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 "cells": [
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   "cell_type": "markdown",
   "id": "eecc8e70",
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    "# Nested turbine type specific optimization\n",
    "\n",
    "In this example, a nested optimization is performed accounting for the layout and the turbine types. The inner layer solves the layout optimization problem with a gradient-based solver, and the outer layer changes the turbine types with a gradient-free (random-search) solver. In this case, the boundaries are not specific to the wind turbine and a spacing and circular boundary contraints are used.\n",
    "\n",
    "The objective function is to minimize the wake losses produced by neighbouring wind turbines."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7069995",
   "metadata": {},
   "source": [
    "**Install TOPFARM if needed**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "46898a59",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Install TopFarm if needed\n",
    "import importlib\n",
    "if not importlib.util.find_spec(\"topfarm\"):\n",
    "    !pip install git+https://gitlab.windenergy.dtu.dk/TOPFARM/TopFarm2.git"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "edc4ac30",
   "metadata": {},
   "source": [
    "Loading Python dependencies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "25330568",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be864b04",
   "metadata": {},
   "source": [
    "Loading PyWake dependencies. We import the two turbines to use (Vestas V80 and the DTU 10MW reference turbine) as well as the site (IEA37) and the wake deficit model (IEA37SimpleBastankhahGaussian) to use for the optimization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "1a591c67",
   "metadata": {},
   "outputs": [],
   "source": [
    "from py_wake.examples.data.iea37._iea37 import IEA37Site\n",
    "from py_wake.wind_turbines import WindTurbines\n",
    "from py_wake.examples.data.hornsrev1 import V80\n",
    "from py_wake.examples.data.dtu10mw import DTU10MW\n",
    "from py_wake.deficit_models.gaussian import IEA37SimpleBastankhahGaussian\n",
    "from py_wake.utils.gradients import autograd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "332e3aff",
   "metadata": {},
   "source": [
    "Loading TOPFARM dependencies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "ac0a5e80",
   "metadata": {},
   "outputs": [],
   "source": [
    "import topfarm\n",
    "from topfarm.cost_models.cost_model_wrappers import CostModelComponent\n",
    "from topfarm import TopFarmProblem\n",
    "from topfarm.easy_drivers import EasyRandomSearchDriver, EasyScipyOptimizeDriver\n",
    "from topfarm.drivers.random_search_driver import randomize_turbine_type\n",
    "from topfarm.constraint_components.boundary import CircleBoundaryConstraint\n",
    "from topfarm.constraint_components.spacing import SpacingConstraint, SpacingTypeConstraint\n",
    "from topfarm.plotting import XYPlotComp, TurbineTypePlotComponent, NoPlot"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d25b981",
   "metadata": {},
   "source": [
    "We first define the site and initial positions for the optimization problem."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "38324412",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Site definition\n",
    "n_wt = 9\n",
    "site = IEA37Site(n_wt, ti=0.05)\n",
    "wt_x = site.initial_position[:, 0]\n",
    "wt_y = site.initial_position[:, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "e89cc2d9",
   "metadata": {},
   "outputs": [
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray (sector: 12)&gt;\n",
       "array([0.03275556, 0.03466667, 0.04457778, 0.08075556, 0.10533333,\n",
       "       0.15191111, 0.08742222, 0.0512    , 0.08915556, 0.23697778,\n",
       "       0.05386667, 0.03422222])\n",
       "Coordinates:\n",
       "  * sector   (sector) int64 0 1 2 3 4 5 6 7 8 9 10 11\n",
       "Attributes:\n",
       "    description:  Probability of wind flow case (i.e. wind direction and wind...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>sector</span>: 12</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-60caa4aa-3682-4db2-b317-fe6c0dffaf8d' class='xr-array-in' type='checkbox' checked><label for='section-60caa4aa-3682-4db2-b317-fe6c0dffaf8d' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0.03276 0.03467 0.04458 0.08076 ... 0.08916 0.237 0.05387 0.03422</span></div><div class='xr-array-data'><pre>array([0.03275556, 0.03466667, 0.04457778, 0.08075556, 0.10533333,\n",
       "       0.15191111, 0.08742222, 0.0512    , 0.08915556, 0.23697778,\n",
       "       0.05386667, 0.03422222])</pre></div></div></li><li class='xr-section-item'><input id='section-e343c662-28e8-4ae6-941a-381f2109088f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e343c662-28e8-4ae6-941a-381f2109088f' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>sector</span></div><div class='xr-var-dims'>(sector)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 7 8 9 10 11</div><input id='attrs-6270c17d-2de1-44b7-8e3b-29cb372e63af' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6270c17d-2de1-44b7-8e3b-29cb372e63af' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5393899a-9d8b-40d0-b16d-2610e4ab7905' class='xr-var-data-in' type='checkbox'><label for='data-5393899a-9d8b-40d0-b16d-2610e4ab7905' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11], dtype=int64)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e6916dd8-834b-48d3-9d70-a53accdcf5e3' class='xr-section-summary-in' type='checkbox'  ><label for='section-e6916dd8-834b-48d3-9d70-a53accdcf5e3' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>sector</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-0a0f52f8-4358-4dfc-bec8-3f1607d97fbc' class='xr-index-data-in' type='checkbox'/><label for='index-0a0f52f8-4358-4dfc-bec8-3f1607d97fbc' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], dtype=&#x27;int64&#x27;, name=&#x27;sector&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-84649c4f-576e-4e47-832c-d7827a71737f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-84649c4f-576e-4e47-832c-d7827a71737f' class='xr-section-summary' >Attributes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>Probability of wind flow case (i.e. wind direction and wind speed)</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray (sector: 12)>\n",
       "array([0.03275556, 0.03466667, 0.04457778, 0.08075556, 0.10533333,\n",
       "       0.15191111, 0.08742222, 0.0512    , 0.08915556, 0.23697778,\n",
       "       0.05386667, 0.03422222])\n",
       "Coordinates:\n",
       "  * sector   (sector) int64 0 1 2 3 4 5 6 7 8 9 10 11\n",
       "Attributes:\n",
       "    description:  Probability of wind flow case (i.e. wind direction and wind..."
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot wind rose \n",
    "plt.figure(dpi=100)\n",
    "site.plot_wd_distribution()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70a9df7c",
   "metadata": {},
   "source": [
    "We now set up the two turbine types and define their basic paramenters such as diameter, hub height and CT curve. A `TurbineTypes` class is created to merge the turbine objects to then feed into the wind farm model as `WindTurbines` object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "7317e20a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Turbine types\n",
    "turb_types = [0, 1]\n",
    "names = ['V80', 'DTU10MW']\n",
    "diameters = [V80().diameter(), DTU10MW().diameter()]\n",
    "hub_heights = [V80().hub_height(), DTU10MW().hub_height()]\n",
    "powerCtFunctions = [V80().powerCtFunction, DTU10MW().powerCtFunction]\n",
    "n_types = len(turb_types)\n",
    "\n",
    "\n",
    "# Merge turbine objects\n",
    "class TurbineTypes(WindTurbines):\n",
    "    def __init__(self):\n",
    "        super().__init__(names, diameters, hub_heights, powerCtFunctions)\n",
    "\n",
    "windTurbines = TurbineTypes()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "f90e23d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[80, 178.3]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "3ac1bfd0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Wind farm model \n",
    "wfm = IEA37SimpleBastankhahGaussian(site, windTurbines)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6fc8582e",
   "metadata": {},
   "source": [
    "We now set up the objective function and the `CostModelComponent`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "6afcb8dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Objective function and cost model component\n",
    "\n",
    "def wake_loss(x, y, type, **kwargs):\n",
    "    '''Calculate the wake losses in %.'''\n",
    "    simres = wfm(x, y, type=np.asarray(type, dtype=int), **kwargs)\n",
    "    aep_ref = simres.aep().values.sum()\n",
    "    aep_nowake = simres.aep(with_wake_loss=False).values.sum()\n",
    "    loss = 100 * (aep_nowake - aep_ref) / aep_nowake\n",
    "    return loss\n",
    "\n",
    "\n",
    "cost_comp = CostModelComponent(input_keys=[('x', wt_x),('y', wt_y)],\n",
    "                                          n_wt=n_wt,\n",
    "                                          cost_function=wake_loss,\n",
    "                                          objective=True,\n",
    "                                          maximize=False,\n",
    "                                          output_keys=[('Wake losses', 0)],\n",
    "                                          output_unit='%',\n",
    "                                          additional_input= [(topfarm.type_key, np.ones(n_wt, dtype=int))]\n",
    "                                          )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9bcd55ce",
   "metadata": {},
   "source": [
    "#### Optimization set up"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "332cc516",
   "metadata": {},
   "source": [
    "The first layer of the optimization takes the turbine positions and design variables as well as the spacing and boundary constraints."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "4832e833",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO: checking out_of_order\n",
      "INFO: checking system\n",
      "INFO: checking solvers\n",
      "INFO: checking dup_inputs\n",
      "INFO: checking missing_recorders\n",
      "INFO: checking unserializable_options\n",
      "INFO: checking comp_has_no_outputs\n",
      "INFO: checking auto_ivc_warnings\n"
     ]
    }
   ],
   "source": [
    "# Layout component (1st layer of the optimization)\n",
    "layout_problem = TopFarmProblem(design_vars={topfarm.x_key: wt_x,\n",
    "                                             topfarm.y_key: wt_y},\n",
    "                                cost_comp=cost_comp,\n",
    "                                driver=EasyScipyOptimizeDriver(maxiter=100, tol=1e-2, disp=False),\n",
    "                                constraints=[SpacingTypeConstraint(3*np.asarray(diameters)),\n",
    "                                              CircleBoundaryConstraint([0, 0], 600)],\n",
    "                                plot_comp=NoPlot(),\n",
    "                                expected_cost=1,\n",
    "                                ext_vars={topfarm.type_key: np.zeros(n_wt, dtype=int)}, \n",
    "                                )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44c0b2fd",
   "metadata": {},
   "source": [
    "The second layer utilizes the optimized layout to then find the optimal turbine types that satisfy the objective function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "21878419",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO: checking out_of_order\n",
      "INFO: checking system\n",
      "INFO: checking solvers\n",
      "INFO: checking dup_inputs\n",
      "INFO: checking missing_recorders\n",
      "INFO: checking unserializable_options\n",
      "INFO: checking comp_has_no_outputs\n",
      "INFO: checking auto_ivc_warnings\n"
     ]
    }
   ],
   "source": [
    "# topfarm main problem - uses the layout problem as component and turbine type as design variables (2nd layer)\n",
    "problem = TopFarmProblem(design_vars={topfarm.type_key: ([1, 0, 1, 0, 1, 0, 1, 0 ,1], 0, int(n_types-1))},\n",
    "                          cost_comp=layout_problem,\n",
    "                          driver=EasyRandomSearchDriver(randomize_turbine_type, max_iter=100, disp=False)\n",
    "                          )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "aca9e29c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO: checking out_of_order\n",
      "INFO: checking system\n",
      "INFO: checking solvers\n",
      "INFO: checking dup_inputs\n",
      "INFO: checking missing_recorders\n",
      "INFO: checking unserializable_options\n",
      "INFO: checking comp_has_no_outputs\n",
      "INFO: checking auto_ivc_warnings\n",
      "INFO: checking out_of_order\n",
      "INFO: checking system\n",
      "INFO: checking solvers\n",
      "INFO: checking dup_inputs\n",
      "INFO: checking missing_recorders\n",
      "INFO: checking unserializable_options\n",
      "INFO: checking comp_has_no_outputs\n",
      "INFO: checking auto_ivc_warnings\n",
      "INFO: checking out_of_order\n",
      "INFO: checking system\n",
      "INFO: checking solvers\n",
      "INFO: checking dup_inputs\n",
      "INFO: checking missing_recorders\n",
      "INFO: checking unserializable_options\n",
      "INFO: checking comp_has_no_outputs\n",
      "INFO: checking auto_ivc_warnings\n",
      "INFO: checking out_of_order\n",
      "INFO: checking system\n",
      "INFO: checking solvers\n",
      "INFO: checking dup_inputs\n",
      "INFO: checking missing_recorders\n",
      "INFO: checking unserializable_options\n",
      "INFO: checking comp_has_no_outputs\n",
      "INFO: checking auto_ivc_warnings\n",
      "INFO: checking out_of_order\n",
      "INFO: checking system\n",
      "INFO: checking solvers\n",
      "INFO: checking dup_inputs\n",
      "INFO: checking missing_recorders\n",
      "INFO: checking unserializable_options\n",
      "INFO: checking comp_has_no_outputs\n",
      "INFO: checking auto_ivc_warnings\n",
      "INFO: checking out_of_order\n",
      "INFO: checking system\n",
      "INFO: checking solvers\n",
      "INFO: checking dup_inputs\n",
      "INFO: checking missing_recorders\n",
      "INFO: checking unserializable_options\n",
      "INFO: checking comp_has_no_outputs\n",
      "INFO: checking auto_ivc_warnings\n",
      "INFO: checking out_of_order\n",
      "INFO: checking system\n",
      "INFO: checking solvers\n",
      "INFO: checking dup_inputs\n",
      "INFO: checking missing_recorders\n",
      "INFO: checking unserializable_options\n",
      "INFO: checking comp_has_no_outputs\n",
      "INFO: checking auto_ivc_warnings\n",
      "INFO: checking out_of_order\n",
      "INFO: checking system\n",
      "INFO: checking solvers\n",
      "INFO: checking dup_inputs\n",
      "INFO: checking missing_recorders\n",
      "INFO: checking unserializable_options\n",
      "INFO: checking comp_has_no_outputs\n",
      "INFO: checking auto_ivc_warnings\n",
      "INFO: checking out_of_order\n",
      "INFO: checking system\n",
      "INFO: checking solvers\n",
      "INFO: checking dup_inputs\n",
      "INFO: checking missing_recorders\n",
      "INFO: checking unserializable_options\n",
      "INFO: checking comp_has_no_outputs\n",
      "INFO: checking auto_ivc_warnings\n",
      "INFO: checking out_of_order\n",
      "INFO: checking system\n",
      "INFO: checking solvers\n",
      "INFO: checking dup_inputs\n",
      "INFO: checking missing_recorders\n",
      "INFO: checking unserializable_options\n",
      "INFO: checking comp_has_no_outputs\n",
      "INFO: checking auto_ivc_warnings\n",
      "INFO: checking out_of_order\n",
      "INFO: checking system\n",
      "INFO: checking solvers\n",
      "INFO: checking dup_inputs\n",
      "INFO: checking missing_recorders\n",
      "INFO: checking unserializable_options\n",
      "INFO: checking comp_has_no_outputs\n",
      "INFO: checking auto_ivc_warnings\n",
      "INFO: checking out_of_order\n",
      "INFO: checking system\n",
      "INFO: checking solvers\n",
      "INFO: checking dup_inputs\n",
      "INFO: checking missing_recorders\n",
      "INFO: checking unserializable_options\n",
      "INFO: checking comp_has_no_outputs\n",
      "INFO: checking auto_ivc_warnings\n",
      "INFO: checking out_of_order\n",
      "INFO: checking system\n",
      "INFO: checking solvers\n",
      "INFO: checking dup_inputs\n",
      "INFO: checking missing_recorders\n",
      "INFO: checking unserializable_options\n",
      "INFO: checking comp_has_no_outputs\n",
      "INFO: checking auto_ivc_warnings\n",
      "INFO: checking out_of_order\n",
      "INFO: checking system\n",
      "INFO: checking solvers\n",
      "INFO: checking dup_inputs\n",
      "INFO: checking missing_recorders\n",
      "INFO: checking unserializable_options\n",
      "INFO: checking comp_has_no_outputs\n",
      "INFO: checking auto_ivc_warnings\n",
      "INFO: checking out_of_order\n",
      "INFO: checking system\n",
      "INFO: checking solvers\n",
      "INFO: checking dup_inputs\n",
      "INFO: checking missing_recorders\n",
      "INFO: checking unserializable_options\n",
      "INFO: checking comp_has_no_outputs\n",
      "INFO: checking auto_ivc_warnings\n",
      "INFO: checking out_of_order\n",
      "INFO: checking system\n",
      "INFO: checking solvers\n",
      "INFO: checking dup_inputs\n",
      "INFO: checking missing_recorders\n",
      "INFO: checking unserializable_options\n",
      "INFO: checking comp_has_no_outputs\n",
      "INFO: checking auto_ivc_warnings\n",
      "INFO: checking out_of_order\n",
      "INFO: checking system\n",
      "INFO: checking solvers\n",
      "INFO: checking dup_inputs\n",
      "INFO: checking missing_recorders\n",
      "INFO: checking unserializable_options\n",
      "INFO: checking comp_has_no_outputs\n",
      "INFO: checking auto_ivc_warnings\n",
      "INFO: checking out_of_order\n",
      "INFO: checking system\n",
      "INFO: checking solvers\n",
      "INFO: checking dup_inputs\n",
      "INFO: checking missing_recorders\n",
      "INFO: checking unserializable_options\n",
      "INFO: checking comp_has_no_outputs\n",
      "INFO: checking auto_ivc_warnings\n",
      "INFO: checking out_of_order\n",
      "INFO: checking system\n",
      "INFO: checking solvers\n",
      "INFO: checking dup_inputs\n",
      "INFO: checking missing_recorders\n",
      "INFO: checking unserializable_options\n",
      "INFO: checking comp_has_no_outputs\n",
      "INFO: checking auto_ivc_warnings\n",
      "INFO: checking out_of_order\n",
      "INFO: checking system\n",
      "INFO: checking solvers\n",
      "INFO: checking dup_inputs\n",
      "INFO: checking missing_recorders\n",
      "INFO: checking unserializable_options\n",
      "INFO: checking comp_has_no_outputs\n",
      "INFO: checking auto_ivc_warnings\n",
      "INFO: checking out_of_order\n",
      "INFO: checking system\n",
      "INFO: checking solvers\n",
      "INFO: checking dup_inputs\n",
      "INFO: checking missing_recorders\n",
      "INFO: checking unserializable_options\n",
      "INFO: checking comp_has_no_outputs\n",
      "INFO: checking auto_ivc_warnings\n",
      "INFO: checking out_of_order\n",
      "INFO: checking system\n",
      "INFO: checking solvers\n",
      "INFO: checking dup_inputs\n",
      "INFO: checking missing_recorders\n",
      "INFO: checking unserializable_options\n",
      "INFO: checking comp_has_no_outputs\n",
      "INFO: checking auto_ivc_warnings\n",
      "INFO: checking out_of_order\n",
      "INFO: checking system\n",
      "INFO: checking solvers\n",
      "INFO: checking dup_inputs\n",
      "INFO: checking missing_recorders\n",
      "INFO: checking unserializable_options\n",
      "INFO: checking comp_has_no_outputs\n",
      "INFO: checking auto_ivc_warnings\n",
      "INFO: checking out_of_order\n",
      "INFO: checking system\n",
      "INFO: checking solvers\n",
      "INFO: checking dup_inputs\n",
      "INFO: checking missing_recorders\n",
      "INFO: checking unserializable_options\n",
      "INFO: checking comp_has_no_outputs\n",
      "INFO: checking auto_ivc_warnings\n",
      "INFO: checking out_of_order\n",
      "INFO: checking system\n",
      "INFO: checking solvers\n",
      "INFO: checking dup_inputs\n",
      "INFO: checking missing_recorders\n",
      "INFO: checking unserializable_options\n",
      "INFO: checking comp_has_no_outputs\n",
      "INFO: checking auto_ivc_warnings\n",
      "INFO: checking out_of_order\n",
      "INFO: checking system\n",
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      "INFO: checking dup_inputs\n",
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      "INFO: checking unserializable_options\n",
      "INFO: checking comp_has_no_outputs\n",
      "INFO: checking auto_ivc_warnings\n",
      "INFO: checking out_of_order\n",
      "INFO: checking system\n",
      "INFO: checking solvers\n",
      "INFO: checking dup_inputs\n",
      "INFO: checking missing_recorders\n",
      "INFO: checking unserializable_options\n",
      "INFO: checking comp_has_no_outputs\n",
      "INFO: checking auto_ivc_warnings\n",
      "INFO: checking out_of_order\n",
      "INFO: checking system\n",
      "INFO: checking solvers\n",
      "INFO: checking dup_inputs\n",
      "INFO: checking missing_recorders\n",
      "INFO: checking unserializable_options\n",
      "INFO: checking comp_has_no_outputs\n",
      "INFO: checking auto_ivc_warnings\n",
      "INFO: checking out_of_order\n",
      "INFO: checking system\n",
      "INFO: checking solvers\n",
      "INFO: checking dup_inputs\n",
      "INFO: checking missing_recorders\n",
      "INFO: checking unserializable_options\n",
      "INFO: checking comp_has_no_outputs\n",
      "INFO: checking auto_ivc_warnings\n",
      "INFO: checking out_of_order\n",
      "INFO: checking system\n",
      "INFO: checking solvers\n",
      "INFO: checking dup_inputs\n",
      "INFO: checking missing_recorders\n",
      "INFO: checking unserializable_options\n",
      "INFO: checking comp_has_no_outputs\n",
      "INFO: checking auto_ivc_warnings\n",
      "INFO: checking out_of_order\n",
      "INFO: checking system\n",
      "INFO: checking solvers\n",
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      "INFO: checking unserializable_options\n",
      "INFO: checking comp_has_no_outputs\n",
      "INFO: checking auto_ivc_warnings\n",
      "INFO: checking out_of_order\n",
      "INFO: checking system\n",
      "INFO: checking solvers\n",
      "INFO: checking dup_inputs\n",
      "INFO: checking missing_recorders\n",
      "INFO: checking unserializable_options\n",
      "INFO: checking comp_has_no_outputs\n",
      "INFO: checking auto_ivc_warnings\n",
      "INFO: checking out_of_order\n",
      "INFO: checking system\n",
      "INFO: checking solvers\n",
      "INFO: checking dup_inputs\n",
      "INFO: checking missing_recorders\n",
      "INFO: checking unserializable_options\n",
      "INFO: checking comp_has_no_outputs\n",
      "INFO: checking auto_ivc_warnings\n",
      "INFO: checking out_of_order\n",
      "INFO: checking system\n",
      "INFO: checking solvers\n",
      "INFO: checking dup_inputs\n",
      "INFO: checking missing_recorders\n",
      "INFO: checking unserializable_options\n",
      "INFO: checking comp_has_no_outputs\n",
      "INFO: checking auto_ivc_warnings\n",
      "INFO: checking out_of_order\n",
      "INFO: checking system\n",
      "INFO: checking solvers\n",
      "INFO: checking dup_inputs\n",
      "INFO: checking missing_recorders\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO: checking unserializable_options\n",
      "INFO: checking comp_has_no_outputs\n",
      "INFO: checking auto_ivc_warnings\n",
      "INFO: checking out_of_order\n",
      "INFO: checking system\n",
      "INFO: checking solvers\n",
      "INFO: checking dup_inputs\n",
      "INFO: checking missing_recorders\n",
      "INFO: checking unserializable_options\n",
      "INFO: checking comp_has_no_outputs\n",
      "INFO: checking auto_ivc_warnings\n",
      "INFO: checking out_of_order\n",
      "INFO: checking system\n",
      "INFO: checking solvers\n",
      "INFO: checking dup_inputs\n",
      "INFO: checking missing_recorders\n",
      "INFO: checking unserializable_options\n",
      "INFO: checking comp_has_no_outputs\n",
      "INFO: checking auto_ivc_warnings\n",
      "INFO: checking out_of_order\n",
      "INFO: checking system\n",
      "INFO: checking solvers\n",
      "INFO: checking dup_inputs\n",
      "INFO: checking missing_recorders\n",
      "INFO: checking unserializable_options\n",
      "INFO: checking comp_has_no_outputs\n",
      "INFO: checking auto_ivc_warnings\n",
      "INFO: checking out_of_order\n",
      "INFO: checking system\n",
      "INFO: checking solvers\n",
      "INFO: checking dup_inputs\n",
      "INFO: checking missing_recorders\n",
      "INFO: checking unserializable_options\n",
      "INFO: checking comp_has_no_outputs\n",
      "INFO: checking auto_ivc_warnings\n",
      "INFO: checking out_of_order\n",
      "INFO: checking system\n",
      "INFO: checking solvers\n",
      "INFO: checking dup_inputs\n",
      "INFO: checking missing_recorders\n",
      "INFO: checking unserializable_options\n",
      "INFO: checking comp_has_no_outputs\n",
      "INFO: checking auto_ivc_warnings\n",
      "INFO: checking out_of_order\n",
      "INFO: checking system\n",
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      "INFO: checking comp_has_no_outputs\n",
      "INFO: checking auto_ivc_warnings\n",
      "INFO: checking out_of_order\n",
      "INFO: checking system\n",
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      "INFO: checking auto_ivc_warnings\n",
      "INFO: checking out_of_order\n",
      "INFO: checking system\n",
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      "INFO: checking out_of_order\n",
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      "INFO: checking auto_ivc_warnings\n",
      "INFO: checking out_of_order\n",
      "INFO: checking system\n",
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      "INFO: checking unserializable_options\n",
      "INFO: checking comp_has_no_outputs\n",
      "INFO: checking auto_ivc_warnings\n",
      "INFO: checking out_of_order\n",
      "INFO: checking system\n",
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      "INFO: checking comp_has_no_outputs\n",
      "INFO: checking auto_ivc_warnings\n",
      "INFO: checking out_of_order\n",
      "INFO: checking system\n",
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      "INFO: checking out_of_order\n",
      "INFO: checking system\n",
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      "INFO: checking out_of_order\n",
      "INFO: checking system\n",
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      "INFO: checking auto_ivc_warnings\n",
      "INFO: checking out_of_order\n",
      "INFO: checking system\n",
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      "INFO: checking out_of_order\n",
      "INFO: checking system\n",
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      "INFO: checking out_of_order\n",
      "INFO: checking system\n",
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      "INFO: checking out_of_order\n",
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      "INFO: checking out_of_order\n",
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      "INFO: checking comp_has_no_outputs\n",
      "INFO: checking auto_ivc_warnings\n",
      "INFO: checking out_of_order\n",
      "INFO: checking system\n",
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      "INFO: checking out_of_order\n",
      "INFO: checking system\n",
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      "INFO: checking auto_ivc_warnings\n",
      "INFO: checking out_of_order\n",
      "INFO: checking system\n",
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      "INFO: checking dup_inputs\n",
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      "INFO: checking unserializable_options\n",
      "INFO: checking comp_has_no_outputs\n",
      "INFO: checking auto_ivc_warnings\n",
      "INFO: checking out_of_order\n",
      "INFO: checking system\n",
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      "INFO: checking dup_inputs\n",
      "INFO: checking missing_recorders\n",
      "INFO: checking unserializable_options\n",
      "INFO: checking comp_has_no_outputs\n",
      "INFO: checking auto_ivc_warnings\n",
      "INFO: checking out_of_order\n",
      "INFO: checking system\n",
      "INFO: checking solvers\n",
      "INFO: checking dup_inputs\n",
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      "INFO: checking auto_ivc_warnings\n",
      "INFO: checking out_of_order\n",
      "INFO: checking system\n",
      "INFO: checking solvers\n",
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      "INFO: checking auto_ivc_warnings\n",
      "INFO: checking out_of_order\n",
      "INFO: checking system\n",
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      "INFO: checking auto_ivc_warnings\n",
      "INFO: checking out_of_order\n",
      "INFO: checking system\n",
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      "INFO: checking auto_ivc_warnings\n",
      "INFO: checking out_of_order\n",
      "INFO: checking system\n",
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      "INFO: checking out_of_order\n",
      "INFO: checking system\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO: checking out_of_order\n",
      "INFO: checking system\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO: checking unserializable_options\n",
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      "INFO: checking comp_has_no_outputs\n",
      "INFO: checking auto_ivc_warnings\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO: checking out_of_order\n",
      "INFO: checking system\n",
      "INFO: checking solvers\n",
      "INFO: checking dup_inputs\n",
      "INFO: checking missing_recorders\n",
      "INFO: checking unserializable_options\n",
      "INFO: checking comp_has_no_outputs\n",
      "INFO: checking auto_ivc_warnings\n",
      "INFO: checking out_of_order\n",
      "INFO: checking system\n",
      "INFO: checking solvers\n",
      "INFO: checking dup_inputs\n",
      "INFO: checking missing_recorders\n",
      "INFO: checking unserializable_options\n",
      "INFO: checking comp_has_no_outputs\n",
      "INFO: checking auto_ivc_warnings\n",
      "INFO: checking out_of_order\n",
      "INFO: checking system\n",
      "INFO: checking solvers\n",
      "INFO: checking dup_inputs\n",
      "INFO: checking missing_recorders\n",
      "INFO: checking unserializable_options\n",
      "INFO: checking comp_has_no_outputs\n",
      "INFO: checking auto_ivc_warnings\n",
      "INFO: checking out_of_order\n",
      "INFO: checking system\n",
      "INFO: checking solvers\n",
      "INFO: checking dup_inputs\n",
      "INFO: checking missing_recorders\n",
      "INFO: checking unserializable_options\n",
      "INFO: checking comp_has_no_outputs\n",
      "INFO: checking auto_ivc_warnings\n",
      "INFO: checking out_of_order\n",
      "INFO: checking system\n",
      "INFO: checking solvers\n",
      "INFO: checking dup_inputs\n",
      "INFO: checking missing_recorders\n",
      "INFO: checking unserializable_options\n",
      "INFO: checking comp_has_no_outputs\n",
      "INFO: checking auto_ivc_warnings\n",
      "INFO: checking out_of_order\n",
      "INFO: checking system\n",
      "INFO: checking solvers\n",
      "INFO: checking dup_inputs\n",
      "INFO: checking missing_recorders\n",
      "INFO: checking unserializable_options\n",
      "INFO: checking comp_has_no_outputs\n",
      "INFO: checking auto_ivc_warnings\n",
      "INFO: checking out_of_order\n",
      "INFO: checking system\n",
      "INFO: checking solvers\n",
      "INFO: checking dup_inputs\n",
      "INFO: checking missing_recorders\n",
      "INFO: checking unserializable_options\n",
      "INFO: checking comp_has_no_outputs\n",
      "INFO: checking auto_ivc_warnings\n",
      "INFO: checking out_of_order\n",
      "INFO: checking system\n",
      "INFO: checking solvers\n",
      "INFO: checking dup_inputs\n",
      "INFO: checking missing_recorders\n",
      "INFO: checking unserializable_options\n",
      "INFO: checking comp_has_no_outputs\n",
      "INFO: checking auto_ivc_warnings\n",
      "INFO: checking out_of_order\n",
      "INFO: checking system\n",
      "INFO: checking solvers\n",
      "INFO: checking dup_inputs\n",
      "INFO: checking missing_recorders\n",
      "INFO: checking unserializable_options\n",
      "INFO: checking comp_has_no_outputs\n",
      "INFO: checking auto_ivc_warnings\n",
      "INFO: checking out_of_order\n",
      "INFO: checking system\n",
      "INFO: checking solvers\n",
      "INFO: checking dup_inputs\n",
      "INFO: checking missing_recorders\n",
      "INFO: checking unserializable_options\n",
      "INFO: checking comp_has_no_outputs\n",
      "INFO: checking auto_ivc_warnings\n",
      "INFO: checking out_of_order\n",
      "INFO: checking system\n",
      "INFO: checking solvers\n",
      "INFO: checking dup_inputs\n",
      "INFO: checking missing_recorders\n",
      "INFO: checking unserializable_options\n",
      "INFO: checking comp_has_no_outputs\n",
      "INFO: checking auto_ivc_warnings\n",
      "INFO: checking out_of_order\n",
      "INFO: checking system\n",
      "INFO: checking solvers\n",
      "INFO: checking dup_inputs\n",
      "INFO: checking missing_recorders\n",
      "INFO: checking unserializable_options\n",
      "INFO: checking comp_has_no_outputs\n",
      "INFO: checking auto_ivc_warnings\n",
      "INFO: checking out_of_order\n",
      "INFO: checking system\n",
      "INFO: checking solvers\n",
      "INFO: checking dup_inputs\n",
      "INFO: checking missing_recorders\n",
      "INFO: checking unserializable_options\n",
      "INFO: checking comp_has_no_outputs\n",
      "INFO: checking auto_ivc_warnings\n",
      "INFO: checking out_of_order\n",
      "INFO: checking system\n",
      "INFO: checking solvers\n",
      "INFO: checking dup_inputs\n",
      "INFO: checking missing_recorders\n",
      "INFO: checking unserializable_options\n",
      "INFO: checking comp_has_no_outputs\n",
      "INFO: checking auto_ivc_warnings\n",
      "INFO: checking out_of_order\n",
      "INFO: checking system\n",
      "INFO: checking solvers\n",
      "INFO: checking dup_inputs\n",
      "INFO: checking missing_recorders\n",
      "INFO: checking unserializable_options\n",
      "INFO: checking comp_has_no_outputs\n",
      "INFO: checking auto_ivc_warnings\n",
      "INFO: checking out_of_order\n",
      "INFO: checking system\n",
      "INFO: checking solvers\n",
      "INFO: checking dup_inputs\n",
      "INFO: checking missing_recorders\n",
      "INFO: checking unserializable_options\n",
      "INFO: checking comp_has_no_outputs\n",
      "INFO: checking auto_ivc_warnings\n",
      "INFO: checking out_of_order\n",
      "INFO: checking system\n",
      "INFO: checking solvers\n",
      "INFO: checking dup_inputs\n",
      "INFO: checking missing_recorders\n",
      "INFO: checking unserializable_options\n",
      "INFO: checking comp_has_no_outputs\n",
      "INFO: checking auto_ivc_warnings\n",
      "INFO: checking out_of_order\n",
      "INFO: checking system\n",
      "INFO: checking solvers\n",
      "INFO: checking dup_inputs\n",
      "INFO: checking missing_recorders\n",
      "INFO: checking unserializable_options\n",
      "INFO: checking comp_has_no_outputs\n",
      "INFO: checking auto_ivc_warnings\n",
      "INFO: checking out_of_order\n",
      "INFO: checking system\n",
      "INFO: checking solvers\n",
      "INFO: checking dup_inputs\n",
      "INFO: checking missing_recorders\n",
      "INFO: checking unserializable_options\n",
      "INFO: checking comp_has_no_outputs\n",
      "INFO: checking auto_ivc_warnings\n",
      "INFO: checking out_of_order\n",
      "INFO: checking system\n",
      "INFO: checking solvers\n",
      "INFO: checking dup_inputs\n",
      "INFO: checking missing_recorders\n",
      "INFO: checking unserializable_options\n",
      "INFO: checking comp_has_no_outputs\n",
      "INFO: checking auto_ivc_warnings\n",
      "INFO: checking out_of_order\n",
      "INFO: checking system\n",
      "INFO: checking solvers\n",
      "INFO: checking dup_inputs\n",
      "INFO: checking missing_recorders\n",
      "INFO: checking unserializable_options\n",
      "INFO: checking comp_has_no_outputs\n",
      "INFO: checking auto_ivc_warnings\n",
      "INFO: checking out_of_order\n",
      "INFO: checking system\n",
      "INFO: checking solvers\n",
      "INFO: checking dup_inputs\n",
      "INFO: checking missing_recorders\n",
      "INFO: checking unserializable_options\n",
      "INFO: checking comp_has_no_outputs\n",
      "INFO: checking auto_ivc_warnings\n",
      "INFO: checking out_of_order\n",
      "INFO: checking system\n",
      "INFO: checking solvers\n",
      "INFO: checking dup_inputs\n",
      "INFO: checking missing_recorders\n",
      "INFO: checking unserializable_options\n",
      "INFO: checking comp_has_no_outputs\n",
      "INFO: checking auto_ivc_warnings\n",
      "INFO: checking out_of_order\n",
      "INFO: checking system\n",
      "INFO: checking solvers\n",
      "INFO: checking dup_inputs\n",
      "INFO: checking missing_recorders\n",
      "INFO: checking unserializable_options\n",
      "INFO: checking comp_has_no_outputs\n",
      "INFO: checking auto_ivc_warnings\n",
      "INFO: checking out_of_order\n",
      "INFO: checking system\n",
      "INFO: checking solvers\n",
      "INFO: checking dup_inputs\n",
      "INFO: checking missing_recorders\n",
      "INFO: checking unserializable_options\n",
      "INFO: checking comp_has_no_outputs\n",
      "INFO: checking auto_ivc_warnings\n",
      "INFO: checking out_of_order\n",
      "INFO: checking system\n",
      "INFO: checking solvers\n",
      "INFO: checking dup_inputs\n",
      "INFO: checking missing_recorders\n",
      "INFO: checking unserializable_options\n",
      "INFO: checking comp_has_no_outputs\n",
      "INFO: checking auto_ivc_warnings\n",
      "INFO: checking out_of_order\n",
      "INFO: checking system\n",
      "INFO: checking solvers\n",
      "INFO: checking dup_inputs\n",
      "INFO: checking missing_recorders\n",
      "INFO: checking unserializable_options\n",
      "INFO: checking comp_has_no_outputs\n",
      "INFO: checking auto_ivc_warnings\n",
      "INFO: checking out_of_order\n",
      "INFO: checking system\n",
      "INFO: checking solvers\n",
      "INFO: checking dup_inputs\n",
      "INFO: checking missing_recorders\n",
      "INFO: checking unserializable_options\n",
      "INFO: checking comp_has_no_outputs\n",
      "INFO: checking auto_ivc_warnings\n",
      "INFO: checking out_of_order\n",
      "INFO: checking system\n",
      "INFO: checking solvers\n",
      "INFO: checking dup_inputs\n",
      "INFO: checking missing_recorders\n",
      "INFO: checking unserializable_options\n",
      "INFO: checking comp_has_no_outputs\n",
      "INFO: checking auto_ivc_warnings\n",
      "INFO: checking out_of_order\n",
      "INFO: checking system\n",
      "INFO: checking solvers\n",
      "INFO: checking dup_inputs\n",
      "INFO: checking missing_recorders\n",
      "INFO: checking unserializable_options\n",
      "INFO: checking comp_has_no_outputs\n",
      "INFO: checking auto_ivc_warnings\n",
      "INFO: checking out_of_order\n",
      "INFO: checking system\n",
      "INFO: checking solvers\n",
      "INFO: checking dup_inputs\n",
      "INFO: checking missing_recorders\n",
      "INFO: checking unserializable_options\n",
      "INFO: checking comp_has_no_outputs\n",
      "INFO: checking auto_ivc_warnings\n",
      "INFO: checking out_of_order\n",
      "INFO: checking system\n",
      "INFO: checking solvers\n",
      "INFO: checking dup_inputs\n",
      "INFO: checking missing_recorders\n",
      "INFO: checking unserializable_options\n",
      "INFO: checking comp_has_no_outputs\n",
      "INFO: checking auto_ivc_warnings\n",
      "INFO: checking out_of_order\n",
      "INFO: checking system\n",
      "INFO: checking solvers\n",
      "INFO: checking dup_inputs\n",
      "INFO: checking missing_recorders\n",
      "INFO: checking unserializable_options\n",
      "INFO: checking comp_has_no_outputs\n",
      "INFO: checking auto_ivc_warnings\n",
      "INFO: checking out_of_order\n",
      "INFO: checking system\n",
      "INFO: checking solvers\n",
      "INFO: checking dup_inputs\n",
      "INFO: checking missing_recorders\n",
      "INFO: checking unserializable_options\n",
      "INFO: checking comp_has_no_outputs\n",
      "INFO: checking auto_ivc_warnings\n",
      "INFO: checking out_of_order\n",
      "INFO: checking system\n",
      "INFO: checking solvers\n",
      "INFO: checking dup_inputs\n",
      "INFO: checking missing_recorders\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO: checking unserializable_options\n",
      "INFO: checking comp_has_no_outputs\n",
      "INFO: checking auto_ivc_warnings\n",
      "INFO: checking out_of_order\n",
      "INFO: checking system\n",
      "INFO: checking solvers\n",
      "INFO: checking dup_inputs\n",
      "INFO: checking missing_recorders\n",
      "INFO: checking unserializable_options\n",
      "INFO: checking comp_has_no_outputs\n",
      "INFO: checking auto_ivc_warnings\n",
      "INFO: checking out_of_order\n",
      "INFO: checking system\n",
      "INFO: checking solvers\n",
      "INFO: checking dup_inputs\n",
      "INFO: checking missing_recorders\n",
      "INFO: checking unserializable_options\n",
      "INFO: checking comp_has_no_outputs\n",
      "INFO: checking auto_ivc_warnings\n",
      "INFO: checking out_of_order\n",
      "INFO: checking system\n",
      "INFO: checking solvers\n",
      "INFO: checking dup_inputs\n",
      "INFO: checking missing_recorders\n",
      "INFO: checking unserializable_options\n",
      "INFO: checking comp_has_no_outputs\n",
      "INFO: checking auto_ivc_warnings\n",
      "INFO: checking out_of_order\n",
      "INFO: checking system\n",
      "INFO: checking solvers\n",
      "INFO: checking dup_inputs\n",
      "INFO: checking missing_recorders\n",
      "INFO: checking unserializable_options\n",
      "INFO: checking comp_has_no_outputs\n",
      "INFO: checking auto_ivc_warnings\n",
      "INFO: checking out_of_order\n",
      "INFO: checking system\n",
      "INFO: checking solvers\n",
      "INFO: checking dup_inputs\n",
      "INFO: checking missing_recorders\n",
      "INFO: checking unserializable_options\n",
      "INFO: checking comp_has_no_outputs\n",
      "INFO: checking auto_ivc_warnings\n",
      "INFO: checking out_of_order\n",
      "INFO: checking system\n",
      "INFO: checking solvers\n",
      "INFO: checking dup_inputs\n",
      "INFO: checking missing_recorders\n",
      "INFO: checking unserializable_options\n",
      "INFO: checking comp_has_no_outputs\n",
      "INFO: checking auto_ivc_warnings\n",
      "INFO: checking out_of_order\n",
      "INFO: checking system\n",
      "INFO: checking solvers\n",
      "INFO: checking dup_inputs\n",
      "INFO: checking missing_recorders\n",
      "INFO: checking unserializable_options\n",
      "INFO: checking comp_has_no_outputs\n",
      "INFO: checking auto_ivc_warnings\n",
      "INFO: checking out_of_order\n",
      "INFO: checking system\n",
      "INFO: checking solvers\n",
      "INFO: checking dup_inputs\n",
      "INFO: checking missing_recorders\n",
      "INFO: checking unserializable_options\n",
      "INFO: checking comp_has_no_outputs\n",
      "INFO: checking auto_ivc_warnings\n",
      "INFO: checking out_of_order\n",
      "INFO: checking system\n",
      "INFO: checking solvers\n",
      "INFO: checking dup_inputs\n",
      "INFO: checking missing_recorders\n",
      "INFO: checking unserializable_options\n",
      "INFO: checking comp_has_no_outputs\n",
      "INFO: checking auto_ivc_warnings\n",
      "INFO: checking out_of_order\n",
      "INFO: checking system\n",
      "INFO: checking solvers\n",
      "INFO: checking dup_inputs\n",
      "INFO: checking missing_recorders\n",
      "INFO: checking unserializable_options\n",
      "INFO: checking comp_has_no_outputs\n",
      "INFO: checking auto_ivc_warnings\n",
      "INFO: checking out_of_order\n",
      "INFO: checking system\n",
      "INFO: checking solvers\n",
      "INFO: checking dup_inputs\n",
      "INFO: checking missing_recorders\n",
      "INFO: checking unserializable_options\n",
      "INFO: checking comp_has_no_outputs\n",
      "INFO: checking auto_ivc_warnings\n"
     ]
    }
   ],
   "source": [
    "# Run the optimization.\n",
    "cost, state, recorder = problem.optimize()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "87a358c8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0. 0. 1. 0. 0. 0. 0. 0. 0.]\n"
     ]
    }
   ],
   "source": [
    "print(state['type'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c12e8798",
   "metadata": {},
   "source": [
    "Given that wind turbine type 0 (V80) has significantly smaller diameter and hub height, the solver selects mostly that type. The type 1 causes larger wake losses due to larger rotor diameter. However, a sweet spot can exist that allows to minimize the wake based on the hub height difference of type 1. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "3a8893ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Z6aBU8D5HoMQFipyjxHkQJeFwoUVYyHXj5y/SvgwRC/q5yzroQYL/wjck8ZMOL1DIlTIuUKmU6yp6lHXL+fAqyTizGI4++hjcfPNNkWP41a9+Bc45vvCFL2CrrbbCBhtsgBtu+BnK5TL6+vpwww0/w9Zbb43NNtsMAPDEE49jr732jpRfmTt3Hl555RWsWrUq07kxMDAwMDAwSIZlWaa/bgN4/Qgtb1TMy1rms7e3N2KGCgCMsaDdgwVDuluEpPBmpXarEOm42m1Rkqp2M0oqQsWrQSfeSuEOw9Kj6rcrZBiz8MPduT+N+yHSSu1uxuBQEvFultodJ95J4dLhvKT1GxsU0KETb6V2q2gHGdavkWtf7c77irdSu1UKAKMyrNzlMkTdFTLkvNcRKLqSeJe5QK/LUXLle849uFz4YePVjyFJ7U5DI4MsQLLaHfnstzFsapx4I6Zoe4lqd5bLtXDhcfjPf/6Dhx56KJh288034jOfORyjR4/GyJEj8cADi3Hrrbegq6sDo0aNwD333I0///kuWJbMxFm6dCkmTpwY2a76vHTp0gxnxKAahP/7U++r0fvTwMDAwCArEke7WwLTXxvEIYTAjTfeiAULFgTXWOGYY47BueeeG3w+9NBDce211+L222/HG2+8gXvvvRfnn38+Dj300IB8DwYM6W4hFNnSw8wVktRutY6alkXtBhCZ5/qKJ1BJvHWirYi1w72AbLv+CJTriYCAKuKtIET/1O74elmJd1ytbWaYub5MGvHOqnbHoeeuqzBz21e6LUbBmBx40dXuDpui06bBwIrFCISIEe+yQFHlfrvcV7xFEKHg+kQkbeAiTe2uFmKurk3Wq56mdqtJvet68fAD9wIAHn7gXvSuW+eTZ08j0jL3O1S7NTIe6//1dZPU7q1mbIU99tgDN954AwDg3//+Nx5++GEcd9zxAIC+vj6ceOLx2GOPPfHYY0/g4YcfxbbbfhSHHnow+vr6Mh61gYGBgYHB+oV169bh7rvvBgDcfffdWLduXUv3N2PGDNNfG0RQq8znkiVLgs/nnXcevvzlL+O8887DNttsg+OPPx7z5s3Dj3/844FscgWMkVqLQGmyUssIAagHIQjgeYHS7cKTud1UQHgEoBSu4LApCcizIua6cRqjyaHVrhCwKIXDPdiM+MtEaQhTxmzCg0393G5KQYUHUAEmCODnInPPA0OlkVmi2VYG6OsJzwMlBMKrrrhy7XwBkojF28NFZdi92n7lfsP96cv0F/JYvOAeYJQA/vmnvhmYItLKrI57Hkpcqt0lLgK122YErn99hfBQFh5gA0WXAEUeLNfrurAoQZ4rEzwPQrUjMZQ72tasEEKS5nqGW9TyxH/jEYLXXn4JD9wrO/Atttoa7779Jv73pp9i733nYYutZoAQDx7UPSHfe57n54XLz3Lb+nsFL/jkIRrIvnDh8TjrrDPwox9djZtuujFS5/G2227Dm2++iUcffTwIS7r11tuwwQZj8fvf/x5HHnkkJk2ahGXLlkWOT32eNGlSHWfFwMDAwMBg6OOll17EnXf+GYQQbLPNtnjzzTdw7bVX46CDDsY222zbsv2a/tpARz1lPi3LwoUXXogLL7xwAFqWHUbpbiFUiHGSqRqQXe1WbuMKWUPMqyne8nPjYeY8ogL3X/FOmp4UZt7s/O7k9fuvdieFmcvPUVM1pXbLMHPm53hTdPiGairEnFISRDGUHYE+h6Poelhb4kGYeZkL9DocZd/5XjnnZ1W76x07yXLVo27i4f8ffrACm0yZii8uPBEHHvYpfOHYE7HxJlOx8sMVctu64o1omLmucEfea/O84L8ojvjcEaCU4rbbbsMvfvFzLFx4XDBw09cnc4D0gRz1WRlv7L77TDz88F/hOKHd8X333YutttoKY8eOzXBGDKrBlAwzMDAwGFpYvnw5Nt10M5x88qk4/PDP4uSTT8Wmm26GFStWtHS/n/vc50x/PYShHPEbfQ1HGNLdIiQ5RutQud1MewHpud0AfBJWHzNSRC1eVqyeMPNapmr9QQXBHqD87nB67WWahSRTNUoAm1E/xJyiwBgKFoPNCAoWrXAxVyHminj3OgJr/TJjPY6LYg1TtbR2JX3OEsFQ7xlTudwzP7E3DvnU4ejsGgEA6OrqwgGf/DR2nfmJGLn2w8srwsx1Qu4lkGwv4R0wYsQIfO5zn8c3vnEulixZggULjg3m7bff/li1ahVOP/00vPzyy3jxxRdx3HELYVkW5syZA0DWfszlcjjhhOPx4osv4v/+7//wgx98H2efvajOM2FgYGBgYDD0MWvWbBxxxOfQ1dUFQPbXRxzxOey996yW7tf01wbrGwzpbiFYQKzS1W5Ahh8z31wri9qtG6opJOV8uxrRVsQ7Kb877mbuCgE3IOAik6laf4yM0oh3GrLmd1cj3o2aqvVX7dZN1fQSYkrtZpQEarfNCDpzVKrdLByEUcTb5V6Q391TkjndZSECUzUhEJiqpbaxogZ56qINI0ntplr5MM3bPzJqrRPvaK53KG9XGLChttq98LjjsGrVKsydOy9S53HGjBn4/e//iOef/yf23HMmZs3aC0uWvI8777wbkydPBgCMHj0ad9/9F7z55hvYdded8ZWvfBnnn3+BqflpYGBgYLBeIp7GV2t6M3Hccceb/tpgvYHJ6W4RCCERMkiJzI+G8AKSJ3N+CRgADj9fmQJcpOd2K3KtHMmlCu5VEExFuF3uwfLXkeHmFPGsXJvK95b/lxIPgIBFZD6xRTxYVOZ1w9+ERYgkoDTMs86S360T5qRogKT86yDnW4QkMUt+dy0ktVffPxdeYmSB3o5a0PO74efIC08mk1tMnlOLEQiPQgggzyi4YChbAh22NEuzqSTcZfj11LmAy8N92IzAdgRYUd4nlk/wc0yAUhqo3RAktd3q/lQEPSnXWw24BD8afsJ0PG86Cfodp+d2E0/mZBMiCTQhUSfygDgTPwcfHjxC4N96keXVdsPG+NuOTZ45cyY4T2DjAPbff3/sv//+VY9l++23x0MPPVzjiA0MDAwMDAz6A9NfD12kRcFmXXc4wpDuFoIQSailcVo4PSTgch7nodkW9yTRc7m/nCcJb1kn3NwLQs7jYeMWpUEut4Ii4BbT1wlpkMPk3z5HMmqbenAF4FJJsFwvNFUDAOabWimSqxPgehTv+Ho6AVYmZ9WIdxxZjNXSDNNqmapVM1qr14SN+uyQ+ikEQghYlEAwgpyQpmo5LtXugk3hCA9F1/Mdz+W94QoPxbJk3muLAGBJQzVHIMc4cpzCcqWa7hIBSihoigN+3PSPEmgDQ6T6Na2DeIMAxINvfOYFyyuyrJPjSO428T8QAg9R8h0wdURN1RBOTmxcSMcNhhLkAFFj6xkYGBgYGBgYDFWY8PIBQlKYuYLK7QYQhJnHc7spIZHcbgVlqKZKUVWDCksGEISaq/eR/O6EMHM1D0BTa3cnGbKl5XdXW1chKdR8KIeZ6yXEqK9o52iY293hlw4rWCQYNFFQxLvscvQ5HGuKHD0ljnUOj9TuVukDWXK7szrRJ6VP1+I9+paDsHJCgrBzQuJL+dv11LbD/O54mHncVE1vnBcuYmBgYGBgYGBgYDDgMKS7RVDqqk6o41C53Uq5Vbnd6r1FJNm2iJbPzcLc71ru5Qo6sdSJN5Cc393niEo38xaaqmUh3pFpdbqZA9mJd9L8ZkAfZInU7vZN1Sz/utrKydyiQW53wXcy77AZLCodz1W7A+LtyPrda4oc3UWO3pipmqrznXZcSbndtdT7JOJdE7HcbqJeJPo+vlkvUEA9v0a3JNMCcWfzytzu9PYbGm5gYGBgYGBgYNB6mPDyFkKvoa3qc6u8Xskg/Bxu7mmh6ADzVG53GFouw8ZlPe2w9nZljreCDEOuJJaUqum187v1MPN47W4uY3zBIPPSs+Y4pyEp1LzZ+d1JNbzjSKoVrud2R9sUJatZwszjtbspBWwAnidN1YSHILy8wMLc7k6bytBx208fcGRuNzgAm6HoyFBzm9kousIPM5e1u5mKlvDTHATJntst2xzWFHdjy8cjt2uFmRMAngonT8jtVveiflcGLVFqtV7D2w8zJ/DgeUot9+ROguhzP5g8nGwwRCFL3NU/GNIfI0cDAwMDAwOD+tBof63WHY4wpLtFUEZUkniTxLBnBUXIhZAsQVe7IShcInOr9dxuVyOCYY63ojxhXnf8xlbkNY14q/xuFWZuU2nAppuqyTZ7VU3Vqh1rmjqdluMdz+/OgizGakmkPrK/DKZq9SCrqZrLNbVby+3utCmKjhyAkTngVJJtJ3RVo0U/ZJ0qx3u//jcToByghMk2JFDPeG633uZqCIh2HfndYVZ3NLdbpm/700mlqZo/1iNvPZ9Me4EZWzwPPKqaJ7fd5HYbGBgYGBgYGBi0Fia8vIWIq55ZSogxEpYNsyr+hrndKhQZqJ7LnURSFRFXoeZJ+d0VYeZVanfrAwr9dSSMr9+fMPP+5HcnzU9ep/byOpJqYqv7QF1fi1FZQiyW291pUxRsP/dbk+xd4aHscBQdmd/dU+JBmHnR5VjnuOhzOTj3ghJiWXK79Vun1mCHF3tT7SzoqdtE5XSrEmIIS4lpi/kx5LHa3BVh5uH8yhJiJrfbwMDAwMDAoL3gZfhn0B4wSvcAIIvaDYQlxOpVux0tzJxXCdlQ6rd0zK5UvPXQ63iYedHlKFgsEmbO/BD3ZoaZA6Hi3d8wcyCbo3kSaoWZ9xe62q3CzJknr6PwZAkxzjx02gyuJ+T5V2q3zVB2eUCE9RJiPZDXdy0VsBlHhy1V8QJjyFHh54+nlxBrVO0GKhXvWghiM4h0NQeiajdJ6ko8DwJyQMCDJ8vqAWBamDk8Emw3XkIs0ajNqN0GBgYGBgYGLcD6SoobrTai1h2OMKS7RVB5sDph0UuIJeV2QwACWr1u7tfHTsvtpopoh2HmkkzKEHNARPK6FVwhIsRbQarA2cPMuU/MWYywJJHfaoiUq4oRaJ1kwydb9YSZA7WJd1qYedC+GiXE6s3tTgszl6ZqMh9f+IMaOU+Gh4+wLZS4zO1ey0QQYm755eVc7qEIybx7ig4AGQHRaXPYjKDAXGnUR2X4erUSYqqN8bryQHJtdR06ra3GvfXcbqlve0FuN0GohksCHm4PQJD/LUPMffINAqqWDRqgxZf7bz3A5HYPYQgv3eyv1noGBgYGBgbNxvpKmg0GHoZ0txB6jWPdVE3OQ4R4h3WRpWLMgIbU7rihWhp04i3NsaL53ar+N1NKuE1BiZdYuztJ7a6XeCskKddJ+dbyfTa1G6id452spjduqlYLcfWY+qkHuqma7YeB5xlFiYW53QWLoM+msBzfydzhwXVUxNtiFDmHYk1REu08c2WeOKeBqRqlUbVb5dsnqd2AvJ955eQKKGKbJb87We3WjNEi9mrh9hW7VoRbqeJB7e6gBBmM2m1gYGBgYDBMYUizwVCBId0DgCS1O56DHCqLAKgHzkOH6yCn20tWu5WRmm6o5mRgR0G4OWgF8Vaku9LNXLJLKrwKUzXGshHQamZq8fUj5mr9CDMHKol3PWHm9ZqqZXEyB1ChdkdM1SiBYAQ5EVW7RxcYiq6Hsi1QdDgsRlF2BByHQwh5QFbJ9cuQ0SDMvGBxWIQix6hvqiZDzZNM1YCo2g1kCzGPoAbjVmo38eBT3kq1O9ijT6YjRml+0xgJw8wJwmuscrmN2m1gYGBgYNC+MMTZYH2AMVJrERQnUwotJSRC8NJM1RT0+t7qb1rdbpv5dbtZ1FxN1nQmFeHSLvciYed6HWdlrBbU7xYeuG+m5ggPZS5Sa3dzz2uaqVqFQZlWv1uJ+ZXO7OH7Rup3N9tUrRaCet3qPoiZqlnMf/n1unOMostmgdqds8MQc3WNORd+zW6OnqKL3pKLtSWBnlJYu1uZqgnPi5iq6YMVqSXFMv5i6MZqNU3V1HsSxloQP9RevSq2r5uoeaHRiECyqRo84ITjFiJnMeQsio5CDhtPnoR5c+fixhtugBACDy5+EIyRqq/Fixfj4osvwk477VDRpjfffBOMEfz9738HABSLRSxceCw+9rHtkMtZ+PSnP5V4DhYvXoxddtkJHR15fOQjW+Cmm26KzF+48FgwRnDKKSdXrHv66aeBMYKFC48FAFx33XUYPXokXDcs7tbT04N83sY++8yu2C9jBP/5z38S22VgYGBgYNAsZDEEU/9Uv8cYQT5vY6NYf7148eLwGSnlpfrrnXfasaItb775JixGI/31cQsXYoePbY98zsZnPv3pxGNYvHgxdt1lZ3R2FLDVR7bEzbH++riFC2ExilMT+uszTj8NFqM4buFCAMCPr7sOY0aPquivC/kc9tlnTsV+LUaHVH+t0sEafQ1HGNLdQsSJNyCV1WphzsrJPAj5DeosS2JtURJxulZO5gB8lVv+raXgAlHyHSfeXCBCvJPczOVfAdcnqIqAe16UyDWK+DZ0IpzkZp4FtRzNk4h3UsR+NcO6etqlD7Qw/9pS//rZTKreNiPosBjyjKHAfCfzHEWHzVDIMb+D0Uh32UWvT7h7ig7WFl3pZl7i6HVdrHNclLmAw4VvhBFtJ4sPBiQMCmVBRUh4EpYsgX3pxSBLlkCzNA+U7sDNPJwbdSX3B2E839DD8+8/nWx7XrjO3Hnz8Pa77+G1/7yBP/7pTsyeMxvnnHM2Djv0UOyxxx54770lweuIIz6HefMOiEzbY489Mh8/5xwdHR04/fQzse+++yUu88Ybb+DQQw/G7Nlz8Oyzf8dZZ52Nk046Affcc09kuSlTpuD//u929PX1BdOKxSL+939vw9SpU4Npc+bMQU9PD/72t78F0x5++GFMmjQJTz75JIrFYjB98eIHMXXqVEyfPj3zMbUa3JPfx7pfw7P/NjAwMBgweEveBy6+EN6S9+si0I06bM+bdwDefe99/Of1N/CnP1f21+++937wUv21Pq3+/rqA008/o2p/fdihh2DW7Nl45tnncOZZZ+Gkk05M6a//L6G//t9Ifz27Sn/9VBv01wb1w4SXDyDiebyqPneSqRrnXhiK7purufBgEQoXPFC7w9zuUOEGEOR4uxnUV5d7AWlTucEOlW3pdQQ6QRPDzLOYqqVBDzFPyyGW7UEkR1z4+ePyXMpz1t8w84p9JhqnVYaZJy1fb263Qpqpmiohxj0PnRZDiXN02Qwj8wK9ZeErt9wvM0ZRLsu8gnJZjpxafnmxVYq827IMWa/LkWMULhGghEKQ+pznk89eZTx5rfxusnQJ7Eu/CfeQQ+FNnozgK4EwtzsMNVe53uG25e2gjNTkX9k+maUd8Hi/w8/n85g4cRIIATbeeGPstPNO2G233TF3//3wi5//HMefcEKQ293R0YFSqYRJkyZlPzEaurq6cM011wIAHnvsUaxevbpimR//+DpMmzYN3/nO/wMAbL311njkkUdw1VXfw7x584LldtxxJ7z++n9wxx13YP78+QCAO+64A1OnTsVmm00Llttqq60wefJkPPTQYuy+++4AgIceWozDDvskHnzwATzxxBOYPXt2MH327OhouoGBgYHB+o2GQ7WXLAG95BLwQw8DJk9ubqMSkM/ng/534403xk47VfbXCs3or68O+uvHMvfXjz7yKL5/1VWJ/fVv77gDR/n99W9j/bUH4CNaf72b1l8fethhWPzgg3jiiScwK+ivH8Ks2XNMkH2bwyjdLUIQOtyA2g2E4eeEkIbVbgCREHMrxqpkqSnhv/eCHG8AkfrdDvfQ64hImLkrPBQ5D8LMXRVm3mS1G6hUvIHqoeBZwszjine8fnfatvV9t0LtVp+Vwh2q3Sq83Iqo3QWldvsh5ozJgQnHkYq3CjFXYearel10l52gdrfD5bUMjslTefrJanet+zbUlBMm1wDR/9fUbsQszgLF2wuJt6cU76Qwcy90QK9ohwfM2WcffOxjH8Nvf/vb2o1sMp544vGKUfW5c+fhiScer1h24cLjcPPNNwafb7rpBhx77MKK5WbPnoMHH3ww+Lx48YOYNWs29t57FhYvltP7+vrw5JNPGtJtYGBg0IaoJ4xX9srrR03nfQawv/ZiryeeeAL77LtvZNr+c+fiiScej0wDgAULF+Kmm28Kpt1404045thjg+0qzJo9B4sfXBx8Xrx4MWbNmo299t470l8/9eSTwYB5YiMHAWGKYgMvE15u0GwkheMm5W0nhfGqMHNAEiCdeFvEN8uqktsN1DYJU4gT72r53Q6XrxIXkTBzRcgV8VaoN88Z0EKEU/Kl6w0zb4R4J+dvJ2xbyzVPamu1dumg2n2gh5lbPum2fEKdoxSdluWr3Qwj81Sq3TYLohUAGWbuOALFooOeooOeolsRZt7r8OB667ndWVGdfnuV75Lyu5XBoOfp0eVhSDlRZD8aYq6vrnd2qm5kPMxcV8eD0HTt8WOrrWbgrbfeRHRq67F06VJMnDgxMm3ixIno7u6OhKYBwPz5X8QjjzyCt956C2+99RYeffRRzJ//xYptzp49B4899ihc18XatWvx3HPPYdasWdhrr73x0EOLAQCPP/44SqUS5swZWqRb/+7X+6oHm222me8bEH2ddtppNde9/fbbQQjBpz71qQaP0sDAwECirfJhg450cAlTtL+O8s44UU6alnVeHMuq9de9fZEV5x/1RTz6yCN468238Nabb+GxRx/F/KO0/trf0ezZs2V/7bhY270Wf3/uOey9t99fL34I8IAnHpP99ezZc7I11GDIwoSXDwBkWHL1EmJyuTDMPF5CDH6oNPekc7bl+aHdKU7minhzWRgcLq9uZ+5yAYtRP8dbRBzNuWCQjugytLzPkS7nkTBzTwCCBmHm3PPAECqjjZYQq3AcbyDMvNr+qzmah9vqf+3urEgKM7cohWCQJcQsoCwEOrmFUXmBtSUu1W6bodcPMXccAc49cP+a9xVd35BNEniZD05RYC7yLvUHfogsIwYSnCu9hJhqEyX1/M6HAeV6aHnwfvly2IvOBgDYi85G6Ze/ARk/AapIGCGhk7ksAaYKe0VJNDzlux+WD0sLM69ojP+5VsrBUMD48eNx0EEH4+abb4LneTjooIOx4YYbViw3e/ZsrFu3Dk8//TRWrVqFj3zkIxg/fjxmzZqF449fiGKxiIceWozNN988kl82nPD0008H3w8AeOGFF7D//vvjiCOOqLrem2++if/6r//CXnvt1eomGhgYtAmGhWq3fDno2WcBAOjZZ0H85g5gwoS6NlHPWUoLTgPC/rrm9uLsOm0HaRtK3Hlsesq6qr/+eY3+etassL9erfXXe+89Cyccf5zpr9czGNLdYqg8bkW89WlAWEIMNKo06iXEhJ/TDQowz8/5FhSgAsKrzO3mqmY39+CXcYbFCFzI8mCAiLiXK7jBKIBcRhFvld/tcA+9EOi0aRBmrmp3S7YKMCEV2Sy1u7Pmdasf2Hh+N5BcRkyhUeKdhKy1u1PXTyDpcaTV7maef225LCHWYTGUXIEcoxiZZ+gtC/SWJPEu2izI65amauH2LUpQyDGs7GUoWDSo3Z2jFIwr9/Robnet8m61ESPe/kf6m18jd8ZpACFwP3sE2OIH0bHDdij/8Gq4n/msdCz3NLVbadYJTUkk3sR3Nvf3zeJE3X+jiPzL/3o5khtdTe0eNWoU1qxZUzFd5YCNHj06y4kBAEyaNAnLli2LTFu2bBlGjRqFjo6OiuUXLjwOZ555OgDghz+8OnGbW2yxBTbZZBMsXvwgVq1ahb33ngUA2GijjTBlyhQ89thjWLz4QcyZs0/mdq5vGD9+fOTzt7/9bUyfPh2zZs1KXYdzjvnz5+Piiy/Gww8/nJjzZ2Bg0J5oJ+Jcl3lsPyRRtRb51a9ATzsVIATiiM+BPPgA6Ee3hbj6Gng1BiprbryB5YL+Oo0Uaxg5ahS6m9RfT5w4CcuWx/rr5en99bHHLsRZZ50BAPjBD36UOAYwXeuvV69ahb323hsegMmR/noxZs8x+dzrA9oqvPy9997DF7/4RWywwQbo6OjAdtttF3H98zwPF1xwASZPnoyOjg7st99+eO211yLbWLlyJebPn49Ro0ZhzJgxOP7449HT09P0tqaRsLQSYnJeslu0yu0GECiQTNtOPLebURJRu21Wney5ypHcHxWQud5aqHmNMPOiyyNh5qp8WBKyELgkAqzCR/X87qQyYupzf5A1zLzZJcSApDBz+KZqJFJCrNNm6LQsjMozdOYoOvMWOvMWCjkWlNlwXYFSyUW5zFEsuujuddDd62BNr4OVvS56yhy9rnypEmIuFxAioY48BfbddWucvvDzWLF8Kd5/5y3EBeTM8AD64gsQe89C8bl/wrn1f1F87p8Qe88CfenF6rndkoVHhWqVs+apl1cZZu7FGqB9fvCB+/HC88/j05/5TKbmf+QjW+Hdd9+tIMvPPvssCoVCXaPRu+8+Ew88cH9k2n333Yvdd5+ZuPwBBxyAcrkMx3Eixi1xzJ49B4sXL8ZDD8n8MIW99tobd999F5566qn1Mp+7u7s78iqVSjXXKZfLuOWWW3DcccdVHXy75JJLMGHCBBx//PHNbPJ6jXbqsw3aG6Z8kXzmUK9tP7I5dt9lB3zhs5/B8mXLU8Ooq73UG/LCC/BmzQb/5wsQ/3s7+D/lZ/Lii/VvtJ9h0Q8+8EBqf520G9VfL122LDL9uedkfz1l6tTMYee77b47Hnjggci0++67D7vvPjNhXQ9zD5gX9Nf7z5ubejJmzZ6Nvz60GA/99SHsPWtWMP0Te+2Fe+6+C08//ZRvqNaCE9oP6OWCG3kNR7SN0r1q1SrsueeemDNnDu666y6MHz8er732GsaOHRssc+WVV+IHP/gBbr75ZkybNg3nn38+5s2bh5deegmFQgEAMH/+fCxZsgT33nsvHMfBwoULcdJJJ+G2225repsZlYpvXO2uDDP3XcoTbkJKiS/heaHKzT1YlFSo3a5AhdptcykZShM1gXIN8y9XyG1L1VuuQzkBIFB0KGCjZpg5E0qN9yrU7v4grngDUXV7MMPMm6V2K6iQbmmqptpIIDw5mJIT0lStLARG5nlE7c7lLDiOCM5PX5/jb9PBat9wLecbsXXaUunOMyoV5RyT7dRora52f+fam2CXV2PjKZsmtNqXpxEq0vJwfXlbe+ecdwFAtb1MmIDy7b+UTwtEKeIyrSJJ7VY53mGKmZwY3N4eomHmRHaCpVIJS5YshRACK5Yvw71/uQdXXvFtHHTwITj66GMqIs+TMG/ePGy11VaYP/8LuOSSSzFp0iQ8++yzuOCC83DmmWeBMRYs+9JLL6FcLmPlypXo6Vkb1ATdYYcdAABf+tLJuPrqH+GrX/1vLFx4HB588AH86le/xB//+OfEfTPG8OKLLwfv0zB79hycccZpcBwnot7uvfcsnHnm6SiXy0MunxtAw52xWmfKlCmR6RdeeCEuuuiiquv+7ne/w+rVq3Gsb3KThEceeQQ/+9nPgutnUBvt2GcbDC7WF/LbH2Q5B6KGMKBw9/0PI2db+OD9t/rFzcQFF0YfpCZMgPjlryINacWVK5WKWLJ0CTjnWL5sGe65R/XXB+OLRx8di0hLJqBz583FR7baCkfPPwoXX3IJJk6ahOeeexYXXnA+Tj/jTDBGg3XC/vpD9Kztwd///hzgAR/z++sTT/oSrr3manztq/+NBccuxOIHH8Svf/VL/O4Pfwx9RTyfcnsApQz/eP5FAPK9pz2f6Jdt1qzZOOvMM+A4Dvbaa1Ywb6+99sbZZ52JcrmMWbPmDHYqvUET0Dak+4orrsCUKVNw4403BtOmTdPCQT0PV111Fc477zx88pOfBAD8/Oc/x8SJE/G73/0ORx55JF5++WXcfffdePrpp7HLLrsAAH74wx/ioIMOwne+8x1stNFGFfstlUoRtaS7u7tpx5S1hJjwfxBUKHpgsEYJXB6q3a7gsCmB46vdqmyYXj6MUiLDxjn3c7gr5VhFvAG/nBj1Ivnd1cLMqfAAcAAMjHiAAFhQjqwyPDxyPqqEmCvoxBu0Mr9b7qc5YeY68Q63lR5mHizTz9xu/b5g/qALpTJFgHsEOUuWEMtzSZhH5RnWljg68xZ6ig5yNkUfJbAsir4+eeUU8WaMoDfH0N3ryDrflgwxLzhMlhDjJCghps5vAC36wv8IzyfIaX2BIsgVxJuknBSfiKvtSXJdmdtNvMo0bWjEm3ixMHN/g/f+5R5svukmsCwLY8eOxXbbb4/vXXUVjj5mARhNJ7E6LMvC3Xf/Bd/4xtcxf/4XsGLFCkybNg1nnnkWzjlnUWTZQw45CG+99VbweeeddwQgB84A+Rv2xz/+GV/+8jn4wQ++j0022QTXX//Tqir2qFGjarZxzpw56Ovrw4wZMyLGL7NmzcLatWuD0mLrG955553I+cnn8zXX+dnPfoYDDzww8fcfANauXYujjz4aP/nJTxJz8gySsT722Qa1MVyJ80Aet1vjQUnNdrkHxrw69dCEpSipbzrQf4LoebjnnnswdZONI/31d78n+2tKaXQfOufVwJiFP995Ny447xs4+ovzsWLFCmw2bRpOP+NMnHX2OZHlP3noIZH++uO77AwAKDkyZW/atGn43R/+iK98+cv40Q9/gI032QTX/fh6zJ3bv/561mzZX28V66/32lv21x9ZT/vr4Qji1Wv7OkjYZpttMG/ePLz77rt46KGHsPHGG+PUU0/FiSeeCAB4/fXXMX36dDz33HOBigTIh8wddtgB3//+93HDDTfgy1/+MlatWhXMd10XhUIBv/rVr/DpT3+6Yr8XXXQRLr744orpa9asqfplWtUbmvTEHa6D8GS9VJOQDyEybFqz4vfC99zzpEGW56HMBbjnoeQKuJ5AmQsUOYcrZMh3nyNQdAQc4aG3LFB0ZTh40eFByLjLZTixCitX6rsirKrGs3TPpqCUoGAz2IygYElDroIlTbk6bVnSqsAYKCEoMIa8JU26ckz+JaS22q06i1q3pb4tZQQG+OHZJBrGrxDfd5qxm068ddIdhvtr2yTReSwyL0wP0JFV7RbaveD4If8O91B2BYoOR7HMsbrkYFWpjKU9JSxb62BZdxnL1/Rh1doSurtL6Otz4Pg53naOoaPDxqhReYwdmceE0R2YOCqHjUfnMKkrj7GFHEbmbHTkWOCarp+jvXaegRkztsbXvvoVfGSrrbHh+AlBCFp4taJKd3hOg3eIvEsJUde363l+CTA/DFCmMPjfF//86LsL7gEShukz4t8n6j4kWug6Ue0jiW0iNbXv9Q+UAsViEW+88QamTZsWqI4K3d3dGD16dM3fwaxQ23vuP0sxcmT921u7ths7Tp9Ud3veeustbL755rjjjjsC4hfH3//+d+y4446RyALh/1BRSvHKK69g+vTpdbd5fUc79dn+mKSBj+FCnNv1ONeVZJ+eVNJUYfePfQRjxo7DRhtNxiUXXYQZ22xb8TveDAzqGWzPy9cSeLw84P31s/9urL8GZJ+90xbZ++zNNtssMiCicOqpp+Lqqyv9be644w5cdtll+Pe//w3HcbDlllviy1/+Mo4++uiG2tsstI3S/frrr+Paa6/FokWL8PWvfx1PP/00zjzzTORyOSxYsABLly4FgEQ7fzVv6dKlmBBzW7QsC+PGjQuWiePcc8/FokWhetXd3V0RwpiEuIqtT8tiqhZXu1VYeZLaDUHhEq+m2q0bqqHKQKlScyUZD8PMLVB/hJX6Bm1e4oYKjAVh5mmmarVACAmItx5uqo5b1gEPDdogQjKcFGaehCyKd9Yw82CbCWHmjTqZA9Ewc2WqRimCEmKdFkOJM4zKMxQdgbUly1e8XeRyDKWSC845PBG6mcucb4qczaJh5r6pmswpZxVq9+1/vA/jN9wQvSvewaoPV2DEyJEoFDpianfyyHeS4h3Mq5gSquhQoeRerIwY5ICUUsL1vG2hbVE5nQuVDw5lqqZtHOptluByg1bCEw2WGWxgHQC48cYbMWHCBBx88MGpy8yYMQPPP/98ZNp5552HtWvX4vvf/36m/mA4ot36bIP2QbuSZR2N/M4pFMvVq9EAwC//dD8mTd4Yy99/F90r3kKpVEQ+L8lY+589g+GGeiuOjBs3Dt/4xjcwY8YM5HI5/OlPf8LChQsxYcKEqpGErUbbkG4hBHbZZRdcdtllAIAdd9wRL7zwAq677josWLCgZfvN5/OZQhSToAi1yu2umF+jhFgcKvyc8zDs2PK3bVGSktsdlhHTy4dZtHoZMdUuFVYeWKk7AGyAC4ZeR6AzyOmuDDO3iBeUEGMZyUytEPM4UY7ndyeFmafldydtTyGNeOvnRw8zr1VCLMv0OCrCzOVUqe4yIO/JgZgu20KvyzEiL/O7u3MMhRxD0aawLArGGEpOCeBAEfK+k6SbomAzdNgMI/McnTZHnnEZZs68oISYwsYbbYJyuQjKGLo6Cij29aFQqHTsBIJ07Ng5jRJvD6GbeRokNZZlwAJlWoWaExl2HofnyfxtD0QOCfmEWnjh9oKa4PD8GHl/Xb9N2iSoQmUG6xeEELjxxhuxYMECWFa0KzzmmGOw8cYb4/LLL0ehUMBHP/rRyPwxY8YAQMV0gxDt2GcbtA7tSpT7Q45bhT6nNukeveEk9DkcXWPGody9DH19RYwY2Z7XYKghY7BiK/Y8WDsedNRbcWT27NmRz2eddRZuvvlmPPLII4Z0Z8HkyZOxzTbbRKZtvfXW+M1vfgNAlt4BZLkdPfdh2bJlQejapEmTsHz58sg2XNfFypUrg/VbhTRTNaA+tRtCTmYAOLzQybyK2q3cy3W1uyw8WIxAbdTVBgVUnrfFqAxFB6ATb8oJ+sDRYcv8bod5YH7ud0XtbhfIW7SppmqKKCfldwOqcyeJOdf1EO8kVCPM1UzVGq7bHcvzF56MXFCmajmLIscpOi2GETmG0QWGVb6TeW/OQi7nos8fMRBcwIWL3nVy27Yt3dALNsMyi6BDM1VjLCwhBuqh1NsL4Ql05HPwPIHe3l6M3VD+CFZq29XzvPTTl0RyI1tJULtlrXA/z1ubru9ReMHWQTwP3D9/Ui33/LINfp3PkF0btXsY4b777sPbb7+N4447rmLe22+/Ddpf58dhjnbvsw0k2oksD0WCnIb+nNe+UnXS3de7DkIIdI0Yib61PSiVSrBzOSRoP3VjQHrHFuykmZscvK9E+o7b9akl7rmRZdBUVRxZtGhRzXK/gBTRHnjgAbzyyiu44oor+tXe/qJtSPeee+6JV155JTLt1VdfxaabbgpAGhxMmjQJ999/f9Bhd3d348knn8Qpp5wCAJg5cyZWr16NZ555BjvvLA0SHnjgAQghsNtuu7Wk3Ulh5pH5vtqtyI2udqvQYkW8A5JOJXHV1W4AsLxsarcyVKsWYg5I8p1EvJWxmkO9xDBzSkhoqkb9MPMUU7VqUCHmSbWi04h3I2Hm+vZ0ZA0zb8RUrR4nc8C/5jwsKaZM1SxGkbcYuoSFMhcYkecY22GhtyRffX0Mtk1RZgxu2YVTdiC4AGMM3d0lMEax0g8zH93LkPfz8nOMwmY0cDL/YMVyfOnYz2Py5En4+te+ii23/Ag6O7vC3PtIiHl1eCnkNo3upqndinxTv9a26gmDe8In3sST6yqlOyxD5gXbNWr38MPcuXNTvSMWL15cdd2bbrqp+Q1az9CufbbBwBDtoU6Qh+Jgw6qVH+Lfr74MPn5LMMsOpsefXd5/7z1cdMaxAIBJEyfgyssvg53Lt+yYmt4zas2sX1VOXqHakTfU/n4c9PryJOGJsHRvI+sCras4Akgfj4033hilUgmMMVxzzTXYf//9G2pvs9A2pPucc87BHnvsgcsuuwyf+9zn8NRTT+H666/H9ddfD0A+SJ999tm49NJLseWWWwblRzbaaCN86lOfAiBH2Q844ACceOKJuO666+A4Dk4//XQceeSRqc61zUSWEmLqWJJKiEliDql2+yo3KMA8AhceLBKq3YAsJaar3Q6XJFmFlqvl3CqDpknE22UkyO8uOnIbeph5SY0cWABAI2Hm3PPAgKqjU1lczINtacRbTVOifD1h5vr2dDQaZp6lhFhWhPdLqHYrx3qLecjbFK5gKFkWRuTciNq9NsdgWRTEb4snPDjl0DUol2OwbYbOvIVlNkPBphhhu8i7NKj/TgnBxptuhrsWPwXulND74XsYPWYcgDCMPEnbTgoxV9CJd7Uw87ja7d9G8JLUbm1/OvEOnM49gGph5opEG7U7GYPhsdlozdyh+IA83LE+9NkGEkONIA/173ur2ve3Jx/Fyy/8E5P2HouOkWMj8/RnFzpuEi7733sAALbnIs+64XoeaMqFTBtQTusFGw2vrrZa2jNhPadS69HrQtKzS7aVsp8IfclMLaz3HA/x70QaWlFxRGHkyJH4+9//jp6eHtx///1YtGgRNt9884rQ84FE25DuXXfdFb/97W9x7rnn4pJLLsG0adNw1VVXYf78+cEy//3f/41169bhpJNOwurVq/GJT3wCd999d8TJ79Zbb8Xpp5+OfffdF5RSHH744fjBD37Q9PbqxLGaqVp8mq52VyshptRu+EQxrna7/vZ0tVuFmUu1OzRUs1hyDW+9ZrdOvF1f2Vb1u1UZsWph5v0xVYtDJ7Q68Y5vr94wc317OpKIdxalWrWzGWq3HmauancLT6YICCHDzDtthhGuFVG7uwsWih02+vpclItlwAFcx4UnPFBGsWYNlWp3Ttb4XpmnGJV3AlM1m8nz18EYchaFS2x4AIrFXhQ6OhCh2xG1Oz3EPIoo8U4KMw+3FrJvmRjhSfKthZ972lY9z9O+Zwlh5iRmqqaZrRm1G+jt7QUA2LZdY0kDg0q0W59tEKK/JHuokeKh1p44sp5vdRhvryoDxb7gmU4f4Lf194zAJgKTR3golcogliQ08ccOEk8Qq+jmohNI7HRmJeFJ/WcwxavVhlrbTn7iyBJ+HIcWwFdryZpL6M8UVZbKvOm0NpXLZQCIVNloB4waNaruiiP33Xcf7rjjjprLUkqxxRZbAAB22GEHvPzyy7j88ssN6c6KQw45BIccckjqfEIILrnkElxyySWpy4wbNw633XZbK5pXgSTiHTdVS1K7pakaSVWagtBw6oF5odoNCnBRv9qtDNX0Nrixv4BGvLnK75b1uFWYeW9ZHqxyS++PqZo6d7qLuY4k4g2grjDzeoh3EtLCzFthqqYjSCtgoamaLTx0WAydloUxBY6eEseqvIURBRt9BRe2TWHnbJSLZbhlF4KFvfxam8L2TdVyWvm3vB9mPnlswW8rYMFCrnMkPlixAgBQKHRGFWZEy4fVetaRhy6PXz8L8TMSkmm5zaC8ngdwD34pMRl5oEqMqe1QIpkzJwSMhiXFZCkxv4QYwrBzkLBdhMTbtf6Tbs/zUCz2Yvny5RgzZkzbdeIGQwft1mcbSAxIePkQIsJDTclPgzpj767sAy/IwVDLJ96W/zATlHwNphNMhIfChx9gNGGgJHzoUY8ekV4tpYvTyWtaXx1ftW7inGH5qsS9wX03osC3Gtl2qyIYBVZ9sAKdnZ0VpqCtBPefwxpdtxFkqTiSBiEESqVSQ/ttFtqKdK8vyGqqBqSr3XoJMaV2c5/UK7UblML1S4bparejTVOGatVCzAEE5cMsRiOlxCwqa38DDLDh1wKP9mCW3X9TtcS87mCQglSErWcJM5fTshHvWmHmcTTDVC1oo3/c1UzVXO6XELMZitzC2E6ONUWO3pKNnqKDzk4bpaIbhJm7xSKEH1LBmAxBz+Us5CyKEXkG68M38NqLj+KM//pGUCteoWvMePR4wPLlK7TWekFdbW1K7E0KEjr+pP4mIN5eSL71+t2eJ78eqnyYp22LEAT1uan2V0bsk2itbo1oa2MCwZv1n3bLczFmzJgBN6sSDeaINZpXZmBgUInBIMRDifg2SggGCu9/uA4lOyTZjBE/YlGSbfXsYvmmqHf3eBjJBNb2vh3p0ypodLW+OImgVyyUbZC6GqGsToCrzKyxz5rrNYWkN7jvhtaKoiNvYerUqYM2SDAQyFpxBAAuv/xy7LLLLpg+fTpKpRLuvPNO/OIXv8C11147GE0PYEh3i1ErzDy6bHa1GwgJearaDQSlxHS12+aqzBiF66vCqSHmmpN5QLxBE/O708LMi6o0GUPDpmrVoIitIt71hJlX3W4G4t2oqVqW6Qr6fRMPM1emajmbStU3KCEW5naPKNjoKViwcyxQu4UDiOI6lAAQSkAoQS7HMIasQ9ebj2Gtv+/Vq1Zi7LgNENTeogQAxchxE+C4G0C4Dhzuq8tKeeZy9DOiSPv3lXqYUedVjcorxZkQ/z1NGGH2PAghB4DKLkcfF1jnOOh1OdaWONb0uVjneFhXctFX5ij5I0kWkSXSOnIUXXkLXTbB6A4LI/MMI2wLI3MWOi0LeVuax+UsGVbPGAGFfJihNOzM641MaDsQYGSHbRRuA4NhisEiwEON7A61wTx1elYs70EP8cAYhWWRoAwoU5VH/M8AwBjFCkpw1WqCMXkg5y8DIOjfAID6P/cWoaFarvpnGuuvg79yXRXmDoTPP5b/DKaHu1Ptvdq2Pt9i0b5V72st7Zkuvpz+nBaPUkyKWowLIZSQCnHASlgvqe9P8+5Ji5asFkXZ6DyAYPstN1jvK27UU3Fk3bp1OPXUU/Huu++io6MDM2bMwC233ILPf/7zA9nkChjSPUhoVgkxpXYzhF9KpXaXuSohlqx2q/JhQkRDzK0qZcSUk5si3sUyRyGHoIyYr1/DpqRm7e5apmq1Qsx16Iqyyu/ub5h50MYU4q1QK8y8GSXEkgZs9HxxZaqWsyg6uQwz19Xu1etsdHTY6FtngyqZvtyHLptjAxThllzsx19A12ppsjZm7Dh88diTYNl2ZL+ScxP/vDIQmwKWT4a5kB0X80CEJNuce4DngTJ5jdR1dOFHL/jHASKzyijzybfq5GPnmngeBBfSM8DloB4FBQe4A8EI1pVc9AiGXu6h6HgouwKAB0oFCmWCTkdgTKeFEgFgUVCLwRIWLFhghIFRBlgUHqVgNgUlcsBJDXCo9qzvxNvwbQOD4YuBIr9DjdQC4XPYUETZkXm7Yu0arHE4KKOwbAuUUTDGQDTyrZNxQJLv1QmEXCnlgOzrFKFlJCTkTAthV88ycjlRJay9kpCzGBGPzgvfJ82PLMuj/a8+L07IrSRC7utAivjrhFsNJJS82DrachFCrjyY4uTb07avwYlvU4Mg6Q+FXg3VaH0n3EB9FUcuvfRSXHrppQPQqvpgSPcAoH9qd3Q7QWgx1xRD6oFzn8hzaX6WSe3Wyoepmt2uqB5nHuZzh8RbGqvF8ru1EHNJwPtvqpYUYh5HWn53o2Hm8W1GpmcIM4+jHrU7bVld7baZyt2XaQKqhNjovI1e18W4Tgur+iyM6cph3boycoUcLNtCmTJsP8HFAVv0+iPTYS3cY088BeM22FC2Qe9QRDTsUEVjyGEWz/cakANCQtmpkWQnfiA8r8F9QJJLv8XBCJG16glBnjG4wpOlzvx89KKjPA3kvexy/17lAmWXo6dEYFOC7qKsZ28Rgrxf79wlQobqE+ldkPPTISir3uEZGBgYrC9oJhkeqiR2KOWUZ8GrL/4Db772LwDApzd6F/e8Nw4vrBkJxlgi+a5FxJmueGvTws+VIesl8AgRtyhFCSJCxMuuJNxlCFBKUHQqiTj3B3Ud7ebQibZKHXREqIY7/jOvzQgchO8BgLvhM5q+HABZ2Ue996+5Is2cJz3bRcM8pDCQ4UEvY3SIRZOfY+XzbeVGmE/E3YQvUhKpH0jIaMbG1x2OMKR7gJBUBqtaCTGd4CQRF/ldU8q35OJJarcrUFXtjhuqWZRUhJgDiljDz/+OEW+u1e9OCDNXBNyiNFC7SxDIMxoxVUsivnG1e9n77+Li/zoZqz5cAWZZOPqU/8KcAz8ZkLek/G65bUnsqoWZZyXe9YSZt0rtVsRb1e5Wpmo5S+7TFQyjcjbWFaSTeXevhS6feK/L2RiV93DgFuVIHhMhBCedehZGjhoVU7dj+1Vh5j5BdnnlgFBwD8dituIDJxXEG4hEKeiDJeFxA9TzkGNUqumCoWwJdNgCnTZFry1QdEiYEsH1SA15H66lAjbjsBlBjlGsc9wgxN3iMjqDEAKXe4FTPPx2RUq4GTQVwmvsIX2oPtgbGLQjkr5PjZDU9997B+ecchw+WLEClmXhzP86Fwd/8vAmtDDWtvX8B2BdTzcee/Du4DMlwLyNV+L1lQRrnQIoYxBcgDIKSqn865NuZjN4goFzAc4VsRYVBFwIDzzweaEQgspINUCmEfpkW/XZLgc488BY+JypnnNcziXR9aeX/eciNV0t73BdpVZE24uEnANIfH6qgBadxUhI0uOwGAnIdyJiz+lSxAonMkorSDMj0W0GpD52XzI/AjS+faBSmFDPm3EizjQ1PImIGwxtGNLdIlRTtJtZQiwISfdLiAXO1r7aDSodzKur3VFDNUpJEH4ThyQhUeIdzGNExs7I1iIeZl50eXDHFcDAqRchW1kMIJhl4ezzLsf0GR/FhyuW4fhPz8Hus/ZDR2dXIvFWBE4fzUwLMwcaI97hdrMR73pKiMWnxe8TvXa3bqqWsyi6bAuj8zbGdXKs6rPR3WthbYeNcZ0ePr/J+xXH53keVq1aiZGjRoXE0h/0UPuJdyJANAVCDgIA8dJ2cn50/YBwpxBvFeKcdF6U2p1jFCUukKM0qnbbDGWXB/ejK7wgiqOnKCfajMBmHB22rEku3dql0k2JvC6MUgiPGLXbwMBg2EAfpOwPKGE4/5v/g222+xhWLFuKQ/fbA5+YMxedXV1N2X47o54xjFWrVlaE1VICjM1zrO1DkI4VR9r0rFDPMnHoz0KcK+IdhppngVre4Z6vboeRg4p4q3lASLyVOl6hfmvv494x+nZ0AQmQ6rceLh6PbFSlc4P5PgHX1e/4/uKKeriuimKNbh+oVK11Es4i7dMGAKqEoxsMTRjS3UJUEKQWlBBTZEipijJPVv6VI44Icn6rqt2aoZorVAiRSHQ1d4MRxJB4U0oi+d3xMHOLkuTa3XWaqk2cOBkbTpgELjxsMH4iRo/dAGvXrEZHZ1eEvKXld9cKM8/SBgVFvJsZZp4FEaU1wVTNYjQoITbKtrFBp4uVvRa6Oyx8RDyLkePWJm6XEIKxY8dV3bca1Imr3dyvGR8MAlRRu3XEiXd8XlKYudqHDcDzCPKMVqjda5lAzmKwKEcZ4UNk0d9G0eGgRVlCrGBR2JQgRyksKr8rlHqwhIxkoMSDIEbtNjAwGB5oloI2bsIkjJswCa7wMHb8RIwdtyFWr1qFjs7Wke71UfUeMXIsIrU5IZ8T16ALha6OCnU7a3g5gETjtWp53mqanucNANFc72olzMKHpbDOeHisWfO948tElo/1y3ZswDye990qxAm9QpJyn0a+gSopjj4BHyzy3Wi1EbXucIQh3UMAzSghFlEVG1S7laGaHmJuMRoQFv1LIkOFPCgmawkvlt8NAAw2lWHmfT75poTUbaqWZKjGKMGL/3wOQnBMmLxxsGxSnrCOLGHmcrlk4l0rv7u/Yeb15Hbr54d5aiBBqtzC8+Bwik5bhpmPWvEMtnzjn8E6xyw8EcuXLcU9d/0pGDyYd+AhGDlqVLjdBLW7WoihujfT1G71wJAaYu6FAyNZwswF5Ah7zpPhXjkeqt0Fi6CPERRyDGVXwBIeimUeGAYCsuPtKRF02tQPM3dRYAw5yuUABiUVajcoQIdF0bDBgSr/1sh6BgYGzYHTJKVbx4v/fA4ud7HhpI1asv31GfmuEdhlr7n421/vASAf+x5aNRl05Dh01iDYACqczfVp4edoDjeQbqYGNJ9k6/P17cdDzZtFtONkOOm5LokAJ+V3J66b8syWFiZfLT+7unO5QTvBkO4WI4vanbpuRrVbLaur3aqEmCTgvqlaBrU7S4g5EKrEingXARRshqLDUQAL8ruLLoEeZl5SJcjsZFM1i5CaSjMjBCtXrcSl/30KvnrpVRXzq+UJZw0zl8dYm3hXCzOvhWap3aHq7bvR+6ZqeYuBcw89rz2Lok+495t7IHba5eMAgEmTN8K0zbfAqlUrMXbsuIBwJ+Vz6/dto2q3ui+TQtSrEe+0MHO1bYtS5KiHAtPU7hxF0WUoOyKilAuffAMALUpVe21JBGHmBeaCUZnn7TIPlHtgxAvUbobQ7M2o3QYGBusjeEo+bKNYs2olvnbmibjoih82fdvDAZQSTNtqOyx95028+8YruLdvBj4sjMHIAuo2RwPS1WsgNEgD0om12l7avEbIdbVlkj7rz3JZlOxWk+ykfYTbSH9G6A/ZNuHl7QdDugcA1Yi3QrNKiFEVTu2TGPWFtjySWe22mBeEmCe5mfNI+TCENbspCYzVXEaqhpmr2t0WoRFTNUVi44RXV7tLxSK+dsp8fPGks7HdTrsFqmkkzyj+HunKaVqYuZyXnXjHw8xbrXanhZkrU7XxoyyMLHRhm40PxD2WiwMOOqxigGDkqFERdVvfbhKqmarxgFjXVruBbIq3pX1OCzOnfpqGciDPM4qReYbeskCfTVGwGcqONHAp++FQxbKM6OgpOvJhxA8zzzMXOUbR51IQos6nqkEeVbsN4TYwMFgfkWZC1QjKpRLOOOELWHDyOdh2x92auu1WYChWXlIDFdSSj+wdo0diQ7uzJUo1UEmo9flJJcCA+kl1fLmkz/1Vr5O2AaQT3TSX8maS7Gr7r7avcP4QvEENMsOQ7kFGf0qIJUGRHuaFarcLDxapT+22uG+uRgkAGgnLVeBcBK6XLgCXSdVb5XdrrYqEmduMQNXuLnJel6ma53m45L9Pxc67742DP3NkhWqqG1oospb0A5gWZp5GvIHKzrga8W7EVC2yzwYInQozF56HMZ02cpYa4aY48OBP1rWtyHYTwsyTFOtICTF52wRqt3poUGo3RNQoRJmVJNVFrxZmrrZZTe3uZS5yfhQGAN/FFejx11ej/p02lWq3xWERKtVuToISYnG1u9ogiYGBgUHbQQhACJQdDr20RaNE1PM8XPDlk7Hz7nvhgE99Hm5cbRgg1BOem/CoM6CoZiiruswJ47rgFmRevF47G0gP906aB2Qj0ZXLxeY1mUzrbY1My0iqgeYR67T9httrnGDX2m+4zNAk2tzzapbxrbbucIQh3QOEekzV+qt2c+4Fy/RP7faCEHOLEbioND/Qibc0UmPw60BIp/OEMPM+TfWu11TtH397Avf++Q5sMWNb/PW+P8PzgK9feS2mb7VNtF018oTTwsyrod4SX/Wi1vZrqd3C81CwGQo2S9lCdqQNBmUJM9dLiFEi70u9nnwadOItDxg1w8zjJcTyFkWnx1B0eaB2K0M1i1GUHaFFakjirUj3Skp9R3O/drcrXc3DEmL+AIJRu1uGRo1Zhqspi4FBM5E7YD+wxQ+C/+NdiI7OcEaDXPmfqr/eals89Jc/AwAu+M512GKrbfvf2AxQBLaamWer0PCzQhUyotILJ47pgCh0VpLZDMqzXK46aU5aJ4kYJxHGpOWA7CQ6bbvBOnWS6Vrbq9aOcNv9I9ZZ2hAul+3GGewa3QaNwZDuAUQtVRuoDDMHQrU7XkJMzgP0+sgAIgSHURKo3rzO3G6LE4gUF3Mek9wDxVvV7BZeoCwCajRWhpkzSmBRD2UqIrW7OdWcrIGKMHNKgR12nYmn/rManj/CptTWpFGzeg26qoWZK8SJ8VBQuyMEtImIh5Kn1e6OQw34yLByElG7wwGj5LzBasRb+fYlDY4EJcQohcsYRtgWSlyq3QWboewbqqkIDM49cB6WEKNUquVrbBIJM89RCkrldbP9UnmUUKN2GxgYrLfodV0I1626TBYSsdWOu+Lel5ZXTF+XsO1WqXmtIie1iFp/FfNExds/RxNGWKCdNoBkkptGfNOuWdryQHWn76oKcC2iW0sBrjFqkeX+q3WN5H6y3R9Z76Nmk+t6928wtGFI9yAii6maUruTTNUkEfEiyyq1GwJ+KGz4I8AoaVjtli7myb2ITsBdKhVtZaym8rslAZdh5kVXLm8zEtTutiiJqN0qn7ca4sZc8RxhIJbbXaUOdJYw82BeP4h3LTSidg8U6jFVy6J26zn3OpKIN2OhgVl8sCRaQkw6mZdYNLe76DBQ6sJi0lyGcw4hPJR9Em4xWS4s59f5VmHmecYDUzXHHzQQHozabWBgsN6i1+HgVmV/n4XExJGV1KTJ6f0lG2XRHPfnOEly+6GeZzqmhOfBrT6+D7b6+D54dXVy2c/IPjKTv+aSTqA2Ya5330B99172e67++6vee6mRwaR2Idh+NkrD6w5HGNI9wGimqRoQqtp6mHmwrG9exXyVUZUQy6J2lxGGgIugdndYSkyBazW79fxuZaymw2KkIsxcupoLlHm4bC1TNaCyfFg8RzjSRi3Pu2J6TDnVyXGjxLsasqjdQ0lBbcRUTWiKdjW1myc8tKjzEc/x1s3wWEL0vCLeqoRYPkHtLtgMZVs6mgM8yO0GgF7mynQHJkPKbT/HO0cleZeOrn4JMQ5QQuW9Q8iQuVbrAxrNERuu+WEGBq1Ar8vBnagabVGCcoZ1KwhXFh+aKijFuH8jxB+oj4hF9kfV4HD90nXasWVVwdMIW5fVvxSyrKQ4cd1+9HGtHbSJ7avB9Ro9vkYjNfpLsBv9PhgMDgzpHkJoVgkxRUD0Uk31qN1Fnyip8mGA/GFQpcSSOgwe1OwGAAoXQBEcBb/dLvd8sgMwyoIw815HJNbujpuqKSKqD1IQInNvPc8LjheQD+CuNjhQLU9YV06V2t2oKq0IYr1h5sH2MpK2gSZ3SWHm1UzVwjQIAhq4qleq3aFKDoAiuGY86dolhJmnmaoxQpBnFCVG0WUzjMwLX+22UHQ4KHXBGIHjqPtWr90tCboMM5ch5gWHIccobEZBuQeLynr0Ocs/L/59ZAi3gYHB+oB1Dgdn0Y6+WpgxoD38V3F7bYaqqhP/ushjA4q35Q+MZ0WcIGZVw9OON43o10uam1XnuRkEr1ESHbShn+v391w0Iw2iGUq2IdvtCUO6W4RqxKiZpmpAstodL9WkCE4Wtbtgk6oh5qruYzyvO4l4u8yTxmqQnTblBEXHC8LMbUaSa3fHTNWA8Bzlbep/9vO6a4waK9UUQGJ+d6Nh5qpNSf1fGvFOQiMlxAYTWcPM9RJi1COBWV0ttVsheNhR5zdjmLlSu7tsC2Uu0GHziNpdyDE4MSdzQK63ep2seUqpcjN3UWAsMFWjhMBlAsz/7gAA1e/RIXatDAwMDOpFT4nDJdGOtVrebzUiU209IJnMl7RHi6rkwn92aHaItCK1WSJo9H27NcqhpR1LGrFvhKA2g2CvD+Q6aEeT+uNmeQ60S+i4QWtgSPcgIYupWuJ6GdVutayudisSlEXtTjJUSwsxr3QODom3cjS3fHdzoDLMHJDumUXOZV3kFFO1jhwLOmiXe4EZVxKBs/wQZQVXKecJP8D9CTOXy4YEuVaYeaOmahXbGUS1O+s8dX9CKf2Qf+Nqd5KhmkKceNcKMweiJcQ6LQtFmwdqd29JEu9eRv3BIw7XjQ4e9ebkMit7GTptip5caKomzdSiJcR0tfuaH1yFm2/8GTzPw5x998N3vvv9qiVgDAwMDIYaekocTox0p5GoJOdrICTbRTd5egBtfi2C1AiB18G92r/FjBC4ovpIvk6cqi0ZV6TTSHzacdci8a3AcCPcv77pOtz161vgwcNOM/fGaV+/rKLPHoolu1z/XhpMxdvzvFT+kWXd4QhDuluIeohRq0qINap2q8+62l0rxBzQCbimeHNprAYgKNsEJIeZl4WoMFWzLIoNR+YA+KHjPDwXzCdzcnthuHJS56aId9C8OsLMGyHezTRVGwoKapYw83rVbuiDFOr8xX0DNOKtpx0khZknlhDjFkblBdaWODrzFnqKDnI2RdmmKJcpymWOcjkk32tZ2c/vJvjAJijYFAWLBaZqFqOgxIPFPAjP/44ygg9WrMCPr70aT//9BeRzOey/z9546sknsNvuM1t5WdYrNGrMMlxNWQwMWoG1ZQGHxCqUJPAOm5Kgbw+mpZDtgIg5ldtIQhLBLro1FHdevY/MQtrdKmRAEZy0geI4yUsj73FSmbRUo3nX9RL7OPTjb5TQJUXv1QM9OrE/SPPzUVi98gP8/raf4Wd/fBjMsrHo6EPx8t//hm123DW2nfC70F8C7iZENfTnWKvdrwZDD4Z0DyIaMVWTy6WXEKvYh+YorUqHMa+22l2wCBxeqXZbwgtCzPXw8miouc5swxwsl5KgjBilyWHmFiWBqdrUm6/DuBlbwPnUZyA8/zgV+aSSJyuCJXxFNQmBu7lSv1OIt66cKiVaRzOJt76PdlG70xC/b5WpmkDUaV8QVKjdQTm72EOMq3VyltbJ6ekBROVSxxzE1fZVCbECkznZI/MMa4ocIwo2eksu+nyDNEoJHEeAcw+9vQ4Yo+j2yXXOYoGbuW6qRgngchJRu4UAXNdFsVgEJQSO42D8+AmtO/EGBgYGLUBP0UXZC1lzYo1lSlHUostCsq2lvqUQdR2KtMcJcdFNUUyddKKetB19OnerE1IngUzr20vK0dbPTRIBSiKuen+XqnJrhL0RUpZVZa/ahgwDEGlICpmvh4jHfXn6g2omu9x1US6VkIPsv8dssGGNbWUb4a2HnCcR8WowIertC0O6W4xaxCi9VFj2EmJAdbVbD8NWyqJSu+ETzXrU7qQQcx2cB5IxFPF2KUHRH8+1KEHZd2iTHZoMM7coQZkKTHjx79jvmEOD7S39TDkYOPA8SbQED8+FUruDEmIpo9C6M3aceFskatgVr92t0CyiGw8zT19u6Kvd+jzdVE0f8FElxGqp3eqhxiI0IN6uJwDhnwQ9v5vIwRZBKiMHKCVBCTFd7R5dYCg6nq94u7BtBsCB4MKv222jr0/W7V5NCXKWzAXvtF3kWUjgbUb9EmL+sQgPG44fjzPO+TK23mJTWJaF4088GZtPn96Ky2BgYGDQMvSWXJSRTrr1CieAGkRH1eUVFFGvJNleJpIOADytDjWVFViStlGNUDsJZFrNixP1OEGNbzd+XDpRTxq8yKIsq2eaenLXeYbwn0Zz14HGCHmjueuNqMPViKxad8y4DfHZhafiqH12AGUWDv78AkycslnD1TD0c5SVnGfbbqxcXZ0kvVVotNqIWnc4wpDuFqFWveU0NBJmHle7VX1kUEDoP9YJajf8MPOCxSA8L6J2M4pIbrceFkZpWO9Yh15CLE68AQaXeSi7qhwZRZ/DgzDzznXdOPrwmWDlEgDA+fjuWHXX/ZHtB6WoCFLVbj1cOf7jlBb25Kn1M7iZR8tkRdFomHk7qd3VwsyBSlO1SAkx7bolqd2B4V3suiURb5XfXS3MnDGCDo+h5Ap02hZG5nlE7S4WHeTzFhxHwHVcOOXwJmeMoDsnjddUmPkI2w1M1Sz/eyKEVLtXrlyJu+/8E55/5XV0dXbiM4cdhEce/is+sdferbgMBv3Ee++9h69+9au466670Nvbiy222AI33ngjdtlll9R1rr76avzoRz/Cm2++ialTp+Ib3/gGjjnmmAFstYFB69Fb4gHpZoyg5PNvRZ5LUCliYd+jE8qyGyXaZYiKgdGiU52cK/AE746sxDogzrHlVbvj29EJvr4tnUg7Cep+pL1uOkFW20xT44NB57T5WfN4M/C9LMRcV8vrUcrrJeSNkPFaRLya67ya19O9Go8v/gt+cvfTyOULuPjUo/DME4/io7vMbCg8vppbfX/U6SQX+2YZxBkMLAzpHgA0qnbXQpKpmiQhuvoIxNVuBgRqN4AgzDzHKBzhBWp3p03BhQjVbhoNMQdCd3HOowqzcjKXi8mKzYzRwEyNaqHmRULwxSvOwfYP3xWs3/3qW+AbbwwqPBk2nqCaqlJU9ajdQLIrdj1h5uG85OuahXhHt1M9zHyoqd3VkHQvq4Ehdd1UlEJg9EcQDJak5eJblFQQb5XfrYeZx6FKiHXaDEXOMSrPMLrA0FPiyNkMhYKNYtEN7mPXceGp75RvtlawWRBmPirvREzVLEYDtfuBB+7D5ptvgXHjxoESgnkHHISnnnzCkO460KgxS73rrFq1CnvuuSfmzJmDu+66C+PHj8drr72GsWPHpq5z7bXX4txzz8VPfvIT7Lrrrnjqqadw4oknYuzYsTj00ENT1zMwaDf0lt2wZJgbPuCXwGHFOjD121mKEe0SRAVxiZNJIaLbcmn0e2wxAt3nUm3f0ThIErFWTdRJdRZCraei6VDbiavu1Ui0k6LoA5KY94s0VSnTbaX0o8GqKqosgxJcjZQ3m5A3g4xHzHMzqLB/e+whTNhkU+RGjgYA7PCJffHSP/6GLXfaTYoB/YR+TPWUngMyqP8Zy9EZDC0Y0t1CDKTaXauEmCKmumt0oDBqpmp55sHhXoraTSMh5kKEed266h3md6uD5wAYymUXuZwFlwuU/V5z9iN/wAk/viBoe+/v/gh+wEHw/PztqqqpX0+8mtrtIjx3KlzZIjSVeGcNM1dIU72Trr0i3mq9ZpmqDTW1O81UjRBUqN3K6I95RDNdS1C6Y8Rbr9/N/BrgwifhVUuICYGReY4ReYbegoXekgwxp5SAUAJRFhD+/dsDuS5jNAgzH5mXTuZ5xpC3GMqukO3nwOSNNsGTTzyO3t4i8nkbD//1IRx3wkkDdEUM6sEVV1yBKVOm4MYbbwymTZs2reo6v/jFL/ClL30Jn//85wEAm2++OZ5++mlcccUVhnQbrFcolTlKcCMlOxXZLrkiQp518zLOYoqyvj6lMWJMgsF7uX3V14fL62lsNGl5oCopTybU0WN1hJeZTKdFyiVtoyaqEOdqluiygkYKcc1gBNcfQh5VkpMJeZZc8qQBh6z58HLfCduMnf9ex0VJiKpRAV0bTsTLf/8bVvf0wLJsPP/0Y9jn00eh5NaoQ6vaV+Oal2OfW11XfqDhNWh8qtYdjjCku0Wolxj111RNzk8vIaYTIeGTn6AkV8xUrdOW5DqudlcaqnkRss0Yiandwp/vQSfegIVNl7yOn3xrfrCsc/YilK/4TkWOcJpqygUB9QcWqE+6dLVblQ+Llw4DZKhyEvFmIHWFmetIur6KKMfLiLXCVG0wUCvMPL6Muj/166ar3WqARd2TEDRiqKYTb6YGlCCXJ7FrFjdVs3wTtE6LYVSeYcNOCz2+m3lvh1S7S77drlN2QuLtk+6VNkUhx7AyL03VOi2Z461KiFkU2GnX3bDv3AOw98ydQSnF7Dn74JBDD2vxVTDQ0d3dHfmcz+eRz+crlvvDH/6AefPm4YgjjsBDDz2EjTfeGKeeeipOPPHE1G2XSiUUCoXItI6ODjz11FNwHAe2bTfnIAwMBhmOI0uG6Up0kFfMZBUTQBJxPSSaa2SFkehyPBYdp8iyIspxQutyHlXGRSxkXfcOSVnHja0DSEKeTKarnZGBg82SiXEWs7ckMl6P0VstgpwltzqJjA80EV/+nxewjubhjN4oskz8um+45fbYardZOG/+gSCEYqtd9sDmu+2D1XHr/YR1K9pVaz4hQV35JGQh2MZArf1hSHeLUY/a3aipWnyZekqIJanditzE1e6cxeByr0LtZkxU1OqO1z4GKBjzUCj34Y4rFmDcyuXy/GyxJfr+9negoyO6dJUc4aRSVIIr1duLlEfTf9zj5lzKFdsVnhww0MpRpYWZ10u8FaqFmTfDVG2okXPdVI0SpF63NLU7LXRK3XsVZcQgYIEm1u5W90WHxVDiMrd7RF5gbIeF3pJ8rcsx2DmGUtG/Jxw3sr5tU6xUYeY5ihE5BwXG0GEx+eDJBBgHzr3gElx0ybeQs4bIE1ybQTRozKJ++6ZMmRKZfuGFF+Kiiy6qWP7111/Htddei0WLFuHrX/86nn76aZx55pnI5XJYsGBB4j7mzZuHn/70p/jUpz6FnXbaCc888wx++tOfwnEcfPDBB5g8eXLd7TYwGIpwHA7ORCySTf4VGtnVibROsAEZZaaICHd5qKJyRELUOecRIq6T5JpEHICF6G9tJV1KIjpJv89J/We23yKbkQr1tVbeeFr+eWpe+gAR8Tipje+3sixabP0k47sYEU9SfGvtJ6m9aQT83X88AgDo3ulIf3/R+XpUwq5fOAt7fPHs4PMHvb6XQcJxJEUzVHPLjyPtmKoRev1alfSojiH0vGeQHYZ0txD9JUZZwszlcnG1u9o2fWXRJzfqR6ARtVtPrEqr3b3BymXYbNX7eHf8JjjxiV/iyKd/H8zr/efL8LbaKtq+uOKfMuCgVFNVQixN7VbhykC6ORej2shyjTDzWqi45lXyu/Xlm2GqNhio11SNeV7FdYur3bqhmh7lpfbjCgA0zO9W9dwZ8VJN1WxGIYRAp8VQ4gwjcjK3e1XeQmfeQqFgoa+PwbItlPpK4JwHD5SEEliWzO/O2QwFm6Fg+bW7rbCEGCMEIwvMEO5BxDvvvINRo0YFn5NUbgAQQmCXXXbBZZddBgDYcccd8cILL+C6665LJd3nn38+li5dit133x2e52HixIlYsGABrrzyStAGa+oaGAwpFGUBrw1WrcA7G24SkGEhuN/Hy+g1AAEJl/P1weik8qGIhFPrJLxiuQqSXOu7FV2+FgmXeeKxdShNzPPWn6Wq5Yknr9taEg5EiWo1V/UKR3VtXnwQoxaprbZPIFupL52Ep4Vc19pPlra+v6ovOO/6ceoDO7qIkrYMED2H1Uh8fFm5fG0Cn0S8bUYqbuChHnJuUB2GdLcI+ghtM9Tu9OURlBCDQENqtwzbIhVqd4ctOyJd7baoVj6M0UiIedzN/DN/vxvf/Mv3wWLH03fzLRBHHlXzHNTKEdZLiOmlqKqp3Qpp5lwAAgLXSJg5UJnnnXb9s4SZV2x7iKvd+rWDSE8P0K+bnpPPvHAgyIUnoxGoiHTkwvMixJv75jt6mHmSqZrNCDyV280FRuR5oHZ3F6TazZh8lYvlIMQcABhjyOUYVtoMIwoWVuYpRhUcdFpS7Z66QSe68ubndLAxatSoCOlOw+TJk7HNNttEpm299db4zW9+k7pOR0cHbrjhBvz4xz/GsmXLMHnyZFx//fUYOXIkxo8f3++2GxgMJtiNPwN94nEAwG9+uADfPGQR/rjrwXIe00uU6up3+D4e7aYvKxF2gpYWQi3D0H2/lSAdDcF+FUHWw9CBkMgoAhnmeEeX15V5fflq6zSbgCe1W6Fek7bUfPMMBDyJ2MXPX2ReRgKeXms8TPNLgyLg1fKda+1Hb6veziUre+V6TBFv6v8lke2p6cFyWluykvZqTvxZCXstsp6FqA8kGjU+VevWg0aqjdx666248sor8dprr2H06NE48MAD8T//8z/YYIMNGmpzM2CeElsIFa5cLzGKqtj1m6rFS4hVbF8jrXpIb1ztFh7xw6ZCtTtnU7hCwOKkaoj5xO4VFYTbIwTrXn0D3pQpNceuw7amq6bxUlQCXpDDG1e7k8KV04h3kOuOxsLMw2W9CuKdJcxcR7up3Unz4gMm8esWcaCPqd1pjp868Vb53XqYuSDR717EVI1TdFoWRuTcQO0eUbDRU7CQL1goFS3QIoVbdlDu8932GQVlFLbNsLJgIWcxjGIulvzl59h71hx0Tf1Es0+rQQux55574pVXXolMe/XVV7HpppvWXNe2bWyyySYAgNtvvx2HHHKIUboN2hvvvgv7lJOCoUrmeTj/T9/DY1vsgg/HTozkc6t+Khp6HiXfqh/j3AtDx7kIQ8+5FnqeQr4r1m8y+Y63Q61TmQPuJeSAZzdkS8of19tdbf20bVSbDlT6yOjrAPWTb0CS2tR86iaR71pGY2nHFW+nwooVPcH1VtVI5HuSOD2cRmoSdLWcPC5/uwmqeS2CXg85L8aWS0g9Xy/RSLWRRx99FMcccwy+973v4dBDD8V7772Hk08+GSeeeCLuuOOOAWx9FG37pPDtb38bhBCcffbZwbRisYjTTjsNG2ywAUaMGIHDDz8cy5Yti6z39ttv4+CDD0ZnZycmTJiAr3zlK3Dd5t+5cRLSqMMfUIdKHvtBIz4BpT75oFQqtep9sAxVZlMEFqGgvvEUJQQdNq3I7ZZqd/hSNbvlXwKLO7jp9q9WKNzE80Bf/488HzVGueIEM4lwUkKCHyZKER4flcesjg1AkKOu8rgVVGfgekI6nAuZU8pFmFvKPU/WYq64pvWN1Kl7QG1XH+kTkffRaaqTVp/1e6miTQ2OOvYX+vUJiC5NXib5upHgusXTHuJQnaok3l7FdfP8vOD4NVMDMnmLodNmGGFbGJFnGNthYVSnjULBRj5vwbItMDXS4hRRXteLvnV9WNe9DmvWFLGqu4iuNx9F8fFfwRMCLz7/z6acQwOE3716X3Xe9+eccw6eeOIJXHbZZfj3v/+N2267Dddffz1OO+20YJlzzz03UoP71VdfxS233ILXXnsNTz31FI488ki88MILQYi6QTqGen893EH//RpIPO/WE5i68j2tGsnwQDz0fCCRVH+86vJVSqMaAB8sW4MPl6/Fyg968OEHPfjwg3VYubIXK1f24YMPerFqVRGrV/dh1apedHfL92vXlrB2bQmrVvdhTXcRq7tL8tVTxuqeMlauLWFNbxlresvo7nXkq68sX73y1Vty0d3rYF3JxZq+MtaVXKwruej1X929TvBef60tylefw9FTkq+i46HoeFhbEii6HoqufN/rRF/tiu7u7sirVCpVLKNXG/n4xz+OadOmYe7cuZg+fXrqdh9//HFsttlmOPPMMzFt2jR84hOfwJe+9CU89dRTrTycmmhLpfvpp5/Gj3/8Y2y//faR6eeccw7+/Oc/41e/+hVGjx6N008/HZ/5zGfw6KOPApCGHQcffDAmTZqExx57DEuWLMExxxwD27ab/uAkfzuTc4LrVbuTpterdqtwX1BAcG1U0le74RtZZVG7qa9wUxqq3Y4j93PSgz/HyYtvTjwmjzGI6VtkPIPZcoTjqqkqRaWr3Xq4chIirthgkfzuamHmWZDlWjdiqtbffQ4k0kzVCEFE7Q5y8kllSTsZYq7ldfuj7sLzgkiFEgTyjFYNM1fpBzkm1e4xBdmxrcpbGNOVw7p1Zdg5G5ZtgTIGUXQwkpUwlpTQU3KxkdONWeveCbb32c8fhc2nbzlwJ9OgKdh1113x29/+Fueeey4uueQSTJs2DVdddRXmzw8rKixZsgRvv/128Jlzjv/3//4fXnnlFdi2jTlz5uCxxx7DZpttNghH0D5oh/56uENssSU8SiPEmxOK9zacElEHgejgfjgvOUc2qeSYfK8tE+urkpTEYF5C2G/SNuPLxtuVdR0gxRArQQipx2Qrq0lXtW1UCy1uxLALqO7AXcu4q5YCXct5O0s5rUwO3/oyPavRLWwwxmS0GqVB1BpjDISSyHsgVL4tK1kNr6aS1xPGrurax+vZW4ygrNW812vXu6K2Qj6QcOFVLVFXa10gm/lpI9VGZs6cia9//eu48847ceCBB2L58uX49a9/jYMOOqih9jYLbUe6e3p6MH/+fPzkJz/BpZdeGkxfs2YNfvazn+G2227DPvvsAwC48cYbsfXWW+OJJ57A7rvvjr/85S946aWXcN9992HixInYYYcd8M1vfhNf/epXcdFFFyGXy7W07Y3W7QaiYdYKWUuIMUTzfeT32yc0PmlVta2z5HYXbAYhvOCvJTzs/PpzuOqaM4N99Py/78PLFzDirFNBOIfHGPp+eC34RpuEqdN1kMNaOcKqFJXgCNVuLbc7LVxZN+eKu2LDP29S8ySx9ZofZg6a3VStWm73YKGe2t0C0RJi8Zx8ldutyocllc1UxNsVXiS/mwsvGCxJCjOXpmpAp81Q5BbGdnKsKXJ091ro8ol33zoLlFF8dHwZB2xe9L8zq4J977rbTMzZd26Lz6hBK3HIIYfgkEMOSZ1/0003RT5vvfXWeO6551rcqvUL7dxfDytssgmca6+H/aUTQCAJ97cOW4QPx00I/M90oqHQLLJtxVhsvL53ZF4/yXYSscxCtrMS7aR15fopyw4y2c5U6qrO/UXWH2Cy/cGbLwfTTp7xPu5+vYB/ftgBMBs0VwChJBMJL2cg4Y4jEkm440hxyo2R8CAM369tr+ra62Hq6lkvcOrXSuuVtWdD3cm/olhQGyGL+Wkj1Ub23HNP3Hrrrfj85z+PYrEI13Vx6KGH4uqrr27ZsWRB24WXn3baaTj44IOx3377RaY/88wzcBwnMn3GjBmYOnUqHn9cmoM8/vjj2G677TBx4sRgmXnz5qG7uxsvvvhi4v5KpVJF+EMWyNBWSYi4FuaqiHOWMOA0IpU+PX2ZpDBzfbrK6Q5yuymB5ZNw2w8vt7X3Su0et/ZD3HnKbgHhLu1/AJavLqL3xFNQWnAcVr34b6y96z6sefk/cI49vuYx1zpW6rdPOl+HocnBsQTHp4Uox8KVLUIrOgIVqpwWZi5E/8LMI+HjdYaZN4KhHGYup5EgzDzpuulpDwAq0gIUXM+LXDtXeCgLgTIXQZh5UhttRtBhMXTZFrpshnGdMsR8TFcOXV055At5jO2ARrhDnHDy6YZwtwjcv2aNvAyGHga6vwYa77OHO/jC4yF2nwkAOOLsm/Cnjx/sp/wQ2DYLCIX+CufLKg6MEeRzLEg9K+SYrHjiv1RfzAiJpKipddUrSGPTcmXVS9+eFVtWLa/apefQqheAxHVsRiIvud/wpRA8B2kdQ9K68XbrSNpGfDtZpuvnM05M09ZJOh8V8wkJXnGk7S9YVz0/+q8kMEqDVxpq7SepneXeHrz97F+D+ZTIPnykWAs4RYi+teDFPpSLJZSLZbiOK19lF07Zgeu44A4P3ssXR7nsolTiKJeld5HjT+NcgHMPjiOncy6C6XI5Ob9U5nC5kCV3edhnyWlyGc49uELAFSJIm1LLAwjmCeEFz5xJ5eDaDcr8VL2SSLcQAjvttBMuu+wy7LjjjjjppJNw4okn4rrrrkvd7ksvvYSzzjoLF1xwAZ555hncfffdePPNN3HyySe38nBqoq2U7ttvvx3PPvssnn766Yp5S5cuRS6Xw5gxYyLTJ06ciKVLlwbL6B24mq/mJeHyyy/HxRdfXHdbhVcZ2tqIIlmvqZpcrlLtTnIK1NVHQWSoby21uzNH4QiODgqcdfnJ2Pb5x4PtLX3lbWDSJLltdcwbbwLXDx9RR16vSlsrzDyumkKEpah0tTsMV65tzgUhibkqG6aHmVOocx0OXiSZtCRtv5qjeZqpWjup3dVQzVQt6boptVtFIFiUyOvjhZ2Q3DCFBT9agajUCS8IM4+r3XIV+XDXaTGMytlYV5BO5t29Fjo6bOQLDLtP+iAxxL9n7VqMGzd47pcGBu2Aweivgcb7bAMAhQIAYPUGk2DbUuOOm0zJaeufki33XTEpc9j4YCrZtdYbzLBxoHlqNlC9vaV1axCvrU4JMLaLYe06AMyWircWTl61Tf5yehg5oJ4fmmvMlmbKlmS6luaYvr6ikWojl19+Ofbcc0985StfAQBsv/326Orqwl577YVLL70UkydPbmmb09A2pPudd97BWWedhXvvvRcFv2MYCJx77rlYtGhR8Lm7u7siByENIkjM9YmbIlE+WWqIhCeEmUfmEySWEAPCHO60EmKch7m0SbndHbYMfTngL7dhnx9cEmx3+R//gvKee8tjGYB60xW1vBUB90tRyeMhibndaeHKkRzhmCs2EA0zjxLiaIRBLfIdPwfVanfr289SQqzWvgYKWWt3B+1LKCEWz+2W61C44DKkXLv+rn8SLABFzlEAi4SZq9rdVOuglKKeY7KE2Oi8jXGdHKv6bEylH2JOx7OJx0YIwdix41p16gwM1gsMVn8N9K/PNpCwbQYRkO7GCfZwyslO20a17RiCXR212hvuUy7XOXIMpLwTjRhcRceCduUyh5brRNuyVMTe0CPaSff7QML1o0IbXTcrGqk20tvbC8uKUlxlkNtombNmoG1I9zPPPIPly5djp512CqZxzvHXv/4VP/rRj3DPPfegXC5j9erVkdHzZcuWYZKvvk6aNKnCuU65papl4sjn84nhDrWgcnTjD/v9VbuTplczVQOqq92ARtBjajd8mun64buTX30en154WLDeqq+ej+6vfEPuA42TxUbU7npVU13t1us/l7XwnDjxVqppoI5TBAZdSQZ50eOLDQro82JKtU68uSBB7W4IVJiqVRvAGEpqdxrx1vPyKfH/SzBVq6Z2lxNCqnTi7Xr+gImfDJUDBRhAY+dHqd2yhBjDKPRh4vO/htLWtvno9thkylTce/efg0GReQcegpEZ6kAbGAxnDFZ/DTTeZxuEyOcY4IeGKzRDuc5Ckmsp180m1unrtyexBtZ/cp2GXOcIbLbzLLz5zGIA8jn0/uUTwAuj0NmGBFufn7W02PqIc845B3vssQcuu+wyfO5zn8NTTz2F66+/Htdff32wzLnnnov33nsPP//5zwEAhx56KE488URce+21mDdvHpYsWYKzzz4bH//4x7HRRhsN1qG0D+ned9998fzzz0emLVy4EDNmzMBXv/pVTJkyBbZt4/7778fhhx8OAHjllVfw9ttvY+ZMmac0c+ZMfOtb38Ly5csxYcIEAMC9996LUaNGVYQu9BeKQFEWdTJvVO1OCzNXUMQ7sk5M7VYu39XU7spQbIquntU4fN7OYL3rAADFHXbCe396ACyfB7z0etNZyWJWJKmm8flpqqlyadcJXPy8AtFalHr9bkaiYeZJtbvTUM1sLU68gexh5vFtJG5/CBFxhTRTNZfL46imdrvcXz8IMZd/Zei5PBFUeAA4AFY1zDwwVbOATTu68MxvZH5QvlDACaeciQ5foZs+fUusWrUSY8eOM4S7xeAeGsrPXg9S29YrtFt/bRBFZ86ClQ8fD5tFqo1anW09oLWKNdC+xDqyzYRlJ0/fJiDdt6/cBiW7A6M2iBLrOIHWnfl1R36dVAPZXMllu5KJdTNqdse9BIYDGqk2cuyxx2Lt2rX40Y9+hC9/+csYM2YM9tlnH1xxxRWDcQgB2oZ0jxw5Eh/96Ecj07q6urDBBhsE048//ngsWrQI48aNw6hRo3DGGWdg5syZ2H333QEAc+fOxTbbbIOjjz4aV155JZYuXYrzzjsPp512WtNHxrmmmjZD7U5DRah1gtqdVEKsYjuKoMfKbHWt68Zee80Ilnvj6X/BnTI10YFbP7aByElOUrvTVFPl0p4UrhyH63lBjrDuig2EYeYqZFmRuCzEOzjPVY63Vph5ZJttqnbHB0xoletWqXaHIea+dB4LcRK+o7mMVIiHmVeUECMEG47MoTPPMGf/gzBx8mRMmrRRZBCja+RIQ7YNDOpAu/XXBlHkbRoh3UD/c6vbTaGuNW+oqtTA0CXUcr/9I9VVtx1rx+hJ4wHUR6bTiLRsT3YynTa/mkqdRqiTjPiGAqQhXGPW6fUOrtdbbQQAzjjjDJxxxhn1Nq2laBvSnQXf+973QCnF4YcfjlKphHnz5uGaa64J5jPG8Kc//QmnnHIKZs6cia6uLixYsACXXHJJla02BiEAjwyM2p3VVA2oT+0e15EDHTUZxY9ujw+//A30HHBIRTuTFNpaZLE/qNdUrZbaHQ9XTjLn0vO76w0zjyNpMKI/aneWAYzBJOJJxFu9jw+YMBUdUiO32/IqQ8z1YwwGTTwBuAAsuX5c7c5ZFDmL+vcssM32O0YjCIbQAIaBwfqGodRfG0TRlbdQttmAE+lWq9K15g0Wka6172A7A0ymgdYo1EB9hLqe9uqYtGGX3FeTQrz1+UA2NTq+nL7fpGWTlk9yrTdoT7Q16V68eHHkc6FQwNVXX121Dtumm26KO++8s8Utkw/s3FO5zvDJGYEi3vUQtTRkNVWT75PVbkok2Zbbk+TVtglGagYEb937OOKIhzTr9aZb7cCdliOsHwP3FXtdNQ3y1hPClSVJSzbnirtiNxpmHp67GsQbSKzdDUqq5sm3C7KYqtVUu4kXhJgrBCZ4UCdTAAxggqAc3PgUNsLIE893oVfrq+8mBIkMiLX7OW8XNGrM0qiZi8HAYSj31wZRdORlePlg16pO20616UBtUtIfAg0MnBoNZCPRWfcJDB1VGmiMPLKU8qFJ2ESR7gaU5viycvnqinPSfZX4PUg47rTSbkmo5xoaDC20NekeyhDCgyCSlAXkCZVflP6o3UnTs5YQU2q3EIqoSvJRyEsWmVbnOansVSsduLMgi2pqQYqeaeHKSYibczUjzDw85nTiDWQLM29ntTvNVE03w6uldguPAJTCFeH1ixNvKrzEMHML4cAY8U331KCGgYGBwXDGyBwFOlnivMHOha61LjC0lGeg+cQZaB15BoYmgdaRtX1Tx8g0lKxkWLapvvu10dz7qvXHaxxf1vvJYOjBkO4WQXhRtVuVmmqG2l3LVC26bFTtTishZtkhka59bOnEG0gOjW62A3c9pmqCQJ7rKgROr/8MSOWswpwrY5h5I8RbRz1h5u2CrGHm8etmMUB41dVuPUXAER5sipB469DCzF0uQAmFIAQe8YJolMD03KjdBgYGwxQj8gwkn0y6B8M4LFhmgMky0BrCDLSWNAMDR5zleq0lz6nrp7R33vyz8O66PvQ6LoA6ogCaPIhSz76B1oXytwoyp7uxKLNG12t3GNLdIghPqsjCJxGKnDVD7Y5DL6OVVkIMCWq3KiFmM1LxBUhT1PXjq2YEFg+NrkUW+xNmrt7XU0KsmjmXyuvWibcy59JdsVWYORKuaVaEgxLZwszl+Wk/tTsJtUzV1HWTHSGFECJR7XYRvRfixFvW9RZBmLms1y6vL6Wh2g3qSfUcHpgKOe9nFIaBgYFBu2FspwWeQroHkhwDg0+EgMbCeQeKKAMDT5YVWkWa6wFJ2caonIV8yj3RbNLa3/MAmDzt4QJDulsEFV4u80RJaKpGmq92J03PWkLM8sPCJRFJV44TjzHFCKyR0Oj+IK12d7USYlnMuQCNeGuO5ipcGUBQjqpRtVtHljBzVbtbX76eEmKDiWph5knpAfHrZjMCL0HttmIh5kCUeLvUA7iARWR6QJkL5AmDIB5cHubqq+8nZSSIZKCx+3ioDVysb+DCq/jdyrqegYFBc9BlM3A7mXQDrSHCQGPEY6AIMdA/ctVfUjXYBBloXi5xGlFuBGm3WKdlIUdb2y8045zWvU/z/NHWMKS7ReCeB+qr3aAy9zethBjQmNqdJcy8WgkxQIYvExKq3vVioBy4a52DppSiiplzqXOr5wjrrth5iwZ5wkKLZAgJWuP53UD1c5mUf5wlXH+wSWO1wSJFvFUbk65bvOybUrsjIeba4IlNgaLLUbAYilwSc2b7aQOgoL6vgcM92EylhRi128DAYPii02Lgdu3Hw0YJQH+UxqGiKvaXBOtoFnlrNiFrJjkGWtePJhqTUQq7jfvtdnjm4A0an6p1hyMM6W4RZGi5VLvhq95pJcSa4WSuI4upWhL5iavd8WX00mNJucj1GoG1Av0tRaXClSPHlWDOpfK7pTGXF8nb189No/ndSWHmqi36uVSKfruo3TrSBkxUOkTSdUtSu8t+7rY8L+E9qsi3VLzDMPMSl+eJEQKHC1DCKkzVjNptYGAwXNFpM4gqSnc1NDtMtpnkVqFVCmUrVchmE2AdrXxWGIiw6aRz0+Dt21Zo5T1h0BoY0t0ieP5DPLiAxWjEVE0vIab/9rdK7Q6X9UlgRX53/Wp3tTxtPcycCxLU7lah0UlksdlqN/NzutX8pBJiaWq3LC8h4Gqh+BHiLbcahJmXuUCO0dTa3Trxjg/uRa9/cn43gMi5rHb+203trsdUTV03xvyBEn+whBIShJgrB10npnpT4qGoudRLZVyGmSeZqim1W0WltMMgxvoA7gkZSdLAegYGBs1BjjIIygYsfHYwQmYHg7AMRh8y0LnCA31e086pEK0PYTcwqBeGdLcI3POU51YQZp5kqqbXBm4GEcpiqpbmZg6EancW1Kw3nTHMvBlIKkUVKJ8Z1W4Fi0TVbkfoiimgm3MlhZmr2t2VqnQl4kp4nHgDleeyEVO1oYb4vZfFVE2/bhaV59yicsAkHmIOxGs3yy+ZSz1Q4aHkiiDMnFJaYaqmlxBLGtQY7IELAwMDg2aj/PPbQF/5F/JdXf3qPIZqv1MLQ9nMaqipmkPlGqdeM+pVn29gMAgwpLtF0EtKqTBz3VRNLyEWqqC+y3Y/1O6k6dlqd0fV7qQQc30dhYGuN50VWVVTXe3WDdWymnOBoSLMXNXuTgrBT0I4CJJwTquEmSeZqqXvY2ip3TqymqpVXDd/sCTPmG94R32iHb1H3WDQxEPR5YAlle4Sl9edczlg4nJ5TmqVEDMwMDBYLzFpEsSkSci9v3awW9JWWN/6hXYiqmnPWUmVggyai0aNT9W6wxGGdLcIZSGQA/XJGCpM1ZLU7v6YizQSZh5fT6EetRuozEdWSAozjy/fKrW7HtVUqd15JtXTpPrPCop4K3MuKrzMYea1UKF6Zwwz1wdu2lXtTgsz103VZI4WhfBEVbVbeCQSXm4zAld46HUEOm1ZEq7s53VbVA62OFwq3S6XyjfRvBfSDOuG0sCFgYGBQbNQq6a2wdBG1gH/9QGmCzZoJxjS3SK4frixKikliAcX8oRHTNUSSog1onanodlqdxqq1pvuZ2h0wznuMbVb+MepVFOLydzdJLVbr/8MeKEpFyOa4h1l9lnCzLOikTDzNFO1YJtDUO2uFWaeZKpmMRJctxyj4J4Hi+jlw0KiDUTdzJ3AUEHuu+RKAq+bqrkqVAEUNozaPZBwG3RDbdRB1cDAoBLMkG6DNkI7KfMGwxuGdLcIXEj1k4v4AzxJNFWLlxBrBI2aqilFURFvoH61G6htBDaQ9abjpmpQ2/LVbpsBDpdKpxDJaneSOZdO4CiRF0/ld/c3zFyei8owcz3tIB5mri8n10/IPx7CareOamHm8fSAyHUjBHmLwvVEVO2mJEq41QWxKSjxUBYiEmZezVTNqN0GBgbDBTYb/A4jnspmYJAFw0nlH2y4nge3Tp6grzscYUh3i6BIGJAcZi6IVEK5QGIJsUbV7iTiXa+pWpLaHWw/Ia87CfXWm67XgVs/XjU/fg50UzU5HYmmahaj4B5PVLuTzLksn8zZ1Avyu/Uwcz2fv79h5kn53QpZIgcqtz201e6kSAXBvcT0AHXdktTusj96ZDN5rfT8IYv6gyWURMPMBUk1VTNqt4GBwXBBf1zL6x2sT8NQVtuHKV9Yr2DGyg0GA4Z0twiuT8IAVISZZ1W7qymUSWSzGuLEOzIvIcxctjtUu7OEmMs216433QoH7oow5Sph5kmmajajEEKkqN1Rcy5FvFWOsDLnCmAhGGgBGiO4afndQH2mas00p2slqoWZx13oE69bTO2mhERCzBW48NDnSPZcpslh5kmmaqqEGEu4nkPxfBoMDSxatKjudc477zyMGzeuBa0xMMiG/qiFrTKwGlLK9wD93DdrAMNg4GBC3dsXA9FfG9LdIgjPkyWlBA3Uz7ja7QIACxVRvTZwtM5z/9XutGUaUbsrjzVGEDOGmevLZjnOWgp40sBAPaZqNiPwUtRu3ZwL0BRvLb87KcxcV7vrfZBR5zUpzFydyyymasH2UgYwhhppzGKqpl83SpGgdleGmDsiev16HQFKSEWYOSGk0lSNeKCMBFEMRu1uDYTnZR5IjK83FHHVVVdh5syZyOVymZZ/5JFHcPrppxvSbTCoaFTpbiUvbiQFb6j+LmRFvQMYQ2pgwsCgzTAQ/bUh3S2C63mRklLKvCkXSK40YqqmSojVo3YD2RVofdmspmpp6yeWDguIu2p3JfEGHdh609VyhOOmaqoUFfEJd5LarZRThyed7/CEWSQ60JLVVC1CNINpUeItz231cxnfTz3h+oOJekzVktRuRbxdT8SuWTS6wxEe4AgUbIqSf6NbhMAiFGUhB00oR2CqxozabdAP/Pa3v8WECRMyLTty5MgWt8bAoDYaVbprddMDTQpr/SavbxxVf35s9wEHg6EP7nkNR2MM1SiOVvfXhnS3CK7PFKnwAHAArGqYeX/U7kSFtwFTtfh6hBDfECz7lyPNCKyR0Ojk7VefnzXMXDdVy6J2KygFIG6qJrfvwaUeityv7+1/uyQZRHCcWY4xaC+ixFu/J9JM1ZLU7loDGEOFNOoEtpqpGiHSDE8kXDeLUOSoB5fKUPM+SJM8JzZgwgKXcsCiHNS/5lx44NyDS6SpmsNldIaqNGDUboOsuPHGGzF69OjMy//4xz/GxIkTW9giA4PaaFWIbDxPe7AfvPWf7/VBJdYPYSj05wYG7YSB6K/NI2OLITxZAocLSbi58GRYsB9GKYQMNZehwvKHP/7bn0SYk0h2HPo0RQ7UNBV2HF0mfB83L42vXw1pfZd+HOp4w3nR41bKpDrO6uHtGc5F/HiIPF5G5QMGJSScxkK12yIUlMgQczs2+s99EqfUb3mtRZBa4Kpr7qcTcP96J7U5y3Hp05POpb68VGar72Ooj4RH7k2fEAPyGtlMRiVYLLxuhBDkGEXeon7t7vDaxeFwD0VXBIMnZS5QFgIlzlEWIrhuroheV6DyvKVNN6gfyg21kVe9eO+99/DFL34RG2ywATo6OrDddtvhb3/7W6Z1H330UViWhR122KHqcgsWLEA+n8/cpqOOOgpdXV2ZlzcwaAUoHZhX6KMxuC95zKStXonXjZhXq14G6z8Gor82SneL4AbuaDKM3PX8JG6lfvpGW0mmahQqfxfoT253GpJM1SipT+2u5WKelI+cNcy83nrT1Y5Rf19LNQUohCcNtSxKg/1bHkms/yzbH77vdaIjA9XCzOPHUY1gy/ZHzydQGWYebCcWOQCBtlS7g88Jpmp6eoDF5HXT1e48Y7LmMxVaiDlJiFAQ2n65H2YeNVUTQn5PVf1uy089MGp3+2LVqlXYc889MWfOHNx1110YP348XnvtNYwdO7bmuqtXr8YxxxyDfffdF8uWLcu8z3feeQeEEGyyySYAgKeeegq33XYbttlmG5x00kkNH4uBQbPRqn4gyXNlMBAfxB+K5le1ogDW99JYQy3yYD0/3Q2BCwFeTRGrse5QRqv6a0O6WwSpjgGKeFPhATRqsqXqBqu6wKBeYNokv+Fh/mgSUYqTpEbDzLPmdqevn6xupxFvoPkO3I0SRkqjJcQsRvyQZS+S220RUdOcS32Oh5kzm/gmX74bfJwU10AS8U4KM9dN1eR6YUfRnwGMwUCtMPN4eoC6bowR5DzfVI3KwRKbCjhUKuWqhBiA4K8s/eahxEUkzFw3VUsrIaZy6Exud3vhiiuuwJQpU3DjjTcG06ZNm5Zp3ZNPPhlHHXUUGGP43e9+l3mfRx11FE466SQcffTRWLp0Kfbff39su+22uPXWW7F06VJccMEF9R6GgUFL0OhvWK3+bKB+G2u2Y5AHSrPwjaE4EJCGVqQJrO+DCgZDG63qr41G00Ko0HJXyNDVtDBzV0inZBVmrsLOAUmWomHDzenU4mHmclrydogWgqXPq+dHUR+1rBZmDkQJfLy8WT2ID0gAsrNVpCoIKSfRMHPLJ1wWpUFud47RIFzZZvKl4PjX1eGyHFWJCxRdHoSZl7hAmQsZohwLM8+KMIQ5Oi0tzDxyvhPun7ROfyiGSCenTqAiPUBdN0YIcpQizxhylCLHaOSaqb8qx7vXESg6An2OqAgzd4WAwwVc7kW/n/5pil8Dg8FFd3d35FUqlRKX+8Mf/oBddtkFRxxxBCZMmIAdd9wRP/nJT2pu/8Ybb8Trr7+OCy+8sO62vfDCC/j4xz8OAPjlL3+Jj370o3jsscdw66234qabbqp7ewYGrUKjpJQGqVqteQ2VdjTarqB9AxS+n+XVDAx2ekAr0w4Mhida1V8b0t0iOMKDE+SDhjmhRc5RckWQNyq0PG7hKUIutxHPy2304T4ptzttGUVmFNJyu7OiIj9dI33x0dF6yWJabm2kvQnEW70PiLf/V6qa8vgtRmXOGQ1zuy0SzQ/WByx04q3yu4uco8xFxUBLfyG8cBBDkb6kkea0PPnK7Q09og1Uku20AZP4dVM5+VbworBpcl6+It4RAu7K61ZyJQnnXA6KuVwE30/hf7d5wv06VM9nO8D1BzoaeQHAlClTMHr06OB1+eWXJ+7n9ddfx7XXXostt9wS99xzD0455RSceeaZuPnmm1Pb9tprr+FrX/sabrnlFlhW/UFijuME+WL33XcfDjvsMADAjBkzsGTJkrq3Z2DQSgxFgjfUyPNAtauZba1o+xAg/q0cEOgPBpv0mwGCEPV6sDzyyCPYc889g+VnzJiB733ve5n316r+2oSXtxhhHWfApV6Q380EQdln15TSoHa3KiEWpIRrtYHVD22tclCUZAszV9PSSoip2shAZW530j7SkCXMPEu96f6WEAvORSxMnhL5X7wUVTy3O8coXM8LwpUd7sGmpCKXu0/7bFGKIud+uHL/andXhNRr+d1A9Fym5cnr2xnqud061P2WFGbOPA+ALBumX7cc9eCq3G5G4fjXUQ8x535uf285vGY2IxVh5ozJ8P54CTGZWuGZ3O4hgnfeeQejRo0KPqeZogghsMsuu+Cyyy4DAOy444544YUXcN1112HBggUVy3POcdRRR+Hiiy/GRz7ykYbatu222+K6667DwQcfjHvvvRff/OY3AQDvv/8+Nthgg4a2aWDQCtTTv9e13Rb8RjYz0qjV/V4zz+lg99EDMbjczn3qcIiAk4JiY/dBPes14sHS1dWF008/Hdtvvz26urrwyCOP4Etf+hK6uroy5WS3qr82pHsAoIh30eUoWAxUeLCIF5hs8SC3mwWmamklxLI+3GftNJMJelShrpbbrXLA4+tU5lxXEu/4sVQzVUtDltzuyGBCCnnTTdVUCTGLUXCPB/WfVW53zidwqv6zInG6QZfrRzoUXQ5YgKXyu4ky55KmanHiXAtp+d1p5zJO7Ns1tzv+PpzvP3xQeR2Edt0q1W5pqsaFT7xFMvG2HeIr4wIW57AoQY7TxBJiKrcbwf1kcrsHE6NGjYqQ7jRMnjwZ22yzTWTa1ltvjd/85jeJy69duxZ/+9vf8Nxzz+H0008HIIm753mwLAt/+ctfsM8++1Td5xVXXIFPf/rT+J//+R8sWLAAH/vYxwDIUHcVxmZgMFTQrN+vVpOzVhGzVpCmgeoTBoQQD5H+bahGlrXzgMFQQyMeLDvuuCN23HHH4PNmm22GO+64Aw8//HAm0t2q/tqQ7hYiMGryH/Cl4h39JVcmW0QQuDw0VfOIb+5FvMCsSUcttTsJSWp30I4WqN06WZKfo8QbQOjAjXRTNaD/ancaeZOEDRBctimumtpMvtdzu3W1W9V/BhB1xY6o3QRlLutHl/2Trmp31woL0sPt4yZzulGaGsSIn8tgO22sdsfvsyS1W5mqWUwOAsnrhojaXSR+3nc8xJx7AOTglsM9FB1pmqe7meeoiJiquUKAURoMiinDQ4P+gTc4cl6v98Oee+6JV155JTLt1Vdfxaabbpq4/KhRo/D8889Hpl1zzTV44IEH8Otf/7rqA0Bvby86Ozsxe/ZsfPDBB+ju7o6M0J900kno7Oysq/0GBu2CdiHvcQx1Vb4aBrLvHmzSO1SeU+IY7PPSLuju7o58zufzFRFqf/jDHzBv3jwcccQReOihh7Dxxhvj1FNPxYknnph5P8899xwee+wxXHrppVWXa3V/bUj3AMDhXkC8KZGqtkWIb7LlG5oRAu5ppcZAYTOV1908tbvatGolxEC9utVuhegAQZQoVgszV8vXW0IsjTAmkbd4KSpdNQX0UlRRtZsSqZb2OdF96MSbcQ8W9VCmoilh5pHBCv2c1ggzr1VCrN7zOJhIHDDxvEh6QLSEmBzosChBwWKBx4LNCGwe3u+KeKtUARWGrsLM84KCCQLKEeTUcYFgUEwvIWbU7qGPc845B3vssQcuu+wyfO5zn8NTTz2F66+/Htdff32wzLnnnov33nsPP//5z0EpxUc/+tHINiZMmIBCoVAxPY4NN9wQ++yzDw477DB88pOfxMSJEyPzN9tss6Ydl4HB+opm/pYOFiFqpfo5WOHMg9HHtQOhHQ59v/KhanRdQPqw6Ljwwgtx0UUXRaYpD5ZFixbh61//Op5++mmceeaZyOVyielgOjbZZBOsWLECruvioosuwgknnFB1+Vb314Z0DxAU8ValxFzqRcLMy1wgTxhErHa3XkIszsuyqN3V1Oi42p20Trwed6O53Wn5yNXCzOspIVYvquUIJ5WiIoQEYeauR1HwJIFT4crJJcSiNaBVmLnlD24wohTW7GHmceINkKph5vp6SXnyct7QD4WqJNte4oBJ4nWjtELttmkYYu5wZeAFdNgMvY4k7CgiCDNf57iBYYkMM/fzyY3a3ZbYdddd8dvf/hbnnnsuLrnkEkybNg1XXXUV5s+fHyyzZMkSvP322/3e17/+9S/8/ve/xy9/+UuceeaZ+NjHPobDDjsMhx12GLbbbrt+b9/AoNlohDC0AxFSaFf1vRrWR0KfhsEktEPpmq8PyOLDUq8Hi46HH34YPT09eOKJJ/C1r30NW2yxBb7whS+kLt/q/tqQ7hZC5frGwyVVfrd+9plN/NBzCkorTdUoFEFFXWp3ErKYqsnl6le7k/ahkJSPXC3MXE5PJ4tJ2077nHjsKTnCumoq/JxhqZr6pcQIDdRuFa4cqf/s53N3+kzeZsSvAa3CzEUkrQDUq8t9Mk7Qq4WZx03V4ucmPoAxlNXuLGHm+nWzKIHQohRKPKp2O8IDtEgFl3voA5fE28/v7i5Jpdsi8hrqpmpCyHUAIfPIjdrdVjjkkENwyCGHpM6vVRbkoosuqhiRT8LUqVNxxhln4IwzzsCaNWtw55134ve//z3+53/+B+PGjQs69FmzZoExVudRGBgMDfTnd65dicz6oL5nQasH5Ycaqa+GodCfD+V7pV5k8WGp14NFh0r92m677bBs2TJcdNFFVUl3q/vrIa5vtS/ipYkA31yL66XEZBkxVctZlZRyuEC8hFi8/JNCUlmtOLL+SOjLRd7HjiWtbrd8X30fYVkl/7PwKtqt15tOWjeynQZ+rCuM1nxn+LRSVBaTZcUYk2p33pK1nwsWQ6ft14CmYR1oLuSr1xFBjrCqAe16ftk4v3a354Ulv+qp3a2XDJN/kbq+Xrs7SwmxdkDygIqcbvk1uSkNrxsjJKjbrQZLbBq9boBPvB2OoiuN1YqOQE+Zo8g51jmuLPVXpYSYQf8gPK/hVztg9OjR+MIXvoDbb78dK1aswHXXXQfOORYuXIjx48fj1ltvHewmGhgMOFTKTiOv9QX9OQftfk6GQ/mxZmJ9uOb1oF4PljQIIVAqlTIv34r+2ijdA4iI0RZkPmgR0tVaN9nKE1ZhqqZSveNuyVl/TCpUwgS1W0E3VQNColZL7c6KmkZgMaVVHm/z1G4dSWHmuqlaRDUVYU3EJLU7DkW8FZQ5lx5mXubSDb3eMHN92Xh+t662NmKq1u5qd9RUTb5UaoCudvf5YeR6SoAQHlwAfeAAGGxHgFGCTlvAorJ2NyM8NFWLlRAzardBVti2jblz52Lu3Ln44Q9/iOeeew6u6w52swwM2gr9/W1tlwG7etDs/mZ9OEcDRbzbSbXvL1zPg9vgvVHPevV6sADA1VdfjalTp2LGjBkAgL/+9a/4zne+gzPPPLOh9jarv24b0n355ZfjjjvuwL/+9S90dHRgjz32wBVXXIGtttoqWKZYLOLLX/4ybr/9dpRKJcybNw/XXHNNJBH+7bffximnnIIHH3wQI0aMwIIFC3D55ZfDspp7KighsP0vuR5eHiXekj27VKqfQBhmTimFK6RTcpDbzaKhxMFWMuR2p7Uxa5g5tPBxWUKsMrcbNF1tjSPNCEwPM69mqqZvo5Gc5Gph5nqOsMXUvlUpKg85L5rbXeICrl+GSplzhdfZLyfGQpJXK8y8UeKt8rvVuUkKM2/3EmIAIkS2YsAk0VQNwXXLa7ndHTZF0RWwuSwp5vrfQUW8HeoF+d16mLl0sRegXA6kxEuImdxugzQUi0X885//xPLlyyG0pzNCCA499NBBbFnz0W59tsHwxHAMi68XhsRnx0Cq6sOF4DfiwSKEwLnnnos33ngDlmVh+vTpuOKKK/ClL30p835b0V+3Ta/10EMP4bTTTsOuu+4K13Xx9a9/HXPnzsVLL72Erq4uAHI05M9//jN+9atfYfTo0Tj99NPxmc98Bo8++igAgHOOgw8+GJMmTcJjjz2GJUuW4JhjjoFt20GCfitgUZJKvG0qazlbduhmzggB9xU011+WtUDtrnedeAkx2a6oqZVcRw0ORPeXRCbj9abjP+5ppmrNcOCuXzWVpNiiFIyIQO3OM1m7OcmcK8zgiNeA1t3Mo7W71blKgxroYEmDFp78rxppF8HITXuWENNRbcCE+KZqlAA2o+DcQ55RcMFQtmRKR8GigcmhKwjKQqZ9WKAoOr7aTQVsStBDw5rdeU6lqRqTA2OeZ9TuZsDxPFgNpD04bfAQd/fdd+OYY47BBx98UDGPEALuD/qsL2jnPtvAIAuMyt4YDIlvDta3sPlqqNeDReVlN4pW9dfE89rzbl2xYgUmTJiAhx56CHvvvTfWrFmD8ePH47bbbsNnP/tZANKFbuutt8bjjz+O3XffHXfddRcOOeQQvP/++8FI+nXXXYevfvWrWLFiBXK5XMV+SqVSJAegu7sbU6ZMwZo1a6om/9/27Ltw/TzPXkcE+dw8UItJoHp22n6OMGPotCzkLYoOi8GiFHmbwqJSsbOZzFe1GA2IBCUh6U7LyY6jaq60iE7jWq6qyhsO5/n5135ecphnHYakx0k3EFVaVTulGzQJplEqSSUhUu0OlqNhzrU6j+G85GPPei6EFw4eyLx6eRwyf1dev7IrUHI5yq5AnyvzfLvLDtY5HN1FjqIrsKbIUXS8oCa7ReW1K1gEowsMI/IMI3IMI2yr4nqr464GHh/0QOzc+atTlaOecC7VeVTrJJ3H+LkbaqQxvC/D/H7heUGt57TrtrbsBtetp8yxqs/FmiJHT4mj7AjpZM6lA3rOYhhZsFCwCMZ1Wtig00KnbWGUbWNkzkJHjiFvMeQsipwVfkf172Z4jw+t81cNHXb1+d3d3Rg9enTN38GsUNv7/v3Po6NrZN3r961bi7P23a5p7WkFttxyS8ydOxcXXHBBRRmS4YCh3GfHSz8aGAx1DFeiOVBot/M7Mp/OwFvVX191X2P9NSD77LP3G7p9dqv667YdJ1mzZg0AYNy4cQCAZ555Bo7jYL/99guWmTFjBqZOnYrHH38cAPD4449ju+22i5zAefPmobu7Gy+++GLifi6//HKMHj06eMVryqXBopIsA4gYNSmoEGTlouwKLzTZckOTrTRTNbUNnSg2+iORbEqVbqoWX57FyIVcJ31/uopbzQiMe15DpmrVlo0jfpyKcKaZqqnBDotS5CiFReRgSZ5RdNi0wpzL5ZJ8KwO9Xt9UrcQFilwadLlCloxzhQhM1bKCawMd6ljjQqHapn4uk4z5apnTDbVOKE5mo4Z4gMWob4KHgAhblCLvD3AVLOYPhtAg+kAfEHK5h7LLA2O1tSWO7hJH0ZXXLW6qpowRhRf9bsYHBwyGJ5YtW4ZFixYNS8INDP0+28CgnTDczNYGGs00tjPnvf3Qqv66LUm3EAJnn3029txzT3z0ox8FACxduhS5XA5jxoyJLDtx4kQsXbo0WCZ+AtVntUwc5557LtasWRO83nnnnUxttPwwZPUlsxJYqCLeAQlz5YO8cjMvc+GXJRKSJPnKsnqwT1Kog88ZiWbFvNgdwWg6kWa00qRLbqO2OppEvNV0fSAhsk4CWWyGA3eFMh53M6dSIWZERRlIUs0oCZzMLRpOk3/lwAsQEu+iI0l30Q3dzMtCoMS5P6AiiXG9SCPeWR21lTqs3sfntQOSohsCN3PdsVx3oPcJuM0IOnOhSg0gJNLcQ9kR6HM4eh2BtSWOnjJHryvV8j6XB4NnqoSYOu/t7A5v0Hx89rOfxeLFiwe7GYOCduizDQyGEwx5HBwYMt8eaFV/3TY53TpOO+00vPDCC3jkkUdavq98Pp9YrD0rLEogPBLkjQI+2Y49kFvUg00lOZMmW9LNnAmSbKpGZF6uyh9VX7qsuaNJubBppmqV89NzuyH0dVChpuqI5iLXNgKrx1StUQfu+HlRxyHVbpkjbDF5TaXa7cFlDAUmUwhsJo239PrPypgLECg6FDYVkf1ZpP+1u7lXacKm8rvjbua6qRpSXOHbJbe74j5OcaG3/WtGaXjdCkzmdnfYsjRYwSJwuFTDLeb5Od0CRX/bRSqvXcES6LTloEmJi4ipGqUyR1yF7+v3sWrrUDp/Qw0O92Dx+gcr4pUhhiJ+9KMf4YgjjsDDDz+M7bbbDrYdjeFv1FW1HdBOfbaBgUFt9Lcfa5cBfYPhiVb1121Huk8//XT86U9/wl//+ldssskmwfRJkyahXC5j9erVkZHzZcuWYdKkScEyTz31VGR7y5YtC+Y1E5QQWABc/700aqr8kVHkuy9SWio02SpzERhtuVw+sKsSYhQkMBWjrDEX7yyoXUKMVCi0VHM7TyKyCnFztSQjMO613oG7GnlTpmryK0chhJCDKYzA8+TgSI5RWFw6Yjvcg2N74EIE5lxCeCgLD7CBoiud8HRzLqWs66Zq/SHeSaZ0SahmqhYsM8SJok5m9UEidb+qAZOghJh/3fIWlaZojKJg08CpPBJiLjy4IjTMsJmNtSUul/P3YfnXzWVaCTHhBYaHNCG9xGD44X//93/xl7/8BYVCAYsXL45ECRFC1lvS3S59toGBwcChGc8UhrgPLhodJFfrDmW0qr9um/Byz/Nw+umn47e//S0eeOABTJs2LTJ/5513hm3buP/++4Npr7zyCt5++23MnDkTADBz5kw8//zzWL58ebDMvffei1GjRmGbbbZpansVkVIhxjJnNPoj4/gmT44IzZ8c4QVh5irsuMxlTrDwZP6ontudaFiWMYc0KSQ3eB8zJYuHmSe5Yyfldke2n5LorYdFy7/Vw8yD9eIh9v3MSVbtDv7GcoRVmLkiZhajIIT4YcosyO22WRhiruAKmfOrQpWLbpjfrYeZl0WYz5+GakScx85H/FxWy5MHouH67ZLbrYOShLz8WHqAft1UbneHTdFpU5kWoAwL/YEjIaTqXXQ41hZdrC2FYeZlIQIPBkczbpPfV3UNTG63AfCNb3wDF198MdasWYM333wTb7zxRvB6/fXXB7t5TUe79dkGBgbtBROWbdAqtKq/bhul+7TTTsNtt92G3//+9xg5cmSQzzV69Gh0dHRg9OjROP7447Fo0SKMGzcOo0aNwhlnnIGZM2di9913BwDMnTsX22yzDY4++mhceeWVWLp0Kc477zycdtppTQ9HswgFqPDDkAnKWlmieK5nMOITUbujtZxdIUNYKWFwlZrmAf0pIQZUDzNPmpa0fDW1GwmGXUnQ603HVdZa9ab1EmIV221QpU08zgrVVF5Tz6/bXdLU7qIjAnMu12+vLP8mgtBzReD1GtCNhJkrcqyuO/fi5weZwszrCdcfaqgaZu55sJgMWeCeKiGWrHZ32hRFx/MHy6TC7fKwhJjFCPocgt4yQcES6HVcrXY3hUsELCa/Cy4Xsra78IzanRFq8LGR9YY6yuUyPv/5z4MOkzov7dZnGxgYDD8Yxd0gCa3qr9um97/22muxZs0azJ49G5MnTw5e//d//xcs873vfQ+HHHIIDj/8cOy9996YNGkS7rjjjmA+Ywx/+tOfwBjDzJkz8cUvfhHHHHMMLrnkkqa3l1GZl60bOenz4lCKt1LJypq7dYkL6YasmarJ8mPJbtVAc1S1WqZqceWaEJKqdif9sHHfkT1oc4IRWBpa5cBd0e4apmoWo2DKnEtTuwt2qHhbGuFSpmou96SxWlnmE/eUecX1VoMYWd3MK8z0MhqpRdbJaAA2FDuZeKSCPl03VYtctwS1u2CHkQzqHi86XCreZY7eUqh2d5c4ehwXJc5DUzWufT9VlIFRu4c9FixYEOmv1ne0W59tYGBg0AiGq9oeVFVq4DXUn4Fa1V+3jdKdxdW5UCjg6quvxtVXX526zKabboo777yzmU1LhAoltzxluuUbqlHfVM3/W5nXEDXZKgsBi8u837ipmqwDTUGJ1zK1O6upmlS7E7YdU7v1XG8FlYsMxM3VkKrQstjx6Wp3f3OS03KEObwUUzUKzj3kGY2o3Z0qv9s353I5h8vVRZIqqs0IbEeAUYJOW8Ci2vWmRJrm1XEtlTIdz+8GSF2mavp5axe1W4eKwIjn5eumasKj4MxDp83gerJ6QKB22wxll0cM8aSxmoRFXX9QhaPDpuh1OfKMIccEXK7SEQBGKYRHjNptAM45rrzyStxzzz3YfvvtK4xZvvvd7w5Sy1qDduuzDQwMDAYDxpRu6KFV/XXbkO52g3TzBrigcIkMV3VFNMRcuZkr4q3+ShdzDyWfxQZhx4SkmqoJL0qYAESIY71f6jTzszRTNQVCCBiAuJN5tW0CUeKtt13/PBgO3Iq8CRW+nmCqRgmRqqmn1G7pZJ5mzlV0OCwuP68tAoBVEWaeZ15golevqVqceAfnL4F4W7FtKlf4tHD96LJDj4SnhZnHB0yUqZp+3UbYFkpcoNOm6LUFig6DRTnKgD9YgtDJ3OGgRTn4o+p85ygNTNUoJbAEwAWCQTHjZD688fzzz2PHHXcEALzwwguReUmlFw0MDAwMDGrBPEc0H63qrw3pbjEYJYlqN6MkUjZMf9+rqd02JShyHoQ4M0JACAGlfi1gLywh1ky1OzIvpnYr4h3Or652y+2jgqDHES97pRPFNBM2hbQSYuH8xtTu4HMsRxixUlTCoxACgdpd8Bg6bJ/AOSIw53J9sdT1Q5UBoM8hWFNExM08x6mf1y+d0QFSMTBR9XxoxDvu9q5DmqpV5skDiJSjaye1OylSQR8wYZ4Hzy8fZrPoddNzu3sYQSHHUHYFLP96uZxH9mUxgk4/lSDHXHntKAXjxC8hFlW7QRFUHRjq53Ew4HAPbD0tGfbggw8OdhMMDAwMDAwMaqBV/bUh3S1CQI6qqt2k4mFRkW+HyYfPXt9cLRJ2LIhvqkYq1G7AA0Ml2czykN+IqVpc7WbUD/8GIuG9IkbShZAEMp6v3Gi96bQSYv1Ru5PIm37ccdVUMIKckKqpK7yq5lyqdnexzGWJOEqxpsiDMPNel8MiNGKqFlevw/OZnMMeJ971hJnrAxjVMNTJYzzMPGqqVnnddLW7w2YoOyJa0k4bLKGUwCoRrLVZEGZeYAy9LpemaloJMTUoxhAt7TfUz5+BgYGBgYGBQRyNDpKrdYcj2sZIrd2gVGn13qK+OzUlQfkwRkkQYg7IUFQuJPHuLQsUXVlSKigjxsPyRJxLd+R4CTG5ndrltupFFlO12iQ2WxiMIuJJRmBppa+UqVpSCbG0z/VAlaJKM1Wj1A8pphSdFqtqzgXIcOWyw6Xi7UhzrqLrBeZcRbfSVE1dz1rGanoupX4PhKZe2U3WuLac2lY75A+lGeKlmapZVKrcOUbRZTMUbIqCRZCzKQo2k99fFvoOKEO1YpljTa+DNUWO7qI0VVvnuOhzOUqONMxTpiHCi53PNjiPBv3DZz7zGXR3d2defv78+ZHyWAYGBgYGBgatx0D010bpbhFUTjfzCFx4sEhU7dZhs2iouQzRViZrArbjm7LRMMw8x6j/MI9ICbFWqt21TNXUZ1VCLIvanYY0I7BaYebB+k1Wu8N2I6KaJqnd8VJUzB9oKcTMuVzhAZo5l8wRttDpCJRsgV5XhiurAZp4/nU1/H/23jxOjqrc///UOVXVPT1LMglJICECCZBclhAWiUE2JRK2CKiIgAHCoiAqisgaDKsRke3rvYByRRHkh4LceGWPcEGQVQEF2WSTEAhbllm7u+qc+v1x6lRXVVd1V/f0OnPer9dAurq2PtPTpz/neZ7P4ziOV3fC3dT/qNR07zkkayEWRatGa5OkmRO3L7dBNXQ4FDnGkaYcHQbDuDRF1naQNziyBvWM1PJ5G9RdPJGGiaYh6roNt7932qIi2s002ExT0e4xyh/+8Ad8+OGHifZ1HAd//OMfcdFFF2Hy5Ml1vjOFQqFQKBSSRszXSnTXEWl8FuVkHk4xN9w66ULKhYYhiyMD0fMZgBshF2nmKUZANQ22xkE04ZKtaaK2G6RQP1ppbTeQLM0cQKC2m2jiP6WEtLcffCLIl2Ie1W+6nBFYI/pNl0oz16n4t+OIKDZ3xH2bTKQa53WOnrTIWsjaDkxdmHNxSmBzFhDeOtUwkBMCXQr1POMBEz1ewe/TL7zleJZLMweKxyZqAaMdhWLUgolOhBme7i5iZXSKHGPoNCiGDY60rmHYIG6kmyDv/i0y17xgAIBOCYZytshyMGWauY2ULSLoOhOCW9PEopGq7Y6H8WQt66KOa0Ucx8HWW2/d7NtQKBQKhaKmjLb08kbM10p01wlN0+C4pmNR0W6DcM9QzaBCeFNSCHyKN6Tm1XRTIv5NNAZd05Bzna2lqZpsIUYJ8b7MVxvtLkWUqRp8/YeDpmqlo91x+KOA5YzAYs/hayFWbr9KDdaiWlHpVCweCLM0t2+3L9qd1gnSOsewz5wLgFsiIM49AEAnBP2Ew6BMuJ67KdEpnQRM1YruK6auWwrvUvXdJFTiMNqj3XJ7XAuxTkNHngWj3fJ3Juu5GXPAXFO1gazllRZsMDSkdYIUtWFSApMQN7VdZLTYTCySqWj32KAaM5Zp06bV4U4UCoVC0Y4kK0VT3x9GSiPmayW664QQQUJ0RkW7iRaMdssUc4NqGPZSjoXAspiDrCvSRPoq89ytw6ZqtpuSrlNSt2h3XJp5VAuxUucvJcLDKeHN7jedpBVVOGoaHe0umHPpRLiZ28xBFkEB158Thmo5gyPPRS1/2FQtKWHhHSauHZtYvAHCLcTaLdodWGzyL5iIZ+E3VaNUOMdndB1Zg3nRblOnMHUGnWqglMCybABAPi+zFIjrVi4yFDoMgrTOkKIFUzWZjSJru1W0e/Sz1157NfsWFAqFQtEiKC+X1qUR87US3XWCytpmhphod7B9mEE1GEyknoq6bw6beyFJwG0jZljiS73nbi3TjqkwViOEBFqIjSTaXU2aOVA+2g0ecjyPcDEHgu7bUfXdUcKbUCnQxSJALaPdSWqEw1HTyGi3a86Vt0JO5mCFVGWqYUO2kGaua4WWcVRzKkozB4LCu7A4UTyWlPrHv7iGvlQrtnYRj1ELJlQT6eMGd8B1IM85MkxHT4oja3H05zjyJnV/b2LRxLIYZAexISpEuE4J+g2KdcM2MoaIdKcoAaW+FmIMIBoR7xtNU9FuHxZ3QKtIFbdaNL1coVAoFKOPVhHPhfsY298d2gUluusI1TRX+BZHu8Ptw7J28fEyai2Ft0EdZC0uvrxrTPQDlkKMaSAaBecItBAbSbS7FLWIdovzFOrAo1Kkw8K7HFHCpR79pqNaUYVN1ahrzmVzB3kejHYPURum4T5ncVgWA+cEA67Lmk7Fe0S2ojKpL7shxlRNjp8sbQCC41ZOeBM3RT6uTj4q2t0ulFsw8coDiDDD69ApcrZI508bBN0pgqxFYRoUhNigVINliTRz2QYOgHA6pxrShoa0LlLMpamadLnXifjb1mkwy2CsC26FQqFQKFqBeovqVhHtI6HaRXJ57FhEie46IcUkIYiMdpvEgU24F+2WzscWd/yG1gHh7a/vNqgWcLc2GXFN1VAU7QaHV7fbiGi3NFUrF+32G6qVolb9pmtRkxz3+qNM1Qwu2nylqGgjlrWZF+2WhmqAeK8wJsy58gCylIFkpejWkDGYW1ZAgtkNCRdSZBYBdcW4VmIMkpiqyQUM/3HtEO2OSzMPmKpR8bbSmYOMQZENRbvTBkXapLAsWdvNvWg3AKx3U8xNnbq9vm2kKQ2YqtlUeC/IRTF/TX0rj59CoVAoFO1IrURuvcRyLVv8KlqXNopVtRdeH2fX7MzvZO717SbEq+32UsxDvbu5Gwm1OUfWcjBkFfp35xkP9O62mAObO7CZ29fZKUSdR9K3O04ESMEnn5f9qys+v6+nuZ9K+k2Xe23+9PeR9puWrzGud7dOxXbpUm9SgpTbt7snTZExiSfe0kYhn5sxjnzexlDWxlDOxkDWQn/W9npA+3t35xkP9O5OghTf8jjmOJFjWRgz+b4p7oHuH8d2xd+7W6ei57pBNa+FmOjZrSNj6F60O22I3xmlxGsbZtscQ0MWLIshm7XQN5RH31Aea4cYBnIMfXkLg5aNnM1c4zzxN1oYe/H7eOWVlzH/kzti3i5zMW+XuZjQ04H//cOK5g7SKOX888+H5n42y5/Zs2fH7m9ZFi688ELMnDkT6XQaO+ywA+69994G3rFCoVCMTbx5cgQ/tbomIL77cA688vIr2O2TO3k/k8d34n9XrPCeL/cjvwvYrPGvT9EcVKS7jhBNA0d8tFvXuFfb3WEI4zMZ7dYJge0LoeUtDhgA4xRDee62D9NANFu4W3MSMlUrtBAbabQ78nUlMFWrJtodTjFP2m8aQMnU6Ho6cMe2ovJFTYui3YaGrCVSlfVAqrLoAy2N1nRKsE72kZatq4iGlGvWFk4zj0oxLxoj9zkeGCcA0MSvJdS7m9DQOcqk67dytLZUmrlOC6ZqOiUwHQcpJuqye1LUjXbrGMhaMA2CvEGQzxPPTG1oSJQGUEowZFD0DefxkaEhbRBkjIKpmk4JiOZAp04g2r3V1rPw+NPPinMNDuI/ttoc+yz4XFPGqVnYrLpWIjYrv0+YbbfdFn/605+8x7oePx0uXboUN998M66//nrMnj0b9913Hw499FA89thj2HHHHSu/uEKhUIwB2lEQ+gM5pe5/5lZb489P/A0AMDAwgLnbzMRen11QEOc1eO2tHNzgvPp2na38uuqJEt11gsjsZ9lSK1TbzXhBnMnabhntdst6PWz57vS2UxgW9/bPc45Byw6YqnGn0EJMuKUX0oerqcWtpakaR/QfaZyhmhSPSYzA4sRiuIXYSGu7K2lFZbi13TnG0WXoyMnabssRxlyuORfARPSTO4BX202gUw39BkXGYMgYonWYbBlXzlQtakwD/c9940moz6zOZ6omx66SBYxWplyaOXUj39wh6NCpaCHGOdIGQ3eKYChtIG9zDGdFbTchGnJZG4QSDA1ZoJSgzxXXhTTzoKka0QCbFRbFwrXdd/3xf7H3Z/ZBZ2dns4dr1KLrOjbeeONE+950000499xzccABBwAATj75ZPzpT3/C5Zdfjptvvjn2uB133DG2nCPMM888k2g/hUKhaBStLJrreW92SBWWE4l3/fF/scden0UqnXHLQgVR32krIUmbXUVtaMR8rUR3nSBSdSM62k2J5rUPk9Fug7rtw4gGRgGba8i7f3BebbcrvGUqrHAztwO9u4WpmvhhHDWLdsfWM1doquaPDMsxStLD2x/1jqvvJiG39lItxKIeVzMeUeJNtGzjwhDNjUpndIocYwUnc0ND2qDI+sy5OHfAmFu7T5nXQszUKdK6iJoSTaQ+y1IFClTsZi6zArTQOPjHkjmoyFStXaLdYQgBOCssmAhTtYIZnvi90aJot2FQGAaF/KO08hYAA/39Ofe8GkxdpKRnDFv83qkwVTMocVuICYf4cG3372//HY786tFNGI32pq+vL/A4lUohlUpF7vuvf/0LU6dORTqdxvz587F8+XJ84hOfiNw3l8shnU4HtnV0dODRRx8teT+HHHKI9+9sNotrrrkG22yzDebPnw8AeOKJJ/DPf/4T3/jGN8q9NIVCoaiYVhDNrXAPkqQR1pxVfkf/V9b/ueM2fOnwryJnJw/httK4KBozXyvRXUcI0cCZUyLaLduHiWh3hyG+iAdSzKXYQVB4ZwmBdE3WiQaTMhArylQNXrRbiODqo91Fr2+Epmo8JiU0ysXcj985m/tSneVzpUzV6unALRcT5IKDQTXXVK3QQqzT0EWk2hftzlrMM+eyLA7GHDBfaYHsAS1rjXWiIa0XWsZ1uA5u4dcTTjGXvxN/C7WkaeZx7SjaSVz7CWcqeH3jQ+UBBne839uQzdCV4uhOMQylDQzlbGSzFlIpHZbFYVs28tk8ONOh6wSEaOgzRe2+TDNP6xZSumuq5rYQM7gWiHb3DWzAE088hl//5tZmD1PDsbgDUkV6uXRCnT59emD7smXLcP755xftP2/ePPzqV7/CrFmz8N577+GCCy7AHnvsgRdeeAHd3d1F+y9cuBBXXHEF9txzT8ycORMPPPAA7rjjjsDfaRTLli3z/n3CCSfg29/+Ni666KKifVatWpX0pSoUijFEM4RZU67ZIunG8rXnfeK5XLS6v78Pf33qCVx53Y3Ilal1Gk06u9r5Wh5bCeeffz4uuOCCwLZZs2bh5Zdfjj3mqquuwrXXXou3334bG220Eb70pS9h+fLlRQvokkbM10p014nCl3mg0mh3xhXfjAJZX6q5zaQq4shaDACFQTjSOodBGXRCRJsjwkU9N3Ggu2YNRBM11swXEa5FtNs7PhTtlnWypT68pdDxR7vBCx9wUXXJ4bZXXpq0g6I086i2WpJ6RLvDNcJRUVOTEWR0HVmDIa0zL9qdNiiGKPH6YgtTNXEN6orutEl9aeZ2oGWcSUlhXEKEU8wZF7+fuPruqDRzFkjrHx3R7qhMhbALPXcIOHfc3xtFl0kxLk3Rn9ORSelIpw1k3TRzAJ4IGx4WRmv9g3mxEOammfekKAZ1CtN1pidEg0WC0e67/vgH7LNgX5gxEVpFPKtWrUJPT4/3OC7Kvf/++3v/njNnDubNm4fNNtsMv/vd73D88ccX7X/11VfjxBNPxOzZs6FpGmbOnIklS5bghhtuSHxvt912G/76178Wbf/qV7+KXXbZpaJzKRSK9qRu7td1VnONEsWtEv0dyic3Crn3zj9itz0/C1AzUYRcUR2V+LDccsstOOuss3DDDTdgt912w6uvvopjjz0WmqbhiiuuKHutes3XSnTXiYC5VgXRbpky7EW7qQbOC6Zqwv24cB2DaujPiZZSKcqgu07alGgwuAabcRDiRtAhr6/VPMorX3M4zRyoLtodJqrtVal+04AQ6I2IdidtRZVyxPXynCNtCyfzrM0xkGMwdSLMufLivmybw3ZXWgmxXFd0ESGVaeYmtUuaqoXxZyCEhbcff5p5uHd32FRN7t/K4roSohZMdEqQ0ik6uchS6EoxdKeYF8U2DApCNGhEA89zcLc8YAAApRooJTCNHNIGRXfK9tzspamaTTkoA4hGwB0Hd9x+G4474cTmDkSb0tPTExDdSRk/fjy23nprvPbaa5HPT5o0CStWrEA2m8XHH3+MqVOn4qyzzsKMGTMSX6OjowN/+ctfsNVWWwW2/+Uvf4ldeVcoFKOXWgvMWgvjugv5FhHYfuQY9g/b3rZy3xHv+sPtOPSIYzGYs0vvmJA4s+CxTiU+LI899hg+/elP48gjjwQAbL755jjiiCPw5JNPJjq+XvO1Et11pFArCiSNdmeMgrGawbRCirmL7YqmrLsKN2y5rY5yLNCzO8VE6jEhGghzhOGWA88t2R8RrlW0O8xIot0g0e2w/NHZqPpu714q6Ddd6yitP8080lRNp8i50e7uFEdXimIoJ6Ldw67RFiBS53O+D3Gdap44z5jCnCtNacBULWwCIbMFssNDOGLhPOy938H45lkXeWPgCW+gKM08PJa69xijOtodZarm/711mbaIdneYGMjaGOoQ0e5cVvyurLxVEN6e6JbtxjT392Z5pmo61aAT8Sewbt16PPO3p/Gb397e0uM22hgYGMDrr7+OxYsXl9wvnU5j2rRpsCwLv//97/HlL3858TW+853v4OSTT8YzzzyDXXfdFQDw5JNP4oYbbsB55503ovtXKBTtgd8ZuxRDQ0PYdcdtccgXvoSLl19WdI5a3UutaZU08ZEYmD1w9wpYmY0weea2sfvo7veewf4+vPD3v+GsK36Bddl8YJ+k4plqySI/8pqjiXr5sOy22264+eab8dRTT2HXXXfFG2+8gbvvvrvsHC+p13ytRHedkCm9flFZSbQ7rRfqu7OWEFwyws25AxsibTxvcQwAor83YW7NL/HqfQumasEWYjSmRjcp1ZiqlWohFudoHpViHk6L9q9ChtPM41Kjay1oYtPMxbNgnLupywQ6c5AxKLJMR4fBMC5NMZDTMZC1PXMuXWcYHrbBmAPOZSsq93hK0GFQpHWCFLXdaHrBVC3KffHGa67AtnM/WbS9lPDmWtCgzp85QELvn1YX16WISzP3R7tN9+8xY1B02boX7e5K6xjK6Rg0KQyTIpd1e3db/lVysfiVNigyKR3r0iLVPKPrrqmaMNyjDOjq6cG/3noPpl7jNJQ2weIOSBVurZXWh51++ulYtGgRNttsM7z77rtYtmwZKKU44ogjAABHH300pk2bhuXLlwMQk+3q1asxd+5crF69Gueffz445zjjjDMSX1NGxq+++mrP8fw//uM/8Mtf/rIi8a5QKNoXO6YGNvx96rIfXYKdPznP60RTiloJ3ZE6bSel1R25X3vlRQBAfnIhk0mnwe83XmYhMXH1vc9gve0Adi64T4xIphGh8ziBHpfBSFtAgFc7X8tjgfr5sBx55JH46KOPsPvuu8NxHNi2jZNOOgnnnHNOovur13ytRHed0LRiURnXt7tUtJsSIG1QcC76+tpuPjbnjlvXLegnHAbVkLa46AdMCFKceKZqOnVFao2i3WGiencDxS3E5L4sJLL9bu9R0e6iuuSYeuRwmnm4d7cUi4WIbe2i3aWiptJUTUa7TU5ccy67EO1O6645l4iOAgBn3EvBp1RDP80jbVBsGLKQ1sW50jpFzuYBUzU/b7/5Ov79xqv49Gf38yYT/xhGCW9KC2nmflM1OQGImu/REe2OIrhgAlEeYDiwuRDL4Wh3Oq1jeJhCN3TkhnNgjHn13RrRoOsE60wK063fT+sEGUOYqpk6Qd7S0GFQdKeN5r3oMcQ777yDI444Ah9//DEmTZqE3XffHU888QQmTZoEAHj77bdBfF+Mstksli5dijfeeANdXV044IADcNNNN2H8+PEVXffLX/6yEtgKxRjGZk5ZcfvG6//CK6+8jH33Pwgvv/hPWD6hXivB2gq6txXTy/288mEW7lcxGD6Ra/gEuF/8xu0T2D/0XcgIi/k4cR9zfLtHv+vlw/LQQw/hhz/8Ia655hrMmzcPr732Gk499VRcdNFFiSPV9ZivleiuI1J4C6GtVRztNlwBzjj3DLYkNuPQQTzhTYiG/pzmGqpp3g/VNGiaBt01U6tntLvUY3+0m3MHlCBxtDuOuH7TcanRYj9xD6VaiNWKuFZUpi5qsFOuqVpPinvR7kxKx5Cpe+ZcVp7BcWdH2QN6/WBetKPy0sytQi2/z1RN8v9+dB6+ddaF+Pvfngym5KNYePvHVo4l9/z7NDhasE5e1s/70/XbkSQLJjYTv7uMQdHDDOQyHBuyItrdlxbRbkrFj3AxL9R3i+0Ehiu6u1MEPWkLJhH9u7eY1Im0EVEwr6gLt95a2hn+oYceCjzea6+98OKLL0bvXAHr16/H7bffjjfeeAOnn346JkyYgGeeeQZTpkzBtGnTRnx+hULR2gxb5Y1szj/nTJxzwXL87anHwRwn4KTd6Chxo6LftaYWddFvfjDglfvprnEtUBDGurswS2K2B8W5+H8l4j0syEuJ93alXj4s5513HhYvXowTTjgBALD99ttjcHAQX/va13DuuecGFtXjqMd8rUR3nRCiUvxb1vhWEu02qDDLslyBqsvaTyZ6d9tcRL113x/7sKVhQxaeWM8zjhzjMCmHzbSiFmK1iHYnSTMHKbSrCpuq+U3hwtFuxqQrenGKuSSq3zQQTDMPp0bXsya5klZUHToVLcS4aENViHaLqCkhGiilyFnBlCXitg9LGzSQZi4MuoKmag+vvAuf2GImNttiSzz/t4KBhD9zwC+8QRCbZu7PHICXMQCE0/XbNdpdLs1cpwQpR7wvZZaCjHb3pS0MpHWk0jpyWR0kS2DnLXDGMCzPTwlMk2KtKdLMO3Jr8e7f78Ondtsd6c33aeprbxUYL3xuVnpcq/OPf/wDCxYswLhx4/DWW2/hhBNOwIQJE3DHHXfg7bffxq9//etm36JCoagz2TKu2H+6905M32Impn5iBp54/C9gjJc9RhGEEA12DRYL3nm/3/0eRrzsQ/FYfB8AxHcDKYDlNuqWeQKom1BvhfTyaudreexIKOfDMjQ0VCSsKRXfXuP0hJ96zddKdNcJ+cVdRnMrjXanqCNqut2It+jrS6DT4KqnzR3AYhhwH+uEYMji6Msxt5ZbuFvrhBS1EKtFtDvqdVdiqhaVhu8dE0opl4T7TccZgTWr33SpVlQycmoz8cGd0SlyjKInRb1od9qg6HDNuSy3/YSVt7x05UE9D0oLrcbSumvOpTOkKA2Yqj3/7NO4/8478MDdf8DQ0ABsy0KmswtLvhmsRY0T3v40c3/mgBazgNHO0e4oKCn8LqUZnqkTZBhFRtfRm2HYkGXoyRgYyJoYHLSgG7r34Q4rC5sL4U1c47QOnWHqh/eDuXUD/SEjEcXo5LTTTsOxxx6LH//4x4EatAMOOMBzWFUoFKObwVxpAf3Xp57E3Stuxz3/+z8YHhqAbdswO7rwtVPPqug6te5OU4paR13DmZ0V435HHOltffD+ADSf6NZ1zRPhANztxdukMAfg+vC4QlnTAsIcQECclxPm/ucK4ntkr7FdqNSHZdGiRbjiiiuw4447eunl5513HhYtWlT4flaCes3XSnTXESm6bDbyaLfFRT2p7aaa60Tz1XQTwGLIEg06sYXzuZtmntYphizmOpmL+g9CRGuiekW743p3e2MSYaoGrx68EO0Op5tH9ZsGEGsE1kr9puWig05FuUBx1JR50e5MSsdA1oJpUrflFIWdt71UZUB8sJumDtOgXpp5xhCpyn5TtW+ecT6+ecb5AIA/3nYz/vXqSzjh22cW1dkDwXR9GS2PSjP3Zw5EtRCr5zjWm9ILJsEWYuNSBoZsGxMyOvpzbrS708DAgAHd0EEoBc9a6MYgevUODOQY5pF3sGV2g3e9k075DnrGjWviK1Y0iqeffho/+9nPirZPmzYNa9asacIdKRSKRjE8NIT1G9bj4yEDetzECeDoU8/F0aeeCwC4f8X/hzdffQlHnXw6BvOl21GVaxnqJ8pstRSVCvhKRHjkvcSYzVVzL5XeT+A2NnyMIW6CUOIumlNoRAv8O0qQSzEu7jc6Ug4kE+RJI+WjnUp9WJYuXQpN07B06VKsXr0akyZNwqJFi3DJJZckul695msluusEcWuXgWA0lzg1iHYTDbIxgc0c2IwhbVLkbY6sxUCybhsxoiFj2NA1TRg2MQKbOl4LMRnt9tfmVv16y6SZA9GmahLqEzlSbBOfMPenmIfP469HlsZq/ghtM/pNJzVVM93fS0b3RbszBoZyNobTNoaHKbLD4p5sy/ai3QO+FVaZZt6dYjCpjbQb7TYpKZqIo+Yrfw90KbIpKTapk2nm3H1NcS3EwuZ07UbYFJA6/gUTwNSFp0KHTtFjGhhMM1GjnTGwftBAR4eO4UEdhBJsNymP/WZkQbQsgHXeNb5w2Few5VazmvQKFc0glUoVtUcBgFdffdX74qBQKEYnf/zD7/HWm29gxn5Hg+jCMDNsghV2td6QszBo2/hwOFhiBlTual3KcKuUKC3VzqqciVe5hQBNBmFiiPz+EEpLTiKoGYrbqcbx9mvPe/8+bvO3cO+/u/FCf68rsCkIJe6ienIRDpSOiot/F6es58CKUtZz4AEhXmYtZtRQqQ+LrutYtmwZli1bVtX16jVfK9FdJ8K1yzKa60W53Vpf4jhlo93U/b/h/sHKtlE6F4Kbc6eo5kenQqinDQKTEgxatkg7pv4WYkL8i+ByIXV4pE7m4vUnM1ULtxDzp+FHRbvj8NKi/UZhvjTzZvSbTtKKSkZNO7mOPOPo80W7O9KyFZWBfDYPWMFWVJRqMAxRI5w2KNamZO9u2zNVo5rjrZoefPhXwblbz+I6xEc5wvvHrlSaud9UrVQLsXaLdkui6vLlgolsIdZp6BiXMjAhw9CfMzG+08Jgp4nBgRT0jn7st3kW4e8lJ3/zu+iuwjhkLGAzsShYzXGtzuc//3lceOGF+N3vfgdAfOF8++23ceaZZ+KLX/xik+9OoVA0gjfXZ6FR8X2tnNv1rM8ejG32OQTv9meL9gFCaceheTWJy3VY5EcJ2CjhXImwjxPFicR8SGBH3YvshFNOVBNSeo6gmobhwX4895c/FY7RgP0268ebzxH08xQYNUANHZxxEf0mpCgKbpgGbIshHxLgjHHvsWVxUBrcxrkDxsT3NZsVouDMZu4YEjDGxPNcRLzDBriNptr5Wh7bytRrvq5YdB9zzDE4/vjjseeee1Z90bGCFN6cFSKJ/mg3d8S2ctHuDsMB4yLqPewaqoVrXvzCmxANek6simVMhg6DuOeiMN3WUrKFmM04iEbqEu2OSjP3m6rJe+XM/xiFqDBziqLdFPFumnFGYP4081L9phvhwO2vEZY1+imDwOYUOV3H+LSO/hzD+pSOrrSBgbSOoSELxF3t9KeZUyrM1kyTYq1MMzeEmZo0VaNUQ0qnopyAO2UXMeKEN3NQNJbMgVcnX66FWLuRyFTNcGBzioxOMTEjfm99wwbWdhjIpDUs2nx1keAGgHXr1irRPQa5/PLL8aUvfQmTJ0/G8PAw9tprL6xZswbz589PnPJWDWrOVihahzc/GoZDrMSGWknNtCptV+XfXqpNVTnxXr1wL3bSkufO+57yny/nbYv+UhEXedc1DSymlF6KdQ4HG9av80odJUQDetMM/UMAiagFDke8AQQi3lHQKr5r24x7UW9F/anXfF2x6N6wYQMWLFiAzTbbDEuWLMExxxyjWp1EUBTZDUW7RZQbFUe7OwwKmznQidsajAo3c8Z44ENFJxpMnWFDVkNaJzCIJnp3u6Zq/hZiNnM8k6+RRrvj0sz9yPpmQCw8RJnOiXMB5aLd/tRo/za/8Ca+NHOZGh0lFuvhwB0l3gwqwszccZDSxQqnbEXVZevo7WDYkGEYyulIpw2kUsKcS3MnFDtveefXiIa+lA7DENHxj1IU+uBH6F/9AhYcfIT7WgpZA9IN3j+xxPVAB9zUfap5ZnRyLP0GdVpogcMbwzaPdkemmUOMnzBVI7AYQY9pIM84ulM2ejpMbGP9C1M63oo8p6Zp6O2d0KiXoGghxo0bh5UrV+LRRx/FP/7xDwwMDGCnnXbCggUL6npdNWcrFK3D2x8MgGm6l04cV9Mb5X49EufrqPZUtRHqrGSU3V8OKa7t3hgv3ld6zBWdr0hM80iBzZyoSDuBHfEd0jve993H7OqBMNstbOMOsIGMg9mZio1u1yu9XPy7fJ23ovbUa76uWHSvWLECH374IW666SbceOONWLZsGRYsWIDjjz8eBx98MAzDGNENNYr/+q//wmWXXYY1a9Zghx12wE9/+lPsuuuuNb1GOI2awfGi3aJ/MyqOdg9ZXLSLcmu4de4gb3GwkP++Tgof6IYr2E1qI21Rz1SNaChqITbSaHfUGFRiquY3nSsV7Q5HzOP6TQMoRLm1Qmp0KVO1Wke7w+JN9u72m6rJFmIZXUeXaWOjjI71Qzq60joG0zoM0/DqibiVh51l4IyDUoo+SmGaFH06w5avr0A/gH4APDcEPd1ZNI7cex+iKMVc9ogP13nLRQyuibGMM1WTLcTKpeu3E+E0c911MWeOyCJgzEFG19HR/y4m/v0B77gDP38omG3jvnvuhOOIBa6F+x+kotwlsDkH4cURkCTHtQu77747dtllF6RSqYoNjaphNMzZjZivFYpGsGbNAKDrRaZbBfFFKnLAHok49wdOw+K8XN/opMI85340e0JaZuqFhLkfFo6eh8S0rhX7A1GiBcS1PCdzCtGoQHCGyW2Fa+npLmz9yc/g1acfBCC+z/zfxxvDyYxDZwKBDQAjMVKT26KczeV4+reP2OV9hFQ7X8tj24Faz9dV1XRPmjQJp512Gk477TQ888wz+OUvf4nFixejq6sLX/3qV/GNb3wDW2211Yhvrl789re/xWmnnYbrrrsO8+bNw1VXXYWFCxfilVdeweTJk2tyjSjTMNG7243maqgq2p0xhJlT3uIwdSLSw+WHi8wBBpClGvSc7X0AyzTzNLUDpmq622dPmqrVM9qdxFTNbzonFyZKRbur6TftT41upAN3qRphETXlyBgUPczAQIphfEbHUM5E35BVMOfKEoBZQH4YncTERJZFdjCP+VPexsQPP/Cu9ZXjTkGqo8tNExc1/KWi3X7CwjsqzbycqRrRSqfrt4sAj3czF3+HOhVZCltO6sSTtwvBvc222+PAzx/qfUBvMWNLrFu3Fr29E5TgHsNwznHJJZfguuuuw/vvv49XX30VM2bMwHnnnYfNN98cxx9/fN2u3c5zdiPma4Wi3ti28GPJr/0Ig6SrIhfsZgpyg2jIuq8hIMitwvP+57K2T3jbyVLa/RFqqmmweUEo60SDfCSj5FEZjjZzAhHycGtZcTtOscB3WOA8k2f8hye6b++bg1wqjfEZNwJdxqEcqD567b/XalqHKWpLvebrERmpvffee1i5ciVWrlwJSikOOOAAPP/889hmm23w4x//GN/97ndHcvq6ccUVV+DEE0/EkiVLAADXXXcd7rrrLtxwww0466zKeiGWwp9GHRfNJVqy2m6DcHQYxIt2mwaBbhHvA5dSYbJgWdwV3wLRXoxggyHSzFPUhklFaynKNLeFWO2j3eGIdiWmajYr7BcV7Y6rzQFKC++o1OhGOXAnMVWTUdNOQ9R2b5RhWD+koydjYLDDQCqdQnYwC1ADcyb3Y78th9xFiQ8g87cO+MJXMG36FuCOA8YRMKcrFe2WYyeJE95aaCzDpmpyASMsqts52h1+/8q6fLFgQrDphA4QTcNhX/kqNpk6Del0OnB8d0+PEtsKXHzxxbjxxhvx4x//GCeeeKK3fbvttsNVV11VV9Etacc5u1HztUKRlHIldGGe//uzeGfV2wCAI6e9ifvfnYAX14/3XLCjUpV1g3oiHECkEI+KoMaJPEo15OzivtB5d1seIoCTtYKiLwvHE3aWK2T9QluKW4tr3nOW+x3UoBos3/HMLpSuWayw3YbjXdN2QuKZFY6xOQuIZoZgTbm/NM6jTEA18D0nROdGveguE6kW/x+5oI56LqpcAIjOPFDUlnrN1xWLbsuy8L//+7/45S9/ifvvvx9z5szBd77zHRx55JHocb9Y/s///A+OO+64lpzA8/k8/va3v+Hss8/2thFCsGDBAjz++ONF++dyOeRyhXYNURbyUUQJS/m3L6O5UgwliXabVPTrltHurOUgbVBkLeZ+oBLAXQ/k3EHeNVXTKXGFtUgz7zAI0jpDijK3TpwUtRCrtZO5fzxKmapJykW7C89HHx8lvAv124XU6EY7cMfVCAPBqGmGiTTz3owv2t1pob8/BzNtwrAI9tsyX2TSdczXvoVMV7dI8Y5Z4PFHu0stXgARqeYotBELp5nLzIHCtYsXMMK0mwAPL5j0dgbTcreYMbNJd6ZoB37961/j5z//OfbZZx+cdNJJ3vYddtgBL7/8ct2u285zdqXzNVD9nK0YG1QqmEdKf18f7r/nTu8x0YB9p67F6y+aGLRMz/HaXyusG7rrVE1DLthOpAu2FOCci22EaLB9Apw5DihzhS91PAdsxkV9sM2YEHyuK3bebWsrt0u3bIsVhDYlQoQb4S8i5fBnFsZ9B/Hto2vBcjfb951PBKfcdPWYKLjtPpZCPhwBD5/Pz8YbddZURAOlU/v9z/uvUW4/Re2p13xdsejeZJNNwDnHEUccgaeeegpz584t2uczn/kMxo8fX/VN1ZOPPvoIjDFMmTIlsH3KlCmRA7l8+XJccMEFVV0rLCz90dooMZQ02p21OdKGhqwlejTnLeatvlkW89oOAMAQtb367n6DYt2wjYxBCqZqFnfTZR3hcF2naHdgeyjNPCobIGw6F47SRlFUhxyKtIbTzP29uxvpwB2VZk7diLepE6QYxbiUgSG7UNudSevIZAzkBym+vMmaSFfsDevXoau7xxtLzpyiBZ7AOLrvNRYznkBBeANBd/ioNHN/nXx4AQMYHdFuoolovqmrbov1YDS3DFu9ejW23HLLou2cc1iWFXFEbWjnObvS+RoY2ZytaG0aLZhrwcdrPxZtOn0QDehN2RgcNpt0V/UjylfHH9Wu33V5kZN66f3Lt9vadKNOANGiGai9cA4/DkftS7WMawajuWVYvebrir85XnnllTjssMOKUij9jB8/Hm+++WbVN9VKnH322TjttNO8x319fZg+fXpF5wgLy1JiKEm0O60TWIaDrEGRtxlMg0K3mLf6yRhzU8x9LcQoQdqgbpq5DaJpSFMKkxLYTIPNtIZFu+MfwxsfoNBiLRztljXJMtpdSb/pqNToUmKxXtHuuBphTuG1ouoxDS/a3TeoYxp9HZtu9G7keTVNQ++ECUXXkgsYxLtWcbS78P4UY2VzJ7AabLtiW7xwxKaZl2ohNhqi3e1yn4rWZJtttsEjjzyCzTbbLLD99ttvx4477li366o5u/I5W1EfWlk01+veensnQNO0gPDmDjCADMy06aWYJ3XCLuWC3cg6b0mtjde8f2vR+4v7ColTnyhO0rIsLIQjW42ZHdikxyyK5I9UKAPRYjmyrVrMd4641miK2lGv+bpi0b148eKqL9YKbLTRRqCU4v333w9sf//997HxxhsX7Z9KpZBKpSq+TpRbt/dcCTFUebSbivZhPoMNAIEWYtmshQH3Q5q46ekdBkGXYSNlE+9DWbQQE+LfM8GKiFhWNA4xQjsq2l2uxVqSaLckTnhHpZlX0m+6HosPwRphsQBi6gSdho5xKQMTh95A57//7O1/0CFfhJXP436fK/Y+Cw9Ed7dbNxzKHPC75sse8Umj3XI1OCy8qVacZi5N1eJaiNV6HBWKduIHP/gBjjnmGKxevRqcc9xxxx145ZVX8Otf/xp33nln+RNUSTvP2ZXO10D1c7aiPK0imlvlPoDC95g4Ort6sM/CA/Gne8XfOHeAh9dtAnSNR1dCgQ0Ea7rDRl1xAhuIbjFVrciupu1YnMD2XxeobW/w8i3HooWrPM/uh5+Cf/cPlTxn3LVLnV/cS3w0Pu5cpa6vqA/1mq/HXI6kaZrYeeed8cADD+CQQw4BINIFHnjgAXzzm9+s6bXKpVGHxVDV0W63fVg2z0CpWO3M5YIzAaXEi3avHaLIGAR9hlUwVbMZiCY+FDl3XDHm6888gmh3qd7dpUzV/OOTxIE7rt+0X3j7BWHYgdv/XCMcuKN7d2swDQLbbSG27oXHkX39BQDAnLk7YuH+izx5vPkWM7F27ccY3zsBnV093j2Fka75cgGDw/f/ULTbS9EnBYOUKOEt0/L9aeZRpmr+FmKjIdqtqC+cO25f+cqPa3UOPvhg/PGPf8SFF16Izs5O/OAHP8BOO+2EP/7xj/jc5z7X7NtrSRo5X48FmiFWm3LNFupGxB0H22w/F/98/u94b/Uq3JXdAcNdGUxoUWEdfK620WugvuI6fM9x5406l9hWuF6PacTuV+r65e4lyb0V31drfj+qdr6Wx7Yy9Zqvx5zoBoDTTjsNxxxzDHbZZRfsuuuuuOqqqzA4OOi5o9aaONEZFkOVRLsNKtqIyWi3qTOkTQrLcg3UdIJ8nnmGapSKdhWivlvDR4aGtEFgUgspmWZOHVis9tHuuPEoZ6oWaLGWwIE7zn3b/4EljcCa7cBdLs08bQBbTunClhP3w29+vRrHHPd1L3qjufcmXbHle6uSBZ5wtFsuXsjxtEO/C7/wprTw3vCnmceZqvmj3XKsVbRbMRbZY489sHLlymbfRlvR6Pm6XainmK3ruRsoiFspIq67PiDdk3rRRY2iVPByfZorFdPiOPH/UpHquOfjBHX4ubhoddRxlYrqyHMmFNVx5/ML61Ln6DGj5VFSARx3nXLXLbu/+s7UMOoxX49J0X344Yfjww8/xA9+8AOsWbMGc+fOxb333ltk1jIS4oRllGmY4xSLIeKKw7hod4chBPIQ4cJMzaQYytm+1gZCbFv5oD1kHyUwDQpTp16aeZpaSFECTfO1EGMA0Ujdo91JTNVKOXBX2286nGbuN1VrlAN3XJr5Rt2md87Ozk587eRvJzufO0ZRrvnlot3UcRc+IswtbIdD14gnvP3j6U8zjzJVC0e741Z+lQBXjBX++te/4qWXXgIg6sZ23nnnJt9Ra9OI+Xo0UGuBWWtx3AgB3Eoi2w/n8DLUNp3UBWqIKGql0WgguYAO75MkKu2/B+9xBZFpcS+VC2lx3MjEtNieTFCXuxcAyCQwS61UAGvVflesoZGvojJqPV+PSdENAN/85jfrnp5Wqn450lRNgyeGqCuIqPvvuGh3xhTp5lmLCjFtMOQNgnw+KLylmCJEQ58h2oilDYqMYSNFCTK6DpMS5G3N7e3tOhNqhWj3Vw77Ah7980PY+zP74Jbf3l71eIT/HfXYT9h0LhClHWm/aWjgTiEC2wwHbhntJtCQNozyB6D8WMa55kct8Pij3V6bjVCNt+24g8vdT/+INPMoUzX/Aoac91S0WzEWeeedd3DEEUfgL3/5i+cUvn79euy222649dZbsemmmzb3BluYRszX7UY4wynMUYd/AY8+8jD22vuz+PUtt5U9T63up9a0Uqp4XD/nRLiHzpzUAVA3dblMXXT4caVu1pVGnsU9FSu8pMJZHB+9vVYCutS5St2XpJzwlS9f48n2q4SRpIlXK9gV1VGv+XrMiu5mUMpUTUZzq41269RtH2ZQ5E0d2aztrZ7alg3N7dEo64d0SpA2qZdmntYtpHQCnRDozIFNRfsF7jhetPvkU76Fo49Zgt/c/OuajkeSbAB/tDuqJrkUUcI7ygisnKlavaLd/v9XemxYeFeywCN6wyMy2h1oE+bDdnhAePvTzC1wGJREmqoRL41fRbsV8dicg1TxLdtupW/mMZxwwgmwLAsvvfQSZs2aBQB45ZVXsGTJEpxwwgm49957m3yHinaiVMsd7jg48eRv4YjFx+K3v7mpor+PWv0pjUigVkGr14g6ruredHwKphFsFRYpais04oo19Coxp5aqOS4lbEuJx3Jp1eXSqZNEj5MI0KSiOFb008jNVd9PHO0cxa52vpbHtjL1mq+V6K4zSYWlpqFIDFUS7c7aFHmDI2tQEGLDMCh0nSHn3odtiZru4eGC+E6H0sxNIn4MGmwhJqPde+y1Nx7988M1GY+i7SQ42ftN1fxisVIHbn8tsr/GO8oILMpUrV37TSdZ4JGt2KKi3aW+MPmFN9WcyDTzojp5rXgBQ0W7FWOJhx9+GI899pg3gQPArFmz8NOf/hR77LFHE+9M0Y74RXfU5/W8T++Jxx55GNwRi/Nx1EqstrLmbYW08z33/Tz6NqzD+7yzSKQlreutJFKatKa40nsAKkurrkSQVipAKxmPkUaKRyKOa2mERir4PSmqp17ztRLdDSAqChlG1tz6W4iVi3YTTUOHQWAxB2mdY9gQQloYqlFkswSEEsACOOOwYSPrXs80KdYP5mHq4pjuFEOnYWPIFmnmsoWYTh3PVItohV6TtaplLpcaXdjmJHbgFgcVzMD85/Ii3iX6TZcTi63kwF0q2l3JAk842u1FvX1jJ8fTHwVnxB3jUJq5P3NAl/dVYgHDe6wEuGKUMn36dFiWVbSdMYapU6c24Y4U7UzO5mXFpMU4uAPk7dJRpVaIEjc6Mi5plDO0merARpM7YK/Plt+5DPVINa5VxHWk46nEcTHqK1Hjqdd8rUR3nSjXJitKDEmHaVm7LKPdII4Q3KFod1qnXn9nf7Q7bVBkDQrTpDAMAivvppnnbXAmJl/Zs7vPFCL9fUMLmKpRKmpwbSaEJ9fqt1qcJDV6pP2mAZ+AlB/KZdLMpVhsp37TcQs8/vdj3AJPVLRb/B5IoaYbrvgmwlxNthHLMw6TEmjuWNoQHy5cS7aA0WrjqFDUmssuuwzf+ta38F//9V/YZZddAAiTllNPPRU/+clPmnx3inZjKGeX3Sdvc3DuIJsvU4M1hgl/Z6h3JNHUm5tP3Mja4HqkTtd6kaQev+96DXGrtQ5j3ClZ5lLu2FamXvO1Et11pJKaW7/DdFgMMZlenTDanUnpyFoMWYNA1wmoW5zCGAPzFUCL5zQvzXxdWqSam1T8GJQUtRDznKhRW+fuJM+NtN80UCy8S6WZt0u/6SQLPIw7Qu/GLGCEo93y+bChmkQK73B9N9UcWAwwAC/NvFQLMUBFuxUFqp3EW30CB4Bjjz0WQ0NDmDdvntc+yLZt6LqO4447Dscdd5y379q1a5t1m4o2YTBXXkhn8xw2d2L3bXY9aT1ExIhFlO+zpFa3J18nc5ymRnJrSSMFYD0XQur9MlpNKCuSUa/5WonuBlIyrTrGYXok0W7T1GGaNoapm2YOUdvtuJPKIKWglGCtmYOpE7xvUKR1goyhY9hmwlSNiH7gnItot+O/55FOHlWkRpdz4JbR7rh+00BQeEf1m25HB+5KTdWoE0zXD0e75f4yldz2fWeTrzcsvKnmRKaZ++vkSy1gtMI4KhT14qqrrmr2LShGAblcDrlcFhuy8aJbfm5nbQabcwzmS0fF6936qKG1ugkW7ep5P4VrFI6xK8gSrLeoboQIbETdcSO+KoyWsVJUTr3mayW660ykoVUJYSlNw0qJoSTRblMnMA0Cw3BTzA0ddt6Gbdmw8oU6BUIJTJNirSHczNemCHrSVsBUTacEmubgK4fsjxdf+AeGhgax9Yzp+M3/dxvmfWp+zccqqalaVE2yjHbH9ZsGAN1nMOLVd4NDBwmkmbezA7d/LP29u/3PRy3wiPF0iqLdukYAwr1FjCjhzYhTlGYeNlWLWsAAVLRbMfo55phjmn0LilHADddfg/6+Pmx50PElWz5d+PUv461XX0RueAgH774tvv+Tn2PWDrsE9q1V2yegekfrkbpZJ20BFSBU4l5OXMkU9Epcs6O+gyRBfveptfhWadn1pZHiWX01qj/1mq+V6G4A5aKQ4X3LiaEk0e6utIG8zZG3OLJZG7ms7UW7OePIDQtfc0IJNlBR3+25mZsEKUrQaUhTNQ5CgFv+5270dpo1FUNJU6PD45PEgRso7jcNFLe9EoMiI7Xw0sz9YjGuhVirRrujnotb4JELGMR9nVTTvAUe2T4sKn03LLwpL/y+vLEUTc+hUxGAiFrAUNFuhcRmHBqromVYhcecf/75uOCCCwLbZs2ahZdffrnssbfeeiuOOOIIHHzwwVixYkXiaz7zzDMwDAPbb789AOAPf/gDfvnLX2KbbbbB+eefD9M0y5xBoSjwxjphyBXV49mgGk74SaG1p5wP3+3PBno716Ovc9KezpUI+xH1cg59NETdS1JRTUhpIU01rWwL0yiirluuo1KlotwruWux+bWWt1Ov1zZaIve1hnEHpMrSrlYvCavXfK1Ed5NJ4jAdJYYqiXan0zqGhylyWVHfnWc5gBfaiVEqTNfWmRSmQdGd1tGdstFlFEzVPrFRR93EUKWp0UkduCvpNw249Va+NHO/qVqSFmLe4yYKxyQLPN720AIGcYQQl675coEHQMFQDcFvFH7hrWtyUncj3iAADaaZh03VVLRb0Sy23XZb/OlPf/Iey7qtUrz11ls4/fTTq2oZ8vWvfx1nnXUWtt9+e7zxxhs4/PDD8YUvfAG33XYbhoaGVPq5oiJe/3AIuvuBKUWBFNFyuxTY7np7pED3i9+o58P/9gubagV8WFhH9Z4uEvyRwr1Ymcpz531P+c+Vg3978XVLRd51J/o5KZp5xCJ/InHsCxSUwi/OS4nyUteMcomvRqxyWdrXArSq67gf9VWmvajXfK1Ed4MYiamav3Y5LtqtEyG+/dHutEmRNiiGTSGqdUOHRjQQSsGtLDizkAOgEQ0a0UCpSEfvSutI6xrMoY/w8ftv4stf+lLgtTRSDJUzVUviwB1G9uwOC2/m5j7LNPNIU7U2duCOM1Urt8Djj3brRIPNxWuVdWq6e245nv42YuE0c3+dvIp2K5qJruvYeOONE+/PGMNRRx2FCy64AI888gjWr19f0fVeffVVzJ07FwBw2223Ya+99sItt9yCv/zlL/jKV76iRLeiIt56fwDULf8ChOiV4kN3F8sBIcBpjCivtVivXqiLxVy/sC4XZaeEBKLYcn/pGVck+iPElB4RuWYRwlqK83DmnHdPoe8Z/muHI99x0XQZNCgFLf10QfxXKMjj2rWVE7PhVnPVCFbHaQ1BWmuxXe/X1GoZC6ONes3XSnQ3kDjhHa65leLRayGmwXPqjot2pymFzR10GARZiyOta8LFPM8wkLXR0WFgeNgGpRSEEjFX5YfBmYUsxgMoRLw3mBybv/o/eN+9H9uyYKaMuoqhakzVolqs+dPw4/pNA9HCm2pOIM28lKlaKztw12OBR7YPsyNS52zHgQ7A5hD13TaQ0kkgzTzSVM23gEFR+LLQKuOoaD/6+voCj1OpFFKpVOS+//rXvzB16lSk02nMnz8fy5cvxyc+8YnYc1944YWYPHkyjj/+eDzyyCMV35vjOODuH+Gf/vQnHHTQQQBEP9CPPvqo4vMpxjbvvdcPXRelYZQS9yf4GID7uLHiPCC45bYKouhGOGpONeTc+csTtIyXFOYsnPYeIaYZLxbmfmHtZco5hYmPRgjqcMRcniMyau44sXXqnMfPdYSUF8dOzPNJIuRJxXgpsVetCJeXqXaqZwkzBRpBPW6jVV5bGJtzaOXqIEocWwmVloTdcccd+OEPf4jXXnsNlmVhq622wve+9z0sXrw40fXqNV8r0d0iRAlL6TBdabQ7bRBY3PGi3V1pHdmsBcMgSHWkkMu6CVacAZyh01qLicggO8gwp/t1fOL9dd59HXfSt0F0vSFiqBYt1rjr9p6037QU3hTUm2BkhNYCh0GJJxYpFSXK7eDAXekCj/QR8Kfrhxd4pKFaOMUcKBbelGuemzmY2w89VCfvX8CQLvKtNo6KxmJzB1oVtV7S4G/69OmB7cuWLcP5559ftP+8efPwq1/9CrNmzcJ7772HCy64AHvssQdeeOEFdHd3F+3/6KOP4he/+AWee+65iu9Nsssuu+Diiy/GggUL8PDDD+Paa68FALz55puYMmVK1edVjC3kF0Fr/cfoR9rNXhOlY5pPcNdakANiLqiFIM+6ZuqUaIBVLMizdkiMs3KRciHG/eZllAfFs3/Wkuns4Tpn2a5QimK5WO+PkkcJauawaGHEI5QsUPCS8aFrWknRHOkB5CuNC+O/n6jzhiPu4fPHRd3D10oqwpMIcP+pK53+W0F41/ryzX49rUYlJWETJkzAueeei9mzZ8M0Tdx5551YsmQJJk+ejIULF5a9Vr3mayW6G0y1pmqlxJCMduuEeELSIFog2p1OG8hkhKGabujIG2kgO4A5E4ex33/kQLQBAB94197vkMOxxYyZRcKnVXpRl2qxFufAHYUcr6I2YqE083aLdpeilKlaOF0/vMADiLo2m4sPD9txYHMu3ns+4a1rDnJu7p/p9nuXKxR+U7WoBQxpTidp1XFUtB6rVq1CT0+P9zguyr3//vt7/54zZw7mzZuHzTbbDL/73e9w/PHHB/bt7+/H4sWLcf3112OjjTaq+t6uuuoqHHXUUVixYgXOPfdcbLnllgCA22+/HbvttlvV51WMHf7x3DMYHBgAACze5F+4/90JeLl/AggRbUGl+KYGLSvCAdQ0Mp63xbY8OAjRkLXEdinCmZsbnbWdIiHOfCnrljsXG6wg0i13/jaoBmYXhLLFCts9Meyey/ZFlBkLC7KECjNmd0q04nakoVOIBX8e2uamqYeOFYv/8SnqftFcKmrtfxnlxHHcOaPOnaRGPGkqeiXR73YS3rW6rBLa8VRSErb33nsHHp966qm48cYb8eijjyYS3fWar5XobgJx/bpLmaqVE0OME+gah040ZAwhvoui3WkdhklhmAYIpehMa9jvP4JmVgBw2LHfQE9PT1PEUKWmalEt1sI1yaX6TQO+tOgSaeZcZE0DtGCqxh0N7RjtLmWqFtUjPkm020sVIsQV44X67rg083C0O0yrjKOifejp6QmI7qSMHz8eW2+9NV577bWi515//XW89dZbWLRokbdNRht1Xccrr7yCmTNnlr3GnDlz8Pzzzxdtv+yyy0BpuWpNxVinv68P991zp/eYaMC+U9fi9RdNDHITuqEL4U0ISJ7AMI1AFDxfQoBz7sCymLeNc+JFjW1XgOtUA7MZKBOPGWNCnBO3lIhosBkTopcLAW4D3jabF6Lg8AxJg8LQYk5Ranks7p9MkfhmBfHt+Y5oIX8X3xwovj8VNlBCiiPgvvMAMRFwHoyA+68nz+MX4f6U9PCxUdeUSLFcSiiXShWPEnWlzlnuvEnOHzxXcvHdKvXe5ajFPY5VsV3PkjCJ4zh48MEH8corr+DSSy9NdF/1mq8rbDqgqBfyw4xoInItBKTYRon4gyREfFDJ2m7N3Zf6jtEJcf9fiHabunAlT6cNdHQYMNMmOlIEx83NRoqavvXrwHhxfY4kri1VrfDfk/w3IYi8VzFewfER+4vHnvu2XKnXSFGdFXcc2NyB7XAwLlKi5Q93x8HmDjgXj+UExbkrGv3nCq0813usylFqLOUXBqIV3ldy0pXvP/84BqLdMROmzbkb/RbjafPiseQO3MwBR7zPHN827ojHLTaOisbAvb+zyn9GwsDAAF5//XVssskmRc/Nnj0bzz//PJ577jnv5/Of/zw+85nP4LnnnitKaa+UdDoNwzBGdA7F6Gft2o+L0oSJBvSm7Mj9WQW9q5L0lLZ9+/hb9JWqzUz6d2mV2K/Uc0B8nXMtKHfuRrQ9CkfARwP1cgivRriK7zjV3Y+mjVxwy+9W7Qhjzoh+AFESNm7cOO9n+fLlkdeSJWH33nsvrr32Wrz55pvYY4890N/fH3t/GzZsQFdXF0zTxIEHHoif/vSn+NznPjei1zzS+VpFuptE0jRzmUataUGH6ahod5qKft3haHd3WkfeZhjK2UinKPYavxpbbvRB8U1BrHRmesYLEwEHRdHcZqf+VttiTUa74yZJIbyBcL9p+cINIJBmPhr6TYfHUtbJl4t2S0M14mYYSLwaec6hayKygaL6bwJCkrUQA9pjHBXtx+mnn45FixZhs802w7vvvotly5aBUoojjjgCAHD00Udj2rRpWL58OdLpNLbbbrvA8ePHjweAou0KRb2YMGEitFDdL3eAAWRgpguRbplWXmmdNwBQSrz0cgBehLsRdd6SUsZrlbYuS+qGXq7XeDIn9OJtUWIqaZuyOLO1Ur3ES0WiSwm78v3JSz5d9vyF8ySfyyuZ9qsV29VSC6FdS1qldVs11KMkTNLd3Y3nnnsOAwMDeOCBB3DaaadhxowZRannjUSJ7iZSSZp52FQtTgzJaHe4ttvUKTZmH2Ce9Tfvt771p/aFxm288vT/AY5I095p932R6ujyzMSaJYZq7cAta7uj+k3L2i9PeIMBoLFp5nEtxFrVgbseCzzMHUNd1D540Y6COV2wvtufZm4xsUJBSOkWYs1e4FGMXt555x0cccQR+PjjjzFp0iTsvvvueOKJJzBp0iQAwNtvvw2SqMGuQtEYunt6sHD/g3Dv3X8EID4zH163CdA1Hl0JBTaAwDb5ZZ36UsgB8d1C94S4FhDYQFBMJxXYQLHIrm3bsfYW2VHXlcSJ4mpFdqlzJjl30muI87S/0G619PF2Ftlh6lESJiGEeLXYc+fOxUsvvYTly5cr0a0oECUswzW3pcSQdDDXiejZnTYIhiyOzT94AtZH7wAAxn1ia0zfcS+kdIoUpZi/yWbgQ33oHt+Lnp5xIl3TV3PbLDFUalGiXIu1KNO5uH7TQFB4A2KfHDhSlJTtN90ODtz1WOCRhmrhHqW2NyiF+m6/m7kcSy9d0a2TV9FuRaO49dZbSz7/0EMPlXz+V7/6Ve1uRqFIyJy5O+GRP/8fBgcG8IfsjrC60pgQilSHzdGSCmugfNuw8Dag8dFroH7i2n+vUeeLO4/YFq1SK4liA0pgj+T6YZolsseKwLYZh8Z4+R1jjh0JsiQsaQswQHix5HK5EV13pCjR3WSStHYiGgKmYaXEkK7J9mEEKerAYg6mjTPhzN4J7z/1Eabv/SWYqRQY3Kik5iDV0Qmzsxuma6gSTqNuJTFUqne3XyzGmc6V7Tetua6kpLQRmN9UrV0duEe6wOOPdsuqwoA5nSu8EfpwFb+HaFO1uAUeeV+tOI6K2lHtJD7SCVyhaBdkBkbP5IkVpYLXqgd38LnaRqqB5IJaXL+5Eeu446OuLalHeni585Y7d9JrFM6lxLVkrAjsZlNJSRgALF++HLvssgtmzpyJXC6Hu+++GzfddJPX+qtZKNHdAoQjt/7tfmGZRAyldNGLWka7x6d15BkHeiZi+gLx5pSRSF3TkHXNVqghBKnFnKKaWxm45BpaItqdqMUa5NgUL0yIeu3k/aal1anp/TJEBLfdHLhrvcAjo9155rglDYXxLKTqC+FN3AUeRhzkGYcJEujdXW6BR6FoR0477bTE+15xxRV1vBPFaOMTk7vqEo2Oez6JgAbi073Dx4l7rExER55zBEJabG+umC51b0nOneT8Sa9TON/oENWAEtajhUpLwgYHB/GNb3wD77zzDjo6OjB79mzcfPPNOPzww2Ov0Yj5WonuFiNpzS0hDnRXBHJeHO2W7TSyjHnn9PpfEiEqs2BIUwrb4ci5PTFF+41gzW24RRZF4UOjmaKy2n7TMtod1W9anJgE6pEBWdcdnWYe10KsFaPd1S7w6BQACJjDiqPdmgM4xSn7fuEdt8ADcBCNJFrgUdFuRbvx7LPPBh4/88wzsG0bs2bNAgC8+uqroJRi5513bsbtKdqYLSdnIoUy0HixDFQnmMVxMeK2jsI57l4k9RTQ5c6f9DqVXE+cr7I5sxFiGhhdghqon6ge6995Ki0Ju/jii3HxxRdXdI1GzNdKdLcIpaKQUWnUfodpnQbFUEongA0vzdwfgQRECw4pvG3iFEUhUxr1am6JK4YADZw7gdrlRomhpKZqteg3DbgtUFzhnWUMadCy/ab94xOmVUViJQs8cgHDoASc86JoN3cKKeYAEi/wMEdkXpRb4Amb0ylGJzZ3oFXRhseu4phG8H//93/ev6+44gp0d3fjxhtvRG9vLwBg3bp1WLJkCfbYY49m3aKiTdlqYkfgcRIhDMSLjDgRWokoLneNOHGc5JylRDLQuPTqJII56fUquW7hvPUVz5JmiGigdn25lZiuP8xxQKpsaVfPVn/V0oj5WonuFiIuCgkUC0tNCzpMR4khMDFR6USkAAOAxRwYVPMJ71C9rSFaPhFCQBzHrZUUs4dO0TQxVElqdHIHbicwNn78wtt23CJu968lLjW6VLS7laK0I1ngIY6IkDgR0e6RLvAAbkaBzFAoYU7XCuOoUFTC5Zdfjvvvv9+bwAGgt7cXF198Mfbdd19873vfa+LdKdqFrxx5DD7++CN8QNKB7eUEraSUsC3at4LP2KRiFKifIK30Piq9F6Bx6dEjmd5GKjhrITBbVTwD9U/xbmwb3YZdakxRr/laie4WpZywjHKYDoshv6AUHwIF4V1AiG5d07woJNWE03dczW2riaG4ftNJHLjL9ZuW9ciyvlummYdTo21XZbdLTXKSNPO4BR5p3kOd4mh31CJG0gUezuWiEAI94lW0WzEa6Ovrw4cffli0/cMPP0R/f38T7kjRjvROmIDeCRNgv9vc90y9P4tbsY621YRkLcVjPb621UMwA42pi27Gon6rf28cS9Rrvlaiu8VI2topzlTNL4ZSlLoCUkQgZc2XxYLim2gOsm6ata4R5F1xJFaMi2tu/dFcQrWmR7vjUqP9LcRKOXCDCOO5cL9pAXcdzYv7TcvUaIBDB2lbB+64sSy1wEM1zfspjnYLkW1xJ/ECj3if0ZZf4FEoquXQQw/FkiVLcPnll2PXXXcFADz55JP4/ve/jy984QtNvjtFu9GZoiM+RyumeErqJdiqpZEGWI146Y0a30YbhzXPY6gpl20qjAFaRKZo0mNbmXrN10p0tyDFfajL19zGiSF/325/BFJGc2XEFiCwiRMwu5JRyHDNrT+a6xc+jRBD9eg3HRXt9o+TTRyvvhuA91cjU6OTOHC3YpS2lgs8spwBQHS0u8wCT0qjsFnpBZ4oczrF6IIxp6qWYazKib+RXHfddTj99NNx5JFHwrIsAICu6zj++ONx2WWXNfnuFO1G2qxMdPMG+B60soivJa22IBBHKzllt9ICeQsNS4BW+n2Ndeo1XyvR3eKUqrmVadSlxJC/b7eIygaFt8QgDrI2g25Ep5n7TdVKRXObQVTv7lr1myaaVlTfLdPMw6nRpRy4WzVKO9IFHp0QrzZeLvDYCJYz+Bd4DOJ49d2VLvD4abVxVCjKkclkcM011+Cyyy7D66+/DgCYOXMmOjs7m3xninbE1KubcGshvuNOUe8vlHGeN6OZ0Tq/taO+VKJ47FCv+VqJ7hYlSRSSuP8vJYbCfbu5owXSyw2qweaOV3crhZCMQlJXLBEiTNUIIU0XQ0nSzMOmav4Wa0C0A3el/aYZd+JTo6n8YtI+NclJTdWKFjBIdDmDH7nAM2RxZAyRxh/nIxBe4CnXI16haCc6OzsxZ86cZt+Gos1JhUR3YkEa1WGj8uSSSOod6a7VfSpan7EkcNslc2IsUuv5WonuFqZcFNJfc+s4wu2K82IxFI52S6EN+E3VOGAII7BAFNJNVbeYEJa2NFhrshhKmhrtzwZI4sAd7jftd9+WNckeukhT17gWmRota95bvSY5yVj6TdXkAgZ3eGy0W/4fCGZUyAUe/1iWXeBxFzBUtHv0Y3MHqCJVvFVbhvkZHBzEj370IzzwwAP44IMPwEMK4o033mjSnSnakfBnXqWfgYHvFlWWh4eFPsHIP4dLCWsasWBQa8ZKivxYQona+mAzDlRRDuYd28LUa75WortNKBWFJJoGnUK0CKPiCc6jo92SQj23wGIOdOITQ7rYR0Yh/TW38jjZQqxVxFC51OikDtx26Lxh4S0jtJRryLsfHMRL3y+IxXbqN13JAg9AYINDp2K8iOPApATMcULRblHOIBYxnEK7Ol99d7kFHoCLXrMq2q1oc0444QQ8/PDDWLx4MTbZZJOKWxspFH50WljErYY4gVxJNDnJ/F7p/VWyAFCPdPORLByoSLwiKer7S2tTr/m6LUT3W2+9hYsuuggPPvgg1qxZg6lTp+KrX/0qzj33XJim6e33j3/8A6eccgqefvppTJo0Cd/61rdwxhlnBM5122234bzzzsNbb72FrbbaCpdeeikOOOCARr+kxJTq3e3tE1VzS4JiqBDtLqSYSwHEuIiKD1tC0Yi6W4480wpRSK7F1ty2UrS7VGp0ZQ7cpftNy9U92e/b32JNikW4LcTaIdrtp9wCj79OXicaOHfN6SLM+/zlDEB0u7pSCzxyPFttgUehqJR77rkHd911Fz796U83+1bqzliesxtNLT/3vM/9GkJ8C861Juq1N7PuO7xgMBZr0BWK0UC95uu2EN0vv/wyOOf42c9+hi233BIvvPACTjzxRAwODuInP/kJANFTbd9998WCBQtw3XXX4fnnn8dxxx2H8ePH42tf+xoA4LHHHsMRRxyB5cuX46CDDsItt9yCQw45BM888wy22267Zr7ERFRiqhYWQzLanXYjkf4UcwBCFEKkaQ5ZPHBNSkREVwhUB5w7kQ7T1LdC3CgxVEm/6SQO3FH9pv1C0SBA1mZI6xSEO8jZ3Eszl6nRYVO1dnHgjkszB8Tr48yJXODRKQFzWGS0O8944L3GQmnA7bLAo1CMlN7eXkyYMKHZt9EQ1JxdfyqZV5OKv1oL+MC56/xZLUV9Kyy+Bkq0FEWM1cWI0fZ+YNypvmVYi5eE1Wu+1hynPd/9l112Ga699lovr/7aa6/FueeeizVr1ngr6WeddRZWrFiBl19+GQBw+OGHY3BwEHfeead3nk996lOYO3currvuukTX7evrw7hx47Bhwwb09PTE7jdsVfvKovF/SHHXLVv+m3EHNhdi2GJCxNhMGFLlbAbGHAzbDEMWQ5YxDNk28oxjyOIYtjiYZ6QmPhDSBkGHQZCiBGmdIqNTdBo6TEKQ0ilSBoGpE+hUpLAbVINONU+gE02IIfkBU+8PmvDYAGIClmPDHSF+bcbBOALjYzMOyx2nPOfI2RxZJv6dO7bReAAAYx1JREFUtRm4E1yEMKgGg2gwKUGaUpiUIEUpUpSgQ6egVENKp2JsfOND3Aiwf3z8Y9MqH8ZJxtJmhf9b7hja3EHO4t44DlriPTZg2cgx8T6TWRWAEPEGFWOSMQhMSpDRdXQaujeW4j0m3mvyPaZTAqIBBiUtPY7tTodR+vmkn4NJkef79PL7oacrdwe1s4P4y9n71ux+6sHNN9+MP/zhD7jxxhuRyWSafTsNp5Xn7FrP16ONVhBJrXAPktGQRt5K46kYOb2Z+LqMVpuvgdafs+s1X7dFpDuKDRs2BFYhHn/8cey5556B1LWFCxfi0ksvxbp169Db24vHH38cp512WuA8CxcuxIoVK2Kvk8vlkMvlvMd9fX21exEVUKmpmozmGtR16vZFu4OGaponhCwpiLz0aIGuadA1DmpocBzHrbnVAg7TrRLtriQ1OsqB23My54V+0/465IDxnA/dzQaI6zfdSi3WSjES13xCEBHtdlPMfR4CVmiF0+IOiOYgzzl0xryx1DQt0jXfn67fquOoUIS5/PLL8frrr2PKlCnYfPPNYRjBlY1nnnmmSXfWGMbanD2aGOn8XQuBV6/vENXcW7PmnFqKfbVAHY1ajFAA9Zuv21J0v/baa/jpT3/qpakBwJo1a7DFFlsE9psyZYr3XG9vL9asWeNt8++zZs2a2GstX74cF1xwQQ3vfuSUE5ZhMSScugu13SZxYBNhrDYMNzXaZ3SVtT1lCINoyDLmRbGDNbdoGTFUqva9VGp0VL9pxgl0jUf2mw5eUxiByTTz0dJveqQLPJ6PANFC5QzFCzyC6AUem3MQhiLX/GYu8CgU1XLIIYc0+xaaxlifs8c6rSDa42glMV+OVllgHg2R/jjU9wcFUL/5uqmi+6yzzsKll15acp+XXnoJs2fP9h6vXr0a++23Hw477DCceOKJ9b5FnH322YGV9r6+PkyfPr3u142iXBRSCkvpMC1czMU2TdM8MeSPdksXc8P9fzCSS7zUap0UopCy5lakbLeeGAovShSuHew3HW6xJsdHpoBHGaoBBfFtEAdZm3l/RdRV1LL23XbHshVarFVLkgUeQ5qchVqxRUW75QIPIIS3+DeBThwxnqEFHs4RcM3XtMIChop2K9qJZcuWNfsWRoyasxXNoBbfHRodwWxkWV2jabf5djQvEjQT7pa1VntsK1Ov+bqpovt73/sejj322JL7zJgxw/v3u+++i8985jPYbbfd8POf/zyw38Ybb4z3338/sE0+3njjjUvuI5+PIpVKIZVKlX0tjaJUFFIKS38atU5FP+VAX2pftLsoAhkwDXMwbHE3+s0KUUjX8dvWWksMVdpvmiLYYi2JA7fMBpCGcxmDwOaijVguIjWac3jjI1uItVu0u9wCD+Oi3jpqgUeMJQlkCRhEC9bJu+8zSdQCj8wcsLloISbT9Zu9wKOoLYxxaFX072Qt3vNztKDmbEW70o7CvRSNmuNa6TVXS7stEihGL00V3ZMmTcKkSZMS7bt69Wp85jOfwc4774xf/vKXIKG/ovnz5+Pcc8+FZVle7v3KlSsxa9Ys9Pb2evs88MAD+M53vuMdt3LlSsyfP782L6jBxEUhwzW3sp8ypRpMJxjtNgj3IpDh2uUhCFE57EW7g1FIKSylGAIIDBSiucTXR7RRYqiS1Gh/C7FSDtwyPVqOS6AFlgxhu9cOpJm7qdHcKYxPOzlw12KBR45jeIHH/16TixelFnj8rvmyJZtOiYp2K1qaCRMm4NVXX8VGG22E3t7ekr0+165d28A7qw41ZyvGMrX6/tJOQrbRC9jtNDaK0UUj5uu2qOlevXo19t57b2y22Wb4yU9+gg8//NB7Tq54H3nkkbjgggtw/PHH48wzz8QLL7yAq6++GldeeaW376mnnoq99toLl19+OQ488EDceuut+Otf/1q0At/qlGrtJLahqOZWthDz+lLLaDclsLgDm2vI2vDEkEwBHkKhH3We8OIoJEOkqRrR3A/PJoqhSk3VqCb+7Y/ShqPdxaZqHDCIZwQGwFuUYMzxsgFapcVatYxkgUdGu1NULFgEUsxZ4X2WdIEnqoVYMxZ4FIpyXHnlleju7vb+XWoSH02oOVuhiKeerdnanVaYt0fbmNaLajPT5LGtRiPm67YQ3StXrsRrr72G1157DZtuumngOdnxbNy4cbj//vtxyimnYOedd8ZGG22EH/zgB16/TwDYbbfdcMstt2Dp0qU455xzsNVWW2HFihVt2e8zKgpZzlRNd8VkXLRb1nVLPGFEC+m/RAtGIXVC3PZRQYfpVol2l0qNjus3TYtqkv1u78X9pnVSiHbrRiHNnBJNjHOEqVq7OHCPdIHHJAR2ZLQ79F5jDizqABZvywUeRW0YbenlxxxzjPfvcmnZowk1ZysUjUEJ+NrTCsI/jPrd1J9GzNdtIbqPPfbYRAMwZ84cPPLIIyX3Oeyww3DYYYfV6M5ag3I1t+Fork5klJsi77b/MmhxijlzxftQPtin2h+FNGm8w3QzxVCS1OikDtwiMh0N43JBggSMwHJ2MDXaZk6RWGyXaPdIFnioU6iR9y/w+PGyKvIcGZPELvAUfATQMgs8CkVSjj76aHzmM5/BnnvuiZkzZzb7duqKmrMVivaj1vOlEoq1Q32XaSz1mq9VXKiN8f8REi0obEVEUPxfp0JMEyLEECEiBVonwvAqrVMYruCW/5cwDq+dmBSYecaR5xw5xpBnXKRRM+GUzhwHjLuuho4TiAjLD+BmuYgSAm+xwNvmpS5DuJjLBQitEO0mmuaNke4TjNJ9m3HHM1aTY5NlDDkmHlvM8bIBguMjRDt3nLZx14waS/FYAyWAToj33iss8BCkKIVJCExKAgs8Evk+G8pzZG2OYYsjx8Q4+seSOY7nmu847ti5b6d2GkfF2MM0TSxfvhxbbbUVpk+fjq9+9av47//+b/zrX/9q9q0pFApFzZGZabX8UbQOjPER/bQy9Zqvlehuc8IfQmFhKYWk5opvogEGJQExpGuyRVZBREmE2BZmV0MWR1b+3xaCe8hinrC0udNSYii8KFH8fFAsyvHxO3B7UVqtIMj9YhEQYjFrcdiuCM/aDNxxxKIE57A5h8V4kVgEihcgmrUwUY5SY+lf4KEEsQs8hWh3dQs8zHECCzzyvelf4JHvtVYdR0Vr8qMf/QiapgUMu8JYloULL7wQM2fORDqdxg477IB777038TX++7//G6+++ipWrVqFH//4x+jq6sLll1+O2bNnF6VgKxQKhaIYJeQVjaBe83VbpJcrkhFOM2dwimpupcN0UbTbkS7bonbZYOE2YoU2TzJKKdPMU5yEam4LLcTCTt0gjU39TZoancSBO6rftL8uGYG2V8Vp5q3WYq1Syo2l31RNthAT6frwyhlsLsz7OozQ2KH4fQaI+nCZZj5ksSJTNceJT9dXtB+ciYWVao6rlqeffho/+9nPMGfOnJL7LV26FDfffDOuv/56zJ49G/fddx8OPfRQPPbYY9hxxx0TX6+3txcTJ05Eb28vxo8fD13XEzuCKxQKhaK21PO7qFr4b29qPV+38Fd8RVKiot0ACtFuLZhGLdLN46PdMgJJ3fOISK2IKlrM8dJ/vSikzb00c+5GcW3OvWh3q3zohKO1UdkAcnwIcR24fdHutJtqLqOzhj/V3HV8ly3F5NjYDg+kmdu8MD4Wc1oqDb8SoseykDlANTfS7Y5XIGvAH+12I96ScGZFOM3cnzlgs2C0G4CKdisqYmBgAEcddRSuv/56r01VHDfddBPOOeccHHDAAZgxYwZOPvlkHHDAAbj88ssTXeucc87BbrvthokTJ+Kss85CNpvFWWedhTVr1uDZZ5+txctRKBQKRQtRj8i8itLXn3rN10p0jxL8NcpAdJq5FEOeuPaJIVnbnTFIUb0tIIT3sMWQtd26W1+a+ZBt+4SlEEOcw/1/ce1yo8VQ3KJE4XlEjo+MUMuFifCiBICi/t1yQcKfgs/cWnfHEeMhx6dV0vArIamPQHgBQ/ONo+nWfddigcdmvCUXeBTNoa+vL/CTy+VK7n/KKafgwAMPxIIFC8qeO5fLIZ1OB7Z1dHTg0UcfTXRvP/rRj/D6669j2bJluPXWW3HllVfi4IMPLiv2FQqFQqGIopni2+Hud/wqfhxe/Xe1JOVgAHDVVVdh1qxZ6OjowPTp0/Hd734X2Ww28TXqMV+r9PJRSDgNWGyTpmoAc4TDtF8MydTfrKahwyBg3IFBNDAK2K6ABoBhMAAUhpsCLNPMTeaKd0YCDtMWc2BAZCE3M/XXn9Ie1W9aPqclcOCWqeb+ftNAoee0QUREO8d4bL/pVmqxVilJ0syLWohRDY7bqi7HhDGdLGewuANYhfPbXNYjAAD3DOxkmrkcS8o0EI16CzwAF78zX7p+o8sZFCODcwdaFZOxzHSYPn16YPuyZctw/vnnRx5z66234plnnsHTTz+d6BoLFy7EFVdc4bmZPvDAA7jjjjvAGEt0/LPPPouHH34YDz30EC6//HKYpom99toLe++9N/bee29svfXWic6jUCgUCsVYJGk52C233IKzzjoLN9xwA3bbbTe8+uqrOPbYY6FpGq644oqy16nXfK1E9ygiac2tTkRbrDgxlGO80D7Mn/bMHdgALOIU6ruzDAbRMGQz6BqBSXih5jbUQqxVxFC49h3EjZAmaLFWqt+01/oKvhZrRIvpNy3EYrv3m65kgYdSIZRTvgUeL9odWuCxmVjcAZB4gce/gKFqu8cmq1atQk9Pj/c4lUrF7nfqqadi5cqVRdHrOK6++mqceOKJmD17NjRNw8yZM7FkyRLccMMNiY7fYYcdsMMOO+Db3/42AODvf/87rrzySpxyyingnCcW7wqFQqFQjAb6+voCj1OpVOy87S8Hu/jii0ue97HHHsOnP/1pHHnkkQCAzTffHEcccQSefPLJRPdVr/laie5RRqmIrl9YGlSYgnnRXJ8YSrlGV1mbw2BudNd9g3HuIGtJQSTEZ19ORHNNQkBtN6WdiQgnc5yWEUNRixLh3t1SLALwjQ+LcOAu7jcNFAtvf4Q2ab/pVliYKEclCzwAB+caONe8/uf+aPewxb0FHv97Tb7P2nmBR9FYenp6AqI7jr/97W/44IMPsNNOO3nbGGP485//jP/8z/9ELpcDpTRwzKRJk7BixQpks1l8/PHHmDp1Ks466yzMmDEj0b05joNnn30WDz30EB566CE8+uij6Ovrw5w5c7DXXntV9kIVCoVCoWgijDlAlSam0jC1kuw0fzlYOdG922674eabb8ZTTz2FXXfdFW+88QbuvvtuLF68ONH91Wu+VqJ7FFMkjHzC0u8wTbViMZRjHGndTQ93o7p5LgzAdBBPEBlEpP/mDI4h24ZONKQYEcKStp4YKpdmXokDt8WdQkYAk0ZeAOBGvmlBUMalmYfHp50cuJMu8DhOoYUYcxyYDglEu2U5g8UcZN2xk+nClSzw+F3hm73Ao2ht9tlnHzz//POBbUuWLMHs2bNx5plnFgluP+l0GtOmTYNlWfj973+PL3/5y4muOWHCBAwMDGCHHXbAXnvthRNPPBF77LEHxo8fP5KXolAoFApFW5I0O63ScrAjjzwSH330EXbffXc4jgPbtnHSSSfhnHPOSXR8veZrJbpHIZXU3IoWWSgSQylKYFAeMLmS+IW3QTUYFveikLJ3d6HmtriFWKuIobjUaL9Y9LdYMylB3k3Ll9Fug4oUc38qvhTeQ3k3LdrSItPMDa55/abl+BDa/IWJaki6wGNQAsbE+4txirzOA9HuES3whFqIteM4jnUY4/IPqPLjKqC7uxvbbbddYFtnZycmTpzobT/66KMxbdo0LF++HADw5JNPYvXq1Zg7dy5Wr16N888/H5xznHHGGYmuefPNN2OPPfZIFIlXKBQKhWK0kyQ7rZpysIceegg//OEPcc0112DevHl47bXXcOqpp+Kiiy7CeeedV/b4es3XSnSPUsJRSLlN9u72m4bpVAN3RDTXL4Y6DOEkPUS4SPslDuCruU0bFMOWSAX2RyFl7+5gzS1ACWkJMZS0d3fx+PCA6Vy5ftOAG71106OJpgXSzHVCvH7TNufe+LRjtNt7XHaBh4tWbA5BjonxlNHurM1BiUhJz7vp+VJ425wja1e+wOM3p1MoKuHtt98G8ZkrZLNZLF26FG+88Qa6urpwwAEH4Kabbkq88n3ggQfW6U4VCoVCoRidVFMOdt5552Hx4sU44YQTAADbb789BgcH8bWvfQ3nnntuYG6Pol7ztRLdYwBhFibaUVGieWLIEM+Cc+61f5Jp5iYjXrQ7YxJkbUdEIG1xTptx+I33N2RRFIWUadQ6hecw3SpiqGRqNHOE8VfAVE1EYf2mc/5ot0E1GEzz+m1L4S3rkQFggDC3xlukRZuUu6ZqYpscH5DmL0xUQpIFHmmqFrfA45UzGMIzgLjv04KpmoCSyhZ4/OZ0rT6Oiuby0EMPlXy811574cUXX2zcDSkUCoVC0aIwXl1mmndsQqopBxsaGioS1nI/p4mtZZXoHsWUikJGmapJMUQ1jjQtRLuzFkda12AxEjC68gvvLCHYkBWGYzIKmWMcJuWwmeaZhol+yq0lhsqlRhei3WIRgWoF0zmbEuikUP9ucQcWExFamwMdhjACM6jmplAz6IRAZ6yQGt2CLdaqJW6BJ8o13+TBBR5p3pc2tNgFngH3/wbRvEWMVl/gUSSH8eqMWeRil0KhUCgUitFDNeVgixYtwhVXXIEdd9zRSy8/77zzsGjRopKeLfVGie5RTlQUUm4vrrmNFkNpg3jCkbiO3bb3JVcIIuIaivXnRJ23F4WkQlgSonmmYa0ihpKkRvuzAcIt1hinyHOOjCF6dxtUC/abZg6GwYTwDtR329A1zUuNlmLRbjHTuUqodIGHkEIrNrnAI6PdspwhyQJPxuCxCzyWm7HQSgs8CoVCoVAoFIraES4HW7p0KTRNw9KlS7F69WpMmjQJixYtwiWXXNLEu1Sie0who5DlTNV05gTEUIchhOWQxaFTTfwwDXmbI++K77zN0J8FKNGRsThyBkeeF9fc6tRpKTFULjU6rsWavyZZRrvD/aaBgvD2HLjdyKxJmZcaTbnmikXSUi3WKqWaBR7HIUjpJLKcQTiea7CZXOQpvcAjI97+BR7OW2OBR6FQKBQKhWK0UK3xqXfsCChXDqbrOpYtW4Zly5aN6Dq1RonuMUC5KKQ/jZpo8GqX/WIobRBkDIKs5bi9lwsRSD8DOSE8ZZq5rmneeWxNRCKp5sBmHEQjLSWGolKj41qsUTeKH452D1k80G+acwc24Alvw+JumzGRZp6zQ6ZqbRzt9pN0gcdiDkxCIssZhmkhxZxzp+wCT87mMAmPXOABnKYv8CgUCoVCoVAoxiZKdI8RSkUhda+8gYBTFFqIMVIc7TY4spYQTTZ3ABYU3joh6Ceidtmgov/3oGUX0qjdWlvNNQ7TaetEu73HJUzVwi3W5Dhl3UhrxiCBftMAPOFtEcdL0/enmRebqrWvA3e5BR6dEjDOyy7wDFkcHQZF3uLQqQObO8hbLHCtShZ4xHUdbxyV4G5NGtUyTKFQKBSKZhNuW1s96vtMO6BE9xikVBTSb6rGqBMd7TYo8jbz6pdt7sDmQhANZMVG4ejN0GGI2twUpYGaW3GdQjS32aIyaWp0uMWa33Su0n7TMs3cZAS6rXmmajrVAi3EWiENvxLKjaVBNSRa4LE4TEOOm6DaBR65gCHHUZb+tPI4KhQKhUKhaA1qJ5AVYxUluscQcVHIot7dbo0spRo6HOHSnefhaDeFThg4JbA5CxpdWQwkq4ESIK0TGESDSYjrNC3SqC0WLYZaRVTGmar5W4iFW6yV6jedtRj00MKCP81c1whMwn1iUQPjiIx2N3tsKqGSBZ6MQWE7HFmbBcoZ0gaFzTjyKNR2A2KBhxAN/bnSCzzhHvHtOI4KhUKhUCiSoQRy/eHcgVZl5xA+RjuOKNE9xoiKQvpbO0W1EEtRgoxOPTFkuAZXaZMi7zqG2SxodKVTDVmLemZXJrWRphQmYSAEol91jBhqFiV7d7sO3Lon2Arj4zedE9Fu0W9apFHL2nA5RiJq6zcCS1EGkxBQW6RKR4lF0mZR2koWeHQiFnhMRwjmLkNHjhWXM+juAk/WYtDdD+yhnA2datiQLV7gkaZqupuxrKLdrY9tczi0ivRyW6WXKxQKRbuiRLJiLKBE9xgnruY23ELML4bGpSmytoO8xb0WYgCQt7grLAU6IYE08zS13fplAps6RS3EWinaXTbN3BWL3E0jl2n4RdFuGuw3DRDoxMGwxUAJYOQKvaZ1oiHl9kL3m6q1a7S7kgUegxYWeHKUoNOgGHYzK2Q5g/+9JjMrCNGg50QGRfECjzBVi1rgAQEImp9RoVAoFApFO6KEcm0Z2Xiq7zHtgBLdY5AkNbfhFmJSDMna7rSuYdggSBsUeTd1Os9Fi6ZsXghJmf4r08xT1IZJCYZtAkoLDtPccVpGDCXp3R1uIRZusSaj3Un6TYv6biEWZWo01QrZAMyJduBuxyhtJQs8acrRYTAv2j2UI97CRDZf6BE/lBMrGpUu8FBo4O7vE2ivcVQoFAqFohqUUI5HjU1l2LYDh1Y3Zswem2OtRPcYJRBJjqi51akQfNL0LBztFr2UKfIGR9agntkVYxyMAQPudURasHD17jAI0joT9cuUCOMw12G6lcRQXJp5ODVathALO3DbVLxWf79pid90zh0hGFRD2uJIUYZBYguDNRYUi+3qwD2SBR5/tHvAINAt4r2f8pboEW8zp+wCj6a15gKPQqFQKBRJGWuicKy9XsXoR4luBYDo3t06KThMm46DDKfIMYZOg6I7xTGU5+iXqdGUIG/xQOueLNWguzW3/QaFQW1kDAKTCDGkE+JzmBbitNXEUFxqtDRVC7QQcx24ueMgY3AMWwRpnWOYilpwmxXMI+Qihd8ITEZy/anRUS3E2i3anXSBR3dd3zscihzjgWh3h+Eu8Fgs9F4ruJuXWuCxWWsu8CiCVGvMMlZNWRQKResz2sTjaHs99YArmxFFBEp0j2FKRSHjHKY7DR155oohk6A7rSNvM+RtjmxeRrsdMFaI5hLXtVxEIW2vHtp0U4K9FmK8dcRQuX7T0lRNisWwA7dJidc6zN9vOm9zEbV2xaJONQxbwgjMcPt8D9kiNZpKM7ASLdbaUSjGLvBwN5rvOMjohQWeYYMXlTMQooExIJ+3wZh4o4QXeAzq9u5ukwUehUKhULQe7SYy2+1+K0GJWUU7o0T3GCcuChlVc0upSHvO6DqyBvOi3aZOQYgQPJQSMMbAuYN8noFSIhymiQbTbQMlo5ApymBSAkI0t4VYa4mhJL27pViULdakA3eacvSkOLIWD/SbJkQDGArC212o0InoS92XYyCaqEWWpmo6RWyLtXaJ0o5kgUeOY9Z2MERtL6JtWWKBB+AYzora7uACTyHN3CQEhMBbwLAZB9FISyzwKBQKhaI+tLIAbeV786OEbuW0y+92JHDmiNYw1R47BlGiWxFARiHDNbeECIdpRh2kdIK0Tb1od9qgIgLpRnQB5tV2S2QKuk6E8M4YNkwijNko9TlMM4BoRKQea60jKpP2m/Y7cJuu6Vxcv2l/bbdObM8ITCealxpN3TGI6jfdCgsTlVD1Ag9jnnmfqVOYukgxl1gWB2NOogUemxZ6xNtu5Bte5L09xnE0wxmAKibjgE2CQqEYNbSKeGmZ+2hzAdwq46hQNAMluhVlWzuJNGohhkzXLCwc7c5auhvJtUEDUchC7bJOhUD/aEhD2iCitZNF3VRsAsIc6ET0szZ1955aJNrtPY5IjS6YqhU7cHvR7oh+0wEnc4uBZIURmEgzt0VqtGfONvocuOMWeHRKYHDHW+DJMD0Q7c7bDEM5G5QSyAUegGB42AJQwQIPIW7GAAI94tttHBUKhaKVabTQavj1WkQIK0GrxkDR2ijRrQBQOgoZNlVL6RTMcZB1xVB/jnnR7rRJYYWczAEgm7UwQEXEnBCf2RW1vZpbommwKQclBLZrjNQK0e5yqdFhUzW/A3cg2h3qN20zx2slJtGpBoPIRQmCQUsIRtlvmjnt7cCdbIFHOJKbOhGmajb3xtGLdhsUpsGQz4sFHlHKICLkA2UWeET2gOYt8Oi0uEe8QqFQKOKptbipp1hqlChuJcHXSvcyWmmVxZZmwRiHU3V6+dgcPFJ+l9Yil8th7ty50DQNzz33XOC5f/zjH9hjjz2QTqcxffp0/PjHPy46/rbbbsPs2bORTqex/fbb4+67727QnbcXIqXZdc92WzEZrgu3QUXNcZoKEdOdouhOFdLMKSVuFBKwbY58niGbtTEwmMdQzsZQzsbaIYZ1QzYGLBuDlo2cLSK/QoiKft9cCkwuJpB77roTO2w7C9tvsxV+ecN/N2dcvHRvYXQm0r7FuPhrhk0qo90UPSmKjEGQNoRglC7lALy+5llL/AzlbPTnxELGoMUwZIufvM1dE7bg+IhzFO6vHSZaKWojx9Jd4JEZASJaTZHRdfSkKMalqfc+0933mXyvyfdZNmthIGth/VAe64dsrB2y3bG0MWwzWO4Y2py77zGIBQwf7TCOCkU7oObs0Yc39zhOyZ+77voj5m43G3O22Rq//MX1JfcFxFxWyx9vvixzn9X82JwX/dTjOonuhRX/1Hos1U/xTzmY4zTsR9EetF2k+4wzzsDUqVPx97//PbC9r68P++67LxYsWIDrrrsOzz//PI477jiMHz8eX/va1wAAjz32GI444ggsX74cBx10EG655RYccsgheOaZZ7Dddts14+W0FLFRSPGsV3OrU+EEnTFoINqdSekYyFowDYK8QZDPE+TzDPk8g22LT6h+mnfTfzV8ZGhu9JJ6NbdCbAWjuYRqsG0bZ5/xPdx9/4MYP348Pj1vZ3z+4EMxceLEho6N9ziUGl2qxZo/2j1ACynmOtWQ544nvCU6IdhgiIi34bq8p+jodeD2j2W4Tl72iO80dOQ5R9pg6E4RZC0dQzlbZA+4YyffYwBA3TFOGxRrhygyho2Um3kgXfPDLcRUtLv5cMarMmYZq6vm7YCas0cndhnvBdu2cc4Zp+OP9zyAnnHjsPenP4kDFx2KCRMn1mRRsx4Lo82MXCrh1H6oVpWKSmkr0X3PPffg/vvvx+9//3vcc889ged+85vfIJ/P44YbboBpmth2223x3HPP4YorrvAm8Kuvvhr77bcfvv/97wMALrroIqxcuRL/+Z//ieuuu67hr6cVKZVmXqi51ZAyCByHemKoO8UwlOcYShsYytkYdutnCdE8o6uhIQuUEvS5gsjUqZdm7q+5JRrcVlkFMfT0U09h9jbbYOq0aSCahn332x8PrLwfX/7KEU0Zm6jU6CQO3HH9pmWWuU40b+GikGZeSI22WaGFWDs7cFeywGM6DlJMvEd6UhRZi6M/x5FJCR+BbLbwWqUAp1S4mZdb4CGa4y7yBFuxvfXmmzj568fjw/ffB6UUDz36BDo7Oxs3QArFKEDN2aMTf3RaPg7z1FNPYNbsbTB5k00AAPt8biFWrrwPX/zyV4LnqoHQbYRgVQKrtXn732/itG9+HR99+D4oofjjykeQ6eyE+rUpWom2Ed3vv/8+TjzxRKxYsQKZTKbo+ccffxx77rknTNP0ti1cuBCXXnop1q1bh97eXjz++OM47bTTAsctXLgQK1asiL1uLpdDLpfzHvf19Y38xbQhMgrpN1WjVHP7KYsUahntFhFvG4ZBAVjgjLt9uw0MD1sgRMOAQWDqIhIpo5AyXd2gpKiF2Jr33sUmU6cBEBP81KnT8O67q5s6HlG179JUzWbRDtzdKYJhK9hvOp8vfOsYcP/vpVabLFD7LuuRpQO3lxrdhg7cSRd4uEPQoRcWeLpS7iJPTqSZm6YOy+LI5Www5njCGwD6KBH13yUWeMRCRjDa/fUTluAHF1yEPfbYE2vXrkUqlWriSCkU7Yeas0cvNnOQs0ur5dWr38WUTabCciPik6ZsgnfeeQc5a2QquxkiSpUbCZrx3SLp2H/nGyfg9HPOx67zd8f6dWtBDNPzBmoUY21hxuEOnCpfc7XHtTttIbodx8Gxxx6Lk046Cbvssgveeuuton3WrFmDLbbYIrBtypQp3nO9vb1Ys2aNt82/z5o1a2KvvXz5clxwwQUjfxFtRFJTNc9hmlJkDL0o2p3NWkildDfSzWDlLe8a1I1ipk2KjwwNXSmKLqNgqqYTkVrMuRRDAJzmR3Gj0sw5c2uRqdjmF4tRDtwdBvX6TQMiFTqfZ+7CBJClGvScLVKjqVbUb1qaqhW1WGtzB27/WJZa4MkYBOPSFP25QjnDMNGg6wSWxWHlmeucL+rt+wzxfkq6wPPKKy/CMAx8evc9wB0HEyZMaPLIKBTthZqzRzfccZAvI7ot7oD5Sqds7kDTUPa4ShnLadm0wXM8R/VjTUh198p5+ZrlV19+EZQa2HHX3WAxjs6e8XAAWE0sOxrDb0tFCZoqus866yxceumlJfd56aWXcP/996O/vx9nn312g+6swNlnnx1Yae/r68P06dMbfh+NJkp4S2EpI5E2Ew7TGVaIdmctjnXDIrqYThvIZkULsTx3wBjzVrekAVbajUKuSwsxZVIR1czbQpRLMTR5ysaByPbq1e/gk5+c1/SxKYwRENVijVIt0oE7k9KRzbOA2zvnwSitdHpfq4sFCH9qtGwh1u4O3HFjGbfA02noGLJZIdotyxlMimxWLGIwVljAAJIt8BACGFwD14DXXvsXMp2d+NKhi/Deu+/i0C98CWecdU6zhmhMwTiDwypvus1Vo+6GoOZsBQDkLB7wIYmid+IUvLt6tSey3129GtvN3QXDVvv9rTZa3MYRFq52HZVdrV8yK+MBEDfGtut7E3tex8Fr//oX0plOnHDkF/H+mnex70GH4Ovf/v6I7lehqAdNFd3f+973cOyxx5bcZ8aMGXjwwQfx+OOPF6V47rLLLjjqqKNw4403YuONN8b7778feF4+3njjjb3/R+0jn48ilUqp1FIXWb+sU1lzK1pkpXSKTi5ql4dSHL0dOoZywujKMCgI0aARDTzPPcOjAQgxZBgUpk7wvkGR1onn+q0TgrzNvWju3J0/iZde/CfeWbUa43vHYeV99+Lsc3/Q3AFxierdLcViyhE1a37TOdlvOm3Son7TUm9QSjDkRrv7DeqmmVtearTmOsvLFmvhmmSgfaLdSRZ4uEPAueOm61N0mdSLdstyBtOkokc8ANuyvfOXWuCRpmqEaLCIWODJWxYef+xR/OXJZzBlyhQcsmh/7LzLJ7HPgs81b5AUihZAzdkKQESrB3OlxfPMbXbEKy//E2/+exW6unvw8IP349hTvo9svvbRR+lnUmukEGQjiPAC1Ud5i3CzBWpBrRcSRvoa4xYQ8jYvG7HO5S387cm/4Lf3/gUTJk7CN47+AmZvtyPm7/nZEd1TLWmVhZtaUu0iOTB2F8qbKronTZqESZMmld3v//2//4eLL77Ye/zuu+9i4cKF+O1vf4t580S0c/78+Tj33HNhWRYMwwAArFy5ErNmzUJvb6+3zwMPPIDvfOc73rlWrlyJ+fPn1/BVjR6S1NxKh+kOnSKn6+gybYxLU6xza7uHOkS0O5cVIsjKWwXhTTXXZVpD2qRYmxI1txndRkanXjRdJ4Bu6Ljgkh9j0f77gHOO737v+w1zLi83NoF08wQO3F0pjg1ZFtlvWqwG+53MC23aRJq55aVG22z0OnAXL/CIaLV/gacrxQLR7mzWgmlSWHkK27KRz+bhcAfDbv/uuAUeaapmUw7KgCkbT8XcHXfG1GnTQQiwcOH++Mffn1OiWzHmUXO24s03XsPaLJBLjS96jmpB9fv1My7ECYcfCO5wfOX4b0Hr6MGGrOW1zEyCnnAe0yqc7yoR6pUIpsj7KBHlrWbBoBoBN5IoeZLLlYpkj0RwZvPM8wWIY/zEjTF7ux0xfqOp4AA+tefn8M/n/4EdP7V3xder1wIOUFvhXen7XdEatEVN9yc+8YnA466uLgDAzJkzsemmmwIAjjzySFxwwQU4/vjjceaZZ+KFF17A1VdfjSuvvNI77tRTT8Vee+2Fyy+/HAceeCBuvfVW/PWvf8XPf/7zxr2YNiMuCulPo5YtxFI6QZehoyvFfNFuHYMmhWFS5Nz0X38UkhANhkGwVppdmQRdZkFY+sXQvgcchAMXfR5pgzZrOAKUq33XZN9pOT5uZFXWJGdtB0M5G3mDum7vBPm8Bf/CISGaZwS2VteEqZpuIaUXWohFtViTtHO0O8pULWqBpz+nizEydZimcM4HRBupoI9A9AJPigpTP7nAs8NOu+DDDz/A+nXrML53HP7y6CM47oSvNWtoxhTVGrOM1JTlRz/6Ec4++2yceuqpuOqqq8ruf+utt+KII47AwQcfXNLUa6yi5uzRy223/gYAsOVBxxc9FxbTs3b7LK78QyHa+FFWGNxFiY84cU1jBHop4V5K3IQXBpKes5z4L4igCgV2KIibRJjxCqZ0ec1y6d2VXL/4GqWPiRP4SS41ZDH0+ebxMDrRMG32dvjoow/wzocfoLO7B08/+SgO+PLR2JDPB/at9LWVeq/E3UvifSu8l2KRrYrG25G2EN1JGDduHO6//36ccsop2HnnnbHRRhvhBz/4gdd6BAB222033HLLLVi6dCnOOeccbLXVVlixYoXq91khlGiBNGpTF+LI5tHR7nRax/AwhW7oyA3nAjW3mmuAZZo6Mqk81qZ1dKdsT1j6W4h1p0WkslXx95v2m6pxR7RYsyOi3bLtleH2m6aUIJezYdscmQyQzVroo5oX8c6YQrQP6hQmId7iR1S0e33fOhx8wL6wbRvMtvGNb52K444/saljVIokCzymLozPMgZFl6170e6utFjgGR4WKeaUUuSYeK9xxqG59fFxCzwduiiD0KkGqhOc84OLsP/n9gbgYMHn9sWBBy1q9vAo6sTTTz+Nn/3sZ5gzZ06i/d966y2cfvrp2GOPPep8Z6MbNWe3N//6eDggiA3/v2n0v/2ix78dEJ/xgcda9HHe8yGBQyMUbfg4cc5gWmsloj5OtMWJs6JzhAR2WHj1923At485BMy2wRjDl485CYd85ZiIMzuJIrJU01Au+zcs5kqZpcVdM07Qj1TAr/j1f8LKZTH9gCXetiixSjUNXzj5DHznq4sAx8F2n9oTM3fdC+uy+aJ9gej3SpJ7LiWU495H3rFlni83VpUuAIxmkiyS33HHHfjhD3+I1157DZZlYauttsL3vvc9LF68uLE3G6ItRffmm28OJ2LlbM6cOXjkkUdKHnvYYYfhsMMOq9etjUqixJBBhXNYkamaTwzJaHdfWkS7KRU/tmXDzvtrbilMk8I0hNlV2o3oZnQhhib3pDCxqzVr9Mr17vabqpk6QYYL07lCTTJH2qBIm9TrNy1NQ4aGxOqu7G2eNinWDlGkdTuQGp23uRtZLzhwE6qhu7sb9z3wMDKZDIaHhrDLjtvh4EO+0NS0/EqRExnnvjp5w4HNRSu28WmGgRxDf4eJgayNoQ4Dg4N5ZIdd4Z3NweEOsoNZAEAqpZdc4MlbGqimYa99FuKggw6EXs9cM0XTGRgYwFFHHYXrr78+kA4dB2MMRx11FC644AI88sgjWL9+ff1vchSg5uzRx2sfDHlCWX5OyoVL/zagILDdJCRPoAcEeYSAH6l4D4ukIhFeRrhHi/Yg4r6DalqeN1y+Hj5fztvujl86gytv+iPSHRkMDw3i+M/vjt0/dyDG9RZ30NCd8pH3RAK6RJp5+H7jBHxcqnPc9UtNq34Bb+XEvP36R+L/pd4vE7bbDaf8/C5vnzUD+aJjgOLXFH4eKL0AFHceIGahJubFxons+IyP5jmxx8EZB6p0iOdVHpd0kXzChAk499xzMXv2bJimiTvvvBNLlizB5MmTsXDhwqquXQvaUnQrGo9fVAbbZBVM1QzuoEOn6DYNZJkQQ+tSOrrSBgbSOlJpHbmsjlw2B84Y8sPiE5xQAkIJDIN6Zlc9poa1T9yFPeZ9EhM/8clmvvSyVNNiLa1TdKUoulMMWUuYzpmmjmzWFqZeFgdjTlB4GxZ0QrxFiTS1vH7TxS3WHBCNIt3RAUD0rnUcJ/KLbysRN5ZyEnSc4AJPlunozTBsyIpod98QQUeHgeFhUdMNCB8BxhgIJejbICLhcQs8BiWYMi7dzCFQjIBwT+ZyplqnnHIKDjzwQCxYsCCR6L7wwgsxefJkHH/88WXFokIxmnn7gwEQonndIQCZdeWKbnduAoQAlyIpSqhHbY8T6sHnRi7UgwKLlYywR4p0n3aQ+0uPueQp89w7P0mlkOcMg9lhcO4gx2zkItSuXS5NPmaul/dUKgIuRXRi0RxzrThhGXXtUjXKb3w4BBqzoBP3/gGK30NAfEZGXNZGeL9KRXw1Aj6peB+N5mylqGSRfO+99w48PvXUU3HjjTfi0UcfVaJb0T5EtXYqmKoJh+kMo8joBTE0lDOwftBAKmVBN3RQ6uZeW1nkeUF4bzAITJNi2tAbyL74MgDgoQdXYsedW1t0h/GnmUtTNULEB6m/37Q/2m3qBKYhFh50nbn9pi0ABoaGLFBKvBZiplsTPmDayOh6bL9pQjWsX78eB3zuM3j9tX/hkh9dho022qjZw1OWuAUeQsTErFOClCNWxEULMRsTMjr6cyYGMjaGszYMQyzkUEph521wxjGMYXHO0ALPuDRF2ukHWf8+dpiXLMVYUR8Yq7JlmHtMuDXUsmXLcP7550cec+utt+KZZ57B008/negajz76KH7xi1/gueeeq/j+FIrRxvvvD0DXNVd4E/dHiAD/NimKpDiXIoRqBbEuFo7df0eIqHLCXBwn/h8W3+VS4CsR5jkeEk2MB44JiyUWF9LlxdcDhFjP9vfhnOMOxbtvv4klp50Ho2s8cpFRQR6bssxKRMHLinWgJoJd3GEFke4SAYHVHw26x2mFhZzQe0luo6H3SNxijzxf3HO1EOxhAR73Xovat1isB98Do0VwV7JQXukiucRxHDz44IN45ZVXyra8rDdKdCsSk9RULaXTgBhaN6xjfKeJwcE8DNOAbugglIJnLXTTHHq1HAaGbHSlh7Fg4Anvep9dsBC77PqpJr7i5MSmmYtnwTgCLcTiHLj9/aYBIJ/NgzMduk681L20QfFRiro9vwumap7IZwDRCLjjoKdnPB57+ll8+MEHOOrwL+LQL3wJU6ZMacIIVU7pBZ5Cj/iMrqMnzdCdIiLandaRyRgYGtShyfR0K488K17gMXWCvtWPY3jDarwBYO9dt1euoG3MqlWr0NPT4z2Om7xXrVqFU089FStXrkQ6XT6zob+/H4sXL8b111/fFgtXCkW9yX38IfppRvhluAucmk9sRwnyggAvCPI4EVVOkFcTKc+6925QDVnbPZ9VeN5/XNb2CSG7IHLCYtxvEuYXu1TTYIfaIjHHKZuWTDKduOx3f8L6jz/ET047HrvscyDGT5wUeVyOVWNAV6rndfzcRzUCu0S6elLBDlSepv7uu32B9xSAku8rIJlAF8eUXvCR1EKgVxRNdyswKxXnjaRa41N5LJB8obzSRXIA2LBhA6ZNm4ZcLgdKKa655hp87nPN7UKjRLeiIsJRSOp9SAthqVMhjDI6RY9pYDAta7sNrHeF9/CgDkIJtpuUx34zshCfPeu8a2y2+RY47CtfBWmzetqRtFiL6zctDeeGhwsTRNqN0mYM4cBtkkK/aenAzR0EnMynTJmC7bafg8cefQSHfvFLzRqixFS7wNOfM9GXtjCQ1pHuMJHP5kU9N7OA/BBSBsNGPIfcIMOE1EfYfuA175qHfeUoJbjbnJ6enoDojuNvf/sbPvjgA+y0007eNsYY/vznP+M///M/vUla8vrrr+Ott97CokUFQz3O3ZRQXccrr7yCmTNn1vCVKBStxz+ee8b79zGbvo77352Al/sngBDilYlRSkENWlKEAwgIpiixVCplPWcXp6zn3W15cBCiIWsFBVIWjidYLPcLv19oM+6AEi3wnMV8x7jHM9tdVNc0r5WV3MeG413TdpyA0LQdB3bIcMwfQQd8wpRxmD29mLblf+D5px/HrgsODB7nE2zSKqwo/ZhHR0OrEerimqVrv0tFv0cq2K2PP8QGpAKLO6UWeoDainJxj8UlEnK7PJ//uaSi3L/I442/FR8xDywGAaCsvb+zJFkor3SRXNLd3Y3nnnsOAwMDeOCBB3DaaadhxowZRannjUSJbkVVxEchRYp5iol+yuNSBiZkGNYN6+hK6xjsNDE4kILe0Y/9NpeCu8DRS07ExptMbc6LqjHh1OhSLdai+k1nh4XZhN9wglLh9G4aFJmUjoxho9OgGLL1ohZrH334IXq6ujCupwcb+tfjL48+gq99/RtNHJHKKLfAY+qAzTi6TR05JhZ4ulMEPRlRztDRYWB40IBu6shngTkThrDfVn3ue261d5158z+NvT6zoOGvT1FMtcYslZqy7LPPPnj++ecD25YsWYLZs2fjzDPPDAhuAJg9e3bR/kuXLkV/fz+uvvrqotV6hWK00d/Xh/vuudN7TDRg36lr8fqLJoaQAqVUCG9CoDO9SBzlQ+KIMe495twBc/+GxWPiGWrZvrR0ZjNQ5nrJMOZFKaVgthkTUXC47Um9u5WfD1L1yLkkKPL8IrsUBtXAnGjxLYW1Pwqua2J7f44VncfP4LqPYKY7kM50YXigDy8/+yR2P+QoDFrMOydQEOny3AACn5vloul+T++AWI9JewdKi3VxnrjIugbmRCtyeZ0owb7mjRe9fx+72Zu4d9V4vLC+xxXZ4r0msibjhTiAyKwLsT0680I+ByRLYc+Bl1z8AVC0AAQAzJ1isrYT6VvAfO8Ny9cjTi4GAeL9Y7V567AkC+WVLpJLCCHYcsstAQBz587FSy+9hOXLlyvRrWgvSkUhvZpbQ7QQy+gUEzM61g7p6MuY6BuykErr2GajviLBDQD5fHSLh3YhnGbuX5SQpmo6ddwWYiLa7Tlwu9Fu2W/aMA3Ylo3ccM4TFcNUKzi9GxRpQ0NXigZN1ahoIfbvt/6NM7/7Dfd7hYOTvvFNbLf99k0dn2oolWYuW4h1GoUFnv6cifGdFgYH8zDTJnRDRypNsN9WdtF77sSTv4XeCGdYxeimu7u7qO1UZ2cnJk6c6G0/+uijMW3aNCxfvhzpdLpo//HjxwOAal+lGBOsXftxkREn0YAJHRxDw+5jN+JdLTR0LA2JUr8YDD8HjDzVNongrob+d15DjjlYm9k08lpStL3z77fxv1cuFRsdB586eDE6pm5ZVqwnSUHOhdYlRyLWIx25axhZZ9lBvPb0Q95jogH7TV+PNz+y0W8bYNQAqAHqegTJxR5/tkWpBR8g6DtgWTyQdWFZ4r3ImIMcCn4EgCgRkOUGjLoLL+54M+4u9pDCwosYQ4I8F347cmHIltkNhMBihWwLMS4iG0OKcP9ikFxgAoL7jGYqXSSPg3OOXC5Xfsc6okS3oir8UUgAXu9uKYakw3SPaWDIZm5tt4EpxjA+0/FE5Dk1TRsVAihuUUKnQjDq1NdCLOTAPZQ2kElbgX7TAGBbdvAaJJhmnjEITEpcUzXRQmyHnXbBnx79K1K6EOjtSCULPD2GgYkZG/05hr7hQjnDoKnjcxv1Ry7y9Pf1jYr3nKL2vP32221X4qJQ1IsJEyZC07SA8OYOMKhlkEqb0M3i6Har13hLwjW35XqOJ3FC9wvNj/7+ZwDAh9t8qXg/32dM12bb4cirVgTuc+2wncjAq9R9Rz1O2jprpFF1QIj1SPftiBpyXdPQv2EdwlkIRAN6MYB+KyNSCJkFhgwc7kBjbvSbExBGwCkX70FOwZn4t8MpGONgTL4fAc6Zl3EhsyA4Z957iLHCQpDMrPCyJShE5oXm21aUUYHA9uIMjOJjhPiWzwVLHQrPF3yDrCprqmtBtcanQMH8NAmVLpIDwPLly7HLLrtg5syZyOVyuPvuu3HTTTfh2muvrep+a4US3YoREd27Wzhsc9dhuscw0GsMYfJL/4sptlhlmjRpMubu/En86b674TgONE3Dwv0PQneCesx2RPbuFi3W4LUQ6yiqSebIpPTiftNWzqvvBgDTpFhnUi/NvDtlI2PoGLaZiKYTsfDR26mL31EbU26BhzsEFhMLGD2miHavHRLR7q7Uehy26YuR5x0tizyK2vDQQw+VfBzmV7/6Vd3uRaFoNbp7erBw/4Nw791/BCAE9yN9mwJd49GdQGADjXc1L1VDW+t2Y6VajUk+6st6r8t/v5UYeIWfr8Rl23+tcvv5zbyiXlMpQ68il3cANEIcRrmvM0KgZbohSgCCCzzrWBowUoAb6Sbu+84wjZKRbgCJar+BwntUPhdOOfe/1pE6iDfTBG20EV4kHxwcxDe+8Q2888476OjowOzZs3HzzTfj8MMPb+JdKtGtGAHxEd2CqZqpE2zSncaTt1/jVVH5U3q33HJrrFu3Fr29E0aV4E5mqkZgMY6MXnDgnpChGMqZGMrZRf2mZesrAOhzJwbZ+iptaAFTtUk9KUzuie9P3I7ELfAwR0NKp2DMEeOYophAhjDxnUL94fY7zMXUadNx/z13jolFnnalUTXdCoWicubM3ckT3XdZOyHXmcbEntaIXAONFdbhYyJ7eId4f+1Q5IKD/zqljLz8r6lSgR7VXg2ovg1W2NDL7/Aedb7wfQHFbume47vRgclzPo0P/vEoACG47/13N/qNnsQp5VrovTgSoZ3UaM0/bnH96KOe84/VSH43Y4lyi+QXX3xxRW3FGoUS3YoRUSoKaXMNE7tNTOxKYc+990Fv7wTM+o9tAsd39/SMWuGTxIFbikUZ7e5OuQZpKR3ptO71mwZEOg5nzO02DfRRCtOkWGsK4d1jAhseXwE+cwZ2OvDzzXvhdSDpAk/GoOj/+5+hrX4dANDRkcHXvvFtzxFzxowtR+Uij0KhUDSSrkkTMa6CKHWcCKmVmPY/H9i3hBCslaAOXyOuhvmjj4ZKmno1U/iJNmqFmmJJbKs1oMhlW+4jCbdQs3zu7QbVPEdzv+M7IMavc/rWgCu6b3hrcwxxE13jay+wC9uCYxp2zS81zqXGuhELIM2i2kVy79gxiBLdipoQFkPjMjp6NdN7/lO77d7Eu2sd5KKE31RN9puWLdYmZCj6hoP9pnVDR344B1hZ2FwIb0IJNpgUhkGwxeArYC+/AQbg+b8/i/1HmegGyqWZCwfcT24xAf2vT8La1a/j2OO/jslTNg6cYzQv8igUCkWj2HxK14hTvMXzWuC58PNJotH+6wOlBbS4x8pEdOQ5IyKMUenS/nP1rx9MJBaj2qr59wNG3vIqb8PbLs8nn/Nvj2q1BhR+Xyz0uwy3XAOCLtueIViZ1muBMRw3ET0JhLV8vUnGy/9aK13EKDVu/uf8r2mkEey4vwlFe6FEt2LEhKOQnbp6W0miIrSF1GhhqmbqJNBirS/F0NNhYiBjY2Aw7/WbJpSCZy10YxC9egcGchy9uUEsHH7Lu95+ByzCnLk7xd7PaCAqc6ArrWPKONG/8bMLFuKzCxY2+S4V1cCrNGap1sxFoVBUx8xJmYYKZSBZCncSwSyOTSaa484ptkcbLcadh/evxbCTSpQaXbPe027adw6sqM85UOy0zSMcuP1O24C/rzliRXacy3ZRWza/x2vMx/jEjTqrilQDyQW1/5h6peyHr1dr4ztF66PUkaImSLOudjftqgdh4S17d/tN1cIt1oQDt46uThODg5bXb3p2F3P7m2cBrPOuMWv2Nvj8oV+CNsrHPzyWBgg6U8phWqFQKBrJ1hM7iraNRAQXzhH9XJyQjbtG4bn4+aHUOYHS9wmg7HxLCLDqXy94j7++9Wrc+2YG/+jr9UzAKKVev+ny7a4cWBYLpKdzHtwmRTNjDuyQAJd9zgGAUQfUFdRRvc7hbpetrgRBp23Z6kpQLL6jKIqA+4W6r/e5n40mZtzXUX8hLe9FMlIxHbVv3KJU3P7hWvhy70tF66JEt0LRYKL6TRe3WBMO3H1pCxvSOsy0ifFpB/t9YrCo9dWSE07GpMmTm/NimoAU3johxZ05FAqFQlE3Tj3tTLy7PosPhoprMsuJ2KL9KxQPlS4qV9rxr1I36nL3MzzYj+efeKBwPxqw3xZDePNZgn6WAagBx40UV9ruSgpgf1urgigGgpMjLzymrmEZE9uE8OYh4e24EW6xnXs9poPb/TCOov7SUVTTW3pST7qkeAZGJqCL9ws9V4GIBpJFpZMI6STZHs3EYbzq2mxH1XQrFIp6Uc5ULdxibShlu7XdhX7T25D1kb2mh4eHGv+CmozKqFAoFIrGk0qnkekAxnEr8TGt1u5+pK2e4iChCXrD0AbACUZ9iQb0duroHzaKnLiT9DqX1ynX71w8DrqjR0WIk5qvjbRmP+75UqUIPf8xDxs++sArHaOh91HSyHN436j9ywnnqH2AYvEMjFxAR2WMxJ1D0V4o0a1QNAi/ERghAHVkDVXIVC3Ub3qq/j72Mh4DjOJzql7TitGEwx3RH6aa4xQKRUMwdYLuDh1c/d2VZPz4CYCmBYQ3d4AB2oNMd7qimu44t/MkxmrlTNXkNnmeqO1REeMo0VuuZrlUanXYWX7cFtuAT9ka6VC6ejnBXO7c/msUbYut36+vaK6m5KLUPShaEyW6FYoGE4x6iw9+aaqmU4IOnaLbNNBNBjDx77/DRLdWatPpm2Hb7bbH/ffepXpNKxQKhaIppNzMLAl3Ri6+W1nAsypfX/e4Hnxqr4V44qF7AQjB/ecN04Cu8cgkENjVRrErbSFWLopdjbgO/ztJH3SgWHx2pyiiaGSEOe48cdcsnK+0IC4nmMtFtkv5FTQC7nBovMr0ckellysUijpTrt+0qQM2I+jVTDy28hbvuJO/9V10dwtxPWPmVqrXtEKhUCiagk4IUr5vj9WKUsAntmsQsauXbh/JF+Vt5+zoie67rJ2Q60pj0rjo3tG1FNVJXLfbop1bmecL95rMxC/uPoLnqj6yXAuhnDR6rQzV2g8luhWKBlO637SG8Z0mJvekoH/hMJimiS1mbBk4XvWaVoxWGOfQqmkZVuVqu0KhqBzpRSLREfzyX1HkOyJiWas/55EsBlRDuWj9uI03AlBI/25mjXWlQjr8XLkIda16o9uh32Fs+neVQrlRIjmpQK63WaCiuSjRrVA0iXDv7u50MI1q1uxtmnRnCoVCoVBEI7KzxL+jBHIlRpdRAp1EZxRXfh6MPBJYyQIAjVhA8LPF5G4A9TEnC+9TTRQaGLl4jjom6rzi2PKp36miPapvERd1/sh9aiyQ6+2ir2gflOhWKJpAOM3crOZbhkKhUCgUTUAKazl1VVvXHSeMK412lxP6Vd9fBVNzuWtsOanQ27we0eWo46NE5kj7qScRy+XOLbZX30Pd2y+BQK2HOK51e7k4WjmSzZkDjVTbMqx1PRzqiRLdCkWT8L60qFVNhUKhULQJ4RIpua1WcMepudjwi/t6VaOExyA8Rlv0pr1/16oGuVlp1bWIGFciROsliKsRwyN5b9Yiih1uTadoH5ToVigUCkVLwBmvauXcYaqmW6FoJNWI7KTR5loL+KLz1zl6KEW9fB3fOeM89GdtvPbBAIDq2jxVa5rVyAhrNYKy2vsDRpf4VbGXsYES3QqFQqFQKBSKujJSMV1NinitM8mS3EOUGNSpht6MUdN7aQaNqDeuZyS3nrff7mNTKYwxaKRy41MAcKowTB0NKNGtUCgUCoVCoWhpmiHaa3UPBtXQmYr/yt3KfcpHA60kVlVUe+yiRLdCoVAoFAqFYlTTTNGuE4K0UezKVouFgGYK9ka3ZasVyiFc0QyU6FYoFApFS6BquhUKRasyEtFOiIOUXpx3PhLR6ontEUZxR6LZayEiarHwMFpRRrujCyW6FQqFQqFQKBSKOiF6m0e02gq1TKtIgIbOVwtX9kZFrv3ReSUs2xOHO1Wv2DhjtJxCiW6FQqFQKBQKhaKO+EV3nEBOKkAjXdkr6Cked564vukVnTOB+KcRCxC1oF3T3RVjAyW6FQqFQtES8CrdUMeqE6pCoWgPwmJaCuRqU6vjxHGl0e5yIr8qx/iE4r8eaeXVLBrUq2+7QhGmzt0Ka8tdd92FefPmoaOjA729vTjkkEMCz7/99ts48MADkclkMHnyZHz/+9+HbduBfR566CHstNNOSKVS2HLLLfGrX/2qcS9AoVAoFIoxgpqzFYrSEE2ryY93PlLbH52KtPha3af/Ryck8FOPayS6D1r4qfX4NeqnGXDGRvQzFmmbSPfvf/97nHjiifjhD3+Iz372s7BtGy+88IL3PGMMBx54IDbeeGM89thjeO+993D00UfDMAz88Ic/BAC8+eabOPDAA3HSSSfhN7/5DR544AGccMIJ2GSTTbBw4cJmvTSFQqFQKEYVas5WKIJUU7ucNBpcy7royNT1Ogo7GWluZm23fM2qvlxRTzTHaf0CCNu2sfnmm+OCCy7A8ccfH7nPPffcg4MOOgjvvvsupkyZAgC47rrrcOaZZ+LDDz+EaZo488wzcddddwUm/q985StYv3497r333kT30tfXh3HjxmHDhg3o6emJ3W/YquAFKhQKRQvRYZR+PunnYFLk+VIH/j9oRkfFxzvWMHJ3fTvx/Vx77bW49tpr8dZbbwEAtt12W/zgBz/A/vvvH7m/ZVlYvnw5brzxRqxevRqzZs3CpZdeiv3226/iex0LtNucreZrxWilFZzBW+EegPZLI69k3Hoz8Tn9rTZfA5XP2aOFtkgvf+aZZ7B69WoQQrDjjjtik002wf777x+YiB9//HFsv/323uQNAAsXLkRfXx/++c9/evssWLAgcO6FCxfi8ccfj712LpdDX19f4EehUCgUdYCz6n8qYNNNN8WPfvQj/O1vf8Nf//pXfPazn8XBBx/szRVhli5dip/97Gf46U9/ihdffBEnnXQSDj30UDz77LO1eNWjDjVnKxStQS1S1pt5D7W8r3ZL+W7G76oimDWynwq49tprMWfOHPT09KCnpwfz58/HPffcU/KYq666CrNmzUJHRwemT5+O7373u8hmsyN5xSOmLUT3G2+8AQA4//zzsXTpUtx5553o7e3F3nvvjbVr1wIA1qxZE5i8AXiP16xZU3Kfvr4+DA8PR157+fLlGDdunPczffr0mr42hUKhUDSWRYsW4YADDsBWW22FrbfeGpdccgm6urrwxBNPRO5/00034ZxzzsEBBxyAGTNm4OSTT8YBBxyAyy+/vMF33h6oOVuhaH+aJY7rfV+Nut92EvitTqUL5bfccgvOOussLFu2DC+99BJ+8Ytf4Le//S3OOeecBt95kKb+qs466yxomlby5+WXXwZ3c0LOPfdcfPGLX8TOO++MX/7yl9A0Dbfddltd7/Hss8/Ghg0bvJ9Vq1bV9XoKhUKhqI5whDOXy5U9hjGGW2+9FYODg5g/f37kPrlcDul0OrCto6MDjz76aE3uu11Qc7ZCoUhKKwrhet9vK7yO0UilC+WPPfYYPv3pT+PII4/E5ptvjn333RdHHHEEnnrqqQbfeZCmGql973vfw7HHHltynxkzZuC9994DAGyzzTbe9lQqhRkzZuDtt98GAGy88cZFg/n+++97z8n/y23+fXp6etDREV2XkEqlkEqlkr8ohUKhUFQHswBSxbTkpqqFo5rLli3D+eefH3nI888/j/nz5yObzaKrqwv/8z//E5hj/CxcuBBXXHEF9txzT8ycORMPPPAA7rjjDrAx5sCq5myFQtFIaiFYW6GmXAnvaMLlP0k+vxljuO2220oulO+22264+eab8dRTT2HXXXfFG2+8gbvvvhuLFy+u2b1XQ1NF96RJkzBp0qSy++28885IpVJ45ZVXsPvuuwMQxjZvvfUWNttsMwDA/Pnzcckll+CDDz7A5MmTAQArV65ET0+PN/HPnz8fd999d+DcK1eujP2lKRQKhaJ9WLVqVcCUpdTkPWvWLDz33HPYsGEDbr/9dhxzzDF4+OGHI4X31VdfjRNPPBGzZ8+GpmmYOXMmlixZghtuuKEur6NVUXO2QqFoN0aLcG85mA2QKl0omWgNWa+F8iOPPBIfffQRdt99dziOA9u2cdJJJ43t9PKk9PT04KSTTsKyZctw//3345VXXsHJJ58MADjssMMAAPvuuy+22WYbLF68GH//+99x3333YenSpTjllFO8L14nnXQS3njjDZxxxhl4+eWXcc011+B3v/sdvvvd7zbttSkUCoWiNkiTFflTSnSbpoktt9wSO++8M5YvX44ddtgBV199deS+kyZNwooVKzA4OIh///vfePnll9HV1YUZM2bU66W0NWrOVigUowmVZl4fVq1aFSgHOvvss2P3lQvlTz75JE4++WQcc8wxePHFFyP3feihh/DDH/4Q11xzDZ555hnccccduOuuu3DRRRfV66Ukom36dF922WXQdR2LFy/G8PAw5s2bhwcffBC9vb0AAEop7rzzTpx88smYP38+Ojs7ccwxx+DCCy/0zrHFFlvgrrvuwne/+11cffXV2HTTTfHf//3fqt+nQqFQjHE452VrwNPpNKZNmwbLsvD73/8eX/7ylxt0d+2HmrMVCoUiSK2E92iJvMsF8iTIhXJAZFM9/fTTuPrqq/Gzn/2saN/zzjsPixcvxgknnAAA2H777TE4OIivfe1rOPfcc0Ga5D7XNqLbMAz85Cc/wU9+8pPYfTbbbLOiVLQwe++9t2rzolAoFK0ItytuJeIdVwFnn3029t9/f3ziE59Af38/brnlFjz00EO47777AABHH300pk2bhuXLlwMAnnzySaxevRpz587F6tWrcf7554NzjjPOOKPyex0jqDlboVAo6kNLRM2dytt1Bo4dIaUWyoeGhoqENaWil7nTxAWLthHdCoVCoVDUgg8++ABHH3003nvvPYwbNw5z5szBfffdh8997nMAgLfffjswYWezWSxduhRvvPEGurq6cMABB+Cmm27C+PHjm/QKFAqFQqEYG1S6UL5o0SJcccUV2HHHHTFv3jy89tprOO+887Bo0SJPfDcDJboVCoVCMab4xS9+UfL5hx56KPB4r732iq0dUygUCoVCUT8qXShfunQpNE3D0qVLsXr1akyaNAmLFi3CJZdc0qyXAECJboVCoVC0CswGtMpSxb3jFAqFQqFQjDoqXSjXdR3Lli3DsmXL6nhXlaNEt0KhUCgUCoVCoVAoklHtIrk8dgzSFi3DFAqFQqFQKBQKhUKhaEeU6FYoFAqFQqFQKBQKhaJOqPRyhUKhULQGvMoWJNW2LVEoFAqFQlE5zAK0Kp3Aq2kNOgpQkW6FQqFQKBQKhUKhUCjqhBLdCoVCoVAoFAqFQqFQ1AmVXq5QKBSK1qDadLUxmqqmUCgUCoWiPVCiW6FQKBQKhUKhUCgUybBtAFW2/rJVyzCFQqFQKBQKhUKhUCgUNUSJboVCoVAoFAqFQqFQKOqESi9XKBQKRWtg2wCqqM8eo6lqCoVCoVA0Bc4AXuXcO0bbfKpIt0KhUCgUCoVCoVAoFHVCiW6FQqFQKBRjlg6j2XegUCgUitGOSi+vE2oSVygUCoWiPVBztkKhUCjqiRLdCoVCoWgNmF1ln25V061QKBQKRcOodr6Wx45BVHq5QqFQKBQKhUKhUCgUdUKJboVCoVAoFAqFQqFQKOqESi9XKBQKRWvALYBVsRbMq2gzplAoFAqFojqqna/lsWMQFelWKBQKhUKhUCgUCoWiTijRrVAoFAqFQqFQKBQKRZ1QoluhUCgUCoVCoVAoFIo6oWq6FQqFQtEacAZorLrjFAqFQqFQNIZq52t57BhERboVCoVCoVAoFAqFQqGoE0p0KxQKhUKhUCgUCoVCUSeU6FYoFApFa8AYwOwqfipLVbv22msxZ84c9PT0oKenB/Pnz8c999yT6Nhbb70VmqbhkEMOqeIFKhQKhUIxCqh6vq7/nH3HHXdgl112wfjx49HZ2Ym5c+fipptuGukrHjGqpluhUCgUY4pNN90UP/rRj7DVVlvBcRzceOONOPjgg/Hss89i2223jT3urbfewumnn4499tijgXerUCgUCsXYpdI5e8KECTj33HMxe/ZsmKaJO++8E0uWLMHkyZOxcOHCJrwCgRLdCoVCoRhTLFq0KPD4kksuwbXXXosnnngiVnQzxnDUUUfhggsuwCOPPIL169c34E4VCoVCoRjbVDpn77333oHHp556Km688UY8+uijSnS3E47jAAD6+vqafCcKhULRHOTnn/w8rBksj6rOyPIAij+XU6kUUqlU6UMZw2233YbBwUHMnz8/dr8LL7wQkydPxvHHH49HHnmkmrtUNAE1ZysUirFMy83X7rFAfedsieM4ePDBB/HKK6/g0ksvrfaOa4OjqIhVq1Y5ANSP+lE/6mfM/6xataomn6vDw8POxhtvPKJ76erqKtq2bNmy2Gv+4x//cDo7Ox1KqTNu3Djnrrvuit33kUcecaZNm+Z8+OGHjuM4zjHHHOMcfPDBNXntivqi5mz1o37Uj/pprfkaqO+c7TiOs379eqezs9PRdd1JpVLOL37xi5q8/pGgIt0VMnXqVKxatQrd3d3QNK3kvn19fZg+fTpWrVqFnp6eBt1hfRgtr2W0vA5g9LyW0fI6gLHzWhzHQX9/P6ZOnVqTa6XTabz55pvI5/NVn8NxnKLP5FIr5rNmzcJzzz2HDRs24Pbbb8cxxxyDhx9+GNtss01gv/7+fixevBjXX389Ntpoo6rvT9Ecks7ZY+Vvt90YLa9ltLwOQL2WVqTd5mt5X/WYsyXd3d147rnnMDAwgAceeACnnXYaZsyYUZR63kg0x6l1voFC0tfXh3HjxmHDhg1t/ccMjJ7XMlpeBzB6XstoeR2Aei3tzIIFCzBz5kz87Gc/C2x/7rnnsOOOO+L/b+/eY6qu/ziOvznm4TLjoggICSImOMG0KSfvNTEim9laWrmclbfEZYs5Y3Oj3BJT8zJXzjaFTTKn1tQZxcSiUnAp8wKKFExCEsQukKWCcN6/P/rxja+AlnE4l56Pjc3z+X7P2ft9PrCX73Pge3r16mWs2e12ERGxWCxSXl4uMTExPVorup8nfb/Ti+vxlD5E6MUVeUof/0RXmd2VefPmycWLFyUvL8/BlXWNd7oBAP95drtdmpqaOqzHxcVJSUmJaW3FihVy9epV2bRpkwwcOLCnSgQAANJ1ZnfX+Y7A0A0A+E9JT0+XlJQUiYyMlKtXr8rOnTuloKDAeAV8zpw5EhERIZmZmeLj4yPx8fGm+wcGBoqIdFgHAADd659ktohIZmamjB49WmJiYqSpqUlyc3Nlx44dsmXLFme2wdDtSN7e3pKRkXHHK/G5A0/pxVP6EPGcXjylDxF6cRf19fUyZ84cqa2tlYCAABkxYoTk5eXJ1KlTRUSkurpaLBaLk6tET/Kk73d6cT2e0ocIvbgiT+mjK/80s//44w9ZvHix1NTUiK+vr8TFxUlOTo7MmjXLWS2ICH/TDQAAAACAw/BSPgAAAAAADsLQDQAAAACAgzB0AwAAAADgIAzdAAAAAAA4CEP3v1RQUCBeXl6dfh0/flxERKqqqjo9fuzYMdNj7dmzR+Li4sTHx0cSEhIkNze3x/sZNGhQhzpXr15tOufMmTMyceJE8fHxkYEDB8qaNWs6PI6ze6mqqpKXX35ZoqOjxdfXV2JiYiQjI0Oam5tN57jLvtzqvffek0GDBomPj4/YbDb59ttvnV2SSWZmpowZM0buvfdeCQkJkRkzZkh5ebnpnIcffrjDc79o0SLTOdXV1TJt2jTx8/OTkJAQWbZsmbS0tPRkK/Lmm292qDMuLs44fuPGDUlNTZV+/fpJnz595Omnn5bLly+7XB8inf98e3l5SWpqqoi4z54Ad4vMdr3M9vS8FiGze5KnZDZ57YEU/0pTU5PW1taavubNm6fR0dFqt9tVVfXChQsqIpqfn286r7m52Xico0ePaq9evXTNmjV67tw5XbFihfbu3VtLSkp6tJ+oqChduXKlqc7ff//dON7Y2KihoaE6e/ZsLS0t1Y8++kh9fX1169atLtXLZ599pnPnztW8vDytrKzU/fv3a0hIiKalpRnnuNO+tLdr1y61Wq26fft2PXv2rM6fP18DAwP18uXLTqvpVsnJyZqVlaWlpaV66tQpffzxxzUyMtL0vTR58mSdP3++6blvbGw0jre0tGh8fLwmJSXpyZMnNTc3V4ODgzU9Pb1He8nIyNDhw4eb6rxy5YpxfNGiRTpw4EA9fPiwnjhxQh966CEdN26cy/WhqlpfX2/q49ChQyoi+uWXX6qq++wJcLfIbNfLbE/Oa1Uym8y+O+S152Ho7mbNzc3av39/XblypbHWFhYnT57s8n4zZ87UadOmmdZsNpsuXLjQUaV2KioqSjds2NDl8ffff1+DgoK0qanJWFu+fLnGxsYat12ll1utWbNGo6OjjdvutC/tJSYmampqqnG7tbVVw8PDNTMz02k13Ul9fb2KiH711VfG2uTJk3Xp0qVd3ic3N1ctFovW1dUZa1u2bFF/f3/T95+jZWRk6AMPPNDpsYaGBu3du7fu2bPHWCsrK1MR0aKiIlV1nT46s3TpUo2JiTGGDXfZE6C7kNmu00t7npLXqmQ2md09yGv3x6+Xd7MDBw7Izz//LC+++GKHY9OnT5eQkBCZMGGCHDhwwHSsqKhIkpKSTGvJyclSVFTk0Ho7s3r1aunXr5+MGjVK1q5da/pVlKKiIpk0aZJYrVZTneXl5fLrr78a57hKL+01NjZK3759O6y7y76IiDQ3N0txcbGpJovFIklJSU5/fm+nsbFRRKTD8//hhx9KcHCwxMfHS3p6uly7ds04VlRUJAkJCRIaGmqsJScny2+//SZnz57tmcL/7/vvv5fw8HAZPHiwzJ49W6qrq0VEpLi4WG7evGnaj7i4OImMjDT2w5X6aK+5uVlycnLkpZdeEi8vL2PdXfYE6A5ktmv10sYT8lqEzCazuwd57RnucXYBnmbbtm2SnJws9913n7HWp08feffdd2X8+PFisVjk448/lhkzZsi+fftk+vTpIiJSV1dn+sEQEQkNDZW6uroerf/VV1+VBx98UPr27SuFhYWSnp4utbW1sn79eqPO6OjoDnW2HQsKCnKZXtqrqKiQzZs3y7p164w1d9qXNj/99JO0trZ2WtP58+edUtOd2O12ee2112T8+PESHx9vrD///PMSFRUl4eHhcubMGVm+fLmUl5fLJ598IiJdP/dtx3qKzWaT7OxsiY2NldraWnnrrbdk4sSJUlpaKnV1dWK1WiUwMLBDnW01ukoft9q3b580NDTI3LlzjTV32ROgu5DZrpfZnpLXImQ2md09yGvPwNDdhTfeeEPeeeed255TVlZmujhDTU2N5OXlye7du03nBQcHy+uvv27cHjNmjFy6dEnWrl1rhIUj/ZNe2tc5YsQIsVqtsnDhQsnMzBRvb29Hl3pHd7MvP/74ozz22GPyzDPPyPz58411Z+/Lf0VqaqqUlpbKkSNHTOsLFiww/p2QkCADBgyQKVOmSGVlpcTExPR0mV1KSUkx/j1ixAix2WwSFRUlu3fvFl9fXydW9u9s27ZNUlJSJDw83Fhzlz0BbkVmu15mk9fuicx2PeS1Z2Do7kJaWprpFaXODB482HQ7KytL+vXr97cCwGazyaFDh4zbYWFhHa6eePnyZQkLC/v7RXfhbnppX2dLS4tUVVVJbGxsl3WKiFGrK/Vy6dIleeSRR2TcuHHywQcf3PHxe3Jf7kZwcLD06tXLpWq6nSVLlsjBgwfl66+/Nr2T1BmbzSYif77LERMTI2FhYR2u8Hrr95ozBAYGytChQ6WiokKmTp0qzc3N0tDQYHrlvP1+uGIfP/zwg+Tn5xuviHfFXfYEILP/qtNVMvu/ntciZLYr5IO7ZzZ57UGc/UflnsJut2t0dLTpapu3M2/ePB01apRxe+bMmfrEE0+Yzhk7dqzTLwCSk5OjFotFf/nlF1X966Is7a8Ymp6e3uGiLK7QS01Njd5///367LPPaktLy9+6jzvsS2Jioi5ZssS43draqhERES51URa73a6pqakaHh6u33333d+6z5EjR1RE9PTp06r610VA2l/hdevWrerv7683btxwSN1/x9WrVzUoKEg3bdpkXJRl7969xvHz5893elEWV+ojIyNDw8LC9ObNm7c9z132BPinyGzXymxPzWtVMtvZ+eDumU1eew6G7m6Sn5+vIqJlZWUdjmVnZ+vOnTu1rKxMy8rK9O2331aLxaLbt283zjl69Kjec889um7dOi0rK9OMjIwe/6iLwsJC3bBhg546dUorKys1JydH+/fvr3PmzDHOaWho0NDQUH3hhRe0tLRUd+3apX5+fh0+fsTZvdTU1OiQIUN0ypQpWlNTY/pIhTbusi+32rVrl3p7e2t2draeO3dOFyxYoIGBgaYrVDrbK6+8ogEBAVpQUGB67q9du6aqqhUVFbpy5Uo9ceKEXrhwQffv36+DBw/WSZMmGY/R9nEXjz76qJ46dUo///xz7d+/f49/3EVaWpoWFBTohQsX9OjRo5qUlKTBwcFaX1+vqn9+/EhkZKR+8cUXeuLECR07dqyOHTvW5fpo09raqpGRkbp8+XLTujvtCfBvkdmuk9menNeqZDaZfffIa8/C0N1NnnvuOdPn/LWXnZ2tw4YNUz8/P/X399fExETTxxW02b17tw4dOlStVqsOHz5cP/30U0eXbVJcXKw2m00DAgLUx8dHhw0bpqtWrerwitjp06d1woQJ6u3trREREbp69eoOj+XsXrKyslREOv1q4y770pnNmzdrZGSkWq1WTUxM1GPHjjm7JJOunvusrCxVVa2urtZJkyZp37591dvbW4cMGaLLli0zfcakqmpVVZWmpKSor6+vBgcHa1pa2h1f7e1us2bN0gEDBqjVatWIiAidNWuWVlRUGMevX7+uixcv1qCgIPXz89OnnnrK9J9FV+mjTV5enoqIlpeXm9bdaU+Af4vMNnNmL56e16pkdk/ypMwmrz2Ll6qqo3+FHQAAAACA/yI+pxsAAAAAAAdh6AYAAAAAwEEYugEAAAAAcBCGbgAAAAAAHIShGwAAAAAAB2HoBgAAAADAQRi6AQAAAABwEIZuAAAAAAAchKEbAAAAAAAHYegGAAAAAMBBGLoBAAAAAHAQhm7ADVy5ckXCwsJk1apVxlphYaFYrVY5fPiwEysDAADtkdkAbuWlqursIgDcWW5ursyYMUMKCwslNjZWRo4cKU8++aSsX7/e2aUBAIB2yGwA7TF0A24kNTVV8vPzZfTo0VJSUiLHjx8Xb29vZ5cFAABuQWYDaMPQDbiR69evS3x8vFy8eFGKi4slISHB2SUBAIBOkNkA2vA33YAbqayslEuXLondbpeqqipnlwMAALpAZgNowzvdgJtobm6WxMREGTlypMTGxsrGjRulpKREQkJCnF0aAABoh8wG0B5DN+Amli1bJnv37pXTp09Lnz59ZPLkyRIQECAHDx50dmkAAKAdMhtAe/x6OeAGCgoKZOPGjbJjxw7x9/cXi8UiO3bskG+++Ua2bNni7PIAAMD/kdkAbsU73QAAAAAAOAjvdAMAAAAA4CAM3QAAAAAAOAhDNwAAAAAADsLQDQAAAACAgzB0AwAAAADgIAzdAAAAAAA4CEM3AAAAAAAOwtANAAAAAICDMHQDAAAAAOAgDN0AAAAAADgIQzcAAAAAAA7yP2LuWwetZ0uMAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1000x400 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# predominant wind directions are: 270 and 150 deg\n",
    "wds = [150, 270]\n",
    "wps = [8]\n",
    "simres = wfm(state['x'], state['y'], type=state['type'], ws=wps, wd=wds)\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots(1, 2, figsize=(10, 4), dpi=100, tight_layout=True)\n",
    "for i in range(2):\n",
    "    fm = simres.flow_map(wd=wds[i])\n",
    "    fm.plot_wake_map(ax=ax[i])\n",
    "    ax[i].set_xlabel('x')\n",
    "    ax[i].set_ylabel('y')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "b103a93f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Wake loss [%]')"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.plot(recorder['Wake losses'])\n",
    "ax.set_title('Wake loss')\n",
    "ax.set_xlabel('Iteration')\n",
    "ax.set_ylabel('Wake loss [%]')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "29320c1d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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