{
 "cells": [
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    ".. _composite_models:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Composite Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from jetset.jet_model import Jet\n",
    "from jetset.plot_sedfit import PlotSED\n",
    "from jetset.model_manager import FitModel\n"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Composite models allow to combine together different models, such as Jet, and templates, including additive or multiplicative models, and give to the user the possibility to define the functional form of the model composition using a very simple and intuitive form such as:\n",
    ".. code-block:: python\n",
    "   'jet1+jet2'*Franceschini_2008\n",
    "\n",
    "that sums two jet models SEDs, and apply to both of them the `Franceschini_2008` EBL absorption.\n",
    "\n",
    "Building composite models it is very easy. Composite models are handled by   the :class:`.FitModel` class, as shown by the following examples. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Combine a Jet model with the EBL model (Multiplicative case)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We start by combining a Jet model with the EBL absorption model, i.e. a multiplicative  model. First, we define our Jet model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from jetset.jet_model import Jet\n",
    "my_jet=Jet(electron_distribution='lppl',name='jet_flaring')"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Second, we define the EBL model, and we use in this case the `Franceschini_2008` model ( read the section :ref:`ebl_model`  for more info regarding the EBL models)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from jetset.template_2Dmodel import EBLAbsorptionTemplate\n",
    "ebl_franceschini=EBLAbsorptionTemplate.from_name('Franceschini_2008')"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Now we add the components models to the the :class:`.FitModel` class, using the :class:`.FitModel.add_component()` method "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/orion/anaconda3/envs/jetset/lib/python3.7/site-packages/jetset-1.1.2-py3.7-macosx-10.9-x86_64.egg/jetset/model_manager.py:160: UserWarning: no cosmology defined, using default FlatLambdaCDM(name=\"Planck13\", H0=67.8 km / (Mpc s), Om0=0.307, Tcmb0=2.725 K, Neff=3.05, m_nu=[0.   0.   0.06] eV, Ob0=0.0483)\n",
      "  warnings.warn('no cosmology defined, using default %s'%self.cosmo)\n"
     ]
    }
   ],
   "source": [
    "composite_model=FitModel(nu_size=500,name='EBL corrected')\n",
    "composite_model.add_component(my_jet)\n",
    "composite_model.add_component(ebl_franceschini)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "the waring message is just telling that you are not passing any specific cosmology model to the `FitModel` class, so it is using a default one"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    model name          name             par type           units          val      phys. bound. min phys. bound. max  log  frozen\n",
      "----------------- ---------------- ------------------- --------------- ------------ ---------------- ---------------- ----- ------\n",
      "      jet_flaring             gmin  low-energy-cut-off lorentz-factor* 2.000000e+00     1.000000e+00     1.000000e+09 False  False\n",
      "      jet_flaring             gmax high-energy-cut-off lorentz-factor* 1.000000e+06     1.000000e+00     1.000000e+15 False  False\n",
      "      jet_flaring                N    emitters_density         1 / cm3 1.000000e+02     0.000000e+00               -- False  False\n",
      "      jet_flaring                s   LE_spectral_slope                 2.000000e+00    -1.000000e+01     1.000000e+01 False  False\n",
      "      jet_flaring                r  spectral_curvature                 4.000000e-01    -1.500000e+01     1.500000e+01 False  False\n",
      "      jet_flaring gamma0_log_parab    turn-over-energy lorentz-factor* 1.000000e+04     1.000000e+00     1.000000e+09 False  False\n",
      "      jet_flaring                R         region_size              cm 5.000000e+15     1.000000e+03     1.000000e+30 False  False\n",
      "      jet_flaring              R_H     region_position              cm 1.000000e+17     0.000000e+00               -- False   True\n",
      "      jet_flaring                B      magnetic_field               G 1.000000e-01     0.000000e+00               -- False  False\n",
      "      jet_flaring         beam_obj             beaming Lorentz-factor* 1.000000e+01     1.000000e-04               -- False  False\n",
      "      jet_flaring           z_cosm            redshift                 1.000000e-01     0.000000e+00               -- False  False\n",
      "Franceschini_2008           z_cosm            redshift                 1.000000e+00     0.000000e+00               -- False   True\n"
     ]
    }
   ],
   "source": [
    "composite_model.show_pars()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since, both the Jet model the EBL share the same parameter, i.e. the redshift, we link the two parameters "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "composite_model.link_par(par_name='z_cosm',model_name_list=['jet_flaring'],root_model_name='Franceschini_2008')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    model name                name                  par type           units          val      phys. bound. min phys. bound. max  log  frozen\n",
      "----------------- --------------------------- ------------------- --------------- ------------ ---------------- ---------------- ----- ------\n",
      "      jet_flaring                        gmin  low-energy-cut-off lorentz-factor* 2.000000e+00     1.000000e+00     1.000000e+09 False  False\n",
      "      jet_flaring                        gmax high-energy-cut-off lorentz-factor* 1.000000e+06     1.000000e+00     1.000000e+15 False  False\n",
      "      jet_flaring                           N    emitters_density         1 / cm3 1.000000e+02     0.000000e+00               -- False  False\n",
      "      jet_flaring                           s   LE_spectral_slope                 2.000000e+00    -1.000000e+01     1.000000e+01 False  False\n",
      "      jet_flaring                           r  spectral_curvature                 4.000000e-01    -1.500000e+01     1.500000e+01 False  False\n",
      "      jet_flaring            gamma0_log_parab    turn-over-energy lorentz-factor* 1.000000e+04     1.000000e+00     1.000000e+09 False  False\n",
      "      jet_flaring                           R         region_size              cm 5.000000e+15     1.000000e+03     1.000000e+30 False  False\n",
      "      jet_flaring                         R_H     region_position              cm 1.000000e+17     0.000000e+00               -- False   True\n",
      "      jet_flaring                           B      magnetic_field               G 1.000000e-01     0.000000e+00               -- False  False\n",
      "      jet_flaring                    beam_obj             beaming Lorentz-factor* 1.000000e+01     1.000000e-04               -- False  False\n",
      "      jet_flaring z_cosm(L,Franceschini_2008)            redshift                           --               --               -- False   True\n",
      "Franceschini_2008                   z_cosm(R)            redshift                 1.000000e+00     0.000000e+00               -- False   True\n"
     ]
    }
   ],
   "source": [
    "composite_model.show_pars()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, now the paramter `z_cosm` in `Franceschini_2008` is the root parameter (flagged by the R in parenthesis), and the one belonging to the `jet_flaring` component is the linked one (flagged by the L in parenthesis)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setting parameters"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    ".. note::\n",
    "   with the new implementation of composite model  (`FitModel` class) to set parameters you have to specify the model component, this is different from versions<1.2.0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These methods are alternative and equivalent ways to set a parameter in a composite model:\n",
    "\n",
    "a) accessing the model component member of the \n",
    "\n",
    "b) using `set_par` and passing as first argument the model component name\n",
    "\n",
    "c) using `set_par` and passing as first argument the model component object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#a\n",
    "composite_model.jet_flaring.parameters.z_cosm.val=0.1\n",
    "#b\n",
    "composite_model.set_par('jet_flaring','z_cosm',0.1)\n",
    "#c\n",
    "composite_model.set_par(my_jet,'z_cosm',0.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And now, we can define the functional form of the model composition, just by writing the mathematical expression as a string, using the model names reported in the model description table, and that's it!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "-------------------------------------------------------------------------------------------------------------------\n",
      "Composite model description\n",
      "-------------------------------------------------------------------------------------------------------------------\n",
      "name: EBL corrected  \n",
      "type: composite_model  \n",
      "components models:\n",
      " -model name: jet_flaring model type: jet\n",
      " -model name: Franceschini_2008 model type: table2D\n",
      "\n",
      "-------------------------------------------------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "composite_model.show_model_components()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "composite_model.composite_expr='jet_flaring*Franceschini_2008'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "composite_model.jet_flaring.IC_nu_size=150\n",
    "composite_model.eval()\n",
    "p=composite_model.plot_model()\n",
    "p.rescale(y_max=-12)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sum of two jets (steady and flaring) and application of the EBL absorption to both (Multiplicative and additive)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Assume that now we want to sum to jet models (a steady and flaring component) and apply to both of them the EBL absorption."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/orion/anaconda3/envs/jetset/lib/python3.7/site-packages/jetset-1.1.2-py3.7-macosx-10.9-x86_64.egg/jetset/model_manager.py:160: UserWarning: no cosmology defined, using default FlatLambdaCDM(name=\"Planck13\", H0=67.8 km / (Mpc s), Om0=0.307, Tcmb0=2.725 K, Neff=3.05, m_nu=[0.   0.   0.06] eV, Ob0=0.0483)\n",
      "  warnings.warn('no cosmology defined, using default %s'%self.cosmo)\n"
     ]
    }
   ],
   "source": [
    "composite_model=FitModel(nu_size=500,name='EBL corrected flaring+steady')\n",
    "composite_model.add_component(my_jet)\n",
    "composite_model.add_component(ebl_franceschini)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "-------------------------------------------------------------------------------------------------------------------\n",
      "Composite model description\n",
      "-------------------------------------------------------------------------------------------------------------------\n",
      "name: EBL corrected flaring+steady  \n",
      "type: composite_model  \n",
      "components models:\n",
      " -model name: jet_flaring model type: jet\n",
      " -model name: Franceschini_2008 model type: table2D\n",
      " -model name: steady_jet model type: jet\n",
      "\n",
      "-------------------------------------------------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "steady_jet=Jet(electron_distribution='plc',name='steady_jet')\n",
    "composite_model.add_component(steady_jet)\n",
    "composite_model.show_model_components()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "composite_model.link_par(par_name='z_cosm',model_name_list=['steady_jet'],root_model_name='Franceschini_2008') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    model name                name                  par type           units          val      phys. bound. min phys. bound. max  log  frozen\n",
      "----------------- --------------------------- ------------------- --------------- ------------ ---------------- ---------------- ----- ------\n",
      "      jet_flaring                        gmin  low-energy-cut-off lorentz-factor* 2.000000e+00     1.000000e+00     1.000000e+09 False  False\n",
      "      jet_flaring                        gmax high-energy-cut-off lorentz-factor* 1.000000e+06     1.000000e+00     1.000000e+15 False  False\n",
      "      jet_flaring                           N    emitters_density         1 / cm3 1.000000e+02     0.000000e+00               -- False  False\n",
      "      jet_flaring                           s   LE_spectral_slope                 2.000000e+00    -1.000000e+01     1.000000e+01 False  False\n",
      "      jet_flaring                           r  spectral_curvature                 4.000000e-01    -1.500000e+01     1.500000e+01 False  False\n",
      "      jet_flaring            gamma0_log_parab    turn-over-energy lorentz-factor* 1.000000e+04     1.000000e+00     1.000000e+09 False  False\n",
      "      jet_flaring                           R         region_size              cm 5.000000e+15     1.000000e+03     1.000000e+30 False  False\n",
      "      jet_flaring                         R_H     region_position              cm 1.000000e+17     0.000000e+00               -- False   True\n",
      "      jet_flaring                           B      magnetic_field               G 1.000000e-01     0.000000e+00               -- False  False\n",
      "      jet_flaring                    beam_obj             beaming Lorentz-factor* 1.000000e+01     1.000000e-04               -- False  False\n",
      "      jet_flaring z_cosm(L,Franceschini_2008)            redshift                           --               --               -- False   True\n",
      "Franceschini_2008                   z_cosm(R)            redshift                 1.000000e-01     0.000000e+00               -- False   True\n",
      "       steady_jet                        gmin  low-energy-cut-off lorentz-factor* 2.000000e+00     1.000000e+00     1.000000e+09 False  False\n",
      "       steady_jet                        gmax high-energy-cut-off lorentz-factor* 1.000000e+06     1.000000e+00     1.000000e+15 False  False\n",
      "       steady_jet                           N    emitters_density         1 / cm3 1.000000e+02     0.000000e+00               -- False  False\n",
      "       steady_jet                           p   LE_spectral_slope                 2.000000e+00    -1.000000e+01     1.000000e+01 False  False\n",
      "       steady_jet                   gamma_cut    turn-over-energy lorentz-factor* 1.000000e+04     1.000000e+00     1.000000e+09 False  False\n",
      "       steady_jet                           R         region_size              cm 5.000000e+15     1.000000e+03     1.000000e+30 False  False\n",
      "       steady_jet                         R_H     region_position              cm 1.000000e+17     0.000000e+00               -- False   True\n",
      "       steady_jet                           B      magnetic_field               G 1.000000e-01     0.000000e+00               -- False  False\n",
      "       steady_jet                    beam_obj             beaming Lorentz-factor* 1.000000e+01     1.000000e-04               -- False  False\n",
      "       steady_jet z_cosm(L,Franceschini_2008)            redshift                           --               --               -- False   True\n"
     ]
    }
   ],
   "source": [
    "composite_model.show_pars()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "composite_model.steady_jet.IC_nu_size=150\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "composite_model.composite_expr='(jet_flaring + steady_jet) * Franceschini_2008'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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Mt2u7wisiuNHpfO4IbPHzGl2cERIfi5D42HO29EX2gTRUba+Ai8kFhcsOwlyUC5NbFpDYHVHXXAW9kb+OiYiIOgO+ohNRh6eUQl2eCVUHi5D/8z44lRlhkGOwuhTBc9pgeIVEwsVztKYZQ+NiEBoXcyYwTibvRN3mXHhtK0Lmtu/hMyoOTv194BrgrWlOIiIisg0LLCLqkJRVofZ4GYqTM1C1Nwe6Ogu8hkSjOsKEikCF2MunI9TFTeuYjRNBt0ED0G3Q6amH5cfyULujGOUv/IJK1xNwGdsf3YcP0jgkERERtQQLLCLqMJRSqD1ZDtPOPFTtK0B1TT48LN+jxNsC49Cr0f2KSPgiUuuYzebZIwjoEYTCga4o/SwT+q9ycGjz5+h5zwQY/Fy0jkdERETNwAKLiNo9c24lTLvyUZ6SBXNVMQRb0eW+PyO34hjcg59Cf08frSPahX9EOPwfmYXy/ALot2QhZ0kKylwPose8W+Dq6al1PCIiImoCFlhE1C5ZKmphSsmDaWcurKY6FBsOIaT6dRwK7o2wcffDKcQD3dBH65itwjMwAJ6TA1DQNwPV7x9AwbPfoXK4J2Kv510qiIiI2jsWWETUbiirQk16CSp/zUH14WJU+1eguuJTdJv/D6DUB06+12Gol6/WMdtMQK8IBDx1Dw4sWwnPrWbkuxxCwJgoLu9ORETUjrHAIiLNWU1mVP6Wg4rtOdA561EZWg6r7jHoiytR1X8e3Nw94eXjr3VMzcRNm4LqMeUoWXYIh3Z/gYh7x8HZvZ0u4EFEROTgWGARkWbMBVWo2HoKpl35cInxRUnvXAQMSIDRpEeBzzz0GzMNPXTn36vKEbkEecLnrmhUvrAT2c+ugOc9o+Af0U3rWERERHQO/uVCRG1LKVSnl6Dg/f3If20XdC4GVIw2IefoPfDY8QiKc4+jW1Qi+l99O4TF1e+4eHsi4omZqPKoQuUbP6P4VI7WkYiIiOgc/OuFiNqEsiiYduZBvyIXJV8cgUusH0L+NBhIcoX7hvtQGXMjQv+8G9EDRmodtV3TGw2IeXQWqsLdUPPZKVgqzVpHIiIiogY4RZCIWpWyKJh25aH8x5PQeRhhHewNvyE9sGPFc5DfjmPI/Hfh8fg+dDXw11FT6fR6RM+9Dtmf7cLJZ1ci+E/XwtWLy7gTERG1BxzBIqJWoSxWVCbnIOelZFQm58JnciSC5ibieOl2ZD8/EG6ZWxB69R8BAHoWV80mIgi5sS/q9GU48a+PtY5DRERE9VhgEZFdKevpEavcl1Jg2pEHvxujEDSnL8wBVgBAXfFJFA97HH0eXYfw6H4ap+3YdHo9guZPgluVL/a9zSKLiIioPeDbxkRkF0opVB8qRtnaDEAv8JkcCZdIXyirFb998Qp67voHMm//Bj1H3oGIiAit43YaXkGBKLwuHN7ramHOM8EYxOXbiYiItMQCi4hsVnOiDKVrjsFaaYb31RFwifeHiKC0MBcZb02Hn7kAxZOXITKyDzIyMrSO2+n0uGwIiuuO4cRr69H9sWtgcDJqHYmIiMhhcYogEbVYXUkNipanovDDg3AfGIzgBwfCNSEAUAoFOSfg5umD6p5XI/xP2xGZOELruJ2a1/Bu0NcWIfW1D7WOQkRE5NA4gkVEzWattaB8YyYqf8mC+9BQhEyOgs5ZDwAoyjuF4+/NAgAEPLoGQ25+VMuoDkNvNEBu6AOfVTnIT89AYK8IrSMRERE5JI5gEVGTKatC5c485L6YjLqCKgTd3x/eV0ecLa72bV2NuldHoNq7J+Ie+FzjtI6n+5CBKPfORsnqA1pHISIiclgcwSKiJqnNrkTJF0egLFb43RYL5wjvs9uqKsshOh2c3X2QP/a/GHbZBA2TOraYh2Yi56UUlB/Og2dUkNZxiIiIHA4LLCK6KGtNHcq+PwHTzjx4Xd0d7oNCIDo5u/142i5YP5mBgr6zMWjSfA2TEgDoXAwwRZXD/O73cH/6Vuj0eq0jERERORROESSiRimlYNpbgNyXUmA1mRH80AB4DAn9XXGV/NXr8Pr4euTHzUTSDfM0TEsN9Zh0JXTKiEOr12kdhYiIyOFwBIuIzlNXVI2SL4+grrgGfrfEwrmn9++2Wy0W6PR61JXloOjGzzC4z1CNklJj9EYDSsOr4ZZcCUzSOg0REZFjscsIloi4iwjnoRB1cMqqUL71FPL+uxNOEd4IfqD/ecXViUO7cPTZQThxaBeG3v4kerG4apd63T4ZenRDbVa51lGIiIgcSosKLBHRicg0EflGRPIApALIFpH9IvJPEYmyb0wiam3mfBPy39iDqr0FCLw3EV6jukEMv/8VkfLN2/Bcdj0KY6ejW2RfjZJSU7h6esL3qmjkreWKgkRERG2ppVMEfwSwHsBjAPYppawAICJ+AEYBeE5EVimleMdLonZOWRTKN2eiYnMmvK7qDvehv7/O6ozqqko47fkQhZOXY0jiZRokpeZSvV2A74qQm3YEwTGRWschIiJyCC0tsMYopcznNiqligB8DuBzETHalIyIWp05pxJFnx2Czs2AoPv6w+Dnct4+JQU5SP10MfrNfBl9Hvup7UNSi3mHBCHb/QTqvjqF4EdYYBEREbWFFk0RbKy4ask+RKQNZVUo35iJ/Lf2wGNoKAL+kNBocZW+52eYXrkc0BlhMDppkJRs5XRFPHwKA1BnrtM6ChERkUOweRVBEfmTUuof9ghDRK2vrqgaRZ+mAYILjloBQH5WBnxX3oJjg/6KoRNmtXFKspeeI0fg2Jb1qE4rhEdCsNZxiMgBpaSkBBkMhrcBJIC3CKLOwQpgX11d3ayBAwfmnbux2QWWiHza8CGAfgBYYBG1c0opmFJyUbrmGDxHdoPHiLBGr7WqM9ci7bf1iB9+LYru/QUDg7tqkJbsyW9kNAo3HWKBRUSaMBgMb4eEhPQODAws1ul0Sus8RLayWq2Sn58fl5OT8zaAG87d3pIRrDKl1Nm3s0XkNVsCElHrs1TUonjlEViKqxF4T18YQ9wb3a+0KB8n35gKi84J1iHj4MfiqlOo66qg/7oSlcXFcPf11ToOETmeBBZX1JnodDoVGBhYmpOTk9Do9hac82/nPH68BecgojZSdaAQuf/eAWOgK4Lu63fB4irrWCrK/nMFKnxikbBwDXR63tqus/CP6A6z4RSOfrFO6yhE5Jh0LK6os6n/nm60lmr2CJZS6hgAiEiAUqqgfuVAImpnrLUWlK4+iur0EvhP7w3nCO8L7ltnroWHbyAOJS3A0An3tGFKaiuV3Z3hdrRC6xhERESdni0XGv7PbimIyK5qsyqQ95+dUBYrgv/Y/6LF1fZPn8eBF8fDy8cfSSyuOq3wiePgpCJhNXGBVyIiotZkS4F1/tXxRKQppRQqfslCwTt74Tk6HH43x0Dn3PhAdZ25Ftv/+weEpL4P/5v/08ZJqa15BQXC2MsbmRt2ax2FiKjN9e/fP/ZC2woKCvTPPfdc4KXOMWfOnK6RkZHxc+bM6bpgwYIuTzzxRLNWDvroo4+8//znP4c05xjqmGxZpp1zaYnaEavJjKIVh2EprUHgvf1gDHC96P5HdvwEl4oT8H1gE7x8/NsoJWmp0HAIbr+YgOuStI5CRNSmdu7cmXqhbYWFhfp33nknaNGiRfkXO8dHH30UmJ+fv8vV1VUtWLCgS3Oe32w2Y/r06aUASptzHHVM7XoES0RuEpH9ImIVkaQG7REiUiUiu+o/Xm/tLETtWU1GKXKX7ITB1xlB9yZetLjKPLIPv638N2KHXI2+j65jceVAek4cB6OlC0pzL/o3BBFRp+Pm5tYfABYvXhyckJDQOzo6Ou6hhx7qAgALFy7sevLkSefY2Ni4OXPmNLp87ujRoyOrqqp0/fv37/3WW2/9bjnWF198MSAhIaF3TExM3Lhx43qVl5frAODGG2+MmDVrVtchQ4ZEz5s3r+uSJUv8Z8yYEX5m28yZM7v1798/tmvXrn3effddXwCwWCy4/fbbwyMjI+NHjRoVOXLkyMgz26jjsGUE6zG7pbiwfQCmAHijkW3pSql+bZCBqN1SVoXyH0+i4pcs+N4YBdfeFy+W9m1djdDv74M1/gEAgOh4v0dH4urlDbPhFDJ/qIT3tMlaxyEiB/X01we6vLPlWOiZx5/OGXYQAG5+45feZ9ruHtEje/F1cVkDnv6+b1FlrREAooI8TN8vGHnw/mU7uq/ekx1wZt8tfxq1p6uv2yUvMF25cqXXkSNHXPbs2XNQKYUxY8ZErlmzxuPFF1/MvO6661xTU1MPXOjYDRs2HHFzc+t/Zp8FCxacfSdz+vTpxQsXLiwAgAceeKDLkiVLAh5//PE8AEhPT3fZunXrIYPBgCVLlvzuRTo3N9eYnJycumvXLpfJkydH3nXXXcVLly71PXnypFNaWtr+U6dOGRISEhJmzpxZeOn/VWpPWlxgKaX2ichNANYqpcpF5C8ABgB4Rim1wx7hlFIHAUCEl3sRnctSVoui5alQCgi+vz/03s4X3X/vxpXo8uODyB7zXwwZcd498chBWGO7w7vOQ+sYROTAFl8Xl7X4urisc9sznpuQcm7bjsVj95zb9p9pA47/ZxqON/d5165d67Vp0yavuLi4OAAwmUy61NRUl549e9Y291wNpaSkuD7xxBNh5eXl+srKSv3IkSPPTgOcMmVKscHQ+J/bN9xwQ4ler8fAgQOrCwsLjQCwefNmjylTphTr9XqEh4fXDR06tNyWbKQNW0awAGCxUuozERkBYByAFwC8BmCIzckurYeI7ARQBuAvSqnNje0kIrMBzAaAsLAwZGRktEE0aorCQr4h02JZ1dCvL4I1zh1qgBdMxdlAceO7Wq0WVJYWwRgUg2Nj3kFA1yhNfw7Y79rSx3ZF3drcNv0eYJ87JvY7tTdKKTz44IPZjzzySEHD9rS0NCdbzjt79uweK1asODJs2LCqJUuW+G/cuNHzzDYPDw/rhY5zcXE5u56BUup3/1LHZmuBZan/dwKA15RSX4rIk805gYisB9DYiiqPK6W+vMBh2QDClVKFIjIQwBciEq+UKjt3R6XUmwDeBICkpCQVERHRnHjUytgfzaOUQsWmTJRvLoHfbb3hEnXxadnVVZXY/+p0WHVO6PPQpwDi2yboJbDftWMJq0P2ikPQmYLQJS6mzZ6Xfe6Y2O/UnlxzzTVlTz75ZJfZs2cXeXt7W48dO2Z0cnJS3t7elsrKyhbPmTeZTLrw8HBzTU2NLF++3C80NLTF98O4/PLLKz744AP/+fPnF2ZlZRm2b9/uedttt/Gesx2MrQXWKRF5A8AYAP8QEWc0c+EMpdSY5j6pUqoGQE395ykikg4gGkByc89F1FFYq+pQ9NkhWMtrETS/Hww+Lhfdvzg/G7lvTgGcg9Bn7rttlJLaO73RgGqnLFRtLmvTAouISEsigilTppTt37/fZdCgQbEA4ObmZv3oo4+OxcfH1wwcOLAiKioqfvTo0aVvvPFGZnPOvWjRoqzBgwf3DgsLq+3du7epoqJC39Kcd955Z/H69es9o6Oj43v06FGdmJhY6ePjY7n0kdSeiC1DkSLiBmA8gL1KqcMiEgqgj1Jqnb0C1j/PTwAeVkol1z8OBFCklLKISE8Am+uf96IVflJSkkpOZg3WXmRkZPDdzSaqzapA4UcH4RLtC58JPSGGS7+PsW/rapQfWI8hf3gJOn2Lf9fbHftde/tefx/O2WZE/d+sNnk+9rljYr87JhFJUUr97l4Qu3fvzkhMTCy40DGtLScnRz9gwIC4rKysvVplaI7S0lKdt7e3NScnRz9o0KDeW7duTQ0PD6/TOhedb/fu3QGJiYkR57bbNIKllDIBWNngcTZOT9+zCxGZDOA/AAIBfCMiu5RS4wBcAeApEanD6WmKcy9VXBF1VJXJuShdcxQ+1/eCW7+gS+5/YNtalB3ZhqG3Pwlcdn3rB6QOJ3DkMFR/lAllVRAdFxEios4rIyPDeOWVV8bcd999uVpnaaqxY8dGlZWV6c1mszzyyCPZLK46HlunCLYqpdQqAKsaaf8cwOdtn4io7SizFSWr01FzrBSBs/vCGOx+yWOSV7+BninPwDJ6SRskpI4quHc0cnxKUXm8EB49Ai59AFLD2jsAACAASURBVBFRBxUREWHOyMjY19T9f/31V9cZM2b0aNjm5ORk3bNnzwVvVGxvv/76a1pbPRe1jnZdYBE5qrqiahR+dBAGfxcEze8HnfOlf1R3rvsQXVP+idKbVqBPfFss5EkdWUn1buR/tR99/jhT6yhERO3G4MGDqy52PyyipmCBRdTOVB8qRtGnafC8shs8LutyyfvAmWtrUJh7Er1HTEJF3yvQIyTcLjnKT+Th2LqdqMivQEVxDSKv6ImI8YPw6b3LAQBOBitCwt0w/LEbcXjFZoheh+ABveDZvbFFQam9qQt2gevJGq1jEBERdTossIjaCaUUyjdmomJrFvyn94ZzD+9LHlNaXICTr9+ISs+eCJn/LlzcWnYDWXNlNTLW/IaTOzORk6tw9f1DUFVUjiMp+XBzF3h4O8HN3ws6gx4Dr+kOAKguNsHF1w0AkHMwB5knzCj7pgRuqhzT37oZpqxCuAb7Qm/kr5n2yDepD1T6CVitVuh0LV6dmIiIiM7Bv3yI2gFrrQXFKw6hrqj69BLs3s6XPCYrIw21S29Ehf9QDJrzerOf05RbhCNf/YqEu8Yi5dU1SE+rRmgwkDSuG7x6dUFAoiu6jep33nFRU0ac13b54psAABZzHYoPHIdOp8P2V9bjWJ4regRVoc/UgQgaGN3sjNR6Qvsm4ORn2TBll8AjzE/rOERERJ0GCywijdUVVqHwg4MwdnFH0JxEiLFpowklOcdQFnUbht72eLOe7/h3yUj5IhV5Zj8EGIoQeV0JBi+ciKF2GMXQGw0ISOwFALjq77ch6+f92P/lbnz36g5Me7UHLGYuhNRe6PR6eMR3h+SagTCt0xARac9iseDuu+/utnXrVi8RUU5OTmrFihXpsbGxtVpno46l2QWWiIwFcDOAV5RSu0RktlLqTftHI+r8qg8Xo+iTNHiN6gb34Ze+3goAdv2wHFUndmLYXf8Aho5v0vOUZ+Zjz3sbMejBCTCbatAt2hPjpw2FW3Drjlx0GR6PLsPjz05DW/XHT6DXmRG0+Aa4hfq36nPTpeWbDkKtLUPvATO1jkJEpLm3337bLycnx5iamrpfr9cjPT3d6OXlZdU6F3U8LXnLeh6ARwDcLiKjAZw/h4iILkophfJNmSj6NA1+t8XC47KwJhVX2z97AV03/wl+fcY16XnyUg5h9QMfYtmTv6Iorxo1ReWInHwZBj00sdWLq4bOXOMz4ZkboNcDy/6yBQeX/dhmz0+N04W6w63CSesYRETtQnZ2tjE4ONis1+sBAL169TIHBgZaXn755YC7776725n9XnzxxYBZs2Z1TUtLc+rZs2f8rbfe2j0yMjL+sssui6qoqBAA2Ldvn/Pw4cOjY2Ji4uLi4nrv37//0nP/qdNoSYGVr5QqUUo9DOBqAIPsnImoU7PWWlD0SRpMu/MRdF8/uPTyadJxO9a+hy4H3kHNjG8RkzT6ovtmrPkNptwiVBdXwCfQGdOeHIzrl9wOz/BL36i4NbkGeKHvwlG46sZQGF2dYK2zoKqgVNNMjqzL0IHQW4NQY6rWOgoRkebuuOOOovXr1/vExsbG3XPPPV23bt3qCgB333130bp167xramoEAD788MOA2bNnFwLAiRMnXB544IG8I0eO7Pf29rYsXbrUFwCmTZvWY+7cuXlpaWkHkpOTU8PDw83afWXU1lpyDdY3Zz5RSi0SkfvtmIeoU6srrkbh0gMwhrgjaG5fiFF/yWNqa6qRf+oYEkbdClPSePgEXHgZ9Iw1v2H7qkOosLjiaicDwscMQPiYAfb8Euyix7WDAQCpy3/CL98XYNwfeqPLZfEap3I8HgEBKNSVoODAUYQlxWkdh4gcxdrHumDbq6FnH9+15iAA4N1rep9tGzovG+P/noXne/aFqdAIAAiMMeG+Xw9ixR+6Y9/n//8u6Q/u3QMf2wuYXr16mY8cObJv9erVnj/88IPXtddeG7N06dL0iRMnll922WXln3zyiXefPn2qzWazDB48uCotLc0pLCysZvjw4VUA0L9/f1NGRoZzcXGxLjc312nGjBklAODm5qYAKFvzUcfR7AJLKfUlAIhIgFKqQCn1H/vHIup8ao6VonDZQXhe0Q0eI5p2vVVZSSFOvDYFlR4RCLv/fTg5N15cWa1WlB4+hQ0rTqBPH0/0n301DG4u9v4S7C721itRVfwtvn73KC7PyEXv6RcfmSP78+zfF051nlrHICJHMv7vWRj/96zz2p8sTTmv7dGje85rm/q/45j6v+O2xtiwYYP7vHnzugPA4sWLT02fPr3U1dVV3XzzzWU333xzWXBwsHnlypU+EydOLJ89e3bB3/72t5Do6Ojq22+/veDMOZycnM4WTnq9XlVVVemUYi3l6GxZRfB/AG6wVxCizqzytxyUrs2A3y0xcIn2bdIxuZnpMP1vCir9BiBp7luN7lN2NAubl2yAk4sOY5+bhjtfD+1w953qf++18P8+Banfp6H3dK3TOJ5iaybMmwoRO/QmraMQEbWp0aNHV6amph4483jLli1uXbt2NUdERJgtFgv27t3r2qdPn6oz+86fP99p//797nv37t1/sfP6+flZQ0JCaj/44AOfO+64o6Sqqkrq6urE09OTC2Y4CFvWZb702+9EDk5ZFUq+PoryjZkInNu3ycUVAJTmnURuz8kYPO8d6A3nF03b/rEKy59NgZOzDpf98fSiFx2tuDojfOxAXP38NJzavBcHP9qgdRyHonyscC2xaB2DiEhzOTk5hgkTJkRGRUXFx8bGxhsMBixatCjvzPZJkyYVJyUlVQQGBl7yl+aHH3547JVXXgmKjo6OS0pKij158mTHfIGmFrGlszn+SXQR1uo6FC5LBawKQfMSoXMzNum4fVu+QvnBDRh2z7+AAVeetz3nlwMIGRYHJ3cnTJwTieBBMXZOrh1rbR22bKiA1fID4mdcpXUchxA2NAmFPyajqqwCrl4eWschItLM1KlTy6ZOnVp2oe2//PKLx4MPPph75nFMTEzt4cOHz45mPfXUU2e39enTp2bbtm2HWi8ttWccwSJqBXUFVch7ZRcM/i4IuCu+ycVV8levIXT9fHjFX33etuLUE/j83g+x5u0DqCkux4D5EzpVcQUA3a7qj/HTw7F1UyXSv/xF6zgOwdXbB1ZdLrJ+Pf8yByIiAgoKCvQREREJLi4u1okTJ5ZrnYfaP1tGsB6zWwqiTqT6SAmKlqfCa0x3eAwNvfQB9fb8uAJhO15E+S0rEd876XfbMtb8hnWf5yC6ixHXPzoBTh6u9o7dbnQb3Q+jS01w8mj/i3R0Fm79B8DZ1UvrGERE7VJAQIAlIyNjn9Y5qONocYGllOI3GtE5KrZlo2z9cfjdFtvk+1vVmWuRe/Iw4kbcgNK4oQgN7np2W862g6irrkWXy+IwMcATwYNiWyt6uxI5eTisVit+/MtyJM29Cp5dA7WO1KnVeFSj+NcMeF4WpnUUIiKiDs+WKYIQkSQRWSUiO0Rkj4jsFRHOMyGHoyxWFH95BBU/n0LQ3MQmF1emilLse/kGZH/xBAxGJ/jXF1d11TX46YlP8NXbR1B0JAdOXu4OU1ydodPpUGe2Yu3T38Fq5cJLranGuQzOeZVaxyAiIuoUbCqwAHwE4F0ANwK4HsB19f8SOQyryYyCd/fDUlSNoHn9YAho2vS9wtxMZP5rDMxGbyTOX/a7bV899Bnycs246eFE9J01rjVidwij/u9G1FgM+PWFL7SO0qmFDUmCTvmiqtykdRQiIqIOz9YCK18p9ZVS6phS6viZD7skI+oAzHkm5L26G8ZQd/jfGQ+dS9Nn3Zbmn0Jh2Ggk/fFjGJ2cYTHXIWXJatSZqnHVw6Mw9bVp8I0Nb8X07Z/BxRnj7ktCVblZ6yidmrO7OyAFyNl7UOsoREREHZ6tBdZfReRtEblNRKac+bBLMqJ2rvpIMfLf3APPkV3hM6EnRNe0hTVTf1uPba/PQ8+EIRh21z8gOh0K9hzFp/M+xaG95ajKL4F3rzDodLb+eHYOgf0jMerpW3Bi/Q7UlnEaW2uxdguDr4HXuhGR47JYLJg5c2a3qKio+Ojo6LiEhITeqampTgDwr3/9yz86OjouOjo6LioqKv7DDz88ey3AE088EdyjR4/4qKio+JiYmLj//ve//tp9FdQe2HrTs7sAxAIwAjhzkYQCsNLG8xK1a5W/5qB0XQb8p8XCuWfTrrcCgB3ffYAev/wZNSNfOttWcvgUVi7Zj/gIJwz906QOe7Pg1rbziwNw+vYArnnpdq2jdEpOod6oyMiDz2DHHjUlIsf19ttv++Xk5BhTU1P36/V6pKenG728vKzp6enGF198MXTXrl0H/f39LaWlpbrs7GwDADz//POBGzZs8EpJSTno5+dnLSws1C9btqzpfxhQp2TrX3KJSqk+dklC1AEoq0Lpdxmo3leAwDl9YQx0a/KxB7d/h26/LEbBpGVI7Hc5TNmFOL5hN3pPH41bHgO8e3EFt4sZu/hafPznjTjyxc+InDRc6zidTmHBThgz6wAkXXJfIqLOKDs72xgcHGzW6/UAgF69epkBIC0tzdnd3d3q7e1tAQBvb2+rt7d3LQC8/PLLIevXrz/k5+dnBQB/f3/L/fffX6jRl0DthK1zkLaJSJxdkhC1c9ZaC4o+Ooja42UInNevycWV1WLBqaP7EZM0BjJ7I6L6XY6jX2/Dx3/ZhMw9OQBYXDWFW7Afho70wo5vj2kdpVNyiegC51p3rWMQEWnmjjvuKFq/fr1PbGxs3D333NN169atrgAwdOhQU0BAgLlbt259pk6dGrFs2TJvACguLtZVVlbq4+Pja7RNTu2NrSNYIwDcKSLHANQAEABKKdXX5mRE7YilrBYFS/fDGOiGwNtiIYamvTdRU23CvlemQ2epRtijaxDQpTv2vrMO236uwojR3ug9fXIrJ+9c4mdchegbq2G1WnmNmp0F9Y1H6Q8HYDFboDfqtY5DRJ3Y87893+WDAx+Ennn83vj3DgLAzLUze59puyPujuxHBz2adcXyK/oW1xQbAaCnd0/Tl5O+PPjIxke6r81YG3Bm37U3rt0T5hFm82pIvXr1Mh85cmTf6tWrPX/44Qeva6+9Nmbp0qXpEydOLN+0adPhjRs3uq1bt85r0aJF3ZKTk93/8pe/5Ig07fprciyilGr5wSLdG2tvrysJJiUlqeTkZK1jUL2MjAxERERoHeOSarMrUfj+frgPCoHn6G5o6i/T0qJ8nHp9MqqNvoibvxxVJ4qg6ixw8fNEbUU1fKIcc9TK1n63Wq34bO7HuPKegQ53b7DWlrH4ewTOHQT3MPtePtBRftbJvtjvjklEUpRSv5trvHv37ozExMQCrTJdyIYNG9znzZvXHQAWL158avr06aUNtz/xxBPBx48fd3r//fdPNmzftGmT26xZsyIOHTp0ICQkpO+GDRtS4+LiatsyO7UPu3fvDkhMTIw4t92mt4AbLs3OZdqpM6pKLULB23vhfU0EvK4Kb3JxBQAl+adQGjAAiQ+tQvpn27Di+Z04vnE/3EL9Hba4sgedToeuES7Y/A7fLLE39x6hQAmXxCcixzB69OjK1NTUA6mpqQemT59eumXLFreMjAwjcHpFwb1797p27969NiMjw7hly5az1wUkJye7hYWF1QLAgw8+mD137tzuRUVFOgAoKirSvfDCCwGNPyM5CpumCIrI+wD+qJQqqX/sC+BFpdQf7BGOSEsVv2ShbMMJ+M+Ig3N3ryYfl77nZ+RveRdD572F7jFL8ONfluNothPGTu2GiGsGtWJixzHojxPwwQNrcOyb7egxYYjWcTqNwuytyFnvhj7xd2gdhToApRRqq+pQVWOCQS+wiMDLw6NZb0QRtSc5OTmGOXPmdK+trdUBQL9+/SoXLVqUl5mZaXz44Ye75ubmGp2dnZWfn5/5rbfeOgEAjz76aH5FRYVuwIABcUajURkMBnX//ffnaPuVkNZsvQar75niCgCUUsUi0t/GcxJpSlkVSr8+iuojxQiamwiDv2uTj927cSXCfvwjSgY/hfLMfLh38UfEsB4Y3L8X3LvwDS17cXJ3wbBR3rBlijOdz+Ktg7GII1j0/5WVVSD5t72wZBtQl18L19pT8DS5wKXOCHeLGywQ1OlqACjolR75EJSJAVUulah0L0Sy9y+o8/PEVSNHINCfK1dT+zZ16tSyqVOnlp3bHh0dXbtt27ZDjR2j0+nwzDPP5D7zzDO5rZ+QOgpbCyydiPgqpYoBQET87HBOIs1Ya+pQ9HEaVJ0VQff2g8616d/OR3ZvQZcfH0TO+Lfgne2JT/76M8beEsYRllYSd/to1JmqUbD3KAL69NQ6Tqdg6BoIp2yT1jFIQ+UllTiakovcAxnwLSmDr8kDdc55KHf3hVtgMKzdfOHaNQQWoxs8/APh7+V5dsTKarWioKQU+iKF2ryTsJyqgCW3Bl77SlH8627sdzWjLqEc0SMGIDy40Uu4iYg6BVuLoRcB/CwiK3D6BsM3A/ibzamINFBXUoPC9/fDqasnfCb1guibdomislqRlZGGXn2GI8dnPQre241DJ2owcoI/ul/Newq1pvTV27FtXS5ufz2cN2i2g4A+cajcfgJKKU7zciCnjmXi+G97UZtWgS4mH5xyFlSGuMDjaj+E9h2ASFePJp1Hp9MhyM8X8AN6RPohI8P77CIXFeU1OLF1B5wOlcD07zS877kGHsOGYNIV/aDX8XuNiDoXm/4iUUotFZFkAKNxeon2KUqpA3ZJRtSGajPLUbj0ADxGhMHj8rAm/3Fprq3BztfugntlJkIe3YDgbpFINe/A1EUD4RPVtZVTU9RNlyPl+0+w+/W1GHD/dVrH6fD8e0Wjxq0Q1nIz9F5OWsehVpR5NBs/r/sZEaeC4GypRWHXPBiGBsK1Xz9cGxho9+fz8HTGmPHDgPFAcUEp/FfVInJdOT7d/DWCJjljVMIY6IS3XiCizsHmt3zrCyoWVdRhVe0vQPHKw/CdHAXXhKZfJ1VZXoL0V6bCBYBLv+ewbM4KTHl6DEY9c2vrhaXf0el0GDYlEhs+z0RfUzUMbi5aR+rwquoycXS7IGrsUK2jkJ2ZTWYkf5cC54MF0JuACq98FF8RhSuuiEOic9uNAPsGeOO6e66DpbIWlV9uh8cn1Xj+h6fhPWwQ7h1ybZvlICJqLZxTQw5LKYWKzadQseUUAu5KgFNXz2YdX5KfDZN3JHTV47FtZQGGDfflQhYa6HHtIFzj7cbiyk6U5KEq3QqwwOoUlFI4mXoSu9b8iuj8IOS6mmAYXo3hw0Yj0d1P02x6dycMnHY5zIVVGP+2DifX5OKRg79hweR4hHq7XfoERETtFAssckjKolDy1RHUHi9H4Lx+MPg4N/nY42m7kPP9vzF43jvwmvAUVj/1AybOGYDgQTGtmJguJnhwDLb9YxUGPXQd9E5GreN0aGZ3ga6EC110dMpixb6fDsDy23HUVZpx1Dcb3rclYFLf9ndzbqO/KxIfHo1u6zMQs/UU/m/Fw7hu1LW4rien/RJRx2TrfbAWNNJcCiBFKbXLlnMTtRZrTR0KP0oFAATe2xe6ZkyNObj9OwStuQeWrn/ET3/9DCP/OhW3vnkLdDpeO6Al0etw9HA1XN/7AYmzx2sdp2ML8ITTcX4/d1R1plps/nIrAvbrcAq1KEzKw9ixE/Cge5DW0S5K9AL/cT3g1t0bf/5kGl5evxxL3L7BB5OeQ7CHt9bxiIiaxdZX0SQAcwGE1X/MBnAlgLdE5FEbzw0RuUlE9ouIVUSSztnWV0R+qd++V0Q4P4guyVJWg/zX98Dg44yAO+OaVVwdT9uF4DWzkBb4Fxz8pSdQfw8mFlfa0+l06D86BLu3l8JqtWodp0PrNW4yvLzb3ygHXZy5qAr7lm/Ckb/9hKz0g/h1cC1GPTka0yfdhaB2Xlw15Brrh+CZfbEwZzr6lnfD7e+vRn55jdaxqBN66qmngsrLy+3yAp6WluYUFRUV39zjPvroI+8///nPIRfb5+uvv/b8/vvv3VuejrRg6zeWP4ABSqmFSqmFOF1wBQK4AsBMG88NAPsATAGwqWGjiBgAfAhgrlIqHqeLOt4dky7KnFOJvFd3wzUxED6TI5u8DDsAZB1LRXhUX+QOeA/7doZiyOUeGPXMrdAZ9K2YmJoj5taRAIBTG3ZqnKRjEw+F2rxyWC0sVDuC2kITdr6VgvR//obk7F04ebPCzY/Nwd03jIVzB/395NzdC4F3xmNBwWjcGZCIP3zxHE6Wn9Q6FnUyb7zxRnBFRYWm75BOnz699Nlnn8252D4bNmzw3Lx5c9PulUDthq3fWOEAahs8NgPorpSqAmDzW05KqYNKqbRGNl0NYI9Sanf9foVKKYutz0edV/XhYuS/tRfe4yPgdWW3Ji/DbrVYsO3V2ShdOgOntuxFzHWjcNPCvkiYObaVE1Nz6XQ63PrvG9BtzECto3RoRg836FUZCrOKtI5CF2EurMKOd37EkRe3YEvBTmTdFIk7H7wfYxPHwajvmIVVQ87dveA3LRajj1TijphYzPj2Tqw5lKx1LOqgysrKdFdeeWVkTExMXFRUVPzChQtD8/LyjCNHjoweMmRINACsXLnSq1+/frFxcXG9r7nmmp6lpaU6AHj44YdDExISekdFRcXfdttt3c/Mkti8ebNbTExMXL9+/WJfeumls8PEAwcOjPn5559dzzweMGBA7Pbt213RiCVLlvjPmDEjHACysrIM48aN65WQkNA7ISGh97p169zT0tKcli5dGvj6668Hx8bGxq1du5aFVgdh6yIXywBsE5Ev6x9fD+BjEXFH6y7dHg1Aich3OD1itlwp9XxjO4rIbJyeuoiwsDBkZGS0YixqjsLCwjZ5HkmtgG57KSxj/ZHnYwKa+D1QW1OFklUL4VRWjR058+H84U4khXsDrkApv49arLX7PfWpjQi7qhc8Y3gfspYy6AqQvi0FlRb7LNzSVj/rDqG0DkWbC+GWXYbkoB0oHBKMm/qMgkFXi+PHj2ud7nds7nc9IL3dMGxrBFJjpuHRLfNRkPcMLu8aaZ+ApJncvz/Xpej990PPPO7+4QcHAeD47Xf0PtPmd+ed2cGPLco6NGx4X0txsREAnHr1MvX65uuDpxYs7F727bdnl+3t9cP6PU5hYRecybRy5UqvkJAQ808//XQEAAoLC/XLly8P2Lhx46HQ0NC67Oxsw7PPPhu6adOmQ15eXtbHH3885Omnnw5+4YUXsh955JG8F154IRsAJk2a1GP58uXe06ZNK7377rsjXn755RMTJkyomDNnztkXnJkzZxa8/fbbAcOHDz+5Z88e59raWhkyZEjVpf5P5syZ023BggW548aNqzh8+LDTuHHjoo4ePbp/xowZ+R4eHpannnoqt3n/y6SlFhdYcnoI4D0A3wIYgdM3Gp6rlDrzFtP0Jp5nPYDG5p8+rpT6spF24HTuEQAGATAB+EFEUpRSP5y7o1LqTQBvAkBSUpI6c1d5ah9asz+UUij7/jhMe6oQcG9/GIOat+xvbmY6siu6Iz13DMKDazHqqZugN3LhTXtozX4/LD8jZ8MJ9Bk3otWeo7M7ZqiEa3mVXfuJv3ttYymvxf7PU2A8bMIWJwNCb/DFrMGPtPub89ra7ypcofD9/XhAjUZIfCT+vb4GQ2f5Iyq4ebfVoPYl+LFFWcGPLco6t7136sGUc9uif/l5z7ltYS+9eDzspReb/I7CgAEDqh5//PFu9957b9jEiRNLx48fX9Fw+08//eSenp7uMnjw4FgAMJvNMnDgwAoAWLNmjedLL70UUl1drSspKTHExcVVFRYWVpSXl+snTJhQAQB/+MMfCjds2OANADNnziz+5z//GVpTU5P5+uuvB0ybNq2gKRm3bt3qdfjw4bMjXRUVFfri4uL2/QNOF9TivxaVUkpEvlBKDQRw3g9EM84zpgWHZQLYqJQqAAAR+RbAAADnFVjkmFSdFcWfH4a5oApB9yZC7+nU5GOzjqXi5Nd/R/9ZbyDh1j/Bd1sa+s4a14ppyZ763ToYq/57ALUVVXDyaHRWBl2Cc+JwhBp4H6L2wFpdh6x1B1H9Wx42eW7HyUE+eGrCHDgbO/40wKYQncD35hjk/nsH7rytP9zH5uD+jXfhf9cuQRePLlrHow6ib9++NTt27Djw+eefez/++ONh69evL2u4XSmFESNGlK1evfpYw3aTySQLFy7svn379gORkZHmBQsWdKmurtYppS54qYGnp6f18ssvL1u2bJnPV1995ZeSktKkGV1K/T/27ju8yar9A/j3ZDajTXfpLi1Nd8soqwxZioKAQkEFQZQhL6KAouL29SeCCDJEHDgQEeWVpeIAWQKyd1voxJbuPdIkbdb5/VFaKS10pX2S9nyuy+uSJ3mSb5K2yZ1zzn0ozp07d00ul9NWP1DGYrS1Mj5FCOlrliQtsw9AJCFEerPhxT1o3ymJjBUxaQ0o+ioepmojXOZEtKi4Srl0DLyvH0B+Qhj2L90Bp3A/VlxZGZdePeAkqkD20Xiuo1gtkYsc2pxSrmN0aVRvQv6h67i+7BSOXErC4ftS8OTihXj/ofldpriqxZcJ4TA+AKU7UzC1dyAeDX0QT/0+H6WayqZPZhgA6enpQltbW9P8+fNLFi1alH/p0iWpTCYz1q6zGjZsmPrcuXPy+Ph4MQCoVCrelStXxBqNhgcA3bp1M5SXl/N++eUXBwBwdnY2yuVy4759++QAsHnz5nq7ds+bN6/o5Zdf9o6KilK7ubk1q0fA4MGDK95///26tVy167hsbW2NKpWqa/3SdwJtne80HMA8Qkg6ADVqpglSSmlkW4MBACHkYQAfoWad1a+EkEuU0tGU0lJCyIcAzgKgAH6jlP5qjvtkrJuhtApFXyfAJtAeirH+ILzmNbMAgNyMJNj9+AT+ynsZ8ySR5AAAIABJREFUeqLAgy8ObcekTHt6+OOprH1+G5QWXgY/ywQghusoXQ41Uagv5iN77zXE8VJw2s8Fzz08Bt6OXXtEURLuDPW5fFSezMETg5/A1osn8NiuV/D7tPXNblrEdF3nz5+XvPLKK148Hg8CgYBu3Lgx49ixY/IHHngg0NXVVX/69Onkzz77LP3RRx/11+l0BADeeuut7MjIyPJp06YVhoaGhnl5eemioqLUtbf55Zdfps+ePdtPIpGYRowYUW9EbMiQIRqZTGZ88sknm5weSAihAPD5559nzp4920epVIYajUbSv39/VUxMzI1JkyaVxcbGBvz+++/2a9euvXH79EbGMhFKWz8SSQjxbew4pdSyVtreFB0dTc+dY12ILEV6erpZ12XoslQo2nIVtkO9YDvYs0Xn5t1IQTefQPy9chuKMkx4YPnDENmxbSfag7lf9zvZ/+I2hI0Lh+dQs3zf06XknTsO3Y5y+KwYa5bb66jX3NpVXy9D1s5E5Ooyscv7GAb0isWUCOv9osfcr7s+X43Cz6+g2wvRKEc1Jn/5K6b36Y8nYsx3H0zb3VwTX2/v0suXL6dHRUU1ay1SZ5Ceni4cNmxYUFpaWjz/Ll0933rrLbeKigr+mjVrGqxHY6zD5cuXnaOiovxuP97WEawbqGlm4U8pfYcQ4oOahhUWWWAxnZf2WjFKdyTDYWIgJGHOTZ9wEzWZcOqbVyC5eAlFfZZg4JJHAbDNgzsDkZSPy7uusAKrFZx6BCAfV1Gt0UMsFXIdp9MzFGuRtecqKjMq8AXPhNAHRVjVZ63FN7DoaEI3GSThzqg4lAmHB/3x1dRxmLP3bYT5P4Lobmx7BsYybNiwwendd9/1fO+99zLvVlytXLnS5fvvv3fauXNnWgfGYzpIWwusjQBMAEYAeAeACsBO1HT3Y5gOUXk6FxUHMuD0RBjEPnbNPs+g1+HCJ0/BEMfDGdMCxFBWWHUmvWYMxg/LL0JbVAGJc/N/LhhAqOgGg/gK9CVaVmC1I1OVAeUHM1B6JhPb5PuRGuaAjyYsgq0Ne87vxG6UL/LXnId8oDt8nKR4feQ4vPjXi/hi1DYEODXWkJhhOtaCBQuKFyxYUG+PgnXr1jl98sknbrce69u3b2VycjLrH9BJtbXA6k8p7U0IuQgAN9dGNb+jAMO0AaUUFQduQHOxAK5PR0Hg3LKOceUlBSi56oZs02CMme4Dr2FR7ZSU4YIiwBNe8iMovJQKn1G9uY5jXQiBzMcDpELX9HWZFqMmCvXZPBT/kY5zslQc63MGT/Z7FlHdlFxHs3h8WxFkAz1QcTgTjrFKDPEaAg/BIDy192Ucnv41+5KMsUgLFy4sXrhwIdsQsAtp618iPSGEj5pGEyCEuKBmRIth2hU1UpTtTkVVYglc/9Oy4qo4Pwsn18wCv1qAAS88j9hX+7HiqpMau3YaK65aSZV5CMknTnIdo9OpzqhA1tqzOLP/NJbyS0FGDsCacetZcdUC8hgPaOOLYVTVfAHw6YOvwqD2x66LmRwnYxiGqdHWAms9gN0AXAkhywAcB/Bem1MxzF2YdEYUb70KQ2kVXOa2rA17Vmo8ildNQsqloYj75hjsAz2hCGhZQwzGuuyavxXF8elcx7A6Rhs9eKXqpq/INItRpUPx/5KQ/c1FrBF9g496HMe6RTGY2CuQdcFrIb5MCGlPF1SeqOkLYCuW4LMJi7DyyO9IL8vmOB3DMEwbpwhSSr8jhJwHMBI1LdofopReM0syhmmEUa1H8TcJEDhJ4DApEETQ/O8ISgtzUbH2GZzQvAxldwMGvTapHZMylkJiQ5Cw4zSGhvtxHcWqmOxEEBawCQltRY0UladyUHbgBg6JKvFH6Ld4fdjLCHMO4zqaVbMd7ImCTy7Bdrg3eCI+wj0VmHqPEasvrMBHIz7iOh7DMF1cq0awyC1ft1FKEymlH1NKN9xaXBH2lRxjZobyahR+ehmi7go4TFa2qLgqyrsBBxd3FLnPQv/Bthj69hQ2V7+LCB0dhLQbfJhMrFhoCbFfd4hgz3UMq1b9Tzly15/H+SPnMU++F6ahPfDtw5tZcWUGAmcJxH4KaM7m1R2b32sOzuck4uNTv3CYjLFmRqMRM2fO9A4MDAxTKpWh4eHhIYmJiay3ANNirR3BOkwI2QngJ0rpjdqDNxtcDAbwBIDDADa3OSHDoKaNceGX8ZAPcIftUK8WnXv6f6tRufc6HAbcj6GvzWinhIyl8r63N2x3J0KdkQ/b7u5cx7Ea3kNHI//cea5jWCVjhQ7lv/8DVUohvnb/GX+7FGD1yP8i0r3RrSOZVpIP9ULJ9iTIBnqA8AhEfBGmBjyLL69sw+zoMRAL7twim2Ea88UXXzjm5eUJExMTE/h8PtLS0oR2dnbs2zmmxVr7Ff79AIwAvieE5BBCrhJC/gGQAuAxAGsopZvNlJHp4vQFGhR+fgW2Qz1bVFxRkwknP12MvJ8rkSUYgu4jWCOLrojH4yH208dZcdVCBBUwaDSoqmSdBJuLGk1QHctG7trzOJFfjme7/YUBI0dg/7SvWXHVDkQ+tuCJ+ai+XlZ3bMHA8eglXogvj/3DYTLGWuXm5grd3Nz0tftXBQQE6F1cXIxr1qxxnjVrlnft9VavXu08e/Zsr6SkJJG/v3/Yo48+6tujR4+wQYMGBVZWVhIAiI+PF8fExCiDgoJCQ0NDQxISEsQcPSyGA60qsCilVZTSjZTSQQB8UbMGqxel1JdSOodSesmsKZkuS5dTicJNcbC7zw/yAR4tOldVUYqCk/aolnTHlJVj4BDs004pGUunzi3G/+ZuZdMEW4BnYwchyUXBjbKmr8ygOr0c+esvIu1cMua4rMavbqX4dupLuN/vftbEop0QQiDr2w3qM3n1jr82JgS7819BkbaIo2SMtZo+fXrJgQMH7IODg0PnzJnj9ffff0sAYNasWSX79+9XVFdXEwDYunWr89y5c4sB4MaNGzbPPfdcQWpqaoJCoTBu2bLFAQCmTp3afd68eQVJSUlXz507l+jj46Pn7pExHa2t+2CBUqoHkGuGLAxTjz5fjaKv42E/LgDSSJdmn6dWleH8+jcQGvs87l35LMT2tuCL2cadXZm0myPURhtkH74E75GsbXuzCESgpBTl/2QCoa5cp7FYJo0e5X+kQ321CH8oz+I74y94JGAx5vUbzXW0LkHa0wXl+9NhVOvBl9X8nfd3UWCUfzRWn/kYy+95i+OETGOO/5jicflgZt20godf6H0NAHavvhBSeyxqpHfu4MmBOV8uORZZVakXAoCDu1Qz9a0B1/Z9Ee+beq7Aufa605cNvGLnJGlzARMQEKBPTU2N/+WXX2wPHjxoN2bMmKAtW7akTZgwQTVo0CDV9u3bFREREVV6vZ7069dPm5SUJPL09KyOiYnRAkCvXr006enp4tLSUl5+fr5oxowZZQAglUopbm5pxHQNbS6wGKY96Iu0KPoyHvZj/VtUXBXl3cA/yxYgST0TdkeuoefTY9oxJWMtCCHwdtUj+UAiK7BaQC/UwlhYynUMi0QphfZKIcr2/oO8bgI8zytFPycdfh36E6RCKdfxugyeVAhJiBM0FwpgO+TfLTemBT+FB3Y8iIkB09DXqweHCZnGDJ4cmDN4cmDO7cef+XREg4Wfs1YNuXL7sdGzwzNGz0ZGW3McOnRINn/+fF8AeOONN7KnTZtWLpFI6JQpUyqmTJlS4ebmpt+1a5f9hAkTVHPnzi1atmxZN6VSWfX444/XDY+KRKK6wonP51OtVsujlNVSXR0rsBiLYyyvRtEXcbAb5Qtpz+Z/c66pLEfqG0twxTQbg4ZJETZjVDumZKyNcmQwrvySwHUMq+Iw4jEoKtgarNsZSqpQuicVujIN/uu0B+eNGdj46Efo78++0OGCrG83lO5JhXywR910TC87F/S3n4GtZ5JYgcXc0YgRI9SJiYlXa/99/PhxqZeXl97Pz09vNBoRFxcniYiI0NZed8GCBaKEhARZXFzcXd9MHB0dTd26ddN9++239tOnTy/TarXEYDAQW1tbNk+9i2B9qhmLYqo2ouibBMj6u0PWr1uzzysryoNUroChx1g8MM2HFVdMAz6jeuHBdY9zHcOqVKjTUZDc5i+JOw1qNEH1VyYKNlxEqrwIMz1fBc9HhIPTN6G/vxPX8bosUXc7wEShu6Gqd/y9e2fhcDxwo7SEo2SMtcnLyxOMHTu2R2BgYFhwcHCYQCDA0qVLC2ovf+ihh0qjo6MrXVxcjE3d1tatW//5+OOPXZVKZWh0dHRwZmYmG9ToQtiLzVgMaqIo+SERQg85bIc1v1vghd+3Iuu7VLj3i8Tgl59ox4SMtbvy5X5UlWnQ74WHuI5iFfSVCeCVuHEdwyJU36hA2a5U6CXAZz48HC+4jvcnrUA/92iuo3V5hBBIe7lCc6kAYl+7uuOutjYYGXMeO9PSsDj6OQ4TMtYiNja2IjY2tuJOl588eVK+aNGi/Np/BwUF6VJSUupGs9555526yyIiIqpPnTqV3H5pGUvGRrAYi1GxPwNUZ4TDQz2a3XXr1JbVSP6uFGqxL8KnjmznhIy1kzjIkXxVy3UMqyH3dofIKEdXXk9gqjKg9KdUFH97FZe6F2OM4DkUyJPx69wZrLiyINKeLtDGFYEa6/+sLu43E9uTtiO/spyjZExnUFRUxPfz8wu3sbExTZgwQdX0GUxXxwosxiJUpZZBfSEfjo8Fgwia92Opq65C7oEqSBQSTNn4OCTOinZOyVi77mOjoSG2KL3Gpr01h4OPHwgodOqu112YUgptfBHyPzwPXZUBn/Tdh1W6VVgY9SY+i50OqYhNALEkAicJ+A42qE6rv62Aj50PpMYQ/PfwVxwlYzoDZ2dnY3p6evzvv/9+nessjHUwyzsEIUQGoIpS2uScVIa5nVGtR+mPSXCMVYIvFzV5fZPRiGOr3kbg6McwdsMiCOQS8HjsuwKmaQKxCN6KCuRdSINDCNv4tSkC1yBQu3LQCh3QjN/NzsJYoUPpnlQYCjVIG2SLV0/noo/cA3se2gWZUMZ1POYOpFEu0FwqgI3Sod7xhX3mY82BBFBK2Z5kDMN0iFZ9KiWE8AghUwkhvxJCCgAkAsglhCQQQj4ghASaNybTmZXtToEkwqXBm2JjdNVVOPri00hK6oPc02kQ2clYccW0yAOrpyJk2giuY1gHoQ14tgKo87vGjBhKKdTn8pG/7gJ4rmK86PMz5qfOx9IHArBm7BxWXFk4aaQLtFdLQPX1v+sdH9oL1GCPQ6kpHCVjGKarae0n08MAAgC8AqAbpdSbUuoKYAiAUwBWEEJYuy6mSdUZFdBlVUJxv1+T1zUZjfh74QIkl4/DoJFy9Hl6XPsHZDqlvYu2ojKrkOsYVoGffxjp19K5jtHuDKVVKPo6AZUnspH1gAxzdS8DkkLsfugHjInw4Toe0wx8OxFEXnJoE+t3DSSEYNzgTBwv/IGjZAzDdDWtLbBGUUr/j1J6hVJa19OfUlpCKd1JKZ0EYLt5IjKdWcWBDNgO925y3ZXRYACPzwfxicbox7wQNo21YWdar6qKIO3Xs1zHsApGoRa05I5NtaweNVFUnsxBwUcXIfSzxfZQHubtS8UYn0ewZexGBDg1f7sIhnvSKBdoLzX88mR6+CTsvf47SjWVHKRiGKaraVWBRSltcsVzc67DdHH51TAUaiHrc/c20OUlhTgw+wUcfP0zDHt1HnxH9emggExn5R0oR0ZcMdcxrIJRbAS/snNuNqwv1KDw8yvQXCxA1WNuuD9jCXZkbMbP80fjqZ6T2XodK2QT6oSq1DKYdPWnCbrJ3CAydMe6U7s4SsZYA6PRiJkzZ3oHBgaGKZXK0PDw8JDExEQRAKxdu9ZJqVSGKpXK0MDAwLCtW7fa15735ptvunXv3j0sMDAwLCgoKHTDhg1sY7wujrVBYjjDO1cB22F3H70qysvE2ZfWI5c3EA9NHdCB6ZjOrMf9UUj68DRb9N4MJKgP7NI6V4dOaqSoPJ4N1V+ZsB3hgz8cLuOjC89hoMd4rBi5EGKBkOuITCvxZUKIvG1RnVwKSbhzvcueCnkGv14uAdgSTOYOvvjiC8e8vDxhYmJiAp/PR1pamtDOzs6UlpYmXL16tfulS5euOTk5GcvLy3m5ubkCAFi5cqXLoUOH7M6fP3/N0dHRVFxczN+2bZt9U/fFdG6swGI4octUgZTqIYu+++jVuTfXIp/0wqQ3h0Lh79FB6ZjOzinMD49v8mHFVTO4Dx6DotSEpq9oJfR5apTsSAZPzAdvZgDeOpmDuPh0bJy0EeEu4VzHY8xAEuYEbUJxgwLr8T4DsP7g90guzIPShU39ZBrKzc0Vurm56fl8PgAgICBADwBJSUlimUxmUigURgBQKBQmhUKhA4A1a9Z0O3DgQLKjo6MJAJycnIzPPvssmyLRxZm1/RohZBUh5BNCSKQ5b5fpfCpP5sAULr/j6FVlRSkK0tIx8O2XMHn5aFZcMWaX/ttZXP7sd65jWDzV1f9BX1wJarLuzYapwYTyPzNQuOkK5P3ccbh/MUYfiIWapGDPk3NZcdWJSEKdoE0sATWY6h0XC/i4d+BVnCrYz1EyxtJNnz695MCBA/bBwcGhc+bM8fr7778lADBgwACNs7Oz3tvbOyI2NtZv27ZtCgAoLS3lqdVqflhYWDW3yRlLY+4RrLcBGAD8lxDiRin908y3z3QCJo0e2qvFoI80PnpVpVXj6HPvoZz647Fv/tPB6ZiuwlClR8KZUkQ9zXUSyyZXSFEBHQwqHYQKMddxWkWXU4nSH5PBV4hh/58IfPrPl/jp6h7MDHoBiwdN4DoeY2Z8hRhCFwmqr5c32P5jjP8DWHl6HWaEzeAoHQMAR77Z5HH+t5/ca//9yNvvXwOA7W+/HFJ7rM+YCbnDnpiTs3H21EitqkIIAE6e3pqZH35ybe+6lb5JJ47WDVHO3vDlFYWLW5vX/gcEBOhTU1Pjf/nlF9uDBw/ajRkzJmjLli1pEyZMUB09ejTlr7/+ku7fv99u6dKl3ufOnZO9/vrreWwmBNMYcxdYYwF4A5AA+AIA28mTaUB9sQA2QY7QS/gNLqMmE44sfB25GIBJr8dwkI7pKvweiMahfRWozCqA3MuV6zgWS2TnBkpyUZxXiW5WVmBRowmqI1moPJEDxZjuKPC2wZz/XYTCXYWdE36Es8S56RthrFLNNMGiBgXWQI/+yK7MxrmsVER79eAoHTPsiTk5w56Yk3P78Re27z1/+7H5X2y7cvuxBxe+lPHgwpcy2prj0KFDsvnz5/sCwBtvvJE9bdq0colEQqdMmVIxZcqUCjc3N/2uXbvsJ0yYoOLxeBg+fLhm+PDhmgceeKBi9uzZfh9++GGORCIxXb16VRQaGto5uwExrWLuHVr1AI4BWAnA38y3zXQS2vgiSHs1/oE27o+9yNIPwv1Ph8A+0LuDkzFdiUhmA2d+KTIOXOY6imVTeMEkqoK+tIrrJC2iz1ejYONlVKeXw/W5XlirOoTxP0/C/eGu+GrCa6y46uRswpyhTShuMLVVIhShj+RZXEi3rp9npn2MGDFCnZiYeDUxMfHqtGnTyo8fPy5NT08XAjUdBePi4iS+vr669PR04fHjx6W15507d07q6empA4BFixblzps3z7ekpIQHACUlJbxVq1axPzBdXKtGsAgh31BKn7j9OKWU9T9l7sqo1kOfo4ZNgD2QXX9vnZyEa4gcMx6B/csgcWINeJj2N27lQxDbybiOYdmcAuDYm0Jg4DpI81ATReWxLKiOZsFutB+MkbZ498JyHMo9gWWDlmF8SBDXEZkOIHSWgCcVQpelgtjHrt5lE0OH4LsL5zF3MFt3x9SXl5cnePrpp311Oh0PAHr27KleunRpQVZWlnDJkiVe+fn5QrFYTB0dHfWbNm26AQAvvfRSYWVlJa93796hQqGQCgQC+uyzz+Zx+0gYrrV2imBdEwtCyH5K6X1mysN0clWJJRD3sAcR1h88zbhyCYc/TEDfBzMRFst+nJiOQfh8nFy+AwNfieU6ikUrT/kF5WUjEDXYk+sod6Uv1KD0x2QQAQ+uz/RCUlU15n/yO6LCdfg9djdkQlZMdyU2QQ6oTi5tUGANCJBjWfwKaA2jIRFIOErHWKLY2NiK2NjYBjurK5VK3alTp5IbO4fH4+Hdd9/Nf/fdd/PbPyFjLVo7RfDWMXcXcwRhuoaqa8WQhNbff8+g1+HCB3shRQkrrpgOJbARIS5NjJKEf7iOYtEU6rOoKmjwmcNiUBOF6ng2Cj+5DGlPVzg+FYYlp77E1F0vYfGwGKy/9z1WXHVBNkoHVCWXNjjuKneArzwQPyce5SAVwzBdQWsLrG6EkJmEkF4AWPsUplmo3oSqlDLYBNVfdPzXOytQwldi7PvTOErGdFU8Pg8u4gpkHI7nOopFM4kNEGssc/22oViLws+vQBtXBNf5PWHoLcXCvxbhbPE+bBy3CA/1suxRN6b9iP0U0OdpYNI0bC5nTyLxXdw+DlIxDNMVtLbAehtANIC1ALwIIXGEkB8IIW8QQiaZLR3TqVRdL4PQXQa+XFTv+IDnn8OIWd0hc3PkKBnTlbl3lyM7qeG33Mwt5HxILKy+opSi8nQuCjZegiTMCS5PR+JipRaTt34Mb1sfHJm6E4N8Q5q+IabTIkIexN3tUJVa1uCyGZETkJfnD0qte383hmEsU6vWYFFKPwcAQogdatZj8QDIAUQAeAjATnMFZDqPqqvFkITUnx74+6J34BIRjOhZUzhKxXR1kdOHwljF9oi8G8XMT6BdHg9qpCB87ictGFU6lO5MgbGiGi5zI0GdxJi5511cua7AqrGzMTKk8T32mK5HfHOaoDSy/mqGQb6BEFRfx+WcfPT07MZROoZhOqu2tmk/BGAmgOUA5gLYRCmd3tZQTOdDKUXVtRLYhP47SlWUmopMVRTsvT04TMZ0dVI3BxReSYcqgzV9uhO+Ogd6gQmGCu5bW2sTipG//gKE7jK4zu+JMlsNZvz2FJLLr+C76RNZccXUY6OsaXRx+0gVIQQD+57E6cJfOUrGMExn1qYCi1IaTSmdTSkdBOB7AH8SQtinZaYBY2k1KAUEzv92bMrZ9hfsDenocd9gDpMxDHD5j+tI/fkM1zEsFknZD42uABX5Ws4ymKoNKNmRjLJfr8NpWggUo/1w7HoxnvhlCYb6DMDxJ35AqJsXZ/kYyyRwlgACHgz5mgaXjfAbiBPZZzlIxTBMZ9fmjYYJIXxCiCeA6wD2ATjc5lT/3vZkQkgCIcRECIm+5fg0QsilW/4zEUJ6mut+GfPT3aiA2McWhNRMLzIaDFDr3dBzam+OkzEM4B4gR3Yj3caYm+SuIPwylOapOLn76owK5K+7CABwW9gLxEuGWbs/xJKdp/FK7/cxv+d88Hl8TrIxlo0Qcsdugt6yYFzIvwyTycRBMqaj8fn8PsHBwaG1/7366qvdAKBfv35Bfn5+4cHBwaH+/v5ht24S7OnpGZGbm9vaLY3a3dKlS1s8v3X9+vVOM2bM8Ln9uFarJTExMcrg4ODQTZs2OfTr1y/o6NGj0sZu404WLVrksWfPHtuWZmqLvXv32v75559maxPbmsfdmDb90BBCcgFIAeQDyAGQC+CXtoa6RTyAiQA+u/UgpfQ7AN/dzBAB4CdK6SUz3i9jZtUZFRD5/rsXCV8gQP//joG/vz+HqRimht/QYFyNvwJKad2XAMwtZC4Q8s+ispFRgPZEDSZUHLwB9dk8ODzcA5IwZxRri/HET4tQqNZgy+xHEOzC1s8wd2cT6IDKkzmwHVp/hDOymx8E6hj8U1yOABeHO5zNdBZisdiUmJh4tbHLtmzZcn3o0KGa/Px8fmBgYMSCBQuKbWxs2rUDisFggEDw78dwvV4PoVDYottYv369+4oVK8wyv/3EiRNSvV5Pap+jTZs2ubbkfIPBgLVr1+aYI8vevXttv/76a6edO3emN3XdQ4cO2crlcuO9996rNsd9m0tbR7CCKaUKSqmSUjqMUvoYpXSJWZIBoJReo5QmNXG1x1AzPZGxYLobqroCq7pKi++nb0B5Yjq3oRjmJtdePRBznwso+ya7ce49IQzvA1ddx3Vc0xdoUPDJZehz1XBb2BuSMGccTclG7M+PYoR/Hxyd/gOCXVgLdqZp4h4K6G6oYNIZG1zW334q4rIs6nMZw6GKigq+RCIxCQSCZv2xMxgMmDt3rpdSqQxVKpWhy5YtcwWAn376yTYkJCRUqVSGTp482U+r1RKgZkRsyZIl7n369An66quvHPr16xe0YMECz759+wa9++67bjk5OYLRo0cHhIeHh4SHh4fs379fBgDl5eW82NhYv9r72bx5s/38+fM9q6urecHBwaHjx4/vDgAbN250jIiICAkODg6dOnWqr8FgAACsW7fOyc/PL7xv375BJ06ckN/+OLKzswVPPvlk98TERElwcHBoQkKC+NbLp02b5hMeHh7So0ePsMWLF9ctBbr98UyaNMnv66+/dqi9bPHixR6hoaEhSqUy9OLFizYAkJOTI4iJiQkMDQ0NmTp1qq+Hh0ezRwnfffdd14CAgDClUhn64IMP+iclJYm2bNni8umnn7oFBweH/vHHH/I7PYeHDx+W9urVKzgkJCS0V69ewZcvXxYDQGVlJXnwwQf9lUpl6NixY/2rqqoIAKxZs8Z51qxZ3rX3vXr1aufZs2c3ex56q0awCCGE1ihv6jqtuf0WegTAhLvkmIuaBhzw9PREenp6B0Ri6tGbwM9XI1dfDKSXIOmb7ajie8PoYstejy6ouLiY6wiN4vVwwLWjZyHrzkZEGqAUBhdn0DPlrfqdbdFrTilIfCV45ytg6qcADZGirDATH/3xN45edcOCoa9ipHN35GZltzgH07Es6Xed5yxA5slkUF9JveOuLidwMDsbPR1PxWc2AAAgAElEQVTmcZSM6Si1xUjtv1944YXcOXPmlALAjBkz/EUikenGjRs2//d//3fj1pGlu1m9erVLRkaGOCEh4apQKER+fj5fo9GQp59+uvv+/fuTIiMjqx9++GG/Dz74wOXNN98sAAAbGxvT+fPnkwDgiy++cC0rK+OfPXs2CQDGjRvX/fnnn88fPXp0ZUpKimj06NGB169fT1i6dKm7nZ2dMTk5+SoAFBYW8mfOnFm2efNm19oRpwsXLtjs2LHD8dy5c4lisZg+/vjjPp9++qnTuHHjKlasWOFx/vz5a46OjsaYmJig8PDwetMRPD09DRs3bsxYvXq12+HDh1Nvf5wffvhhtpubm9FgMCAmJibo9OnTkv79+2tvfzz79u1T3Hqes7Oz4erVq9dWrFjhsmLFCrft27dnLF261OOee+5RLV++PG/Hjh1233//vfPt93cn69ev75aRkREnkUhoUVER39nZ2ThjxoxCuVxufOedd/Lv9hxGRUVVnTlzJlEoFGLPnj22L730kte+ffvSVq1a5SqRSEzJyclXT58+LRk0aFAoAMyaNaskLCwstLq6OkssFtOtW7c6f/bZZxnNzdraKYKHCSE7UTM170btQUKICMBgAE+gZi3W5qZuiBByAEBjn2heo5T+1MS5/QFoKKV33CX0Zkv5zwEgOjqa+vn5NRWJMbPqf8pR5l4Btx7dAQDnUwEXRRacXWLAXo+uyRJf99O7f0FBWgnGbRjAdRTLY9DB8M1Q3KjeBj8fXxBey6dRNuc1N1ZUo+THZNAqIxwX9IbAWYLSqlLE7nwWFdVq7Hn2G/g62jV5O4zlsJTf9YpIPoxl1XC4x6/e8fE2A7Hq7GqLydlVZC091sfct+m1Ysj5u13enCmCOTk5goEDBwZPmDChQqlUNrn736FDh+zmzZtXWDu1z83NzXjy5EmJl5dXdWRkZDUAzJw5s/jjjz92BVAAADNmzKi3IPCxxx4rqf3/v//+2y4lJaXuW4DKykp+aWkp7+jRo3Y//PDD9drjLi4uDYZj//jjD9v4+HhpVFRUCABUVVXxXF1dDUePHpUNGDBA5eHhYQCAiRMnliQnJ9s09dhu9c033zhu3rzZ2WAwkMLCQuHly5dtagus2x/PraZOnVoKAP369dP8/PPPDgBw5swZ+Z49e1IBIDY2tsLOzq7usURGRgbrdDqeRqPhlZeXC2oL4mXLlmVNmjSpIigoSPvwww93Hz9+fNm0adMabnB3l+ewpKSE/8gjj3RPT0+3IYRQvV5PAOD48ePy5557rgAA+vfvr1UqlRoAsLOzMw0aNEi1fft2RURERJVeryf9+vVrdqen1hZY9wN4CsD3hJDuAMoA2ADgA9gPYE1z10RRSke1MgMAPAo2PdDi6bJUEHn9OyJNCUFI7FAOEzFMQ75DgpFw5TJbh9UYgQhEBGh0JhjLqyFwaNF7c7NorxajdFcK5APcYTvcB4RPcCAlDisuPY/BPiPxUv/FkInETd8QwzTCJtABJT8kNjgeoAhGfFESVNVa2IoljZzJtIemiiGueHh4GMLDwzVHjx6VNafAuvl+QW8/dje2tramO/2bUopz585dk8vlDW6zqfclSimZPHly8ccff1xveP/bb7+1b8t7WmJiomjDhg1u58+fv+bi4mKcNGmSX1VVVd0So9sfz61q17EJBAJqMBhI7WO5kytXriQCd16Ddfjw4ZTff//dds+ePfYrV670SElJaTDAcqfncPbs2T733HOP6s8//0xLSkoSjRgxIqj2sjs9P3Pnzi1atmxZN6VSWfX4448X3TF4I1q1BotSWkUp3XizPbsvgJEAelNKfSmlczqi4QQhhAdgMoAf2vu+mLbRZVVC5PVvU5nYLS8hYBgbJWAsi2uvABj4YpTGpXEdxSIRuStyqRY6Mze6oHojSn9KRdkvaXCaHgq7Ub6ghOLjvy7gpR8yMTv4Zfx3yFJWXDFtInSXwViph7Gi/mdmB4kccn1fnMm4cYczma5EpVLxEhISpEFBQc3afX7UqFEVn376qYterwcA5Ofn83v27FmVnZ0tio+PFwPAli1bnIYMGdKsFqyDBw+ueP/99+uaS5w4cUICAMOGDav48MMP644XFhbygZrCpbq6mgDA/fffX7F3716H7OxsQW2W5ORk0dChQ9WnTp2yzcvL41dXV5Pdu3e3qKNLaWkpXyKRmBwdHY2ZmZmCI0eOKJo+68769etX+e233zoCwK5du+wqKiqa1QLWaDQiLS1NNG7cONXGjRuzVCoVv7y8nG9ra2tUqVR1t3Gn57CiooLv5eWlA4DPPvvM+ZbrV27dutURAM6ePWuTnJxc10FwxIgR6tzcXNHu3budZs2aVTfS2BxtbtNOKdVTSnMppY0O1bUFIeRhQkgWgIEAfiWE7Lvl4qEAsiil1xs/m7EU+ltGsH6Z+w4Ovb2e40QM0xCPz0P//mLwpeYfnekMeJFTIHOzQXW++RoC6PPUyN9wCSaNAW7P9YbY1w7lVSqM3DoLW5LXYff8e/BoRFsmOTBMDcIjEHdXoPp6w48qI10WIKNAxEEqpiPVrsGq/W/+/Pl1XXJmzJjhHxwcHBoVFRXy6KOPFg0ZMqTum6SoqKhQNze3SDc3t8jbmxwsXry40MvLSxccHBwWFBQU+uWXXzpKpVL66aefpk+ePDlAqVSG8ng8LFmypLA5GT///PPMCxcuyJRKZWhAQEDYhg0bXABg+fLluWVlZfzAwMCwoKCg0N9++80WAKZNm1YYEhISOn78+O59+vSpev3117NHjhypVCqVoSNGjFBmZmYKfX199S+//HLOgAEDQgYPHqyMjIxs0bdkAwcO1IaHh2sCAwPDpk+f7tenT5/Klpx/uxUrVuQcOnTILjQ0NOTXX39VuLi46O3t7Rt2oLmNwWAgU6dO7a5UKkPDw8NDn3766XxnZ2fjpEmTyn799Vf72iYXd3oOX3755by3337bq3fv3sFG4793t2TJkgK1Ws1XKpWh7733XreIiIh6b3IPPfRQaXR0dGVj0zLvhrSlDwUh5PlGDpcDOG+JbdOjo6PpuXPnuI7RpZg0euS+fxYebw0EQPHNk98jcqQUvWc8jPT0dDbvvQuy9NddV14JkaJBkyUGQOXJHOiyK+EYq2zRebe/5pRSqE/louJABhQP+EPaxxWEEMQVJOG1Ey/CRRCCD0e9BYVNm7ciYThkab/rqr+zYcjTwGFSYL3j35w/jPOFf2P9/a9zlKxzIYScp5RG33rs8uXL6VFRUS2aYsV0TlqtlggEAioUCnHgwAHZggULfO+0Ns4SDB8+vMeiRYvyJ0yY0Ogo5OXLl52joqL8bj/e1s3Tom/+V7v31VgAZwHMI4T8SCld2cbbZ6ycLrsSQg8ZCI/g0o6foOdLEPXog1zHYphG3Th4CX99dw3Tv3qM6yiW5+THOBUvgndhBBzbcDNGtR6lO5JhVOng8p+eEDrXrHvZczELyw4exeIx0zE1dLJ5MjPMLWwC7FF0ouE2PUN6+OL79Pc5SMQwXU9qaqpoypQpASaTCUKhkH722WfpXGdqTFFRET86OjokJCREc6fi6m7aWmA5oWbtVSUAEELeArADNdP3zgNgBVYXd+v6q9Jr6XAm1eCLWraRHsN0FPcBwaj8IQ9VxRWwcWLd6urRlsGOXw5RZTBMWgN4kpa/fVSllKL0x2RIernCaVoIiIAHrV6Hx3e9hcwiim+mvopQD/a8M+1D4CYFrTLCUFYFgf2/U4H97PyQoyrGjbJC+Ni7cJiQYTq/iIiI6mvXrlnsiFUtZ2dnY3p6+h27lDelrQWWD4BbV4zqAfhSSrWEkGYtEGQ6N12WCtLImrWEw99YxHEahrk7ocwGdrQU2UevIODhwVzHsSxSR3QT5SBDTOCeqYKNsgXrpI0UZb//A+3FAjhMVsImsObcIm0RFh9eglK9HjumroWPAyuumPZDCIHYX4HqtHII+vxbYPEIDxJTAE5kpLACq/2YTCYT4fF4HbdbOcO0M5PJRAA02kWxrU0utgE4RQh5ixDyNoC/UdO6XQbA4qtTpv3VNLiwReE/6dg+fS1Mpjt282QYixAUaQseG2VtSO4GJ5kQuXIBqjMqmn2avkgL/p58GAo0cH2uV11xdT6jFNN/XI1+3aLx59Qt8HFgH2yZ9icOsEd1WsNGF/c5vg6Nyp2DRF1GfGFhoeLmB1KGsXomk4kUFhYqADQ6ytWmESxK6f8RQn5DzebCBMA8SmltF4lpbbltxvoZVTpQvQl8Rxskfv07dMQWPF6bG1cyTLuKfo6tEWxU+ETIwidi3NViVDayjqUx6gv5KP/1Oky95HAaGwpCCEwmE17Y9wmOXJbig/ELMSq0sX3mGaZ9iAMUUB3JbLCvkE+3CsSVnAAwl7twnZjBYJidl5f3RV5eXjjM0MGaYSyACUC8wWCY3diFbZ0iCACGm3dCUTNFkGEA1EwPFHrKQQhBWXIRZHzWBpexfNqicvz48h94/PPJ4PHZ54A6lQXA1Z/wP9VwDM+oADWYQASNPz+magPK9qRBl6WC86wI5OiKQAiB1qDFogOv40xOAjY+tg4DfVlxxXQsgbME1EhhLKmCwOnfjYX7B8jxZ+EBsAKrffTp06cAwHiuczBMR2nTpwdCyEIA3wFwBuAKYCsh5FlzBGOs360NLqqrbKDwkTRxBsNwT+KsgB5CFF5I4TqKZdGpgb/Xo8BgQLkND7qsxpsq6bJUKFh/EUTAg+uzvSDyqGl5n1miwfRfFsBJJsZfj+/EQN+gjkzPMABursPys0P1jfo/v/52AUgqSYNGz5aPMwzTdm0dwZoFoD+lVA0AhJD3AZwE8FFbgzHWT5+rhrRXzbqK2C0vwmgwcJyIYZrH2UaNrFMpcOvLioA6UkdAW4JwDwWuCkvgm1YOsZ+i7mJqoqg8ng3VX1mwnxAAaeS/a6p2ppzHttNyPDnkGTwzuFe9qVkM09FEvnbQpZdD1su17phcLAMx2ONERiJG9YjiMB3DMJ1BW+e/EAC37mxsvHmMYaDPU0PYTYbkg0fx28IV4AvMMSOVYdqfd4gD9FXsC4F6xHaAoQrh3SQ4adDVaxRgKKlC0eYEaOOL4PpMz7riilKKF/9ci803PsQbD7lhwT29WXHFcE7sawddRsMR2EjBK6jWOnOQiGGYzqatn3i/BnCaELIbNYXVQwC+anMqxuqZqo0wqXQQOEnwz74TUJWw6YGM9ei9gDW6aIAQYO4ReDnKsWrxIOS/dwZ5q84BlMKkNUAe4wHb4d4gN9etlWmq8ObJ15CrzcIHvVbh/vCe3OZnmJuEHjIYSrQwVRnAs/n3Y5CnIx+X8xIxNsKTw3QMw3QGbe0i+CEh5AiAQagpsJ6glF4yRzDGuunz1RC4SkF4BJoCE6RyLdeRGKbZKKX4+dnv8MC74yGyZ3sz1eEJQAxaHPlHA8/pwejhUPPFicBeDCLk113tWm4xntkWh14hYfh2zDLkZeVxlZhhGiB8HoSecugyVXXbBgBAVGAhEoriAIzkLhzDMJ1Cq6YIEkJUhJAKQkgFgCMA3gOwDMCxm8eYLq52eiAAqI0ucFS6NnEGw1gOQgjKq22Qe/QK11Esy97ngdzLOJtRgj8zSyB0kULoIq1XXG08uQ9TfnsY02OcsXrMU7AR2NzlBhmGG2JfO1Sn1/+44mbjjQu5yRwlYhimM2nVCBal1NbcQZjOxZCngbCbFAAwdGFfOHb35TgRw7SMk70JOZdvwJc1Fv6X1BHQlCDC0x8/X6q/F5bBaML2pO+x9Z/P8ErfNzE1MpyjkAzTNJGvXYP93LpJvXFDdYOjRAzDdCZskxemXehya0awrp84jexTlyF3cmj6JIaxIK4BjijLZ1Nb65E41HUSjM8urzucXabFjM1HsTvpELaP+w5TI0dxGJJhmibysYPuhgrUROuOBTl7wFQ0HmVqHYfJGIbpDFhbN8bsKKUw5N/sILjxICqKJRjIdSiGaaHes4ezjYZvF/YwILGHr5MUW2b1R5XeiP4rd4EqDuDxwGfx/KhNELDnjLECfJkQfDsR9Hnqur3aeDwefCX9kFxYin4yN44TMgxjzdg7IWN2JpUOIABPLoS6mAeZHdu4kbE+fAEfJ1f9DC1r0PCvHiMBzz4ghKCHqxyJpXFwCvwU8wb1wov3hbHiirEqNaNY9ddheQX+goyqkxwlYhims2DvhozZ6W9ODySEQA03uER4cx2JYVolLc2I3GOXuY5hOeJ21DS6AJBTmYOFhxfirYFvYn6veWx/K8bqiHxsocusrHcs2MkfKaX/cJSIYZjOghVYjNnp8zR1HQR9Q7UImTia40QM0zpOjkBeQk7TV+wq+CKYVHm4UngFHnIP7J6wG/d438N1KoZpFaG7DPrc+gWWSuWAAykJHCViGKazYGuwGLPT56kh9lcgPykFyjHDYOvizHUkhmkV1wBH5Jws4DqGxVALbbC0OhXai+vx+b2fw9HGketI7UKdk4Piq/HQZGfBKyAIxQoZEv/3PSqzs6HTV6OnyBbOr76Cfes+gCE/H3weD91t5Oj97GKciTsP3ZUrkCoc4OzrB/+RoyH09GAjfBZI2E0GQ6EW1Giq2yC7n0cUDiSncpyMYRhrxwosxuz0eWrIYzxwfuWX0FSIEftNKNeRGKZVQh/qgx5De3AdwyLkqfMwP249IkQKvD7yE/CI9U+AUOVkI/PgnyhMugZBaRnC7x+HPy4cR158HKQgcLCRwlmrBxk8ADInZzgoHCC2s4OrXw/IXNwwdPpT0GVmwVBdBVuZHEJPL9jnZqKIx0dJThZUaalw1lTjlE6FrFMnYMvjw9nNHQPGx8Ju6FAQnvU/h9aMJ+KDrxDDUKitm3UR4xuC4pyBoJSyophhmFZjBRZjVtRIoS/UQuAmhbZcCKmDketIDNNqUidb5J68BomdGGI3F67jcMZETZAIJJgW/iQmBk60yg+eOrUamQf3I+vcGdgVlSJwzjxs/Xgl5GotHJ1c4BMSBklkBCbeOwoCsbjBY/QJj2pwmz59BwB96x/rOXYCMHZCvWMPGPQoirkHBVcuozDpKrQnTiC5ohgXd26Hh8wOPYYMQ48JEyGQSs3+uJm7q5kmqK4rsBQSIXzCtiBbFQUvO3eO0zEMY61YgcWYlaFEC76tEDwRHxo4wytExHUkhmmTE79mI6akAAEzHuQ6Cid+TvsZf/zzBzaO2ohJ/g8CPy0AHvqY61hNMhkMyD72F3hJKRDdOxLfv/kSZHoDXJ1c4dF3IGz8fDFv03cdUizyBUK49e0Pt7796465mUzwcHJF0i8/4eSe/0G/6St4fPM1yjNvwK/fQPAE7O25IwjdZdDlqiHt9e8xNzshctSZrMBiGKbV2F9wxqwMBVoIXWu+hbWX5iLw/hkcJ2KYtnFy4iHvWj4CuA7SwUzUhHUX1uHPjD/x0YiPag7yRcDl74FxawG+kNuAjTBpNCgpKsCxDWuQdT0FQhDEBIQg2HYi5n72LWzkcq4j1iE8HjwHDYHnoCEAAGOlGtnpaTjy+QZUfbgcPXwDELPgeci7d+c4aecmdJeh8kT9Rjal5QrsuHIR/dz7cZSKYRhrxwosxqz0BRoIXKUwGgwY9+lrXMdhmDZz6+GE7L+yuY7R4S4WXERcURy2jdkGexv7moOEADZ2QFU5ILOM5jWlSUm4+uM2XE+4gpDMAgR8txXdBw7C0Omz4BQRyXW8ZuPLZfAJj8RTm7cj6/BBXP7xB+QsXAj7dWsAgwGugUFcR+yUhO5y6HPV9Y55yQKRp1JxlIhhmM6AFViMWRkKNBD7K3DknY9Qkm7E5C1LuI7EMG3SY1QoFFID1zE6TE5lDi4WXMRY/7H44r4vGjazsLEHtGWcFljq7CzoL15CoYsj9n20Cu4iCSKH34fgyY9C7OAAB1/rHvXxGj4SXsNHghqNSDr9Nw6uXwV7vhBDnnwaPqPu4zpep8JXiECNFEaVDnzbmintIzwexom0Io6TMQxjzViBxZiVvlAD2QB3VOaoIRTyuY7DMG2m8HKE8J5I6MsrIVRYzhSz9nCl8AoWHV6Ep8KfAoDGOwXOOwYIZR2cDDDodLi6fRsS9v+KAq0ao7r5IXD5+/jP5u3gCyxvuqI5ED4fwTFD4R/VB2fXfoCDGz7EAxcvw/X558Hjs7+v5kAIgehmo4vaAosKc6ARnwXQk9twDMNYLVZgMWZDTRSGAg2ErlJUVcvg5E25jsQwZrHrv0cxMJog4KkJTV/ZSp3NO4sXjryAdwa9g2Hew+58xdwrgIMvoPDqkFw3DuyH4MJFqO8ZgqsXzyAgKhoTZjwJqatrh9y/JRDJZBj02tsYoFbDkJeH/73zCpyqjRjy4quwcXLiOp7Vq+0kaKN0AAB4OOlB869wnIphGGvGCizGbIwV1SBiAXgSAQQ8HTxiejV9EsNYAQdHHgqS8zplowtKKSr1lQhxDMGm+zYhyLGJtT5nNwHBDwIRse2WqVqjweVvvkDc4T9RbTJiVP970COqNwL7x7TbfVoDvkwGfkAAHpj1DP586xV8/fR0jJ4+G/4TJnIdzaoJ3WWoTi2r+3eZmiClkE0RZBim9ViBxZiNIV8DoasEADDpm5c5TsMw5uPS3REFxzO4jmF2epMey04tg9agxftD32+6uAJursEqbZc8uSf/hu7Pg+A/NB7ZudmIGT8ZykceBV/Itnu4lcLHF7Ffb0PcV5uQv/4jdAuLhNjPt9NOlWxvQnc5VMf+bWRDqBil2koOEzEMY+1YgcWYjb5AC4GrFBe27UTGgRQ8/NVSriMxjFn4DgoEzbvBdQyzUulUeOHICxDwBPjgng+af6LEvqaLoJlQkwmJP+3C+R+3oUxXhWE9ByDEvwcefud9s91HZxXx1ByYHpmKxAtncGbV/+H+p+ajW7/+TZ/I1CN0lcJQrAU1mEAEPAQ59oCk5GmuYzEMY8VYgcWYjaFQA6G7DAU/psBgYN84M52HW7Ab3F6bCkpph2xM2xHiiuLQXdEdL/Z9EQJeC94KIiYDaPtzUFVcjNI9eyCMGYhzRw8iuO9ARM2dD5Gs4xtoWDOeTIaQwcNQdvw4flz5XwwdPQ5Rc/7DdSyrQoQ8CBRiGEqqIHSVQiYWQEPzuI7FMIwVYwUWYzb6fA0kUS7QlphgI6vmOg7DmNX253Zj8CAhPB95kOsobRJXGIf44ng8FvwYYjxasabJOQgwaFt9/6rMGzi1dhUSM1IR5eCGAaNHY/qaT1p9e0xNJ7yYV96Ax77fEPfROvi5ekAxofM2ZGkPAldpXZMmhYQHvvuXAOZzHYthGCvVSA9ehmk5Sin0N9+cdEYp7HwduI7EMGYlsBGiID6L6xhtciDjAJ45+Ay6Sbu1/kYS9wJ7Wj5CUn0jE9rMTHz3zqtQqyow5eW3MHTT1xB5dUw3wq7Ab/QYPLDle2iVgdi37gMYNOqmT2IA1BRY+gINAMBGYAODyYAqg47jVAzDWCs2gsWYhalSDwDgyYV45NtFMBq6zsasTNfg7G2LoovWu/D90I1DWH5mOT659xOEOYW1/oYkNzcabqbcE8dx6pP14BeXYPTilzHroy8gtLFp/f0zdyVwdISTTIqy+Dh8P+txTFr/GaQuXaelfWsJXaSoTq1p3kIIgckoQqFaBW8Fa4PPMEzLsREsxixqR6/yE1Pw67PvgS9gtTvTuXj29gVPKuU6RovpTXoUagoxwH0AvhvzXduKK6Cmi2BV0wUWNRpx4LOPsHPNctg5u2Ls19/B7t57WXHVAYRiG8R+vAl2dvbYNX8WDIWFXEeyeAJXCfSF/059FVU8BL2hc6y3ZBim41n0p2BCyGQAbwMIAdCPUnru5nEhgC8A9EbNY9hCKV3OVU4GdXPXE/YfQUm5G9dxGMbsegzyQ49B1rUmQ61X44UjL8DL1guvD3gdUqEZCkRbd8B/2B0vzj35N+K+2oRwx24IeeoJDJk2E7lFxZC6sb8LHYkvEmPcJ18ia/06aK4lQiAWQWqn4DqWxRK6SmEo1ICaKAiPQGEcBKPBoj8iMQxjwSx9BCsewEQAR287PhmAmFIaAaAPgKcJIX4dG425lb5AA4GrFKrrhZCgffbIYRiu/fbGzyg9dPufI8tUqCnEzD9mwl3ujqX9zLhlgq0bcN+7DQ6X5GRh59MzsWP1MgidXeD62mvwDAqBWG5rvvtmWoTH48Fn0WIUSIT4bvE8lCYlch3JYvFsBCBiAYzlNQ2a5H6foNhwneNUDMNYK4susCil1yilSY1dBEBGCBEAkADQAajo0HBMPYZCLYSuEmjLCWzkeq7jMEy7KKsUIO9EPNcxmuV6+XXc53sf3hzwZsvasDeFUuDL0YCxZp1laUI8Cj/5FLkpSXCL6oXZn2zB8OUfQGDHCitL0aPvAAS6++B/r70AdbZ1N2ppTwJnCQzFNdMEHaRSGGkVx4kYhrFW1jr+vQPABAC5AKQAFlNKSxq7IiFkLoC5AODp6Yn09PSOytil8HMqoNZJ4D2xJ3g2omY9z8XFxe0fjLE41vy6S+2BnJQiSCz470hCWQKSVEmY6D0R99rdi4yMDLPfh3d+AtKOHUDCth+QWVKAwUFRcLh3FGS+AchXVwLq+s1ArPk17yz8Zv8HZcv/i2OLnkXQsvdBRO2/V6G1ve48kR7alGxQQTlScqvwJ70GT0MbOm4yDNNlcV5gEUIOAGjsL9hrlNKf7nBaPwBGAB4AHAAcI4QcoJQ2GM+nlH4O4HMAiI6Opn5+fmbJzfzLpDUg15ANr/AAZB/6C/2efhxCG3GzzmWvR9dkra97aS8tshPjLTb/vvR9WJm0EsuHLIefh1+73IdJo0EJXLD/y8/hI7PHE++vhyKgR5PnWepz1pX4bPwCJV9vBvg8OPn4gPDafxKLNb3uFd4E1ECh8PODsygY9rbuVpWfYRjLwXmBRSkd1YrTpgL4g1KqB+geqj4AACAASURBVFBACPkbQDQANmGaA/oCDQQuUhSm/YMr8R4Y0AFv2gzDhcixQYh6MIjrGI06mnUUH5z9AJ/f+zmCHM2f0aDV4uzaVf/f3n2HV1nf/x9/vs/KhkBIAAFlilpFVMSFuK21WrV11TorVfvVVm1tXaWOqrW22tphHdVarXVUUbFqnbgryhAFGaKC7AwgIfuMz++PHPgxEnKS3Dl3TvJ6XFeu5NzzBXfuJO/zGTeN095g3IS+nHXVpfT+2iGen0c6TyAYpN+k83li8s8ojDuOvuV3fkfqUoJFOdQvaOoM87XckyiOaFIQEWmfTP1L+CvgcGuSB+wPaPSuTzbOIPjla++S21BKMBL2O5JIp5l53+vUffyx3zE2SbgEpbWl7DdwP/557D87pbj67I1Xuf+sk1n88SyG/exK8n75moqrDPaNC37EF4vm88HvbvU7SpcS6pNFfG3TuKtI4SzKEzN8TiQimapLF1hmdpKZLQcOAJ43s5eSq/4C5NM0y+CHwN+dc13nL54eJlrWNIPgukUryCGz+tyLtEUgYMye4yh/5U2/owBQH6vnijev4A8z/0BWMIsBed6OF6me/ykb3nqLLxbMY+LJZ/C9R55ix6OOhgXPw+rMmOxDtlUwZEdO+Ok1zHn3DWo++cTvOF1GqG8OsWSBVdK3hkhOqc+JRCRT+d5FcHucc08DTzezvJqmqdqlC4itqSVv/EBK9tmZ3us0maN0X2ZG794ByheuYojPWdbXr+fH037MgNwBXH/g9Z4eu2bFcqbdfAMrVi/n5NPP5esXXbrlBotegh3GwoDdPT2vpM/AAw7ilAsvI75+PQ21tWRl4EO0vRYoCOMa4yQa4iwpi1FWU8EPx/qdSkQyUZduwZLMEC2rI1SSw8ijJnLAxef6HUekU/XdsZB1K2twzvmaY0nVEvYu2ZtbJ95KJOjdjHBfzPqQv19xMYlEjO/+/q8UnXrathvlFELdes/OKf4oPOYYPquqYOovf04ikfA7ju/MjGCfLOLr6knEI5TX6g1DEWkfFVjSIYnGOPGqRkJ9c3jyZ6/y6X9e8zuSSKfa99u7sPfvLvft/PMq5nHfx/cxtmQsl+1zGQHz5sf4gkf/yYIzzqB3QS9OveUOvnX33+m109DmN84uhHoVWN3BnocfTeXSpcy4/Td+R+kSQn2yia2t58CSYxlhZ/sdR0QylAos6ZBYWR2homyqytbQEC5kxKH7+x1JpFPl982mrqKa6PL0P7D17eVv88NXfsjw3sM9O+bahQt4/OzTmPbUo0SO/yZ9R+5MybAR299p7Bkw/gLPMoh/Qjm5fONHP2X69LepWrTQ7zi+C/ZtKrBcoIp1aJyhiLSPCizpkFhZ0wyCn7/6NjmNFWTl5/kdSaRT1axv5MVHl1M5dWpaz/vh6g+Z/O5k/nj4HzlipyM6fDwXi1FfUcETt99E35IBnP/Avxj+3e9hZq3vHM6FWEOHM0jXMGjioXxzwtE0Tusak7f4KdQ3m/jaegaVVBPp+5bfcUQkQ6nAkg6JrmmaQRCgKHepz2lEOl9BUTaNLkz1vEVpOZ9zjvK6csaWjOWfx/6TsSUdH3X/xX+m8uJp36b6qSmc94d7OOp3dxLp1YZn/iz/EJ7/SYdzSNcx7Mc/pmLMrnz04nN+R/FVqE82sXX1NEbDfLV+nd9xRCRDdelZBKXri5XWkjOmmL2POoW9z/Q7jUjnCwSM3kVZVHxQxrBOPlc8EeeW6bewunY1fzniLwwuGNyh49VtqOLln1/O8rJVHHDo0RT9YFJqLVZb0yQX3Y4FAhT1H8ijd9zKDiUDKNlnX78j+WJjF8FoNERZtSa5EJH2UQuWdEi0tJZw/1yePvcW3vvT3/2OI5IW4741ioE/OLtTZxJsiDdwxZtXsLRqKb85uGMTEDjnqJ0xg7lvvEr24MGc9+cH2PtHl7WvuIKmSS7q9O5+d9Nv+Ej23HUML/3ulh47q2CobzbxdfXsWDCISOW3/Y4jIhlKLVjSbi6WILaunlC/HCrdjgwt0vgr6RlGjetPfPQEEpWVBAsLO+UcSyqXkBvO5TcTf9OhadjLP57Df399PaMbYZ+HHyFwvAd/NOb3h30ndfw40uUcdO31rD/jFNZNnUrRiSf6HSftAtkhLBSg0GXRUFvsdxwRyVBqwZJ2i1XUEeqdRX1tDXVZxYw86mC/I4mkxZJPynnuxleoePBBz49dWlvK/Z/cz859dubmCTe3u7hyiQQf3H8P/7rxagYNH8VejzxKwKuHyWblw0E/9uZY0qUEs7L4+q23w+hRNNTW+B3HF8G+2YSr6wns9Gu/o4hIhlKBJe0WLa0jVJLLitlzyK9fTkFxP78jiaRFr6Icqlxv6j/+2NPjLqlcwtkvno2jY10P6z5bTM3s2aytq+bUK6/jsJtvI5Sd7VHKpDvHQr3GqHRHWaNGMeu1l3j95uv8juKLUJ9sIhsglogSjcX8jiMiGUgFlrRbrLRpivaRh0zgrIfO9zuOSNr0Lsmhtt6onrsA59FYla+qvuK8l87jwjEXMmmP9k0+Eauv5/VrfsajV1xMbNkyjrnkpwzYdz9P8m0jWgeN1Z1zbPHdgSedyuKFn7LqvXf8jpJ2G8dhJRJhKuo2+B1HRDKQCixpt2hp0xTtr99wJ+/c/je/44ikTTAUYOf9BlBw1nm4+voOH68uVscO+Tvwh8P+wEmjTmrXMSqWf8VDk85k5ZeL+davbqOws8fPZOVDgwqs7ipv0GD2/NpY3rrrTr+jpF2wTxbxygZCNQdQ1xj3O46IZCAVWNJuG1uwKhbHqF6lGcWkZzn8rF0Z8qMfYB3sevfc58/xvRe+h2HsWbxnm/ePRxup+Ne/qF1bwd6nnM4ZjzxFvzFtP06bDRrX+ecQX+3/82sZUxsnunq131HSKpAVItEQp1ftdzCX43ccEclAKrCkXVzCESuvI1SSQ50VUbRLx57PI5JpPpuxhhl3vcSqX0xu9zEenPsgf5r9J3478bcEA8E27182Zzb/PPMU5vznGQbuMISxJ5xMIJCmH+vfvgeKd07PucQXkbw8hj/8EO++/ByJeM9pybFIENcQp2TEv1nbuNzvOCKSgVRgSbvE19UTyA0Td3Fqsvoz4siD/I4kklYu4Sit70XtBx+0a//S2lJe/epVHvrGQ4woHNHm/T+aOoXHfnUtw0bvxqEP/YtQvzRPMjP9XlgxK73nlLSL5Oax9I1pfHTXn/yOkjYWCeAa42TlVGKhWr/jiEgGUoEl7RIta2q9CoSCHPP9fvQduqPfkUTSqs+APCqrAySqq4muWZPyftF4lCmfTaE4p5iHv/EwA/IGtOm89atWUvn880RxnHr9rUy88dcEQj480nDZdCj/LP3nlbQyM/Y9/OvMeOtVEj1kRj3LCuIaEywrjzHzq9TvbRGRjVRgSbvE1jSNv1o87R0qFn7hdxyRtCsckEtDTZTep59OfH1lSvvURmu55PVLmPbVNKKJaJtnCvzkb3dz/yWTqJr/Kft+6zv0331Me6J7IysfGjXDWk+wyxlnkWsBlj/1pN9R0iIQCZJojBN0fahpiPodR0QykA9ve0p3EC2tJbJjAZ89+A6N9SHGnet3IpH0CkeCnHPrQZhNSGn7DY0buODlC9i5785M3n8yoUDqP36jDfW8fMMv+GrRfL5x7oUMOf6E9sb2TkSzCPYUgUCAky67inhVFc65dj1CIJNsHIM1JutCdswt9juOiGQgtWBJu8RKawn3z6W+JkJO7+79y1akJV98VMaqmZ+z/Ec/2u52CZcgL5zHObufw/UHXN+m4qp27ifULlpE9tChnPvXBxneFYorgEOvgv0u9DuFpEnegQcyfdnnfPbm635H6XQWCeCicUL5CyhrXOR3HBHJQCqwpM2cc03PwCrOpZ6+9B5e4nckEV+s/Gw9q8sC1Lz7HvHK5rsJfr7+c0597lSqo9UcM/SYlN/9TyQSvHPDZKZc/VOCa9dxxAWXkFPchd5NrymHsoV+p5A06h/K4p277iTh0cO1u6pAVpBEQ4KSkq/Iyl/mdxwRyUAqsKTNEhsasVCAYF6Ywbs0MPpbR/gdScQXhSW5rK9oIHvPMdTO2nZGvU/KPuH8l87nnK+dQ69Ir5SPW1tVyeOTzmThvDkcfeOvyT/kEC9je+PLN+HD+/xOIWn0tTPPpjER54tnpvgdpXOFAhBPULbeMXdVud9pRCQDaQyWtFl0TS2hklyqKyrY9dtHU6QZBKWHKuyfy+ezSsmfcDCxsrIt1kXjUa555xpuOPAGDhmSeoFUN28ey6vWMWDvcRz8/QsJ5eZ5HdsbGoPV4wTDEfY78FDW/uc/8O2T/Y7TacwMiwSp2xCiLLHW7zgikoFUYEmbxUprCZfkMPvBf7P0oyBnPjzK70givhgwvDeHn70rvfrtvcXyOWVz2KPfHjz5rSfJCmaldKx4YyOvXfMz7ONPmHjP/ex8yU86I7J3sgqgUQVWT7PnxT9mw+uvE22oJ5yV7XecTmORIHvnH8qCWj0HS0TaTl0Epc2ipU1TtG9YUkFOYJ3fcUR8E84KUrO+gVg0TsUDf6fqlVd4ctGTXD7tclbXrE65uKpatZJHzj6VNSu+Yvxd95I1bFgnJ/fAgDGw///5nULSLJCVxfyadbx1d/d+8HAgK0hBIEJ1Y53fUUQkA6nAkjaLltYRKsmlvipEdkHc7zgivnrz0YWsW1VLeNAg5v/5Vv72yd/4+zF/Z4f8HVLaP1paynvP/Jshe+3DGQ89Tu+RIzs5sUcKBsDQ1Kaol+5lxMhd+PTtaTRWpfb8t0xk4QB9ClYS7veS31FEJAOpwJI22zhFe26RY+B+O/sdR8RXhSW5rC+tJf/ww4hUbOD+Ha9mp147tbpfvK6O1y67mAXf/z5Hn/9DDrtyMsFwJA2JPbLuS/jLeL9TiA+Kx+5N36wcPrrvbr+jdBrLChKKRqiorfI7iohkII3BkjaJ10RxsQSBggjH3HGV33FEfFdQks3Umf/lWyMOZKf/u4zgmtbHJVXOm8vU664mkZ3FuL/8mUAkgwqrjSIFmuSiB9vv+O9Q8eSTfsfoNBYJUl0Ji8s1yYWItJ0KLGmTWFnT+KtVn87nld98yDkPneN3JBHfNMQbeNY9TGOvKIPyTyH3jKYJXxJ1dQRycrbZ3jlHvLqap+/7EzvstjtH/PJXBEMZ+mM4K1+TXPRgI045jZKhw6itqiS3V2+/43guEAnQP9KPnIb9/Y4iIhlIXQSlTTZO0b502nSM7v2wSZHWTH5nMvG+Nfz6pOvIDecCUDP9A5aeeRaJhoYttq3//HOmf/8cyv5wJ6fe8BuOvvHXmVtcAYSyYfQ3oJs/dFaaZ8Eg85Ys5q2//MHvKJ3CIkGKw70I1qgbrIi0nQosaZNYcgbB9Z+vJocKv+OI+KKyoZJ4Is6P9/4xN+5zM49c8wHOOQByx+9L1siRfH7U0VTcfz/OOZZdew1PX3oRC4IJCn90Mbm9C33+F3jADE55EAL6NdJTjdx1DxbNnE60psbvKJ6zrCAWryUw+A6/o4hIBtJvRmmTaGktof65uIQjv2/M7zgiabe6ZjVnv3g205ZNY3DBYHLzm6Zir6+OAk0PKd3hN7cy5N57sHCY+uoNvFZVSu+jj+Z7d91PTmEfP+N766lJsGGN3ynEJ8V7jqV3KML8fz3sdxTPWSRILmFqE3ojUUTaLoP7p4gfYqV1hItzOPbOa/yOIpJ2y6qW8YNXfsCpo0/lyJ2OBJoKqsL+uaxfU0tOwf+frCJ7l11g8GCycnM5+NwLGL73vpiZX9E7x6qPoW4tFPT3O4n4ZL9DjiI6cxb8wO8k3gpEgtAQo7K++7XOiUjnUwuWpCzRECNRGyXYJ5sp595K+RdL/I4kklZTv5jK93f/Pt/f/ftbLB+5TwkW/P/Fk0skeP+px3jsup/jcIzYZ3z3K66gaaILzSTYo40661yG/N/FNNbV+h3FUxYJEo4bifpBNMbUW0NE2kYtWJKyWGkdoX45VCxbxprwWHrvMNDvSCJp8WnFp8QSMS4ee3Gz68ceueOmrxtqa/nvXXdQs34d37n2RgKBYLpipl9OX4jV+Z1CfBTMz+Od/06l5INixk+6yO84nrGsAEQdDcsuJmB6L1pE2kY/NSRl0dKmGQS/ePVtchtLCWdn+R1JpNPNLp3ND1/9IeV15S1us3Lxet598jMA6qoq6V3Sn1Ovu5WCvv3SFdMfZz4Jwyb6nUJ8NnyHHZn78gt+x/BUIBIk0RBn6Oj/EEuoBUtE2qZLF1hmdoqZzTOzhJmN22x5xMz+bmafmNkcMzvUx5g9xqYZBBetIEcDf6UHmL5qOpe+fim3TLiFw3c8vMXtgsEAn334Pi/f+ycKBwzk0LN/QCgcTmNSn3z+Oqye63cK8dnO3zmVmkSMstmz/I7iGYsEcY1x1tr/CAa6YfdeEelUXbrAAuYC3wbe2mr5DwCcc3sARwG3m6kNv7NtbME64uafcMxt3/M7jkin65vdl98f9nsOGnRQi9u4RIIF7z7D2mUvMObwr6cxXRew4HlY+q7fKcRnoZwcDhg2mrq3tv5Vnbk2Flhx52iMx/2OIyIZpkuPwXLOzQeaGxy+G/BacptSM1sPjAM+SGvAHiZWWku4fy5v3vQXRh47kV4DS/yOJNIpnv/ieeaWz+XK8Ve2uu1nH/6PlQs/oWjH8+g7eHga0nUhkXxo2OB3CukC9vzplWyoXO93DM9YJECiIU4iBInkM+5ERFKVqa0+c4ATzCxkZsOAfYAhPmfq1lw0QayygVBRNkuXlLB+2Qq/I4l0in8v+jd3zLyD74z6zna3qyor5au5cxg1/kBOve4Wzrn1aCLZXfo9K+9l5UOjZhEUCPXvzxO33UjpJ3P8juKJQCSIiyZo/OIXhKwHdPcVEU/5/teAmb0KDGhm1bXOuWdb2O0BYFdgBrAUeA9odhSqmV0AXAAwaNAglixZ0tHIPVNFI8H8IAs++Zj6SF+ydu74/2VFhcZx9URd+bp/sv4T7l54N78a8ytClSGWVC5pdrvyLz9n+iP3s8sRx5DI7w3AqgXV5BaG6T2g50z+Eux7EJaIE2vlZ0FXvubinX4uwIwH/sZul/4UyPDrXhUjWNtAIOdLvlz6Jbk9YUyliHjG9wLLOXdkO/aJAZdvfG1m7wGftbDtvcC9AOPGjXNDhw5tX9AerrayjLpBMcrmLyS7sZ7Re3gz1kTXo2fqatfdOceqmlUct9NxTNhlAn2y+7S47WfT3+ODfz3ANy/5KUPH7rNp+fIPPyMeiDB0/53SEblraOjX1EWwV+uPbOhq11y8V3/UMbz39BMcu9m1ztTrHt/QyBpXTv5OjzNs2CXkhHL8jiQiGSQjuwiaWa6Z5SW/PgqIOec+9TlWt7Zxgosh++3F1yaoP7p0H845fjfjd1z99tUALRZXiXicxvo6SoaN4PQbfrNFcQXQq18OG8rrOz1vl7L4VXjxZ36nkC5i5InfYdDaKhpXr/Y7SodtmuQiAdF4wu84IpJhunSBZWYnmdly4ADgeTN7KbmqBJhlZvOBK4Gz/MrYU2yc4KLyqxUMO/xAv+OIeCLhEtz0/k3MLp3NHw//Y3MT6gBQX13NlFuvZ8ZzT9O7pD99dxi8zTa9+mVTVd7DHrqblQ8NGoMlTUI5ORx8+x+IhjL/4doWDuCiCaKxhAosEWmzLl1gOeeeds4Nds5lOef6O+e+nly+xDk32jm3q3PuSOfcUr+zdnfR0lpCxbnMuv8DZt/zpN9xRDxRVlvG2vq13HvUvfTO6t3sNmtXLudfv/gpRYN3ZP9vn9bisQbt3IdDz9yls6J2TRFNciFbWllXw3O/udHvGB1mAYNggFDZyYQDvo+mEJEM06ULLOkaXNwRq6gnVJxDXaCIoq8N9TuSSIfEEjEeW/AYRTlF/P6w35MfyW9x22XzPmbfb32Hw875AYFgy+/Mh7OCVJbVkUj0oC60vYfAbif6nUK6kCG7fI01X37OhmVf+R2lwwKRAFkb9iTg/3B1EckwKrCkVbG1dQR7RWiM1lMbKWHkUQf7HUmk3aKJKFe9fRWvf/U6sUSzk4/inGPm88/y2QfvsedRx7LH4UendOxXH5hHzfoGL+N2bb0HwYGX+J1CupCswkL65+SzcMq//Y7SYRYO0mfYncTpYWMrRaTDVGBJq2KldYSLc6hbX8ng0Cx6D+zvdySRdokmolzxxhXUxer40xF/IjuUvc028ViMV+79E/PeeIX+w0a26fi9+uX0rHFY9ZVwz0S/U0gXs8e+BxL8/Au/Y3SYRQLkkEXzIzNFRFqmdm9pVbS0llD/XAr6l3D8X6/xO45Iu4UsxKFDDuW44ccRDjb/XJs3/3k/NevXcfqvfksku21TMzcVWPUM2tmLtBkglA1r5oFz0MIEIdLzjP7eWdR98TnlicyeHKJpJsEgNY1Rem37XoyISIvUgiWtipXWEi7O5YVLbmXqBbf4HUekzepidVw+7XK+qPyCk0ad1GxxtX7NauqqN3DAyWdwws9+0ebiCmC3CTtQslOBF5EzQygLMIj1oG6R0qpQURHPPPEQ6xYt8DtKh1g4QHbtaAKa5EJE2kgFlrQqWtbUglVfm0WkIPOn35WepTZayyWvXUJ2KJudejX/EOAVCz7lsV/+jOXz55KTX0Ag0L7v8x1GFVI4ILcjcTPPwDEQ0xgV2VKf2gZW/fd5v2N0iEWC5Kw7kuygHjIsIm2jAku2yyVcUwtWSS71FFE4cge/I4m0yeR3J7ND/g7cdNBNhJp5J3rBu2/y7O038/UfXsaofQ/o0LlWfV7JM7fP7tAxMs4PXoecQr9TSBczfP+DKF25zO8YHRIIBxg46H9EEz1oXKWIeEIFlmxXvKoBywoRyA4RcVUMO7xjf4CKpMuGxg1EE1F+vu/PueHAGwhu1SrlnNv0+ZTJNzNs7D4dPmdB36yeNckFwLt/hMrlfqeQLmbE8SfSa30Vsaoqv6O0m0WCJBKlhEM96NELIuIJFViyXbE1tYT7N3V5Ou3hy+g/um2zqon4obKhkvNfOp8Xv3yR/nn9CdiWP+pi0Sgv/uUOFs+Yzq4TDqV4x6GenDevdxYNtTGijXFPjpcR5j8H6zO7pUK8l1VYyJ6nnQ3xzL0XLBKAxhBV9Y1+RxGRDKMCS7YrWlpHqDiH9//6oCa4kIywtn4t5790PvsP3J/jhx+/zfraqkqevOlaYo0N7LTHnp6e2wLGrgcOJFqfuX9UtllWPjTW+J1CuqBVRb1447GH/I7RbhYOkhXPIhbP7NkQRST9VGDJdsVKm1qwKj5ZQbRRE1xI1/fc588xcfBELt/ncqyZqcPf+Md9DBq9G8dfdhXhLO/nXj7kjNHk9op4ftwuK5IPjRv8TiFdUL4zPnvtZb9jtJtFAuRUfp3eWX38jiIiGUZzj8p2RUtryRlbTH1NiJze6ocuXdfa+rUs27CMs3c7G2Cb4mr5p3PpO2gwR190KaFw88/A8sLcN5cTyQmx8/gBnXaOLuXY3zW1YolspfeeexF7xFH+8Rz6jfG2tTgdLBKkIHsNjfF6oAe9aSIiHaYWLGmRc45osgWrMZFH7+HFfkcSadba+rVMenkS7618DzPbpria9+ZrPPeHW6ksXdOpxRVAtCFB6ZIe1KLTWA01ZX6nkC4oEAgwsrAf697/n99R2sXCAbKzlhEOR/2OIiIZRi1Y0qJEdRQzCOSFOfXvF/kdR6RZG4urQwcfykVjtv0+fX/K48yd9jKnXvdrigYN6fQ8vfpls+rz9Z1+ni5j9sMQyYOJP/M7iXRBB597IbjM7P0QiAQJxkLUNsYp0qOwRKQN1IIlLYqW1hIqzmXN/EW89NPfEgypHpeuZ0PjBr457Jv8aK8fbdFy5RJNA9NLhg7nu7/6XVqKK4BexTk01sfScq4uIZwHjbV+p5AuKrz3WP771kskEpk3UYSFA4TjYWobe9D9LCKeUIElLdo4wcXil96iYp26B0rXsq5+HXfMvIPB+YM5f4/ztyiuGutqmfKbG/h85gcM33tf8grTN0i9eEgBJ16+d9rO57tIHkRVYEnzIjm5rJkzm4qP5/gdpc0sEiS7fjh5YY0xFJG2UYElLdrYglW1pIwcW+d3HJFN1tWv4/yXzydkoW2ecVW9toLHrr+KXkXFnjw8uD3mvLaM+poeMm5j2ETY5Ti/U0gXZWb0y85j6Zuv+x2lzSwcIDuRRdD0p5KItI36fEmLYmV15OxaRH1lgOw8dZGQrqE2Wsv5L5/PoYMP3aZbIMDbj/6D0ftPYPyJpzQ7TXs6LJy+mv7DezFgWG9fzp9WA3b3O4F0cYNH7kzlZ4v8jtFmFgmSm1WKC9QAasUSkdSpwJIWRdfUEirJYc/zDyGrQL9cxH/OOXJCOfxkn59w0A4HbVFALZ8/l8L+A/n6RZcSCPr7zLZeRdlsqKjvGQXWwv82TXRx+iN+J5Euap9Tv0fVCy/6HaPNLBwgKxHGZegkHSLiH7V7S7MSdTFcQ5xg7yxKP15I/1129juS9HDr6tdx9otns6J6BRMGTdiiuFrw7ptMvePXVJau8b24AsgvymbD2nq/Y6RHKAINVX6nkC4se5dd+LS4Fw21NX5HaZNAJEg4HmZ9XaPfUUQkw6gFS5oVLW1qvSpdtJg5H5UwPqBaXPyzrn4dk16exMTBExmUP2iLdTOff4aZzz/LKZNvpnjHof4E3MpeR+5IIORP98S00yyCkoLlb07jy0gOu5x2ht9RUmaRIFnxPLICmqNdRNpGfzVLs2KltYSLc/ny9ffIbSglGOnch7OKbM+171zLxMET+fFeP97UcrVxGvaiQUP47q9+22WKK4BwVpDqdQ1+x0iPvH4wcIzfKaSLKy7ozbIP3/c7RptYpGmSi7yIusiLSNuowJJmRctqCfXPZf3iVeRQ4Xcc6aE2NG4gmohyxrzZTgAAHGdJREFU04Sbtiiuog31PHv7LSz839sMHbsPBUX9fE66pcqyOl57cL7fMdKjaAQc93u/U0gXN3js3qxZsczvGG1i4QBZOOrjmkVXRNpGBZY0K7amqQWreOwIBu3b1+840gNtaNzABS9fwH8+/w99s/tuKq7qNlTx719dS1ZODiP33d/nlM3L75tF9boeMgarvhKe/qHfKaSLG3XcCexfT0ZNGGHBAAZkaTCFiLSRCixpVrSsjlD/XHY9/mgO/NF5fseRHqY2Wsv/vfp/7N5vd04ceeIW69779yMM3nV3jrn4JwRDXbPranZemHgsQWNdD3i8gQVg/lS/U0gXFynqB9dcSVXZGr+jtInDUVpV53cMEckwKrBkG4nGOPGqRkJ9snnssv+y4KVpfkeSHub5L59nROEIrt7v6k0tVxXLl1FVVsqhZ/+Aid87z7dnXKXCzJhwyii/Y6RHOBeitZBBLRPij/lTp7Dgycf9jtEmDnCu6/6sEZGuSQWWbCNWVkeoKJv1q1fREO7N8AldsxuWdD8N8QbmVczj5FEn88sDfknAmn5ErVw0nyduvJo1Xy4mGMqM/jpfO3gQ4Sz/p4zvdIEgBMIQ6yFdIqXd+mblsOqj2X7HaKMA/bJL/A4hIhlGBZZsI1ZaS7h/Lp+/+jY5jeVE8jRFrXS+aDzK5dMu59H5j2Jmm4qrL2fP4Jnf3sQxP7yMUeMP9Dll6t5+fBHz3l7hd4z0uGYFhPVzQrZv4Ni9qVifaZMmOerjG/wOISIZRgWWbCNaWkuoOBeAopyvfE4jPUE0EeWKN68gEoxw3YHXbbHOzDjp579k2F7jfErXPjkFETas7SFTtS94vmmyC5HtGHzwIeyxdA0uGvU7SsqcJcjO0oOGRaRtMqOvjaRVrLSWnD2LGXfUaXCO32mkJ6ioq6BPdh+u3e9awoGmiSs+fG4KgUCQfb55gs/p2qegKJulczPt3fp2mnYzFI+G7N5+J5EuLNK7kGH/fJi6ulpyw5nxveKAdTVRhvTxO4mIZBK1YMk2oqW1hEtyefq8W/jfXf/wO450Y/FEnH/N/xd9s/ty/YHXEw6GcYkEb/7zAea98Sqj9sucLoFb6zswj15F2X7HSI9wLjTW+p1CMsD/HnuYhf951u8YbeCobewBs4GKiKdUYMkWXCxBbF0DoX45rE/sRE7fzHiXUTJPwiW44X838OpXrxJ38U3LP3rlBVYunM9pN/yGXv2KfUzYMcU7FrD/iSP8jpEekbymmQRFWlEYjbPinbf8jpE6F6Iokrk/h0TEHyqwZAuxijpChVnU12ygPlLEyKMO8TuSdEPOOW6ZfgtLqpbw58P/TE4oh2h9PVVlpex+2FGc/ItfkZNf4HfMDnHO8fxdHxOPJ/yO0vkOnwzFu/idQjLAgD3GUl5R5neMlDkcCXrAPSwinlKBJVuIltYRKs5h2Yw55DesIL9IHc+lc+zcZ2fuOuIucsO51NdU8+Qtv2TOKy8QjmQRzsr8rnVmRvmyDdSs7wETXfT/WlMrlkgrBh88kSGrK3DxeOsbdwWBOKFwld8pRCTDqMCSLWycon3nIyZy1kPn+x1HuqFHlzzK7NLZnDr6VPIj+dRWrueJG6+h//ARTDj9bL/jeaqgbzbVa3vA86FevBLmPuV3CskAuQMGst9ttxOPZsrMfI7GmFqwRKRtVGDJFjZO0f76DXfy7p33+x1Hupm/ffI33i57m2G9h21atnblckbtewCHnXMBFuheP5IKirKprcqcKanbLZIL0Tq/U0iGmPbmyyx4/RW/Y6Qk4RzlNT2gFVpEPNWl/5oxs9+a2QIz+9jMnjazws3WXW1mi81soZl93c+c3cnGFqzyxTFqlq/1O450I08teoopn03hpjE30Se7D+tWreDD56YweNfdOeDk72Jmfkf03JHn7cbIfUr8jtH5InkQrfE7hWSIvLWVfPXfF/yOkRIjREGGTCkvIl1Hly6wgFeA3Z1zY4BFwNUAZrYbcDrwNeAY4C4zC/qWsptwCUesvI5QcS51gX702XWI35Gkm3DOMX7geO47+j76ZvWlbOmXPHHD1WTl5vodrVOtX1PLko/L/Y7R+YbsB8W7+p1CMsSA3fegomyN3zFS4giQH+rldwwRyTBdusByzr3snNv4AIr3gcHJr08AHnPONTjnvgQWA+P9yNidxNfVE8gNE3cxaiMljDzyIL8jSTfw0pKXuP5/1zOkYAiD8gdRtWY1T948mUPOnsSYI47xO16nqiyr45M3lvsdo/Pt8k0Y3b2vpXhn0P4HUrC+Cuec31FaZ1Hq3Gq/U4hIhrGM+AEHmNlzwOPOuX+a2Z+B951z/0yuux940Tn3ZDP7XQBckHw5GliYrszSqn5AD3h7X7ai697z6Jr3TLruPdNo51xmP2dDpINCfgcws1eBAc2sutY592xym2uBGPDIxt2a2b7ZStE5dy9wrwdRxWNmNsM5N87vHJJeuu49j655z6Tr3jOZ2Qy/M4j4zfcCyzl35PbWm9k5wHHAEe7/N7ctBzYfIDQYWNk5CUVERERERFLTpcdgmdkxwJXAt5xztZutmgqcbmZZZjYMGAV84EdGERERERGRjXxvwWrFn4Es4JXkFM7vO+cucs7NM7MngE9p6jp4sXMuQx4LL5tR182eSde959E175l03XsmXXfp8TJmkgsREREREZGurkt3ERQREREREckkKrBEREREREQ8ogJL0sLMHjCzUjObu9myvmb2ipl9lvzcx8+M4r0Wrvv1ZrbCzD5KfhzrZ0bxlpkNMbNpZjbfzOaZ2aXJ5brfu6ntXHPd692YmWWb2QdmNid53W9ILte9Lj2exmBJWpjZRKAaeMg5t3ty2W3AWufcrWZ2FdDHOXelnznFWy1c9+uBaufc7/zMJp3DzAYCA51zs8ysAJgJnAici+73bmk71/xUdK93W9Y0+1iec67azMLAO8ClwLfRvS49nFqwJC2cc28Ba7dafALwj+TX/6DpF7J0Iy1cd+nGnHOrnHOzkl9vAOYDg9D93m1t55pLN+aaVCdfhpMfDt3rIiqwxFf9nXOroOkXNFDicx5Jn0vM7ONkF0J1H+mmzGwosBcwHd3vPcJW1xx0r3drZhY0s4+AUuAV55zudRFUYIlI+v0VGAGMBVYBt/sbRzqDmeUDTwGXOeeq/M4jna+Za657vZtzzsWdc2OBwcB4M9vd70wiXYEKLPHTmmTf/Y19+Et9ziNp4Jxbk/ylnADuA8b7nUm8lRyP8RTwiHNuSnKx7vdurLlrrnu953DOrQfeAI5B97qICizx1VTgnOTX5wDP+phF0mTjL96kk4C5LW0rmSc58P1+YL5z7o7NVul+76Zauua617s3Mys2s8Lk1znAkcACdK+LaBZBSQ8zexQ4FOgHrAGuA54BngB2BL4CTnHOaUKEbqSF634oTV2GHLAEuHBjf33JfGY2AXgb+ARIJBdfQ9OYHN3v3dB2rvl30b3ebZnZGJomsQjS9Ib9E865G82sCN3r0sOpwBIREREREfGIugiKiIiIiIh4RAWWiIiIiIiIR1RgiYiIiIiIeEQFloiIiIiIiEdUYImIiIiIiHhEBZaIiIiIiIhHVGCJiIiIiIh4RAWWiEgbmVl1B/bNMbM3zSzYwvqImb1lZqG2HmvrXGZ2rpn9eTv7p3wuERERSY0KLBGR9Po+MMU5F29upXOuEXgNOK2jx2pNG88lIiIiKVCBJSLSTmb2EzObm/y4bLPlk81sgZm9YmaPmtkVm+32PeDZ5HaFZrZ6s/1mmllv4Jnkdq3ZdKwUsl5kZh8lP740s2nJVameS0RERFKgbiEiIu1gZvsA5wH7AQZMN7M3gSDwHWAvmn7GzgJmJveJAMOdc0sAnHPrzSzPzMLOuSgwBxgDvAfs28r5tzhWUo6ZfbTZ677A1OS57gbuNrMw8DpwR3Kbua2dS0RERFKnAktEpH0mAE8752oAzGwKcDBNPQOedc7VJZc/t9k+/YD1Wx1nDTAAWAbsAqxxzsXNrNHMCpxzG1o4f3PHqnPOjd34wszOBcZttc2dwOvOuecAUjyXiIiIpEgFlohI+1gblwPUAdlbLVsJ7GBmBwLlzrlFyeVZQH0bj7VdyYJrJ+CSrVa1di4RERFJkcZgiYi0z1vAiWaWa2Z5wEnA28A7wPFmlm1m+cA3N+7gnFsHBM1s88JoJXAicBVNk1ZgZkVAWbLbYLNaOFaLkl0arwDOdM4lNlve6rlEREQkdWrBEhFpB+fcLDN7EPgguehvzrnZAGY2labxVEuBGUDlZru+TFP3wleTr1cAJwOHO+fKk8sOA15IIcbWx9qeS2gakzXNzABmOOcmteFcIiIikgJzzvmdQUSkWzGzfOdctZnl0tTSdYFzblZy3V7AT5xzZ21n/ynA1c65ha2cp9VjpZA1pXOJiIhIatSCJSLivXvNbDeaxkj9Y2NxBeCcm21m08ws2Nzzq5KzAz6TSsHT2rFa05ZziYiISGrUgiUiIiIiIuIRTXIhIiIiIiLiERVYIiIiIiIiHlGBJSIiIiIi4hEVWCIiIiIiIh5RgSUiIiIiIuIRFVgiIiIiIiIeUYElIiIiIiLiERVYIiIiIiIiHlGBJSIiIiIi4hEVWCIiIiIiIh5RgSUiIiIiIuKRkN8B0qlfv35u6NChfscQERER6ZZmzpxZ7pwr9juHiJ98LbDM7BjgTiAI/M05d+tW6y25/ligFjjXOTcruW4JsAGIAzHn3LjWzjd06FBmzJjh6b9BRERERJqY2VK/M4j4zbcCy8yCwF+Ao4DlwIdmNtU59+lmm30DGJX82A/4a/LzRoc558rTFFlERERERGS7/ByDNR5Y7Jz7wjnXCDwGnLDVNicAD7km7wOFZjYw3UFFRERERERS4WeBNQhYttnr5cllqW7jgJfNbKaZXdDSSczsAjObYWYzysrKPIgtIiIiIiLSPD8LLGtmmWvDNgc55/amqRvhxWY2sbmTOOfudc6Nc86NKy7WmEsREREREek8fhZYy4Ehm70eDKxMdRvn3MbPpcDTNHU5FBERERER8Y2fBdaHwCgzG2ZmEeB0YOpW20wFzrYm+wOVzrlVZpZnZgUAZpYHHA3MTWd4ERERERGRrfk2i6BzLmZmlwAv0TRN+wPOuXlmdlFy/d3ACzRN0b6Ypmnaz0vu3h94umkWd0LAv5xz/03zP0FERERERGQL5tzWw566r3Hjxjk9B0tERESkc5jZzFSeTSrSnfnZRVBERERERKRbUYElIiIiIiLiERVYIiIiIiIiHlGBJSIiIiIi4hEVWCIiIiIiIh5RgSUiIiIiIuIRFVgiIiIiIiIeUYElIiIiIiLiERVYIiIiIiIiHlGBJSIiIiIi4hEVWCIiIiIiIh5RgSUiIiIiIuIRFVgiIiIiIiIeUYElIiIiIiLiERVYIiIiIiIiHlGBJSIiIiIi4hEVWCIiIiIiIh5RgSUiIiIiIuIRFVgiIiIiIiIeUYElIiIiIiLiERVYIiIiIiIiHlGBJSIiIiIi4hEVWCIiIiIiIh5RgSUiIiIiIuIRFVgiIiIiIiIeUYElIiIiIiLiERVYIiIiIiIiHlGBJSIiIiIi4hEVWCIiIiIiIh5RgSUiIiIiIuKRlAosM7vNzHqZWdjMXjOzcjM7s7PDiYiIiIiIZJJUW7COds5VAccBy4GdgZ919ORmdoyZLTSzxWZ2VTPrzcz+mFz/sZntneq+IiIiIiIi6ZZqgRVOfj4WeNQ5t7ajJzazIPAX4BvAbsB3zWy3rTb7BjAq+XEB8Nc27CsiIiIiIpJWqRZYz5nZAmAc8JqZFQP1HTz3eGCxc+4L51wj8BhwwlbbnAA85Jq8DxSa2cAU993GRx99xIsvvgjA5MmTKSws3OZj+vTpAFx66aXNrl+4cCEAkyZNanZ9eXk5AKeffnqz6zc67rjjtllXXFy8af0RRxyxzfpBgwZtWn/AAQdss37YsGGb1u+1117brN911103rd911123Wb/XXnttWj9s2LBt1h9wwAGb1g8aNGib9UccccSm9cXFxdusP+644zatb+7/5vTTTwegvLy82fWTJk0CYOHChc2uv/TSSwGYPn16s+snT54MwIsvvtjs+ttuuw2Axx9/vNn199xzDwD33HNPs+sff/xxAG677bZm1+t7T997+t7T956+9/S9192/90QkxQLLOXcVcAAwzjkXBWpJoaBpxSBg2WavlyeXpbJNKvsCYGYXmNkMM5uRSCQ6GFlERERERKRl5pxrfSOzXOAnwI7OuQvMbBQw2jn3n3af2OwU4OvOuUnJ12cB451zP9psm+eBXzvn3km+fg34OTC8tX2bM27cODdjxoz2RhYRERGR7TCzmc65cX7nEPFTql0E/w40AgcmXy8HburguZcDQzZ7PRhYmeI2qewrIiIiIiKSVqkWWCOcc7cBUQDnXB1gHTz3h8AoMxtmZhHgdGDqVttMBc5Ozia4P1DpnFuV4r4iIiIiIiJpFUpxu0YzywEcgJmNABo6cmLnXMzMLgFeAoLAA865eWZ2UXL93cALNM1cuJimcV/nbW/fjuQRERERERHpqFbHYJmZAWcB59M0JfrLwEHAuc65Nzo7oJc0BktERESk82gMlkgKLVjOOWdmlwJHA/vT1DXwUudceWeHExERERERySSpdhF8HxjunHu+M8OIiIiIiIhkslQLrMOAC81sKVBDUyuWc86N6bRkIiIiIiIiGSbVAusbnZpCRERERESkG0ipwHLOLe3sICIiIiIiIpku1edgiYiIiIiISCtUYImIiIiIiHhEBZaIiIiIiIhHVGCJiIiIiIh4RAWWiIiIiIiIR1RgiYiIiIiIeEQFloiIiIiIiEdUYImIiIiIiHhEBZaIiIiIiIhHVGCJiIiIiIh4RAWWiIiIiIiIR1RgiYiIiIiIeEQFloiIiIiIiEdUYImIiIiIiHhEBZaIiIiIiIhHVGCJiIiIiIh4RAWWiIiIiIiIR1RgiYiIiIiIeEQFloiIiIiIiEdUYImIiIiIiHhEBZaIiIiIiIhHVGCJiIiIiIh4RAWWiIiIiIiIR1RgiYiIiIiIeEQFloiIiIiIiEdUYImIiIiIiHhEBZaIiIiIiIhHfCmwzKyvmb1iZp8lP/dpYbtjzGyhmS02s6s2W369ma0ws4+SH8emL72IiIiIiEjz/GrBugp4zTk3Cngt+XoLZhYE/gJ8A9gN+K6Z7bbZJr93zo1NfryQjtAiIiIiIiLb41eBdQLwj+TX/wBObGab8cBi59wXzrlG4LHkfiIiIiIiIl2SXwVWf+fcKoDk55JmthkELNvs9fLkso0uMbOPzeyBlroYApjZBWY2w8xmlJWVeZFdRERERESkWZ1WYJnZq2Y2t5mPVFuhrJllLvn5r8AIYCywCri9pYM45+51zo1zzo0rLi5u079BRERERESkLUKddWDn3JEtrTOzNWY20Dm3yswGAqXNbLYcGLLZ68HAyuSx12x2rPuA/3iTWkREREREpP386iI4FTgn+fU5wLPNbPMhMMrMhplZBDg9uR/Jomyjk4C5nZhVREREREQkJeaca30rr09qVgQ8AewIfAWc4pxba2Y7AH9zzh2b3O5Y4A9AEHjAOXdzcvnDNHUPdMAS4MKNY7paOW8ZsNT7f5G0Uz+g3O8Qkna67j2PrnnPpOveM412zhX4HULET74UWCIAZjbDOTfO7xySXrruPY+uec+k694z6bqL+NdFUEREREREpNtRgSUiIiIiIuIRFVjip3v9DiC+0HXveXTNeyZd955J1116PI3BEhERERER8YhasERERERERDyiAktERERERMQjKrAkLczsATMrNbO5my3ra2avmNlnyc99/Mwo3mvhul9vZivM7KPkx7F+ZhRvmdkQM5tmZvPNbJ6ZXZpcrvu9m9rONde93o2ZWbaZfWBmc5LX/Ybkct3r0uNpDJakhZlNBKqBh5xzuyeX3Qasdc7damZXAX2cc1f6mVO81cJ1vx6ods79zs9s0jnMbCAw0Dk3y8wKgJnAicC56H7vlrZzzU9F93q3ZWYG5Dnnqs0sDLwDXAp8G93r0sOpBUvSwjn3FrB2q8UnAP9Ifv0Pmn4hSzfSwnWXbsw5t8o5Nyv59QZgPjAI3e/d1nauuXRjrkl18mU4+eHQvS6iAkt81d85twqafkEDJT7nkfS5xMw+TnYhVPeRbsrMhgJ7AdPR/d4jbHXNQfd6t2ZmQTP7CCgFXnHO6V4XQQWWiKTfX4ERwFhgFXC7v3GkM5hZPvAUcJlzrsrvPNL5mrnmute7Oedc3Dk3FhgMjDez3f3OJNIVqMASP61J9t3f2Ie/1Oc8kgbOuTXJX8oJ4D5gvN+ZxFvJ8RhPAY8456YkF+t+78aau+a613sO59x64A3gGHSvi6jAEl9NBc5Jfn0O8KyPWSRNNv7iTToJmNvStpJ5kgPf7wfmO+fu2GyV7vduqqVrrnu9ezOzYjMrTH6dAxwJLED3uohmEZT0MLNHgUOBfsAa4DrgGeAJYEfgK+AU55wmROhGWrjuh9LUZcgBS4ALN/bXl8xnZhOAt4FPgERy8TU0jcnR/d4Nbeeafxfd692WmY2haRKLIE1v2D/hnLvRzIrQvS49nAosERERERERj6iLoIiIiIiIiEdUYImIiIiIiHhEBZaIiIiIiIhHVGCJiIiIiIh4RAWWiIiIiIiIR1RgiYiIiIiIeEQFloiIiIiIiEdUYImItJGZVXdg3xwze9PMgi2sj5jZW2YWauuxts5lZuea2Z+3s3/K5xIREZHUqMASEUmv7wNTnHPx5lY65xqB14DTOnqs1rTxXCIiIpICFVgiIu1kZj8xs7nJj8s2Wz7ZzBaY2Stm9qiZXbHZbt8Dnk1uV2hmqzfbb6aZ9QaeSW7Xmk3HSiHrRWb2UfLjSzObllyV6rlEREQkBeoWIiLSDma2D3AesB9gwHQzexMIAt8B9qLpZ+wsYGZynwgw3Dm3BMA5t97M8sws7JyLAnOAMcB7wL6tnH+LYyXlmNlHm73uC0xNnutu4G4zCwOvA3ckt5nb2rlEREQkdSqwRETaZwLwtHOuBsDMpgAH09Qz4FnnXF1y+XOb7dMPWL/VcdYAA4BlwC7AGudc3MwazazAObehhfM3d6w659zYjS/M7Fxg3Fbb3Am87px7DiDFc4mIiEiKVGCJiLSPtXE5QB2QvdWylcAOZnYgUO6cW5RcngXUt/FY25UsuHYCLtlqVWvnEhERkRRpDJaISPu8BZxoZrlmlgecBLwNvAMcb2bZZpYPfHPjDs65dUDQzDYvjFYCJwJX0TRpBWZWBJQluw02q4VjtSjZpfEK4EznXGKz5a2eS0RERFKnFiwRkXZwzs0ysweBD5KL/uacmw1gZlNpGk+1FJgBVG6268s0dS98Nfl6BXAycLhzrjy57DDghRRibH2s7bmEpjFZ08wMYIZzblIbziUiIiIpMOec3xlERLoVM8t3zlWbWS5NLV0XOOdmJdftBfzEOXfWdvafAlztnFvYynlaPVYKWVM6l4iIiKRGLVgiIt6718x2o2mM1D82FlcAzrnZZjbNzILNPb8qOTvgM6kUPK0dqzVtOZeIiIikRi1YIiIiIiIiHtEkFyIiIiIiIh5RgSUiIiIiIuIRFVgiIiIiIiIeUYElIiIiIiLiERVYIiIiIiIiHlGBJSIiIiIi4hEVWCIiIiIiIh75f4a8/S8itOgwAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "composite_model.eval()\n",
    "p=composite_model.plot_model()\n",
    "p.rescale(y_max=-12)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}