{ "cells": [ { "cell_type": "markdown", "id": "5a16aca4", "metadata": {}, "source": [ "```{try_on_binder}\n", "```" ] }, { "cell_type": "code", "execution_count": 1, "id": "fdf7537f", "metadata": { "load": "myst_code_init.py", "tags": [ "remove-cell" ] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The pymor.discretizers.builtin.gui.jupyter extension is already loaded. To reload it, use:\n", " %reload_ext pymor.discretizers.builtin.gui.jupyter\n" ] } ], "source": [ "from IPython import get_ipython\n", "ip = get_ipython()\n", "if ip is not None:\n", " ip.run_line_magic('load_ext', 'pymor.discretizers.builtin.gui.jupyter')\n", " ip.run_line_magic('matplotlib', 'inline')\n", "\n", "import warnings\n", "warnings.filterwarnings(\"ignore\", category=UserWarning, module='torch')\n", "import pymor.tools.random\n", "pymor.tools.random._default_random_state = None\n" ] }, { "cell_type": "markdown", "id": "15acc45b", "metadata": {}, "source": [ "# Tutorial: Projecting a Model\n", "\n", "In this tutorial we will show how pyMOR builds a reduced-order model by\n", "projecting the full-order model onto a given reduced space. If you want to learn\n", "more about building a reduced space, you can find an introduction in\n", "{doc}`tutorial_basis_generation`.\n", "\n", "We will start by revisiting the concept of Galerkin projection and then manually\n", "project the model ourselves. We will then discuss offline/online decomposition of\n", "parametric models and see how pyMOR's algorithms automatically handle building\n", "an online-efficient reduced-order model. Along the way, we will take a look at\n", "some of pyMOR's source code to get a better understanding of how pyMOR's components\n", "fit together.\n", "\n", "## Model setup\n", "\n", "As a full-order {{ Model }}, we will use the same\n", "{meth}`thermal block ` benchmark\n", "problem as in {doc}`tutorial_basis_generation`. In particular, we will use pyMOR's\n", "builtin {mod}`discretization toolkit `\n", "(see {doc}`tutorial_builtin_discretizer`) to construct the FOM. However, all we say\n", "works exactly the same when a FOM of the same mathematical structure is provided\n", "by an external PDE solver (see {doc}`tutorial_external_solver`).\n", "\n", "Since this tutorial is also supposed to give you a better overview of pyMOR's\n", "architecture, we will not import everything from the {mod}`pymor.basic` convenience\n", "module but directly import all classes and methods from their original locations in\n", "pyMOR's subpackages.\n", "\n", "Let's build a 2-by-2 thermal block {{ Model }} as our FOM:" ] }, { "cell_type": "code", "execution_count": 2, "id": "4311a98f", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "e290211508c8478c9f33f854723162e8", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Accordion(children=(HTML(value='', layout=Layout(height='16em', overflow_y='auto', width='100%')),), selected_…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from pymor.analyticalproblems.thermalblock import thermal_block_problem\n", "from pymor.discretizers.builtin import discretize_stationary_cg\n", "\n", "p = thermal_block_problem((2,2))\n", "fom, _ = discretize_stationary_cg(p, diameter=1/100)" ] }, { "cell_type": "markdown", "id": "2304eabb", "metadata": {}, "source": [ "To get started, we take a look at one solution of the FOM for some fixed {{ parameter_values }}." ] }, { "cell_type": "code", "execution_count": 3, "id": "f3cd5c09", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "0f72f37d51b744fd81a38d25e483c059", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Accordion(children=(HTML(value='', layout=Layout(height='16em', overflow_y='auto', width='100%')),), selected_…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "72a8123f44ca449cac85642ed17838ce", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Output()" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "U = fom.solve([1., 0.1, 0.1, 1.])\n", "fom.visualize(U)" ] }, { "cell_type": "markdown", "id": "27a1dbe5", "metadata": {}, "source": [ "To build the ROM, we will need a reduced space {math}`V_N` of small dimension {math}`N`.\n", "Any subspace of the {attr}`~pymor.models.interface.Model.solution_space` of the FOM will\n", "do for our purposes here. We choose to build a basic POD space from some random solution\n", "snapshots." ] }, { "cell_type": "code", "execution_count": 4, "id": "9fc7da0a", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "c10d7f44381940039c0c8406ad3dc140", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Accordion(children=(HTML(value='', layout=Layout(height='16em', overflow_y='auto', width='100%')),), selected_…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from pymor.algorithms.pod import pod\n", "from matplotlib import pyplot as plt\n", "\n", "snapshots = fom.solution_space.empty()\n", "for mu in p.parameter_space.sample_randomly(20):\n", " snapshots.append(fom.solve(mu))\n", "basis, singular_values = pod(snapshots, modes=10)" ] }, { "cell_type": "markdown", "id": "34675cd3", "metadata": {}, "source": [ "The singular value decay looks promising:" ] }, { "cell_type": "code", "execution_count": 5, "id": "8181cb0a", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "_ = plt.semilogy(singular_values)" ] }, { "cell_type": "markdown", "id": "db6de1ba", "metadata": {}, "source": [ "## Solving the Model\n", "\n", "Now that we have our FOM and a reduced space {math}`V_N` spanned by `basis`, we can project\n", "the {{ Model }}. However, before doing so, we need to understand how actually\n", "solving the FOM works. Let's take a look at what\n", "{meth}`~pymor.models.interface.Model.solve` does:" ] }, { "cell_type": "code", "execution_count": 6, "id": "792ec15a", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
    def solve(self, mu=None, input=None, return_error_estimate=False, **kwargs):\n",
       "        """Solve the discrete problem for the |parameter values| `mu`.\n",
       "\n",
       "        This method returns a |VectorArray| with a internal state\n",
       "        representation of the model's solution for given\n",
       "        |parameter values|. It is a convenience wrapper around\n",
       "        :meth:`compute`.\n",
       "\n",
       "        The result may be :mod:`cached <pymor.core.cache>`\n",
       "        in case caching has been activated for the given model.\n",
       "\n",
       "        Parameters\n",
       "        ----------\n",
       "        mu\n",
       "            |Parameter values| for which to solve.\n",
       "        input\n",
       "            The model input. Either a |NumPy array| of shape `(self.dim_input,)`,\n",
       "            a |Function| with `dim_domain == 1` and `shape_range == (self.dim_input,)`\n",
       "            mapping time to input, or a `str` expression whith `t` as variable that\n",
       "            can be used to instatiate an |ExpressionFunction| of this type.\n",
       "            Can be `None` if `self.dim_input == 0`.\n",
       "        return_error_estimate\n",
       "            If `True`, also return an error estimate for the computed solution.\n",
       "        kwargs\n",
       "            Additional keyword arguments passed to :meth:`compute` that\n",
       "            might affect how the solution is computed.\n",
       "\n",
       "        Returns\n",
       "        -------\n",
       "        The solution |VectorArray|. When `return_error_estimate` is `True`,\n",
       "        the estimate is returned as second value.\n",
       "        """\n",
       "        data = self.compute(\n",
       "            solution=True,\n",
       "            solution_error_estimate=return_error_estimate,\n",
       "            mu=mu,\n",
       "            input=input,\n",
       "            **kwargs\n",
       "        )\n",
       "        if return_error_estimate:\n",
       "            return data['solution'], data['solution_error_estimate']\n",
       "        else:\n",
       "            return data['solution']\n",
       "
\n" ], "text/latex": [ "\\begin{Verbatim}[commandchars=\\\\\\{\\}]\n", " \\PY{k}{def} \\PY{n+nf}{solve}\\PY{p}{(}\\PY{n+nb+bp}{self}\\PY{p}{,} \\PY{n}{mu}\\PY{o}{=}\\PY{k+kc}{None}\\PY{p}{,} \\PY{n+nb}{input}\\PY{o}{=}\\PY{k+kc}{None}\\PY{p}{,} \\PY{n}{return\\PYZus{}error\\PYZus{}estimate}\\PY{o}{=}\\PY{k+kc}{False}\\PY{p}{,} \\PY{o}{*}\\PY{o}{*}\\PY{n}{kwargs}\\PY{p}{)}\\PY{p}{:}\n", " \\PY{l+s+sd}{\\PYZdq{}\\PYZdq{}\\PYZdq{}Solve the discrete problem for the |parameter values| `mu`.}\n", "\n", "\\PY{l+s+sd}{ This method returns a |VectorArray| with a internal state}\n", "\\PY{l+s+sd}{ representation of the model\\PYZsq{}s solution for given}\n", "\\PY{l+s+sd}{ |parameter values|. It is a convenience wrapper around}\n", "\\PY{l+s+sd}{ :meth:`compute`.}\n", "\n", "\\PY{l+s+sd}{ The result may be :mod:`cached \\PYZlt{}pymor.core.cache\\PYZgt{}`}\n", "\\PY{l+s+sd}{ in case caching has been activated for the given model.}\n", "\n", "\\PY{l+s+sd}{ Parameters}\n", "\\PY{l+s+sd}{ \\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}}\n", "\\PY{l+s+sd}{ mu}\n", "\\PY{l+s+sd}{ |Parameter values| for which to solve.}\n", "\\PY{l+s+sd}{ input}\n", "\\PY{l+s+sd}{ The model input. Either a |NumPy array| of shape `(self.dim\\PYZus{}input,)`,}\n", "\\PY{l+s+sd}{ a |Function| with `dim\\PYZus{}domain == 1` and `shape\\PYZus{}range == (self.dim\\PYZus{}input,)`}\n", "\\PY{l+s+sd}{ mapping time to input, or a `str` expression whith `t` as variable that}\n", "\\PY{l+s+sd}{ can be used to instatiate an |ExpressionFunction| of this type.}\n", "\\PY{l+s+sd}{ Can be `None` if `self.dim\\PYZus{}input == 0`.}\n", "\\PY{l+s+sd}{ return\\PYZus{}error\\PYZus{}estimate}\n", "\\PY{l+s+sd}{ If `True`, also return an error estimate for the computed solution.}\n", "\\PY{l+s+sd}{ kwargs}\n", "\\PY{l+s+sd}{ Additional keyword arguments passed to :meth:`compute` that}\n", "\\PY{l+s+sd}{ might affect how the solution is computed.}\n", "\n", "\\PY{l+s+sd}{ Returns}\n", "\\PY{l+s+sd}{ \\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}}\n", "\\PY{l+s+sd}{ The solution |VectorArray|. When `return\\PYZus{}error\\PYZus{}estimate` is `True`,}\n", "\\PY{l+s+sd}{ the estimate is returned as second value.}\n", "\\PY{l+s+sd}{ \\PYZdq{}\\PYZdq{}\\PYZdq{}}\n", " \\PY{n}{data} \\PY{o}{=} \\PY{n+nb+bp}{self}\\PY{o}{.}\\PY{n}{compute}\\PY{p}{(}\n", " \\PY{n}{solution}\\PY{o}{=}\\PY{k+kc}{True}\\PY{p}{,}\n", " \\PY{n}{solution\\PYZus{}error\\PYZus{}estimate}\\PY{o}{=}\\PY{n}{return\\PYZus{}error\\PYZus{}estimate}\\PY{p}{,}\n", " \\PY{n}{mu}\\PY{o}{=}\\PY{n}{mu}\\PY{p}{,}\n", " \\PY{n+nb}{input}\\PY{o}{=}\\PY{n+nb}{input}\\PY{p}{,}\n", " \\PY{o}{*}\\PY{o}{*}\\PY{n}{kwargs}\n", " \\PY{p}{)}\n", " \\PY{k}{if} \\PY{n}{return\\PYZus{}error\\PYZus{}estimate}\\PY{p}{:}\n", " \\PY{k}{return} \\PY{n}{data}\\PY{p}{[}\\PY{l+s+s1}{\\PYZsq{}}\\PY{l+s+s1}{solution}\\PY{l+s+s1}{\\PYZsq{}}\\PY{p}{]}\\PY{p}{,} \\PY{n}{data}\\PY{p}{[}\\PY{l+s+s1}{\\PYZsq{}}\\PY{l+s+s1}{solution\\PYZus{}error\\PYZus{}estimate}\\PY{l+s+s1}{\\PYZsq{}}\\PY{p}{]}\n", " \\PY{k}{else}\\PY{p}{:}\n", " \\PY{k}{return} \\PY{n}{data}\\PY{p}{[}\\PY{l+s+s1}{\\PYZsq{}}\\PY{l+s+s1}{solution}\\PY{l+s+s1}{\\PYZsq{}}\\PY{p}{]}\n", "\\end{Verbatim}\n" ], "text/plain": [ " def solve(self, mu=None, input=None, return_error_estimate=False, **kwargs):\n", " \"\"\"Solve the discrete problem for the |parameter values| `mu`.\n", "\n", " This method returns a |VectorArray| with a internal state\n", " representation of the model's solution for given\n", " |parameter values|. It is a convenience wrapper around\n", " :meth:`compute`.\n", "\n", " The result may be :mod:`cached `\n", " in case caching has been activated for the given model.\n", "\n", " Parameters\n", " ----------\n", " mu\n", " |Parameter values| for which to solve.\n", " input\n", " The model input. Either a |NumPy array| of shape `(self.dim_input,)`,\n", " a |Function| with `dim_domain == 1` and `shape_range == (self.dim_input,)`\n", " mapping time to input, or a `str` expression whith `t` as variable that\n", " can be used to instatiate an |ExpressionFunction| of this type.\n", " Can be `None` if `self.dim_input == 0`.\n", " return_error_estimate\n", " If `True`, also return an error estimate for the computed solution.\n", " kwargs\n", " Additional keyword arguments passed to :meth:`compute` that\n", " might affect how the solution is computed.\n", "\n", " Returns\n", " -------\n", " The solution |VectorArray|. When `return_error_estimate` is `True`,\n", " the estimate is returned as second value.\n", " \"\"\"\n", " data = self.compute(\n", " solution=True,\n", " solution_error_estimate=return_error_estimate,\n", " mu=mu,\n", " input=input,\n", " **kwargs\n", " )\n", " if return_error_estimate:\n", " return data['solution'], data['solution_error_estimate']\n", " else:\n", " return data['solution']" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from pymor.tools.formatsrc import print_source\n", "print_source(fom.solve)" ] }, { "cell_type": "markdown", "id": "c30a0828", "metadata": {}, "source": [ "This does not look too interesting. Actually, {meth}`~pymor.models.interface.Model.solve`\n", "is just a convenience method around {meth}`~pymor.models.interface.Model.compute` which\n", "handles the actual computation of the solution and various other associated values like\n", "outputs or error estimates. Next, we take a look at the implemenation of\n", "{meth}`~pymor.models.interface.Model.compute`:" ] }, { "cell_type": "code", "execution_count": 7, "id": "992f5310", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
    def compute(self, solution=False, output=False, solution_d_mu=False, output_d_mu=False,\n",
       "                solution_error_estimate=False, output_error_estimate=False,\n",
       "                output_d_mu_return_array=False, output_error_estimate_return_vector=False,\n",
       "                *, mu=None, input=None, **kwargs):\n",
       "        """Compute the solution of the model and associated quantities.\n",
       "\n",
       "        This methods computes the output of the model it's internal state\n",
       "        and various associated quantities for given |parameter values|\n",
       "        `mu`.\n",
       "\n",
       "        .. note::\n",
       "\n",
       "            The default implementation defers the actual computations to\n",
       "            the methods :meth:`_compute_solution`, :meth:`_compute_output`,\n",
       "            :meth:`_compute_solution_error_estimate` and :meth:`_compute_output_error_estimate`.\n",
       "            The call to :meth:`_compute_solution` is :mod:`cached <pymor.core.cache>`.\n",
       "            In addition, |Model| implementors may implement :meth:`_compute` to\n",
       "            simultaneously compute multiple values in an optimized way. The corresponding\n",
       "            `_compute_XXX` methods will not be called for values already returned by\n",
       "            :meth:`_compute`.\n",
       "\n",
       "        Parameters\n",
       "        ----------\n",
       "        solution\n",
       "            If `True`, return the model's internal state.\n",
       "        output\n",
       "            If `True`, return the model output.\n",
       "        solution_d_mu\n",
       "            If not `False`, either `True` to return the derivative of the model's\n",
       "            internal state w.r.t. all parameter components or a tuple `(parameter, index)`\n",
       "            to return the derivative of a single parameter component.\n",
       "        output_d_mu\n",
       "            If `True`, return the gradient of the model output w.r.t. the |Parameter|.\n",
       "        solution_error_estimate\n",
       "            If `True`, return an error estimate for the computed internal state.\n",
       "        output_error_estimate\n",
       "            If `True`, return an error estimate for the computed output.\n",
       "        output_d_mu_return_array\n",
       "            If `True`, return the output gradient as a |NumPy array|.\n",
       "            Otherwise, return a dict of gradients for each |Parameter|.\n",
       "        output_error_estimate_return_vector\n",
       "            If `True`, return the output estimate as a |NumPy array|,\n",
       "            where each component corresponds to the respective component\n",
       "            of the :attr:`output_functional`.\n",
       "            Otherwise, return the euclidian norm of all components.\n",
       "        mu\n",
       "            |Parameter values| for which to compute the values.\n",
       "        input\n",
       "            The model input. Either a |NumPy array| of shape `(self.dim_input,)`,\n",
       "            a |Function| with `dim_domain == 1` and `shape_range == (self.dim_input,)`\n",
       "            mapping time to input, or a `str` expression whith `t` as variable that\n",
       "            can be used to instatiate an |ExpressionFunction| of this type.\n",
       "            Can be `None` if `self.dim_input == 0`.\n",
       "        kwargs\n",
       "            Further keyword arguments to select further quantities that should\n",
       "            be returned or to customize how the values are computed.\n",
       "\n",
       "        Returns\n",
       "        -------\n",
       "        A dict with the computed values.\n",
       "        """\n",
       "        # make sure no unknown kwargs are passed\n",
       "        assert kwargs.keys() <= self._compute_allowed_kwargs\n",
       "        assert input is not None or self.dim_input == 0\n",
       "\n",
       "        # parse parameter values\n",
       "        if not isinstance(mu, Mu):\n",
       "            mu = self.parameters.parse(mu)\n",
       "        assert self.parameters.assert_compatible(mu)\n",
       "\n",
       "        # parse input and add it to the parameter values\n",
       "        mu_input = Parameters(input=self.dim_input).parse(input)\n",
       "        input = mu_input.get_time_dependent_value('input') if mu_input.is_time_dependent('input') else mu_input['input']\n",
       "        mu = mu.with_(input=input)\n",
       "\n",
       "        # log output\n",
       "        # explicitly checking if logging is disabled saves some cpu cycles\n",
       "        if not self.logging_disabled:\n",
       "            self.logger.info(f'Solving {self.name} for {mu} ...')\n",
       "\n",
       "        # first call _compute to give subclasses more control\n",
       "        data = self._compute(solution=solution, output=output,\n",
       "                             solution_d_mu=solution_d_mu, output_d_mu=output_d_mu,\n",
       "                             solution_error_estimate=solution_error_estimate,\n",
       "                             output_error_estimate=output_error_estimate,\n",
       "                             mu=mu, **kwargs)\n",
       "\n",
       "        if (solution or output or solution_error_estimate\n",
       "            or output_error_estimate or solution_d_mu or output_d_mu) \\\n",
       "           and 'solution' not in data:\n",
       "            retval = self.cached_method_call(self._compute_solution, mu=mu, **kwargs)\n",
       "            if isinstance(retval, dict):\n",
       "                assert 'solution' in retval\n",
       "                data.update(retval)\n",
       "            else:\n",
       "                data['solution'] = retval\n",
       "\n",
       "        if output and 'output' not in data:\n",
       "            # TODO use caching here (requires skipping args in key generation)\n",
       "            retval = self._compute_output(data['solution'], mu=mu, **kwargs)\n",
       "            if isinstance(retval, dict):\n",
       "                assert 'output' in retval\n",
       "                data.update(retval)\n",
       "            else:\n",
       "                data['output'] = retval\n",
       "\n",
       "        if solution_d_mu and 'solution_d_mu' not in data:\n",
       "            if isinstance(solution_d_mu, tuple):\n",
       "                retval = self._compute_solution_d_mu_single_direction(\n",
       "                    solution_d_mu[0], solution_d_mu[1], data['solution'], mu=mu, **kwargs)\n",
       "            else:\n",
       "                retval = self._compute_solution_d_mu(data['solution'], mu=mu, **kwargs)\n",
       "            # retval is always a dict\n",
       "            if isinstance(retval, dict) and 'solution_d_mu' in retval:\n",
       "                data.update(retval)\n",
       "            else:\n",
       "                data['solution_d_mu'] = retval\n",
       "\n",
       "        if output_d_mu and 'output_d_mu' not in data:\n",
       "            # TODO use caching here (requires skipping args in key generation)\n",
       "            retval = self._compute_output_d_mu(data['solution'], mu=mu,\n",
       "                                               return_array=output_d_mu_return_array,\n",
       "                                               **kwargs)\n",
       "            # retval is always a dict\n",
       "            if isinstance(retval, dict) and 'output_d_mu' in retval:\n",
       "                data.update(retval)\n",
       "            else:\n",
       "                data['output_d_mu'] = retval\n",
       "\n",
       "        if solution_error_estimate and 'solution_error_estimate' not in data:\n",
       "            # TODO use caching here (requires skipping args in key generation)\n",
       "            retval = self._compute_solution_error_estimate(data['solution'], mu=mu, **kwargs)\n",
       "            if isinstance(retval, dict):\n",
       "                assert 'solution_error_estimate' in retval\n",
       "                data.update(retval)\n",
       "            else:\n",
       "                data['solution_error_estimate'] = retval\n",
       "\n",
       "        if output_error_estimate and 'output_error_estimate' not in data:\n",
       "            # TODO use caching here (requires skipping args in key generation)\n",
       "            retval = self._compute_output_error_estimate(\n",
       "                data['solution'], mu=mu,\n",
       "                return_vector=output_error_estimate_return_vector, **kwargs)\n",
       "            if isinstance(retval, dict):\n",
       "                assert 'output_error_estimate' in retval\n",
       "                data.update(retval)\n",
       "            else:\n",
       "                data['output_error_estimate'] = retval\n",
       "\n",
       "        return data\n",
       "
\n" ], "text/latex": [ "\\begin{Verbatim}[commandchars=\\\\\\{\\}]\n", " \\PY{k}{def} \\PY{n+nf}{compute}\\PY{p}{(}\\PY{n+nb+bp}{self}\\PY{p}{,} \\PY{n}{solution}\\PY{o}{=}\\PY{k+kc}{False}\\PY{p}{,} \\PY{n}{output}\\PY{o}{=}\\PY{k+kc}{False}\\PY{p}{,} \\PY{n}{solution\\PYZus{}d\\PYZus{}mu}\\PY{o}{=}\\PY{k+kc}{False}\\PY{p}{,} \\PY{n}{output\\PYZus{}d\\PYZus{}mu}\\PY{o}{=}\\PY{k+kc}{False}\\PY{p}{,}\n", " \\PY{n}{solution\\PYZus{}error\\PYZus{}estimate}\\PY{o}{=}\\PY{k+kc}{False}\\PY{p}{,} \\PY{n}{output\\PYZus{}error\\PYZus{}estimate}\\PY{o}{=}\\PY{k+kc}{False}\\PY{p}{,}\n", " \\PY{n}{output\\PYZus{}d\\PYZus{}mu\\PYZus{}return\\PYZus{}array}\\PY{o}{=}\\PY{k+kc}{False}\\PY{p}{,} \\PY{n}{output\\PYZus{}error\\PYZus{}estimate\\PYZus{}return\\PYZus{}vector}\\PY{o}{=}\\PY{k+kc}{False}\\PY{p}{,}\n", " \\PY{o}{*}\\PY{p}{,} \\PY{n}{mu}\\PY{o}{=}\\PY{k+kc}{None}\\PY{p}{,} \\PY{n+nb}{input}\\PY{o}{=}\\PY{k+kc}{None}\\PY{p}{,} \\PY{o}{*}\\PY{o}{*}\\PY{n}{kwargs}\\PY{p}{)}\\PY{p}{:}\n", " \\PY{l+s+sd}{\\PYZdq{}\\PYZdq{}\\PYZdq{}Compute the solution of the model and associated quantities.}\n", "\n", "\\PY{l+s+sd}{ This methods computes the output of the model it\\PYZsq{}s internal state}\n", "\\PY{l+s+sd}{ and various associated quantities for given |parameter values|}\n", "\\PY{l+s+sd}{ `mu`.}\n", "\n", "\\PY{l+s+sd}{ .. note::}\n", "\n", "\\PY{l+s+sd}{ The default implementation defers the actual computations to}\n", "\\PY{l+s+sd}{ the methods :meth:`\\PYZus{}compute\\PYZus{}solution`, :meth:`\\PYZus{}compute\\PYZus{}output`,}\n", "\\PY{l+s+sd}{ :meth:`\\PYZus{}compute\\PYZus{}solution\\PYZus{}error\\PYZus{}estimate` and :meth:`\\PYZus{}compute\\PYZus{}output\\PYZus{}error\\PYZus{}estimate`.}\n", "\\PY{l+s+sd}{ The call to :meth:`\\PYZus{}compute\\PYZus{}solution` is :mod:`cached \\PYZlt{}pymor.core.cache\\PYZgt{}`.}\n", "\\PY{l+s+sd}{ In addition, |Model| implementors may implement :meth:`\\PYZus{}compute` to}\n", "\\PY{l+s+sd}{ simultaneously compute multiple values in an optimized way. The corresponding}\n", "\\PY{l+s+sd}{ `\\PYZus{}compute\\PYZus{}XXX` methods will not be called for values already returned by}\n", "\\PY{l+s+sd}{ :meth:`\\PYZus{}compute`.}\n", "\n", "\\PY{l+s+sd}{ Parameters}\n", "\\PY{l+s+sd}{ \\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}}\n", "\\PY{l+s+sd}{ solution}\n", "\\PY{l+s+sd}{ If `True`, return the model\\PYZsq{}s internal state.}\n", "\\PY{l+s+sd}{ output}\n", "\\PY{l+s+sd}{ If `True`, return the model output.}\n", "\\PY{l+s+sd}{ solution\\PYZus{}d\\PYZus{}mu}\n", "\\PY{l+s+sd}{ If not `False`, either `True` to return the derivative of the model\\PYZsq{}s}\n", "\\PY{l+s+sd}{ internal state w.r.t. all parameter components or a tuple `(parameter, index)`}\n", "\\PY{l+s+sd}{ to return the derivative of a single parameter component.}\n", "\\PY{l+s+sd}{ output\\PYZus{}d\\PYZus{}mu}\n", "\\PY{l+s+sd}{ If `True`, return the gradient of the model output w.r.t. the |Parameter|.}\n", "\\PY{l+s+sd}{ solution\\PYZus{}error\\PYZus{}estimate}\n", "\\PY{l+s+sd}{ If `True`, return an error estimate for the computed internal state.}\n", "\\PY{l+s+sd}{ output\\PYZus{}error\\PYZus{}estimate}\n", "\\PY{l+s+sd}{ If `True`, return an error estimate for the computed output.}\n", "\\PY{l+s+sd}{ output\\PYZus{}d\\PYZus{}mu\\PYZus{}return\\PYZus{}array}\n", "\\PY{l+s+sd}{ If `True`, return the output gradient as a |NumPy array|.}\n", "\\PY{l+s+sd}{ Otherwise, return a dict of gradients for each |Parameter|.}\n", "\\PY{l+s+sd}{ output\\PYZus{}error\\PYZus{}estimate\\PYZus{}return\\PYZus{}vector}\n", "\\PY{l+s+sd}{ If `True`, return the output estimate as a |NumPy array|,}\n", "\\PY{l+s+sd}{ where each component corresponds to the respective component}\n", "\\PY{l+s+sd}{ of the :attr:`output\\PYZus{}functional`.}\n", "\\PY{l+s+sd}{ Otherwise, return the euclidian norm of all components.}\n", "\\PY{l+s+sd}{ mu}\n", "\\PY{l+s+sd}{ |Parameter values| for which to compute the values.}\n", "\\PY{l+s+sd}{ input}\n", "\\PY{l+s+sd}{ The model input. Either a |NumPy array| of shape `(self.dim\\PYZus{}input,)`,}\n", "\\PY{l+s+sd}{ a |Function| with `dim\\PYZus{}domain == 1` and `shape\\PYZus{}range == (self.dim\\PYZus{}input,)`}\n", "\\PY{l+s+sd}{ mapping time to input, or a `str` expression whith `t` as variable that}\n", "\\PY{l+s+sd}{ can be used to instatiate an |ExpressionFunction| of this type.}\n", "\\PY{l+s+sd}{ Can be `None` if `self.dim\\PYZus{}input == 0`.}\n", "\\PY{l+s+sd}{ kwargs}\n", "\\PY{l+s+sd}{ Further keyword arguments to select further quantities that should}\n", "\\PY{l+s+sd}{ be returned or to customize how the values are computed.}\n", "\n", "\\PY{l+s+sd}{ Returns}\n", "\\PY{l+s+sd}{ \\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}}\n", "\\PY{l+s+sd}{ A dict with the computed values.}\n", "\\PY{l+s+sd}{ \\PYZdq{}\\PYZdq{}\\PYZdq{}}\n", " \\PY{c+c1}{\\PYZsh{} make sure no unknown kwargs are passed}\n", " \\PY{k}{assert} \\PY{n}{kwargs}\\PY{o}{.}\\PY{n}{keys}\\PY{p}{(}\\PY{p}{)} \\PY{o}{\\PYZlt{}}\\PY{o}{=} \\PY{n+nb+bp}{self}\\PY{o}{.}\\PY{n}{\\PYZus{}compute\\PYZus{}allowed\\PYZus{}kwargs}\n", " \\PY{k}{assert} \\PY{n+nb}{input} \\PY{o+ow}{is} \\PY{o+ow}{not} \\PY{k+kc}{None} \\PY{o+ow}{or} \\PY{n+nb+bp}{self}\\PY{o}{.}\\PY{n}{dim\\PYZus{}input} \\PY{o}{==} \\PY{l+m+mi}{0}\n", "\n", " \\PY{c+c1}{\\PYZsh{} parse parameter values}\n", " \\PY{k}{if} \\PY{o+ow}{not} \\PY{n+nb}{isinstance}\\PY{p}{(}\\PY{n}{mu}\\PY{p}{,} \\PY{n}{Mu}\\PY{p}{)}\\PY{p}{:}\n", " \\PY{n}{mu} \\PY{o}{=} \\PY{n+nb+bp}{self}\\PY{o}{.}\\PY{n}{parameters}\\PY{o}{.}\\PY{n}{parse}\\PY{p}{(}\\PY{n}{mu}\\PY{p}{)}\n", " \\PY{k}{assert} \\PY{n+nb+bp}{self}\\PY{o}{.}\\PY{n}{parameters}\\PY{o}{.}\\PY{n}{assert\\PYZus{}compatible}\\PY{p}{(}\\PY{n}{mu}\\PY{p}{)}\n", "\n", " \\PY{c+c1}{\\PYZsh{} parse input and add it to the parameter values}\n", " \\PY{n}{mu\\PYZus{}input} \\PY{o}{=} \\PY{n}{Parameters}\\PY{p}{(}\\PY{n+nb}{input}\\PY{o}{=}\\PY{n+nb+bp}{self}\\PY{o}{.}\\PY{n}{dim\\PYZus{}input}\\PY{p}{)}\\PY{o}{.}\\PY{n}{parse}\\PY{p}{(}\\PY{n+nb}{input}\\PY{p}{)}\n", " \\PY{n+nb}{input} \\PY{o}{=} \\PY{n}{mu\\PYZus{}input}\\PY{o}{.}\\PY{n}{get\\PYZus{}time\\PYZus{}dependent\\PYZus{}value}\\PY{p}{(}\\PY{l+s+s1}{\\PYZsq{}}\\PY{l+s+s1}{input}\\PY{l+s+s1}{\\PYZsq{}}\\PY{p}{)} \\PY{k}{if} \\PY{n}{mu\\PYZus{}input}\\PY{o}{.}\\PY{n}{is\\PYZus{}time\\PYZus{}dependent}\\PY{p}{(}\\PY{l+s+s1}{\\PYZsq{}}\\PY{l+s+s1}{input}\\PY{l+s+s1}{\\PYZsq{}}\\PY{p}{)} \\PY{k}{else} \\PY{n}{mu\\PYZus{}input}\\PY{p}{[}\\PY{l+s+s1}{\\PYZsq{}}\\PY{l+s+s1}{input}\\PY{l+s+s1}{\\PYZsq{}}\\PY{p}{]}\n", " \\PY{n}{mu} \\PY{o}{=} \\PY{n}{mu}\\PY{o}{.}\\PY{n}{with\\PYZus{}}\\PY{p}{(}\\PY{n+nb}{input}\\PY{o}{=}\\PY{n+nb}{input}\\PY{p}{)}\n", "\n", " \\PY{c+c1}{\\PYZsh{} log output}\n", " \\PY{c+c1}{\\PYZsh{} explicitly checking if logging is disabled saves some cpu cycles}\n", " \\PY{k}{if} \\PY{o+ow}{not} \\PY{n+nb+bp}{self}\\PY{o}{.}\\PY{n}{logging\\PYZus{}disabled}\\PY{p}{:}\n", " \\PY{n+nb+bp}{self}\\PY{o}{.}\\PY{n}{logger}\\PY{o}{.}\\PY{n}{info}\\PY{p}{(}\\PY{l+s+sa}{f}\\PY{l+s+s1}{\\PYZsq{}}\\PY{l+s+s1}{Solving }\\PY{l+s+si}{\\PYZob{}}\\PY{n+nb+bp}{self}\\PY{o}{.}\\PY{n}{name}\\PY{l+s+si}{\\PYZcb{}}\\PY{l+s+s1}{ for }\\PY{l+s+si}{\\PYZob{}}\\PY{n}{mu}\\PY{l+s+si}{\\PYZcb{}}\\PY{l+s+s1}{ ...}\\PY{l+s+s1}{\\PYZsq{}}\\PY{p}{)}\n", "\n", " \\PY{c+c1}{\\PYZsh{} first call \\PYZus{}compute to give subclasses more control}\n", " \\PY{n}{data} \\PY{o}{=} \\PY{n+nb+bp}{self}\\PY{o}{.}\\PY{n}{\\PYZus{}compute}\\PY{p}{(}\\PY{n}{solution}\\PY{o}{=}\\PY{n}{solution}\\PY{p}{,} \\PY{n}{output}\\PY{o}{=}\\PY{n}{output}\\PY{p}{,}\n", " \\PY{n}{solution\\PYZus{}d\\PYZus{}mu}\\PY{o}{=}\\PY{n}{solution\\PYZus{}d\\PYZus{}mu}\\PY{p}{,} \\PY{n}{output\\PYZus{}d\\PYZus{}mu}\\PY{o}{=}\\PY{n}{output\\PYZus{}d\\PYZus{}mu}\\PY{p}{,}\n", " 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\\PY{n}{mu}\\PY{o}{=}\\PY{n}{mu}\\PY{p}{,} \\PY{o}{*}\\PY{o}{*}\\PY{n}{kwargs}\\PY{p}{)}\n", " \\PY{c+c1}{\\PYZsh{} retval is always a dict}\n", " \\PY{k}{if} \\PY{n+nb}{isinstance}\\PY{p}{(}\\PY{n}{retval}\\PY{p}{,} \\PY{n+nb}{dict}\\PY{p}{)} \\PY{o+ow}{and} \\PY{l+s+s1}{\\PYZsq{}}\\PY{l+s+s1}{solution\\PYZus{}d\\PYZus{}mu}\\PY{l+s+s1}{\\PYZsq{}} \\PY{o+ow}{in} \\PY{n}{retval}\\PY{p}{:}\n", " \\PY{n}{data}\\PY{o}{.}\\PY{n}{update}\\PY{p}{(}\\PY{n}{retval}\\PY{p}{)}\n", " \\PY{k}{else}\\PY{p}{:}\n", " \\PY{n}{data}\\PY{p}{[}\\PY{l+s+s1}{\\PYZsq{}}\\PY{l+s+s1}{solution\\PYZus{}d\\PYZus{}mu}\\PY{l+s+s1}{\\PYZsq{}}\\PY{p}{]} \\PY{o}{=} \\PY{n}{retval}\n", "\n", " \\PY{k}{if} \\PY{n}{output\\PYZus{}d\\PYZus{}mu} \\PY{o+ow}{and} \\PY{l+s+s1}{\\PYZsq{}}\\PY{l+s+s1}{output\\PYZus{}d\\PYZus{}mu}\\PY{l+s+s1}{\\PYZsq{}} \\PY{o+ow}{not} \\PY{o+ow}{in} \\PY{n}{data}\\PY{p}{:}\n", " \\PY{c+c1}{\\PYZsh{} TODO use caching here (requires skipping args in key generation)}\n", " \\PY{n}{retval} 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\\PY{n}{data}\\PY{o}{.}\\PY{n}{update}\\PY{p}{(}\\PY{n}{retval}\\PY{p}{)}\n", " \\PY{k}{else}\\PY{p}{:}\n", " \\PY{n}{data}\\PY{p}{[}\\PY{l+s+s1}{\\PYZsq{}}\\PY{l+s+s1}{solution\\PYZus{}error\\PYZus{}estimate}\\PY{l+s+s1}{\\PYZsq{}}\\PY{p}{]} \\PY{o}{=} \\PY{n}{retval}\n", "\n", " \\PY{k}{if} \\PY{n}{output\\PYZus{}error\\PYZus{}estimate} \\PY{o+ow}{and} \\PY{l+s+s1}{\\PYZsq{}}\\PY{l+s+s1}{output\\PYZus{}error\\PYZus{}estimate}\\PY{l+s+s1}{\\PYZsq{}} \\PY{o+ow}{not} \\PY{o+ow}{in} \\PY{n}{data}\\PY{p}{:}\n", " \\PY{c+c1}{\\PYZsh{} TODO use caching here (requires skipping args in key generation)}\n", " \\PY{n}{retval} \\PY{o}{=} \\PY{n+nb+bp}{self}\\PY{o}{.}\\PY{n}{\\PYZus{}compute\\PYZus{}output\\PYZus{}error\\PYZus{}estimate}\\PY{p}{(}\n", " \\PY{n}{data}\\PY{p}{[}\\PY{l+s+s1}{\\PYZsq{}}\\PY{l+s+s1}{solution}\\PY{l+s+s1}{\\PYZsq{}}\\PY{p}{]}\\PY{p}{,} \\PY{n}{mu}\\PY{o}{=}\\PY{n}{mu}\\PY{p}{,}\n", " \\PY{n}{return\\PYZus{}vector}\\PY{o}{=}\\PY{n}{output\\PYZus{}error\\PYZus{}estimate\\PYZus{}return\\PYZus{}vector}\\PY{p}{,} \\PY{o}{*}\\PY{o}{*}\\PY{n}{kwargs}\\PY{p}{)}\n", " \\PY{k}{if} \\PY{n+nb}{isinstance}\\PY{p}{(}\\PY{n}{retval}\\PY{p}{,} \\PY{n+nb}{dict}\\PY{p}{)}\\PY{p}{:}\n", " \\PY{k}{assert} \\PY{l+s+s1}{\\PYZsq{}}\\PY{l+s+s1}{output\\PYZus{}error\\PYZus{}estimate}\\PY{l+s+s1}{\\PYZsq{}} \\PY{o+ow}{in} \\PY{n}{retval}\n", " \\PY{n}{data}\\PY{o}{.}\\PY{n}{update}\\PY{p}{(}\\PY{n}{retval}\\PY{p}{)}\n", " \\PY{k}{else}\\PY{p}{:}\n", " \\PY{n}{data}\\PY{p}{[}\\PY{l+s+s1}{\\PYZsq{}}\\PY{l+s+s1}{output\\PYZus{}error\\PYZus{}estimate}\\PY{l+s+s1}{\\PYZsq{}}\\PY{p}{]} \\PY{o}{=} \\PY{n}{retval}\n", "\n", " \\PY{k}{return} \\PY{n}{data}\n", "\\end{Verbatim}\n" ], "text/plain": [ " def compute(self, solution=False, output=False, solution_d_mu=False, output_d_mu=False,\n", " solution_error_estimate=False, output_error_estimate=False,\n", " output_d_mu_return_array=False, output_error_estimate_return_vector=False,\n", " *, mu=None, input=None, **kwargs):\n", " \"\"\"Compute the solution of the model and associated quantities.\n", "\n", " This methods computes the output of the model it's internal state\n", " and various associated quantities for given |parameter values|\n", " `mu`.\n", "\n", " .. note::\n", "\n", " The default implementation defers the actual computations to\n", " the methods :meth:`_compute_solution`, :meth:`_compute_output`,\n", " :meth:`_compute_solution_error_estimate` and :meth:`_compute_output_error_estimate`.\n", " The call to :meth:`_compute_solution` is :mod:`cached `.\n", " In addition, |Model| implementors may implement :meth:`_compute` to\n", " simultaneously compute multiple values in an optimized way. The corresponding\n", " `_compute_XXX` methods will not be called for values already returned by\n", " :meth:`_compute`.\n", "\n", " Parameters\n", " ----------\n", " solution\n", " If `True`, return the model's internal state.\n", " output\n", " If `True`, return the model output.\n", " solution_d_mu\n", " If not `False`, either `True` to return the derivative of the model's\n", " internal state w.r.t. all parameter components or a tuple `(parameter, index)`\n", " to return the derivative of a single parameter component.\n", " output_d_mu\n", " If `True`, return the gradient of the model output w.r.t. the |Parameter|.\n", " solution_error_estimate\n", " If `True`, return an error estimate for the computed internal state.\n", " output_error_estimate\n", " If `True`, return an error estimate for the computed output.\n", " output_d_mu_return_array\n", " If `True`, return the output gradient as a |NumPy array|.\n", " Otherwise, return a dict of gradients for each |Parameter|.\n", " output_error_estimate_return_vector\n", " If `True`, return the output estimate as a |NumPy array|,\n", " where each component corresponds to the respective component\n", " of the :attr:`output_functional`.\n", " Otherwise, return the euclidian norm of all components.\n", " mu\n", " |Parameter values| for which to compute the values.\n", " input\n", " The model input. Either a |NumPy array| of shape `(self.dim_input,)`,\n", " a |Function| with `dim_domain == 1` and `shape_range == (self.dim_input,)`\n", " mapping time to input, or a `str` expression whith `t` as variable that\n", " can be used to instatiate an |ExpressionFunction| of this type.\n", " Can be `None` if `self.dim_input == 0`.\n", " kwargs\n", " Further keyword arguments to select further quantities that should\n", " be returned or to customize how the values are computed.\n", "\n", " Returns\n", " -------\n", " A dict with the computed values.\n", " \"\"\"\n", " # make sure no unknown kwargs are passed\n", " assert kwargs.keys() <= self._compute_allowed_kwargs\n", " assert input is not None or self.dim_input == 0\n", "\n", " # parse parameter values\n", " if not isinstance(mu, Mu):\n", " mu = self.parameters.parse(mu)\n", " assert self.parameters.assert_compatible(mu)\n", "\n", " # parse input and add it to the parameter values\n", " mu_input = Parameters(input=self.dim_input).parse(input)\n", " input = mu_input.get_time_dependent_value('input') if mu_input.is_time_dependent('input') else mu_input['input']\n", " mu = mu.with_(input=input)\n", "\n", " # log output\n", " # explicitly checking if logging is disabled saves some cpu cycles\n", " if not self.logging_disabled:\n", " self.logger.info(f'Solving {self.name} for {mu} ...')\n", "\n", " # first call _compute to give subclasses more control\n", " data = self._compute(solution=solution, output=output,\n", " solution_d_mu=solution_d_mu, output_d_mu=output_d_mu,\n", " solution_error_estimate=solution_error_estimate,\n", " output_error_estimate=output_error_estimate,\n", " mu=mu, **kwargs)\n", "\n", " if (solution or output or solution_error_estimate\n", " or output_error_estimate or solution_d_mu or output_d_mu) \\\n", " and 'solution' not in data:\n", " retval = self.cached_method_call(self._compute_solution, mu=mu, **kwargs)\n", " if isinstance(retval, dict):\n", " assert 'solution' in retval\n", " data.update(retval)\n", " else:\n", " data['solution'] = retval\n", "\n", " if output and 'output' not in data:\n", " # TODO use caching here (requires skipping args in key generation)\n", " retval = self._compute_output(data['solution'], mu=mu, **kwargs)\n", " if isinstance(retval, dict):\n", " assert 'output' in retval\n", " data.update(retval)\n", " else:\n", " data['output'] = retval\n", "\n", " if solution_d_mu and 'solution_d_mu' not in data:\n", " if isinstance(solution_d_mu, tuple):\n", " retval = self._compute_solution_d_mu_single_direction(\n", " solution_d_mu[0], solution_d_mu[1], data['solution'], mu=mu, **kwargs)\n", " else:\n", " retval = self._compute_solution_d_mu(data['solution'], mu=mu, **kwargs)\n", " # retval is always a dict\n", " if isinstance(retval, dict) and 'solution_d_mu' in retval:\n", " data.update(retval)\n", " else:\n", " data['solution_d_mu'] = retval\n", "\n", " if output_d_mu and 'output_d_mu' not in data:\n", " # TODO use caching here (requires skipping args in key generation)\n", " retval = self._compute_output_d_mu(data['solution'], mu=mu,\n", " return_array=output_d_mu_return_array,\n", " **kwargs)\n", " # retval is always a dict\n", " if isinstance(retval, dict) and 'output_d_mu' in retval:\n", " data.update(retval)\n", " else:\n", " data['output_d_mu'] = retval\n", "\n", " if solution_error_estimate and 'solution_error_estimate' not in data:\n", " # TODO use caching here (requires skipping args in key generation)\n", " retval = self._compute_solution_error_estimate(data['solution'], mu=mu, **kwargs)\n", " if isinstance(retval, dict):\n", " assert 'solution_error_estimate' in retval\n", " data.update(retval)\n", " else:\n", " data['solution_error_estimate'] = retval\n", "\n", " if output_error_estimate and 'output_error_estimate' not in data:\n", " # TODO use caching here (requires skipping args in key generation)\n", " retval = self._compute_output_error_estimate(\n", " data['solution'], mu=mu,\n", " return_vector=output_error_estimate_return_vector, **kwargs)\n", " if isinstance(retval, dict):\n", " assert 'output_error_estimate' in retval\n", " data.update(retval)\n", " else:\n", " data['output_error_estimate'] = retval\n", "\n", " return data" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "print_source(fom.compute)" ] }, { "cell_type": "markdown", "id": "3b17f7e0", "metadata": {}, "source": [ "What we see is a default implementation from {class}`~pymor.models.interface.Model` that\n", "takes care of checking the input {{ parameter_values }} `mu`, {mod}`caching ` and\n", "{mod}`logging `, but defers the actual computations to further private methods.\n", "Implementors can directly implement {meth}`~pymor.models.interface.Model._compute` to compute\n", "multiple return values at once in an optimized way. Our given model, however, just implements\n", "{meth}`~pymor.models.interface.Model._compute_solution` where we can find the\n", "actual code:" ] }, { "cell_type": "code", "execution_count": 8, "id": "2785d95c", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
    def _compute_solution(self, mu=None, **kwargs):\n",
       "        return self.operator.apply_inverse(self.rhs.as_range_array(mu), mu=mu)\n",
       "
\n" ], "text/latex": [ "\\begin{Verbatim}[commandchars=\\\\\\{\\}]\n", " \\PY{k}{def} \\PY{n+nf}{\\PYZus{}compute\\PYZus{}solution}\\PY{p}{(}\\PY{n+nb+bp}{self}\\PY{p}{,} \\PY{n}{mu}\\PY{o}{=}\\PY{k+kc}{None}\\PY{p}{,} \\PY{o}{*}\\PY{o}{*}\\PY{n}{kwargs}\\PY{p}{)}\\PY{p}{:}\n", " \\PY{k}{return} \\PY{n+nb+bp}{self}\\PY{o}{.}\\PY{n}{operator}\\PY{o}{.}\\PY{n}{apply\\PYZus{}inverse}\\PY{p}{(}\\PY{n+nb+bp}{self}\\PY{o}{.}\\PY{n}{rhs}\\PY{o}{.}\\PY{n}{as\\PYZus{}range\\PYZus{}array}\\PY{p}{(}\\PY{n}{mu}\\PY{p}{)}\\PY{p}{,} \\PY{n}{mu}\\PY{o}{=}\\PY{n}{mu}\\PY{p}{)}\n", "\\end{Verbatim}\n" ], "text/plain": [ " def _compute_solution(self, mu=None, **kwargs):\n", " return self.operator.apply_inverse(self.rhs.as_range_array(mu), mu=mu)" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "print_source(fom._compute_solution)" ] }, { "cell_type": "markdown", "id": "d5dfe69e", "metadata": {}, "source": [ "What does this mean? If we look at the type of `fom`," ] }, { "cell_type": "code", "execution_count": 9, "id": "723a1453", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "pymor.models.basic.StationaryModel" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "type(fom)" ] }, { "cell_type": "markdown", "id": "f4c652ea", "metadata": {}, "source": [ "we see that `fom` is a {{ StationaryModel }} which encodes an equation of the\n", "form\n", "\n", "```{math}\n", "L(u(\\mu); \\mu) = F(\\mu)\n", "```\n", "\n", "Here, {math}`L` is a linear or non-linear parametric {{ Operator }} and {math}`F` is a\n", "parametric right-hand side vector. In {{ StationaryModel }}, {math}`L` is represented by\n", "the {attr}`~pymor.models.basic.StationaryModel.operator` attribute. So\n", "\n", "```\n", "self.operator.apply_inverse(X, mu=mu)\n", "```\n", "\n", "determines the solution of this equation for the {{ parameter_values }} `mu` and a right-hand\n", "side given by `X`. As you see above, the right-hand side of the equation is given by the\n", "{attr}`~pymor.models.basic.StationaryModel.rhs` attribute.\n", "However, while {meth}`~pymor.operators.interface.Operator.apply_inverse` expects a\n", "{{ VectorArray }}, we see that {attr}`~pymor.models.basic.StationaryModel.rhs` is actually\n", "an {{ Operator }}:" ] }, { "cell_type": "code", "execution_count": 10, "id": "75dceadc", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "NumpyMatrixOperator(<20201x1 dense>, range_id='STATE')" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fom.rhs" ] }, { "cell_type": "markdown", "id": "289c0f83", "metadata": {}, "source": [ "This is due to the fact that {{ VectorArrays }} in pyMOR cannot be parametric. So to allow\n", "for parametric right-hand sides, this right-hand side is encoded by a linear {{ Operator }}\n", "that maps numbers to scalar multiples of the right-hand side vector. Indeed, we see that" ] }, { "cell_type": "code", "execution_count": 11, "id": "dfe87eeb", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "NumpyVectorSpace(1)" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fom.rhs.source" ] }, { "cell_type": "markdown", "id": "2b97c1a5", "metadata": {}, "source": [ "is one-dimensional, and if we look at the base-class implementation of\n", "{meth}`~pymor.operators.interface.Operator.as_range_array`" ] }, { "cell_type": "code", "execution_count": 12, "id": "cc915853", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
    def as_range_array(self, mu=None):\n",
       "        """Return a |VectorArray| representation of the operator in its range space.\n",
       "\n",
       "        In the case of a linear operator with |NumpyVectorSpace| as\n",
       "        :attr:`~Operator.source`, this method returns for given |parameter values|\n",
       "        `mu` a |VectorArray| `V` in the operator's :attr:`~Operator.range`,\n",
       "        such that ::\n",
       "\n",
       "            V.lincomb(U.to_numpy()) == self.apply(U, mu)\n",
       "\n",
       "        for all |VectorArrays| `U`.\n",
       "\n",
       "        Parameters\n",
       "        ----------\n",
       "        mu\n",
       "            The |parameter values| for which to return the |VectorArray|\n",
       "            representation.\n",
       "\n",
       "        Returns\n",
       "        -------\n",
       "        V\n",
       "            The |VectorArray| defined above.\n",
       "        """\n",
       "        assert isinstance(self.source, NumpyVectorSpace) and self.linear\n",
       "        assert self.source.dim <= as_array_max_length()\n",
       "        return self.apply(self.source.from_numpy(np.eye(self.source.dim)), mu=mu)\n",
       "
\n" ], "text/latex": [ "\\begin{Verbatim}[commandchars=\\\\\\{\\}]\n", " \\PY{k}{def} \\PY{n+nf}{as\\PYZus{}range\\PYZus{}array}\\PY{p}{(}\\PY{n+nb+bp}{self}\\PY{p}{,} \\PY{n}{mu}\\PY{o}{=}\\PY{k+kc}{None}\\PY{p}{)}\\PY{p}{:}\n", " \\PY{l+s+sd}{\\PYZdq{}\\PYZdq{}\\PYZdq{}Return a |VectorArray| representation of the operator in its range space.}\n", "\n", "\\PY{l+s+sd}{ In the case of a linear operator with |NumpyVectorSpace| as}\n", "\\PY{l+s+sd}{ :attr:`\\PYZti{}Operator.source`, this method returns for given |parameter values|}\n", "\\PY{l+s+sd}{ `mu` a |VectorArray| `V` in the operator\\PYZsq{}s :attr:`\\PYZti{}Operator.range`,}\n", "\\PY{l+s+sd}{ such that ::}\n", "\n", "\\PY{l+s+sd}{ V.lincomb(U.to\\PYZus{}numpy()) == self.apply(U, mu)}\n", "\n", "\\PY{l+s+sd}{ for all |VectorArrays| `U`.}\n", "\n", "\\PY{l+s+sd}{ Parameters}\n", "\\PY{l+s+sd}{ \\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}}\n", "\\PY{l+s+sd}{ mu}\n", "\\PY{l+s+sd}{ The |parameter values| for which to return the |VectorArray|}\n", "\\PY{l+s+sd}{ representation.}\n", "\n", "\\PY{l+s+sd}{ Returns}\n", "\\PY{l+s+sd}{ \\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}}\n", "\\PY{l+s+sd}{ V}\n", "\\PY{l+s+sd}{ The |VectorArray| defined above.}\n", "\\PY{l+s+sd}{ \\PYZdq{}\\PYZdq{}\\PYZdq{}}\n", " \\PY{k}{assert} \\PY{n+nb}{isinstance}\\PY{p}{(}\\PY{n+nb+bp}{self}\\PY{o}{.}\\PY{n}{source}\\PY{p}{,} \\PY{n}{NumpyVectorSpace}\\PY{p}{)} \\PY{o+ow}{and} \\PY{n+nb+bp}{self}\\PY{o}{.}\\PY{n}{linear}\n", " \\PY{k}{assert} \\PY{n+nb+bp}{self}\\PY{o}{.}\\PY{n}{source}\\PY{o}{.}\\PY{n}{dim} \\PY{o}{\\PYZlt{}}\\PY{o}{=} \\PY{n}{as\\PYZus{}array\\PYZus{}max\\PYZus{}length}\\PY{p}{(}\\PY{p}{)}\n", " \\PY{k}{return} \\PY{n+nb+bp}{self}\\PY{o}{.}\\PY{n}{apply}\\PY{p}{(}\\PY{n+nb+bp}{self}\\PY{o}{.}\\PY{n}{source}\\PY{o}{.}\\PY{n}{from\\PYZus{}numpy}\\PY{p}{(}\\PY{n}{np}\\PY{o}{.}\\PY{n}{eye}\\PY{p}{(}\\PY{n+nb+bp}{self}\\PY{o}{.}\\PY{n}{source}\\PY{o}{.}\\PY{n}{dim}\\PY{p}{)}\\PY{p}{)}\\PY{p}{,} \\PY{n}{mu}\\PY{o}{=}\\PY{n}{mu}\\PY{p}{)}\n", "\\end{Verbatim}\n" ], "text/plain": [ " def as_range_array(self, mu=None):\n", " \"\"\"Return a |VectorArray| representation of the operator in its range space.\n", "\n", " In the case of a linear operator with |NumpyVectorSpace| as\n", " :attr:`~Operator.source`, this method returns for given |parameter values|\n", " `mu` a |VectorArray| `V` in the operator's :attr:`~Operator.range`,\n", " such that ::\n", "\n", " V.lincomb(U.to_numpy()) == self.apply(U, mu)\n", "\n", " for all |VectorArrays| `U`.\n", "\n", " Parameters\n", " ----------\n", " mu\n", " The |parameter values| for which to return the |VectorArray|\n", " representation.\n", "\n", " Returns\n", " -------\n", " V\n", " The |VectorArray| defined above.\n", " \"\"\"\n", " assert isinstance(self.source, NumpyVectorSpace) and self.linear\n", " assert self.source.dim <= as_array_max_length()\n", " return self.apply(self.source.from_numpy(np.eye(self.source.dim)), mu=mu)" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from pymor.operators.interface import Operator\n", "print_source(Operator.as_range_array)" ] }, { "cell_type": "markdown", "id": "9d8c753b", "metadata": {}, "source": [ "we see all that {meth}`~pymor.operators.interface.Operator.as_range_array`\n", "does is to apply the operator to {math}`1`. (`NumpyMatrixOperator.as_range_array`\n", "has an optimized implementation which just converts the stored matrix to a\n", "{{ NumpyVectorArray }}.)\n", "\n", "Let's try solving the model on our own:" ] }, { "cell_type": "code", "execution_count": 13, "id": "f9d7ed49", "metadata": { "tags": [ "raises-exception" ] }, "outputs": [ { "ename": "TypeError", "evalue": "mu is not a Mu instance. (Use parameters.parse?)", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", "Input \u001b[0;32mIn [13]\u001b[0m, in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m U2 \u001b[38;5;241m=\u001b[39m \u001b[43mfom\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moperator\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply_inverse\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfom\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrhs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mas_range_array\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmu\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmu\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m1.\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m0.1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m0.1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1.\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n", "File \u001b[0;32m/builds/pymor/pymor/src/pymor/operators/constructions.py:194\u001b[0m, in \u001b[0;36mLincombOperator.apply_inverse\u001b[0;34m(self, V, mu, initial_guess, least_squares)\u001b[0m\n\u001b[1;32m 192\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m U\n\u001b[1;32m 193\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 194\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply_inverse\u001b[49m\u001b[43m(\u001b[49m\u001b[43mV\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmu\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmu\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minitial_guess\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minitial_guess\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mleast_squares\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mleast_squares\u001b[49m\u001b[43m)\u001b[49m\n", "File \u001b[0;32m/builds/pymor/pymor/src/pymor/operators/interface.py:221\u001b[0m, in \u001b[0;36mOperator.apply_inverse\u001b[0;34m(self, V, mu, initial_guess, least_squares)\u001b[0m\n\u001b[1;32m 219\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m initial_guess \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mor\u001b[39;00m initial_guess \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msource \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(initial_guess) \u001b[38;5;241m==\u001b[39m \u001b[38;5;28mlen\u001b[39m(V)\n\u001b[1;32m 220\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpymor\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01moperators\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mconstructions\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m FixedParameterOperator\n\u001b[0;32m--> 221\u001b[0m assembled_op \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43massemble\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmu\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 222\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m assembled_op \u001b[38;5;241m!=\u001b[39m \u001b[38;5;28mself\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(assembled_op, FixedParameterOperator):\n\u001b[1;32m 223\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m assembled_op\u001b[38;5;241m.\u001b[39mapply_inverse(V, initial_guess\u001b[38;5;241m=\u001b[39minitial_guess, least_squares\u001b[38;5;241m=\u001b[39mleast_squares)\n", "File \u001b[0;32m/builds/pymor/pymor/src/pymor/operators/constructions.py:140\u001b[0m, in \u001b[0;36mLincombOperator.assemble\u001b[0;34m(self, mu)\u001b[0m\n\u001b[1;32m 138\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpymor\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01malgorithms\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlincomb\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m assemble_lincomb\n\u001b[1;32m 139\u001b[0m operators \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mtuple\u001b[39m(op\u001b[38;5;241m.\u001b[39massemble(mu) \u001b[38;5;28;01mfor\u001b[39;00m op \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moperators)\n\u001b[0;32m--> 140\u001b[0m coefficients \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mevaluate_coefficients\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmu\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 141\u001b[0m \u001b[38;5;66;03m# try to form a linear combination\u001b[39;00m\n\u001b[1;32m 142\u001b[0m op \u001b[38;5;241m=\u001b[39m assemble_lincomb(operators, coefficients, solver_options\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msolver_options,\n\u001b[1;32m 143\u001b[0m name\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mname \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m_assembled\u001b[39m\u001b[38;5;124m'\u001b[39m)\n", "File \u001b[0;32m/builds/pymor/pymor/src/pymor/operators/constructions.py:78\u001b[0m, in \u001b[0;36mLincombOperator.evaluate_coefficients\u001b[0;34m(self, mu)\u001b[0m\n\u001b[1;32m 66\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mevaluate_coefficients\u001b[39m(\u001b[38;5;28mself\u001b[39m, mu):\n\u001b[1;32m 67\u001b[0m \u001b[38;5;124;03m\"\"\"Compute the linear coefficients for given |parameter values|.\u001b[39;00m\n\u001b[1;32m 68\u001b[0m \n\u001b[1;32m 69\u001b[0m \u001b[38;5;124;03m Parameters\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 76\u001b[0m \u001b[38;5;124;03m List of linear coefficients.\u001b[39;00m\n\u001b[1;32m 77\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m---> 78\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mparameters\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43massert_compatible\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmu\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 79\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m [c\u001b[38;5;241m.\u001b[39mevaluate(mu) \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(c, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mevaluate\u001b[39m\u001b[38;5;124m'\u001b[39m) \u001b[38;5;28;01melse\u001b[39;00m c \u001b[38;5;28;01mfor\u001b[39;00m c \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcoefficients]\n", "File \u001b[0;32m/builds/pymor/pymor/src/pymor/parameters/base.py:208\u001b[0m, in \u001b[0;36mParameters.assert_compatible\u001b[0;34m(self, mu)\u001b[0m\n\u001b[1;32m 198\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21massert_compatible\u001b[39m(\u001b[38;5;28mself\u001b[39m, mu):\n\u001b[1;32m 199\u001b[0m \u001b[38;5;124;03m\"\"\"Assert that |parameter values| are compatible with the given |Parameters|.\u001b[39;00m\n\u001b[1;32m 200\u001b[0m \n\u001b[1;32m 201\u001b[0m \u001b[38;5;124;03m Each of the parameter must be contained in `mu` and the dimensions have to match,\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 206\u001b[0m \u001b[38;5;124;03m Otherwise, an `AssertionError` will be raised.\u001b[39;00m\n\u001b[1;32m 207\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 208\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mis_compatible\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmu\u001b[49m\u001b[43m)\u001b[49m, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mwhy_incompatible(mu)\n\u001b[1;32m 209\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m\n", "File \u001b[0;32m/builds/pymor/pymor/src/pymor/parameters/base.py:220\u001b[0m, in \u001b[0;36mParameters.is_compatible\u001b[0;34m(self, mu)\u001b[0m\n\u001b[1;32m 212\u001b[0m \u001b[38;5;124;03m\"\"\"Check if |parameter values| are compatible with the given |Parameters|.\u001b[39;00m\n\u001b[1;32m 213\u001b[0m \n\u001b[1;32m 214\u001b[0m \u001b[38;5;124;03mEach of the parameter must be contained in `mu` and the dimensions have to match,\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 217\u001b[0m \u001b[38;5;124;03m mu[parameter].size == self[parameter]\u001b[39;00m\n\u001b[1;32m 218\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 219\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m mu \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(mu, Mu):\n\u001b[0;32m--> 220\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmu is not a Mu instance. (Use parameters.parse?)\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 221\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m \u001b[38;5;129;01mor\u001b[39;00m \\\n\u001b[1;32m 222\u001b[0m mu \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mall\u001b[39m(\u001b[38;5;28mgetattr\u001b[39m(mu\u001b[38;5;241m.\u001b[39mget(k), \u001b[38;5;124m'\u001b[39m\u001b[38;5;124msize\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m) \u001b[38;5;241m==\u001b[39m v \u001b[38;5;28;01mfor\u001b[39;00m k, v \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mitems())\n", "\u001b[0;31mTypeError\u001b[0m: mu is not a Mu instance. (Use parameters.parse?)" ] } ], "source": [ "U2 = fom.operator.apply_inverse(fom.rhs.as_range_array(mu), mu=[1., 0.1, 0.1, 1.])" ] }, { "cell_type": "markdown", "id": "af836e63", "metadata": {}, "source": [ "That did not work too well! In pyMOR, all parametric objects expect the\n", "`mu` argument to be an instance of the {class}`~pymor.parameters.base.Mu`\n", "class. {meth}`~pymor.models.interface.Model.compute` and related methods\n", "like {meth}`~pymor.models.interface.Model.solve` are an exception: for\n", "convenience, they accept as a `mu` argument anything that can be converted\n", "to a {class}`~pymor.parameters.base.Mu` instance using the\n", "{meth}`~pymor.parameters.base.Parameters.parse` method of the\n", "{class}`~pymor.parameters.base.Parameters` class. In fact, if you look\n", "back at the implementation of {meth}`~pymor.models.interface.Model.compute`,\n", "you see the explicit call to {meth}`~pymor.parameters.base.Parameters.parse`.\n", "We try again:" ] }, { "cell_type": "code", "execution_count": 14, "id": "171b46ba", "metadata": {}, "outputs": [], "source": [ "mu = fom.parameters.parse([1., 0.1, 0.1, 1.])\n", "U2 = fom.operator.apply_inverse(fom.rhs.as_range_array(mu), mu=mu)" ] }, { "cell_type": "markdown", "id": "1c763d4a", "metadata": {}, "source": [ "We can check that we get exactly the same result as from our earlier call\n", "to {meth}`~pymor.models.interface.Model.solve`:" ] }, { "cell_type": "code", "execution_count": 15, "id": "f3a48eff", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([0.])" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "(U-U2).norm()" ] }, { "cell_type": "markdown", "id": "1fbd17fd", "metadata": {}, "source": [ "## Galerkin Projection\n", "\n", "Now that we understand how the FOM works, we want to build a reduced-order model\n", "which approximates the FOM solution {math}`U(\\mu)` in {math}`V_N`.\n", "To that end we call {math}`\\mathbb{V}_N` the matrix that has the vectors in\n", "`basis` as columns. The coefficients of the solution of the ROM w.r.t. these\n", "basis vectors will be called {math}`u_N(\\mu)`. We want that\n", "\n", "```{math}\n", "U_N := \\mathbb{V}_N \\cdot u_N(\\mu) \\approx u(\\mu).\n", "```\n", "\n", "Substituting {math}`\\mathbb{V}_N \\cdot u_N(\\mu)` for {math}`u(\\mu)` into the equation system\n", "defining the FOM, we arrive at:\n", "\n", "```{math}\n", "L(\\mathbb{V}_N\\cdot u_N(\\mu); \\mu) = F(\\mu).\n", "```\n", "\n", "However, this is an over-determined system: we have decreased the degrees of\n", "freedom of the solution, but did not change the number of constraints (the dimension\n", "of {math}`F(\\mu)`). So in general, this system will not have a solution.\n", "\n", "One approach to define {math}`u_N` from this ansatz is to choose {math}`u_N`\n", "as a minimizer of norm of the residual of the equations system, i.e. to minimize\n", "the defect by which {math}`u_N` fails to satisfy the equations:\n", "\n", "```{math}\n", "u_N(\\mu) := \\operatorname{arg\\,min}_{u \\in \\mathbb{R}^N} \\|F(\\mu) - L(\\mathbb{V}_N \\cdot u; \\mu)\\|.\n", "```\n", "\n", "While this is a feasible (and sometimes necessary) approach that can be realized with\n", "pyMOR as well, we choose here an even simpler method by requiring that the residual is\n", "orthogonal to our reduced space, i.e.\n", "\n", "```{math}\n", "(\\mathbb{V}_{N,i},\\, F(\\mu) - L(\\mathbb{V}_N \\cdot u_N; \\mu)) = 0 \\qquad i=1,\\ldots,N,\n", "```\n", "\n", "where the {math}`\\mathbb{V}_{N,i}` denote the columns of {math}`\\mathbb{V}_N`\n", "and {math}`(\\cdot, \\cdot)` denotes some inner product on our\n", "{attr}`~pymor.models.interface.Model.solution_space`.\n", "\n", "Let us assume that {math}`L` is actually linear for all parameter values {math}`\\mu`,\n", "and that {math}`\\mathbb{A}(\\mu)` is its matrix representation. Further assume\n", "that {math}`(\\cdot, \\cdot)` is the Euclidean inner product. Then we arrive at\n", "\n", "```{math}\n", "[\\mathbb{V}_N^T \\cdot \\mathbb{A}(\\mu) \\cdot \\mathbb{V}_N] \\cdot u_N =\n", "\\mathbb{V}_N^T \\cdot F(\\mu),\n", "```\n", "\n", "which is a {math}`N\\times N` linear equation system. In the common case that\n", "{math}`\\mathbb{A}(\\mu)` is positive definite, the reduced system matrix\n", "\n", "```{math}\n", "\\mathbb{A}_N(\\mu) := \\mathbb{V}_N^T \\cdot \\mathbb{A}(\\mu) \\cdot \\mathbb{V}_N\n", "```\n", "\n", "is positive definite as well, and {math}`u_N(\\mu)` is uniquely determined. We call\n", "{math}`U_N(\\mu)` the Galerkin projection of {math}`U(\\mu)` onto {math}`V_N`.\n", "\n", "You may know the concept of Galerkin projection from finite element methods. Indeed, if our\n", "equation system comes from the weak formulation of a PDE of the form\n", "\n", "```{math}\n", "a(v, U(\\mu); \\mu) = f(v; \\mu) \\qquad \\forall v \\in V_h,\n", "```\n", "\n", "the matrix of the bilinear form {math}`a(\\cdot, \\cdot; \\mu)` w.r.t. a finite element basis\n", "is {math}`\\mathbb{A}(\\mu)`, and {math}`F(\\mu)` is the vector representation of the linear\n", "functional {math}`f` w.r.t. the dual finite element basis, then\n", "\n", "```{math}\n", "\\mathbb{A}_N(\\mu) \\cdot u_N = \\mathbb{V}_N^T \\cdot F(\\mu)\n", "```\n", "\n", "is exactly the equation system obtained from Galerkin projection of the weak PDE formulation onto\n", "the reduced space, i.e. solving\n", "\n", "```{math}\n", "a(v, u_N(\\mu); \\mu) = f(v; \\mu) \\qquad \\forall v \\in V_N\n", "```\n", "\n", "for {math}`U_N(\\mu) \\in V_N`. As for finite element methods,\n", "[Cea's Lemma]() guarantees that when {math}`a(\\cdot, \\cdot, \\mu)`\n", "is positive definite, {math}`U_N` will be a quasi-best approximation\n", "of {math}`U(\\mu)` in {math}`V_N`. So, if we have constructed a good reduced space {math}`V_N`, then\n", "Galerkin projection will also give us a good ROM to actually find a good approximation in {math}`V_N`.\n", "\n", "Let's compute the Galerkin ROM for our FOM at hand with pyMOR. To compute {math}`\\mathbb{A}_N`\n", "we use the {meth}`~pymor.operators.interface.Operator.apply2` method of `fom.operator`.\n", "For computing the inner products {math}`\\mathbb{V}_N^T \\cdot F(\\mu)` we can simply compute the\n", "inner product with the `basis` {{ VectorArray }} using its {meth}`~pymor.vectorarrays.interface.VectorArray.inner`\n", "method:" ] }, { "cell_type": "code", "execution_count": 16, "id": "a1f098b4", "metadata": {}, "outputs": [], "source": [ "reduced_operator = fom.operator.apply2(basis, basis, mu=mu)\n", "reduced_rhs = basis.inner(fom.rhs.as_range_array(mu))" ] }, { "cell_type": "markdown", "id": "41838304", "metadata": {}, "source": [ "Now we just need to solve the resulting linear equation system using {{ NumPy }} to obtain\n", "{math}`u_N(\\mu)`:" ] }, { "cell_type": "code", "execution_count": 17, "id": "f3a9a45a", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[-15.16166755],\n", " [ -1.06060498],\n", " [ -4.3360103 ],\n", " [ 3.81207226],\n", " [ 2.74026133],\n", " [ 0.98606744],\n", " [ -0.1756243 ],\n", " [ -1.26817802],\n", " [ 0.55510353],\n", " [ -0.0470637 ]])" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import numpy as np\n", "\n", "u_N = np.linalg.solve(reduced_operator, reduced_rhs)\n", "u_N" ] }, { "cell_type": "markdown", "id": "170390d0", "metadata": {}, "source": [ "To reconstruct the high-dimensional approximation {math}`\\mathbb{V}_N \\cdot u_N(\\mu)`\n", "from {math}`u_N(\\mu)` we can use the {meth}`~pymor.vectorarrays.interface.VectorArray.lincomb`\n", "method:" ] }, { "cell_type": "code", "execution_count": 18, "id": "d30c7d27", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "NumpyVectorArray(\n", " NumpyVectorSpace(20201, id='STATE'),\n", " [[0.00000000e+00 0.00000000e+00 0.00000000e+00 ... 3.51308241e-04\n", " 2.29395339e-04 8.64727182e-05]],\n", " _len=1)" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "U_N = basis.lincomb(u_N.T)\n", "U_N" ] }, { "cell_type": "markdown", "id": "ab65ead5", "metadata": {}, "source": [ "Let's see, how good our reduced approximation is:" ] }, { "cell_type": "code", "execution_count": 19, "id": "18e0fa63", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([0.01961789])" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "(U-U_N).norm(fom.h1_0_product) / U.norm(fom.h1_0_product)" ] }, { "cell_type": "markdown", "id": "3bdc414d", "metadata": {}, "source": [ "With only 10 basis vectors, we have achieved a relative {math}`H^1`-error of 2%.\n", "We can also visually inspect our solution and the approximation error:" ] }, { "cell_type": "code", "execution_count": 20, "id": "2d2930f4", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "88d7b63f25454a1baac9fc776d4f1125", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Output()" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fom.visualize((U, U_N, U-U_N), separate_colorbars=True)" ] }, { "cell_type": "markdown", "id": "a24ab460", "metadata": {}, "source": [ "## Building the ROM\n", "\n", "So far, we have only constructed the ROM in the form of {{ NumPy }} data structures:" ] }, { "cell_type": "code", "execution_count": 21, "id": "48c44d1f", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "numpy.ndarray" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "type(reduced_operator)" ] }, { "cell_type": "markdown", "id": "889fa836", "metadata": {}, "source": [ "To build a proper pyMOR {{ Model }} for the ROM, which can be used everywhere a {{ Model }} is\n", "expected, we first wrap these data structures as pyMOR {{ Operators }}:" ] }, { "cell_type": "code", "execution_count": 22, "id": "d7b83320", "metadata": {}, "outputs": [], "source": [ "from pymor.operators.numpy import NumpyMatrixOperator\n", "\n", "reduced_operator = NumpyMatrixOperator(reduced_operator)\n", "reduced_rhs = NumpyMatrixOperator(reduced_rhs)" ] }, { "cell_type": "markdown", "id": "491059fd", "metadata": {}, "source": [ "Galerkin projection does not change the structure of the model. So the ROM should again\n", "be a {{ StationaryModel }}. We can construct it easily as follows:" ] }, { "cell_type": "code", "execution_count": 23, "id": "0021b0d1", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "StationaryModel(\n", " NumpyMatrixOperator(<10x10 dense>),\n", " NumpyMatrixOperator(<10x1 dense>),\n", " output_functional=ZeroOperator(NumpyVectorSpace(0), NumpyVectorSpace(10)),\n", " products={})" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from pymor.models.basic import StationaryModel\n", "rom = StationaryModel(reduced_operator, reduced_rhs)\n", "rom" ] }, { "cell_type": "markdown", "id": "0a43ecd1", "metadata": {}, "source": [ "Let's check if it works as expected:" ] }, { "cell_type": "code", "execution_count": 24, "id": "30fc3588", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "908b4cc937ed44c1a371a5dc6589f2db", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Accordion(children=(HTML(value='', layout=Layout(height='16em', overflow_y='auto', width='100%')),), selected_…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "array([[ 0.00000000e+00, 6.66133815e-16, -8.88178420e-16,\n", " -8.88178420e-16, 0.00000000e+00, 0.00000000e+00,\n", " -5.55111512e-17, 2.22044605e-16, 1.11022302e-16,\n", " -6.93889390e-18]])" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "u_N2 = rom.solve()\n", "u_N.T - u_N2.to_numpy()" ] }, { "cell_type": "markdown", "id": "8237d147", "metadata": {}, "source": [ "We get exactly the same result, so we have successfully built a pyMOR ROM.\n", "\n", "## Offline/Online Decomposition\n", "\n", "There is one issue however. Our ROM has lost the parametrization since we\n", "have assembled the reduced-order system for a specific set of\n", "{{ parameter_values }}:" ] }, { "cell_type": "code", "execution_count": 25, "id": "a0eecadc", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{diffusion: 4}\n", "{}\n" ] } ], "source": [ "print(fom.parameters)\n", "print(rom.parameters)" ] }, { "cell_type": "markdown", "id": "82f1b617", "metadata": {}, "source": [ "Solving the ROM for a new `mu` would mean to build a new ROM with updated\n", "system matrix and right-hand side. However, if we compare the timings," ] }, { "cell_type": "code", "execution_count": 26, "id": "cc06e5f4", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "0ce9fdbe1c0243e89a47257566c6f2c3", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Accordion(children=(HTML(value='', layout=Layout(height='16em', overflow_y='auto', width='100%')),), selected_…" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "FOM: 0.10337 (s)\n", "ROM assemble: 0.00879 (s)\n", "ROM solve: 0.00124 (s)\n" ] } ], "source": [ "from time import perf_counter\n", "\n", "tic = perf_counter()\n", "fom.solve(mu)\n", "toc = perf_counter()\n", "fom.operator.apply2(basis, basis, mu=mu)\n", "basis.inner(fom.rhs.as_range_array(mu))\n", "tac = perf_counter()\n", "rom.solve()\n", "tuc = perf_counter()\n", "print(f'FOM: {toc-tic:.5f} (s)')\n", "print(f'ROM assemble: {tac-toc:.5f} (s)')\n", "print(f'ROM solve: {tuc-tac:.5f} (s)')" ] }, { "cell_type": "markdown", "id": "1fefa81a", "metadata": {}, "source": [ "we see that we lose a lot of our speedup when we assemble the ROM\n", "(which involves a lot of full-order dimensional operations).\n", "\n", "To solve this issue we need to find a way to pre-compute everything we need\n", "to solve the ROM once-and-for-all for all possible {{ parameter_values }}. Luckily,\n", "the system operator of our FOM has a special structure:" ] }, { "cell_type": "code", "execution_count": 27, "id": "1836d16c", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "LincombOperator(\n", " (NumpyMatrixOperator(<20201x20201 sparse, 140601 nnz>, source_id='STATE', range_id='STATE', name='boundary_part'),\n", " NumpyMatrixOperator(<20201x20201 sparse, 140601 nnz>, source_id='STATE', range_id='STATE', name='diffusion_0'),\n", " NumpyMatrixOperator(<20201x20201 sparse, 140601 nnz>, source_id='STATE', range_id='STATE', name='diffusion_1'),\n", " NumpyMatrixOperator(<20201x20201 sparse, 140601 nnz>, source_id='STATE', range_id='STATE', name='diffusion_2'),\n", " NumpyMatrixOperator(<20201x20201 sparse, 140601 nnz>, source_id='STATE', range_id='STATE', name='diffusion_3')),\n", " (1.0,\n", " ProjectionParameterFunctional('diffusion', size=4, index=0, name='diffusion_0_0'),\n", " ProjectionParameterFunctional('diffusion', size=4, index=1, name='diffusion_1_0'),\n", " ProjectionParameterFunctional('diffusion', size=4, index=2, name='diffusion_0_1'),\n", " ProjectionParameterFunctional('diffusion', size=4, index=3, name='diffusion_1_1')),\n", " name='ellipticOperator')" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fom.operator" ] }, { "cell_type": "markdown", "id": "c5279747", "metadata": {}, "source": [ "We see that `operator` is a {{ LincombOperator }}, a linear combination of {{ Operators }}\n", "with coefficients that may either be a number or a parameter-dependent number,\n", "called a {{ ParameterFunctional }} in pyMOR. In our case, all\n", "{attr}`~pymor.operators.constructions.LincombOperator.operators` are\n", "{{ NumpyMatrixOperators }}, which themselves don't depend on any parameter. Only the\n", "{attr}`~pymor.operators.constructions.LincombOperator.coefficients` are\n", "parameter-dependent. This allows us to easily build a parametric ROM that no longer\n", "requires any high-dimensional operations for its solution by projecting each\n", "{{ Operator }} in the sum separately:" ] }, { "cell_type": "code", "execution_count": 28, "id": "279742de", "metadata": {}, "outputs": [], "source": [ "reduced_operators = [NumpyMatrixOperator(op.apply2(basis, basis))\n", " for op in fom.operator.operators]" ] }, { "cell_type": "markdown", "id": "92cf38df", "metadata": {}, "source": [ "We could instantiate a new {{ LincombOperator }} of these `reduced_operators` manually.\n", "An easier way is to use the {meth}`~pymor.core.base.ImmutableObject.with_` method,\n", "which allows us to create a new object from a given {{ ImmutableObject }} by replacing\n", "some of its attributes by new values:" ] }, { "cell_type": "code", "execution_count": 29, "id": "edac9cb7", "metadata": {}, "outputs": [], "source": [ "reduced_operator = fom.operator.with_(operators=reduced_operators)" ] }, { "cell_type": "markdown", "id": "74c48da2", "metadata": {}, "source": [ "The right-hand side of our problem is non-parametric," ] }, { "cell_type": "code", "execution_count": 30, "id": "e723c720", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Parameters({})" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fom.rhs.parameters" ] }, { "cell_type": "markdown", "id": "132db3ba", "metadata": {}, "source": [ "so we don't need to do anything special about it. We build a new ROM," ] }, { "cell_type": "code", "execution_count": 31, "id": "be5ae3bb", "metadata": {}, "outputs": [], "source": [ "rom = StationaryModel(reduced_operator, reduced_rhs)" ] }, { "cell_type": "markdown", "id": "193191e7", "metadata": {}, "source": [ "which now depends on the same {{ Parameters }} as the FOM:" ] }, { "cell_type": "code", "execution_count": 32, "id": "0aa32169", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Parameters({diffusion: 4})" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rom.parameters" ] }, { "cell_type": "markdown", "id": "feb59bd1", "metadata": {}, "source": [ "We check that our new ROM still computes the same solution:" ] }, { "cell_type": "code", "execution_count": 33, "id": "895cd583", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "123dc3c6ecb44520bcdb5a88027a51c5", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Accordion(children=(HTML(value='', layout=Layout(height='16em', overflow_y='auto', width='100%')),), selected_…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "array([[ 0.00000000e+00, 6.66133815e-16, -8.88178420e-16,\n", " -8.88178420e-16, 0.00000000e+00, 0.00000000e+00,\n", " -5.55111512e-17, 2.22044605e-16, 1.11022302e-16,\n", " -6.93889390e-18]])" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "u_N3 = rom.solve(mu)\n", "u_N.T - u_N3.to_numpy()" ] }, { "cell_type": "markdown", "id": "b9ffc746", "metadata": {}, "source": [ "Let's see if our new ROM is actually faster than the FOM:" ] }, { "cell_type": "code", "execution_count": 34, "id": "ece9e11a", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "43fed29df8c94dfcaaab1b6c0985bf39", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Accordion(children=(HTML(value='', layout=Layout(height='16em', overflow_y='auto', width='100%')),), selected_…" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "FOM: 0.10470 (s)\n", "ROM: 0.00153 (s)\n" ] } ], "source": [ "tic = perf_counter()\n", "fom.solve(mu)\n", "toc = perf_counter()\n", "rom.solve(mu)\n", "tac = perf_counter()\n", "print(f'FOM: {toc-tic:.5f} (s)')\n", "print(f'ROM: {tac-toc:.5f} (s)')" ] }, { "cell_type": "markdown", "id": "efc7c2e0", "metadata": {}, "source": [ "You should see a significant speedup of around two orders of magnitude.\n", "In model order reduction, problems where the {{ parameter_values }} only enter\n", "as linear coefficients are called parameter separable. Many real-life\n", "application problems are actually of this type, and as you have seen in this\n", "section, these problems admit an *offline/online decomposition* that\n", "enables the *online efficient* solution of the ROM.\n", "\n", "For problems that do not allow such an decomposition and also for non-linear\n", "problems, more advanced techniques are necessary such as\n", "{mod}`empiricial interpolation `.\n", "\n", "## Letting pyMOR do the work\n", "\n", "So far we completely built the ROM ourselves. While this may not have been\n", "very complicated after all, you'd expect a model order reduction library\n", "to do the work for you and to automatically keep an eye on proper\n", "offline/online decomposition.\n", "\n", "In pyMOR, the heavy lifting is handled by the\n", "{meth}`~pymor.algorithms.projection.project` method, which is able to perform\n", "a Galerkin projection, or more general a Petrov-Galerkin projection, of any\n", "pyMOR {{ Operator }}. Let's see, how it works:" ] }, { "cell_type": "code", "execution_count": 35, "id": "e6549468", "metadata": {}, "outputs": [], "source": [ "from pymor.algorithms.projection import project\n", "\n", "reduced_operator = project(fom.operator, basis, basis)\n", "reduced_rhs = project(fom.rhs, basis, None )" ] }, { "cell_type": "markdown", "id": "9d79dd88", "metadata": {}, "source": [ "The arguments of {meth}`~pymor.algorithms.projection.project` are the {{ Operator }}\n", "to project, a reduced basis for the {attr}`~pymor.operators.interface.Operator.range`\n", "(test) space and a reduced basis for the {attr}`~pymor.operators.interface.Operator.source`\n", "(ansatz) space of the {{ Operator }}. If no projection for one of these spaces shall be performed,\n", "`None` is passed. Since we are performing Galerkin-projection, where test space into\n", "which the residual is projected is the same as the ansatz space in which the solution\n", "is determined, we pass `basis` twice when projecting `fom.operator`. Note that\n", "`fom.rhs` only takes scalars as input, so we do not need to project anything in the ansatz space.\n", "\n", "If we check the result," ] }, { "cell_type": "code", "execution_count": 36, "id": "3d809f02", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "LincombOperator(\n", " (NumpyMatrixOperator(<10x10 dense>, name='boundary_part'),\n", " NumpyMatrixOperator(<10x10 dense>, name='diffusion_0'),\n", " NumpyMatrixOperator(<10x10 dense>, name='diffusion_1'),\n", " NumpyMatrixOperator(<10x10 dense>, name='diffusion_2'),\n", " NumpyMatrixOperator(<10x10 dense>, name='diffusion_3')),\n", " (1.0,\n", " ProjectionParameterFunctional('diffusion', size=4, index=0, name='diffusion_0_0'),\n", " ProjectionParameterFunctional('diffusion', size=4, index=1, name='diffusion_1_0'),\n", " ProjectionParameterFunctional('diffusion', size=4, index=2, name='diffusion_0_1'),\n", " ProjectionParameterFunctional('diffusion', size=4, index=3, name='diffusion_1_1')),\n", " name='ellipticOperator')" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "reduced_operator" ] }, { "cell_type": "markdown", "id": "031a5615", "metadata": {}, "source": [ "we see, that pyMOR indeed has taken care of projecting each individual {{ Operator }}\n", "of the linear combination. We check again that we have built the same ROM:" ] }, { "cell_type": "code", "execution_count": 37, "id": "3419bf49", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "01be2966aa40431d9aff547be5a19403", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Accordion(children=(HTML(value='', layout=Layout(height='16em', overflow_y='auto', width='100%')),), selected_…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "array([[ 0.00000000e+00, 6.66133815e-16, -8.88178420e-16,\n", " -8.88178420e-16, 0.00000000e+00, 0.00000000e+00,\n", " -5.55111512e-17, 2.22044605e-16, 1.11022302e-16,\n", " -6.93889390e-18]])" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rom = StationaryModel(reduced_operator, reduced_rhs)\n", "u_N4 = rom.solve(mu)\n", "u_N.T - u_N4.to_numpy()" ] }, { "cell_type": "markdown", "id": "e06ab420", "metadata": {}, "source": [ "So how does {meth}`~pymor.algorithms.projection.project` actually work? Let's take\n", "a look at the source:" ] }, { "cell_type": "code", "execution_count": 38, "id": "dedc2795", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
def project(op, range_basis, source_basis, product=None):\n",
       "    """Petrov-Galerkin projection of a given |Operator|.\n",
       "\n",
       "    Given an inner product `( ⋅, ⋅)`, source vectors `b_1, ..., b_N`\n",
       "    and range vectors `c_1, ..., c_M`, the projection `op_proj` of `op`\n",
       "    is defined by ::\n",
       "\n",
       "        [ op_proj(e_j) ]_i = ( c_i, op(b_j) )\n",
       "\n",
       "    for all i,j, where `e_j` denotes the j-th canonical basis vector of R^N.\n",
       "\n",
       "    In particular, if the `c_i` are orthonormal w.r.t. the given product,\n",
       "    then `op_proj` is the coordinate representation w.r.t. the `b_i/c_i` bases\n",
       "    of the restriction of `op` to `span(b_i)` concatenated with the\n",
       "    orthogonal projection onto `span(c_i)`.\n",
       "\n",
       "    From another point of view, if `op` is viewed as a bilinear form\n",
       "    (see :meth:`apply2`) and `( ⋅, ⋅ )` is the Euclidean inner\n",
       "    product, then `op_proj` represents the matrix of the bilinear form restricted\n",
       "    to `span(b_i) / span(c_i)` (w.r.t. the `b_i/c_i` bases).\n",
       "\n",
       "    How the projection is realized will depend on the given |Operator|.\n",
       "    While a projected |NumpyMatrixOperator| will\n",
       "    again be a |NumpyMatrixOperator|, only a generic\n",
       "    :class:`~pymor.operators.constructions.ProjectedOperator` can be returned\n",
       "    in general. The exact algorithm is specified in :class:`ProjectRules`.\n",
       "\n",
       "    Parameters\n",
       "    ----------\n",
       "    range_basis\n",
       "        The vectors `c_1, ..., c_M` as a |VectorArray|. If `None`, no\n",
       "        projection in the range space is performed.\n",
       "    source_basis\n",
       "        The vectors `b_1, ..., b_N` as a |VectorArray| or `None`. If `None`,\n",
       "        no restriction of the source space is performed.\n",
       "    product\n",
       "        An |Operator| representing the inner product.  If `None`, the\n",
       "        Euclidean inner product is chosen.\n",
       "\n",
       "    Returns\n",
       "    -------\n",
       "    The projected |Operator| `op_proj`.\n",
       "    """\n",
       "    assert source_basis is None or source_basis in op.source\n",
       "    assert range_basis is None or range_basis in op.range\n",
       "    assert product is None or product.source == product.range == op.range\n",
       "\n",
       "    rb = product.apply(range_basis) if product is not None and range_basis is not None else range_basis\n",
       "\n",
       "    try:\n",
       "        return ProjectRules(rb, source_basis).apply(op)\n",
       "    except NoMatchingRuleError:\n",
       "        op.logger.warning('Using inefficient generic projection operator')\n",
       "        return ProjectedOperator(op, range_basis, source_basis, product)\n",
       "
\n" ], "text/latex": [ "\\begin{Verbatim}[commandchars=\\\\\\{\\}]\n", "\\PY{k}{def} \\PY{n+nf}{project}\\PY{p}{(}\\PY{n}{op}\\PY{p}{,} \\PY{n}{range\\PYZus{}basis}\\PY{p}{,} \\PY{n}{source\\PYZus{}basis}\\PY{p}{,} \\PY{n}{product}\\PY{o}{=}\\PY{k+kc}{None}\\PY{p}{)}\\PY{p}{:}\n", " \\PY{l+s+sd}{\\PYZdq{}\\PYZdq{}\\PYZdq{}Petrov\\PYZhy{}Galerkin projection of a given |Operator|.}\n", "\n", "\\PY{l+s+sd}{ Given an inner product `( ⋅, ⋅)`, source vectors `b\\PYZus{}1, ..., b\\PYZus{}N`}\n", "\\PY{l+s+sd}{ and range vectors `c\\PYZus{}1, ..., c\\PYZus{}M`, the projection `op\\PYZus{}proj` of `op`}\n", "\\PY{l+s+sd}{ is defined by ::}\n", "\n", "\\PY{l+s+sd}{ [ op\\PYZus{}proj(e\\PYZus{}j) ]\\PYZus{}i = ( c\\PYZus{}i, op(b\\PYZus{}j) )}\n", "\n", "\\PY{l+s+sd}{ for all i,j, where `e\\PYZus{}j` denotes the j\\PYZhy{}th canonical basis vector of R\\PYZca{}N.}\n", "\n", "\\PY{l+s+sd}{ In particular, if the `c\\PYZus{}i` are orthonormal w.r.t. the given product,}\n", "\\PY{l+s+sd}{ then `op\\PYZus{}proj` is the coordinate representation w.r.t. the `b\\PYZus{}i/c\\PYZus{}i` bases}\n", "\\PY{l+s+sd}{ of the restriction of `op` to `span(b\\PYZus{}i)` concatenated with the}\n", "\\PY{l+s+sd}{ orthogonal projection onto `span(c\\PYZus{}i)`.}\n", "\n", "\\PY{l+s+sd}{ From another point of view, if `op` is viewed as a bilinear form}\n", "\\PY{l+s+sd}{ (see :meth:`apply2`) and `( ⋅, ⋅ )` is the Euclidean inner}\n", "\\PY{l+s+sd}{ product, then `op\\PYZus{}proj` represents the matrix of the bilinear form restricted}\n", "\\PY{l+s+sd}{ to `span(b\\PYZus{}i) / span(c\\PYZus{}i)` (w.r.t. the `b\\PYZus{}i/c\\PYZus{}i` bases).}\n", "\n", "\\PY{l+s+sd}{ How the projection is realized will depend on the given |Operator|.}\n", "\\PY{l+s+sd}{ While a projected |NumpyMatrixOperator| will}\n", "\\PY{l+s+sd}{ again be a |NumpyMatrixOperator|, only a generic}\n", "\\PY{l+s+sd}{ :class:`\\PYZti{}pymor.operators.constructions.ProjectedOperator` can be returned}\n", "\\PY{l+s+sd}{ in general. The exact algorithm is specified in :class:`ProjectRules`.}\n", "\n", "\\PY{l+s+sd}{ Parameters}\n", "\\PY{l+s+sd}{ \\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}}\n", "\\PY{l+s+sd}{ range\\PYZus{}basis}\n", "\\PY{l+s+sd}{ The vectors `c\\PYZus{}1, ..., c\\PYZus{}M` as a |VectorArray|. If `None`, no}\n", "\\PY{l+s+sd}{ projection in the range space is performed.}\n", "\\PY{l+s+sd}{ source\\PYZus{}basis}\n", "\\PY{l+s+sd}{ The vectors `b\\PYZus{}1, ..., b\\PYZus{}N` as a |VectorArray| or `None`. If `None`,}\n", "\\PY{l+s+sd}{ no restriction of the source space is performed.}\n", "\\PY{l+s+sd}{ product}\n", "\\PY{l+s+sd}{ An |Operator| representing the inner product. If `None`, the}\n", "\\PY{l+s+sd}{ Euclidean inner product is chosen.}\n", "\n", "\\PY{l+s+sd}{ Returns}\n", "\\PY{l+s+sd}{ \\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}}\n", "\\PY{l+s+sd}{ The projected |Operator| `op\\PYZus{}proj`.}\n", "\\PY{l+s+sd}{ \\PYZdq{}\\PYZdq{}\\PYZdq{}}\n", " \\PY{k}{assert} \\PY{n}{source\\PYZus{}basis} \\PY{o+ow}{is} \\PY{k+kc}{None} \\PY{o+ow}{or} \\PY{n}{source\\PYZus{}basis} \\PY{o+ow}{in} \\PY{n}{op}\\PY{o}{.}\\PY{n}{source}\n", " \\PY{k}{assert} \\PY{n}{range\\PYZus{}basis} \\PY{o+ow}{is} \\PY{k+kc}{None} \\PY{o+ow}{or} \\PY{n}{range\\PYZus{}basis} \\PY{o+ow}{in} \\PY{n}{op}\\PY{o}{.}\\PY{n}{range}\n", " \\PY{k}{assert} \\PY{n}{product} \\PY{o+ow}{is} \\PY{k+kc}{None} \\PY{o+ow}{or} \\PY{n}{product}\\PY{o}{.}\\PY{n}{source} \\PY{o}{==} \\PY{n}{product}\\PY{o}{.}\\PY{n}{range} \\PY{o}{==} \\PY{n}{op}\\PY{o}{.}\\PY{n}{range}\n", "\n", " \\PY{n}{rb} \\PY{o}{=} \\PY{n}{product}\\PY{o}{.}\\PY{n}{apply}\\PY{p}{(}\\PY{n}{range\\PYZus{}basis}\\PY{p}{)} \\PY{k}{if} \\PY{n}{product} \\PY{o+ow}{is} \\PY{o+ow}{not} \\PY{k+kc}{None} \\PY{o+ow}{and} \\PY{n}{range\\PYZus{}basis} \\PY{o+ow}{is} \\PY{o+ow}{not} \\PY{k+kc}{None} \\PY{k}{else} \\PY{n}{range\\PYZus{}basis}\n", "\n", " \\PY{k}{try}\\PY{p}{:}\n", " \\PY{k}{return} \\PY{n}{ProjectRules}\\PY{p}{(}\\PY{n}{rb}\\PY{p}{,} \\PY{n}{source\\PYZus{}basis}\\PY{p}{)}\\PY{o}{.}\\PY{n}{apply}\\PY{p}{(}\\PY{n}{op}\\PY{p}{)}\n", " \\PY{k}{except} \\PY{n}{NoMatchingRuleError}\\PY{p}{:}\n", " \\PY{n}{op}\\PY{o}{.}\\PY{n}{logger}\\PY{o}{.}\\PY{n}{warning}\\PY{p}{(}\\PY{l+s+s1}{\\PYZsq{}}\\PY{l+s+s1}{Using inefficient generic projection operator}\\PY{l+s+s1}{\\PYZsq{}}\\PY{p}{)}\n", " \\PY{k}{return} \\PY{n}{ProjectedOperator}\\PY{p}{(}\\PY{n}{op}\\PY{p}{,} \\PY{n}{range\\PYZus{}basis}\\PY{p}{,} \\PY{n}{source\\PYZus{}basis}\\PY{p}{,} \\PY{n}{product}\\PY{p}{)}\n", "\\end{Verbatim}\n" ], "text/plain": [ "def project(op, range_basis, source_basis, product=None):\n", " \"\"\"Petrov-Galerkin projection of a given |Operator|.\n", "\n", " Given an inner product `( ⋅, ⋅)`, source vectors `b_1, ..., b_N`\n", " and range vectors `c_1, ..., c_M`, the projection `op_proj` of `op`\n", " is defined by ::\n", "\n", " [ op_proj(e_j) ]_i = ( c_i, op(b_j) )\n", "\n", " for all i,j, where `e_j` denotes the j-th canonical basis vector of R^N.\n", "\n", " In particular, if the `c_i` are orthonormal w.r.t. the given product,\n", " then `op_proj` is the coordinate representation w.r.t. the `b_i/c_i` bases\n", " of the restriction of `op` to `span(b_i)` concatenated with the\n", " orthogonal projection onto `span(c_i)`.\n", "\n", " From another point of view, if `op` is viewed as a bilinear form\n", " (see :meth:`apply2`) and `( ⋅, ⋅ )` is the Euclidean inner\n", " product, then `op_proj` represents the matrix of the bilinear form restricted\n", " to `span(b_i) / span(c_i)` (w.r.t. the `b_i/c_i` bases).\n", "\n", " How the projection is realized will depend on the given |Operator|.\n", " While a projected |NumpyMatrixOperator| will\n", " again be a |NumpyMatrixOperator|, only a generic\n", " :class:`~pymor.operators.constructions.ProjectedOperator` can be returned\n", " in general. The exact algorithm is specified in :class:`ProjectRules`.\n", "\n", " Parameters\n", " ----------\n", " range_basis\n", " The vectors `c_1, ..., c_M` as a |VectorArray|. If `None`, no\n", " projection in the range space is performed.\n", " source_basis\n", " The vectors `b_1, ..., b_N` as a |VectorArray| or `None`. If `None`,\n", " no restriction of the source space is performed.\n", " product\n", " An |Operator| representing the inner product. If `None`, the\n", " Euclidean inner product is chosen.\n", "\n", " Returns\n", " -------\n", " The projected |Operator| `op_proj`.\n", " \"\"\"\n", " assert source_basis is None or source_basis in op.source\n", " assert range_basis is None or range_basis in op.range\n", " assert product is None or product.source == product.range == op.range\n", "\n", " rb = product.apply(range_basis) if product is not None and range_basis is not None else range_basis\n", "\n", " try:\n", " return ProjectRules(rb, source_basis).apply(op)\n", " except NoMatchingRuleError:\n", " op.logger.warning('Using inefficient generic projection operator')\n", " return ProjectedOperator(op, range_basis, source_basis, product)" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "print_source(project)" ] }, { "cell_type": "markdown", "id": "1da7d32a", "metadata": {}, "source": [ "We see there is error checking and some code to handle the optional `product` {{ Operator }}\n", "used to project into the reduced {attr}`~pymor.operators.interface.Operator.range` space.\n", "The actual work is done by the {meth}`~pymor.algorithms.rules.RuleTable.apply` method\n", "of the `ProjectRules` object.\n", "\n", "`ProjectRules` is a {{ RuleTable }}, an ordered list of conditions with corresponding actions.\n", "The list is traversed from top to bottom, and the action of the first matching condition is\n", "executed. These {{ RuleTables }} can also be modified by the user to customize the behavior\n", "of an algorithm for a specific application. We will not go into the details of defining\n", "or modifying a {{ RuleTable }} here, but we will look at the rules of `ProjectRules` by looking\n", "at its string representation:" ] }, { "cell_type": "code", "execution_count": 39, "id": "e2898df2", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Pos Match Type Condition Action Name / Action \n", "--- ---------- ----------------------------- -------------------------------\n", " Description \n", "0 ALWAYS None no_bases \n", "1 CLASS ZeroOperator ZeroOperator \n", "2 CLASS ConstantOperator ConstantOperator \n", "3 GENERIC linear and not parametric apply_basis \n", "4 CLASS ConcatenationOperator ConcatenationOperator \n", "5 CLASS AdjointOperator AdjointOperator \n", "6 CLASS EmpiricalInterpolatedOperator EmpiricalInterpolatedOperator \n", "7 CLASS AffineOperator AffineOperator \n", "8 CLASS LincombOperator LincombOperator \n", "9 CLASS SelectionOperator SelectionOperator \n", "10 CLASS BlockOperatorBase BlockOperatorBase " ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from pymor.algorithms.projection import ProjectRules\n", "ProjectRules" ] }, { "cell_type": "markdown", "id": "5f4abaa6", "metadata": {}, "source": [ "In the case of `fom.operator`, which is a {{ LincombOperator }}, the rule with index 8 will\n", "be the first matching rule. We can take a look at it:" ] }, { "cell_type": "code", "execution_count": 40, "id": "e6b2cada", "metadata": { "tags": [ "hide-code", "hide-output" ] }, "outputs": [], "source": [ "assert ProjectRules.rules[8].action_description == 'LincombOperator'" ] }, { "cell_type": "code", "execution_count": 41, "id": "cd640579", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
    @match_class(LincombOperator)\n",
       "    def action_LincombOperator(self, op):\n",
       "        return self.replace_children(op).with_(solver_options=None)\n",
       "
\n" ], "text/latex": [ "\\begin{Verbatim}[commandchars=\\\\\\{\\}]\n", " \\PY{n+nd}{@match\\PYZus{}class}\\PY{p}{(}\\PY{n}{LincombOperator}\\PY{p}{)}\n", " \\PY{k}{def} \\PY{n+nf}{action\\PYZus{}LincombOperator}\\PY{p}{(}\\PY{n+nb+bp}{self}\\PY{p}{,} \\PY{n}{op}\\PY{p}{)}\\PY{p}{:}\n", " \\PY{k}{return} \\PY{n+nb+bp}{self}\\PY{o}{.}\\PY{n}{replace\\PYZus{}children}\\PY{p}{(}\\PY{n}{op}\\PY{p}{)}\\PY{o}{.}\\PY{n}{with\\PYZus{}}\\PY{p}{(}\\PY{n}{solver\\PYZus{}options}\\PY{o}{=}\\PY{k+kc}{None}\\PY{p}{)}\n", "\\end{Verbatim}\n" ], "text/plain": [ " @match_class(LincombOperator)\n", " def action_LincombOperator(self, op):\n", " return self.replace_children(op).with_(solver_options=None)" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ProjectRules.rules[8]" ] }, { "cell_type": "markdown", "id": "d4125fb5", "metadata": {}, "source": [ "The implementation of the action for {{ LincombOperators }} uses the\n", "{meth}`~pymor.algorithms.rules.RuleTable.replace_children` method of {{ RuleTable }},\n", "which will recursively apply `ProjectionRules` to all\n", "{meth}`children ` of the\n", "{{ Operator }}, collect the results and then return a new {{ Operator }} where\n", "the children have been replaced by the results of the applications of the\n", "{{ RuleTable }}. Here, the {meth}`children `\n", "of an {{ Operator }} are all of its attribute that are either {{ Operators }} or lists or dicts\n", "of {{ Operators }}.\n", "\n", "In our case, `ProjectRules` will be applied to all {{ NumpyMatrixOperators }} held by\n", "`fom.operator`. These are linear, non-parametric operators, for which rule 3\n", "will apply:" ] }, { "cell_type": "code", "execution_count": 42, "id": "4a49c29e", "metadata": { "tags": [ "hide-code", "hide-output" ] }, "outputs": [], "source": [ "assert ProjectRules.rules[3].action_description == 'apply_basis'" ] }, { "cell_type": "code", "execution_count": 43, "id": "e27c0ec3", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
    @match_generic(lambda op: op.linear and not op.parametric, 'linear and not parametric')\n",
       "    def action_apply_basis(self, op):\n",
       "        range_basis, source_basis = self.range_basis, self.source_basis\n",
       "        if source_basis is None:\n",
       "            try:\n",
       "                V = op.apply_adjoint(range_basis)\n",
       "            except NotImplementedError as e:\n",
       "                raise RuleNotMatchingError('apply_adjoint not implemented') from e\n",
       "            if isinstance(op.source, NumpyVectorSpace):\n",
       "                from pymor.operators.numpy import NumpyMatrixOperator\n",
       "                return NumpyMatrixOperator(V.to_numpy(), source_id=op.source.id, name=op.name)\n",
       "            else:\n",
       "                from pymor.operators.constructions import VectorArrayOperator\n",
       "                return VectorArrayOperator(V, adjoint=True, name=op.name)\n",
       "        else:\n",
       "            if range_basis is None:\n",
       "                V = op.apply(source_basis)\n",
       "                if isinstance(op.range, NumpyVectorSpace):\n",
       "                    from pymor.operators.numpy import NumpyMatrixOperator\n",
       "                    return NumpyMatrixOperator(V.to_numpy().T, range_id=op.range.id, name=op.name)\n",
       "                else:\n",
       "                    from pymor.operators.constructions import VectorArrayOperator\n",
       "                    return VectorArrayOperator(V, adjoint=False, name=op.name)\n",
       "            else:\n",
       "                from pymor.operators.numpy import NumpyMatrixOperator\n",
       "                return NumpyMatrixOperator(op.apply2(range_basis, source_basis), name=op.name)\n",
       "
\n" ], "text/latex": [ "\\begin{Verbatim}[commandchars=\\\\\\{\\}]\n", " \\PY{n+nd}{@match\\PYZus{}generic}\\PY{p}{(}\\PY{k}{lambda} \\PY{n}{op}\\PY{p}{:} \\PY{n}{op}\\PY{o}{.}\\PY{n}{linear} \\PY{o+ow}{and} \\PY{o+ow}{not} \\PY{n}{op}\\PY{o}{.}\\PY{n}{parametric}\\PY{p}{,} \\PY{l+s+s1}{\\PYZsq{}}\\PY{l+s+s1}{linear and not parametric}\\PY{l+s+s1}{\\PYZsq{}}\\PY{p}{)}\n", " \\PY{k}{def} \\PY{n+nf}{action\\PYZus{}apply\\PYZus{}basis}\\PY{p}{(}\\PY{n+nb+bp}{self}\\PY{p}{,} \\PY{n}{op}\\PY{p}{)}\\PY{p}{:}\n", " \\PY{n}{range\\PYZus{}basis}\\PY{p}{,} \\PY{n}{source\\PYZus{}basis} \\PY{o}{=} \\PY{n+nb+bp}{self}\\PY{o}{.}\\PY{n}{range\\PYZus{}basis}\\PY{p}{,} \\PY{n+nb+bp}{self}\\PY{o}{.}\\PY{n}{source\\PYZus{}basis}\n", " \\PY{k}{if} \\PY{n}{source\\PYZus{}basis} \\PY{o+ow}{is} \\PY{k+kc}{None}\\PY{p}{:}\n", " \\PY{k}{try}\\PY{p}{:}\n", " \\PY{n}{V} \\PY{o}{=} \\PY{n}{op}\\PY{o}{.}\\PY{n}{apply\\PYZus{}adjoint}\\PY{p}{(}\\PY{n}{range\\PYZus{}basis}\\PY{p}{)}\n", " \\PY{k}{except} \\PY{n+ne}{NotImplementedError} \\PY{k}{as} \\PY{n}{e}\\PY{p}{:}\n", " \\PY{k}{raise} \\PY{n}{RuleNotMatchingError}\\PY{p}{(}\\PY{l+s+s1}{\\PYZsq{}}\\PY{l+s+s1}{apply\\PYZus{}adjoint not implemented}\\PY{l+s+s1}{\\PYZsq{}}\\PY{p}{)} \\PY{k+kn}{from} \\PY{n+nn}{e}\n", " \\PY{k}{if} \\PY{n+nb}{isinstance}\\PY{p}{(}\\PY{n}{op}\\PY{o}{.}\\PY{n}{source}\\PY{p}{,} \\PY{n}{NumpyVectorSpace}\\PY{p}{)}\\PY{p}{:}\n", " \\PY{k+kn}{from} \\PY{n+nn}{pymor}\\PY{n+nn}{.}\\PY{n+nn}{operators}\\PY{n+nn}{.}\\PY{n+nn}{numpy} \\PY{k+kn}{import} \\PY{n}{NumpyMatrixOperator}\n", " \\PY{k}{return} \\PY{n}{NumpyMatrixOperator}\\PY{p}{(}\\PY{n}{V}\\PY{o}{.}\\PY{n}{to\\PYZus{}numpy}\\PY{p}{(}\\PY{p}{)}\\PY{p}{,} \\PY{n}{source\\PYZus{}id}\\PY{o}{=}\\PY{n}{op}\\PY{o}{.}\\PY{n}{source}\\PY{o}{.}\\PY{n}{id}\\PY{p}{,} \\PY{n}{name}\\PY{o}{=}\\PY{n}{op}\\PY{o}{.}\\PY{n}{name}\\PY{p}{)}\n", " \\PY{k}{else}\\PY{p}{:}\n", " \\PY{k+kn}{from} \\PY{n+nn}{pymor}\\PY{n+nn}{.}\\PY{n+nn}{operators}\\PY{n+nn}{.}\\PY{n+nn}{constructions} \\PY{k+kn}{import} \\PY{n}{VectorArrayOperator}\n", " \\PY{k}{return} \\PY{n}{VectorArrayOperator}\\PY{p}{(}\\PY{n}{V}\\PY{p}{,} \\PY{n}{adjoint}\\PY{o}{=}\\PY{k+kc}{True}\\PY{p}{,} \\PY{n}{name}\\PY{o}{=}\\PY{n}{op}\\PY{o}{.}\\PY{n}{name}\\PY{p}{)}\n", " \\PY{k}{else}\\PY{p}{:}\n", " \\PY{k}{if} \\PY{n}{range\\PYZus{}basis} \\PY{o+ow}{is} \\PY{k+kc}{None}\\PY{p}{:}\n", " \\PY{n}{V} \\PY{o}{=} \\PY{n}{op}\\PY{o}{.}\\PY{n}{apply}\\PY{p}{(}\\PY{n}{source\\PYZus{}basis}\\PY{p}{)}\n", " \\PY{k}{if} \\PY{n+nb}{isinstance}\\PY{p}{(}\\PY{n}{op}\\PY{o}{.}\\PY{n}{range}\\PY{p}{,} \\PY{n}{NumpyVectorSpace}\\PY{p}{)}\\PY{p}{:}\n", " \\PY{k+kn}{from} \\PY{n+nn}{pymor}\\PY{n+nn}{.}\\PY{n+nn}{operators}\\PY{n+nn}{.}\\PY{n+nn}{numpy} \\PY{k+kn}{import} \\PY{n}{NumpyMatrixOperator}\n", " \\PY{k}{return} \\PY{n}{NumpyMatrixOperator}\\PY{p}{(}\\PY{n}{V}\\PY{o}{.}\\PY{n}{to\\PYZus{}numpy}\\PY{p}{(}\\PY{p}{)}\\PY{o}{.}\\PY{n}{T}\\PY{p}{,} \\PY{n}{range\\PYZus{}id}\\PY{o}{=}\\PY{n}{op}\\PY{o}{.}\\PY{n}{range}\\PY{o}{.}\\PY{n}{id}\\PY{p}{,} \\PY{n}{name}\\PY{o}{=}\\PY{n}{op}\\PY{o}{.}\\PY{n}{name}\\PY{p}{)}\n", " \\PY{k}{else}\\PY{p}{:}\n", " \\PY{k+kn}{from} \\PY{n+nn}{pymor}\\PY{n+nn}{.}\\PY{n+nn}{operators}\\PY{n+nn}{.}\\PY{n+nn}{constructions} \\PY{k+kn}{import} \\PY{n}{VectorArrayOperator}\n", " \\PY{k}{return} \\PY{n}{VectorArrayOperator}\\PY{p}{(}\\PY{n}{V}\\PY{p}{,} \\PY{n}{adjoint}\\PY{o}{=}\\PY{k+kc}{False}\\PY{p}{,} \\PY{n}{name}\\PY{o}{=}\\PY{n}{op}\\PY{o}{.}\\PY{n}{name}\\PY{p}{)}\n", " \\PY{k}{else}\\PY{p}{:}\n", " \\PY{k+kn}{from} \\PY{n+nn}{pymor}\\PY{n+nn}{.}\\PY{n+nn}{operators}\\PY{n+nn}{.}\\PY{n+nn}{numpy} \\PY{k+kn}{import} \\PY{n}{NumpyMatrixOperator}\n", " \\PY{k}{return} \\PY{n}{NumpyMatrixOperator}\\PY{p}{(}\\PY{n}{op}\\PY{o}{.}\\PY{n}{apply2}\\PY{p}{(}\\PY{n}{range\\PYZus{}basis}\\PY{p}{,} \\PY{n}{source\\PYZus{}basis}\\PY{p}{)}\\PY{p}{,} \\PY{n}{name}\\PY{o}{=}\\PY{n}{op}\\PY{o}{.}\\PY{n}{name}\\PY{p}{)}\n", "\\end{Verbatim}\n" ], "text/plain": [ " @match_generic(lambda op: op.linear and not op.parametric, 'linear and not parametric')\n", " def action_apply_basis(self, op):\n", " range_basis, source_basis = self.range_basis, self.source_basis\n", " if source_basis is None:\n", " try:\n", " V = op.apply_adjoint(range_basis)\n", " except NotImplementedError as e:\n", " raise RuleNotMatchingError('apply_adjoint not implemented') from e\n", " if isinstance(op.source, NumpyVectorSpace):\n", " from pymor.operators.numpy import NumpyMatrixOperator\n", " return NumpyMatrixOperator(V.to_numpy(), source_id=op.source.id, name=op.name)\n", " else:\n", " from pymor.operators.constructions import VectorArrayOperator\n", " return VectorArrayOperator(V, adjoint=True, name=op.name)\n", " else:\n", " if range_basis is None:\n", " V = op.apply(source_basis)\n", " if isinstance(op.range, NumpyVectorSpace):\n", " from pymor.operators.numpy import NumpyMatrixOperator\n", " return NumpyMatrixOperator(V.to_numpy().T, range_id=op.range.id, name=op.name)\n", " else:\n", " from pymor.operators.constructions import VectorArrayOperator\n", " return VectorArrayOperator(V, adjoint=False, name=op.name)\n", " else:\n", " from pymor.operators.numpy import NumpyMatrixOperator\n", " return NumpyMatrixOperator(op.apply2(range_basis, source_basis), name=op.name)" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ProjectRules.rules[3]" ] }, { "cell_type": "markdown", "id": "92025a91", "metadata": {}, "source": [ "This action has special cases for all possible combinations of given or not-given\n", "{attr}`~pymor.operators.interface.Operator.range` and {attr}`~pymor.operators.interface.Operator.source`\n", "bases. In our case, the `else` block of the second `else` block applies,\n", "where we see our familiar {meth}`~pymor.operators.interface.Operator.apply2` call.\n", "\n", "If you look at the rules of `ProjectRules` again, you see that\n", "{meth}`~pymor.algorithms.projection.project` can handle many more cases.\n", "If all rules fail, a `NoMatchingRuleError` will be raised, in which case,\n", "{meth}`~pymor.algorithms.projection.project` will return a\n", "{class}`~pymor.operators.constructions.ProjectedOperator`, which just stores the\n", "projection bases and performs the projection for each call to the {{ Operator }} interface\n", "methods. Thus, even when offline/online decomposition fails, still a mathematically correct\n", "representation of the projected {{ Operator }} is returned to allow testing the approximation\n", "quality of the ROM before taking care of online efficiency in a later step.\n", "\n", "## Using Reductors\n", "\n", "Instead of projecting each {{ Operator }} of our FOM separately and then instantiating\n", "the ROM with the projected {{ Operators }}, we can use a {mod}`reductor `,\n", "which does all the work for us. For a simple Galerkin projection of a {{ StationaryModel }},\n", "we can use {class}`~pymor.reductors.basic.StationaryRBReductor`:" ] }, { "cell_type": "code", "execution_count": 44, "id": "b8b7febc", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "8b9738211efb4b6093b900090b5f002d", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Accordion(children=(HTML(value='', layout=Layout(height='16em', overflow_y='auto', width='100%')),), selected_…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from pymor.reductors.basic import StationaryRBReductor\n", "\n", "reductor = StationaryRBReductor(fom, basis)\n", "rom = reductor.reduce()" ] }, { "cell_type": "markdown", "id": "07af35eb", "metadata": {}, "source": [ "Again, we get the same ROM as before:" ] }, { "cell_type": "code", "execution_count": 45, "id": "dcc74646", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ 0.00000000e+00, 6.66133815e-16, -8.88178420e-16,\n", " -8.88178420e-16, 0.00000000e+00, 0.00000000e+00,\n", " -5.55111512e-17, 2.22044605e-16, 1.11022302e-16,\n", " -6.93889390e-18]])" ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [ "u_N5 = rom.solve(mu)\n", "u_N.T - u_N5.to_numpy()" ] }, { "cell_type": "markdown", "id": "4d2d14fd", "metadata": {}, "source": [ "As an additional feature, {meth}`~pymor.reductors.basic.StationaryRBReductor.reduce`\n", "allows to project the model onto a smaller dimensional subspace of {math}`V_N` by\n", "extracting the ROM from a previously computed ROM for the full {math}`V_N`. This\n", "is useful, in particular, when assessing the ROM for different basis sizes. The\n", "actual projection is handled in the\n", "{meth}`~pymor.reductor.basic.StationaryRBReductor.project_operators` method,\n", "where we can find some well-known code:" ] }, { "cell_type": "code", "execution_count": 46, "id": "dadc9170", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
    def project_operators(self):\n",
       "        fom = self.fom\n",
       "        RB = self.bases['RB']\n",
       "        projected_operators = {\n",
       "            'operator':          project(fom.operator, RB, RB),\n",
       "            'rhs':               project(fom.rhs, RB, None),\n",
       "            'products':          {k: project(v, RB, RB) for k, v in fom.products.items()},\n",
       "            'output_functional': project(fom.output_functional, None, RB)\n",
       "        }\n",
       "        return projected_operators\n",
       "
\n" ], "text/latex": [ "\\begin{Verbatim}[commandchars=\\\\\\{\\}]\n", " \\PY{k}{def} \\PY{n+nf}{project\\PYZus{}operators}\\PY{p}{(}\\PY{n+nb+bp}{self}\\PY{p}{)}\\PY{p}{:}\n", " \\PY{n}{fom} \\PY{o}{=} \\PY{n+nb+bp}{self}\\PY{o}{.}\\PY{n}{fom}\n", " \\PY{n}{RB} \\PY{o}{=} \\PY{n+nb+bp}{self}\\PY{o}{.}\\PY{n}{bases}\\PY{p}{[}\\PY{l+s+s1}{\\PYZsq{}}\\PY{l+s+s1}{RB}\\PY{l+s+s1}{\\PYZsq{}}\\PY{p}{]}\n", " \\PY{n}{projected\\PYZus{}operators} \\PY{o}{=} \\PY{p}{\\PYZob{}}\n", " \\PY{l+s+s1}{\\PYZsq{}}\\PY{l+s+s1}{operator}\\PY{l+s+s1}{\\PYZsq{}}\\PY{p}{:} \\PY{n}{project}\\PY{p}{(}\\PY{n}{fom}\\PY{o}{.}\\PY{n}{operator}\\PY{p}{,} \\PY{n}{RB}\\PY{p}{,} \\PY{n}{RB}\\PY{p}{)}\\PY{p}{,}\n", " \\PY{l+s+s1}{\\PYZsq{}}\\PY{l+s+s1}{rhs}\\PY{l+s+s1}{\\PYZsq{}}\\PY{p}{:} \\PY{n}{project}\\PY{p}{(}\\PY{n}{fom}\\PY{o}{.}\\PY{n}{rhs}\\PY{p}{,} \\PY{n}{RB}\\PY{p}{,} \\PY{k+kc}{None}\\PY{p}{)}\\PY{p}{,}\n", " \\PY{l+s+s1}{\\PYZsq{}}\\PY{l+s+s1}{products}\\PY{l+s+s1}{\\PYZsq{}}\\PY{p}{:} \\PY{p}{\\PYZob{}}\\PY{n}{k}\\PY{p}{:} \\PY{n}{project}\\PY{p}{(}\\PY{n}{v}\\PY{p}{,} \\PY{n}{RB}\\PY{p}{,} \\PY{n}{RB}\\PY{p}{)} \\PY{k}{for} \\PY{n}{k}\\PY{p}{,} \\PY{n}{v} \\PY{o+ow}{in} \\PY{n}{fom}\\PY{o}{.}\\PY{n}{products}\\PY{o}{.}\\PY{n}{items}\\PY{p}{(}\\PY{p}{)}\\PY{p}{\\PYZcb{}}\\PY{p}{,}\n", " \\PY{l+s+s1}{\\PYZsq{}}\\PY{l+s+s1}{output\\PYZus{}functional}\\PY{l+s+s1}{\\PYZsq{}}\\PY{p}{:} \\PY{n}{project}\\PY{p}{(}\\PY{n}{fom}\\PY{o}{.}\\PY{n}{output\\PYZus{}functional}\\PY{p}{,} \\PY{k+kc}{None}\\PY{p}{,} \\PY{n}{RB}\\PY{p}{)}\n", " \\PY{p}{\\PYZcb{}}\n", " \\PY{k}{return} \\PY{n}{projected\\PYZus{}operators}\n", "\\end{Verbatim}\n" ], "text/plain": [ " def project_operators(self):\n", " fom = self.fom\n", " RB = self.bases['RB']\n", " projected_operators = {\n", " 'operator': project(fom.operator, RB, RB),\n", " 'rhs': project(fom.rhs, RB, None),\n", " 'products': {k: project(v, RB, RB) for k, v in fom.products.items()},\n", " 'output_functional': project(fom.output_functional, None, RB)\n", " }\n", " return projected_operators" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "print_source(reductor.project_operators)" ] }, { "cell_type": "markdown", "id": "eb25f53a", "metadata": {}, "source": [ "We see that the reductor also takes care of projecting output functionals and\n", "inner products associated with the {{ Model }}. The construction of the ROM from\n", "the projected operators is performed by a separate method:" ] }, { "cell_type": "code", "execution_count": 47, "id": "cca91fa6", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
    def build_rom(self, projected_operators, error_estimator):\n",
       "        return StationaryModel(error_estimator=error_estimator, **projected_operators)\n",
       "
\n" ], "text/latex": [ "\\begin{Verbatim}[commandchars=\\\\\\{\\}]\n", " \\PY{k}{def} \\PY{n+nf}{build\\PYZus{}rom}\\PY{p}{(}\\PY{n+nb+bp}{self}\\PY{p}{,} \\PY{n}{projected\\PYZus{}operators}\\PY{p}{,} \\PY{n}{error\\PYZus{}estimator}\\PY{p}{)}\\PY{p}{:}\n", " \\PY{k}{return} \\PY{n}{StationaryModel}\\PY{p}{(}\\PY{n}{error\\PYZus{}estimator}\\PY{o}{=}\\PY{n}{error\\PYZus{}estimator}\\PY{p}{,} \\PY{o}{*}\\PY{o}{*}\\PY{n}{projected\\PYZus{}operators}\\PY{p}{)}\n", "\\end{Verbatim}\n" ], "text/plain": [ " def build_rom(self, projected_operators, error_estimator):\n", " return StationaryModel(error_estimator=error_estimator, **projected_operators)" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "print_source(reductor.build_rom)" ] }, { "cell_type": "markdown", "id": "d9aaf50d", "metadata": {}, "source": [ "More advanced reductors, such as {class}`~pymor.reductors.coercive.CoerciveRBReductor`\n", "also assemble an a posteriori error estimator for the model order reduction error.\n", "In the case of {class}`~pymor.reductors.basic.StationaryRBReductor`, however,\n", "`error_estimator` is always `None`.\n", "\n", "Reductors also allow to compute {math}`U_N(\\mu)` from {math}`u_N(\\mu)` using\n", "the {meth}`~pymor.reductors.basic.StationaryRBReductor.reconstruct` method:" ] }, { "cell_type": "code", "execution_count": 48, "id": "a0296c43", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([2.28488749e-15])" ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ "U_N5 = reductor.reconstruct(u_N5)\n", "(U_N - U_N5).norm()" ] }, { "cell_type": "markdown", "id": "7fb0820f", "metadata": {}, "source": [ "Again, if we look at the source code, we see a familiar expression:" ] }, { "cell_type": "code", "execution_count": 49, "id": "e02e117a", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
    def reconstruct(self, u, basis='RB'):\n",
       "        """Reconstruct high-dimensional vector from reduced vector `u`."""\n",
       "        return self.bases[basis][:u.dim].lincomb(u.to_numpy())\n",
       "
\n" ], "text/latex": [ "\\begin{Verbatim}[commandchars=\\\\\\{\\}]\n", " \\PY{k}{def} \\PY{n+nf}{reconstruct}\\PY{p}{(}\\PY{n+nb+bp}{self}\\PY{p}{,} \\PY{n}{u}\\PY{p}{,} \\PY{n}{basis}\\PY{o}{=}\\PY{l+s+s1}{\\PYZsq{}}\\PY{l+s+s1}{RB}\\PY{l+s+s1}{\\PYZsq{}}\\PY{p}{)}\\PY{p}{:}\n", " \\PY{l+s+sd}{\\PYZdq{}\\PYZdq{}\\PYZdq{}Reconstruct high\\PYZhy{}dimensional vector from reduced vector `u`.\\PYZdq{}\\PYZdq{}\\PYZdq{}}\n", " \\PY{k}{return} \\PY{n+nb+bp}{self}\\PY{o}{.}\\PY{n}{bases}\\PY{p}{[}\\PY{n}{basis}\\PY{p}{]}\\PY{p}{[}\\PY{p}{:}\\PY{n}{u}\\PY{o}{.}\\PY{n}{dim}\\PY{p}{]}\\PY{o}{.}\\PY{n}{lincomb}\\PY{p}{(}\\PY{n}{u}\\PY{o}{.}\\PY{n}{to\\PYZus{}numpy}\\PY{p}{(}\\PY{p}{)}\\PY{p}{)}\n", "\\end{Verbatim}\n" ], "text/plain": [ " def reconstruct(self, u, basis='RB'):\n", " \"\"\"Reconstruct high-dimensional vector from reduced vector `u`.\"\"\"\n", " return self.bases[basis][:u.dim].lincomb(u.to_numpy())" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ 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00:01 DiffusionOperatorP1: Calculate gradients of shape functions transformed by reference map ...

00:01 DiffusionOperatorP1: Calculate all local scalar products between gradients ...

00:01 DiffusionOperatorP1: Determine global dofs ...

00:01 DiffusionOperatorP1: Boundary treatment ...

00:01 DiffusionOperatorP1: Assemble system matrix ...

00:01 DiffusionOperatorP1: Calculate gradients of shape functions transformed by reference map ...

00:01 DiffusionOperatorP1: Calculate all local scalar products between gradients ...

00:01 DiffusionOperatorP1: Determine global dofs ...

00:01 DiffusionOperatorP1: Boundary treatment ...

00:01 DiffusionOperatorP1: Assemble system matrix ...

00:01 DiffusionOperatorP1: Calculate gradients of shape functions transformed by reference map ...

00:01 DiffusionOperatorP1: Calculate all local scalar products between gradients ...

00:01 DiffusionOperatorP1: Determine global dofs ...

00:01 DiffusionOperatorP1: Boundary treatment ...

00:01 DiffusionOperatorP1: Assemble system matrix ...

00:01 DiffusionOperatorP1: Calculate gradients of shape functions transformed by reference map ...

00:01 DiffusionOperatorP1: Calculate all local scalar products between gradients ...

00:01 DiffusionOperatorP1: Determine global dofs ...

00:01 DiffusionOperatorP1: Boundary treatment ...

00:01 DiffusionOperatorP1: Assemble system matrix ...

00:01 DiffusionOperatorP1: Calculate gradients of shape functions transformed by reference map ...

00:01 DiffusionOperatorP1: Calculate all local scalar products between gradients ...

00:01 DiffusionOperatorP1: Determine global dofs ...

00:01 DiffusionOperatorP1: Boundary treatment ...

00:01 DiffusionOperatorP1: Assemble system matrix ...

00:01 L2ProductP1: Integrate the products of the shape functions on each element

00:01 L2ProductP1: Determine global dofs ...

00:01 L2ProductP1: Boundary treatment ...

00:01 L2ProductP1: Assemble system matrix ...

00:01 DiffusionOperatorP1: Calculate gradients of shape functions transformed by reference map ...

00:01 DiffusionOperatorP1: Calculate all local scalar products between gradients ...

00:01 DiffusionOperatorP1: Determine global dofs ...

00:01 DiffusionOperatorP1: Boundary treatment ...

00:01 DiffusionOperatorP1: Assemble system matrix ...

00:01 L2ProductP1: Integrate the products of the shape functions on each element

00:01 L2ProductP1: Determine global dofs ...

00:01 L2ProductP1: Boundary treatment ...

00:01 L2ProductP1: Assemble system matrix ...

00:01 DiffusionOperatorP1: Calculate gradients of shape functions transformed by reference map ...

00:01 DiffusionOperatorP1: Calculate all local scalar products between gradients ...

00:01 DiffusionOperatorP1: Determine global dofs ...

00:01 DiffusionOperatorP1: Boundary treatment ...

00:01 DiffusionOperatorP1: Assemble system matrix ...

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00:10 StationaryModel: Solving StationaryModel for {input: } ...

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00:11 StationaryRBReductor: Operator projection ...

00:11 StationaryRBReductor: Building ROM ...

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00:03 StationaryModel: Solving ThermalBlock((2, 2))_CG for {diffusion: [0.4370861069626263, 0.9556428757689246, 0.7587945476302645, 0.6387926357773329], input: } ...

00:03 StationaryModel: Solving ThermalBlock((2, 2))_CG for {diffusion: [0.24041677639819287, 0.2403950683025824, 0.15227525095137953, 0.8795585311974417], input: } ...

00:03 StationaryModel: Solving ThermalBlock((2, 2))_CG for {diffusion: [0.6410035105688879, 0.737265320016441, 0.1185260448662222, 0.9729188669457949], input: } ...

00:03 StationaryModel: Solving ThermalBlock((2, 2))_CG for {diffusion: [0.8491983767203796, 0.29110519961044856, 0.26364247048639056, 0.2650640588680905], input: } ...

00:03 StationaryModel: Solving ThermalBlock((2, 2))_CG for {diffusion: [0.373818018663584, 0.5722807884690141, 0.48875051677790415, 0.36210622617823773], input: } ...

00:03 StationaryModel: Solving ThermalBlock((2, 2))_CG for {diffusion: [0.6506676052501416, 0.22554447458683766, 0.3629301836816964, 0.4297256589643226], input: } ...

00:04 StationaryModel: Solving ThermalBlock((2, 2))_CG for {diffusion: [0.5104629857953323, 0.8066583652537123, 0.2797064039425238, 0.5628109945722505], input: } ...

00:04 StationaryModel: Solving ThermalBlock((2, 2))_CG for {diffusion: [0.6331731119758383, 0.14180537144799796, 0.6467903667112945, 0.2534717113185624], input: } ...

00:04 StationaryModel: Solving ThermalBlock((2, 2))_CG for {diffusion: [0.1585464336867516, 0.9539969835279999, 0.9690688297671034, 0.827557613304815], input: } ...

00:04 StationaryModel: Solving ThermalBlock((2, 2))_CG for {diffusion: [0.3741523922560336, 0.1879049026057455, 0.7158097238609412, 0.4961372443656412], input: } ...

00:04 StationaryModel: Solving ThermalBlock((2, 2))_CG for {diffusion: [0.20983441136030095, 0.5456592191001431, 0.13094966900369656, 0.9183883618709039], input: } ...

00:04 StationaryModel: Solving ThermalBlock((2, 2))_CG for {diffusion: [0.3329019834400152, 0.6962700559185838, 0.3805399684804699, 0.5680612190600297], input: } ...

00:04 StationaryModel: Solving ThermalBlock((2, 2))_CG for {diffusion: [0.5920392514089517, 0.26636900997297436, 0.9726261649881027, 0.7976195410250031], input: } ...

00:04 StationaryModel: Solving ThermalBlock((2, 2))_CG for {diffusion: [0.9455490474077702, 0.905344615384884, 0.6381099809299766, 0.9296868115208051], input: } ...

00:04 StationaryModel: Solving ThermalBlock((2, 2))_CG for {diffusion: [0.17964325184672755, 0.27638457617723067, 0.14070456001948428, 0.39279729768693794], input: } ...

00:04 StationaryModel: Solving ThermalBlock((2, 2))_CG for {diffusion: [0.4498095607205338, 0.34421412859650635, 0.8458637582367364, 0.4210779940242304], input: } ...

00:05 StationaryModel: Solving ThermalBlock((2, 2))_CG for {diffusion: [0.3528410587186427, 0.5884264748424236, 0.2268318024772864, 0.8219772826786357], input: } ...

00:05 StationaryModel: Solving ThermalBlock((2, 2))_CG for {diffusion: [0.16709557931179375, 0.9881982429404655, 0.7950202923669917, 0.2788441133807552], input: } ...

00:05 StationaryModel: Solving ThermalBlock((2, 2))_CG for {diffusion: [0.10496990541124217, 0.8339152856093507, 0.7361716094628554, 0.7561064512368886], input: } ...

00:05 StationaryModel: Solving ThermalBlock((2, 2))_CG for {diffusion: [0.7941433120173511, 0.16664018656068133, 0.4226191556898453, 0.20428215357261675], input: } ...

00:05 pod: Computing SVD ...

00:05 | method_of_snapshots: Computing Gramian (20 vectors) ...

00:05 | method_of_snapshots: Computing eigenvalue decomposition ...

00:05 | method_of_snapshots: Computing left-singular vectors (10 vectors) ...

00:05 pod: Checking orthonormality ...

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00:10 StationaryModel: Solving StationaryModel for {input: } ...

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\n", 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1ODL26v8/EbFPAelkncb0ljmegu/b0c/XZNKjeFEAya0D58itsx82qWLcZgvNOFKbO/cyvg9DWfHg5voHff5hKjeGyS+HhuOxb/1VgDFdMm5GX5P5V1N+RLMb8wxGCTGtnQsR4boWbb4wU6/Tg0fod03zOtVRh+u9J44BDkCHabQiwKr2zXKxEcQCoh2JY+0W6FUXpTD5Azi4wLC6aXwDJ9BVobes/31N4YSacJjLjzr2t8KtpB2QY92eqBNNPoYAdJ3wEU4BPaptaeQzHAvCcfE+dls8fSxqH3WVNPkfO3BA0BQClCpklaBP15WqqbfVbC0lU+erJ3M2zu2Cg2+75PyKuVWYuEyqXStzEGXO1Tk9CwKbayQ+yIhvT41k8GB0htfAVUxtoGmuI35KWgHHseC17tOkiNgW6Lgz4q40sZxyDVT6Kmx9T7UM7c7j5KXL8HNU5ae2BvQUTH04hrZlgIt2dYbTAT4tKilcbGja8vgrLbgPUzkz+bHcGCY/zM85Wj7uO3MPkbv8K4LwhReNXgELoIsPR8LXjEWCuZqJLp/BgmQUYUWw502GYFstmas4U3O+i6YJJ85c4Uy00INmzFEAqLRgwp7yJNfTVvzk0sbFoMAFkxRgFWzaivG7yDHsbYRrTXMsLuB0U7moNPXQRLS25XBcI3FhQoSor6cVLqLUkdsL1ftI3LHOOuwhAykhbn3oVLpaLgEtr5Uvgu4GcexqNMS76pxLitCG54A62NgJcgwxX+sEV2Gq5lznsaZeGEJHdDt7MrCoiq9+R+QndPWcohOsrt9ai0E3Bb3SgXJ9FVPLtjajN3/Dfm3vY4dhT/TOS/wp9rYZpxkD3RUyWj1GBMUCOMkGea2pU8yANknjrdl/x1m5167EMzs0ncsjU24Ok+/+UdQ9yq6pK8QWce5xFxnM+VW75++QqofAvMrEud/b2pfWQBJ4XwB04ghFKmN6exPjBd2ZVU0C4nCiDidFkVjsh3Sgu3ZrmjMy4h6Jr6kxYQBoHVhLuwK4X1vROtAXWxyEloOI4Efw3LYaHMThxOAkfO2uoHdgdeYvXXMM3bzenUEv3fjiuPvAzOmNY6Nwmtzf6W2FYYN1QEb6u/YtrqgqxMbJ69pKk7/7+FcKEuraPSDCTHW+rPZuFgcbOLMWKCxeYe2AdogcAKy51a5utQS2GifLrI3uZL7zyVbgZFy0JUS1EV8wmsIiZldOPI9432F9CZ4yMVDO13lHCBk8kVVlsyijnOPw4ZRpaCg8GNyIPwQLnYQWls02vB3mP5fJWnBPLoIr7tfXYQhPPTxXVOFlbGGgSSlYO/ufrTm1Ha1iwtTaJDg+zKWroJ9MAHTvOB6VcmOYvDSYObouOvS7L4W5Ly0ix23PdQmyYzQ8HI7+NGmu+fmSFVvt1ILWpPi3D0wVC/SD+/+7MSA0D6RToB/s69cO6G0JhkSGaeZKMnGHW2CaZVfX+n2xUwCPObMPgcHHA4p1MXza1ds1kzxo1q9wgEX53nYcx4KvW1/6c5y+Fei+q09WMmqDk644Vnw+5GwLDAoknJrAYMKD4YM6TR0mNbCPRw1zvXQ1Ae+2Cz4rgFv+3o822ErBYlUba1XgCKw015NR3/Z3sHY0Mo9VLQeAuItjFcjB67rnOlBAjvS5H2wngADAAdCee+6Bral9MGtz+kkKCLPJu/jzOWdGM3fRKK8ya++Zq0+Zv8M1MqV2VeQ3Ekx8i0MH2gvz3GXAZZyuhKt1mtc6wdV+DePeU8pgqyFHaQpSfX5uGqf4Bsd3EDi5LtT3UftSnxmkgvwnZbsq0FF8qkJbIA+Y0VpX4Sqj9zXkbK6/luXmMHkRhH+zmjLbFBFPE2bV5EXQJc3tASfcrsU/eKQ3PxyZzPUILV4bEo6MWySTn8V2ME0LAZmkmzgVCYeAc+tAsRB07ycAw0dJnlHDrOOijKTH+jrCoeXioxdlgRWFLjmeWuFkhBt2HlS/M9sSAeP4wqROXz/KolTGM+AuCGZjlnU2ngF3SDjD73AqwEGLbjx+8AIX1BhsF0ZPQbh0OhDBUd1jDrhwNkEHfffWSdFpUY/Gyvy60sxcf9fxPAWP07/JsO+muc2MaIOrtrHXN06H6fn6fRaa0gdf6NyBGxjnrJcK/zieV4wLv5MBgSaSimvGUWmTyZ0yw9CFskdDFW7YXlhnqgRCGB3oHUzyUmFxNdxQf5d5cC4PdbkxTH49MtGFlRCKgyG2gakPDLww/Niu5c9qE/dfI7VxEddaWzIlEXSP7tYGM98CKRgEYxRaQg1OJLbCcZubwMzhqhJwprUimBogYcpXAIu6+dvhALMCqJa2ILY7jIzRLchKOGVbLc3aTUwz1wLXC02+t1d34JZeaFrtpXgsmuFYTDgKc72YKwAK6EVpSwCo5DWsj37bxqnnuLSjwy2AdB/fgwA0//u7bUcNmqDuKmjGbFp34QkCOVKr5Hg4QyMQTGDBpQfp0ToUrgfmi89FPxGWMrlaBu04bNz2fnK7ni/8e+Wq7HJz3MpOvv1dHPV+7cumLXJPMtIdOq5iepv+s0zCRGHSw5bBqbKw+zTz7+DYMP420RhkzHRUhj316UTdsBPghJC0+cHAFC29qdPjKmV8gNtwetyLa+JhKCsa1m2Uxn3ieHTKjWHygwWqauWVwdc/mvAXqxcy2eaMngycGjej0oGMeBcyJ9apm9ddK2c0OEpQnWsea8tlZw2NWKDwaHv/CI/0LKgFwQVngwZzAoBLyhTqWrVk3XHJj//Ib0MBvZXPAIojfdoKrFHHbYK5MBxdUmgKrLf0JNylw4kCeksBOKPrhSYtNK1AL/ig5tOn8LCSWXSJRZYa8gBH60Nvkc3OpYEIypMOrIsz797CpSIMvqMpxYUHAczCyzTD6gstYwPWDq1auyCzdswaGzXRYdKyemKoJ5lHYfolN3683LqiD+1OcFVwENmQY2A6PkMVfaar7jCocBVHMdlvLAVzWzsm8GB2JU5AHFea7/kM4CfUuJyU41KZftDu9FoiJzhDxfS++GDb9n/vnfn1MOp7/v4qJEV7kjjYH5neLZWEMM3X8SpETxaArQUj6XrYiz4LPnk9++SvXzlcLFhXX/iBZPDBsP2llmx4s2ZfDzcZ4WCaOWOXXNNXdYbfysfYJKO7F8nvcdD6NZ9RuF9/CwcgTNhr3eXkban/VjUaAV/TWrYb/M3bCgE4tHGuB/ZMwBF3L3VKHC6crNnWVXCWyYsLKOlHuEZDqRT/T8EhAmAxHML6tbZlTL/CxaLe1dwo7FiJwh+z7qlp/ECxcNgzEfsAOGNZksGvvQTRC+RgE8ROfGO0oHdyKYNdmQfnTeAvDLRezwyPCzpNMJs6vseKQ07AFXyYaNowWuKY2KTqFENwoi2WUxr9qWcq41TNb7OO04Z1axFeEh/zOwz448AiCg/jN7Sllcx87Nau8DP1PdxdVwkJmLtS6IrxzfsiZb4qduiQ6NueFr/tyMNbzj75sdwYJt9rghXxRV4E4UMNBizJcFz6r353FUReeGmCtVwrjHHzujcUOESbCmcWBU78Q+yOn+sDGTW8jtbCLkm2AuiHAAscjXCCENR1SVmANIUQ30Y4INuqcORBAMLHHXAocNX/fRWcnK7DksH7XLcENJUXOHF3QulHwLURLpVhgfhAdtjzFiRFOAn3gjj+rkyAV+C65GIfTF3RVEJQYUyEJdUpHVFPkhJZUMrgzlp1NQ3fjSkWYWniQuPiHgxjXtT2mMCJdgd85YJM8Mp+VXon5r8Hu0f7XtmzAlQa9nAU3G2zpZECBGdqolR/Vgf0I95ZFtilfWLqsgMXbc34Nk/uM+WN751gNd5hj66rxvpcHupyY5i8qmWRa1UzrwucIE9Eo6m+uc+9mc+9e/Q6WkNvkkF14hHai5nyum9VMz+6E9AaOnr42SFqmr/YdrVQDpuAkb3qW9lUEVvDVhhT0LKIUQHrSGVM4ZsJIujfaBFxphlMBqGhhmJJuMiBjzCd1zruW9e+g8PrcAIu8IktPIwnCJrYVq9w7AuCg1ertPYCF+Za8S15Eo9F0jxFbNdTSY3dpoTvMPA2ZeWBMoJVNRi7QIAjzfyCduxQl6y6dNP6GyC6QI8rIqWprH5otZiWeLlCwa2Q3in3/6u/P0Fqd0NUOZp3imMjpf8tk/JUjR3ldzxTt+ThBAPcERLCNDPhq9plxT+3W83Pgm1dvO/CbIbT3zAyorJFb6B9FkhmHJs+7ZQd+sT7F0mQSl3NYjmUPZfETO8OHN2MdS5sYfmbzxXaKxcf/Pb1fRGXBg4qIdehrNqwXpnN715wPEvEPATlxjD5tjTLzhYfim+jYlBdGz+iOMVtacZoRVwIcE5EMzzhufWs+TUzsQ1Jb5qZ6AGoCvRCYl00rd+JVeLzuoMYo+gWSJdwCJM6k8CENi5IAYPmeklGWLNiBlzHsEVNDwimq4Tjer4kPhwQLghwK59m3R6cZeHz9o/w7HJwSSVpinNHBG5qx2B5JU3aATmUZ5biZ+UWPXUcizoTdziO5SqBQ44KHBwH3Q7+QnQVs94qoKtCAk7RDy2EB6ABzff8HztkaRabwH2MIhacdzRzvQjsgB5J/7GdPeBzofe0LjljyK1hxeWkmrsXVLE1SRc4KXDhtsrF3ebFvK1vYkhhXjqRaY9wLbeWbuBYPLhxbIvP77Q79+OqurmtWreXLvgqfHvt9jIWbuaxPfwavw2u4J7N+ns0sVTauFYB2NueN9ORdUWQFCA/Li0oyjNitGvcf/hLh+1eeTAc16Ov91JuDJO/p8KPKzQCZ8Jk+L5AhnTuWrQueV2fjzSzcKa7IPz2zE3Putgax3Zbfvdk0CEITHBA8hRIBnTTlMh1PnDN7l8pazrxLSMOxvMweDeYJuHYjxym/MPEgyocYFHupJVCCzCY6APO71NI2KzdTlusm12c4VmdgbnvvyGNl0XoCBO9j134AdTemx1RwHvJFC1zHSKfgcQiqinoKcdPcrHnggwyeHa4MiSOmQx1HKPKk4fnAN++ZwARsU0NrQIGY4jRz/v5Y3iv5U2N387us5U8yevZMlBxk1gKH7VuZsZDAzt1e5oy781wM85TjH+Da2KygLuOyqAJ0mQ+vOPpMKBZIBm6mG3XqT+846hVzIDcZXDvrGznPZ7LtSg3hskrM5TFVjj7mPI0KwGkueZkCyP98+p56lUkEs5EUNoi4VMfrpv/kVE2+F57h8MIRxKUjBSlzpkxlljrN3ChcTujJmPW6boyTaVVEwnXegoTcSJbc35UgvFkRSTBoRk+mE2F6wg/OU7ACca2tBccegLu4LQXEz239Ym3GxaHWgdAjgmH1ZmuHzrXaFFeHK4BstgWP2bbWxvQVp81BwWOTEWswApLeiuCVTraqhb4dwDapdPUGngynoh4wpw8RzRN8pSsWFUkv2KJGRheqXO9beDXKRX5HAiexMlUNLxoq+LnJMumk1/tMNS9EozSEYgm/trWJGsMp7fMXumBjkJjvT8z6qtovFfffy1VeNnEKUyCEhXyqJkFPCRAFShOCCrU5sNSOZvuAXAHSQwKhRKHzYNrCn4Kg3uCz0NazoF3Y7kxTB6wBY8LdegwImmuF/+9NNd4BTg0SBN0adCLBmr2evCAPMCCww4M4LPr0LAWuIleva6krV0kA7rJMBUAmHnOHtMFsUeezJ5IerkeMs9JqVO/BjJdLOEwwq3FFdAvEIy6wknPOtAMT3xOb5juCbeehtMVnufemDEWp5E+92KSDwtANes7DrlV8C0JZ/vgCw5aBNwMH/i4H181femCiNxnjIGsFuhoWfLorzS42L692mAqo/VWQb/w0/COno+/iY3Lqp4xURAn14nHCPjif8qXLqRXeR9Rsv/JJIzfMNAD2VYwBdZVhkKE3meaznVbF9cF90j79AzpqPnbKz6aoTbPFIbN3yEgyLZOpODbSA8j3J6FoNbRtaG6NalX+IE5l7pO07iFwYUZpjLgWvYY/ETbfl78uR+TYFQGdTzvYpJAlPEoE10PaXl2fPLXo6/3Um4Mk29NYAeG0e9u99O0XCLraVqr16FtC6hxp+btyNx3P9QNWegclusjrQFxLYEjTPkFDoIwtYdSRzgtdQsGv/sAhxFXwLFStvhCcSOOlnB1LELprPAFLv6V0lQdaiAtxX5/2OItZQlsEw4U/BiXyoCbeZALMDE+XsGEReQn6d7zxXoxpiWNW/4cse+WiNMHVX19dyaqKK4O8ZD+Fsekko4QQBugHOA9c3XpR+xjINGhrVrAqfgcFzKcgalMA0dhYbOVinPZaeSglvlfOIO3KylgxLo54htfWqWdYJJ1/A2M16R5ZogDnqnNmedVOoZJOeMrbdYo/FPug41ZH0gzXI6jlHEzfDN95eaepYF+dX7zG62/vM+Jpo3cU7+ga6LBn8t+uTFMfj12jCerSSys9q0JYg+7oETBM0MdmTQsExuZNU30rWjIzbexVXxLbkXTlqY5FaCXVLODib8wT8Dx87ulOVzH+6yLxFdlK9zJgDvk8xG0V79rV1rIm0zLKjh21pAZbqPBARF5T2Y7lKrUFEFF+gkczUztA5zCZrimWx0LIpNfRcM2I5Md4bgeEqcAop5Bz60Y7Ygw13cfQ/G2sPrWx0VzyyAsErvRQd9gJn2naNziFJsQi5lgGs/5N60+mJgxUNxTGBf6SKKyMzLDbU7OkaZoKybSljGkUCbYzIeJ9rG9HXpPtLGLo5bKzbacrcBNz+yBzcx/006Bm+uimh+j5Dug+Zy3Kq3DNpMyHjoN5p5ZvwqJk9ChWp7dTC6ScD2YvQXePRitD/r8w1RuDJPPaFIUYd65c9XgabqPf5EBeAu31Iltl2NdM9M7mu9zXwrDrpp500jjqoI4IS60/+KfD+YsuSc+t+P5bxQzPOEmZk84LXUDc9f8Lci2jK4cM9E05Q97bVnKmpJrwbhYDGyEgoA1NJoIicMXnqosBw6FMXhGw/eUJ9C3cFHn/ZWda+HpeRQsyIu8rjKP7gfkmFDArW5iaXfF5ltbLZWxZbbjuQR2bK1o9/fSLBN+Xx21lLVYcpw0acmhDc6Yed2jnzTNS+l8jmGasIlPQgjhYwnn+H1QzRpRXyC/J8L7D+6M4AvtuaXMYilSNBit+YX2WfagX7mm+J01/+zlyKDjujDZiAUp+HaZnGa7nJDVnbFjQs9JLFsBZXaJlI6K10uJQRi/IofaEzL8nmj5Qqt1Q0cc9sqqFWFup6I+JTk9XKU/C2ltz9H117BYxjvkh9ZsUocZE/n9x29BRtVP11qYPwWBMGO7uTwi7920rA0Rja5krlwnPPIe3m7NNhcR7PV6D242j/MZIALQMOGD01W15YBrEw7Sxz3YZQ0b8MUCqvt1zkSjrbXAzZo/t1QBI7OqDJpk1L4POMpzjG9jW9MaG8x/Hccs/iU+Txm8Qt1KLS5caJg8VlUsktuohAIeNKL3AaB3RVtaCiVVuoi1X8q41wXergcNnXUUDugKqu+E2jhTK6szYNoNeApbETSCPsJVph7+6rldtqWxMwRASbms6MWnrPVF0tJRmemMr2qze+b12XdfmTOQ3zAZeBJRXjwKjnIdbdX+z4y3jtMOTXv0houjYilJlIL2qf9DDEBZ02ZhIhY6dfmjuJMGMav2/9Fhejet3Bgm33mYuxROKALmWksGb3/Vn87rYNySCWhWZ/4qXJ8l6lXSRN8BT2iTGe/I/CMrHa9LXd1utpZr4t2D68CwjUwBj13SiGOKtkTTH6yamfsUQLO67p987PnlOBFOSh2A0PSjX4z+LXVA5rOPLHyC3uhXlLAkxHJTtG7zCztTUZifnAwpmL/Q7R1rm1TaOR2ATJ9bhCJxHhN+eTL4ErQIiGXz63yXYmZ4lHW6tOeP5DvzfvOeQ8b8hCC1Mmcm4vMstPsqqVRGyLZirHwMw3KSzGDUSqeBcqSkIX5t2iocqLy7vDf8ikEZzcCafCu+zw0VgY/veVMqj5JC34aGynDjgVLNCZBMcdNc5X8yjcUp7Xdm6vegJVcwo2in3xPgwKpPtDHiBDZWuqvcEg9hOQfejeXGMHmltrOTGtQ09oYhVz3PhBfxOrtmcpvw0y+wk96aeBQ8wuy++ROEdkxmTjN9r2Z6MpUS+Ea/ezCHlvvhKXB0LqwUJERdS/dPl227FmQSfGHIfIY4FMFobfyyLveCGwPPw2cIO9bdDa57VHnAVbs024KU9MQAD6JRIA6soR/TkqsZrHYJEz4OgBwR6XN1zUVQVwymfj264r8AWOF5/w0/T8OTBdBL31a32DPKpg+WNU8BtAsG5SEUOwpcuAD0ji/bviqToUpXC9oEIB4coZy7NH/bBA9NLzQz2LWQSfnkqRp7xVeFiZHlFe5CfKFVtqFu8CcUk/FMU5r1MTEfyRfgAk5QQUG9tGXC32ieDnpNGhrxz1r0XuELotSs5fdAL2HKdS2nmOOe6X6wzmAcnzou3q7MQsmepWCvn1OdxmSt7L3gdmGrBis/7KWjnZPhlHJjmHxbmi38k5kzst/BtAZ4GttYSKnNQ+KwmQhgWxxG/LoIAiFIxv2si7WCGeBQ8CHrWDrrFKOJn3BiDCi34TljZkY51TRgKMBz3iOhSzXRL/5s1yHi3LRlh1GFXjhD7sb8Y33pia+7aVgaMk8BaVAZzP/aEh9QaFXLKAfA0sw6HOjTJZxngzM4hBBj2+Ich3qdZ9er+/uhGXBHHOLnATBjrCywdhnspzDmfxAXLNQYvTNgwK6lA3pUz5II4OjCAeBb6DBkvDM25UyM5mR/jyHRqXpWP78uptzMmkfexKx+inwJcCaZdVq+i5j/jg+SAshg+q0JfTTbDZqcKNUraKqMaDZ/V8ZVxgKnMgOqJkkuIMY3GjTVtmS/rUpTxTG4OxQlMm7aQkjGfaqPV9DBsd8IAt4npGUs3gO39O3hnkuRJLMrgz2owMLng04VD29ZVbAOkcMfHo5HpdwYJh9F8nuqTBwCoFGStYU2mAaQUe+Og4ICA4kQ2nPCxbVIuUYI5Vqviz++mgRNU/HrVp/3BYYf9KT5k6mrm2TT35+LZSTOaN5SZfittAu1RC+xWDp+aLgFYmFjdjmU+0AyZ8AXypF2jrSZ2jn+ZQ2ttKHkBOf7KE3RX26MSjPbX9SBr9i3uhX85XpQcoqJnmu3+DsRIJid0eUCS4y/PyTcz44kWLhHGYk8eklTemEkGOGoednPZBCxYMeFbttCOKpQzeLDBCzNql/720nBYgcfQsPlGNaofg2SqE1KPR2t9HEQBOKFIIUOtqX1GS3vX1AqUUdtbmvXVz7jGNGNjG+AGzqf1+zjrG3fjTFPLHhGMWjZG5qmvtzVRTDdv8rqcS4PfbkxTL6vHdoWtKK10yQNwBh8W3yhlmBWdshM8bM3xLY5LnB9gWnHTWIbnQXdkTGbRlaz4dXnq+ZP074IBsEgou9Fg7nkljzHxQV7yZgye76nsDAxAyXncqatEKcXcYiMuQZ8Cxh95t05cEMc6mLHuFlGGbMWALJKCh081Ybact1vtkquf6EhOqU9+43uTIEnwbGjTeNgGGvXF9SDZwGLXL+wKHpVyCKWmCTSD9oWuuDLQJjUmTefClxzM7/QuuF9FFUbhuaskJn2DohT7NpCOUcgomiLuLVAg3GQkfYmefSnM8JgG/EazaQ/HBFKOOWdso+eReDuGrvNV1rbEnBsdHiuBtiRFOH8cZqKRJCdCgSlA+0EvupKQI7pgILQ8WyIX0NHgz7BiaNUC9KNSb8yao6Hw9dsfQNJVdhhp9jHuzDR2bJAGgYXxySE6YmxKW1xTOv9KjclpCDMV0QrgFwTZr8+C9H162ZuXN9yY5g84BNcjXNxukbQWWwGF9QjZ8WZbp4dT4YGVP97RNc3jYh6MvBqLRhywjPSXhJXwBWtXEWTMRLWiLc99tUKEHU6rANouUCQAYMmumKWF+lp+ZU60QWQ3D0qi+Zad6hw4/hi0UxEtni9L5BC/B3Qg6ZQgaSHq5A4HBZnV2odDk8sTe8uINgYWcPS3U3COiDM9abN+1isksKW45MG6BE5Too8MU8dbqEQA9NlKQgcEUKTHdxjzKqvHv6xGAEa76cN/VWNHuUL5hztOeYc7SGOoS7cAw+ctj9uFvjSFiGHypJQx+vMIkTfPJmHDNllUzoi7RQ7irYe+HxUZOxH/kxmPAghVYoJKXeLb6CpW7Bp+vALc5236A2Mt07Ucsl3FFp9FRD2nikCVNWw6wl5gyvB/8NvFEVRgWIrkEky9tZc0E76qmxEP7/NWdZk36/NFjpt6A8YeNevSV/vpdwYJr8sDWv3BYlfiAKxJ14QDJzfujrDD3M7GbwATJ5D5TEC62gJCAbtCNv4e9j+Vhg+f6d/XvPEt/IMyPyLjz6ZtQ7mdkC311wABcZsg3sXZikJJxWHcush0vwcq0VJ87m3Jtbrsm6dgguXQrwYb1QxboFLlbUg12IFoGAA0+79GVFfJDuAxbk34bi9jO+GCXYUG9N+MCPyBTXLTXNmZu7gYiHpgHIhXwFZ2oBAnV4Z+pQDOmyZi0GcrgWbsR4DyMp9BiYG0m1cOxl1n9tmpj8Fak3h+cm4ShtVOKla4sDgB2ECE8PlOG23ENYA2w2+gaZi1i/MtB7CFAQUoWPoZGXAdENxnAemWxk/Yjz3NfdSBobr/+pULTBLlk6H3BSrxLB3PuipQ2y4OV4xVR4hhncTy41h8uuxw86FLx/ScPAGEOyNzBT+SRb/8bCdDshDZASWyW7Ydjcx9SoQ0AqAEY71IhYwpsX3vWXyvj7wbHp2oan5wFG2oXGhoGAAuMzuHzTou064GCl3OUTat2D83hbHjtdUcz39K1sTEhgMuUVbJCoMvloW29gdkPgUcFN5YcTqAUn0n6vf7xi262UCFIRMos7EI83tAgvuUkmBpmGIsNeOODOF/IeH3sUCGe9JU4AT22q3cMkljlkbdTzBawYmMy28UvrI3xMocSSf01EI2OExm3ZnjbLADe6vQdssz1WC88GhxX0BZmKCgjK3yv2IPYiv+YQESRxDBwJHmPUjQ9CE46RJfaaxSCph2qjPTYy+4uZ1NDvDJTlaaM1gTA2mX39vyiRDBMVB//Vi8mdz/VhuDJMfzlyuH3d8gMYx1TV3cR+6Mec2MOdg0ov95T3Z3T4X92SG9+ZZV+4NZviA06Aj1sjC9IPJBjMh01Dk8WoC8evg+9LzuvXUBCThjNms6RdVCg+og+m3exEYetwf34e1q7GAZq5ZpXOYjNRjCoRMHJJMdljoepJCRq6w1LJuf5cufvALTChYR8GE4QWi5k+vC3ns4BJu0StrrhqDb5p5Dxrg298UTQS9A42mj67mn3ffCCPYSXNlHUETfB4X60BUrJoykx9jq0C6jeoi7n5kmTXT4GeGn30chDoUHK63031rVWO2Oh1oT3yyuXYG5OZ8+x6mzHilD0OJe2UM+7g1MF8W8puooyw2tnEMMGl3c35EnxMHaQ0hgW2Xfsz0BY3FtSHR+CBkJL0zs6/tIOoEmpaOWfjgb+HE1bH7Orc1j7HduC4++Y4Hj46fs2xf53JjmPzhYsFaJ271wzVySU2tDkD44HmvIbSESHkbDBYOJ8HYhfcrMy9m+TADA7nVDv7vknSkvz4D2ow+mLYcDDrrpGjsgEKW4IQe6e7fdAOaCweqiqVuu5P0u9saYXBdNUIUerfnq/JAq2z307b2lJRqou1li5Zpys5knJnQ9Nq7unblr64w8upSrWfDczFL/7YDinimPe8gt/gR32oLtQBhyZGOwaePZv518cQ52u39Fj7uZJgwIYsLMMhAQBUAlxjS646L7yhA0awQ6zgX5KZDXexmmHhZGgtk5AVFgDEcuhnDzTVpKp6G7ZG1lfbyvmSP1zq9Sz6oxVqTjBYjU594GsioveGwDAw0cVLu4KjCxLKMFhbVgr8sKHsMeSiOtEqF7O9Ae2HIVTI+Zc4nGVGlOW3oepBxLLJ/k6SleZljnP28Lj75cxnLjWHyOqwywQnjQ6C2oMhI+tCWC2xumTOGzuxyUefoFQgTPQWHwQdf8K/ePs3rCafxLzPKDdcLGaAiIt1F4Ieap8RPGlWh0tForVDX4EON74NZ37osppmH2q5oohl/2Dqaj41U9QwauYXgGr04ExLRZMoB53kKJJlTk5LpjV0ocQHc/mYaWL6sZNfOaRv9InUfN1czRLt52Ir3fYGr0UwEJPk++ZI5fCSrrvVsxuHrWQAWt1G1sTLXhlK0NOdENaCNbaC1uhzHwj4E5s8MGmWf/PDmBpLiZn0XSbrT5M9FExu4gjN5TvDNwtYHfAGuZS7wYv6cdcRRmZ86oDHAkTmPgsM4RlVbjsdmgezD4n0boj9MPBXf3REElCqG6VAfHdBMcNegPDvJcB7s+Yep3Bgm393P1tyfLsDokxcBWssAuqVFIB0j5zvM566HNMky85wCm8x2zEcfyWzI4GNrHVJLb8bM1dOz8pqakdJ8z6j5xvBvhBnfguN6rkCS2rjxaPrdTQW2uK8eDFrI3ChIuOZkcHao+uJ1a1e0RjO0tdmceXcVNIez47b9Wo07pUWA7bY0fx8YAOzL0SJYeULbIuirjYfBCbRLLEO9G0OUpbkZ3sZMuZ1OLVmN5QdeAXU4qtye8Q6unVoCG3t3ckzBLeqa7764RFpo/US6sMYc7Zl2QCqOzRi8wt6dXAD0NEg1V9sgBT5Bs9/uCtEIpCrCChDvTuMaacEI4YTCTwm36jpgSp5Xff0p3AQOPkfaA25kpkNQWJjhUSQVDDRZE4W5kygKpgNjYkIcj/Svef4312SwhSbUQimKwvMWR8gZ8fAVrLBKfdlhv25lnEpbpO8Uvsm8Ti07xhea0lPBQ7flKOzoVshRFHxABCtfg/LspLU9M/lrV5o0W7+7+hGfyI+FZk5wMjPzWivR654Sl2tLDYDbY+LUIIKBj4x/E1FPHMHgER8jFo32ROx3fHxL8R4tPQLBIBpmeMDN8M6EF5rXo46+ccXBBYaugsV99VwLDkt35iE4+Ba6rvXaYG/5ma9dBRes8zyyEoIAcBHXq2URdqFgEcRZ7ceuOHi2umMXLC3hRMROA1Qz5S+LjVdfuQ1Q3I/enYkhtuGZud7D8xdxuUcjGE9V4pjayFfPuhXQC3/GD7IR5NY4ACEs6AHmV2YWPhjD1cVZ6dGfW5DuiqVkjQOCoZGRkqdEUKDyndukywx1zuy5SBNHTDqHQwlYdLPT6CJmACKfcRxkBEvJ0MeDdwZ/AlAz1Kla/4d0uUW9H83rSevARktWPyiZssFZ5sKCoxzCw2+YZnet/Z/N+uTkasI96+za25LiFSvbGjem+8qwubbUPg7vccJRC9erXnexlHgBIN7LHo4qvGdTst8WcqjK5DmXa1ZuDJOXOqvDjiwY8tVX5YCMV0oSHF5LgZFYF1PbKHWxns71WuuSiYdJN2C1CBQaPnipcI6Qa0WY7lu57wzftHmNxZG7BGnPZXa4BT387iqKBo3QBXUzv7WnoZk3b7s5IU1LdrU2rhC0CMRuaTHml3jNSXDguiUmnFDDEpF0u5ZUulCx/jFIr1gO0o8qwUzoxrCxlxzPTpO3j3MjI0AIPiEo+UXwNI4Txuusl/KuuWUp5w2nB1ByhgfTqQpXYZCFhwwMZcO4Chdx03/iqG3V91Wz1XG7XJUCchxqkBt5Q7RVyQoyZGoqGXrSzj5pwvL3Dn3iBGnBN/YjaR9SQVRy683ar6mtweowvIbpQx+sGtiW0scrAQfBr1y7lM05MeQCmJ/z3xlINzHwYUxqFsPrUc7nyY/lxjD51TPeiataqkh/tv9FFD0Qi6qZOf0Z+KYzZ9rqC3UXiQU798tjMMtDSoKbMOtraPCVuffmUnrrHuDnH9miiNO9BG6aF8AZdwoFGuZySPdrY/Ct+IUPzZ9TQZMOi2JvaNJTuYVgQbe2tIV2v6p9/MtiUeIKM+UrFKqGownc1K7p1tAWcF0b6CI4+gk9F75wr9rsWsVNZ4oLGBz1oK5iOLhdsNu7aRCsfmQfrRSqLSKn++oSwQHA0S7jPVFgiPdvgoDCg+VqQBfE8ttfasI5aoHhYZ77tuQ1JDVAoXWn7lOPNXXcTlbkwvhtjZGR+O+G3Ao4a4ZNTilt+8xnLoM6nb95OzZejo72gTybwBPT22G2okzMklN74r5+i1veTtE7wW1HEMGIac6uTHAIetOkd6c9rUxRgPANblwXpUNDsESO5MlCOuvv2o9h259Oj+rQSqU8rgTFKqHR5+sSXX8214/lxjB5mqjEONKw7gEIrYGZ7iANw1nyvM+gm4iY52otaYqfzPcROV+i6wfTfbPALj4jAujSg7mguYZdmDm1Qlk8eA7qDB3G5BsgnqFORI05u2bfRCOTqDH0HJBFLl1JEBy8jgtXk9UX64YLPXqCNkFzuZfSb5PVx1Zw4QJKB/3vKzKVqokRq7flvfDS0VUMh58J1bsJI1bb0H1zu5n8FxtHh7NXan56c6cSrkE8vgBdYKYCSZM6J8YqFhOhyIW6bE2Tg0tB3YS35utuHAoEZ+woS205xrcV4Y6Jc2IJLevrxPMn67bUHwE4Lu3kRjYZBUNlZv3D+F3YHUmEBc4C/0qbVcbQZL1QFI0SnpNAna+NaXbDJAwAvQRHKpuZtvwJbCskv83SFuMLqBgraeLzO1qtkSvFeiPD2oDus3syR0RKZ+KPI63r+APDATLRb8mxpTbN1InxFiWFjEHY2CnaI3jUpST+EzRI79wSM+4DbQ2eLQzMRDg4SPT6ZLx7dvbJPzpM/tHpyV3K4WCrb2yH4UfTmmUeY3FmzrU95rggGHsXpKmXuMLcjvS5Fwa/Md27dhs4Zv88ffDO4ONvUbTFTe7N/O68XrxuaRYEtzT7a61jWfjbNPj8bdeHpji0Xu51XPjvQ+v253CLrOW627OL4qLZM4ZLsfhzF0vHoa04SLZzaKu3Z9cXC+HGvyXgrB+HhbR3/3N65Ri/CddaHQMTfJp0LIcctxZWD9tmKIuPt9iWN4uH0AyMbGqLpycconC2SVG8II4T3uRBiKOKYWcaIN896nUImeWPxxpX0zp5X72OOSRuobJne7U2VTj4rpI4p8GF3EjnXPDxPv+WEU5bgSHdtHQId1GwHa/jdwTvI+uibaedfxUOMoxRCFSS40SatNU+0hUn6d9vO+MOjPRWwb9NzxT8A73xruZ3OreHccyAXVp2/+Z1DFvXAq93RYV4B5O0dy73VL7/+78fL3zhC/HYY4/hiSeewE/91E+dhP2n//Sf4ou+6IvwcR/3cfi4j/s4PPnkkxt4VcXrX/96fPInfzKe85zn4Mknn8TP//zP3zddN4bJr6sbn0uSCq3XQDJ0ANVaw5z0EOSC7HKuMXwdF/iygFKortvmtGnR9H17FkbmwTo4U7G/ntetW9CdALY9rRtjEtO4l7b6VjdjlrZ+GDM0U3rHrXaJg8MtsmIRuxZ0HJxpNum4kOMAd6sdsQSOoz/XcdGOuPC6RY641Y44OI7b7YiDwx7aERdtxeIWhgtZA99B1mjrICsuSj/s2ulo60CTbeVTiPSgu0HR2hr7+EV6uTZYcIwXjq+NZ7pG6jvwCTG842S2wzuucJJzJmcOJ5cO+JS7JwSDoFnnkO61JVscdCdtBIGA12D2IVgWXPEtkI46j2tdnePZs13a2W5lIoO2P5uXW7krp/8GHDODOsmwdPjvBsfec6oThRXoHjTdMg+uJG5GdTctejbZO5ItGh3+3W/rKkIe/mJuvAf/u5/ywz/8w3jta1+LN7zhDfjpn/5pfPZnfzZe9rKX4Vd/9Vd34d/5znfiFa94Bf79v//3eNe73oUXvOAF+NIv/VK8973vDZjv/u7vxj/6R/8Ib37zm/GTP/mT+L2/9/fiZS97GT70oQ/dF22i18AG8/TTT+NjPuZj8Bu/8Rt47nOf+2Hh+NLDlwPLArl9C/LYY8DFBXBxAC4uoI/dskNlLg5Yby+2fe4gWG836CLQRbAeBP2WhJl9vXAhYQH6AvSDRFR9PyBM8+GXb2bW1UUj0U1ktWvO3BfukXezfGjzZrqnCT7M93BNFOrehdUFejJ70+ihrs0Ktdk1FQYPsGsuaFiXzOwnvluUx4AK8bkpsfEmVjTXxsIt4v9RNTgm4aAJuBfOoWEaNdyqafqvH5yihauwQ9B7i3gAhWBdG7wXWP1UO7svcWBF1+b+epi/33332mG++s6AMbGtdgpLYLOKmWZVLNe81zUV4NLgpQNydFnBr9taft+xE+pkBeQSaKta2MUKtDvWLxHk+fUcEl9oBRitvQNHzUuryxs8r2F4pliA99oCNFLtiWBMATbRdIqONN2fqKtR7Jo0jbQXNni3dhkYCeRWyAkflC4Yr+ulYWZCnMdCJzjSjoJbC6xfqMoIp1p+b+mKf7lNJe5z0uv2mQk/f2uF7WvS1bsHpOrmeT5jpzY6HN0IUPx/nvl/43Dx4Xl4n401/F7b+K7/35/EYx/1YJ7oD/3WEd/0ef/fe6b3iSeewOd93ufh+77v+wAAvXe84AUvwF//638d3/RN33TX59d1xcd93Mfh+77v+/DKV74SqornP//5+Bt/42/gG77hGwAAv/Ebv4HHH38cb33rW/EX/sJfuOe+3Bif/HKxWKrDaVGjdhFbS6rWE4AGHHvWvSJchFKvESfXCcp+ev4V071wq52Im4Q9UYzDhdbjpnp7xs3Jrvnb9rXCyB2uuWncipnQmzvnqNkDxtAXZryD4sLh1IPxTFE1jWARWwRt15i6i9p98kW7I5xvJU9mEuNsdYs/dHQfLPfS2w4w86UfVXDhrGLt6sfrWl035zC6ApcqaIvFD6y9m6kd4r7fBtUOVcExjoATrCsiw1/3CcEDZLRbAKG9Z04a8YVWgjn1o9p2La6FxbeuRTNWBSSOlIXFhni/dOWWMh8naq9keqBQkhxvjgUjSVlXXojTP7t0a7zXXluylDr2JZ4pNIHMGYWIEW6QJmqdlnuFJn6fgwu6EDzDlXB91F0Q2WjpZKXRXRAD7ZVOllbwELgOaExydkSG8YwrwtVn5xcjMz7ZwpD5Vkmt4I+2S/AiceQ8qR3MDg9ka96/Bvrgs16efvrp4fft27dx+/bt4d6dO3fw7ne/G6973eviXmsNTz75JN71rnfdUzu//du/jcvLS3z8x388AOA973kP3v/+9+PJJ58MmI/5mI/BE088gXe96133xeRvjLl+CKKh744fE2EW0wjzFDmHAwYfX62v2+rC9El8QDB3BtDxWkULHOvsTxaSqJVcIM9TBaT4kqnRE9YZN3G0AtuaRc7bX4/nTCiwQLsGmI87nnX/PM3/0qOOcDS9Z13HhdBvzzq1XFSy4kK6B+zlM4KOBdaWwXVcSHdTPtuGmeHhwYT+t0CDJvHdAdFn/5dZ/JZGa8M4vpE4iAvrnEJYdPxD4s3ffN/TmoyCJ+bhtKAiBcD5L5ZfNlDnrYw4qvZsdW18ttZVuopcMDQ+zfGRJvg3Qv964p6/B7bvo1/qtsxDHUcfcEyMENu6Gpg4tM175T7bCLhS3wfcOo5PjIcMcCdZoJv4B7j6KnYnSu2obC5PTrCJEZ+iacuwtVZuaL9OhUfNPugfALzgBS/Ax3zMx8TfG9/4xk17H/jAB7CuKx5//PHh/uOPP473v//990Tz3/ybfxPPf/7zg6nzuQfByXJjNHnt3cKd+RuwxclVq8HnCPVgPP84W8v7Ih4N74tUDWICwhTPoLvw1zfLmJfauSBOhIvALoQZnr5gC7iz3xY8tkIg7oO2VLHmEwdaMyP30nLbXMNqAWYiOMgRTSSY3iLdllwRLFjBzHQNjLi3Xi3iy15soTPtOZPlVBN9h2oLk39Xj8xvpgR3WDpdbbY1rjlD7WqmdoEHrXNfGQSX2nBQBZaGo7ZY1Lsqjt1U4C4dR12wqOKwNBx5AIw0CDrWvhiDF8GxLxA9QqRhXZl5D36uuL9HWYHVaNClLqa+X777/OF2NbV31SmvNXcBUIBwd01b7ZpCXhdFW8S0+Tpf4XsaqEhRcNCcq1JN6KV0qfvfK7ZBMRu+hfG2Djeqkisi6NA4VCZyobs5ISPc49bY/qwyCzLS3vHJOBCBbOpS8jgBujIhjOb5AILBWhF4B94YO8ujv1HtCGw8ib8M1CShjXqxFtpHZqwFmFY4q9gQMDamBcOswe8y9xROdH6k/pi5uIitl/A1SWnFvEoYeXjKCsGKB6OVz//yL//yYK6ftfhno3zXd30XfuiHfgjvfOc78dhjjz3r+G8Mk5dWP2ZNC500X4gkFw5q7+aEzg+1XjsTp/QfUdSKjKhveZ9pTsFMdh6VHdoio7nFPnxZqBWa9t0cjlHzDLK7WCx4TGCaOLPcHTzqXZxBX4S5vnswnI1Lc1M+9aoLf8aY+FrOClEc/DQ466K11VVc6zao7m2TD9Iauno3mbVzdXeAqn9QuoJb3uy9dIODb6FT4KiKxdPwrl2wquBiWaEKXPaGg6+rx27PA4JVO5osOCyrmf97M5cIGo4ejHmAYFWgr+ZKWMX33MMEFjusBqnY8v27iVgXn1+rM2EuMEtZ4xUhyPUjjLEjx0wOzugitazs+KPLetxh2eWIuyzUdB0YbyS2CpNqcOGhw3VFyD5JoMvn8zkyoe02vRGuhXYYzxSmxr3xUp7D0Ee2rUMfY3udjzNflvDgIfI0cn3+U8e4ZMmrfnshAtYFkyWTljqqu2xfSkcFSLdO9GnmtKwsY12ln8qs6791PIJZZ2ZAcdjhVE5/OVHngn9mxvOBuoHm+uc+97l39cl/wid8ApZlwVNPPTXcf+qpp/C85z3vyme/53u+B9/1Xd+Ff/fv/h0+67M+K+7zuaeeegqf/MmfPOD8nM/5nPvqw40x12cih0kibflb27SOlHWjmjbnCOPBCtpQv/J4lrnDh8/EtXzmlKfJOEgc/sTN7cngmYGO5vbmvnBe08S9lN80rc/XhGVU+oHm9TC9r46r4wA3ocMi4AMHOi6wuiWgwvm1Hx2xwKLom0fCH2TFQdJEv3i0Pc38C8q1w6X5n+6FdDW0oJ27m/JQHWszTfgLx9LniAhd9opwnwjC4g1xU67wL+HmvyGyfZ4jZf6NrtHNi49pO07oaV2f/qQAemgmdJio22eT15Zn/E924OmuCi2P5niZ2ppcZCUM0PFVczbrHBfFz8BZBqA8kyaE+qw9d2os5n4h6AhpbhivSZbaG8or4E6Pe2rcOy+5vv/Rt1TGfO9Z3pZsuggrInPfT5V7hXt4yrNprr+XcuvWLbz4xS/GO97xjqShd7zjHe/AS1/60pPPffd3fze+4zu+A29/+9vxkpe8ZKh70YtehOc973kDzqeffho/+ZM/eSXOvXJjNPn12C3KyiXSFKAtmnRg4tCyoGiY1rloVOGajDuYOYXeEmCnALjP2rgwijBALZEfulo0vaDshe/B4GmGT396YdiukccWMsK5WT18397WIUz0HoDnpvgGY75WzMxvRtolNXiYGTyj7c1tAOnouvj2NcXRs9o1UbhzwDR4ACsOEA/au+x+KKt0mHfeMuMdVHDEArMkCC77gg7BAsUqgtUj1loXXALeF8GlZ7VrTdFUcNTFXBUALrsAWHGxCI7qwYPNNJi122lubWlQvszmeUPISxYFVlfFmCCHyp2/Q5Sz0FUkAiIjZk9yzrUGx6GbxdrMpfEqCveobDk1S8LNShcVwb2dQWSXWie2bFrYaXeuyypx2jHRzumSdfeGr9JU8dll0f53SvZ5akvL+O4w9DK1B1qjS0P0H6slJkO1NGz7MsElMOIFDu97HhSvqHb4SZufpQ0VsWQ4p8ap0Jo++0RybTLeAc+Cuf7+ymtf+1q86lWvwkte8hJ8/ud/Pt70pjfhgx/8IF796lcDAF75ylfiUz7lU8Kn//f+3t/D61//evzgD/4gXvjCF4af/aM+6qPwUR/1URARfN3XfR3+zt/5O/j9v//340UvehG+5Vu+Bc9//vPxZ//sn70v2m4Mky9LGCDpn7TvzTSMjJ4vknwwdSlwSCbdpuv6ryAPqIkkKIWpx355an/IpDeHEljXMrELU85yO90FhQAg9o0313ovWg8mfsE98KKel54+e9N6afK3oDfTrZi/Hg63iKWEUwia3rEofGkeQEfBSHHwT6Sj4ZZeQgB0abkuiTHWx/AMFOIZ7/gMI+LXyEF9oS4UeEwAIDhq+vG7ClZpOLgmduwNoj2OnGzdBJqugqM0qGfdW7UBS7mGpePtaifrdfc1WB785hq4Iv0yzh6bmXe5pdIOPIHFZGiauVVMJqhzjXEZijLXIrscGbNrXwCUsZfu80/mWQVT5JY3jxkYGD/Ny95WpGRWZ5Wc8pqCbGShc2GCGqhIpSlpJxOXiaY4eU5dR6QVTeOmZ8bLvulEU0wknk7IfiD7pdwO1wCtcLU0gEcJW//5qWv0uTJ20XxGaqQ+B7aJZ9qbmG28V412Y8zCb1gHu3DmIMEfqv2oAkBl9Buhgn3EVD8JNQ7D3Rnj8dzXJ+Pd70b58i//cvzar/0aXv/61+P9738/PudzPgdvf/vbI3Dul37pl9BKsqIf+IEfwJ07d/Dn//yfH/C84Q1vwLd+67cCAL7xG78RH/zgB/HVX/3V+PVf/3V84Rd+Id7+9rfft9/+xjD5w2GBnT7Kj84W7ypx97IdKL43Li4tP1aHGs2usvPH++EjVAwLDVctX+irRp9WxzQ1M4DNPAypwdu321ObF8WF5N54cUZOM/8BzGGvEZVO3/3Bmb3CcHAdMCZv9CoQlgLV1Z836V+RNHUecgOgY43YB/WFuzk+Do/CAvUI17VhjeXH9u3T1NoUWNXy33cRPzVwcYuKpe3sUHT1a399vXccmgkgtmfYNfbOHQ/W4e6CFAD0bpkE1feNq0X0WSXn0wILPOCE8Vz1sR3Or2M9p5wAGbbQUYbYK7HmJu+JhZv+UxDHktda5lP1QefWuMJoal1lJnO74LvUfZo2xCetm+10gbMQuGmr0jR3xL/FUG7H9yOF3g1RpKkqAQN+YU9HhozEXwdqP989NvSKJD6Z+Lo1ODHjWJCQwKT9bkfTep1UAWmujtuCPXKuU7lfc/spHPdbXvOa1+A1r3nNbt073/nO4ff/+B//4674RATf/u3fjm//9m+/b1pquTFMvq8eXV+kqdhC5/65/KjTdwgR38aTW37oi1VYHffCq6AcUKNxCI3h08yGJ76oOxMf/+UJb7YVLPzkgvCvi6TfWcLvzqxxcH+3+6exB2cL2iJrZuR0Ez0Z+QJq/mIR+qJokBAAOIrNI/shAga7weGWopIuUDehtYBjQI9ZvM1XukA9JfwBDUcwJY/6wtjFtXN/I5FuXkzr7xrp6EHxYnGu11WxuG9X1RwOTUx0oCBl0f+2G6GHNtNAHz0ZJjXP0MoLg1PBEPkellc+ywqxyozvSCHBqzbXQ2EdAEhLZdCxsU0GedWFW0c0TkMrWjo8CM6hw/fLuiLsVrpEC021ocr0yJSTihBw/L1VnhZ0nOI+hTQbpwJ4QqmNu8WyMPLQimNqdIfX7uHb8O24GmvGo3Udcta2ByFhh4hApuMzQ5sn6uo7h8aOI1buTb2HuZwPqBnLjWHyOn00ZOAaC4gCsqQv3U+o62URG2J7uOXNfzMZWjBxh4EgU9UCU1S92+wYBS8mh0hsgbNc80uY65PRW0IbY+DM2X4o2nymibUPlnvYWwMWZLCcqPnYabJn0J0I0DTN/3Bd2hLfiGtIPQQEjgtNfI1ajYU0Q6VFHaP3jfsYE+26ugDl2jxMKOodWLEYg0XDqgrFESqCY29YPVZiRcPRTdZ2up3FGXQ0HFeye8HSzOy5CNBlgeCIVRu6+Na8WEAbunb0bifZrWi+Tc63zXmUvyxiGrxrxEqNssGSHfkzAgArcpuk+PIpDrfCxwbhTq3XG3401/VIUOd1MizOPZbxopG2giMQxwORdVB8S0RicLO+el8KvdC6S9/7qJi2J/IZifNYYixq0EDwOhkwZmMz7TMjRMZMADs46niSvjJO4DhVposJbqpjqf0dSCSjHZlsPj8x+E27/qNKdLtMvcCGqFZPyOME0gFKh7qRrOvikzfb5YPRqg/4/MNUbgyTb4fFGEvnAapwv13RLgaTp61g1VImgpLT3hdkKXVcZN1G3WGLv+2LlrimdIxFwIA+EfrZncVUs3zTSG4T17D96xdLFQg8Wh6ZwEZ8yt9ejClDNRLbwH3zdlKcpfW4tdAnT4sA9SoXBJDZ7xhMF1vo1ISYxbkRT2YVACvcXeCjt4qnzwWcOdu7WNU0aPHtdZdN3NcuWLsJOYDvv28mL3UVoCkWNcZ02dWVUsFRTahqquhdcKnWX0XDsa/QZlaBY+/WF1X0BlyuHUuzgLl1hW9NVPRe3mcHcFTbysaFn8yeq6abjXnwV8yeFWBQiHZLaxy+/Kq8lbWGPnj6wWk5gCIyLPK5LQ4ZtUHwfWHSorMuXA3eD4kfI36AW66cPj4PJDN2YW+wemh8ZoFQu0YfQXI3DM4a3tI+MzvvcyvXMw6g+J+BwVhdBIbc1ldfysCdN3UyICGJku0mctCi6EcmbphwHctRSJH8vYEjTd6zAVeF1fJY8cVXgYaBoedy7cqNYfLDohOmRylb6MROj1KJz7NK+jXhTS/XUScpCfMeKlxoGQbRUm4e2rKVzet46IxXM0mNacmZCY6meGPuGnACbh8jntwmZzJHbq8D/GCb6H3iAPGGRq8lttD20tv6QMHBnqq7CU22zkXKZB0b60VWUAM096qC7pEDGhTGgLsolkiGY32DdqiI3bdwdncxmJnf4hxz4WwuUHTNTH88O5yWCHtVYrsI4ijRfD/UaFLAm5hHea9ab1OCK3NGuKAjccrESHPu6ghXqgflcL52IK2SKOliNHVFpkifMfFn7/NXZcJB+9TngTnLWMfKKpVMGnqMh2zhTtLu7WnAVdoTuw19zlAKsClJ1aGa3mkOYN6UykjLAKhWVNFaYI6+Te+xjMcGAUq7WuEmQAFS4ix1u/x6T8DRAf11KGdz/VhuDJO3YBxTlbUsotozwQsXoMhIh9HnGoFZDkPtvJpNGYGtxNHG+7wOn7wfPsOsdmTYBuZM2c33S2S588Q3fiDNwZPWLK1HLnr63hdZ/TmLMKd5X6C4QIeIJaE54Jh7yZnxjjRADQ4NDUe3RNYkOLb4NvemKxhxT7OZRlyUaf422mYut4i0Fcbwb0FxhNiRNwIcVHCpBxzQcYGGo+v/KhZBf8SCBQptgjt6MPq14U5360ETNF1xqRfmXlganlnF+tMlBLED7J1eNtuidxBFX43ZHZaGowpUOlqzTHu6ek8WWEAeTfmhiZIZq0XnL8i6ssRz3oxbOH1E67zyMU5WlMmdqEUPvCGemHgjCpzfZ1tGr8/tYJZVoHVrQ0HSC5/D0E6eQTBWqQt0ZGqjz7fCDb11gqJeU7DmVjZ1gYtZFocx5oiVQekiKCJ9KYST6Xn/bzW5K5PHYKt072nh/qw6YnEhgHWeSDHluUFwK8LObMq/KviuTqTB357jYzuHEk59QtHEf50y3n04p8jt4XhUyo1h8gBA/zAlePtmqgYFRCSaayxcBsQX7jCLFoVm2EbnTJzpP2MBV/GzyZEMvx57Co+cd//8ssD3wUuJmgday+x1BxHcbn7ynCgupKevHrw2xn27HZ1ZK24544dq5IoXZ8G3Wnqz6Lu37qpvc3NzPdYNs1cf4oNcwoLiaoBcC1nHRAFBw6VntVMcfA1ZVdDUzPWrClaxjHd2WM2C5r7wY29oaLhoWbfgiK6COxHV3nHUhsvesMglVmm4o8AFjlBtuHRqGoBLVUAWFxwajgpcOCM4dkVbzE6/dmMqTf39domdF4q0ySvUM6hJ7o33LXldgWXNNdsH1OcoPMgQ00lqKPyFg12uJ44cfCjTHQRjjOWrtqVzW8mgJOqEn1BEs4ufvTzjsPY12on6oL08cwJOZ7iuw0E5SbuE/Bw4KIxznDg2ddshgGoGGPbaE0e48LZiCG/TVRNjMbyAiSEjrwc6a7O86BjqEHhb4q9wM/MfcEoGGlOIKfV1d0bmcmAGQsd9Ntdfy3JjmHxr4lvoMEbYLw3MzjVbaJgVCxhN9BZkJ8nA+XXk+jVqaIIIxssTzRBWWkX63+HMPs30HZkFr2aooyZPX3c9Tz6z2dlv19JBU34x7YvGWiGTiX4Bt+DltY+MP29L4+IJbbj+hVlf19jJ1NTTS/gibzKRmdZpylfHLZIm+RhaEQCrR+Fz87dRYvutV9/B1nBoHdoN6+LmebMUGK3qAtgiik4By5lSgwlU61qEL+8Tzeu2nUpz63K4f8Y/7j8PDbQw5Ii0F45oKWX+pEApExDAeI46ToNAsMGBZMRzW6UbxmBH7pIuicIXXWqTvoMDxUETvG7sgPKBCGbZwsUVXWsThg3b8XHabYuDwfeiQNgUBu1fxnYxwznGgZ+K/z9N/ilMVenJcNY6qR8PXRBEuXnnWyY9vPxTjDiayDlYNfPBX1+FhcEFcT3Kiob11D7U+8DxqJQbw+TX4wosh5jsSgnbz4nWUOOtjolFmLGMJZl6Md2zDsjfgtTYAaQ65Ay75VMiCp5CZwF33NOeKVhNcy/mekbLC1O6ss5S0PLQGqaPbTDt+6LR726aeXODupnyu7PMNbRzgZv80cHEN81N7Mb81+AiS+S2N5N8a901+AzS62i48EQ3K8wMLwCOsdFN4yO17XTimfEUBwB3zDjvTTasuriJXpymFUD3PCbclihQPYBqT/f3uTSNY8IXXzzv6C0PWrQEOIqOJi3eRxPGbpg2FYzeI/spkMFdORalTrmyHNTCtTYWXGyURM63LWvLIuWenKqbgE7CYYTbb7NUUgGXHRw7ZcYh99vWPBBxewuXfSkMXSYcWtBOboO5kV24zPgTbSWO0eURRRIOKHx2kNYmZh84K4PX8bkrBl5jHPxf6CgMbGIGJLfRKc1JwHWJrj+b68dyY5j8EGUstsRkMJ19/bEISF6HubUqHPNiITt/ZStV/HZzrWmhqQ2SsTfJ1LVmomc0vfvZ/a8e4RoH0UBxaKvni7dI+Fsly90tMntRXMhxCNKLbHgwOHHBo3H3uphedsAxxrDhTgS0NTd5M1iOzwCCC73jTC1X3rSQ3AFgO9kPegnADodZLDUaVBrMQWALzurhdKoNa2sWCQ9mtlvQcIlVBUdZADlC0XBsgtYtlmBFw1EWaF/t1DossUdZpAFdcKtd4qh2klDrHaKCY7PQCRPwbNuDavc99oCIh9xXl424r9zfvWLU3sOKUwS+ECCLiT0YcvKqoh2PgWw6Lf5V6Cwxe7HVDorUwglXvxn+x9uSmPxsRcMlEDi00Bc0Oe01U9xUBiajFW5kvQNNcDieelSF7nB1FAZf8CehIxwHIQSQ2qFoi89wK6HGc1s414arpu5aciTYmUWuOmgy9T6EQt9fOkhu+5zeWijt0Ok/SRo1H0Fa/RP3Obr+epYbw+QPFyXjHUvXOJ1OELvr7Hr64GMBnic/8n58Z1zgpOLggmwftkJLYD8T3CAEg5TwNeUDT+wSB9FAPbK9HkQD8BjZyG1f98XD/PVk/gzGk4DLLXQXQv+8RkCfkWinwdF8T3q4CFFg6LC1iM9wEdTQeGxMjh6Rr7BMdt0HYI3BpFBgFgSVjqZGc8eCVTuOogAWY9ba47Ch1gV3fJFtYvveD40JdlYsnlsfvaP77oqlK1Z0gxMLputlm1fvZnFhPnt7OWrb4koGOV3Th1yz30FhrqEl63Yz3p1Y+2O9Lkwqf9bFvOCoDK2UiDGZ2sLc1nwN2Dz2LWoDjp12NfrlzE92aKwlJJoTfdEJrlXNFMEgT/uRyyDG5S4h2dhM0+ZFeOMD3E4d0y8OL3XqlwsuW3EIiSNoKgLLjLM8AqWwvRUuaJLPaUJBqLR1TUoPtePBcDwq5cYw+c4Pa5DYi4ysCkbMq2tgZOyZ8S63eSnsr5drBYK5VO3I2spUssYJeZ2+dAiGRDjcJocwFWc++yZrOXK2u3m+u1n/mH530KzvDF5Kfnus6btHPk8zPBPa8IgYm/YmaNB/LWKeV/rWU/ax9LkhAaEjtl/54qdqmvDBF2VFc3yWVtLOu29QbZ7MhTv2eVwsFyrm4FNYohtmzbOI4QOAS2e8i3Ss0sDzwZsfiGM5/cXTWbqrQhpFM2TsgyICn/iKuYCCe7cpwtg9e7WSgqKO0zDmYMyViY/pWBf/ahFAK5LwI5e6om0mvrIdcEPExH9l/DdxMAsismO7cAUhB60K0lpwoOJoE033BsfMe7x3T2xqV2jag5MEQvXlT21N/vPKh6EjnNHqa0NpeKCjuAX2haMyuQIfMoCuwg/X1X0gcZZ8rTs5Fg9hWdXybTwojkelfFjiyvd///fjhS98IR577DE88cQT+Kmf+qkr4d/0pjfhD/yBP4DnPOc5eMELXoCv//qvx4c+9KEPi+APuwyMXcuC67nAGkC1K04DU0WvKKSgwLCeIjQ6lN/1r8H9/gpq7gKl9bcwa26hcz97y2x1B9fQD+Axq9387FhxaIoLWXEhR8tb74z/wv3pC1bcliNutY4LOeIWjrglKy5wiQMucUsucQsrDlhxSy5xWzpuyRG3cYnHZMWFXOJCjnhMLnFbVtySI261I26L2jUu7V+9xAW83uMDbsklbjm+W3I0OkB8d/Cc5eh1z1i7ODoN1v5BWL96Hy+9D0b3RbP+HGTFrWb0XcD6dtFWt1ZYzMGtGMsVt+juwIpbzcZo8diEi8XGV2TFYWEego62aBGu1DIUUlBr0zv3w4dCa+dcWUY4OaRASZP/bObf1Ml07XDaTtVJBpgGPrvufI445udLuyHgDjS48NzaBKcjjuk7ibYGfDKO4SmaduA235z3MYT7+CP8hCP6weNzd+DaibaAsQ0GCRbcWheL+kwtQ7sTbSNg/tV+VTDehy9/m8DFhAthL6wMmgxejPlfF5/8uYzlvjX5H/7hH8ZrX/tavPnNb8YTTzyBN73pTXjZy16Gn/u5n8MnfdInbeB/8Ad/EN/0Td+Et7zlLfiCL/gC/Lf/9t/wl//yX4aI4Hu/93uflU7cS5HFt+RMGe/g+7FtJaHWJqGx+XSPZ0AwMZkgBYTS2JgFpiwMEqZdOINPH21ukxOBM5a8Pxwr23I//aEV7dt98U06DrAtdfSt32pusnfNfgnTvfnybavbatvzXDu+wDqsVXbkrI0G3QaR8Q4AYAxtkaNrKkxvy0AWjfXDstqtULWYeVujBKseTYMWe19H8Qh5CI5uQVCYpC7ip76q4E7z/egAjgAuGjwZjtrJcLpi7R13cMAtWdHF9tIfmrkIjt1T3oqa/713XCyCpi2y3xnhQG+WPV8EOB472rJY1joFhr2E3Rd7QSTciVgNMlYPzFuqKZ/rvGIUHEtdzEEkvgDrJ+pIUnDPMWtcNd1vttqdaGu7RW/Ow544BjdEK8+4pcOmlg701mvb5lpuzHCDZlv6uHFJTIMIjA9Xa8MM5+6JIVlAgYtteKFtJ5yhlIQL3EiGrFPA3vASkLAV6NTpseH7t+v5VLlKU2yhi2XQ+hQWgGvkkz8H3o3lvjX57/3e78VXfdVX4dWvfjX+8B/+w3jzm9+M3/N7fg/e8pa37ML/xE/8BP7En/gT+Iqv+Aq88IUvxJd+6ZfiFa94xV21/49YKdLtLJlr0bA4z+PbbpMW42W0gCnqOeEJkRp8/lvxa5DE/PFWxTS1iN9UTOIEOrcK1ONjzX+eDJ5MneZ77l2PZDswhr+Ib5WD++CdR9G0n/cpWHjiHBQBxLPmLYwLEG5DWzFm62MEP60TuS2PJ+FJbJjz39SmwTgFjb/FaYttgqWfUnAzV4AAsa2QHv9sB+W5nCbi74fvIt9hLrgNCHzqcwKwhVODg5YFmvML49zSvb/CeAe4Mk83OGvdpi1xlkSK87tQp3yv3equSvgZzgIn8w3I0PaGlkJTjP6kiY40Fbj4I035XxXZbweYaJroG8oIl21t3XfDpx9auQQtMe7DexkZylC3wVfWrpMCSoWT+JOpqZMse4+ZXyOep34K3YP86SOU8e6+enLnzh28+93vxpNPPpkIWsOTTz6Jd73rXbvPfMEXfAHe/e53B1P/xV/8RbztbW/Dn/7Tf/pkO8888wyefvrp4e9BS197fHQM/FKPko6Fy1fOaq6PgDFqGcDA/GmGj7S1vpDbqXMaC6aVXE6kJSNjBrrwc/tvZrvjaXSHZkxOgokZIztgxYHZ7mIrnDHRW3K06Hl0LDji0FZnxKtnyjtGhL3BrWYeb0cscsSCo5n0He7gpvDFo+1v44gDjoZPra6h44BL3OZ9HHELKxY4DpiJnHC3uGUPK27h6HDWxoUc0cRouu19W+iWwNGFB++j5w64iC2F1kfDYb9vNcsZIO0YuGyrYsciRxckzHzfPFaBCYbiQB4KIdzZ4FaRxoOIxP/1P1UG4Nl80mYRCpZBcZpP/ns2lQdTlIyOH+JGyu96vzJgzH8VnwDa1HNA2PyfXQE1w281n+t0f8gEXOmYrumSmCWpitPGCZlwaKJp2NIa/8qmv/Zb740mCuqn/tiWj1PFqfE/IH3dFU7ynXAca8KZUqogh536QUgYIvx3imvxBKPFxZT0IhQ5Ep41QMHlOpnrV8iz8veolPsy13/gAx/Auq54/PHHh/uPP/44fvZnf3b3ma/4iq/ABz7wAXzhF34hVBXH4xFf8zVfg7/1t/7WyXbe+MY34tu+7dvuh7S7ljiEYuDFXFkohWvAGqDtGvfsp2ACnBCiaQYED6vRaTHIJsJXapu67UfLrWbMTMdMuExFCzXT/eKm/dY0AulaAy5aBuRdNNPabQ+8Za/jVrnb7RhCBZkgk9xcFOHhdhxqY8LD4gtXU0uly+AgatvwcWuxMFvgH9Seu/DxX2GTjQfPmLkyzfXNA/MsCL3hAuo73hsu5Ohb46wtDxOEoOEgilUFl2rjRrhbAC7Qccffr8iKY2+4FOCWnzx32QQXECwA7nSL1L+lK1Y0XCpwSy1I707vWHi8XO/ozfcfrILe7D0Y7WrbyZi9jmbdxRdPbqE8wk9v8zpq+Vx1HYyrbpxsR0NQqYOv77H+auJRQZhy5QTcEGitcxY6GfK4FFctnDflKXSFxoG+ShMkmNoAN8Pz2wkumDTNB9zkUlyTBfsjtV/EQdN2oane2CztHKccEoM7sRWQGvOAr1G+KB2OHSllTWGTSmtSxce2i71g8NnMg7lTyjomzOuAadiFW4bF37PnCdF+bcz15zKWj7hN4p3vfCe+8zu/E//4H/9j/PRP/zT+5b/8l/jRH/1RfMd3fMfJZ173utfhN37jN+Lvl3/5lx+YjtbKhyH12i5t8hNaY5Hkxx3WG8ltsuC/khJw/SdLWZFcm1Ch9g6APmmPvufJbSYfJNMFaO422MXr2gRXo+S5Na6arSVgS/Y7cL97mrkzY54lthGUCH9UE32azQ+85n2ZrqNue6BO/vnOd0H0MfLwO30Ldw2QZtfkwyXgb+SA4oIQ/kvXRB7csyAP62mMS6j9L9dh3m+MWHatR5j+GGVBLtYcKdNBcgdHrP9RV3jedH+3DjknT+Gb526Fq1ql/RVHBTszt1toGeb7jG+PJsdLk/xg9p7HYaKpPlNGuLTpddM3aPVi1rs67qVugKs0kXR+3mVMGVwYOAJ3xVHj7wsSuhJiLvi+jHESlXGvL7Pg0J26ucxSXhEebKpuKEy4a8bcu6Zf/sP/+93uxbNX7kuT/4RP+AQsy4KnnnpquP/UU0/hec973u4z3/It34K/9Jf+Er7yK78SAPDH/tgfwwc/+EF89Vd/Nf723/7baG0rZ9y+fRu3b9++H9LuWtZjB5YlVJDQrPxlDqZIVIZftBSg7PetcGTc2F9cUbdWAcxsx7pqAq4MihnvuFyQ4Ym6Jh5+cd/rHgyxauqp3S+xTY7afiaAXArDNxwMxkNh/EhGDwx1prXECeTRB34rh3JN7cF6a7jWct0BdGloagFyzMt3UOA4yFEdEaquZKQdB2qxagtng/iWuRUXUKfYhQh3yZiBpUNctabQYuff8QQD2hmM4UMtsHD1norDR1h8UZuFjKcwhziTPe5lIVMZdktNMmTR6TZFsQN04oHh9qxNzzQRWncY/PjoqFjKiCvgdPtQzTC5RxPvWSAeUFTsbVuRjGbCxZ/EweuZdraF7P8YGFdfUtluJqWhYeJPBMgEw6Z3mMzu6wt8daLs1Ie5vk/PjcLOhj6tKs31KPSrPyiOR6XcV09u3bqFF7/4xXjHO94R93rveMc73oGXvvSlu8/89m//9oaRL4vZM38nzT/7baWEzehum+gMSMLItIE00Q8SflnApwWVEfRx5k2V6RnEJeKaovl1pXkwmgsDi/9ePPvdQTJI7iC2fa6JmdMPPH0O3FJnsBdy9G1k6tvarO7gdRfNt465b/4A9ZPfVlx4DIA913EArM63pRmOjguBt2v37L5n6AsYLXUrbqEXmno8ewvdn+24hRW31LYGXsS104ujP9NtOx2OuGhHHLi10Osu5BK32ooL30JoffYYA885sLD/rZuLxAMCaV2gpaG1tDa0BahnCzBnujTkljrATqErmm3MFXflXOUzP+kfRuIbA7nKFJ93ekjSMc7hkSbCJT5q3i193i1x1HZ1bkt24DY0SWirG7jSVv22UuqRwD+0NQhaFZ9kLMBAu4xwm9/ILXl+X3jBtlhX4bDXFkaGunl3GuMSfRzgyu+JpqHUtbeStHlOsi9DSZjr4pM/l7Hc9xa61772tXjVq16Fl7zkJfj8z/98vOlNb8IHP/hBvPrVrwYAvPKVr8SnfMqn4I1vfCMA4OUvfzm+93u/F5/7uZ+LJ554Ar/wC7+Ab/mWb8HLX/7yYPa/EyUz3sEmNH1O/D470Ms2EtR/w15mEvaQbCpa4AqnuShsNJAUBsLv1gA6Cps7KW3/fGoHDbnF1QLzjBCa40nkeKBMT3M7TdEABBn9Ls7I08Sd0ewABtM3/fd0G7AOYDR8dnfxKyaEG4ciI6QjQYxr8ICNLd3V3cdTpKGJ4KgrDtLQfWsj1ALFVl3RRXGAoHme1th5oMxVL+ju9z+I2Fj3FYtLYZbq1qwJCzq6ChZpkEWhvePYm8dcdKwiJnzBLEStWXKeFYD4FjqtnQGAoyZzUUBjqx2gx52BmqfOXinzc+B7s8lkguXvTQa9U23swk0IJxyDy/qqtqp6yriZvbqTNPEDTvW6bgFLbj/j88rNuBd8Q2MFX7UcRP9LJ2Nh2HluhoN9E2FG4HOOQ1CeCTxzW4WeagKoJnqRkqLR7/lCJvS5J0llOBLuuvjkzbH4YALJgz7/MJX7ZvJf/uVfjl/7tV/D61//erz//e/H53zO5+Dtb397BOP90i/90qC5f/M3fzNEBN/8zd+M9773vfjET/xEvPzlL8ff/bt/99nrxT2UIeNdldBdmt2NTkaCUpOISGSu0eWjiKj9YZHwlVySWYr74KNeajNjHU90Y7JGKXjmjHdk+maWp/l/DdO9+eAZyW5m/no6XWiimgl5mMnOBAg402VsgICfg9k+uitdLqzEQs9wOQ6oxsh0qMch0h9pZuyuzQ349iwz3sVZ9EHPAhULDGQbFHKMsSuISf2OwDLWEbv9iQs5dvwO6A7xRTezEsp032jWjPLyl2k5BMQZQpiVC0NRvy+cbzoMz5bf7KyxUubgUC8z0ImfJ9ayDX8tc3QkQ5LA8llFF/QubckMd5XEcVV1zTxn1N+dJclE4D01hIgYHLsdJeWPhNtgGuKCPGsgp04wbv7J9pkBrhDRd+DmayT59kn7eInNR52eOyVrPazlnPFuLB9WWtvXvOY1eM1rXrNb9853vnNs4HDAG97wBrzhDW/4cJp69oqCm74B5NYl7Z7xTtJEz7q4pvZVJXMv9p1pXhchYMxs5guKM21j0MZIal55cQbBlLa2V109aY3vBxeeKZ9m9AsPTDuAJvE1MuUtyP3yB4FHzRvOJscIcGvwKPqSEtf89S4EKFxgEGfmmQSHgXjkVgLYGRqujeR6pKHoqFsppMHz1Nv4rmq+bVXFIoKj2oaEgzTcUQbeNazouARSC8LqZ9EDKqu31QA9ensNtwToYmLDBQQrT2gRtwqgoangQgTH0PwbjmIn6jV3NRylQ6ThYhH01bbDtdYs1z01JQv3N9oOAnfeD6ZnzhM5YphXWi+qwFnXai1zlUOP5EHxTDmXfFjqZzjim1f1HbhA1vI5HtbCb0ZLuxscPPCn3puLC5Up7RR4Bm9U2l3CPmlWnmmvghXHaQOn44AKSi73UlfhZloDrvQn8PmH4d9J0lSkn5QKS18kccU41fsFbtD2dez+vG1vj8GLv9tHh+/dqHJjctdLc1MgTVTw79LZlK0Z21lcvx+f6oPwrPkVYFgIvJLbUfwrNs2YcVmoeb/dfSZMdEMTvZvW3TjC1LeMLz64GR7OUzJDHaPKARFmwrMFI9yR/uyhRNtfCLV4CgFGgzFZCjNMQON0x/dv/10KQ1mk8BnJZSaELK87hAZv47Q4zBHm0zezfMfBLTFHtTC9W4Lc0yqujXSeZmdWcnXG27TjEoKD+BMqHsAIc9eIb+QTc90cWodog3bPJrhYerqjWtS+qOJSzbXSulkXpNn9WNf9paofVAcY87M6JGPhYPYis8zMdl7rT8ANO6vIiP1advDFdcxbjGWCkwkutmK5IC0kop3GsclCt5E+wA90y5yBQXBJ2iUFFdQhmfDxltNr+HSLDzODZL8kJ3HfgZPa1iRdVRM68RGsChokRYrqEQuFFJ/hjLs2VvHL0H8aMCKdbbgqi8tEas57xWZePKTlHHg3lhvD5EepvXx45V2qJ9wITbNmuStJSYYEGsBm28+gpZGJa/qzkyZn/CgBW/k4ALjxmHW9pJ92k7TjaLp6gh2rs25qwKX5vpjlKUD4ujRkgwOtC0ZfHJoDwqI8V2iWXDcr7xlgeD2sGy4muLDCveN2Kp2iQdBFsagJY1xnu8c2NFjSme79tmNovb9usO/CKHua3k0QYjR97HBosBPA/N3AGT/DJGh15xiS6cXrl5IuNkz0Y5DnMI/qrBAM7uBhzZZxDGP8KkPnUJa6+jLGyPCJR1S4gagC53XVLV3dwPHYjsm44ti+/722ykcWN5O57dOexOuMbxiMbVshcG+IqsL4NKDsvCYO+81AwoH4Hbg6VhjWFZDxervplvF1YzD503RyFSceX5pcAVma3T+c7yEuHc9CWtt7G51rUW4Mk+9rB5YDuNIp4CZFTUbtEyPcWvziW104cqXNRW/KkBUfq4JPtViF0o9rzFh5rge4xz397t2PKadvPZn0wTO1iXb3wTMtK7fCrRDNKHDRHvvIids0+BVNUeB02m8+muuZrIfMnXl9rLtmKqWfuS7CNZ8Lx5F1BxRLtubpcsRNTeICwKXYmfKLisdANDRtaJKb3RYgAvBUGhZPcLP4mB/VcFwIcPREKU0Ei65ovlHxlniufNCdYvEJTQSLqLsXzFJxGe+xY+2AHXmbQh5UUnP1yaE+XiYVjfOG064yYz46rLeFyVZeMUhXyXO2y1bBVxlw1MmE5hRNO8LHTGPAFd4288s9uIH2CgeMFrWdNVnm8RSMUk5tSwo+2XZ+2K4X461cOEbBinXRHGf9juwjgPQ8ennY8la0atl0nsKWd0z3fA3zgHALXfrZK6QO/9Wp9vpkvDuXsdwYJm+qYU7cyotZjfigBDqo54T360F9MIk6GD3v+8qjJfJcPGiLUrgd0aqxYNAEbwxU4/z2SMLi/1qUu2mz9Qx57rNnwpzIHR9wZNo9ouoXkMG76V7SnL+45cCC+SUi9AHTrKnN012QDH7MHFbzalVtiEwwLJQK3zKUjHDVtAysAFQ7VMUj5u0o2I4VigUiFhV/KcAFVhwdbYfgFtYw6194DoBLwA7x8VFfRHDACj9WB4uYT741YOnNfPlih9U07Vik2WE1YinNtJl1Xrtr8n4efUg4jXPCFK/B7FvmE6/JU4Z7NPX7c1KeGyLY+1iXjGnEV+E2Wn7lG4WmDRPvY/0gbJAJumsirKCazHXAQRfGDBd1hamWfBWDAAJsJAWpVyG8l7YU5mqo3G+gaZRkhjVgYPQyjHtmxlM/XCclE+FgCQU+a0tX+4ZyPXK4ecsdf873c8EZ62ml1MQZR/yWb9bkG24n9iRP1yjjnatHD4zjUSk3hskvB99C1wtr7wgOMnyY04ooijDXi6QbDkiNJEz84nnsvZjF0VbMuAYs6KyYw0uV51Y3xILVNX+JumD4IIM3HKwD6nY6gAls6nUbnqEyyUQ3FBiK1h7PGFUNwEKacjiM6RdOkut9wpKvieapXYsDU6NRVaziZnhJHrnAzZbKVDpmPejFmb2oGtsXBdS2vClaaFUco66Z2KfFrgHTYpqmQLV2z4LX+Wz3VLYKWf1eU+jqSXOM53t8QXIf7i0XF/4iydKKYUtdZZIDb6l1Zbw4RytDH+rKI33CX924gzY84avPzHCDK7i0OxMqhXHbeIxaa+BrkzAx9SvwFbPArMlLgwnq/s6H3QsYcWz4NYGaDu9koKkhG21OQMVXhRsXdmYLgQq2L2Tzfiqjrpx9Yv6DEOBtVH88kFvopgklQDlDfowTggikT4ECD3k5n0I3lhvD5Ne1wzOXIL52foD8Z2LcrOvlWoeFAr5WFDVi+IDyenykjxnv/LnwfYPMNBmrMe5enunFj5/+csCi40W4nc0S21A1YVAdr21hUUhkzUuLALeWUcsXwM35kr/LH7DNvYK4v91EF+ullixnarDMZQfZKnnRjniQG9SYfwRRmh+ewlPT7lvw6NIA6O+MLXTux6dZlJYR8J2UmIrgVcMrLoeRYDtP+FPmSmrmhJCCtzK3Cc9cqLhWBku0gzl9enjmezsceoDb2653iqY9HEmcFkVbNnD3StMpmJDRteA7BedXFDj3WrprH52pVny1veHZapLfbQ2jq58Xu3CToHQXbTtqC1w9oIZb+Ta0XqNyDrwby41h8hE9yt+xGOd2Op5HDv/NB2pcDGT8bWAZFR6FkVoVnzOJzKantti1opU4HBPTmCJIxkz/vJac737CnCC21AnM7G6nyFFD9XzwsGj6hu7aak1Tq1jUTPSMzGfedxMYzKTNoUlNX1wYSc9hnFkOCiKt6gdUblxAarkrSRQr0ozIt9YDr8bwdh8bmuY8HQ0OWLBKd6ONhy7qERDb03jU1TCJnVUPWd2oI5YIB8DiPnzVbmZ4NbN8E3OjtHVFk8WupUPF4gIgiwtxDdqd8wosSY4gTfVFKqqKbB2fTREbiJyvO8/sCKEVn2x++Dq+s6bNzPGq5b4KxhtmSPxar09w3A3cTjsgA79CAqj4+j3AXeVv5mSrYzuPsyKj3kMiqjiq8MYfOwMr0yDWd7UHF/iQwqNUWr0tby+j5e15LTgCY1FOattnn/z1LDeGyR9orvcPQYD0bwKAwndF528AG2k8UCC/rbjm4lR4uHolP6zI+KbUkvkRwk2McPM7dUy7TtYKjwGQrPHAG/riSX9s3UPNmqfOxK2FapIHfOsemboUJg4dtv1Xbd7gJMaoJAVmD0s/6tJC3diFHYoyish4t2jms1+R2/OE74tR0Zrb9Y4emMdx6SGVmek+thNqx1FMfLjQji6La/zdTfnmf+/dBR53xRzFTgUEFP1o46meE6C5GV5oqifzjGRMKCcSel9XRN2GkWD6XUzeQ0AaCuweYyj8ZLNUn2BcVwXybcrMhFIC3vZlYEBAdWnsw21pzaC7guMUTXNbM1DQrhMNrMt3Z9JmwTHRyw0BKVj4O3cL2GYRGaQ1WwvSRD81sIEDirQ70jQLQdXH7+MRK6H3MVdGPsU+26/r4pM/m+vHcmOYvIaUW1cxgMnl6zvNCHr7YindRs7virf8G2Z7X0wGBUFy21Zkiau/IyAvt7fVZ3hmea7qdUueQqRH9wRrWRDG7WFhFXB8jPQXT2wjvpaE+V/IjyQC6uKPMQtlOGPUJsm/fjIlxAcCBtfl4lKVmeFTc8GIfWuCwUJTc5xJGR9xbmp9yzFDsZDY2+PvzNAnibC8fHUIj7+QxMBBG+YJF3XCMmEPyq3CEJBg+fwwfknPzN/rDy03Z1N+xRvNzIx6t+/79RuQq2ivVTMz2hdD7oZ2InKWVjYN3rUtGTo0DraW3zUuoLzFEYmbobTAxRKzwziHmMEioG7gUE6Iq0EDc8BdPKABpwO+mmp6YuZ6D66Kh6yc09qO5dFxPNylKLkHtSnYb2a8q0t6ZCyDDstBmraSTQglfeS3RYFAwtxoW+GIXQR2TKl9bWbKhbGGhYt/DayDJXJZGsJv3ApV4mZ4M8mvOEjdTudMXs1kfyE0+dtBM7FtDmaaF+W+8/wLDV7zN+sO8FPe+K+mdaGBmfEsSK8qsKZ1G/yFtMCxoPkCJ55hzwPzVHHhOKC5fU+gDkfGzrHwbYKCiFFoUE/2Y0LPIlLcHnYYDuFutcxCaOOZR/n68UpoAC5ajqH1kbsgONVcUFuSa9g66zNKYOfPy/hX99CfrONfy/uK/B3wwHCgTOCdAytqIb4Tbc04NnAFz+avlboBRuP7rHA1hfTdaZpwVFruBlfpQGXKM+1a4MoGOeJDgcMEtxmf8kwIidP4VDgpcOL3W86teGhPKGueFHryx488fYfBC0xgf3T43o0qN0aTtwNpjKnSJKa8DyDZtjEfdUdl/bYjtK18i7GAjigQe17FfuXH4/u7CzPMIDDx7G/+PNLHLS0XCfrPzRfufSFjLjbWYHbiEfuwDWcUEoJpuSAAYGOuJ/9Jxgxn4NVEnwydv2kGTN88whLAkWii4K5dwllOegkTPXMWmHROWgWr2rjbHntbjJZYTDsWGMM9akeD4AKKIyxd7uKb6UzHZxY9oGmzIEXpWFU82ZB5/RfhFjpj/odmloDegdYWLGLR+pDFmHozQW6NSHGFNnddqL3PkOi6nDbDc8D8mnVpri5wPtTFMzAu9hPcXBem5h0FEDNNBW7UXifcOgN4H9tEn+40vguHbQn8J7jQFNV/Eq6a86Sg1HKjaMMJJwhzuE5wGFAWOqaBmk30O++BX1vWjWM1oJzfQ1lnILkencx45wLE9cx4dzbX13KDmDzGCQ//HVq0oDeuNXVVszKcNy+5JmngK9L1pnGA5tqQiqnhiTPbpqFBpJZumjvb4RY3tsEgOyO5e6Cf/VnEfmr6oVWGIIOACxxIGqqiQAGk7n+vXW0FzgJ4Roa/NxzsYT3AhSza4uLVcVmF7TCTMGNGf5TCA60VKR5pEWZivCcGaHhpwdDASdrsN90aZITc8siFUiNRivAdw/H6QHV/H9xFUHmHQ6JWbNZTVs8M2OsqI5zjuWLuCwatbaZDiX9uq+KbGUkReK+kXec+b2khcGzhmgSLk7QPN8Xb2st4V25gBy76VgX/HRylw8EASbs4U6ySf118bGKNZv75BZGQusaEr34HritsT6fuD+psvvc1aIDUsa8xg6W4BK5JOTP5sdwYc/16dB+US7Sxi6kbA1DuYUX5mNWWXw2Hpo6LlF/H6XPlOdQ7qsGU7LqXOBgtC4OZjrlSNiTjrl4m075XgCfFecY7Nh6H3ahH0ZMGj9gnbWSGxhw1LMqCEoznfL8B4T827d4aXJylW10NDxx/n/6TwphhB8zE/SJgQAY3waIa9DAoMN0QuQUwxt+vF7dkqDJHf48FTtzsDuQuBSgFBYcFU+X2MtYAPHFQjGnMhq05lO0HR3TBIUoRIvk75pxMf3XK7T3jMFenXi5lrpvr9QTc/PwJ2k+1tQ+X2Sh155m4fxUOmeAm2IQrbH0XX/2e94SGqCr4dLeuyknM+TDICvM1AFSmPMBprGlbH/wEB1+rlPPPaajme6eZ/1Ie2syDc7k25cZo8qErxYJeMtQNK4H/Dnm2yrV+XVdWdak4zOYaX0Yy8YxkZesmTRNNZsmK9iR/pyRWMTTTYIsJPPZ8O72N9YIMpEMe1xq+d0F5Ji0JpqHLVGdYjNk2Z7Iy0h5XOYLmiki3hPUiBZdIZCZmbrf2DVOXDtXMWU9zNM+tV03mz3fdxBcyMdN9h2LRFSoHNLWcAzTXNwiaNCyKSAYUp/AJdxYoVvGUv21BW8090IVC22LPNo18S60J1tXfZfPENk3gVn2Ej2Va/YuXZywCe5aCgSITzKCs8QLbf68jjnADTIkHaha6SssQzI2JJh1xBNxM+wRXrfJBn8w0JXdRIKwkW7j8hrZtlQ7oBFeL8svO7zxpckLrNjx+l3Vs41pzkEkrB8SF4CELnwI0+4gmbsvxTGbro1Ci42POrIWGoZBuR7CWNmme9/iHmvEu+y/u3kR2tCuuSzlr8mO5MUx+3kIHEUiHZRoDJ3cRAPgBwpTp1d+5+YfzmkWIelTIslBpGwSA8ZrMuhW4unKPa7iU9UIiYG4QEpA48rdF4dfYp1irdHpuqpepJupKu3O/ydRDGBjqfcHxRdbWMZrrEf56+vdFM1YAmkKDMX4twgXvq6/RnslOAG41hD/DWAPVjkunkKfXEXcEI3LdLMGJUmiogZmtmfVIpFl2OxdgIriM3W+A1mNm6zuYmHTUSanbg611kwDRFWn1meFOMsIdxr2H4woUV9G0i28CGSWIq58ZJJL6zB5NAOiH3ggM8zcoO/gG3ieFpsLQJ5jqxhnqKBiUtnId2K4nI017xOsIV5uiMC3IeCX6o5jhjm2fM95d63JjmPwm452GEG/zurxTBTIDG4BexPYp+DQY0baoa/fO+aVuyqj72cfFpcXXnNvduK2uSX5sEqoa8RXzO0Y4rieZQU+d0fMUO/XocMfh/r/KoCt7rntpc5+yt6dSFih/docBeBdt5OoiLY7Dx2JYA8u6GX7xIiCR4RpV1M58bFy4Y/Q7JTK+Hw0hyevoX9cOhiyKKNAzC6HyKHoXBHqs2mqJcEqnB2bl/9AN1GiKnQVEweb+gEtKlY/Nrr7VUBXI03xp79k9uJk57SLexzFc34NyOPLv+tBVUsnc6H5DI6MVnPqORzQT8x4Gr0ovOlI3BUpES0WZCAGiau3RhPvHJzgLJrZ7Mj4wHlurOjBr1RFwGKV5kTuXa11uDJOPj6klgzIhPaNHB8O4++jJyETHo6MDzus7SvYo/3eE44fLr9yYiW/TT1iJdDnxW2ABeEvj0Slr5F+H+9lbcECF6cwrYhVyjpTHyCIEAY7GqN0xep79S8bOkn708d/AKbm17upiwkD3XvNIWb6gtAabhh/paTWturReUrM/iGcghDFe8f7a9r8FljbXIua5tXCRhkU9xkEEB5iJvsMi6Y/Kk/zEtxp2NDQcBLjjreRxvJ7rfika4iKe9EbTP5KDueFXm6W1MJSZ1wzPzLxvwisVeHYV7OCTud2ZsMqfdjLUbdqa2900uHN/F276CK/Ed6JztezC6aAUA6S93Jil0Prt7o0nmxFscc+diQjOPUBB7J3fCAajkDDQF+Z5JG6apVSA3jPYbsJxXTLe2VrwYLQ+SqLNjWHyy6HZ8aCrMzf/RjwYF6JlWwlyfvN6ENpLDB8LNcjhEMf45soeWbJVarxlQchfyTprjHDvgC71Y/N8cjQ3RlvWNvtmC7CkoCFk0tlaFUyqRsC1oF6TJvrkh2cwRtfbcyc+OCJWLbjLegONoD8Tbaq2knvwAc+OF/EJHU3NY9/EBSSne0WPbXOqHUtEIKxoMAYO6XYErVgwIjPeNbGje4/iJ9NBceyZ8a7RjN+QgXUCz2TYEckS6haBBvTV9tjvMitOp1pXptdmZHfgdnFhWydT3Sn+uFtkbPZkW5W5YbreqRuEkjoRq8RxSjgYaJfxGX7389gOcKUtxgRUOADcHRJ1Ax+uEpKW8ZSKoiw2hkT4UfX8puf1ZPgXgMwvafD5zLClmxkYFHD1G6vlnPHuepYbw+S1fiDVfsxAFCD88/H1lflPhgnFEKg0ZsqbP7R6nQlS4Fva6pOxpYYfOWB7ssuiNOdxmiPltcB5ppgJt/+OZ7TcTQFnr1St3NbFMo5lNR+H9+4fCte80X2Zwo04QM9VqPSDZnmgmur5osRfUDWcxitX63Oa6CXX6mCg3E4ng6tAae4vgiGfGIak9mTQ/mSQjFLcsucG03p5LJhEKfOo1I5ulmQJGW8YDy3wNYi8Au26l4GNNWEPZpe+E3Bsa69fO2RtcZT+7ck6UhgmA9rmmJgZyQbfIKDMDDjr4tjk6pKbiaovpJeZuoErDVctm9e1bi7U8gucXhVIV6Ptr5k/Hjgz+bncmC10qgpyWZroFWUS1zUYPJoyTfCqeT++V0ncgzJRvnneaMJvh8xgZNBkTE2JrzAtKMzsEI/4pe/vLqwxctirXZOh01UQAXelr7HKoygVXAg9oC3cC0hTfdBerumjl3v8H/hMPl1wJV0tcObrip0S4U+vNPHd+dj5VrnGve+e5c620LH1jIO4EGpU3czzJT6Cef+1maYvhTPWrXbBMX2BVimOGG5jUgzmevV5tUlpq+aeqMJAGaT6Coe6mKNSYErd0Bbr5yx2/nyffgNOU7m/W3SCO0WnTP0vvyFbeN2hEdP9LVyeFtjDwuaZB2VMU8wStJPm8snE+xQt6wHhpu2T/Hd+Z8WE3ud+VDgUNWLQErimDatQIbjQEHD1JjaFc5TtSGoo53LNyo3R5M08n4wqGavEDyZ9oA8+WVWajBWYAlpSOwh9WuLbNXxqPuJWvpHuh4mrNnRNPdo+rjTXR9icNPtAuQADABpUzBOtbnfMw2vggW9OvZsOGVBoC0hu3cuBYrdYly6ImgyHo6Ng1Hky6Wz3Lu+kwHC3T8Q+BIR75EXQVDM5nIovluZ3Z5CejyqaZORBbLXz/y3ud7fo+mT8izKzn51sZ0f7ClYx/7uZ/mHb6ETRVdH9nPlFgN4Usor57cUCNrXVXQiSe/3EFk41P0KYjWsgIR9x4scRVYRBoAoCpXrgp9iBG9rQqV7GtgcLQH4y5Tsa2y2vcEv7Dlzgk5G2gc7NvRoCui9nDHRKfcZ/Nr/Xt/hGHGOnt7QX91cZWAvWtEGIXSJ+oIwIIrCkWv3H9zNH2OdLMpJywKo7breIoGa4GzqgExwQvnkVMWXobK6/luUGMXkpE7l8pU08mNoy3lnR8lFbqcHS9fqqaT+YbkOhpkl3WmkdqEOGxTXM9VuSysollvGO7Qyr+xhtD82sa3mA64leDCtZ/tgcWVEX5unbuBuzd504nq+UVPdHroeV9qysJldDZANuQ+GRXlPUckMKfBnrp+CRw5HRjgunFhrFYzh92Gn2z0OH2BcXriLo0l4iBRTxOi2DWM3VMdI77Wcb5aIy5unWAM862YEbh2mLo86xStOMoz58Cg4THMb+DwScEBaGTu+1VabM4LZzphmyPtsNs0fBFzimbHiTb0XcWjNMT+FM9AZESh+1uBCmTmohfvhu7Xed6xlXM32b1d9fI/ZFxjE+FVEvct5Cd83LjTHXr8de5j4TpSAm8CbjXXyTNePdqQU2zf9RplUxMKuCPnma76tLoEn+FuPyQXPE3TvDiJ0BlUmAdRV/6XfQTtOx5tqAiVnW1VcTZ+o7A2b7n8OkUfTepH8pyYh056lYuoJWYUPpnmSVJn2Wc4D5+jONb2T+Io0CmDrnFgGutXR5lLGUgoMZ76pmFPm+3VozbG8KM32+w+F8eSd/z0RdujYN3hYOBcdgasYIR15W4Tc4SdP0bKV3oH2HxAFu7t9M04m+z3Djs6MEsIltm/s0j2d1pey1O7SvpX5cDPKTmYW9Oi7D/pn98drlueNHqsN1YeZXIMnvVHfB6DoIobQw+OsSXX8uY7kxTD59z4CxKZk+0n2dc/QewwTtntcJhTE6uQtU55hXGYAowIehLxh/eNILa09aFQ2qzrKEAj/rBPFaBX69lOdrTL3T4mQJMlGKlr/oqNQ1pK6o+TvG4T5KvotKV459jG9oVDUbX6a7rUHHuQaP+fkZSy/CnWw98tbTkp6n//UIlmw8oU46DvTrC8AcBonfGIA0YFl8pJv62QTWgIi/EprvK7Oqr25mNG2E2zDlHEKbE1Ui4auuX3xLXDPzHALFSNMe853x7cHt0De01aY6tjs/P7U1MuScy9jBd+V4Fljh84039mgqSIm4PkMNnH90z8TE9G8OOm6nPNHfyHgXdMr2LzoqI13TxzisZjPOgltiPBPHdYmuV5Vn5e9RKTfGXD9mvIN9BDzijD9L1RDDkkp+PAaUb4QfVRXP+RH7j5nZV7PhIEjnJ2gM3gmp4XWA7wOtOMIXR4HBtMiOhgXH0sCo8WS7+RfVBW4IFgpKkiPZEOSCNQbVXV2oXcQYYrjMoY11qGx3FBdsGBQothgJwkgPij7EY+un7YFnWt+mObpteobJgcwfr55dbzTXN0EcHiR+fVRFa6YMpbUAA7OWBuiqcdzw+AKmVzWYEab3NM1XmTs84+D13vO1nMJ9gqYN7TrV3Y2mU+3u0D5GuZcHyzeuJ+EmmiKV7gxbBrtOygoogkiPSyYeJhLDsQmkRYXbwT18ALILJIFDh3EZAHppq1bRzz7DYRyGa2mux/k8+VpuDJPvTHg+L6ZSGLeoB0TL4P8at8khvgLF4HorErTVqopnbzPIghHxNWrZXgPX6adGtAb9BKQObRWDHR8yOOnZ1+EZWjeKj3HAJyHchGxEl0bpf017zgds7WKeuCq2bMtoPvRUm7twZciGkQAwmR4jQ108M67etI1EJLOO+KxOY8wUqbmnYKjQnol6AEXXEmRXSXKCY6mvEqTTpzMRXr/hf9NQxtq+w4DIowZ+s4Nvw7dwUhYM+L2tcxscW3JPlmHqYZ+mq56rcz95M+fUvZQT0pXyq9wZwGluCWwHRQo3ZXKV9yADjio2E1EVVBxw3X4XdetpEjZPvHI9ZLw7AQeMTP2aaO/ncrrcGCZfNV0u3ACgK3dgZ8Y7HjZTvs8h6nuOeCbTiS1RG7jchkY/uvj9MN8K2bTEuRZGS4Ni9fWkoastJICMuKQBhPOQsg54HniH9f6TwdnxLJmrj8ycAo49n/1j3vPs/sjCOVbkYXdb4E953svbKYIChsUvh7Zg8YsdC3JQW8+3z4RAnrGOW9+gOAhw2e19Lk0gXSMq2jLeGaUXAlw61a05DJgUR7F2u16Whr5676QMjsBM9zxs5NSaqlP9zOxr/Tzwp+6dyninyOx194Pv1Au/C+33RNM9taVTW9MknPo4TNwmm10zw3NVCpkd/hWOU6jE+NR5PVgVAJvTQ2KAwuBJMM0Rm4C4ukAhpcnAP/W/NWDlWkIhk8IE4nfNeEdYE3LvVWT73S3nwLux3BiffFu8q72kc3UzLATO1J2BkNthI4zbc/X8BuT1qMHJ8OEG4wv/loBmPt/Zh2DwwXrivDhj2rFuCFa1UDKIHyCr3MndsCqgak7Crh5bL36umia1ud6lO4Ca+3a90xBEylJiIoKi3FMOYBEgtqF0fdooV0/UyzV11KBo8h5colI891GnAx7+Na1BjQVOEAF0duRuYdSOj2b9hW9EFIsfA2ynz6Vbln784ONS+iHIQDv61RVbYisfcdjsyBZuU1eKnIIDrgy2O9XWSd/8vdA+fzR3owk71zt9jMrAqfc4TvOE2o7FOHOFE2/TdP30x3p7yTLATZ0svm9BPi8VV9C0Q+/e4Ff/PC2Y4W9nfcMQ+SnF7kZYVV+jTkmgD1c5++THcmM0+VFylrxHjV0RGe/C/+0L8KChEk0Vvvn8XpPO/cRztYZQDp9MJU87K/NT55nmhYGqBLNr0QdvJrT8cs9/aJKCkGFiFdZ4Pq5Bhq9liUr1h+Zl4QCwz+XjOCX532vE/eSAuBLy1K/xutCGHJt4t+QRnf3OrHkDfgba9W1fjEFRc0MqYAqIRw4O0dInFpMQaAYYGZhJ9EFSedz2r761qZT5Pd+fzfW1Lc7Jkd77oEm231MFOpXxb+gU4tOdCEmqTzw20MO+2jwoIa618+UbGxGV7bClsVCoJZ8f4wKskxp1+SLKJzkRvu2B1KpqEaiN9Qqpm2f33DxDmSxn53L9yo1h8pbxriGC1AAoc4ojF3eAC2wDzVW5x35alLXgBtJCF1/vHpx9vcKtcgrT6CgpT1IzLXLaAV3EzenGfFY1P3AD0GsErLjWD9+eBde2fQVRpUncDoNhncKOnl6KgBBauvhxrlzkycRgbaU5r67QzuyEbgOKKoo8W34cw6r3C+iA0AFlWBM0BRK6OOrGh6hTFCzWagcZsUcB0PwJBUSxeHo3M/GrvQCvsyHt6CKutatbiJqZ/JW7GThnyoof194nLs6cYxNziTWbHLNwyeArlZuV/u8x4xk/ZIQNP++0qte2goS7wF1F02Al3ukzsE//xDsHuBhZGZve4JuEmyrzG9z0nVehh8/Ktt0UGDgn+P7sgXmjaywRXGeqkEF87EsT88uHdKRDf6VzrSodIcCeRDWYd8a6lCsKPdco493ZXD+WG8PkAZCTJnPhagVjGMx41yCeHc+9tspvRcaENLHQ8esuq4FKERJgQXgM2ALsKFK1jHVdGeu9BPNVNGPA7l9XWTzDGqwOCpUDulz60SoWSW+uRUuq08V2FDRZPIGPHcNiGd/sTLcqIDCYz5i6eoxZsQK4laH7WNKNaTuHqvoxDLo/sxeAx4Wq+uH5TnI1zkUHw7qkNIOXBdh85RJYRIw+xhPwdLqMVogZ4GfHJyJRO9hmlawTwFPZWga+5kx/aYKjzwFxxr80xarILdN8mNee5k+7DNHww2K9x+SqjIDx391XMJcyvDM4ce8xWDlxvcFxjzTJ/KPAnerCwKivoGlmYEPdqTHiNtq9usmZXmMgkvnn+2ejI1yRDMgzI+OdcmsIsEqORwgG/sBAu+OJ+brHtKuwMnWszoNSl9txuZTJtcp492yY28/m+mtYWpOyhc5fYMxiLR+gV1ClUf5MxtH5LBdkn/sZUFckcOOOSHXZF/ZW1/BMysIIbbvNaHsPnHPRXoVb6NRN9A3QdWwGUq594sOD+CSD76oJ29k8Iuqe+AYanfGKDPcrcyyjCHKSwpd8rBR0TVQGP5jodaqT/D3AUcZic5rC0cioUpeyMMV4ZHMoSuyF8Pkx1vm+ehFT8N1fw2x3tm++CIeSLec0qFyq6ngzA610cJ1Oajjs8Q4K44lXwkemcQKG15VDVZkXpvrKR+4CJxPcSHnBN92cb/Fijkeb59R+3SY2fnhwaIuN1ax2knXGcykIqH/ntWNjtsocK5ng6pgpRt7sqXADB9eqXixlsqnLnpSehuDhP3jsZjYddaI952RsrZt+X5Oiz4Im/ygx+RsTeLce68T1j54qHsbDP+rWKszXO7jD7C7j3fzAasY7/isFTAbwDMTroWUPIoFbHeLT84hY0hKBey7RdqV3WYI5q49FyD3QYur20+jVxmIFcHS6usORAfvRL35fXVOeGDrr0NG1D0F3fI7b6Ixuu65wYcZXBFwcNFQYtNGcmgj3zKaAIt5ujmhHpirmc1Vw4/tB9E+iLt7OsL5qxF7wfdUof+EcgA3qYDlFmW9S/gptyZUmDltgT237nCSILfObf+89N/3WUzRcgWMT8Iepzu/vBuHNfdwbp+k6YiVOtDXc3qOXtwYhawSKL0vL5JkZL3QzlLWRELp2pJecXgVn+NPY9ohv8yMWmSnV9d71BHddouvPZSw3RpPXEkk/3Pd/BWbAXbG/duLEb5qHg9HrCJU6mq00xkBsRTItnFvc3HisqWXTjM+tcLmMJNPuamebkzFzI1iHa+IWL44jFAuaJ4ExLiJ0F4j1wXZyZfpX+q3JoMgI82x23/GkmUvEnAC5TAwKDTCYMicPZMo9Uhg5yOATRwgZIqhb6LqmlSXgynMaYxhvJCqF2hEMeSuqfQ3wEhEsmhp6a4C4ghTTSwWtqZ0vD9PqZZhUZVT2mBjHgmCTAMm66vceeI9M16XJ3a12s4BaYfx68EnPz5S2duFmmubteXv/7tGOnefugSaobwmrnbyKvj18Xm9zWJJZOn57IZwoZYIMUlnBRRzV1cc5Uk1Me/TNN3i6E+CKC3ZeBNtlv7y9UXo5fe2C93UoIfM8II5HpdwYJn+4WLCquubuZuKO8DmLIjKZ7S6CXgRTAhyMdUV5j3vG0Cvjk+mDyQ/SzPBxlhq69mTowdyBFQ2Hwae/+p569VPtGlQUa2zEs610RxUcRAAsWNFxCPO/iTixlQ5arsejbMjEydClDFqVc3IIc9HY/3h0tz60exnrmHSklshhAIARkFxLwx1TKKxrd/jqfd1bxIIaAa6F9Mkzix633nE+OcNHuS7MSFpRiLjQthQKdS0CaGVip4ZKNpf75R5wDPLGzOR4fRWD34Pba2eC2+Ca/90RbjY49vDtwZGp8UPfobFaXk6u8LuCAR8UVyS8LbpxCJgReQbn1+PiUnEAPL1uS8eIb+BodREa6iZpZZRcId2/aqFAVNq6ZuZ6y3px5ZdxTzgelXJjmHxkvJujlvxdVtM3o+s2Wb3Uv2kuHBSEZ642+PO5grApZ6FaP1+xQDyxepNEBSo9zdFKppe+8txTr2GaV8nodMLC6wTd6kukb/rdR225/hmD17g2imVg/BCnXJ0JhyDgig4ZINi2xHVYQSBbE72Ua6eI7oGkj32q7oe63qWpk1SwL4FDx77nfCi7kIQuBW+nZ/ohwBcX98v36KdrQJwT3EJHQlXKvIjBBbZ3o64uP/WtzQvbKcOwJMDYRpnze5pQ8LgTvOde4Eba77/MPbonHANzTqJkYnZ64pkBThFzctu3YtHz33xDswARY6MYttAFiP9neFd0yfVtcii63uDrnACTb12jLhaUwJ3uPv6Of6+J9n4up8uNYfLK6NVWFk8BtPfNQhGbU+oc1803DUcx3i9RYMMxqhNXsW9Nc8EXMjILEIwPXTx7nVqE/aprnN++wnfVSENvQMfqUeQLOrqb7wWr44Q0DzjrQd8RzPpmTHeN9cyFAoxMIePFmDyG+n9dgA0/t83RVaKaKWVbmNo1AtM4VjVVLAP9OHSV+ZpAIyGUmR9fgpFSCKJf3rb6tbgXGQ6R66DFDzQ0r+tOKxRl94VNDIVgIQ5qYD5RLDCvxRxZFmD1QIfWgJWau8C+wsiUVibUzBFP1dUbuleHbZ1izC431Q/R/TO+qthdpfCcghOkaaj26V76P18D+zj28NX+hqn8RB/quFQ4msNr3dwvwD54929F8Cz2mHNtSwfFIzBP7Ya1YK3E940Qswmyi4MURgY/POUMf5SLrth18BCWc3T9WG4Mk29LM5NsL5O6Ixb6pqOWWnLKjCdL4sT3yV+80UuiGxWon0KmSvleAG1Q7a6FW10XgaWvVazS0HTFqg0iwFEVB3gdFItvjVt9O1fXBV0EXY9YQYYONGnuq+4G14z9rgCaNnQRMOyuSebb6z46AvvwuS7HmNpoxBoE62oJfPP0r87Qq8bQNVn5LD1VX30E3AHDH1wQ8eF1a0S2Ox4sS1wSAgTjL9KmQLjosf+SyDgL4UE2JrQsTbB26+0iZvJvEDf/Z8cs47ALAZ4TISaUIAMaZolRi1LPurL6xs/KFOWKuloIJwVuj1nu0TQeWLBfKtwMWwWM2qedtq6kHVf0sfZn4FiElWT0ezQWrpYC3FynQ9uDG1zLdluNL/5qQS3qOI9dQJiLAIP2wf74erPLnvZM/BQWgKFOZji4O+uq9/0Qla48M+TBcDwq5cYw+aHUL7acCFb9vOVby8c0F+i4VoT5vk9awfA4uRGQPrmhkqZmKQttQtVjaweGRzM1mV4N5gPC+2AMWjygT0IxMKGA/vXc926Be+rtJfNk++Zy1JD0w8I3+zgkNZjcf5sDNa9383Y69rP7uEVdXXOmJmvyGy2/ax+CivJOlMIWgDXUKUXv6pL9SLO5SRry3PCkmFsypc4Vvmd1g1KRGrdb6ArH2AhB45gFSIGrOLZbCQvQibV7yAeRoJt2eVOvgruKdmzbn3jdVgDZwy8TXIHfwDlOOjiCppPjaFA640BxtVRm77cGkieT/KmtdlZZBpVm+M2aIaWt+fCrUna09nRbnoAbW9lZr87lOpUbw+T72oHlENKpArZXvdf86tRNdYQrX/+wYMa3qCEYq3MkWTQXjK6ZlYpcRMUC/dQZdTffclss4133PfJrs+A7EcUCwdobWutmrtfmPmGByIIL7R6Ya5r6gm7qVG+Wj10AyIIFiiM6DmgWmBcaTcOiPdaa1fsq7ou2I9DpUbdtdQLgENKOCQwLTDllLneuYzWuvbvBPNfG0ccucY14DyagqNOWfvRe6hic2F0oWn0sAxd4X5wOE2hWtAxsVA+cizaauQLUxoSBk6oStgL63evaTcZOdwXdEoK0bBhJkg+V6RVb7YpWuWEmAVeKFN6RhICMIYpuYRk6sIePVFRL1rz6z3CDQFDhdEvORFZIaco+nmgLAOKwxeHmHtzoI6+459dRaUk4G6SBNyPHInFo0j4/Q34f46PY6V7SrhQnCFcacoJVOyxHQ3EL7I1WnXdEPvvni0++CinXZQvdsxFK8CiFItwYJg9gePvhI4/PzE4ag5p2x+QuNN0LYciJvEQSGMcf0nu31TKsYtQEw/wlWBgsp8ZsxZmTHSrjgWnqfnI35XfY4TTizH4VuhsURz+wpukRHR1HWXBQ89GvQgM8cNTFGDYAwQJp5qk+ADgKo8RtL76KYilqnagx7iMsGYxdI65jKdLCsIuPL8PtEAMpGAPYTC6igFVkKh2D5VQ0DuChub4jtyOaNSK3KPJEGO5WQHl3GY+pED+CLd7dyCbs9DpnhkxcUpUjf9NYBDg6Jws4eHAkF02aVAa12bOXbTS8iQGSIe8yS3LspHvgShVf4TAbfFU+qHClrjLNiqOSchVcxbXlnPvMjx9atDWPgZ6Ck1F4Grh4gdujB9mByqzz2llwpWm4Fn+39kAIBmDWyDpQtf8aEzTasvxXpc9FZB7orwMu2zp3JWRypmTmMRQeYX9tttCdffJDuTFMfllayXgHDBKswvxQUsxvFAB8gajrQi/XygXEP1ytjD4kcHtKi1M2Pb3EKFA0Z1a5bc7kksV99+a/72Ia/OICge2N9y10nlNdqX1K84C8FlvF1OtXAAs6VC1Nrp0a59vwsKILfffJhSZe5IFp8KA3D1LLEXYBp65ZjPzXYTGpy8ewv72YsgfzvWLYUtiFH2Zq52T4KJRb7Fum53VxxoUSKW35dQ26iueSO2kf8Ysz9Cb+Fj0z3uoCgTYFevMtc7l4j0xYCl+vWlawhlisY4t2fTmci1GnUaeTHb4ytSqvDnUY2xqY4/gIyb93uJ12BhxBzPxw4VFzgB/r9uCkAPKdEK7SVNqhcEIcUj7bMdK+pCfuLsrG9hL36zMAb8DHqPyE47uIILsaAe/WQHvnVkfcNo3qXOIcAyIz3vARWyNSA5Cl1AHXbgvduYzlxjD59diBQ5rrAQz5/up6ohgXmbpdNaLhEd8auJLF9pmNJlMkdAUYCUtmJRF4B9/ixsdofjbGQg2/KxXA5ozP9sozuv4oYhH4IFOSwNs9gA4qEaAr8L3hEBygHpDmIWchvAAXTYcjrJkPvzKOrool1WJP4qMZTO3b/LgWah15dWZdxjPa0pppz8313krV7lf2N5g24mXwKF4TsUreAbW8A6TE3CWMmnefvmth3YU/E1SKTULztyLHNWdHnVwUMMW3Q52YiDtTiebiofiUluleZZiD6BVcZMI/MdpdmmYGfgoH7gI3g1WmPl/v0VTxzW0Cg9thgNujybl8RJXv8DTCycleFIFs7sfg0+aKcdrPHW3Ni9I0CAZncymCA7vuU8gPhIvLxLjrlBrm0jVk8GdNfiw3hsnXA2iGQLESwUVmCx0/kiHVJKVrJJx6eHfV+LmWdnI3N99bEhRbZHvvlq2OGrl6tDs6jqvg0OyQmlVXQMXOOOuCtSma+9OPXYBmWvpRG1SAgwKr9EF7XlTcpy44as8Fyf0WqoKDAJew8LwDFEdVrGK+eAomZNorkBnvynVDCinU8AfloWxbCIUBrh0H7+H2OgpbKQqsSOGoS90mZ/7yFbRSuI/ddw8cnakjmDv8On31FKLUe9mVWw99FP2dSnnJrYSaS1NI2eYUf7DdS8xkJg0RoLjJwiiV8UylMj8U5l1X9YmJDVHs7Moppj09X5njQNPMLKWg1Hxml56r8O1dn8KBqW7v+ip8mzrZvz/9DkFgoMkvBvMBxmv/FlLYkKyrxT6GQm/BXYmvL5Jb4/oevhSnfbFB5LCv36JYLszRGV3aLZa3h72co+vHcmNy1x8u/PhP+uT9YwpGQ4nbF86BsZd5P0TVA7GoAz6x8rspz8l07YFX/vWnz1gyqQ3EtHM1rVJ1cRO0paldfYuYZcVbsKpg1cWv7Z6i4agNXY3pHbFg7QanWHB0+I4FKxYclcxxcYa5eFvWbkcLRkqmyd99+NfWqVVT4+a/85/u/i458bsOz4ZCIpO5HrRstMEMn3DcHw9ojLu/Efo7fVENfVyQC5vUjXa+8U4yEFDC1+tMPSYPNruXAigAkxGE8BMSmgx18QyFzfqH6d/YOSJbmDb9ntud8I80nWj3bvdKncyw2Lm+ap0dxu+K9k/hijoBrjpGVcoFt0Ts0l4cKzIR4XUSgRs7A78zNjp3JOAk4Tzvx6lhjNImiD1fvV/LDOeT9/r45J+dv/st3//9348XvvCFeOyxx/DEE0/gp37qp07C/pf/8l/w5/7cn8MLX/hCiAje9KY3bWC+9Vu/1QN28+8P/sE/eN903Rgmr7urbP6OF+uSOTWNee2o94HUpqxuSgqj07+sCelfQrDO9t38zYhwRTCyMEHHRGSAmSI1WkkGhwzUMzO/hDm7o/quYWfTQ8KqwAj/+qfT/VXhwgXCrL0Wuu2gGV7T7O0R777ToKulkNVap8nMe3k/R93rZ21XAgeFppW0g5r52EfVBm6do4Ultit6A1Wqry4ac6/kHKuGTb4vzr3Mmsf7khOK+Mr/4q4UwSbGZQdu52+/bgtZBad7widT3cRV9p69K31yAt8pQUDLs3t/M418ZMPw2X8d6vwTHGmKNvN7zMVAN8+NzHOuw/DcQNHAgGd6S1t1HErkp1awYQ3SXP8mjV17+a0nrs/lZPnhH/5hvPa1r8Ub3vAG/PRP/zQ++7M/Gy972cvwq7/6q7vwv/3bv43f9/t+H77ru74Lz3ve807i/SN/5I/gV37lV+Lvx3/8x++bthtjru+qg8QeH/Xa6R7FuI+7FYYLs1arQrg3zOe+AH72PAYtP31fxO23OqBLgfWFwDR3i+xe1c4hRxe0ZUHXjrUL2tIs+UoDBAuO0rF0QJt531e9BCBofiCN9G7mZFlw6BaEB1xA9BIK851fOpdsrj6Imk15kQscsMI2FgqAo/VLLLp88Tx33CZHS2Eri6bB+XgUZUTLvzUWisuJ7cF3zV3sOg7fIaMTnpTnjFyMaR9Bcz0ZecMq4sJI1plZfylWhBYuCNapGhzbXcFgtjzZb0HDJcxaGnp+zA2JAM4l6AWkCXQtcAvyUJKr1tS9DHUsdRCr8DAPOP8p632UnnUDYzlF015blR6d4Gpdxy5twz2naRfnqXYrvj2Vdm/MoCO/lX04MmchQ4z4CN0+NwlwpjgwXwSSAVftgS/FFySpPhvZwV/rQvqc6Bk67nUTE9degv2GQL3MgzEYIB7yQgXoQXHcT/ne7/1efNVXfRVe/epXAwDe/OY340d/9Efxlre8Bd/0Td+0gf+8z/s8fN7nfR4A7NazHA6HK4WAeyk3hsk3zyJHlZHfRZinGNUF+BY6BbQFM3YWCCRYSXJCkT84kP11hSzJ7GkORjcNlab+Loqm7j9WOyqm6wLICunAigVoHa0rlqWhq+CIFaKLbXlT08ePulh/lL7iAxY13fdS7LrpiqMs3t0VaLaZrqkb1T2iXp3Zd2eFioPT2bGIomOxGAZYngGTfTIVjBWNdNlDFi14xLnDZfiamcppvQBgVg6aO6Fln3oy5y7U0hek+d7dGb46aYgjeU1au5vy7S0bDqi1KxFcV1Qy74YlsnORsFmK4dJFy/jW08rDusjDIDaJugdB7ilskk3aLVHIFjLhZga1YbD5I4URhyuZ7E7xyD2aqoAxxAncI01SL2a4WbApcIFGd6pPrPGbfmkK9rsW+5khD0xW8n0IRp+4TkikfBV1DNmPYKwTrbsCh69Pta1d4UbGxuo3WK5lnryp9QQcFZnrUJ7NwLunn356uH/79m3cvn17uHfnzh28+93vxute97q411rDk08+iXe9610PRMfP//zP4/nPfz4ee+wxvPSlL8Ub3/hGfNqnfdp94bgx5vrhA579UYB9A2LBeTUmexa047GipaSQuyPu0vSuGPztI0ziTjM83JTMagmTN03WBiOAw+Ue++Z+/EzwUgPUaOqmtsv2wrQdeGR6xjVhTXN5h/iWvdyvnmZ5ieC43NJmNK893Q5BA/sQuE0A4lBbu4g6xgCkGwLej3RDhJsg+pzDzcA8Hd6PtXVEroNpoq/uAKRSFGb+8bXWDIiD8lSvqgA0TAnLUTBPle0hNPtwbEelTPeTjG80/QfN5a/iG+QdGeuGJk7R5HUV9ym4+JPtvUE4KH97AspeP7Q+S6HyxN/Q1ybD80HbZC4P87x43SQY2XP2gggztxU0kePXNvhbi5CAGcFmFTtRdiQlON4bbLJ/wQtegI/5mI+Jvze+8Y0bmA984ANY1xWPP/74cP/xxx/H+9///g+77SeeeAJvfetb8fa3vx0/8AM/gPe85z34oi/6Ivzmb/7mfeG5MZr8unbTWgWA+4DjOha5kvFOJeAiccrshB0Yc7k3RMf6bxGYWpowGtuxBN21vqWJBcp69OvanOl2y363aoelwRcce8PSzLetbbHtby5E8FDU5hrtot3Myjjgtly6OdxgqA2Z9s/4dIHIJaDAEQsWdNyCmhsADbdwdIuruNHbjJErBAfVGKYDx8ipsmx4dpPnssRQqSXjATLtrsK2AzKWAP4+LOOd7Te/VNYx+K+FOf0IywyoMFN+18UDEe0+hY9jichfXXACBSukQFItCUM2Pc1XG0KB/8fcML44q/oujvIAZe3KnYZxq5Npmlun4MqaXi1Pm1K0P8atDHvxazMan8wpeWFgFRuYoS37N/DV/sj472AdKHCDpdyHU+oz83jOdRWllD5KabT2lcCRNc/ghnbZjGhm4Zu6FP1X+/rrc7OQxPEM/DrioICYMUelLcGojRjx81spEzYhk4B8Udclur4KdA+CAwB++Zd/Gc997nPj/qzFfyTLl33Zl8X1Z33WZ+GJJ57Ap3/6p+NHfuRH8Ff/6l+9Zzw3hsnH9pVYyKaFC5nxLtOTZjw1YaDI5Dd+PUfcKxJGO4CFC7uUD8oS16ArughaF0S1GjMHzF1w2TuWZterLpa4QgBpiqNaAhrRjme64CANBxFctiMUwAUAkQV31FLmQFbc6QsWAQ7oWCF4Bh0HZ3d3YHAH6bjTF89kV1PldBxEccddAws6VjWmvKCjieKO99/M6zbei68PR9jC1ADcyXXJjf4Si/UxWY1p1f4rr91KoGZqp4BhFg9j2hFRL2T0zXGMzD43G3oOAV9RaVWgIMYPvyF988UQlH/ORJrD8Ax6Q+2V1MLc3znIjtUkz9V6Z9Xamu5lYKJkBuWf8jDhxq1RwjleFT8yuxkJGWVhmmQytY2TNFXORt7TEDu8gqaJDsLRFTLgK3UxhLXdPeafnHmA2xO6rCF3tREf7w+H7khpS31tGAdqpEkTx4osgd8GUGqHueA0m0ASDLnEGEjpcM18FFYAFxLqHAgXV/1AS7rbh7w8m+b65z73uQOT3yuf8AmfgGVZ8NRTTw33n3rqqQf2p9fysR/7sfjMz/xM/MIv/MJ9PXdjzPWHA7fQ8Y5CPODE9qfrsKalKqaexzxy0IE+fcJx+zsZewrP3HZlQXxNxf39EsIEF2bTDhvWTpM5Te40Kbcwp3M7GwPL1LfaKbfEqTExe2ZB774dz7fUrcaOcQS3wtn2ussJ94qDB6RRy/XtdSo4wpjkpS5+3xjrscDbn23Ru1Rrz4Le8ppb7oi/JqkxnAgNm31Xauqa2wjXSjvqljkGyTEJDvy53KbI34QDGHnPxY6zwhbm1S06VWjkYkx3DH3vIhqnsYnY72FLm2BcTIXMgFPI7UyimA//CTjHUeFse1VOeU63Xq7NlH9FWzHJvY0ybQcchXZM+Me2MKw4ezCpge7gkhEOAjt/YoaZ6I12dWpzwpHXsunPAAf2Q3bxDQKCjHDj9rcyceI92kAxHXMKg8aIadJPfls6GfNKchD3+HIx+4egWRn/ADs0di475datW3jxi1+Md7zjHXGv9453vOMdeOlLX/qstfNbv/Vb+O///b/jkz/5k+/ruRujyYe5Pgq1MftVp/FkvULn6uB1IQDwXjxYfpQvUUOVyCrpABaCS+KV9Aura5MRea+2Xa25FtGkYWmuAmnDgg5R01Rb57G1AkjDIpbwRqWhicF1sVWrSXdPhOGACi5lwcEz3ikWJ617EpqGgx9kY0NhZngzrSsOLRPicMS9u4VnCBZm4QNzvmUw3mCu98j51VdR+v+J7agMtLOxO5JJI/fzK+z5o1pfulbNP83wAH36c8Y7f+WanIqm/AoXc4Z75hSbNTJzJHC+1OtSZLqnLjjEwo79UuFmPPVar0azW2aaUHBcxQvuo60Bbqe9Ae6quhnuKtpRx3aLlDhkeGoWuk6VhMu01+oJW061VW1H2MwDmStirSl124mX96a6IU5oXgyvW6nr8IPguI/y2te+Fq961avwkpe8BJ//+Z+PN73pTfjgBz8Y0favfOUr8Smf8inh079z5w7+63/9r3H93ve+Fz/zMz+Dj/qoj8JnfMZnAAC+4Ru+AS9/+cvx6Z/+6Xjf+96HN7zhDViWBa94xSvui7Ybw+RjsloattCe8vhS//g6gKaulUt+UOp1iwu73BqnxrB7+a0drr2Jpy11gaKr++EBXSRMlGY183S16gxoVbTWIEu3QC8I0Jv5zVvHAYtphl2wSIM0y2S3QnEBwR0jGQePIr/Tj2giOKDhssMy2amiyxG9N097I0BfIWg4wLYPHoXR9YrejW0cxMz3qsBBFJfu71/EzPnduZ2IR5+LxbL3agGB74+HJXk4KmPYbXCPDsn4CBuq3EJHDTs0d5VierdtbYBp97Z9rnlyn4ajprWAPvwVZgE5uglfPalQJh3KHANA+uJ5LAh5dch2s0LkZlgqXVAdzcszh6hMmb9nZl3x1+jsCa6CJgO4Gt8gKFzFmetiSNnjbrQDGxP/ADdfz+3UIjv1e+3u9YHfccA58Mx7qzIgOCkIFAk2pdTK4FEj+MsA7NEu/p+6BYJMWqYHKH3QnM9JKAWhqk1CUYypav1ramLnMKhCW+78oZIA9Gvjk8ezYK4/uT3jRPnyL/9y/Nqv/Rpe//rX4/3vfz8+53M+B29/+9sjGO+XfumX0Fqasd73vvfhcz/3c+P393zP9+B7vud78Cf/5J/EO9/5TgDA//pf/wuveMUr8L//9//GJ37iJ+ILv/AL8R//43/EJ37iJ94XbTeGyS+HxTTB1cPFTJ0rC4Kk3x7lGzZOYszdfzNgrmov9TlB0czdLCA91xBurzP11wQB9Qjb3s1HaiZpY+K2hU4hYgfNiAqO6JYC1zXL1o8QLH7ymenDRxF07bhAQ9ML88nrEdrsFDo7mPYAiB1CYwFzdiKduqZuyS4FB+Q2Nu1HrGIatirPyzOGfpDQH31bnjGKPHLHBmGBZghQcEUO0AIelGPDyLzypCA1eTJ5Ezt8C53ymbIDAAfQJN8L49b4zcQ7EkKFxkJbfPCkWSTfM5zJuo9YBOHigRShkD1kV/kX+ww5P3JCxWl0e0rf/PsquJnx7cANJ98hZvhpXDtlN9htr60ZZ70549g8cBea6jN7uGpduQ5/9yCpFLjB1+9wZQGwAMIUtsbYihSYDE4DxyBMFeZsl9MABHodeHhKczEho934rVNd7XwRWja7PTxb1/Xxye/LYPeL437La17zGrzmNa/ZrSPjZnnhC1941/H8oR/6ofsnYqfcGCYf4zn4IGulSbqRsmJSYMZ1QiLYjoJ1WdNjYalCQ9eCRY3lxcEY9P1OHziCybms0QW98ejbzFonavvHm5op3kzqYnvvIX5qnUkZq1iMABluhzhDMrie0gkYMgaoa8FuFG/OdEMTb/RkY4WlzhH2UQy/aTEUjGRnP7UUfsfxEPcTczxSzTHxgb50HjAzbvkDxE3vnvSmmOjV6xIHt/FlPEBeI65pcbF10U38ysWaQsK2Lkz5XOQh4fdWfx/b2YZdBlmzClxVhmC/K9aT+mlwsun0QG1rj9I9Xlyt0VfC8YamkBD8Mz6gfT6/6ZbutxVwMsHtCUXgu+Q4uwSgU5vCcdEBR7Yr5R2UylnCKf0d+5h10qf3odgy7tnPuNOrhDsBOGnr14Svn8sV5cMKvLufHL0A8Ou//uv42q/9WnzyJ38ybt++jc/8zM/E2972tg+L4A+3aC+28bJMajFdCT+S2Ixt0qvwXkTfx0pouKOez9qqzueUC1RscpchQh9q2+a0C9Ab1tVBu6Cvnvxm9ajxblu8jr3hcrX95pddcNkXHFVwZ11w2Q92TwWX2nCnLzj2hju94Zl+C5d9Cbg7veFSxesOuNSGZ3rDHT3gji64ow3P6AU+pAsudcGH+gEfWm/h6NfP6AXu9AV3/JkP9QMu+4Jn9IA7uMBRG55Rq7vsDZe9Gc7ecOxj3TO94VIP9i/ruuXbv3RT+qXa80ddcKfbeNzpDce+YPXnjrpYH/uCS73we9bPVRtWvz6q9feomRGvK3DsmSuA2+RYF2mBNffLW1pgrrEa/1JBqgIe50KtHxl8mRi85O2i/Q+pbeeFeIbTAjdLrpL3Bh5xqi0p9UAJKBso39DeKxxhJ1yAw014OHwbC8hEPyY6Kn1a4Uodn5uD9hKv7X6x+twaOr8xQLc4Yp2xgR9e0wbOqZYtTI5VYqjR8OqSY5lmJXYI5YbjqJx7Fgy00Fq1+6vy+z9kJYT1B/x7VMp9a/LM0fvmN78ZTzzxBN70pjfhZS97GX7u534On/RJn7SBv3PnDv7Un/pT+KRP+iT8i3/xL/Apn/Ip+J//83/iYz/2Y58N+u+5SCsTt5sux+1Ded9hgfDJS/herYY+88EnX9dlBdDFThrzo9JC4CYsTX4dHiPQoZ0Hq3Qs0twn74F064KlWapby4BnF10XXHZugeq4sx5s3zwA6GK+dnGNupuWf4EVd/rBz5ZfcWi2B1+gOGBF7wsaFBc4RkDcwfXz1Y32B10tza4IFrXz50xjN5159QXpoB1H1/HN0+0Z+WD689HZnfnrxetsc7FKpjqj5t3QIxAxZbElth6GuR5wvzt38PuWOdgCcCw7AtaAM7yx9U5tF4D6Hvs067vuFvveuCBImnvF546vi00yN4CULWLeZWjDcNDR+O9YaAKumzzvG65olZOFvrSeFQMOPb3Wz5Rv8Mpd4Px72n12r+gEVJVa3p7xlb5vaKp9I8eUrEsT+HAZQaPcwjZs/2MjtOpNdBKvtTERW2kUdkw3Y5LTzjvO3RV1jIZBkdKWDzqZmq8lOcets9r7KBw8zCWkugfE8YiU+2by95uj9y1veQv+z//5P/iJn/gJXFxcADB/xO90kXmSRwVSeh2Sz2fu6Di2MOZ4+trCws5vxv+lSZbyu297z8WCEnSI6RptUIpkmltR1w61BOchNcrmO9lpGl/R0FTNdA8/gMZXilXFtQD1veg03dvRsheiUHSsyqVL/dnFMVhCnMVXK0a/G00CHkYrUFzC0tfasmHCAyOKe6T5NH96vpOWwU2gudP3t3vaWiAj740h5z57mvZ7Md+nib6cNx8MPn3zxzDXcz++rxed3Nj+1rKIrINSNLKtYU2silQVBgS5v3koZYUv1fMRmtSuY4EPCmSEYZP1+cJUhzIziFNtyRYmHhuY4xVw2Kdhc2sHxylhY4gwrzBVu5WE2/BBrc86g54EI16PZMno8vAfwTChOR7xYCIT31kjMbw7A+M0jQpKR7gN+LZLZzJgjpoJXPKcBq3+W8ek79BxLtem3Je5njl6n3zyyURwlxy9//pf/2u89KUvxdd+7dfi8ccfxx/9o38U3/md34l1XXfhAeCZZ57B008/Pfw9aFmPDGs3Rq5k5p3mTCSfVcIhJz33UcVCkdvExlPkUDYjI0z4snefFgH/MzyCvloArP155Hj3VLBd7Ez53nB5tH31ZrJmdLiZtY+9Gcy64M568OuGZ/RgJutue9zvrGbKf6bTrC9uKj/Yvne16w912x9/qYJLP372jgqeKSbvZ3TBM0q4hjt64abxhjtu7jd8i5nr3RxPN8Gl190JuKT3jtN0WfDd6dnW0ds9donng97O6wWXekB3eo/KXANSTPkGz3wDq7tHVn8feYqfBrMf0g2rLbOZf79kTYSLRVoY724ofv1xxQJLZoGC7xQM4cLsNJa7LuOFpisDhircXn1lcv6nE43Dv/X+Dn6tP0r9rIgFP51wDyb7DZzdCVRFeqj8eWq6/FAX2vfhDEf2Quv9YjbfDLdu4XI9qp3UCY406bjfcyJ+O64ujl8bc/2z8/eolPvS5K/K0fuzP/uzu8/84i/+In7sx34Mf/Ev/kW87W1vwy/8wi/gr/21v4bLy0u84Q1v2H3mjW98I77t277tfki7a9HhY0Xo2IyIpeY9zHAF6D9vzgxiexyzcvHf8hyD8eDau4Zp35i6QoEm0FXznGdhmI5APLWtnSGsWDs1CTMFdIXtj2/AnW77zZfWcWdtaE1xIQBEsWrDoRkhH+rdzfCrawGutYtg7QsWMUnj/3bTxC/air4aDQd0LNJxVIuKPxRN/4COVWwQFteLL9X6cpCOo8MtUBy9nw0dIorLLqFjc81hdrg7tDAIcKnU+dMi4UOP7vQCiK1xgGvl3M8OmujtuaNvjSOzrwl2jli8jtvuSj1sa51AfFeDOHPIA25olKGZnkfvNlqFXH1OG4+MWc4kJmXOwUHdzHsW2CnDvV3OWut5g3PVtflh/T7V1rzIV5wBh0Hr3dBGhVaQWeNqm3RnYIt773oD5zgGLRUT3NyHCjfglzQzVK4uBU7ynaoCUt/jZDOXig9Tdr1KyCANFLgKE8JJvvTQ/OOWjHB0gZE2MReDBp6BsmyN7+y6cL59Ofb+cTwi5SMeXd97xyd90ifhn/yTf4JlWfDiF78Y733ve/H3//7fP8nkX/e61/3/2XvbkG27rC78t/ZxXvf9jIg1JTOaDEzah1F8K81BQoIaGoMCqUAl0obwQzElDYVMhGZGYzXICEoDlvUlUfrSpzBIGiiaEBQhCi0EUawZ80sjms99HXuv/4e1fmutvY/jvO638e9z3de9Z67nPs5jr732y7H3Xi97rbXxoQ99KH5/5jOfwbve9a6XaseFLnRFvy62+9q+oXDLct8jyvk8BqCbrWoBwrp6FYqo7ec+TRiBSeTiQWJk+NEAU+XA1YLWMHvEmbugDyPmCiMe4nBmYS8YskFULaq8WHia3Y2ubsTviFMFZDOiqwMqgg3qamrFJpsxGwN41IxM3jpR2+DmV+qhdEUwMOx2OxtRNElmyo4SEo77WUeD6Mj9J4wUXHWfI+/XYOZmObTFxmYEn/cNjHImb+503PaSUDMWffV9Zxm/aEeLdb3jV+Vvj4FfGIi0W6oUhHYYniPFNXkKMerN31eivoiIRL8Q34noroR5bs2REBJHLXPGLKx1PbVNpZ5rzIeUeisuFBzXGJa5Y3NdKL/Xfq1tX/HM0668D3+bp7SpDKhgZjJEPF6GwYXXjeOvSKO5ssCdVcVMSvNkBEaFy3rnzaoQdW9fpoWZeK2uv9fpuYj8i8To/cIv/ELc3Nxg2zLa3Jd+6ZfiU5/6FJ48eYJHjx4dypxd5/eyacQFNTm5lf8txiyitCPOU80BP0tWvzpVZxc6ru1Q12upYPpNlhhpRjw0Da9ivVZjQIG2XKCDRn1qZ81N3BDPDeEAMwwUNLQ2LCxAE2zifZMGER5XCG4wII1ud5ttCjowpKFpw6bOAIhYqD01a+MLuqud7QydbngNGpfmCDZcPLpex2bBcpQbGSPeKYAtFBpkBTZRP++2QD1mze6GZG4UR2Jq6vJLaGT2iFNPNzlrPy3labx3qymR744zb/KjRb3l1Xc1DK4FzqE3BADNaHiqDQzMkayPLO6UiLxKj2KWPgux0wOtmHFMoEdkB8nNC1+LJpc+oxVvlQSRc/lKe2v77mz7GSFf+ePTNl7JfBqzc4qU9joFBZdyZU4g0/gmw6PHZykfbcKh520aBaHGzoWw+WGFlSCvcAeres9fP9QKd8/SZzN2/auQnovI1xi93/RN3wQAEaP3WhCAP/bH/hh+7Md+DGOMiPjzP/7H/8AXfuEXnhL436mUtzQJpvneuXg54ZFc8lDoSGMarar5CC1r77XDAuY4HDy4FK2m2+Yw4nRAYSralpKWKuxiclFotz2gufU7AFw2wd4VY9h771lYaEMsBr/5sfeIsbINgbqt/KUptNt2tIliaEdXv6tOBoY3/oZwYgFxbtFxGaZC35D3um+i2McIOJGBzW/pMkJtsrmprssGiBH3BTTnkIKxik3RBvtWTRpvyHC1QDJfA803LQnpPog70ge+w5iEXiznb53I18h4ZtBI9T3zWiHwRtB3/01YRuSza3QbebhgajgPyYyUwch/TqTjCW6VOCnEXYNb013HADVpgjy1TRUfcLWN8Vwvcjmp+rQf1+pd8876dXUsrsCdc0fn2oIVH/FUIh5FdEZ9GvGuEOq1j1jgYgB9AtTgXkcu7MAk2ONJXa0Bo1PiiHL35UwewNMZ4weUnltd/7wxev/qX/2r+KEf+iF853d+J/76X//r+J//83/iH/7Df4i/8Tf+xme3J09J7bK5ml1Ds6Xlwm+TzH0RKvxGuhYSe3DWjoPOSVPQvOJW57Fn4jIaJaCKB7YQPxdw7t9oRG5+zrkPR9qaoPttdHYWnbuaSINgYIdZs7fhqnBpEBlhzd1EgdFNSheB6rDjCj9/uEjHaB4QaHST/l0+uYBR5hQ3umMXc5vblOTXGQftfl0scNEdt2J5TY0RoBVwQwdvbmm6A6C066fbsUEOZys8bp5KbHBGRHnxkIZbHKAh6VMrUy3zuxNxWucz4p06Qac0X0PaMt8+vzMW8R7xDFzZf0nqhVJX+YPZRsT8uotY8vf6vBKgq1LpXK6q1yeCLjO9UEFpX6mMzMB0A9uC7672Pithrc9kulHatPRrqvcaQS5w0813p/gy81q9Oo2hTANqvI04XDmCqoXc7Xblw2I4iLJJ2XjEmWIpba37C8FKA2lZ70duczPsmMDGxRHSOPkeSvWv0wsQ+eeN0fuud70L/+7f/Tv8zb/5N/GVX/mV+KIv+iJ853d+J77ru77rs9eLZ0l1B6aqKjZZz4JMmshQvunsjcwz+eSIJTfMstHNayKl1AznimQKyl+o/eFSuvrC41l+bHJ5mt0BY0pEwehuIibpUnJUADtaXBpjhjfZsy7NLsYQO8sHZIpkZ/vHwN42c9GDkesmvKkPoAudDcjmvAul9CRkw53wADs/n+5bdyNEOpLn+bcxDNl3+4DsG+G6kmNCqtjRQgtAdX1XBDPA44Ak8OV8XtPtztT2+f2mS2kcv5Kx8ylyTmfU+8S5Qa4AgFaGoUzISnkr4Sz4DjgqcV7AEkclGEewmKNXUMhUwZV6T1LAncA8CzmRsx/lLHxiutZ3eix/pl2pa/TAYy2MUMUzfR7fGzTw1crKYcl6Bn/CDN3FE80QFcfCZVXzcTZMB85S2JVcre+tl16r6+f0QoZ3zxOjFwC+/uu/Hv/lv/yXF6nqs5Z0+MULqtNi1j6mDdleOoFxvyhp+UyJS9RU+cYta0jlsbojyl2u1eGbaaiixerSFssc69NQV3V3gfj1cw2GY+/ARWGRuHQDWnd4e1ZXY3dFXF3XILjZyMgLugA3TTFgV+E+bh0WeKZhNCftYqFrH8nA0IamwON2a/2HWeZvzlwIFI+kO5NhVvokZJv0gFMANx7cRoC4kU5dXbmJYveFdhF3V4MxLiToQxWbALfD8pq4qtwJjQgikp1dgiNAqNFdNe+MxA5zlwMsgqC6++AohD9c5cgIhLo+jxEYMyQCJLpNw0RkJyYxxC+3TJ8pTbETRV6AcLIBTcRgJgWxn9fqClRAVqa1EsaFf4gH9SkfPEJdWHPb9aRsMKuV0K9dY37p3okmPIjwwUsgGOKZ6VAkXuJL//Tj4KjoNH4r/XW+ahp6Wq7LWsY/YXzv0vZRhjHw1/H17zvvFf5G1PaqtAZNvs0ZDdZNaV7r2XwMDPfIrOU+Rby7wlU/P45XJD2Y2PWAOoFPhydbZG7FOmwD1bHFTXXwy2B0KGQTmKodk/Th3mOQIRh03fGzdgBxdm9rUPJ35FmmihyjnjXDq7vCb4WNqG3SLCLeLSVftbNrEcHmhLUPgcjADQbeVI9q1wZG92h1MjCc+DUoLq3j/6lFtbtp3RgLETQZ2NCxy8XhBnq33eIi3dzk3DWuQbHLjdWFjr2bseOGHbdqFu8NA00Gbl3Gv8gIBqq5RKFKe33FEzU4M8AzIk4ty5sjg/YM3XxDdJsBxzEA6Nhi6HdtyKA3zQk11foXqNfT/bxdkffdD9vxp8h7qqyLLfY2ujp0clGbmDxX37uqVZxSGPEpBSKt1A5zHv8JHAk3YeN8BEB3vpkCTY+nLZlr9cJr2wvRm8pWwr4QfGAm6lNZzXbJ0q+1DuWYn8BN8Myv7VyINZSVZiPqZ+BSF4cLzUYcwcxloi5JvNRcyQmrP3ssOFzlinwwohQJeRnItHEpnaK63g15CUJBRl2jpiL3K+JdDMDL4ng10oMh8q1JutDB/pVwyAW0LFuBQkaxlaUkpsW/Od4Z8VZFnt8bmviXxnipNXP1e91Y+dv94xOHhlRk6nqJNVpVSl2BzX/30exWSa9gH4KtISXdlmfQ6up7FUBHw9YUTYB92Ll98yFT4aV5bvgnbnSnzYzulMPhZ+ri0fhcAhq42PEBzIWtaZ4H3iq1I64W1xyDuK0LwF43QKVGgMQVQWgButeJn5/z+6ZaHqBVfIl4h+pCl+M7XV5DVX7BR6lQqxbB2xj4PNAR1eg2FzIWeqWoElLU/H6aE9zEmbdInlLgtBDxg8Q8qfXrnCz4a9UL8xDz+aTturTvQC8dNJpO8DNaIgWuEOQDaNKvKfMMjoxADBWfT87n4/dgX9XbOpvTzQTZn5uY0S2FC4eLPWNqeyHgUj5BMIo2yHWNgOf4FVEsKB4BSG5OE8FH7G2TpG4L1f+9L8T9dTpLD4bI971Dt0sxMPXJ38mhlgh2XLiVKegALuKLRoBNkXHpNaVy10ULYNJpDZwDTW2rArIZ8Z+sXcV58iqq0BmexIX8QAMuFys+BiCXEjdbxb25TI2dQXUaLtKxqeIWDRcR3DSLb98APJZhF8JAcLOZBfzQCzYZBqeAyIZHrWO4b/pN85j1fnv9jcfLNyt9G5ChphGg6l1E8ahRDb/h4tFgdliI3E1owa941BDW8rx0dncmy67WtXG5iMF1Mg3wQDq+IVoYW2cSnPlB8X0fo0Gava+udrtahEHEs30X3mIHZ+4iKKL6jcaU7kMtz09N9lEOZ8jr2beEbnpJlRG46/xwYRgm1fgCV+nSIRUicoC7llcjy3g3wqBcK1xpYsFxtT1kcmWuIgqzbIFLInlS19p2WdpX4Zw5mxtTnwrV1vI39a042lVNSq1Tz8YqCXXCkaFKo+HTsajn8NCJcNPwV4mvgoZx8kkwpLdqWsb8hXG8IunBEHkNgu3LZbXGUQ885ofglGpEy1Ws3JDrhSLim4dL2K0BY0dYygfHzoP0DrsbvgFjt11KtiIeNV9tuzeVrnekaF3cRc/U3n23sts2cNutSNuMYendz7pbw2/vRt4294kXsfNsFXehE8UmDWO3wTAp3SziL1B0MVW2qfwVvXvEOxkBt8Fc6J6oq+tl4HY3xuYiGhtRg6I1xW1vXtfAm24lz4A79DNsAtx222TsIhsSe9ucnrg6XQA8gcfwB43lzAzQJGqHE8HtgEe/s3C16qaIA4LeW4mMR2M7D5yjefe8MQIZGY9x+4MRyy0fgJxskDL9E4/CDbvEQV+IYAArJ+AJ3IFKyjEPdxPKimMleqd1AkdkJ3VGNvnYK21fz9crAbyKr9ZV4K4xJafvynI84AZwlIqRY9RLhbXSNQrfWX/4Ysy/JwIf417qaVI4mWUcJum8NLZyemKbmMDR8BlIS34ls3oP0msiP6UHQ+QvN5vHGq+iU+4kMtwtjRz0qIvQOdmQxhT10ppYL3Sb8/fMCxfWUH8ZLy/uI6+DalsJdSeN8dAdlzmaQze3Xh8KDU7CpNfWGtCA0U18aaLoTlxbM7W6doTqXkVD7T7E4sS1zVXqOrD5mfGAYmu8yAbQbu5vTTwSXnOCCUXTUYLyULHtroBCMjiwFdsEEm7iqOeSGMOMfrwcb3zb4yOSmCoYkU6QhnqD76nKVw2iTSO66k9/mucq+uHR7+xaWT/XT5Fs3ltk3rvjIpvYi11bJD4A5Yhm9uWQqdwp0cECvhA/0St5JDSBU54RbsEn5bngOGgMBOf9eBqTsjIhZ3Br3tSmK3Xc0aagoyvcWN4VwSHbZB9fpSjyJ42NCxEc1FJ/1ZrTGPDYN5ki6CXHhmMKi/3CmPD3gtaerZOTcui+EPfX6TQ9GCI/fCMVqXfy+PRW96b2O+BD6q+qK1f71mco5tvlgJmLrM9rnO4hk5M9LV8VSJNlRar3R1n0Ht9bIS74p8qvBcdtkfGGCrSNZEZEIJoBd7aG8HFWabiEf2CDNpNx7fxbcXFfv+EW9Srmdufu9hjD/Pkv4pEBJSPeDWyuLVDoaOhkNFTBiHdG7wStpQABWNQ9EmeG091dZR5wCrtOWE1VbsKJgPcSNbcRGKP557OLfmggtw/7rupwfZQ4+C61Z9Q8D3Wr6XancK0Az+7HfI7POUOaPhOG3FFXeqYn6vr51zyVT1M5+DV+9RzfbLG+Bm7x/+gKd2xTMjpHFbKura9MxBmOtStX4K6p4qdCZ0yHzj+fqQ2VGajMmcxjUyPgxTm7M3hRWTHMNVA9Hqn4pNFSJgw2oYsvfGm5zy11w9zJCKFa1Rfr+sOnvY8EnuvtZXG8IunBEHkaOeUC9cnbh/mhAcmZqxE5KRHvhGft/qcKoFsku+GR7MCw6kWi93tfLOId2yIwK7ZwryOz4WtwK4uu0Wfa1fpwxsIrCtVwUwxVjG5GhjqGaQNEjbA1I8pNNgwdEI96N8bAaNYpUxZ0qJqhXh9uviYw/3gxD4XWzB1O1ZiEXbrDiMPZ2X1rgu7udM0vBGiiMK8Fd7tTBvnL/kvP+ACAohFOBIztD8rChBOgdZfgRTxiINX0ioZ0pYtbA8UN4pAGelAJP/vuan3AJflhqnlVRIhc0IUOQMS5T5qaWl3SgnEiTEr+vnNruYuQ3wknJ08L3MpdSHGBm0W9pfEv0KaVI5Dl+VnhsOTXus7wNRzbWpb+6eATB/GcXH5zSAc8hoTzNUR2tqnGmhcxjR2S8Z/qPes71fVTSNuTtrRmZ3g6AsD4gGxPqOTJcEBAL5D7ciY/mR+8BI5XJT0YIi8bibYml1zV9Wp5UErzJrKIh2gV9ys1d3i3vm9u8EZJmAZ2/u8gcyCYbxnjrWO+eGrkPRUauLhPODl8v7XOJE84q98Cp6pxFiLA0OIvG1xJM7jWocOCzwjsWMCqZ1hbxl7vEDG4puZD38W2qk0HdrkAIthGD+na8sTyoNh6x63cGI7OWL+2a11UsLtWZet7biZqp94qdKEbUJ7Xa4f6xTMUfYKIDyujsNC+eee7fezd1fAy0ipflL749Jgo1vawW/x4eNCpyle/WU430A0vmA5F/K4BOQ70dtlADhsKD6krkQ1EZZdfJdQzInhGnNc8f7cS9FNLcV3e135VUC1Ik4c9SPZT+Wtwd/VlqT9SJfDX2lv64kvgvF1rvSuhLj9PxwZiR2Nc45pHfwAJuiE/GF6etWdqRmnH2taTyHioZaS2QgJfHCOVPWiM1xHv7mt6MER+Ttcna6xj0sapSOFyi/qdkj2lygjMQZiGSfsTwTfC+l6yslCneWv4XmHLkIF32I5oYOZZUwXDO5Nr3VTlVKPaviKl0+7T7QSuiZohoW8p3IchLeCGNlxE3e7QQssKUfqNdsb3uPGZj+st4QQe8U6jjwMeWtcl6vAUQisbpW9MQik8N869iEGEMymbQ2Z5ZnTXAs7uDCKTk+p6woWUXqR1RsOLujhFlrrChQ5kDJh3NgErEa+TMF6WDb1u4ljyEjyJ3tOoY0FVcFx161Mcqj7FEXnZ3nlt3dkiA1v6EnB1iNjWyqycIb9C0Fc+6ozhmeqqfViYlGnNYy63Mk2255D4ZodjKtTjPuis5icSAaagDC6onHNFa9LzZ5/U177JWzJN++JL4HhF0oMh8qMPaNssZnuV2PtwCqimQoea/r1tJqYPi9EeEe/onlXuvTaa4oRQ/VY5bmnH/dD+3Yr00ErG8MA4F+PxpYl9JVc/o0iH0mF5xLEh7IB0AOoO8AOK7WJEU5pCm8Y+IBi4XDTO03UbEa1ua3Sh28w1btPwqLnZRnA23SPedTXi/GhT7K7heNRM/a8CD6SjHpVO8bgN7K4Rubi7glqsPWxteNAa4JHDQSQuhu2+M9bIeDfunlcNmxiN7iIIHMacIIPniClWxhBscY6fPu/dz9jBM/nhxnnDGI/hvtPD3etI3G1/1LAtAOAaIncE1LyU53RPufpSTl4dyOwRz0L8+DIIzB07+Yxdk4otDEU8rhutlJaTy2HG0qapmJ70uBBTgMwq4ix7er/iGzO+E3qeXg1ng+CM0ikOQSqPBKkFdCIcHvVOmIOeq07DEXxQbZfjIwGTKdfYaDsOMAS1TRC49qByXBJt4nEB5ySUn0fD5kDamYfIWzS9PpOf0oMh8kIVlXOl4npwEW68eiDqos2s7qERBlKcU5aifheFRbxToF0A7RL+8XmFajLwxW7PCBHspQgwmthtdrvDbCXProjP6KYbYLfB0K/fKVYbgCikN7PEa2pufb56OwPuiF2zcjsEaArVgeGVtKYWVAcWNW+T4WfVdpVsHy0Ygd25lCYe8U6puh/4f4x4J2a48KQbvgbFb7qq4aIDtzD1f/P75tRvvKtwmyie+Cg235mekKBDI4KemztwtKEAboer9RURh55W+DWo0O5EWhQl6I2VMeJOXsyfi+GdjbAYEfePG1b9ZKpc4hLQIAppK6I5J5gm+l3zIqOKi5XKVjhcT5IM6fJ6IeC6wB3JTBC3FdlE+QuTesqsrNgT99SmM7hrfa1l7+Zlnk1iDTU3L54p7UcyF9SMETHZOZ7LS3RsjowXR4gMuMN3hJ9ceLJDscfJkefjEWAgpLpe7Sgh2iAS89GicGbEu9fq+vuZHgyRt1vcME/U6XzeF6Hv4kH4SZGHPXMxUlLRciYvgJ+1I6n48AWLtPuJTcBV60ILfS7OOL4ODsB+d3Gi7XUNSb/W7m535CJaVqgRYNwM0oSaAxEMMQW4DLOU3zyATu/DuXcY0RJnWEQwurnJ2Y2U5oKXwWYU4r/pGSYKDNnKPmVX23KzYzheWuEjxig3RYGY3YHv1MMHIUeXLoXAmFkq/z/d5AhvY9+1lfIIoswwt0aokefsnAqa6vka/Q6FaQjBS9PYz6dPJoEznGXeFFIzER3/tiHZrWqi9XlNa96KDziWDWahwmmRaguxrowKknE54Fj6FVUGvpMusIpS76G55f00JOR9qoq74DsbumifP4SnXFiy+3+UM6f0mQ0rSPKiF2ciw9q+DHwT1w5qojhrVIOt48ma0+sgM8YNpy8jGWcDAroTC+Z90VedPVGLeY8Sj1pfFserkh4Mke/7gLaW684l+elb1qAPIW5rhKUN4AGP8ZpEOY6+4O+5qRuFmw1qJPHEIuafbyS45H6f61FLFcYAyIULNDdaVZia34PioDm+UfCpq+8FkM3lqqLKBprdTR/4NmzNqZpsuDQ1YxyY9sEs8y0i3c3FrPxVGi5txHBvYcFv4W4vLeu6NMPVXfth2gJr643DqTZnKFz1DjgOe94aQg0fsYfUpJW2ZXDDTUjg7aMJeFMdsEn6yHNA7X54GzvLy5vrktCbun/4vCHBZ99D0l83D7ru1XeT7nZN3pBCZ1G/v2L+d31eUZ3huytpoUsnbYrEeXOF6Yi6DowA7g7QU9Pa2GWNFa+1Y116B1zFv9LIsz5EXo7D/F30vIwub+6oC6jq/YqDe5YT/VE7p+V5xj1FvKNl/Rngldvp3tKpdPulcLwi6cEQ+Xrb0rTTjgHpZlpWrVFFzG+eoqgogN0okIhAd1jkui2vHm3N19hum0e7AGMHtLn3im8o4oZ46ip5abDLbVo+6609ywaMW1vUskmsTQlJ3TeJDWFbEJb1XUw60OEVGIUbPFgXAOJeBWJn49p9hTTYpRQ+Lk0HRjcJXZpiuJuPqfWtUSJGvPcn1tEmFhlPfWhvndRZmN1hkezEIu896XA4l7k7m6jYu50dbs3gWJfIwL4XitOLkELJiVTm1iT5JsBtnJVKGB/Ton53SV7EQ9NGHvzWPZOYemUCnLBXq/o8ky9b9cQJ4kDcIglh62+cJH5HuXtTmsrLKWE9bUet6YzwntSjwKwqPtK5Q5smRrfCru1b/qXzyAFv7cc1fHflncE5rDLaJQlt5R5o3AEfr9XyfcV/0gYR5Bm52ByFS/l0M53wCWKPmoz2Av860MTXkJaiVrGAPyX7LI2hQF+fyd/T9GCI/OVmQ+84+JLGWlESBj+tDRFNTRKnrn3A3PAEEDWCyXNWINeRAKEa5zJrAKO1UiAOKZ2LW9UD2hS45mp47R5p3Z+tDyV+vlFZ9sjc8dzQS1wND9VQ8UPcyM/brGG5zx3UXd6GYjBiHmCMEQTSBGN4NHjfTIakUd/ACNV9+gjDIuNJGvKMogPllZktNkhNOFJdb58UAlfMmlL97d9R+IxZDQ9wDxZOhyTKWvNSlW848qxdw2gqz+WD2sU8Kx4XKBJTqFvLebd4GyTL8v3KCMjZRrQScCy/n5fAVZDapit7oKw4nmWvJNzapqcQ55W+nuKseQdG6jq+u+Cm7keezzX2I2Jl5DcmDtJRg5MJX+wFADJUra9bZBtDSwIn8KGGL42qqvYzRjAYYcS+IIysGf0njtcudPc1PRgiH9IrgFwEcNLAeOn1j8TCN/FQ16ufmbuKlcZ4tLYncY68+Rku4UVAHTLSQBwBxGbCcqgbixRLH5jBHBkLZ87B2PnNCiYJYxmhw7/DadH4OlETYy4EpoYWFbTmkQHRimTXgrdgzAATEoxJ2JoZu3UXHrhn8Eyf5wkic154E5EJUTicGyEOc++z/cl2Xka8gzMUBifxrIoMbgQ/rgx87L6EDSbL9S4Ol/fFc6NllLvMS+aBEn1I+Zx96h9U+Z1XEzRWUF8dKeb0ZvL3jJf5z537s7MdnGhz6QOx1hN884GD3AFXa811KCprNYe2H/BxTeihiXO5mldo7krUpx3iDqYoHqcCOr+fi6RGSUFW9qypEKk3E2pyTb4WlHXJYSi4GOJdMh2soZRILsFx0hi1QpFbwf1KS1dfGMcrkh4MkY+NgCp57uJdoBfOCv/zS2o8bmm5XGaY5AsLEoFBdb3tNrmObWGajznCHz4ixpJAMtEAzzee4Wf+AiOa44JwtVPHx/1YN2NGpIlFynPpYTR/xw3hInlu1woHLwrlIbXAcBiFxyiNVFGz9B9G0aUNC2fbTH/ZnThKs6h26oRb28iz/z6ckLsl/sbPYBb3lHtERoarFVPTJxw/pFFmnkwA5lQQ2seQl+2jxEkMpSInzNoVIT07czCYN7Ku4bHCh6vsAZ7b2xjToI++8DzpIAPCjTSIe07Lq8Ku5fn3uEsknogNjkRdT/LO4BZ8oQ6/u4HHes7gamK9U/161EzU/LU/NW8NKy3l0enU2paJwJ/174wKXyX8BamUvGrHEyicuHIe10h2zCMzUBiIQ6o4fB3TvoOVSm2k5hvnNBw1F4+ElrIyLqrqRwXt+jx4q6XXRH5KD4bISxP3My1sOzldqqNEIXYxu4uezANoQLeJYjSgdQDNrMkb4GFUjaiInxPTaD42TKsyiHM15uKFLepHYFy8A8B2W+BK84UXwcOt2l3cV4Hd1+5n1LoBcutc+2bR+gzO+ijut44GW8ywhjIKXUgUNOpRxegWDW/AHfLL5tR9Y7EzfreCD2phedr8ylYwL+EaJK6ZDZ942Hm9fwwY4ZasC3blrMGN2OboL79rEnsawlk0r2yTEXa2Cd4+ibYPt6i3brZpKnEHnM7hSfhjvp3My/zUy9vy3yvEWKYHqcN7JPprwYAjRS+Ej2AVbq234jzjBCq+E6ZDz1zyzpgTmSDy4Wl9rIzKGRyJ8F1MipzAnTIF/PY6MQ/BZMDa0YC0oK8ZqICJMphy7lGsNy92mMdMMI/3eh5PKYJqLSH+cqwlxaW0dEbHPZToXycAD4jIx4SNzVbnZ9j59XQWHxTZcThVZpALm/+Sanmqn30jsMAb4ofsSNc4ANr97N8NedL9DdNiku54CZf0cLLqF/rm87fCJXbJDUIcnxCHuDUgx4bm9wB6bvIhHHCDqOeGPdvtoxMFKSV7kSQaXo4bWEjf5CMgsQ+pJCnoFII4TMooeunrC8Dufpfcy6JdylvuDG50RJnca+ffgEvl/q3PbDcz0E15xelVeco6jYpx0IFmnKUrhP65UiVUgbNu6Dl3bVlk3qlLUeBbXeMKhV/Kcd1IyVt8XMiaHesqdK7OuVO4ZbxOh877FW2PfmMe76X/VZU/fzsDJI5JpV5x1xJah7mc65fOXOVhKvciaRGYYWlh2kbJA4JYn6sRwzp565jcMxe615L8nB4MkR/dZG7xzYwRoHQMk/AprSr8sFbNmv7ii+F2QPzAXVUgN0bEwl+eGwF90v1fYUQ6nn9vtvr9uDwI5ti42/i5WI1kd0GqHEtkO5GSRzgSqwZXr/siZ54gL8fhRrR529NiziJd1Uh8IlYvGZqoq0gIgIWv9Qh9KG586gMkTUNabuHWx/N+wzcAtE0xuru1XdTV9Q2NLnl+tk6Vv6pg2xBqcxG6/xlhtgt3DMcm3iYPn0sVfYXjWX01zBPQR9422IPhtOYpkLpRQfKQCwUJRvNICPlCIefEdYJCCtLn+/SzpbWsTA/Hus5S6dZSKv9ZmKSJnnkRMxgtzKMcmnPav4M7nCx1VWZVlzJyArc0LX6Mgl+Pz9Z/DeaHvG08cw9yfIxkNxnH12kyNznwZecEdnFT7B7TGCnquGh+H+VvLeBa+pF59Cq6F+m1df2UHgyRN9U8rc2TOLs1VmzEsg+/f9UdtnsH0CBbw3AXugZg3I44Bx9okFtAmxE/vbWFNup5uft6C6PLbQK9taaNjUK3SeMqgNzCzuA1Cevg+Z275YU7D4xQR5hdGgHuYhqCBuAJAFi9YWTWbJELpXG/BAeAhd2lu1pzSSCkezG3Q7rXBGcB68gTjjkt4nOXjo1NgEG4puhdA07EXA9Bn/gnbIdi7L7FeV4vLnW8VnaijGQ+SoTCEdvtnASK21EKrUF1CsJR4PRq3kLAi+QeRm4r0Qr4EhGtWvwjhjKHtfZ1oaunxP4M7gRkEkXXupZu5UlKUcOT+hUiWumE1KeVmlW4K5RuMp4j3NrvnC7z+XeFK3We+ucfqHSBq+PSYGsE3m+Z2y51oJr42i2DUjtkoRkDh7o9yoHZkexXGNmt6glSdZfgq10LDR4RGrO09leq3miP9Nq6/l6mB0Pkt8uG3l2CL+r4JPZIzlV9gdF/fPh5Pbl4rlUFMCwAjG5GwAcjTIlHl+XGr07InRhDB2SzBdU6TPJzK3dtVm+osV2qbtzvqboH0iarqNdjE6gqfi7yTj91bwePCQTmGkh3vT3xmQox4WzTcMvAiCXg7yvhGgoRi2CnIe07fhR3o1Cb28CmOtRtHrgHUuefInbUlXG72T4sz7mRJhmaKZ1WuFCjLLs7u3mgElIko0IlirX90uAl1RZdp7w1yp2U9+fEvfS9MhGywmGGi29Z4a5HxpuHTYrn14xDlr4T34HQMpfjfDYclWHQExApQyAIrdna76qiN8KGWXsiyZysOKqqfcInyNDT7FowEensqfyeHFA/rw8OorijErnRZs02wW2N4qy/MtzIfSHaJ7mGHKe0Bt330uVyXGJqrpMP8NZNryPezenBEPm+0zK+JLV47UH0adk2XP891Ch195XeXEdNouRqNoECrfk1tlxErpberB4BgI1csu0oBIUirNpjr71BucWu7CbFRY/q+thHyy2sVMMHXL0Qp8A11wiElLWV8aEbnmsfgkFxpsMYBfEjCCSBvziTBINTgbsXStnMxY8dbMyUbXKjQKEbHiT6ERu71+VOABFdrjkcNR9xFTBMra9uI8GNWIt73VAj/QmHPJ/3vmRdSHfFRRqPzdcpWNgFnFh4xzOZg8KIXCX0IRaXb3vXhqSlDJ6O9llw3AUaicTxrnZUBqMwITP+67XFfH6G5p8eaSzlFl7jal13texslOY60tVuNco98KZn44OEn/rPd+uYy9ySeC7GJTXineGolb2OeHff04Mh8hb3vDiY5+Epwt/J1VKhuuowCticLd7dbGujPhwehc7uqpfNadut5W2bYHDBbB6IxQmLbskg6ybO+ZtF+iBRJJyr9ezmOydu4kZqTsRkS5V0RN5zqV0FJpmzHd5d5sHxKTySH/y8vNu2kDjE49cXE4GG8CwIaacDQeB39Sh/5D6QlGk3qSG8GEi1aLzE556VqR+I0u+4O1dkn86ZrlbG3dUZfVjjROAeAXDJiCJOg8oIewHTpBDOLrchsQfK3uffIJLIBDcOxL1yBLg7PQs11WeAexojUJt19qJSwac1RxbixHILUZ+aXPGuxP9p9Z7BPAvVJhzV91Tlr2lt+wq31uuwGur4LKe9SOZlDGQtX0+JyAWs7SnvtSE1i6UNAcd3tMr3OQ0ZqYloDdK7rQdZBjDU/E9l7V6nt2B6MET+ctmwV3U9YDtwDe9Iwt9MBIxrGxWmBusMQuHn9E0sCl0xsGrhGmdEIqLVKdCKP/pwWGtGUZMPf1/hfJ1ZsBkF1XYBB4SLDvkWEmOuZ+4kFV9oEjwvVP8CC6vrBNlw6AQXddEdiHUFEUdG/BKEVB7q0VDxo1j/J4H1mub3A4gAAnUDAibreh01T6Fit+mxVKgh6q7obgs6bWSt5DGinc+hOENYJtoqhT+jAc8h4h1mNBWS/xyI6VXwMtZre2vZM7H2aXkLPkHNKwRmhTsjwCtcpTd3pbO2Fnxy1lbmrc9rWqbJ08YoLofhhuA4dMCjOLKY1qmVKIvnCs/hpwq9wYy6aUzBScuDESgRKUfpb4BxM+Axls9xIhGEtenrM/n7mR4MkddVPVX1W8Ak2QvP5ONwh78lVgmvnDVV/IBJgvB14S5dcPAalCY2FCd+UpiExvqRqnY1Iq1NLNgLELu73V0vaaXfDIfsLBNdDiUG8cVG5Cr5EDKjD8jzfwV4Ex07JRXO8YmPKeuNAXBf/MSt0XcAtrm0PMu1Mgqq5C0WP5y4Ijc2HhmgbIjsSGzKkt/NB9TqA9QNDkO9Xp9h31tIdINnIFMAb1OORWoiUDZzLMRFymBjwSMnan1DPql0T4jm3akQilp2JVJPxWuZYTS4FD1AnxH3QvjCXuEpbWe/y4p6ehKcq/IXgj9Z5EuO8XrWvqCOh4OHBQ0Pq968upzWBun8CgAY8e5wJlx19DHn2AAywyiZAH0/s/9FXT/GOX6RhZjfP8IuOBm/F8DxqqQHRORh838rlI+HxVV1r2om25vrvwUh0Zt6S9BUoH7fo4UztSkhcCO9iFAn5hrXBJv4ukoNsBN0I1bDr5BtiukiqSZmfW/MQpoLRHIzAcLRupe+9dYuhMtbE7fSlzzqV96oR7hqzV/oI8/rG5B321c4Gme5O2CD9393AkbjO+6m7l63wSP0EYcozw9sv2psX4uIXKLuCdDYL79qV+e6hJQmoqLZDqq0q6Chn8PpCpcjWOhkbrhxCQnL6JIX1L7AVUpSN/pKfMtjRsXPNplxVALW39NzSI9TTyJv8p8uGGfbRh9PTXwZbc0Az9qEpS6ABCzDD1deB4q4XMmqkCS49CKZ+rXCqTO7Mg07mSgIwmAVNY+MbDFSlfI9xA3YJvMLTRy+tZj2bCCZ0W53Q6z4GvERdzAwbjw6lJGzQX4yjGidgiUPQi8M9xmR5VvV7xGMACsuhD5+06+e42aLUBhD+j6k1y50U3owRF7CGM58tSMpYmUL1feNFlsUpxErWUCiYLsPrbqFm4xKnKnRqGzdcIIIFMvr2GS4kRRizvvmQzioZ3bkT2CSfmwKxAnE5sbuTlyuZt4Ep2D022h7FZSjLsVUrwg8gp7DubSs3MADI4AhQfMbo/DBCc6ImssGT2LE5pbOavEp59jGhlbgkHmz4dxcl+VzA+ULIkduAsEQsO6yOTCPz8X3uw7DYUMpKtrZpats5Kox94KIO+7hk5WheauBJxVSAKa8CHjiRGeM/F7aFdoSn1E0b3/F0Utdw/sV+DS0XzVcMHzeB7HzmxQDbmheDtUl1oTuBhdtqjjoBiqSbqAkwGUsdMBOfwQGV09xOoKBVt6IKFZGFBYLYsDvdbc6ut/xzvEMxZ9aHo/n+n4GJwEnF1Obj64UroHhOPxcffRcDTrsfgiz2VG7wbH5XteHuQTD+6TqnR5wlyDv2B62RxbKlseYzky9jnh3b9MDIvInPxa1lBFbZ/trgdjUnKsNlX/FVWgJMJct0kECaKiBgyCvzGMh0pPosMBYUdrtZl1a27QwK3UooruKc0mmDNeszp5pFvtXGZqDG1FUykGb+z6zARJ9CxzTGLJMXtxRXk5jNL2kpINKXxcGpIwJs7NNa0BWXQbhSpL1h8zNCxhZfiMaK1OWYGlIGU/PaAXlOjlbgoV2iDgqJ8ioxdA0Ca+wFa7gLhxoHmnMkxrBdYaGoOAvtiSGv/ivbQlXtVZEb/NNo005BbySgKP2CZOUr9vc3Op6HvUI0huFe8Q0nwqDx/YQLnCQcSFBzaGBMyGEo8eJRcZDHk8p/P4JDZ96gETf72FgXeJqiyZ2tMfgXeXMaXKzkwYNy9d7kpY97oVxvCLpwRD5vrvVvFMbU6kVozvYpD/QWlqxTQzBvJOH6jEW9bSr5UL0Dc3Un4KJoiYiI9aquTiPlSXuidBmG8IEQZYNauFdpocDxUYS6pPuT7zQQtwn2ELsJ7izustQH75Fhaltl5PnK7hnuER2xmOt4zDhXcYxiOAzqfkYM3/GfWh+TA0HaGd583MQYmqJGpkQmZ4jSqEfNdmryuEBeU6L1KYIIJoqJ7vvQeZvX7+R/8d8x/3lxXEXF9Nk+iTuZCdnGLaM1ADAcEjPJQQgojIDlse+CCVYN3wlMRfA7qrQCoeYg9RQQTNPGHSqaNcanXM2WKAq9nH4kRjXCS35N8Nn4axtHLZi5d92g9WLWAyNoeH22ro406Vou7P2m3+zjrxGons7txbEn+eBtg1JSuviz9LLdwt2B6mSuCfpNZGf0oMh8jFxfUMR/ia1uWsST0Q7T0j5W1z6iA1nlbZ9TzViInNGrUZSQlSutpVIet5KS9ckCTwJFQCOWoNKNAtfMrVdFvqlS+vLPlA1GjzBqDQ1pKNyJlzzJqEuGqVzv87azt35GlGPd/kj4o4UHFOblrpO28BvDzl8+pq0DKpMv2e8DBTEeRVNLtxIPfrJOldOJHEAowiZxZjNz2RayTtl9IqUTmYVcMK9jfy+LKeICG8KJ4pbtk+1hFwp+LQ70Sr44oyZUifgBC25iZghXu9g7IbuxNcbLkODadUhnqemdKhxJnjkpDCC24LnyCEiw8B2FtU980bBMbiA3G6GKn8ZannOMAxH0AZg9ztJMgWbeqCuhmFBpP03jInpmvE9hv9WBcNbA67C7821EDl4Mkn6DREEqzVT179O9zI9GCJ/uTTs+0DctnRK2WYSXLNmUpIhLfK/M3HLMgeKZYm+bAX1XSr7PA+fT6XXqtbnAzNwQkwDrgBO0bzWdKWummq9gfukTWfGWudVFQJ+hr/WXC2bryK8ziY9rU13pfWk5yrcHXmzSl4Lk1aIHDCrh9X250DOMkGNKpxmW72+a0czK9xhzdT2HXiMIlpPyNkvm4DT3AhK6pOz4FQZWA9KyPjEylWkQeaaFAetRxy91GlT+By2QS6lS2Pq/dxvHo/QSI74ynKPprPDRe3e1WJUiHq0x+qBUNctNRN1TpDQ6zAtJfwI0o3m7J4Occs/ZwbEGyxuj4QBUXcl5d89I/CvI97N6cEQ+d5toqYMvO5Isjz5r1U9jrKhwKV6mu6HiLDs9LJsGkA5h0NukGWzPVCwyY5AFnxnFLv8e+xYvAuQhWjrWZlCCKZmntSrS/2nZfyhEpn1aGBqsuSVmGub6zG/ut3ElM7G5GmEPmlIHfE70zSrVoJ/qB8zhQNid4lyQeALBarNdyk9AhdUgi5wly4Fo7iYQoruCmldIPFsOKQp6GqQ5/Nm4h3HSKp2dTHXzoSD+n0nNoKEI2nV5oS7wDWkR4Rzvea14ZSV7g88kojzKHs2ozGki0r844zbYqgY5/BBK+nuqo4v88SHRARoKsHC0LW1quRDbc48j5+hOjNjdrV0S7gLzCCS66asC5o5qMA1EBLbTmOwKQVqeFwR5HGjrw3OIwnOjXX4+X2ZgPfSN37d3l8UxyuSHgyRT0LkmxBXJVexLw6DVUyUp0oVsU94/jBOOeZVEJ6ywy/osk2esRDYg7BS8w6d4kZ3UoR8RwFfDedqUyr/I/SdX4SxKFeGBpjXxNV9Ya03xre0z3/ftcZWBmJtgNR3csyf864TesIIVrgysMvYVDq8Gu7N7SCOFUEd3GKtvtQVNgAR35wfjvPTcLRAN4rhVjfCIIhvYPPCJGXxOxSomw43QSe0lieRFxbxJO6cls78GtFsqEEAtPjFBcMgJrmuc1HhtzYOYApRV8MOjoE44mJIag4zkEZvw8eGhL/CKdIuYSDyDA7JnHagE8eQSdClJT4G7DydHjI9BWJReDRJr5fXP7OoX1zVmqQ7LcsKgKbmjcJP1LJeUevr8HZb27sfTTTvn889N9aDiLkM925MAMNPczI1MnuHDeh1ugfpwRD5LdT1po6yVNhq3/BkIvCF6FfVetw2U+KoqQVjqQZVUnDkOabnFxclDJh1bDYjN3Q6zBaBJeBQlJe0xGVzHIh7xDXCN8EVviRc+IqAEAR9YJZGUjiY+Y3KZJR6sx9lLMKHfqlraW+NKKaLTm2qd+p8gatEdyL0NXPBFzkncCcM0yHviPo81TYJysfQ7EMQdcQezNSohofFDQh+gUGD1Ma5lVAR1MhyfrZmH8C0uoZvuBsb4VSRcKqmXhaHQ4GDQtwncziFi7q88sAnWZd9f5dGC8HnxU0AwqUrVNe1H6PcwlTOzGPtUprv9hwSOFyKJYzvjuauZ23iuogYWbQhcGIt3FEFces01G9edkt5j5xtU2YscOFCxzAdOjsEeIxpc69zF92R24S5HHoDh2LsirY1ZwRMJW9alG4BLmIdezlvENeP0Lp+3KOIdz6WL43jFUkPhshP85MrLK2b/PWyU4dR3ry5K28nWXSx9VwwFmWRVhkJa8ROiMwrZYJgAubmggK6wKWxH+utYU+AuklmmzxrpVl3MOqTvcEVfJWuTvhr0nO4A/nksAmWb1cf8+w8VcVndZbazgwfDh3DPNBLOyxM7oJgHUPFeV1Rn0z4p3ortyLEA0xubdBl+ma54Fu8LBkj4dwIOC1TnGp5m4xChgpkGLJjwrksioY0Nqxw001pmjQ3hqlxLShqo6SxXu8Pfd+r2yMEIn7VM3DC4PL7FOIMmGReYjVoa7HWYtwVLs2WzxABktxojx2hDz9x1MEtn0V53F3hCqPGf0OWIBMhS34J2sP9K2or02k96lEa3BGPS+XRF34zL2DdsPfTHnZP0usz+Tk9HCJPtZwTaFvXSrECgJ/nUZqPcyz/8zMunveGul4KCg+1Gnry2NRst1PfKBgZjvHcq+FNLDIvI2zrELeEZoV5Shw0bCK+GhvWgj4J80S4vHgNtMNCLd9zo5nqRAxF4K5HpYdvwWJncLICLX1jXVMfC4Gv9Sk3PT4oeMvdXEFt0AJ30gk5aVtsrmPBFTAVkOOV82TuJ+dqeW75r3gjIrSyn7VnZMZiSV24FOGZPIlk4Vxay/lI6Vu1ORwmDVX9cCKwS334vMARh60vb5OXGSpoHsZtnMBhc0l/mC2Awp6baLjDDS03B7rFvm40Sx+mIfPLiWJtug5cLgYXwV6H96sNawMt7BvCTRAb3GLd11aDWeyrHXOEG95mbRHX0mnzs3v/ZLSWJzMRZgWbudDtPp+awxmDJjm/LoJNFbsCTQSyOwJndoSW/huwdS+7+TgwJHTzGPiAneM3xDFHhP7WYZftSOV6Xqf7lB4MkWdoSr9EPTfqoHI4EAgXAxIH6jOCcPOMnuhC6imUK87OUk4pEoZEcI/Y72VxkyMbLitcpUO+SbPQqi2o3ZTccCZax6bXetnHM8KtpRwJPd2NTuhXfRFSkGSPdCJwM+44qgh6fkYg5+HKQaqNKJlTuRO4evyCY+Kxw/lx5UljsvFz3fU7TM9yIOxxwiRI6RuKRodtAK2RONvvrQ1Q63FxuAFFI9F1LK0N2mhha3sSUif2wwkwj3MHBG2zvK7GSNTLzjYxfF0lbAM7iVuz8+yhis3tADojwAkwhqns5cbyhhvEqSJCSV8Ix4h8zNtszIb7ojPPLo+UMDHQZtSwRrxTt6AnE5mR8sSfNeJcaIfpDVShXWLuT5cuDTIhPiv28n3p/sZ6BjAunt3h9cAvuHEcTvjHja11U8m7ENDNKn9D7iHjImiDdzYIcGn2EUYrmghbkBYHQI0BaM1U+GNAdxol3INEg8yXxfGKpIdD5CcrqCkDSTzXjVyLJGWrwYTCKo7JodRkObQYxUW1WiLezTUuD96qiSDo+ftyYJ9RsY5FKw/A5k5q8XWoFkJaNayno1rgzgAOTAYJeGGQQg2PlNIPGoSgoV6m6tiW76nTeNVBWBu/wJ2lYihAZmaS2iNH1gZgyZ2qO2b6GTe5p6m9VV2vmM7qlzyjNYkr1OvQsLHSgOM30QhuJ6Ep8LbAiKaIzWPi2zjKwqPeBY7NC8TK+CyhPhYiADzD57KDC9zjv2lo0VWyf1U1rd5HahcUSAZJgyW2nAggpLGktX6/s7lgFeS6aa6lC46Je4fBhmHgmatjgTv8ixwfQKmMzDnOWP20bfFx4PFMjG1ofgB+SYiUIxbO2dIo47aOfX8rJ8XLMyT3haF5hvRgiHzfiwudlAWuwKpeL3ulpUIBFRqR8QIs/FxIVCV/EE9s5F5XsOWVENSVW1tfEJAozhVO8FmPtb1qq/UAV/u5EPo74GpX6vO0F5YurozCSqgDPsaBr+QIf+zy1D4VN0g6SxXX/GHuSCuzJofsp7XpKqoT8BiqINSWIy3V72GoyI0/VPTD1Nkkyu5CJyJoGA7XHC6bt1EtD4drCPW3tLTzMG2BQtGCsA7CgUbgYsS1wDUA+zCqtTlLoWi4YLhG4IKGDmxAH+atcnECTVW+DvgzcCMOp0C7qJ+q2VnS1gRjtFiro1vrNsdHoi8bTPpWAS4K6U4sNyBvcnIRuvtx3ib0Rox/ZRTXvZ0DCpPqXUoXmFGhGej6F3VbAhGDC9tKWt57efUpp83U9xAvp0gNITUHDbG3hEt8uPgJhmhcfONowaA3ufbsy1kbB3RiAt766fWZ/JweDJEvlywW+lskpMmMey1cOWCAl2EEzSOXbmJI1DFzAlyUhWi1tT6ZCfFVqpFwK82aLN25i58Q02gTMF0Nu2j4s0oAaDOaOny1IbZxHPHNSHGqEZDyItSlRFA/w7oI17E46ycwj8cJgzSlFC3nTh+rzrF42j54B4Ffi1vEuBGbtwhSEm1p/U5CTpV6EnaENHdpvFdcscleaiGDYH3YyuVNLaiIZTZoqICb7E6oBaK7ETFnhq0eeudbjNqBhpvGZ5N2GwoOvAnA1Po3YpRMQW1UjzJkI/po2OLYQXzRdFfrN2DrDudnzRAMz6O5pg7B8Ni1OiSJGdep94U+57QV0C5FzS15eUt1s4N4ZBv/PcRV85r8d12fbvcyRdAesBsofV2bYb0xFG0gLs80Zx9BqM62dKFTmAdA22yPsqh2hqddWnoieAAAaeL9a1DpYBx71vs63b/0YIj85bKZCx2Ka9G621aVIk7y4QtH5uwqdwYzEZROAnYmkAXuWp2n7ayVF9xER2m2EG5dil7t3nnTp7Yzj9U/DS66WJmXtYvLUCSO65HnjuNZ2y7HcaxlVJaOPE861Dynu5iGF6nKP2qdCge1PJIBSII/W983J/iAucklPkp26sTbJThVXOhChzy7Z9+bP5vBujqxyTrVDUbJx3btXo8YHBLOPNZ8LtMQzImyPYqf6wOmIwCADh7TDCWs/e5+jzr8qGAMOyhvCgx3KKctbfO5MHjOznr9hkRS7ZhSA7l/OGGfVCL8SpSsHc7uynACy4h5LL8Ua5ck7HSts2/iTJ4KxtBwyaOfvTizAEr7dALajIkB1G7y4zjuI4m48tnO4ZXjOd3vcU9SFQxeBscrkh4MkR8lsMXVpLi+R/tEN2aeUbFqnHNKVUWVTzW/MEJY1pPUzeCKxj+JoouuUtbYDHflXF9B+8Ig/Ifz9lqmEkgc4ab9i91axm0qtuLmu3XhFDyTdX5S48lWYD0OiLp0zlvjFdT+RNsVOI2M90ypduZs8Oas03Qlb1YeqBNf/zfeJTEX0FK+vJMk8pARbm6CgdYULSRzozaW3x1GQGv95lEZ46wdtNCnct61B45/SzYEZv4lMVahKofgQkt5uBpem0v06jTF4Jqbnw+fxAyUM2AaB5PuDUdD8yXqZ9Z+YCAiEErrAdfc99+N8RyOZ+XN17mqB9WJ6HqIgHv2xwXSinsetWwKjqZCgziHNMy5Qs3MKMa5NbhOOZcH17VzT3kTJtwY0OGKXUvMpdyKfM1wfpDo10lY7BJGDTRwT5Li5dXt96zLd6UHQ+S1ENRpStcJvJ45cRF5HokDV064wN11/ks8y/l8svJyJJoFQSWaQfDW6uo7cvtnOBe4u/idQmePhJvlgantlUFZ82QuZs9L5tze44icM18lr3yb03RC9184Tab+ev4B76rgSl6rc8MJd/WTp4o+JfXhkrmGIZ39HgfpfROPKuflLeKdE3SHs7N7MhdwCd4Jv5ejCcvmeSa00oI9jwlMM018EoZhhBt+Pq064lwfImYd73YFQ82dTUQxhqS63eHUb3vpo4XlP9CcqVeMraHThU4UXQd0tFDh7715W5tZ4vt3HWp3tmtTNDSM7t8EwOhi7nUC+7ePZAS6n2cr4iY4zsvRXYlej71G+UdgdgK3SIZ0h98mZ++Ki3ww8gJASfSbvUvjfgoY1CSQ2gMRWrvB7pzfc07zqEQBYNvcwPKlVszr9LuUHgyRb1tD5wU1lbrUiVvV9WqbRU1Js2kAlT7tPP+aNvuhfolEZsxwknARSnKujxqAWNT1/uoKT8mCixozUyAVbnkMgi7l5cKXnEnhazsOW0DFcSLwTjzQoXkJWeGmSmTJK2GJJ3zruJI3mzJXceYZE3FU7uZFk9Tvzf6LZzlxhYSqXESxFXe6LdT19tzKRzfVuzECl41ysxncEc7wDedZFDctDeuadJvKasfLW1Nz3wJw4/YCYwha68H3dBFs3p5dBW2jWt8IbvO5uqtg84myD4FsyTwMkTinvgVcuncjPoezo+90k9vHwGWztb0PqyeYkeGSvXpdF8fRh9+660cDfuavavdetM3julPK3mh0PoKb1THItqcanoZvfq97SONcx5w6DDZXb83rBkfpPph3GvhdvJ7QXjhzxDnZBHpr8yQGsaFcbNPiClvtvUj93lbjGKH7bn27LxK9r6GXxvGKpAdD5DM55TnZ0I8R754V5wJ4ct5eGQTLkhQAr1HgYNmRlPDMICBoTHFDKnWBYv1MN2YULp1VIrz2sErsB63ACpTC0ymuKHaN8Puvykod2qYznszMikl7o+2Fr5vxyZGbWRu2NvLQlmVAVoPNZ51Py3yg8smmbbqs5b9Uz5O5qVb4LtEjVflUtvM8nscAm9BtTgHVIP4bjMATjgbcpkKv7RihYVCH45q6Ke1uVPn7nLuJryy40GvA22/++Davb1z6VjWVv7M+VtpV8sZ0mI6b9gKt9JgcnjFLafXR/F53gMfsyb4rY/nD2uuu4676lmDAk5j6N6QVvo/V9H3LOVS4j3K8yBhIzgVZ4DiPQ+XudZhxXV6ni9LG5JLTDHnim6sRR9SKaOe9Sa+J/JQeDJE/RLxTUGjYjgAA10dJREFUX5Q0Kinn61PEO6kLI3HUOaA8KBQkhw6UBcMIeUhqagUxua64EQ+3Fz2sahSxlxL7zFCQka/0DlpC3S6UkgoLBSAR9AN+JumwNeKdYNpwKl2rxDZG7YT/mfaRk8XEblVPASxlYgOsuxUwtYfjHQFFKtOx4gPnwx34cCXvrANyAvAs5XgGKwDPx/maKmyFuhU8z9+B2eAu1fqbKBjdbkM3Vb6Ym1xlDDZYMB1Fw0V2/3YtyjCZe56FW9lg1tckt5u72uV5uqLjgg07pAm6q9obA99IM1W+Ajs2P30XdJUgvEOBLptZ6atg14v1B4JdrcQN1DUCW+Tdjg0Q4BGAW3fDg5oavqtZtl2aYHd3PahgR3OtBaCi6Gp3qrcmuN1NjN5cgpc4TFeoB5XRJjDHBTXPluHeCE1o/G8ftmm6tdUwu06gxYPg8LJAuuuF9T1g1vLcdjbrV1OYu+OQcOulFwDtAuLsXqSsoWTAqKVUWvyp9UW27dmZ1NfpLZUeDJFnYmCRO+drHCYXVhq26VY3G57Ti5cJwsfVoxJwK1etrGepN5a8nES8I4Gs0jyZhIqmELeQ0Atc1KFT02ZhNPpVCPOah+RbpsZWlzxNuOhy4UvOaOmBPkpCygnwpMavCArjcxRbljzAz36Rkksd2AP+K/nR3vpu+UJ3EXviLwyVeEP5TLrQJK3mTdWu8dyc0Jtwaep6agJu2gAN+rY4mzdV+MXd3Exd350BUmwyPJKsuOX9rRFNCDbpzo4YcTf6ZvPtbfLECDoEj2Gq/O4LZZNuRFwb3tbM/e12NDxyRrMP8SvSh8MJ3nB8t9pw40zG7iFy32gGt4+GbaNaf3NtQ0cfRtQftd3V+nTDs/fiDNAYVtcF5pu/jwa52CLowxq3XQZGB4Y0YHNf/y7Qi3+loRZa9wJ/9g/G54sd8aE7E9rEKHb3qdFgUfjKfBP63vOs39X36PA4/gYcV2t4cYvCZ8cVWxO7BhfGDLTRYs5AxC+34Uyzdtkxxf2JeFfMWF4Kx6uSHgyRb83cTuxMvmRUFZUi49kHYeGGb9Qkjrwq1XBKuRrQZYx7nEiEFEcDOvKqLZfUPN9kUVGw3lKxylyGzwcbsYVwhRW+zM07MAWFSN0FhyXvbN3E+7O+T/2aB/DQxTqcK9fiABPcQmhnHkYczZWVflBHXOncyrHIkrdyCStHBFo5F7j1OffimIoChIQ+Wd4LXeFSgg8DPhxV/gyqA6VGIOFYX9NU0TdNq37quuKooaj/peQJgI1+8QLctB5zPoZYJI+SQYM30zoAYkfTXpGB0CrfosfQWl8EkGZW/3avul2tSgsFGWblz4qNQAqajrxOWpBMrHU6bDsaNRla8oAIamOcFGJeUosWNkKMZOcTNFTxnlfP+SdbXiEjD2cO/cikAbyDQQUm5YcgUQzr/MMEbSdhFxi30e+hhf3rFOnBEPnrEe/qprkIe7liY3PVkpWSZKHwhdJprELCocAsRMIzFJgvQKkI+BwESo7ZWX1s0LRGPoW7VoXMMGfS8ETrvMyp1HyNAMtM5iba7C8q4zR7Cy5uctfaMXXyWPeBY18ZADViL1PlcuRa1s95lk7G/zqILsPo0lZAlfN3PRJncWv4TVItbyp5Phd1vQAX6fHcYBK+aZaGu8nZ782j5pnjneWpX5u2yQgiuUmHHXBdjNw2xa4t2mQh25uXAYZe7E1T3DqxvfEgOAOb1aOCjg2KgctmUvqA4EYGOgRdLXLMBcA+Ll4S6Gjo2nBpAx0Nu14A7bhswO24YKjiUTNbtqFmLNhV0PUCheLRBtwOEmWbC3YxjgAdXosTcD/7CqnaGYwMiqNxuY4vTlft+89mMKrizIEzCmJBfTb6xPPcnmsw1PBw7kmLPQDnMMJ4TgvTdfD+qWucd8wDeG1dfz/TgyHyukxkgSYlrET6jApWouyLNCRYPdKSUJGzCh7fIdf2hB9HHmGCr7yISwkrjcFSjtTumnffDHuga0f8J3hmjcj8PoaydGqyal+JKf85G8+KRvJ7HeisXmdIDj2qDEqtcPo24vT8hFt4FsHmrrE/MHojXs+8hE7wjGpHS/oG9We4qtyI/NZmon5TiP3FiXZrigt6nutDcaHq1gk9Df0EIwPiqEXNE2+T6JOQOsVd8vidNnkThlnwCNcj44ndHoWuGx41i8g3hEFcurupC4DdiX3DDaPfiWDTBjXrNyPouLU8NHObU1OttwHc4AlUrUcbbqGw83ia4QwImjZsuhtdHhtEWK+53W1qbNQQwRjdjidU0J2ybk0wXK0uqlChtV4y+2Y7uDCqA3nJjfqzR+0TRsCj250grqZlzH5TeZingzTFxr3ImYm4ec4t502bgZDegykoG5XZFBQf/7d6Wtfxi+J4RdKDIfKXmyXiHQCQ0JeFJjNlmBch4OdTGv6p5NkJOhOmxHUgmsUCf6VHMxwAlcKfyJxXntN4BgfcU5lCdAtout6V5vH3aQOvEPHI4rnhWV5FcUXCPYUDCke0EF0pTbxG6IO4gHzQXOGVnzq9+Z3YAbK31eCQF43YzHTpkEZV3KfD6A6pUgfz3KhO3O0Nw//1M/lGiY4MA5XXw87xYWN5kRHzyxgFg7Mz82JRTzgfd4bJ7S7AirgPNwmPMqJeelLUPPVO2bm+rz/4rWpmvo9Ngc4xUoDx5kVM1d4M0IzuGuPmm6sdz6BFI3yOufgpjJCrYJfuZ94mmduYNAxV7Grn2KKm1d42F9iH2depuNvdGNBmxmvCcLccDGfcYzvaYITbb8aDIK6l5dSjS10Y9Pm5O+jPz1C+fud9hOUhF9n86/Ecvu/8uEgG2gzudL8ti++tn16fyc/pwRD5iHi3qpyKZAgUmnCY1Jr/XQk/N+BCMYIouJ9QKY2JGgEMpnUkyAGgIfFMz9DSYORmUXDNGgDHvxLcBcekWs9OHiTvAFkbXZida9yLeL+vdtzfHeBqKofrh0tprtJjOTIHa71THVNzcPx1hUN5Sta1vJl3KS5vRcK2r++SNRDEWqiml4RnlLvV+t4Yge5EWSDFTc5kXI+UBwXQnYEwuHChw8AFeVudtcnWA++kT3IxPNKe+dwLWy8DF2c5jPi7QRoEKgMqzWqQATGR1ae5SdK0SG/wgDYC3ACANNRokBq3ySSB3sSM1AZaRrlzUn9hb9T9+b29Q60ffrKPJs2eVSFhys7APgA0rdnrHhL7kCAt2UfO95gDZV/i1I2pM8GhwBXAMs9s66h55HQ1GMpgPGIKjvn3fUn3sMm/U+nBEPkp4l2d/JUKTqozJ9CFsukKV4gqf8IlmpmQl5VYF6XOcHq2YCuKoNyF+JUmaXketQzO4Q7E+qQNZ6kOW9VeBK6Tek/xnRLQpUEr3Mp4xKPM9dy1yJ+hj3c1Z05PGahnTQ7bCjGohAGaUjvgKnqcEG6hdD5Clb9hYPMz8yYDG0xl35qfzSPP8c3ALX3rNwyr15mHiJIHKYF0tPi+axwdcEHYrXZSYuALVO1yGkbQM9c1BaSF9b2503VnN2wcujSfB4I9FoC53fW4NcZd40Sh2LD7TWoKoI+ODpOoBxr2oRjaoZs9dwzcwILh7KNBN8ENBE+GQtGxtYa9KzoaxrCb7W7dte0C4Ha3i11aM7U+z8IB2Fk7FHLTgF1TpaES0jgagFv4fTGumfCId2EoB0AugD7xY/fiVx/PMFxyMTpN/35I/tktfDZe0hrMwMDbVNWC25ZMy+t071J7OsirkZqHBkt/ec+YDE4qB7sQfXBtFE5XoiALwdTBvNziSBqCL1dKRqhG0hO6w5k311+Bm8oRu+KgbqpwZ3VVuhJwC56VZznA1TaVPohvVFNaiHi06a60wik40nM/TuCmvjL3pO2Hv7kE7nrzXGktSlquJzb92VwIBI2xFMpeDKTVuwCunkf4w9tVsuYzf9NMZU8Du4uHvL0I8Ih50vGoddy0Ea51j9rARTouTfF423HTTBPwSHY8ah0X6XjcdjxuO26k49IG3th2PGaeGI4NAzcy8EbruMHARQbetnU83gY2x/HG1nFpO27awNscx9Y63rbteGPbcWkJZ+30vLYbju0Wb7t03LQdj7aON7aOx63jZht449LxeMv2vuFwN60HvktLuE0GHreOx21gk4HLZmN4sw20NvBoG7jZbGwv28BlS2brchlom9k/tM0ulRGo6fI3mCFfA3CxdyqAbvY3xI4kdENa9fufwt6DeWKMAf8qXMyfIPCS80kEEhHw4GcqLe0qmgX+0H5/XOiuruXn/XvO9MM//MN497vfjTfeeAPvfe978dM//dNXYf/bf/tv+PN//s/j3e9+N0QEH/vYx14a57X0YIh8poV4r4T9zqIk4vk8oT3FicOEsSXF8pos+pn0WgjRgQnASZlC4JLYlrpwXlfALjwQMOfV9pzCLW2veYcFtA73SnSBWZmy9DPLCE7vj6/jVssGjsw5JdknhPjIFFQmcfkbOO/z0zaQ0Ebo3Dbn3li8lY/MPdn2aiqy02eeVvPhQgdK7u5KB56na8CFVb5QLe9hcWHaALPxGuFrv0k3jQE0tAVhDyAOB/OPv4gfCciwgDve5ovDSdTVA+5GqDRXXKSbMaHD3cgIj4GbArvJyLrieMEZn+bGiEiDROKPNlQ4gWtEaIdQAhGVP36zCEx0WDTLoub3bpxkmBdYKUuiXUJxFLgyVflb3Wgu7FjyN10JzWhvlIIaFVnWyqG/tdM0vC/x9zzpJ37iJ/ChD30I3/M934Of/dmfxVd91Vfh/e9/P37t137tFP63fuu38MVf/MX4/u//fnzBF3zBZwXntfRCRP5FuYsf//Efh4jgm77pm16k2pdKdjkEUCPe0d0nlk+cqRUKE5vzSrEw0WfAI1ZFuaVs0Nb0qw2YReQ0wlbKMysWn2aTJqKhB3BgIUpswxlaFLjKE2jhR+qgLgSNzT4leKXM1brWNpFAn7Xl2OUjUS2f8ZTgsrzH/74Tbk2nbdBj3lpGT55XPPx34eoopZuRWI41CTp0hN86iZQRWSOUFxm4aRoE78Yl04bhhHe393KLR83LoOOR7LhIxyb2fNNuXYJ/gjfarTMBt3hDdtzILS6y47Hc4rE8wSYdN/IEj2X3Om7xNrnFI7nFRTrekFu8rdnzI3mCN7z8RXa8Ibd41G5NMp/w3eJt3iaDe4JH7YnDPcHbWoW7xY3DmsbhTWcQrL3GBHS8TUya35pJ8o/bLbZm2gTTIhhzcuMaBXosPHJtxiYdj1yybzKwte5x/AdETJqXppA2PDa/MQcWrWjYnyiwaTJyDRZSV1zC5zPznG6jmaW9xpzxaaSICJzqLr22/3BjsL0gYvTyN+0IVC0IjquFXrvQXU8/8AM/gO/4ju/ABz7wAXzZl30ZPv7xj+NzPudz8KM/+qOn8H/0j/5R/JN/8k/wLd/yLXj8+PFnBee19Nxn8uQuPv7xj+O9730vPvaxj+H9738/fuEXfgHveMc7rpb7pV/6Jfytv/W38A3f8A3PW+VnNYmbpbpiGyGZUx8OpKR/5ey9bsLiB9BmeesUO28GMdzTwTUN6CIXaU7NCHkozMeE6ACnKwiSOPIyr4iep2UPkPKbXXf0i6lA5EVIWySnWyWJaPsJjiizDPPBnQ4pkbJghYt+V0LIPKEFer5j+WmsJiJaxpTjco1Ylykw2yLIOdzZnriqPta0fPTqvhczlRJ7SI/if/A75SVc6UxKL/7yMMv4zd3otpC27e/i0jgwcAkc9t4Mwsk87HaeDgkJGvCzeQC8YGfzEG4DgkdqdgDD52/D7q5ZwGPeJoeGG+3G2nieuAvdQMMjfQLAzuY3vxt+wIz2HsvAgJkGtvEEIoJbbWgek3lX85d/JE8wINjHBvHg+/toaCq4wUBXwa6bGeNBcDuAx23HUA/NO+yIY0Cwm+m62wgAN2rrrqvY3e3uvdCHHYyrB7IZ27BLa2BlzdVWI3gNb6qjmx1d6OreAkGc1ccc93cRClfVzuYb0hCRwXQ2gdwKwybm3ApuErZXSoOO/UG60H3mM5+ZXj9+/PhAlJ88eYKf+ZmfwYc//OF411rD+973Pnzyk598oeo/mzifW5J/Ee6i946/+Bf/Ir73e78XX/zFX/y8VX5W0sYzef7ndIPVO35fmTUy589a44VYhOS14A51vQHIBIfMO8DJpEZfJ7fUelHwIfFXeiQrXG1Hbf86LCeL6oBD57yzelecVdW+wlUcc5kT1X3Fd/ZJFUtbr6v/nS884jOqlOKV0cnj+OiVv7Oka5arg4XP3I/tPDgvMkwjuQhxG0zBKMTfw9o2Demft9fVs/pNiu/9Andx1TvfX6LcmN5vMnBp9vviWoXNDf/iWUw9T1U8n7c2/Ihgd7X/wAW74zd1P58vXu7G67op7buR4ZI46+zeJkrepY9tTM8t2miMSvM4BOLvm9s4RJ6PK/Mk8vi9CjNrHI+7yymw6UGCR6PLIPJMni50ku+nvAbQF0Pdqj4s6MVcgdV97BVAcImt+fynX/39inj32VTXv+td78Lv+T2/J/4+8pGPHOr79V//dfTe8c53vnN6/853vhOf+tSnXqgPn02czyXJvyh38ff//t/HO97xDvyVv/JX8B//4398aj1vvvkm3nzzzfi9clMvktaId4DT+nBRUUjbTkpycp9v+jqJdEZ4cznMzyGwKpIjD4ajcB6T+HvWHnECQADFEg5ukloXQTnbnk07JTR6PeucWSpDtdJSWWFKvdVLYOkG1i5OcCuu00ZezztExjtLPs513tzxSe5Adda5Y1snHDJFRShCXGzd8Zx7farqgZTYwy9eynm8DPD8fpNuzACAhl7Op0dY5Vue3z/vcIbfUkbDE7+8BlCPjPcIDEMLNHFJHc0j5knAXVyaVwAXUY+Mt1l7IX5yrNgA7P5lDM4j1GlHE8GODXETHhSdfdeGrhcMUTyG4k2YL/yNGMPc4URQ1SLgQcydTgQMtsMTahXBRS2QDmCMz+gCXj9rngPmOB+KwLgzFsnF8o8R6vj5g7OV1HiJo+D2Uda3AuGGl3OqcJf1rGtyk1u40N5DatcHHtL2V37lV/B5n/d58fuaav2tnJ6LyN/FXfz8z//8aZn/9J/+E/75P//n+Lmf+7lnrucjH/kIvvd7v/d5mvbUtEa8s4WXKypcsM7Swbd+/lfAdVA2cc2fyhdnQd79x0RMNcuvmcFTSNYrMHXe2kTCBc6aIfNmoFOhte2YmYWC49D2tS4kMRZg1nTU92zTGaUscCuBP2VCBMZsxZnCnIe1rivUWWKcyAjekTT7J+t3fgr5z8pyv58bmwZdQdQl7ycRH5gmJMhIi/qQ0in1apzDX1oax5m0ntI3z+nNJ17zOZgH4hE0j2QXfvowhkQxcAFZXfOxnw+heBlOZYHNvQ6Aq+vF4MQJMOwimyEbLn65u4pfiCMdEFPXbx4O1m7SGyA70zAwYC51XTeTZr1Mg6nzR2vYhx2P9wH0tsVFMrs2oNuYjmYjIrqju1bN3AHNDVC1Ac3jEMgG9TvrN7/QBkM8NLzFFdBNvDP+YfmBgaObXJksyih4amWUkfRELbIdJXhK910tWFDBM523h5rBOiSteVCmp8zht0qqPMvL4ADweZ/3eRORP0uf//mfj23b8OlPf3p6/+lPf/qqUd3T0mcT5++odf1v/MZv4C/9pb+EH/mRH8Hnf/7nP3O5D3/4w/i///f/xt+v/MqvvHRbLjfuCnKgGKvMuaYT4l+4YFlm1GSfV62jJ67aAC1oyEzLr6muo8mQAiczXNSLIDpn+EJ4WHGvOE7aJGc4kL8P+IqAcAZ3aBPuSCuOpU1HuHqmfaWupX1zmlt1rX2n+KbnZ9gci3FDZawU3IAz4l3AcC/mW/8dEe/cFoPBayJPUm0cKvtQo7uxHsxCnRK9Wcsnw3ATFvc98jYoLqCle8cNrPxFBpr0otp3I8BQrw/c0FpeTNV+4+56m7vH0cju4vkXd9t7tA08cjU83fg2N6J71EYYyz1qXl+zW/huaCxX8jbxPHRX5Rv+C0ytH3/uQndppubfmuLGVflbU2ybq+k3e7YbADsum+LiysLW3DutKWRTYIOdpUONu9j8uleq4N1dLn5zKpy418VkbKZ1mPKDeXD9j8Pl9BPzi/cJI5fNFQLFYO+tnvSz9PeM6dGjR/iar/ka/NRP/VS8G2Pgp37qp/D1X//1L9SFzybO55Lkn5e7+MVf/EX80i/9Ev7sn/2zU0MB4HK54Bd+4RfwJV/yJYdyZ8YNL5umCTpRCJfSSGwP5tsniZZgB4kNCMaB51xUewkBY+fOeoqIS6tWoqLEmZfduLRDuKoCVkwSoTJ2vSauCpfEpCxyh1t5ktLEY6pwpVvXJPLgT/jb+1gl/6C9Cx+zMhzXo+FVOJfqFc8RGW9N2WAjt5JtKniCa9bpHxhLd0cl0aSZS+BnFjG1aVhkLztSdQWDpPW2hApdYWf1VN/nM5BwEU2v5JGQG+3gM+ujWl8AN8Yz1srj6Pu4bYyGJyaNM6odXKrOyHg88jLRtZEyCd9z3lJnLRiiAJpJzmLx2ptI3AUz/N55u5jHsA56KIiFu7WIf3QqJJzE2G/wu+bBAEANioHml74PhffZftvNsrYXdFg0vDyL1yIVZ3Q89UA0UvcKyTlGkw+orzOBzWWul5Xnp1Ext47BGeXjx4mnyCA4EAvB6whN43C/XOh+N9KHPvQhfPu3fzu+9mu/Fl/3dV+Hj33sY/jN3/xNfOADHwAAfNu3fRu+6Iu+KM70nzx5gv/+3/97PP/qr/4qfu7nfg6f+7mfiz/0h/7QM+F81vRcRL5yF3SDI3fxwQ9+8AD/nve8B//1v/7X6d3f/bt/F7/xG7+BH/zBH8S73vWu52rsy6RBqbq13Ecndf2SglpVirVQk0rMmSsA3eSmO9wL4UwrdVJxOaCe4Ujoj01gvdMPWd4R39IOkWOMmgPcmlfaFMNykmJjKsN3F77akep6fsbLnLYXcDXllTYpUJwc54Y+U5q+8rHt1/Dd1fZD4gZP5o/cj/1uLQK+esS7lMaDuEM9PO2A6MCl0Qee/t2YfN1Nqk6L+83P4Ofn9KnfhKfkbrEPI1otQtqayvyCAXUF+eb3vivofw9A+TV4yt6c0cgzbxGLeOfk2LHbszSLchfshQ50sbN7SMOTsQFiZW9dRQ8AuzY7rxfgRhreHMCQjotsuO07dmw2Tqq4hTEBTRre7B1djIjLAHY/PJCmuPWbYh61Bt1Nb/5oa7jtwM5zFSieDOvfZWu4dZfNrZklPlXoDWo0VSxKYBhv1glETrJ7llvTk9DHTXZiebpbXmsMN6wJB2NEZGtQdNM+VnX9GPcu4l01nHsZHM+Tvvmbvxn/5//8H3z3d383PvWpT+Grv/qr8ZM/+ZNxtP3Lv/zLaC0V5//rf/0v/OE//Ifj90c/+lF89KMfxR//438cn/jEJ54J57Om53ahex6O5Y033sCXf/mXT+V/7+/9vQBweP87nZo0dAy7gYn2dasD+Oo6t55BnUnudULZYWHe6FQmSnUVyyzJzMUQ78B+kLJynqySbykz2QcUPiKIbqk2rt49gZNSVQzHCTNygCvNXQ2EVnxVal96nL8rnFyHnZQdC9AhMt5aZs077fzSpiO6Y/smuBVpSZyGA/Coq/Ge6vqhiUeHmvpWEHOiMQ+prud1LJsTWAARvCZU98IAORkJD1BcWgaHuRQXvIa8yQ6AqbddM5SSruOQHZw0DKE7QBc/XzKwQDr2bJH3ROxSGxEjfKpGMC9iV9N2FWdmGPPebqVTtZP+x80M/XrY4gx0v95tU8VQwS2Ax+3WGIGhwLbh4i50t7rZpbZD8EQ3PN4UAw1PGCJWnDgrcCMd27C8yybYIB4u175PH4yHrxgC7H04wbUzf16OMzri7BxA2BGF1O68js3VPFPXjmKpj4iVLw1xmRaAsD9AYQJMPd+g3Zg0EnirUzwWLqB7fw6G+Hc5JV/8cjieM33wgx88FXYBBOFmeve73/1Mxx934XzW9NxE/nk5lrdKkrprln+mFGpyxYHAH8quDICm5B5qeKfmMkuQRvAUOlFmNjR/G3FIyTF0drU/JPCVWFZCTYZCcahrIsBOiap/+2GIiO8KVT6QsAJXeZ5DlzX5pxO0U7kDMUZpe4VbGBF7by91ZdPJgMjS9rVN03dPsq11sE7qPcKdEHsp/87cWsmoehf6yHtuOaYxv3CNv5TCGGNe4zmJf0aaA5xYIy3xqa5nubwcJ6PB1fdsX/7Oq2o38MgAgHhse+cw07iPNgWc4IKLfxQbwvxAArgtPdxYr3sP/DhBrRWMjkd38w12Uc6A3SE/dMRoGJxJ7hvsLnu+t/gAdnzgJ9Z2DCSKpsreOOOEGO860cl+JUOsuUZiDvjC5jNiKCK4DRnzw3YkWVxxhcn2Yzohl1850mJPpL3jXqXfJSL/Vk0vdEHN83Asa/qX//JfvkiVL526R7xjkA51iiWqeb/yQXLnBsNnf194GJ2eCrUJsRcA3eoGzNCGRi9xrk9i7xtG1cvzvdZnEnZbxbxvui50DTg5MABz2wvc3KFc8Cthv4P/mZJkVsV9Rj9j6HxjujPJEWdta23T9ClWArwS5yv1Tm2qO+Xy7PzcDDchKviicWcAcz9tAy4E3ecu81rZyS3CmsEwJG1I6ZTa1S+wcTU7wKtjjfjeuJqeKvqLu9ABJrU3V1Wbfzq1AOpE2DrVXK0vTkY3fx/2AJ67+VrkZbMNCBv8zQkhL6gZ5VkA7P7UBBjqF88A2CC4FUB0+PMWDnyigt1VeZsKnuAGgo4mHcCGHRcn5B1P9AY8cjAXOuBGTNViDHqP3ggEj5rdhAcd2FxzaFqLlgyTDFxasytgYdqMIQMiLaR1Hf6hbTAQ3LVwxboU35NpmLaSsk+sptWMeEdbCQWgI68RVlWIjphbFvGupXbydbp36YHdQpfUytaBE/p8UQuEqiophac4+5WkwyEDSTmLDzGr0IMiLrZl0RS43MRZFxLjdIZfKGkR3YX4l/LZ0myHtoygR36mEk0pOFIKKFLHKdWODkfWtEdkUwPfNUJ9pgUonZnh1jZzbKb+pCFe4Kg8FU7KrX0tTNzVNq2fd3o3M3bxbuGmRCjx6ZQVn9QH1aLcZbCyJpQ6S8x6MG/ktbJCa3gzNgv/eOGZuzMLrpKP+PUlzv2GHqr8KvEDCD99OH4m+tsDvtSEkfEiEqvpLbRBpCNZBmM2TAZHMNBdBDfOFOxiUenQgFtsEDW1+xBz+LvAbpHrIng0bu3O+LbhkQIXVb/prkHGjiEWye6R7ujYLMLdMLfBXRpEGzCcmYDgIt1ZI+DSGuA3241mRywDwO7MFmDq+9b8yIHu86Jm8R5E2gnycEJb3enio2JxvfNFUPYpu9nO8BtD4ZqSJmHYK+Lh8Xgu78TdTjafxn2/NdLvxpn8Wzk9GCJ/uWzY93FFilzenKrt61fX+JOicj/Qg8pYHLjgbImQDVcN9VlVocdfxS8prQdRY7S1snmu6nqjbVJwlKashD7GJ9+FarzQqDPVeNR1MnziTa0MQO0XuwWkFC0zikO52s5oE/EXJqESesNfGTvM6QqOVH3yw+tpX6aGljpmuAJAbVH5y6lTEbmyV2hwZ8RvPpKi5M9od/zzM3Ma003n8xk1T5yo04o+w+TmBS9znpXbkMFxqNpmIgNAA7xUT8/HWUG3FFA35CMNS2U+5744baMlgUuqzQ4PLtrNB741DLVIMRFWR40x6hLO5TaeOoyJcHX7gLnEGaFVjOYhaof7vbcGUWAMh1PFZSiGWJQ7KLCPYceYwwhrcziFrX9pCokrYiXnghNnKM/JOYZGpLl+hcTd54yS0+uaAxoDaz546uPBiRPqfzfwVBFI2zD0ybIhvMVT2S9fCscrkh4MkQ91/ZX8ifhzR4wcLJRMIkt9Iw0HKRchtVZ2Vmkh+sQxSfmkVlyAmOfdRJwjT+PscKr3AJfPK/FbYc6I1ESXTrrGjDVCHdEdytyBjxK5LnBX08KwnL474/Se8m41nDyHS9e6F01EJWW8MiefSeqMaZjDnOVjBtGx/X1EORJniTIjn5UE3uB4E5wR8V5csQdqRL2thJzZaH0OSt+pSaALnqL5cQFzTeXPVm1Qv3jFLe+jJjrYpVrf1Pmm4L+BYIdANBkOc6GzduzuincDk75blPQei+KidhwwvM82Ah71z9vURNGUdgNww0I7zVeYTYHRcmOgzMPH3eyca+YY++eaJ8I6MTg7GOymwNW1nFuXlnJIS30AGHWmJlctgKnomXPPQtq+Tsf0YIj8ZD7OVPSFovPvKdEgJX7D15uX05ZrUllcU1S+RkC8TcY1F8k3FulMGaW8WnFVqZyC34GI4wSuwkjp+hmBXPuPZciWtkl5X/edU3xnbVo/xwnTMqUSFrTWPXdyxkHf6wPMtb7U8oc2yaFrV9u6pDjgKX1ejUVpT1L7aOe8JKe8HKZobEGDM6rbEURtA43leF7POPAeIMd94hndLi6ycUn+4mSRVvM04hPZ0aJtPC6wfuT0Nt/0HK+e40wyL/Bv4/9VDc2Lgop7+7dD0PyDD9qhiHm8GwPQodi8/7t5KoSPu2CTDRaglr75gLmUCUQu6NLRdGDfNg/AtwOtQYdgoIN3sG9u9IcNuHSByg60DTcqGGIxAbAJNoa3bUAfdla/NWN5xkjpmzY1cT4vSEt7f4Zr17NMmTP1TJ75qin5O4EXPzpUzflE7yNpHs/+npzJv1bXz+nBEPmN6nqdJZ2JI6Zb0pTkuFHHQmBRVzeG7++CXwmFRKQWIIN1T1btK3FamgkkQ1DpgKrMRLcwBgu9mAh6lBko7oVzGyZHgBJ+m0LAQY1fun8NLlLGNZlxrH0chQlamYcytBMDctIm9j9xkOyUlX2FaQkca8WV8Ttr00mam8vGzjB1zugY5uiuFkudnRxIgjuHak4syX7QZc5ypkBoau5reb6OkPZJ7OlCd4NieR9W9BqMhHpZtktR1fUI4q9T+2beKZ81lif71KFo3okBhMZnRCl70dFxAx4ydOs/1M/aFRcYsd+1uw5C0ZQBeoAhDU+040YUw6+GG80p6RgYIrigobWB2wFcpJn2ZCi6WDwDjIEurtYXwW0f1nY/g/C7s6ztXDPrGTt0suHRXV2djpyryUEFp8ebYhMPghHQgJM4BshzHXuWrWHc2ty7L2fy3DNfGscrkh4MkT+dn6tUXzbOGe5auaIap0vLlTpzw9dcXH64O23k/K8vXC1U0ngF3/4WhiAuypm64MZah6OB2pVC2hYcdcdNEoGJQK7pYMl/Da6gX4PF1Lw7UwE6lKmfdm3TFTz1qlrlOfEJ1anakGV48/uAxKk25ErbyXDpCqfnz+Izb+XcRMGodhRn4qze58Osou/IIDp2Cxst5VtY5RuppEU7CXngJrHnGT9II4rUrmkEGO0Aj5BtgtQpV2f8xIAVIkffecJvHEMAqi3GPuPjaZaLU/zNf6nTwHympX/Y97vxGZkcc8+T+G3zwGPa+8cUfhJU+utrd/2Gwg/rxpa+KPLOghJYq4xYeGksE5vr3E0CbI0VDQFdE2P0a1Q7NQ8MhY/p64h39zo9HCK/TGITuoxt5kaREqSGcYpH6EhjGN+RNazi3Aq2iK1hcEcLWVngsMAFBXU4y3VVnQetWCzYtFAae3ZxmAZlfp80fHGrIKzogw6lvZFZ3mrRNiyEnkQNRXKovrcgXkn4qa4FTuoz8SGHosIdjgKq5LIyJaW++Fwy17UyECTU6lQjQt/KTGg4NOR2qmtdfH5WcNqpE6ZgbXcdL/+uqjSy48CMlOBITAo1MRU9wqALWAgyGM3OVfc67OpYdSc1WtWrXUxj6no7i9/IECjKpTWuyvezZzcOD0LPm+qoBG/TIJC7kejL/F8OpxNTWc/kBRssME335ybqlgM7mrq1Pez8vMEi40GBG1HYFqi4cXhaHDyCRbyDAo9lw5vxsTcM7cFA3ADBGtwI4tx+E0GXDnH3uSZWt6LhAtO+0HFQMOxYwHUJdRLkXC3jZRQbHGEa5KkzClwz1SvGzzaMHVF1rZ+Nrg7aOyA5JT6Pbhb2W43Q9BZPlWF+GRyvSHowRF5oaQqAZ6fcaIKZjh1FktBPSECAykf75l457eDZZzgiIR0IpoCZpT0hVUtFZGWCIEgSUSnS0AmRESkEnpUEYSqNLecCbBKJuVbVYCXwIR1E0USp87tKxIREd8F3BndgCLQARx+9LinlylAEXGlnjIGWsUUCSBmPtS45qSs5k6XtWODOCD1Q4t2IHy1lLHPrs8CcqXvMRMunXxWCAWU5+6wS8zRc7ZwQ5530eVmNQPyyF7rNwX3pXV0vyVwwRK4dN/P8nXlkG9VvpPP/CD3erfOcWpS/01ku4YbXQb570Prdv+FF7LkXHHuQY/GI+Zsb0gnsrrsdGxoYl8+usOWi6GgwI74b6diwWYhcEYh0NL/QXXWgDXeha8bQaBdsItA2gCEYbZgP/VCMZgwRLdxbY7QAsysYLdeumiM9gktSmf4VzhlJGN2A5kZ2yvm02TP3FmkahhtisXUNBc/e6Ua3bcDtbgFx7gnh49d7WRyvSnrrhab7HUqnRiPTmbwidaZXZnNka4HPvJhc/l7uggvL16WdgaPg0vp7aV7gm/OoapYCN/eBdcl13Cf9j7rOxmXFX+Dq6K+L8JqRy11wh4V8Vu/Sn6nter0fouKSFaa/AHuGutZS03d42ma5fMsg7vECmG0/YCp6ftHYzS0zL6gBaOluDJi7zHknwiUbs0veGv0uwuCGGx1ANTP5RBENmTSt8ZnnzInnNS3PEJdu8zfLxa16woh1KHl5bNCE/vp5PMFyEe0vmBf+ZR/tXP7IAEVcgGCEeEShk+bC6iplfHFJ/SvtpZam8RiktHPaWDiBGwMIXZlKy90zcf4OfoDcxgi3zt16g+Zrdf39Tg9Gku/77EKnLqlLJcBXpDYvgCKPhQRiBjiaamymkLoQa9VeeRmq/yEToxFnuFP99hASunpbq2HfwsPE+aSrnvMynKWJ8aCJE7kRsJjWApLlJje5MoRzY5Yya3MXfFctWxcch7ylHuAEX4Gr/T+0vc4BLLfOrX0s+O5s+9qmEzz5WgvjpUDxPydHZ1m26Q/C+R3ylagwNVf7m2am3jqnHqFOAbVob00GoHa+z8AtULe0x3BVPoPh0L3OiasC9IOnlT3P4AGbj3G8ruox9pOOBY0qx2kxFOwL4JK43TonMtupdX41n/vqdW1uqm5q8+5HXepBbGyNWYS6HQ03ULXLeLqfxG/iRu4qfpGNqegZc38Pb34J5mHADPK6JhNi5/Fa7EBsBKa5VcZo3ooy+lxuAWTcEoGOIsUFz2BxBGRoCXlLRhBQHYD66Kl6cH04E3e2uN+C6VkY6WfB8YqkB0Pk52UCm7CznxIBTzhbHDZkA6mUpVANuqNMZUkmWlGZJ0Vi7O7ajLmu0qyiej9rqpUh5ZEUd9aukIClZRYYiCfgOEzluSIqPEg0JNt5zFv4jGhG9OeE+BVaO58zapZfB4rtPSQ95Z+wPFoa3qByFDJXeDIGh+S1lTE8hY0+yTRkVkZQB04VJToa3M1LXBp0IgAEETHC5xIimhNxokxpXJCX15g/OOO0O8GSBQ6UtrX8m+WFOHzQTGKXOCYQJxyGMwck1f0cmCRinAdGOG2EVBD6CVPl2wnz8E94gceeF9avGNohskG1ww4+jOgLFF0AxQaonakDwKZma7CLaRAUHTuASxO/PptMgBFHbcPO3kczrYPYmEEtSI6IRpCnrZG35pi4JQgHW1CeJeeKv4/Idz4WYlyJ30a3zDFX0aupUHJdezSjFnPN1PaGuyVDeQ+ScJ94SRyvSnow6vrLxXzD7vx2MYl9O6kb8UnBUFXmi6lMlaIODPrB9P5YZc07IdELvrWMnEMXuANEresAN+fJM8I9SzpaIJ/AXClzWteVNt2J73rr7sZX8672V+74VfFIGQMJgs9S3GQFgkFCDp4+uyRWuB+6zc3feRQI+y83gRr6FrSUB8BgOxK15bznxSr8ndI75veQULEbA9Gwgar52u+kRXWGEq5B3IjNYtRvKDiRV+masWBqF+pVupZv/aJrIJmTraXff4YDdpimgW8DLfzdMl+SqdkcDv5+c/V6EzPzCaZIEBJ0MGi+uCQHfznv8KlBz5xg9px5WlzqJhuc5Ozs7J7vw3gFiJvnONfa5gap94jq6Wfp7xVJD0aSH88T8S5WCOad5wBPFfopkpkFoATJxRLW+kCq3rmWWLfmczACKaqK4lj/JKpKvju00zdQVqG2gU9zu+AQlA0j6lrG5QxuadpZOkjwV+BW3JPRXu0q8dVyS5cObdVyJq3ZLsOXZ5RTO+qwcp/Ua80vjdJjm8ip2Gf3zrSiokdFXCLewYlwgSuHSUi1MIPYIM/MKfKInbtLuNCNcJMDimQuPJ93HJq+8yIpyZvmQ6kEMSt/SuySBP1saRFWFxjFfCLWIBgyfxMbIl51gxij+dsPNDXr9g0S35Y34QXDIWbpLjqcwcgzeGoD8sze1jU1H8PbEcclmip65ZFc6sqTaQrmWcPTI75l+fbQjHqQU4peNmVu8RNWxoBplDWiCt5jbBd25bzT0ZHWe6/TfUwPhsjH9L5yriTMCwpQWeKKIOHsPLRsRRX3GeFZ2+IUusLNRJPuMYhdrhKHqXOV+BX0K2GOutJOa6ZUml2Z4ErDa3vjGP+ESK9wc99Le1nO64o+rp096/wo78eCT49lZOrvQpQLjjouEsyOntd1RtXPGL8Kt7RJvVD6RTuJ9gE0FT0bXcvY4FYJHtIg6KBe3+zKaYxXjMXgl7xggNH0mGcuZzXQDYPI0rKcBm7wSHcpUdN4TYBJ8hZg+pfHIHWIJH0DseonBhDjAeGZ/DykClMx0161OZPBwLoe4w5cd8Ov5RXZoOjmgCMt1P6KhosMP1m3RTggGH7D3QUDHcNi6YkGE7FJwyYD3Y0bm5hBpFncK26H2jiGXZC5wG6toXt0H2llwdPQAJaHrUFo8C7xaee5h2W7kxnE9hW1qssRYxwNcBC3DRicI/ckna3JB5oeDJFvW0NfI96tiWopEedet3w/EXC1hdlmmelAbYuYXSVVCZS+sCYrf+SiJQoucm+SzKgP5XRa+VlndMXzTt3QJjhMcNURgb/pnv9UuNq+tU0nDMe1Pq7MAje3NSLfhG/p/zMF31nrqoR+wbdOqKntsuZk6yccfK0Ftn4EwIOsAFAp0d+cEWhZodJQS3gyDXACJZSUb8KjgNm4KizGfSxIwMXz4qwd5RwdXDVGosMCHghiD29bhqwxZqCOk5Q8swjQyfq/a95qF9fIivWpw2Pgi6BrqtJp2GrEXdFh5/VDGm7VIunbWTkj2RmBvlULQ0sWwQLkGmHeReK5qx0fQOBGdqaq5010m9hp/kBe3EMb3Oacz77TQ0Gg4iEom+8f3AP8RjuI5Ll7OasPBplxMMqUIpfFG+mskQLt3JzEiLpzktKa1dXvT8S712fyc3owZ/LPxNnFBrcS6hMEoU4jtPL/WUYJ95TKNf8ESNc7vnA8Ed1KS8ECdzYxZWn+KYOj5f0BrlRYiPbJSM39uQbnC3AlsmfpQCPvwLGWu5piw892nuGravw5b7bEmMatjL99R0zfNuHmntH1DMjpwqljftjla9Rz+jK49TZELmqdWqvOK6oTgqXBoKsXkrgKCUF1odPpu9TjYhJ8Nq3RsK4a2JVngyPLkf9LF7p8XudCk/mbTK52kHC3yznoa6QcL0jgSfV71lXj7afbIFiWRxllTHKy6NRmiWM5Lmc3tI3Wa/CPdhOc49FksgpwUnGZx6TCxBRbGN7gGf13znPNDz545ONxOF670N3r9GAk+UPEOz/7AyiklYA1NF+exFkgw8gBuSpT0osIdcSZfkII9X7AATo0rFqtQHGhU56xkdAXuMDnoGIbSUTGE13aVLfzQrDbiuNcojUBhjmCHKgzuOXdyS50p5vcWdkFj32v5G/uiox3Fl1vin9/DZ/OXdUTfHPffJPUklfqWNselDxcKo265xGHlstFDAmJD+Hs0fCMMNDQ2LhFk3iAZaH+XsuxhrqtlROuxs66Cx3hNQkd+A6pmqcrm6imil5JeFf7sWJsp1KI8fLN/V/Sq0xJJjef+xy3zdeGIK+iUJgLYXweNev8W20Q2XFRcwS0O2BM1T5gqveLCLoykp9Ahp/X02CPLnQAdiUjoGhq4XfQNrSufmygzvzwIobCBYqk8SP3pxBLObfmgTE3udyHBDkHtWM6urJp54AcTFVouZHOXOjyaAdjANuWjMp9SCv/+qI4XpH0YIj8vNvNaklZ/p3P5aVkLrOchi7ixJQykONPn/fm6CSJrosZVhP9Y5OqKAocq9Zsx8GVbCJuaXkd/QX3h/LuBEcliOxctiPbV2zIUkqp7T2BiyEsdQXrUPFVuPI5qpFd/DvzUQciPfXxSl3R1XWciRMFT+DLBvLM/nD8UYabTELaBCxUzQdSh5+/q8TRksKezTZKALRQyavw7jkP9Mr54ZUrGlSc2JhdOWpI1CTq1pHQ+ooUKdcNz/x3W5rP2u2bGpHlytmkwsyW9FVFP6vrl/HNlRVDxeh3gFhIV4flyflFLMDlAKBi9gc9ei3oUAwILm5gtyMD6HQ/AKClO9Qv2hEPWyvNGQvF0IELBszNjFfW+l3y4scGavfOt6bYOvPc6l7NZc9U9sb0i5+R21l9Sy5pA8LgwCeWbAAvpoqIje5CNzHd3KPEJ6zALPNFbH+y6D8QaWYL4PsYXenGGInrLZ5eq+vn9GDU9a3J3QCTWv3kC+vJ8yrdLimi2lW8PNNd6ws4XXDYhj9POpcW/FHilcRvcbiq4k+4rKvikLWPJVTW5Cm+9rU0W9b3OL6PuvS4IBd6PFV1WHjXcDwr3Mlfbd+1vBxDKVzNse3X+l/blDgqnsIZxLfnOymfT8o3Vy8iENS72KpJnkmdOU6ukvbyW9QiHseeeEdR3RerccKTyXNCI/FbnFlIP/4cz/K0qPUbGQwRI3YVL5KpWP/InMTVu2wHe8U2QsN9LXHV4wiujXJJDdXwypgDbpcTjBGPOIqGo4zLyhtO8PwegtCmyDoBgbDaV8LVqVMnWIkKRBsUrcSdg0jj0VUDJ7nfSAN038te9Trdt/RgiHzf875qACEJTcRd5KwophXA1Trl+o6tJWsh7AzUMeexfl+8fE9xJMB0ZixUZ36gwrGoJg4ZeuhGwOEkb4VTpJqvgOtSptZ5Fd+Rj5lxrG0/9DHh7sIxtekuuLM2n7R3atMVfNN3udbna20yWjrBCepztQTQ6b8R082JDW8utKmaCEVT4gXS+EsKDlFTM9eGpYp++J3t2abEUdqoRybJGIg8gWcSnQ1hJeCknMnP5/JJrwRka+QUTiigeplqT5Bhd+2ZbnRpf8CObvwwOko4Ww0CXRke/m6sz9dr1CXmb1+/YwwYj2GAZX3r+iI1kWV+6srdztGNHc5XPQ1mAXi4xJzDvds8UUD7ML/5Rfv5lk7rmn7Rv1ckPRh1/Xpee4h4d6eBnGBmAGTJ9e2tEAEtkApM6t4zP3IBYnOGzDjAZ2789aiBxErymZsJUPz4C7KIqIV8TxwTHHhb1dIOPhTJqmYGmgO+GUeFo6q/7lPxWA5jYzxxTDJO8q5WynFacCxMAgE5VutYT5UUgzJuktUlL/o4dQ4pXfkP5Vl6OQKyY6Dmt5wVUy+Fz4cGVb8olRFSOBrRaIHZfzMgDp3UTj7S1MhiNIc0ypvc4VAIvZTz9oI4CbihDgk9yq5fNUcz8gSxbnWBSLgkvvQpsCMxg2Z7B3iZTfbDJGXFRYEhHVA74mAMAHV1vbgh7AbF7kRanEmwIw5zObNzfKeTY6Bpy7j2MQYa+8NkUFgJPnzQZc6LDvlkVnJUdjaRUwGcexxHtwdCemJEtfUgH6ZNMfp/Pyjfa3X9nB6MJH+5OYl4d8qZXvm6ab10ZAbK7woWqvmT3wcxmHBlz637bOSh5JX/yljhVgig7h6ncHNMFfBs/6xNUp6nvNq+RTK9Clf7uObVVJioa4vw0KaVK/cNYO1XrXDFcaCBaz9O4ZbWX+ljbRMgxZJfYJb1DqKSLnRIOZMGo9yQFYRzwl9qo7wez8Go+Ek163I/8WjxxLxNMyrtB0vv1m9IUlL7PjECIlOpo1Se7EGLsuJqdssr1jNRb3PC3iBxFDEzJkf3P8YOqB4G8KtyLYIemYdyCQ4QMfsFeb5u5T00sGsRnK4Wu4Zc+/U261Y6lKZB/BD5HePz1M7XWA/Th5D8IJvkflTiL0TlfrYhm7vQ3RMC/zod04Mh8mMUAgucS+5UnStSnc+0LpjcfRGFYIsaLBsLcDrBdNAF4USYdG6f2n8mCXM43CGV9gTx0etE6zAGc362sLZpwV/KnqrXT5p4gNP8q3n1eUV5mqfzyB5wXGufj+mhDbVtZ20/wxM4JN6FZH/SRy2F1ftlBGOUprI854ppWVRr3jwOVk8dkFSvA1TDAxCv68C1sMP5do6uh0LtHSdmIp1glWSnZqBqPxLumKTkJ72q9czsU9o1lDNvkJ2xwbEx9tGNQTcLeAiZEy8r/Ar0TFDnwySXbzFcJJ9lxnplNONT+X+VjITGGMKZAubV7YaNkjpRlrUoBS44MIGr5T0NBYPeq8KsFLndFDgd4/650Oln6e8VSQ+GyJ+eu4c1EZY8neI3n+Lhcyk+0Y9JutepiNWFWJx1W1PmYZlntJivm/k6IcvztPcW5FHXImWvPT1Is2veXXB6Je8uuDU9DZ8ueYoTTcQdOK7BrW14lrbftTGEhqV81LXtgbMejdgmLAzzpsThZ+dSA9vyNJkbfIv4dODlIkiFNpQHAEkIIwjspG1a2hcUA2XOpBSY3urHcToJvx6lq6S+sgKtvG2R0yxwzQRj5RjTPgVWKe/ndqTlv+KCPLqwCH62QNwfweCEIX6TUTA43sLHDjOMrbmjRbhfZZkcXwYcIuXe2tx2sgMyncAoSiSgo0KycCfTGb/M81ViD1LIVgG5yfjf5QK5bK/P5O9pejBn8rI1oEa8O5uwMcHFwjhuW1JlSlBcGNXwpWi74gYwGHGnNEc5PNSrqu6q4kwAXWIKEzCdt7N5rCvakv9OC7qKIoLSJt9Uzly+JOtXzY2F+FbG4Wwo6tCuJg/n1Hxp+9Kkypic4ajSVpVezvAB5TNegZtwX8urbSKcXsMhvl/qTN0WHGyLDh839xdUSfeyiHsORqgzop3aVLu4xi9Fg8IivtEEu6ug2fVtUGwYursdSIPyEJdGe3H+u0TGK5RfovUGoIfRzmAzKDlcf7ML3TRq02dmGWNFih9BnWeRJ+5eZ25zQ/Mq2+EGcPRCo886x5a4Oc2o0k+HurCOCBX9TgIuw2+cVr+80BkQkbDGzzsAUnXfYziljCc7XUJc1oGo9gge1U4k8xXLMzK/uuSqZ9iZe25iti+5sV1zW4+9vz6Tv6fp4Ujyn5VUKOCUZIaZ1OwJIV42TlQ1i9hfYSFrWS1AE1z+HaPhYSIksXdw09eSUdtUe6SJ40xYiHoPffRypT1rO065ZS0L9GShXuMRztp3V94Z7FUcbFNpsy7lruGY4YqUqne3aSVxVMnrVLlv85qMxnQ+ntFhDLrkpXZ3/m/dDNb5cHe6NvrX8479zb+z8/XS0HNkRe2flvf5XgLu2jebbQYsL5Tq8Y5jnTZws/8+y02TuXCr0cayWGh8B7jix9X+Iz7m0nXOw/qJr32CaT/gAEhyTUweJpcTSWo83H7P1PWv05QejCQ/6kRVDemZEepiE/B8iNh5VGtpic9F0cp2KIjNl9aredNc80047ZjtvC2lB8DZ+8CRK9csgunqJ7EpBFzxh00qxK0n4US5USW3fsZnRNS8asW70JUpahwwAWmrosOcvdYp6yZTynDjuisK32nkuXWzSzp4jk9O4NbENrHtcsLl17yC79C++O1zoLapzTBxuuTzUzXjvusweErwgrhEzOa296XmkYgTFzCgLsGpt6OrBW8hFeF8jdvclKFvde6jpwifr6S7UvrPeekE2CfQHOKWUBJ9nzRfxeDswGhxHZcjNFPla9zrUguJWHtH2N4wCLyxGdkvhRQZX3wc1MeZGhcaOzKf7ec3Bpa9hkwbWYlQ5ZutgEWh88F0jaDyo0ZNOdbxwePjlzxwLnh7qGHk2f+oWhFFBLxXNRe6bYPoPbqghv1/WRyvSHowRN4ur0hVPTeGyfCHexeJemsTXGxDQcjsWbiJceMTSXc44gOXcwt6lxfUINoVu8AVffMR7oRW1va56laQalco35VU217xVGIsBc6RpfpPEt77hEIQpyHUKDK3qXa11lWbcK1NHDJuqgvBWfHF0K5wfL+26RojUX8u7WBm9lGutqluzDMhlNiYh5o6vnnEO6grknVA1W3CpZiWiWBo833fIrKJNKjYBSwk38ZYNkCaRXGDmqOeD/bwaG4zB2MdLNc4YQjigpZgEyphUE5dWp5TIT7H26cFfRxhleEBsryTrTKWGqGlJ1Ob+IbuAqjFot4JtTgxb+yHUk0vNm5oFs1O6AZna8DO5yXO1hlUqEu+bwq/1peMi7V/E6BzPlLj4J88u292Gpr+dgkAWLCaljggmAJ1Rohjic6CNw5yvVfbJAsm1LKe1mwbGSWOx1s8RXyCl8TxqqQHo65nxLv4dKvRHV+XfLkKVyhXeSclS4DFSn7e8MJaHfOEjFZShV5U/1XVXglLqMWnCHVsU6mHz2XHnGyTC/e/uutJfdasoRKndRyPLn934TumGE/v47U2HcrdgW/C8Yxw19q09vtam474EpJ58Wb6njGrDpir0Da0KvgbUl4sJ8iOi1bbqnwWqHpo3PikEsyGsjLk1Jl68sx7YU5aozfXvt6VJBIKJlnG71DTzFesiCY4a4vOoxXSvfMOXnfwNuyHwi3q05iO2wXj/XMUabAe9NVxNAmWzlX0mnDRB6u8FfxEmPtZgefLVraFGLL8CBTYpW5c/oL9lCYmzb9CRO+hpQdD5PtOVVymA2e67BixwS3EfCX6QYenM3WdilAteqhs2siTH0gUJa/+t8KdQNT840as5CMK3Nre8nyFqM076Mw8rHAV351wOsPJnD3jO2vXtbafwD9Xm8rfoU0V3/O0aRnPeX4s41mQRNx6x5Vnx4BO5tYKhoKhqreeIOv0VNy2lk5OPvXrXJqWQpmQhzk3s3m5rJ6NeFT2OJihBeYpn7GMUu39EUd1XPTW2hutd9qbxF/Y8hn3RHSLWV0MkY31CLUTPdUK3DJpol/lSMK65NyezHPoMPerv3scvTg+wg1T0cexyH08j1/X14v+vSLpwajrpzvb7UVYt2e6Sz5cESaoTL9lyqvwsZzPJhDfXWsCy8sJ3ILPmmGNmAiZZwpIHLLh6hk8s66opfRzwlff11vyajAOLW1am8I7LyghsS63NdAFTVXHywlejPO2q9d1V5tq20/bxL2RkhhKHut6jjZN38w3Z5YfdZBIeJUSOaPaSTQmHboQ79OxywjqaMBAQy/l0w3P4YotBuO3GVEVV1gvda39KhNC6u8DHF/qAmW/SCqv+cwT30rEaz1npKkpCbPV0xCB4SZjP9dqg/HpG6jO15yDWpcgg+Jk66e2+v0vKPOJ5+3VQwBSL3zkt/AxWtbDmRbywPgoyk2GKA32EZ6i4ZVvx8FgbJFtg+77vTmTf21dP6cHI8kfIt6dLpKZhYvFtMLWWPBF0pd1965q+AX3Ma/UWYmF6qzOjTIrXMKEWp/Ps1iWsBVuqfegul/qWtsRcDUNZNtXHLW/a2Q8XIGr7cN5uqvtxH/WpkO919q09vFF21RLDRJxEm/kMwkwXL0ev1m9uE881e6tRMYjoTeXKPrOK4AhLW61C4KufG7ZhulvjaBXDP4wC0D2PGsLtJYswWXmz5NYVqv2CULmz3pYH/G7/FfmED1jgjNsq2V/KwEYIkqfkFBbuYjEh9QWVrjm3KMgI95J4OPeMUe8i0BFKEcFfEdAVsYA/ctAiLvX5ftA5DiQ+GJRFCrfGPFuOLP5ClG+B5QeDJEf4ykTtFqUe4oS09l6yV12lYN6DLKUQQCZEKYJysJO1Oedr2yZlVjyearTXoRlb7R/wUk4vixlTTVYN9ssMqnT19GqBi96AlJA5RqeE4J8yFcccCzFph9xSckZviv1ntZ50nYlDbirTSf4BBVOrgMGc7B+Cz1MxwnCJfpzAyKjNErPDUliSxU6n23qrGS4+FWzyoAlrrSDn58dZm08UKDqkmN/NI8ppv+dpDqlD+zBYcAOS6Nqr1VLX3UOGpdjY//tlQEqa5QrDU5Xh+Qo5pEcx7ysnzJG9eiuDl0wpCf9n+bZCqdIBmAAUiPelb3udcS7+58eDJGfpO1plRzkxcw/EHZ/J5jgFAvYaqFUyzg8pfSw+GcGTuAiT04nX+JDEJyzOTrBrfsdCdQozV3gDjjLZnK2fx5wPA1uXIGrbVqT97c9pe3X4uiz3uqf/9JtIo5a/qzewzhJwgx3c1LLtLoy5h1V7cPnWndJ3n56vDbVfGYbhK5YQERiV0Ao6Stc1d/KNDYJ3vBI4OZvNj+IWO2yZpfU207iPFDJPyYpe2IyVcPV7UjWyxpkhcsyyTxkjZoqcN8ZAMzSvSrAuysVRpzppcP+SoxNcXFE7jO84M28ZG3EwhvBNTVB7BVo7nY7APc+sAEMJSHnHANVjZLnKY4UClzgKAPS/GxAVDN+ffhl+j45FLhskMt2ZbK/9RLX8sv+vSrpwRD5s+hyUyocNCXw8zNBn/yLir2q/Hz1z5RfC2wsVt8YtATiqItVp58GJzOcaJ73hUZuKrPADcx1+X3lVN2bUhanxKlqDqKMx9Bf81r5fUZIJ7ii1sdazt+3MxyY+3IVbtnx57bPjNQ0nk9r01lebRPKg573X85w+9xIDsAwjvCVFg81bq2gqn1A0LWFq13Hhq4C1Wbn8Wj+LP6MuFyVkfGGbBjqV6+IB2SNeUip1vLs3zznXqe7o4zhn5ZYgRsLoSYRNZLoZFmAvHhnDYRbUlWHA4e8YEomwpirV8pvhszlGzr8zbfsuTtc6Vl+e4G0gl2SSQNcS87PXCmw8Ldi6oWUP7aL41uFCC9Wz/F9iiSeOJER6CYYae7vzIVXtHmv975sRq/TfUkPxvDumbhQimaxmSyLbIJdslWRfu9lt5/Kq691BsxZxPsA18AtmhtT8g3FYGcqn4SBfvH0S67DwGA8k9EYq536VOuSGU4KvmlrLO5HiileQG3D2uVapkpitTmylJn6v+A/G861AQG3Io22P1+baoNO2yfHPJkecuONX0W6N8/tKonD+c36IamiT3JS+6jhA2Y2+eXo1wksSkAmotWAn/kRCaZZgcyP8WZJt/8XrxMlwE30t1RYuNhZU3CFymipm4uGWVqZh0OxeAq7BtSwN4Z3nK3TqLdmpMzEuAam7qcxpY+7W9tzzGq3w4UOmt91/hyWPBxeBGC6llZm/XwAPOJd/mRgLIET+PuUyvp4KRyvSHowRD4i3lVC7BviMZ69LyCX2CcuWxe4iVKVzYpGLg5nmxrFjMoMSFnErDefT0VzJ/yT/EFi7iuzxjY/Ci1rPQ53hYpm20rGygOthDTqyzbduXCk4CBBwskGtsLJM8Bdyavpmeo6g+PeXqfGMi4rn1f+OTRCANPqipr1c7HH2ALOo78NBCPS1fbpTRVdLM4bXa2Gq403gUv5Fndd0SKOu0pDh2BTMbh4DzCQTv62vBHMpMdrRyGq4ipntcAwDcDmDITFZ8r5JCTSMbXqIM4jNdFYn+NU+9e9vdoS5Fl+Ggqe/UbgkYLRtG07yniM7KMZKzqTEPdUcM6oq/w9KJF/hyDumoTcjj9SM5Fn/1apDORZeXG1q/Yg8weydrY6D/3j6IDdYcB3PWN86FA/g9dwp8O22V0er63r72V6MESeIioN0oQ7khSFny+6mMzMCxV/lZhIYUoenyUXe8i48SOln+pwQ7g8TVjzarWFtZ8C7GRXiaPyHFHkMIHl/DFVAZGpDiOFwNQAIUwV7lQSQdnCC9yd6S645d0ZHOuqB69XF/OztmmpayL0/jwphpbmHtpUKJXQ6Ms34+Gb/WhiEciaS4MqgBoh7mhoDjvECItKsyh2Kv4s2LF5yFe7RGWoR8BrIyzyuywx33yeqzaMxhP5vHzWupFrSoEg/nMEZiduPncZrz2/U2VN8wMM5KoQiGklgmmqIW/dqNC5s2BkADc2LDErBEWyTlfSphLn8dSmseb64eLyIJ9M4RNf9osGJ7YeZCaWrMQ0zOeyVUz7gPhoVwt5TNtNvmuJD6Uu+gaK2Jg0D08oWZl9Y54jNHH1vTNKqyrkrZoqt/cyOF6R9GDO5LdLPTVb1Y7cRyebYV8kZRVVg7wAWvNqeee8T/AyGl6EYAy4VKEGg+GbvNRnzPWdn/XOcEK4ZD2Qykktdd0NR4vtxFf6VcsUaeRamMgJjm0veVM61LXgqH93wT0F35R31qYyxmv/20ke62UdZ22Kd2EzYbs9I9JBZ7e2oUmUhpavQ21R+MvTva65ex2g2qAqcV7Pc3prgJ3DVzie8UcZsF00EpNo56pen2XsysEYlMKt5nMJLClP5usHizLxtjIJJOgTfzp/WiFmiYhvqa5PAs8WgIQ+CL6NQ1cF1ENVlyWU90DYFhFujcHI2fdJoi4BV2huND6OEfhpqamT7A/s8+UY+nPMxeAmBGgusEdliY/MCAC7hW6cfpjX6Z6kByPJ9z2jksR856qB/9vaJDtwsUYqEn4CeNkSTAJRfiFTAX8FrlZc4Ytsw5dVEmF+rabWJQDq3s8HPWlL4Khtjf7PbQocU8Unfaz18gNUuIqilj1pe6BZx2vZxCe4a20667oc82rwnaj2Gr6zNj2lLmYcDNemsZkJXPy2SVrmw5j84aubmUn1x3AtQZBjgMdEkmmUFb+jnFnYb/5thn/auD1NgUt8u8SR6vp5sNhSUZfS515P/R+l/0OyjxZ3P93tGKFuZhE4thnJL8ZMwyve8dHDnXlZhjjgfa5wHakh7Eskuzq2vSygseBLvkCnurguAEAYYyEHIOeZzvN28v/r1XA0Om3MyRi5Du7bebynV0nd/rLpwRB5LZtC/AVXXYj0XKjsQ2ezhrt3m8v4v0GgTnFKUNIDITshvleJFqxwaNW5AUybdsJWD8KpGYHTGiF+KVfEY6m7e6l7Gp7ngcNT4KrBUDWnmHbqLFAJ8eRCtDJOwCRhRxb5rFLXOk5149Brba/9kyttP2kTJS4+YwCyOUEfGjee2VS1j6LDCGvTlOib31bS3Wtic2v7rkCThqHDVPQKO4f346lNG4Z0dLUZOWQzKRWmpjei1bxLAx3qCn9F9wVlqnstA2G2AiK20eQpCee9wTQBug9UI451TvoYUiIOBiaY3JmQ55Gzzs+yBvAxqZ1aCG8RFII9r58xLQhSrZ+BhNweIuAyUuBQMjwSkezUO0Ic4m0g/c04N0XDwE0hOqiAtPS+ERcByhZH2APTqQD9+rR8J5S7PZI9sQZKEyhv47wP6Vwl9Pw4XpH0YNT1l4v5BB/EmJpWtfo1An+3yHXAJ0Cq5CuOVdUPTO5hgf8kTapghwu1rwJ0hxOHinYcotDNIjBNf4LYRV0F30kku4TDM8FN6uu17RVHeZ7ajmNd05hdw3cyvrV90/treU9pU6Qx97EygGub1oh32dZ8b1OoquhJTFKFPrTZeT0Eig3d1fEKM6wjOe5i7nWmwm/YdbOyaOjYXDI1t7s+DM4IosCI4OaEL93wrAyiDkBCndwXQlwV8L0MzVjhNJ9Xo7myanAyvPYsRauxEKmM0J7tpAdAvCcTAGOufCn4s/VRhJfRJjOSH9eYreEZSYzToFe8jIiEFiSumIZg0hI6MK+6PS6S8tfKspCCi3+K8ptwktZ64hHvFMAY9+dM/nWa0sOR5J82QX2yk+EFZqFshsVMpCcgjTyTvrijFx9Z9Q3njNCT22ZzF3em2oZU481tqJufEQyd1KWEiufJgm7uSrIJOFHvV/gkTncdA8D7HwCE4ybkzZj8mMsQTUxYqavuU9DsUrRpKbd2fYLz8vKMeTXVNq1qftxRhgWqWlbrIFKleqRwM07ViZiR4Np8y/C17FS6jpUzaSWv4YQaJLaGl80whz6T9Km6TwtzDckdoIuaTu2a74fn2suPrtGCJLx5sztbLKGiD0t/5P3uo9SawXskDRGVTAo/nNkWTKGCi73BqJTV61JUtb6PkSbDFvy9/zvIvJFpiz8fP/4Ntmn53IqJMY4PcpgLy/wLFUax7/F+gDfNKaAjnQZVAfT7pbJ/bV0/p4dD5CP0VLVMwRVCBKAS4TC+q5TLCdSZLsTPA6WayoLryzZh4xO8HW2GCzSYiQvaMS+ICQqfIKUM0/B6ZYYba5umkF9ljHwnW+s6UFC2CVfgTngZ9iOKS+KwtpdhWuFKm87U/4c2rRycYmpvzaoNDNONpU1rXWwTEJ51px1WlI2kcCdSxloa3Ti93TxD8bEYQzCa3SY2nAA1Z5bMWG6YOxwQqv0h4ip5oMuGMTp6AxoadpgLnQU933AZ3ZhT2VxF3yHYsEPRtKOJOKH2S2/QHCqJu/A4AGkdnzey+em3CEachKcHOQl9ZXdGMA+V2LsVgTpxd2a9EuAkxP75SLRdG5ER5pMZGMozcw80ZI3FGM4ciQDFiBHOLPQxM0fEPzQj6A1gUvmTEbAYGnz2GTS4X9h/JBahz5M6t3SeuzHfh+EWjGA2miPVoWjuqSGjTGwS/8vFPW1wP1LZZ14KxyuSHgyRl+bmpG5gdwpTf6jmLXUhcXO3dxFtIeJxxl6JeKG4JuXlWRpDSapqMATqDamEz/dtC2FZY2cC9IqJctGMtW8iBc43UK7baGP6yh+UDG1tU8FRmZhlHOvQhBoTQD1OOCWw9XfJPMCtzMgJHLt4aKQWHKWcLOXW/U1Pylyr66z/B+2LIjmVemBsFmqgxGdGUQY3GiXohq5qZ+pQdG3Y1F3oPM+i37lCWU0dDwiadvTWnACb3zwtyZs09GAYzPfevljHDZpHuRsQzyO5IgFv7KBUfUAScSMyPgBDQ0Wc9vkHNrBoBkjcc6KHC52rsmsUvvUkZyBba3A1+p/EOIQhHezognDVCl8BdNeShCReuFtqUAAy0oLpDIkSdZ3MAvDYIBZesYS3Krwev4SmHhHE/KyTtgHaNfMa0pJ+a9De7ZvVs3cRi3i3K7S/jnh3X9PDIfLrTrwm1bKd5Lu5cH1/QuBXMfGAMOFCXbYyCkgUQk67Ln7fMCYCHFRJpuamKjTo+glc6bciVL1J6BgoQ5Lg3gUX5J9wCLg1Ba0LuHMcE4Fd8E11VeQVTuchPjsKOBDqMl7BZD1Dmw4nHiVPWVaOcLz+bOqPHhVFIflR5GwAJddUN/tIqviZNJ+zjxk5j+fRTtD8I1OajLNqdZU586ItaYk/YITbCKaGJsNoVGEgUfhKjo1m25rkeC0hn3IctHgNoD77n86/jVkp7RZzf1OX4Pmec482CJDiNhh9ThV7p2od4jYPmd+1xe/Oj0tGoNx3F99OgNFz0kzXv8+dgY5kqMrkmP84DuV3HVneLnc2wFL2w/tmYS8DebX0S+B4VdKDIfJ9d6WdZAx7qTsNEJslg8BMdCBWi5wQ88IMaD0nz40z6avGTqalnEYdB/JVKI69N3xlRXJnLD+xrN141/LtQeKP3VamZiSrEKNR94oJTq7AHdzf1lTgQgo6bmMJdwVNrbqOwRnMyiCcMSIBe74XHtokJ+/XNPGD0d4yb3yqxVAEcXB2zzfgoYjzx96B4UJfd7X5UL9DXd2SWwU7BBe1a1U6jdKU6mjBBjMiG26JLzC1fscI48AmVOsDQMNWle3lqEqhuFGTlhs8/oNUplFnWwoOmUq40FXpfVhx1Fj3on4M4WNDIzkbH8WgMRs0jQTV4KqxYPcxGgB2SJzTZywB3g3gRyHwS4FgGhSLiNfizL0jret5eVAaKTrDpH7koqYtGcOIv8KUjjEu/o2Vi1iR0rbnBWcTXBkO7nQJYwMQmr16Hq8DEfFO3Z2uNRwEnbdympial8DxiqQHQ+RXOT0Id528QXStxERKJ11zZJcHCXw1kl3hK/xtC4IrlZEg4a/Iq7q/ctlVP14I2kx3575NkuThZSXhtU1HahSqe7rXncAFDkm4aajW6gWQcUr55uT4zgSYpTszj1ThBEuwoQJQ8ljgLk3EXW2qvZi+8TVEw0eN+ytLk2lUANpM1JM0CGsDplVpzQmdRbnrQ9CaudR1HdghEG3uGjcg0rBB3YWuudp9wy6c9w1d7KIaoGNXC4F7USPrtw24RK4RsAvEzusF2GDnv7ce0haeZ5itQwPu5lfoklkd6GE+iw8HZUpVYJQoRUqLc8/f+Q4IBoTPxghYpR280Me8D7qr5QU0SrTW9vBOaIVQu8QdUr86g+W//V+6wOlIw73wjqAKv+BbmW/OW1D1Hx4XWOavPUjzTpZxjWyq+0Uhm0BuNSPcwY9QRIBySY20hqVJr9M9Sg/MhQ6FkOfErik2/sNK0+PzGdyEZ6GgJ3kr3KFFR73jeZrwIRf8+t+CY41qx7x8rqVL3nhGuIJvjfh2hJPSppI39fE6/vr+6XUd0zXaexizp7XpJG99v7Yrv60sY6tzqaLhUM0eUoLMc2JayjdXNRsMr4+1s2Vx/25/9n/VXeiiHNKyXIu/uNK9DiRyWxBBuuvVS1/SoA3+uxDdMQmfMRzr8xRnvtA4+9MJdv4EUvJKmwpxNGP2cs7uxL62KfC4LQRAl0Jn7IMjlFLOI9kNJBEfbNvMhBg/rxWFP5Oo54SaL8FCaEREygAcJn9Zp5uFRp4LextpnCyw8/qq1r8HqUacfJm/VyU9GEm+LxfUXDtvDzX+2bZ/du7OxVf9vsIAzxdjiHMumU3aAyBupXM4gUtnvlkXC7mFNUf+FkybgB+Q+s8SES/KJFPAW8fCN7dKB16nQCd3NS2EGWVIKly+lxklcRzg2Cpd4CTy1n2LUm7Fd2ddzHOggwtdxVG/xxV8p22qBUhd6pSq3zAQaLbPy/DMnMwk3avEo6hMBK40cAyBenz7tiGJtOYd8DxXp1W+wCzvTVXu5+xUUYur8ZWqcRJ+jTLDO0JjNYFG1Deq1+kbYCfhUuiOWnCeMoZpeGZaAeKQqR+WKk0z6/u0SyBzYAQ53dy6XlOpSzIooZpPdf3uUr9CZlX+oGqfVvkepV/F4wxsMe4deW3wcE5DvZ10r8twxvbtoeYBAcfBoxMZfnkN6fAo87u6EwBmRc9J0zUlPJ4xcK734kLX7xeBB4A4knhZHK9IejBEPqhXjU53OBCU3NmDuC7EPt7Vndzjijdg3slbPFavl1TDez2VyIlv8r6RF+P8yXBppjwLHJukV+Aq4QHy4NPEmXM4bhq1bg6nLnVVfqoSVlyHW09KQlAFjKiBzNBJ273ph2h1J3C1TfCxPhg3PQ3uWl1AHGUw6VpvJf5sm3+7OCNVMZdPEWBoHAeIDyBhxxAMMaZ0ODGRBjTZLLKdAG3YRTPdicSQhn0oVBqaXDDGwN5MEr1VQVPBxVX1t2q31Yls2NWsmS7hQpfT3X4rIGrPsGh4cHctuvXt4UwmJFdGdERwm8PnzJsGEzDHfU+VfDUHU5806gPFIwSBYOcUUHEXwtQydKVGomGnpsKJ+O4ajqEbdoejlmJ3w7rhxnd7aDNSizGCSUiCHq522tDJiDiTQEJ+G/PGmdvBOYEMviRO3Mu4xdrn/Krbnc5zsMU8tIldr8mO7Y2Wf1sDxv2JePfaT35OD4bIb5eG/daNSrYtiXf9mLxOcSL0KxCSU6xagTMJv+zkh1Cqcbg9l+e5bYWLxeU3j1VCGMsuLK2XJp3Bleb5Hvt0OCnBLqvLzgC0VRxyjuNZ2wTb+EIamQfNf8sBh7L/Xi6kxNrHFSftBa58xmtjc7WuNQ9zHyvjMnVrwHzi4d9fGaZ0CVg0vCEKqHqoVLjE3lwlr8AY3VzsdMOQDkbC66JoLpkCgl0Vm2wQSuJirmO7mgHd4C1lY0drfnudDmMivAd9ACIbNlFAO0R4hm0DYdohdendL3Ipkn0Hov0+TMjT6Vw36pnVVnb+N63koRRiqQ53TYbwfcs8V70rJXY0qPDMfAttgBY4Hl2on7lHOZCG8r0cXOjstkC2eot5IA43MTQe/o62O7HmQvNXA9B6jITCOB5MXMS4VTbJ5q0BamxQ4i7G9l62Br0F0F9HvLuv6cEQ+WeZn8mp1tWhy28sq8d/k6a3JW+CS8oRKnpWF8Qnyyvx1SZwF5O5GFXa3BUZJexKQ8rT0RtgThXfWryItv5+blNKnyv+Fa6imzZyXUa/UH9RmYZtii1feKyQkKc2HXtaDVQmJqS2V++oi20vhKkyALWPE7FnefKOWvhIUHvB+G6TaWTyomxc8KYJFa5hNAAj0YmO0pqcqntX99fatJ5HU+2dgXga0nIcygtq85xcYAS1uaqdU6Hyb4Sb7kCX8iga/SI8+LsUSQJPuwPrP63qCR/qdVTpHnHZDMeD9gpgGRJxpYW94dmd+QJKlDyvbPBbIY8PrN9Fde/Poa7nOb5i/nPJPhjMMc9HKOLIh3niFce0qwswGxXP9JKwI4B75k9WJ8TL4HhF0sMh8q4PU7ckniRk54pj6+SGSsm57kQ1kI6WB7LaE+Em1dMk1qQSSVlrK2PjmuBcTV3IbC7+5kE2dKTUoAqlGl2WwDxBmDH1WQHrLzn+2kfxllV8x0HINtUNPJyls96Q9svGruuwHvmC3Jhqm1jv2hyXarS0JzbFVmDqcGjmVaKO8rl07rx/g9qmk7q0IKxEa2krBky48z6acKzAVooqQpMy3IJa1Cy31SPg0WVuDCOs+wAuDelCJ6zO1PAyALQNT4ZCmvo99A0b54NfRUM52yz1xc/jN4gobgFsLpluGGHIZt/YNAUbNAi9nZ4bI9AgcZMd4LfaaTJTHBqQmahjrYCKeQk0ALvP61CRi/j5eLrKAXY0gVDTm3RPAn6red3ubcTzb66y37C72n1Xxuy3Z7iGgIzFcMK9w+8UUHFvNavLjPE0GJaYY4rU9KkGg2VzokwmZwQaZ5QCvAqQzGKsjM6tiHCIyrRrznca2QWnaS50sm0HWeetml6r6+f0YIi8mMhhi2S9UelEMhcp/9pL+6fqdEMkXESPkKopyUkhNlJ2sNzxpZV2qL1IgrFw4EijJYVHshMpdNSfi0gcEfUk8w5S8gSHjBeg2Y/A5x1MX3YyHZgkZwzikCOO+glGopAKVhYba5zbnkimr3AoVxLrUhw+/UT0UeBYQfTf4aTwL0veKb7apcMLHwduUrG5K6SLMxhk2jyKuxP0MGpThWjzW+UG9tGwbc2kTAFEN+xOhETMNc4G21TtXS9gpPlbsdvpRHbsTo5VPTCrKEQ7IANPVLBBAT/9HrKh6cAWIqYTeFWHsw6bYZ7HmieBhxoBFvI+nGFZhrwzj60Bs5Tv2sytzom7+oE0PQUoqXfd3Coe2HVztzfx95sb6wlu9ZLSvDZYj2ictzkjYJf7KPJ8fsDCyUUMfHULe0i6+jnzo35kpx4KN1dTfm8D4H7h+Tzey1dWWmwvSdW+T6xmdUg5N1QaVWwNcEIvdc9rArQNIjvG64h39zY9HCJfpdgpA7k7g2viQBbmn5M+topmJZ9SeK1ykpCLuMLylEl9I0hCUssBVSpm24KoE4e6Tb0sTVDiO4sup2kLqFkvx2SSJkueorS9bNCGMjejGU6RDMJSVyWWpf3RpiC6OrW9kv2K75mi0JXhzfEseYUpOI5n4jvUVfBpwbfyFhWeU3LS67Cx6mMa4p+/A4MkNSg64tITQT4jf0N445khpno7VPRR3tXJrvXpYPAbO2Ovd9R3e+OCIo8WkiBTqWEhdJmXzKlZ5Ns79slueOMokDDSGC9Xa3gNYFbnz++L5TzyiCKM38hWuCFdWOJPeKW0waT0dLtL1T21COrHH/ugtXz5FqBBXj0OKO3Vap1QJhMA7eoWDhw7yflQXA+0+iGiTEJoSPfGLCbzBbhRn1pLxr7jXiXurS+L4xVJL+Qn/8M//MN497vfjTfeeAPvfe978dM//dNXYX/kR34E3/AN34C3v/3tePvb3473ve99d8L/TqWMeGe/lZO6fkyqr6sUGLsFzbgXETHe6Ty5VANEK3wl/A4fkFPZhZAvcNOanBZxbbf9Dmvv2hDSiAIXMGPGF91aO6PLT9v50nVHl7z6fKXtUz1ncEHUTuAO7dMjvrW9Oj8Lx2vN47t6/rm26Vp77sJXP/XyLtSOB3w6u9kB0B5Z5kJHSdJVw90DsezDpdZB6TStwAljqmgrtw/BLTa3Jre/22JVfgsa5AE77JIblg8c2nCrW7igvakNt6M5rsRxC3vuxIFap+CJmip8resJrE1Uoddytd177ZdL4F0bdmX55vis7QOCJ6OZhH+Cw1zh0liRKvnh7WM0u9uRRpFdGV3Pz/RHuvGNctauquGZgVHnhrqb3EzULRCQTvMp3OmAMg990nQyXRpn8FZ2QIddDKyAudB5sJz7Zl3/2k/e0nMT+Z/4iZ/Ahz70IXzP93wPfvZnfxZf9VVfhfe///34tV/7tVP4T3ziE/jWb/1W/If/8B/wyU9+Eu9617vwp/7Un8Kv/uqvvnTjnyetRmgZ0naZuFVKBxeTZt7KJU4EWbFUczJZUhSUg/RaxcTy7PVRUhSVdOFaCaH/a+dxMi14goTkpInPVrRMRGW9e/6M4BhRNKLTTghTVT8HPtZVGAHm3YUD5X3gKH04tj2lxDPiWxd0W+tc2j6P15U2ndRzNp5tbZN/4sQr3rf8LqGbHhL5JBQ6TO07ehL3fdjfcGLalYSrYR/N3b827KMFgdt1wxPdsI8NAxtudcMOy7vVDU+UBG/Dm3rBrT8/8fyuG27HBU/0EnBPdLO76p0QP4Hh3tHwxGEGmhF+d1V74oSbBNjyrB1P4tkI/5vevo5mxNnd4G79d8cFt2i4hanie3kezlRY+y7YvRyDBT3RWsb76L9ZDwDcDhvLUZgbBh/qVQuijGfvzMBISZ2BiKhJgKv4uW6F6iRqq6gVGlWD5NqGcv1hCO6BA7AAPeJHazxGa1FAXOdPraKUUOCv0/1Lz03kf+AHfgDf8R3fgQ984AP4si/7Mnz84x/H53zO5+BHf/RHT+H/1b/6V/hrf+2v4au/+qvxnve8B//sn/0zjDHwUz/1Uy/d+OdJFvFu1bUuKnxNVfIs4a9lSqpE9grDMBnAEExLibsWz0TAEu6g1l5RyHkeN4NVZXwXPpapeRPxf1Z8lfAVuMB1hmN91mcocwf+2nYssAG/tH1Vw2uBfZE2gbAcixPtwIFhgMSGD1fzAsYIpDKmqJXhhB+Aarp6peV4KxHvUnI1lXZDH3Q121yVbYTwFjzDzsh4XS8YuLhmwBkJN1azcpR8k8B3vkdLAzZK8QWOeDol94BrpYy3XXlG7swILh6HfgvYdHcjk0TfeIk2DdajCaeFOHdkgJxd032OoWrrLXYk3LxWFqDVPEDNYUQv1NmQPZc7GTu1uSGF4ErCiOeF5m7Z3jgzBcg4Gt6O+NGMwMfe1Vpq++5LOmHoX+jvFUnPdSb/5MkT/MzP/Aw+/OEPx7vWGt73vvfhk5/85DPh+K3f+i3c3t7i9/2+33cV5s0338Sbb74Zvz/zmc88TzNP0xTCEchJuxBtxYFUn6fVaqtasU15jlGNz469XPwmOmB2p7M30T6zRDccdq6rgVZKN9bDYvaj0GdA+T57KVgi2ZERisLKFhU3vexnxWQ4TiLercxV4KttOq9rakalnhWflu5P9RJ5+QYBVySiUq5KPofBZF3Ldb+n3MjaJkzDdoBD2Uf5fQbg0ryGpB+bPeAxCqzRJgkCqiMi3pmBV543yxj+XjAwQNcxnu129YtnYJfXDLYPFiSHdh+7CtpGY9JmLVX/Zmi4QGH+4IobJ0hDGi6q0TcLWuNeLqCfvSLjutlICIzucPk27349gRoBmW5yZIT2cH/j2XpazjOgjVnUu8GcuBakRLWjFsGOG1xqd+YkAuIM0yzQda4PYxKgeTQyud15pDwa3MW8QFXblwm/zneXwMkkxiRdtW+U9O0D5/MozEC57s6uMy4R7+LCmvuTXlvXz+m5iPyv//qvo/eOd77zndP7d77znfj5n//5Z8LxXd/1XfgDf+AP4H3ve99VmI985CP43u/93udp2lOTXiHqV1NxX5t2cdW8lYn5ZygnKy0yAEnKpMLwJXDw456UDeU5cZTmlowDrVq7vxCaqd2gBffSp5G4p3rPiCKba5QnrfZr9WPBUQn5WAekjEVlZpbNb2pfqUzZ1zIwB3yCWT1a27Q86zUcRUKaCHytl22qTa84zNfLiJvbN/C7DZ7JNgGGuIsdDbjgniNqEe/8VrMmA23ACLIqtqHY3MOk+y1jF/8eDXZhjGDD7TAzO20NMoAhaiFmBcAw+XlIA9Tk5YuY9X1Xj2YvMNdVMfyKHVDBJmpMRsy1EUy4iLuDkmzLKCFvFRjGfDQpgWOQluhdBRAyNtbn3WEYu3+4eh/wIwGq+LVBh2B3Vf4+UmNA97mu1dbgEnYLu1vf8yik+5l8MgKmHbGradOdLqz3R7oJQpvZWJCxcud+HZKO/jBCpDwyd2IvdQ6NnHcRwIoGABRKyEBK2ZvIIajDXi4QruHX6d6l/1+t67//+78fP/7jP45PfOITeOONN67CffjDH8aHPvSh+P2Zz3wG73rXu16q7rY129B0AOKX1ZzJ7UGc1aNyLOJapWqrZX3d3aXgWhJdzIRRPwochc6gv0uUu2jG3CqXiKxg1eTpCgcsmbLApVQhQqmrtKlsuAwzO+MuFvNqHaqaitRLYIIT1zXWobVHhlqxTwdZeKd1CEmAK1xtU8Wx1jVmfNHIE4bmTCFU27S2b2JMyifg7/i+xfjKY9EarG/UGP6N3W9OILYRt2ZzBQp14tuaQrW7QZh1sA21K2lVMEZH3zaIulW7qLnaeadFBU0U0I4mGzZ4/HxR7BBTSo8OyIZNeEOehc4VDNMuy+ZGXR0qF1gI3oGWXxV2PMz5PwDdwhvG4uUnJ5Wq9mGSsmu6jPdpBquuOne1cw+VummedlxAP/pU/7uRorvQ5fECVe8b0oXOzvozWM4W6vs4ElAJ2Iz17+f9MW9afmsIoJz/CkhL48y6hTgzQw26CCLs7XT2Ko6+Mq0C5wjqhBPfh6RMTL+cRzzi3ZMB7f3+nMnHJQAvieMVSc9F5D//8z8f27bh05/+9PT+05/+NL7gC77gzrIf/ehH8f3f//349//+3+Mrv/Ir74R9/PgxHj9+/DxNe/Z0x7c7cKqaEqCtiWWHj4JnlVTq40Cr+BdsMwrsTKVlFZVn3XrCkfhKdY0rQS4IWjbxaaGvcCBc9l3jh5/CUxhwPFlvadcdcFNdVX2uOYIhwQXjk/gATHUdHlc4QeKh/UUdCpE6pHMFUoSfZZwmHJj35UP96zvC6dxv7rmrFf2BdauNcsRG/+vhEIKwDInQBaA/+JAg68ZSFWOxuPfcifIQ2Nm3HQBgNIHfQA+BnWU3VYe1VlizzJO/BcfYon3NCR/9/lt4uShMd1BHykjZjox1QUmUangF3Ee++K2DrnHpnsarZVmul7P1OOcHIhpeWNGXM31GzQPM6n9oKaMpNHfYHFetbnIIYRmRX//4vckAoRhjWp64Uea0BWn5q14uVNGXimNs6x4z1LUpxHG/1PU5Pi+J4xVJz2V49+jRI3zN13zNZDRHI7qv//qvv1ruH//jf4zv+77vw0/+5E/ia7/2a1+8tS+RtNvsViea9g0lJ7za+SA3Fys0prxlty3IcVgkM2EmDgQuKe/rX7js1X+ZSJSjPfztbYDGolZfxHmWu8DxYNP/Aq7e51lwxGYxYBfGaNbF96owCZP11hiiSDjfBU9d7VacYZFfNycfS42+ev7qlsZhJVzd5KZ/HV/XdAFcNslDXXr+dw2uSurRplHgUOu0KGTxHWPeSGmTlHa6pfYQdFfzDj9K7aNh75bPC1KGwlXMW7jX7WPDk24q5idD8GRs9r5bnqmnLeAM3d/e1IY3x42psHvDk3GD20GXtM3cz9x6/1YvUfZWN8MxDO7NvqEPs7Z/gou51Q3BE1zcyl7wplv0p7X9xfAOK/PbY7MynrePhjdHwxO94HZsuNWG39YNbw5zB3xzFGv+YYFvzD3OzuD3YRI4vQN2beatABtPnsFzDNPCvvkZvJ/LD+uL+cKby5z6+zHoOufSvs8HzoGIXjnUuTOuyST2OqZt4UDgg/j7OlpdZtUaFe90KDQIvEJHv38R70CG6CX+XqDe53EtB4B//a//Nd7znvfgjTfewFd8xVfg3/7bfzvl/+W//Jc9OFH+feM3fuNzt+u51fUf+tCH8O3f/u342q/9Wnzd130dPvaxj+E3f/M38YEPfAAA8G3f9m34oi/6InzkIx8BAPyjf/SP8N3f/d34sR/7Mbz73e/Gpz71KQDA537u5+JzP/dzn7vBL5yqu5qEUtrerzpfMgKtVWEX8emrGj4k9bNbScqLKMJySGlcsl1nSGigZ6CS5aJhhjyixikygp4SW1G7j1LXIlbOEe+WNlZiLNEtMDJelSbJnLAuquTjJjkT36Mbpk5MmS3PYSX4mPlbeK4W6btoAaa2VE0H+yJsYgUEUPCxXfVIgvjq1ahEweGqUkDYSUYfMfVnOkMVTK5/QzWs5+Mogd+uGt2NYdrdAbQm/m/zOOgDoop9CDaxUM17M1V7GwO7bACa3QYvsOh10iFqz6as7oBe7LxeBy7SobqBt8m/qRc0AW50h2LDjovZ4svAb8N8sG/Q0fUGIoKmHQOCW7mY1C+KN33iNgx0mCdM0w64+xr1CubiZhIoz9U517Rc/kIJHQB2CGgI2CG4HaZ2h8L9961cnLu7tH+rN+i+aPZw94Nb+m8hce/uPkfL/uFGdTy/V1SjvfViG9+J6E5HzYB4JDx1aR1wC0TJLavOqTqXeL8Mcr7BR406E4ECfvtg3QNJTODrEW2DtB3jdkzz+nWaE13LP/7xj+O9730vPvaxj+H9738/fuEXfgHveMc7DvD/+T//Z3zrt34rPvKRj+DP/Jk/gx/7sR/DN33TN+Fnf/Zn8eVf/uUB943f+I34F//iX8TvF9FwP7cL3Td/8zfjox/9KL77u78bX/3VX42f+7mfw0/+5E+GMd4v//Iv43//7/8d8P/0n/5TPHnyBH/hL/wFfOEXfmH8ffSjH33uxr5MavX8u6RZzaXTvxPJ1SWff1k4/4m/Fc4yQoqvRR3ONn49rUMK3iUW1lSHQKO9UhokvuFODY16meewB3xLm5xAZiztQrQKRzzhILOjmP3na79U0y+dmHUhiFFOo0zVVBzgSl2Hf2ublrFgJDAJzUtt69LHpS9yAlufz+IBpJROnBIEP6wK+NmKxmDWaqQKO4eFFvUZ+MZU0ghClReqZFAXniN39/umiptx2cPfm+5k4aImEdCGeG5pjEbih20J1IPwM48ws6BR3Py8utANBrIBz8Q3VJU63eLCXc4ZALr8mRV91juUAX+sjzvHA3AcbugI3i/PULc+no4jVp1SWHYXRe/rPPb+eYvGyuaJRw70uRE8dL1sRue1gk4GnXsAaTb3NTUC3stC07JvUGBQZ8rv253yJxrSF/p7jvS8ruU/+IM/iG/8xm/E3/7bfxtf+qVfiu/7vu/DH/kjfwQ/9EM/NME9fvwYX/AFXxB/b3/72597OF7I8O6DH/wgPvjBD57mfeITn5h+/9Iv/dKLVPFZTxnxLmT4kG7J7Eq7xvNowEN1vqSGyOrDZJlfwJRZJS9p5AxUUUp9TwLrdWyy5IlnFeKrgG61LoNbq6516l1wzBvk+NWblTH1p0LKnwuma3B1DLhDDaSF/MmwVRxLV07hDtI5sw91OYbSJlHMF89USH7j2vbaJvZxrbxKZrUfJOrqzMpW9h8yBfQxC1gSJICq/O7fyG5UG249bq5r1mWPX77R13vDBd0vudkgOmyzUJMwpcEs+OWCx+hQntqLYlM7S1cAj0RxqyZD3qDDQtpu2FRxESsFKB4Jj8s2MLJ8xwUCi2fPWPMXUY9Fn8N769FfGjIELWP572pircBd3PyZQWvgZXZNw7nMa67C35zBaR4Bj8FyeMe8W9GPFgzULRkWzZvrgGRm4iSwGFoOZwTg302Hr6dyxTC/+SGMbT0aivlBhtnnRg+uL9zpgOW9KtB7Tr8S0va+WNf//+1C9yKu5Z/85Ccn43IAeP/7349/82/+zfTuE5/4BN7xjnfg7W9/O/7En/gT+Af/4B/g9//+3//sjcMDil2/Woae7bErgZ1/PsME5+bPJVJ3c003skAVFpxyQE+6vjyA6jW+klEM4rigIbOleTMCQbiQlKsqb4GrtIbPAb4QLhnkLXh7X2bL8i8ZJlS42qbS+RUupJNluKKNHIuob2Y6Jobu0C4bkLBGjmFPZiO0G1SXs1hBeFDvn4zFmmghLQ4vw2kpmY3h7KjDRROGlG+YhKN5Hi3luigwPAJ8A54MxSZ2G92tCLoAN9LgpuzYpOHSOn572L1qFygwNnRR3IiiC/BEAcGGSzh+NVww4hKUDRYvoXvbNxl+Axz8NrqBXW3ONAF+29fCJsOJtvNRfvbEQ5QnynWQdK3B3OkYlKYBdpbvBN3MG9JPfejF8yxaHf3n7arYrUjzWxwJPHGtA2+Qu9VLaDyeuI3CUPO3txvm6CZH9b2d6ffh0fA0w94OZ8xE3eBRG/3pbF44gSdvHXNJk6mMucSBozQPgGf79FgIhruu4wkJr7BUZ+haXKzz0NIao+XMKPxFXMs/9alPncLzOBswVf2f+3N/Dn/wD/5B/OIv/iL+zt/5O/jTf/pP45Of/CS2bVtRXk0Phshfbjbsh3MlnTm2M0bAN/qQHVYpvW78EwWRpa4Zrx5wVJFQQbuBaNJCnSrtF8CMcwTZPs84wiFFoKDYJW/DVG8Qe1ngFik1mQJnMnzstB27KStcxbfC6UCE2ASmMa12+qrZ9oA7wRfST6W4Be7M5e0IV9qkJwzNOjVOcEz98HqVY0FJpG7sLv0xzzZ/zf5wvgnC7dIk+oG4MEUbug5cXOXcpaOpWa/vsLP7QYRjoMkGiKBrh7inO+gaR5t4NduVTbyNoB291dvI/Ckg0pw/GmjaPG+g6QBDp3bNi2tGcKActIE8YRygIrqTbFEdDl644+fwHojGWl/c2pAudDxGCEt7SvCgFT3d5xDHF7xilur/iKCneTwwMxf5zGMU+2D+B6H6JRjJFKnzN9e7zDMr5wINOVefT+GaM04wiHYreETirF5FIG3D0NvD3viWTrEmXhIHcHDd/p7v+R78vb/3914S+bOlb/mWb4nnr/iKr8BXfuVX4ku+5EvwiU98An/yT/7JZ8bzYIj8IeKdp3o/uqwEvMh+05I6VVudiIpnDIHnSzEyMyO3MiurB8DBrS/hBMvaO7jjITeFCa52j5Rh3hOyD6WuVRPBzZsQCvOrXtFjKadAjYyXWaUdtQ1OzDS6uMBFA0vXCwUWcWdCLfRiKoxDvcH/FLwznN4Bt47TYdimvNIl+00iwA1bcwz+v/bePtazq6of/qx9vvNStS3wNO3M4CgUihhAJ1I6tED6aBonDxXtEwMDmFIJioZKgFGxlNIBQUoImEaoNsUX+IM6iIHGQFPEKjFAhQBtRHlRaBV44lTwJ0wpnXu/37PX88d62Wvvc+507rx17r1nNdN7vmevs/ba++yz18tee+0YQOgBWayHmdgxvxa8pWMoZ0JKKvxBmttcrjNZjnTysqwdmJPQMoUzQTy7BJLMp6aocQe4epDEUc9i/c2kFpgLPanGZSchMick22qn7iSCrUCQnY2iTq8k2+tUyQCE997OqaUSKwDYVjYRrESERYYLXTtwxgSxbX8DobjoFW+eiwKxyPDsd56LniXYcR7W2Rd2AJDGHPRKI4droF6r90x3oBBNT0XAw7bMcYnVEE2lGqM2HO1MCYkpsefkGepZy/VvXJ/PllEAksxojW2hI+Z6Pj1GGgDwzW9+E2eddZbfHwt8O5at5du2bVv1VvTzzz8f55xzDr72ta9NQn4MfAtcJXjLTFsJ8Mq00hnZBVRUAmIFqAX8StZ+xMsSMk1R4FAhV9GOTDYCs7DdKgpDGi7IvY1jSsRRZrzjhl4Dw7oK7+bWH8OzV2WFtEJdTejhCF7TXabwEDTRDK2MZzR64zfi0Yp4jhbmTDBKOnAur9+R1TtKub72jHdmsVsXWR9q4LsnOPFlEynrmZCS+G5zr30JwkKtQEYHECNnoCMJ2mDKSBDLfJ57AB1mqQPzAkxicffcI3MvbnLqwFiAcpKEOayr1ASNlM8Ad+jSJsxY1nclQ1tGYsvkkDXRTikzRRjMSGxr9/BrT7DE8j5Y158tNS2zWPAgyTQHQIPuxK3fZ8HrueyVt0BBWYOHrtV3gG0bhAp6Fe6WyW4BW9NPupVOAwGzbVe0E+5EqEPLRPG09LYoZ8r7GrsOvLCUI7+pKHxZg+n1GQ/ktG2sNiazxCyIV4X9PaiLpyTmylYJFU2z68SHMTLtrXc466yzKiE/BnFr+RVXXAEAvrV8pdi1iy++GHfeeSde/epX+72Pf/zjR9yK/q1vfQv/8z//g+3bt6+qDRtGyJeMd1xlsvMAlijEk+XistsumYoAb2Fg5RpedaPCK9nl4tco/2tkeKyoViYi6eoZrp6p9QP2idBpNpisTYrWat30mPFO6Y0wbEF0zKx6lT1T60CRJynTBZLgoW3rMgtFrkNXWF9YVzPVcY7Gk0UoG73QExWNKFi1rop3qpWTOBScXq55qnrdhLMKbmuzWefQACwmDsGOWrkpSolcCWNbg08Ac1bXslpnmZF03Rw5g0gEPSH7Hu9M2S3HRAzKGaAO4qLXbyjLdiymjJ5n6AjouIeEAoizfQY5HAYANuUePWSrXeKM5Eq2BszZej33kAQ4kt7Wsi6ahmiWeLK93CgvImuwHzF7Ehxiscw90xzYA+nA0BPopGxu7nWG7pEXPwQDfoKfuf8XPKvc+pZV0AS/72DQujgTsl7bGrwtozAXAc/ZtswNtf2oqEqTQ5a7Rvm2G6YIuWcL8o0TgqewI3BfvgWod4eJdC5k8GINbaEzJel4aawCVru1/FWvehUuvfRSvPOd78Tll1+OAwcO4HOf+xxuueUWAMD3v/99vOlNb8Iv//IvY9u2bfj617+O1772tXjiE5+IPXv2rIq3DSPkHewjoPamzeZR4I09PMSTopXu1cK2WLVFirgLFuWeP8Hs6+GV+RfNQa+XKr7Z/xalgbioEKQ3IllDFPLl/ybsPdBLhYwIHwqst/Qo1Cs3hUbdzxWNamYrUnElPNI2+qEzhlf1ZGh/oGG7CORPyxOqV8L+PGo8DNsv9EqHtjxVSkndzIpp32cPVVbMeuWCaFnpXB5mAqWCV7KnQQOojCa5S1zmRfJseZbhjiECjyislXNyy04OuFF3O4kwFcGtz0EUswUIyQK6YNHmwmPW+Abpy66MVa9RX4ZvaxChWzLeMTxlLIuSY2vy1elvDD9dzrPZwba11ZnsPDMeNAkQyha66prLFrieyzM53JdseuwOmZx9FARPkg4AZnflw3MmWFmtGFbXRsfwbDzkMN68rvJcecbC/QHXPOyd9McrMU8tnEh3/dHC3r178e1vfxvXX389Dh48iF27dg22lqewK+uSSy7Brbfeiuuuuw7XXnstLrjgAtx2222+R77rOvzzP/8z3ve+9+G73/0uduzYgZ//+Z/Hm9/85lXvld8wQj6HjHceCAS15qzvKZqM5AOdKUkUe7KJvJQ1Jh1qaVg+XIzt048BZ/6oTuRt2GxbL2xNvx2MpsFLXeK+DZLZJXawvAnlHBjLeWoSDdAT86AZYAyxaTOM99AelLqiUuLWrxCvhBqj+ilKTkJzN7CXgvANXWDvwXlKsSx0h7Ufpb0tT4bv3RLjv1reJSbP5+26UYEnqumqxBFDyqLqCZ4Zz4YVGH7YCPfGA6nbXnGTCgpzy1OJnchZD5gh6MEzLIFqJAKSUsaCEroELcvoqcMmaLQ8JXQANidgDkZHCZtTj8yMOSXM0GNGFpjI2JwyemYQOmxKC82NP5OofcqyNa5nbPboe5LnIZZ5AmNGWU6Ty5DofhWmSdd7erWiZ2Tb6aSsV0vaxLydQ98pHusHLSfSdbIjQcvMnT/XaPgeJY1tzuXkur63A2yoeEJs7b3X7XU5Ifdlfd6y3TED0N9gFmte/4lFr++bWQ4Y0neK3vbNsyh0NiYqQc/gHuhU0BdXv2odis+6Jl++TS47dlhOpENKQFeUqgnGYTVbywHg+c9/Pp7//OeP4p9xxhn42Mc+dkL42jBCXoDMLiiWoLsNmwHMmvGObPtSebaQo/raLLdGSFbCOe6xDxa6JaEoAqFcx4x31g4w+8ESbjAWE81d45Uk179U8QZ/pqoXNU9uSLkSA6/b69LHTZ4VunF9MPQiAxbAZjQG0wiZAOeKp6JsFUSq6rS7RTob+2Q6h/HW8BTbWAVIhi4a3HS9oCxHxL6s8UogYKlTlAOyLXRKL4aIJJY1XYKW2/54IpnNs72HJIjJttFLVHgmwqwDFjnDtlMtIFZvR6LsLmVx0Ys1Kfb8LGVwniGR7G0H9TicxfrmlOVcGgI2UUYmySLXEaNLGYsslnmHjL4nQBUBooQ5ixM9EeMh+zwg7nS7JmLdNidBnfMs76MDo+eZ4kk3LKuSQLDgOKm7z/YOxVOxpG55AmPRa2wCwwV1Vmt83hf3/TyX++aiN6Vk2bfJkafDtSQ3PSeAS7CfHXvLOakAVw07kyre5POBb53jcg/uHSn/GJpcyb4EMjyuQ27Y814Kcmqy29mf+IzGDEm6W8aaAFN0jpfGOoENI+S7LmFh6rOuO3lQVzXDm+BVl4/P6gFcmJoACcLc6difqAiEZ9FeB5QiMWqBFV334fniQo9lIhhFvjXPNC5vjjRiM8m5KZHckV1NpMLq16a2fUaDo1y0ckJsZlFGAk8ovDtepcyYZyYWhhq8D8vRpW3dzDT++gO1VrBzs8Zf8Vc1HiWeQP9XyJigLbjeBRo57S54DkMs8KC+abHYSaPmrZdNeOiavMREsESik2x/k7S5cvxsjwxiiYyX3mJkTkj6DCUVirqNrVMBvMiERDN0YMwBzCBr9T0yOCdXqKSPpK+X0WlOOtVLuPRFVv+yqBi2xVXK+rD8kYMbLIPVUC2dbQLZAuhM4YpueVt7L2755NcSAV8y4JXtb0VY2973ksa2ZPizPfCeKTADlnbXveEm9E3YwzLNaU+pgC+7KMp3GC33qNySdI7KbdJ5TJUGV3V0HLo7wQagKbfiTWQAlBJyhbcGICw3HBeNdQIbRsi3Ge8AVEJCbKtxK9ttvVaYjrns5Ubzt6m3hRD4VQ2tuM5frfmbRKD6oSAA2fhiVQ40M17Uadw6lxlVICbz0wVoabIiVooJqVtZhY3RqhoiN2uLQvlM5E2hth3ebQGv7cJGp4r1UrwPlOh2KzBibad3ze9aJyl90cQKDPDiC43DJOqNKTxn5qgpjUHQOw0PxAN8fSKWmQKgSwZguFfA0CiRV2WJa8jWiJn0zAOAFY8zNCFMFiUiJ1m26uQQHSLCpiQx61KW0ZGu9YOxqRMvzgKETUly4ouXWax9Se9AmCkec4dEbPlckAA9f17elz2z0Fz6BBXiDHTqwJCsdrJOvwjW/UKFKhHpljkNKkNSD4HtPrCT65JumSNXBua5XFuZRdwXN7zSUA+CCXiw9GfOVIS8b5uzd0jBtR7eb89i7UM6JoVy8/7YM0W3ZT31TweoZbaDDAD/1C1wwPH6okfLiUdYS+DxCMdJY73AhhHyA1cTs7jOw1xfpBSqX+TCTe8cQV77g060FY5RuBSePN7VJvkc0MOHWgRIg5dqAWwuNjOlKBct3qL5Tdj4KWiARHoDJfsdcdWMlqe6txoaAbx/o3DN5kUw3gu1KjDN8LQfozeDQrdyaGP1kgglut3bojTj+rtIjFrp8Bmz8Ro0PEUlrrjrUUfp2lCw59p+J0LKqJIN+XYqBPmf1QWfC1EmllP/kvZnr21M8iKz1YmMfiG0Z12HvgcyccmHwoyUpD3zhSz6d7rc0mWgSzLmMsu4mCUg21Y7YuQ+y9ICZRBl9Bo3MKOMvk9Or9fAAkuoI2v1jI6KvEkAyAMUAFOFbQliyfoF8ix6Eaa2imEWfJ/hVr58Mkm34UFO6FO8hW1rUyt/kTvt+oR5rylz2RLbSDY8zoLXAwALnngJbMucJc4Bcm/R9EnW3VXQIxPI1uODkLdjZEVw69gLmexc0NtwsTHOMj5j8J0dbOMCPch0mXZaY8Lw2DPeTWvyaxM2jJCvMt4RfFKu3PWNm2c48FGE80rWfIRm8q+FRlMvjQsxVJns5EETigO8hIovx2u0kjGHQFUOlG1dhhpx+Ag0gvD3JrpSUAh6+IL/QCijih6h4NUxcTW96hWEG9G9PtA+4ovg0OZa5zBfTgUmXCrU9h0Hz0hDcgDJdDYmn5zJPAsq3CmTnzjnlr8Gj7Kt7SpXlHQfnrmEsykEMtZ6DaqyDHV2wIEoVb3ujwZ8MzaAnDNygp4Tx+A+I2nsCnPGjMqa+EwtfP9kdOz2zJoAR3glziWPHWcd11KWPGUiu3UOAMRZ1ruNBqT9EmiZPboe4OCGl360rXbgOhtevOYcM96xH2Zjuects90iw7fMifyMSwEh451a+KyC2wW8u1mCgI8DKLjqbcucjU+O1zbcg0dnbAnOaadQRCGOhJIcLUskgn3WIS8W8u2tFYt+ctdXsGGE/OgAjZM6ALc3qb3j8hXxq1lRs42CP9ZrX10l/WIFckHGw2DNua5jgDdmPjveSNX6YVdlA2EYJlHDo6AcudVKrqx4N1FFomqrxwpU9bKW1RnqSj4B1GcDeL1BB6rqXoleea5I9JrpVnDH2Ib2MZFJXNzoPo5Qr6eOCf9WKcjQ9VAVStF1G96TWHrhdyaYb1bc9QFZo8bdBat9yJlsJUTYyATubK1et8cxdNsd6QE1BEICJfZVmo4l4t4OkemUvzkSZkkm3AyxsJMqIB2SLxtoglwRutBgV9Iy3V6XWcUlFQEKyJp/r+0AkWaF7fxdmTsdZILW8rCXMnHRwxPp9NmMXT0xTiPjmTRrngfSwdfgmdUrkMXi7fuCl7MoDSbg68x2cEHPDLXoddyo1e4Knx1Uo+75wTY5G0PxPluUgSqDiufZ7qB/LdcuGMjZlXBmxprLeGfLF8dJY73AhhHyOeaEBhBU1xrRBnVKRT0eNdNQygfQ4qEW8FamGe8GygAg7mAKH6491iga7To7govevQCGx6qYBKHBVhfgViHs2qWJ4UmRLyVb+xkN/6UuBuTEK8suV6FKf1iqUmtIjAWyVVOnZ+5J3+3IKvz1QUapi51MTU/7xs+2RyWbEcIFinBt+TWdxtb6+/IaCaWs6os4BKOwDuT9fWfPgyOWX6ondXRwt74bvra8YhvfAc3qF6QAS+ITJjkpqydNYEMdmDQfPHXoOWETZyyI0aUOKWtXaMa7TazCOiVwzpgz0KWEnrK490FIOSNzFmGdZui49/eT0CMRSypZyME3mQlIkixHukHevkTSi2Y5I7XgwxIPqwBLJOvupIFjvQpmNl0nJw1rkDV0uMKglrn2p7ntba2d7HAaVQT6Xt7/ojervQTqCZ7e06WKbGlx1W0OO4BGFTBWdz1ZeloX0OW3ueYp3LexkAhBqIcxYt+yXhNIEhuppVu24RVPTVmbD2VdgpwwiAnWIGwYIZ+SZbxDbTnFgeuWp0wg1VpxsEbrqGjGQEjH/fZGt5ihpeLRoDopr4Q76sdiA3zu9vLASxDAlsyE44KxCX1no1aCSOvihl0RRqyTJGv3lK1mzlNWGuZSNp68brssMfRRr4pC0HgnBGEf9a+eC72+1MsQqyha+mTCQdtIbK5cquomDoqAXQOAHqgibVT+XMAHpnLsztL+2J/lvbFbsDZR5ziRQw1yU86MtkgtoalWJLu2oC1JpA/b4oLuD9ctcuKazZqsQyP7EmMO7TfOslZO8pa7pHWAwZzR63ptzj0SERaGR4ReD7bpWPa6ExFSn7Wtwl8yIQ6g6zMWkINxSDWVuSXYyRkLkix0HdkzNvJY968zOsCT2VBwyZtCPNe0tSDbXif05x4Nr7q+egQYrGvrxQOQs6a+zfBMdqZIeCY7AGy/M3TbnCnZFK7tdy2swRDhb9eubcPHXmqUguqfvn/yT14+LrKxaEAJUAXMvjYiiJdQIzHXVMa7yV1fwYYR8it51iuIAnvwADdoRyBYDRAKf4IYHPMkMFfPkCoH1dr7Cuo0tTwzfLKP68lBV6mfH6HM+v/xMhPLzR2uhX31jPaB77933kMZ4P3ggj/S0z7xMhrW4zqVcykzulvQtALvUd1whcmI1s/U+/bhyojxO7oe2rSxWucnE1Uo1lzzfNxWF6083wZoMQscPBnMus+ajXHYugtrtLZF1CsJFdjWZOHTMuCJckCupPWcdNUZACcgeehaUJrIt+OJXmJZ8oTXDC7OJ3XBS3+mslzD0Mx4wujc3N3aJqEsC81zf49C25QBOS9eBDlYlgCyrnKzCmtA1uAlgl6WEfwMeNSZ7OKWOWaPU/N+HAjy6hrD+zClkayjy/syQW7tDca3e230fVfjpg/X2hgOv8mUpDhnWaO1F7mPRNYAjHw7x0RjncCGEfJ9byGrZdoXo0uDlNR9F4GjsGGUnPdmIRZE+dta0W15BBMCrUSINKySSvGIH3EjScKzdlcEABdmAz1GUQ68PRWeVaf7d1NoNQU+iqRC2chd6nIrVu+ZhVVJZ618ILSZ4dKlhRVoRJ5MKMYMdbYdkAOe8exOmMCwKw0BtygMdt8ax0qDSpk9G7fMtXUxSyYz40kte7aIeetuhq/Vlu5n8JyATZAdER3cIJdzyvUdkz2nDKlwz5o5LSdC13Fpn3UlEWaJfQcXZWBTB0+Ju7nr1bPSoUOPGQG9HmG7KbEk8EnAJvX5M0kUfUcSVQ8GNifWc99tS15ZeZoRe1Y7wbN0vBLJv9DEMrME3wonzwA9OtiOxzkDnO3wHcFLJDvLFhoFnwia5Y7EK6Fb5mwsLfwEuZK9DmgC61h+5941F7XiuQj+bIKbyilyPeqIemZZo/e/CGXlWp7l8tvc83GrneOKQKdex4ouT5b8CwzKvYwRZrBlOrLEOROsOdgwQp59ljZ7Cy5gy8Rqt4rAd5nXuNPrfdZcC+JRd354vA7pD7N1FOalmHIu1rwrBf6j8FHMLxVCjRKgiKRr9Uf+ZMldw0AM7BP+yNf7ydnwSPMQis+69m0syt/SJ950ebCyjF0Rc+lW2s8w/PgeTIAZvfBufRIrbSrvvdAYrtGzl9XvqeCxSZGKBmPgXg0n1UnfaFnYFE5JswdrsJy55DlBttf18PTKyfoBkDX6nsSSNsMroaRTXuhafUclbR5svZ7Ug6+pZQlIqYRpp5Qx505c7SSHxkhQHIOJcZhFFepSRs4Jc5J3nFKG7sJDl7MfCdsR0JN5MiTj3Q96LUMWoU2mlzGWFY8APBgy3llSG9OdlhaF/g96eJkkwCmf6rK579XKj0Kb/SUS5lwEvAftIQbbqQWfzcVv6/FA2dGgQt0EuwnrXv+yfleWqpbhgWN2nKzRimvxBFTBd77FDmUJisK34LnYlb68WKpd++GTDhOfzi1rJ7r+kchdfzrDhhHys5ltoWuFaBDYiOvwrWBH/UxlsQbpYOUGrRUuNxvTsBHWgwFWPuCWd/ZnMFAOoiDn8Ewlaaj6M4pHQL0coG5hNy2pTBDRa8BsvAtVjsqOChfnpFKEStBhlZEOcKuXrIKoK1X0UPrR+K1aFCFoAFURuQ5TdT0XZcrLnEbs9IDXvI9aObOfXDKeMYvFFsLf2QQAuTdVLTrSgDVJR+vvhuH5EZhIlAUwqFOxKBFxwnnO4irXCP3cJ32XstadVLL0pG53EuWGKSNRJ797ICVbhGBwr8l2SAxNkApkzpUu3OeyTz9D8IjlUBdyqWO9bf2uQYIQl7x4qUUt6K0fQJKX39z1+h5MWGuztYyKW19fnWeoQ3HX+/o6SxAf91Tw2JZA4nKJ7lXr4cJ66IYvItorBDleFUVv1zFIL/zzKHqjYQLPv88w/OzgGSIweiTmooDbEjyleh1iLcC0Jl/BhhHyvQ9ou9OKtUb4tXcqt3b4IB/OhcUcDqdBED424zf1jsYDtM+gDEKd0J2VMQUjXI+qFg+DFxAHgnCURiggyIRr67shQs3xBHXkozLhGRP9GB9xxgq6k9Pj8nvYYK5/uiSHu9RZefdELG1dwfVe629c6BmPLZ4xqJOp04vmptGwa8PrUD9jPJmXIHvHwXIdSH+xskfqKlCGXFktsQSshzEJGnmTSLfdSw581nNOOsy6DGLNgQ891U7d6TN9BujQJfH1ZE5y5om61BMIXWLdtiaZ8Aiy3k8kCXjcC6B4mTuJKocknCHIhphehbiE4EkueQJAREGmyVa7PiiQ855EuSJSGuKuly10cIWiVwucCL41DoSSyc7eTW+diaGA74sAr9bTe/YtdG5xBzwfumrpuwLQQwV5FPAoW+PsurfxpHVZxcxhyxgX4Q/CWts+N8EQNoyQLwIozIxuTpTJvBKiwXJ1N3gU1D6BB+Eb64ou9mDxOiEumnRR/aPEiGUK2ZgIv4FwWEloRPhASa9J18iFHa0rl496DE+4lyQjjqftLwl8OOCxYWnXsTffBWyQjDQQ/AQKbWTf1mMCh8MrK31NfaAHDt1IVZdW11T3p012RV/iqkud96gzDhSJoHXEDHp9g8cBzx7V9XdIEjnhx1z5gLvrE0T4gFGGtNIns24JSCkE9JlislD8rugQKt3F6lWPSK/mHHVisUubE4gyLGFRRxaVLlZ8ziGILhGyBnrJ+ra9H9ujjfI74M2DJ41hyw/6Tnvdnkek22INL+sac9JPhv0YXNtUoyqn9I++FNslBl3zBwA76nWheARdY3crXZPy2NY4BvxoP3snfk2CB4gA7+uyykK3CHulQSbo9S9lLcuhLDxLPSNlWcIiSfknr9ai9+2biNvkZLN/mWf0WoZnljkglQQ5awKi8nQ8NNYJbBgh342566PwBcKMNwKtO35g2g0VghWtcnvMSZhbNygcKykbsa6oW+QYhd/WGyrjskbu7v4V8UpdPOin+pnCbo1Xu1vjUyG2XJWEQCXUG58eEYpeNMKf/4xt1vvt6xsyWNGou0bXt4dFDQ12gTk2tApprlLV+ja5XhWWHOohmdSzBrNBdUXSPXaiR+qeaLWiY5iox6aYD90i6y06UYuZNRAAck2pC96YDlmVMnHXCz3O0Q3PSEwerOeJdckCQe2d6I50FSwJQZF0j0vBM5d8RtZrLaPiNrdviFXZie56MVyL30j0WN3yZvvj2ZS85PKCe8Mjp6FGMKDb7qA0XKnUdXXPZGfr8SrAEf7F3PMu/AMuAUWgx7IwCMlGFbM7iuLhUaS9Kjodl29VFTx5nHx4qBsFPJ9DEuSsDck3rcnXsGGE/MrvLMy8R7O27tdBDhCaskbIMg9pm1XfymJjya5TRGgbVIT9YD2+aiKNX8eqGE12vUoLCQddwDX9IRrLhG/8pTJNu3WjfZX8GnVmuFbRSLGuWpAbDV6pv5Ve1UbL6VG9vIDQ9lNUBFnrDa56E7xj9DzvP+p+LneU9+Sr2FpC1et1YWB0ublPLY54U7iqi2F76iOt8sJtPGoFYdudCYpKOfLmku/nZ5Dmvddet7V7hioCCP2RlEdx71v3Wk758klREFbx9ZdobxHKJZNdaXMJnIP1aUaFZz1u0fGGx5k8hsSWeBkUDpQp6+7sQjz0reFpWUxig0DDBT5qPI+eD89Uwt1kspVZs7nmwdbjCw1nWpUHXYc3dwcDsDPnGeJFW2sCT7/T46axTmDjCHnxybkgK9ZYOCQlWjwVXuPiDyMgTtzUCvIWogLgH5t+npau1ct0EoiutWipRynLhBKRFfFCk/xWxkDouzu9CF4pQ7GwTTJag6s1ZCoTO7O7503Ql3ViE8pQa4FLk4nCcgMAMiEVBKybK3LPA7iMrRT6xc2fEonvE6rJLCJfZij9xC7s6jMC2HmnSA8Ap1BgAsu62RLSk/UvK0/Z20XMstbek7v1ZZc2IS+sXrPAlFfJIyvV2uQ8Q9gmV9rKmUGdvsswnNgUSGt3D2CmgillcBeUDRuqaomTZtzLiUCJfVjr2Tbq9ZV1ePM6JCp4ZFsEs1zbCXJyZpQqF1pxorJM3FnefvcIQF3mRqN4C8r+eN2fr9HvKckRua4kWPCd9o0JfCJC7kXpsqHPZo2Tbm80MDe8KQss7nMmgPryPPVlCYLA7sInsEfY1254uJuezP2u7vwU8GQ9H5KuNpP+5aJc6G/7S70ukWQGFnrGH7N4Yxi6VCcWcU4E6rowh0ywlmDDCHlL9elBRFQ+Vt/qRBTmepmNynqxCRrFK3O60HMaihOF+cALYMpCFMatgAcqglHQBUu3SOUSIOVutco69p4I9UsZDehZVSVSun5G17+9D00CWBS70YPiNW0EBta375tv22hZ81CUCOl/USYsW51PmIF3eZ8aMmXeh0r6leWEgT7h/WG04HWVaHibuM1dHGgbvUDCFAd9W0CsawFfg08qxFxfsxzzpBN9F6LrdQWKTTkggDsgmWAhdmFuBjlXShsVIZ+UDzDcBQ0AQYgDDE7s1r3sq9Z6KatOqhUlYKEKngTm6XeHHJZxNPpe14xzWJIyYb8wPMAD8AiV3wOAZa9T1UDd9RL4aXVJX/WLMk44Q9UpLsJeC7Nlv7N3bjTcEodmnSP/RJBJPEwsOGV/e9nrLhHwZfuaueerLXR2bfnrrVei1W5jRZ+Vf6qEmZKg2kVR1AOuzltkEYv+fvR7UVc+gfTIWawNMNfL8dJYJ7BxhHxjgZeCcB3Xbo8EjlcmJL8OisDAbd/QGGXEJl/74TS4xhujZ5aho5Q66quHb+MYxsPRcOUoPlO1nwpeQ7je0l/wavWE3YVqwi/MXVUtntUu6kmFKRew0m22Jc8mOOXJ6EXlgwqe0xSp4lzzCjwVLQPwdXN/xYVfygAn9uq8u3USN0FgHemsWT9WbvnS0DaY0vG8X7Qi8wgZA1aXLYF73UWR8N8UFJiYXji0obw0xfOQFHKBHLvcFOKoGJqAj91qp9fJWQd2Xepkfam+iMKGp0qB40EtcuuX5p3b3npGcL0Xwex9y+FfzH4XBHK1zt4+NzigJtTRx2c41MUa1KfjrJetlT4GnEdSBUVvlDWJMBbs/a+xCPughB8XjXUCG0bI9wtTee2jZk0GEdXkMDIC3qgbXid7SxLjgWRGolk/HsCYEmAf2VAuhsluBXoRfNYP0q2lyRgKI0Y96cd2M8KsEmigUaAqJaXQdfY5kApZ6CILMVCMxuo1wTDgt/kb62raH1+7SxRbXgjNt7oAqYs0B3zpQy6IynBcjZEj2VD61ib3+AipUM8q3sI1L+r2SBkVgR77dRPElRv7yJ6DtoNCeCMBNNM2MGnynNBPs9BpKbSJqMKjLrzxJEqO1ZU6LtdJM95r+yUwUOroOlZZkpA0617OukRhLnompM7kUYLtwc9qIaeuRMpb0KGv5SdIsiBtp1nddmIRm7vdEhNlbYsJcRsj8Xdv4yHcj8K6RB2W8cUUTkkr7nqnxyju+rDOX9zu8ltc9VzKei7b6aw+2yZndftau7jpJa8CxH1vyidnSb6lZehzseiPxgCa4LSDDSPky85vtYSSRRJTmC0RZiu5oDFhbO56Qtku5jNt/BC4PDuqKATao4JbJYO7sluBMkLLroGKJo3hRUk9KMOg3RTw6pS/Ea+WPmWrnbXHrsj38Jrx6PSqtjQsqklormOz5m0hmI1e+MvGh/EelgAqQQ/yGAgO9TpPvdTJJqy1T8qBMIWgipaybquTuAy/MPkCRbBYn/byjp0lm+g7SJR3lmvfTtVBDjYzhSABvNA+9XASFaydlAGM1BHyXPWapAIxWulzZTApE+rCZQr7qjsCz1WboATu3SwHJdZtZ0Kz96UbVZQWUOHBnhkPBOReO94CEhflXUY8X6fQdfxFXwR6XpYyNt51kBGzCnsV/MtqzZO9+9D389JvJahOn2UUoR6VKYuOR7hmLWyy2g220NkQXQhOUqENFOFfsuEFZYBZvQIlqj666/0ZBP7VCCDWwA7VfH2sW19ZVwOYouvXJmwYIT/IeFe5wU3jDpZcuD2AgVt/BeR4e8xqj8J4Rbe+Ri09HBw1vVJmH3HFY2xHVHqatrh7dUD/CPQqspHfIxBRl3ItjFEEf5mB4FZqnJmsLoS+wTg9IOgn+ojjRVdxE7toMqtqIEyYlzbGIPWq7rjGr3EQxFKP5a2P9frWuOA+58hDVtnuiWhKH2Zd0zehZuewswo9UwqqbYLZ1vVl4vcATQKwYDCl8i2RBWjFJEjaARQ6NLz7slaPesxUAn3kPZpL3peLy/vx6PVeFWUVbmzCGaYw6Zozl2tvP4fxlAu/ANzVzgzENM7+DkyYc7lOHO9TVV655Q3PlLuWHqxefQ3Kp++GNKsexrcJcetffed2Pj0IzH1RAqBzAwF+5PZaEnrTmnwFG0bIZzugpoiYIiWKiQqAq8Hurzq67RuLt6Rr1amm0h1GBPtKrnynGyWP8tcKEbdijF7VhHF60Toma6utW2qZ0mOgTObR8o91Rc0/tM/o8aCo0EuBRqUH6EOcgJKkg2p6JGW+vc55D/XaviwTZNauqNvFvshcDsJp6iqviwrvFOjZgd6+dk0NT01dzMHCRsFz16/0CWd4sBYDIgCsjRpxDcTFjSLb3Pq0LqfyPDtPmnSobDcveIE1NgGhrm0QgI7LuqWmnvN+injJzjMAOGkMgvGViivfXeg6ngiCx1TkDFj3/uu1e9FtGcKUmtghvkQR1vutrbm0z5c2iGqXfFwjBw1iFWpFwPqOSmIi30IX8CzZDaEkrNH7VWR9DLqLbng9fKbCs3V5X6tnrZc16A9An0FQK7fPxZPGwTMDibA3hYjt/PkJ1ixsGCHPYz/sq6u2sSFMaI0AHhA1IaQPOT1/CIN1djtEhrkEtIy5yQOJyg0ftVTCkEZs6BjNERqeXY5ItxqiKDSKJxN1Q2OMXsAbZNeL9NjEE2qhqH1Gsd+Uv9IVSiMXGpzKRCent/nMDEceCHirV2/0UhdD93XbRGm4lAub1i47xszfB8pSBsWypq5qr1tRGBKZi1/d6bYNKwGpk9SsnIE0E6vc60W5tnTjdu3VWtu12pRE8KKPwj/kFdBMbakrgrlupxAhda9Tp21yvtgPQQElzVZXytgUOGLflk/KsL9xsnMShCsP0LQ+C2Om0FBco5d1gcQRUI9J205nWzhz6NCseK1VjZiwRgV5UAp8/RzwHRBg+E4E6yMX6laXCm2z/D37XXDNI2tZVAhUoHu0fWYvS5r9LsHuSxpi2baXyxi1LXXQRprGpFrWmlmTnyz5CjaMkE9dQu95QN3uKdcqQTx47uFc3oBPNMXKC6Qdz+jFahvaR1NXtKKjm5vKB1qt11cWd3ympcHVMyXBiD0f61J6JngIdRvbtnjfFMFXtdOuw9q8VxSFaQzy0v9zW1cuOPGdeO/ZWrvTodBGfxDmsGWdlcvWu2aSMyvQUqu6paiTOgDbMmftp4YeYl/DUtWyZ6+z+m3i9iNZEhW3u6+hwwWNnjvjQ8aEjrhfm2Fh3a3tSaxR6INXSvpa9H1wCv0UFSsZGI4XBGbZNiht1w1uWhcVj4NZx9YpVZxL+V7NTe5DkENUPkjSJkR6KpwrevEZd9crhbgl0yx0NQrM3S4KqXUmiiWtwj+F/o+u+2odPjflKsSBUmbPedAeN2WZ9L7tgSfERDjeflNKgkFQf5Jl65x4R/Q7WUMZ7yYhX8OGEfI4infmk/hAUI9cD4SyChGu71OZXWrCY0JvbGCZpyAKVKsvCmF7Plo68V5NNNQf2KcKA06MTVwqLzELX/Vcq+WgTCRuRgPu5o9oleKSva5yxG3FGKihYWfEGw3PuMbVQ2X9tC3T/hxUZfVET0kYJ4W3sIVO/z+g1SzreLW2j70RBqyTumWbtUCteN+XFkwgEYr1HoazDwNXQADSPPimRJWjhXVyN7c6o2zpU6vWh3kqApnUhS5dKcLU8MT7XRQiVM8UjaLwoGBnsus79c8l4JEKOcOrhFZ7XoAtD6gC4E4ss7qN2cozhaIo+D59o1fW8d0Nb2WtcA6CG+F9jK7HxzJA+AnldWY8XYNnLgqzjdUmTz2ZN8DoWZlF2HP4B53P1pHA24iwYYS8uaErgUlJJw8GUQoWgSEqaMY7D6Rqhb25+YjCVxpSbNrk6DP3iGC3rHrtWr34cMP6dKFvAth/eyQxbFYdaXOoa1BGw+vIp5XZdquM0mFkYi5kvNP2sFnWBMRUr6WdFDLDhf406zkuDyTy9+B0Oa61x5eo67YWsebtotIewwGV/d6tYPc2UtNPKO89dKij+Vp3NbAgVqy5nlkERQcVVOSR9u4dSPK85Qkg2wZmCXQI4lGYmXBVVqzZJHiioJVrT/FrXhDFc6vfntF3J+vm0n6LDSBdT3ehabj2qGbmk74IvCk9SeDDfroeh+cd1/CA+hS+8Kn4dsoceIjeHftt2+Zi8GRQrAyPLN6l2cI4arVH/mLfB9c8xWdCn/sBRASQ7YgIbngX0n1RWGihw1Fd8imjLJlYrEZGlQXPFAFbY0+ZAc6S4U7jlSzLnXxPpgjIFjrqYvTraQ6tonisNNYJbBgh74uxOs+40CWqrqNwdzyb2EygruRqN3e/2XT61Vfb66JlHiEKdRNEBpWAB2oJEzSJHHmLgjbSB3xLEVDz0XotVtLg2QJ1Yv1y7VnpIm6wYOM2vOKm52JxubXsBIpVAQTro+Eth3vEUZ4qvdBPbqXIHTmynV34enMCf1V1CWVngrWLqKCEJQSPGVAapkyI7ma7G1gz3okikDSgLpMuhy5077mNq8R2MB1kmxjAHYCF1Ma6hk4EZFU0zHVtPLO135wm+s+WGsiVOKUHAvVAVgWnLEnArW237v0bY03jimK9Rjx7H0SApXv1YUl1n5kXQLfJtf0JBmgRlwlCmbnatd8tzazjmVJjY0vHi21lc8GvCqG50306WOjQVSHuywR9iKjX12yemHbbneOpcuBu/uj+B5CUJ1u7L2vwuvbOMub8mtlT3AoPZXuZpP8N45XLuJbPhlzJ5kV2Hk53mLbQ1bBhhHzSdUygla+1wC4CqZHl8RHHawZCoyToTBc8801ZC60V394P9TuRislW6KJtbHm+tZbG4IhLCVQEJ9uUECyWgFaENIV6g6Lh0qKpiyDbmqqmlDbHD7FeDrC6ojSjoJNQoaczvPCeKxp1H5F7IdyN3bQxopO1IxmvjugTMRAivX05oNlL3QTnmVtelrtVuDIkD7nphhmlXnPr5yK4Hc80Bar/iWcg3OtLt1DEs1bYPQTahkzePbWlS/Y96Faulp4PgFBXNbbaQWZXVI7Std/W85ZcJ2yho8hX5b4uy0QWed/i2ViILv94cEy7Du/vFaiE/Ep4tt7vrvxqDT4osplrGn1dj495fal2SI3lqYe6+V1NjfMLEM6XXyMwrclXsGGEfO9ZNBTcXa/CQScQ+dkIT8d3BNTHlzbCuyv0irqve5HLrFXwCUM6BisJfns2RqZXOkZsF4d9SE1dRsMItFvyWvy2/rbe9tk2ziHijfAu7BYahMB720/WNoMqSp/L+4z1h2vv+mipdxSWX2Jjs/cngYGuK5Z+zK0AlG1zRiN6BPyPtMsFVwyIs2Q7HqWuDckAbQI8yYp5BMzCtLIewKby/i1ZjmdW24QSAd+hUgK4864D2+zAihPc3BHP3fqAL1GYsOMZiuVrePZ5RYt7Bo8AZ6Ohrm3uAg2l58pLi6eC2+vKgScT/sGCtzwE1Co1GTI79qUuCu+EUqnLDgvybWgEwFzqpnRxecbrDnW5wLfgOeXdPFTUU3D/s2+bE56Cpb4I483ua5Y76llc85ndlW8Kg8V21O56gNVdL8OwnRQmWAuwYYR8K4fdEk9RQtk11chAWO9tiI657j34JVg2Opv7unVFp6m29RK09SDgrbQNr3Xvx/zTVRMbQVvFLsT2jvVT4K+qN/JXaFfBcUCxEAKvXsrsLmpyPFR/KxaJKosjdmXdt/B3Ub8Xo6c0zF0d2KU4GBahPzOX4DEOPCWUrHHt6yOJRidAxmDPrpD4tqnEyJSABYuQSQRe6Lp8x6CFHlyTJFkNL0s93AF5Lp0jXav70zVnDS9r/1meEwQ9yhSBJHheZkpB0jEd8MxDZnhRqYrPmYVtySYRlN1KAC9KNzOrizrgsZVFPAQ8ArBUyjAvPPn6veHNA41c806HQ5uX9BGjEb0FC5SxEodqDJyDtivXZdBrzy8PKu+fubjrETLXmSdBYzdq174K9EVZj089q+XPQagLTTlyT5kO11VYTSI5enetWLd29vHx0lgnsGGE/GxTh8VcZpwo3yq5U62vF5wBFP/7yoLeH1dJD1QDTyb0KEECI806/wDGBH7kaZRG8wzHi/jMkPQAz7WXEX5yU294ZEBvRf6kjELXjdOI/Z6bfilCt/hZGxqhbKDDxajsAeut0qWO5pa/QKPoW3YjF8tIT/RzQaOL53YMK0isuAyW/fsqWBNESbUyc48DYQsdyRjk2J/molf+3Hljgs/4SIV3K+M+4EF++3nwJnxMz6OquS5Xy4E0w+70rH7h9cQh1F6viMelzD0O9gqaMUAr3Q9C2ASr1WWC1RVNRh01r8/ZVjhmVBnv2jX5su7OwbInzSsPt+xdGYgWPyNEzSMoF+xanNcVdU4XZARkzXinL8Kt95TAC0t9u0ZgctdXsGGEfB7TzBiyzsoja4GGEP5UEAOtuLXMuZ4ZTYIwfEZl+3JdwNleXg4zZVNxK7iJCp5dW9lKzyjvR/QWtHg5B5e04YW+iXhRMlhXRq8JobjDR+uqy1LklQM9zXgX2+H55smWUpRewKsEli25KL3xLHxUu+VtuUbxpPlNXRUNKpOxSy8O2+LUTSomuUdFe+0qUSpyhIoe2/qsHYZjTgY9XhnQ+jJAGqnv1qpFvdv6vOHFAc2lDDng2VC1jSEuAVHeleEFSVzhNQK/wosxAyb4bCkj4kVXe1xXb+ghKighEND7LOJpX7tAReEpPuP7VHLTlj7wES36kKQm8hTd8xTc9bJVlMQyt/ZochvKOn4X6oIHAb1Y7sRQtz7DEkGhz7p2z57xzrfT2bfBQBVln0+AwJzgEYUNI+Qr1/WwUO9WtsC4cB/QQzMjNmXtdc46x5rw4CCAWMnR0A3PXF+39R0L3hiPEa918bd42fjL7c1AQx9OJO70Sik5Au/R5Ih4OZQZbZSuAqvwN5edoVsENlkWPsULdRX9hYtAb/kzGRnrivz2XLWR2zZWdSlPSVz3zBnUJZDufiAi5AXEItMx0wOyttqRnL2ull6WLQLuXs8Seq+Jc8hTyjPIlQFS1zugbn31ElQWPyuebd9CKTNB5Xvj7ZOhmsZYMGb8GjlcOA0elgGo4wL4KGjY8DSeVGGhSCO+MqDeORCHbKBHFPD0GX/VXOoije734FRTQvpCo7bgqfIcoDehz3piHxdFQ93xNpdQzr6VDhnhZLqQsCdn+Q7NZa8Z8OR7ynpufBHy9u0SJaydNfkToZisH8Vmwwh56mzxMMw4xaDBUKhzPTE1SW6it3pFgb/SQOPmgnUvKtnPGNRX2Bu45KPVu9LavJWl1veK8d/xfixr3fBtG51G+MCiYpWbewPPQbwe4bGtd4zfyGv1Prm8v+B1YTsVbayumKrW6x+py4R6NVbKeyU1cwcZ/yoa8kfkl+39BzhB09yy60tJvQWZJe0tMmnagqKAZNa90xRzCWjcgQldiCJgXg8pk+h9Bnw7nXWn61oq1EkPhbHvgOx+ELK+dq/1ekY+CvT0WY9kNwEcaXNQHvrSN9Elb3hA4CO0o6rLgvMMj+tnfOUjB56CZR4j2Z13LvwN8DIAZJAlM0LAUwGforA3oYxaQBN0fX2kDGE7HXquzpDH4NoS34T1eAC2W6WazsDyXz9lvFursGGE/GqgZMSOJkf90mNSHA5CYrC/ckyAjAhVckHnB5E29RLMEnUzpWZ6XICsJMQjP+31GE6Q4eP0bCJpBJ4rISNKQhSwPkO37VqJf6r0tYFp6Pe5/j1grZhzlUsdCNJq2J+D3vJ3Qq4YeIa2SiGU/5G3P7bdUpEGBU/5Z5L1WSTbpaHzcyrPertUwFvymqRC0flXhhzPBXdJWuTS2gRrHJIUlFAq6Wgdhcqf6L62NlWvHeH1KJ7TCXqrDS+nHTbLuECk0rzoRq/6nsNmEi7PjuE5TxYpD+lPCZ+VscFmSRsNDjTMnc8AW4pboETFBzyK9UblINBzqz+MCxPqJYsdl90CDNjhNEKf3UsQt+CBud4mFy15pTHB2oUNI+SzDWKXpXJRJicu5ToJxvzlrSXvajxQPggitGeXDyzi+LsyL4rSUC2K6oxo1r3lW3clg0Z4sb9u2TaLrA9nyY8pI1W/hbaFyb7i3dtI5V7ko3r2YXiqfLONBtGWuc+VhnW1eDgCHpFlyVGwdmlnxCyE+rxn19OyKlsfAcV3rXUZjQRfHiBOUkXW+nsCNiXQQvFnAPUZnBJyYtnyBxHQPCMg62lsekpcxyQpaWcQK50AnrHgEaSMgGRb8jpIAhsCWLfkEVRJIPbT1HimwgKs16UPECxf3xpHUdHQP7r1LEU8q8uEneKRRs4bnikW7lJnSLY+zRpXbbuzoWJ4M4jgtue5wTOHX+DPl3tsLT9s17N6wwCW/s1ZDu2ZQxQDOwxIhTSZ8mN4C1LditWNrmPChl9fniFTSlnX3XXZBgv25R5Zg2dxw5sy0Pcyl+Ts2e9I/3LOHmFvZczlVMc1466PS3rHRWN9wIYR8uKSlFmD9TeAYEbYTw5uNCqWc7udrPkZBYUXqbCgMSFlWnLAs7LaG6Au14gX+HQXmtUVzagxoQbU1xHGXPnOxgiNSjEZoT9wya+AF/21Y2Ur8d4qX0fEW6FspTYzF79yKXR65MFx7DzQGA3ETHjFJDbLGYCvrUKPzk2AuOETgbsEmueSencu4yR3GV0WocEJcoxrlnHBHSQKPhFyUktuIQI9qwBmcNmPDkLWhXNaAEyKt8Tu4odndWMXhHWZPqOCOasAtS1vvr4PixcAaF4ENc2HeC7sFY8BpHmpN7r1AQLNJXgMCcByoBHc9Qg0qox3Ac9eZsVTTGHLDJrrqySA5votRzxSQT8vo8fx9N173EImzWTHdRnYrX7WcZI0cA9Uu/XRm8tflZE+ww0K2y/PUMu+zD0m4GUcG7FyTI90La0xd329DHHMNNYJbBghv3LGuxqKgIfKcvlBlSVvgsHKIoGGehD6g3pXEqiNgKMBHrWXzlehUgo4Zz8ERciuUO8YtEL64eBIywNjeNHHulp6biGjVtZacrmh0ZJrla2V8MZ4ahVA0SArGlSV6e2Ghp+Wljnk0EfJZAddm9fIduohFi5QhJjpib294yAElI/kyqDKAFWwZDI3pVTXd/U6rq6wMWyvzoIvYc9oPU37o3PK8Jwmubz0ffXWLvvUXOjQCB5LYyIN7/OQ1S5+h47HQzxYWeTdaPTsngloH6b4jHkZVGimcFKer6wMXOhc5pcYdMcRD/C97oAL7kLDXqgJfy5C3o0EtdzNz28CXmkXiEcCNwryBGsONoyQ7xdDzcwPnNFfQylRrDRJaUrDsoHA4PrDCO7fUVG2ksUKDN3s7TMAypFjpVXKcNnKZLfNshywXTR7s1RGeapmyrasabMFAsaAvyPhVe20PkShMTbRMMK2ttAXcTa3dndd4CkI6JanWJe568eWKAyvFfggPUSlpcf1M6C6rxMByPBTDDmBKIM5g9JMFDVKavnptirApRQTgWaFd54R3L1MAGZU1sVnVPopZrxT17uvTBhehrinCe6McDyGLA0g1NVFPAIyl4x3QH1+va5bs2YadGuZIFneoPQzIYELXiaRmoZHXOgxihcgY5jxbkYanIaSuc8i1qtMdgXPPQwLrTOxLxuQ8RR2INBC3jkFb4Ht+qAQ1Jl63VXTSdInz5rH8Pzzbt1DlD0R8GE+W+RK+PvYs8j5XrcB9LmKpC8R9sHq53rbHNsRs2tJ0J8IftdSex8GNoyQj64mFXVFwNvkTO0zgOebj3nhXdYXJaASrhTxOBKr5U/gqbLsgHqQtW7viBeFZNTGiWU9V4taerV8o8JT39BwfmP/tApJUF8IQG9mVhDiY7xHvMFHFQTrkfBi6tc+pEAD6meMBlDtQ6/wcq4VARMK1bjQ99sHvOrAH5Isd8aDvx/U74pIMtn5tUhW6mQDPSeWrUsMSGo3VR7sXXWahYxMF6HSro4kL4QqQTkKZ4Ks6auA50VROJigPFk7srurWd3TrnuZG9rK2HQsa5OOiuXCEy8XxcKdMIanY1LaZfREANrhMgB87EpKX+GdiGuHStJxbDzlwDsDyXhKhSfBE4vaV+iWONAzQQ1kVVqM97Rk69baf94XQDkSWZQlV6D0XbneOO9L4KcKe8MjlPmC1LqXMSNrJmS02Tw3Oj5VcXMhzyyd0asQtPEehXzWzueypdfG6LQmvzZhwwj5OuOdCaUgnDD8ueKYjjMUwtfuZUF5oOa5UFVFkgij1Y1asEdzLyglMUlLw1T0lB/Ra76i1wPhHo6skEQ+Hw7P8c0UG2vjCN14HS3vh9PMI19A2RoXmuZ+07h0E7fajdFZ0RNDlRJIgG5wZzBlUNZsOfpPBE+vAXMkilkIhqLgtWG2JELl2ptCtf7hKyYZIYkQkOO1WaIm/FI5ra8qy6Yf6Fo+MRKMRokodyFO1jxyl3dWy9lHL6HUheIaL4Z8UFg5lsl4zdmurXuKa1wSYZlyG+iZnmOvKwOpL7x3MTI91Cvb9bgsBYSyIoD1GXe1FyUHKqBtHR5BSXCFwfDMzW/jKRsNDuvu8Z/yEO+3wt3HIpUdQ/6SgtA/3WGy5CvYMEJ+kPFOPyibSKLcJ5sxQ+EQz0RenIVHlAbTzqNlWgkA9iJhKWRRG7PuTaNuB2Hlbm5Ml8iLW1ot/dBeQh05njO4dXFbm6OkUDznmGsasV7vqSDwaASvzIQ8pJfSeH/GNfgxPMNt64rvx5S4iAeq+6UV5G3fxoFg1pfzTvB0cM6TTrZIQGcTVSrKAOs57onERdul4vLnpNHrOuF3ErhH6ho3GpSoSoAjSw+QSHArUzxSvBTaRcywDILMrJHnQodsp4C2P2mXMes+fgJASYLCDC8DlMrOkWSvTj1Tds2akdACYRPzynj+SuTI1SEeipWr75ks3oFCUKWOJ084Q/EcBXJrz79HHuKR4TFXeHLAEInVHscd57ILIBc+nB4rXlYrnki9VCqM4zo7SzS9f2Mx0U0Q8Ky/WRURbmisJ4G3EWHDCPkV3cHN5fDGSnjqSowTvEH4OGvhhOFHEy1Nv2/CP9LMvqbrmfGMJQ7b6/reJxML/qFYtwkZ4894iAI/N3jAIJPbgHfnE3VdOnEVYYmiKLkEKO0qvId2Vdc2sQLoez+iVfQuqvvCnmOuywI9z/TiXW88hXcQBb9NmKnmvcYLf1dS1jJJBFpUGvoemHVlsgcASmDupG8SgZMsGlNKekSrnrZOug2LoRn0VECRWmWRD11DTilJkJ+P6Cxrw5pMiDsV6pSk34ndrc4daWKeJMsBpiSo8kdZLOKCZ/b4QixF62Nrf9h2yADs2F+j59+UrX+7fV94qmgQyrXSMp6chuJRlnMEvD+Nnj7jNFSI25KEnfzmFrzhAYKnylxcgzeFwbfnZnhfUJ/L2GX1OBi9rGvlmjnSFWLTU3oNKujFTSL3wvq7CWwT9L5tjku5RtAz27a6XHhjVTbXAlhbj5fGOoENI+QpWca76m41944Ke0Wo0p+wyakiTEpykEaAm0Az4VDpD1z/NRJR6EThl3Ox8g1Fn81R8EdLGUDOGakNgLPrVviMKS1Gq+W34p2ODs/QOfzoe29X6xLMzN6O2C62yrRe2VXEA7xStUyqWS1BO7RFeGn6AKjX7d0P7FzBN31zGFNk7zesw8eXGtumVmxVllLY26yCOQE0VyWmS2KZUQKSKZgqFBNAGoPBXfIYD8ukaKwxgLTQLHydrvGCSia7RelbWii9xD5+zd3v28s6tZZB6u0x0at4GtfgAXNArZhp+93lrcFujoeCx5DtbwCQE0mOdqA6/0l4CjQ0ZqLCm4/X5S50mOCG06A+FzyOrvdyXePp8JxbI4OrvRX8gATq5SLsfVwYPa1HAvqsrC8dHSzzyiXvlr7yMViDt3fL4fkmSJl1D/1aEXwnwvuwjrwXG0bIr6yFDguKtRgn/+Yp0ptcJg8A4NwoDv4sV3N+kf4NHzwsipn3XAEwpYHC00GqtR6GlYZsqSocgjKGR3F5YgRLH15hD8FQgag7AzGDYMRrg31K9wUFikzAh/7g8EDsy8rrwEXv4rBMMuC95knFXdA2jF6jhEUdqpk0Wi+Qt9+C+GKUv0ppstz/iX1Fxre/ea7YICRggl7vw9zJFtVl78EUROFJfQOIh/cUesaSRr6HMo+uL42saHiRJZZxvDyOh+x9X4WsEpqsceV7NZ48kNXc4S1PwKCuKlud41E5yMV4ilnuWnqVgmvvtVj4ACRhTVASTMZKWW7qsi5jpRHqMpJ2RKzNCzHwrO/L+4vCz6x4q7wNkDWBDwwU7wnWFmwYIZ/7PHI3CIo4Fei9YqVbOdc40PscHjKaYVIqJTaZGjoHNK5pjgk9J2sfqq0VmzQJeG12uaiJUyOqqakrXDueWZphHduT9KBYOEwUDogxes5UMCm5uGuBUaHmk03rEl+prN1qF13UA55CD7T0xtq4Ur3WT4YXywgDnjwjYlVXAqdUhGRKug9MeZ/Nyjr+rCsZ31IqNBjA5k5dtAR0SYeVCqFZByzEY4JNMyBrNPdM8UyB2CR4xAA2z/zkMu5U0VioebupA+a9KASbZHsiZd3i1iXQXNPGbZoBix4JBNYtfmRnBsy6cbyQxQ8E8CZJCARm8KbOt5pVeGDnyevqM1JmseY7EhpjeFm8Eax9T4tevv3NM2kjQ7wDHOrqrJ9Y+n2hSkKi0p9g73fKem2Hw/gSTZaYh1kHzNWN0uk7jePVjnu1en3cxh0hCVgs5LkulWf889NMdubWj9vpfI4oS2XmRbQxvnai63Ubw3HTWB+wYYT8ym7qIJxRH+hRoUdaZPTkueJeNw0/TOoIWr8KbjVO6qRKA9lU87vi9xUthxbJlJSoMGhdgSOvnAiDfqrw3O0XlBWYsRX6y1yRlbITKtdnq+16YYtb7EEikpOx2vY6jX5w3YLQ0Lpd6YjXUAORypJOtDabbiVgMAl4PwX3KBHKuqjpYhHZy7jEF6jQtgAudAlYXhY8defbNdn+QXvuIfWNpw6Yq0+6k6g7Xp67skPzhZexXvt6+vJc+isl4PuHhdVZByzr+m/SvfpLsraOjpCX5hKAN0tgx6OCRwC6DvnwXGRWl2R8PzTXOAMCDpe68mE9ErXTWICHdL07EXB44coJH9a6DO9w4Omh5cJT3/B02HhKyIfnsh2uS+AQzMaJgCXlqUs1T5yBrLwTAd9Xnrqkp7hpPyEDh+dOj5bmLsQ596LUJQnypeV5eZfLy4LnCpyMVQKX90qprK9bfEiML1ks/FvhOFbDHnh3w1f75MUsYfOUsc5gvJYy3nE1TxwzjXUCG0bId7OyhW5oGZcfbmW7dSyQkYOLrn44uuh5RCB7MJJHpZkQH5fcY+NrbMhVLt8jAIfT0I5E40j0amWlhlaxORo4EnYs4+iRaPGCYsOtkhOVjlbBG722APj2Hdt1o/St0O2MjBgzsRK9etjpuEvJA6gsWM7d5pFfW4Mlcq2BLPAKCIGfcDzfamd780G+Vg0C0JMHu8lYDfTmxQ3NvYR++1iw3O7Quuwb6qkop0R+iIqUNXh2HXgSRaoHqnEXrpeLcjfA6zWzH2hYly0NaFmyvup7VHzEa42TsLqSW8NxGQHFO6J9QbkcYE/xpWeWvvY+Qz0WPZlNru5njksPueAtahq2nz7WVx4rAr86Vjby4MJdFfJ1JPA2IozP/A8DN910Ex73uMdh69at2L17Nz772c8eEf+DH/wgnvzkJ2Pr1q142tOehttvv/2YmD0eGMt4N5zJ9d/YoB+gFc3XEeLiWljXkn+5fK/xA4w44OFHx+ZK4+LCC641MMo9c68ZLqxMeTTXnuV2jnmrqwxYXNyKfq1N67OXWWQuO49ZJly77vsS6NP3xfKI1w+HF12JLZ711QBP/3Fox0rtinjVdV/cm4teLSO7vxLvi8LHGO92b7EY9AVnOcubFwuZfA1vrjQXvV9zzsDyXKy6RQ8sL4tF3uszy8tCq5cyLC/L/aVl8Hwudepvp3F4Sej3We4vzUudDx12PFpakrK+Fx6WlgufDy2JtTrvgYeWCo3luZTNF1J2eAm0tKzXoa7lBfDQMmh5UWgcnivtZbmu8EbqOrws/+bK0+ElwbM2Li9EKC7NheYi4EXera6lOXB4GbRYSPsfWpL2LPrST732k/fnYrw/De/wYXkPiwWwtCTvJ2d51t6dlvFc6uWl5ULP6vJ3PJd/OZd2Zx1b87nQyz14eVno5ux/4zfE+k2zJZWyeW6twWDuPcZ/6wRWbcl/4AMfwL59+3DzzTdj9+7duPHGG7Fnzx589atfxbnnnjvA//SnP40XvehFuOGGG/ALv/ALuPXWW3HFFVfgC1/4Ap761KeekEYcDTDGXhqhTjxfbhdhrbfigK8MV1YtncKjAc/R1FWv9Yln3K7VVep1cYkEBonbz+mw05WEe2rRcNlexlCX48LcxgRWJce3CllT27LFIuDlGs/41bUGCozUvZhHaUhRHlwfF97IM0RJ8HT7sDIJqFUsyVi4vMeAx2aNEqn+U96FZwODLSFw3Z8EwOoWNLeWiNTN64j67ljbZQpekgh63/ZmyxXMoNSJcGAGW5pe3YzuWwPB4q5XFz/HuApZk1Cdj0FdV5Y4YhyBLRUsSZtpNhPXOJQ/61KjkXvZjTDrSnxAlS9B3dwmVJo4Bc9LkTOom6nbXOsyt4n24cp49oq1n0wh62ZFUR7DCzQq3omAw6IM0qxzN7xF6yvj8m+AF+iVRirv0mfodd2fkn6vLJ+N7QJiBkt6PXUk6fxh/Z4SmOey7S1p4qR5edMMEeJlq52ODvajrfwduifJvQr6v2KVaBMe3mt4WsCU8a4C4lUutOzevRvPeMYz8O53vxsAkHPGzp078cpXvhLXXHPNAH/v3r148MEH8ZGPfMTvPfOZz8SuXbtw8803H1Wdhw4dwtlnn43vfe97OOuss1bDrsP/s+WFxV1/RCgfyjHBmC/Xfz8yH8nRuvVXC2Xb4AknfYJhpeWE9v5RLjscaYiYK/hoWWhJqgveKdgE7rS5CJf46cb36wmRtOIQMMhRSEcaKvyr7WDmKVopeBCQQLCoKER3cEplO1bXhXgHSzbFLji9ri7S0C17q8TzMue9CK+KxizwTkfgPbbLllDGeGr63ba9Dfos2TICaqXHr4MSEQOG4xiyoB62dwzvz3LNg3FiSkI0UqICUJDqMjDj9qW/xKbNm3AscCLm8KOt47LHvBSztPm4aC3yMv7u//zFSeX3VMGqLPnl5WV8/vOfx+te9zq/l1LCZZddhrvuumv0mbvuugv79u2r7u3Zswe33XbbivUsLS1haWnJfx86dGg1bI7C0esyRyOxjjDL0wief5gZ9WxKFVq5Ds/RkNw4XkCMigY3a+lNWXUdpctR4HmynRBnYIKqVSwGa/9oeI54kUbAW4nGAA+o8QiD5+V3PgIevLNXjlvQbYexTPtqwFM+Au9ETbeT7yWPCYIqKzt4dOBFeiOsz3Mu29MsWZKXWTAXQn4C8wj4mQAALxYV7+KxEE8HmWsXBJ7P/bRDSywDY3WxCDzNJW+F8heVFl6E/oy7HI4Wj9nzBQAN7y3egKch7+Xb1TZGYd8o9Nz34T02/YnwexF2pTTvh3JxK3mZ0vPPvJ3KckY8CKrawQEUHkN8S9wZ433j8xEXRcF/n/aavANzRrVl8RhprBdYlZD/zne+g77vcd5551X3zzvvPHzlK18ZfebgwYOj+AcPHlyxnhtuuAFvetObVsPaw8L/teMx+J//7/8ABPSqHc9mHRYLyRCX/AMHAAYlknV8ArouIetHYHiWJjcN8Mr9iNd1CYtFBhGQOlJlmjylvOPNDI+Q9NQuCZqVyb7XbUVdRzJfp0CDATAjdYoHoJvpYSVkeDTEU3pZBZEJuKx9UXgS3gH7/rWftK7ZLEl/gtA1eCngWV0gDHiXdomFMsQrPJW6BM8mwbY/Zx1hsRCh3Wna18wi3CTOzXiSa0pku88KTw1e1Z/U4On6f5dIAqVMkBJ5wqJZR1jo/u3Oxp12ViLSsQbvCzLXt87yNvm7oUk6f1uGOlBNT+u1DLjQZDkiL0RodqTxW0RIpPvwu6TZ4ICsaxYdsRiXKSHpsoy5lRMBWQVsIhb+kpweB0Dcyr3haV1g6T+rC6J8QNPY9lzweAW8zvECT7EuVYQ7YsFLpIF50g7uR3hXPA841H5yI1zHpPcT51X2p40ZQq/fnQULsrrYE5XxLu9Y3PSuNigegZB7OcTIx0I4X8Az6BGhVyVCxieX8dmNfe8k3zsDZ597FrqZHSN4mgPz8bvbV+fgPq3htIyuf93rXldZ/4cOHcLOnTuPi+b7/+NPjpetCSaYYIIJTndgxtDdcSw01gesSsifc8456LoO999/f3X//vvvx7Zt20af2bZt26rwAWDLli3YsmXLalibYIIJJphgggkaWNUWus2bN+PpT3867rzzTr+Xc8add96Jiy++ePSZiy++uMIHgI9//OMr4k8wwQQTTDDBMYNtaT3ef+sEVu2u37dvH6666ipceOGFuOiii3DjjTfiwQcfxEtf+lIAwEte8hI89rGPxQ033AAAeNWrXoVLL70U73znO3H55ZfjwIED+NznPodbbrnlxLZkggkmmGCCCSZ3fQWrFvJ79+7Ft7/9bVx//fU4ePAgdu3ahTvuuMOD677xjW+UE88AXHLJJbj11ltx3XXX4dprr8UFF1yA22677ZTukZ9gggkmmGCCjQir3if/SMCp2GM5wQQTTDDByYFTuU/+537ohZjRce6T52X8/Q8OrAuZc1pG108wwQQTTDDBMcHkrq/gmHLXTzDBBBNMMMEEpz9MlvwEE0wwwQTrBzJj9EyS1cA6suQnIT/BBBNMMMH6AWYAx7kFbh0J+cldP8EEE0wwwQTrFCZLfoIJJphggnUDnMuR3sdMY7LkJ5hgggkmmOA0BM4n5t8q4aabbsLjHvc4bN26Fbt378ZnP/vZI+J/8IMfxJOf/GRs3boVT3va03D77bfXzWDG9ddfj+3bt+OMM87AZZddhn//939fNV+TkJ9gggkmmGDdAGc+If9WAx/4wAewb98+7N+/H1/4whfw0z/909izZw/++7//exT/05/+NF70ohfhZS97Ge6++25cccUVuOKKK/Av//IvjvP2t78df/RHf4Sbb74Zn/nMZ/DDP/zD2LNnDw4fPrwq3qZkOBNMMMEEE5xUOJXJcP5v+n8xo03HRWvBc3yCP3zU/O7evRvPeMYz8O53vxuAnOmyc+dOvPKVr8Q111wzwN+7dy8efPBBfOQjH/F7z3zmM7Fr1y7cfPPNYGbs2LEDv/3bv43f+Z3fAQB873vfw3nnnYf3vve9eOELX3jUbVkTa/Kmhxw6dOgR5mSCCSaYYILVgs3dp8KmXPDSMbnbKxqYAxjKnLETUpeXl/H5z38er3vd6/xeSgmXXXYZ7rrrrlH6d911V3WcOgDs2bMHt912GwDgvvvuw8GDB3HZZZd5+dlnn43du3fjrrvuWn9C/oEHHgCA4z5TfoIJJphggkcOHnjgAZx99tknhfbmzZuxbds2fPLg7Q+PfBTwIz/yIwOZs3//frzxjW+s7n3nO99B3/d+fovBeeedh6985SujtA8ePDiKf/DgQS+3eyvhHC2sCSG/Y8cOfPOb38SZZ54JIjomGocOHcLOnTvxzW9+c8O7/Ke+KDD1RYGpLwpMfVHD8fYHM+OBBx7Ajh07TgJ3Alu3bsV9992H5eXlE0KPmQfyprXi1wKsCSGfUsKP/uiPnhBaZ5111vTRKkx9UWDqiwJTXxSY+qKG4+mPk2XBR9i6dSu2bt160uuJcM4556DrOtx///3V/fvvvx/btm0bfWbbtm1HxLe/999/P7Zv317h7Nq1a1X8TdH1E0wwwQQTTHCMsHnzZjz96U/HnXfe6fdyzrjzzjtx8cUXjz5z8cUXV/gA8PGPf9zxH//4x2Pbtm0VzqFDh/CZz3xmRZorwZqw5CeYYIIJJpjgdIV9+/bhqquuwoUXXoiLLroIN954Ix588EG89KUvBQC85CUvwWMf+1jccMMNAIBXvepVuPTSS/HOd74Tl19+OQ4cOIDPfe5zuOWWWwAARIRXv/rVeMtb3oILLrgAj3/84/GGN7wBO3bswBVXXLEq3jaMkN+yZQv279+/JtdUTjRMfVFg6osCU18UmPqihqk/jgx79+7Ft7/9bVx//fU4ePAgdu3ahTvuuMMD577xjW8gpeI4v+SSS3Drrbfiuuuuw7XXXosLLrgAt912G5761Kc6zmtf+1o8+OCDePnLX47vfve7ePazn4077rhj1csRa2Kf/AQTTDDBBBNMsHqY1uQnmGCCCSaYYJ3CJOQnmGCCCSaYYJ3CJOQnmGCCCSaYYJ3CJOQnmGCCCSaYYJ3CuhHyJ/qYv7UOq+mP97znPXjOc56DRz/60Xj0ox+Nyy677GH7by3BaseGwYEDB0BEq96ycjrDavviu9/9Lq6++mps374dW7ZswZOe9KR1862sti9uvPFG/MRP/ATOOOMM7Ny5E695zWtWfSLY6Qj/+I//iOc973nYsWMHiMjzpx8JPvGJT+BnfuZnsGXLFjzxiU/Ee9/73pPO5wTHCLwO4MCBA7x582b+8z//c/7Xf/1X/vVf/3V+1KMexffff/8o/qc+9Snuuo7f/va385e+9CW+7rrreNOmTfzFL37xFHN+cmC1/fHiF7+Yb7rpJr777rv5y1/+Mv/qr/4qn3322fytb33rFHN+4mG1fWFw33338WMf+1h+znOew7/0S790apg9ybDavlhaWuILL7yQn/vc5/InP/lJvu+++/gTn/gE33PPPaeY8xMPq+2L97///bxlyxZ+//vfz/fddx9/7GMf4+3bt/NrXvOaU8z5iYfbb7+dX//61/OHPvQhBsAf/vCHj4h/77338g/90A/xvn37+Etf+hK/613v4q7r+I477jg1DE+wKlgXQv6iiy7iq6++2n/3fc87duzgG264YRT/BS94AV9++eXVvd27d/Nv/MZvnFQ+TxWstj9aWCwWfOaZZ/L73ve+k8XiKYNj6YvFYsGXXHIJ/+mf/ilfddVV60bIr7Yv/uRP/oTPP/98Xl5ePlUsnjJYbV9cffXV/HM/93PVvX379vGznvWsk8rnqYajEfKvfe1r+SlPeUp1b+/evbxnz56TyNkExwpr3l1vx/zFI/mO5pi/iA/IMX8r4a8lOJb+aOEHP/gB5vM5HvOYx5wsNk8JHGtf/P7v/z7OPfdcvOxlLzsVbJ4SOJa++Ju/+RtcfPHFuPrqq3HeeefhqU99Kt761rei7/tTxfZJgWPpi0suuQSf//zn3aV/77334vbbb8dzn/vcU8Lz6QTref5cj7DmM96djGP+1jIcS3+08Hu/93vYsWPH4ENea3AsffHJT34Sf/Znf4Z77rnnFHB46uBY+uLee+/F3//93+NXfuVXcPvtt+NrX/saXvGKV2A+n2P//v2ngu2TAsfSFy9+8Yvxne98B89+9rPBzFgsFvjN3/xNXHvttaeC5dMKVpo/Dx06hIceeghnnHHGI8TZBGOw5i35CU4svO1tb8OBAwfw4Q9/+JSf5vRIwwMPPIArr7wS73nPe3DOOec80uw84pBzxrnnnotbbrkFT3/607F37168/vWvx8033/xIs3bK4ROf+ATe+ta34o//+I/xhS98AR/60Ifw0Y9+FG9+85sfadYmmOCIsOYt+ZNxzN9ahmPpD4N3vOMdeNvb3oa/+7u/w0/91E+dTDZPCay2L77+9a/jP/7jP/C85z3P7+WcAQCz2Qxf/epX8YQnPOHkMn2S4FjGxfbt27Fp0yZ0Xef3fvInfxIHDx7E8vIyNm/efFJ5PllwLH3xhje8AVdeeSV+7dd+DQDwtKc9zfOKv/71r6/ykq93WGn+POussyYr/jSENT8yT8Yxf2sZjqU/AODtb3873vzmN+OOO+7AhRdeeCpYPemw2r548pOfjC9+8Yu45557/N8v/uIv4md/9mdxzz33YOfOnaeS/RMKxzIunvWsZ+FrX/uaKzoA8G//9m/Yvn37mhXwwLH1xQ9+8IOBIDflhzfY8R/ref5cl/BIR/6dCDhw4ABv2bKF3/ve9/KXvvQlfvnLX86PetSj+ODBg8zMfOWVV/I111zj+J/61Kd4NpvxO97xDv7yl7/M+/fvX3db6FbTH29729t48+bN/Nd//df8X//1X/7vgQceeKSacMJgtX3RwnqKrl9tX3zjG9/gM888k3/rt36Lv/rVr/JHPvIRPvfcc/ktb3nLI9WEEwar7Yv9+/fzmWeeyX/5l3/J9957L//t3/4tP+EJT+AXvOAFj1QTThg88MADfPfdd/Pdd9/NAPgP//AP+e677+b//M//ZGbma665hq+88krHty10v/u7v8tf/vKX+aabbpq20J3GsC6EPDPzu971Lv6xH/sx3rx5M1900UX8T//0T1526aWX8lVXXVXh/9Vf/RU/6UlP4s2bN/NTnvIU/uhHP3qKOT65sJr++PEf/3EGMPi3f//+U8/4SYDVjo0I60nIM6++Lz796U/z7t27ecuWLXz++efzH/zBH/BisTjFXJ8cWE1fzOdzfuMb38hPeMITeOvWrbxz505+xStewf/7v/976hk/wfAP//APo9+/tf+qq67iSy+9dPDMrl27ePPmzXz++efzX/zFX5xyvic4OpiOmp1gggkmmGCCdQprfk1+ggkmmGCCCSYYh0nITzDBBBNMMME6hUnITzDBBBNMMME6hUnITzDBBBNMMME6hUnITzDBBBNMMME6hUnITzDBBBNMMME6hUnITzDBBBNMMME6hUnITzDBBBNMMME6hUnITzDBBBNMMME6hUnITzDBBBNMMME6hUnITzDBBBNMMME6hUnITzDBBBNMMME6hf8f8WM7DuCh0FMAAAAASUVORK5CYII=\n", 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