nbIdR0llTUZGk6SzNg4SuExdKI+a0aHNJ1X7OqD33Zm2NZlj1B1QxasrTN5y0qT9fUmDaFTZxQpBVnXJK9XoA10oQ10koouJuuvQ9dFBdhj6lhmgcFUYDEZuj4yFFTRWR3jr3oSJ7EcGzPchKFrmGM32LwORj/Exqv+V4e8LKkNkcm7BSdeeqQ6rourpCWNv8xAq8fU2bS8lk3LaznUleD+XR08tK+LX+3uxG8ZXDi3gg3zYqyfVzliY4yFoWtU+D0opehLZulJZFhYE2JO1H/+A7BnG64jNkg5wmkge2j/YREQPBf0WqWEaeILj7ZRGWobzzz5Q0Y+Jcwfvcz7Uy7Ocu35ucQfndNzjETzJqr3/g/R47+me90HSx4zLgOq1GkZE64rjtqKhhAmDmAS9JgEK01ywUYGk10cztcST49+jKkX+g/qOl5LJ+YRNTyvNUrN9Rj65AU+0SmRm1CtROl7DwoHvvDGmqYRC3pYP6+S3mSWQ91JhjJ5KvylVQXN+DHySidjhrELDjOIE+oLGPhNjbyjcBxF2nZwZIdFAUGvyeJYgIjPkl6GypXFOp8Gq2ZqZ0bTxfkZOAbKgepl0hi573D5heu6Ie8Tb4XqxTMfX+o0NA2/x8DvMagMephfFSSZtelP5uiIZxjK5DEKDcxn2ug1TWNFQbp//bwYtz98mL/40Q7+5qYVLK4dpR3G/BZtgxlMQ6O58syqrJaN7j2iihmsnn7cmz4I+crq+Zi1XVKun8SBLbSEL8RWBsW8gddJER46RMRJoqJV8l7KRXPzaNk+tHQnmlK4xmSqvO0qnmy1uedAjv19ef7E8zi3++8i6CYZXPhKBpa+UVgRY6EZJJqvI3b4HozMAI4vJu0obcVQbweuL0q45wAZfwy/oRH2eQpZcwPTkCy6kdYwq22oGh+wGlsjbbsuOdslkRG2wlAmj+vKt9bRxs1djQlVBGq07rOYXXEch0zvcXLeGCaizFqX3EfNU5/Ebr4C/frPoZvTbCe+qDio7VthzkUy/+ws9B+D+MnC/ZwFJXH//bLGbPyU9BONt8AjX4DXfn28MNFEtG+X4zQdllwPS15Wfj3y4d+JA7r05bDuT8Y/t/r1sPsnsOVOuPHL5X+P6eDa8PAX4OgjUmvdf0ScUssnlOKpakjt3ORAmumT2rLM4JlXr54CVSEfYZ+H7qEMrQMplILwFE6q1zTYtLyOTcvr6B/OsbN1kB2t0orkmaN9gLRFaoz5qYuIGm1dxEdt2Et9xEcs6Ckr+GIZOlH/6HqZs13aBzO09KdG1J4rQx5sR8o/8plhot3tOL5qLNMh6DVn3I5bhxye67B5vt1mZ4+D7WpYeo4LagzescbLRXUmi2P6KesyOI4iV2DboCgELA0qgz581XOwlIOVb8MI5rDqVo4vZ7CzknXPJiDZJ/PAG5XgheWTcWJYQhEcPgbRmtJCOVZA5l8+PcmZGbt31YS9I4HW4ZzNYCpHX1LWIxhtoTNTBntJXZiP1IW57ZqF7GyNs+V4P1tODPD0ERkbC2uCXLmomutX1hGbUL+saRpRvwfbcTncnaRjMM3Susi4ntN/cEj1Sy1iuS3udEOO7T8mCtRnG+mB8UJNvugoA+YsCLe96JAZOqNvV2xh1zMwjOV1mdd4Rt/+vOCPzuk5husJkWi8mnDLo/SueseUokUjOM0odyKbpznoEvrlBySKdtNXR97T4/VTayeprtdFha/QTuKUsmZOXpxRX6G2JlAl9FZPcJLxp+satREflUEPHfEMx3qTOEqhjVSiaej5ISoGWtAC1dKXzWPi8xgjyoylTlEpRgSILGPCAZou56R3zrz4aXqBntsqBmJ6ULIjs4lK+aLiDEebzghVZZwSa1WA4ZxDf1JaLsTTeSxDJ+Q1ZzR4Ni6rpSkW4AsP7OOTP93Jh1+yhGuX1ox8RixgcaI/haHrzKk4h3WNrjtaIzMbJ6WEY+q4MJwVp2wwlSfnSNQ+mM8Q83TgxBZKHXeyFW/8KK7pw/WNMeQ1HWV4J7VaytqK/f0Ou7oddvXY7O1zyNiwMXiC78TuZE76IOnYSk6ueR+56NT0qETzJioP/QTr+CP0z3sVhqZREapgjncQrzeDNa8OyzeNcFGwsJFXNI2rWdE0DcvQEOaaXJexmfisLdTxVM4uBHsoUMfl/8U4mKnrI5kSQ9fQnQw7TjgsbqjAClViFcfY8z8E04fZ8jQ8+gXY9OnpAwW+qNBN27dLtrPvsDweqJrd3LIzUuPZsBbmXCgnff3n4Wfvgd98Cl77zckZA+XCjrtg87dFsCzSANt/IO9TuwKW3gALr5s609C5Gx77snzm1R+bvC57grDmzbD5DujaIw7z6cC14Xefh2OPwaXvharF8MBfSma28SJZk6YKXOamENqwfCIwdo6cU5DsV1NlgJqIl+6hLG1jnNSpUBn0sHFZLRuX1aKUoiOeYUfrIHs7huiKZ9h6YpD+1PiengGPwYJCH9+F1dLXtznmn7H20VOo7y8iZ0tm1dA1fKZBTEvg8Xlx/FObSnlHsbPHGVHWbU9KdnRuROd1Sz00erK8dGlkVMholhhLmUeBaYiiccRn4veY+EyjhP/oFwP4xFMydvJpCWjkM4VolC6OKIij4NpAQbjL8sl+7g1PP581TdgOFU3Tnv9YZ7U65GVxrVznVM4mnhLKdSKZBw38BUruVIGGgMfksoVVXLawCqUUx/tSbDnRz+bjA3z/2RP88PmTXLm4mleubmBZfXicLWMaOlVBL+mcwwsn+5lT4R8hUfzBYeCYBBBmA19UAurRxlE77myh7/B4ZoduAJr0KD+X1OLzhWSXBHVOPif7SsVc0U6oXiq1t5Hy9AHSeYfBQtmCoxSGbRPU3XPwBc4+/uicngfEF9xE9ORDhFseIb7wFWftc9JZh7DpUPf030OfUITp3DW+954niN5/BE9zGW0MpkO8TTa4YsZX04R21rNfREFKUNBMQ6e5MkBtxMtQ2i5kbDURQuk8jhmsm1X/LU0D80xR1jRN6h0TXbJIeGcpbFJcbM8CVUXThNob8po0VwYYyth0DWXojGdwlcJvGVP2wgRYXBviX9+4li//ej9f/e0BjvYkufXy+XLamlChjvYm8Rga1eFzVAOSGQQ3D8FTUwxUCobzNgPJPL3JrCzUmib1R0URKFWJmThOxhvCGjqBkR8uCGiV3gTSecWeXocd3Ta7ehwO9DvYrth2Cyp0Xjsvyzvyd7Ok+zc4bpTOiz5Kovm6KeeR7ShSORtlNVAbXUx952PELnsrAbMgrJTsKVyDGZxz3ZSsfqKj7KyfVrwWlkGsBAVuEooS//EWSA9gKJtAeAwltGOHtO25/IMy75/7JpgeuPaT0zua/oKD2ntQqGTlRvbHYu+9olj70s+Mnk+oVhzUX34Efvc5yV4W3zubgEe/LIb6wuvgmo9LNinVB4cehIO/gSf/FZ7+mvSMU0rug1P8yYkBH6yF6z9XurYZ4ILXwa4fS/b0FV+d/fcqwrXhoc/B8cfhsg/AmjcUHE4NuvcKNVo5Qg0rVbOU6i99XT0hUeXMJme/np0mvKYxstZ3DWVo60/jHc4S9lnTl3ZoGnMq/Myp8HPjBaNK6jnbpTuRoWsoS9dQZqSn72/2dIoThwQo51cFWdMUZW1TBSvnRGZU7x3nrCqFNdyOW+Iau0qxp8fhdyfyPN5ik8gpLB3W1Rm8bqmPS+eMUnWPdmRn7ZjajiKTd6TmXdOI+EwaK/wFynyZgRxfRMZS/xEZs6Zvivs+wYlxbZnLM7VW84SE9TCDc1rypaaOx/RQEfAwrzpY0FvI05XI0j+cLYgrGQQ9U+9jmqaxoDrIguogb1jfTOtAil/t7uShfV08drCHhTVBXrG6gWuW1IyrNS32++5NZEnlbDoH09RGfH84VN9sQlgqs21RUlQY7zkoa8zZov9n4rIHhCacny8CA0fFOTbL2KN+X+E6kOqVIOHhB4XN4ORg7y/kN4jjWrUYGjfAgqshtmDkfriuaMh0DWVIZGyh7lsGhqGRT+em+eDfL/zROT0PyFYsIRNdRPT4r4gvuOmsLAKOo8jZeVbt/2e0rt2w8a/h2W9ItmCsc2oFxChOD0xfwzQd8hmpV5ioSKib0u+vY4f0mpxCLdVrGtSExxgNw31ifIanb25+TjBVYbmdAbQZah0LGa5I41kr9BfKkkXUb7GwOshgOk/bQIr+VA4NpjT+KgIePv+aC7jjiaPcs62N433D/NmFFbLIaRoVPov9XQlW6hqnr+dcBuKtp1Qvkcm7DKZz9CSyZPIupq4RNBw82V7yoQncFk3HNX34enfiWsFxSrgAGVuxt9dhe7fNji5xRh0FhgZLKw1uXuphdY3BqhqTiJ6l6YnP440fZ3DhK+lf/icjLV/GQUl0M2u7eEyNxpifWMCDd9VN8PR/YCVOjAYvArHS1+DksxBuGE/j9EWFghVpPH0FVqVkwyw6PEMdIvGvlMzZYLUwDsae29b/kc11xatGKbov3AmGF676i+nXtBLKpWUjn5Y1rHG9ZDHHov4C+ezH/wme+xZc/n5xgh/8jNQcX/EhWHXz6LkFqmDtm2HNmySSf/A3crxpgR6SrHTxxwrA6jdMn1GwArDuLbLOduwcv86WCycPv/t7OP6kOP6rXy+Pe0ISiOjeN3psbgrnNNkl923ghDgjRZVZTZN63nirOOHnAV7TYG5lkLqIDzMU4mR/iqFMHp9pEJjGEZkIj6nTFAvQFBu/pziuon0wzZGeJEd7hznYleAX29v56dY2LEPKG9Y1VbC2uYJFNaFpmSZ6Polup0TvwbWZ89zn6fQu4NvaLTzUAj0phc+AyxtNNs6zuKjOxHeGsqMeU3QAKgIWfo8h9Z6nkubTzUkqw2W9phRyw3D8KVjy0kLm1SvU3jMQ7ChmVmsjPmxHyhHa42l6Ell0TSPsNWfMgjfFArz76oX8v0vn8ejBbh7Y1cHtDx/mzqeO85ZL5vKK1Q0j97tI9e3XNY70DtM5lGZhTXhE8ff3GvG28fdwy52iz7DsRli0afqaUk+o0Fqmd3YMptlg4GRp51M3ZYwnOs5a26uSSA+InVruPuq6oomRS00IYuZB2eJURqcJ2GQTo3O57QWYd4Wwjlxb2IY9B2Qf6t4vLcpeuBMijaj51zDUcBknjblkHPCZ+h80Nf2MOKeapt0A/DvCJfu2UurLE573At8D1gN9wJuUUscLz/018E7AAT6slPpN4fHjQKLwuK2U2nAmzvVFAU0jvuBG6rZ/DV//fjJVK874RyQyeVYf/i+MlmfEYFv6cjHQtnxncssDbxC69sLcS09N2rr/GKCX3tRMr0SDOndKFMiYYcgVBXH8Z7hQ/EzCzsLP3ifZjJf9w9QCKGcxe1oKZkF5tzrkJZN36ElkaR1IEU/n8Rg6IZ85ji5lGTrv37iYRTUhvvHYEf4jleEvrmrAWxDLiHgt9nckaG7KEz2bm7adFYO6TOfULShmdiYyxFN5dA0ClklFwEDLp2h85u/wDRyk5dp/HRUiKsCxgvS5AdoGXVoTOdoSLm1Jl7aEy8khF9uVVg7LKg3esNzD2lqTVdUGfmuM0akUtS/cjjd+jPbL/o5U/cWTv5IjbWiUgsqgxfyqICHvmNYzizfBM/8Jh34LVe+Tx0rNn+59QlWtmAuv/+54BdZ0XBQPZ9OfrihEke6XzdDJiWKixuiGaXimdpRB2BdtW+Gy948GZy66VQI2O34or7/8A2cn8r7n55Jl3/CO0s8vf4V8v10/EiPgyEMiiPXq/5iaaqsVxM/GCqCdKla+BnbcLevsK/9tdtcgPSiO9Ymn4IoPwwU3j3++doUEKpSSdTQ9MNmAzGckwGBVwf0fleDFq/9j9HlfRAyrygXntbbLaxrMqQzQWOFnMJ2nZSBFXzKHWRB8O53ez82VAZorA2wsLMvpnMOejjg7WgbZ3jLI9549Ac+eIOgxWNEQYXVjlAsao5OcVTXcw8G4zt62HGbb83xo4AUW8QJ/5j4D0Q8wf80KLm80x68Ns0DedsnYbkERXCPiLwiKeYWqq+US0LNb1oDuvfLbG5G2SadLG58tlILH/hGOPS4BmrmXyuOaLo7MGczEm4ZOLOghFvSQzjl0D2VoGUhhu4qgZ+Z2QH6PwY0XNHDDqnr2dgxx9+YW7njiKA/u7eR9GxezsmHUttCQWuZM3mFn2yB1YR/NlYHfX1VfOyfBp0CBup+Jy5qsG7K2PPOfUmu/4lXiRJWCLyI2WKBydoFPpSR4OF3LtlxKxNqmCpj4o9JeJnJOQuJyvq1boHJx+XTiRIfYy55AQcxJE/aV6QE8Mk/9samVeNOD8rq+I7KGNxXsB90UUb/KhRJIAEj14R57CvvIY1i7fkR05w9Z7qsi2XQN/cvegjuB9ZB3FPn8HwZX/bSdU03TDOA/geuBVmCzpmn3KqX2jjnsncCAUmqxpmlvBv4ReJOmaSuBNwOrgDnAQ5qmLVVKOYXXXaeU6j3dc3wxItF0LdW7v0P0+AOzd06VQnOyUzbdTmZsFh//P3zHHoT1fyYGE8Cq18KOH8hitenToy+wAkIF69kv6pezMaiyCRhqkT6SU8EbFgpdz76Z3z/RMb5Q/sWI5/5LMqL+Srj3Q3DtJ2DxS0sfe54K/X2W0OiaYn6GMjad8TSdQxmUYpIA1ctX1RPwGPzTbw7wjWe7+OAV9Zi6hmVIC5ttJwdYXhc+e9SnZA+FQqhpD8s7isHhHB1DabK2wmfqVIyJHGp2hjnPfg7fwEFc00/tjv+k5Zp/omNYY2unzdYumx3dDvHs6OJtaNAQ0mkM61xcaHK/qtogMI3BWXHkF0RaH6N3xa2THNNs3i2oREuPvVjAg7eU2IevAuZeJrTSS95d2jF18vDYV2TjGzgujsm8K8a8R7jQB7a2tGhJKfQfFZqfrxApNkKzz7y+UMiarnz16GOaBpfcJs7u7p8IhfCSd83ufWdCLiVrV/Ol0xvnl79f5tzBX0lAbNOnhUJ8LmD64MI/gadvF/GnxvXTH29nxRk99CC0PC/Z6yv/HFa9bvKxtSvg4K+lLjtQKWvqRBTpvwPH5PlUn0Tgi5nSYsAh0XlusxNTQNc1KoOi6J7OOXQVxJNsV1o4+S2hsZ6OerjfY7BhXiUb5glTYiCVY2drnF1tcXa3xdlyYkCOs8RZbYj6ONqb5Gh3gqxjABm+7n2chB7id00f5Kaeb/PFxKcZGL6Zfv2tKMqjII6l6rpA0DJoiPoI+UwClomR6YPWp2XcdO0RSj0AmtyruZdLP+d7PwwXvwPWvuXcqXPu/6U4piBq1UXn1BuS84zNOyvBKL/HYF51kKbKAH3JLCcHUvQNZ+W5GcpXNE1j1Zwof//qCM8c7eOOJ47xyZ/u5CXLa3n7FfNH1OpB9kuvqTNQaIUzr1qy+6cqWnXekOyGguI8IO22nBy87rtiV+37pTy29xdQswJWvQaWvHz8vTO9BbuurXyxOBDGTdcuEcGsmFt6PMTbZD+baqyMLVk5Fxg4XnAUD4nNOVOQJZ8WOzlYNTXDwPSKg9q4vvT3TLSL43rw1/J3U+m8m+0qBuwQbZFrsNdeTVhliPZtIdT+FBWHf0Go7Um6Lvpz0jXrODnk8MCRPA8ec7llaZ61l5Z/CV6sOBOZ00uAw0qpowCapt0FvAYY65y+Bvhs4f8/Ab6myYryGuAupVQWOKZp2uHC+z1zBs7rRQ1l+kk0byJy4rf0rH43rqe8TKGVaKV+67/gHThEumoVw3MuJ9lwBXagBteVlhK1x35O7NBPYeVrJaNRhDcsEbNdP4EN7xRRkCIClVKM7quQFijlorfQYmOmjclfKVRB15Gi71LRNScPvYdOz5BMdEpGaDpaxemgdTPsuQcuuAUu/H9CGXz4HyQKdvG7Jhv6I4X+rdNnT123fAdjFhhH+60J0TWU4WBXgvCEXrNXL6mho6eP72/t5bubu3nXJbXohTYkEZ/Fvs4EbfE0S+rCRHxnMIuqlPTl8oaAgclP51JkOvbSE1pOTzIvSsweE/+EPoCak6PhuS/g79vLiXUf4+hgnk3H/4177v0F30hL4KDKr3Fxg8mSmDijTWGD+qA2KwPE37OT6j3fJdlwBQNL31A4SUjlbHKOS9BrsqgmNKoSPR2W3iCOSdsL4nBNxI4fipNx/efg2a/D9v8TA3VEVtsroicDJ6By/sxzcKhD5uvY3rGzRdceaNsiIj0Tg2OaJlRUOwfb/1ciyWPXn9PFnnsgOyQBt+mgm/Cyz4shP/eK06c9zxbLXyniS1u+K8rEE++Lawvt99CDIniUT0md8Zo3inpw5RQR/NqCimb3Xlj8EslYOfb4Y4Z7Javavk3+NrxSB/uSvx09xlfITkTLE904V/B7DOZXB5lbGWA4Z5NI2/QkMwwW+g3raHgtUYs3T6Onbyzg4dqlNSNCcP3DOfa0F5zV9iH2tMdZWOXjVfMcltaFWBnNccUTL5Bovo5V666iJX8h1bu/Q+WhnxDsfJ6ui/6cbGwye0YpUcp3XEU8lcdr6lSHPET8BQVulZVxsHeLzKn+o/JCbwTqVwvbqXaFMHOKJQPZBDzxz/D8HcJeuO5Ts6ftzhYDx6Ueu3E9hOrgyMPCkjB9hR6ZQzIvz6KIjlEQUayN+EjlbIbSebqGsiPlK4auE/QYJTPumqZxxaJqLpob4+7NLfx8exvPHuvjbZfOY8UYqnRRdNBRiuO9w/QkMyytDeP3/J5Uvykl96qo0+E64oQ2rJVsHMi4uvwDwtjZd5/U4ru2rFlj4a8Qeqk/Vp7uRzYpSvu+qDhv6UEZu2Ppu3YO4ifExiyic7fUmI4VafNGxJ4qp3Hw6SA3XMgyV8r/u/dNX2tb7L+uGdNrJXjDEnAfapfvNhZ2FrLDsge3bpH7MmH+KgVDGZsTfUlyjrAFgoYGWCQC15Fovg5v/wFqX/gXmp76NL+0XsYnEm8mq/m4rB5W1bx41vTTwZmYdY1Ay5i/W4GJltbIMUopW9O0OFBVePzZCa8t3k0F/FbTNAX8l1LqW2fgXM8fDK80ZVDOiABLfP6NVBy7n8iJhxhccvO0L0cposfup3rPnSjdw+CiV+Pv3k7Nrjuo2XUHyegS4nWXU+X1Un3gv0X448oPT55oq98Au++BnXfDVR8Z/1ywUiaoL1LeRpPqF1phOVlOTRPRksygGOQ1ywqKZGM2k8EW4eyfErX4qNSiHXlYZvfaN8OGP5tZ1CHRKVkO5cCmv52+KXImLot5bL5kiUwvvOKf4ZmviSPRf0TeY+JiPl32tNiMuXu/GKVnMZthGVKnFfZa7GofJGe742psNi6Mksq5/HR3PwGPzp+sqy4owApdOJWz2XKsn+bKAHOrAjOKi5SF7FDBOB/NvCsFqbxNYqCPyKN/QyB+mKroMtTa95CrXDr5Pdw89c9/kWDPNu4Iv59/2nwhOUdxl3cVH9TuJrTmKpY3VdEcPr0MjJnqpn7zl8mFGum88CPkHEUmb+MqqAp6qI16Cc3GkJl7qWzEB38z2TkdOAFbvy/zeME1kgF76t+FHj+21jJQCX0HpfalesnUG2t6QOi4wcrTy7Zs/R8Zz0U2xkRoGlz9FxKt3/JdmX9r33zqn1dELilr1twryquX9IRg/tWn/7kzQbmyBvorR9ct0yuBqyf/Fe56qxh/RdELJyevAWGsLLhWaHYNa2d2FCsXiLNZdE4B8sPjjxnukfdt2yrUuHlXSSb70vfI+guF7IQrxlHNsnOXVS4Tuj6qTD4n5sdxFcmsTTyVI56xSWdtElmHsW05dTQ8po7X1GdNCa4Merh6SQ1XLxndx7z9BzEyKVyPRbjlSXQny1DTRgBcK0j3hR8mOedKarffTvNjHyfRdC3K9KLsHKpwrw03j6XZzMtkCXs1dOXIPHXtgihKv/ytW9CwWvaUpg1Ct5xqjnrD8JLPCCPg6dvhJ+8UPYm5ZylVYmdFNdryiSM8eBIOPCC1p8UxqJuQ6D77Cq8FBDwmAY9JfdRP3nFJZmz6hrN0xrPk3TwByyBQYh32WQZ/esV8Nq2o5VuPH+Wbjx+lKQRvuzjD4qrRQJuhacQCHoZzNttODrKoJkhtxHf+en+XA6Wg/7isB97CPG99XjKQl9w2/lhfRGrZL7hFmF+bvwMLN41PGOhmYR3ZBs0XT1+n6tiSMbV8svaFaiHdJ0yQhjWjCuiJDjnP4jqX7Ib7/hyaL4EbvjT6foZVKDk57asyPfqPSSBP06d3KIsY7hF7sRwtlEBMmIL+2Pjrmk3I73xa9uMJpRvpvEPrQJrBVI6AxyTqmbwOtAw5/PzYXJ4c+gLvUz/infyKK8M76LjwIxBbiOn9o3NaRKkZO5H0PNUx0732SqVUu6ZptcCDmqbtV0o9PunDNe024DaAuXNnQUE41zAssoEmwtkenELkKBedT7pyJbHD9wCKZOPV2IHJA99I91K39d8J9mxjuHY9Xes+TK8KE1j+DqrsLiq6niXQ+hShg9+TFzRukI2k1AYXrJHo/IEHYP2fjo9Y6abUn7bvEMrhdIppxdpQ7yz7YfqisvB0HxDaSO1KeSyfFudutm0OuvbAtv+Dk09LJHf16yUKtuOHsjhf9zejUcNx5+/Anp9JawmQrO39H4Ub3nHu7AAAIABJREFUv1J6k1UKnvgXcVBv+PKok2lYUtNbtVich5+/D172hfFO5lTZ0/SgRCczcaFo9uwXY35sRvssIBqw2DCvkr3tcfqGs1QGPCMb703LYwznXH59cJCgZfC6C0ZFgwIeE79l0BnP0DmUYUlNiKqwt6yG6lMi0Qm6ieOKAmb7YIbeZAaV7GPp1s/iTXXRv/SNRE78lrmPf4yhedfTt/JWHK/MoWP9Weo3/yOh9BY+lX8n98Wv5oaFJpvmWdR6Poj/0Q/xttT36Yp87LSuGXaWuue+gObaHFz7SXKOh5Cl0Riahro7FYotQAyPCFQceECcr2J2RLlSH2T5RMQHpAblhf8pCJqNcU51Qxz7weMyhmuXT3ZycsOSSfOFT00dt4jufWJwXHLb9MaKpsO1HxdH7Llvyhy54JZT/1wQtkc2IQGnFxNSfUKrTvWNV8dcdpMYP7mk3GfDM6qAangkUDXvisnZ5+mgm+JMFkWRNE3ubRG5lGS0LJ+061m0SYyf3T+RgORl7x09tpgtaHlezqVy4VkTbTtdGPoo+6MIpQq9Pm2XvKNI52zimTzxVJ5MJj9iWHgNYyTbWjZcGzPViVMIMoZbHiXvryFTNb7/Y6puPSc3/SexXd8h0v40SjdRhgfN9KCbXnRT/p+2feiBoBjC+pgfX4W0BWpYM7txoGmw4pUiAPa7z8GvPylB50vePXMwdrZ4/luyL7/8i5Lh8cdknB/+3ahz6g1JHXPVorPC/pkO1pj61IXVoZH65d5kBtPQCXutScyY5liAz716FU8e7uVbjxzgS4+0ceW8MLesriTqG10fgx4Tn+lyuGeYwVSe+TXBMxOQPdOwc7ImTKzl3PNz+XvBFEE6TZMs6s/fJ/vKxDIMT0Cy4h07JGs+VdKg75BkA8eKR/pjsh6dfE5KMEK1kkAYGwjb9RNJCpx8RhhzY2v+fRFwemRPO5VkxUzIDIkjOnbN9lcUam2rJq+Fdk7qTMsV8yvO8Z4DMGfdaNB4uEf2gY6dEpgq1JvarqJrKEP7YAaPoY2jnBdxeMDhh3uzPNFiY+pwdXOA2MLbaDOuoW7rv7Hi+U/RP+8m+i94+ylckBcfzoRz2gqM5YE2Ae1THNOqaZoJRIH+6V6rlCr+7tY07WcI3XeSc1rIqH4LYMOGDS/qSuBssAFtqHNcf7qe1e+idsc3qNlzJzV77iQdW0ay8RoSjVfi+KsJtT5G7Y6vo7k23WvfT3z+jQxlbOojXuZWBoAozFsKl9wqhn7HTlhw1fQTeu2bpe5g90+FijoWVgDyA7LYNawpnYlx8lI7kEuOb3vh2vDbTwuVbc0bp/583RRaQ25Y6uhiC8SQ1a3xhrXryKKlnIJYS+FHKVlcdv9U2ll4I7D+7VKnVXQs510pBv4975FFd/UbRp31viPw+FclstV8mWSQ+4/CQ5+RSN5NX50sw37ot0LBu+TdpYVTVrxKHNIHPyOL/eu+Ob5eY2z2VLlyDkPtsgEUM88BU6JppvfUlZPLhM8yWNsc40hPgtb+9LjG5a9fXclw3uGX+wcIeHTeVj/qLGuaLJx5x2Vf1xBal/TG9ZkGXksUN31WwSDUdQxD+mWaulDxdF3DcaX2KpXJkjt5iCE3yLA9QCbv0DmUIZLrYd6WT2Pkhmi//LOka9YwsOT1VB64i4ojUmvxVM2b+NLAdbxv+BusMZ7ne4G3s2TlK7m7wRzpcevQzMDiW6g6eDdD864nXVO+empRPTNnuyilWLD7awTiRxi8+rMsWLQcv2nOTNudiFwScmmZJ+E6GY9LXw57fw5HHxMxH5BWKV27RfikOA6KgZfN3xZl2bFiFpouNd+Jdtnw6laNzn87J46KYc3OAC6Frf8jc23la2c+Vjdh09/Id336dpnbY2tUJ8K1pR6q50DBifOOd+h2/hjmX1N67jm5M2+Ul4NUPwTrpGn9iafHn4dhTWamnAnUrhB6s5OXzxpbd1rsb9p7SDIojReKWNaCa2H/fUKxHhvF9wQLau1dktWoXnJm1J/PAbRCycGIsxD0jFCucrZLOu+QydnEMzZ9ySxDmTwgQkx+y5iWym9k42jKBc3AyA4S6NnGwOKbxwV7bUeRzju4ykN2zQdxrvwYYZ9Vss3Lzj2dbFx1ai2ypkVsPrz2G6IOvevHEmi45uPitJ4JnHxG9thVN4/Wums6LHqJOBaZuOxruinzNzN41vet6TC2fnk4a9MZz9A2mMZxFRGfNa6fraZpXL2khtrkIR7ujfCbg4NsbR/mtStjXLcoOjI+DF2nMuAhns6zo2WQpXXhko7DeUM2Kc6jkx1lRoAEwluelwTEdAHJ2hWil7Hrbgl4TBTX80WEddO1RyjBE9eGZLcw3kq1rfEEZP3u3CXPO/b49l7775MylY6dUrLy0s+OvtbwyCZ87HGhkocbZKzNJKhZLvqOiAM61r41LGE19h6crLTef0Ts0Nnohviicn2GOiA6Z5Ql5wlC62aU4SFduZJUoV+97cg4nRjf2dNr84M9OZ7vsAlY8KYVHm5e5iHmkwMzXMDJTbdTvee/qTp2P4bmwsUvO8UL8+LBmbjTm4ElmqYtANoQgaO3TjjmXuBPkVrS1wMPK6WUpmn3Aj/QNO1fEEGkJcDzmqYFAV0plSj8/2XA587AuZ5XuKafvL8GIzc00nYiG1tKy8Z/xRzuJNz2BOHWx6nZfQfVu79NPtSIJ9lKOraMrvUfJR9qJG+7mIZGQ0WJzEW4vjzlzoq5Ek3b83MRVZhIZQ3EJAo3WDE+A5hNykSLnxQHa2KWc9994mwWW19MFbErwhOUDEz8pCxcoQlZ40e/JOILUyFQLYqhK145ueH0vCvg9XfCE1+VzfvE03D1x1hw4mfw9L1C49j0t5Jd0DS5bjd+RZRR7/2w0HWLGcxEh2RF69fAmmkoivVrxFj48dslyzW2zquYPe3aIwalbsqCPXZx1E3ZDNq3QtMlo3SYswRD11haFyHstdjfmSBrO3hNEZi49aIa0nmXH+3soyrWwY0X1I+jNVmGTnVQnB1XKRxXkc27pLIOtuvijK3lYZQOYeoajgugMNO9VCSyaKEwUZ9On65RlWuj8em/RXPytF75BbIxofG6VoC9C97Os+lrubT1O1zTcScX8iPCxjCty/6Uy1e8vuR3HFj2RsKFAM/J625HlQjaGJkBqnf+F/6+PWT99WQCDWQDDYQqGvFXziUwsBtv+yOw/u1UrNg4u4vsOkJddm0Zr7WrJIg0XKDB1SyHaLNQe5e/Qjaz5/9LIqpLXj7+vVa+RiLc238AL/m78c9pmoynVJ9kSRvWynjq2i1O02lSN8OJIzKvL37X9GqMY6Gbcp4P/i08+S+y+RdVCItQClqehWe+LsIq/koxApycUAqLFFjdgg1vn/wZmSGw0zKfZ9t0/nSQictn1q0Sg6lqicztib37zjRqV8DOvBhX1YvFQS4i2SPGU/tW+XvOhfJ7zRvh6CMiEDUxg61pMjZcWwIDAyfF2T6PTsbpotirNOq3qIsCdWHpo5mVHsh9yRyOo9A0Rnr/jlUxN4c7cAtBhlDrE2jKJdG8EdtWZGypIfWaOnMqfET9Fv7zqexqeiUIMu9yYfbc+yEJ0l7yrtObD6k+KWGpXCSU8LFY/FKh2B97XIKyIM7EwIlCT/DI2euRWSaCXpNFtSHmVgXoT2Y53DNMMpenwu8Zd6+9hsYtq6u4cn6YH2zv5Yc7+njieIK3rqtmWc2ojRX2WeQch93tcZoqAsyrCpx/mm+yW8o8TN9kW2zvvRJIKN6f6XDJbXIvN397vFBmEf6YZPx6D8p+VfzeuZQ4nv7o9AJHoRpxcMd2YNh/v7DlNrxDArPb/0/GzzjGmSlrU7q/IJBUKA2LzBHWwak6qunBQqucEmu1LyKfFW4YTRqkB6Z2wGeCPwa9+yEQw7bzZFNphnNeoieeI1uxkkM9o+JegQkU3u1dNv+7J8uOboeIR+Ptq728ZomHkGfytVamn5617yNetQ67cjEvrkKNU8NpO6eFGtIPAr9BWsl8Vym1R9O0zwFblFL3At8Bvl8QPOpHHFgKx/0IEU+ygQ8opRxN0+qAnxUmvwn8QCn169M91xcD8uFmrK6tk3oi2sF6Bpa+gYGlb8BKtBJue4JA91Z6V97KwOJbZNFXMJyzWVoXwTpdFbm1b5UFad99pWvCAlXQe2C0fnLguDQO1q1CtHQidTApPZnq10j25tEvQvTrU4t7FKHppQUduvaIY7riVVI3pWmFBbDwWzfFUJsuY+KvgOs/L6poT98OP7qVeSBCNJe9bzJ9d86F4pT+6pOyyb/iq+I4PFKoh7jur2fOKoTrxTjYcZdkKib2pkz3y3lNp/SmHKkZa76kfEfgNNBQ4SfgNXmouxOQ7IKuabzr4joy+Q6+8dgRnjrcy23XLGRe1WQat65p6IZGOTaa4yp0TSLX/uFuCIZQhYh2JHmEpq3/hDIsWq/+ErnIfJRS7Opx+PnBHE+12UAN2+b8DVrNDtZ33EVv49Wki8JEJaAMLz1r30fjM5+h4vA99Cx+I3nbJe8qUC6xjieYu/8OdCdLsuFyfLkBAoM70NsfHv9Gc6+YnbiPa0urF02TbHmkcVQJUNOEBlf8/9IbYPMdkkl/+j/EYbvqo5M3fG8YVrxa2qRc/E55z4kIVMrntm6RjXEi3fQUMa/lHvn8Uiqy08Gw4KV/L4yKx74ifxeVrfuPidBT62aZZzd8SZgMY7+3a0v2V9MmU4mVEhpr/VpxwnXr7FDAJiKfAjRZ64oGUrhOMtp29uyocueScl9rCsru3XuFwu3kQBX6A6Z6JODXtlVoukWDtXaFqKTv+olkvUutYcVgWT4tBu/8a845RfNsYqSPZtiH6ypSeYehVJ6+VI7B4dxIMC2g2wRSPeQ9MXI5h4aTj5AKzafHaiSAojYivUcDlnm+/a/xaL5UgrGb75BylRNPwdUfkz1kLFxH9vLufVJWE24oaB3MH93rlQuPfFHaEr3kbyeP56rFUDFvdH8GeW1mULJ1VkCeD1afd6q4ZejURf1Uhry09Kc42Z/CaxiEfOP33/qwh7+4qoGt7cPctaOPrzzWzksWR3n96ko8BYqMxzCI+XVaB9Kixl55DoNhY+G6wsLqOyy2xEQbyM5IqciCa8oTywrVSq/nbd+XMoDalZOPCVRJvbHhk5YrriNrrmHNzFrRtPHBLicvpQaNFwlbI1gjf2//PylHG/faQj2oNyzjMjMo2UcrIK+frrykFJQSZolninGpaWKnde8F/xXy+V17Ze8uc8LnHUXOccjZwhDLJNIk+jeTNKIE48PoJKhNtDC06vqSfXU7ky7f3J7hqVabKr/Gey/0ctMiD/4yeignYqvx+M7TuDzDOCM5cqXUA8ADEx77uzH/zwAlLUil1BeAL0x47CiwttTxv+9wPREcTxjNTqPM0hMrH26if/lb6F/+lnGPJ7M21WEvUf8ZuG21y2Vy7/qxLEgTFxjdkAWh5Tn5v+UfT+GdiO0/kGzC5R+Qheye2+C3fwOv/ebsM4BKSbbTHxMn8nQiwJom2Zo562DbD9hhrGHtlddPfXztSnjVv8P9H5MM6oJrxFjb+NeykZeDNW8SA2Hr9yZnT8vZLKwAuAnJgDVtOCctaKJ+i1WNUXa1xdELIkiWofHhKxvY1qfxv8+e4MN3beMVqxt46yXzJm3u46CUjO8S922kCXo+hZ4dxPHL9fD37uLifV/E9UVpveLz7M/X8eiODI+dzNM5rAh7NN6w3MOrFnuoC+rAFbQsv2LS+0+E60JfbB3+uiuoPHA3vXVXEq2eS9COE33ha1gtT6FqVqBt/CsiYwMJ+bQ4i/FWyCUKGfYyjHWlJNKqaVCzFEL1k2u3vVHZ5Iv1NEteKlHrhz8vRuNlH5i67njNG4Rqt+NuuPqjpY/xR4U2FW8bXwd0qujaQ/XACxLlnsiycHLyPaYTEjO9op77q78So9fOShR+333yuss/KG2uSgVsdBOmEpnKDkF4jlwrTReKf7AMJWKlhAZneGef4XFyQs1uvmS84a0bcr+LtPwziWxCqAeBSkCTNaRYdwpitOWGC03gXTmHiRmTNW+EB/9OnJYF10z9WZZf3is79OISSnJdyV7YaQkEeKOn7DzrhV6qIa/JnJh/xFlNZvIMdh4nlXNQpkvM7iEUP0h2/btZ11yB+WJvK+IJSBuiRZukpOVXnxD2xfyrpISla68EnPNpOV7TR5kJIMyOygViC7S9IAGy2PzJn6NpUm+65buSvQvVjmbgQeZ3z37oUWIzRJsLQaMSVVeG95w4sJahs7AmRF3Ex+HuJL3J7CTVeU3TWN8Y4oK6APfs7uehw3H2dae47ZI6mitkTuuaRkXA4lj/MF5TpyZyHpzvnv2yL0211h3+nQSzVpVRflHEurdINvOZr8Orb5+8LhaZOb0HZX3Lp8TeO5XA55GHJXN5zcflb3+FBF13/0RoyKWCrjDeUc0moPUFsWFnE8BPD5TuDz0WplfWwL6jMhcmCDZOhOtCyrZJpGx6h7Nk8u7I5TM0DdMIEsj2EvSmUaEw4fYnAUjVXTTufbK24kf7c9y1L4sOvGONl1uWefAY0687SsFw1sZ2FQEdasMvTu2A2eL3RCP7DwiaRi66AH/PTuwpnNNSsG0FGjRVnIajVpR/L2LtW+GBv5SWBsV6t7EwfcL3n8mAS3aJk7v4ehHsADFG7/tzMbhv+NLshFiOPS5Ruav/8sxR9cINcM3HGNjTOfOxlQtlgb7/Y9LfbcG1IiJVLvwVo9nT9bdKFHm28IaFftKxUzK6Z6rWYhpE/BYrG8Ls7UgQ8ZmYuo6ha9y0uoGrFlfzv8+d4P5dHTx2sIdbL5/PS1fUlazdshIn8Q4eIRNbih0u3dLHTPeMbqyuTf3mfyRpVvKN6s9w36MR2pPD6BpcVGfw/1ZZXDvXwldG5BDG1IO5CkMXY0K/8kNo972T5UfuBO2lkqG0M3Dpe9FWv2FyNsnyi8DHdO1/JiKXkk0t2gxVC6d2UnRdqPX9R8ThCNXJPW7fKpmxCQp+4xCokkzrwV/JRj5VsKO4iZ8q8ik49iQc+g20bSVvhrFKnVcmLht4NjH955k+uOGL8MAnxHDWdMnirf/TU1P5VEocxSIzI1wHucVyTacLork2DPeLONRwH+iaCFGV41C6jmQv51xYOuAWrAXTf2azp0XHtGlDIVjSIpnQnqIoki5Mi2yCkbIBJze5v+q8K2UN3Pmj6Z1TkLU62fPicU7zaWk5kR6QQE+8RZydcKMoZ54mjXTEWfUY1A/0oxY0olleeOEXgIZ32fUyTn5fUL8abr5DMmHbfyhzWDOEBr70Bhk/tSuFHpnskkxq/3HJxg0cl59Fm6anhBad0yMPT2Zemd4CA0hJkKN9K5O1LwvFHropY3um/pJnCEGvyZqmKD2JLAe7E9iukjZFY6m+ps5b1lWzuj7Ad7d08w8Pt3LLBVW8dEkUXdNEzddncbA7icfSifrPYQ2qY48K+ZQa80pJYLxyoTA7yoUVkJKNx78CRx+FRddNPkbTRe29a5esSadSwqCUUMJjC6R0qYg1b4S9P5Pxes1fzvw+RQe17QVZ68pxUJUS57ocEU9/hYgManpJkU7bVaSyDvF0jt6kMC90TcNvGVQESjFTKjDSveSD9QS6t2H7KsmFxS5Ujs0zHQ7f2Jajc1hxbbPJbet81AanD77lbJdUzkHXoTrkpSrkIYgXzToH7KFzgD86p+cBji+Ga3rRnHzJGrhSSObyLK4NjYi9zBrpAYmse9UoFaJxvfQc3XGXbFql6F7lbPrPFxRvx6q91a2SWpjHvyo92S57X3nn6eSl5i42H5bdUN5rzgaiTeKg7r1XFs7ZGj8j2dPvl67jKAf+Cqkp69wptR7ngOIbC3pZXq/Y15Ggwm9hFLITEb/F+zcu5uWr6vnW40f52iOH+eXOdpbUhqkIWMQCophYTZz67AnC0QiR/gPoToZcdOH4CK9ycQZa2Jrwc3AwS6D9GT6SHeSjuXfx6KEw62o13rTCx1VNJhFvedkR15Veo7ar8Joacyp8RHwWPtMoJFiCkvl75mvCBqhbBdd+4tQCBxPh5IVu5A1LRq0coz5UIyqHRax8jVCJrv34zNTxtW+CA/cLTXNiPdjpQLkinnTwNyL+ZWfEobnoVrZoG7h8QimCKMMGhZXQ+sJ4xeFSsAJw4z9K5nfBtafXNikbH0+VBjF4skkpPyhVM5lLyU/9asm22hlxUAdPQLIXjAJbpBhIU6qQWVKjGfHaFVNH3XVdaGqdO8+Mc1p0OJsKxpe/QqjQtSvg+JNSb2v5wI1LBsvyCttC0ycLeuiGCGo9fXuBElyCuleENwSJtvOivjoJyW4JVGrG+Ovu2jDUIgak6S9kzw25X8VsYPF3ZE55zk+hrZUWrJb7ffghuY4TtRB+H2B6xdlYeoMEOauXlB6T4Qb5mXv56GNjBBunRKRRxtDhB6duFaVpMwfJcsPiYDRtmJ59cQahadIzNRb08NtDOoPpPF5DJ+gdbw5fUB/g769v5r9f6ObunX3s6kzxjotriflNDF0n5DXY1z7EmqYKAt5zZErnEsA096drj9B9S5WFzISlLxextef/q6AkXmK86KY4a5p2ai3Jiv18N/7V+PMLVsOyV0gy4KJby5tzIxnULYU1cobxk+qTYOrY93Zt2TMm7Nk5F3J6kLztYg/bZO0cOcchm1eoZCe+3r0YdgrLSTNXZTDsYXQ7hTK89K54G653fMBVGRb5UAMoh0D3dobrL2Ewq9jTa/Org8M8160zL6z45ytcLqx1UAa4rrekLZDOOWRth6DHZGF1gEjAM1rml8/NfN1+T/BH5/R8QNPJRebjGziI7Z9ZeCKZsakKesYpqs4KmbgYhjVzJQJddE41Dda9FR76rDhSq0uLykyLngOyQa17q2SAxmL5K6H3sETKqpeM1ppNh70/l8jgjf94em0vzgRCtZPl1cvF2OzpRW87dScoUCn378RTIrpS0XzW1TSrQj6W1ioO9CSJTYgKL6oJ8eWbV/P4oV7u3dHGCycHGEzlcMcxtjxABlP3ErU6iPp7CAWDRAMeDF3jWPcQJwcUCllIv+97mAE9RuPCNdx9QYgKX/mbXibvkLVddE2jKiRKjSHPFPVgq14r2YGK+ZKdPBPXMT0IKBE6CjeUb8x7gpLxyadlPi68FuZfWd6YjzaJc7f3XrjwT6Z3CCci3irZjkxcNuVsQgyebEICIdkhcTgXv0QogfWrQdPIlmIcZIYki+gJinHQslmMzemMBE9wdrW7paAU2PnJlENdFzGflucnZ3JT/UIhnHvpaNbT8kNFk/S1yyYg0SVOmSPKrmhGwQgzxBCLLZSs+HQI1shaO5GlMltkE4A+nrZmBSThVHQse/aJcJbbL4aXLyL1pjXLSo+JpTdKtmvnj+Gln5n6s3VTrsH5pPY6tgRvBluEpj6p7MQczWg4OamHK0Ib+aegIhsX52cmY32oY5Sh0ntQMrQTVefLcdxeTIg2yc9sUO73W1xgoPQfm1lbYip4ghLUatsq92i2NYSnAcsQ4aylzRUc7x2mP5Uj7DXHtR0Kew0+eHk9jx9LcNeOXj77YAvvvqSOC+oDeAwDx1Ts6xhiVWMU37kQxkoNTL9v7f25rN9LJthayp3ZmdQNEZi8/6MSQFw3Ude0gKkSKuXMjR13CX180UsmP7f2zVLqseMuuPLD079PEd6wjJ/WLdC4HuUJlhaqcl2xVQtrv1JgK4X+0D+gtz1P/JV3kDQrSeUchrP2OEFHHQddA8PQsPLDLHn645jZwdGvrem4ZhDXCmCme9HtNJ0bPj7h0ijak4ruE/tZkk/wr+0ruPOQqKsHTY13XjmPV62swqNyZPNJzOwgejaO5ubQ0HDMAEnHJO8qKgIWC2qChM9VQOQ84Q/7272IYQdqUIOHhZKlTb3Y2I5MksbYKWbNsgnQTGhYJxu8dWQ87WzBNSL28tw3JZtUu6L89y7WhvqiUy9kV3xQHILHviJUxuql05/r1u9Jn9axlI8XK2YyxM9E9hRGe8P2HRLn4hyoadZG/dgKjvYmqS3QY4vQNI1rl9Zw7VLJZLhKkRroZrhtDz0qzEBWYzCriBd+Eqkcg5kkR5Meso5iUcRlY73O0hofqwJx1j62nYFFN3NVpVGWYzqaJXWJ+CyaYgHCPnN8TVg2IYbOWGdPN0frXM4E0gXF1vrVp5Ypq5grWaGiQTaVY1rMzI6lq657qyiw7vmFOKjlYLgH7vuIZBatQCGrEQJPWJyu2pXCpph/1czfx86IAxQoULs8QTEuWzcLJfhsKudm4nK+peaeYUkmt+U5Wed0UxzTUJ2sbaV6N2uaGC2+iFAflTr1jGExe9q+DUKn6JwWHdOm9eONdcsva3nlYoTCu1eEcED2ETsrtajFTFaRVlmkTXsCsPxVIqiV6Jxe2f18UnuzCSlnsNNT0xfHwvDAdNTK4Z5C259p6q+dvAgEFa/V4YfkGiy4dvx55YZlbJ9lJfWSUK6c5znQIJgSmSGZY5YfFm4UJsqR30HlKQZxQdaRbEIc1Mb151xEKeg1WTknQv9wlqM9KYazNmG/hVEYd5qmce3CCMtqfHzj2S7+85lOPnHtHBZU+vBbJsmszcGuIVY2RDFPp+d3OUh2Tr22pvqFkrvyNeOPcW2Zy4YlwRdvZGpHtfEiKQHY9r+i1VFu3/lcCu77kOxRG/9K1HQnoveQZMkveU9pBzdcL9nb/b8c2dPyrhIBQ0eRsx3SefnJ5R2cAqFFodDtFLQ8RKJ6Lcr0Y6kcHjeDxx7Ga8fx5BOgXNJmjLybIe8q/IOHWXn8UQDUk/9Gz0WfwjINAh5zyuW/dvd/Y+SGaLvi82Qj83DNAGqMdkHFvh9Qc+AHbPZdztPGxbQMufKTcEjk4APGC2BBa3gd71rsZU2kbX4jAAAgAElEQVQsz6KaMG6dBD0dAuCrGC2HyqdIx3sxk+1UmUPURD0Egl7KUp/8PccfndPzBd0iF27Ck2jB8U29ACSzNotqgnjNU1j0cimx5Js3jC74VYslexoqbHCaLovJPe+WDOrNd5S/8Z58RkRIrvzzqbM3uin9q+55j7Rp2fgpWQBLYev3JZtz2Xtf/NHpTBzQhHYWqCodzfRXSF3drh+dXvYUxqtptmwWGlv1krMaaZ5TIWIh/akslQHvlL0BzVyc+uG9OLVR5pWMqvoxskMo3SYbW46/Zzu2T6hBsYOPoimXoXnXQ2L687EdxXDORtOgNuSlOuTF7ylx3dODcr3SA9PXH54O8mkZo6fqmMJovehMEef0gBgbY4Mh1UuEQrz7J7D8ppmNiHwKfv0p+X3Ld2ZXS1sKmYQ4gWN3cW9IjMu2LUAJdd0zAeWKsRWbO/UxnqAE41o3y981yyUQUM6aMqIKfhoIVIvjX8yKlwvXLhj/3smOafHcAjEZB7H5o3WnIOt4505xUostZPIpUVw1vaNZ3AtuFn2APfdIlmQqeEPSN7d68bldi3PDct8MT3niceXAExIhIP9lUwcdUv2yV+qG1BYfeVgc/7F7oZ2VOr5kpxj7gdjZZffYWQkCjc3km97CnlN57plF+TSgCg66X86hcb0I8Gx45+mNE29Y9tSObdInfeKa6jqyDg6ckHOoWlS+41QGNE2jKuQj4vfQEU/T2p/GY+oEx4ix1Yc9fPTqBr74SBv/8VQnn9rUSE3QIuQ1GUzlONKdZEldGP1s1SfnM3LtpxLn2X+/rCErXzP+8cyQJAVCNRBvlyAMSq55qb3r0vdKO7xnvzmZflsKSkmrsP5j0orqp7fB9Z+dXDqw80cybla8cur3WvdW1MFfk95yF+nIa9nRMpqh1NAwdTANHcsw8IwED0DzRtHtFNGhnSg0XOWCUriY2IaHrB4EXcdA4TVN/DrMOfoDHE+EwQU3UXXgLjL9z5NsvHLKU/P37CR64rf0L7mFVO2FI4/3pl2eacvzdGuevd038FPzSVYd+iYfyS7E9IVpjuhcO9diYYXBn7XsIcMiPrFRAoNmephMqAa3xOfF0/+fvfOOc+yuzv73FvWu6W1ne19vdQfbgE3sOHQSSugQEnpCEhJIgQQIhDckIcQkOIFASF4SyAuxsTHGxtgGbGyv1229xdu9O7PTm7p0y/vHkVaaGWmqZpvv8/nMZ2akq6urq1855TnPKVAwNdpbu+mMbSCgFnsJj/eU2xGeL7oAS4CLRyv+AoQRaEOxTZncVZDMGmfq+aahkClvWlVPnpWfUr1SCYEmiZ5VvtYbFgcyPSy9Re1qU2UKLEOyppGu2Xtp+WIihqK5hTJy/+dkklViolcMpnU3iQN9PsPMyz3qvBQa14lhY+SqH7v1DfK593yr+vOFDOz+N/jhHxYporPA5ZNNJj0idWennxY6Wi5ZcxwtBh0xH2tbQoxn8oxn8thT3kPNJ/ANPoXpCsxYP216woCNd/BJYdwpCtg24efvIdOwiUKwhkKfLdTdsUyBgmnSHfNzSUeUrri/umOaHZd71HmpRG/zyYV/+Foo1am0b1tcFkN3S0ZvpmvMTogx0ryx2MKkAjvfJQGo2z8kY6Dm9Zpw32dFLOhln1y8Y2rkxAH0VzGSvGExWAtpWX/qjeyE0BRnqy/yx6XFTNdlUtt6Nh0sVRVjMDdLtAWK7REmRL2y5HTORG/0N0hGsWUjDBwoZnk1WWN790g7ndYtcqyRlXrLXMX4CjaL2Mn+H0h2sNaaoeqyzuUm5vXRFwWzAKefKio017H+0OWTe5AarH3M2AnwFPfJ3idkL1xToepuGXJdoVZxnlo2yVqTreP9MXLigKWG5Me2RY26bZvUAK68Tn43b4L0mIyXswXLlHWq9RK5D5Ypj6++XvpCDuxb/Ht4I1DIyf03irVzuQQMHhKRxN49MvaNYoD21G65X3WES1NZFg+wrSuKW1cYSeexKuZIxKvzu1e3YVg2X/r5aVJ5uQ9Rv5uhVJ7TY5m6Xs8klGrQqyE9DPtvE9ZZtCJwZ9vyXYXb5P62bBC2XMtmWXtSQ2X15hKiXZK5PHS3UGxnw3M/krVk5zvgVf8o69/tH4a93yuvL8kBCfis/7WqNci2DYmcwTEjxkjri/Ac+gHuQoKIz3XmJ+zT8Xt03LqKpimoqrxVqcOg5fJjeCKY3gi2vwE70IgSiOLy+vG6XXhdGi5dRVXF0QwMPMHImtczsu5NZCMraXrmq6g19mLFzNH85D+S97cyvPaNnBg3+b/P5vjQj5O86bYk/7A7S2/S4sbVfvau+xBNygQ/Xf0d/vvVIf7mpQE+ssvHq7oNohMHJjm2YGO6JyeDsgWTwWSOqF/n8pVx1rWGpR7a5ZX1p3MnLH+x7AWJAVmnL0I4zuk5hK37KPhbUQvTJ4RpiopcV8w/2a6yTFlQQDaz1NB058jMQzYpGcqpC4Gmi3E6dVNt3iCR9OcflgjXbNh/h9TkXP47c4vgNqyWPmzb3gKH7oXvvB2e+3F58Xr0VjnPrnfOfq6ZYBlyP2Zy3Ku+zpzbcbYtTmTLZnH648vlPudT1Z0MX0yyp0fum1wXZduikvydt8Kebwql6Ucfn75RVIOiSB2WLybCMP175Xs78lPofUoc/Xx69vPMAYqi0Bnzc/nKBhpDHoZSedJ5Q54zMvgGn8TSvdhzqK+zXAEsdwijKBbgHdmHO9nD+LIprX2KDul4psB4toDfzrA2prG5PUpT2FNbFKykHNu+XRbypvVyP+cSbJkrbFvqflo2L0xldioinbUDG5Yhc7lpvThbpWxcCc3rpS9vdhxu/yAMH6l+nke+KjXLV35Qai4Xi1xC5nOtLJQ3IkZSLln7sy0EtiW1iHNlIIRb65pdmRf8DeKo59PF77FQzITl5DvMJ0WMKVOka3fslN6iDatmpjV6gqKU2bxBHMeJHs70fu7ZI05TKWBi2+KYq7pcQwmXvluCivd9RgIbQ89Vfy9VK+81Sw3blhYZhcziVKZrwRuWz1ltnc+nJFhaokIevlf+rhQJyiXF0VeLdciRDimH8QTF8K68v7VQEtiyzPKYyE6IGFdySJ6LrpDgxMqXQPeVxXZUTeKslyzxaCd0XwEostctQVByGtIj0LBW1qFwe1GYBykB0Nxyz+oBX0TmzOkn4flfys/ESbnPgeJ9cAflnhgZcVCXwEn1e3Q2tUfpivsYTefJGeVx0xZ288GrWhlMFbjloT4KxdKriM/FiRGhBS8JUoPVyxL6nxVmWjYh3QEqUUhJQKoy2KO7xVlddqUEWnKJ6WNo5ztEsfnRr0pmvBZGT8AvviTn2fabUu/+mltlDD/0D9KtoZAWhg82bH7dpJfn8gWGhgbY2zvOwb4E46kCifVvQDOzLB+4e163ByjOz9lbiTXs/xaGN874yptB1RjY/mG07BiN+76BYdmcGDf52ckC338ux9eeynLwvv/Anerl44V389ofGLznrhT/9kwOG3jnFg//clOAb9wc5H07vGzbtIGxta+j4dS9+Af2nHlb39AzKLZJurnIHLQKWJrnTEtJ07IZSuYwbZvtXVE2d0Tx12qj5vJKELJ9m6xfmfH536vzHA6t9xyjEOrEle6fltZP5AssjwXwuiomWiEtm2TDWqG1KWrZyJnoKRoSSpHataO2YRZsFf5/KRpcwqbXSOT60VslMl9LijwzBo9/A9q2SiR3rtA9IjC0+qXwsy/C/X8Fh34kQh1H74cdb184DdPMy0av6mJ4jT8vWYTZKMq2VdzgEcd9NppEZlTOX6n4FmgUCtjpp+T5qfd96xtEqGDPt+ClfyIZj4e/LJtK41rJZmXHpQ/hT/5S2vDMxeFXNdmoS5Rqy4T8BAwMgmVLO5PostoCBvOA16WxvjVMW8TH4YEEg8kczdmTgF2zX281VGZXwyfuwdJ9QqUp1o+MZwrYNoS9Oh1RH0E7gdvXIGM6MyjZ0GqfJzsh33clJcwbFvGj8ZP1q9FND4tzFGmvz/m8kbLzMPU7T4+JcVoyLOKrRE23MqvWuhle8Q9w1x/CDz4Mv/JXMi9L2He70Mo3vXbmFjVzhZETddTZ5qkvKg7XqcfBS33q5DLjEtU/C6rVi4aiCKuib6/Q8UpGk6KWG9c3Fp3n+bSJcgUQAa4iZW5gH3CJrB/Dh0WRGmQtUIv1ZfGVUq9eosmG2uA1/wwH7xIl9e/9trQSu/Q9k9c/T0goZPGVS595HntegmpLpYyre2TvnDgtzl0lEgNl3QcjJ1m6FddMHrNmYbrgn9sv681ErzjWU4NgRXaIBEyHAbWscqqogCKfN9BUm2JZC56Q7DnDh+Te+aLTRaPqhey4rJ+ljFywpRxodQfEBjjyU+lxXg+qsT8mDlOplKUWSntfPimZ1ECTrId12O9AenIviweI+twc7E+QyxQIeXUURWFdk4937Wrm1kcH+LfdA7znsmY0RcGjqxweTLClPVpfeq9tQ2pgOqNg/w/EOQw0w6u/Mp0Vk89Ipr0aFEXqsCNd0lKocu6Xyr3Sw3D/52XtaN82+fVGDn7yF1Iy8JJPlEubvGHZh578vyLANnxEHOtVL8UItJDJGkxkC4yk8ljJIVAU3D4Vr0+CcgVvN8m2q1h++k6SexIk268m3bxN9vc6wN+/G9/Ifo5ufD+HJnT6UwWOj3dxhe9XufH4Hbz30GU8Yq4/c/xG9Xk+476de7RrOB3eynU+hRURjSs7dBr91R3hkXVvItj7MM1PfJkTL70F2+XHP7AHS/OQjYuui1pIYwRasYFEtoBhWqxuDtIe9dUsoZoERZEWat6IrD/J/rMqKLbUcJzTcwzLHcb0xtEzQ9iqjukKkjUUgm6dhmBFBDw9IgNv2RWTMzYlqfb4ctl8MyOgeWdpMuyWtgujxyYb7YoirSyGD8O9fwmv+9fJC9bIUdj7fVHnNfPSHqZqr61ZlOFKfUT3/wAeuVUi/r64OHHzhZGTjUzziMEWbBFjL9Ipzt9MtUH5pCzeDavEYOvfK/e5liNTyMgC2bhm+nOeoFBJB/ZJJD3QUL4HvpjUgTzzXTFUjv5UHrv2j0QAoHTc1b8rtRs//zvp8Tpfg7DSWbVM+X7HTooDHGqtS1uIiM/FjmUxBhM5+vYOMWq5wCpgIzUhqiKbeqkfnKrKY1PZSFYuRbDn5wy3voixgg4FyXSvaPAT9Lqkxjo1JPexdYsYj4k+qR2zLRmXpfuWS8jzHTumZ53iK4R2Vo/ek9lxcY4b60g7VzUId0p2oDKokU9JL85KdVh/Y1EJdspnia+AV90Cd/6B9C1+2Z8L7efUbvjF30PXFXDlDPWF80FuQuiycxlL/rh8J717ZCwvxni2LQlQLKb9zNmGPw4rZ+kpOl/obgkOuIOyHwzsh+glErQAcZag2Di+qWjAtIoTU7kuK6o4pCuuERG6vd+TdWnnOyRIqepFam9usqjSUiA9IsZVvWpMa8Eblr0t1FJ2YCwLxk+Ug5jPPyz3rlJZ3izIva52D0pZ1ECjrLlnWmxUOKE9D8Lq6+r/eTRdMuj+Buh7Rr6vegs1lUpYWjaV57wnLHO5FFBbfb0El4/cJ73O6xHImE/2vLTnpUdEJKx1S11bIIV9LrZ2RjkxnKJ/IkvE50JXVS5fFmIobfC9vSM0BnReu7kBv1tnNJ3n9HiWjlgdHYV8UlgjJTvGyElm8sCdUrrwkj+d/t2b+eK4nSXgHl8hyY2pAVLNDS//DNz2Qfjxnwplt1Ih/ZdfEZvwxr+eHkRQVNj+FuymDXDfp1EKaU503MzQqTFpb6soeHXwet3kIivxjB3GoLx3D1zyXjy7VVp6Hyby/L1kVT/P+nbxgHo5d+e2MG66MG0wLTAsUd41rSKpTFfw6eDVFfy6gs8FHk0hmbcZz5r8U+4bHLdbePmeKzAoU+Mf8L+OneojfNn/Nb6/4W/pinpp8llsfuTrkA6y8vrf4S/mGBi1NTf9Oz5C54Mfo3HfNxnc+j78A0+QabwEW3NhGDZmNsu4L0ghnacl7GVFY2Bhis8urwSkE31iwy4F8+QcwHFOzwNkGreg5hPomWG0ZC9mOs2qtiiqXZCZl52QbE3Dqpmj7J7g3JtZh9ulBm2qI+kOSv3pbe+Hn34WfuWzcOIhUZ09/ZQsWKuvlyxMtdrQXAJyKWnWPJMxqqjisHVfLcZR91XVVejyqTKnfir1xDZB98tmFGiaLErk9kvmZvyk0LlcvnLU0TKEmumNwLKt5UW9fbts8qnh6cqOtiXOf9eltSOzuluyzSNHpJF55YK99Y2w7zapE936Jtj+lulR0I2vlAjjE9+Sz7PzHbXv32xQNTFazDwM7BUDrGl9XWiOiqLQHNCIN2jkPDEsS5pSG6ZF3rDJmyYFU/7PGiamZWPZZf/UBlp7f4ZmZnFvvJkNbSG8us7PkjkJyNh20TFtlkh46XuNtMs9LTWKd3mL2Qmgc0f1qKHmEgOu98mZAzZQpOYp1cWtCpnihW+pfyufUIt8nhJsS8b9sisnG1mqKmtApaBZCcEWeNWX4a6Pwz2fhO1vlT6oseXirNYjozHXrGklAg0yr3oeL2YJZ4h+5xJSc1bZCqQEy5Cg1kUUGV4wAg2yTjSuKzqnSADA5ROqN8h3VfqedI8EQBKnpzNDPCHJeK3/NVFeffgWmXulvtSqJuvhUjmnhYzsK97IkrfIQnPJOBrvkWAuCJ3XLJTnx8EfSRCoJCoFMi5js2SPz6WCbrBZ1orB/RIYnW2ezRW2LeyNju2T552qStBs9LgEfrsuk/Xnp8Vs2bqbxEldYkX5afDHRaxqJFDfACLg1lXWtISI+V0c7E8S9uroqsqvrosymCpw54ExGgMurlkRJuxzcXw4Rczvql//00raZnJA1vjB/WJH7Hxn9bmTnYCmDbM76i6ftKkbPjTdyfSE4KbPw/++H+76I8nO+hvg6ANiz1zyhmmlIoZlk8mbjKULjLIK+/Iv4k2dpBBcRVhXzyztWnaMfKiDQrAdd+J5FLOAoeg83mdw5xE/j/a+D9U2uErdy03qY7zc3M1O5UHeh5fvht7Gz4M3oCugqaCrIpZk2ZA1bDKGTbogf4/nbLKGTdCl8Fr3I6zPn+C7LR/h/U1Bol6FJr9Kd1jF5wqT7/8AHQ9/itfnb2Mk/mYiR+7EN/YcfTv/AMs9v8BPNr6BsVWvJHbkNhKhVbhTvfR13cR4poBfV4kHPLQv78Tv8y2+DZGiCFXbF10arYdzAMc5PR+gqFieCHlPhGG9neVdJn5vRuhCti38/Xov9C6vbDDVDJbGNXDVR+BnfwP//hqpWwi1Sn3puptqGyq2LROjZYP0lAo0zs7/DzTCiz9a/TkjJ9HCeAVVRalIw7n8shHXWnxVVTItvphElFJDYBtCS25eLwZb5Ws1l0Sg+vYKhcbfUDZI0iNiHM/m3KmqZKUnesXoKRkJvphEHl0BWURqYde7xPB8/Btyb9bPoGw3F2huMVLzaen/WGqevtjIcj6Jrqro1WoipgQ8bBtMW2qoTctGV1VcT/4Uot1Elm+bbPTZtrQ6CXeKMz31OnW3UF3DbRJ0yKWmi35NRaBJfrIT1TMLti107NI1l5xUir9Ul4zFrsuWps2BJyTXX8qIpkck8FPtWoPNoLsmj60SvFH4tb8VeviebxaFyD5XPxpsLrGwrESgaOz3PlHdcDayUi8VaJD5p7mLmdLST7FWz3MO2necj/DFxcFq3gDPfBfVygv7pHXr5CBEZQQ90imlDrWUoWPdcNMXJENy6B64/LdlPriDklWJr6g/tdcyJRioqLM7d+lh0ShoXi9jZLZ9pRZ8ERg5LPuZyyvBy9J7pwbh1KOw9c2TjX3bmj2wda5xhmJ8WpwWRZPPuhikR6R8qBrVOtgkQViQ+fr6rwm19+BdIpT4yK3C8lp3YzHIVhyXts2ZGg6of0Ai0CjX5fLXr/SiAo0hL6qqsK93gqjfjaYovGV7E6Npg2/tGaQr4mZF3HuG3ru5XvTeZD+4fbLn3fVHsmbe8GlY8eLqx9vFNOJcafKRThEFq8YwCrXBjZ+HH3wE7vpjeMnH4cEvQNMGrJ3vIV+wyBsW2YLBRFZ+LNtGUxS8Lg091gKxFiad1bZRbFOEEFWN065l3PfMUe58XqcvZRP1KFzTDlvbQ3QEr6Yt+GIGvCbp4b1ED32Ptw3eyk3LbcZWTVEmngmWQfdPvkMuvJxtV76MbVXWkHTLLhKd1xJ77jtkY+to3PfvpJp3kui8tsoJJ8MuamUUzDK9P7HizQROP0Ln0/8IQMO6q2lriuKycoAfwvPoUT4XuHwXTQDXcU7PI2QLJl63Tmdrk4SD4itlkVmqiHK0S2pHqhks62+WyOj4SdjwStloZruOzJg4vLHlEsIaObS4Vh7ZCWi7ZOZ+fHOBNyz1OSPH4PghydbWMthVTQzwgf0wcUquv5CSjPRcG41rujgXA/smRyLnokKsKHDNH4iz9LO/FUN0PnW9teD2y6KVOC2G6GKpH9mJ6sKB6WG446Pyua//FHhCKAropaCChogo9D8rwY5J484WGnZsuTigMxnDnpAYY5Yxe5ZAUeR8Jx4q1uJVjOOSYxRdVmQmFB0/IyuKkYWMiH/44ksn264owowYKtat6b7aoj+qJutCKfgzFS6f1Ps8812Zs1Pr5BYK25LaooXO52CzqI6efqpMs7cMycy4fMJy8MfP/xZS5wNKa1fzRrAM4iNPFNfpYiDLMoo9rSuMFE9QmAi5idpzX1FEBOXELyRA13aJzIfsuAQm6k0ZHXpOzj1TXSHIvH3gr4vtu5DjV71MGDwN82x1o+qAIvterLvcCgzgubtlnK+7sXx8SZl6roykcwlFEYfMH5f1IdEnDmqls2EZRVGuiuxKKQCnu2XcKGq5XUxDlRIWKNLKKwJq7qCo9m94hazvB++CQz+WsVTzelVheCxWBHHqOf1xCUa7fUsiiBYPeFjdFOTwUJKYz42uKvz2FS38+Y9P8vXdA/z5y7oIuHVG0nn6JrK0RxfpLJiG2AP+OOz5D7HXXv3PM5c45CYg1FFdQKkaNF3EjE4/XT0Q07QOrv8U9t2fgO+9F1t1c3TzR5k4nTqj4K+g4NIVgh591impFpIk3c3s7slwz77jPHJsBNPS2Nas8J6tXq7q0Dk5MMHKtsrrV0k3byfduJm2x/4PTc/8C1gmY2vmpqUQfv4nuFOn6b38z2YMbg1ufg/+/sdpf/hT2Jqbga3vr7nGWBZk8gaGbaMqClGfTtjnxaNruHUVl6qiev8I7vg9CDQTaC4G+bLp878rxTmG45yeJ7Btm0SuwM5l8XIjZ0UpCzUsBdwBiYplqtC2FAWu+uDcz2UZgCVZQyjWwE5IFmwhWd9CWmruAnUSyFA1ofq4Ts2eSVJVyUqoujjoiirqiPMJEgRbpL6pWoZr1mvV4fpPyoJ271/Ar3xG6lkXi9ICm08t3jlNDU2P0OUS8MOPSZR3okdqVW76vIyxShy8S8b1mpeXHysJhzSum7sAi6LM/d66A5OpS7ZdrM/2TGcmaC75OZtMvUADDNhCHe/cNTN9P9QGQ4eriyiBXPu2N9fv2syCUOgb1ywuUBZqAWuzZMs0Xb6/5vVlFVQHc4PLLw5Fk1B4u0/dJo+X6k3zaVl/ps6hWLcIx8w095ddKePn2APinILM1dRwfZ3T8R5QR2bOSBo5UZp+9nvCnrnpCzB+StRDn/kfePq/JahUclJtU6zFUn2yZckatfxFk++FLyoO1Jna0KJw0cEfSlY2UiGYlE9C4/ppl3Zew+WV7y7cJjWY2YniZ7WLvRHjIhRXcrgLmWIAYkLoo7Ypx3ddVnsdUhQJbg8dmp5pi3VLr/LL3iNsncEDpReVX4siQjl7vinXtOOt9fv8qi62Q++T8hnq2ZaoiNaoj5xpcWo0Tdzvwe/SeNuOJr70iz7u2D/KazbHifhcHB8Seq+vlurqXFBq52Sb0Pu4tBSarfbeNKQWej4INMt9y6fP2EilbGAiW2DYtQnfht9m2f5/4flN7ycfaCGklWm6s8G2bU5MWOw+bbCn1+Sp4THy5ighr84rLmnn5uWw2j6B4ZslEKS6OH3px2jd/Tc0Pft1FNtidO3rZ3yJYuaJH/w2mdg6Uq2XzXis6Y0xuPk9tD7x9wxteBtGoAVsoSuX2F+lvzVFoSHoJupz4/do6NWy5O3bpROGJ1Reh0q6GQ5qwnFOzxOMZQp0xnxE/PVRJJszYt1Sp7FYZMbFQCjRHhVFFH9PPiobvHuekedcSoz0OgobzAuqKtk2rRhRnq8zp+liUA3unz0zUA0uf5lK88M/FAPsivctXjRE94ihuZhstGVJrVbl4mpkpRXO2Am5blWDH/+Z1Krc+LkzhjSWIf3Tuq+c7BDmkxK9X2wPzpkQ7RLjNjMmWdFYdzlbeq7h8sk4cXlnD+ZorqKg2dGlq+sq9TU0C5Ix1b31CRSVqHb5pNz/c1mrd6FC1YTibJsQaCKUOir/l+aOWai+Tnij4pAYWfk+q8HtF5bJsQekFlVR5TUTpyTgWI/MdqJfgisrZsh6jp4Q5fKRI9J+4rL3ylhpWgerXybO1LEHpYXJ7q/P/H4v/TN5TQmKKuvzyNGyw3262IJrxzsmv9a2p+sPXAgoUTq90aKKp1++W907/Z77ouVSE9uWoIBtzR7E9TeCfbD286ourJ9azB/bEiXY3V+T67rk1+f++WaD7pV50PukBHbnmkGcB5bF/eQNi6FEjqjfzSVtAa7qDvLDg6Ps6AjQHfPg1lSODCbZ2BZZOL03MypzfvCgBJZnC1QX0rIezDeYpKqyT598jLzqYTCZZziZI2/aKIhif2HtzRxddT225mEuu2aqYPN4n8FjvQaP9xkMZiTLuiyscePmNrYvi3JJRxS3roqwYu/J2kHXSdeq07frD2GPRuO+b4BtMDziAgkAACAASURBVLrujTUPjxz7Ia7MEP07fq/2mmNDwbQomBbjzdcy9KI15Pxt2JkCqiI9cD26RtCr4tFVgl4dv67PzUS95DfKf1umBPzcF4dw0VLBcU7PA+QNC1WF5Q3ngDrkDYshk0ssPJtm5ostXKZE6jSXyI8//0tQ56GWmp2QjfVsCypMhaJIO5aFItS68OwpCCXptf8CT/4nPPltOPGwUKA2vXrhAjcuX7GtwSJQSE2uKzULItDQ/6xkfDt3yeOvugV+9Efwg98VUZ7uq2QsZEZh3a9OOWd26VohlKBqEjAZ3C8ZknM9vqaiZePcv9dIu9TOTaUpLxS2Lc5OqQ+nokp9cqhVWBUnHqhfoGgJasFecPA3SiCoeQMcG5To/Bmqml19LVcUCZj1PV3bOQVYca0It/XvK7fmyI5LQGGxjIv0iJRM+KLVx7ptw4E74KF/lEDNjZ+b3G+0BG+kTCNNDcqaplS061FV+f/eT8JjXxNV4so12BuRz1Pakw7eJQ5cpbpyPi3XeSHXcOluCcrNFYoy97p6t1+coEJmYfdIUeHaj4n98Mtb5Fo3zqOGcDZ4QiJ82P+srPd1DnQrisLKpiA5wyKRLRDyunjD1kae7c/wb7sH+NOXdRLw6Iykc/RPZGlbKL030Sf3+uSjgFJmSNRCPj25ndg8YHmiDBGl73gPpjuM16Xhc0925mytth1n2zanEha/7DV4tNfgmUET04agC7a36rylVefyaIJw91ZM75T9V3WRD3fjHj+G6au+N4v4ooVl2djAsc0fodNSaNz/HxTyBfpWvxFFUdCsPP7EUXwTR/GPHyHU9zDJxm2MRbdg5UWk0SyqNGpGGkvzoaoqXl0jFnATcGu4W9bh1lU0VZmcES1pVBRsKEz69MXPMUtLQiMjgehzlXi5QFAX51RRlBuBLyEVZf9q2/bnpzzvAf4d2AkMA2+wbft48bmPA+8GTODDtm3fPZdzXiwwLZvxbJ4tHRGJHp0LNKwWp8HlW5jTkx2Hli3VHTB3QBbKnj3iDMxmSNuWGMgXAx+/VHu60OwpiPG0611Cgf3Fl0RR87m74EUfFXn/+aLUHmKhBgUI9bQEy4T7PwcnH5H2NyuvKz8X6xYH9e5PiNDKVR8SWqEvLnSrEkrZOXVqt98lgD8Oy646P2sb55NF1D1Slzp+Smo454oSndmGM5spyP0o9QVuXDf//psOzi68EZl7zRslg9hRNFjNvNQs13IwAo3luupaAbPuK2UsHHtAnFMoU3sX45xmxqWW1Ruuvc889GWh8XbslN6Jc2GKlATPquHS90qA7MCdEtQrQVHKnyWfklYoa14+2WkvpJeWyXExILpM6jsXupeoOrzkT2TP//nfybq29sbpxxk5CZicfFRq6VdeOzdRLH9MSlAGD1QX2FskNFVhTUuQZ3vGSeUNgm6dt+5o4h8f6uOHB0Z55cY4Ea+LY0Mpoguh9xYyMg7djdCzWz7DTBnRUtZxAQyr8XSBQ/0J0oVm2tVT2B511n0ylbc5Nm5ybMziyJjJE/0GvUnZV5ZHVF6/3s3lbTobGzU0VUExcyiWn7Sn+p5lBNpwT5wA08SyIZE1ztS02iAiS24Nv1tDVRUUXKSu/H08j7toO/LfxJOHUDPD6BMnURB7wnBHSMc2cHrju3HrqvxoKm5dQbcN9EIOl27iDsXnZhZkRqVsItRaDIRpsj6qmuyvpx6buXVdIVu7ltvBGSza+lAURQNuAW4ATgGPKYpyu23b+yoOezcwatv2akVR3gj8NfAGRVE2Am8ENgHtwL2Koqwtvma2c17wMEyLkUyetc0hmkJLoAI6V3jDEoHv3weheVL3Chmh7M4kvBJoFDrW4IFy771ayIzJhnchCFDMBYvNnpYQ6ZSaq2MPioN62weE6tt99QKygIpEVxdqUKSHZeG1bem1duQ+od1tqKIs7G+AX/t7+MmnxbkGaaVTaZzmEpLRef74wq5nvjgfHdOFINIpNdG1FFirIT0MoXZR4SxtqKru1HxeaHD7ARu6ryKx90eEStTJQhpCnbVfp2pCCR86VJuu6i72bD76QLGXtQqegARCYt0y1oxcUTQsJ8GqkX5hu7hrrCm5JPQ/Ax5/bYbEwbvEMd38ujKleLHoukzWxz3fhLUvr96u7Mh9ErBbX8HmKKnJ1sjgOCiiJDg0nzVoKjSXiOfd/Ql44AuiA7DqJfLc8BGpBT7043I/80N3w57l0mptxTWzjxN/g4xd2y5qSdTXQfXoGhvawjx9apycYbK9PcDlXUHu2C/03s7IIui9uUT598B+2PabMx+fnZC2R/NYz7MFk2NDKU6PZwi4dWKRGKa9HFfiFGaFoJRp2Tw7ZLKnz+DImMWxMZP+dDnAGdBtNjfY/PoquHRZmJbA9Pus5ZJkGzbWHCt5dJJ6K96J51EVD8viPtyahktT0HUVV617d8PH4eEInuM/E+HK1ddJf/fGNeiBJkKKQtWwWmoCuraIWKY5Q7nDmZuQl32zcW1tqnjLZukxHpzB1nWU52eFUopKLPgEinIl8Cnbtn+l+P/HAWzb/lzFMXcXj3lYURQd6AOagD+uPLZ0XPFlM56zGnbt2mXv3r17UZ9nsbj//vu57rrrpj2eyhlc/eJrcWkyWG1b6Al+t37uMqZTUeopOh8nyixIBHouGdfS+VWt+oZSEsXxRupjmFTB2NgY0ehZLkQ3cmI0znRfbeYsLODTLN66coRXd43h12X+nki6eGrUx5Ojfp4a9TGcm+H7MA1xLqc4p/l8vurhbveURTg7BorGu1aP8LZVI3z7WIyvHpo5M6xi84F1g9zUMcFvPbyMnkzFOc0CeCOMjU+c/e/mQkc+LYb1XOasaRTrFYPMebAVcU7mjYOZUZyHY+kC0UBxPpkFcS5nXGssYbuoek3j6eVtE3xiSz/ve6SL/ePe8rlVXTK2lVl3lPLaobnEgVC18rlL9ctTegi73eVrXBfO8uVLT/HMmJeP7enAtOsXQNoYyfCVy0/xtUMNfOvYdGfzK5edxKdbvPOhZZyZFyW6/Hy1Eqrgop87uaSUBCwywOXVLL6wo4eNkSzfPh5jV0OaDZEceUvh5/0B7uyJ8OSoj2tbkrx95TDdwQJHE26+eTTOg/1B7JnWNBuwChIYqRBIGh0dJRCoj2CSYVqk8ga6qmK5fCSvfD9qboLAo/+KYouIjldX8bgq58AspSz5FFgGL27N8Oltp/nQo508M1YjAFT6jPOwn/KGRTpviqL+FOqqlp/AdPnJN64j17ieXOMabJcfLBMtPYQr2Yee7EdP9KEnTqNlx7DcQVQji2LlsUvK2BUXqFgmpifM1P2n1AtdVcCjK7gKCSZydnldWwqU2pR5I2J35hLCGJlp6TELMn5mK0GqtS+X2qItVd9oxAc5G6jl68wERVEet21711yOrQdvqwM4WfH/KeDyWsfYtm0oijIONBQf/+WU15YKF2c7JwCKorwXeC/AsmXLFvYJzjIsWyZjwKPj0s4TxxQkqmwZc69js0yZzHOlArsDYLpk4mJOXwgsQxymJXJMzxk0t2SYq0WXbaT3qm0hBRCzOxkZU+XWQ4187XADa0I5tsYzbIuleWlrkld2ibLf/X1B/vlQI32ZKudTRXwAFpA5LS7oWxsyvG3VCHeeCvPVQ7NTiCwUvnywmX96rgmj0vC0zKKRfJF952cLLh9gFR2HGTZWy5Sx5w4wX8fUwXmKM45ilcdngqIK9dfIlGlpU/DQYICCBde2JMrOaYm2Vul4lqAVe1daJphJOUb3SJYhn2KqY1qJiMvgL7eeZiSv8ZdPt9bVMQXYN+7jwf4Ab1wxyu2nIowXytexPJBjYzTLLQcbmTQvbAu0OvUHvtihe4rBh8U5p1lT5Y/3tPPFXT28deUoRxNuvnygkXtOh5mo+M7u6wtxf1+Ql7QmePuqEf5iax9HEm5uOdjEnpEa31mpXY6ZhzwzK/iahswN3TMvHQRdU3FrGnnTQi9k8B34Iemtv0Gu+2q8x3+OripkDQtdkzrGOcEsgKpxaUOKtKGwb7xGZq/S+Z7DXmoD2bxJzjDRVXVyq3Eg13oJ2ZbNFCJdoGoo+RSewQN4hg7gHj6MauYnnU2xDExXEBQVy+VHMTVUI4OtlBMRimVg6T5AkcstOqQALk3Br2voWulCvNJuZSlhmeXgk6pLUM3M1y5nKbWtm8uYcPnkXFN6vmOb8j4OZkU9nNNqs2xqOrbWMbUerza7qqZ4bdu+FbgVJHNa+zLPPb70re/jc+lkCgZbu6JE/UssALMQZCfg+UfAX0OwogTbljYxy66cfx2SkRcVxrGTQhcr9Usz8iKas4S1bguJ9tQFY6em154W0qJKHGqV3p6DB4oiHAuMqlmmUIiP/5zrnvku13X0wJbfECrQVOXF1BCsfMmke93b21v1tO3tFQI2qSFRQNz3v/Dkf3Lzn/4XN8+m6jgTUkPS/zLYdO6+mwsdliVjZ/ykjK+pxomREweh6/IF0+Wd7+Y8xNgpGDrI/cfyXLeptaiyakvN6FyQGRWqYD4pqq5TA2N3/TFvDB/jjZ+9dVbKZm/f4OQHjAzkiwE53VWVxtbe2iQG310fg75T8Movc9tHlqhty+gJ+J93ctuHdwpluISHvwJ7e/jAZ/6VD5RETCxT9sEV19RlL7ro545ZEAq4P1afIGMhA4nTrIyt4EOKwodmOtYy4ch9rHr8G/xtdEBKSGbSYrBt2XPCbdC8kXt+ch+bNhWPzyVh5LiUPqiqOC4d2+d36abFkyfHcKkKHl3jKw/38ZT6Mv7gA++gPewmnTfQVYXNnVE0VZm8t05FdkK0HPwN8F9vgvgWfvL+z1a5B4YIjYU7pXxqljGbLZjsPz3BeKZA3O9GqZjbB/sS/PODRzg8kKQr7ufKTh/XhPtY3dqEprcB1007n54eJhddSSE8ub2NmhvHN7QXsDHdAazUKIMNl2KoblRFoTHopjHoIexz4XVNCWwUstx/3z1ct6ltaUpOCuliu6QrymubkZOeyu4qpQeWIWyTZVfNrmJdQnIQevdMLmVLDYs+wPkmxngeoh7pilNApRxcJzDVyj1zTJHWGwFGZnjtXM55wSFv2uRNkx3dsfPTMQWpP23ZKCp3tSjflllc4LsWJpChu6X2o+tSCTmkhmTiz2FhvWARai2KERXkJzVYXBwvlZ503rDURmmucp3JfKFqcg8vfTf8xr+L8/nkf8J/v0XquewpgkP5ZPXzzITMGGiaKCDGV819oa6GkqT6EjRKf0Gh1Jc3vgqSQ5OzaaVNtX37xVPH7UDgCU5eowvpmWv/p8IXE+OseZMEL9Ijk9eIlddKG5KhGdqF1ILuEwPMH5+5vuqxr4lY3os+Wm43tRSIdYvQzrP/K+qnIOvwoR9L3X6lumY+Kb13L9a9qN7QXDLu8qn6nM/lm3uva1WDNTfAq28Bf5MI75W+32pQFKkFTPTBQFHCJJ+RNi09j0MhKbXYvpg4h4XM/C5dU1nTHCSZFyGf39zeiFdX+fpjA5iWjd+tk8ybnB6bw3mz44AiPcMTfWUV/EoYWZm3zRvEbptlzE5kCzx+YpR03qQh4DnjmI6l83zpJ8/xB//zFCPJPL9/w1puedN23nrNBlatWoMnPzrdfkAcUNMboxCarghdcIUZiG5j1PSQHT5FIdhJd0ucHd0xXrS6kY3tEZrD3umOKYigm+4ReyM1JDZRlfdfMHIpEf6rHGO6R+5jZmz68ZlROX4+9k6wSdaR7Lj8b9vUVFJ3MA31cE4fA9YoirJCURQ3InB0+5RjbgfeXvz79cB9thS73g68UVEUj6IoK4A1wKNzPOcFBU1ViAdc7OiOEfKeB30VZ0K4XdpIZEYnP27kZKHIjEmmb7FKhr6YKO81rJEeirUUFy8GaLp8ztSgZEebN0Pn5ZMdM90jToRliaG5GASbRe3y1V8Rx/iBv4bv/065IbqiLswJTg2C6paNvaTkuVDkEtJ+yDECFw9FgcbV0LxeIv9WkSqeHhElbSdSe/HBHWASochaQGN3VZU52H01RLpkfS8FrbqvluDR0QcWfo0zORhH74envg0bXgnrblr4e8wVO98hXK3H/03+f/6XUre7fsp7m3kIzcPJdyA2Qz49b2eubvBG4ca/EvbV3Z8olg7NgECjOHyFtCjIp4ZE/KqyxlhVJEg/T0T9btoiXiayBhGvzlu2N3FsNMddB8XpifhcnBhJkcgWZj5Rsk8Exk4VdVSm9jfNTkAhJ6Jf0WWzOvP941kePz6KW1MJF21Q07K5/alefuc/HuenBwd57fYO/uktO7huXfMZx9UIdZILL0fPTr4XiplHsW2y8fVnMuZ5w2I0nWc4lSORLRAJB+necjVrt7+YzZs20xX3E/a65iYKpblF/b99h3w3JUc1O1G9nGGuyKdknawWFA+2iB1aciihzCwJd0w/fjY0FvVdzbwEEnxVGCoOqmLRzqlt2wbwQeBuYD/wHdu2n1UU5S8VRXll8bCvAQ2KohwGPkpZCOlZ4DvAPuBHwAds2zZrnXOx13ou4XVp7FgWwz9fKfFzAUWBprWiXJZPihORGpRajOaNElFvmkGtbD5QNWnu3rH94u/7FGqV7Gj3VdLrsdrndfuF9pHPSDBgsWjeKO1cXvqn4rTc+yl5XPcKLXs+MAsyHsZPykLbskjn1DQg1La4cziYjFg3tF4iTmlqSLKpTl/RixOaq6g+ay0+Kq+7ZU1fdiXks0WhkLCsRUcfqM2iWSD0xEm4//OyPl01I3Gzfgg2w6bXwnM/hpGjcPBO6RdbafSXWvF4lk6w5KKEPy49zRVN6IzpEQmQnU3Elkuf7dHjcN+nZ3dgAo0yrv3RoojQFIfJHYBEz4IupSsWQFMV8qbJpV1BLu0Mcvu+EZ4fy6EpCj6XxqH+BIZZIxtoFsQZ033SmiTUVnaOStRklw+WXT4r88gwLQ4PJHn29DjRCgrt8aEUv/vfT/AvPzvKmpYQX37Tdt559YqqNmohsoKCvwU9OwKAZVqYyVEG/GsZyikMp3IMp3KYlkV33M/2ZTGuXt3IhrYwjSEfnobuhXUH0HTJZLdulv7LHTvFeUwPy1xdCPJpaFxT3ZlXFGGfmYWy/ko+s3CVZ90ja1xmXN432Lqwa34Boi6ekm3bPwR+OOWxP6/4Owv8eo3XfhaYRqSvds4LHcpCpdbPBTSXOFKnHpPFr2Wz/L6QPsP5Bk2X9h+zwRuWDGrP7mJv2EVOU0WRtjPZcekjmByQjTk9KtmWuS66JWGT/r3yf+uW2sfO1Oer9Lwn4FBclgLhNpm/qWGhxzm4eOFvAHtUgkXeyOKj8t6w0NHyCckirbwOHvw/UsveWJ/efEo+QfzR/yPG6g1/Mbdrtm0x1i1Tskku/8L2om2/CQfugJ/9rbA/prW1Skpv6os9UFpvKIoEX0Otcg+T/RLENPPFHtZT97BisEN11TeT1HUZXPVh+MXfw6NfhSveP8t1q7XrZDWPONn59LzLV9y6yuqWIHt7xmnwq7xleyPPDWX42mMD/OlLO/G5dMbSeY4Pp1ndXKXcoqRubZvQ+wSsfll5vKeHxY5oXDtrPeZoKs/BvglyhkVDwINaPMcvDg/x9z95Dp9L4xM3reeKlQ0z2qc2CmOBVWjJJNroELpqojWspLW1k6BXx+vS8Ooq+lIKfGp6uVQg2Cz3xROaX3/wXEKc25kcendA7u3gc8K0aFi1ODsl2Cw/46eWVKX3YoOzAjuoDU8Qlr9YIqL+uOOYnk0EGoSOmR5dHIWlEqVMZ98zsiHb5vzow7mEjIG+vULDDtboiZsaFkd2Ki186rmi3c6YWioEGqF5nWNkX+zwx8VxK2TmV286E8Idkj0FWH61rBVH76/LqRUjS8Mjn0PLDML1fzG3Ug4zL9m4cBu0bxXjMT0s2aP50ki9Ydj6Zgmw2dZkOnGpzcPFXF5yNuAJikG//Bro2CWUTM0tToTuLf745Cc7LoGVemLTq2HTa+Dp70ggYjFQFHFQF4BYkd6byBoEPRpv39nEqfE8t++T84V9Lk4Mp+gdzTCtpWNpXA/skz26lN23TFB0qX+cwTHNGSJ69MTJUTRVJV50TC3b5lu/PMHnf3SA7niAv/uNbVy5qrGqY2paNsmswXAqx0g6j8vton3dpazrbGDTii42bNzK8sYAjUEPQY++tI7pVAQahe6bnZg7w8y2oZCdWzlapAu8QRmj0e7Zj58JpWxspKsuraleKLgAOKYOzimcesBzh0i7GGaDB0XBdz4RwmpoWCWGQf9eicSCOJFzFcpJDZZfX4vSmxkV1cbmjTBwQAxIf8NkJ7S0EVcqFztw4GD+cBUzOqV+ffWALyoZA9uWWqv27eKcXvqexQWTLIPY7i/iGj3M6K7fJ952yeyvyY6Lw9ixvRwMCzaLQZoeEcX3VEV5gqJMpyB7grJulbDldfDs9yX7VMlkKaSLdYdOC5m6QFXLma5ayIwWayqVxe9vlbjyA5Kp+tnfSbClfX6qu2fgDsBEr9RlL2DsL4sHGEnlKZgWW9sCvHh5iLsOjrG1PcDqBi8NAQ8H+ieYyBVY3RQsO3jZCckon9otwaHS9RfSwmyoEXS0bZv+8SyHBpKgQGOF6FEqZ/DFew7y2PFRbtjQwvuuW1W1laFl24yl8yiKQlPIQ3MoSMjrwq0Xj41cJvdiKVR054NAg4hEnXocvMw+fnITktn3ziDQVoKqSnmMbdfHBnb5JLDmYM5wPA8HDs5nxIq1GgP7y4X8C5XrV3Wpnegvlm/rHsiMzE38w7KEVmdmxUmtRunNjoMrIIu65hIV4qHnYPR52UhKm1k+KbUX9TRGHDh4IaLknCrUjyKvuSR7WEnt/dkXpU5zoSJ4tkX0iVvwDjzJ2NbfJttetW15GZYpzqc/Lq1Bptar6R7JpIbbhHaZS3CmO90ZJ0KR4F7/XghWOKe6V4TipraLKKSXVjHYwXT4YlJHeGq3BGDn0Vd0Rqi61J/e9kG458/hFf8A8RXzP4/mFpryfIK4FXDrKqubAuzrSxD3uXnD1kb2DWT4+mMDfPL6TjRVoTHgoX88SypnsKk9IjWh2XEZ46cekz27NLeNXE3G0nimwLGhFCOpPFGfa5LjeWo0zWfu3E/fRJbfuWYlv7qlrWq2NJkzyBZMuuJ+lsX9ZYe0Eq4avVbPBfxx6NwpSstQ26awbRHLmk+Zy0y9cB0sORzOlwMH5zMURZzH5RVqmgttNQOS8Rw+LIaY7p2cdZgJhbRkMPqL8vtTlXpzCakfat9WriFSNTH2GtdOFsgoZCUS7cCBg8VBVWWe+eL1zWSEO0QJFGD5i6ZTe21b5nTvE3DgDvTEqdrnsm3Ce7+Bv+fnTGx4M+nu62d+70JGaLsNa4S6N5uQitsva2SoVX6XaryCTbLOBFskE1WJYPNkZWPLkPXLaWt19uGPi/BWZkyEaOoFdxB+5a/Ewbzj9yS4shAoqpSqLBDxoJeWkNB7fS6Vd+1qpj9Z4P/tFXqvoijEAx5yBYvHjo8wlspJALdQbHHTUdFCxp4semZZNiOpPHueH2XPiREyeZOmoGeSY/rwkSF+/7tPkcgW+PSrNnPzJe3THNO8YTGUzOF1qVy6Is7q5mB1x/R8hD8uGdRcskwRN/Nik6SH5btLD0ug32mndsHAyZw6cHAhQHOJmmaoVTas5EBRlnyekebWzeJkDuyXiHV2XJzF2aKh+RRgS72q7p0cgcynJGnRuWN65FJRoGGFnL/vGREz0b1CF3TgwMHioepSA15PeCOALcawLwZt2+C5u4WGOXocxk5MCpI1A9nmbaRW3kyuaeskCmTw0PcIHruL5MqbSa5+9czva+TEwOy6rH6OYsMqeP5h+Sy1qJm5hPTtPtdUxRcqAo1CXe19oroIoG2J82HmZK9x++eW2Qq3w6/9vTind/we3PxFEbyaD9wBSJyG2OztWmqhuyHAWLpA3jRZ3+zj+tUR7j08zktOjrG1S/bCkNdFzjB58lg/G/NZmlLPodhWud7UzAtTwuXDtGyGkzmODaVIF0z8Lo3G4OQ9fDiZ46sPHuXho8OsagrwiZs20ByefIxp2Yxn8uiayqb2ME0hz4Ul3FmCLyYOas8eGSfugLA/vFEZKy6/08LlAoPjnDpwcCHBG5ZFONkvNZ1WQrInLv/caLLNG4GiqFHHTvm7kJ7dOU0Py/n7n5Vm3yXjoZCRaHfnpTNnOMJt8vqexyUj4gj1OHBQH2huMcLrCd0tDkM+JYbe2pdL65djDwo9cuV10roj2g3BJiaevovAsbtp+OVnKQQ7SK28mUznNfhOPUj4wH+R7ryGiU1vm9m4twwJlnVeWt8MpicE4c5iQK9GXa5lOr1NzzWCzVIScvppCBQdVCNXDoKE2iQTbtvC/kkOzs1JjXbBK0oO6kfFQa2lPG1b+HoewtP/OONb3o3tDopTk0vIz1zqFavArausaQnyTO84DT6V126O80xfmr+99zk+9YqNrGiUjJ5H1/B4bHoGMujHHiKi+zlid6ENJNELE9iRLoyBBH3jWQzLJujRaQxM3vdNy+ZHe0/zzYdPYFo2b7uym9ds65gmWJQ3LMazBZY3+OmK+6vWn15Q8EXLLA9HK+WCh/MNOnBwoaEk2+9vkA0zMwapgTJFV9Vkw66WVfWExKgstYNRVXn9TKIVIM6pbcPIEdj+FnnMyInx2nXZ3Ogy/rhsHottjePAgYPJWIpgT6gd+p4uOqc3wopryjWuU5Bc+zqSq1+Jr/eXBI7cQfTpWwnv/0+UQppsyw7Gtr1v5lp524bUCDSvn30tWghiy2GiRzJwU6/DyMpnXKDj4aCOCLfJd9T3jPRM9QQkoBpomtxX3XepZPHPOKmBmYWsIp3ioP7g9+DO34eb/0bKTUqwbTyDTxLe939xTRwHIB9fR3rFjfK8qsn+uogxEvW7aY94GRjPEfW7ed8V2XFA5AAAIABJREFULXz54QH+4H+e5sMvXcO1a0UlWreyBLwawaEnSTVeQsFWyBVMtEyWpN9PfjyH361XdSaPDaW45aeHOdifYFtXlPdft4q2yPSgcSpnkDdNtnZGaAheRNoPep1qlh2cczhWogMHFyo0V1kNsWGlFPznk9Lwefx5qbPyxaZT1Vo3w+H7JFvg8smm2zCDUEAhKwbc6HExHEpKvbmkRLrnoxK6kEbcDhw4OPsoZS9LdNgajukZqC4ynS8m0/Ei3CMHCRz7IVgGYzs+MntAKjMq6uTRZfW59qlw+yG6XNbFqc5vLllbfdzB2UekQ/YJVQNPuHq2XVHke5zqpPpnqL0Od0zPoOLHNXqI8L7/xDP8LIa/mdEdHyZ46Pv4en5edk7PUHuXLyoQtCweYCxVIGeYdEU9/N0btvHXPzrA3/z4IIf6E7zz6hW48xPo2RHcmQHG1r5eaj9tG92lY4Zi+KrMpfFMgf+35xS3PdlDyOvi929Yy7Vrm6pSdEfTebwulV1dcfxuxwVwcH7CGZkOHFws0N2gF53VaJdI6Q8fEqEPb8Um37IF9v9AnM34ShE2MY3a582n5Hf/XkARWi8AtpNtcODgYoXuFnZGITO/9iqKQr5hPfmGOSrf5hLijDStX9q+x7Fl4pxaRtlZti357W9Yuvd1MH/MNXte6aSOPQ+DByTLWmschdvLDuqdH2Wbfy1NY09gusOMb34XqeXXg+pCSw8SPvBttPQgpr9JxotlSjsS38L1ElyaypqWEE/3jOPSVGJ+N5951Wa+/otj3PZUL0eHUvz5jjzxYVHUTzXvkI9pZjE8kWlBnt6xDP/7ZA8/2T9A3rS4YUML77hqOWHf9PpKy7YZSedoDnpZ2xq68Gm8Di5qOM6pAwcXIzQd4suljmr4iPRqK9XntG6SY/r3lltDFFK1z5Udk02xb684s+6gGHia28mEOnBwMSPcXhQyW6Len0ZOatY7di69YInugfgqCdiVeiznklLL6NABL2woimTdC2kJys7UQzvUdkYkKTaxn4l1v0Fq1a9h6+W9LNNxFeED38bb+xCp1a+SBzVdWEaLcE4Bwj4XXTEfPaMZWgFdU3nvNatY3Rzilp8e5v33mtwRfYp8oA0j0AqAWsiQi3WdOceB0xN874kefnl0GE1VeOn6Zl69vYOuWPV5WjAtRjN5VjcGWdbgvzBFjxy8oOA4pw4cXMxw+YTGG+ksqvwOQrBNKHt9e2FjcePNJqjaWcrIQ6FfHNGBfbDmBnm8kAH/DAaAAwcOLnxMpfbWE7YlJQidO89eT8FIhzBGzII4w2a+/mJSDs4NFEXqSAsZ0VGYyYkMtcLrv8bPnjrJus7pWVoz0Eo+uhpfzy/KzqkrAIk+EQRbpKpzR9THSCpHOm+coda+dH0z3RGNL9z5NMGRZ/iR61q+dl8Kr67gRcUVHMPjyXB0KMX+0xMEPTqv39nJKy5pJxaoHVwZzxSwbItLOiI0hc6jHqUOHMwAxzl14OCFAF9UhIt6HgczK9TekiiSywvpIaQhRAUyo6II3BwTQaRCGlqKWVcjtzTCJQ4cODh/oHukh+p8qb1zQXpYnImZslz1huYSpdb+faLcq3mctlYXE1RN6odPPSb6C+4ZhPpcfgy99pjOdLyIyLPfQEv2YAY75Ny2VaT2Lk5NWtdUVjeH6MkbeHQNTZXAz5q4ztcv7SHwWI5j7rVYNoxkLLIFhcxEkmxhgpBX57devIIbNrTic9d2kvOGxXgmT3PYw+rmEF6X0ybJwYUDxzl14OCFAkWRepzhQ5JNPf6gGIjeKKRHwVes1TELkl0Y7wFvSDbiow/IOVq2lM/nNLR24ODiR6Sj/tTezCi0roNYd/3OOVeEWmHkqFA0mzc5ba0uNuhuaNsKJx8BNTe3FmtVkOm4kvCz38TX8xDJdb9ePneivy6tjkJeF+tiYQ72TRD2ufDoGqqZJZbvAeDVl63lV6MB1HwS09tELj63Gm7LthlLS+/SLZ0RGoMXaO9SBy9oOKuyAwcvJHjD0sS8lAHt21uMCBtlOlTPHqEv+RskswCSZfU3iGFXasfgOktUPAcOHJw7+GKyZth2fc6XnZCMVss5cgxVTTK2mguCTWf//R0sPTxBaN8ufXOtGcT+ZoDljZNv2Iiv5+flse/yQ2q4bnOhI+Zj+7IYmYJJMmug5hO4UqexUSiEOgBQzRyGb27sgnTeYDiVpz3q47IVcZpCXscxdXBBYlE7g6IocUVR7lEU5VDxd9VwkqIoby8ec0hRlLdXPL5TUZRnFEU5rCjKPyjFWaQoyqcURelRFOXJ4s+vLuY6HThwUIQrANhF48xdpvYCjByD3qfEYPTFJteY9e8VupSiSFsZX8zJODhw8EKA7gF/FIzM4s+VT4pT2LJx6QWQZkKgSZyXpRJ6cnDu4Y/LnpUeLasyzxOZjqtxJXvRi71PUVSwTWmvVifEAm52dcdx6QrJiWFcqdMUAq3YWjnja7lmZill8iZDqRyqAju7Y6xpcdR4HVzYWOzo/WPgJ7ZtrwF+Uvx/EhRFiQOfBC4HLgM+WeHE/hPwXmBN8efGipf+nW3b24o/P1zkdTpw4ACEluQOyGbdtF4ypyBGWmYE/DHQpyjwpoYkk9pa7AVYyDitFxw4eCEh3An59OLOUUhLxql184KplnWDqp7dWlcH5waRDulNmhpaULYz034FtqLh6/nF5CfqEaipgM+tsa0zQpMrjzZxinxQlHkVM4+lB7D16UJGtm2TzBoMJXOoGlzSEWFnd5xIlTYyDhxcaFisc/oq4JvFv78JvLrKMb8C3GPb9oht26PAPcCNiqK0AWHbth+2bdsG/r3G6x04cFBP+Bsl+9myCYaeE3Ejl7+YLa2yJJSyq6V6U9vpb+rAwQsKJWpvCZYpQarsmBj+6RFRvq0FIyPPt17itJ9ycHbRuEbazKQG5+2g2u4QuaZLxDktZV81TejCdYaOQVdYw5c5TcLfSSZvYmSTpD0NFEwL07KxbPkZzxQYTuUIejV2dMfYuSxGQ9CDqjoUXgcXBxbrnLbYtn0aoPi7ucoxHcDJiv9PFR/rKP499fESPqgoytOKony9Fl0YQFGU9yqKsltRlN2Dg4ML/RwOHLxw4I+DaUDrFqEoDR6Y+fi+Z6T2tHF1+bGZVBAdOHBwccHlFVZFclCc0VxCygLCXeJwNqwWIbX0iDitlTBzkM9IcMvj1Kk7OMtQFGEJRZcvyEHNdLwIPTOEe+Q5eUD3iDZDvVHIoCT6USyDaPsaAm4NXbEwPXEM0yJTMEhkC4wVFXgvW9HAls4oEZ/LqSt1cNFhVrVeRVHuBVqrPPUnc3yParPGnuFxELrvp4v/fxr4IvCuaie3bftW4FaAXbt21UmxwYGDixguP2BPFkVq21r7+P690LweVF2yrO7Aua0Xc+DAwdlH80YRl9G9Uh5QiYgpYmnZMRg9IU6qpsux2QS0XSKtWxw4OBdQFGhaK3+PHZea41oOXSEtwRRNB2+EbNul2E+58Pb+gnzDelA9kB0V9sAi+51OgpGDccnj+JpXsqoxAOkcrOyUa3Hg4AWEWUe8bdvX13pOUZR+RVHabNs+XaTpDlQ57BRwXcX/ncD9xcc7pzzeW3zP/or3+Bfgjtmu04EDB3OEyyfOpTsgdKf+Z2oeqhhZGDoEW98sDxgZCHXWPN6BAwcXKWYTD1JVYWX44+KQJk5Dsk+CYE5PZAfnGiUHVVFg9Nj0mmMzC9mUtE9rXgeDkim1dR/Zlp34eh5mYtM7iur2CEOgnu3UsuOQ6JW/o8uK2g5xxzF18ILEYmm9twMl9d23A7dVOeZu4OWKosSK9NyXA3cXacAJRVGuKKr0vq30+qKjW8JrgL1TT+rAgYMFQlFE0MjIipph/76aaoausSPyXEkMyTCE3ufAgQMHteANiSPQfbXTrsXB+QNFkRrU2ApIDsljZl76fVs2tG0RFedQqwRxzRwg1F4tP45nqMIUnUpfXyxyE9JbvPTeRgaCLfV9DwcOLhAs1jn9PHCDoiiHgBuK/6Moyi5FUf4VwLbtEYSa+1jx5y+LjwG8D/hX4DBwBLir+PgXii1mngZeAvzeIq/TgQMHlfA3CI2oZbNsimMnqx7mHt4vf5QowAqScXXgwIGD2VBP2qMDB/VAyUFtWCU0dSMHzRugY6dkKkt033D7GYXqbMs2LN2Ht7eo2qu56i+KlEsIrTe2XP63bfA6VHgHL0wsii9g2/Yw8LIqj+8G3lPx/9eBr9c4bnOVx9+6mOty4MDBLHAHy20dQKi90cvLz1smwcP/S+i5/5FaM09INnLNLXVkDhw4cODAwYUIRRHn1HUYOjdWp876YmXxJM1DtvUyfL2PML7lt0QUKTtav+sxcvIzdhI6LyvutS4nEOzgBQunS68DBy9EuAOyQYc7wRMu9zsF9MQpGn/+J4QP/BeZ9ivgxs/JE6UaGEcZ0IEDBw4cXMhQFMns16rpdPtFXdrMApDpvBrVSOMdeFKCtPmMKFTXA0YWkgNgFSDWXdxrZxBtcuDgIofjnDpw8EKEqgllyCxSe/ufBdskcPgHND3wMbT0ACO7PsrYzt8tU4vMvPRIdeDAgQMHDi52hNohJ9TeXOMWTHcI7+lHys8Xab+LRuH/s3fncXJVdf7/X5/auztJdxYCJIEECLLEhABBcMCBEWTAUUFRBwe/oqC4/NTRcfmC4sCI+gXHmXEct4nAyKDighuOOk5A4goOixsSMUEDCdl7X2q/n98f93anOl2dXqurO/1+Ph716K57b906t04t93PPOZ+TG8jUy/wVYdDb0DI5+xaZgZQGTGS2ajwM2p4Mu/Y+/XMW/fQDpNo3kz3iDDrXXEOQOeDH0VE3IxERmR0qA8RYgsKCE0l2bN6/rJQFJmFcaL4bup8J/29ZHk5nk2yY+H5FZigFpyKzVWbeoHGnie5naD/1rWSX/fnQ7kTuSoYkIiKzR7Ih/J0sZSHRQLFlJQ27HsKKvXgyDdn2MLvuROW7oHNHmJ031RgGp8rtILOYglOR2So1B3A4fDWc9z72JI8maFhYfdtSDjItyr4pIiKzx7wlsOf3UXB6HBBOsVZYeDJkOybnOXKdUabe5dGFYFNwKrOaxpyKzFaJFCQawiQMz7pw+MAUwgQNTZqvUEREZpH+nAvuFKLgNNW+BWKJcGxoMTex/ZcKUabep6FlRZjbIdkEMZ2ey+yld7/IbNa0aHSTibuH08mIiIjMFol0mKW+lMVTcyg1HUmyY8v+9aVR/H4eTCkLvfvCoHT+8vBvet7E9ikywyk4FZnNGheOMh2+R92ARUREZpG5R0IhbCEtzF9Jqj84NYN878T2XcofkKm3sL+1VmSWUnAqMpulGkfeplwIEyElUrUvj4iIyHSSmRcmBPSAYsvxxHPtxLKt4bjQ3ATHneZ7oHtH+P/85VFW/FH8LoscwhScisxmyUawBATl4bcpZsMWVhERkdkmnoyGwPTtH3fasSXs8pvtDIe9jFeuAzq3h/vvT1KoZEgyyyk4FZnNzKLxNAdJ6lAuQsOCqSuTiIjIdDL3CCgVKDavwC1Osn0LWAy8PLGkSPnuMDhtWaFMvSIRBacis13jwuF/XINS+Ffzm4qIyGyVnhcGjhanOG85qY4n968bb1KkUiG8MNzxdDjeNCiGvZmUqVdmOX0CRGa7zFzCgS4HKPRAtgsOO0FjYEREZPaKJ2DO4VDsozh/ZZix14Nw7u9c1/j2WcpBb2v4d/6KMDlSWsmQRBScisx2qTmA7R83E5Shtw3iaVh2OrQcXdfiiYiI1N2cw6BcpNCyklgpS6JnJyTTkG0f3/5Keejqz9QbTSOT0TQyIgpORWa7WDz8QQzyUOyDXCcsOAaOPAXS6s4rIiISdu2FYstKgLD1NJaGQvfBkwoOJ98Nnc+E/7csDy8QawiNyMSCUzNbYGYbzGxz9Hf+MNtdGW2z2cyurFj+YTPbZmY9B2yfNrOvmNkWM/uFma2YSDlFZASNh4Xdi2IxWHIqzD9K415ERET6xeKQmkOpcSFBPEOqfXM4DtUJs9qPRRBA727o2hEmHOxvMU2kJ73YIjPNRM8+rwXuc/fjgfui+4OY2QLgBuBM4DnADRVB7HeiZQe6Gmh395XAvwC3TLCcInIwcxbB4lVw5KnRGFQREREZpGE+lIsUW44LW077HSzjfTXdu/Zn6l2wYv9yZeoVmXBweglwR/T/HcClVbb5S2CDu7e5ezuwAbgIwN0fdPedI+z3buB8M7MJllVEhpOeCy3LwivDIiIiMlSmGcplCvNXkux6KpxqLZ6EbMfo91HMQtsWyLRAx9ZwGplyIezSq99gkQkHp4f3B5fR38VVtlkKbKu4vz1adjADj3H3EtAJLKy2oZldY2YPm9nDe/fuHWPxRURERERGIdEAhONOLSiFAWoiDblRJkVyh9YnwyA02x4Gqv3JkNLqtSQCkBhpAzO7Fziiyqr3j/I5qrV4Vpm3YnyPcff1wHqAdevWjbRfEREREZGxS2bAYhRajg3vdmyhOH9lmOG+vxX1YPpaoWdfOJSm/U/hsv5pZDIttS27yAwxYnDq7hcMt87MdpvZke6+08yOBPZU2Ww7cF7F/WXAxhGedjtwFLDdzBJAM9A2UllFRERERGrCDDLNBKUs5XQzqfYt9B1D2KRS6AnHpA6nVIC9f4CGqIW0fWv4d/4KKJcgqfnERWDi3XrvAfqz714JfLvKNj8ALjSz+VEipAujZaPd78uBH7q7WkVFREREpH4a5kMpT7Fl5f6kSMkG2PnbcGqY4U5XO54CL4dziEMYnDbMD8exQtgqKyITDk5vBl5gZpuBF0T3MbN1ZnYrgLu3ATcBD0W3D0bLMLOPmtl2oNHMtpvZjdF+bwMWmtkW4O+okgVYRERERGRKpZvAncL840n2PIMVe8PgNDMP9m2BXY8NnVom2wGdOwZ33e14KpzfFABXpl6RyIjdeg/G3VuB86ssfxh4fcX924Hbq2z3XuC9VZbngFdMpGwiIiIiIpOqIikSQLLjjxQOWw2xBDQthHwXPPMILDoBmhaBB7BvM6Qbw27BELautm+FlS8Ix6omGpSpVyQy0ZZTEREREZHZIZmBeJLCvLDVM1U53ylAeh4km2DX72Dv76Hj6bAlNQpqAWL5dij0RuNN89DQPIUHIDK9TajlVERERERkVmlowfNdlJqOINm+Zej6eDJsRe1tDaeJaRw8G2KyK5phcf7yMFFSet4UFFpkZlBwKiIiIiIyWulm6NlLoWUl6dbHq29jBg3Vp4dJdG0N/5m/AoIAUnNqUkyRmUjdekVERERERivVBEBx/kriuTZiudHPdmiFbuZsuQcWn7Q/QVIiXYtSisxIh3zLabFYZPv27eRyuSl5vubmZjZt2jQlzyXVZTIZli1bRjI5wmTYIiIiImOVCuckLURJkVLtT5I7csGoHjrv8TuJFXvgee+KEiQpU69IpUM+ON2+fTtz585lxYoVWH+WtBrq7u5m7ty5NX8eqc7daW1tZfv27RxzzDH1Lo6IiIgcauJJSDZQTCzFLU6yYzO5I88Y8WGpfY/R9PT9dK+8lLkLV+7P1Bs/5E/HRUbtkO/Wm8vlWLhw4ZQEplJ/ZsbChQunrKVcREREZqGGFnCnOO9oUtWSIh2onKfl1/9OqfFwuk94ebSsAGk1aIhUOuSDU0CB6Syj+hYREZGayrRAuUixZSXJjifD+UwPYu4fvkGidxcdp1wD8WiMabmwf9ypiACzJDgVEREREZk0yXDe0sLCk4iV+mh59BNYoafqpomup5mz5dv0HXUuhcPW7F8RBJBumorSiswYCk6nyK5du7j88ss57rjjOPnkk3nhC1/IH/7whzHv5/Wvfz2PPx6mLf/IRz4yqsesWLGCffv2jfm5RERERKSKKDjNLvkzuk68nIYdD7J447tI7f3N4O28TMuvP0uQbKTr5NcM3Y8y9YoMouB0Crg7L33pSznvvPN48sknefzxx/nIRz7C7t27x7yvW2+9lZNPPhkYfXAqIiIiIpMoFof0HPASPc+6jH3P+zBBooFFD9zEvN/ejpXyADRu3UCqfTNdq15LkJ53wE48TIgkIgMUnE6B+++/n2QyyZve9KaBZWvXruXUU0/l/PPP57TTTmP16tV8+9vfBmDr1q2ceOKJXHnllaxZs4aXv/zl9PX1AXDeeefx8MMPc+2115LNZlm7di1XXHEFAJdeeimnn346q1atYv369VN/oCIiIiKzRcN8KIUJGIstx7H33FvoOfavmPOn77Pox+8ls/Mh5m36ErnDTiG77HmDHxuUwilklKlXZJBZ9Yn4h+/8jsd3dE3qPk9eMo8bXrzqoNs89thjnH766UOWZzIZvvnNbzJv3jz27dvHWWedxUte8hIAnnjiCW677TbOPvtsrrrqKj796U/z7ne/e+CxN998M5/85Cf51a9+NbDs9ttvZ8GCBWSzWc444wwuu+wyFi5cOElHKiIiIiIDMs3QsW3//Xiarme/ltzhp9Pyq0+x4KGPEsRTdK55QzSnaYVSAYa0pIqIWk7ryN153/vex5o1a7jgggt45plnBrr6HnXUUZx99tkAvPrVr+anP/3piPv7xCc+wSmnnMJZZ53Ftm3b2Lx5c03LLyIiIjJrJTJVFxcOW83e8/6JnmMupvOUN1FuOnzoRuV8GNyKyCATajk1swXAV4AVwFbgle7eXmW7K4Hro7sfcvc7ouUfBl4DzHf3ORXbvxb4R+CZaNEn3f3WiZQVGLGFs1ZWrVrF3XffPWT5F7/4Rfbu3csjjzxCMplkxYoVA/NzHjgdykjTo2zcuJF7772XBx54gMbGRs477zzN9SkiIiJSK8kGsFg4jYwNbu/xZBNdq68a/rFBGVLK1CtyoIm2nF4L3OfuxwP3RfcHiQLYG4AzgecAN5jZ/Gj1d6Jl1XzF3ddGtwkHpvX0/Oc/n3w+z+c+97mBZQ899BBPPfUUixcvJplMcv/99/PUU08NrH/66ad54IEHALjrrrs455xzhuw3mUxSLBYB6OzsZP78+TQ2NvL73/+eBx98sMZHJSIiIjKLmYWtn+X82B7nHv5NKhmSyIEmGpxeAtwR/X8HcGmVbf4S2ODubVGr6gbgIgB3f9Ddd06wDNOemfHNb36TDRs2cNxxx7Fq1SpuvPFGXvjCF/Lwww+zbt06vvjFL3LiiScOPOakk07ijjvuYM2aNbS1tfHmN795yH6vueYa1qxZwxVXXMFFF11EqVRizZo1fOADH+Css86aykMUERERmX0yLVAcY0+1XAc0Lwuz/YrIIBNNiHR4f3Dp7jvNbHGVbZYCFaPF2R4tG8llZvbnwB+Ad7r7tmobmdk1wDUARx999FjKPqWWLFnCV7/61SHL+1tHK23dupVYLMZnP/vZIes2btw48P8tt9zCLbfcMnD/+9//ftXn3rp169gLLCIiIiIHl5mzvyV0NMp5sDgsOK52ZRKZwUZsOTWze83ssSq3S0b5HNUGS470Kf4OsMLd1wD3sr91duiO3Ne7+zp3X3fYYYeNskgiIiIiIhM01nlKcz1w2LMgkapNeURmuBFbTt39guHWmdluMzsyajU9EthTZbPtwHkV95cBG0d4ztaKu58Dbhlu20PRihUreOyxx+pdDBERERE5mGQG4slw3tLYCKfV+U5oWgiNmuZPZDgTHXN6D3Bl9P+VwLerbPMD4EIzmx8lQrowWjasKNDt9xJg0wTLKSIiIiIy+dLzoDTCuNOgFGboXXDc0DlPRWTARIPTm4EXmNlm4AXRfcxsnZndCuDubcBNwEPR7YPRMszso2a2HWg0s+1mdmO037eb2e/M7NfA24HXTrCcIiIiIiKTr+WocNxptn348ae5rjAwTSlDr8jBTCghUtT99vwqyx8GXl9x/3bg9irbvRd4b5Xl1wHXTaRsIiIiIiI1l5kHS0+Htj9C1y5omAfxijGlxd4wM+/cI4ffh4gAE285FRERERGZ3RIpWHwiHLEKin3h+FIAD6CQg4XHQ0yn3SIj0aekxlpbW1m7di1r167liCOOYOnSpQP3C4XCqPdz/fXX8/GPf3xSyvTqV7+ab33rW5Oyr9G69957ufTSatPgjs2tt97KO97xjkkokYiIiMgkm3NY2IqaaobefdDXDvOXQ2ZuvUsmMiNMdJ5TGcHChQv51a9+BcCNN97InDlzePe7313nUomIiIhITSQbwhbUrp3Qsweal9W7RCIzhlpO6+iOO+7gOc95DmvXruUtb3kLQRAA8N3vfpfTTjuNU045hQsvvHBg+9/+9rece+65HHvssXzqU58CYMuWLTz72c/m6quvZtWqVVx88cXkcmHGuEcffZQzzzyTNWvWcNlll9HZ2TmkDBs2bGDt2rWsXr2aN7zhDQOtuffccw8nnHACz3ve83jb297GpZdeSrlcZuXKlbS1tQFQLpc59thjB+73++EPf8gpp5zC2rVrOe200+jt7QWgu7ubl73sZZxwwgm85jWvGbEMv/jFL3juc5/LKaecwplnnklfX9+g57nnnns4++yzhzy/iIiISF2ZQfMSWLoW4moLEhmt2fVp+f61sOu3k7vPI1bDxTeP+WGPPfYY3/zmN/n5z39OIpHgmmuu4ctf/jLPf/7zefOb38xPfvITli9fPijw+sMf/sB9991HR0cHJ510Em9605sAeOKJJ7jrrrtYvXo1L3vZy/jWt77F5Zdfzqtf/WrWr1/POeecw/ve9z5uuukmPvaxjw3sr6+vj6uuuoqNGzdy3HHHccUVV7B+/Xquuuoq3vKWt/Czn/2Mo48+mle+8pUAxONxXvWqV/GlL32Jt771rfzgBz/gjDPOYMGCBYOO7R//8R9Zv349Z555Jj09PWQyGSAMlh9//HEWL17MWWedxYMPPsiaNWuqluH1r389l19+OV//+tc57bTT6OzsJJ1qYuykAAAgAElEQVRODzzH3XffzSc+8Qm+973v0dzcPObXX0REREREphe1nNbJvffey0MPPcS6detYu3YtP/rRj3jyySd54IEH+Iu/+AuWL18OMCjwe9GLXkQqlWLx4sUsWLCAvXv3ArBy5UpWr14NwOmnn87WrVtpbW0ll8txzjnnAHDllVfy4x//eFAZNm3axPHHH89xxx0HwGte8xp+/OMf8/jjj3PCCSewfPlyzIxXvepVA4+5+uqrueOOOwC4/fbbed3rXjfk2M4++2ze8Y538G//9m90dXURj8cBOOusszjyyCOJx+OsXbuWrVu3DluGTZs2cfTRR3PaaacB0NzcPLCfDRs28M///M8KTEVEREREDiGzq+V0HC2cteLuXHXVVdx0002Dln/jG9/AhpmcubLlMB6PUyqVhl3uw82zdUAZxrIcYMWKFcyfP5/777+fX/7yl4O6Hfe7/vrreclLXsJ3v/tdzjjjDDZu3Djmcrr7sK/DypUr2bJlC5s3b+bUU08dtqwiIiIiIjJzqOW0Ti644AK++tWvsm/fPiDM6vv0009z9tln88Mf/pCnnnoKYNzjKRctWkRDQwM///nPAbjzzjs599xzB21z8skns3nzZv74xz8C8IUvfIFzzz2XVatW8cQTT7Bt2zbcna985SuDHnf11VdzxRVXcPnllxOrkhb9ySefZM2aNVx33XWceuqpPPHEE8OW82BleOqpp3j00UcB6OrqolwuA3DMMcfwta99jSuuuIJNmzaN6/UREREREZHpRcFpnaxevZobbriBCy64gDVr1nDhhReye/duDj/8cD7zmc9wySWXcMopp3DFFVeM+znuvPNO3vnOd7JmzRoef/xxrr/++kHrGxsbue2223jZy17G6tWrSafTvOENb6CxsZFPfvKTXHDBBTzvec9jyZIlg7rPvvSlL6Wzs5PXvva1VZ/3Yx/7GM9+9rNZs2YNLS0tVVtXRypDOp3mrrvu4s1vfvNAYqh8Pj/wuJNPPpk777yTyy67jD/96U/jfo1ERERERGR6sNF0/5wp1q1b5w8//PCgZZs2beKkk06asjJ0d3czd+7Mn8uqp6eHOXPm4O688Y1vZPXq1bztbW8D4MEHH+S6667j/vvvr3MphzdcvW/cuJHzzjtv6gs0A+zYsaPq8iVLlkzJ86tupi/VzfQ1HepmuO+Og5mq75V6mw71I9Vt2LCBVatW1e35Z8tnYDz0uZnexlM/ZvaIu68bzbaza8ypjNpnPvMZvvjFL5LP51m3bh1veMMbAPjwhz/M+vXr+fKXv1znEspk0w+liIyHvjtkJkomk3rvikxDCk6lqve85z285z3vGbL8/e9/P+9///vrUCIRERERETmUzYoxp4dS12UZmepbRERERGTmOeSD00wmQ2trqwKWWcLdaW1tJZPJ1LsoIiIiIiIyBhPq1mtmC4CvACuArcAr3b29ynZXAv2pYj/k7neYWSPwNeA4oAx8x92vjbZPA/8JnA60An/t7lvHU8Zly5axfft29u7dO56Hj1kul1NgVGeZTIZly5bVuxgiIiIiIjIGEx1zei1wn7vfbGbXRvf/b+UGUQB7A7AOcOARM7sHyAMfc/f7zSwF3GdmF7v794GrgXZ3X2lmlwO3AH89ngImk0mOOeaY8R7fmG3cuJFTTz11yp5PRERERETkUDDRbr2XAHdE/98BXFplm78ENrh7W9SqugG4yN373P1+AHcvAI8C/c1dlfu9GzjfzGyCZRUREREREZFpaqLB6eHuvhMg+ru4yjZLgW0V97dHywaYWQvwYuC+Ax/j7iWgE1g4wbKKiIiIiIjINDVit14zuxc4osqq0c4nUq3FcyA7kZklgLuAT7j7H0fzmAPKdw1wDcDRRx89yiKJiIiIiIjIdDJicOruFwy3zsx2m9mR7r7TzI4E9lTZbDtwXsX9ZcDGivvrgc3u/vEDHnMUsD0KXpuBtmHKtz7aB2a218yeGumYamwRsK/OZZDhqX6mL9XN9KW6mb5UN9Ob6mf6Ut1MX6qb6W089bN8tBtONCHSPcCVwM3R329X2eYHwEfMbH50/0LgOgAz+xBh4Pn6Yfb7APBy4Ic+irlg3P2wcRzDpDKzh919Xb3LIdWpfqYv1c30pbqZvlQ305vqZ/pS3Uxfqpvprdb1M9ExpzcDLzCzzcALovuY2TozuxXA3duAm4CHotsH3b3NzJYRdg0+GXjUzH5lZv1B6m3AQjPbAvwdYRZgEREREREROURNqOXU3VuB86ssf5iK1lB3vx24/YBttlN9bCnungNeMZGyiYiIiIiIyMwx0ZZTGWp9vQsgB6X6mb5UN9OX6mb6Ut1Mb6qf6Ut1M32pbqa3mtaPjWIop4iIiIiIiEhNqeVURERERERE6k7BqYiIiIiIiNSdgtNRMLPbzWyPmT1WsewUM3vAzH5rZt8xs3nR8qSZ3REt32Rm1x2wr7iZ/dLM/muqj+NQNFl1Y2bvNLPfmdljZnaXmWXqcTyHkjHWTcrM/iNa/mszOy9a3mhm3zWz30f1c3OdDueQMxn1U7FuvZn9Iaqny+pwOIcUMzvKzO6Pvqd+Z2Z/Gy1fYGYbzGxz9Hd+tNzM7BNmtsXMfmNmp1Xs68po+81mdmW9julQMZl1E62fZ2bPmNkn63E8h5JJ/tx8NNrHpmibqgk8ZXTGUTcnRr9FeTN790j7kYmZrPqJ1rWY2d3R+cAmM3vuWMuj4HR0Pg9cdMCyW4Fr3X018E3gPdHyVwDpaPnpwBvNbEXF4/4W2FTLws4yn2eCdWNmS4G3A+vc/dlAHLh8Kgp/iPs8o6+bNwBEy18A/JOZ9X8/fczdTwROBc42s4trXfBZ4vNMTv28H9jj7s8inBrsRzUu92xQAt7l7icBZwH/n5mdTDit2n3ufjxwH/unWbsYOD66XQN8BsITC+AG4EzgOcANtn/OcRmfSambCjehz8xkmazPzZ8BZwNrgGcDZwDnTuFxHIrGWjdthOdlHxvlfmRiJqt+AP4V+O/ovO0UxhHzKDgdBXf/MWFFVDoB+HH0/wagv7XAgSYzSwANQAHoArBwbte/IjwBlEkwWXVDOK1SQ7SuEdhRy3LPBmOsm5MJv/hw9z1AB+HFgj53vz9aXgAeBZbVuOizwmTUT7TuKuD/ResCd99Xw2LPCu6+090fjf7vJvxxXwpcAtwRbXYHcGn0/yXAf3roQaDFzI4E/hLY4O5t7t5OWKcHXpCQMZjEusHMTgcOB/5nCg/hkDWJdeNABkgBaSAJ7J6yAzkEjbVu3H2Puz8EFEe5H5mAyaofC3tb/TlwW7Rdwd07xloeBafj9xjwkuj/VwBHRf/fDfQCO4GnCVt9+k8APw68FwimsJyz0Zjqxt2fIbz683S0rtPddbJQG8PVza+BS8wsYWbHELZsH1X5QDNrAV5MFCRJTYypfqI6AbjJzB41s6+Z2eFTW+RDW9Tz5lTgF8Dh7r4TwpMJYHG02VJgW8XDtkfLhlsuk2AidRP1PPgn9vdOkEk0kbpx9weA+wnPB3YCP3B39XibJKOsm7HuRybJBOvnWGAv8B8WDmG81cyaxloGBafjdxVhs/cjwFzCVjgIu06VgSXAMcC7zOxYM3sRYde3R+pS2tllrHUzn/Dq0DHRuiYze/XUF3tWGK5ubic8MXiY8CLOzwm7mQAQtWjfBXzC3f84pSWeXcZaPwnCluyfuftpwANU7+Yj42Bmc4CvA+9w966DbVplmR9kuUzQJNTNW4Dvufu2KutlAiZaN2a2EjiJ8LttKfB8M/vzyS/p7DOGupmS/chgk/C6JoDTgM+4+6mEDULXHvwh1Xci4+DuvwcuBDCzZxF21wX4G8K+1kVgj5n9jLD726nAS8zshYTdReaZ2RfcXUHQJBtH3TjwJ3ffGz3mG8CfAV+Y6rIf6oarG3cvAe/s387Mfg5srnjoemCzu3986ko7+4yjflqBPsLxqQBfA66ewiIfsswsSXiS8EV3/0a0eLeZHenuO6Puh3ui5dsZ3NNgGeHQhO3AeQcs31jLcs8Gk1Q3zwWeZ2ZvAeYAKTPrcfcxn8jJfpNUN68GHnT3nmif3ycch/djZNzGWDdj3Y9M0CTVz3Zgu7v3t2bfzTiCU7WcjpOZLY7+xoDrgc9Gq54mvMpmUVP2WcDv3f06d1/m7isIk+38UIFpbYy1bqLlZ1mYGdaA81HSqpoYrm6i174p+v8FQMndH4/ufwhoBt5Rl0LPImOtH3d34DvsD4DOBx6f6nIfaqLvoduATe7+zxWr7gH6M+5eCXy7Yvlrou+2swiHJuwEfgBcaGbzox4iF0bLZJwmq27c/Qp3Pzo6J3g34dhHBaYTMImfm6eBc6NhDEnCZEg6J5iAcdTNWPcjEzBZ9ePuu4BtZnZCtGh85wTurtsIN8LuhDsJB/5uJ2wZ+FvgD9HtZsCibecQth78LqqQ91TZ33nAf9X7uA6F22TVDfAPhIHqY8CdhFl96358M/k2xrpZATxBeAJwL7A8Wr6MsGV7E/Cr6Pb6eh/boXCbjPqJ1i0nbFH4DeF44KPrfWwz/QacE73vf1Pxvn8hsDB6jTdHfxdE2xvwKeBJ4LeEycT693UVsCW6va7exzbTb5NZNxX7fC3wyXof20y/TVbdEGbs//fo++5x4J/rfWwz/TaOujki+l3qIkzAtx2YN9x+6n18M/02WfUTrVtLOAToN8C3gPljLU//iYeIiIiIiIhI3ahbr4iIiIiIiNSdglMRERERERGpOwWnIiIiIiIiUncKTkVERERERKTuFJyKiIiIiIhI3Sk4FRERERERkbpTcCoiIiIiIiJ1p+BURERERERE6k7BqYiIiIiIiNSdglMRERERERGpOwWnIiIiIiIiUncKTkVERERERKTuFJyKiIhMETNbYWZuZokpeK7XmtlPD7L+pWa2zcx6zOzUWpdHRERkJApORURkSkTB0m/NrM/MdpnZZ8yspQ7l2Ghmr5/q552GPga81d3nuPsv612YkZjZVjO7oN7lEBGR2lFwKiIiNWdm7wJuAd4DNANnAcuBDWaWqmfZZrHlwO/G80Azi09yWURERBSciohIbZnZPOAfgLe5+3+7e9HdtwKvJAyQXh1td6OZfdXM/tPMus3sd2a2rmI/S8zs62a218z+ZGZvP8hzZszsC2bWamYdZvaQmR1uZh8Gngd8MurO+slo+xPNbIOZtZnZE2b2yop9fd7MPhut7zazH5nZ8oM891+Z2S/NrCvqNntjlc2uMLOnzWyfmb2/4rExM7vWzJ6Myv5VM1tQsf5rUatzp5n92MxWVaxbaGb3RM/7v8Bxw5QvbWY9QBz4tZk9GS0/KWpV7ohe+5cc8Bp8xsy+Z2a9wF9E+/lYdBy7o9eooeIxl5jZr6LyPGlmF0XLX2dmm6LX8o9m9saKxywys/+KytBmZj+JXpM7gaOB70T19t7h6ni4ehERkelPwamIiNTanwEZ4BuVC929B/g+8IKKxS8Bvgy0APcA/cFjDPgO8GtgKXA+8A4z+8thnvNKwhbao4CFwJuArLu/H/gJ+7uzvtXMmoANwJeAxcCrgE9XBn7AFcBNwCLgV8AXD3K8vcBromP4K+DNZnbpAducA5wQHcffm9lJ0fK3A5cC5wJLgHbgUxWP+z5wfFTORw8ox6eAHHAkcFV0G8Ld8+4+J7p7irsfZ2ZJwtf3f6J9vw34opmdUPHQvwE+DMwFfkrYEv4sYC2wkrBe/h7AzJ4D/CdhS3kL8OfA1mg/e4AXAfOA1wH/YmanReveBWwHDgMOB94XFtn/D/A08OKo3j7KMHVc7ZhFRGRmUHAqIiK1tgjY5+6lKut2Ruv7/dTdv+fuZeBO4JRo+RnAYe7+QXcvuPsfgc8Blw/znEXCgGWlu5fd/RF37xpm2xcBW939P9y95O6PAl8HXl6xzXfd/cfungfeDzzXzI6qtjN33+juv3X3wN1/A9xFGGxW+gd3z7r7rwkD7v7jfCPwfnffHj3XjcDLLUqg5O63u3t3xbpTzKw56mZ7GfD37t7r7o8BdwxzvNWcBcwBbo5e3x8C/0UYqPf7trv/zN0DIA+8AXinu7e5ezfwEfbXx9XA7e6+IXodnnH330fH8F13f9JDPyIMiJ8XPa5IGFwvj1rYf+LuPkyZx1LHIiIyAyg4FRGRWtsHLLLqGWqPjNb321Xxfx+QiR63HFgSdd/sMLMOwla1wwGirp79t6MJA9sfAF82sx1m9tGodbCa5cCZB+z7CuCIim229f8Ttfi2ReV5X8XzfjYqy5lmdn/U/biTsEWvMgCvdpz9LZnLgW9WlGMTUAYON7O4md0cdZHtYn9L5CLClsZEZTmBp4Y53mqWANuiwLPy8UurvQbR8zUCj1SU9b+j5RC2Zj5Z7YnM7GIzezDqttsBvJD9r88/AluA/4m6/F57kDKPpY5FRGQGUHAqIiK19gBhS9vLKhdG3WkvBu4bxT62AX9y95aK21x3fyFA1NWz//Z01Or2D+5+MmG34hcRdrUFOLAlbhvwowP2Pcfd31yxzUArqZnNARYAO9z9IxXP+6Zoky8Rdkk+yt2bgc8CNopj7C/LxQeUJePuzxB2q70EuICwO+uK/iIBe4FSZTkJx2iO1g7gqKj7dOXjn6m4X/m67SPsQruqopzNFd2Ft1FlzKuZpQlbpT8GHO7uLcD3omMgahV+l7sfC7wY+DszO7/K8zNCHYuIyAyk4FRERGrK3TsJEyL9m5ldZGZJM1sBfI1wfOGdo9jN/wJdZvZ/zawhakV8tpmdUW1jM/sLM1sddXftIuwCWo5W7waOrdj8v4Bnmdn/icqWNLMzKsaBArzQzM6xMLPwTcAv3L2yJbHSXKDN3XPR2Mu/GcXx9fss8GGLEi6Z2WFmdknFfvNAK2Gr5Uf6HxR1g/4GcKOZNZrZyYRjMkfrF4RjZd8bHf95hMHhl6ttHLWwfo5wvOjiqKxLK8YA3wa8zszOjxIaLTWzE4EUkCYKps3sYuDC/v2a2YvMbKWZGWG9lRmm3kaoYxERmYEUnIqISM1FCWzeR9hi1kUYDG0Dzo/GT470+DJhsLQW+BNhy92thC2I1RwB3B091ybgR8AXonX/SjiOs93MPhGNl7yQcLzkDsIut7cQBlH9vgTcQNid93TCbr/DeQvwQTPrJkwQ9NWRjq/CvxK2uv5P9PgHgTOjdf9J2NX2GeDxaF2ltxJ2D94FfB74j9E+qbsXCJNRXUz42n4aeE3/ONFh/F/CLrgPRt2M7yVM8oS7/y9RsiOgk/D1Xx691m8nfE3aCQP3eyr2eXy0nx7CFvdPu/vGaN3/A66PuhG/m4PXsYiIzEA2fJ4BERERMbPPA9vd/fp6l0VERORQppZTERERERERqTsFpyIiIiIiIlJ36tYrIiIiIiIidaeWUxEREREREam7ahOiz1iLFi3yFStW1LUMvb29NDU11bUMMjzVz/Slupm+VDfTl+pmelP9TF+qm+lLdTO9jad+HnnkkX3ufthotj2kgtMVK1bw8MMP17UMGzdu5LzzzqtrGWR4qp/pS3Uzfalupi/VzfSm+pm+VDfTl+pmehtP/ZjZU6PdVt16RUREREREpO4UnIqIiIiIiEjdKTgVERERERGRulNwKiIiIiIiInWn4FRERERERETqTsGpiIiIiIiI1J2CUxEREREREam7Q2qeUxEZvx07dlRdvmTJkikuiYjMJMN9dxyMvlek3orF4rjeu5NFnwGR6tRyKiIiIiIiInWn4FRERERERETqTsGpiIiIiIiI1J2CUxEREREREak7BaciIiIiIiJSdwpORUREREREpO5qHpya2UVm9oSZbTGza6usT5vZV6L1vzCzFdHy55jZr6Lbr83spbUuq4iIiIiIiNRHTYNTM4sDnwIuBk4GXmVmJx+w2dVAu7uvBP4FuCVa/hiwzt3XAhcB/25mM3Ze1iBw+gqlehdDRERERERkWqp1y+lzgC3u/kd3LwBfBi45YJtLgDui/+8Gzjczc/c+d++P5jKA17isNVUKnL3deYrloN5FERERERERmXZqHZwuBbZV3N8eLau6TRSMdgILAczsTDP7HfBb4E0VweoAM7vGzB42s4f37t1bg0OYPPlSmc6+Qr2LISIiIiIiMu3UOji1KssObAEddht3/4W7rwLOAK4zs8yQDd3Xu/s6d1932GGHTbjAtRS3GF3ZEoWSWk9FREREREQq1To43Q4cVXF/GbBjuG2iMaXNQFvlBu6+CegFnl2zkk4BM4jHjXa1noqIiIiIiAxS6+D0IeB4MzvGzFLA5cA9B2xzD3Bl9P/LgR+6u0ePSQCY2XLgBGBrjctbc5lknN58iXypXO+iiIiIiIiITBs1zX7r7iUzeyvwAyAO3O7uvzOzDwIPu/s9wG3AnWa2hbDF9PLo4ecA15pZEQiAt7j7vlqWd6rEY0Z7b4EjmhvqXRQREREREZFpoeZTs7j794DvHbDs7yv+zwGvqPK4O4E7a12+esgk4/TkSuSKZTLJeL2LIyIiIiIiUne17tYrw0glYrT15utdDBERERERkWlBwWmdpBIx+gplsgWNPRUREREREVFwWkeZZJzW3jzuB86uIyIiIiIiMrsoOK2jZDxGvhSQLar1VEREREREZjcFp3WWScTY111Q66mIiIiIiMxqCk7rLBGPUQzUeioiIiIiIrObgtNpIBWP0Zkt1rsYIiIiIiIidaPgdAqVvXrraCoRI1soUygFU1wiERGphd58kY6+Qr2LISIiMqMoOJ0ixXKR9nzrsGNLY2b05EtTXCoREZlsuWKZ3Z152noLFMu66CgiIjJaCk6nUK7YR66cq7ounYzRlS0SBEqMJDKbuDvZQpldnTlyGns+acqB1yXRXKEUsLMjSzoZI2am1lMREZExSNS7ALNJQEBnvp2GRMOQdTEzyh7QVygxJ5OsQ+lEZCqVA6c3X6S9r0ixHGBmABzRHK9zyWY+d2dXV5ZELMbiuemB17bWSuWAnZ1ZEvEYiXiMeMzpzpWY11AmnahdvZYDJx6bmmMUERGpJbWcTqG4JSgEBfLDtJ5mEnE6lBhJ5JCWL5XZ153jqdZe9vUUSMSNuZkkTak4vYUSJXUDnbBssUy2UKYnV6K9b2q+U8uBs6sr/G5PJcKfVjMjHjPae2tThnwpzFXQndPvhoiIHBoUnE6xhCXpyndWXxePkS8G6toncggqlQP2dOfY3palt1CmMRWnKZ0gEdsfyBjQVzg0xp735kt1GaYQBM7e7jwNyThN6ThtvXm6siN3rXV3+golcsXymLsDB4GzpytHqRyQSQ5uIc0k4/TmS5P+vZ4rltnRngXQVGQiInLIULfeKZaKp+gr9lIoF0jFU0PWJ+JGT6445ASnlnpyRWJmNKb1dhCZbEHgdOeKtPYWiJkxJzP85yydjNHeV2RuJjllXVFrIVcss6Mjy9xMgsVzM8SmsMtpb6FEOfCB79CmdIJ9PQWS8TgNqerfq+XA2deTpztXxIB4LMa8hiSNqTjpROygdeEePjZbLNM0zHdoMmG09eZZ0tI44eOD8PV9piNLJhGOa80VA9x9Rr9nREREQMFpXSRiCboLXSxsWDRkXToRoytXYn7T1Iwh6skV2dWZI5mIcXQqrpMbkUmULZTZ15OnUC7TmEoQG+HzlYjFyBWK5EtDW+BmkrbePJlknL5CmT3duSkLUMuB09pTGPTaxcxIJ2Ps7MyydH7DkLGfuWKZ3V053J250Xj/wMMLCu29+YFANZOM0d+g6kAQBARO1K22dPCLDok43bki2UJ52AB5tPoKJXZ15MikYmGru4UXQIplJ5XQ97eIyExXKge09xVIxmOkEuF3fSJmU3qht54UnNZBKp6mr9TDvKCZZGxw8qP+4LA3X2Rew9CW1cnUVyixuytHYzpBtlCmN69kTCKToVQOaO0t0JMrkU7GmJMe/ecqEY/RNcW9JyZTtlCmr1BmbiZJKhGjL19iV1eWw+c11PyCW3euSOBDL+wlYjE8ATs7ciyd30AyHsOjAHRfd4FUMkYyPjigzSTjkIwTuNOVLdLRN7irrxkYhhk0pUeuq0wyTmtvnqXJhnFfBOzJFdndlaMhlRh0jAYUy8HAWNeRBIHPmpMcEZGZJlcK6MwWScRiBBXDTJLxGOlkjKZUglQiRjJ+aI7OrPlRmdlFZvaEmW0xs2urrE+b2Vei9b8wsxXR8heY2SNm9tvo7/NrXdapZMTpKXRVXdffta+W0yDkimV2dew/yckkY7T11vY5Z4vefFEJSqapIHDypdqOzwuixDhh5u3EmH880okYPbnRJUZq7clPqzGq7k5r1GrarzGdoFAK2NWZpVzDMailckBbb2HYlslkPIYZ7O7KUSgF7OnOs6c7T0M6ftA6ipnREI0Prrw1phI0pOJkkqPrcZKMx8iXgnGND3V3urIFdnflhwSmAPG4jel90Nqbp0ffUZNmV1eW1p58Td/fwymVA7KFcl1zVeSKYQ8RmVnc6zPdloysO1cknYgP+e2Jx2ygt8+21j62tfXR0VsYV66E6aymLadmFgc+BbwA2A48ZGb3uPvjFZtdDbS7+0ozuxy4BfhrYB/wYnffYWbPBn4ALK1leadSOp6mp9DDvFQz8djgaqh1176B8Uqp2MBJTiIeI1cqkS2G3Q9lfAqlgN1deQJ3csUyC5vSaqGYJoLA2dOdI1sMOGp+A4kaXHF0d/b25CmUgmHHH46kP9DpK5QO2nuirxBmos0WyzSMMkCqtWwxPEmee0APjIZUglyhzM7OLEfMy9Tkte/MFokZB+06nUnG6cuX2NbWSzxmQ8pZa5lEjH3dBY5aMHJ9uTv5UkBfoUxXtkjZncZ0vOrxJeMxsoXRZXl2d3ryZbKF8D06Hd43M1mxHNCXK2NWpjtXYvG8dM1+Q8uBUywHFEplssUwKA3cB7qbz29K0dKQnNLfnFI5YCrI5vYAACAASURBVFdnjnLgNDckD9mWnENRe1+RzmyRuek4czLJEcfXy9QIgnDu88YqF1rjMSMei9N/elEKAtqzBbwvPHdY0JikubG2vS6nQq2jkOcAW9z9jwBm9mXgEqAyOL0EuDH6/27gk2Zm7v7Lim1+B2TMLO3uh8TlOTPDYkZvsZd56eYh65OJGB19xTHNeRgETke2SLZQYm4mQSaZGNLNK18KE5Vkoj7slTKJGK09BRrmT48T3ZmmP/hJxI10IkF3rkS+FHD4vIx+sOusv276CmViZrT2Fjh8XmbSn6ezr0h3Lvz8TUQ6GaMzWxo2MVKpHLCnK09DKkauGEyLi0ruHrZcDnNBLZOKky2U2dWVm/QAtVAK6Owr0jiK7rWN6QSB+4jjf2shEY+RK5boyZeGBMYeBRmF8v6AtL+c6WTsoOXtv5peKgcjvq75Upg8qeg+Ld43M11vvoRZ+L4qBQE7OrLMSSdYOCc94e/9IHAK5TCDf28+/D3p19+9r/994e509BXoK5RYPDcz6i7eE9GfDAwgZuFr0XIInBjPBuXA6ewrkE7G6S2U6cqViMeM5kyShnS8pvMyy8EVygEOozoPT8RiJFLhZ71YDugrlhkaUcw8tf5VWgpsq7i/HThzuG3cvWRmncBCwpbTfpcBvzxUAtN+qViarkInTak5xG3wF0E6Eac3V6JYDkb1A5crltnbnadYDkhHQWbg4RyK8zJJGlJxzMIxV6lErOoJTCIeoyd/6LWe9rdAtPcVoqvOoxub5e705kvEY7FRJTFp7ysMajFrSifIl8psb+vjiOaGCSdCkcHKgVMoBWSSB7/aGwTO7q4cuYpsqt25InMziUl9n/dEGXnnjCJAGkkiFqOnWBq290RrbwFwEtEV1P6gsJ4XlcLpUoKDBuYNqTi5YpmdnTmObJ68ALW9r0A8bqM+/noEpv0aUnF2d+Vp6w2nt3Fn0JgiB+KjCEirKQXOSOeUuWIZM0jHY9PifTOTuTud2SLp6DOaiMWYm4mRK5Z5uq2PhU1hoBYEjkfbh38h/A8qe+L1Ly+VA3ry+7vqmkEqHjtobwwz2/+b097Hojmpmmf97swW6c2XmZMJA/PObJHmhpmdaXy26M0Xw++aqCUOwt/U9myB1l5oSMU4Yl6Den7VQV+hTGKWv+61jkCqvboHdoo+6DZmtoqwq++FVZ/A7BrgGoCjjz56fKWsk5jFcJy+Yi9zU/OGrDcLv/znRYlFqnF3OvuKtPbmSSfjAz9e/Sd95SD88WzvK+DOiAOo04lD54QlCHyg62OhVCaViOPA9vY+FjSlDvojmi+V2dedJ1sMMOCwuakRu1h29BWHJEZJJ+KUYuHV9IVNKZob9cM9GUrlgF1RwJlJxlnYlK4a/IfjP7Pki8GgqZIaUnH2dOU5akF8UpL05IrlqCVz8j43iVj1aaV6csUouA5b3pLxGD25IrliULcLIOFY0wINqZGDzUwyHk01k+PIlon3KsgVy/SMkC13OonHbND3hDG6K+QjMWPg83AwvfkSqXh4gbLe75uZLl8Khp3bNojGXxdKAVtbe4c81ql+8tMvlYjROI7vk3QiTjLu7Oku0Fcos2hOuibd6HPFMq09+YHv1UQs7BUw0zONzwbuTntfkXRy8PsiHrOBC7a9+RJduWLdWsKLZR/0OzebdOeKs763Xa1/zbcDR1XcXwbsGGab7WaWAJqBNgAzWwZ8E3iNuz9Z7QncfT2wHmDdunUzbjRwOp6mu9BJU3IOMRv8ZmxIxenOFunsC9+o8zIJMhXz7oVJPXLkS+Vhxw7FYzamE49kPEZ3jU5Y+oPeydpv/9XoIcujFs+OvrBbXDoRG8hC3J/cpLU3T0++xGFz04O6r5SDsGtUZ7ZIIm7MzYRdAPd05ykFML9KcFksB+zuDKd2qFYHiViMprSxr7dAvhzUpDvpbFIoBezszAIwN5OkWA54pqOPxlScBU3pgROjcuDsrhKYQlgnBS/R3ptn0dyJ1UcxGnOVSsYmNRtttWmlSuWAfT1Dk/6kEnHaevMsTU3OPJpj1ZMvUSr7qE9K9weoWZa0NIz4Q5wrlikHTv/Hqz9LLoTT1iRn2BQqtWi5TcZj9BXKHGwq1XLg5EoBc6LPQ73fNzNdV644bOAXM2NOOhz/Od7x5+MVs/C3K1css62tj0Vz08yZxPHF5cDZ1Zkjkxw8Bnq4C2oyvWSLZUqBk4kFUCpCIj1km/A8qUBTeuxJ/SYqVwzHUrf1FmhKJWZV622hFES/pbPnmKup9TfmQ8DxZnYM8AxwOfA3B2xzD3Al8ADwcuCH7u5m1gJ8F7jO3X9W43LWTcziBJ6ns9DB/PSCQevMbOCkur+7RdAXdvlqTMfpzpVIxm1M01SMxmRMeXCgXLFMe2+BDqPqXINj1Z0rsqc7P+yVZwPSyeqtYv0nDYVSwDNtWRY0pZjXkKSvUGJfTwF3H3TFOtw+QVtvgXIQDEpy5O7s7c4Ri9mQMbyDyhOdLPTkSzSPonVDqssVy+zsyJKI5v6C8KQ8zIRa5pn2LHMyCZobkrRFrRYHBqb9GlJxOrMl5mQOXh+FUkCxHETdn4y47Z9rrL/LsBmT/gNemRip/+pxa9QV9MD3WirRf1Fp6t9bQTS36FgvOmWS8YEx8Ec2N1TtHVKIuuP35EoDwajj2AGf/Kk++Z+OEjGjrxBmbBzue7tQCgb1XUolwqEc9XjfzHTlwOnJlaomLZkuMsk45cDZ05WnM1lk0Zz0hOs5TPqWw2BIYJ5OxOjOlVjQpKmKprP23gLpOMR6dkIpjycb8Yb5kGiAivOeRMxo7clzRHPDlJavrTdPzMLPWG9h6Pj8Q1muWEIfnRoHp9EY0rcSZtqNA7e7++/M7IPAw+5+D3AbcKeZbSFsMb08evhbgZXAB8zsA9GyC919Ty3LXA/peAPdhS7SsTSNyaaq21R2twh8fyavWl2Bn8zuXv1JE1KJcCqHXZ05lraMP1tqqRywrztPwzDB52iFXZyNtr4C7X0FAhh2n/3BZXcubCFaPC9DPGYD2VJHe4EgFY/R2pNnScvkBf6zRV+hxK7OHOlhxkynE+H4y/7kITELs8QOx6JxfXu78yxtGTq2JgjC+S3begtD+t/FYzYwV2bxIAHwRKUTsTCbYiY5pDvv0G3DVrAlB2s6q4GefPW5RUcjndgfoC5p2R+glsoBXbkSHb3hWNKZ0mW3nvq/TwrlYNiLf735sEdIpWTc6OgrTPkJ6EyXLZTCCyXT/Hs8Hgs/P4VSwDPtWZobEsxvSlf9vPZnAy4H4ec5EV2QqzzGrmyRnly56thyM8MJW+Z0wWh6yhXLYW6AchtWLuLpuVi5gHXvgFgSb1iAp5rAYmSScbpzRbKF8pR1/c8Vw0zi/b/PrT2zq/W0J18mOQXJzKa7mn97uPv3gO8dsOzvK/7PAa+o8rgPAR+qdfmmAzMjE8/QmmslGU+RjB080BmYIL6GJrO7V88BiVLKQZixc0nz+Abbt/cVMGNSulD2J5EYbfbOpnSCbKHEzs4sLY0p2qNuL6PV38J1qCWdqrXuXHFgTOdI9T6Wz0YyHgvH1mSLtDTtH1vTP4a0FAQ0VJm+I3AnCJzAqVlgClGSslyJ3nyJvd35g75nUtH8qFPZClYOnLbeiXXjSyfiFErhuOwjmjMDvSzCDKgzc+x7oZynp9hDOSjRnG4hFR/abW4ylIIS5aBMoZwfeI5i2RnuLdlbKA9p4U8n4vTkSuRLZWXoHIPObHFGvV79F2N78iV68mUWzkmRjMcoRtmAs4WAYhDmWKgcEGtmpOJGOhEjmYixryd/0N+8ZDxGZ3Zsv4sydTqzRZKlHizfiafnhgvjqfAWlIj17oG+GEGmBc+00JCMs6c7x1HzG6ckQOzoKw4M00jEYuS8OGtaT8uBD0rcOJvpFZgO3IlZnLjFacvu47DGw4eMP62lbCkbTlcQ3z/2brK6CZajLn+VXZ8yyXh4st2TZ/Hc9JhOPnPFMl3Z0pDEQxM1lhbohlRioHvpeBJWNCTjmrJnDDr7CuztztOUSdSkp0BjKh6Oh07HiZvR3legK1sinYzRlKz+FRkzIxafmrpLxI193XliUSvGwSQTYfmPrGErWH/2655cka5c2EI90QtFqURsoGXHLOxyXc+MuuPh7uTKOboLneRKORKxBGDs6t3B3NS8qnNaj0fZy+RLOXqK3eTKOcpeZl92L0c0LSERN/oKpYExpZUKpbBFrNpYpkTc6Ogrcvi8mRNs1VOhFJCfwFzG9WJmNKQSA119+z9iibiRiMdIV/m+c3fK7vQWypRzpSHjTA+USsTGNNOATJ1iOaC3t5e5+X2QrNLwEEvgqQT4/8/em0dJdtV3np9771siXkTkVpt2hBCItQFLiEUYjAEDxoDHG25z2vsybm9MT/eZtj1uu93Ttnvc3eOFtttuN3jDeMEG2w0IA7aRWAXISAgkISS0VVXusb793Xvnj/siMrNyr8oqVZX0PSdPLRkZ8SLyvfvu7/f7LgaZLGOEwGvMkFfnxhwprzRJUW24rpq+97iZnuaV3uDe/XjGhbWyXmywFlHGiGwVEx0m8CKyKtlSf3o2UJmKbr5KWiUAHAoP0wrak++PaYKXTp8+BbWXOA3nqZvXVugosoGSzLb2tuBtpAc/totUw1enXbSvn4a1HwfdwDPB2FUwCs9OYQpuw+Z5gsVBjjYWsLTOo4ld6Lks0+3yQzc+Vp216WleaeLc5W9q4/I0T6c5sx0CT56TbMaDhraatEwYFH0qWxHIYIM8w5c+cZkwKmNmwpktze92grEGbTXaaJIqJi5jBBZP+kReCykk2mr6RY/pYJa00Fs+T16t/X9lKqSQk+No+IpRVjIbBRfk7+BcI8nXNNAXIsZU371ACIEnBPs5LcQTmafnJYZpgZ8sQhCA3OH+ICQ2aCHiJawKaAbNc2KO1E/LTXtFJQXGWkZ5uWNiwvkMY1zFuVtxneTVmuzCWqgyN9He6Xd1keKJ4vSxQpUh0xWELrAqQMXL6M5lTn+aDwhlg2irztYBwFhDXI7oZV2UVEReC2MNK9kyBjOJtQlqc4N+enods7zS9JLt6T3t0E2snCvu7kXaqfTgCxmNQLISF0SPg27gmSAtNfo09Yz7Qeg599jAO1jH3YOAEPtz3B5PwS6Z3t8NzWUrVhgL2hgqs0ZdLirDo910Xf7m+X2zNNYwLAZYLE2vSSB3ZmhoU1GYgqRKMMYga8Mp6XyB3W7bWjQGYzTa6knRiHXf9mW4JX1XCEHDa2CsoZ93GRYDZsJZpFCs+Y2PMy8t2moqU1KYgspUdQaq+1LCp6Eam95LQzUZ5n0iL0Jrj0qbTbrsUV7hK0llKhaTkwihONQ4TKDc2q6kZJAWZ+xefbHDWkvvAqP0nmuEvnoi8/Q8gzaW4cpJmtKA2gOzRkjwm6jhPExfgScd4+uS6bOzPhSVYZRWRGNWnLVgzUT7uhqXE/frc4GiMljsvq7zJK/oZ+XkvqmNnWRYKyl2dKa31jLK9STeRxQxYnQCIRTGCyFoY72GK1bPIbPyscKFv8u/0GBKRNpDlCNQIbbusFtTIdIViI4Sqgar2TKBuqymhu0Oay25zhmVQ0pT0lAhoWrgSQ9P+pMOeVZldLMVtK0Ivcbk/6WQNL2Ibr5KZStmgtlaj6lYGeX7nhRaa1kZ5fg7TDnHm+6lQY6v5I7PvxU9+EKGy4QrL+hu4LlAPy0JzhE17Jy7lRrt1gOjocqwZQatQ+Cd2c3fTcH2pyHUxjI/yMgrgyddTIsUzhNXSTFxrL4QUOiC1WyZypSAZFgMEAgiv0XTiwhUgBKK0pTkVUZSxeRVDsIVf1IIxkNGWxeG4yJSIOvPReJJRSD2riWVQtLwIrTRrGRLCMTEeXg9k8u9hkQJtWtRPfkZIQhUyGq6QksdodSW9b96Y5yJXujDcroIuGtqITnBTDhH2+/Q8F100XT0BB1zJ+SVOScNswsZSgrSwjyReXoeIe6vIrI+ojOz9x+SHlZq5GiextQVDPOStPD3bY6kjUWKnfOcB2mBHJtv6RJ0gUhXsdHhcz49HacwlNpy5Vy0p2u90oaFQYan5ETq4qs1M7FxLvCxqc3NRXBGdnrsfWItIlsBv4mVPsKUkK4irEEgMH7kilW/CQcgFTkfcXG+q/MR1iKLPrJYASnhVFder4koYyhGqLCDtpKVdGlX/ak2FWmVTihlnvDxpCKtMkblaLLx8aSHJxRplRKokIbaPJUVQtBUEaNigDGG2cac21D5ivl+xhWze3fYjfOKpNAbJqKVqRBCoNZNXZR0k5gT3ZS5dsBUY+vO2Hb04PMNY4qfsYbQC/Gkv+H9rkfT91iNS1qhv+P7MubxactfakOaa1oXwaQca10hWuVQjBA6w+iK0lYUuiA1BbktibIlpo88B3mGNB5PifpG2Nz1mjF1HmylzfYF6D5PP20qhuUQX/gEXognDi5jcTtYaxmVQ3r5Kp4MaHjRhu9lVUZSjrAIlJBUtkIg8KVP8yyxVLaCkoqm3NqV/UzgSZ+sSkj0gLxsbNhAFtpgjKGb99BWT/wFlFB0s1UynTEXziGEc2OdawVPTLy2wSAr8R6H6/F+4dWu/08Up489bJkxXH4UP2rv/uBT4YWIIkHESzQbh/dtjqSN5Xg3xVNwpNPYsvFVakM/q2gFCqxBxC6UQ2RdV4D5rcn0dLf90kEgLTVpqfGkYDXOObILm2QsORNCbCuLcM7HFVG2dYGdFnotQqZKQRcQ1IZV0ndf9WsJUyLiRdc29RoQTGH9BhdTSXfxvJPzHaZEpF2YOsq2Oz2/iUhXsL6jh2VVwnK6hC89pFCTbroQjmyWVjGjMgYswSmUskApYO0CMFZTWb1tVM0YQgiaXoukitGp5lDzMJ5SVEazOMz2pD81xrK8LvfQWMOg6DMs+kgkU+EMkd+aFG2ekkgpWI0LhlnFkc7GLLbd6MF7gbUWY10G4E45gOsfX9mq1mYJfBns2iSIy5hB0cdR8CSm0AgkoQpoehGhCjdMscfdwOE2RgN55VxL88rsSAe5WBFnFTtEx9a0H733zqG1iGIEOsc2D3HWRWOmAp0jygSZxxhTUtmKFENqSgqjQVDnBQY0RJMsX6XoP8Dc9NW7unYD5DpDCW8Tw6Lhq4mr9LGprTcEMM4szMnKgzN3SauU1XQJM6aqZtYVZF5E02viy2DPjJC9ojIVq9kKuU4JVdNdY1XqtDoyqCeLAeM10Vi9r6nnhYJQNYmLAd20tcF9Oi0qBmUXK7INRbsUkshvkeuM+fgEs43D9BJICs3hdnjO4iMuFGybbVomIBR4F985tRu01SRlTGlKpsOZyX19nHk62zr/m8oXLXQFZUzam6eUPi11euuuDSJkPsD3QnLRppsUHGrvfq6PJ5DWWkoNj64mHOmEm7w2hlmFqierIllBVpmjrnpN1GgRPX0lSno77pcOCtZalocFTV/hKRfl1gq9HZ3ynQP27o7CrVCxPCpo+N6mInaUVxOWmEi7sJ3DuxCgAmwtxxCmRCRLbngjPETrMHDhx4I9UZyeSwjJziMICdJzLmmtS2h4EZUpSasKi3GFFXbyDELILfVHWz6zUPvSiTW9iFznLCYLzISzNLwGSaFZjXdflAapyz10E9y0phFrGirCYunlXfp5j6lgmpbfQklndtMKPUq9MYtNCrakB491WWNns/XarfW6rcqUlKZy2jAspSl5dPQwnvTx131JoWrdWU6uc0pTYmuKhcEikDRUQNNrEagQXzotTWlKRsWAURkjBAQq3FTEVqain/ewWJSQzDYO0/Tc4tEMFN24oB16k6l0URl6acEwrfA9R7E82cu4bKZx2tmwFxomuq6tuu7WQpUgk2WELjHBFLYxvfPGsEyQyRJCFwAYOHsFqjWIdBWZ9RzdHkNqDbHOMGgEAk94NLeg74bhLGW6wrzyOBxdMjlPTkWhc3p5j0ynCCRz4RyR39pwjYxdpY93XUTLqROMcbd3lJd7zundCdpqBnmPYTEkFB5e3sWGU+A7TXtWpfXkEjzh0Q46hKoxuZZ2fG5TTYrd9ccPUOqC1XwVJSRNr26+VRlyOI8QEt0+6sLl1+Gsa2atQWQ9sObcNEJquGzABvPxElfOTuEp9z5PjlYpbEzH72z5c6FqoI1mOV2k5bdBN3loNWe62WCuFTyhr6yRFBVwCj1Rl6jhCdcrCzvYxsxZLVLH9zdt6xghU1DqEjD4KiRUIUp4KKm2Ze0cBNY3ZMd5r4XOOdI8ipKOKWF4IhrjnMNaKFPI+pAP0VbQywVBcGZykbFBUtTx6SYWJcSGBthW6Cclca4n5ltj+ch0qZmrs3YrbegltWSrGNXT0nrCKz2sKZ0xU/sSmoGil7is77PV8Bjlzml67FzdDBSLg5wrZrfOVy+12TXmbQwpnOP+8mjjoKfShqI0jiVWZYgqhWCPU27pY4N6qpqniHwAHN7bz57HeGLFeIxx13zB7Y/k/Isb2u5EVQGiTBBZH9ucxdvD9ORsIVQhlSlZShcIZMBUMMNqbAg9uaXLbFEZ4rxiNc4JfVhKF0mrhFA2JlNdgaDpRW6aWvbpF306QYeO30FJ5wTnybUstummt4EebK0lKWN6+Soagzyl2B9vX8e6LSkknvQnU5KxttZY7TRnOsNYA1iEkEjcDb3pbab5VaakV3TrolUSyKAuDtSOTQKn+13LeF1KF+j4HabWdZkHWcVUw6OflvSTEqU2uilmpcuGvXR6d5rmxYCxEdIGh951RSm6BM9ptkUVI/p9rN/CNmfBX1eIVDkiWXGUea+BDTq1nqMLQrnHHySqHBkvUBQxI2FJdFrP0UXdBNml4yskgfAQZcZSMs9MOEcnmJqcW6UpGeR94nKIJ4MNZmaJTpgN5zZMJRu+cg2fXsolUyGtdUVoNynppweTH5frjJXUGao1VROZLCKrDFum2HAa2diY9amN3tCwifz2ZKpqrKEyFaUpyKqMwuQYq9mqsedkC4ZQNdYKTlOgRgtYP3RtpeE8tnUEu9eb/ZmiylzjxFRgLUYFrkg/R/Clz9BmrGZ9jrbm6KdDlpMV5po7H4OSiqaIyHVGUo0AQX8gODlocLjd4Wi7RcN/fOvjXbbpxg2qSFewUjlpTpUi+sMDKVLHDs3rDbIKXdRaatzlYJkwqgCKcjTRWFtACUlQF6ye9PGkh6pZWFvBWoupG+ECJiytDY3hmrI/LIabGrKFzllI5jncPEqgXFPjTFlPT2CPMMYVpFnPTUyVT6EiluOCyhqaZ9rYrg2SvNEC7anLWR7lqB3MLJOiYjkuaIcKqhykQkmPTsNnlDnZ17GpBlmh3blmSrdue9HGZp7XROZDTD5ENqYw1tJPCtoN/8CdxU3tbbKeMeJJSUnFSlxwbGpjgT9u8Ko9xLyNMfaEGKQl0/UEOK8MVtS+Btng9HWkF5FR0hMrxmOMD3wp4e+/kvGCq0KeccydqNZvIrKe49qfoTnKmcLd0HwqU7KcLaKET9Gd4imHZ2n4njPaKDWrcUZaVlg0QlbMxz2klETe1jRiKSQN1cRaS1yOGOYDmn5E2+8QqpBm4FEZw0q8tlDkOqObrVKaglA1CM+gK7w2Sd77xtyTPl79+LFTZ0NF+9JmKamIZIukSkjKmLnmEZpBg25c0EsKpIBoixiThq/ICs3CIOWSqeZFr0HdYIQ0KUpXnA6jdq6bwGuCB6LKEYNHwW9gG3NQxm6hVz6E6yZGQoDfRiTLrkBtHEDhYC1kA4rRcQY6JRcWhSLco6HNhqfyG3jFkGbYoV90yU3OdDBDXI0YFQOkUETjznKVIqU3oWaejE9smqL6ypn4zPdzDrUtM1FALylYjfMzMjkaT3BG5ZBB3iNQoTPwSVddYepHgEXmA2yVYKIjE6qSkgolXRNh7B4+LAbueWt2iEC6htUulPoNMBVytIBVHoj6vQVNRLwEunQFw1a/D2tBZ67rLAO39u7XFdEaZziS9erGSQQYRLKC9cLtaVpnAaFssBiv0PR9TsQLhHtk2IyNlcAdq7GaUuc83B/xcA+uO3YpR6IDbuhcIMgrTV6ajREsZYIoRiRSuTVKSKQfosoRMhtAY8o5jlY5CInGOPk54LxAzaQANVZTGY1BO5dmYya0f4yjxro7lqtKhbFOOmAsWFeMeH4b1Np90VhDaUqyKgWcc6gQAk/6hCpEwMbXtAZRH5s7WyTgmrESiZSSoiqQcgvWljUEKqQ0JYvJPEdqc8dRVrEau8lYeB7EwF20KIYwWoSgBV6DpChZGmV4UtLcw1RvT6gNkrzhCVrtYywOBFKwoekJblCx0M9oBhKZDxDxonOdjeaw4RTRhCWXgBBEft1ElN6WsSk2aCGSRawXEgUB/bSkl5Z4UjLV8Ggc0Lk1yBzrT0nhDAsBpKIZeAyzklagNgxmhpmbDO83QSIKVb23dfTesZM6ukQUg82eNI9DPFGcPsZ4sOtoQn9zVzIpTkGAH6LiJXTnsvMi42itSK0YlMvcOd9nutGgl6YU2tFP15tErHcC3glCCELVwEpLoQsWq3k84dEJpmh6EZ2GT2lKltJl0jIhqKm1jzXWZwRuwjj6YYf3P6bRjaeokT+FLz23MZh0zM2kS970IhqBR5JXLA4zjnYaF22BeqoRkogX3YKtGjtTXbzQfekSOTqBFcrdqOsblrWWzDjKdqSaeEELES9gpdz5ea1xNyrpbVnYWF2RDR5hkJygVD6BDGmekaZSOHp/1qPZOkauMxaSE0ihNjRDRDFyxhF+A9O6hFA1MFazkm+eoiopiELF8ignLzXD3FHtTqXKa+PWozFNfkybN9YwKoY1Td59aauxWCROpy6EQORD11gLWpP3Yv3IFY2DE9hoDhtMbfgcpZATg54zgjXIZBE3Tlq/WZIQRMi8hzEVNjq0dm1aUzNVemBK93M6R+Q9hJAYr4UNI5DhzuuwqJCpzQAAIABJREFUzmvaeIUNIk4ONP/1H1f4ruvbPP/S0OmmOpcevLOi0e6ryjY0Mn1PUmlYjBepKoWvTm+tkEIReorQa5CWJY/2F4m8gFbw2K/B5xpxrjdOR6xBxksMbEE3i1FC1sWf+7ZAoIarVGXKycXPYrQBsdGb2SKdQaJQSOkhhEJKRYBAWoOwFUJXrLVsxkVjvaaJOuYI6fT0WR/TmHXXn1hjD52qXzdWk1UZMHblljs2gZykyBXWDe8U34maiSKqDNO+1NH0ESwm8xxqHCEKIwZpSTcp8KVkqumd9bzMxx2shWQVghZWSnppST8paPjq4JlWXog1Jd7gOFHzCAt9uGx2LW3BGMviMENJi58uI/K+Oy5wDea0h4kO4QdtvNCjMhaVOomO3W5dERJUgIwXMFNXEIVrNOFeWmKSonaUV8zWdOH9otKG7ngYYg1iNI8wFWbqcpBOc7o0dMkVnpIUlWFpVNBaF30j0hVkPpxclxbpjr0+ftucra9LgafExMclKTRNXyLSXv343T1RDKb+x9r9ujQlnr04rqsnitPHENpYHulVBApufSDjB1+kOdSqT3ThYamQ8Ty2MescuQ56ZG+0oyHlfazXcNqoXeBJj6mwTVLkdNOUpufRCvY/HToVY8OSgABTB8r38lVC1ZiYvuxm5nRewFTIZBGhNaY566KCtvlsTp2iSumtUbZgsgkRQtDLV2l6LTpBhzi3LImco50z/9zPR2wwQioTN80Kt9bKbQnlY9XaZkxbTVpl9MsB2mokgr7t0/ZbtFSTYDSP6Vy+kQ5sLegc8gEm7VGZ0mmolMKqRm1I4KGNZtR/kMpWBH6H6KCKDxUiihgbpoRb6E5F3kekqxBEiDKbyACkcLnFbor6KKFqEHktQs9pO9uhR1JqokAhhUBbTaFzRsWITGdMiPHjDXb9z8pUdPPVCSVwPVV+gipz0+ggWnuCMaQHgUSmq5gydQXiQUoWrHX07ap0U8/NnxjWbyHLBBuXmOYhRJEic2dg5iab6yirqi4HdIYcDd3PK2/jZmNcWFiDyIeTaWk/M/zcB7oc72t+++MDfvvbDqNwUWE2Onow+lNrndFWugqmQuQDd4+o4UtJXkHLb9CLs01U1NNBw/NIS8tiushl6jLCczgJ3glpoemlzsAkCjYbjRwEtHFUwvV0P5EPGGSrdEVFpLY2CjTSYMUI359CBlsdl6kvObvW1NRj2q50+wDfZ/31ZLd4FsAZpFiDTFcg6+14/5FC1aaJe4MQAoHazKy3BpEsI8vYPa6MsUEbr/aRWE4XmW0couW3kUJRGVcAjOPpWqHHdnXEuXBlvWhQJqArKhmwPMjIKk0UnEWHdOlDoPCyJZp+zonuLJfPtQg9xUqcU+Q5nWoZURVOSlPDBi23R4rnsVmIiQ4TGO3uX7vJLsaStzpeBlzTdc140zLKK7LKcMnU/v05BplrzEpw57TOsUIihycwncsmOurlOg5meZTh1TFr7v6zhMz7k3hIrK0bUQaMQZQxRhfY9jEQktBTjPKSlVGOMQZhcT4VtXO8sYZMZ1TWsRq0NVS2xBjj/Besra/HtUiysshpmyku39c7Pz/xRHH6GOLEQFNq+K6vafGu22Ped3fCd9+wbhPuNeoLeQGEcsYvYQRyC92Prak9xnVZEV496dniAtUFohgi8xFuYxbUdDYPG07v6dij4OxtTNyEyG0wK1Pumzq7CaZAlLmbvukCqsSFUO/lOU3B2KhqL4+VowXAYlWATJawsottHsJ627/eeOIF4G+hdR2j0AWLyQKe8IiHEZYpjnXO8LM5hzDGsjjK8KXcNqpigxGSNch6MrgVdL1oO62wK+Ila9Se0pSMytrRWkAoA8L62rHWklQpwyomEh7T/QdRM9dgEeiij05WSMohma3Qwk1MhePAgV11jR1rscISBG0CuY/i+RR8dbXkf30x4YGVil983SydsL5mvRCZrmDal284d0Q2LkybgFwnA2hMjH/Gk8hxUWlziy8D2n6bUIUuVqWK3fREWBe9sgP1c6zV3hamRMWLWD9knKG5xbM4jbDOkYPjmOjwjs2b/UBkXWQ5mmwMtLE83Kt48tzGAtj6TTflHJ5wlO4dj3ejKyLWuM2GNc4l2lrQdalQF+RZZfn5m7ssjTTf9twW774j5u/vS3n1dRGyjDHFYM9r7LbQOTJdQZR5nXMnkWWCNnoy3RXCOYHnlcGY3d3J9wIhBFgBxmMhXuDS1qX46rHzRNDG0o1z+mlF4Em6RcFqXOArSafhHWihmhQVljUjJKtLBr2vMqAkktuvwVK49Wh7FpFcK/gOYhkX0m1urd7z/ee0MW7EVqW77qxBpqtorwnSyWYaXkQ3W6Wbd/GlT6Om/geeT2Whm5gtn9oaZ0yzlyisJwAkq5TSY6HvpuGt4Bxcl0JC0MEvRtgq4+TKMWY6EYPhgOlyBSvE1pNQ6bmCVZfIwXFXWu3xPmC9pvOL8BruPF93XUkh1kwAeymXTjf3fP2X60yZRN53zb5gnXSmLlDH9F4xzEmLmuJvDSJeQhbDDYX4qdezVT6yiDGjhUmBGgUeg7TEUxJRDOufE1hr6Rd9hmVcsx/c3sY1hrdvOghpNhkHXqh4ojh9DPHgquvUvPjqBvcvV7z/Synf+fw2wXoKlvQcDx+DKPqT3KexTk6UBejMdXmMmVzfro8CViq3uappaZMCTao6F6n+AV8iklX3WufRhHJHQyhrt1/QTIEoU0d1qgqEkFjPbTLlaMFNihuzW2t6x/qzrO+eQwjXhQ4620+vdY4azjut2ziPym+B1Yh4AaECTHNuk2voGHtxDx1HYWijKe2A+5Z7LMYtrpieZarRPPB4joPEuDBNco2loqgMR6camzYe642QRLKKMHrDRMha57gcVwmjMsaK9TQ3i1MqOkqNm5JKGmrzhNnRyV2DpTAF80WfYOF2KjTGAspHqQBfNAjX/84PiGFfasvHv5rxvi8l3DVf4isoNXzkyynf/Jz6+pMeoowRxQgbrhk5OZrU+unkWAawvIk6eqoZVz/vjT9JvIPK97TGbYSFWNN57gQVYpWjRBp/tLcpqimc4YdwatRxxxiE+4zy/qTjDPD7nxnx7jtifvJrp3jdM055j+o09Z/j82CbJUcby698uMeXF0t+9tUzvOTqkDtO5Pzx50a8/Nomgd90a6wKT89LwJSItI8oh65oDlpkleVLK5YnXWUQOsfKje81zvWWxyvSnivMt1mPtoNSkryEtieZT+a5tHXpY7LuZKVmYZBhjKVVa/THG9HxdG5cqK7foBq7tnHzpeBQe2/sk/VGSNZa+r0HGFYjonD2/GwOCjW5/7jmtodpTLn/O4jfl65NxwRrTAUh3RqVDyZGc+OoInBU4rTKiIk3GS4pqZAopJQoJFIoRrkHAx4XHgtnhCqHKiMzIcbqg9OX7hE2aOFXGTZ+lJWsQ8cM6qbZLsehfMdy2mkfdyqEcMZjo3kkYLwG+C23R1ABSEXDVxSV4URva5f6rdCracGijJ0/wameFusK1ChwxpWdcWE6WnCMnD0Y7tnAsXfMaBHbPooUklbDQ2KRvd5krzOsRq5xvs/1+WLC+bubfRzgq6slUsCVMx5venbEp9/f5Zb7M171tK1OSDnZSAhT1hM64fRywqtParGhZ+L+bhBGQ9V3kx4v2Kb4dE5sMl7CdLxzat5xOhB531HaJvzDeuMg6iJF67WCtNY7uMcJ8FsuG2p0Eus13Y1UhY4eVyZu828qt9gFLSyuI2yLIaZ5ePPGsoyR8ZJ7rVM350K5z9tUyNE81o+w0ZEzomg7M5mIUFmSMueuhYfphB6HWi2mG53a9XT3eI5zBWNclmaSr0UKZMVaxMn6zePECKnKHX2nXvC10aQ6YzCh5m5ddMJYH2UnU9LdEMgAgoDKFPjC27v5zmlgJda87+6Em+9O6aaGS6cUP/iiDt/wtCY/+4FVPnhPypuevTaJsV4TmXXR9XRU5kPsVrRZ4WEpd6SOrjchOjDsSqfdDtKZXIynqM1D7ne9/rhNVTeYBlAV62pxsZHbaG09RXYPWBpp/vqumFDB2z424Ghbcf2VZ3c9s9byto8N+PTDOT920xQ3PdmtEd/3gg4/8/4u7/9S4poOEy+BXfSn1oItnd6wjhYQusAKOVm/48LwCzd3uWseLrtc8s/8Aawr0JUUZKXePD0wGpn3oJC1p8HetwGBkowKzWyrSaYzltNljkZH9/5BnSGMsXSTgl5S0vAl3ljnZs3kuvWkxAvXCtWiWpvOrT+94rwi2iW/EFwhnFeGdug5ZsfoOHE6TxQeOm/W2G0xKVINMu1C2sUEbecerU5ZHyeMAOO+xvR1oTber6rUFaYq2HTuWL+ByHvYsLWJ4bUVlXi9llVTUWmw1mDqXPLVTJJVh7hqdmYSi/QETkHaB6EY5dVjFzXnNQhkSVgNJ3rnPWO/15D0IGi7VrQpIesicLRYqwJMdIjAb1FqV6BeNtPcsUDNK80wq4hk5QYMfrT5mNYVqHQuZ7rpO/bUaB5Z5dtrZbeA9SM3QY2XsK0jjl2Rx475KBtkOqOb92gehA/DBYwnitPHEA+tVlw6pQg9wfMuD7hyRvE3d8W88qm7OCtKH7tnvVati5Le7sP+WrTtzDsuOWBNmNOmoEJ3YzyDm7ooRq5w8ddv0u3GP5Xc+f1KH6SP0AVieAK8EFG5DEzXhVu/mXV0RFdgnsT6bVfQSs8ZwCTLTq+404IsHc1algkm6+5J37sbhBC0ggaRH5JXmpP9jGGREfmuAx3WEQIuRsCrdUDn9uZlrStM47zaECfQCFx389FuwqXTTZqBwoIzQgoVcrjoNHzg6C3FCAQE0nfF5A5w+qj9n1/eHovZ00FRWf7yzpg/+3xMUVlecFXINz0z4vorg0lczmufHvGbtw64Z7FcM0erpxEynt/ZMAKc5X4RY7x60no2YI2b4OkSyhRZpWD1ms6mxh0ncgaZ5Wuv2eUGO56ipsuYMsY251wESz5AVJmbh9cNpr3ijz47wlr4tW85xK/+Q5//+OEe//mNc1xzaOf17J7Fgi+cKLjhqpCrZ/en1/qT22Nuviflzc9r8U3PWisQn39FyHMvC/jTf4r5huuaRIGHFRUiWXIGX1utUrpwTBhrXCSIlG7NX/cZ9zPDz71/lQdWKgTwxUXD846lztCqLhg8KRimFdPRxvctqhRwDN396mBFTTkrKkPTb5KUCcvpspuEneVCLa80x7sjSl0R+DCqCso8pzAl2hoXmyJ9fBngywAl66xPVcehnBKJIoClYc6Vs2rHydwgK/GkwFhDN10hGz5CFGzj+ny+Ykz3xSKqBFEM3X1O+q4JZAqEMfUUq+ah1E0gR1IQjoUlPESZOtbVlmwf4RhaaR/bOrL7YW3Qsm5+vkqVHB/O081WuObQEdphe5O5E7j7TKENgXqcuQHrCrIe2m9R6HRPWZtnDevyNs/la06YagCmdMZ7QRs/OozwFSe6Kcem12LUjLGUxlBpJ3sYZRWe0Kh43rFatjO+85ouL310AhMddeZ7tQHefmGDFrIYubz11hFE5hzdnfnnCo118UyPVzxRnD6GeHC14smHakdSIXjTs1u87WMD7l4oeeYl5yZP7qurJX/02RE/8uIOxzqugLK1XsVExw7GKbiersgygTLBVjEmOry1dnY3VElNu1iblDicpnBHBe7LVPXkZ6emgPt8RJUgBzEmaDsjlFr7txdYv7kWV3FABYQQoo71USSppqokMw2fEk2hh/RtfyKY96RHy2/R9JoEah8RHacBay2Lw1MK0zGtrqbhSQkneimH2gHGWGdcmQ8RVYb2W3TzLnGV0NxjFMb5iNsezvjvnxhycqC56ckh3//CDpdNbV56v+4pDf7HJ4fcfE+6zrnbTSPQxaYCcCvYoIlIlzcb/OwF1jgTKGxthGQdldZot4EdnkDqwhU1tt6segGnnvtxYfilD/UY5Jbvu7HNdzxvN7rTmhZVDI67wagKwI/2rZ55cLXkI/elfPOzI66e8/n3r53lre9d4d/d3OXXvvkQh1ub1zNjLe++I+YPPjPCWHj7bSOunFG87JoGX/uUJk+a3fk2+cF7Ev74cyNe+dQG3/OCze/1+25s89b3rvKeLyS85fo2qAZCF2Cc0dImCLUlE2aM5Vjzs+/vMj+o+HevmeF3bu1x90IBz3HPaycOzZKp5mYGhTPAcywP18zYnw5WSUGcV4S+IvIj4jJmkA7w63gSKV3uphKbI7H2C2stuc4Z5DH3L6+A0HXkwlq+py8DQiHreC9DWSVoOwQB1jiJwNjpFqiPyznYKlqMcp+p5tbXSqXd5tX3DEvJCmW6SkuoyWd84UG4pojCNYGqzDWvVYBdN3XbzMByBizCmt3vd6qBKEfYqnPGUXie9JmLfEZ5wf2ri8xFPXzPqw/HUmhLUpYMswpjLJ0w4tLOFDPNiEBt7WtwUSEfgpQU2mD3u1iex9BWU9mKQTGg5bcmOb67QvoQ+ogqRfYfQkSHEX6H+X5Ou6EpKkOhHZNC4NYyTzhXYbCOarwT/AjKFDV4uJbAbc0WGrvpGusaPhoD1hKoYPJexgWqNRXoAu1HLGdLeFLt/f1exLhQV9gLHlllOTnQvOLatcX7lU9t8I7bhvz1Xck5KU4f7VX8zPu69FJDO5D8q6+rNygqdHS6dKWmoJ7BAj+2115nVoIuUIPjmOjI/gxRdI4cjQ1yDrio2s9mw2tice5rWzqT7ggBfhORLDtH2QPMsZVSTPLDFkc54DIuo8Cn4Uk8JRDCMigG9PM+AJEX0Q7aB25ssmVhWqbO4EgITHQIvAhPSqLQOeBVxhJKgxwtUXkNVvIVcl1csLqLE/2K3/nkkNsezrlyRvEfv3GWr7lie3ppFEhe/pQG/3h/xg+/uENr4u4p9kGzry33k0VM+7K906tMgYyXnFnaemfQiYjd6dnHus6d9kHv/ULCILc87/KAd9w2oqgsb7m+vftGsdaCnske6x23jWj6gjc/3xWJh1uKX3ztLP/6b1b5+Zu7/Oob5ojWuab2UsN/+ccen32k4GXXuOLy9uMFt96f8Se3x7zz9pirZz1uenJIpyEpNRTaUlSWUluS0vJ396Zcf0XAW18+veV7vO5owEuuDvnLO2O+6VkR0w25/8ZBjZMDt2b3M8N/eN0c/+yygA9Nw+cXSrTsuMbXOmrvpomgzp3nQL0Wu2bG6hZske0xpvbOGOvWHC9ixay4IrLOqgUm5h2RFzmpwR7XGG00uc6Jq5ikTKi0ZnlU4iuP5g7U8bV4r3Vr+Sl7vDHlHyyFzilMjO6WXBcc2ZISmRQVWZXSLbpIa2mVqTMXuhiwrwLb6fj3vJ6osZnbZQcyYW6HAUkhSXJFRwrywjLIK7SxKKGYCnwQUOiSe5dOopRgpukx22zTCiIiL3pMzbvOCoyBtAtekzQpN0T5XcgoTMlS5tgY/XLIsBwxE0wTefswf/SaWGuQyTK+6iOiI+SVwBOCtmedF4jRzhm7iF0u9ynsHG1cVNomTb3vnvvUa0EbzbAaEVcJ1roUYyenXWP3CSRtv0Xba+FJz0lbigSjQlbzLtoaGjLEWstv3DogKSxPmvN40qzH1XMel3TOQjTQeYqzXpwKIV4L/DruNvF71tpfOeX7IfCHwPXACvBma+2DQohDwLuBFwC/b6398bN9rOcSD3ed+9/V69wkG77kNU9v8t4vJCyNNEfaZ697sjCs+On3rWIt3HR1yEfuS/mur2lxST3RsX7TuUtmnjMOOs0bzJpObt2FX8dwiGQJ/NhRXHe7UZrC6QG8YBs60bmG3LeZyIaf9Ruo0QJ66tLTmyDvAF/JSYacNoZRXtJP3JbfU5KZyCeq7dcLU7CYLGKxpGnKTHPmjF9/XJiO8pJ26LvCJu06h1mv4ZhfwxO13vcQ0m/SafhIASrrUmFZyleprL4gdRfaWN75uRF/cUeMrwQ/+KIOb3xWtKesydc+o8kH70356FcyvvGZp2lWJH2nnU5WsM2Z3en5VYIaLU7M0LYsDsVgT2ZHg8zwl3fG3HR1yE+/aoZfv2XAO2+PyTV8/417KFDPAF84WXDbwznfd2ObqcbaxuGaQz4/86oZfv7mLr/8kR6/8JpZlBTcNV/wKx/pMcgMP/7SKb7xGS4O5LJpj296ZsRqovnYAxm3PuAK1fWfixIQeAJfwfMvD/iZV83suDn87hd0+NRDy/z5P434oRdPndb7e6hb8bPvW6XQll9+/SzXHXXrxjVT8LETlkcGgie1M9eJ32Y9FfkQjaSfaOYihWtmhLXXwN6aGeupvY1ATWLAToW1Fm01g3xAL+uhlKLjd2jUsUbjx1SmojQlaZWSVimVqSabwlCGxGnpsnT9M9+urKf8B0rhSZ+VdJn7uyXXzB3bQBk1xvBgb5GkGhB5EV7eq02/Nn5Gth5ZXfRTuv1AeogiRpTJvvR4OyEKPJKyIu1rhIRQeUh/42fe8EIaXog2hjirGGUjWo2YyFe0wxYz4cyW5+oFiTJxkgohiHNN4J9Zw95ai7EGg5k0cXzpn1N6aVzGrBZdfOEYH03VwFjDSt5lWLkitbHXPYGQzsdAl3jD43jSdwaLwNgy1ApRuwdvPEeTMqFb9DAYIhXR8dsbz5t1n4m2mriM6RdDhBQEwkfKrT8zYw2jKmZYDF2Und8mCCL6RZ+0yiaN+Ie7FTffkzIVCm55IJv8fKDgqlmPaw75XH9FwNdcEdIOz93v51zirBanQggF/Dfg1cCjwGeEEH9jrf3Suof9ANC11l4rhPhO4D8BbwYy4OeAZ9dfFxUeWnVZZlfPbfwVvOGZEe+5M+H9dyd8zwvOjm5sNdH89Pu6ZKXlP71hjqmG5NMPL/Fnn4/5qZet0busHyGzPsbojcH1e4TI+s4cYUtOvjP2EGMXtOjwlgYLQK31XHBOuHtxA30McaJf8T8/PeTbntvaQM3cBKGwUrlNYeuSrenTY9dgXe3fZKCGkhK1bpigjWF5lBN4krkoIPQDAhW4grJcpBk0zzi/sNSWUVbRbvhQ5ch4gapMSaQgKVaRSCIvItQ5/vA41ouw0RwCS5WusmgzhIXGWTDl+twjOdcc8piNzl6D4/c+NeS9dyW84toGP/DCzlp28R5w3RGfq+c8br4n2bU4vfWBjD/8zJBfev3cpkaW9aOafj7ChDNO533qOWYtIuu5a9TbTkO2P/zFHTFZafkXN7RRUvDWl08RePDuO2JKbfmRF3fOyibeWsvbPz3kUEvyxmdv3gzfcGXIj710it+8dcBvfXzAsY7iDz4z4pKO4r++6RDXHt5cwM9Fijc+u8Ubn91ilBu0dZuDQIl9d6+fNOvxyqc2+dvaGGk/jUdrLV9cKPkPH+yipOD/fcPchqbmNfWSfc9CwdVtEFW2tXOk0chixPvus/zOJ5b47W87zBUztVShTDfkB+4GJQWjoqIRbP8+hBB4YqNb9Ji1IYVExYpc55PiTkmFJzwCf23dHKQlo1P06gcJKSRzzQ5LoxFKllzaPkoraFGZiuODBXppj7loymkys0HNlNmIv74r4U9uH/E9L+jwumc0Jxryc4FhbrhvqeTRXsVLntzYkrZ+tjHIDP/3+1d5w7MiXn3d2udj/YaLltnNj2EfiPbYoFBS0gqdVCTNK9AKJTOS8gSRH10cRWqyCiqk0AaNPa3z7qF+H99PahpqnbNbN28sFoWi47dcc+YsUtmNNfSLAcNqRENu1FtKIYm8JpWpWMqWaagGU37HMSXqKKb1EXKbULsCc6rz/xYPrUxFt+iR6pSGbCAQNYsjoeGFTPkdQhnWUV2u0BwUQ8AS7kEmJYWkIUKsWIuyC2VIpjMitTbsuO1hx3x727ceph0KHu5qHuqWPLRa8VC34hMPZvzdvSlSwLMu8XnBlSE3XhVyNLp4uN1ne6d/I/AVa+0DAEKIPwXeBKwvTt8E/EL993cDbxNCCGttDHxMCHHtWT7GxwQPrlYECi6d2ngzuWTK40VPCnn/3Qn//PltAu9gb3T9zPDT7+vSTQy/9PrZiUnIa57e5IP3pPzzr2lzdLJxEhPraxuXmOjInk2SRD6ssxh3ob16TadxjRfcYiF9F5XjN+vXks4Kv/7e+YxCW37pwz3uX6m47eGcH3nJFK9/xtbh7ICj9lWZ0wiuNyUxVR2PMXB/B2wxcDrdMyzYlJREgaQyhvlBRtNXzEQ+gadQUtHNuhyLjp1RAVFo5/ZYxovko+OMbEWFC6v2hIfB0C16bjoiFO0qo5F3sVXOoh4hpX9WKFjvun3EH352xDOP+fzqG+fOyiby/XcnvPeuhDc9O+J/f8n+J2RCCF779Cb//RNDvrJcblk0gdMd/sYtfUaF00v+6E1bvJbXxGKR+QDyAaY5sxaHZCoXNF6lpxiLnT5WE83f3hXzimsbPKkunqQQ/NhNUwRK8J4vJJTa8mMvnTrwz/4TD+bcs1jy1pdN0dhmzfzGZ0TMDzR/cUcMwMuuafCTL5taR5/eHvvpTmc65x8XbuWTS7fxnJln8YYrXkuoQt5yfZt/+ErKOz834q0v313j+Uiv4pb7Mz56f8ojPc3RtuSXXz/HZdN1sWc1X+h+kTv0HXQaX8eXFkpe87QWFMONUQg1xkZIn34opzKuYTA+DueyOnTnzB4mXYGSxHnFXBTsOebDuUWrybFrq3fM1k2Lim5cTFgeW8JaJ68ohu7+oEInl6j9AfYCIQRNr0FewnwyT6fqkFUZy3FOp9FGADJdAS/g1OvEWMtf35WQV86t+SP3pfzES6d48i7mW6eDUlvuXym5d7Hky4sl9y6VHO/ryff/6HMjfvJrp3c3ITtAGGv5L//Y577lij/7fMyrnrbuficU2ByRD7GNM8z2PU1IKYgCn6SoULnHXKtJrnOOD4/T8ls1xfsCRJm6CJmwTZ6Vp7V6f/grC/z0+5e5dEryimvnsYMHAAAgAElEQVSbvOLapmtWrYOxhkE1ol8OaKombb81Kc72AmstuckZlCOnuZQBgfLxhOd06VJRmYrVvEtuCppy+/XAkx4enst718vrXwSEi5CTUtJUTZqqsXnqu4N/iss9T+gWfQQQqbUmS6ACAlxu+lK27Hw7VMSwjDEYQrl/7471UXalKTd5atxWN9HHTczrjkquO7q2pmhjuWex5DMP53zmkZy33zbi7beNONISfPvz4DkXQdV0tovTy4FH1v37UeCF2z3GWlsJIfrAIWCZPUAI8cPADwNcddVVZ3q85wwPdiuumvU4nj7K57t38U2Xv2Zygr/h2RGffCjno/enGzqRO8Fay4fvy3jX7SOunHEF7gufFNbULYe4cB3O+UHFL75udjLZM9bwHc9r88F7Ut79+Zh/+dKNG91xcL0ankS3ju6qkxRF7Ci7e9Vjju3uwVFAywSZDyZ8fSvkgWozzxb+56eG3L9S8X9+3TS33J/x3z424N7Fgh9/6TThdk0Gr1HTp3uTDaIoE0c58dZp8EzlXOgaM+5Gf4adaE9KvEBOXH7boYfEo9AZcRHTDnfP7NoOvSRmdfVOPJuAHxGoBsEpx+vVS4+xhoEp6FtDiUHJ4Kx0aN/5uRF//LkR1x72+NJCyfu/lG5wVT0I3HEi57c+NuCGKwN+6EWnz3r4+qc2efunh9x8T8KPv3Tzps5ay6/f0qc0cMOVATffk/Dm57c2XOtrELXRVx0lkQ0wjWlk1scKuyeTpb3iT2+PqQy85YaN544Qgh96UYdACf7s8zF5Zfk/Xj59YNoZbSzvuG3IlTNqmxiuNXzvjW08Ccc6im+4bofG0WlgJV/l5hMf5sPzHyWuYo41jvLnD7+Hj8x/lLc8+du56ciLeP0zI/72iwnf+twWV67bCGrjNKzLseHjX8245YFs4sT7rEt93visFi9/SoNOQ/Lg6GFuWfw4H1v8FL3Sacdnryi4e+GNIKcRZYw15cZmnrWIvEdOwBdODAgUfOS+lLdc3643QE4LL5NltBfs2ggcf25jau9+4YyMtn+NotIsDnNCf5uJiLVuncxWEaZyLptVhihiLLXhiVAYL8QpinRt6FO5P43GSg/TPgbSJ/QkSa6ZbkbkOkdYhdEejVA5d/gq3/JauetkwfxQ829eMY218LufGvDjf7XCtzynxVuub9E4A6qlNpYHVio+fzzn8ycKvniyIK9r0dmm5OlHfV71tCbXHfHpNCRvu3XAL324xyuf2uBHb9pb0+VM8Z4vJNz2cM5zLwu440SxyczR+g1ktoqx2rmvS88Vrbut8fvJvtwDosBjmFUoKZmJnIN9rnNKXbKcLNMJO2fMGDqnSHsT8544r1z82h5hraVX9nnnP3WZiyTHOh7vuj3mT26PeeoRj1dc2+TlT2kwF6nJpA9qCVCWoIRHx2/TkOG2kXXWWjKT0y/6lKbEEx4CQVIlDCfRThaJS1WQiD1LeLabeI+pyHEZMyxHrsj0mjS9iED6G0yGxoZFWNAYekWPTOebprbr4UsfH5/KVPSLgTN/OwBGwKnr4DA3fGm+5Nufu/29WUnBsy4JeNYlAd97Y4elkeazj+R8+sGE01iOz0uc7eJ0q9Xl1FbVXh6zLay1vwv8LsANN9xwwbTBHlyteP7lAX/+0Hu5beVznEhP8iPXfh9CCJ53WcBVsx5/fVeysRO5DXqp4Tdv7fOJB3Oeetjj4a6b3Ilb4bqjPi++OuT6K0J+6+MDvrriHB6fe5lbnN/98F/zwZN/z3dd/W286mkv4OZ73UZ3ExVRhVhbIYcnsdHhjU6z1oIpEFUGZYIos9qU4zQuXCHr7rdbEO06msnp4qFuxXu/EPPxr2Y895DlrVeZPd24vzRf8KEvp3zH81pcuoW76np84sGMv/mim5i96mlNvv6pDd51e8w7PzfigZWKn3v1zETPeyqsHyHyHiLHbQq3cimVHgQKmfex5chNUQ/AmCP0FNZaRnlFmpV0opBe3qPhN06rSKx0xYnVhwlsQdjYnSIohZzQd6UY7Pk1rbV8+MspV856PP3o9vQsay1/9LkR77o95tVPa/JTL5vi5z7Q5e23DXnhk8ID03Uf71f8Px/qcdm04t++cuaMCq9OKHnpkxv8w30ZP/jCzqYN7gfvTfnsIwU/+pION1wZ8kN/vsxf3Rnzgy/aaVJbR0lYjUydbf1BUuQXhhUfuCfhNU9vbulELITge2/sEHqCP/zsiMrAv37F9IGYeHzw3pTjfc3Pf8Pun7sUgu8+YLnEA8MH+V/HP8gnl2/DWMMLD1/PN13+Wp42dS139+/l9x/4E37j3t/hAyc+zLc+9c188J5p3vqeFTyFM1iqLPqUC/4Zx3x+5MUdXnpNg6mGZjFb4h+W7+SWhY/zcPIoSii+Zu65vOzoS/jEQ5/jk8mtJPkzGWaHmFICUWbYcN2mxxSgS7644JFrw3e/qOKdnw74qztjfmQ84RcSK6XLYw6nnPGP9LctEvZC7T0daOM0676SqFO1W9a6zNd0FWFKrNeY3CsA7LpDsRjnSGvLOq8Td87L2g1aF8h4EdO6BCEVnifoxgXHppsMitK9bVO5jOtt1tq/uzcl8gUveXKDhie48aqQt9825N13xtzyQMq/vGmKWW3ppYZCW/Jq7auoLKVxE9HKWEoNlbEkheWL8wV3niwY5e7EuGrW4zVPj3jOpQHXHfU53NpctP/nN83xrttH/Ok/xXzhZMG/ecUMz7707FFX714oeMenh9x0dci/+rpp3vLHS3zoy+kpZo4S6zUnTKDxpFIIhRnLeOpcVVFnrApjAIsJp7eWI5wmokDRS3KUhE7DnxQXhSlYGC3Q9Jt0AqeJPq+hy5od0XE5vtrsOULGWkuv6HHn/IC7Tmp+4IUdvu25LZZjzUfvz/iH+1J+95NDfu9TQ77lOS1+YF2TNZABAYHTkRcD+nVx2fSazv1f+kgkmc7olX1KUxEIn6bafp8yLihPLfK0sSynlift42MZ68nHTQZrLZnOGdXNfiXlWlGKmDRArHUMrmiH41yPcSzf2cI/PZpjLLzgqr03S460Fa97RsQrrgHPP8/P3z3ibBenjwJXrvv3FcCJbR7zqBDCA6aB1bN8XI8pBplhNTFcNav4QP9upv0p/n7+FpqqyXc/+TtdrMyzIn7zYwP+5osJX//UJp1taGWffijj128ZMMwNP/iiDv/bcyIEbjL7yQdzPvVgxjtuG/GO20ZIAf/2lTPceFWDO7t38T++8gcsZEtc3ryUt9//x3z90SX0vS/n3Xes27Csh/AgkIh02bk++lHtdJasFZHK31cm4e44vc2rtZbPHy/4qy/EfPaRgkDBcy8L+fgjOXf/xTI/+bVT3HjV1hdxL9W8/dMjPvRlR4P71EM5v/Ca2Q20ivVYHGn+v4/2ufawx/e/0C3mUgjecn2bpx7x+dW/7/ET71nh//r6GW64cqsFR0zcM3eGqAuMym0ggw62MXXGhkpCCBqeYrU0zAhFKUp6eY/Dzb3pz8aw1rKQrFBkPTrB2XO0rIzl1z7a5yP3OaOAm64O+Z4bOxsmUePj+YPPjPizz7uMyZ96maOT/sTXTvGjf7HMb318wL/7hpkznp6NcsMv3NxFAr/w2tkDmVi89hkRf/8VZ8aznj2xMPz/2Tvv8DjKq4v/Zna2F0m76s1ykdx77wZXMDZgCJCEEkhCSUIgDfKFEAKBBAIpQEJNCKF3HJrBxtjGNu5y70W9S6vV9jLl+2NkuahYbpSE42etfaSdslPeee+9554j89SaAEOzTVww0IYoCEztbeH9XRG+McyhK8F2haNZCmcQL24KIQhwxfCuK+7fHKFXLp9ZH0RWNG6fntwtoajOEE2ovNBK1R7b48i9tb15F581rqOvq5ARKUNwmToP3DVNozxcyYamYspDlXrfo2hEEiU9Wy4akQQDESVKIBEkKAeP+xnCarAwJ3sG52XPIN1yxN+xf1Jf/jDsLj6tX81LJW9w/57fM2jIGMTgaCSDhkFUEEUF0SAjCiqiIY7T7iekNbEh2sgH2xvbqqMAhc5efLf3VUxIG4vTqB9rtzeD3fH9qFlvsr12EBN6WPXJ61EJRCEaAFFiU0UUa86rvN2ylZ5FU1i0dxZXjDjqujGYdSXLqA9B8yIIEqrJro87BtMxjA2TQSQck1FtZy4AUlWNxkAUNDBKx13LchQx0qhPziUzmuFE13Grx3dnOFxtDTeg2dMxGQyEYgkicZmWSAKzZECIefVwqoMKSTiusqokxrl9LG1UcqdF5JYpSUwvtPLoKj+//cjX+un67h4CANIdIhMKLAzLMTE029QJK+JYSKLAVaP0hNWDy1q47V0vlw61c9Uox2ndYx0hEFO5f6mPVIeBW6cmYTOJTO5lYcXBKDdMOI5a35ZwPvIrDb16jRxtVQFuFZoSDWit+yrG/BAPoFo9+vXX1TitynoCRpRA6DihIggCNpOENxhHEgWsrQGdyaDrLsQVXRzQLJlJMiW1aTEcvc9wtCr0F4Son8OJ/4Sidv3Zo6ALC3mJKjEW7ZKxSHoLCeiq5pcMsXPJEDvlzTIvFwd5Y1uIsT3M7RIcBsGAwWBoW2dUjhJMhBBaW3cUTcUkGrsV7HXmSf78xiBvbIUn8uR2VOPuQhAETIKJw49jVVP17X3JhcvWl8dwmgX6dTLf/F/B2Q5ONwCFgiD0BKqAK4BvHfeZd4BrgDXApcAn2tEjwn8hSr0yABZHFaFAmFv73cQe/37er/oIm8HKN3pcxLmFFv6zM8wTnwV4ek2AQVkmxheYGdfDQobTQDiu8vRa3ROxl0fivrkp9DxKJKOn20hPt5FvjXDQEFRYVx4j3SHSLyvB3/Y+x6f1n5FlzeSuwbfTP6kvzxx8gcU1H5FfVM+iPZdw+XA7ydaOHoi6kJEYD0E8iCZKbZ58XWFtWZTXt4T45fTkM1KtiiZUwgldqlttvVrUVu+z7TVx3toeptQrk2IVuXqUg/MH6BYOy4treeWgyF0f6vSnG8a7cFoOK9tqLNoT4d/rA0QSGpcOtTOll4X7PvZx+3te/m96EmN7HBvQyqrGA0t9KCr83/RkTMdNAsbkm3l4gYf7lvj4zaJmbpniYna/06STChIYDTqtLa5POjWjQ6dvHjeB7C5EUUBRNWRFxSJZCMfDRIwRrCdRnW2Jt+ALN2PXVOhmFvJkEUmo3LfEx6bKON8eYUcUBd7YGmLN643M6mvlypEOPHa9Gvyv9bpi7px+Vm6efKTPMcslceUoJ/9cF2BVSey0erQUVe8zrg0o/H6uu8Oq4algUKaR3CQDH+45Qu1XNY2/rNCtOn4yNant+1w+3MHyA1EWbg+dNRG1rlDhk9u8Rbtzb39jmAPJIPDUmgD3LfHxq5nt75uj0RBUWHkoigAYDbpCrkkSMIoCW6vjNEdUfj1TTzLE1TgvlbzBB9WLkQSJpbUrEBDo6ypkpHsYozzDyLZmoaKyu2UfG5uK2dC0mYZYIwICGZZ0VFQSagJZlYmrCWRNRtEUzKIZp9GBU3LgMDpIM3twGB3k2rKZkj4Bm9TxfS0KItMyJjMudTQLK97n3cpFJAzr9T+qrS/5yOcNYQOpZg/pllRGuIeSZkklzZJKH0cvsm2Z7dZvEk1cX/gd/rj7Id6pWciEgquOpfaqSquVl5VVLa8hubYyIKkvu1o+xZC7l+e3XcePxvQ9skLB0MbK0C2zggixFgRBRLFntLVY6Kq9EJOVdvt0KlBVjcZgjKjcQSUoEdJtqAzmbibyugnJgpgIo4ab0GypmI0GmoLxVs/lzkWQQBcki8kaM/u2H+sGZZn42wIPS/dHKK/yk5mhswaOfplar2VJFDAaBCQDGEUBkyTgMp/6JLp/hom/X+LhqTUBXt8aYn15jB9OcjH4DFVRNU3jL8tb8IZVHprvbuvHnlFkZcm+CJ+VRDm38ETjv3BCaq9mtOqV1HA9SBY0q/tYzQVVQVBiEPMjyFGEw27egoAqWfX+acl8TPVfEATMRpH6QIxM17HH9/ggtaOgCfT7Od2W/sVZ08T8bf6aobjSLfaJoik0xbzElTjhqJEVB33MHWDrsJc+P0XilikudtbG+ftqP39b4OmUkSIKYltPJugBoPk0A/dATOWdnWEUTdeJ+MW5p+8gAHyxCYVuQtU0NlbEGZln/p+xjOkMZzU4be0h/RHwEXrjxzOapu0UBOEeYKOmae8A/wSeFwThAHrF9IrDywuCUAq4AJMgCBcBs45T+v1KoqxZV+ptYQ8CAoOSBzAudTRRJcrr5QuxGixckDuHxy/1sLc+wdqyGGtKozzxWYAnPgvQyyMRjmvUBRQuG2bn2yMd+BKNPHPgQz6t/wyX0UWuPZs8W47+sucyq18Gaxs28JONLxNWIizIm8eC/HmYWqtu3+19FemWVF4oeQ1Djo9Xtt7IjeMyOv0OWhd+c8cjHFd5dKUfb1jltx8189B8N9bT6MUpaUrw83e8hBOd5zAK3BI/nepiah/rMRPfni6BRxZ4eGVzkNc2hyiujPPDSS48dpG/r/JzoFFmaLaJH0x0kZ+i3x5/vtDNbz9s5p7FPn4w0cXco1RUX9gYZFddgtvPTSI7SaIsVMH6xk3k2XIYkNwPl9FJtkviTxd6uGtRM/9YF2BiT8sZkP8WjvThaipC3K9TgwURVbKBydbqUXnY+qA1O43YJU1KVlQkg4hJMuGNeMlyZHVrb0LxEM2RZoQEujrwGRDYOR6+iMJvPmzmYKPMLVNczGkN8s/vb+OVzUHe3xVm2f4IFw22E1c03t4e5vz+1g4FeC4ebGPFwQiPr/YzLMfUKTMhFFdZui+CxSiQ6TSQ4ZRItYttD44n1wTYXBXn1iknP/nzxX3satnH7pa97GrZiz/h57reVzI+bQyCIDC7n41/rgtQ5k3Qw23k3Z1htlbr28pwHjmHPVp9ON/ZEeaSIfbPXVr+hY1BzJLAN4bplTxN01jdsI6aSC0Dk/pR6Ordrq/m4sF2jAaBv6/yc89Hzdw5K6VdX3YorvL6lhBvbw8R7yL+mVBgZkCmidJgGY/ufYqKcBWzs6bz7Z6XUR2uYaN3MxubNvNi6Wu8WPoaGZZ0gnKIkBzCKEgMThnIxXkXMMozjGRTxxMhVVNPe3JjMVi4ouASZmWdS1W4BmMrPeywou3him2yKemktzUqdRCWyDgOWT7hQGAihZasNmqvkAgD8G75CsL2pfQ0TuCuwd9nS/N2HtzxNCsiD5JXfhnz82Z2EBCJYLCAATRVbuddKUkCwdjpB6eHA9NIQmkXmAqxAEK4UZ+Qn4UJpma0ISaCqFEJyZpCXJYxS0KnIkiHsWRfhLxkQ6cVDqNBYE4/G2VSgB59zjxboStYjXoFd1wPvZ3ntne9nNvHwnfHObtVhe0K7+wMs6YsxvXjnG12RgCDsoxkOg0s2RfpRnDaTQitqv5qAsFfrTOFJJ0ZICbCgKALYB3VCqNXZRMIkYieXEFANbvQzEkgGjCIIiYJ6gOxDvvHDgepneFw8Jphzzir9M4OoSRamQMWNDTCMQVzF3MpRVOIylH8ckD30DRYeGVnAEWF+YNsSKEGZFtqu0qzxShywwQX9y7x8d6uMBd2oIDeEc5EAPjezjCRhMZgDyw/GOXy4XLbXOy/HfsaErREVcacBKX3vxVn/YxrmvYB8MFxv/vNUe+jwDc6WbbgrO7cF4RSr4zDJHAwtJsCRz4uo17tuKHwWqJKlOdKXsFisDAjaxr9M0z0zzBx7RgnlT65LVAVgD/Od+Ny1vPkgZdYXb8OQRAYmzoKVVOoCFVT3LS1lV+PLrCDRpGzD9cXfod8e+4x+yQIAvNzz8djcvPInqdZGvwTs1p+Tq+k9NP+vs8WVxNOfp3UgkNUNY7gvqXTuXtW3illhjRN47HVfiQRfjjRhdgacx1elSjoYieDs0ydZp5NBoGrRzmZWGDhLytauHeJTr1y20RuPzeJqb31gb82UkdDtImejh48MM/NHz5u4W+r/NQHFa4Z7WBLVZzXtoSY1ddCRlo5f9jxPpubtx2zrR72PAYm9WdQcn+uGduLny2M88bWEN8ZcwYrXIJ4VKVDQ1BiCKHQ4SPWSksTjnRyixKa0XZEEbn1AWsQBWKKhgW9ryKhJAjEAyfcfFyJ0xBpwGK04AvVH2MFcaZQ45f59QfNNIUU7pyVzLijKtjJVpEbJ7i4cJCN5zfqNF7QbZlumtixdYlBFLhlShK3vN3EM+sCx1gogX6drSqJ8cRnelLlmGUFvccj2Sqypz7BJUNs3aqGh+UIW5q3sc23k90t+6iJ1AJgFs30cxViEo38Zc9jHAyW8M2CS5lRZOXfGwJ8tDfC3AEC/1oXYHSemVmtlZpAIshe/35GuIfyzREOVpU08c7OMN8acepiVieLA40JPj0U5ZvD7SRbRXzxFp7c/y82ebcA8Dp6Za+fq5BByQMYnDyAno4eiILIBQNsGEV4+FM/d33YzG9nJ2MxisiqxqLdYV7YFMQf1Tinj4UrRzlwmUUSikZc0fv09Pcaecki/6n4gFfK3sRldPKrgT9lmHsIAL2cBfRyFnBZj4tpjDaxybuFzc3bcEgORnuGMzRlULd8885k1t1tTsFtTjlj6zuMkdaLWZXYzWP7/skDw+7EGPejmRwIsRa2Bg/yYtlzyMFCrht1FYIgMNw9hJ8X/pZ7tzzFi2Uvscu/g5uKvkuyqRNl1Q68K42iSCQuI6lat1V7j4eqajSGOglMoy0IkabW6uXZS7poRhtCtBlEEZs5qUsRJNB7zHfWJs66d+/pYmwPC0NzzLy6OcgbW0OsLYtx9WgHFwywtXv+xmWNQ94EBxpkBEGvoOUlSyRbjxz3fQ0J/rE2wLgeZi4abEPTNHa17GFR9ceMdA9jRtFwXtwUpC6gHJNAO22IRjC1Xn8xP5rB1EV7gqCr/h/20kVDjAUgHkKxe0CyIYkiqqYgK1pbQra7MBlMxJQYDeEG0m3pbQrUnwvkWFuCJiarrf2ax55HTdOIq3FCcohQIqJ3DQkSFtFMVNb4YHeY8QVmBtR8QNa6vxJN6YN3wCX4C87Re7FbMaHAzIhcE89tCDK5l+W0kxrdQTShsnB7iDH5Zi7Lj3HnOoGXioP8cvqZqZ5+2bGhPIYowMjcr4PT/410xJcMpV6ZfLfC3sAB5ubMbvu9QTDw4743ElUe5ukD/8ZqsDAxfVzb33OTJS5NlrhkiI19gQO8XfE8xfu3YhbNnJczk7k5s0g1e9o+n1AT1ERqqQhVURGuIsOSztSMiV1OtCamjyMWc/D4wb9x17Z7+c3QWyh09T6l76lqKq8d+phl0TcxuRT6JfVjm/Ype9XV3LZmPP838iJSLZ4Tr+gorDgYZUdtgusnSozpHdIN3DW51chdRtZkQnKIRdVeGmNemo56+RIt2AU72aFM0i1pZFrSuXRiKnuqXIQTMn2yG9kbq2TJ1grKQ1XEVN1rSkCgj7Mng/sPwmTvyWtb0qn1K2yriZKRsZdG10ru2nYAp+Tk8h4LmJE5jbpoPTt8u9jZsocltcv4oHoxAgI9+o5g4a65zB9kO0uDvQAG0zEPmXbQdKqeGGtBa+0T1iQ7BhQicYEkq/5QN0tmWmItJJREpxQmRVWoC9UhiRJqolVp2XbyFipd4UBjgjsXNaOoGn+4wN2pf2yWS+K2c5O5ZEiCEq/M9MLOZekB+qQaWTDYzhvbQpzTx8qQbH29dQGFx1b7WV8eo7dH4tczk0myiNQFFGoDCnWtr9qAwgUDbFzbRaKhJe5no3cz6xs3sd23C1mTsRms9EsqYnrmVAYk9aXAno8kSsiqzLOHXuKdykUcCpRya7+bGF9gYem+CLvrEhglgVumuIipcT6oWsx/Kj8gokQocvbhB0XfY2y+mYXbQ1w0yIbtc1DqVDWN5zYEcZgFFgyxs6GpmCf3/4uIHOHqXt9kWvok9vj3sd23ix2+XbxU+joAdsnO8JQhjPGMYGrhYCQxiT+vaOHORc1cMFBPMFS1KAzNNvHdsU76pEr4Ei1ENQVN1BBEDZMRjJoGSoQHdr/Ebv9exnpGcX3hd9p6MY9HqsXD7OzpzM6eftaPzReBwZkulqy/mErjs7xV8QGXZ89GSAQpD5bxp92PYdYy0BqvpDD1SDA+IstD3+LrKWn6lB3C+/yi+E7m557HoOQB9LDntXtWHO9deZjaG5UVDK3iIrq4j0pC0VBUDZtZwmY0tO8hRZ9IN4XiROLHBaaahhBtRoi1tGoYnO0AUACTDSHcBIiI0eZORZAAlrR6DJ6xCuFJQFZl6qMNVEdqqY3UUR2ppSHWiMfsJt+WRw97Lvn2vLb7wCIJXDPayfRCK49/5ueJzwIs3hvhypEOfBHdJ3VfQ4JSr9xOmAvAZRbIaw1Ut1TFSbGK/GSKi62+HbxV/i57/PswChLrmzYxJ+MCNCaydH/kLCTJhNaK4ckvp1OEFcRAHZrJgWZNwWSQAI3GQJxUp+mkAlSzwUxMjtEUbSLVmvr5UUbjobZkciyhHnNXqJpKSA7jTwRQNQUDBiyGY21flu6LEIhpXFtQTcb6vxNJ7Y8gR8he/QDpxU/j7XshvqL5KJYkBEHgpgkubnqjkWfWBfj5OWc/QFy0J4I/pnH5MDv2YJz5A228tiXEFcMTx3g7/7diQ3mMfulGXCfSjvgfgPDf1N45atQobePGjV/oPixfvpxp06a1+30oHGDr/tUkO1K59Nl6hvY5yA6e4teDfsGQlIHHfDamxPj9jj+zL3CAAns+MTVGTIkTV+PE1LhuXI6GU3JwXvZMZmdP73RCdqq48+Pd7BGfRDD6GOUZzoK8efRx9ur28oeCpTy1/1kOBUvRwn343ejr6OfOpiZSyx82LqRG24AoaEzNmMhFuXM77KU6HuG4yvffKMOYuoSodW1bVbgzGAUJj9mDx+zGY3aTbEqiuqmOkDFIfX2kCu0AACAASURBVLQBb7y5nc+ZQ7LTw55Hvl1/yHvMHvb697O1eQcHAod0b04sRPy9MZgbEc11pJk9zMs9j3MyJncoRx9X4+z3H2STdwuLqj9GTlgZJF3BXZMndft4nlVoCoIco7YpQNjoJivZ2pZVjykx8nLySDIntZleC4LQ9jBuDDcSVaJ6n6rfR3NjNRbbqVWFyw7U0aPPsVTy/Q0Jbn/Pi9MscO/57naiR6eLqKxx0+uNGER4dIGHD3ZHeH5jEICrRzm4cFD7CsOJEJbDLK9bxbrGjezx70dDI92SxhjPCMZ4RlLk6tPlZGZZ7Ur+ceDfuEwu5npu4O9L9UrWz6Y5wLmR18sX0hz3Mco9nCEpA3m17G0SaoIZqRfz6oqhXDfG1UaxPVM4+tyUeBMs26/7b9YHVa4cLeG3/4dldSspsOdzc98byLPntFuHL+5jh283W5t3UOzdSkAOttFqk5RBvLexAFW2k53qZXK/JgRzFSWhMkqD5YSVSKf7ZjVYuLb3lUxNn/ilrmKdLRw+NzV+meteaWTYsLcpjW/kD4NuJ8mczB3b7kfWFPyHfsDwzAxuP66Ha2t1jF++18y3xwXZqbzIwWAJoCcRBiT1bWV+9CPXloMoiAiJMKo1RadJoouyVFfVtK1PEHRmgtgqdhJXFDRNw2KUcFokTJI+hmRkZtAYjBOOJ7CZjra9UREiXsR44Ix58HYbmgqJsB4AdKLaqqga17zcQC+3kXvOO3EFvKNxrTsIJkJUR2qoDFdTFa6mKlJDdbiW+mjDMc8+p+Qg1eyhMdZEQA62/T7FlEy+PZd8Wy559lzy7bnkWLLYUK7x5Bo/jSF9HQ6zQGGqkaI0I4VpRvLdqi5u5zdQ6VMo98lU+GQqmmUissJ3playIbCIg8ESPGY3F+aez9T0iTx76CWW1a3EER+NWn8J/7ri9PyyzxrkCIIGqs3DwR0lmDM8aBqkOswdJlC6QiQRwWq04rF4Tum7Zmdnd//DmgbeQ/p1KYhUt0QQBN0WTtM0vLFmQkoIs2g+xjblMFRN4/rXGkk1Rnid/0NQE5Rc8DSK2YW9ZiPuXW/iqF6PajDR0msmTYO+ScKZzbPrA7y6JcSD89xnVf05rmhc93IDOUkSD8xzU3agDnduGte83MDIXBN3zDzzbJMvE7xhhW+/0MA1ox0nFBbsDNFYGMloYXCf0Wd479qjs1inKwiCsEnTtFHd+ezXldPPGQ0hXchHsRzAGDfSL6mw3WfMBjO3D7yVZw+9SEvcj0n0YDaYMItmzAYTJtFEqtnDpPTxbTYcZxrXDu/DD9/+MaMGbmB3y6f8qukehiYP4uL8eQxI6tvpcmE5wqtlb/Fh9cdYRSeRqiu4dsAk+rn1my3LmslfJt7AHYtnsje2lFXCWlbUrWKEeyjTM6cy3D2kw4FVVmXu37iIePb7KIY40zOnUujqdUy/liRKGAUJq2Ql1eTGaWxP6SxTjkwU4mqcxmgTtVFd/KCHPY8UU3v11qEpg7isx8UEE0F2+HazxbedjYadWA02Lut5PeNTx3TZe2ISTQxM7s/A5P5MzZjE3cVPspN/8MD27fyg31VnPLFw0hAMOq1NbgCDjCyrGFrtIcwGM3ElTl2ortPFbUad0hoN+xC7qtieAl7dEsQowp8v9LS3NzoDsEi6eu8dHzTznZca8Mc0xuSb+cFE10nT0nxxH+9XLWZxzTIiSoR8Wy6X5M9njGckPex53Z68nJM5mR72PB7a/Sgv1zxIRvbFZNgcvOdfRHVdLX1dffhJvx+2jR1jPCN56sCzfFD/KumFG3hz9zeYN6jfsYqZpwlvVGPdliDLDkQp9cqIAozIlZg5tJo1oRdpqGvkorwLuCz/ok7vhWRTMpPSxzMpfTyKprDXf4ANTcWsb9xEcWwr9kIBAxIBEnzQpPu/FdjzmJQ+nlxbtu6pd/ifAKAbrw9I7ncMY+R/FZlOAylWkaTQfBzW3Tx26HlEDQJykO/n38YftjkY0QFdbEiWiX7pRj7emcQ/L/8NvkQzO1v2sNO3m50te9jQVAxAmjmVXw/+OVnWdMSID8VoB1HCaBCxdmEnY2mlPSYUlYZAFEk04LRKeEMdBKaqghBpRExEzoqq9AkhiCes1G6pitMUUrlhvF41bYw2Udy8lWRjEmmWVFLNHhySvcP7XVZlWhJ+WuJ+/Ak/fjlIIBEgkAjiTwQIykFa4n6qI7W0JPxtyxkFiWxbFgWOfCakjSHLmkmWNYMsa2bb8+Owf2V5qJKyUAXloUrKQxV86PuYhKYrbgkIZFrTGTwsF2Q3khQlQQB/ws/GRICl1S3EKuMAbTYhNosVe66N/gVWWuIBXqmpJsOSxg2F1zI1fWLb/X5j4XWkmj28Xr4QOdlHcfXNjMw5syyaMwLJiqapCOEGBFXGiExCkGgIxEhzdh6gaqpKLBYmHo3hStHHG6vRSjgRxoCBFOtZDp6UhJ48EURkVSUhq9jM+rFvSfgJyeFORdlAr8pVtcg8l/00xuY6ymb/FcWiJ5hC2aMJZY/G5CvFvfstkg4txlm5hpK5T3DFcDef7I+cUBzpdLF0X4SmsMpPpx25750WkYsH23ipOMTBxgS9U7961dO4rBGMqyRbxXYU7KOxoVxn6n3db6rj68rpGcaJKqf7m13c9WEzvYY+QpYthV8P/sXnv5PdxH1LmllbFuO+C+yUJFbyXtWHtCT89HcVMTPrXOJqjMaYl4ZoI40xL42xJppiXhRN4dyMc1hTPA2LwcbfLvG0U5QLxVV+9h8vjVEfM0cXs9G3ipaEnxRTMtMyJnFOxhQyrXq/a7F3K88ceJn6WC1JWl9+M/LqDisz3cGpZrHPJBpCMW5Y8gZG9ye4THa+3+caxqSOBPQJRiARoDZaT320gfpoIwWOfIalDD7r1KGGygqikh27y43TeuQhkJl14qo2ikxt+T4MVgfiKVY6jj833rDC1S82cOFgG9/v0sOze/DGmlnVsIYsayYj3cOOOZ6PrmxhXXmMG8a7mNTTfFJZ8NpIPe9WLmJ53UpkTWFc6mguzDufXo6C09pffyLAw3seZ7tP14DLsWbz7Z7fYKR7WLv90zSN5XWreObgi0RllTH2i/nZyNndvmYUVePFbRVU+gOEEwrhhEwkoVdKorJMSA4jmppxu3w4Hc3IYhPeuBcVlQxLGj8sur7DRFt30Gbl0lhMUA7Ry9GDno4Csm2ZHSaqvsaxOPq+uWdxM2VemRtmlPPQ7kcREPjFgB9TUlHIM+uDvPDttA6TPGvLotz9kY9fnJPUjqraEG1kh283L5S8il2yc+/QO3CJplZ6pBuA+trabu+vomrEZZW0rAxsxqMSGUocMVSnV4i+xF6T9y/1UVwZ44Ur06mNVvG77X88JpAEvY881ewh1ewmEA4RNUTaAoiOICLqStBGB07JSaY1nRxbNrm2LLKt2aRbTp06qmgKdZF6ysOVlIcqW9t8KmmINuIwOnAZnSQbk3CZnCQZXa0aGAIRJUJYjhBWwvpPWWcvTM+cwsT0cZ3emx9VreAfB/+NnWz+Mu7npHQiMvZlwMFdlThdCppkIWF0oGAi1WXGJB35bpqqEo2ECfgaUGQ9QEzNyMNo1YMoTdMIJ8IkW5JJMnfSr90JTqpyGgtCoAZMDsJxmYZgDJtJIpgI4Y03YxW7bmP55XteJjW/xy+0Z6kbeQPegZd3+llz8yEKFv2IaEovymb9hdXlCvcu8XHjBGe3xZFOBoqq8f3XGnGYBR6+SK9CHx7XgjGVa19uYHCWid/M/nyqp5GEyrs7w8zpZ+s2xbYuoPDkGj9NIZVQXCUU1wjGVORWksPgLCN/mOvuNLi/d0kze+oTPP+tNJ2dFg/qvdWdJPw1TUPRFGRNQVYTJDSZUDSAw5zE+P7nnNL3Phl8XTn9L0OpN4Eg+WmIVzM750tC6+wEN09OYl9DIw8tjfDoJXM4L3sGS2tX8E7lIh7Z+wSgZ2LdphRSLW56O3syNnUk41JHs25fOnX+IPdf4OxQ6txuErl7TjK3LlRZu/kcHrrwIg6Gt/NJ3acsrHiftyveY2BSfyTBwFbfDoxqGkrtd3jggkm47V/tyzbNbmZ+9nxe3z2ArP5v89DuR+nvKiKiRKmL1hNRou2WybCkMzvrXKZlTMZxlioKmmRCkkNE4q5jgtPuQI6GUBAwnkEK3pK9ERQNzjsN6x1N09gfOMgH1UtY17gRRdOVRXOsWczLPY/J6eMxikZ+NMnFj6Ddwz2hJigLVRBVYvqDoLWv+fD74uZtrGlYj0EwMC1jEvNy55Bl7UYw3w24jE5+NehnvF+1GKfkYErGhE4nhIIgcE7mZAanDOCnq59kQ+RVfrB+MWM8IxjtGUn/pKJ2FU1N0ygLlbO2cSOLytcTEer0gpGp9XUUDocrktGF05JGhqWP3rdtTWesZ9RJWQ51tO897Hn0sOed+MNfo0sMyDCxpjRGkX0YV/S4hHRLGqM8w3lzrZcCt9Qp+2BMvpmCFInXtoSY1sdyTIY/zZLKOZmTybFlcff2B3hg18PcNfg2TLEWNLPjpH2WDaKA1WQ4NjBNhDCE6vWJ2Bdl0dENBGIqn5VGOa+fjcpwGffteAhJlLhv6J0YBAMNsUYaYk00RpvakrUakG/PJcmYRJLJRXLrz8OBoENyYJOsZy35aBAMZNuyyLZlMS717NP9ZudM5bMDJnbxL+7Y/Dt+Nfhn5NpOIgj7PCEIYLQhqAlMkUZkwUCjnIQn2YVJEomEAgRbvKhyAslowmh1EIvHiAUaMJotIBp0/1SjDV/Uh0k0YT0JJ4OTQiyoC0NxxEImqkTxxpuxiF0nVA82JtBqdvNTywsEcsfjHXBZ15tK6UX1hNvI/fQeMjf8jQljbz1lcSRZ1aj1K136la48FKXGr7RZggGIil5JdJhFFgyx89zGIPsbEhSmnf3x4dXNIV7dEmJPfYI7Z57YC13VNB5a7uNAg9yqWm3EYRawm0TsJoFgTOONbSHe3Bbisg5abhKKRnFlnKm9LW2BaeTdq9klQY0rizp7Cg1GC14S+OJ+AnIQWZXbtaUBXJg793MJTs82vtqz/K8gyrwySe6DKMCQ5IHYq9bhKltJ7dgfdy1i8wXAZRH59cwUfvZOEw8s9fG781I4L2cmM7POoTRUTpLRRYopud2kty6g8NqWBib3sjA0u3OKQoZT4jezkrn9PS+3vu3jBxMH8suBI/HGmllet5JP6lYSTASZknwp768Zxg3jU77ygelhXDrUzvu7s3E2/ZjLB37G2sYNuE0p9EsqItOSToYlnQxLGkaS2R/czuLaT3iu5BVeKXuLyenjmZ01nQJH/pndKUHEgEosFkVRLSdF30mEm9vUEc8EVE3jwz0RhmSZOn2oVYdriKsJnEYHDsl+TL9vQk2wpnEDi6qWcDBYgs1g5bzsGczIPIdDwVL+U/k+T+x/hlfL3mJuzmxmZE7DJllJqAkOBkrY0bKbXb497A0cIKEmOt1Pq8HCvNzzmJsz6+QrBJqGFKrH7CvB4itBTERoGHLVMZNzg2Bgfu553V5lqtnDrYU/5a5VK5DydvJJ3Uo+rFmKXbIz0j2MMZ4RJJuSWN9UzLrGjdRF6wEBOdyTIUmXcH5hNpJoQBQMSIJBN1wXDHirggwr6tctZduv8cWhX4Z+7eypS7CgYB6gK2DuqIkzf1DnSR5RELhsuJ0/ftLC0v1RZha1n2AXufpwc9/r+cvux3hk71P8tOh7CBEfmv00FN01TfdQPSxA9CX3IlxxMEpCgaK8Ku7e/ggOycadg29vY/n0cha0W+bLwNb5vPGNviO5fbGRUO9/89NNvyLDkkaBvQcFjnx6OvIpsPfosIXmC4NoBNGIpMoIMS/e2hZEUUOTZSSzCaPpSEAhGU2EYxHsUT+CTa/kCYKAWTLjjXrJkrLOfKJB0yARarOQicQVRINCQ6QJs8F0wu19tLWOx0yPIFs9VE+8vZ11TEcIFEyj0buf1B0vE3UXctOEOSctjqRpGvcv9bG6JMb8gTauG+tsZxmmahqvbg6RnyIxvkB/hqdvfJzCPe9Snv43Yim9mD/IxtvbQ7ywKcjdc85u9bQppLBwewi3TWRNaYxlB07s2/ufHWF21CT46VRXmy/50dA0jdqAzPMbg4zON9PzOHGnnbVxIgmtjdK7b8c/uSPNiSYIiFoId8hPqqKQpQn0M7ux2XtBUj4GkxNJkNpsyTRFpdcpCph+2fDfMdP/CqG0WcaecgCMTvItmWR9dDvGUD2aaKB23E++6N1rh8I0Iz+c6OKvn/p5YVOQa0Y7kUSpS3Gkp9f6EQSB7407sTBO/wwTD85z8/Cnfu5Z7GN8gZmbJrhYkD+fi/PmEZVVbny9iYIUkXkDT72C9mWDwyxy+TAH/1wX4LJhc7hkxPxj/t4cVni5OMQHuwMMyynirtljqYqU82HNUlbWr2Fp7QoKnb0Z4xnJCPdQcm3ZZ+ZBL0oIiSCy4uq+RL4cJxaLYjCcuYzx5qo4tQHdsudohOUIqxvW8kntp23CLYdhFI04JT1QbUn4aUn4ybZm8t3eVzE1Y2JbYJVty2Ri2li2+nbwTsUHvFDyKm+Vv0OBI58DgRLiarytB3lm5jn0TyrCKTkwiAYkQcIgGFr7nA0km5K6HbAZg7XYqzdiadqH2XcIs68UQ+JYmp9sdtE84NLTOHIwPNfMudnjWLxrKNMKRSYPrKC4uZhN3i18Wr8a0IPeQcn9KRBnsHRLL2YXpvHjca5Or6Gy+rqvA9OvAApTjUgi7KpLMK5AP1/baxLIKh32mx6NKb0svLE1xJ+Xt7D6UJTvjnO2EyAblzqaq3tdwb8PvcxzZR6+k38hmtye6dEGVabNa/n4a6utvzT8+QsfnSKW7I2Qk1nCsxX/wm1O4c7Bt33d79wBBmYYyTT1ILn5ViYO2UVpqJzSYDnrmo60XTklJ25zMjaDFZtkwyZZsRts2CQb6ZY0JqSN+fzHHFHCYJZAkQENg6n99g2CQFw0EQ/7MBttYNTvq8P2a+F4GIf5DOtIHNVvGpMVEmqCgNyMUZRO2PrgDcnMr3iYTLGZiqkPo5q73yLTMOw6LE37yVj/KNHZvbhkSB6vbgkxviDKxJ4nPjdvbguzuiTG4Cwj7+wMs606zm3Tk44JztaXxShtlvnFOUmIgoCzbAWeXa+jIZC98j5K5z6O3WTi0qF2/rU+yJ76OP3Sz14h58VNQRQN/jjPzZ+Xt/DYaj+Ds0ykOTo+zhU+mWfXBxibb2ZGB0k90JMXP5yUxI7aRh5a1sJfL/JgNBwZ7zaUx5BEGJZjojpQxu8DxfTByM/GPkiyKQlTxIutphh7TTH2mk0YIzvRBJFI2kAC+ZMI5E8m4chsE0T6b8DXwennCEXVKG+WSUrfz6jkAbhLPsYYqieUOZyUfe8SSe1HS5/uV0k+L8zuZ2NPfYJXNofom2Zsm/R0hM2VMVaXxLh6lIP0Tm7m49E33cQjCzy8vS3Ei5uC3PB6I9e0erG9uTVMfVDlgQuSzloj/heFeQNtLNwe4tn1AR6a70YQBEJxlbe2hXhrW5i4ojE6z8y68hh//MTHL6fnc2PhdVxZcDnL61ayon41L5a+xoulr5FmTmWEeygj3UMZkNwP00lS7dpgMCHGA8TjcczdpSclIkQTYLCcufOzaHcYl1lgQk9LGzV3ae0KPmtYR0yNk2/L5Tu9vo3HnEIgESQoBwnKodb3IXJs2ZyTOZkhyQPbsspiLEDKvncIZY8m6iliWMpghqUM5mCghHcqF1EXrWdG5lQGJvenv6sIx2kKVQmJCPa6LdirN2Kv3oDZXwmAYnISTelJS6+ZxJJ7EkvpRSy5gJwV95C27Tn8vWa2CVWc0nYFgVunuMh0GnhuY5DGQAF3zhrOjYUqe/z7aIn7GZoymI1lIg981sKEAjM/mtR5YPpVhCDHsDTtJZI+uFtVgv8WmCWB3h4ju+vjbb8rroxhMsCgzK7HBIMo8JcLPSzcEeLVzSFufL2Ruf1tfGuk4xivy7k5s2mINvJB9WLSzCnMlWaCJuqxpaqCGkeQowiJMKgKh122EQyookFXwhUNiCEZVPWLET46BZR5ExyMbMOR9yI5lkzuHPxzkk3JiIkwppYyop5+/1PXWlcQBIEZRVae26jwiynnsSBfn2qG5QjloQpKgmWUhSvwxwOElTBNMS/loTBhJUJYDqOh8e9DLzMtYxKzs6Z3S83/TMJg6HpqbBBEooqGOdIMUkbbeTdJJlriLViN1jPrf6rEOJy8icRlfPFmJANIJ9hPKVCLsvR5Zoqb2DfoJpS0/ie3XdFA1ZRf0/P9m8hdfhdXznmCrdVGHvjEx73nudvs1zrClqoY/1ofYHIvC/83PYlNlXH+vLyFW95u4roxTi5sZXK8sjlEptPA1N4WTP4Ksj57kHDqAHannsfIPX8ivfgp6kb/iHkDbby5LcQLG4Pce7775L5HN1Hhk/lob4QLBtjISZL42bQkfvBmEw9/2sLvzktp94xUVI0/LWvBLAn8eErXz9Bkq8jNk138brGPVzYHuWrUkeLNhooYQ7JNqEKEh7Y/hElT+VXPb2Nv9cOWban4e8/C33sWaBrm5oM4y1fhLF9FxsbHydj4uO5XmzOWQM8ZwNmn759tfB2cfo6oDYBqrCOOnyFJ/fGseZqIpy/lM/5I/se3k7n2r8RSehP1FH3Ru9oON01wcbAxwUPLW3jkYonspGMvHX9UZem+CG9u0weaS4ac3IRDEgW+MczB5F4W/r5a92Jbui9CWbPM1N4WhnRBD/6qwiwJfHukg0dW+llVEsMbVnipOIg/qjG5l4WrRznITZZ4e1uIp9YGeHSln1umuHAY7VyQO4cLcufQFPOy2buNTd4tLKtbyUc1SzGLJgoc+fSw57f18uXb87qt7CwZDMQiIZz2bgSnmoYc9iGLRqxnaGLmDSusLY1x4WAbB4L7+OeB56gIV2ExWJiUPp7pqeMYXbUV99pniLty8Q68nFDu1M4nhkqClH3vkLr1eaS4H2Xna5TOeZh4cgEAvZ09+Un/H5yRfQcw+UrI2PAY9rqtCKqMajATzhxKc9F8QtmjiSfld7ivdaNvote73yd167PUjb3ltPZBEAS+OcJBlsvAn1e08JOFTdw9J4VByQMA2FQZ40/LmxmcZeT2c5PPeOInaf8ijOF6mgZ98/NvV1BlcpffhaN6Pc19zqd23K1t3oCfF0y+UlK3PY+3/yVE0wZ8rtvun2Fk0e4wsqohiQKbKmMMyjK1o9N1BJMkcNkwB7P62nhxU5D3d4dZuj/CFcPtXDjIjql1HVf3+iaNMS/PlbxGqtFFgWEgyDFEOYYm6IEoohFaxxyt9X9BU0FNtE62XSB1/9rQNI2mmJfKSPURe5VwDVXhGkRBOErBNqP1fSYZlrQO7b1OBc/v+Qxr7vPk2fL5zZCft6nkZq2+H1f5KsKpA2gceg2h7FFfB6nAjCIrz28M8vG+CFe2TsRtku7x3C+p8znO4WTkh9VLWVzzCYuqlzA0ZRBzsmYw3D3k8/MU7QKSJBBJGHDKMcR4CForpQbBQFyLE0wET1ocqUvEjvibVgUaUQUZk6ETJpkq46z4DOfe93DWbqK3Bitds0gdfmqMHNXsovKceyhYdDO9V93NPTMf5Gfv+7n7o2YenO+ml6d9O09DUOH+pS3kJBm4tTVoG5Vn5rFLPfxlhZ8n1wTYVBnj3EIrexsS3DzJhaTGyFl+N5ooUTXlThpqwdtvAe7dbxHMHgM5Y/jGUJ1ttnC73hufbD2zgnnPrg9glgS+OUKfv2YnSXx3rIPHVgdYtCfC+f2PPeZvbA2xtyHBL6cndasPd0KBhemFFl7ZHGJsDwtFaUZq/DIVPoXz+lt4eM8T1MoBHg+bsOdP63glgkDM3YeYuw+Nw76DMVDVFqhm7XgJSVNh5ILTPRRfOL4OTj9HVLSAwb4fgIn+JkzBGirG/OhIduq9G8lZfhelc584rcrJ2YBJErhjZjI3v9XEvUt8/PkiDyYDbKuO8+GeCKtLosgq9E03ctMEZ9sk5mSR6ZK4Z04Knx6K8uRnAQyiwHfHnppv5lcBs/paeXNbiN9/7ANgaLaJa8c46Zt+ZMC/eIidQFzl5eIQdpNOlz6cofOY3czImsaMrGnElTg7W/awpXk7JcEyVtavYbHyCaDXLjIs6fRx9mSYewjDkgfhMnVM7xElM/FwC6riRjyRMbkcJ5GIo53Byf9hIaTsrN3ct/1pUi0ebii8lokpw8g+uBjPkl8jRZsJZQzF7Csl/+PbiLgLaRp4BYEeU+BwxlrTcJavJL34KUyBaoJZI2nufwlZax4i/+PbKT3vUeTT6Zc7HppG8r53ydj4GKrRjrf/JQSzRxNJH9StAC2e3JPmonmk7HsXX9F8Yik9T3uXpvWxku4wcPfiZn76nybunJWCyQD3LvaRnyJx1+yUU75XO4Oj4jOy1jyEgH78qyfdcUa+S7egaWSuexhH9XoCueNIOfABpmAtlVPvQjV/DuOIquDe9TppW/6FqCaw1W2j5IKnUM62zcRR6J9hZOEOONQkk2IVqfApzD5JUbFkq8gPJ7mYN9DGP9cFeGZ9kMV7Izy6wIPFKCIKIjf3vZ57tv+RRw48yyTHSNLNGaSbM0k3ppJsSOqgiiDoQethGmI3xgx/IkCxdysbmzaz3bfzGLE4p+Qgx5bNmNQRqJpKbaSOYu/Wdsq5BsGAxWDBYjBjEVt/GiwMSRnIvJw5XdqAAaiayqulC9kmv4tdLeDuob9os+ywV2/AVb4Kf/5krI17yV96O+G01iA16387SE1zGBiWY+KdnWEMosC5hRYynCc+54IgUOTqQ5GrD1fHr+Dj2uUsqVnGA7v+SqrZQ7IpCUVTUDUVVdP0n6gkG130TSqiv6uIvq4+XdqqnC5EBFRUEoIJc7hZV5durWKaJTP+mB+7q7ujtQAAIABJREFU0X7Ca6tbaOs3tRKWI7QkQrgt7Vk9xkAVyfveJ/ngR0jRZurw8G/5YoQhc5k+suC0rsVYSm9qxv+cnJX30mfHE9x3/o/46X+a+PUHzfzpQjdZriPfM65o3Pexj7iicecsNzZJwxDxoljdJFsN/HZ2Mu/tCvOPtQE2VsRx20RmFFnJXPcgZl8J5ef+ng8Du1nWtJpg4TwuqN1M9uoHODTvH1wwMJllByI8uSbAU2sC9M8wMq6HmfEFli4Fl7qD3XVxPiuNcdUoxzFB79wBNj4rjfGPtQFG5JjIbP2uJU0JXtgUZEovC1N7t0/kR5UYvrgPb9xHc6yZDGs6fZy9uHGCiy3VcR5a5uNvC1JZ32ohUyu9x5bG7dzV1ETekJvxd/N8JZw5eAdejnfg5ci+SiSjhTM4q/nC8LWVzBlGV1Yyv35zJR/6nyEvtYX3KqtQjVZK5j5JQtUwGARsjXvp8eEthDOGUDH9/iOT7C8RNlXEuHNRMwOzjHhDKtV+BYdJ4NxCK3P6W9s1ep8ODstxd5ce3B18GcUpNlfGeGNbiAVD7IzIMXVIDdE0jSc+C/DOzjBXj3LwzREnppxqmkZDrJGyUIX+ClbotM6EHwGB3s6eDE8ZwnD3EHo5Cmisb2pbNhYOkJKRi9lq79pKJtSEr6WZiGrEdJIG5sej7EAdeb3T+e4rjVjca/Ha3qLI1Zvb+/6QvJJlpO54CSniJZQ5nIah3yGSMRhBieM69DGena9i9lcQd2TRNPAyYsk9SS/+B7aGHUSTC6gfeSOh7NEgCJi9B+nx0a3ItlRK55xcD05nEGN+stY8hKt8FcHs0VRPvB3FevLUI0O0hd4LryLi6UvFjD92OaEQlDhm70GiqSemE1b7ZX77YTM1fgWLUcBhEvnThe5uqy52974x+UopWPQj4q5cmgZ9i8x1DyPGg9SPvJ7mfhefdcEbz/aXSN/8DxoHf5uG4d8l6eBistY8RNyZTcW595FwdmxBJShxXKXLMPnKCGWPIpwx5KSrrUZ/JdmrH8DWsBN//iSaiy4kb9kdhNMHn9Xx/Phz0xBUuPqlBm6c4MQiCfz1Uz+PXeo5rbF52YEIf/ykhV9OTzpmIuaP+/nrnsfZ5z9AXDsiHGYSTKSb0sgzZzPEMYh+tiKMxx3P9Iy0DrdVE6llQ9NmNjVtZo9/PxoaKaZkRriH0tPRg1xbDrnWrE6Ta2E5Qm2kjppILfWxRiJKlKgSJarEiCpRYkoMfyLAwWAJvR09ubnv9WTbsjpcV0vczyN7n2C7bxcJ30h+PvgaJha0bldJ0Ovd7yFoKofm/xOApAMfkrr9JYzhesJpA2kceg27g1nkF52a9dkxUBUM8SBiPHjkZ0J/L8gxAnkTkB2fL/31RDjUlODx1X521OrXxqAsI+f2sTK5lwWHuftjgazKrG8qZnXDWhJqAlEQEdGTJPp7gfpYIyXBMhRNQUAg355Lf1cRA5L6MdIzDGMHgn0Hd1fhdJ8asyOhqEgGEbdJBaMF7Kltf4vJMaySFfcJngHdspKRY+CrQDPaKA3U0BCI4TQfywaQwg30XngNghKnNGUM9zdMZoNhOLfPSDmjrLP0TU/i2fkq3v4L2Njre/zsvRYcZpE/zXeT0vos+duqFt7fFeGOGclMzY6S8+k92Gq30tJ7Ng3DrkW26/d9mTfBE2sCzCyycjHLyV7zIJWDv8UDdoHldSsxCkYSWoIxziLu2LOKtPRhVJ5zHxpwsElmXVmMNaVRDjbpHr45SQZm9bVy6VB7l36iHUHTNG5710tVi8I/r0jFIgnUhatJa7Uzqw8q3Ph6I31SJe6/wI2iwq0Lm/CGVZ74Rious8CGpmKW1C6jMeqlOd5MWIm0205fVyEX5p4PoX7c+WELlwyxUeqVKZfXEU15lUtlM79qaubAgpdOie1zuOd0cJ+zT+s921YyXwenZxhdBadXP/MJ++x3M9/Zk/t2fEzFtHsI5k8iFFcwCGAxGkje/z5Za/5E46Bv0TDie5//F+gGXi4O8tzGIIMyjZzX38bEnpZuUca+DPgyBqfdhapp/Hm5rqZ50wQn80/Bb0zVVEqCZWz2bmNz8zYOBA6hoZFkdPG9zKvpae0BQCwawemwY3dndh6cqiq0lFMbBFESMXTjgaBqWqcPjrIDdTSYk/jdhtcxpy1lhHso/5c2nd4rfocx0kQoYxgNw64hkjG0/cKairNiNZ4dr2Bt3A2AbHXTMOxafL3ntAsMbLVbyPv4dqKeIspnPoh2Gr6K1rqt5Kz8PVK0mfrh38M74NLTCsJSdr9J5oa/U3HOvQTzJnT4GTEeJO+TO7DVb6dq4i/1XpQTIBBT+f3HPsqbZR6c7ybb1f2HX3fuGzHmp+cHP0BMRCiZ+wSyPQ1DpJmsNQ/irFxLMGsUNRNvQ7aldrmeU4Wr5BNyVt5LS8G5VE/+Vds5sNZtJXfZXSAIVJ7zOyLpg9qWMUR9pOx9h5S9/0GKNqMJIoKmopicBHPGEsibQChnDKqxiyqMppKyZyHpxU+jGYzUjvkx/8/eeYfJVZb9//OcNn1me8luyqYXAkkIoYdQEoogVcFeEARULIjy+lNexEpRQEWUJqiIIk2QEnooCSmQ3vtmd7O9TJ/Tnt8fZzLJJrvJpmleL7+55prNzDlnTn2e+3uX7x2vOxNXQNG6F6l5/xe0HfM52o/53CE/Zj3egL3kFeTJvVWeP/NYKxOqDKSEFc0mf/5U+UHVFDuu5DOPtTG+Uuf7s/aMAjc3t9Bjx2m12mgx22g122gxW9mcrSfrZvErPiaExjEpPJHxobH4FIOKynJMx2RTcivrExtYn9jEhsQm2nOek2xoaDBTSyZzXOkU6sJD+9z/7XGb389NkLUlpwz3c/IwX8FI3hfmtS3ggQ2PknNNPj3s45w96MxeKaNretZx15rfkrRTxJKXkOo4loevKC+kwJes+CuVH95P/Rk/JVV7QmE94ZjENryUJ6ltALiKjmuEcLUArh4svBw9uMdnruZDzcXR0x1omQ60dLv3nun00qL7gVQ0ukZfQPvETx2QY+xA4Tllfk73yI/QM6pvzYyWhM0b67O8sT5DQ4+Drnrti6oi3rUSu4phCajNE439vWezTo4NiY2s7lnHmvh61sU3kHNzlBolXDT4I5xedWovPYaDIacAGcuhIuJDs7MQroC8gJKUkoydoTpUjb6X1kgDIqfZOCRbSSBpTHWQzioE9N73ePnihyhd8Ti3D76H+9aVMq5S53tnFVHWT+uoviClJON4/Xh7zASlvmLK/buN1a5D5Qf3UbL6aVJVk3hz3E3c8KqkNqZy2wUlzN2S45dv9XDZ0SG+Mmo7tW/djJrtJjF0OpEtc0AIOsddSsdRn8DNKyD7Ojcy7KWvsLFiHDeURtmc2sqlgz/KFPM4VgaW8sy2f2I6OT7R08NFIz6BO+5jvXapNekwf2uW9zbnWNpkcvpIP9+aEeuzhWF/2NHn+SunRDlheIaHlv+SD7KNVClBzqu7hBmVp/L2Boe75sT58okR4lmXxxen+N9ZRQRjG3l8y5NsTG5mkNQYg0GpMChV/JSqQUrUIEV6hPnBEM92L6Yt10FNoJpgZgaL10xAC2wnOPT3TAjW8OjKd+jcRw/aveG/5PQIxZFOTk+79w/kKh7gF3HJdFdn8/n3g1DIWA4SCOgKAkHVvF9QvP6FAnk9nNDjDYSbFtE94mzkfvTnSmRdIgNsTnwk4XCT08jWt6n44PfE686kY8LHCwPwoYLjeikz87bkuGFGrF91uIEibiVY2rWcv2x5El3q3DT0G2hCw7JdNJmlpGoYVbW1fa9sprF7ttOcUQkY/U+ClmuxvHsVf1s3n02Jeo4pGcNFI6YyNjqqV9rTpvVN3N7wPJ3aPE6rOJVrR3ySkf+8BsXO0XTKd0lXTd73AUlJsGUZRryenrqz9npPR7bOoWbOrSRrT6Bhxq29Cax0CbYsJbZhNuGG93H8UcxoLWZ0cP69FjNSQ9H6Fylb/mescDWN039waOrFXZvhz10JUnoRmd2MGzXbzeDXvou/axNmtBYt1crm8+/Hiu47OiOlxHbppRQ4EOzzuXEdBr9+E8GWZdTP+kUvAoiUFK3/J5ULf4ur+mg+8Vte+vUhRKBlGUNevZFs2VjP2bBbGrUeb2DwG99DT7aw/aQbyRWPoGT1U0Q3vYriWiRrptE57jLS5RMIb19EeNtcwg3z0HJxXEUnXTUJK1SJq/mRmh93l1ds82uEmpeQrJnG9hNvwA6W59s9uAgpGb7wDmKbXqP+rNtJDzr2kByvYiYpW/ZnStY8jXBtGk/5HvHhZxW+/9lr3axqMcnZkuOH+rhhxn62OeoD970X5+U1aR7/TAVBo/fY39rS1uc6lmuzLrOBJYnlLEuuIOWm0YXO6OBI0iJNfaqh0Hu43FfKqMgIxsZGc2zJpD0N413gSsmLqzI8ND+BIqAkqNDQ46AImFhtDJiodpnd/G7dwyzuWsbEoglcO/qLlBol/LPxZR7b/Hcq/OV8qvYabnk+wOePC3P5ZG8819LtDH/2c6SrJtFwxk/63LZwTCJb3iJVv5GSqIpqpVF2vOxd/rYyKFYa1e4dabF9UexAGXawFDtQ6r37i3GMMK4Rzr9HcIwwwjUpXfE3ija8iFQNOsdeTMeEKw57KvsOx49mxnH0EBsv/hOOv/97TUrJ+nabN9ZneHdTlqQpvbRVKHRsdCXYLlx6dJArj48clFPFdm1W9KzmqfrnWBtfT4lRzIW153Fm9WkYinHQ5DRrOcQCOkENkA5EqkHxno2cncOn+SgL9H8fD4icxrdjmxkacx3kbIVU1sG/CzkVdpaRT13BQjmWT8S/wQUTglx1QmSfY3xTejt/2fIk7bkOekxP3d6W9s7tIphUPJFZ1WfsUesb2/gKVfN+gR0o4ZWx3+frc0sZWa6zucNibIXBb8ctYtD8X+L4i2iYcSvZ0tHoyWbKlzxMbNNr2L4o7Ud/hnjdmQx76WvMUyxuKi/BRfLVMVcztXRyYc7pNnt4YuszvLH9LcKuy2W153L0oNMJa0FCeqigViyl5IklKR5ZmOS4wT6+N7MI/wCCJo4rue6pDmzX4bJTVvD4lr8h7CyfTmRY4NNY6vcR1kKcXX0my9ZMZUWjge3CcaOaUUpms6JnNeV6lK+0NnN+OocMlqHYWRQ7i3By3rt0cVWDLTN+xOu6w3MNL7IlVY9wojjSpdjv4zErQm3jIjZc+tcDthv/S06PUBzJ5LS9p5uTH7wZX+lbvLe1np7pN5MYehrged9ChkrWcjE0BeGYDH356xjxbTSdfBPJmuMPbWNy1yHcOJ/itf8g3LQQgETNCTSc/qMjMpX4YKFmOolufZtg8xK26mPRTvzYwI/TdQa8bPHqp6lceC92sBw93YptROk46gq6xl6M1A5dao1pS25+uYulTSZnjfbzxWmRAUcL+sMHHUu4bdXdfLTsXGaVnIFEYmbTVFZUUV03qvfCUoKZhFQ7WVehPSP38ORmnSyLO5exoONDPuxcQsbJIh0fwqpGGtsQikNIDTKp5GiOLZnEhKKx/OLD+1lnraJOmcXPT/oE5cv+SPnSR6k/8+ekaqYd1PH1h6K1/6B6/j10jzyX7Sd+Gz3ZTGzjbGKbXsFINuPoIRKDT0JxchjxBox4Y6E5+A50jziblmlf23t0bQCwHBdVFSgIQg3vM+SN79Ey9Vo6x+/0FGupNoa8diN6soWGGbeQK6pj+PNXYUYGseWcXx3acWIX7IucViy8l9LVT9F04o39Rk+MnnoGvftTAh3r6Kk7i5bjvnJIauuNnnqGvnQ9jj/GlnN/3W+atpKLU/vWLYRalgDgqj56Rsyic+wlmEVD91zBdQi0rSCybS6hpoVo2R4UO4Owc576bB6OFqD1uOvoHnkeCIFEkjYdigMGjpRkUwnGvPI11FwPm8+//+Aix65D0caXKV/8MGq2m56R5+Cvn49VMaYXSXpmeYr75yUA+M4ZMU4fefBtnlY2m3z7uU5uPD22R9+//sjprnCkw4bMZpYklrEmvZ7KYDmjoiMYFRnByMhwioyB3QstCZu75sRZ2mRybK3B16fHKAspbOmyeWdTlnc2ZgtEdXS5TkAXuHkOJJFI6RGgc8YFmTk6gJSS15vn8Oimx1GFwvDwMFb0rOb40qlcO/qL/HGBzQur0vzpU+WFWrRB7/yUyNY5bLrw4X5TxXdgwA5R6XpE1c7i+CIHJCSmxxsoX/oo0c1v4OpBOiZcTue4S/fL8TxQRDe9yqC5d2BGBtF67DXUvvl9ukZfcNBiblJK7nsvwfOr0lw+KcTnjgsftJK4lJIV3at5sv5ZVsfXUWwUcWHteQzvGU1J6YGrRTuuiwTKw36wMhAsLogjAaTNNJXhyn5FufZJTqWEzo102CYpJ00qK7AcF30XLYiidf+k+v1f8vHcD5g4bRqXHbPv49mU2MJPVvwCicuoyAhiRpSoHiWmR4jpUSJ6hPWJjby2/S26rR7KfaWcVX06Z1ROJ5ZPp/e3r/Uio7k4rw2/nquXT6Yy6PLM8CcZtOFZUlWTWH3it3i+Yz6re9ZSGaikNjiI4TZMWf8Kw7cvxVF0HooGubc4Rm2wlhvHf42qgPes7P7cNHau5IkPf8Y8X++Mn4DqJ6SFCGshyv1l6LlxvPphHWPLirnlnGIi+0gff2VtmrvnbmL0+H/QZK7n+JzDD+I25tn3UDX/V2xoW8R9dVN4P7cdTWiYPZNRtBQyuJKoHuHyoqlctfhJVCNM/cw793QSS4ma7WLIq9/BSDSy7cyfkqqcxPLulTy+6QW2pLZy2+irmPXqjXSOu4zWqdfs8/r1h/+S0yMURzI5nb+hiS+8diXHqA08khReREQouEgsS1IWNWjpyRHMR6C0VCvDXroePd2KY0SID51OvO5M0hUT9yBLXtuEdQTaVuLv3oTtK8KK1GBGB2FGarBClaCoqNkeija8SNG65zGSzViBUrpHn49UDSo+fIDOsRfTMu1r/4rTdNihpTuI1L9NZOscgi3LEUhsfxFatptcpJb2SZ8nPmxG3+mXrk2k/l2K1z5LqGUZ3SPOpnXK1f2LmkiXig/up3TVEyQGn0zjqd/H6KmnYslDhBsXYAXLaD/6s3SPPOeQqYZmbcnjHyZ5elkKQxV8ZmqYCyYED0p19SeL72RVai3/b9i3KdNLyJo5SoMaQ8bvIuzh2JDq8AQa9CA9OYd0zi7Um7rS5eGNf+bN5rexpE1Ei1AhJrJ842hOqDyKG2eUct+8Nl5vWEZ15TqcwGoStmdEIwXZlvP57cwLGS6aqHv+KhJDTqFp+g8O9nTtFWVL/kD5sj+Riw3F17MViSBVPYWeEeeQGHJKb8eCdNFSbRjxBnzxbVjhKpK7pPTtL1wkpuUZOJoisN2dRH/wazcRaFtZiEbo8QaGvHojqplk2xk/IVN5NOBF62vn3EL7hCtoO/bqgzkV/WJvBnZsw8sMmns7nWMvoWXaV/e+IceibPljlC1/DMcXpfmEb5AYcuo+f1/JJbzxUvP1eobUTBfDXvoqip1hy7m/wYrsNPayloMiRO9aaMeifOmjuHqQ7lEfOTByLCXCMT2PuJ3BzUeydiBtOkT8GjG/juk6tCVMIqltjHjxOrIlo9g665cH5AQMtCyjauFv8HduIF1+FC3Tvkq2dDT+V+9kWMsrrPvYU4VI2dpWk2882wnA458p7yXwoccbKFr/Iu2TPr9fBMiVks/9pY0RpTq3nNN7LBwIOd0d/dWc9gcpJS+tzvDgfG+8uPrECGf3kfoppSwQ1WVNJlJ6w5cQnpCNENCecmhLOjx4eXmhf2FzppV7193PhsRmPl33cc4bNIucLfn0Y20cN9jHd8/0IoKBlmUMm/2NQl3zvvDvKCXxdW2kfPEfiDTMxTHCdI88l64xF2FF+q6t3S9ISdnSRylf9kdSVZNoOO2HuL4IlfPvoXjd82y64MGCEvqB/4TkN+/GeXF1hk9OCfVqu3GwWNm9mr/XP8uqnrWERZizSk/j1KIT8SkH5kDOWA5lEQNjh8MqurMMxnRMVEWlIlDRJ8HeJzm1c5gdm2iyEwTUAM3xLKoqdpbQSEnd81fSmRGcEv8xf/1s5T7J2KruNdy26m7CWojvT7yR6kD/dcq2a7OoYzGzt7/Byp7VqELl+LKpnFA2laOLjiJiZaid80OCrctZXXshVZnNFHcsY+3Yj/KH0nJea55DzjUZEa7zIrS7iJUFhE6p49CguJxSfgJXj/pCr44CfT03oW1z6Zx7K/X+EB3RGjrC5XQFYvRoPhKOl4nhlQUInMwQIvZR3Hj8SUwo3dOB5EqXeC7Lta+8gFM0m6Cm862kw2XtDWw959fkSkYgHJOat24h0vg+84+7mse1DHNa3kMTOhcOPpePKWWMmvMjrFAF9TPvLNTT9gU1282QV76FkWxm2xk/I111TP4SSqoW3Uvxmn+w4ZLHDkqk8b/k9AjFkUxOH3xnMfds/CzXdvdw4cSvEa87AwBHSqSUVEYCtKWyOI7c6RVzbUJNi4htfoPItndR7CxWoJT4sNPJlowksIOQdm1EuF46hhUsQ80lekV3pFCxwlVoqVYU1yJVeQxdYy8iMfjkgqFXseg+Slf9nebjvkrXuCNbhlrN9lDxwe9Qc3GkonlKsYqKFN7fRry+QEizRcNIDD2N+NDTMGNDySx8kfHNT+Pv3ky2eDhtk68kWXMCCIGa6aRo/QsUr3sePd2OGa4iXXE0sc2v4+oBWidfSfeo83sZlsIxqX7vNmJb3qRzzIW0HPfVXt8HWpbmhXlWYkZq2H7iDaSrJh2yc9HQbXPf3DgfNpgMK9a49uToXnuP7Q1rG9bzoy13MCo4gmsGfYGc7RLTLYaPn+IJPpgpSLZ5Vp7meeKb4xkUZedk+cTWZ3iy/h/MqDyVGRUns6a+hgfeT3FKnWfcaYpASsk/V6X53dwEg4sUPndKF9vMVTy7oIQhgYn8/CPFDHnlBvxdG9h44SOHv35KSioX3kto+wf01J1Jz4hZh1bFtw84rkvOcVEQhP06QUNFEYLtPRn8+fR+o3srw5+/ku5RH6FrzEcZ8up3QLpsO+u2PdKHq+b9kqL1L1A/8w7S1VMO+f72Z2AH2lYyZPa3yFRMpP6s2wZMunydGxj03u34uzbQM+x0WqZ9bY90QC3dRnTzG8Q2vY6/a0PhcykUXNWH1HwIx0K4Fltn3UV2lx5+jnRxHC/woKqgKf+aEoSM5eDTBEG/WxBs6c6YmJagquEtat79Ge1HfYK2KVftc1tqtptA+2oCbasJtK0g1LwEK1hO67FfJj7sdFwkOTdHeukHnLLiZppOurHQJ9tyJJc90sKQYo1fX9I7Ulsz54dEt84ZmDNhN9w/L87zK73U3l0FbQ43OU3kXH7+ejcfNphMqjH4xvQYlZEDzxZpSThc9UQbp43w90p5dqVL0k4R1T0yNHtNmrvfjnPHBSUcVW2A61D3wjWouQQbL/zDgCKS/06dA3/7akpWPUl06xyQkuTgk+gce7FXItFXNFK6qNluECqOL7rHMsIxqZ53J7FNr9E94my2n/CtQraGmu1hxDOfJlMxwRMAO0i4UvKrt+PMXpvh08eG+dSxfac6SinZ2GGjq4KhxQN3/q7qXsOf1/ydDdZGgkqQM4pP5bSikwmo+xdlzlkOYb9O2KeBlYZIVa8WSWkrTXmgnEAf98q+yKnMdNPSvgZbNdAUnaaeTK8spWDTBwx97UZu813H24HTufOjpXvd3qKOxdy1+l4q/BV8f+K3KfUNfG5tSDfx6vY3ebt1Lik7hSpUxsfGMLnoKGY1LueYdbOpNwLcO+okXslsxpUuJ1ccz8W151Mb8shhwkrSkG5kW7qRhlQj27MtTC2ZzKzqM/Yg7/09N+GG9wlve49g63J8PfUAuKpBpmwciZrjWTzkOBZ2Left5g9oMbcBUGZUENEDZJwMSdsTSLPlzl7Qo4NHc3sqzogtb+9RTicck9o3f0CoaRHbT7qRpmGnoqBQ3vQBNW//CDM2hPqzbh+QIrua6WToKzegp1qoP/M2MpUTUXIJRj11ueeIP+V7A74efeG/5HT/duYc4B5ABR6UUv58t+99wB+BY4EO4HIp5Zb8d/8DXAk4wPVSytl7+60jmZxe9cTveT/zGx7skUTPe7hgxDmuC0JQEfaTtR3aElmCxp4DrLCzhBveJ7b5dUKNC1BcC1f1kSkbQ6Z8Apny8WTKxnsPiJRomU70RCNGohEj0YQRb8T2F9E9+oK+2zq4DrVzbiG8bS4Np/+oXyGWA4XRvZnoljl0jr/soOowhZ1lyKvfxt+xHjM2FKSNcG2E63gvaWP7i0gMme4R0t3S9bZuaGHo8DKiW96ifOkfMBJNpMvHY4WriW6dg3BtktVT6Rp3sddbS1ExurdSteAeQs1LyJSOofn4r5MtG4tiJql962ZCzUtonXIVHROu6GfCl4Qb36di4X1o2U62nPubg/Ys9968ZO6WHPfPi9OadJkxws+544KMqdD3S6iqtaWN17vm8EzbP7mq+rOMD05Ax2Lc6BGAArm4J5mfd2jYrktzPFuYLN9vX8gvV9/LjMpTuXbUF3lupUdATx7m46azivYQKFjckOOnr3WjCLhgQpDHPkzx3TNiXMgcBs29ne0nfIvu0ecfsvN0JMB2XUxHYqiCsE8joGu9BKI60zly+fR+gMoFv6Z47T/yoikB6mfe6fVJ3Q3CzlL3wrUoZpLNFzyw17qvA0HBUJASI9GQJ0wriWydg6uH2HLeb/c/CunalK14nLJlf8IxwjRPu57UoKlEtr5DbPNrBJuXIJBkysaSGHwKUlG9iKVjIuwcipNDOBY9w8/ag5CnLYeykIGqClrjOQxNoB5mpeB0zsRVbCrCQUp8UYJ6AIGg20ywpq0Fn64xfMFvKV7/T1pVvs9yAAAgAElEQVQnfwk7UAJ4+aae0I1EsdJeFkz7GozkdsAj47miOhJDp9M+7jJyioKLi4KKkH5aN7dw6rLrsaKDaZh5e2F/nlyaojqqcnLdTrEvNdPJqCcvx/FF0LLdbDvjp/sV+V/TavLNZzv51owYM3epdz+c5NRxJT94qYvl202+fFKUj4zbf6GcvvDQ/ARPLU3xm0tL++zVCHD90+2YDtx3WSlCCIrX/IOqBffQMP1mEsNmDOh3jgQRPi3VRvG65yha9zxaLk62qI6e4TNR7Cx6qgU91YqeakFLtaG4nrKuq+jYgZJ8zatX++rvWEuwbRWtk6+k46hP7jHflax8gsoPfkf9mbeRqjl4A9mVkrvm9PDaumyvml+Aph6bNzdkeXNDhsYer275uME+Lp8cYkLVwBy0G1c30h7YzuzO11mRWk1A8XNa0cnMKD6VsOqlx0opcfHa1kgkxm6qv46U2LakMuZDWFnwRyGwcyy0XRvHdSgNlOLfTXhvX+Q03bmJ5kQTYX8RpuvQGs/1Iqe1b3wPX9sajuq5h08eV9zr/OyOt1ve47frHqIuPJT/OepbBQfM/sKRDmvjG/iwcymLO5eyLd0IQKUWoc1OogiVGZWncGHteVQGDtzJO5DnRs12E2xdTqBlOcHWZQQ61pGL1NJyvDeXzG9s5hcL5kFgPT5dks4aOI4PXAMhfRT7AhxdPphbjbVULn2E1slfomPiJ/f4HWHnqH3z+4S2f0jTKTcBgkHv/ZxM6Vi2nfmz/artVjOdDJ39TbR0O9tm3k6weSkVix9k0/kPkCsZsb+nqRf+S04HviMqsA6YCTQAC4FPSClX7bLMdcDRUsprhBBXABdLKS8XQowHHgemAYOA14DRUubVE/rAkUxOL7v/Uhr0Nfyt9lOkR5xd+HxH/UBpyIdE0hLPoghQ9+LpV3IJ9HQrudjQg04TdaVL2s4gEIQkDHv1Bnw9W9l69t2HRtwFL41s2MtfR8t2YQUrBi5us8fOOtS+9b+EG+bReNr/HpCoSq8Bz7Up2vAyZcv+iGKl6RlxDl1jPtqn8Y+URLe8QcWi36FlOukedR6BttX44vU0nXQj8eEz9/nbWqqVuheu9VoInffbQ9LCZFdkbckTS5I8uTSF5YCmeH1nj6oyOKraYFylTsjo/75qbWnDkQ63b72HlJvme8NuQHUMjhlajNA02K1vXNa2aUuYBA2V+tQ2/t+SHzMkVMstR9/Ey6tNfvteghOH+fifM4v6FWdo7PFanDT0OIR1+OvHfYx9/guYsSFsPefuw9565F8FR8o86RQUBQ18at9Rn5zj0Brf6aBScnFGPPtZXCNC/cw7sPbSKsLXuZFhL15HatBUGk7/8X73tRNWBi3blRdzyKBYGUT+71T9RqqdegLtq9ByXmqWo4fIlI+nZep1eziBHCnJmA6GquyzxZCvaxPVc28n0LEOKVSEdDAjNV4Ue/hZWNF+BLn6ge16iqYVET8CkXf65fAbXsuJ/mA5LqbteumfQqD3Q2hl3kh1pVt4z1g2Pk1ndGklIc2/B3nqSGWoj7fjugnGvXYTwY51/e9HsJxM2TjSZWNIlY4hXTISWzPyStcKIS1IQPVj2QrFIT9LV6xlYuc/KFn1GOsu+zvuXjz4O1rtrDnvXobOvRM928WmCx4ccHaClJLPP97GkGKNH527c53DSU5/NzfOP1ak+cb06H73a90bkjmXL/61jZFlOj/9yJ7Hv7bV4hvPdnDdyREumBDKt3n6LNmSkdTPvHPAz9eRQE53QNg5olveoGT1M/i7NiAR2MFSrFAFVqgSK1TppSZKWVAJ9lSD29HSHSBdmk+8oZD5tcf2HZPhz30RVzXYfP4Dh0TDwnElv3irhzc3ZPnM1DAhQ/Dm+ixr2ywEngDWjJF+ujMuz65IEc9KJlbrXD453G9rth3YVRBpW7aR2Z2vsyS5HJH/J/P/dsXk8NF8vvqTBSEegIxlUxr24VMEOCbEanrdH7ZrY9omYSNMzBdDzZ+XvZFT13VobJiHpoVRVY2MZdORMgvkVI83MPLZz/J+zSe4YuMF3LsXJ8uLja/yyKbHOCo2jhvHX09AO3Q1yK3ZNj7sXMqy7pVU+iu4oOYcSnwH39f5QJ6bUNMiKuf/Cl+igZ5hp9M69Vo2m0Xc83YPuioYXqoxvERneJnG4CINTRFEts6hds4P6R4+k+0n39Tvcy3sLIPf+B7BlmUgJemqSWw7/ccHVM+tpdsZMvtbaNlOpKKTLRnJtpl37Pd2dsd/yenAd+RE4BYp5dn5//8PgJTyZ7ssMzu/zDwhhAY0A+XATbsuu+ty/f3eEUtOpeSsh45mWM5i5Z+DOHLnze9KUIUoRJUcKbEdyb8iC22HJ1BB9Qwu4VJuuPzhpAY0IfnC+7W0Zg9OYKXcZ/PACQ0EVJc7V5Vz9ahOBgctHttSxO/WlWLJgRrQkv+Z0MbFg+PctrKcp7YdmJBKNmPhD/Q+JgWJIsAewL6EVJerR3XwsSE9ZF3BdxdXs7Bj4AbTxKIM901rZElngK9/MKjXvXCo4Gp+rNgQrNgwzKKh2JFBXuN76aJ3bya86TX0ROMe65mm5zGXVRL3chAfAm9DKOBn19tR4t07tiO9cdwvyV1mIVXwP6mTjU0lMeZCjPbVxFY+gZAOCpJPDuvm/No4z2yL8cy2KKarFPY3OfJcaN/ML6tnM7M6waffG8Lm1IGrKB5JyHMlNFWgCLEXeuSdW9NxEeycI0t9NmlbIePse1D4+NBuvj2unTtWlfH3+oFHT8+vifOd8W341f7ng81JneXd/sJrS9LA7edoXBdUReTHGNhXKbQqJJcN6aHKb/Nqc5hVPT7Y65nqH44r884QWdiGK73P+6rJltJLAxZ41wi8/XZcr+RCCE+5cqfNsuMaep9JKVCEgl9T+zeEJeRsB0+Sx6bSb+Lgrbtj/1zAcgVxa4fR6xnI3j0jEEIg2FHbLdEUBU0VdCdSTCwX/Onkzfx0RRnPNvR93QWSp6ZvpSWjcc2CWurCOf54UgNLOv18/YNByAGe7+TwWaRrT6Rs7u0oeYXZHWPH/sAw9j23ZKqmkBh7EYGGeUQ2vLTfv7EvpGtPJDnyXGJL/4hvl9RxgPiYi8hVTKB07p0oTo6bJrTy0Zo4n547mE3Jgdcn9jXn/PshKTUcui11v+Ygj67tffkZlUlun9zMz1eW8/QBztO7QwqF+NhLyOXr7LXEdvyty/C1LkfN7axjlIpOpvpY0kNOxvXF0OKNhOrnYLSv6XOvMykTVd+tZrlEIkcDCvnJLv8ugRDIY0CsAPH6zpFASm+M0xTFU+1VjT0cq54Ylze+aoqGqqgYRv9znO1Y2FYKNR+ptV2JI2VhLL1hXBuXDO5hRtP1bI+Mp3TenXsco1vmYh/l4oxzUTYJjNc0hPN/o+3fgT43huLy6bpuvjC8C0vC79eX8mR9rHCfK0jK/TY1QYu6kMXXx7azNu7jugU1+7RH/arLT45pJuMo3Lq8omDDHAjKfTa/m9bI4JDF1xYOYn4fdqTMT2EDvWKu6/KHv9z/H0FOD406S/+oAbbt8v8G4Pj+lpFS2kKIHqA0//n7u627R1WzEOJq4GqAIUP6iHgdAejubqTc0ck0RnBkb6VPz37yBnwB+RQ/WRBxOByQknxamIKKhqGpOK7EkQ5tps03F1Xz4AmN3DVlO1fNryU9AKO4L8R0h18f10hUd7huQQ1r4n7eaQtx/Zh2PlPXzQmlaW5eVsnGAUz0V43s5OLBcR7eWHzAxLQ/uHhqjgNBylG4a005T9XHsKWgKbN/g+fy7gA/W1nB/05s5Rtj2/nF6v0TBRkIFDuLr2Mdvnx0xlUN7GgtZlEdmepj6Tr2y/halxPa9BpatmuP9UWzQCyXyEnAKpDJnTejC9iOi5ufjFEk5iwbGQLfsxqkFZKTZ6J3bS4Q00EBi/+d2MLkkiwNaY0bxrXzqWHdPLChhBebImBnia55hmPCcc6rSfDQhuL/CGK6QxVUVTyjZV+kdMcooCliJ/EHOnIDH6af2BrjhNI014/pYGPS4MPOvTtODMXl2+PauWhwnAXtAV5qipBxFNKOIJMnxBlH0ByXWANUnN7h71QVgYqnLum67NXh5kjB37YefCqymx9PhVDQFb0Q/XBcB4mD5Tio+WuxIyYiJeiKik/TvMhqQWzEE6xyXY+ouoVR2sOOv4Qi0FSx91RTAZqqeIrMwqC11zXtvU1DyRu9fVcI4EpPl2BXor055WNL0mBmdZJntsX63JdppRlqgzb3rStBVzQa0jp3rynjpgltXD60h78O8Pz7WpeTHnIKubJxBJo/HNA6BwIzOoTE6PPROzcQ3rjXip4DRqBxAema40mNmIWxaGNBhdnVAmQrJuJvWYLi5BgXzXJRbZy/bY3tFzE9ciHoMPff/BuIA+OtlhAfdvq5emQns7eHSdkHHz0V0iW65mmynevRE01o6b4j9cK1CDa+T6BpIdmqY0gPOZWeoz5JaPMbhLa+NbDf6hSI9/v/3s1J5DQgRWE5IXY6+wXCI6i7kdMdDiaJxHItXOmio+8kuLuQV6/ll9mrfYsrd579sOZwQU2c2dujNBVNwte6ovCdNCTOaBd7rIssl2CDukxBn6siDoMz/EiD6So8vLGE2U0Rbhzfxg3j2vlobZzWrEZt0KI6YLFrAll9Suc7i6sGFCjJOgo3fDiA9j8DQFtO48sLaphUnGF+x57RV1d6U4B0e9sHO6aGw8UPjhQc7sjpx4CzpZRfyv//M8A0KeXXdllmZX6Zhvz/N+Kl8t4KzJNS/jn/+UPAi1LKp/r7vSM2corX53TpuncpjvbOwc9YDj6fTUBXCagBVKGQyFr0ZK092nMMBJZr4UinIMax47UD2bxQUkyPIlwf5REfYZ9OxrJpSeRQVZu2TAfFzUsY9ub3SVdMpPG0W/a7nkyx0gx55QZ8XZvYdtZtpKsm4Uq3MECHG96neu4dKGaStilfonPcpf2mcBate57q9+8qtPs4mKfycKdYWa5FzjURKChCoAkVTWh7GIs7BKj2Wlfp2gRbl2NGBx9c+4ldkDZdnlqW4qllaRxX8pHxQT4xJUzMr/RKzUs5aX605XZKtVLumHYLxUE/qZxNT8ZCU0VBtOuRjY/xYtOrXDf6S8yoPIWN7RZffbqDb8+IceYoP7ENL1G58F4QCs3TvkZ8+EyC2z+kYvGDBDrWkosOpm3SF0jWnsDgp7+IrqtsuuDBQ9p659+BrOWgqf2n8DrS9WqRpBdNU4SCX/WRsjIIodKRdAp9j/cG23URYhf1Rrw6nGEvXoeRbCYx+GRaJ3+pz1YpemI7NXNuIdC53lMePebz/abh7c9zk7ZsQj7w6TujgvG0A66Cv496+kMFRzp0ZdOMLC2jMlDca9zb8X1TPElnJomqumj4UIVBdThI2L9vZ4iTNw53RFL3FxJJY1cGVRUHJNDkuJKs5VAWNgj7djrF3lq0gmlHj8V851fElj7AsosfxYjsmQo96K2bCbYso/WTLyPxoyqeIyQ0+3r8DXNZf/ZduOUT9n0cUnLl39qpiqiFdNhDndbbmnT4+jMdBHXB3ReX7lOB9GAwZ2OGn7/ew7dOizJzjOfMeWZZivvfT/CbS0oZUaoy7KWvoidb2HjRo31qJpi2i6r0XY5zJKX1Hi64SGzbxdC88cPfsY66F645rAriA4HjSu5+26tZ/eZpUWaN6e2sO5A+p1JK/tLyJPPiC7i84mJOLfL0ObKWQ0lI98Z7O+Ol9u4lrTlrZ6moqkARCq50e30nhEBPdqAJQPH2r6k7g6F7WRQlq/5O5aL7eGXar7n67VK+P7OI0pJ6Zm9/gwXti7CkTV1oKKdXncop5ScS1g+8Xc6/C4fkuZGSSP07lC19FBQVMzIIMzwIKzIIM+K9W8HyI7KFYiY/1vs1zcuodF1cV2I6kpztYNrePaMKgaZ5Lej+k9J6D3fktAEYvMv/a4GmfpZpyKf1xoDOAa77fwv9kCpNCMp9JbRnu9BVnYCh0pO1dnpKBgDbtTGlhV/1EVEjWK7lvaSN67ggQEqXkBYipkfJ2pKSkK9g5Ph1FV1RUDCoDFbQVj2F+hNvZPD7v2TYi9fSMONWciUjB3aYeXUzf+d6Gmb8kFTlMeTcnKf9gUdQZc3xZD76ENXzfkHlovsoXfFX0hVHka6YSKZiItmSkaCohOvfpWr+PSRqTvCUAQ+QmLrS9WrEpFcjtrvhuiuk9KLIjnTy0RLQhIa2l/pe0zVxpIMhDCJKad5IcbDJkXWyO6Ni+e20TrkaX/cWqubfQy42mEzlMYVtaclmite/SGzDS+iZDhw9SOuUqz0Se5A1mEFD4TNTI3xkfJA/L0ry/Mo0r67N8PFJIU6rlgVxnpAa5JLyC/hj8195fPOzTCqaSEALUeqP4FO8ie7N5nd4selVzhs0ixmVnrrd8u2eAt6UkhS1b/6YSMM8UlWTaDrpu9hhb6JJDzqWLdVTiGx7l/LFD1P79q2FNj9bT73zsBJT03YxHddTVjyMv6GpgvKwv5fYkeXaWK4JeLWMAS1AUPOjKzp6/t4yDZOWTAeKYmM7+l4bqVuuQ8LMIKRCcWCn0eX4i9h0wUOUrH6K0pV/9RR/R5xD+6TPYQc9QhBueJ9B7/4UpGTb6T8+ZAJolmtjujmqfVHKAyW4UmI6Jn41TVM8Tncmi9/Q0BW9V83WwSLnmpiWQ12siupg3440VajURKPowiBtOfg1lbKwD0Md2DOlHqSrWiAoCRm0JnNoe6n93h2262LZXruhiohBsI90WE1V6Bo2i6Kl91PduIjG0eX4d2mNoaRaiW6bS+KoT2DoIdI5G1tKNFUjPeNWfE9+jGHv3c7Kc+7C5yva6/gohGD6cD9/X5qiO+NSFDi0xDFrufxwdhemLbnt/JK9ElPFTFK89h+ouQTZkpFkS0ZgRofsl7E5fbifZ5aleXRRklNHBPCp8MLqNOMqdUaU6RStf4FA+xoaT76pT2Lq1R1LcAWm4xTUtg8nvGwAr7RCAtKVuMJrQ3W4hb92h+mYxE3P6R1VfBiKQbZ0NN3DZ1Gy+im6R19waFrYHABURfD16TE6Ui6/ejtOaUjl2NqDm1+EEFxReQkJJ8kTrc8SVsNMjhyNIgQ5h7wIoQA7C0b/pNCv+Qu1n3tkOrguuFZB58F23XzkVAHXoXjNM6QrJvJKzxA0JYXlX8bNy35LSAtyRtVpnFE1nbpwH72bd4PluF4ZxL9AMO7fAiFIDJ1+QPok/25IQM87tlUhUFUVVPAynfX83OqStRwyppOfJxy0I62C4ABxuMnpQmCUEKIOaASuAHaXwnoO+BwwD7gMeENKKYUQzwF/EUL8Ek8QaRSw4DDv778cMi9yETHC+FSD1mwHlswRNlTSpluIPvQHj5SaGIpBha8MTRi4riRkBAsDnitdHOl4N7vQSJk2JUEfsV3y+QWC4qBOSyJH2KdTFaigfcRZrI9UMfydnzDspa+x/aRvE687c+8H5DrUvP0jQs2LaTz5Jrprjsd0skT0EFEjipSSpJ0iYSYRup/6024hVv8O4YZ5BFtXEK1/B/Aa22fKxxNsXU62dAyN03/Qy+BwpYvlWrtIFci8UMnuA71HClVFRVN0hBA4OJiO2VvoQFJY10utM/CrfnyqgSNd0naajJ1FComCgq5oKCiYroktHYJagDK9FNtRiPh1Qj6VtngOKf2U+kqwpI3pmCTtJBkng0/x0Tj9Bwx78Tpq37qFLef9Bl/XZorX/5NQ40IAUjXTaK27htjGl6mefzfRLW/QfOINmNFdfTYHhpKgyvXTY1w0McQfFiR4ZGESfbLglCE7z91xkSnMj3/AMw3P8UzDc4XPBYKQFiTjZJlYNJ7PDL+88N3y7SYXh5Yx7fX7UKw0zVOv81oT7T7xCUFiyKkkak8itvl1Spf9mabY1MPSBmUHTNtFUSCiaWQtB98BZCbsC7brIiWUhnwIwHQtbNdCSghofmK+Enyar0BGd4ehGgwKVSBlO1u7uyn179mA3pUuOdckYzqMKKqgNZUibqUIaf4C4ZN6gI6jP0336PMpW/5nitc+R2zza4UMhbLlj5EtHknDjFt69QY9GFiuRTybY2RxOZXBosJ++1SDiBGmIlBKU0+ChJlFYpJ2PCE2fS+Onx2OIls6yN2iC4VlhCSoBIn6w1SG964ErghBWdhH2nQI+3orJf8rEDBUjHx6r74XUuy4EtN2cCXoikIsYBTW7Qu6KnCKh2OXjKJo6xyaRp2HIx1U4ekJhNe/gJAu9vjLsV0XXVOQ0kvR1/zFJE7/CbEXvszIJX9k43HXFso+NKH2eW2mj/DztyUp5m7Oct74QydSJKUnfrO5w+aWc4oZ0k9rEMVMUbLmaUpWPoFqpXAVfafKrGqQK6ojWzwCMzYE4VooZgrVSqNYaRQrhWKlSQw5ha5xlyKE4MoTInzn+U6eXZ5iTIVOY4/DJyaHUXJxyj98gHTFxH5F7yxbEjJUogGDRM4mnjHRVWWv13d/4fVDd7GkgyNdpHTRNTA0BUPz7gvb8YS3kOAzVNR89pQq9lILfYCQUnoOWVw012B4rAZVwLZEK1LN4VN9tE25kujWOVTPu4OuMReRLR6OFa7+l0epNEXw/2YW8Z3nO/nJq93ccUEJI8oOzoJXhcoXqz/Frxvu59HmvxBWQwwP1GFaNvg0UDXIJvZKTqEPUroDdm7HAoCXtbHD3xFpmIuRbKb12GtYtCDHiJomHtj0AGOiI/l/R93Yq1fovmC7kphfJ2Xa5BwHRYCuKQftiPsvDg626+JTxV6vgyIEfk3Fr6nEAhLblSTTDrp2uGndvwaH9SjyNaRfBWbjtZJ5WEq5UghxK7BISvkc8BDwJyHEBryI6RX5dVcKIZ4AVgE28JW9KfX+X4WLi5ZvhG6oBtXBCjqy3WTVJLYEH3tOcDsMNtO10BSVcr/nJc9ZDjaSgE8lnXNwpYsqFHTNq7+SUpIyHYp2I6Y7EDBUDEXBdt38dsvoqZrM6nPuZvg7P6fmnZ/g71hH65Sr95hghJ0l2LKU4nX/JLLtPbZP/Qotw6ajCUlloBzfjgFTQJERI6KFSdkp4laStsEn0jN0OopQ0FJtnjR46/ICMW2YcWtBEU1Kr7efQBDSQ4UaMSWfRuv9hOiV0qygIITAtB1U2qkOVBUiqA554p6PpmpC6zWZ70h7D+uhAiHOOllSdpqcaxLWg4T1CIaiY9oOuiooCugoQlBdFKAjmSNtOgQNDUPXCWlBElaCHiuOo/loOP3HDHvxK4x49nMI6WIFSmk/+tN0jzwXO6/MGq87g9jGl6lcdB91z19F2zGfp3P8xw7JJD+kWOPmWUVc8adWVrfDKbuUbQshuLbmiySDKZJ2iqSV9BwL+XdVKFw65MKdhEhK1m1P8qDyK+xAFY1n37XvljmKSs+IWfSMmOWl8Rz0EfWNHcS0NOTdh1nLxZHykE7CjpRYjqQkrGO6nnER1PyU+GIYioE2wOulCpXacDnJDCStOEHdXyAZOWkiXYlfhKgpilIZCRAxQtR395BzE1jYvYwTx19Ey3FfpXPspZQv+QNlKx4HoHvkuTRPu/6QRKmllGTdHIpUqQlXURmJ9Bk50hWVwUUx2pN+MpaD4YOckyNlp8nYGRCgonp1ntLL9lAQGIpBVAv0S2AVoYCjEQ4Y6ANIl9UUhaj/3xMl8JyABs3xDK7raQsUal8BkRfAUIUgFjDwG0q/ys67b9enK6Trzib6wW8ot02aNZWA4idtJRm18RXMQdNwYkOxTYdowIvKtyZyXi1szfFkJn2B6JKHGRUoITV8JsnoYDJurpdTzqd46qd1JRq1MZW3Nx1acvrHRUne3ZzjyuMjTBuy570prAwla56hZOUTaGacxOCTaTvmc+SKhuHr2YavawP+zg34OzcS2fYu2gZPLMdVdFwjhKsHcfUguDZVC+9Fqgbdoy9gYrXBCUN9PLEkxehynahPcOpwPxWLfo9qJmk+/vp+s3ZsKQnkHR0xv07AUOhOmaRML4q6v9jhTDZtB9Ox86KFCkGfSrHhI6j7CekGuqKhCRVVUQtjcC5q0ZbI0JPLoSouDhY5N7ezXlrKwvK7jts7VKdd3J1OXnbWSe76broWEpeQFsSvhFDRqIoEsByXVK6UjIyTdXP4g+W0TvkSlQt/S6h5iXdsqkEuNpRcUR1m0VAkAtVKoZgpFCuFmn93fDHaJn3+kLVcCxkKPzynmG8+28HNL3dx10WlVIQPbv40FINrar7IXdvu5fdNj/DNwddSqlR6qtqK7vUFty32O5Tl2JBq9USV8rCdndJzxaufxgxVsrHkBLYmN1Ba/QdKjCJuHHf9fhFTy3Hx654zPeLXMV2HrOmSzNrkpIOm7Ftp/b84PLAd2aeN3h8EAl0RhP0q2iF0iv07cdgptpTyReDF3T67eZe/s8DH+ln3J8BPDusO/pshpcTYxehShUq5vwSfapDItpAyHVRV7GK+7LgRdcp8JQS0AK6ElGkT9umUhAxUIZAhSc52yVguqaxF1vJSj4qCBkWBvi+7QFAU1GhLmmg+j9gV+4qx9QhN59xN0ft3U7Hq7/g6N9A0/Wa0TAehpoWEmhYRbFmW771qsH3SF9g+5jxiRpSwFuozRUxVVKJGlJAeIm1niJsJHBy0QAxr2Ol9StWbjomDQ0SPENHDA0oLlPkWHi4Sv+6RzmTOIqB7ankqKtD3IJCzHGzPesSvq6iKgk/14VN9xIxYr/Rgx/U8V9WxnamcqhCUR3zEMzZdaROfrniGsRHFrwXoynXREy6nfsYPKVn/AvFhZ3g9B3cnMULQM/JcUoOmUbngV1R+eD/RLW/SfOINh6TdjxCCo6oM1rbm9vhOExqjowNL567vsjnWWkLQSFM/9cuHtJdrX8haDq6UqEIp1OL0hZzloqoeMd1R61cU1GhPmfVmmz4AACAASURBVIQOUQ2kRJLKmYQD3jNcZBQR1AMHnLqqCIWaSAktSY2UG8eUJgiI6BECShDpKgWiHfRpRIwgEeEnaSdIOSn8ir/Xc2dFqmk69Xt0TLgcPd22X70t9zjWvHPM6/3n4kiXYiOGkH6qIoG9pjTuiFy2J3NkLIeQESKkh3BcB9M1yThZNEXDUPQ9HEX9wZWSnOMQ8R15dUN9IaCrlAZ9uJAXwQNF2XnWVFVgqPufGhrUVRJDzyL6wW+IbX2LxOgLiFsJBrWuRk+1ED/hBsCrd/bpCj5N8cS3XBdNUUhNvQ61axPhpY8QXvoIZeFB5IaeRnbodNIVR5PGJmElCChen9HpI/z8dXGKzvSh8Rk//mGSvy5Occ7YAJcevRvhdR1K1jxN6YrH0bLdJGuOp+2Yz5MtG1NYJFdcR664bmeEU0oUK4VUDaRq7LG92jd/QNX8e7ADpSQHn8QXj49wzd/bWdpkcunRQaI96yla9zxdYy8mV9x3/0FHuqhILJnDsXder0hQYFiSeMbCdSWJXK6Q/itxQUqkEAgpkWKnU0JKgaoo6EIn5gsTC/jxazoBTUcbwLPgU3VqijSKcgE6Uiaa4ukD7ChT8dLuTXJuDtOxkPnxU1G8CKuu7PgdBSm9VFIXtxCttaRNSAsS1kOoQiNj2pTFfKhCoGgKPlXHr5TQY3WTcbIw7lK6R56Hr6ceX/dm79W1mdD2Dyja9Ip3mYSCq4dwCs6DEKHmxUSef4/OcZfQfvRncfcRgVSz3QjXKpQt9IWykMqPzi3m28918oOXurjzowNrn7Q3hNQg19V8iV9uu5d7Gx7ihpqvY7l5nQFFASuzf+RUSkh1eH/v0k/Vsl0UIfB1rCfUspSWY6/hva1xAoMfQREON034JlFj/1rTWY5LcWhn31VDUTH8KhG/hum4dKfNfWZ4HE5ICY7r7rWt4n8qXAnGATi2/pPwnxH//T8MF3ePps5CCGJGhJHFKls7O1GkRkDzCqM1RUMRSmGSyuUN9Iqwj5Cvd5rujpB/UcAbbBxHEjDUvRo9QZ+GlrYKBgt4sufFgXKc026lteIoyubdyai/X4b4/+y9eZxtV1nn/V1r7fHMNdedbxJCEhKSYBJIDGEI8yC00oqC3SKObzfqq3ZrO/Sn7dZ2bLEd2n4VmlZaRdsRBBEBEwTCFEMgEEggJLn35s411xn2tNb7x9r71KmqU1Wn6lbdW/dyf/dTn6pbdYa991l7rfU8z+/5/fJCdrt+iDNPfzXze26hNX4Dnl9j0qvhrjivNNNIKZZR6ZRQVN0KFadMpCMWk2ZeRRF4eV9aqlMSkxCogDFvdNXr9r2uxhAllspc9R0qgYunJF9SgrGKz3QzttfIW72hTTNNJ80oew4TZY84yTi7GOM5cllWqnfz305Sxvv0rwkE9dDFcyRnFjpoaSwNS7qMB2MspIvMjF/HwsQzUULZ7HUWd2nYQogu9dsPh3nqBf+Z+Sf/iclP/SZXvO8H6TQOs7j/Dhb330F79LotV1Nv2ONx3xMR023DcLi1auJDJxJeqz5O7DdoTu4cPResWEDgSmqhS7OT2iSOEKsyvXEemI5WArTJaKYdWxGQktCVxKk+5+xwqlNmozZjpRIHaiOETrBuz96gKPkKvxVQ8X3aaZvQCXGly2KUMFF1u/enTSq5nG1GjATDhEnAdDwLGDzpLTuWaPiqvkbfuhBoopc6W9DehfXyzDpgbIDpSIdABvl3H5EvJcEAVOkiQJ1qxjTjhLLnoqQilOGW/Pc6SUYj9LYkMnShUNsBaxHXkSS1AyRj1+M/9gEaN3wnSipGH3s/OhwmOnx3bo2zFPwW48bxJEiX+Zf9JrJ1Bu/IR/GeuJfwy39F6YvvouFVia58KUef/W9YSDuUnJDnXRnwJw80+djXOtx+jqLjf/7gIu+8f5EXXR3wlufWlgVhIo3Y908/T/XYfSzuuYWzN7+J9gDCTQjRt0cUAKl46nn/kUP/8GPs+6ef58mXvpUDY9fxiutC3v+lNq+8NmDyvt8kCxqcuflNa75FJ8kIvIwRf4xQ+WS5tkGsE1InJXBjpqRgpOzhKQcpXFwprZWIEEihUNJaBCkhuv6XcouiW2CfV/FdPEdZ5k6SErp272AZTDbQKyqmW52rFiPbHhTkIkgCQTVwmWvHjPojzMazzCeLlJyQzug1yxIJYKnZRkiME6yqSqvOHGOf/V8MP/wX1L/2IU7d8gM26dDzOBnNUzvyUWqP30Pp1IMIo4krkzaRMnEj7YlnElf3L3vO4WGX//jSBj/7dzP8/D/M8OYrt3TqyzDsDvEDe7+bXz3ym3x49h6+s/LN+AorZBQvQFAdXCujMw9pC1YIGMWZXccaX30/WvmcveplvOeTv4Pypvj3z/h37CttrjUj03bd69cmIBD4SjFU9jg9H+GowbVPtgtZzljrpHpdb/ZLERpjk0or1jNjTJdVZIzuKvj2itpmWYSzier5bsbl4PQCQxuNr/p/DA2/RHUyJE4zmnFGK8roZBolrB+fvXHtJLIena2YbBggbuluWPLqaS+UVHD9dzA9dj3el/+a9sjVpPvvxFQmbd+skDTWqBTZ6obGpIayt1q9VghBoAICFZDpjHbWYT6Zp5NFeMplzB8lUEtZvlTrrlrZSuEoAygsLa7sq1Wb1orvEriKmVbCYpQQuPYx2lg1TCUEk7WA0LWfi+tLlJKcnu/YjNaKYKYVp9QCd1lyYCVCVzFZDzk11+n6LQohqLlVAhUwE82QmNT2ugoHN/dBU0JhgGbSpJm2rBDGwTtpTj6L+mMfoHrsE4x88f8y+oV3kfp1FvffzuL+20kqe/JMdIXMK8M6Yk4AN0zaysKjU4bb929tIfrqUzP8sPosC1e8esf6igyGVqyp+opGyUMg8EqKcpAx305oxZZa7SpJnGiENFRDRSdtETgBE+EornSYj5vEZp65+Q5ShTh9xm1RIUx0kvf7rLwulpapteKK2iR7a9VtCUoLuFISeg5JlnWz4p2cIbFSFKfkOahmQqYNJbeEr3wW0kWaaQutLSVspQhRQVPXuUhZ2SlbWliXHk/+XTAlF5kMx1FC9T3HZpQwUVu/atoLG6B6sEg3QF0LcapJMr3m/s4RkmpweSkrKhzRlS+j8qm34i4eZ1h6+Ec+SvvG7wLlkqSawFXdBGHvuCmsaXRpjM6130Ln2m+BtI137FP4X/sHwi//JXuGr0Y/7WW0sjaHhgMODTl89ByD07/+fJN3fHqR518V8KPPry+zyJHRAgfu+VnC01/g5LN/hJlrX7v1N1oB44YcvfsXOfz+t3DgH3+aJ17xO3z/HXt51XUlrjvzIcKzX+L4GiJIQDdhc1VlD3WvuuxvvaHF15wprmqscsHbcXhKMlkPmG+nzLZjlBDL+uwLmu5WEMVZ3me7/L4r+YrZln3thtdACcVsMo/DcvoxsG41NAvqnLzjx5i9+lVMfvq32PfxX2bo0fdy+pbvx1s4Tu2Jeygfvx9hMqLqfqZueANpUKd06iEqxz/drcqmwRDNyZuZeuZ3Eg1dAcBNe31+9Pl1fu2eOd6ZCN4ybLo+81vFgWAft9du5ePzn+BF7bt4mjdp18AkgjQGd4CAIelAawrc5awBg1VoDV1F+eRnaU7cyP988i9oqq9wDW/khqHrNn28UWoYKbvrfv6eVFR8h2aUDpR03E7Eqbbj1ZHLCiVfD0hTW0TKdJY7P9jPSArbruYKB6U8ZE61L9ZjiUCaMs4WEry7EZdX9AsIg0EYs64KrBKC0HUIXadL1e0kmlacMlr2qATOtme1Sp6DI5M1JwUzfiPR+I1IYFAR9k6SMRR6aGCuFVNeRy1VSUVFlik7JRKT4vaxYokSzWjF69nI9GTahV2Y1xM7caRkrOJTciXTzYSYFIBG6FENVgulBI4NLk/Pd4h6xHTi1F6jodLGV8JTkkbJZaoVLduMe9JlIhxf55ng+Q1qXpVO2mEumSd2HDrXvobp616HSppUnvo0lWOfoHr0Php9PAG18tFembg8SWvyZpqTz6I9fkO35/DKEYfAgUem4PbVLhQbwhjDxMmP45Nw/IoXb/4FBoDG0I4z6qFH1VfEWdK1YhFIKoHAdwXz7ZRWWyOVZjj0KLsBNdcKjhUYCRrUvSohsxyZmyHwJF7el90bjLrCwxdVHOkgsIkPKaxsu5QCoyF0PSZqwbrjbauoBg6nFlI8bEKGXPF1JaSwSaXpVmwDDqloeHXqbo3EpHmfdJMos2rKApBIyk6Z0Anw8l7CtVC0EvRDnGp8R23a+kpgA1TZhIVOumpOSLUmSnV3k+3nIj7WmzT3A6RYtC8LeChhLWqaV7yYyqfeSvDYB0CnCKNpX/c6wF7Terg0flaOm1VwQuLDLyA+9Hxk6zTlB36P4atfSaY8oizieVcF/NH9i3zulKQZG8604HQTzrQMp5sQOPCCQ4K7DgnKfcT93v0Fa9ny3Ct8/v0LlwemTussBz70k3jzx3jqef+RhcMv2PZrloVDHHnxr3D4/W/h4Id/kide/ttcVXEY/9Dv0xq7gbk1RJC00SzELfaVRxkKNkenPJ8omDs2aMyTsc659aWlma3aDJe9VXsPV0r8HkZKzavhKo922ibSEVEa50kmO4cWtP3uvxX3cWf0Gp54xW9Tf+wDjD/wNg7//Q8DEJcnmHrGt7Jw+AV0hq/uJtRmrnsdGIM3f5TSqc9TOv15ysc+Te3IRzl7w3cw9cw3YpTH3VeHzLQ1b//kAr97v+b/uUWuq4w+CF458lLuX/gs7z799/x4/U32l0JC0to4OM1SWDwFjr9KPDDNTdhVexp/7kn+576r+cjpjxKduZtvuvWuTR9nobUQuBtv/6uBSyvKtl2fYT1kxuTrK9RDjzMLnU2pm1/sSI3BcwSRjpgIx3L2kxgs8Z3FoC4Nud7LwekFhDYgpRy42rKcqrtzA7DYsHTpXueINNPd6oYQ9v/tOCXcoN9PCIEnVp9nJ84o+84yr7+touy7+K6iGWXrqmHCUib69EJEO07xXUWqNZP1wQOTsu8w11478F8PSijKbpmSUyLWMQvJIu20A8qhffBOZg+/AGkMwdQjOO2ZXGhisftdxk38uScZ+eKfMfqFP0FLl/bY9bT2PIvFPbdy9fAQj05tzff4qbmMl2QfY6a0h87otVt6jfWQ5b3DNV/hOimdLKXslig7YVfcKjUZmaOpuAmtNKXuV6i55TWFiByp2FcbxmifVtKhYxZxpMuQW8EVDllmaXfDZZfQs15jmTZobYWP4swq8w6X3R0LjgJXohBk2p7/RM1fc5NQ9h1mW8urYMU95EmXmrtkMeUIa+eyHSqeSWbvga1A9ATbC52UkqfQBjppiiMkE1Xf9opTnE/xxMvBaD+EnqJlJkkmbsb/6vsR8QLxvtvRucK3MXS9KAuUfIeZVmyFXNa6rkLQfM6PMfTX30Hlc3+IvvXfcqpzmjuuUPyf++Gtn1yigzcCGC/B9WOCU03Du75o+MsvG+48IHjxFYL9Nfse73u4xf933wJ3HPb5yRc1lgWm3vxRDnzwJ1DRPEdf9Es7quKd1PZz9O5f5NA//DgH/vGniYauRMULnHzOj/QdZ5mxFY2aM8Se6ub8vy8U3DwZW/EdphYj4iztVtBNPq9l2pDmXuSFIFIBa2Vhk0BxppmoBWuuX7XA5cxihJeLOYYqIMxZT915WqdEOibRKdpkpCa1x4FGGGH7bzH40kMJxdzTXsHCwbuoPXEPnaGr6Ixet/YcIARx/SBx/SCzT381qjPH+P2/y9jn/w+1Jz/CiTv+He3xG3jdjWVmT8/xF1+T/NanNT/0bIl3DgHqkNvgBY27+ODMPTzWvJurygdB+RAtQlC3Paj9sEafaYEsD07LJx/kw6WQ/9V+lAlxC0/OvIQb926ewhmnmnqQr1k6BXTXS3UllBDUSw5TzYRynxaonUBxfGfz/a7vbKxufqnAYMAYMiIm/FFKl0gVdCu4HJxeSBiDlOxKf6l+dK+topNmTNbC7sZnpOxxMtPLKpCDQue8+0Zp+4JzR0rqA3r1OVIyWQs4uxixEKVMVP2B1DQLSGEte870oU0PCiFEV5ipoJ220w6ttI1G0xm+cl0xGZm0CE8/RPnEZymffIDRB/+AsQf/N68f+yF+9vQdLMaGire5z/yxJ0/yffKLPHnoDdseOKQ6YzGKqJckpSCk4dYI3YDt8MkUCMarISdmYcizti1JZinj1cChUfK6waAUgvOtUSAQ1EKXMwsdhspel2reD1LYx86216iCQe6puj33Tppfp5Knun1nW0ERoAohmG1ZEZeRkk/5Ali9XOwIXclCxxBd9VIq9/0qAIt3/ARg504rkLOivy9XBp5rx+smDNOxZ9B52qsIH/oj2s/4NsZKoxhzhv/88hqzM4uMl2G0xKoN/pOzhg8+bvjYEcM9TxieMQpXDAne95V5nn3Q56de1FhGqwzOPsKBD/8HEIIjL/uNdUXfCv/qQpyu+NosOmPP4Knn/Sz77/1PhFOPMH3tN/ftzbYq+TEj/ghKe/jnMO4vBEJXsbcRstBJmWnH3QDUU5JK4OI7VpDJkaKbjEtyb+g4tVoMjdBb9363ySS6/c29kELiCdvfHLJ64130wRpjaGeWJRTpCFe4uF6F2ad/06bPOQvqnHjuTzF/xYuZ/ORbOfz3P8z0Na/lzLO+lxfsM1Srgj/4nOGtn9T8v8+RBE7/OeexGcMHHjPcOA7PPdh/jL1k+IV8fO5T/Nnxv+Wnr/63S2th2gFvDVXrNfpMCySFUu+Jf+a/jo5wRfkQZ7/yOm7a6695rGuhsM8LPQVGQ9axAXTSBKHszys+s5Jnqb3nI0AsfOVLPSyaesnj1Hzn6yI4TTODkTFD/iiVDUTALnVcDk4vILSxGwPRxy7mQmNDuteAiBIrLNRL+ZNCMF71OTHb2fSEV4ifDGIZsVOQuQpvNXDWDRbWQsl3cNuprSif44SrhEIpRaACGl6dxKREWUQ7bRPrGF2I2uQWAo5wwC3R3PccmvueY1+jM8ehv/8hXtz6AD/LHTw6Bd+wSc/06hP3ooRBX7uBD+4mkZmM2ajFlY1RRkvVZdTc7YKvFI2Sz1zbUl4daSvk5xJwbSdKvqKSuDQGoI5XAscqhK5XBTsH2M2qlU1ylWS47C3bSGwVhc9y6Frfxss+e1uD0+07fSnl+34NHY4QH34+YBU/S2sI4pV9xWy7f0DRi+Ztb8F//IOUP/M76Bf+AmPBKM/cd5o5b21hnUMNwfc+S/D6Zxg+8qThw48bHj5ruPWAx8+8pLEsWC4/9Rn2feTnyII6R178qyS1pR6DVKfW77ZHud56Trt4yrPCedqOfXuKBsFyyuhSBd7+3HvMiwfu5MTtP0bjsQ9w5ubvXnUexeuPh2OYzKES7hxjYichxRLV1xjrkdtvTBTJuN55cKW+w1qvX/EdWvHmk8/dz0jQbe2JdMR8skgrayN72gt6hWAKrKfu3dx3G197zTsYe/AdDH/pr6ge/TiLe76dF1x1J64SvO0Bw3/7hObHbpeUeijoX5k2vPsRzZdPp7xCfprPT+3nGw8c7vvZl1TIixt3857p9/LQwpd5ZvVaq/fQnrW9p0KCUhSNFcSLeZ/p2oFInGqUFPzZwhc4U5K8YfIN/ML9ktdcv4WqaWKoeLkOR7wIlTHwq7Y3Npq3vxMCVNClFwsEjfMkjhQnmlrg5vO/TVL4ShF8nVRPm0mH8XKJIe/iYGTsJC4HpxcQxoCSYldWTmGJJrjVIMoKypi+m2pHSsbrPifmOkghBqrO9tKDLzQEYkuBafHc4bLLyfkOlW2cbHspnFXXinhk2lKmrIVARCeLyLSl4DnCBqtZULfCE//8e1ynjvHI1AG+Yc/gC5AxhpvmPsIT7hUk22gfk+qUThqzrzzOvurQtr1uP9QCh3aSUnKdvj3HFxKulEzU/IE2BWqA6ul6sBTpooKRfze26taMUxwhqIXehhT4reBc7qnLsHCk7eXLwlFaN38PWeNwlyqYaiu00f95klrosdiOCdYZN7q6l/YNbyT83B/QfuYbYfQ6RvwRzuqprraz9eW0gV+vgFbVF7z66YJXPM3w1Rl4zjVDS1VWYxh++P8y/sDbiBpXcPRFv0wSDpPqhDTvK7d+txUr7CUdVP76K5GZrOsVmuqMzKTWlgKrbG+VLg2xjkEIArm0yZ+7+pXMXf3KVa8ZaZu4mgjH8ZRHM00pXeR9cFtJ8A4amJR8h/ko5Vx1Q3uFEhOdWH/xzFqe9SYWCuubSEfdcahy3/Lexxk35PRt/5b5w3ez5xO/zi2P/Q7Nk3/NxBWvxb/lG/ndByS/ep/m390hOTYP735E89jZmDd59/DO8nsZyqY4Zkb5+zO/ztXj/VsZnj90Bx+b/zh/evxvuf7pT0cqF7LE+p5ibMWyuI6L5H2ma1/XONXMzj/CHwfwYm8fZ6f3AwvcdmDzV1djKAcupG3wK1CIfbk+uGOQDdnj7MzaDaoT5s4JOy+OVPg9h54izmybQTtr4wiHesnj9ELEedZl2jS00cRZPv/oJSsmm0gRlD03V8xejUQnCCHYVxvblnabix2XdwIXEJnW+M5qsZ/dAikEYzWfk3PtVRYwg6CdVznX2sT6SjFe8Ti9EBN6asPXX0kPvpgRuJYKuR1WJuuh8HLttRAoKgDNtGXtQYDTh1/A2ANv5/vCe/jtqX+1qfeYPXGEb+Qx7h1/MxPbdNyJTsiMpu4NM15eww5iGyGFYE9t9/Z3bCZbXQkc5tob9BD2QStOu0JPSgqksEFLQfObrFlRovNtK3AZg0NgFVmTTNN69g+t/OOqftNeVHzF3ADV09azvofgy39N+ZNvZe5Vv0/JCZkMJ7oBoUYT65gk96412lL1lFDWQkUKrhlZov+KtMOeT/w69cc/zNyh5/Hk7T9K6vgIHRMon4ZTx5Ne12ZlIyhhVWFd3HUV6jOTMR/Ps5A2u72NK2GMoZW1KTkhw/5Qbm1m5+zNtHN8vcF3JI4Q26q06kqXxgYVJWMMiUlJdUI769BJO2RoJGKZrVZn7Doef/Xv0brvb7jm9N9y7Rd+l0Phn/OsK7+Jtzz+PP79Bz1k2uL7/A/x5vLfUc3mmateyyOjr+Car/4RE4/8JYy/sf+5K4+XD72UPznzZ3xi5gHuHL51bZEadw2qbw6dJ/j/5LE/QWF4w6Fv4Vc+G7Gvrthb39z2Pcmsv7FLaquipZHVQbFyIKzbampr2lZS82PcaXGkONFUA5dYd/Ckhytd9pYmmI5maWcRSmYkmThv1dO5qE1mNA0/3LBVwBib7FqMYmpBSFkFuMrFkw6+Y5Pd7TjjTGuONm0EEr9HhFAbTZQlTATjBJeIoNG54nJwegGRmQxvjUb03YLAUQyXfKZa0aYEiFJtF4SNqpwlz2WoBNOtiNB11qyg9qMHX8wQCIbKHifnO13hiPMFR1rPu9AJu3YibbfCqbFbeNnZj/LTc6+nkwYD97OIL30IbQTm2rs3fKw2tmq73mQfZzFGwHgwSpbJDYWzLmM5ih7CzVRPO3GK70jGq/3FvaQUu4bmfBnrI3Ql7SRdNq+kmcZXct1NpSslNd+hGWV9/Z8LGK9K85YfoHrfr+Ad/RjxwbsQwiqvqjwa7LX9SnRCrBOaaZN21kEgcHJPZ7lwnIMf+TnCmcd56qY3MfXMbydUJQLHX+XRu91QQjHkD+EXVl6k1kopR5qL9gz5DapOpbuRjFPN8AAU+69n9Hqenk+l1V72UMkpgW8/x1baYj5Z7IosSSFBKk6M3M7i0+5i+MwDHPraX3P3U+/g/tJfco/5Bu72Pk2om0w3buLBK/8Fc8PPAODUsaO8vPk+PjN7F0njYN/juLl8Ex9b+Bh/fvJ9PLtxM+4GVm5rITWaL849zH2do7xlIcIZuZnPHT/Dq65bP6jthyTT1lUgi6C2v2v3VjANCmHBrnemG+CkLby0DU54TuJI7aS/D3kBgxXjMjKi4tYY9us8Ks7gKY/J0jjttIPWZzkx32IoKO3ovKCNZjFtE0ifkucz3Vmwyag15qM4i0nJcE3A04bGmKz2T6ZngcFoB01KR7dZTJrWWUJ6tLM2dTXMUKknQW7MkvruLmVX7iQu7/ouIDIMvrP7P4Jq6BANqLBboJNkTA5or1EPXZSE6aYVaFhJK9PGkBrDRB8LjYsZgaMouYo4zdasaMRpRqbNjgVoUsiuuNLJgy9jz+lP81JxP1+dfi43rO9uY2EMV5y6h/vFdUzs7d+oWog26bzvlZzqUtiT9FYsIh2jpGTUHyHLBBX/cv/hVlAJHJpxSidO16Vpgk38KCUZWyMwvYyLC64jV/XjJdlyC5m1UAlcmlFGJ87wXblmBbVz3bcSfuFdlD/1G8T771j/eHIRrrJT6gZ87bSNf/x+Dn/slxAmY+qlv4E69Dz2bIPI2WZRckJ86TEdz9DKWgTSUkgBJsPxZTS8gu6+Fj36MpZQeJ5eaDjSoebVKLtlmklzWZAKgBBMj9/C9Ng3UJ95mINf+xteOXUvZ8dv5eErv5nF+nJhrMeveSM3PPjPHHjo7XztuT/XN3CQQvK68VfxG0d+nw+d/SivGH/hlo49TlL++Il3sS8zvK58Ne87kZBkcNvBzVF6s7za7+kOVMbB8dBG00pb3SqlUpZx4Ainy1JYUD6t6ccQOsV3y1sSR4oTjefYoK8VGwJPIlewb1pRguNm7AknqBZUY5NToIUkdAKurO2DbJqpziyOEmAMQuSvJWz/eZEk2yo6OWW8IhscqtfxPQd3JiQlopk284KSiyMdYh2T6oySE9JwKkjtMFZZm32lhGC06nNyztDw6tTcKs20yXyywJDXQBmfoFdxMW2D8myAqlPsSbp5sHrpr9O7PzK6hGGMxr0ISvgC0VXYXS+QKhCnGaGrNtWbUPFdAlcx105Y6NgqTtHn5GbzDwAAIABJREFUWnikXkgRpJ1CveRyYrbDyvghyTRRfh19R9GM03MSphoE2cTtNIMxvl3/I386dSc3jG88AQbTX2EyPc57a6/hzp4JM9UpiUkxGGtj4tUIVYBbeInm3putnFpssFlbVzqM+MMooYiSlPIu6C/eMRht/e1MahdisOIZzrl2atmFcKIWMLUY0YwTSm7/9oE4zQAYr65tUXMZFxc8tTqo1MbgDyA17SnJnkbAXDthMUpxpOg/3yuX5nN+hPoHf5zg0fdA404ARBZTnnqI6unPUDn9Gdz2GdJgmCQYJQ1GSPIvlTQZ/8q7yOqHmHvZf8fUD63HwN1xKKkY9UdopS2m41l85THiDa+iEieZpuQ620ZVvZThSkng7XzryqBQQi0FqWmL+XjB9gTmSs8Iwdzw9Tw0fD1Cp5g1qp0HJ2r8tngD/6n1+7SeupeT+1czhpQUXOVezQ3Va/ibU//A84afQ9nZfLXzvUc/zFPt4/z3s2dJr38Nnz0W4yu4Yc/mEvVxZhh2U4RvKbuZyegkHcbCMap+dc3nldwSiVdjcepLLKQdtBBUQ8X0YooUGrXBfRBlCR0dMxmUkEIw246YaSd4SuIqhSMUqclITMY1jf1UvZxxofM1MW5aijHk/ZgN0C6uo/Nqa4bWuttPnpmUdtYG7OftisHs0gp7qLIqUVZVlFSUA9eq+VdCTi9K9pQq1mc+naeVtQmUz6g/gitdWnHG6Do2bwUCR1ELXRY6CSXPJk0qbgWBoJPopRY4Y8BkUJ20wWgaW8XnuGmVlbWxAarj2z3DJYhL86wuEmg03kUQnILtyRut+JyYa6PWsZfJtCHNDKPVwQRceuFIyUjZWkicXbBebG5ORdsNIkg7AV8pyr7TtdVJtSZK7SQ1WQsJXWUDt0VDa4cDVKVcpg++lG989I9525njcN3+DZ/jPPJBYqNYPPy87u+iLEJKyZDbIFA+zorJc6X3ZqqtwvBZscCoP4IUkjS7BPu6shh0kguNmjwQDcCpgeMBAuaPgfG2JTOqclXpmZZkLqf49lZGk0yjNUzU1/YsvIyLD1IIPCW7/X7GWCVdf8AAoZiHK4HDTDNmMUoIHLVKFC8+/CKSiZspfeZ/MPK0s1RO30/l7IPILEJLl+bIjSyO34rTmcbtTBHOfJla5ywyr0pGh17Iwgt/AePtfE/5IBBCUHbLBE7QrcKsRKINw+VLcy3aCdQCh1Pz0a4ITgsooai5VcpOieNyikQnGMCTbpe2uVZgCvb+mjrwAj795D9x8yN/zNT4rSRebfl7KEGcar5jz2v4mUd/jbcd/VO+78C3bypAnYsX+JMn/oqbvQnubh3h8cmbefyxlMMj7qb8WDtJhktKEPhQGraBadphvDROeQC7EjeoMjRyDfX5E3Qcj5lojjDQTC9GhJ67rHeyQKpTOllMlgmuHp1gyK8ghWRfOaMZx5yeb9PWEVJopBZcUZtYCkyNgaRtgzKvYn92bUUycBRV3yPJ9JpFkizvdW/2JL4dFEqqJasiDBgrEFUIuI36I5SckMUoYbTidfevZd+lkmg6cUbJKxE6IalJu6rR7TjNnRsG26s0Qpd2lHWrz1JIW9Dp1V1JO+DXl3qVHc9+BTV7fdLIXpfOnP0u5ZpetRcrLs+yFxS294ZowZbr3a0Z2Z8veEoylgsYlbwlyXZjDEmmSTK7GRqpeOcUVAROjxdbK2JiQHrwxYp66HIiSmnGCY6QjFd8wh7LB4FgtOxzWncGommeCxYOvYrs0Xdx++I9JNl3rvJEXAadMfTkvdyrb+bqAyOAXZRAMO6PDSxgUvTA9ip7RlnGWHmDCmJB+xnwfeyk3rbPccs7T43RmQ1IjQaMDUTLY/a7dPubsocj0JlZ11pgMxAIhksejhBMt2J8V+JIG7gkmWayHmy76u6uQdKx13gH7Id2OwLPodlJcCQkmcm9Jzc33n2lmKyFtJOM6WZMM0qXb6CEYPH2H2fo3f+KvQ/9D6LSXqYPvpzF8dtYHLkR4/RZz4xBJQuoZJHhK2/elfS0tWiB2hgUYjn1rkDStvPQ1+FYWw++o5CCbRVG2i4ooZBIxsMR2lmHhXhhVZC6Fu48KPnpr76ZD8if4spH/ohHnvlvlv1dItBGsz/Yy7fteRV/ceL9fKX5OG/a/y+5rXHTQMf3B1/9c9ppmx8V42R+jU79MI9PneUbrxhsn6gxdKKYssyolzxkZYIUQ5RGTJQmKG0gxrQMQR2ZRJSiBUrlSZIwZcRv8eTsDC1jHRcKhe7EpDjCoazqHBiqUw2W7gklFDU/pDIaMNOKme8kCCkY6l3rk6ZdJ8VxS0GefdJWUvOEQb3kcnouohklKCFxHbmsWKKkIpRhV1Mj1jHttEOs42VeyBJpj1soQhWgpOqy/lYGmkMllxNx1h3HrsgV0DOd2y4Oft9LIRipepyc6+BIa3GVasNQb6uATiFs9H8BIWys4AZQGsqrqm1oz9mg9hKZgy4HpxcQwhgcjFVIAzvInN09sEqeS70Ec+0IP1ebNUDJdRip+Num5ll4sZV9tesWte2GpySNkocU1r6nryCNEIxVA07Nd+jE6wuWnAuycJQn67fxLbP/xLunXs/V42uPx9Kpz1FJpviAfCNvHnasuJKxinODBqb9YIzNZK4ZhHeDzMwGeUnLmoevd+8kHTAJhMP2/63pLl1o22G0pd8oF4K6zfo6/mBBdNiwMv46GzzoHgC10MV1BKcWIjJhyLRhoh7sXGVaZ1bpUXndrPd5RdKx10+noDvnP/FntB2Xxtjs/3kOwgJHMJ9TxVOtaZS2ztAJXcXeRsBsK2Ghkyzrf08nbmTmNX/I6UVNXNm38YsJQebVyLzargxM10OUZNRCb/X6liX5uYilMX+JbBDPFVLYlqDZVkozTVEIPFfuqmSzEoqKU6akQtpZh/l4AYNG5oJihuX920IIJiseprGfP45exb8+/h5O7ns+c8PXL3ucwTLJXjvxUm6sXsfbjr6L//7EO7itfiPftf9fMuSurTz82MKTvPfIh7h7/IU88wt/R2viZmY6MB8ZDg9vtG03ZHGHOElolAMqjT0It0QiDEkWs6e8h6Bf4mgjlEetmFLSwXUDxko1QhVyeqGDcjRR1iExKQ23gU4VtdBbFpj2wo4Ln9CVdFKztA4lLZuYLQIz5Vhq69xT3XnUV4p9QyFxpmknmmYnoZMYKy7kyGX7RSlk14poECSZYaSyOiluiy4upxYiKn4+LowhSm2Cd7MtMYGjqJc85gvRQsMSuyCNwSsP3t7TrarW7VzUx//3YsSlvevf7RDgmAy8GtT2Wspfllzoo9oQjdAhdBx0BsMlj/2NkPGqT+BsPju/ES71wLRAPXSpBuubuishbG+gEkRJtmPH0rnqVYyJeeTRz6z7uPrj/0iTgDMTtyOAjo4Y9obwznFjFiWaiu+snvALuk+yCF4VGodh6DDU99sKWbRo6S69yGLLTHAD+/jyqJXQDxr28duNLF7K/A4dhvIIeKXBA02p7HPT9rYfWug67KkHCAGjFW/n1HeNsZuM2h5bJY4W8urxeUIWA8a+f+OAvaZJ8/y9fxrZ8y+P2XGWnH9VmJUU3HO1XxAIyr5Dv21POnnzYIHpDsEYQ5zanrMdfR+g1C8pmHagMmHHWm0f3SA1i3f0eC4WlH2XvUMBk7WASugSJ5pmnBIlWe6j3P/rfEMKSdkpMRGO0fDqhI6tvpXdUver4lVwhEOkY+48IPjF1jez4I9z9cNvR+jlezeJIMnseVxROsB/efqP8/o9r+bB+Yf5iS//EvdOfaJ7nrFOON46xYNTD/PBpz7KW7/wNspumdcP347bPE1r8mYen04B1glONSJpEbebJDJgbO9hqhNXIoIaMZpMZ+yt7N1aYAp2ja1OArq7T634LiPlAJ0pGl6d8WAUqV18R9EIexJiWWwTlitQ8twl5essAYStlvau/V4ZSsPL5lEprHr8UOiybyhkTyOgEXpobWm2W0GUZJR9Z811seS5VH2XTv767SSjFrpbXkfrgYOrJJ04w+0NqnVsz3crUO6uL3ANisuV0wsMVwi7eXV8qO+DuaNW/W0bqybbDYFgvOZ3f76M8wdHSsarPifnOgOJU20F2b7bOPXACDfN3Mucub3vY0QWU3nyI7w7u41r9lbp6I7t49mAjhqnGVKIVZvnZe9vzHIhJGPsBtCk4DdsVrV3AvZK9ivp2IpotJBXzbS9rxoHllfvhIDKmK2qJc1to9CSNEE4UD94bpU6vwrt2VxGfoOFpqgQDti77itLmd/R+zZezAOzOvg12xfTPA1yg+r2dkCnNjgcOrR0Ter7YfGUHRdrVTGLPh6dgsB+jo6/ueqe0RC38jF3yJ6r1nbsptG2CF0NCldaFcs009bCYRuo21bERO4qimaa2R79kqeIEk1mctqds73VuTjVBK5afR2Tjh1TXk6R9ErgHrBJtObZPDGTW0Ig7NpeVFk3O7Z0ttTKgO5TIcnpfhfKdiJXVu0HgQ0mAkfRCB06eYDajvonWQ0gMDvawrIWpJDWhmYNlFTIVDTNN0ym/PFDPv+78t388NSvcODxv+XIVd/SfZyjoJOkVHx7Do5QvGbiJdxWv4m3H/1T3nb0T3nv2X+knXWYjedXHcMPX/u97Dn7KADNyZt54kgRnK4x18dtmqqCN9RgrFpCSUGkY1Jt+yP3lPecuwCncm0CZv4pOx7dgHroooHZVkzgSIyB0aq/dP/FzSXV2VTbtXjlODHaJmQbh5aYhL0IhyFq9mUXCmw11Q8V1cDh7GK0aX0OYyybqN4bUJv8HuvZiw+VPdpxRpRkOEIuD8A3CSkEw2WP47NtamG+NmRJzgLb3S1+5wM7ducLIYaBPwMOA08A32aMmenzuO8Cfjb/7y8YY/4w//1/Bf41MGSM2R2KCduIzGgUwt7AxUB0Q6jugbnj4Fd2tbfR5aB0HRSbhh2iLjlSMl4LODXX6QopbSuE4jPVF/HK+T/nI4vHgcllf1adOfZ84r/hJE3+JruT104KAhVQd2v9Xy9HlGQIbH9Fpvsfd18hpHjRBjrh0PrBjRtAfa8NBDrz9v9rBSNC2CzwwvGcSrRO/43R+cZL9X8tndnXCBq2UnquiaUieJ49un5wmrQsrTnt2H6cAcfbjt67SdOKNhS0LCHsz24IC6fsRsUt7cy9YbQNCur7lweCRcZfedCaspn4Ym4tNkXG2EA6qFu6eLQIcR5YCGk3DOt9rmkMOrJBedhYOr/ivWefBO1sf9LR6DwAXh38Bp5ioZNsqh9qI1R9h5nW+fWu7AdjTNc7cbIeEDhWOC5KNa0opRll3UBVrhDv6wpjy8GZOYnWDJdXbBiNsQmz8oqqcZFwLoJUccRu6nUeYFJ8L4JLG4rZr/xnk6txFo+Rjv1SXv6zsidQzElC5sm5KfsyTp8AYKdgjL3vjbaMlg3ubYFY6utbIy+YGcOp+Q5RnOHvMtseJRTD3hCZmeKmCcPbz97E6ydv5+DX/oqT+19I7A8B1hs6SjTamGWJkj3BOD/ztLdw7/QnebjzFYb9BmPBCGPBCOPhSP7zMJ0Iag//V9JgiLh+iCem5xguSepBv8/VEGeGoD5EvewR6w5oqHgVKm4FX/kDqdYOBDeAxkGYP2Hnc69MI3TQWjPfTtnTCKyzQtHeEjQsYwlsorJ11o5NJ1waK3HTsg/WSupKCbV8HjXOmmNb5iKA082YhU66TBtlPURJRjV0e9Ry82Mv7qW837Wwgzm90GGi5i9PgBX3wSoUjzH52rd07IGjGK/6S3uhLLIxwC6ivV8o7GRa6j8AHzbG/LIQ4j/k///J3gfkAex/Am7FzsL/LIR4Tx7E/i3wO8BXdvAYLxi00bZq6paWC6P4VUtrWDxtfz4fgzSL82rt5UL6OUNn+aQm7AaidwLeRnhKMtkI1lXTPBdMHXgZfPHPGXryPqIrru96/ZWf+hR77/s1ZLTAXw6/iX8+cyM/OqwY9ofWXQSiJEMIwURecT+90D+7uUoIKWnbALM6MfjBO74N7jaClHky6Cm7sVu5MKaxpdgIYZXwstbyaoWQ2I1kZimk29nD6ob29fodF+RBXmgDn848NM/sXA/toEgje53K46vHvOPboLE9YzfQTjBwtXcgFLYD1YmlKlYvhLCJA+XAwkkbbGaJHQPhiL12vRl7rwxmPJfvb0E0D2mLpSBixXt3q6V9qqOOZz+n+eMDbd4HPt8iqJaOPZcV7x26ktmWGVhFchD4rupL7T2fKKqltdClES61QvRW54bKeaAaW1XMlZBC0IkzcDauAmd6DSGkpAWl0bUTZkWQKh3btrMSxuTziVmq0hTiaWADT6l6qq0bwA1tYqgz3z8A2AkUm/iSFcTbLjG3wgprpzUWtgpHOoz6w9y+f5oHTio+MPRt/OtTn2T8+Ec5dsVrADseDZAajbdCZEsKyd0j38gbJv/Fmu8xn7aonHqQ5qQVDntiOl2b0pt2aKuAhqdt20YwSuiE56T9sC6Ua+fz5lnozCK8MsNlb4kWmyV2fqpMLBf2KQ3lzKAZO1aEC2g7boO1e3ABO7+VxuzYXkfhW2ArkkII5toxZa+/jVoBbQwaqyzdRZy35ji+VdDvCVBDV7GnHi5nURhjk+ilEZtEp/fezr8nbbuv702OYqnR9kAye89vRqjqEsZORiOvBV6Q//yHwL2sCE6BlwEfNMZMAwghPgi8HHiXMeaT+e928BAvHDKt8YXpv6EsDdlJvz297k14TuhSJTO7UUzaeaC8uxaBbYHObEZKF1WGTQhPdc2PB7guWQJZx1bvnMBSMzszliK4A6IwrpSMVwPaScbUorXeCVy1LXS2Q3v3cu9DN3HrqXt5TL+ZTjrDvs/+b8YffS/t+iGOvPAX+I0PD3PthGSiNLau8XVRMZ2o+d2N4GQtsNnNKKXck92UiCXBFaPt+Bwk0NwqpLKB5exROy6kzPtWjV1EgtF8YZL2ntFZLrKT2s9bp3ZR3Qm6amkEZp8As4JeGi1aZkVlIg+uGjZju1Ygez6QJfazqu/rr0IM9vflkbyKetIG/s42VVGTfIO80QYnqNtqc2vK9vV4lbWPVwh7rG5oH1tUu1ZuOkyuxLxekONX7fG1Z+24OhekkU0ohkP2Sx6zgfUKES3XkYSO2lYbjy61N9PbmgwbBMYYOoltCyiqpWuhN1BdC6045fRitGEVOE4z6iuFkHRqN5gbjbf1IHqpvdu07kq1FAB05uweYqeCVJ3aAL06aa+DzrZVzK03QN0RhtA5wpUuzzvQ4A8/N897T0/y2sbTmTz+EY4d/qbutRZAlplNq7sYDGL2GG57itbkzWTacGQm5dXX9w9csiwh9cocrO6l5J0nATop7dqsXGieQbghgePYdQhtq6v99j3Ksc8L6nYezpJcnXeA8RnmPfxFcnYdGvlwyUMJwXQrWmWjViDThk6SMlRa2pt0q70lWwGntn9VgLo8MNVLwWzxnH5wfLtWNE/njK4Vx5527HXZJS0TFxo7GZxOGGNOABhjTgghxvs8Zh9wtOf/x/LfDQwhxPcD3w9w8ODBLR7q+UemNV7hc9gPpWErkNSZt1l+5W588xqTbxL1EuVn5SKhM3sTYOwNGNTsTRMt5pn98po3/EWF3oBUOfZcvTKoY6By+x7HX5s2mUb2+kvXbrozs/5GOs09LOs9/Y3FBNyezj9Hb0f6zqyaprXemW3HKCHOeSEfKSn+j/si7k5/nasefR/hV9+PO/cEc9e/nrPP+l5mI8WxuZO8+hkNPLl2BawITMfr/goVPcFIxcNVkplWTOBaD7JKr1pxISqz017Ays37vY9Z6mVlzGb/V/a+CGF/168nZifgeMutZQraUFC3x9hj6UFlAmae3HaV34HQ7Rc6ONhn5ZVsT2hr2mbQB6miFvMaIi9c9mzs0ygP/gYUkSh6lDeD4rM/F4TDtgq71SSCTnMmQSkXm+qZS4K6vZ49ga+nJPWy219JMm7mWfrNH0fVd5huxec9OG3GGbXAoZFvOs8VoWd7SONUrxnAm7yqUvZX3FNJe/1EzIWGcmwiKKjlSdJZrAJjsD3zQ8EqqR9YupcKMbfFU9uWVC9EAE8vRGsGqKnWJKlGAwqB7/b3qN0JlF2fuw56fOjxmCM33MUzH/1fVOYfZ7F+pT1+KYhSzWZbE1NtqJ5+EIDW5M0cn8+Is/79pjqLWDSCvfWD5y8wLSCEDcgc3+4fTcsGcbX9G8/pjmfnsS6FfcD3q0zYoDZe7FkTct2FFWO7HrpIAVPNGM+RaG17S4s8kyMktcClUlRNk5adEwsaMuTzbb4/cMvL32PQwLRA8ZjmmeUtR4Vg4C7xfN4NOKfVVgjxIVY2pFn8zKAv0ed3m2INGWN+H/h9gFtvvfVCM44GRpaleH5lfUpQedwO1mghvxHJffvy/idjVij85tUe6S4FV13Vz2IjK20GfyWNza9YOvHCqfNHJz4XpO2lquYy5H06sicg7RU2EcLSUeLWkmBFsTkuetbQ4OZUUiewvy820v0sS5I82G8cWB18FpS+cAiaU+uLspwDCuudkq+YbSW0ohQjDI6QOGq5D9igmB17NmdONhj75/9JVh5n9pW/R7L/dh4/0eIXP3wcgOceXsOLC1txKAJTt88mTmCP2VVwejHGwJIQUhrba30ulYnNwPFh6Irdt9nsWsukdpyVhuz9u3L8KLeHOnoerUu6dNrJzbEDpLIBtle2m9mkTxVVp0sJJje0zA6di8EU3w127upHJd5tkNJuxmaeXObb1xfG5BX6ZGnjIvMkSr+eXa9s56geCARVv88GUWd5AlJvfBx9EHgKzrMAcStOqfqOpettU890Qf87OddZMziNU03JdZZTf9Mo7ym9COh3yrX3WWnY7iFa03aNU97qdcwYm4jVRY+9zJPccnnCOmnb8dc4uHq986vQmsmFXbYnqViIAPYGqJkuFJotg6geeviupBWlLEQpEktBPx9B6kuuKPH3jyW8J72V6+U7mTj+kW5wWvSdssmYsRmljJ36HEk4QlzdzxOPWxX6K1bQeo0xdOIFSpWnMVZZX/NhR1EkHKNcH2Iz6+hmPyPl2L2ZGc/ZarEdk/FirsOwnKlmXRBgIcoIAwdfCZQSVjSu973TvL2tMrn6+L3ykhBUEaAWWhPVic3tU/oFqGknZ8HsLnbAhcQ5BafGmBev9TchxCkhxJ68aroHON3nYcdYov4C7MfSfy95aN3BKx1Y/0FS2o2XX7ELRhb19D/lQacTQqWRV1e91TdVQUU0mV1wlL/2xBE27GalPbMzGZykCWwtW7/8dXIBmKEDLGs2h6WewI0qzV3BihYsnl1SeA1zSlTvwi3yjbRftT0D8WJO8VBLojS1Pesvxo5v6b7NaWhP7ViGzJWSsYpPUnJJcx+wdpQRpZllH0qB5wyWWb52LOQXjr6Bn772FOq5P8K8qPA/7jnOXz40w2TV5a3fdJBrxvqvunH+fhNrBKa9KHkue+qSx6RV3bMMgMhufs5nwLHbAlNYqkbMH8+zs8NrXxO/klvkzJ87dXQtFOwM3WOTURreehLBK9nPuT1jN87KtyIzWtv7KRyx57XT1fPzBZXPFXNP9WGoFMI4+c9OaKutTs6cWe8aKG9pw7TRBifr2OvqeMv8AweFKyXeeaT2RkmGq+S2BqYFAkflSr9rV+VGw54AzBg79msXmWiJVEvq2UlrSdW8y0QgF/5yc09mJ0+OxHn7T9G3K+xYq+3tzyQoxNzmjm3rPVsEqKfmIxajxFa8Qo/QWy6eFziKauiyGGXMt2MENkjdSV/Vq4cc9lcVHz3V4M1jz2LixMf52jX/CiOtHVpi9KYUrjtpxkI74crTn6O55xYQgsenE6SAAw1nxWObVP0GTjhOcKFpz8odrHq4XRBiyePTrwBjNsBsTS0vOmDtjMr9EnUFirHeOLg2Q8avLAWoTmDvi9rerWk9LAtQy3Z/HlzA5MIuxE7y094DfBfwy/n3d/d5zAeAXxRCFCP6pcBP7eAx7R5ogxtsYlBLCTLvfyqP5MbfauMNdZeONuBHXRqxN2q8uH0WG9BVdcNk+WuX+mzOBkAa28V0rcVxMxDCHtNQyU406wXukKvUHbAT3+JpuxH0y1ZUZ9CMV2nIToLbfX1XHqqUuFJaOlEppz5lhnacstBJUXJj6u/1Yy6/pZ/LMyb2UD0Cv/5PX2WmnfKGm0f4gdvH+nv+Yfs4tIbJQrWvF2soGftKLVV305Ydhxeqf3K3wa/aRXMQKmp51GaRN9NXXcDk/dj0iLQUO1et7ZgV0t673joJsc1CKnvcXtmyGdyG3QicR+uV8wqvbOeRlVYgXXaHtNd1MxtqIWxiolAjXg9GLwX8peEt9cFWzhO1N800xsBY1d+xAKNR8jgx28YYsyxpl2qNKxV+b1U1beWB/UU6Nos1zyvbCnBhlbHRXqI3yS3d9R/rlWzCY5t74B0pmaj5pNrgO3LNRIUrJUOhpOormlHGXN7q4u1Q8CaE4O7DPu98qMVXDt7FnlOfZvjMZ5mauK37mEwbBmn9zoxhejGm1jyK05mxYkjAE9Mp++oK31k6546OCI2kUjlMqtQ5exlfEiiownEtLyTEG6vDd1Xe9218X/sVu/dcOGUDVf8cigxFgLpw0s7Dl0oCdpuwk8HpLwP/VwjxPcAR4FsBhBC3Aj9ojPleY8y0EOLngc/kz/kvPeJIvwq8ASgJIY4BbzfG/NwOHu95hEEbg3MuIjk7NZALOnGaUyW2Q8in4PFXJuzrt2ehdWbzvodZYmlu62W3toJC+GTQxwY1O+ElrfUFVdZ6fjnP8J1H70NHShxp+1OroctsK6EZpbhKrOmVuqciGQ4kv/WxUzQTzbVjAb/xmoNcN77+tUozTTlwVgemBdUcs7ZIlE4BuVzh7+sdhernIBhQcn8VCnXFcDhPtIjlSqHqCAw8dqTwAAAgAElEQVQd3pRlzabhhjZo+3rADgik4ZWskuV6SCOrGFysH0Uf7CaTGYGnoJ9rwjbCUjc1E/U+Sa5thKck1cChGaXLfDXjVDNc6qnW6gwQl87ctJm1Z7NJ7vIIzDyxWsztHFGsY4M+th5Kyr7i9EK0Y77gAM+a9HjnQy0ecJ7Ns706k8c/0g1OBYIkM/gDXLrZlk0OVk9/DoDW5E2ADU6fNrq054uzGEcIhv0h2jKkHuzkVv4ihFe26umduXyv6S0JEnWp6+mSQnZ5bPAEnV/dPvHQ0lDuaXWee4UvAuzYiDbGTAEv6vP7+4Hv7fn/O4B39HncTwA/sVPHd0GhM4wb4uzWTImUNjs0d3RrFZheJG2baa3uWQriSkN2I7UZ30Od5Uq4B3dGGXWzUA6oLdIwugqxR7a1N2dQFNTfauAw27JWNH6fRVsIwS17XD56NOZH75rg224awRmgdzU1mrDXdqHoSyzsNmBtkaiu0Mjl3ostw/FtImj+5JKVxXpI8haBtdQVYYnydxm7F45v59r1+kizxGoLFJDS9kxtMpnhSonnSJJM70jFxvqYpoxV/HUVd7cLtdBlIUq7npQmr2qXeiOKpG0TP5fnpo3h+DbxEc1f8N5cR0rGqj4nZzs7Nl4P1xWegq/NupzacycHjnwAJ54n9WooBZ0kpeyrdWnprTihGaeUPYfSqQdJSuMklb20E82J+YwXP93OzalOMRhGVQURDqGFJNxlVju7AlLme82yVciNFnLNlnzP4dRyESVn8xX+7ZwDzpeuxkWGyzyACwCRWfEPZzcvcsqxtAWd5NYaW0DSsXSh2t7VN3PhexgOWbpgFvd/DchpFy2o7r10qJ7Ktdclbff086yAMfbax83VFMBtQOAoJmshkzW76HWSdNVjfvAbKrz/e67hDc8aHSgwBZsp7tL9shjiBUvbbBxY6hGpTloBBeXZRSON7TkGtZ3rl/x6QlCHxn4bqESLedVnBYyxf3O89QPTy7h4ENRtv3Y/6MzO6ys/5yKZEW9O5agaOH09RLcDzTilUfKXPAB3GI6UNEKPTmLvkzjVVH1nSRU4jezac1lNc3CEQz3erRcWrpSM132SzPZ/bjccKbh6yOGRqZSFg69AmozxEx/P/yZJMsPZxYhkjfdOtWamldi+UZ1ROvm5rr/pkzN2XT487GCMITEJY8EojpBoz7aGeZcpvWvD8exeduQqGHmaXetqk7nlUuXS2VNeYrg8oi8AMqMRToDajQIsvSg2rULmYkabQNEbWl+nN7TwPWzkwkbxot0sx00b1BQm5XHTZvfPhd+/G+GGNkhbGXzqLPetzKuN4ZAN8NJ1AvhzQOgqRis+uk/86ylBZaWNwjrItEEJgSvE0nk1DtmFYGV1vBCJahzIq+pmycz9Ms4dBbWpOpGLqTWXNopFX3lp2C7c58se5zJ2Fl45VzLug6xj+1L7sVT8mp1fu+ruGyNw1Sa19QdDJ68eNcIBxqQxue9hsXYs5u0DfZIxG6AaOEgEmTak2ixVTY2x61HlIlCE3k1Qjp3Pk8HH1E7CV4rxakCUZOgdSPZeO+Ly2GxKXHs6C9XDTDz1ke7frFUanF2IaMXLk8AGw2w7RgqBEoLRz78TJ5pj4eBdgKX0gg1OIx1Rdat4xmC8CgmO9fDcghr/1xXEgF71l7FrcHlHcr5hNEZKlBPuqILctsHxLM2yecbSMAfxQc1iy+evHxiMCuiGMHQwp+7GtuIa58GZ0bYH9lKlPgR1S7VrzdhrlcU9KqU9dj9e2Tb4R4uWqrnNXrSuEkghurS2rSLNNCVXLgU+4fDGPblFr6F67DJ1dLsh5VI1ujOf9yTmdkv1fZer1JcaHM8qSfaj9hqztrJk0Qu/iVYDp4fau12IkgylJCMVf31l3iIoNXrJGs30rB9JJw+0e19jRUBitO31ys9VCsFQ2eXUfEToqSU6cdqyc9nFKoJ0IRHUbd9fvIjtY3fyrwsTKBSJ2DMLEeVBmkA3gWtGHNJH4LGZlLEDL+HAw2+jtHiUVsX20btKIqVhph0TpZpq3ifajFJasabsKcrHP8Po5/+I2atezuLBOwEbnAaOYLwiyIyg6lYgaWP8cZJMM1zeBW1Ol3EZ24zLwen5ho7JnBKOtN5LFwWksrQv5dsg1Q379zQVFhMGS9ndbG+oVEuKxKWhJduKSz1gKRSSjbHZeTdcnaEvlILbszbAkO62bpYEgsBVxJnGU1sfmJkxBCKx1d5eI+vLuLCQKqcxVXOv3fLu6N2+jO1HWLfzdO8cnUaWkrpehVw5lskxd8xW2qW3oSVXNXCYam4PoyNKMqQUjFf9JTrtShiz1AoRNKw4Ue/64IZLiUytc59YY89BSKx1ilzyFpw7mvt52mCp7DsEbrokMNMVQTqPFhmXEqS0e4Estp9F3LZjMW0BcumzAfqW4QvLtm1ExXfJNEy3tpeJdM2IHYdfnkq5/vBL0F96B6PH/pEj135X9zFKCEquQ5xoziYxQ3HCTDsh9CRO6yx7P/ZLRI1DnPz/27vz8DiqM9/j37eWXrRbspE3wMaAWcIODhOzGBgcwubsJDeLfRngZuZOQhbCwJNMTDZiliSEMBMukyGQgSc760xIhgAOITFjm2CIbczuYIExRgZvsqTurnP/qJIsa7MltVqy+vd5nrbUp6qrT/u0uuutc8573vmZzses3ZRn/6TXdHy6Ht9F8XSYMAtt+T7X6BXZmyk4LbWoQD6dIvSDkiwQXTRm8cltkI7XXPQK8UlBPuklhfiKfeWE+AulGCe+HetYjXVm8UnhnuzXfYJ/l7W8hioTeuzI5WGI81dCz429IdhjhR+Udi06Kb2woufQ3o5hqbuTqoD66fFQzLatO6dzWBB/9nf7zirW2optuQJmcWDa53qQ+bb44mdmXM+gtDeeF2eE70uYjRP1bX4t/ryyeHmSibWZnReOcy3xZ7OGBA6eH+y8KLLLhYN83Nvd/aJBx6igti2wbWNyHtDLBduuOkZdeX584XY351Y12YB8FBU1lUN91mOfCo81zTkKM+vYss/xTFr/J1456GM9LgqlQo9CFNG8vZ2U7+FFEZP/8A28fCuvnrIQF8TzIJ1zvNyc48RpKTJ+moqgAsttJ6qcGC915HtaQkbGJAWnpeQchkfBfCq9vTToSlXEiWy2rI9PFtLVSS9MWl/gpdIxwb9tK7S+veuQqYGuj9j1sL4NaQpZ5Bw+jjAI46BZRErPT/7+OkadRMmFxD1NeOWH8S1TEz82n0yzaNuyM0hNBJ5HJvRp7pIF1TkXr3VMvN5x4FvfASdJYIrRWNNPYJpriYOOumnFvWCZro6D9u0b457lZN4fEF94DbN9D4WWwfO8uGe+P5naeC3wlk3x95zfbem5jvcmLn5vZGrjiyq57cTBLmBhr73/hlFfmYpXtsvlyYbFORU+pCFgdXN8sX7zvu+mbsP/UNX8JNv2OaHHvr7nUZksXTThyX+ncsPTvHrSlbR3ZLQH3toRsaXNMaUOxqXqkg4Nw6UqyOUcddkxPqpMypaC0xIyIqIgjXMeqWAv/q/3w53rEe5Nvb9jScd6q5kaKOTjL+m2rXGg6uhlGBvJT6/P+aop38PDeixGv6dy+Yisl4NMvd4XIiMpWxv3Ovlh/NlQ0TC4v0nPjy8+pirjYbRvrwW36wWw6nRAW77QmQXVMALPSAc+vuexvS1HeyEfz1Ht1svTno8TF+1T219guj0OZGr6Sa43FBXj4h68HW/tzMbrXDy0uWZ/fZaNJD+A6n3i77ltHcuB+HHPa0fCpVTFrlNcoijuRe1IApdridsz3DVXQ8f7NBsGtORyVIRDD/RmNoQ8uq6d5pYC1jiLfFjN5NceY82E4/D6+N6tfHUZ4/9yJ28deDZbDjhzl20vJ8mQDplQScpPQZQjCtJgHoUoF683LDIG7cUR0l7IAamq+HPS38s/VPSFPXr4AfhV8dC0KIq/lAvt8ZXljpsr7EwYEuUAL17mxw87e7wNixOcRG5Q804LzpEJTUN6RUZaWAFEO5fyKEbvX5CKh9R2W7syE/o01mTibKOe9ZgvWpPxac1FbGvLsyOXxzDSgUd7voBz0FibIewrMO1YB7t64u4Tqw1Fx7z/tm1xIK4kSKNLR86Ftq1xO4UVvQ4zB5Je2Uz8mExtsmTWlji4NX/XEQRm1FeGWEucmKgi1f8psXOOgnN9XkiZ2RA/fs2mPLOnptk8ZQ77vPJbVrdvxkv3nE4RbN/I5MeuprXuADbM+nSP7S835wA4sjF5bCEH2Xqcc3ieR1rzTWWMUnBaQs4LcUGKqL1t7w9OZXTyvJ1JpfpSyCdXlZMlGHIt8RXlQp5s6PP2jvbBzTuN8oRhWid0IiPND+N5eu3bd836PVTZcfEQSxd19kJ5Zjsz2/bCMLKhTzZJuNaSi9je2t5/YOpc3GOaro0Tqw33smtmcVb4KB//n5mSII06HaOFBvW42jig3bYxDnC7JHU0jHEVca/ptrY8FaHfY+SQc462fIRzjgjwQus1q/0BdQGhB882x8HpW/ueScPa+znkhV/QXHcQLqwlSlWRCyophBVMefJfsEI7r576FVwv35svbWqjocJnfGUyTcZFuCBDeyGiItWzniJjhYLTEnJBGrwUkVNwKiOoI0FFqhKYEF+Nze2A9esJ/D2cl9ZN5Bx+lCNV2VjcuorI4GRq4xPxbF3xjukHUDEeWpoHtQxRyo+H9lanfSbVZXvPyuvczqWoBjsceTA8L0mQ9Gr8f6YcCmOLH8brardtjXtRCzuz9XYEqGbG1tZcZ4AaB6UFHFCZCqjKBGxrzbMjVyDdywWZ0DdmjAtYk/R47qibSUvdTKY0PcSUpod6rdarJ3+J9tr9epTnozx/3RRx4Pid+RsMAz9Fvj2ioVKn7zJ26d1dKl5IlK6Nr7w5RzrQRHYZJToSoPgZUi6eNzbQeaf5QkQmMK2bKTJahBXJ0NQiJyfL1MbzM3tbS3UP+dZz+C8QT0HItcRZ30ciq7Tnx0ufDHdPrYycdHU8qqBlU5JUqR2CFIZRl41XUdiyow3fjIh46ZmqtB/38OfayFo721zfFy4OaQj49Yut5AqO0DdePPkGgrYtePltuLa3advxBq5tM9monYqJB7Bt33f1epwdhTaa3o74m/2Sv99CjijIxCMWXKQhvTKmKTgtFYuveDkiwr1pjVMpH5kavJY3Sfk++Sj+Yt1T+Vw7dVUV5bH0j8jeoGPd0mLzPKiaAFtf35lAqBgK7fGtZtLIZshVYDr2dSRa8sP499x2IMDCNHWZAA/iwDTlEZgla7PmIF1DOkgRbH+bgu/j93IiN7Mh5J7nWnl5c56D60Mwn3xmHDAOqvbFnKO1sIM32rcwoXECQZQnchER0S7Z8t/anqK94Hb2nEbxfNN8FJEKPQItISNjmILTEotchGd+r/MVREZUqgJaHJnAY2tbbmDrp+XbCSuH4URYREafVFW8tEehPV6+aqg65r3X7ac561I65sUXQ3Kt0Lo5Hk5uATXp5D2Y3wFEkKqN57sGKayQp3LbNrbm2vHTPd+rhyRJkZ5tToLT7k9pRkVQQcpLURFk4kSCFhB4Ab55eMltzastABzYkDxHx3zTfMS4Cl0ElrFNwWmJRc4RmIb0yijk+ZCuJVXYTMSeXzyJnMM3CLPK0itSFsziREWbm4YWnDoXJyBKVUBV4/AsFSOyO2GS3Tc/Dlq3QPvmeHWF7LgkoViXczY/IFs3nq3rX4VegtPxFT4NWY9nm/Ocd1DfTxl4AfXp+j63v7ipDc9gWn0anOucb+oKEZlQ86FlbNM3QYlFLiLwAvWcyuiUqiTc8Xb8RbiH8u2tpCqqMV8XXUTKRqoi7kHNt/W9TyGfrLHs90xs1DG/tNSJj0T6EqSgajwUagHr82JJmK0lSL9JLtcWZ6jvZmbDzqRIg/Xim63sV5ciE3jJfNMsmIcj6rFesMhYo3d4iTkifPM151RGpzCD76cJLSJfiPboIYX2dipq+74CLCJjVGVDPLTXJbPlXAS5tngOX257HJg6IN+6syzXEt/yLfGc2MrxCkxldOmYi9oXM6rGTaLQ3rrzvd/FIQ0BG7ZHvNW6Z9+hvXn+zVYObIjnm1qhHVJVFCJHyvfwdAIpY9ywBadmVm9mD5rZ88nPXlPvmdn8ZJ/nzWx+UlZhZv9lZmvMbJWZLRquepaew/M051RGsWwNGSuQj3p+6faQfDGn0srSK1J2gjRk6pL1krfHmU/TlVA1Cer2h9opUDcF6qdB3TSo3RdqJkPVxPj+YNatFBkFMtkKokxdvAxbNzMb4lFEzw6y97SlvcCrW3LMGB/3yjoDF6TiZEjK0itlYDjf5VcADznnDgIeSu7vwszqgYXAO4FZwMIuQez1zrlDgGOA2Wb2nmGsa+k4w0dXvmQUS1WSCjyc2/1VX5dvw6WrCEPNEBApSxX1ULUP1EyBcfvFvampbM91Qj0v7pEK0vF2ZfaWvZjvGdnqetoi4mWVuphRFxB4cVKkwXhpUzxU/sCGTDzf1AF+ikLkyIYKTmXsG853+Tzg9uT324H39rLPu4EHnXObnHNvAQ8CZznnWpxzjwA459qBPwNTh7GuJeNwhL4ms8so5vmksjXxUKLdyOdzZKrqBrQmqoiMIX6QZDJNa3iulJXKTJpcpiEett5FOjCm1w1+3ukLb3YJTqP8zvmmDp0/SlkYzuC00Tm3HiD5uU8v+0wB1nW535SUdTKzOuA84t7XHszsEjNbbmbLN27cWJSKD6fIKTiV0c/PVhMQkY/66T11jnzkqKhUll4RESkvmdDDS1UQBRVQ2DVAPaQh4PlNeQp7Mj2mmxeaW8mGHpNrQ6yQ22U94WAA64+L7K2GFJya2e/MbGUvt3l7eoheyjr/ks0sAH4C3Oice6m3AzjnbnHOHe+cO37ChAkDfxElFC+x7BEoOJXRLsiQSqco5Pu58ltoJUrVkNKQXhERKTNmRlUmoDWsw6JCnBAsMbMhpK0AazcXBnzcF95sZUZ9Gs8MR4QL0jjnMDMCTQmTMjCks0rn3N/2tc3MNpjZJOfcejObBLzRy25NwJwu96cCi7vcvwV43jl3w1DqOVo4F+GZrw8XGf3MyFQ30PLGaz23OYflduDMcJlapbUXEZGyVJHy2dwSEGXG4bVuxoVZAGbWx6fXzzbnmDFuz0+1nXO80NzGnBnV8XeteZ3zTVO+aQqNlIXhPKu8D5if/D4fuLeXfX4LzDWzcUkipLlJGWb2DaAW+Oww1rGkHA7fAgJPJ/My+oWZSsDtmio/ykNuG1G6itbKqWQyGX1ZiohIWUoHPqHvkfOzOHb2nDZWetRlbMBJkR59eSubWwsc1piFqGN9UyMfOTKhRt1JeRjOKGkRcKaZPQ+cmdzHzI43sx8COOc2AV8HliW3rznnNpnZVOBLwGHAn81shZldNIx1LRkPH3U0yd4gCFOQrqKQS+bS5FqwQo6oejKFigm0RlCV1pBeEREpXzUZn3bn4/w0FOKpMGbGzPpwQEmRNrXk+eZDr3Hw+AznHVqHFfIQxsu0FSJHWsGplIlhO7N0zjUDZ/RSvhy4qMv9W4Fbu+3TRO/zUQcsl8vR1NREa2vr7ncugtraWp555pke5ZFz1BUctn0z6zZvLet1TjOZDFOnTiUMw5GuiuxGtrKOQr4dnwIurCSqnEBrwci3F5hQlVZwKiIiZS2bCmB7DjK12PY3cH58bnNIQ8D/vNbOlraImnT/vRLOOa5++DW2t0f84P1TCH0PV4jnm3bQlDApF2P+zLKpqYnq6mqmTZtWkuGHW7dupbq6ukd5IYpoybUSWEg6CMp2nVPnHM3NzTQ1NTF9+vSRro7sRiZbSZ5Wwopx5MMqWtoLVKV9JlWmtRi4iIiUvcD3yKQ82vIpsnjxVBgzZjbEQeqzzXlOmNz/ur73r36b37+0lc+e1MiMjvVNk/mmHUINu5MyMebf6a2trTQ0NIySeXFWpP7gvZeZ0dDQULKebBmaMPQpVE+hxaugveCYWJOhsSajwFRERCRRmQrIOyMKKyGK1wg/qD7AM1ixof81w1/d3M63H32d46dW8tFjGuLCKEcUVoAZkXN4Zvhl2qkh5WfM95wCoyQwjZkC1FHVHtK/0PfwAiMT+NRXpvXlKCIi0k2YXLB1qUps+1bw02QC44RJKe57vpUdecfFR1eRDXf9Di1Ejq8++CpmsPDMyZ1TvqyQw2XGde6TDnVBWMqH3u0lZN1+iuwNptRWMKE6o8BURESkF2HHKgxBGrwAonh90yveVc2HD83yu5fbuPTBt3i2W4KkO59s5snXWvjiqZOYWL1zCK8DXDKkN19wZAMlQ5LyoeC0RJqamrjggx/mHYcdyoEHHsill15Ke3v/Qz2uvvrqIT3n4sWL+dOf/jSkY4iU6/xoERGRPeF5Rsr3yEfgUtUQxUFo4BmfOKKSb51WSyGCyx/ezE9XtVCIHM9tbOUHS97g9Bk1nH1I7c6DORePMEuC08g5Uuo5lTKid3sJOOf44Ac/yHnnn8eq1Wt49tln2bZtG1/60pf6fZyCUxEREZHRL5vyyUcOF1Z09px2OHxCyI1z6zhl3zR3rmrhykc285X/bqIm43Pl6ZN2ne5UaCMKq6BLmUYuSTkpizmnHb56/ypWv7alqMc8bHINC887vN99Hn74YTKZDJ+YPx+AIAj47ne/y/Tp05k+fTqrV6/mpptuAuDcc8/lsssu4ze/+Q07duzg6KOP5vDDD+eb3/wmZ511Fu985zt58sknOfjgg/nxj39MRUUF06ZNY/ny5YwfP57ly5dz2WWXcdttt3HzzTfj+z533HEH3//+9zn55JOL+tpFREREBNKBz2aXAz/EBWmsEP/eoTLl8YUTqzluUsgP/rydllye7563H3XZLqfiUQGLCkTZus4iR5dhwyJlQO/2Eli1ahXHHXssHn7nhbCamhr2228/8vl8r49ZtGgR2WyWFStWcOeddwLw7LPPcskll/D0009TU1PDv/7rv/b5nNOmTeNTn/oUn/vc51ixYoUCUxEREZFhEvhdejfTNViU63W/OftnuOnddXx/3v6cNL3L0oPOQb6FQlVj55DeQuRI+Z6m10hZKaue0931cA4Xl8wfiIdtWI/yPbXvvvsye/ZsAD7+8Y9z4403ctlllxW7uiIiIiIyAKHv4XUs/RJk48JkzdPuJlT4HDGxapcyy7UQZcZBamd5PopIKxmSlBn1nJbA4YcfzhNPPAHs/IzasmUL69ato7a2liiKOvftb/3P7oFsx/0gCDqPofVDRUREREovE3rkCw48L543GvWf+LJTvg0XpHDZ+l2KC5Ejq2RIUmb0ji+BM844g5aWFu78j//AgEKhwBe+8AUWLFjAAQccwIoVK4iiiHXr1rF06dLOx4VhSC63c1jIK6+8wpIlSwD4yU9+wkknnQTEQ3g7gt9f/epXnftXV1ezdevWErxCERERkfKWDX0KkQPApSog6n3q1i6ifDzPtHIi2K6n5c5B6KvnVMqLgtMSMDN+dddd3PWrX3LoITM5+OCDyWQyXH311cyePZvp06dzxBFHcNlll3Hsscd2Pu6SSy7hyCOP5GMf+xgAhx56KLfffjtHHnkkmzZt4u///u8BWLhwIZdeeiknn3wyfpcPsfPOO4+7776bo48+mj/84Q+lfdEiIiIiZSTwPVznnQxmAbio7wc4B/kdFKr22SV50q7H1HxTKS9lNed0JO2777786p57CX2vR0rwjoRH3V1zzTVcc801AKxduxbP87j55pt77HfyySfz3HPP9Sg/+OCDefrpp4tQexERERHpT6prcGpGlKrE2rdCxxzUbnqbZ9qhIy9JoGRIUmYUnIqIiIiIDJHnGWnfIx85As9wYSXWtgXLbU/2MJxZPHw310IUpHvMM+0QZ+q1ASXOFBkLFJyW2GA/YqZNm8bKlSuLWhcRERERKZ5Mymd7az7u8QxSRFWTwBWwjj7VKA9RHpeuxqXreswz7ZCPHFVpnaZL+dG7voSs8x8RERERGWsygccW53YWBPGapa7bfq5iQr/HKUSOdKhkSFJ+hi0hkpnVm9mDZvZ88nNcH/vNT/Z53szmdyn/jZk9ZWarzOxmM9NfqIiIiIiMWoFfvFNrzTeVcjSc2XqvAB5yzh0EPJTc34WZ1QMLgXcCs4CFXYLYDzvnjgLeAUwAPjSMdS0NU8epiIiIyFgV+h6eGZHr3lc6uGOJlJvhfNfPA25Pfr8deG8v+7wbeNA5t8k59xbwIHAWgHNuS7JPAKToOSJCRERERGRUSQce+cLgHx85h2fWY3UHkXIwnMFpo3NuPUDyc59e9pkCrOtyvykpA8DMfgu8AWwFfjl8VR1eVVVxinCD3WZdu+qqq7j++utLUCsRERERKbZs6JGP+lnfdDfi+abqNZXyNKR3vpn9zsxW9nKbt6eH6KWss4fUOfduYBKQBk7vow6XmNlyM1u+cePGAb+GUrJhHtRbKAzhMp2IiIiIDFkY+LghDOvNFxzZQKlWpDwNKVuvc+5v+9pmZhvMbJJzbr2ZTSLuAe2uCZjT5f5UYHG352g1s/uIhwk/2EsdbgFuATj++OP7/yR44Ap4/S/97jJgE4+A9yzas327xKb3338/3/jGN2hvb6ehoYE777yTxsZGAJ566ilOP/101q1bx+WXX87FF1+Mc47LL7+cBx54ADPjy1/+MhdccAGLFy/mq1/9KpMmTWLFihWsXr26uK9PRERERPZYyveGtD5p5Bwp9ZxKmRrOpWTuA+YDi5Kf9/ayz2+Bq7skQZoLXGlmVUB1EtgGwNnAH4axrsPOAK/LB9VJJ53E448/jpnxwx/+kGuvvZZvf/vbADz99NM8/vjjbN++nWOOOYZzzjmHJUuWsGLFCp566inefPNNTjjhBE455RQAli5dysqVK5k+ffpIvDQRERERSexu2PUAABGSSURBVHieEfoe+cgNOuOu5ptKuRrO4HQR8HMz+zvgFZJsu2Z2PPAp59xFzrlNZvZ1YFnymK8lZY3AfWaWBnzgYeDmIddoT3s4h4GZ4Xf5nGlqauKCCy5g/fr1tLe37xJYzps3j2w2Szab5bTTTmPp0qU89thjfPSjH8X3fRobGzn11FNZtmwZNTU1zJo1S4GpiIiIyCiRCT1a2gqDCk4dEHrqOZXyNGzBqXOuGTijl/LlwEVd7t8K3Nptnw3ACcNVt9Hg05/+NJ///Oc5//zzWbx4MVdddVXntu5DQcys37kLlZWVw1VNERERERmgbOizpTVHeoDpXQqRI+V7eOo5lTKlyzIjZPPmzUyZEicmvv3223fZdu+999La2kpzczOLFy/uHML7s5/9jEKhwMaNG3n00UeZNWvWSFRdRERERPrhe96gEmHmCpEy9UpZG85hvZJoaWlh6tSpnfc///nPc9VVV/GhD32IKVOmcOKJJ/Lyyy93bp81axbnnHMOr7zyCv/8z//M5MmTed/73seSJUs46qijMDOuvfZaJk6cyJo1a0biJYmIiIhIH0Lf8JKRb3uaHClyjkLkqMumhrl2IqOXgtMSiPpY62revJ4r7nQd3tuVmXHddddx3XXX7VI+Z84c5syZM9QqioiIiEiRmBmpwMgVILWHZ9st7QX2qU6TCtRzKuVL734RERERkSKrCH3yfXRQdNeaK1CZ8qlKq99IypuCUxERERGRIgsDf4/2y0cRzsH4qvSQ1kcVGQsUnIqIiIiIFFnK9/DNaM3133u6o73AhOoUga/TchH9FYiIiIiIFJnnGRNrM6QDY1tbgaiXZQFb2vLUZkMq0+EI1FBk9FFwKiIiIiIyDALfY0J1hvGVIa25iPb8zgA1X4jwPKO+Mj2CNRQZXRScioiIiIgMEzOjOptiYm0Gszgrb+QcrbmIxpoMvqd5piIdFJyWyOuvv85HPvIRZsyYwWGHHcbZZ5/Nc889N9LV6teKFSv49a9/3Xn/vvvuY9GiRSNYIxEREZG9UzrwmViToSYbsq01z7jKFJlwz5ImiZQLBacl4Jzjfe97H3PmzOHFF19k9erVXH311WzYsGGkq9av7sHp+eefzxVXXDGCNRIRERHZe3meMa4ixX4NFdRlNc9UpLuyWkzpmqXXsGbTmqIe85D6Q/inWf/U7z6PPPIIYRjyqU99qrPs6KOPxjnHF7/4RR544AHMjC9/+ctccMEFLF68mKuuuorx48ezcuVKjjvuOO644w7MjCuuuIL77ruPIAiYO3cu119/PQsWLODcc8/lgx/8IABVVVVs27aNxYsXs3DhQhobG1mxYgXvf//7OeKII/je977Hjh07uOeee5gxYwYLFiwgk8mwatUqNmzYwHe+8x3mzp3LV77yFXbs2MFjjz3GlVdeyY4dO1i+fDk33XQTf/3rX7nwwgvZuHEjEyZM4Ec/+hH77bcfCxYsoKamhuXLl/P6669z7bXXdtZLREREROJeVBHpqayC05HSEWB2d9ddd7FixQqeeuop3nzzTU444QROOeUUAJ588klWrVrF5MmTmT17Nn/84x857LDDuPvuu1mzZg1mxttvv73b537qqad45plnqK+v54ADDuCiiy5i6dKlfO973+P73/8+N9xwAwBr167l97//PS+++CKnnXYaL7zwAl/72tc6g1GA2267rfO4//iP/8gnP/lJ5s+fz6233spnPvMZ7rnnHgDWr1/PY489xpo1azj//PMVnIqIiIiIyG6VVXC6ux7OUnvsscf46Ec/iu/7NDY2cuqpp7Js2TJqamqYNWsWU6dOBeJe1rVr13LiiSeSyWS46KKLOOecczj33HN3+xwnnHACkyZNAmDGjBnMnTsXgCOOOIJHHnmkc78Pf/jDeJ7HQQcdxAEHHMCaNf33MC9ZsoS77roLgE984hNcfvnlndve+9734nkehx122KgfuiwiIiIiIqOD5pyWwOGHH84TTzzRo9z1st5Vh3R6Z1px3/fJ5/MEQcDSpUv5wAc+wD333MNZZ50FQBAERFHUecz29vZej+N5Xud9z/PI5/Od28x2zRTX/f7udN2/63P29xpFREREREQ6KDgtgdNPP522tjb+7d/+rbNs2bJljBs3jp/97GcUCgU2btzIo48+yqxZs/o8zrZt29i8eTNnn302N9xwAytWrABg2rRpncHvvffeSy6XG3Adf/GLXxBFES+++CIvvfQSM2fOpLq6mq1bt/a6/7ve9S5++tOfAnDnnXdy0kknDfg5RUREREREOpTVsN6RYmbcfffdfPazn2XRokVkMhmmTZvGDTfcwLZt2zjqqKMwM6699lomTpzY55DarVu3Mm/ePFpbW3HO8d3vfheAiy++mHnz5jFr1izOOOMMKisrB1zHmTNncuqpp7JhwwZuvvlmMpkMp512GosWLeLoo4/myiuv3GX/G2+8kQsvvJDrrruuMyGSiIiIiIjIYA1bcGpm9cDPgGnAWuDDzrm3etlvPvDl5O43nHO3d9t+H3CAc+4dw1XXUpg8eTI///nPe5Rfd911XHfddbuUzZkzhzlz5nTe70hIBLB06dIex2hsbOTxxx/vvP+tb32r1+MsXry4z+eYPXt2Z7Dbob6+nmXLlu1StmDBAiDurX344Yd71KVr0iSIe3tFRERERER2Zzh7Tq8AHnLOLTKzK5L7u2QkSgLYhcDxgAOeMLP7OoJYM3s/oOhGpAQmT5480lUQkb2QPjtkbxSGod67IqPQcAan84A5ye+3A4vpFpwC7wYedM5tAjCzB4GzgJ+YWRXweeASoGeXoxRN995OERERERGRUhvOhEiNzrn1AMnPfXrZZwqwrsv9pqQM4OvAt4GW/p7EzC4xs+Vmtnzjxo297qOMsaOL2kNERERERLobUnBqZr8zs5W93Obt6SF6KXNmdjRwoHPu7t0dwDl3i3PueOfc8RMmTOixPZPJ0NzcrIBolHDO0dzcTCaTGemqiIiIiIjIKDKkYb3Oub/ta5uZbTCzSc659WY2CXijl92a2Dn0F2Aq8fDfvwGOM7O1SR33MbPFzrk5DNDUqVNpamqir17VYmttbVXgtRuZTIapU6eOdDVERERERGQUGc45p/cB84FFyc97e9nnt8DVZjYuuT8XuDKZg/oDADObBvznYAJTiCe8T58+fTAPHZTFixdzzDHHlOz5RERERERExoLhnHO6CDjTzJ4HzkzuY2bHm9kPAZIg9OvAsuT2tY7kSCIiIiIiIlI+hq3n1DnXDJzRS/ly4KIu928Fbu3nOGuBvXqNUxEREREREenfcPacioiIiIiIiOwRG0tZbM1sI/DXEa7GeODNEa6D9E3tM3qpbUYvtc3opbYZ3dQ+o5faZvRS24xug2mf/Z1zPZdV6cWYCk5HAzNb7pw7fqTrIb1T+4xeapvRS20zeqltRje1z+ilthm91Daj23C3j4b1ioiIiIiIyIhTcCoiIiIiIiIjTsFp8d0y0hWQfql9Ri+1zeilthm91Dajm9pn9FLbjF5qm9FtWNtHc05FRERERERkxKnnVEREREREREacglMREREREREZcQpO94CZ3Wpmb5jZyi5lR5nZEjP7i5ndb2Y1SXloZrcn5c+Y2ZXdjuWb2ZNm9p+lfh1jUbHaxsw+Z2arzGylmf3EzDIj8XrGkgG2TcrMfpSUP2Vmc5LyCjP7LzNbk7TPohF6OWNOMdqny7ZbzOy5pJ0+MAIvZ0wxs33N7JHkc2qVmV2alNeb2YNm9nzyc1xSbmZ2o5m9YGZPm9mxXY41P9n/eTObP1KvaawoZtsk22vM7FUzu2kkXs9YUuS/m2uTYzyT7GMj9brGgkG0zSHJd1GbmV22u+PI0BSrfZJtdWb2y+R84Bkz+5uB1kfB6Z65DTirW9kPgSucc0cAdwNfTMo/BKST8uOA/2Nm07o87lLgmeGsbJm5jSG2jZlNAT4DHO+cewfgAx8pReXHuNvY87a5GCApPxP4tpl1fD5d75w7BDgGmG1m7xnuipeJ2yhO+3wJeMM5dzBwGPD7Ya53OcgDX3DOHQqcCPxfMzsMuAJ4yDl3EPBQch/gPcBBye0S4AcQn1gAC4F3ArOAhR0nFzJoRWmbLr6O/maKpVh/N+8CZgNHAu8ATgBOLeHrGIsG2jabiM/Lrt/D48jQFKt9AL4H/CY5bzuKQcQ8Ck73gHPuUeKG6Gom8Gjy+4NAR2+BAyrNLACyQDuwBcDMpgLnEJ8AShEUq22AAMgm2yqA14az3uVggG1zGPEHH865N4C3iS8WtDjnHknK24E/A1OHueploRjtk2y7EPhWsi1yzr05jNUuC8659c65Pye/byX+cp8CzANuT3a7HXhv8vs84Mcu9jhQZ2aTgHcDDzrnNjnn3iJu0+4XJGQAitg2mNlxQCPw3yV8CWNWEdvGARkgBaSBENhQshcyBg20bZxzbzjnlgG5PTyODEGx2sfi0VanAP+e7NfunHt7oPVRcDp4K4Hzk98/BOyb/P5LYDuwHniFuNen4wTwBuByICphPcvRgNrGOfcq8dWfV5Jtm51zOlkYHn21zVPAPDMLzGw6cc/2vl0faGZ1wHkkQZIMiwG1T9ImAF83sz+b2S/MrLG0VR7bkpE3xwD/AzQ659ZDfDIB7JPsNgVY1+VhTUlZX+VSBENpm2TkwbfZOTpBimgobeOcWwI8Qnw+sB74rXNOI96KZA/bZqDHkSIZYvscAGwEfmTxFMYfmlnlQOug4HTwLiTu9n4CqCbuhYN46FQBmAxMB75gZgeY2bnEQ9+eGJHalpeBts044qtD05NtlWb28dJXuyz01Ta3Ep8YLCe+iPMn4mEmACQ92j8BbnTOvVTSGpeXgbZPQNyT/Ufn3LHAEnof5iODYGZVwK+AzzrntvS3ay9lrp9yGaIitM0/AL92zq3rZbsMwVDbxswOBA4l/mybApxuZqcUv6blZwBtU5LjyK6K8P8aAMcCP3DOHUPcIXRF/w/p/SAyCM65NcBcADM7mHi4LsD/Ih5rnQPeMLM/Eg9/OwY438zOJh4uUmNmdzjnFAQV2SDaxgEvO+c2Jo+5C3gXcEep6z7W9dU2zrk88LmO/czsT8DzXR56C/C8c+6G0tW2/AyifZqBFuL5qQC/AP6uhFUes8wsJD5JuNM5d1dSvMHMJjnn1ifDD99IypvYdaTBVOKpCU3AnG7li4ez3uWgSG3zN8DJZvYPQBWQMrNtzrkBn8jJTkVqm48DjzvntiXHfIB4Ht6jyKANsG0GehwZoiK1TxPQ5Jzr6M3+JYMITtVzOkhmtk/y0wO+DNycbHqF+CqbJV3ZJwJrnHNXOuemOuemESfbeViB6fAYaNsk5SdanBnWgDNQ0qph0VfbJP/3lcnvZwJ559zq5P43gFrgsyNS6TIy0PZxzjngfnYGQGcAq0td77Em+Rz6d+AZ59x3umy6D+jIuDsfuLdL+SeTz7YTiacmrAd+C8w1s3HJCJG5SZkMUrHaxjn3Mefcfsk5wWXEcx8VmA5BEf9uXgFOTaYxhMTJkHROMASDaJuBHkeGoFjt45x7HVhnZjOTosGdEzjndNvNjXg44Xriib9NxD0DlwLPJbdFgCX7VhH3HqxKGuSLvRxvDvCfI/26xsKtWG0DfJU4UF0J/AdxVt8Rf317822AbTMNeJb4BOB3wP5J+VTinu1ngBXJ7aKRfm1j4VaM9km27U/co/A08Xzg/Ub6te3tN+Ck5H3/dJf3/dlAQ/J//Hzysz7Z34B/AV4E/kKcTKzjWBcCLyS3/z3Sr21vvxWzbboccwFw00i/tr39Vqy2Ic7Y//+Sz7vVwHdG+rXt7bdBtM3E5HtpC3ECviagpq/jjPTr29tvxWqfZNvRxFOAngbuAcYNtD4dJx4iIiIiIiIiI0bDekVERERERGTEKTgVERERERGREafgVEREREREREacglMREREREREZcQpORUREREREZMQpOBUREREREZERp+BURERERERERtz/B+Zxatronn0yAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 936x504 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(2, 1, figsize=(13,7))\n",
    "\n",
    "# Filtered states confidence intervals\n",
    "states_cov = np.diagonal(econ_res.filtered_state_cov).T\n",
    "states_upper = econ_res.filtered_state + 1.96 * states_cov**0.5\n",
    "states_lower = econ_res.filtered_state - 1.96 * states_cov**0.5\n",
    "\n",
    "ax = axes[0]\n",
    "lines, = ax.plot(dates, econ_res.filtered_state[0], label='Capital')\n",
    "ax.fill_between(dates, states_lower[0], states_upper[0], color=lines.get_color(), alpha=0.2)\n",
    "\n",
    "lines, = ax.plot(dates, econ_res.filtered_state[1], label='Technology shock')\n",
    "ax.fill_between(dates, states_lower[1], states_upper[1], color=lines.get_color(), alpha=0.2)\n",
    "\n",
    "ylim = ax.get_ylim()\n",
    "ax.fill_between(dates, ylim[0]+1e-4, ylim[1]-1e-4, rec, facecolor='k', alpha=0.1)\n",
    "\n",
    "ax.hlines(0, dates[0], dates[-1])\n",
    "\n",
    "ax.set_title('Filtered states')\n",
    "ax.legend(loc='lower left')\n",
    "ax.xaxis.grid()\n",
    "\n",
    "ax = axes[1]\n",
    "\n",
    "# One-step-ahead forecasts confidence intervals\n",
    "forecasts_cov = np.diagonal(econ_res.forecasts_error_cov).T\n",
    "forecasts_upper = econ_res.forecasts + 1.96 * forecasts_cov**0.5\n",
    "forecasts_lower = econ_res.forecasts - 1.96 * forecasts_cov**0.5\n",
    "\n",
    "for i in range(econ_mod.k_endog):\n",
    "    lines, = ax.plot(dates, econ_res.forecasts[i], label=econ_mod.endog_names[i].title())\n",
    "    ax.fill_between(dates, forecasts_lower[i], forecasts_upper[i], color=lines.get_color(), alpha=0.1)\n",
    "\n",
    "ylim = ax.get_ylim()\n",
    "ax.fill_between(dates, ylim[0]+1e-4, ylim[1]-1e-4, rec, facecolor='k', alpha=0.1)\n",
    "\n",
    "ax.hlines(0, dates[0], dates[-1])\n",
    "\n",
    "ax.set_title('One-step-ahead forecasts')\n",
    "ax.legend(loc='lower left')\n",
    "ax.xaxis.grid()\n",
    "\n",
    "fig.tight_layout();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## References\n",
    "\n",
    "    [1] DeJong, David N., and Chetan Dave. 2011.\n",
    "        Structural Macroeconometrics. Second edition.\n",
    "        Princeton: Princeton University Press.\n",
    "    [2] Ruge-Murcia, Francisco J. 2007.\n",
    "        \"Methods to Estimate Dynamic Stochastic General Equilibrium Models.\"\n",
    "        Journal of Economic Dynamics and Control 31 (8): 2599–2636.\n"
   ]
  }
 ],
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