A simple yet extensible NPZD slab model implemented with xarray-simlab

npzd_xsimlab.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# A simple yet extensible NPZD Slab model implemented with xarray-simlab\n",
    "\n",
    "Benoit Bovy, January 2020."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import xsimlab as xs\n",
    "from scipy.integrate import odeint"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create process classes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Common"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "@xs.process\n",
    "class Time:\n",
    "    days = xs.variable(dims='time', description='time in days')\n",
    "    \n",
    "    # for indexing xarray IO objects\n",
    "    time = xs.index(dims='time', description='time in days')\n",
    "    \n",
    "    def initialize(self):\n",
    "        self.time = self.days"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Forcing environment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "@xs.process\n",
    "class BasicPeriodicForcing:\n",
    "    time = xs.foreign(Time, 'days')\n",
    "    \n",
    "    mld = xs.variable(\n",
    "        dims='time',\n",
    "        intent='out',\n",
    "        description='mixed layer depth'\n",
    "    )\n",
    "    \n",
    "    mld_change = xs.variable(\n",
    "        dims='time',\n",
    "        intent='out',\n",
    "        description='mixed layer depth change per time unit'\n",
    "    )\n",
    "    \n",
    "    par = xs.variable(\n",
    "        dims='time',\n",
    "        intent='out',\n",
    "        description='???'\n",
    "    )\n",
    "    \n",
    "    sst = xs.variable(\n",
    "        dims='time',\n",
    "        intent='out',\n",
    "        description='sea surface temperature'\n",
    "    )\n",
    "    \n",
    "    def initialize(self):\n",
    "        self.mld = np.cos(self.time/365*np.pi*2) * 100 + 200\n",
    "        self.par = np.sin(self.time/365*np.pi) * 50 + 0\n",
    "        self.sst = np.sin(self.time/365*np.pi) * 10 + 10\n",
    "        \n",
    "        dt = np.ediff1d(self.time, to_end=1)\n",
    "        self.mld_change = np.gradient(self.mld) / dt\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ecosystem components"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "@xs.process\n",
    "class Component:\n",
    "    \"\"\"Base class for a component of a ecosystem.\"\"\"\n",
    "\n",
    "    label = xs.variable(groups='c_labels', description='component label')\n",
    "    \n",
    "    c0 = xs.variable(\n",
    "        # only slab model (scalar)\n",
    "        dims=(), \n",
    "        # support slab + 2D models ?\n",
    "        #dims=[(), ('lat', 'lon')],\n",
    "        groups='c_c0',\n",
    "        description='inital concentration'\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "@xs.process\n",
    "class Nutrients(Component):\n",
    "\n",
    "    label = xs.variable(\n",
    "        default='N',\n",
    "        groups='c_labels',\n",
    "        description='nutrients label'\n",
    "    )\n",
    "    \n",
    "    k = xs.variable(\n",
    "        default=0.85,\n",
    "        description='half saturation constant'\n",
    "    )\n",
    "    \n",
    "    cb = xs.variable(\n",
    "        default=15.0,\n",
    "        description='concentration below mixed layer depth'\n",
    "    )\n",
    "    \n",
    "\n",
    "@xs.process\n",
    "class Phytoplankton(Component):\n",
    "\n",
    "    label = xs.variable(\n",
    "        default='P',\n",
    "        groups='c_labels',\n",
    "        description='phytoplankton label'\n",
    "    )\n",
    "    \n",
    "    m = xs.variable(\n",
    "        default=0.2,\n",
    "        description='mortality rate'\n",
    "    )\n",
    "        \n",
    "\n",
    "@xs.process\n",
    "class Zooplankton(Component):\n",
    "\n",
    "    label = xs.variable(\n",
    "        default='Z',\n",
    "        groups='c_labels',\n",
    "        description='zooplankton label'\n",
    "    )\n",
    "    \n",
    "    k = xs.variable(\n",
    "        default=0.6,\n",
    "        description='half saturation constant'\n",
    "    )\n",
    "    \n",
    "    kn = xs.variable(\n",
    "        default=0.75,\n",
    "        description='net production efficiency'\n",
    "    )\n",
    "    \n",
    "    beta = xs.variable(\n",
    "        default=0.69,\n",
    "        description='absorption efficiency'\n",
    "    )\n",
    "    \n",
    "    m = xs.variable(\n",
    "        default=0.1,\n",
    "        description='mortality rate'\n",
    "    )\n",
    "    \n",
    "    m2 = xs.variable(\n",
    "        default=0.34,\n",
    "        description='mortality rate (closure)'\n",
    "    )\n",
    "\n",
    "\n",
    "@xs.process\n",
    "class Detritus(Component):\n",
    "\n",
    "    label = xs.variable(\n",
    "        default='D',\n",
    "        groups='c_labels',\n",
    "        description='detritus label'\n",
    "    )\n",
    "    \n",
    "    m = xs.variable(\n",
    "        default=0.6,\n",
    "        description='remineralisation rate'\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "@xs.process\n",
    "class AllComponents:\n",
    "    \"\"\"Group some component propreties as 1-d arrays.\"\"\"\n",
    "    \n",
    "    c_labels = xs.group('c_labels')\n",
    "    c_c0 = xs.group('c_c0')\n",
    "    \n",
    "    labels = xs.variable(\n",
    "        dims='component',\n",
    "        intent='out',\n",
    "        description='component labels'\n",
    "    )\n",
    "    c0 = xs.variable(\n",
    "        dims='component',\n",
    "        intent='out',\n",
    "        description='initial concentration'\n",
    "    )\n",
    "    \n",
    "    # use component labels as xarray coordinate/index\n",
    "    component = xs.index(\n",
    "        dims='component',\n",
    "        description='component label'\n",
    "    )\n",
    "    \n",
    "    def initialize(self):\n",
    "        self.labels = np.array(list(self.c_labels))\n",
    "        self.component = self.labels\n",
    "        self.c0 = np.array(list(self.c_c0), dtype=np.double)\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ODE terms\n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "@xs.process\n",
    "class ODETerm:\n",
    "    \"\"\"Base class for any term in the ODE system formed by\n",
    "    the fluxes of each component in an ecosystem.\n",
    "    \n",
    "    The term is represented by a function of both time and\n",
    "    the component concentrations at that time (even though\n",
    "    the dependence of the actual expression on those\n",
    "    quantities is optional).\n",
    "    \n",
    "    \"\"\"\n",
    "\n",
    "    func = xs.variable(intent='out', description='ODE term expression')\n",
    "    \n",
    "    def _func(self, state, time):\n",
    "        \"\"\"\n",
    "        Parameters\n",
    "        ----------\n",
    "        state : dict\n",
    "            Keys are ecosystem component labels and values are\n",
    "            concentrations at current time.\n",
    "        time : float\n",
    "            Current time.\n",
    "\n",
    "        \"\"\"\n",
    "        # must be implemented in subclasses\n",
    "        raise NotImplementedError\n",
    "        \n",
    "    def initialize(self):\n",
    "        self.func = self._func\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "@xs.process\n",
    "class ForcingTerm(ODETerm):\n",
    "    \"\"\"Base class for all forcing terms.\n",
    "    \n",
    "    The common operation is here to retrieve the index of\n",
    "    the 1-d array at the current time, and return the value\n",
    "    at that index.\n",
    "\n",
    "    \"\"\"\n",
    "    \n",
    "    time = xs.foreign(Time, 'days')\n",
    "    \n",
    "    # to be replaced in subclass\n",
    "    forcing_var = xs.variable()\n",
    "    \n",
    "    def _func(self, *args):\n",
    "        _, t = args\n",
    "\n",
    "        idx = np.argmax(t < self.time)\n",
    "        return self.forcing_var[idx]\n",
    "    \n",
    "\n",
    "@xs.process\n",
    "class MLD(ForcingTerm):\n",
    "    forcing_var = xs.foreign(BasicPeriodicForcing, 'mld')\n",
    "\n",
    "\n",
    "@xs.process\n",
    "class MLDChange(ForcingTerm):\n",
    "    forcing_var = xs.foreign(BasicPeriodicForcing, 'mld_change')\n",
    "    \n",
    "\n",
    "@xs.process\n",
    "class PAR(ForcingTerm):\n",
    "    forcing_var = xs.foreign(BasicPeriodicForcing, 'par')\n",
    "\n",
    "\n",
    "@xs.process\n",
    "class SST(ForcingTerm):\n",
    "    forcing_var = xs.foreign(BasicPeriodicForcing, 'sst')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "@xs.process\n",
    "class Mixing(ODETerm):\n",
    "    \n",
    "    kappa = xs.variable(\n",
    "        default=0.1,\n",
    "        description='cross thermocline mixing'\n",
    "    )\n",
    "    \n",
    "    mld = xs.foreign(MLD, 'func')\n",
    "    mld_change = xs.foreign(MLDChange, 'func')\n",
    "    \n",
    "    def _func(self, *args):\n",
    "        return (self.kappa + max(self.mld_change(*args), 0.)) / self.mld(*args)\n",
    "    \n",
    "\n",
    "@xs.process\n",
    "class Uptake(ODETerm):\n",
    "    \n",
    "    n_label = xs.foreign(Nutrients, 'label')\n",
    "    k_n = xs.foreign(Nutrients, 'k')\n",
    "    \n",
    "    def _func(self, *args):\n",
    "        state, _ = args\n",
    "        n = state[self.n_label]\n",
    "\n",
    "        return n / (n + self.k_n)\n",
    "    \n",
    "\n",
    "@xs.process\n",
    "class Grazing:\n",
    "\n",
    "    max_rate = xs.variable(\n",
    "        default=0.8,\n",
    "        description='maximum ingestion rate'\n",
    "    ) \n",
    "    \n",
    "    pphyto = xs.variable(\n",
    "        default=0.67,\n",
    "        description='grazing preference for phytoplankton'\n",
    "    )\n",
    "    pdet = xs.variable(\n",
    "        default=0.33,\n",
    "        description='grazing preference for detritus'\n",
    "    )\n",
    "    \n",
    "    @pdet.validator\n",
    "    def _check_preference(self, attr, value):\n",
    "        # pphyto + pdet == 1 must be True ???\n",
    "        if value + self.pphyto != 1:\n",
    "            raise ValueError\n",
    "\n",
    "\n",
    "@xs.process\n",
    "class GrazingBase(ODETerm):\n",
    "    \n",
    "    max_rate = xs.foreign(Grazing, 'max_rate')\n",
    "    pphyto = xs.foreign(Grazing, 'pphyto')\n",
    "    pdet = xs.foreign(Grazing, 'pdet')\n",
    "    k_z = xs.foreign(Zooplankton, 'k')\n",
    "    \n",
    "    p_label = xs.foreign(Phytoplankton, 'label')\n",
    "    d_label = xs.foreign(Detritus, 'label')\n",
    "    \n",
    "    @property\n",
    "    def comp(self):\n",
    "        raise NotImplementedError\n",
    "\n",
    "    def _func(self, *args):\n",
    "        state, _ = args\n",
    "\n",
    "        pcomp = {\n",
    "            'p': self.pphyto * state[self.p_label]**2,\n",
    "            'd': self.pdet * state[self.d_label]**2\n",
    "        }\n",
    "        \n",
    "        num = self.max_rate * pcomp[self.comp]\n",
    "        den = self.k_z**2 + pcomp['p'] + pcomp['d']\n",
    "        \n",
    "        return num / den\n",
    "\n",
    "\n",
    "@xs.process\n",
    "class GrazingPhyto(GrazingBase):\n",
    "    \n",
    "    @property\n",
    "    def comp(self):\n",
    "        return 'p'\n",
    "    \n",
    "\n",
    "@xs.process\n",
    "class GrazingDetritus(GrazingBase):\n",
    "    \n",
    "    @property\n",
    "    def comp(self):\n",
    "        return 'd'\n",
    "\n",
    "\n",
    "@xs.process\n",
    "class Light(ODETerm):\n",
    "    \n",
    "    v_max = xs.variable(\n",
    "        default=1.1,\n",
    "        description='maximum photosynthetic rate'\n",
    "    )\n",
    "    alpha = xs.variable(\n",
    "        default=0.15,\n",
    "        description='initial slope of P-I curve'\n",
    "    )\n",
    "    \n",
    "    kw = xs.variable(\n",
    "        default=0.05,\n",
    "        description='extinction coefficient due to water'\n",
    "    )\n",
    "    kp = xs.variable(\n",
    "        default=0.03,\n",
    "        description='extinction coefficient due to phytoplankton'\n",
    "    )\n",
    "    \n",
    "    p_label = xs.foreign(Phytoplankton, 'label')\n",
    "    \n",
    "    mld = xs.foreign(MLD, 'func')\n",
    "    par = xs.foreign(PAR, 'func')\n",
    "    \n",
    "    def _func(self, *args):\n",
    "        state, time = args\n",
    "        \n",
    "        p_term = self.kw + self.kp * state[self.p_label]\n",
    "        mld = self.mld(*args)\n",
    "        par = self.par(*args)\n",
    "\n",
    "        iz = par * np.exp(-p_term * mld)\n",
    "        psi = (self.v_max / (p_term * mld)) * np.log(\n",
    "            (self.alpha * par + np.sqrt(self.v_max**2 + (self.alpha * par)**2)) /\n",
    "            (self.alpha * iz + np.sqrt(self.v_max**2 + (self.alpha * iz)**2))\n",
    "        )\n",
    "        \n",
    "        return psi"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Component fluxes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "@xs.process\n",
    "class ComponentFlux(ODETerm):\n",
    "    \"\"\"Base class for the expression of the flux of a\n",
    "    component C, i.e., dC/dt.\n",
    "    \n",
    "    \"\"\"\n",
    "\n",
    "    # let's reuse the component label\n",
    "    label = xs.foreign(Component, 'label')\n",
    "    \n",
    "    flux_label = xs.variable(\n",
    "        intent='out',\n",
    "        groups='c_flux_labels',\n",
    "        description='for consistent building of the ODE system'\n",
    "    )\n",
    "    func = xs.variable(\n",
    "        intent='out',\n",
    "        groups='c_flux_funcs',\n",
    "        description='component flux expression'\n",
    "    )\n",
    "    \n",
    "    def initialize(self):\n",
    "        self.flux_label = self.label\n",
    "        \n",
    "        super(ComponentFlux, self).initialize()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def temp(te): \n",
    "    E = np.exp(0.063*te)\n",
    "    return E\n",
    "\n",
    "\n",
    "@xs.process\n",
    "class NutrientsFlux(ComponentFlux):\n",
    "    \n",
    "    label = xs.foreign(Nutrients, 'label')\n",
    "    \n",
    "    p_label = xs.foreign(Phytoplankton, 'label')\n",
    "    z_label = xs.foreign(Zooplankton, 'label')\n",
    "    d_label = xs.foreign(Detritus, 'label')\n",
    "    \n",
    "    n_cb = xs.foreign(Nutrients, 'cb')\n",
    "    beta = xs.foreign(Zooplankton, 'beta')\n",
    "    z_kn = xs.foreign(Zooplankton, 'kn')\n",
    "    d_m = xs.foreign(Detritus, 'm')\n",
    "    \n",
    "    sst = xs.foreign(SST, 'func')\n",
    "    mixing = xs.foreign(Mixing, 'func')\n",
    "    uptake = xs.foreign(Uptake, 'func')\n",
    "    light = xs.foreign(Light, 'func')\n",
    "    grazing_p = xs.foreign(GrazingPhyto, 'func')\n",
    "    grazing_d = xs.foreign(GrazingDetritus, 'func')\n",
    "    \n",
    "    def _func(self, *args):\n",
    "        state, _ = args\n",
    "\n",
    "        return (\n",
    "            state[self.p_label] * (- temp(self.sst(*args)) * self.uptake(*args) * self.light(*args)) +\n",
    "            state[self.z_label] * (self.beta * (1 - self.z_kn) * (self.grazing_p(*args) + self.grazing_d(*args))) +\n",
    "            state[self.d_label] * self.d_m +\n",
    "            (self.n_cb - state[self.label]) * self.mixing(*args)\n",
    "        )\n",
    "    \n",
    "    \n",
    "@xs.process\n",
    "class PhytoplanktonFlux(ComponentFlux):\n",
    "    \n",
    "    label = xs.foreign(Phytoplankton, 'label')\n",
    "    \n",
    "    z_label = xs.foreign(Zooplankton, 'label')\n",
    "    \n",
    "    p_m = xs.foreign(Phytoplankton, 'm')\n",
    "    \n",
    "    sst = xs.foreign(SST, 'func')\n",
    "    mixing = xs.foreign(Mixing, 'func')\n",
    "    uptake = xs.foreign(Uptake, 'func')\n",
    "    light = xs.foreign(Light, 'func')\n",
    "    grazing_p = xs.foreign(GrazingPhyto, 'func')\n",
    "    \n",
    "    def _func(self, *args):\n",
    "        state, _ = args\n",
    "\n",
    "        return (\n",
    "            state[self.label] * (temp(self.sst(*args)) * self.uptake(*args) * self.light(*args)) -\n",
    "            state[self.label] * self.p_m -\n",
    "            state[self.z_label] * self.grazing_p(*args) -\n",
    "            state[self.label] * self.mixing(*args)\n",
    "        )\n",
    "    \n",
    "\n",
    "@xs.process\n",
    "class ZooplanktonFlux(ComponentFlux):\n",
    "    \n",
    "    label = xs.foreign(Zooplankton, 'label')\n",
    "    \n",
    "    beta = xs.foreign(Zooplankton, 'beta')\n",
    "    z_kn = xs.foreign(Zooplankton, 'kn')\n",
    "    z_m = xs.foreign(Zooplankton, 'm')\n",
    "    z_m2 = xs.foreign(Zooplankton, 'm2')\n",
    "    \n",
    "    mld = xs.foreign(MLD, 'func')\n",
    "    mld_change = xs.foreign(MLDChange, 'func')\n",
    "    grazing_p = xs.foreign(GrazingPhyto, 'func')\n",
    "    grazing_d = xs.foreign(GrazingDetritus, 'func')\n",
    "    \n",
    "    def _func(self, *args):\n",
    "        state, _ = args\n",
    "        \n",
    "        return (\n",
    "            state[self.label] * self.beta * self.z_kn * (self.grazing_p(*args) + self.grazing_d(*args)) -\n",
    "            state[self.label] * self.z_m -\n",
    "            state[self.label]**2 * self.z_m2 -\n",
    "            state[self.label] * (self.mld_change(*args) / self.mld(*args))\n",
    "        )\n",
    "    \n",
    "\n",
    "@xs.process\n",
    "class DetritusFlux(ComponentFlux):\n",
    "    \n",
    "    label = xs.foreign(Detritus, 'label')\n",
    "    \n",
    "    p_label = xs.foreign(Phytoplankton, 'label')\n",
    "    z_label = xs.foreign(Zooplankton, 'label')\n",
    "    \n",
    "    beta = xs.foreign(Zooplankton, 'beta')\n",
    "    p_m = xs.foreign(Phytoplankton, 'm')\n",
    "    z_m = xs.foreign(Zooplankton, 'm')\n",
    "    d_m = xs.foreign(Detritus, 'm')\n",
    "    \n",
    "    mixing = xs.foreign(Mixing, 'func')\n",
    "    grazing_p = xs.foreign(GrazingPhyto, 'func')\n",
    "    grazing_d = xs.foreign(GrazingDetritus, 'func')\n",
    "    \n",
    "    def _func(self, *args):\n",
    "        state, _ = args\n",
    "        \n",
    "        return (\n",
    "            state[self.p_label] * self.p_m +\n",
    "            state[self.z_label] * self.z_m +\n",
    "            state[self.z_label] * (1 - self.beta) * (self.grazing_p(*args) + self.grazing_d(*args)) -\n",
    "            state[self.z_label] * self.grazing_d(*args) -\n",
    "            state[self.label] * self.d_m -\n",
    "            state[self.label] * self.mixing(*args)\n",
    "        )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Slab main process (build and solve the ODE system of fluxes) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "@xs.process\n",
    "class Slab:\n",
    "    time = xs.foreign(Time, 'time')\n",
    "    \n",
    "    c = xs.variable(\n",
    "        dims=('time', 'component'),\n",
    "        intent='out',\n",
    "        description='component concentrations',\n",
    "        attrs={'units': 'micromol N L^-1'}\n",
    "    )\n",
    "    \n",
    "    labels = xs.foreign(AllComponents, 'labels')\n",
    "    c0 = xs.foreign(AllComponents, 'c0')\n",
    "    \n",
    "    flux_labels = xs.group('c_flux_labels')\n",
    "    flux_funcs = xs.group('c_flux_funcs')\n",
    "    \n",
    "    def initialize(self):\n",
    "        # pick the right flux expression for each component\n",
    "        funcs = {\n",
    "            label: func\n",
    "            for label, func in zip(self.flux_labels, self.flux_funcs)\n",
    "        }\n",
    "        \n",
    "        def ode(c, t):\n",
    "            state = {label: val for label, val in zip(self.labels, c)}\n",
    "            return [funcs[label](state, t) for label in self.labels]\n",
    "        \n",
    "        self.c = odeint(\n",
    "            ode,\n",
    "            self.c0,\n",
    "            self.time,\n",
    "            rtol=1e-12,\n",
    "            atol=1e-12\n",
    "        )\n",
    "       "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create the NPZD model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "npzd_model = xs.Model({\n",
    "    # common\n",
    "    'time': Time,\n",
    "    # ecosystem components\n",
    "    'n': Nutrients,\n",
    "    'p': Phytoplankton,\n",
    "    'z': Zooplankton,\n",
    "    'd': Detritus,\n",
    "    'components': AllComponents,\n",
    "    # forcing\n",
    "    'forcing': BasicPeriodicForcing,\n",
    "    # ODE terms\n",
    "    'mld': MLD,\n",
    "    'mld_change': MLDChange,\n",
    "    'par': PAR,\n",
    "    'sst': SST,\n",
    "    'mixing': Mixing,\n",
    "    'uptake': Uptake,\n",
    "    'grazing': Grazing,\n",
    "    'grazing_phyto': GrazingPhyto,\n",
    "    'grazing_detritus': GrazingDetritus,\n",
    "    'light': Light,\n",
    "    # component fluxes\n",
    "    'n_flux': NutrientsFlux,\n",
    "    'p_flux': PhytoplanktonFlux,\n",
    "    'z_flux': ZooplanktonFlux,\n",
    "    'd_flux': DetritusFlux,\n",
    "    # ODE building and integration\n",
    "    'slab': Slab\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xsimlab.Model (22 processes, 26 inputs)>\n",
       "time\n",
       "    days         [in] ('time',) time in days\n",
       "n\n",
       "    label        [in] nutrients label\n",
       "    k            [in] half saturation constant\n",
       "    cb           [in] concentration below mixed layer depth\n",
       "    c0           [in] inital concentration\n",
       "p\n",
       "    c0           [in] inital concentration\n",
       "    label        [in] phytoplankton label\n",
       "    m            [in] mortality rate\n",
       "z\n",
       "    m            [in] mortality rate\n",
       "    kn           [in] net production efficiency\n",
       "    k            [in] half saturation constant\n",
       "    label        [in] zooplankton label\n",
       "    m2           [in] mortality rate (closure)\n",
       "    beta         [in] absorption efficiency\n",
       "    c0           [in] inital concentration\n",
       "d\n",
       "    label        [in] detritus label\n",
       "    m            [in] remineralisation rate\n",
       "    c0           [in] inital concentration\n",
       "components\n",
       "forcing\n",
       "mld\n",
       "mld_change\n",
       "par\n",
       "sst\n",
       "mixing\n",
       "    kappa        [in] cross thermocline mixing\n",
       "uptake\n",
       "grazing\n",
       "    pphyto       [in] grazing preference for phytoplankton\n",
       "    max_rate     [in] maximum ingestion rate\n",
       "    pdet         [in] grazing preference for detritus\n",
       "grazing_phyto\n",
       "grazing_detritus\n",
       "light\n",
       "    kw           [in] extinction coefficient due to water\n",
       "    alpha        [in] initial slope of P-I curve\n",
       "    kp           [in] extinction coefficient due to phytoplankton\n",
       "    v_max        [in] maximum photosynthetic rate\n",
       "n_flux\n",
       "p_flux\n",
       "z_flux\n",
       "d_flux\n",
       "slab"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "npzd_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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iAAUoQAEKUIACFKAABbolsPS0OUSdL3v4ziG49ZEfsWlnHqaMi+h8uNfXahHUr6THZunSpRL8eAInJWdzk5TZ+6W355c2Oxk2py6dm0poYGfX8yxyna/vzrUKwv5P1kFSayVFS09T123b9h3w9PDo+nCv7pdVNOLbDcdw69UxDIZ6JcgXUYACFKAABShAAQpQoGcCZw2IBg/wxK/nx+KdFZmICvOUi1fPSu6yd0tzRyrvV2Sez4WyGGpAYOCJTHMqhfWfH/5zl1eYx129LP6qb23F1q1bESSJIKKiImFnY4t0WRD2gAytmzRpElxcXc+7ss3NeixengKV5e/X85hM4bxBWQAFKEABClCAAhSgAAW6IXDWIXPqtWow3Z+e2Y2UtErcK0PnAv16/8NfLXL6mczn2SsJCTIzMqRnxw5qAdTp06bhUkmjreYJmet28OAhrFz5jcx9StHSbXt7e2PkiJG44orZGBwff97VbtO3463PkpFfXIsPnpnE3qHzFmUBFKAABShAAQpQgAIU6JbA0nMGRKoIXZMedz++A/klDfj9TSMR5O/WrZLPtVNzc4vM+3E41y5m+1yz9HQZcs5Sq8yDem95KjJzKvH64xcgYeD59cSZLRwrRgEKUIACFKAABShAAfMT+OWASNW5vrEV9z61C0fzanHb3CEYPNDX/JpigTWqqWvCu8sOori8Dq/833gMjfW2wFawyhSgAAUoQAEKUIACFLBYge4FRKp5La1tePL1FKzafBxXThuEGRMjLbbV5lDxrOM1WLwsBV4e9njxkbGIDDn/njdzaBfrQAEKUIACFKAABShAAQsS6H5A1NmopV9n4eUlhxA3wBc3zI6Hr7f5zv3prLM5Xashcqs2ZeOHrTkYPzwA/7o/CW6uZ81tYU5VZ10oQAEKUIACFKAABSjQ3wR6HhApgcPHqrFw0QFJAtCA2dMGYsrYCNja9jcbw7fnWG41PvnmMNRQufsWJGLuTPayGV6ZJVKAAhSgAAUoQAEKUKDbAr0LiFTxra3teHtFBt7/IhP+Pi64/KKBGJkY2O0jW9OORaX1+Hb9MRw4UooJIwLw198NQ7A/F121ps8A20oBClCAAhSgAAUoYJYCvQ+IOptTWNqI/36chtWb82UNHU/MnBSFoXEBsLHp3MN6rwslEFq3NRe7UgoRE+GJe29JkIDI33pB2HIKUIACFKAABShAAQqYl8D5B0Sd7UnLrsGbn6Zj0+5iBPi64qJxYRg/MhROjnadu1jN9eHMcmzYkYfDRytkQVt33DF3EC6bHMYg0Wo+AWwoBShAAQpQgAIUoICFCBguIOpscG5hPT76JgvfbMiTAMAGw+MDMXZ4EOKifft1QFBS3iA9QUXYk1qMsopGjBnqj1vmDMSFSQGdNLymAAUoQAEKUIACFKAABcxLwPABUWf7aupasGZLAb7acByHM6vg7emEERIcDY3zw6AoH9jZWf6YuoKSehzMKENKWimyJY22n48TZk8JxxVTwzEg3L2TgtcUoAAFKEABClCAAhSggHkKGC8gOrm9OQX1+E7mGG3YWYSjubVwdrLTFncdLKm7YyK8EBJkGcGDyg6nMsWlZ1fikAyLq6jSwcfLEZNHB2HmhaGSRtu/X/eCnfye8jYFKEABClCAAhSgAAX6gUDfBEQnQxWX6bB5T7FcSiTrWgXqG1vh6myPARIYRYd5ISzYHaGB7iZf36hB14KC4noUltQhp6AWWXlVKJWhcCq9eFy0tzYUbsqYQAwZ5H1y83ibAhSgAAUoQAEKUIACFLAcgb4PiE62aWsDMnJrsP9wBfbJ5ZAMrVNZ69TmIkFSaIAERr7O8PNy0QIkP1kE1sfLGe4uDnCW589na2ltQ0NDC6prm1BerUNFpU6uG1Fe1Yji0gZUyGNq83R3kADICyPjfZCU4IthcT5SN+tLFHE+1nwtBShAAQpQgAIUoAAFzFTAtAHRmVAadHpkSpCUmVOrDa87XtSA/JIGFJU1oKlZIqifNjtbG7i5OmgXFTzZS9eNmpdkZyfXcttGenLa9G1obWuXNZPaoNe3o7lFLz1SLairb5bbP5elen38fZwRFuiGsCAX6alyR2yUp8x18kCgBGTcKEABClCAAhSgAAUoQIF+KWB+AdG5mCuqm1FS3ohqSdhQVdMsvTtykdt1Da1a0NMsgY/q+VEBkOp9crC3lYsN7OXa0cFWm7vk5e4ILw8HuXRc+3k7IdjPpV8keTiXHZ+jAAUoQAEKUIACFKAABU4TWHp+485OK8+4D/hKAgN14UYBClCAAhSgAAUoQAEKUMAQAjJYjBsFKEABClCAAhSgAAUoQAHrFGBAZJ3vO1tNAQpQgAIUoAAFKEABCogAAyJ+DChAAQpQgAIUoAAFKEABqxVgQGS1bz0bTgEKUIACFKAABShAAQowIOJngAIUoAAFKEABClCAAhSwWgEGRFb71rPhFKAABShAAQpQgAIUoAADIn4GKEABClCAAhSgAAUoQAGrFWBAZLVvPRtOAQpQgAIUoAAFKEABCjAg4meAAhSgAAUoQAEKUIACFLBaAQZEVvvWs+EUoAAFKEABClCAAhSgAAMifgYoQAEKUIACFKAABShAAasVYEBktW89G04BClCAAhSgAAUoQAEKMCDiZ4ACFKAABShAAQpQgAIUsFoBBkRW+9az4RSgAAUoQAEKUIACFKAAAyJ+BihAAQpQgAIUoAAFKEABqxVgQGS1bz0bTgEKUIACFKAABShAAQowIOJngAIUoAAFKEABClCAAhSwWgEGRFb71rPhFKAABShAAQpQgAIUoAADIn4GKEABClCAAhSgAAUoQAGrFWBAZLVvPRtOAQpQgAIUoAAFKEABCjAg4meAAhSgAAUoQAEKUIACFLBaAXtLbXlJhQ6FJY0ordShtKIJFdVNaGzSo7lZj6bmNujb2uHsaAsnRzs4yrWXuyMCfJ3l4oQgPxeEB7nCzs7GUpvPelOAAhSgAAUoQAEKUIACBhCwiICoqKwRew9WIDm9Euk5tTiWV4P6hlat+bbSx+Xh5qQFPI4S/Njb28JRLrCxQau+Da0tbWhu1aOhoQWVtU1okftqU8FQVIgbYqM9kRjjjaREXwyO9oIqjxsFKEABClCAAhSgAAUoYB0CNu2ymVtT9fp27D5YjnXbC7FlXymKJSBSgU5kqKf07LgjOMANIYHu8Pdx1oKhngQxDToJjKQ3qaikHgWldSgqrUfW8WrU1bfAzcUeSQl+mDYhGBeNDZIgy8HcaFgfClCAAhSgAAUoQAEKUMBwAkvNKiA6fKwaK9bkSiBUgNr6VgmAPDAk1l96cXwQFeYJB9XzY6RNBUZHc6twKLMcR45VoE2CstFD/HD1xZGYPj6Yw+uM5M5iKUABClCAAhSgAAUoYEIB0wdEbTKCbdXmfHy8MksCkWqEBblh/IgQDE8IhK+Xs0lsmmQe0mEJjHanFCE1owyeMv9orgRGN84eAG9PR5PUiQelAAUoQAEKUIACFKAABQwuYLqASAVC30kg9NbydBm+1ohRQ4MxcUwoBoZ7GbyV51NgVY0O2/bJ0L3d+dpcpOsui8aCOTHw8uBwuvNx5WspQAEKUIACFKAABShgBgKmCYhUcoRn3kyV5Ai1GDciGDMnR8Pf28UMPM5eBZWMYbMEReu25UgGuzbcc2M85l0apXI3cKMABShAAQpQgAIUoAAFLFOgbwOiBp0eL75/CF98n4v4GF/MmxWHQD9Xi6JTab1Xb87G+h25iInwxMI/jMCgSA+LagMrSwEKUIACFKAABShAAQpoAn0XEKVl1+CRf+9BdV0r5ksgNDIx0KLfg+KyBnzy7RHkFdTggduG4NpLIi26Paw8BShAAQpQgAIUoAAFrFCgbwKilRvz8eTrydKj4oWbr0mUJAVO/cJaSwixKQtrfszCzAvDsPCeEZIenGPo+sWby0ZQgAIUoAAFKEABCliDgPEDove/OIpFS49gxsRIzJk+qF/OuUnLqsDiZakYMsgbzz88WlvPyBo+PWwjBShAAQpQgAIUoAAFLFzAuAHRG5+m450VGbhWhshNGRtu4Vbnrn5BSR1e/+gAQgNc8PrCCXBxtjv3C/gsBShAAQpQgAIUoAAFKGBqgaVGW+l02eocvL08AzdeEd/vgyH1LoYGuuOPC0bheFEDHnpuD/SysCs3ClCAAhSgAAUoQAEKUMC8BYwSEO1ILsNz76TiyhkxmJAUat4CBqxdgK8LfnvTCKRIWvFnFx80YMksigIUoAAFKEABClCAAhQwhoDBA6Kqmmb845X9SEoMwsyJUcaos1mXGRHsofWKfb4mBxt2Fpt1XVk5ClCAAhSgAAUoQAEKWLuAwQOip99KBWxsccPswVZrq1KKXzgqFP987YCkGW+xWgc2nAIUoAAFKEABClCAAuYuYNCAKFmGiq3bXihJFGLh7Gxv7m03av2uviQW7bDB4hWZRj0OC6cABShAAQpQgAIUoAAFei9g0IDolQ+PIG6AD4bG+ve+Rv3klc6Odpg1ORqfrcpCcZmun7SKzaAABShAAQpQgAIUoED/EjBYQHQ0txb7j1TgkknRZiFUXlaG7777Dk88/gSWL1tmkjpNHBUGV2cHfLkuzyTH50EpQAEKUIACFKAABShAgXMLGCwg+mr9cagsa6qHyBy2qupqHD50CLt274K+rc0kVbK1s8HYEcH4cn0u2pmF2yTvAQ9KAQpQgAIUoAAFKECBcwkYLCDasLMQo4YGnetYffpcTEwMps+YoR3TztZgzexxG8YMDUaJDJlLz67p8Wv5AgpQgAIUoAAFKEABClDAuAIGiRQqqptRUNKImEhv49a2p6Xb2HS8woQBkVqw1VUSTCSnVfa09tyfAhSgAAUoQAEKUIACFDCygEFSwR06WqVVMzrM08jVPXfxzc3NWCbzhY4cPgxbe3sEBgRoL7CRbG+m2lRMFiUuHUbWty6Tqdx5XApQgAIUoAAFKEABCnRHwCABUUVVE5yd7ORikOK6U+/T9mlobMRfH3sMvj6+eOSRR1FRVSkJFR7X9uvsKDrtRX30gLeHE8oqm/roaDwMBShAAQpQgAIUoAAFKNBdAYMMmauubYGHm2N3j2mU/T5fsQKZmZm448474ObuhojwcFwy8xKjHKunhbq6OqCqprmnL+P+FKAABShAAQpQgAIUoICRBQwSEBm5jt0qfuXK7+Dr54ewsLAT+8cNju24beouohM14g0KUIACFKAABShAAQpQwJwEDBIQeXk4oLbedD0gVVVVqK2tQXDQqVnu2n+aO2TKOUTqzW5oaIG3p2l70MzpQ8e6UIACFKAABShAAQpQwFwEDBIQ+Xo7Qdekl0urSdqlb+04rhoy19LSYpI6nOugVbVN8PNxOtcufI4CFKAABShAAQpQgAIUMIGAQQKihIFeWtWz802z1o6fvz/c3T2gsswdlMVYO7c2vV672d5umoVZO+uRIy5DYswsJXln5XhNAQpQgAIUoAAFKEABKxYwSEDkJz1EIYEuOJrbkX7bFJ7XzrtWO+wrL7+ElJQU5GRn49NPP9Ue275tGw4c2G+Kasn6THVo0LVi+GAfkxyfB6UABShAAQpQgAIUoAAFzi5gsDzZ08YFY+3WYsyeOvDsRzPiM3OvuQa1NTX46quv8Jik3/b29sbIESOR7ZKFMMk4Z29vmjk8u1OKEOjnjLho067RZER6Fk0BClCAAhSgAAUoQAGLFbBpl80Qtc/MrcWND27CHxckITbadL0hrTKfqKy8TBIsBEOn08FGMsw5OZlm/o6+rR0LX9qK+ZdG4rfXxxmCmWVQgAIUoAAFKEABClCAAoYTWGqQIXOqPoMiPWRYmC/W/JhtuOr1oiR7e3stGFIvdXZ2NlkwpI6/fV8B6hubcdWMCHWXGwUoQAEKUIACFKAABShgZgIGC4hUu+67JR5pxypxMKPMzJrZ99XRNevx3YYs6R2KRrC/S99XgEekAAUoQAEKUIACFKAABX5RwKABkUocMG1cCFaszjBZCu5fbHEf7fDl95mATTvunPfT4rB9dFwehgIUoAAFKEABClCAAhTovoBBAyJ12Ed/OxQq3fVnK9O6X4t+tueBIyXYsjsff/3dcHi5O/Sz1rE5FKAABShAAQpQgAIU6D8CBg+IfDwdsfAPI7EntRg/bM3tP1LdbMnxolp89PURXHNxJKaPD+7mq7gbBShAAQpQgAIUoAAFKGAKAYMHRKoRF4wMwEO3D4EaNrZ9f6Ep2mWSY5ZVNuL1jw5gqCzC+vCdQ01SBx6UAhSgAAUoQAEKUIACFOi+gMHWIep6yOsui0Z5VRPe++Iw9Po2TBwd1nWXfnW/sLgOr3+cjFBZoPb5v4yRdY9s+lX72BgKUIACFKAABShAAQr0RwGjBUQK6+4bB8PZyQ6vfpyGimodrpwe0x8NkZ5ViXeWpyBhgBdekGDI1dmuX7aTjaIABShAAQpQgAIUoEB/EzBqQKSwbp87CP4+zvjXm8nIK6zFzVclwNPdNAulGvrNU0vart6chVWbsjBjQige/+MIONgbZRSioavO8ihAAQpQgAIUoAAFKEABEbBpl60vJA4fq8YjL+xFXV0r5s+Ow4j4wL44rNGOUVrRiE8keUJ2fjX+dGsi5s+KMtqxWDAFKEABClCAAhSgAAUoYBSBpX0WEKnq1ze24oX3DuGrdXlIjPXDvEvj4O9rWYuWtrS0YfWP2Vi3LQcDwz3xj3uGIy7a0yjvDgulAAUoQAEKUIACFKAABYwq0LcBUWdTDhyplCF0KcgpqMOEkSGYOTEavt7OnU+b5bUKhLbszddSibe26vG7GwbjulnRsOUIObN8v1gpClCAAhSgAAUoQAEKdEPANAGRqpi+rR3fbsyXZATpKC7XYczQYEwaE4qoMK9u1Lvvdqmpa8K2vYX4cU8+dE0tMjQuGguuioFab4kbBShAAQpQgAIUoAAFKGDRAqYLiDrZVGC0UgKjj1dmIyO7GuHB7hgvvUbDZY6Rj6dpki+o3qBDR8uwO6UYqWllcHezx9UzIvGrKwbC14uBUOd7x2sKUIACFKAABShAAQpYuIDpA6KTAQ9mVmHFmlys21GI+oZWRId5YkisP2KjfaTnyBN2dsZb26e4rAGZuZU4nFmBI0fL0SprJyUl+OOaiyMkg1wI1xU6+Y3ibQpQgAIUoAAFKEABCvQPAfMKiDpNW1vbsTO1DOu2F2HrvhKUVujg4GArAZKXLHzqjuBAV4TIdYCPCzzcetZjo2tqlTWRmlBUWo/C0joUltQj+3g1auqa4SLrByXF+2HahGBMHRsEbw6L63xLeE0BClCAAhSgAAUoQIH+KGCeAVFX6fySBuw7VIHktEqk59QiK68GDTq9tpudrQ28PJxkbSNHODrYwV4CJ7UWkI38p3p5WiQBQktrG+rrm1FV24ym5p9fFx7shtgoDyQO8sboRD8MHii9UFIeNwpQgAIUoAAFKEABClDAKgQsIyA601tRVNaIgpJGlFXqpAepSev1aWzSo1kCHl1zG9TySo4SHDk52snFFl4SMPn7OkuvkhOC/F0QKcGQvT2DnzPZ8jEKUIACFKAABShAAQpYiYDlBkRW8gaxmRSgAAUoQAEKUIACFKCA8QSWchUd4+GyZApQgAIUoAAFKEABClDAzAUYEJn5G8TqUYACFKAABShAAQpQgALGE2BAZDxblkwBClCAAhSgAAUoQAEKmLkAAyIzf4NYPQpQgAIUoAAFKEABClDAeAIMiIxny5IpQAEKUIACFKAABShAATMXYEBk5m8Qq0cBClCAAhSgAAUoQAEKGE+AAZHxbFkyBShAAQpQgAIUoAAFKGDmAgyIzPwNYvUoQAEKUIACFKAABShAAeMJMCAyni1LpgAFKEABClCAAhSgAAXMXIABkZm/QaweBShAAQpQgAIUoAAFKGA8AQZExrNlyRSgAAUoQAEKUIACFKCAmQswIDLzN4jVowAFKEABClCAAhSgAAWMJ8CAyHi2LJkCFKAABShAAQpQgAIUMHMBBkRm/gaxehSgAAUoQAEKUIACFKCA8QQYEBnPliVTgAIUoAAFKEABClCAAmYuwIDIzN8gVo8CFKAABShAAQpQgAIUMJ4AAyLj2bJkClCAAhSgAAUoQAEKUMDMBRgQmfkbxOpRgAIUoAAFKEABClCAAsYTYEBkPFuWTAEKUIACFKAABShAAQqYuQADIjN/g1g9ClCAAhSgAAUoQAEKUMB4AgyIjGfLkilAAQpQgAIUoAAFKEABMxewN/P6nbV6JRU6FJY0orRSh9KKJlRUN6GxSY/mZj2amtugb2uHs6MtnBzt4CjXXu6OCPB1losTgvxcEB7kCjs7m7OWzycoQAEKUIACFKAABShAgf4vYBEBUVFZI/YerEByeiXSc2pxLK8G9Q2t2rtjK31cHm5OWsDjKMGPvb0tHOUCGxu06tvQ2tKG5lY9GhpaUFnbhBa5rzYVDEWFuCE22hOJMd5ISvTF4GgvqPK4UYACFKAABShAAQpQgALWIWDTLpu5NVWvb8fug+VYt70QW/aVolgCIhXoRIZ6Ss+OO4ID3BAS6A5/H2ctGOpJENOgk8BIepOKSupRUFqHotJ6ZB2vRl19C9xc7JGU4IdpE4Jx0dggCbIczI2G9aEABShAAQpQgAIUoAAFDCew1KwCosPHqrFiTa4EQgWorW+VAMgDQ2L9pRfHB1FhnnBQPT9G2lRgdDS3Cocyy3HkWAXaJCgbPcQPV18cienjgzm8zkjuLJYCFKAABShAAQpQgAImFDB9QNQmI9hWbc7HxyuzJBCpRliQG8aPCMHwhED4ejmbxKZJ5iEdlsBod0oRUjPK4Cnzj+ZKYHTj7AHw9nQ0SZ14UApQgAIUoAAFKEABClDA4AKmC4hUIPSdBEJvLU+X4WuNGDU0GBPHhGJguJfBW3k+BVbV6LBtnwzd252vzUW67rJoLJgTAy8PDqc7H1e+lgIUoAAFKEABClCAAmYgYJqASCVHeObNVEmOUItxI4Ixc3I0/L1dzMDj7FVQyRg2S1C0bluOZLBrwz03xmPepVEqdwM3ClCAAhSgAAUoQAEKUMAyBfo2IGrQ6fHi+4fwxfe5iI/xxbxZcQj0c7UoOpXWe/XmbKzfkYuYCE8s/MMIDIr0sKg2sLIUoAAFKEABClCAAhSggCbQdwFRWnYNHvn3HlTXtWK+BEIjEwMt+j0oLmvAJ98eQV5BDR64bQiuvSTSotvDylOAAhSgAAUoQAEKUMAKBfomIFq5MR9Pvp4sPSpeuPmaRElS4NQvrLWEEJuysObHLMy8MAwL7xkh6cE5hq5fvLlsBAUoQAEKUIACFKCANQgYPyB6/4ujWLT0CGZMjMSc6YP65ZybtKwKLF6WiiGDvPH8w6O19Yys4dPDNlKAAhSgAAUoQAEKUMDCBYwbEL3xaTreWZGBa2WI3JSx4RZude7qF5TU4fWPDiA0wAWvL5wAF2e7c7+Az1KAAhSgAAUoQAEKUIACphZYarSVTpetzsHbyzNw4xXx/T4YUu9iaKA7/rhgFI4XNeCh5/ZALwu7cqMABShAAQpQgAIUoAAFzFvAKAHRjuQyPPdOKq6cEYMJSaHmLWDA2gX4uuC3N41AiqQVf3bxQQOWzKIoQAEKUIACFKAABShAAWMIGDwgqqppxj9e2Y+kxCDMnBhljDqbdZkRwR5ar9jna3KwYWexWdeVlaMABShAAQpQgAIUoIC1Cxg8IHr6rVTAxhY3zB5stbYqpfiFo0Lxz9cOSJrxFqt1YMMpQAEKUIACFKAABShg7gIGDYiSZajYuu2FkkQhFs7O9ubedqPW7+pLYtEOGyxekWnU47BwClCAAhSgAAUoQAEKUKD3AgYNiF758AjiBvhgaKx/72vUT17p7GiHWZOj8dmqLBSX6fpJq9gMClCAAhSgAAUoQAEK9C8BgwVER3Nrsf9IBS6ZFN2/hM6jNRNHhcHV2QFfrss7j1L4UgpQgAIUoAAFKEABClDAWAIGC4i+Wn8cKsua6iHi1iFga2eDsSOC8eX6XLQzCzc/FhSgAAUoQAEKUIACFDA7AYMFRBt2FmLU0CCza6CpKzRmaDBKZMhcenaNqavC41OAAhSgAAUoQAEKUIACXQQMEhBVVDejoKQRMZHeXYrnXbVgq6skmEhOqyQGBShAAQpQgAIUoAAFKGBmAgZJBXfoaJXWrOgwT5M1r1Gnw7uLF5/x+LGxsZg5c+YZnzP2gzY2QJS4dBhZ37pMxvZl+RSgAAUoQAEKUIACFDgfAYMERBVVTXB2spOLQYrrVXt0jY3YvXs3brvtdkRGRqCquhoL//F3tLW1Y+rUqb0q01Av8vZwQlllk6GKYzkUoAAFKEABClCAAhSggIEEDDJkrrq2BR5ujgaqUu+KcXCwxxVXXokpUyYjOjoaa9eshl7fhjlXXYXExMTeFWqgV7m6OqCqptlApbEYClCAAhSgAAUoQAEKUMBQAgYJiAxVmfMpx93dA3OvuUYrYvv27di0aTNCQ0Ox4JZbzqdYvpYCFKAABShAAQpQgAIU6McCBgmIvDwcUFtvHj0g9XX1eO3V12Ajk3fuv/9+ODqatudKfXYaGlrg7Wn6evTjzzGbRgEKUIACFKAABShAgV4JGCQg8vV2gq5JL5fWXlXCkC96+523UVFZoQ2VS0hIMGTRvS6rqrYJfj5OvX49X0gBClCAAhSgAAUoQAEKGEfAIAFRwkAvrXbZ+aZda2fv3r34/vvvTxsqt2TJEuPodbPUHHEZEsOU5N3k4m4UoAAFKEABClCAAhToMwGDBER+0kMUEuCCo7kd6bf7rPYnHahBsswtWrTotKFyecePY/+BAyft2bc3C0rq0KBrxfDBPn17YB6NAhSgAAUoQAEKUIACFPhFAYPlyZ42PhhrtxZj9tSBv3hQY+zw+YoVKC0t1Yp++623tetGXSMKCwsxatQoYxyyW2XuTilCoJ8z4qJNt0ZTtyrKnShAAQpQgAIUoAAFKGCFAgYLiK6cFoGPvslCRnYlYqP7vjfk5ptvhrqY06aXNZB2JRdj/qWR0nNlTjVjXShAAQpQgAIUoAAFKEABJWCQIXOqoEGRHjIszBdrfsxWd7mJwPZ9BahvbMZVMyLoQQEKUIACFKAABShAAQqYoYDBAiLVtvtuiUfasUoczCgzw6b2bZV0zXp8tyFLeoeiEezv0rcH59EoQAEKUIACFKAABShAgW4JGDQgUokDpo0LwYrVGWaRgrtbAkba6cvvMwGbdtw5L9ZIR2CxFKAABShAAQpQgAIUoMD5Chg0IFKVefS3Q9Gm1+OzlWnnWzeLff2BIyXYsjsff/3dcHi5O1hsO1hxClCAAhSgAAUoQAEK9HcBgyVV6ITy8XTEwj+MxH3/2omwIA/MuDCy8ymruD5eVIuPvj6Cay6OxHTJvMeNAhSgAAV6LlBR3YyCkgaUVupQWq5DeVUTGmUB8CYZjtzU3IZWfTucHG3h6GAr13bwdHOAv68zAmQR7EC5jghx057r+ZH5CgpYvoBK6nS8qAHF5Y0oq2jSvkdVtc1olu+O9h1qaYOtZHvSvkPy/XGW75Kft3x/fJ3kO+SMYFlKJUgy5HKjgLUIGDwgUnAXjAzAQ7cPwXOLD8LN1QETRoZYhWdZZSNe/+gAhsoirA/fOdQq2sxGUoACFDhfARX87DtUjv1HKpGeU4ujeTWormk+Uaynu6P0tjvC0dEeDvY2cHCwg62tDVrlR11zqx56CY7qG5qhfvCpH3tqs5XxD2GBbhgU5YmEGC+MSvDFkEHesJfXc6NAfxJoawMycmqwV75DBzOrtdu5hXVobW3XmukgJw28PZy032OO9nawl/v2dvIFaW+X74+cXJBLc4seNXXNcmmCKk9tbq72GBjuKZmDJWlWnA9GJfpqa052PMv/U6B/Cdi0y2asJr32cRre+yIT8y8bjImjw4x1GLMot7C4Dq9/nIwgfye88fgFcHW2M4t6sRIUoAAFzE1A/dVJSa/ED9uL8OOeYuQW1msBTESwJ8KC3eXstBtCAt1lDTdXeLk5wtau+0GMrqkVVTVNKCqtR6FcikrqkV1QjcrqJu1s+PA4X0wdF4Rp40O0s+HmZsP6UKA7AjV1Ldi0uxjrdxRhz8FyyWjbCnc5AR0d7iVBS8f3JzjAFb5eznB16f7QfRUM1TU0Sa+STr4/dSiU70++LDCfk1+jBU6B/s6YODJQvj/BGDfMH3Y9+G52p13chwImElhq1IBINerdzzPxqgRGMydF4crpMSZqp3EPm55ViXeWpyBhgBde+MsY7ayKcY/I0ilAAQpYnkBOQT0+X5uDVT8WoEKGwKkfbEPj/OUMtC8GRHjJsB3jnUgqq2rE0ewqyYRaIWfRy9Goa8WQWB9cLcsizJoUCmcn4x3b8t4p1tgcBVRP6IadRfjf93nYnVoGG+kljR/oKz2gvhgk6z+qQMhYW6u+DdnHa5CZW4WDaWXIKaiRAMweMyaEYO7MKCQO8jLWoVkuBfpCwPgBkWrF1+uP419vJsvQBR/cfFUCPN2d+qJxRj+GOsu5enMWVm3Kkn8UQvH4H0fIcA6D56kwejt4AApQgALGFNi0u0RbuHvPwTL4+7hgnAyjTkoIkB514/2AO1d71I87dSJrT2ox9h8ugaP8uz37onDcPGcghwSdC47PmUSgurYFH6/Mwv/kZIIaFpo4yB9jhgUjMdbPqCcRztVY1eN6QL47Ow4UIl9GyAyO9sINs6Nx2ZQw2Emgxo0CFibQNwGRQjl8rBqPvLAXdXWtmD87DiPiAy3M6tTqllY04hNJnpCdX40/3ZqI+bOiTt2B9yhAAQpYuYAKhN78LB1pWdUYNtgfk2TodHyMH2Qut9lsDY0t2Ck/6jZJZtDKah3mTIvAHdcO4vpxZvMOWW9FqmVY3JKvjuHT77Jk7psdJo4KwwWjQuHjaV4nlbOPV8vQ1wLsSSmSkxzO+PW8OFx+EQMj6/3kWmTL+y4gUjxqjOsL7x3CV+vytDMb8y6Nk6xAlrVoaYtM4l39YzbWbcvRJhv+457hiIv2tMh3n5WmAAUoYAyB7Pw6PP1mqjbJe2RCIC6ZHIXwYA9jHMpgZaqsXDuTi/D9j3IWvkaH264ZJJcYZqozmDAL6q6AGn3y+dpcLFp6WDLB2WLaBZGYPCZMkoqY97DOiiod1sjvI9VrFBXqjsfuGiYnv32622zuRwFTCvRtQNTZ0gOSSehfb6bIGNQ6LQPdzInR8JV0j+a8qUBoy958/LA1VyYW6vG7GwbjulnR2kRgc64360YBClCgrwTUHAfVI/T+l0e15AjXzRqMyDDLOmGkAqONO47LUOhj8nfJCX+/ezhGD/HrK0Iex8oFjubVYeGiA5IprhpTx8v8tikDtLTylsSiRtCsWJ2OQxnlmDM9Ag/clgg3F6MkNbYkFtbVvAVMExApE/VH59uN+ZKMIF3y5OswZmgwJo0JRVSYeU3MUykot+0tlO7gfOiaWmRoXDQWXBUjXdaO5v3WsnYUoAAF+lCgoKQRj764F5mS/veqiwfJv+fhZjU0rqcU1bVNWL4qHclHSnHntbH4zfw4ngDrKSL375HA/77Pxb/fPait4XjjlfEmm2PXo0qfY2e1SP2yb9Ph7m6PZx4YhYSB5vX77hxV51PWJ2C6gKjTWgVGKyUw+nhlNjKyq2VYhTvGy4Tb4TLHyFTjZFVv0KGjZdidUoxUyabi7mYvmYgi8asrBkoKSwZCne8drylAAQoogZ3JZXj433vk32xn3HrtUFnQ0bXfwGyRk2H/W5OBYZKu+/mHR2uZtfpN49gQsxBQPatPvJaM7zYdl4y8kpjgogH9JjFBbX0zlnxxSHq8KvHoXcNljl64WZizEhToImD6gOjkCh3MrMKKNblYt6NQFtlrRbQMtRgSq1Ky+kjPkadR890XlzVIOslKHM6swJGj5bIKeptkQfLHNRdHaGkluZjfye8Ub1OAAhToEFi1uQAL/7sfSYlB+NWc+I4FH/sZjsqi9eYnB+Dj5YRFfx3H9Yv62ftryuY06PT483N7kJxWgdvnDdNSaJuyPsY69jfrjsr8ohzcLdMNVNISbhQwMwHzCog6cdTqyjslx/46WbRv674SlMoCYWql5WgZThcqi/UFB7pqi/YFSPpWD1m0ryebWrSvQtJFdiza17HomMqQolZodpHFVJPi/TBtQjCmjg2CN4fF9YSW+1KAAlYm8JUsqfDPVw/g4olRmDMjpl+3vlIWe33jo/1oa2/Du09NlPTh5pXpq1/j99PG6Zr0uPvxHcgrasBdNw5HhJknHjnft2Gr9LZ+9l2aTDsYhHt+Nfh8i+PrKWBIAfMMiLq2ML+kAfsOVcgZlEqk59QiK68G6qyK2lS+ey8PJ1nbyFGyAdnBXgIntRaQjfynenlaJAFCS2sb6qXbVuXvb2r++XXhwW6IjfKQnP7eGJ3oh8EDpReK+fO78vM+BShAgdMENu8pwUPP7sLFF0bhin666HbXRtc3tOCl9/Zoi2+/888LuAh3VyDe77aAmi7wwNO7kZxRiftuHS3zhfrPMNNzIew8UIQlXx7Cg7cPwQ2XR59rVz5Hgb4UsIyA6EwiRWWNUJN4yyp10oPUpPX6NMrZlmYJeHTNbWiXvJWOEhw5SZpKJ0dbeEnA5O/rjAA5qxfk74JICYY4DO5MsnyMAhSgwLkFjh2vw4K/bNYWh7x+dvy5d+5nz6oFKf/z7h5ZbsEDi/42rp+1js3pK4Hn3jmIr9bn4Q8LkhAZYlmZGM/XaN22PHyxNgMvPTYOFyYFnG9xfD0FDCFguQGRIVrPMihAAQpQoGcCzZJ05pZHtgDtNvjjraOsslc9Rxbk/s+7e/HHmxNw05UDegbIva1eQPWuPvDMLtw6dwhGDw2ySo8PJdFCZnYFPnnhIiarsspPgNk1eqmt2VWJFaIABShAAbMVeEPWGSosbcCCa4ZYZTCk3hi1PMTl0wZi0UeHZT29erN9r1gx8xOok4RRT8i8uwlJIVYbDKl3Zf7lg2WUjj3+9UaK+b1JrJFVCjAgssq3nY2mAAUo0HMBNVT542+ycLmkBTb3xbR73rqevULNnQryc8PLHx7p2Qu5t1ULvPt5pizu3o65M2Ot2sFZpjPMmxWLjbuKsO9whVVbsPHmIcCAyDzeB9aCAhSggNkLvP5puqSedsbk0VxLxMYGWma9TbuLkJJeZfbvHStoegE13/mTlVm4dEo0nJ3tTV8hE9cgYZAf4mN8eVLBxO8DD98hwICInwQKUIACFPhFgZq6FllHpAAzLoiErZ1EA9ygftBFhXpi+eocalDgFwVUEgVH6RmZZMQTCjt27MCdd96Bffv2/WJ9zGGHSycPQKpk2kvLrjGH6rAOVizAgMiK33w2nQIUoEB3BVZLMGQngdAoK50EfjanC2QuyA/bC7TFxM+2Dx+ngBL4al2uZGYMMe4i88XFKCkpRbFcW8IWE+mlpRz/al2eJVSXdezHAgyI+vGby6ZRgAIUMJTAhp1FGBoXoC1lYKgyjVHO/gMHsHTpUmMUfcYyk4YEQWXe25Fcdsbn+SAFlMDR3FptqZCxwwINBqKWF/nrXx9DW1vbiTLnzJmDxYsXY9asWSceM/cbo+Q7tEHmEnGjgCkFGBCZUp/HpgAFKGABAvK7SxvWos7mmvOmzoz/+/nn0db+8w9EY9fXReaChAd74IAsHM6NAmcTUAvLOzvZISzIcGsOffDB+0hOTpF1F089akCAZa3tExPpjZKyjjUlT20J71Gg7wQ4q6/vrHkkClCAAhYpkFNQhwadXtJNG+7H3LkgysrKsHr1aqRnZMDF2RmBgQG44447T7xk29at+HHLj6irb0BIcDDGjRuHkNBQPPboo6iqqsL2bdtRUV6Byy6/DHGxcSdeZ6wbkTKP6FAmEysYy7c/lHvwaLX2/bE10Gno9z/4AMuXr9BoXnr5Jfj7+2HBLQuQd/w4Nm7YABUUXXrppdrzdXW12LhxE+rr6jF9xnR8/fXXOC77TZo8GRdNmYLikhJ5fgPSj6QjMTEB8+bPP4V82/bt2C4X9d2KiRmIeXOvhaub2yn7nM+d6J/+XTl0tAoX+Vrnukzn48fXGkaAAZFhHFkKBShAgX4rUF7VpLXNx9O5T9r4/PPPYcSIkXh84ULs3bsXzz333ImA6MiRI3j5lUV4883X4eTkgn8+8TjyJCgaOHAgZl9+OdQPxdi4WEyfPh2BAYYbnnSuhvt4OiHnOHuIzmVk7c+VVzbBy93JYAwXjB8vgf9WCWzyMfPii2Uoq6MEQhuxbMVy5GRn47rrr9OOpQKkV156CYfle+Pv748tW7dg2PDhUCcdXvj3v7Fnz24cyzyKIUOH4khaGnbt3gUXV1fMnj1be/2SJUtw8OBBLFggwVZeHl597VWoExIvvfSyJIhwNEh7VKIJNxd7VPz074xBCmUhFOihgIHOVfTwqNydAhSgAAUsRqC6tkWrq6uLg9HrrIa9HTx4SBZ9tdOONWrUKCTEJ5w47rp162BrawNbGzv5QeaAG3/1K7To9fD29kbUgGhtv+CgYAwbNkx77MQLjXjDzdUBNfUdRkY8DIu2YIGquma4uhomgFAMcYMHS++pqyaiPuvq/kVTL8Ltt96mPdb5v4jwcCx84gntrp2dHZ566kn8+s478Yj0pqot/UganpUTEPfccw/+8+KL2mOZmZna9bFjx7Bs2TI8+OCDSEhIwCWXXIIRw0doQdi69eu0fQz1P3f5DlXVNhuqOJZDgR4LsIeox2R8AQUoQAHrE1Dr7vTFpobHBQUF4cMlH6K0rBQ33XQTHnr4zycOHS8//L777jvce/+9uOs3d2G8nCkfIL1Dp2x9VNdTjsk7FDiHQNd5PufYtUdP2XT5YtrLSYKum6uLi/aQn58f3N09tNtqqKmdnS38ZWidu5u79lhgUKB2ktHCahsAAEAASURBVKFUhtCpbcOG9VrChqeeekq7r/7XpNNpw1QbZLgqNwr0JwEGRP3p3WRbKEABChhBwMvDQZu43dDYAtUbYuztvvvu04bJrVq1Chs3bcLdd9+NaVOnaoedNHkKklNT8MP3P+DJJ59EUlKSdgZbzTXq3GzQtxFRfUMLPN2M79LZPl5bnoCPhyMaGgzfA9I1IOqJjIPD6T1Wqme2Vd+qFZN/vEAbPvfiTz1HPSm7p/vWN7bCS4y4UcBUAhwyZyp5HpcCFKCAhQj4enXMfaiq7ZhLZMxqqxTCgwfH4+WXX9aGADU2NODFF17AgeRk7bD6Nj3uv+9+PPzww/CVM95qAcpnnnnmlCr1dUCkXPx8fg7ITqkM71BABPx8nFBdZ/jvT5cOIoNaOzo5Qn3/1Dylrpsh1zlqkbT1dXJSwc/bcHOsutaX9ynwSwIMiH5JiM9TgAIUsHKBqFB3uDrbIft4tdEl1A+txYvf0eb/PPTgQ7j33nuld6pdyxynDv7aa6+hsrISkyVD1n8XvaIlU0hNTYXKpNUZCLW09O18npz8GgwZ5G10Gx7AcgUSY7ygPicnLRl03o1RvUN6fZt26U1h6nt1ri3mp6GoH3744Sm7qQQN77333imPnc+d7PyOf1cSxIgbBUwlwIDIVPI8LgUoQAELEVCpgocM8pHFJY0fECmSTZs2a0GPuj127Fh1paUDVtft8gPwm2+/UTe1+RBxcXGIjIyEm8yD8PX11R7fLZmzMjIz8MUXX2j3jfk/na4V+cW1GDHYx5iHYdkWLjBcPh+6Jj0K5LNiqM3Xr+PzvnbtGqxdu1bLAtcgPTpqK5PkJJ2bSpettvqfnlO3m5ub0dTUhLLSn/fTyfygJnm8TtJzq+3SWZdCzT/aKpnp/u9vf9Pm7qkTEs8/9yx+8+tfa/sY4n9Hc6sQ6Cfp9X3Zy2oIT5bROwG7hbL17qV8FQUoQAEKWIuAGuP/zYZcTBkXAXuZjG2sra6uDqtWfweVQrigoBBrv/8e4yRxwjVXX60dcqukGl6/XtZMkTWK9stwufKKci0lt1p3xcPDAzt27IDKjqXSdd9www1GzzS3I7kQhzMrEBroqs0j8ufQOWN9NCy6XDXs9JuNx9Esw8MSYvwM0pb2tnao78OuXbsQEhIi63LV45OPP5aApg5Fkhihvr5O1vAKwksy/LS4qAg11dWoqKjQ9n3m6ae11Ntq3yxJ0z0wOhov/uc/KCwsRLXsp9YmuuCCCzBy5Ejs3bMXWVlZ2nGqq6tw331/QvSAaIO0QRXy2co0XDQmCBNH9U2afINVnAX1J4EUG+kyPXefaX9qLttCAQpQgAK9Eqiua8Gs36zFdZcNxoSk0F6V0Z0XqT9JekmjreYSlZeXI1iyYZ08cby5uUXLhKUCIQd7B3h6nrpYrHp9ifyYCwwMPOV13Tl2b/Z54Z3dKCmv1xauVa8PkLPck+SH3aTRQRg/3F/WhzFe8Nib+vI1phN4e3kGPvo2G0/cN1EyvBkm8UdNTY2Whr4ze5yxWqcCJflCaRnmDHmMY9Lr/J/39mDJs5MxeMCp32VDHodlUeAXBJYyIPoFIT5NAQpQoL8JrNpcgMamVrg42ctaJnZwdrKT23ay0GnHded9de1g//MP+n/89wB2p5Tjsd9NgK2BftBZsu2RYxV4dcl+WRPJBm1nOLfoJAtOjhnqhyly9vuisUGcNG7Jb7YB6l5SocPV96zHVRfHaD2tBijS4ot47SP5/qAd7z19ocW3hQ2waAEGRBb99rHyFKAABXoh8MxbqVixJqdbr1RnslXgpIIje3sbFJfp4CPDfwJ8XODgYIfxI4MxIt76hrqo+OfZN3fK2fl25BV2zLlQgZGDg43Mw2g7zVYtJjsy3hczLwzB9Akh8PViiuHTkKzggZc/PIL//ZCLv99zAZydrXvlk84TCm88fgFGJXbMh7KCjwCbaJ4CS38+9WeeFWStKEABClDAwAJTpLeiu5te366lxC2r1KGotFHL+FZRpcOxvGoMjfO3ymBI2a3bmouisnoMivTQhiypx1QvUWcwZCN/Xd1d7U8M22uT+R57D5Xj/72dist/+z3+8M8dWLkpX5tor17LzToEbp8bAzUF739rM62jwWdppa5Zj+XfpWu9pwyGzoLEh/tUwLpPT/QpNQ9GAQpQwDwExsowLlcXezRIooTebI4Otgjyc8OEkcabS9SbevXVa3IKaiTBxFHc86t43DJnIMqrmrB+RxF+2F6EfYfLZQ5UO9qlk6iuocNX9RypxW3VPCwVGKnndySXaZdn3rLH9PHBuGJquDa8rq/awOOYRsBDFvD9++9H4MH/txuDB/pi1BDr611V8stXpsv3oxn335JgmjeCR6VAFwGLnUOkxuIWljSiVM5allY0oaK6ScbE6yWVpF47Q6eXPzrOMplVjeF2lGsvd0dtsmuAr5P8IXdBeJCrwSY1djHlXQpQgAJmL/CX5/di3Q6ZKN3Dbe4lUbh6egR+8/etGDs8BNddPriHJVj27lU1Ovzn3b3SM+SO//59/GmNqaxp7giOthVij/QIqeDn5E0NPVQ/issqm7TetpOfiwhxw9UzIiQ4iuCQupNh+uFt1VP4zYbjuHfBKISHePTDFp69SRu25+LzNR09ZO6uDph7SSR+NXsA59idnYzPGF/AMuYQFZU1Yu/BCiSnVyI9p1aGatRIPv2fzrxJ17OHm5MW8DhK8GMvE4Ad1SRgOSPXKutVtEqKy+ZWPRpkFeRKWU1crYisNjUuPkr++MRGeyIxxhtJMn51cLSXDH0wvjqPQAEKUMCUAuqE0n8/SsNKSQPc3U39iP+/u4drvRnqNZt2F+PPz+3GJROjcfm0gd0txqL3q29swUvv7ZVFam3xzpMXakPiztWg6toW6TUqxLfinJxWedqu3h6OkpABqJAg6uRN/R2bNi4YN1weDbV+Dbf+J6AC5fuf3oWDR6tx362jZB0e1/7XyDO0aGdyEZZ8cUiG2/ogVX7TdW6OMh9x9kVhWHBVDMKDrcOis+28NgsB8wyI1D8Uuw+WY538Idmyr1Qm8TZqgU5kqKf07LgjOMANIYHuUOs9qGCoJ0FMg04CI+lNKiqpR0Gp5OovrUeWrL5eV98CNxlCkpTgh2kTgrWMQF7uDmbxLrESFKAABc5XIOt4HTbsKsbGnUU4JD/CerLigvpR/tT9SQj2dzmlGl/+kIcnX0/GzElRuHJ6zCnP9bc7qmfojY+TtZTgi5+aKCMOnHrUxLyiBi0wWrkxH4WlHYtndhag/oapkQsV1bJYpoxyOHmLH+glgdEAXDoxVEtqcfJzvG3ZAmqh1t8u3I4CGe3y2xtHIEx+3/TnbdveAny68ghunhODO64ZhOWS2OWTldnSW6o70WyVfOQS+az/el4sokLdTjzOGxQwsoB5BUSHj1VL5qNcCYQKUFvfishQDwyJ9ZdeHB9EhXmekv7V0DAqMFKrJR/KLIfKfNImQdnoIX64+uJI7YyoodYMMHS9WR4FKECBMwmoLGgpGZUSABVjgwRBuT9lQjtlX7UUyqkjuk55Wv04ue3qGNx1fZxMBD/zuikqMcATrx6Qfy+DceOV8f1yKHJhcR1el2DI29Mei/42HoGy1tD5bHsPVWjDpdRJP7Xg7cmbr6cj7KSHqFR68U7e1PpGN8qworkzI7WTdyc/x9uWK6De/z8/uwepmVW4Y95QbV6R5bbm7DVfuf4YVm3Oxl3XxeE382NP7KgWqv1Whg5+8NUxHC/qyNaonlS/uWZNCtMCI/YYneDiDeMJmD4gkrX35EuSj49XZkkgUi1nSNwwfkQIhicEyhjq8/uj01s3dYbusARGu1OKkJpRBk+ZfzRXAiP1x8hb/lhxowAFKGCOAi2tbdiVWq71Am2U3iA12b/rpuawjB8egKnjgrQz028tS++6i3Zf9cA/ce9IqAQMv7RtP1CGR/69R+YAuGDB3CH9avjPVjmr/fnqdAyN9cHzD4/W5v/8kkd3n1cZ6dZuLdDOlB/MqDrlZSpxhZ+3M4rLG7VEDJ1PusvQxfmXRuGmKwZqiRo6H+e15Qq0trZjoazxtWZLPi6dPACzpgzo0cgXc255bX0zln51GGlyovmR3wzT5sidqb7qt6AaXrp4RQYyc2tP7GIvKfkul6F0d14bi9DAU3uoT+zEGxQ4fwHTBUTqw/+dBEJvLU+X4WuNGDU0GBPHhGJguNf5N8uAJahhEtv2ydC93fnaXKTrLovGAunuVRmDuFGAAhQwtYCaT7llX4n0AhVjq1x37XFQ9VNzVSaNDpQgKBgTRgRIspmOyZI5BfWYd9+G05owMSkQC/8wokcngPKLG/DoC/uQlV8rPeuDcMGoMDWV02K3mromrFiVgf2HS3C7DO85Vy+ZIRqpTgguX52D1VsKTkvFrXqHamRYd5MMsercVJbA6+Xv0U1XSmDE4d2dLBZ9rUbIvPDeQUTI9IAbZscjyN+y59KkpJXiM8km5+pii2f+NBqJg7r3+27t1kK8tSxDpjOcFBhJr6k6EXCnDKXj592iP+bmWnnTBEQqOcIzb6ZKcoRajBsRjJmTo+EvZxbNeVPJGDZLULRuWw70Es3dc2M85smX05L/4JuzN+tGAQqcXaBKJuGr+UBqyJXqEWqVnqGuW0iAqzYXcpqkdFYLgp5truW19244MZxOTeb/g6SSvunKAV2L69Z9dab79U/TsESGv0RI5qx5l8chMsSzW681l51UhtLNO4/ju01ZEhA64G93j+hWL5mh6q9SdX+9/jg+X5uD7Py6U4pVC+KqTKonB71q7qsavXCLTEZ3dbY7ZX/esTwB1TuycNEBHJXkUdPGR0qPUbRkyrWs97WsslFOJqTjYEY5Lpd08g/fPgRusiZXTzY15Hf1jwV4e3kGcgp+/h6oHtLbr4nR5tWpXlRuFDCQQN8GRA06PV58/xC++D4X8TG+mDcrzuKGVqg/RqtlHOz6HbmIifDUzqKqhfm4UYACFDCmgMoMt0HWulknl/1HKk5L56yOHRvlqfUCXSTD4QZLBs3ubC99eFgLYMKD3fCvPyUhQSbxn+92TBI4PP1GilbPpMRAzJIfdSFmPmFcjVpQw6TX/piF8mqdZLsahDvmDjrRm3a+Jr15vQp2VXCpev5O3tSCr5JEFY26n+cf+UqwpOZmXCNzjM423+vkMnjbfAVUMKB6C//78RHY29lh+gWR0sMbBgczDwBUwqq1W7KxfX8BIuTfEzVE7nwXXVXfS5Wl8Y1P07Xho53vWnCAC+6+YTAunxLW+RCvKXA+An0XEKVl12hjzKvrWjFfAqGR8kfSkrfisgZ88u0R5MkCfQ/cNgTXSh59bhSgAAUMKaCGoakAaJ0s+HlQJl13zQynkh6MkAxwqhfoorHBvRpjv+9whfRG5OLRu4YZvIdBDeN787N0ZOTUYHh8ACaPCTO7SeMq8+guSQW8aWc+KqoatQVS7zCz+Qqq1+DDL4/JHJMCbTmJzs+Y6jlQ58h1J2Wmiwp1l79JibgwKaBzN15bqIDqCf5QAuLPVmXLciJ2mDQ2DBfIYshenj3LcGjs5quFirfsyde+R2p456/l+3PFtHCDBuZqvt1H32bhgy+OyoKuLSeapDJgPvxrSUbRzRNAJ17IGxQ4VaBvAiKVZlSlZo2J8MLN1yRKkgLz+jKfatL9e+rMxSoZVrFGzijOvDAMC+8ZwbSo3efjnhSgwBkEVO/Kei0IKkS6nEjquqlJxqMl0cEMFQTJnCBfL/NP9KICo4++ycK+w+WSrtoV40eGYGRCgMlGCKgsomnZldiTWoz9h4rlLLwNLpsSLsPOBiIs0HznbRSX6eRH4TF8IenOG07KTqd6Dtrl75Fae69zmzImCA/cnmjW7emsK6/PLaACIxUM/E9G19TUNUuCjwCMGRYkc3L8TDacrrqmCQeOlGDnAZXBshYxMlJGLa46+yIJhOT7ZKxNWbwlw+jUSZzOocLqxNC1smD03TfEGTTpibHawHLNUsD4AdH7Es0vWnoEMyZGYs70Qf1yzk1aVgUWL0vFkEHeWhYiNaabGwUoQIHuCqgJ9aonSAVCXeeNqDKcpCdgwgh/6QkKwZQxgRb7R1+thaR+yKz6MR/qh01IoBuGxcnSCgN8MCDMy6g/7ipkGFxmTpWW7UplD22UgCJBFuW+ekYELpscBhcLmn9TKwkW1JAqFWRW1f68qKuaA6aXoEgNuer83Nwuw/5U6nRj/kjtOBr/b2wBNUdP/Tuhph3sOVQmgbyt9Lj6yW8PX8TI8iRBRlzcVa0PmSs9QRnyHTqYXqat36h+66je6bkzo+R77G3s5p9SvkoI89zig9hxoPTE42qO3R9uiscc6Z3iRoEeChg3IFJjPt+RFIrXyhC5KWP79we0oETWqfjoAEJlXOvrCydY1B/XHn5ouDsFKGAAgVRJs/z9tkJtOFzXhTpV8SqLmMr2pn5wTBoV2K/+TVE/2NU8KDUUcPPeYuTLoqVq3otafDtU5hqpQClUFt8O8HXp8eLbap5npZy9Li5Ti2/Xa4twZ+dXy3A4nbaW3dA4GWIoPWvTxTXI3zRLOxjg46MV0Sjzcj/9LhtLvj6G6lMCIxs5e/5TVCR7DpK5ZX/73fBuZ/kyVP1YjvEEqmtbsHGXmlNYjL0SHKnPgocsERItJxbU9yckwF1+j7jCR1K3Ozv17CStSpWtFkstlN81hbKIvbpW3yG1ZpC/LEg8cWTHv0sqfb+9vfF6g7qjpwLEF987hKKyxhO7jxnqj/+7exh7R0+I8EY3BIwXEC2Ts1fPvp2KX8lCfROSQrtRF8vfpbSiES+9u0cSRnjh5cfG8oyc5b+lbAEFDCqggiCVUlZlhzv5D3jnQTwlffJkGeqkfqyr9NjWkkWprLJJftRVyBCcCjkDXatl2Kqp65gnoDJ5qmHWXpI63MnRXoIaG/m31U7791UNmWlt0aNFzl7XNzZrQUFnogH1OpVpb1CkpxYIJCX4ylAj735pqhIWfSxDqlQChpPnV6ihRG2SNU9t6rZau+juG+OMush552eZ130noDIjpmXVyHeoXBaXr9bm7OXJQszqcbWpf0e8PZ2hMrQ5SC+ig8xHUr1L7fKfWrusRX2HJNipkUCourbpRMIW1Ws6INwTcdEe0gPkg9GJvrJWpPkNKVXzi9TJd3ViQLVDbWq9td9LNuAbLo/ulyOTtEbyf4YUME5AtCO5DH98cgeumB6DmROjDFlhsy8rr6gWiz7YJ+PRw/Dob4aafX1ZQQpQwLgCKemdPUFnDoJUdjC1SOr0CSEYM8SPJ1J+ejvUorIqqYTKrlda0SSLzOrQKOvwqB8/avFsNYTHUdZTcpIfe2pIoYf82FMTugN8nLRrlVygc70l477D5lN6tQSR736eiWWrcuRs/s9rFp1cQ5WJ8J/3JcmcXveTH+btfiageghzi+pRUq6+Pzqtx0cNr+z8/qjeHhs5a+As3yGVnMNZLr7ejgiU75BalDlERruoiyVtahjdE68eQHJa5Ylqjxjsi7/9fjiiQt1OPMYbFDiDgOEDIjUu/IYHNyEqzBu3XTvkDMfs/w/tP1SCxctT8dyfx2g/dPp/i9lCClDgZIHuBEFqKNzMC0OQlOB31jWCTi6TtynQXQGVfOEVmburstJ1zUyoynB0sMO9t8RrC7t2t0zuRwFLEFDDcT9ZmY1XJWW57qeFjNUJk/sXJGhrR1pCG1hHkwgYPiD6y7/34kBaFf7ym7Fwdu7ZuFWTEBjpoJ98cwSp6aX4/JVpXFXZSMYslgLmJKAWnP5B5gT9sK3olPUyOuvo5+2kzQe6+AIGQZ0mvDaugBqi+YLMr0iRz+aZthnyWfzb3cPBREBn0uFjliyQX9KAJ19Lwe7UshPNUEsTqN4iLxmazI0CXQQMGxCpHwR3/nUr7rpxuIzV9u9yLOu6q9aF+OeibbhSVmn+060J1tV4tpYCViLQ0RNU0K0gaFSiH8eyW8nnwtyaqZa+eHnJYRl22HRa1SJD3PDsQ6O1tMmnPckHKGDhAirpyMsfHjkxhPT/s3cV8FWWX/hIDFh3AyM2RndJg4gYoCJ259/CblFswQRbwVYsFBSVkO4YtZHbYKw7WAID/8/zXu64gyGLe7cb7+E3vpvf937P/eI95zznOaTVvjSph/QGPVmbRsAEAfM6RHdMXiclh/+V+27oabINx324alOy/LY4Vua+N9Lm1Ywc91fUe64RqIwAm0yTirR4TZpUpQ7HTBDrgQyZIG/tBFWGTz9rIASKS8rlIyi//rIwoaJo3jgUKhq+NKmnknQ3vqaXGgF7QYCNjZ9+ZyukwgvVLlFg5PYrwuWOieH2sot6P+qOgPkcongccKwdojMUgZ4S2kTY/O+56WsUT/vOK/WJp48JjYCtIsDeQIvgANEROphadNpusAiZNUHaCToNGv2ClSGwGz2vXv5ox2lNfzlJvO/aSNWc1sqGrIejEagzAhSTePurXfLrooMV6xrWN0BeuL+HpoxWIOLQD8znEL3z1W4lJzv5voEOjeipOz9vSZxE782U+R+O0pHiU8HRzzUCVowAOejMAi2EExR38NBpI/V0d1KZoDGDgiGMoDNBpwGkX7BaBCjH/M28/TLzl1il2Gc60MtHt5In7+iq71emoOjHdoMA+xa98MF2KUFjZlpYiKu8+XgfrUJnN79wrXfEfA7R+HuXStfIALloeNtaj8Yev5iSUSRTP9ko304bIh3auNvjLup90gjYDQKUeP4HfYKYCdoZl3/afrGPx3BEFc+HE9Svm69qJnrah/QLGgEbQeBAcpE8//422R1fUGnEw1F8/urDPXW/okqo6Cf2ggCP+0enbZZE9GqiuTo3lZcf6CGD0ABbm8MiYB6HKLfgiIy5fbHcc30PiWzr7bBoVrXjlIB86g1QCa+LlIkXOFZPpqrw0K9pBKwNgTy0CqA6HJ2gbXvyTpMpbgG1zCG9/ZUTdG5PPz1JtLYfUI+nTggwW/T5nDiZhWyRsZEnV9i5vad8PGWAanBZpw3oL2sErBAB1tQ9O2ObrI7KUKNr3PgclRm9dFRLKxytHlI9IPCdWXSxd8UbIqlhIQ2XAYmPj5fVq1eJt7e3+PsHyKpVKyUjI1P69e0nE66YgD4fjeoBz9M3wW7prYGLASPtEJ2OkH5FI1D/CJSUHZPloE4sWJ0iG6OzTysyZ58WOj/MBA3tE+BwDT7r/xfRW2woBBqjdojF5QO6+8kTb0WpJp4cCzOkNzy+Sr59Y6g+/hvqx9HbtRgCLs5N5O0n+sjHEBr5fA6CAaj5fuXjHer412ILFoPdqldsFocoF1KezZuh03Ezs6yuxoDFxMTIzJkzhU6Rq6srHCJ/adeunSQnJ8uePXskOydb7r777hqv11xf8HRrhi7Rp8udmmv9ej0aAY3A2RHgDW/ttixZsCpFVm7OqGjaZ/xmk8aNFA2OThBpcbxhatMIOAoCXSM85ad3hsnk6Vtl9ZZMtdsJqcVy2f3L1Ouu+nxwlEPBYfaTAeu7r46QIL8W8vpn0cop+vSnfULq9FOoo2ugOLrD4G9tO3oOuliD1FU3Y3HmbHQGbkhBBTpFTz31lHTs2FGmTZumdiglJUUmTbpfysuPyeezZomPb8P0Rpr3T5ykpBXIN9MG1w1o/W2NgEagxgiQBvc3nCDS4goKj1T6/jm4I/bq5C0URhiJJpW6YV8lePQTB0XgM9DnODGUE7MDd9TO0Vny8WrmoIjo3bZ3BBgEeOrtLRWBspH9g+TVh3oKqXTaHAIB81DmrAGqZs0NF+o2bdpUDCckJEQGDR4iy5YulYMHDzaYQ1QxIP1AI6ARqBcE2AaAThAV4tKzSk/bZnhrdxk7JETOHxwsAT7NT3tfv6ARcGQE7kCPlvahbvLkO1vkOGqMDhUflcsnLZOvpw7RalyOfGDY8b4PhqACa+YefG2T5KOudOmGNFBI/5XXH+4lTZpop8iOf/qKXTMLJ8TDrakUFleOvFZsoYEfBAUGqhEUFZc02EhKSo4KJXq1aQQ0ApZDICO7DA5QChyhqmWyg/yc5YIhwfgLkbahrpYbiF6zRsAOEBgxIFBmvnSu3PHcWkUlYt3dNY+ulOlP95O+XXzsYA/1LmgEKiNAIZFPXxgo97y4AWUOZbJiU7o89maUTHu0lxbTqQyVXT4zi0Pkjc7sZYeP4a+8weqIzvTrJCUlqbeCQ4LO9BGLv55feFjatnS2+Hb0BjQCjoZAEZSCFiMLxGxQVQpxnm5Oct65QXLB4BDpHqkbRjva8aH3t24IsK7o0xcHyl3PrZNy1OAdPXpc7ntpg7z6YE8ZBYqpNo2AvSHQBsEyZor+N2W9coqoQvfotCh547He4tS0YcS57A1ja90fszhEHdt6qP1LSDlkdbLb0dHR4ufnJ2GtwxrsNzgIXC4ZHtxg29cb1gjYEwJGcYQ/VyTLqs2ZcuTosUq7R4EXdiBnJmgglLM0B7wSPPqJRqBGCHSL8JIZz/SX+17eoOhzpNA9/e4WmXy4u1w8PLRG69If1gjYAgKtg11UIOBuOEUZOaWydmumPDx1s7yFBq7NnLRTZAu/YW3GaBaHyAcZIqp0xCfmN7hDtGPHdiktK5MWzZvLmjVrJD8/Xx56+GFwQM2yqzXGODWzSErKyqVbBx2drjF4+gsaARMEdu8vkD9XoC4IUtnkeJuaUSGOlDg2lWzRvLHp2/qxRkAjUAcE+nb1kZcm9ZBn3t2q1nL8uMgLH+BeC2bIxDG6nUQdoNVftVIEWgY6yycvIFP0wnpVh7phe5Y8gmau7z7ZV9cUWelvVtdhmc1LGNE/UBavzZCLhret65jq9P2yssMy6f77xdPTU1JSUuWee+6RkSNG1Gmddfny5uh08UfRdkRYw/Voqsv49Xc1Ag2JQEZOmfy9MkX+wt+B5MLThhKJ7PSFQ0NVbZCXrtM7DR/9gkbAXAhQjj4hpVg++xnqcyds2swYpUSnm44bEdFLe0IgJMBZ1RSRPpeaWSJ0ip6C0Mjrj/QS9u/SZl8ImM0humRES/l+/gGJTciT8LCGy4b069dPbr7pJsnNy5Pg4GChrG5DGbt+b9qRgQhaK4yjoUaht6sRsC0ESlG8vRRNU0mJi9qZo2g6pnvAAAPpcBcNC9XiCKbA6McaAQsjcOeV4Sow8Q8k7I02bdZOcXVpolQbja/ppUbAXhAg++mj5wcocZFMBOiWb0yXFz/YIS/c391edlHvxwkEzOYQtW/lBlqYtyxandCgDhH3q4Wzs4Tgr6Ft/dZUKS49IuNHtWzooejtawSsGgFScDbFZCsnaBmcIYq0mFqL5k1kJLLQFw4LgcKVrw4wmIKjH2sE6hGBKff1kOSMEtkDCqvB/lUTRFfnpjKkt389jkRvSiNQPwgE+7eQDyb3lzufXy95BYfBWEgW5xaN5Ynbu9TPAPRW6gUBszlEHO0DN0TKbc+ulZ2x2dI5vH6boBYeMtBpcnJy6gW4s22k7Mgx+Xv5AWSHwiTQt8XZPq7f1wg4JAIHU4vlj2VJihKXhe7gptYIlATK+16ITBCdIYolaNMIaAQaFgEWlU99pLdc//gqtNs4qgZTfuy4amrJSDqV6bRpBOwNgbAQV3nv2X5Kfa4Ix/0vCw/CKWoi918XaW+76rD7c86/MHPu/eNvbJHouHx5/I6+9SbBvSUqSmbOnClJycni5OQkQ4YOAW3uZlVHZM59q8m6fvxrr0TvyZRf3xshHq5Na/JV/VmNgF0jUFxaLosglf3HsmSJ3pd32r62a+mmMkFjh4SKn7eh4fJpH9IvaAQ0Ag2KwEooPD6KInPTKQQFlr6eOlj8vXWz4wb9cfTGLYbAjr15ci+k540shvvgEN10aTuLbU+vuN4Q+M7sDlEe1J+ufniFtG3lJTde1rle9uTIkaNy9Ehl1SkXV5d62XZVG9kOR2jWTzEy9dHeKrJd1Wf0axoBR0NgU0yOygZVRYnz8miGXkHBqi6oQxstQOJox4beX9tE4L3v9sjXc+MrDZ5CJ2zoquWJK8Gin9gRAht3ZMtDr29WLR9Yp856orGoa9Vm0wiY3yEiHOu2ZckDr26UcaPay6hzW9k0QjUdfHJ6obz3zVa5AIo8T9/VtaZf15/XCNgVAqmZpTJ/ebL6S8sqqbRvlMo+t6efUJCFtQe6X1AleBz2CSXVqS7I4FreocNKYp2vlR05LkfLj2MSgiX+jqHwrGmTRqpZYpMTS9axUG2QDXk9sTS2hNANFS1zOLEn2C3PrJHd8cZ6IsN2xqAR8ssP9LDMRvVaKxA4VHRUnSdFJUdRr1yu/kpKj0kxn0OcpgSv8Zzh70SRp3I+xpLPSXMUOUdJSDcBPbkxziG1bIzXcG12AjXSBbWbpIW54I9LV2cs0dLADawXb/dmDt3egMI/T729RYn+8Poz45l+iuJd8ePoB7aGgGUcIqLw098J8sbnO+XacR1lQA/H6GidnVcq734RJR0gsc2To0kTLS1na2eEHm/dESCVYOn6dPkdtUFbduVWotRw7aTE0QkaOzREvD2c6r5BvQabRCAFMrZ79x+SvQcKhLVkiWnFkpJRjL5tJwU1eA11dXYSNzg6TZ0aK6lbTj6aYsJ2DiZxamJXfgyTO8MEr+xwuZDfzwmiqfmCetkywFVaBTlLOwgAdWjjoa7TLpjgaasbAgkpRXLDE6sVhYhqqkYS/uS7u8m4kVpQqDboHobzn55dqqSe07IMy+y8w5KTb/jLRWE/gwR0bk41Ov/NmzWRZvhrwXMG5wqDTfxtKBXduFEjaYTnjc5pJP/iHxvtMrjwL84hrk45T/8agg+HcS3nOVWKP75+qjEL6AXHyNuzufjgWu7j1UzRJYP9nVVvyhAs/UCfxCbt0n7EPPdNzHNpDMZ89tJAocCYNptEwHIOEeH4aPZe+XJunEwc20EG9bbvdGJaRpF8PHuHBPg2QzOvgSqKYpOHhB60RqCWCJBb/Tvqgv5ZS3XF8kprcXNpKmNAiaMj1KmdR6X39BP7R4ATt11xBbIZSoKbQZ3cFZ8Pp6VcTdIC/Vxw3XQRXy9MqrxaYOmMzE5zNcHgxK42RtVCKnweKjoiOQhUZeGPy+zcEknJLJZCvE5jn5HuUEftA/EONh/VAji1QVvEdGJodIqoDPnttMFwQhuOvl67vam/b9HZ2Z9UKPuTiyBnXqSWqTg+8wpOlgAQRzocnm7NxM3FCRLnTbE0POZ11RWvOTdvCgeoEWiKTSzWH4cZpTI0mS87ashAFRYfUaIaRWrJx0ekGOdVLrK6uQWlyEYZHCg6YWyVEBrgIm1auqJVgpu0PbH0cLP9+urp3+yWb3/frw4a7ufnrwySACy12RwClnWICMcXv8bJh3CMRg9uLZeMtM/Cs30H8mTWL9HSEVHHt5/oIzrqaHMngh5wLREgrYmUuN+XJqFpY1GltVAlrl9XX+UEDe8XoKhNlT6gn9g1AgWg86xAz46lGzKQKcwW9pdirVj71t7SOsRNWga5Y5LkKk0R0a5vyz9UJsnpRZKUVijxifmYjBYoGh7ldQf3CpCRAwKlZ0cfu41sWwLvO59bJ1t351ZaNeuJvsAE0dHZEgwIxCcWIhBQIDshOrV7f74cRJNbY2G+B5ydQD9nOA4u4ofAgLdnC/Fi1gV/dHZs0QrgGOXgPMvLK5OcgjLJzCmWzOwSZL5O7jdpre1auqsgWaf2HtK5vafKLNna/j79zlZZjEAgjRki1tDpeaCt/YpieYeIkFBN6tVPd+BG6CXXj+8o7q72oRxFasDCVQdkwcoDMmpAsCqsI6ddm0bAnhHgcb8eHbvnLUmSFZszFC/ddH9bIiJ8yfBQuQh/Wm3KFBn7f0ynhwqCC1enShScINJ1Itt6Y6LjI+3RsNvfp+H7w1X1KzD6nZBM+l6uxOzNUtQ9D0zWRvYLlItxHHfr0HDNxqsarzW+xgzHtY+tUteDpqBkHT1Bsbrzygi5Y2K4NQ7ZYmMinW0LnMPte3A87SuQvQn5Qhoc6WwtAxEICHKTYH8XOEHIjsIRslWnp7YAsjYwPatYMrKK0NMKfwhMpOE57y087zq381Ty7T07ekuXcC+rF+hgndZ9L21E4MfQ9qV/dz+Z8XQ/HVCp7QHSMN+rH4eI+7YbTdyeRAFaUVG5TLwoQrpH2nYDt6zcUvnhjz2IihfIQzd1kokXtG6Yn1BvVSNQTwiw0J2ZINYGpYPqYWrsETT63GBVM9AjUk8eTbFxhMd7Ew7Jr4sTERxKliOgynSN8JUenfylExyhZqhjsDXj9Z1qoVt3ZqosUptQV5lwfmu5EHVvpClpqxqBj37YJ5/PiVVvMvNH8QsuZ785VFoH2y91jpmeqJ05CBRly/odWXCuixQdNIR1ayEeEhbshqU7KJmuepJc9aEDh/GYOtcSUwslMfWQytrmIbPE7GLXcG8Z0N1X+uOvE5wl0jKtzdiT67Zn12Hchp6YV1/YRh65pZO1DVOP58wI1J9DxDGwruDtL3epSVWncB+5YkyE+HrbVtNSXuAXrk6QpesOggvrLs/f200iIKKgTSNgjwiwkHZlVIbM+ydJ1iErxAJcU+uIeqBLR7VS9UFUItLmWAhQNGPWL7GyMTpbghDxHtgjWPp2D4Qqlf04DYlph2TdFmS8YjLURGziBWFy/SVtlZqdY/3aZ99bKgBe+dAKZNhK1MSftVy0Xp18UFs7wPDETv7Pyj0syzelyyr0Y2JtHLMEIYGuEh7mLRFtPEEF84QKm74m1uXnzs0vk7iD+cKyhH0JuRCSOCysOxrcO0CG9gmQQT39rSp7xOP+pqfWSEGhoQbsmf91w/1RC4vU5Riox+/Wr0Nk3LHte/JAoYuGslCRUqAbPShMFQ0a37fGJR2hNVtSZMnaRFACjsn/ru4gV+LGaK/qKdb4G+gx1R8CVPyah2zQfNBdqWhkaoyQX4CeC7zQ62CAKTKO85gCGu99u0e2gRIUHuYp5w8Okw6gxtmzlSGCvWZTiizbkIhodjkyRmFy+xXtdcbolB998do0efqdLepVd1cnJWzBJ8/d0x31hKGnfNq2nrJmkpTQRavTZMe+XKi5NZaO7XykIzKhndp72005gLX+KhSv2hmfIztjc2Q/av9IQRzUM0DGDAlWrRusoWSBmULS50jDpSLmh8/1Rz2ifV8brfV4qeG4GsYh4iBZZPjnihREF/epnhN9ugTK4D7BKLa1LgWqQ0WHER1Mk9VRKSiAPApqXJjcOL6djg7W8EjTH7d+BMhxX7I+TdUGGbnQpqNmlHc8nKBRA4KsKipnOkb92LIIUPZ3+jd75G9Q4zq09ZKxw9siU25d12zLIiAqE7B+K+qkViYgY/Sv3ItO9cySWiONx9JYnGn9tzy9VmJi89TblHxmppkqgr+9N1w5EWf6njW+zrnKmi2ZoIQmyfptmYoC2K2Dv3Tv6CeR7bxVLyxrHLe9j4mqdjtQ77cdtNa9CXmqVxL7X102upWS1G/I/Z+zKFFe/yxaDYHCEV+9Nhg1Y7bFhmpI/Bpo2w3nEBl3mBebv+AYzf4rQWITCiQUKef+6FvUDTVG1LdvCGM2aFc85GGjM1Bgmw1ZyybqhnftxW1135SG+EH0Ni2KAHvAsP5j/vIkRHMr92/xhioYi8rpCGn5XIv+DFa/8t/+SZR3v9olzugLdOl57XGN9rP6MVtygJQg/ntVgqzcmCQRrT3kJTQitec6mZpgyQzibc+uVV+hFHEm6g9pd10VgayabQgsFBQelTmLDspPCxMkF4EAOj/9uwer+riGUEZUAOr/qkSAgevN0ZmyYXuqpEG2vCPqjK69qA3qWoMarOH3tFk75ecFCWq8VJ6jHHcLNLXVZrUINLxDZAoN5SjpWS/dkIZOy+UShiLEzuG+oGR4IXPkbtEDOwNykHGJebI7Llf2ICXLdGfPjr5y2XmGiLijy4aa/k76se0jwB4RyyCJzBs+U/ymRrnsAVDJoRM0DDxtRni1OS4CdJJf+miHrEC9xKhzW8mFyAqxk702AwIZkBH+Zu4uTPpL5NFbOqvzRmMjMumVjbJuW5aCwrlFYykpPSbOqDP8dcZwyEk3TLCzOr8LxWO+nhsvc5ckihMEQQb2DEbNSqiSjK/O9/VnGhaB/ZDQX70pWbbuysRv5iQ3jW+PeVyremc1MNh//8sbZRPqK2nnDQyS1x7u1bDg6K3/FwLW5RAZR8rJ2kYUKbLb/dqtmZKVW6bS1GGg0wX7u0oguyBj6YcmfmxUVhNj12XWRFDyMQ2Sj4wmJOAEYgM/eu89I31kBHpQDO8bIEx1atMI2BMCqZmlwkg/1eJOrQ1iR3FSf8aju3yAr24sZ0+/e233hQ0j739lA+omBS0TOqF1gmdtV2XX3zsOSthfK/bL4jUH5cJhoTIZxdSOHkgwzRK1DHSRpPRidQyw9vax2zpb3fFAqeyZc+IQJEoAO6W5jBzYSvp1D9KUOKv7pao3oILCw7JsfaKshSAKHfE7J0Ygs93SYo1rqxoVg0k3Pbka0uIl6m0qEl97cZuqPqpfa3gErNMhOhWXlMwSePu54ItCaeRgoRxIOiQl6HdBYxdkNjVj8aZT08bSBEV2LKw7B/+Y5TkKAQSqvxSDb5oP5Q9KOxq/F4qLdHhrNxRDekpv1Ed0aIssFNanTSNgTwhQ6Wn1lgyVfWXE9l82ezhh56DwoX83XyUpPKSPvz7+jcDoJXpq5MojUzehT4qr3HFlV0wq7Ec5zlI/7664HPlyTgz6FnnLG4/1Rn8Zx6bI3PPihooIOZuM5kA1jDLs8z4YYTVZIkbyf/o7QT7+Ya80RxPUMUPb4JoYpAWTLHWS1PN6i0uPypI1ibJyUxL6PrWQJ27vohqG19cw9qElAemjlGZnZv2jKQNEt6aoL/RrtB3bcIiq2qX07FJhtDsbXZApf8lodykOuCNweMpQHM5JHxVIePFt5tRIPOAw+SIC7ufVDNHvFtIKzpCmwVWFrH7NXhDIRGaVzVNJ/TBy+I375oXaICo+XY4C1BBkXLVpBEwRoIz2A69uRL+4ALlufKSmyJmCc5bHyemF8tmPOyQI95mPMflx5LoBHkf3wimiscGmUWjhOsiWP3hjx7Mgafm3YxFgfe69beh/UyyjB7WWEcgKWYNSmeX33PG2wGawv/8Tp+Tzxw4Jlcdv7yyuzvUjiz5/ebK88MF2BTrFRb6dNthqAgKOdySccY9t1yE64y7pNzQCDo4As0CsxWNWiOpOpta7s49ygkb2D9IBAVNg9OMKBNhk9c7JayEl7Cc3Xa4bC1YAU4MHbOw6/YsosA885N2n+zp05vXKh1aqZpUUInCHZD+VCilXPf+jUaqnTA1gNetHZ/+ZIDO+3QUaqJdcfVGk1bf+MOvOO/DK9uzPlR/n75VzGh2XqQ/3Rja3fhqJv4ZWMxQvolGGm5kizUiyqgPxu8ZTYFY1JD0YjYBGoMYIsEs2naDn3tsuP4L+wR5fRmYc+wZNOL81mgh3l+vHtZV2ULyhcII2jcCpCHCyesfkdRIa5C63TuiCSYM+Tk7FqDrP2Zg2oo0XMrT7VXZ2CBpJOqrxWrMastVs6tyzo4+qJSpHoIYMjj5dfOodFlLon5uxTX74+4CMG9leJl7YQTdQrfdfoeE26Iva8wFQMk5GTdtnP+1VmRo2GLe0Uaho3bZsyQKriQwnln0M7OHYSp2WxryG648+B9SyyiHkGq5Bf1wjoBFoOARI+aC059+r2CfLUB9nHA0pKhPObwXp0eB6V9gxjkEvbQuBe9FQMC6xUJ68sx/qKeqHTmJbCNVstFvQI4U1RVMf7S0j+wfW7Mt28mlel8betUSKELQJBj23pLRc1fNStIhZIlLa68s4lgde3YRa5EOoi+smbVpafiJcX/umt1NzBJashcDQkji5A4ILd0y0vBw8HaEbHl+tjn+O9q0n+shQKLlqswoEdIbIKn4GPQiNQA0QIA1uCRQYX/8sRj74fo/s2V8AARFDXINUlEtGtJTJd3eT2ya0l4gwd9R/6Ch/DeB12I/+tTJFvp+/X+64qpv4++q6MnMcCEH+LqhzLZX5yxJVw0hmRRzNmkDkKBUqW7xOMZM9Cr1hYkHLpHMSBAcpsm39OCVUr33gtU0Sn1Qkk27qhZ6Hbo72U+j9PQWBtnCI/byd5cvf9ihRrh6R3qd8wrxPXZ2ROQ7zkAWrUtWKN+zIlguGhKimsubdkl5bLRCI1pS5WqCmv6IRaAgESGf67o8DMhl0jz+WJam0u3EcVEy8bUK4vDSpp4xCvwNr7vNhHLNeWg8CnCw+PG0TRBT8ZWi/UOsZWC1HkpeXJ6tXr5Yff/pJ4uLipGfPnrVcU92/Fg6pckaiBcqnrOFzRKMK7O+4ZtHahrqB0lusKHRpWaVyBei89WEMIK1HfeWkm3tLgI92+OsDc1vYRnCAq3hCqXjWL3uUc9462NWiww4NdFZqx9v35Knl7vgCuWhYS4Hgq7aGRSDa8cJVDQu43rpGoMYI8ML5zLtb5eK7l8qnP+1TyopcCSWzB/Xyl+nP9JPf3huu+hvUl2pOjXdCf8GqEVi0JlVyoNZ5wdAwqx5ndQeXnZUle/fulXVr10pRUVF1v2aRz1GufPiAlqjtO4AJEDTwHdC6R3pJaIDBCVmDeqIR/Qz0wThQ17buzrU4Iis3Z6r+azde3lk7QxZH2/Y2MADNd4f2DZUXoQSXh35Ulra7r+mgVBe5HR7/M3+JtfQm9fqrgYB2iKoBkv6IRqC+EeDEaS4ks697bJXcDsUvTljLUQxMc3dtKpStpRP07lN95VxdmFnfP4/dbW8eGvV2jfRDM2rrbMjLUtdnnnkaWYXqORThEREy/tJLreZ3GtQ7RIpKjsrKzRlWM6b6HgipQTTi0Dn8JE1uzqKDFh0KKcZvfhEjA3oGQfXPMTN0FgXYDCvftn27fPfdd2ZYU+1XMf689lBebSKf/Liv9iup5jepLvfKgz3FFYJHtFlzYlXft2p+XX/MQghoh8hCwOrVagRqg0BGTpm8990eufCuf+SVj3cIm7oZjfVAz/6vm/z1yXmqh0fIiYir8X291AjUBoHiknLZtidHenbyr83X6+U7X3/9lezYEV2hnFidjTZpbD1NUd1cnCQ8zMuhHaLhJqISiaDM8XpGW7ohXfItGJVfsj5NMrPLQEtqW53DRn+mnhHIzMySt958U47/W71gh6WGR1n485Ehn7c0UQoKj1pqMxXrDfZvIc/c1VU9pwLj5BlbpaDI8tutGIB+cBoCWkboNEj0CxqB+kcgel++zP7zgCzD5KD82MkbAwuSRw0IlIljwlDfUT/9Eup/7/UWGxKBnXH5yLwI+rF4mm0Ys2bNkpycHMWLf+yxxyU5OUV++HG2HMex7eHuLnf9739qW/Hx8aj1WSXe3t7i7x8gq1atlIyMTOnXt59MuGIC5OEbyVdffy2//DJHfX76jOni6+sjN95wI1TLilSdUExMjKQmp0pEhwi56eabIaFcdZZr+44dikJnzDINHTpMunTpLAUFBfL7H7/LgfgD0sK5uYwaOUp69e5tNiyMK2qHfjdbY9KMTx1u2QEOUKBfC0lH3RApbKx5nDozWo4ePY6ayGS5YbxlHJaFq1OlU7gPjrtmFse8vLxc5s37XXbt3Cnlx49J7169ZOjQoeLpefLc2rtnjyxfvhznRLL4+PniM71lyJAhFWPbvXu3LF26VC6//HLZu2+vbNywUTw8PGXcuEskICBA1q1bJ+vWr8O50VjGjxsn7dq1U9+tzrlk3MjZxhAXHydr1qwV5+YtpHef3rJgwQJJSUmWUeeNlpEjRhhX85/nTnFRsWzcvEnWgrZ61513ynqMeQP2xdfXV2688Ubx8vKStPR0efqppyQ/P1/Wr1svuTm5MvbCsRIRHqHO1dVrVuM8L5GgwEDp16+f9AKelrQ+nQPkt4X7ZNnGdLl0VEtLbkqt+zzU+244r5XM/cfQPP2lD3fIm4+b/9pj8R2xkw1oUQU7+SH1btgeAqRyLF6bJi8jEzTz51jZn1SIKJlBLY6iCDeMaweRhB5y8fBQCUTXe20aAUsgsBaF5qxTu3C4+Sak3bv3kI8++khiY2Pl2muvFXc4QR1AY3vvvfckNzdXLgWdjY7Mxx9/jInXGtT77JP9+/eLn5+f7Nq1SzZhInXo0CHp27cvJmXN8dpOPC+UO++4Q8JatRYfTKpeeeUV2bxps7z84svSpWsX+eTTTyUjLU0GDhyoYGLt0B9//KEmjP379xcnp6byxRdfCOl0l2H7YWFhcL4y5Kknn5Q+ffrIuYMHYcK2QQkxcBzGiaa5MC8qOSKrN6fKLZeFO2wfsLTMUomJzUcPlnJc39oqtUxSgVPhJF01NsxcUFdaz7TPY6Rv1yAJCz1J06v0ATM9KS0pwbH0lDrO7rnvXvHDMfrWW2/J/gMHZOTIkWorf/31l0ybNk3GXHABHJzxkpSUKDNnzsQySQYPHiy/zJkjH374odAp2gmnKjc3Dw1jPeXPv/6UtXCEdsCpT0hIkLKyw8phWL9+vVx22WXqs9U5lziIs42BY/nwgw9l5cqVymmL2hIl7m5uEr0zRlatXKUcJB8fn7OeO/PmzpOvvvxSjXdLVBRUBY9IUWGhbNy4UVIQIBk2bJgcgwPJTO52UOb69O2jghHBQcHqO9OmvSEvvDBFRowYKfNxHru6uEhkZKSZfq2qV9MYiqyxCXmKnj6sb/3IYffv5ifLN2Wo2iX2Dwz0c5YObQzZ06pHqV+1EAJaVMFCwOrVagTOiADT4l/8Fi/j7l0qz07fKjsxQTBahzYeMuW+HvLHRyNVXwStFmdERi8thcAhHI9GLru5ttG4cSMJDgmutDo6MR4eJyelXbp0kdtvv119pmXLljJ9+nSZNGmSvAn6DJ0XRqVzsrOR+WHjTENBfteuXdXzI0eOSHR0tPjDcWncpLHw+6HBIbIb0feqjJO8Z59+RjlUNyE6zSg1bSYyWV2wzgswQe2IyRbfo337zbdqac7/XJ2dFOWvEDU0jmrn9jzZiJJZ8fMHGY6RpLRi2RyTY3ZY2IT1EOhPPp6WDyh9+/33cjAxQR3Tzi1aKGVDOt9JBxPxu/+rsiGzZs3EBH+EnHfeeciI+snNN9+ispRURFzyzxK5YsIEOX/0aIXDKDhRzzz9tMqmDofzwHOhd+9eMnnyZHlhyhQZOWqUyqxk4/XqnkvMyJxtDDyXHn7kETUGF1cXdT4+9PDDKivL/di9c5d672znzsQrJ8qAAQPUZ6+44gp56qknZdobb6hsGZ0rGjNnrduE8aEEBgQKz2++xgwZG/o2OqexuhZcg6DK0WOV++ypL1ngP28cKxnoF1Rfxj5cLyLwSTYI7e0vdgrVF7XVPwKaMlf/mOstOigC+5OL5AfQ4k5tosoLP5uzXXNRG+nVybJ9EBwUer3bVopAs+YGGlObNm0qRhgSEiKDBg+RZZgUHTx4UGWD+CZVFY3m5OQkHyCS3rxZM2FkfsmyZZKSlipH4Sidavv27ZNHHn1UZYJM5bdLSktB01knnPQdQBSfRjod6TnngKp35MhRNRk7dX36ee0R6NnRW038mBXaFJ0t914bKfOWJKoV/gbaUJ8utit6sGjhQunWrbs0wzFptDeQ5TiGiTyPXVLCeEx1gINvaqTMxcTsRAZoLShpo8QFmRBaK2QwjRYSapDCDws7eZ6EnniNNTjMaFbnXDqYmFStMTAgQfPFepufoKC2atVKvUaKaXXPHacTWBjHyhXQ4WIw4+jRo9K0qWE7asUnT2+JBEZ///23THpwEoIYdwozvG3ami+DrbZnRf+RTnrnlRHyIfoKFqNx8fPvbZNPXhioKMdWNEy7H4p2iOz+J9Y72NAIrN2aJd/DEdqwPavSUNjfvzqiAABAAElEQVSkbdzIUFBF2qCDu+UjmJU2rp9oBE4gQNXCIjTMtCajU0Jj/YDRTB0ivsZs02xE5Um3uXT8eOGk6wBod6daAihGtMWLF1fqR5SamqJev/qqqxWFTz2x4H+kzNGnc8N576jGxtFdwz2V1PD2vXmoW3MTiitQPIZ1GxRX8HR3Mhs8TRF1d3drKjn5lo24Z6LuraysTDnXpoNnppR/tDQ47DTWxZlax06d1VPj8Wj6nvFxUwQATrUmjQ3Tt/Jj5ae+Vem56blUlzGYjts41tqcO41P2X/jYM9Bny6jDR4yVHbERKus2csvv6zO20eQtTpTfaDxe+ZY5uJYCW9t2V5EVY3zpvHtZE1Upmzfm6vOj2//2K9opVV9Vr9mGQQqn5mW2YZeq0bA4RA4cvS46ntxxQMr5IFXN1ZyhthE9dFbO8ufn4ySh27qpJ0hhzs6rGuHWwW5KCnkwuLTsysNNVJS3GjBIUEVQzBJEKlMztSpUyUKtQmvvvqKog81BXWuKiM9qVu3rhBsWIWaot8rPtLcyRDJj43dV/Ga8cHhw4cVHcn43BzL9OwSVQvYpMnJiZ851mtr6+jb1UBXPHzkmFDQ4zIUldOUuMLyZLPvTvcIb4lDXYglrXFTw7EXtTkKdTGVqV2kd1JswcPdQBfdiXo4U/PxNbACTIUXTN+v62PTc8lcY7DEuWPqEB2DIMWDDzwojz/+uHijXmnr1q3y+uuv1xWKs36fx2BCyiHpGnFSBOOsXzLTB+gnvnB/dzh9Bkf3ox/2SnxioZnWrldTHQS0Q1QdlPRnNALVRIBynWyyxiaqr34SjY7sJ5tC9umCItsn+sqvM4arAmLn5lVP4Kq5Kf0xjYBZEOjS3lNlLuIPnqxlM8eKjZFgUtqMdgwqc0bhEONrVS1JqSENKKx1mHqb2SF+l380Km2xUJsUOFdXN/UaC81Z43CqMbL96KOPKVWrWZ9/rug6/ExgUJCKOK9evUao0GU0ruPtt9+RzKxM40tmWe7HpLx7pKbEmqplUmBh7NCQikkgaXPmtjGDg+F45UBK+bC5V12xPh9vH3Fzc1dNgFesWF7xOimYb6AmjoqLEZER6vWdoMeZWlxsnHraMbKj6cuVH1dxXFf+wJmfmZ5LdRqDySbMee4YHSFS6IxGQZa8vDylvvfB++9JW9DlKMJSVGRZByFqZ4aw7mxk/5OBGOOY6mPJVhoP3WQ4DuicTZ6xDc706de0+hiLI25DO0SO+KvrfTY7AsnpJTJt1k44QktUY7e8AsPNl70Nxo1sKbPfGiofPd8ftUL+mhdsdvT1CuuCgItzE+nZEVHYXeZ1AHr26qmGNQPKclSNe+2119WEJheTQ6q5mTovO3Zsl1JQjmhUnaMM7/U33KAaJfI1bx+DI7F48SJFfaOCHI1KWFwXpbnp1DASz7qgg6DJHUKtA60EDhklfh977DH5F/0+Xn31NVUzxCaMF4+7RGWbKP37Peh3c+fOlScQlQ4M8FfSv2oFZvivCNm3WDicQ3rXj3KVGYZssVV0bkcH3JAlo6CMS4smQqeFRnGFDTuyzbptShv7eTeXv5Yb6sTMunKTlV177dXq2fQZM5RACBUN77jjdglv117JZfft01e69+gh6RA2WAcpaqMxy0kVxsshM0+jCiOttLhYLflfYeEh9TgLAgpGKzxkOL6LT3ES/utcqu4Y8vMMwZESkzEYAwSFOPeqe+7QqaEVmwRFMjIN15lDJ/aJkvu0zVGbJTYuVp2D/yLwMf/P+ep1BjwiIE7BGiYXF8tR2egILVqVIONHtRIP0CwbypgxHdTL0BMu9uAh1bS1ocbiaNvVstuO9ovr/TUrAoxwvv3lLpk6K0apxZVDSpvGuoxrLmorr6IbNTu0e3sY6Dlm3bhemUbATAhQ2OPnBftlYI9gCBUYKBt1XTVrF7aivicaUsGUEB4N9aw8yAgHBweJJ/qqUD2OE6aFKEZ3An2NS9LalixZCqWu2yrUtjgOOjIsOt+0aZMEIbND1SpG1imGEIN1d+nUCYXX/WTLlq2SCLqdj6+fcnC4fvZPKSwqVkpcGzduUNtcBMeK6ly33nKr5OTmCIUXGEknNYcS3DffcgtqP8yXwV26IUnSMgvlGTRWbgJpX0c2JwSJ2G6A9UIsIL/u4rYS4NNCfl1syA4Vlx6rUJ8zB048tkP8neXzOXuV9Laft2XqNcPRO4e1PpTMptw8HfRL0CfoqquuqtiN/v36I/OYIT/9/LOkQiJ+DXpw8XNUjfPFMTsPDvn8P/9UggOJ6FPk7+8va+A8zfv9d/Ua10vJ6xjU18yFrDWpnTwHqODo7OxcrXPpbGNIz0iX6e9MV+cFM1usjWoO1bxPIJFfDAeJ4/ZE/d74S8b/57kzZ86vKnhBUQnW8bVHv6QffvhBtm3bpvDgfvfr11cpPjKoQdn9LVu2yNVXXy07d++UZcuWyz7s7zackzxHb731NpU1rgDTzA/mLo6TlPRDMvWR3shYmu/cr80wSSv9fVmSHD5yXHZAjXFY30AoJeo5RG2wrMF3os9BlE7n42qAmP6oRoBnzKqoDPl63n70bzFE84yoBOPGS7W48cgKNfRF1TgmvdQInA0BRkcvvW+ZhLfxkasu7HC2j9fo/awsgwoWv8QJnKkKFyPCDz/0sFx44YVy8003SS4cmODg4IoMgumG2JeIk1sjRY7vkY7XAhNBo5363Pj62ZbMTqWlpioaHSWTzWklZUflpffWqevCXVcZaFPmXL8trutFNKD8AxM+2sKZoxEwcpJbnl6LHkV5cETPkbnvjzB777WXPkaR/tpUeejWPuLvc/KYMTd+pHUym+IPB4eS8FUZMz4HIcdNWiibrZrDanIucXvmGoM5zh1OQzOROaIDyOyhUeGRjlDTJk1VBs0cGJ1pHeu3pcn3v++WNx7rI8P7mef3ONO2qvv6XytS5Pn3Dc5jZFsP+fK1QdIY1z9tFkPgO02Zsxi2esX2hoASSkAUc+KDK+SRqZsrOUMd23nIKw/2kt/eGyFXXximnSF7+/HtfH+oxnXvNZGyNioFhbwGKo65dpmTPqOZOkPG14xLOjaU3DbSqYyvG5ekFZk6Q3zd1Bmq6rnxu2dbUr2KdQrmdoa43V8XxAl7jVx/SduzDcNh3g9vfbLxZDwaUtOuHNtaLdmw+ueFB9Vjc/731O1dJAJ93j74dptk5Zaac9WV1kVVOWZHz+QM8cOsN2LvIHM5Q5UGgCdnO5fMOQZznDs854mF8dw3yn6zNovnvSUtKiZTfpi/W+65poPVOEPc3wuHof1ATwN1bs/+AhWAtSQOet1QgNQgaAQ0Av+NABtXVgglfHpSKIEXb3J92S/g69cHg+YRhAj2f69Lv6sRsFYEeAPu09lXvp23C13l/1vK11z7UHjIMBkmNccebRvqsjbuSJPHb+sirNXSZkCgXauTtSBxBw3HwOiBwRXU4rkQVyg7XFmtra7YUd1v+lN9pXWQs7z7ZZQcSDbU5dR1vdbyfXs/lyyB87L1SfL1bzFy6+Xhcsvl7S2xiTqt86m7ugrbc9Bm/hwLBbyTIk11WrH+cpUI6BqiKmHRL2oERLLzDstnUIx7Dk3S1m/LqrhBUyjhkuGhqrs06XFBfual2GjsNQINhUD/7n4yZ1GCyhL17ByATvGWo2hQJW7mzJlCKhxpdWnpaRIZGVnRCLKhMDDXdpPTC+XTH6Ih4eslt05ojyxR1fQpc23PltbDfkTss0LzR/0QG1OTDkknKGpnjqqdoBBCZyggmtOYCWVNZ2xCofz09z5pgXq51iEGOWxzbqe+12Xv55K58SyD5Pt38/bICtT2PYZgxY3oAWSN5oogigfqkVehP9Ex1FHu2X9IiTRZ8LJsjTDU15h0DVF9Ia23YzsIUDHu63nxMn9FsuqNYRw5hRImnN9aSWbrAkcjKnppbwiQnnHn8+ukS4Sf3HBpJ4vtHusEjqJHi6m5uLqYPrXZx9mgZDELcQyyy8Ul5cIgCukvzMINRlaZE3NHt9G3Lpb8wiPSrYOXzHr5XAVHHoQWLkHLAvYoogTxrzNGWCzr/t0fB+S973ZLhzZectVFHcXLhoVv7PlcMvd5svdAnvz45x6oXB6X1x/uLT0ivcy9CbOv7+4XNsjmGIPC4MM3d0Y9YpjZt6FXKN/ZrKhCZi6KYDNLJSuvDHzgw5ILmeNSRJeO4EJKZQ56083B22ZUzglLD1cnJb3p591MKdqE4mLL4k1tGgEjArGgbnw1N07+WZeGficntUZ8vZrLdZe0kQmjW+vaICNYemnXCFD6+MHXNkrPTgFy7SUd9bWyBr92fGK+fP3rLvHxcpIDqI/hvcjU3FyaCqWg2X+nZ0eD5LDp+47y+LZn18qOvXmKJrdw5nkVu/3aZzHy6yJDDdFLk6jSaZDkrviAGR/sTTikGACpGSUyenCYjBjQEiqA2lk1I8RWs6qCQ4dl3pJ42RydDnp7iDxxe2elBms1A/yPgaRklsjVD69UGVRnyNT/9M4wzGOb/8c39Fu1QMA2HKL07FLZsjMX8oN5sg+T1v1Jh1TUjTvMmg03l2bK4XGC89MEkTcnRt+QUyyH2ks5mlsdKT+GXhRHJQ+N2djsikZnqDU6tIeHuUsn9EXo2clbOoR5WCwapTaq/7NKBLbvyZMvfouTNVsyK40vNNAFqfS2cjHocTqiWwka/cQBENgckyOPvrFZgv3d5LYru4hz84brzWErcO+Oz5VPvt+mMkJXjAlTVDD2Evl7ZQqagxp6u5juSyvcgy5F35OLR4SKl7uT6Vt2/5hUZOJCW/nNBRXBJk7+JkxaroJSYSGuavJnSYoQg1+z/zogn/60Dz2RnGTMkDDp203Xg9rLAUiVxyVrEmXFxiTQM5vDEeoqA7r72tzukWI6/evdatyU4X7z8d42tw9WPmDrdIh4gdoMHvHS9dDp35olGXCI6Oi0CnaX0ABXCfRzkSB/V2Hkns5QTQrZeXKwaWZ6JvT0s4okPasYxZUFUlR8VDWIY4PCEQMCofseoLibVv4D6uHVAYG1OLboCG3bXVk6mwpIN1/WDlHc4BodW3UYiv6qRsAqEYhPLJRJr2yU4/+eI9eP7yRtW9l+vYUlgGYWaOGKA7JoTQKwwhZMkkJtW7qpvjpdwj3VtWbBqhRJRkbC1Hh/G457zmWjW0k/9CBxBPv0p1j57Od9ale/f3OohLd2q9jtFz7YLvOXJ6vnL9zfQy5ENs3SlltwRInn/Lr4IHq+tJCRA/FbdA/UGSNLA2+h9R8qOizLUCO0ZnMKeqs1ljsnRsjlOL9slRnEa8yNT6yWfchq0t56oo8KuFgIPkdcrXU5RLvBXZ+zKBGOUKoUFpfDAXKTzuG+yOJ4ofDR3aJRejpGpDrsistB4VquHIdT1ruzj1yKrsEj+0NCU9Pr7OIEAaVflsDR/hKOkPHCYtyx7pHecgscIWOXaOPreqkRcGQECqCy+OIHO1DYmy6jB7WWscPa6uuhyQGRkV0i3/6+C0G2IrnjighhhmPphnQpQH3MqUZ5/vMHBSOw5yyrt2TBgUqV0rLKin58bzyyRpeOaimedpw1+hN9Vqac6LNyav8X1nGyvQFZHuztNmf6cARF64fiTkbKl7/Fy+9Lk5C1aiIDewXjnhCM30JTlE49nq3xeQIC3Ks3p8qWXemgxDnJTRBMYO0vpe9t3ZhlvvWZtXIczlGgbwuVPdX9Ds32qza8Q8QJKiNmTFmzmDckwEX6dw+Sbh39wS1umAsQCzp3wzEi1zQmNludVJfDMaKimD3foMx2WFnhiozH2ee/xsnB1MrSlSx2vhmSm7ZQXGmF0OohOQgCDFa9+/UulZW/dHR76drBMTIZZ/p5qVS1cOUBRcVp19JdXn6gh5DiRSPLYf12OjxpsnxTupSUVnZ6KNnfHcXcZCIwm7RwdarsOVC5/xPrX6mIxvtOu5YnZarPNB5be51qcv+bsl4Nm7LkEy9oXWkXXkct0ZwTtUQP3dRJrr24TaX3Lf2EAg+/oB/SLxgDWSWd2vuouUnnCF+LBmctvV/2uP7C4iMSFZ0hG7anSkpGMYQyPORanDcMPtSXI11fuE6dGaOOS27vOvQ2e/DGjvW1aXvfTsM5RJyg/g1H6LNf9iGyViq9ugTKoD7B0jbUuigZ+YfKZN1WUPeQdmUt0pVjw+TGce3Ew03z6W3h7GCamR2f6QglpxdXDJkSr6MGBClqXATqyLRpBDQCZ0eAAjbvgse+aE2KRLbzlouGt7EL2eKz7/nJTzBrsR73hAVwhujN3IOGtpchYHYm6jZFflifSKdnzdZMpaB2cm2GetY+XXwhz+2p6OFL16dL8SkOVP9uvnAI2sq5PU82uTVdhy0+PphaLFc8sFwN/ebL2su913aotBs5+Yfl8knLlTPpCiGKuWh63RD3Xd5DVm7OkN8WJ8mGHZnINDSRbpF+0qOjn3Ro660pdZV+tfp7Uoy68Oi9WbIVvb72HshV2bzzzw1WtNOOba1rHmlOVKhaeQWyp9kQFCNziT0Q9RzGLAg3jENEcYTXP42BOEKh4uiORhGjLzi71mwUY1gFp2jpuoNKSpVd3a8Y05raDdqsEAFGaP8AB53UuBQTvj4VhCh9ewtuwKGBzlY4cj0kjYD1I7AVdXfvf7sXQje5EgnZ4vOg0BWBpT0bmQNrolJl2fpE0NyOoh6htdw+MbxGtaZ0dFZsypBFcI6o5EfnytQoz90fNURe7s1k254cSQJ1zNSYgWLG6BKIMNi60Etp2TEZesMCtXsXQbhmyr3dTXdVPf4CgawPZ+9Vj0kjfPZ/XU/7TH2+QCeNNMeFyPztjM1Tk/CO7XzQL8lHItt7I3vqWMIY9Yk9t8XShp2xOaq0IT4xTzmj54LhMWZwsKqnccL54wjGzPMz725Ru9oZtYlfvjrIEXbb0vtYvw5RCS6A73y1S9iFmtHFKy6IgOqHbU1KKeu9cFUCivUSQWNwlyn3dZf2rU4Wg1r6F9Pr/28Eysv/ld+XJSkOeFrWyckEi5bZTJWRyGB/63a+/3sP9bsaAetBgEp0n6GD+pZdORIS6CoDewRJn26BdqVIl4QGq+u2pKJhaIb8i2wBA2HXg6pS115krM1aCon/hZhg08FkXYCpsRCcke4iOFGxJwqpje9TUIgKmHTKbLk2YviNC1U2jBmw9yf3N+5exfIIApFXPbRCiVCQZjjz5YHSDY1urcEycspk+cZ0ZI8ycfzDucW9p2WQG6L1XhKO4EDblh4o5m9iDUO12THkQSo77mCeLFp1UAUhDhUdUVLZrPNlM1/S3R21hoZiN+vQMJ42+e5uqmGrzf7Q1jHw+nOIqPf/5FtRUlBUDq5whPTo5G8dENRyFCyk/QHNvZJSDwkbZU04v1Ut16S/Zg4EjpYfl7lLkuQrFMNm5JRWrJIR13EjWqqMUIBvw9SkVQxGP9AI2CkCFMT5dXGiqgclxahbBz/pDkpR5/aot7DBqG12fqls3006ToYkphRC4MdFrkBh9kXDQi3SuyQ777AsXpumsg8xyDycai3Qe8TPq5nqvcdrndHYTPQ61NZMvCAMTmhj48s2s6S8dmJaMRTm3OX7N4dUOW6qgT7w6kb1Xnt87htQhKytLoTBXgYHNqBubP2OLEkEHZAUytBAN0UpbQVHqRWEoQJ9XTSrpMpfWVRLlGQEHw6mHMIxUYgeXgWSg/OQ9HZjsKA7mvg+DtnsiDAdhGb2+KqHVyjceB34dcZwcXXWDvgZDq/qvFw/DhFrOF7+eAcyKh5y/WWdcENpVp3BWf1nVKE+eOSLVh+Q0eeGqJS/tV2orR7EOg6QEcTfkHH8am48GvSWVazNqWljpdJ0E1Tj/L21I1QBjH6gEbAgApwYLkRt6AJQwkj5IkU18gSliFFzXy/rzM6SYpuAidg+1CKwLiE5vUhR4Yb3CwTFNlR6oU9dfVkqampJy+Ifexidas2QOeJ4y00cI3fXpopKRzqdC5wnWzEqZkWDQs/+MH9+POqMw37izS1Q7ktT7992Rbj876qIM37WGt4gtY5Zv23ocRe9Lx8ZvgKhI0uhDGaRQoPcwVRA+xA/ZwmAk+RomaQC9IRMzy5WFLhUnGuJcITSoNLIORWPZfaG7IpMIPtDLkGgYA6k0I3GTCEpcndf3cHh2R4ffL9XlQUQm6subCOP3tLJCJNe1hwByztEnKi+/90eGTWoFVJ67e0yOsKCvs9/jkE01FM1y7KlG1LNjxnr+AYdISoQfT1vvyouNI6KNBMWON8IqU1fRFS1aQQ0Ag2DQD5UupaBUrR8Q4ZEgVJEcQEqh7YHpahNqLu0RPSc/eQaIoPEHiV0ehiJ3g9Kzn5Eo3lNCYCU7WDQcUaiFx3bLjRGdLoh7UByUYVzxEzKqdYIk8Pj/56k2nm6OcmtE8JlImh9thCce+j1zbI6KkM5Cqu/u+DU3at4TgfjqodXKilzOtlfvHquRNpQ4TwdWDq3u+ILVINeZlQToXbKc4LGCD8dI2aQfECH9PZocWLZ3GadJZ5jeQVlyPIcltyCUskCqyYjt0TSUAdUekI0hM4PSw8oR8/5U+f2HlAaPr2Mgtk3Tv5Ns6e8bjBrexuO94YQ26g4OBvwQdnhYzLhgeWSCfomBRa+mzZE2ukSjtr+IpZ1iD75cZ/MmhMrE0CRG9o3tLaDtInvpSK68fH32yXYr4V8PGWAw/JaLf1jKWrcP0mqoappRoiOEHsN3DCu7tx+S++DXr9GwNEQ4IQwJjZfNsVkg1qUixYL+ap2hLSiIDTaDvA1NNpmBsnX21k5Tq7OTcUJEfXaGH0ENuFmL6CcvFL8lUgWVJmysUyFLC8j1LQgXK+7dfCWvl18pE9XHwlBzxtrNbalYDH14rWpiK6fpAVXNV727mEE/YIhwVW9bTWvTXl/u/y5IlmNZ/V3Y/+zHmrBqlSZPGOr+mzrYFf5Zupgm77P8hhNRc+q+KQiNIeHY35imYLa10OFRyt+IwZY2SjWA0IbrhBtcMN5oZZoSu8G9T0+dkG/JGYOqYBnKaEn9mYsO1KOv2NSAoU3Sl0XoqG9YXlYNbfn4zwo8+bklykqF3eC5zhr3kLRUqUt5OPbhrqpZRssvT1qJkJBtb8P4RjFQ5DLaLxO3HhpOyWzbcv1dMb9qenSVGCBQRzOP7XVCgHLOUQ/Q79/GvTSr70kUgb0tO6Lcq2gq+JLWbmlMv2LKFBEPGTG031188IqMKrtS5xQUSzh8zlxlSYDbJzHaOj1cIS87LiJYW1x09/TCFgrAmy+yd47nOgnpBRDUa1YTRAZ9TQaVaM44VPOEWiwTZCxaQyBFIqkkDpzHCptR/FH+hhrl8rQ5LQQk7XikiOKfmNcj7dnM9XjrnWQs4qgRqJPSYc27mpCafyMLS23g4pFSt0/EGXIRY+cM1mblm7yyM2dhKIF1mgUWfp+/gE1tD8/GXVWevOTb2+RJdhn2sUQyXm+CmU69aaN/8dzIDWrFDQyOPBqiQwLHPpcZFuy8UexATr7pJidagwO0jFqwSX+mjRqrOYiTHY2wnnDrCLrcpj9pFPG8+ZfPDiG84jZRq7zGP6jsi6dn7LDcILwx+enGpUOed/1RoaLmS0fTyf1GwbBIWdwOAgCRmwgas5MK8c3H070pwi4m9YLB/i0kHsg3X7h0JBTh2n3z9nPi329aK882Av9l4Lsfp8tsIOWcYgoJ3r/yxvk4pHtVGdzCwzcaldJRaL3v94qY3FSPnVHF6sdp60MjBc/RhCZaTSVzzZmhG5GZEg3y7WVX1OPUyNwdgRIsaGCFyl3bI7JyR8fl4FexAwxJ2akt3ECx1pBUmeaNjkHj+E8IVpM2hivCWqiBkeIEzNeL+zReH3cjKwbnaOlG9JVxL6q/WwT6grJ6u7IhnlW9XaDvUaFwk9/2qe2/+M7w5A9+O8GtEXowXLto6tAuzIoiDqyuhadGTpFuTg3ihAEKCk9hkAAggGo42OvmhIEB9gQmOeMqjnD+aKWcICoiMfzh0EFUivpsDQGFbEJaFekXnHJeidnBByZoeKfM5co2mc2yg1UN2+cY3zeUEa64Y9/HwBbJF5lp4zjIJWSjXzrs+7PuO2GWsYnFsp1j69Svy/r8eZMH2631zwLYmx+h4g3rqsfWQllFU+5eUJnC47dele9DY3CPv8lRt54rI8M74dO5NpqhQBVl0i7PAiutdE4AbpsdCuoxrWrs+ytcZ16qRHQCGgEbB0BTnLXQoaXTXOXb8w4rQEs948tB9jdfsygEKuou2B2iFki2uevDFLNadWT//iPfQzvem696uHE+8FnLw6UTqg90eaYCFC+no41a4pNhUbYeH3SDR0dRnjhzS92yY9/GbKtd0yMkDuvDHfMA6L2e/1d4ymw2n//9G8+D05wRs5h+d/V3RSt4fRP2P8rgeDEk6P+y8L9cikK/JvXkgdv/0hVvYer0dX9KVAjflqQoCJg/BQpMhRLmPpoLyj6BanIVdXf1q9qBDQCGgHHQ4A0qNaQBx+JiSCdHkpU5xYcUdk2IxqFxeVCGevv/tgv0ajpYoaJRewN1dCSdTOsC6GNHhSMOpOz13CRGuWO2hnuB+lea7dlKgePGQxtjocA51fn9vQDTSxYiQskpBgCqBQkYSsANlSmYh3nEPZsXSM8VesR7u+u+Hz0XWxpU4qTVvDbRJvVIWLkZvrXu+UGSGsHB/x36tsKdt6iQ6CS0oqNySplPbCHn0W3ZS8rJwd28oxt8jWUCUmboTF9fzFO7GmP9paxQ0L0CW4vP7beD42ARsBiCJDy1A71Q5eMCJVrIMebgz5H+zFBNCrSkW7Fmi02Fv1+foLsRS0Xa0tCUPjO79aXJaaVyNL1hpqg4f0DpU1I9eYNncM9UW9WqpTbikELi4IKGe8PrGnR5pgIeIAqS6eIwgL70PeScwg6zNsgf07avR8EW+xZgY0UR/4xUFCOmut8NLFl2wBt1UbAvA7Rs+9uE3e35nLxiHbVHoG9fpDSoE64OM9ZFK88dd0w68y/9N4Dh2TKB9sVPY61AzRym88bGCRTH+kt40e2tNni5zPvtX5HI6AR0AhYHgEnp0ZqYjRxTJjq1RaHegNT46SRUXUKNPzwd4JSO2PGKBg9cph1sqSlnOi5xG0M7h2gGrRWd3vn9vSHI5SrCuspNLAPstbMMtGx0+a4CFBh8fLRrcUP/Qdj4gogCIGaKjjNdLy37MqFxLdnjdXtbAXNjqifYqkBa8viDhbKkN7+SuHPVsbfwOM0n0PEoq73v98j11zSEWoj1tl8r77BZp+N1VEpKlXLqIW2ygiw0zKVCMl9TUa00miD0Afk1Yd7yVVjw1SBtPF1vdQIaAQ0AhqB2iFAYYkRyMLQkdiz3xBBP3VNLMCnw7QQjXV/XpiALFIJpK2bSJCvs0XknDPRTNsou30umBTsR1NdI3tgWN9AJSZxCHUkSejTlAJVNu6jNsdGgD4xnQPS7Cm+wL5PzIqmQbFvLtp2UICiWwcvu8soMoARiEAGRVZoB1OLVZbYsY+Gau+9+Ryir+buhxxymVw+RhdyGeE/BwdnIeRfV25OhUZ+W4vcUIzbsqUlU9kzvtkjL3+0o1In9p4dvSEZ2VNugnKcL9ShtGkENAIaAY2AeRGgChWz7pw8Ub7bSKNjLYYPrruMptM4kSSVjg7Lb/8kSjqy964uTYQ1POYy3gt+X5qkVte3q1+1RBVMt00njwE0RsVLkQlgVDwfPXz4mjaNADOdrC8a0T8IvZ6KlEPE433H3jz5a2WKkgWvLk3TVtBkHeHW3XmqhQEdwIgwDwmrJhXVVvbRQuM0n0P0+mfR0qNTAMD3stBYbXO1buihsWjVQRXJ8vVy7Ek+b7SzfomVZ6ZvlWjUmxlvxCz+fe6ebnLvtZGqU7xt/tJ61BoBjYBGwDYQoDNE1sIQ0NQ4OaT4AusOeI0e2idAhuGPzsohNN6klULKeSdEGOi8zF+eonrhUNq8roErOi8sfKf16uQjPRAUq6l5QAKafZYWwSmiHPuuuHyh8tggZMK0aQSIABvAsm9VqyBXdbzzeOaxTkea9UY87igtbi8WEeauBBaYFWOvtwmgEFqa/moH2JnHIeLF9KPZe2XMkDDwFc0XPbIDgFH74iQrNyZB/cdVOre3rh4Q9YUvex/8gka9j78ZJesgC8sbL41c30dv7SxP3t5FWiGqoU0joBHQCGgE6g8BBukuHdVKFZ9Hx+YpWhFpNoXoY/Pm433k4mEtVf8ZNsA0Zo7Y84aZJToyhnqFo6pOgf2famrsl0M1UVp3UJj6dKkdtZyZrV6Y1HI8lF7eCadIZ4oUrPo/EwTat3ZTNLoSOERGGh2P93lLkpRybSfM0eyhBI3nQ3p2KTK8h4R0Ui84hF0gRKLtPxEwj0MUtStHcY4njo1oMGnDuPg4+WP+fIndu09atGghs2fPlrlzf5NzGjWSNm3a/CcKlnyTJ1dsQh4aZh1zSMUPdhV/9I0oWbAqRRU3EmsvdLW+77pImXJvD9Ut3h4uQJY8hvS6NQIaAY2ApRBg5LhfV18Z0N1P2FSdzU/ZEPfPFSnKSbl+XFtF+e7TxVepWJGGw0J1Wj6Kt6kOSqdm5eZM1SA0wLeFapBbnfEyUj/7zwPqo13CvVSmpzrfq+oz3G5PZJmWoHietVDMFHGsQ5Dt0kILVSHmmK+RRkdKJf94jDATyuOF6mwbd+RI90hvu6hdZgBe9WZCAHo3agYnnN/a7mqmzHwEm8choqzhxuhsGTusrZnHV73VJSUlyYcffCgrV66U5ORkidoSBbU7N4neGSOrVq6S3n16i49P7SJP1RvBf38qDg5RSelRuXBoyH9/0I7e3YYI4pPoJcSbHSMUNPK9b7y0vbwOwQTWC+kUrh394HpXNAIaAZtGgLVFFw8LlfikQiVQwEni0g3pyjnq391X9QgaDNUq9jhiQTpFDSh9TZoajRPLjXCoeM2nY0WnKcivxX/2jON3v0VPJFok1L9Y71EXC4RT1JtOEQJxHD/pULFQn6P8MMerTSNgRIAqdOwT6QzRkO2gjZK5wkzoPNBCKd/O3kW2HKxlX67DOAe3Yn7Oc5H7pMW9jL9+lUvzOET0qvehmHFYv5ZVbsXSL3p4eEjXbt3kjz/+ED8/P3nrrbdkyJAh4uzsLJs2bZJWoS0lMjLS0sM44/r3JxVIVk4JpCBbnfEz9vJGSkaJvPzxDogm7FYSr9wv3ojGjWwlbz7WB7VUATpKYS8/tt4PjYBGwK4QYB+TC9DPx6lpY0gU5ygK3a74AtkMeWvWFjGoxWxLy0AX5WRce3EbqHl54nP/SiqyMUY6NNsnMOL+PZwjTsiOlv+rKNJcv6mxMexX6DtHi0AtKbM5dTVmipjtWr4pQ00ESYmiPDcV6Th+bRoBIwI8lrtHeqGxbzAc50KVUSTFnw49/3qhzo41arZqnRBk+A10QDZrZS0R6bEt9Dlwpp8z2m66mDk5GQ5aXzhEzZs3VzvcqpXBASkoKDgTAPp1MyFALvi7aMo78aEVsgxRRaOxt8T3bw6VZ//XFTxzxxaVMGKilxoBjYBGwJoRuPmydvLhcwMqephs35srtzy9Rsn4mo6b9KPh/QLkNWT9F80cLS8/0FMJNTTF67Tj6HG0CeyRVxAkG3PHP/LQ65vlb9CnSZWj8ftGM2aajM/rsoyE5PJnLw6EBLGhppnjvxnjT4Q0tzaNwKkIhAQ4y8dTBsiTd3StEFeg8NO1j65U9c+nft5Wnrs4N5FbLmuvhlsCEYmZELXSdmYETl6NzvyZs77j4dZUCouPnPVz9f2BRqgfsgYrQREqFXns1f5YliyXT1ou34H6cPQEfaJDGw91gXnnyT7SNrR63cftFR+9XxoBjYBGwNYQ6NXJW76ZOljoXNCSkf2nU7QpJqfKXWnRvLGMGRwsb+Oav/Cz82Ty3d1UbZKRqkaxg9VRGfLcjG1y/u2LFaV6zdbMinUxim1Oo/zwF68Mkkjci2jsdcfxM/KvTSNQFQITzm8lP7w9tIJaRqrZVPRKnPTKRjBeDlf1Fat/7cqxrYVUUhqFUNirS1vVCJjFY/CGogUPnLLDhv4FVW/KcV/NLzyMZrX2lx2hggmjbi9+uB2yrYaLha9Xc0hod1c3Us1XddxjXu+5RkAjYPsIMKvPTAupzrRCyHBzcsganf8yN5emoEm3lA+e6y9/fXKePAY10e4dvFGTYajj4XyB63gcgjtGS0f9xjFklMxpHP+nGD+ZCjTWsz7w6saKuiVzbkuvyz4QoPPAbNFDN3VSIiLcK6rjXv3IioqGp7a0p6wduuuqCDVkBiU+mr3PloZfr2NtPAVW1y1Sv52FkeFtvBpMdru0FB2I586VoOBgGTF8uNqlrKws+eeff6Rzp07SvUePuu5mrb8/Z8E+VaxqL7LblF+d8e0eefWTHZKBZrw0UiRugBrR1Ed6S2fIO9pyMWKtf2j9RY2ARkAjYGcINMGEavS5wZDdPmboHwenZdnGdBV1Zr+TsxkzR7wn0EG6ZESo+CFoVoD+Q9kQYTC1TNQdsT1DCoQaOKfgxNQc9xFOCM/H+MuQgWLPJfZm2bA9WxJSimUgehXxfW0agVMRoKjCiP6BEo3+W9l5h1Wj4qXr00G7LFFqiKZ0z1O/a23Pw1Gfx7FTPXJ/UpEKcFCaW1slBMwjqkCVjvnLk8XJqUmDNWZNS02TBQsWCAUWzj//fLWXO3ftlHVr1wlrifr161dpz+vrSWpmkSxbnyR3X9PBLmpoVqBQ9YFXNyk1Id5YaOfipvLOk31x0wzSNxcDJPp/jYBGQCNgNwjQMRnYw0/1JKIzwWs/ZbZJBa9JoM/VuakqYqfA0JjBIer7VKk13kvotOyGiAPnE3NRDJ6ZWybuKGr3hyJYXYzjp9ACG3NS7IGF8/uhpsd6VxbOe6MVhDaNwKkIeOH4Hg9HnlMdozMdn1goi5HdpEw8lRltwXj8+/u0qMhwkf5Hequ2SgiYxyHiKtkEak1UJpTmQittoT6epGeky/R3pktObo7k5ORIWVmZNEcvok8+/liKi4slNS1NPOEotWvXrj6GU2kby9YnojfDYbn/+o5miXZVWnk9PmHz3RdAjfvkx32QEDdQI3kxID3uHjh7tqzEUo8w6k1pBDQCGgGbRYBy26RFr9mSpZTl1mzJFC/3ZjVyiow7z0aupFX/uSJZDoGKR6eEPepIy6PxPhO9L185Rn+tTAEt+4h6vy7OCxtzDoJ0OOXBuc0C9FGi80Wni005tWkETkWA7UHYMJgBgc07cxXtksfon8tTpDHe64G+RebIZJ66XXM/Z03dum3ZKshAcZHBvQKE0uPaKhCIPgdymSfi/BUv1upBHLzmax5ZKfff2FPCw7xqtQ57+xL50FOmr5WJY1pVcDhtcR/Z/XsaCgvZhI/GC8TEC8LkXjhCpENo0whoBDQCGgHHQYAOCmtHmWlhXdAL93eXsZDrro1x3sD5Q1iIq/z87jDlBC1akyr/IAqfnWegZJuut21LNzkfMsn8axnobPpWtR9TFZUBPlNFVMqKM8BHkShtGoGqECiBOuLUz2Lkr5XJFW/TqX9pUk84F9afZWQg4N6XNqixM2P63rMNw5yqAM+6Hnxnlhoi7hOjNhvYjyghX/p1C7Ku3Wyg0azbkirb92TK8/d2r3bn7gYaapWbZRTkhQ92yGc/71P8a36oHW5Gbz3RB3r2LTU9rkrU9IsaAY2ARsC+EWBNQrC/s5BCzZjqKtDnqCzKKHRN7c8VKSpqzR5B7GsUAOYBG7Red3Fb6YUmq6zVSEOPI6MKHesgNkPp7se/E2Q1MlSsaWXvIVdIDFfXuE7WRXkgS8UeSwxesl8Rs1Wt4ZjVZj+qu239OdtFgPVmrCtqGeSisoxs/stjk8cNHfrWwdatqEt5cZ47ZHRRNbJ/N78KBTrb/VXMNnLzUeY4pDY4ID6fEyutgt3AV6xd5MZsu9bAKyIX+vOfolUnZF54bc140tz/8kbZAS1+GqVTb7q0vbz6YK+K3g62tk96vBoBjYBGQCNgHgToFJEqzZqc43CKlm/MkL5dfZVDU5MtLFydKqmQAm7cuJHcOP4krZ00JDpdbNZK56gLhBnYSoOywZyI0ljszpqm2X8myEb0Ozp85Lj6TnWbT3Kdw6Ggx+axdLTYH4njSYdYUB9E/m2pcL4mmOvP1g0BHvuc15HSmYU6N873mNWkc94P5wBZNNZqIQEuiibK8fFcunh4/Ze5WCk25nWIGKWJO1iEDtGp0r97kFCdxlFtzsJYycwplqmP9pbmp3TntmZM2Dn8k5/2ycsfR6P2ycDlZtTj3af6yUXDQqz6RLdmXPXYNAIaAY2AvSFA9ThO/qJ25ij6HDM256NYmypx1TXS1pidIXn/tgntq/wat9EKUXlG56+9qK3KRvFelYroPGl7NFXHjO1//+cB2bYnT8rxOiPiZ3NqyG65ZERLNamNiTU0cd+XcEj+BC2QmYCzRf1Z20TFPF1HW+VPZ7cvsu6Mx80ROOc79uar/aSDtB4O+kDQ0WqSsaxPkILQrHg7zg86Q8xuMQvLwIM2Ma9DRED7dPWRnxccQMSnWLp39HdIjEmTm7c4Xl4Er7TjiaZ2tgBEDi7qj06LQrHgSX4s1YDefLyP8CTSphHQCGgENAIaAVMEOKHKhGrV3gMFKsOyZVcugmeh0gSsgurYKogxUbmLtLXbJoSfNejG9bZBs2+qml51YRgeu6mG4JzcMVNFxyoFdKCVoPPROaJqncg5yjk6U5CW62TRPOtBmC0iXZyiDoz6H0gukp4dvYVqulXZB7P3yoez98ioAUFWOwmuatz6tbojQEe9fzdfodjIWvQqYn8tKiPSmWZ5AZ14azSqLc5bmqSGlpxeoiTxrXGc9Twm84kqmA6cTazY/GzcqPYy6txWpm/Z/ePk9EJ575utcgEKPp++q6vN7C9vYs+8u7WiiNUVjfWeR4Hp8H6GhnY2syNWMlBGK0m7YG8NFgazcW0Z6BzkwR/BkjfuZuCxN0P2kH+e7k2VehNVX8ihD0VaG+wQbRoBh0WAkXdSqbJw/mThPGLAphQTDp5DpEYxA9DMqZHKAPAccsc1yxfnjx+acVKmmdH9s2UHHBZcM+94efm/cveL64US2jRKar/8QPV6/732abT8ujhRfW/JF+crxTf1pIb/FaDp6tL1aYryRqfmOBwsU2ONEoUTKDdM5+dM/YdIm6OT8/OChIp18H5INdUrzm9dSVGMffgum7RMOWSsIWETW0qRN7RxUk4qF2mAPI/yDx0W4sN7DzMaR49iiT/eY5zA5GEfQZ4rZLNw/FQO9PLg0kkFQ9loV9t/I0Ap66fe2YLsi+EcoNjIjePb4riJtMp7+YNon7Jma6baqfcn91eO3X/vod2/+53ZVOZOheonFDy+8flOuXZcRxnQwzFEFrLzSuXdL6KkA5rVzXimHyiD1YuQnYpdfT//8rd4+fjHvRXUg0hktV5/pJeE6DRqtX4KRiOj4FBGo94q9mChHEg6JFSjoVGW0x2Fu24uTrjhNFY3YXUjxsXyaPkxOYaJxBEsixCRzC88LOwkTeNnWNgbjmOJfT4YhW3fyq3SzVh9UP+nEbADBDhp27orR1Gd9uEcisc5VIDJnNHcXZ1AScI5hF53TXFdbYpzidHZck7seB7BOSouwcQPSpjG4ntO9kL8XaQ9+P4d23lIL0T5eS7ZynXZuO+2suTk+8YnV0s6MjW0yXd3q1bkecY3e+Sb3+PVd37/cKRZ2AisLaI6KjM8MbGGOli1gRP/0cEZ0S9QOUd9u7Dmw/Rdw2PWz778UTQyRIUVb3Zq5ylP3N5FfOB0b4BiFye/v5+ItPNDkRCW+HjKAHGpgcBDxcpr8YC0J2bA9uwvUI1mk9KKFBWKAQOjMVjA+w/H1LRJY5W5Y70Ws2WkHR7Df7zvqHvR0XIpBFW+COcSnVyjkR4WEugiYbgnMSPXCecT5wmaJmhEyLBklvPD7/fieN6vxEb4KrOLrz7Uy+r6UO7CcXMTzldad0iHz3xpoHrswP9ZziEiqB8hyvLl3DiZOLYDtP9rJ8lpKz9OWkaRfDx7B9RumsknLwxEet365aiZ3p3ywXZZAnlTo10xprU8fHMnNSE3vqaXlRFgQS9vhuS+r92WKdmIDDG6FhbiIUH+rhIIR5JLX8/muBE1q5ETU1x6VPLyy8CNLwa/t0jSM4pxoytQxZpuLk1A6fCVkQMCZUjvAE3PqPyz6Gc2hABpTQwgLEH39NVRGej+XqwmpS0D3THxwjnk56LOIYrzeGAy16ia9CtCUHa4XEXE09U5VCzpoG8npBZIHrK0zCh1i/BWme8R/YNsQirXhn5WNTG/9dm1KgPRAhSzb6cNPitt6PM5cfLRD3vVbs5+a6gK/Jhzn1MzS5VjROco9uCh01bN3kfnDQxSMt49Iiu3DKFTQGft81/jFB2KX2bkv2M7d9kVVyDnwCn/95RMVA9MgN9/tr861k7bWB1e4DlDWiLFIzZC0XdXfB6ofeXqvAnC+RLgi3uOdwtkSFtg6Qzl3+a4RzRV2Z/abLasrBy9mo4gM8tsUwmYDqW415Uia1skuXiNFoi6cU6m+6FUoh+oY3yuTYQ00Cnvb1M9i4iHj2czee3hXso5siZ8TLNEdORJGXVgs6xDRGC/wIXkQzhGowe3lktGnlSQsSfQ9x3Ik1m/REtHRIfehiR1fUWH6oIh+daPTN1ccYPgzevZ/3VVN4W6rNeev8tO1aR2LNuQpjJAbVp6SOdwXwlv7QllRXelxGeJ/eeNkM5RXEIeboK5su+AobM7JTNZ40XnqKoIpyXGotepEagLAiye/3XxQVkAJa9cUOAC/ZylSwTOoTBv4flkSQGa7PxSiUdbiL37c2VnXA7qXcpx/nqpFgIXgEZFSpW2uiPw7R/7ZfrXu9WKmEX44pVB/5mVM7JJ+AUGE3t18q77IM6wBtYD0THiH53wUy3Ap4WMHmRwjkzrf+lUTZsVg4a0BorRqd879fmgnv6qPQXVWetizPSQ1sSeTBu2Z6kJNrOl7PXYFudLaJA76NWutXZ6aju2IjhKSWmF6i/uYJ7sTypQFLwQ9IUaivvReajv6hZR2bms7bZs9XukzT/51hZcawyCC02QlZt0Q0e55qIwq9klikDc+swaNZ4+yJR+9Hx/qxlbAwzE8g4Rd+qPZcny6qc7QF3wkuvHdwRH2PobWFXnx+BEdeGqA7Jg5QEUVAar5nRn4iVXZ3319RlSAR6DeALrWmhU4qFwAilZ2iojwAjhH8uT5Ie/DuCiXyStQtxUn63uHfzEAzzrhjBG7mJic2RTdJrsjstFNLAZuO1hctXYMJ01aogfRG/zrAisRJ+a7+cfgBpZNqgjLaQfaNQ9O/ohqt0wRcflx44jsJAnUTEZqHvJVHUUFAK4flxbs1C2zgqInX/gPjR/ZBaddueVEXLHxPAz7vGCVakyecZW9T7vQ8Mgg10fRprZojVpoNZRZttA8zPdLmvQjA1g20LEgUYVvWmzdkoaqGpnM8oyv/JgzxoxBLhOzitYhz0f4karkD0lBbQ9gm5dI/ygruclQXCArM14PiUkHwJ9L0ei92aBNlmC7GtzJU09Hj0LjfhZ27gtPR7OH978YqfMWXSwYlOsryOdlNlqa7B7X9ygso4cy6yXz1UCEdYwrgYYQ/04RNyx3bj4PPn2FikqKpeJF0UgzWrbCnRZSB3/8MceRWd66KZOMvGC1g3w+9V8k+ww/goktY8cNdS4MCrAeiHNBa6MpXKEliXJzF9jVSS7b9cgGdQnGPQP98ofbOBnOSg4X7s1RdZGpVBISUnSsrmhtUp+NjBcevP1jAAdoU8h40+qT9cOvjIY1OnIdj41niRactgloKlu3J4mKzengFZXJuMgpXsr5J81/af2qFMA48qHVqiMBgv2Z7859IzNTtnHiCJMtMkQ8hk3ov77olCGmFkjZmKMgULTvWcdmsE5CpL5CPDO/CXW9O0zPr70vFbyTDXFlQoKj0L5K1F+weQ5DRmpcASQe3Xxl26RfqoG6IwbscI3WEKwdXeWREWnQxSlFMEPHzVHGgnZ9Lpmzaxwd886JCYFps6Mqahv5PH05mO9VTD6rF+28AcoqHXX8+vUVgZALvy9Z/tZeItWu/r6c4gIAZtWvf3lLlWE2CncR64YE6E4r1YLTxUDozrLwtUJsnTdQUQ93OX5e7tJBArfbcE+/SlWPvt5X8VQSbd6/LYuDnmBqgChigeb0JT29c+iwZX+P3vXARd1/f6fFCcoCChTEFFw4MK9ym3a1nJUtmzZzpajYcPyn/0yzWxolqVppU3Lbe4NCooDUMSBLNk4QOv/fn+Ow/M44A4OOOD7+DrvuPvO57s+z/O8n/f7IhTTPWVQbx8w79Q1MaXtfMW+iS17zgDOdxpNs4LSfJsKGVjYjke0LalID5w8myUffHVIQkGU0BHyC0P6+oq3u21XoNkQvSc8XtZvi0UP0iV56K4WePlrTHUlPJFINvDu5+FqbpLCfPl2D5NLYi/Og5N1zd3P3t/qOnFWkzOU4ZckGdh3KFkFRxvRI0r6bWMjoUduXkLR+DdTf4+73R/341amflLfkVCEfUpktSOsqis0HNlz7VZFxO2PAqK6HQm7Q8eSxQVohkdBrc6kQ3ULjFiRfPWjUMDfddVFMve9+1xH6R1c8cWBJ97ape7VPCEXfdBH2rRwLPR8rcI/LKk5DVZeO8jGc5bD2fvAPow//zkh6WDW8kQDOntYbNkYCG3dd0a+XREhsWfS5Zn7WquypyvYZmzdWO0gecKPYP6jkZ3pxQfbygTQiPKzZjoPZICW9J15B2XO90eQzXSSJ8Z2kM7t3NBbYNvnJreejEH+gFX0CfYCS9AVWfxHJATizqsmTkew3GmmeaA8PEC2ty9/jAQE6oDYYeD42Kj20q9H00oBk66BZvmmCNr6dPFCEGQnv6w9ISs3n5EAZHM14ULLz55A9NSGRKQo8Uf2rHqgXyzQr2DyMBfPJ0KSaUwuMktdUYZTQGXtb8Q45b5bm2Ng6KRIFJgc0zOAGtN5F7et7D0llJ5sY4bGBDGvlddnhwKOnS1D+zaTB0cEqZ46kiFUFSNENrgtxn0dPDHey8E45DgqYWcUvXplSSZb41i4NqorwwHLZbWczLSkPV8DyCaZaDuVYd+cOdvu5lpX/t4MlAmM1V1S01dDKxsdInMcyWzcXzgAXy+PlARoTHQJcseDyFN8wdRlS5aRdVl2hp4DE9JZsMzkouzbTGWwyM9fGYyD/FdmhuRH/ww8iWvu27nisxK25D8+tKghwIfz6Fta4UFYudlWzgKysPTPI4odaNJj7WT4jVWb5dGWzqXqui0cNPIaigaT1x2DWuB+7m1T0DhLjwuTdctXR0r40SQlGPrYPQEaeYmFTiSJxtiXtyjWOQ4If/20XwHyChIH9LlvlVry8Bu9VS+uhasp88m5jVv2JcisRYeVvk9JVvgq6LrvAYsrjfA8omWoTcdAiBWhytB/XJL9Np6H1de1qMLuCD0rQS2cZdLjQYAH2nb12HgfSvM3q5AU8l30m45qnssiy+G0ZzpWaF/RI1N3KOZPbs8PgLhWp2PCfYaVb4VIt07d/8zGMVs0Co3gHq71Ze8hNBH+E6Ma8tig59igrtSroMw8q0GHopLkL1SwfvrrGBousxQT0fsvdlbaBfUqCRsRsxAT3t6tqFDpdT6Q5r3ZvUxZfAyPcWX5/PPqWJmCgZxfUyd58t6OyBDaXtOqpb4kExEzctkgYPhmxTE5D1gGmY+YAdVM84C1PbAHDfRPvr1LatxQQ54a10klFCr7ucbKMDPbvJZ+/DsaUKoU0HW7axA6C04eJ1Sns1CxZsLpAu5FRIkQPmdoUmdMIAAAQABJREFUdmBiW/rXSZUxpygoyS1szbiN/k0bKMZcJnNLYuyVcmxQC8uIRAX/uHTCufXoaELuG6kqQUmWWRnn4XVFdlayS4YdOQ9K82MqERkMymeOC6u6cRdJUc4+ou2hSaryeALshyTsIHyuoqqDRDuR/ZPGa5byHtXMKq5CZMrRpCdcsfaUbAScLhsHpJkXRClJa4wbhi8+lyXmNCH5gkSfSlWsXUfBlMKgrFNrV7lrUFMwyHkUSRtqal8q+js+gF76cB+w8DpxQ158n0zuClyybffClLffPoOI2re/RsutoIQfAmr4qmjhYP357pcIBUWhQJytsNtURV9Xx30iS9i0zw4A9uEGIe5Wqg+iqvmBFdevloUJNWvmQnS7MXohNDPPA+zDufOZfxTBQv16gCLO6ad0WQznHj1xC2BjmUqzaAV+t0U7FJUmD0/ZXqpN43C/AVh2HxsdZHNomFLtWClmZn/Rr2ujIGrrBAHTTmCerD5jlOhTmUr+JC6PtZD3l/8DyZUxvLIU7rVo1rEvb1UVfo61f5nTH1DhaqUrVXEVIlNHqQloGtljdP9t/mBWaaR0InaFxYPA4LRs3HVK0aSegRJzKkquV4BVZwMiVZgtMTafJ4IhLgp6FKGHE1Qj+i9romT99lg5cSpNfMGjfx+oV994sj1IH3wVFXVl67NhOf6VmaGKxIK+6dmRzCHdpbLA/Cw5nqWZdubCCIVdv++ONnJjV9vLSpZm3wznJbVxAOhaV246iabxZLm5r2e1ykga+kL7bF0P/AH2JAoQDuzlK6OGV92eRFaJOiLg273/nPwGJrAhoFTm4F6z4j3AZzThYLugo0NRa/ZOGDeSb4eQ5en4bAUhI8OfLRr1lRgUldZYYWLl0cWpWg02C3UbNfzIpLfzQDwqhSdUPw31oKqDOSMAIkyUxQD22V26fFVWIcHkgu9b+5d/+4g97mkUnCf1O89T4+u0ih8T26oQFebss4ie94MakFWPyNhMiTmdoYQxOT0b0hwb1FGwhtpo4rVDSZ433xvwj1We3CtX1U04G0JiaWjoI6e/fj5vd3uFk2TjZGeU8QObowpVyUkGqPr9BRo1/+MZDRsxxBdMcm0r/X6pnbHifxQMpjr6wyODMNCpHv1UzHLP+S5UeoLUhFTr1QCdYMUzRluUsQeoxv7yh3shwuirKqzGv1fFv7Mv5Mrsb0OU+PbX7/asFCLctnAcSOwz4rlNimGLAdLvn/W/rkr0wfxDILGIVZu69uvBNpe84+N0+BMbJBkyB6UxJnFbNHNSY5QRQwLEBTB2zXQeYKvCol8OgXQgBc+nziAIqh7PZe49yWioV7R8je4a4HdMyL/8SPmO3RgE3fWM7jqlUPUf8wbY3LVI35SRlS/ttjV3gkJqbOLlDSop5bLSDriI6DoHAQ8bFRkQEK/Mmy8hQo7I8LmiAtUYOEk313rig2DIzo4F7KphfOC890U4iCrOqB1iVYsUpvff1rxq7KAV92IDtCaoiTXmlkDphWbW6mTHT6XLvCX7oVfkB6bEVtVp17V9taIHiHl/4LWt0qWduyIhseKibX5RqRC0/uSbEPR+NJC5b3Sz+e21lQ0kHJ5yBrT78Fx64YHW+Zv27a/H5bMfjqq/v3m/twS1dMr/zRY+cIzxDaDVasSA/25ANomfayhtTXxW31EKTveZP/J3itNGncxQ/TLuTezlBgRWN+DZ3LOTh81LOVSE30k48PPqY7IrNE5mQqS3upE//brulHwI5Iqe0bBbO1f5v5c7l6uuIHuqP/z6kDr8j4AifcKYgIo4FSpinZU3IKoIb9nqOtkn9DKY5MKOpqhNZGT/7nOd0ABcPorftuoXU9uVkHxJxry0WToA/jIaEJ/qaDvwsPnxr6Mg2OgBdsfrG5yroz+0fbbMA4Q8jZuEXor/kHR5MLhaVp9jz6YjKApF0qk1Bvd+ljmwmk5NuNwdT/+jWNr4jFr5+UBFMkB3UPPntY9ClGfItnXLTZU/UfX9Hyfk08VHqhUKwVqn9lKI3odGxMv8d3tJ6+blDx2z1n6UZDn7j6TIa/8LhUj0ZTV7My8HmTWpK3Tc6pdkcRbPQ0bFWydsUP3n1Epa+cVAqV/XstYUi1dqGzMsUfkN29gWbStK4gE25T2AwYk+GCKT3Ffv9NSCoUKc+Tb0mBqiqXXE4JaFTFH1v+4V7KnEMt/8dL/CLFf9Pdb20Joe+PKnSAV9euCu8oVzWHMfSrssykMM799c5v5wREgtrVnxHiCUnSKlNPZK6LWH+Lev5zVmT4r6VnYjnJR6diOHtqw2kGxrHrNRQG+08G0kL36wV+niWHPZtr4sEip8iyppc7Aa0ng9PAQyj1C0jZSHEVE1ZrguyUNClF/WxZbHam1iHVpAZBOHoWQbsXlvgox/fYcanHAJrZBJWTSjd7XLqJjrPdKe7oUK+ajhraQW4JTV2e4eFgAV9iuyZGVMdXaDtu8WeoBQ5aU4Z4bf5CfOTtW7/4G9U24u9hj46qBeFrqyWk4+YrCPkIqbthw9Q8xG03w86uezyMbGVe6AiIiNt8G6SDjpjd2aqv3T/rPMA+zlfhAJl/9QhX5nXrhlM1eBqcnutnB6L0WIxd2hoO3T7+6WP0FiUx426mbffNKYpX/FqB6n8lhvRa+jeo8KK9r7pVj//J+jlODqBahd0wZC2GsBystk6tPMtAdmLz4qHVo3luY+1asEb8obDexry6DezeS736IlHeK9mmkeMMcDJGxp5FgX2P6qy8pojh84DftGbh/oD8HOeAgalp59zNz1VubpmH0emSdOysBh1dazandYPfJqooMEseemMtscPGfQXSR3D6u+KARrHL+6EJEfe1sr2bE/UUEqrbHMyrQMMr59Mrmb0urkdrOv6J15YfLpkqOKBa4s94VQuTsH6oL5xPMQ0t2h0ycqy3XawrK1gMgWjoIF23Dh0lUwO4XIV4CtkDiCzZ2PjwqQGRM1fZmi3HjgaCpo1TNkSN9mRU1WrX7r181bqDH4dx4RR7Xa+Wq6s1Fg6QyPTC3R3mcgcF4L4b6BPX2kBnQqSmPHjx+XxYsXy/PPPy/Lli3LX9Tu3btl/PhHZP/+/fnfFfchPT1dVq9eLe+8/Y788uuvxU1u1d9bt3AB3KvhdexQVl1BFVzYPQiI7BAA0X5cdVK98z8iHGhnIChOYciKsh0HkhRhU0nWT3jTyk2n5c7BLdB3UaskiyjzeeLi4mTu3Lny1FNPyetTp8p3ixbJpEmvSXJycpmv29IVBDZ3VpU2Em7kEedauohKPT1JO14B09yrjwblV1C/++24vIp+O311tax2kLA5vfbnD39WDySJFhCV1dlUBsvlzfbBSdtk8954tXRmEGa+0lkeu0fLRBXn7j82nhYfzwbS1F2Hyy1u+urwe20wMFIP4zf4RrPq4YEjx9Nk/NQdqrl93tJjchw9iObaGgRDfEAGB5WerOXSpcty+vQZOXHihErs6LchISFBEhOThO/mWmZmloSHh8vefXvl8qXS0SKbu07D6cgYtmFXnBITN/xe+2zaAy5OdZTYOX+Njs0QNpHTWvnpAiJ+jqzAKtHv60/j+tgoj+A6+QkBW0q6Ttyc21WcEV7k5lpfOlvhGiluXSX5/fLlyzJ50mRp6u0tUxEMXbhwUX5evlwiIg6XZHHlMs9QJDFPoU9vW2hiuazPFlfCJAKrRQ6o3NA27YmXJ97aadG5ael+eTSuJ/27uavZjsakS0jEeUsXUemm1wKiSnLI1oMq+sHJ21WDHTe5qYe9kJ6UQraaFe8BBpHBbXUXd/FTV58piHM/gUExKew1qz4eoDI6tbjGvLRFxkzcAkrh48WeA3wIBwU0tlgM25RX27ZtI4MHDyrw0+233y4LFy6Um2++ucBvhX3h7e0lw4ffUtjP133PqvrUqVPkX/L7Wsk6IalA5j1SLGtmngdGDfPNn3BFnv4QdQD1dhiBe0XbQVRSKd49/In18sx7e1D5OVNk0EvpCyYNenXyrOhNL3T9mzdvlpTUFOncpat4eXnJzI8+ki5duqjpS1fzLXSVpf6BAWZgcwiLl1P/TKk3uIwW0KODqyx8r5d45kFLKeb6MMgWypKE5P7bm+fvzeJqUCXSAqL8w22bHyjYNWvREZkM3Rx9v1Dfzm7y3Yw+4ud9jZnHNrfeNraKLFCE+/j7XstA2saWVfxW+Ho1VHpcFD3WrHp64PjpTJkHSEpRWXHCVQ5FpYq/FfvvatY0TeXauHFjiw9ETQhemmPffbcI1aSDVoXf1EOvgzcqz2HaNWTOIVDTtA9oBB0nXQBEym32E7X1d4Kuj25YHgaIs60Yn8G7w5JAlBAmQx5dD3rwUNXTwiDY0PZD9iIbPb0dWttukjIyMlJtsoODvXrndVO/fj3D3bDJz+0DG8vOsEQkMmxy88ptozjmYyK8bQsntU4mtljJLCsGOq6nQ6CzWtd2VOiMGTUJwV7854ly2/+yXpFdWa9AW37JPUDNnMmzQtGwq3s48GHxBPqFHhnZouQLrYZzMttIzDoHLWVlFy9ckF179sj2bdtk/KOPyj8bN8oB9EE4u7jImDFjpFmzZvmrPnL0qOzasUMioyKlbt16MmzYMOnWTSfwSLjQxn82QFS4tprn999/l86dOwsz52VhbGb2dmsgR06kyc19bTezWRb7ri2zoAd4r+Hr40WHoVHlKjf38VSwiSQIYLN/kQF0WdrpM2dk86ZNwqBo6NCh+avKzc2VFStWSMQhCAaibzIgMEBatmippjMOoEJDQmTz1q2SmpIi/fr3lwF40RZ9950sX75CfZ49Z7a4urrIA+MeUH+X9j8f9BEdRsZWM/M9MGKwrxJqzUVgQfascXc0F39QDUcBRmerwWVO7lUEQ+fUy6F+LSVvMbSPl1BA81BUmmJebORYx3wnFDMle+22bdsqzs7O0qSJm2zdugVw0kTp1rWbjLx7JAJI8xIBXM3ChV/LwYM6YdwffvgBzx7ddsacuL4/5Ouvv5bz588r0pBXXnlVzpw5K8t+XCr/Xv1XHBs2lCeefFL+wTV65LAOZsc+5oEDBsiOnTsUBI/bxGeek5Nu0F7MLpr1sz8ouC/i/nPiTKa08Cm757hZG1PBEzk71pYv3+4pUz/Zr9onSI39zHu75Y0J7WVYX+vrd913u5+EzUxR0OYfVp6QV8cHqYTAT6tPygHAXYlWuh9Cy1XBtIDIRo8io/G35oYpukVuolPD2vLe852ke3tXG91i292s82mXoT1Uu0wFJL9fvETWrFklOTm5cvLkSWnVKlBq2tWU7du3SwgGaGxidXNzkyNHjshrr70mL6CZfNwDD8jXCxbI9OnTZd68eZKMh9Ans2ap5lZivHOvXJGk5CRJTU0ts4CIR82xYR05dS5b3eRs9yhqW2YNDxw5kW7WYvRZcWbGP/jqEDKSuupqg/o6ymSzFmLhRJs3bZafVyyXWFw/o0aPum7u6e9Pl4y0DJn+wfvCgGfGjBnq9/bt28no0WNBo697lK1du1a2IBgKattWJRxIztAU0KCWAQHSs3t32YVBGwd4gwcNAvTPevvSCNfQidgU7Rq67qgV/Uc9iD2yjzEn56os+euEeEF40qVRHQREokQpf8SAqzF09crbElPM60PLupCrYHSE0jkjCHJ0qCUN8LKWHULwvwDPBwZFDg4OCIiaiL+/P87fM3IUSbXk88kyYcIEs1fXq1dvPH+OCkkV+vbti2tGt60nYk4KExF6e+ihh2XcuHGSmZkhDIgISX34wYfk4UceERck+BgQ9e/XTyUcvvnmG/Hx8VHbERYWLvtCQmXaW29ZNRjidrm51FcBGp9T1T0goj/I1sj+8Y+/Paz0vJhUeHPOAQV7Hm/lhHm/ru5ImtZXZCfsxd60JwG9SzrRWG5LZhViqdUCIh5RGzIORNjsTJVr4t1pHVo5y/svdtIotUt4nNIzc8WhnvUeVKY24/HHH0MgFKMycE8//bR06tRJTTZnzhxZt24dMtPLhd+H4oHB4+qLipGdnZ0Ed+ksK//6S6Kio6TfTf1k4sSJMmXKFJVp+2r+fMnMMG8Aa2qbzP3OHr6JjUvPV4o3dz5tuurhAWbF9Y3v0z/fLe1buaJp3F0C/ZyRobaeD27qd5Ma+E17e9p1C40+Hi0h+0Jk7NixUq9uXendu7caGHKg+NLLL4tzI2eVaOBMPr6+akDGrHXz5s1VouEwMtkMiAICAzG/jtq5Xbt2162jtH/Yo1qQhoHBa2B/0sxyD5xPvVzAdx99HWH5gipoDg4Q9YPEaXN2SGf0lXUBsYKHW8lh7UFBQfIo0AaTJ4MEoWlT+fDDD9XejRw5Up577lnFrDjqnnvExdW8JGmrVq3EPg8qF4hroXZeQsCx4fUVF8LoPL085djRa/TnXIejoy4ponfxiBEjVHIiDIQmZHdcuXKlTH//fVRezdse/XLMeSeZC2m4CX3XTOcB3OLkpYfbiBeClVmo6P8Lytgvlh1DUHRBpjzRzmoJ4IOASuvJHK5gfKo/z/XHoSIZIfXbYK13Kz7OrLVJ1Xc5FD187M2d8t3vx9WgmQ/1B+7wR3m0hxYMVYLTom4dXTbTz88vf2tHjx6tPp+I0cESRo8ZLR/P+lgN6CKPHZPVf69Wvycl6RqyCY2gdQruhAdWLfWwM/eBp2bU/tM8UIYeYDD/H3D86l10CRtrrs4O57yxZaTpkgJxcWfzfwpAgEPLAsOcoTVDtpr3TZovgiMaK6+Gpv/d8Dvts+YBa3ngPwxM/8V1cjUvoVma5dbJg7UZPlNIhtC7T19FDBIbG1uaxZd63mefew6BSl35BkQod424S1WTSr1QbQEWeWDM8Gby4cudpW4dXU8mqzgvzdgnly5ftWg5hhOT0vsPwlhfRQsAepSOFoEsuAIoJeGMVcG0CpGNHEWWISm6RTwojWrebz3TQfoEN7GRLay8m+HYoJZkXayYzBJhDhyAXcjWDdxYFbqQnS0TX5wofs39JLhzsOzZuyffuXpMuP49/4cy/JAN3/gCB/zU2MAyXIu2aFvwwL5DybJi7SmLNqVWrRrSBhox7OuYOqGHOFmxR8KcDWkD+BvhPQcOhMkl0GpzAEbYUBO3JmDK8i50ETXzylfGYVtZBETZgE8RMvXmU+0L3R7tB9MemPP9ETmXdFFRuk9+rB0YD6PlLLSICAt686kO+UQLpue2/rdLgM44ZGE/GCHtjoBls2T60iNdrL9RRkv0cNcxpmZlXzD6pXz/JAx8CHr9/kCva1kGZ0TOXLp0BdD3ggmT8t1j21wb2Ya/nNZTXpixV8FNt0PM9om3dsknU7pKI5ybxsbrbVtIotxzsy5pZPh7NjTAxry8ReIxjbmWifsfIbCV3bSAqIKPYC7Uh2d/f1R+/Dsmf0s6tnaW6S9oELl8h5TyA/HdGVk5chWZu5p5LEalXKTZs585e1Zl0z09dM2O7Cki9IHYb1ILE25Q0ZaeeRkDXhcZ1NOjojdFW38Ze4BMlSuk+ICIBC6d27rI0N6eMqCHuyQD0jTqxc2SfSm33AMiBkBvTZsG4dW3ZcrkKeLfwl/S0tJk6pSpGERbDnLIKyBZ1dNpuIbcXOtp11AJvEqGuf9bcEg46M1AQpAkBQtXRCnhSVf0EAW30VXNS7DoEs2ybvs5CCQVT5BRHzqA7K8YCuKR7qBEpmDmj6uKv7ZKtFFGM50+rdOO8/Sq2Hs2+1u3gUiIxCbr169XvUnBwcFGW1v6PxPOX8BzFHIj7jp2vNIvseotoQ36PEnL/ez0PXImPltIJkUGurmvd1OwOv0ep+J6e+bd3ZKEezrv7dQFMzT7+nby+pPt5YUP9soVjE/NsczsK0AxmTOlbU9j+dPEtvenUm3dmXgdZaI+GGLm8pERLVSk38S5/JtJK5XzLNhYUkfywj4Tb74IpQWLL3LSg3kBT5euuqzhCrBcUQOlLyAPNL2Q5A1WgFcUuSGF/MiAnH5p7X89PryQybWvq7gHeK1MfKit/P3lQJn3Zne5Y2BTaQAxQF9PB6mPDODJM2Xf02bKxQnx8dKrZy95/oXnQaIwWvUGsUfIUuM99iogHnxZ02LPZuRT4VpzudVhWcNu9EJ2WZeb/XX9KenZ8Rrt+s4DSTblAlZLb0IQ9P6LwbJ2wWB5+9kO0B1qrBJtQaASP592UWXoy3qjyRbHIKSZb7MyWZW+ukr2VL3xmiEU0NA+nfOp3DJsuEyaPEkx3n366aeQBzG/smC4rKI+nziVpioQzZuWvCerqOVXld+8QUyycHovaQMKexoDIwZFejId0sI/B00tklNcRMXt6xXRJned5F3Tnu6QDz82OZHBl1l5yCaDryrlR61CVEGHbe32OHn/y4NKt4CbwCrGu891lG4ai1yRR+Q5ZD9IkuDZpJ4SKKNImQc/Q1XZo3F9BbMwXoCvp70qtZ84lY6BXdnSBnPde0C/PWTIEAXvWb9hg1IFHzJ4iNqsGmCeoy1e/L0EobF7yZIl6m/qQ+zatSufnYcDwPIwDuQoKNg+sFF5rE5bhw16wM+7gcpysxrEB6opI/qsbYtGchzXUO/Oumqnqeks+S7lfIqaPD39WpB1IW8Algz6eb0lJCQolsauXbuCtCRc6qBXj1Ujdw93Rb3N6fQ9eEnJul48fqfvHTpv8J2zC9KYUQKik7WoLtUEG2Qr1bDO6UtqhPKcTciEXoeur6mky6mu89mj0sIqy28Ihk5joHYB/mQQTvj4xl3n5Ol7KxbKq6+WkoJ+QA8PcUAG3ZR1AvkR9yXsSKL069HU1CQl/i48PAwD2EsICuoq5lJWSF8EAQ8h2JbYhTyIXWZGRj4ZAym2aVmAcuv7VdnDSjKSOQhwBgwcIOvXbZCsrEyFdti9e7eSiVi1apUkggV16khUavFcG4bA6K+/VsqXn3+uts2S7Spu2nD4tGeHJuWO8Chuu2zxd0Lk2Hf+2v9CZQegcyRBIHzu3ec7ytKVMXI05tr9lgmI+271u66CpN8nXpOcl0x2xRkhc1XBak6DVYUdqSz7wGY1wgPm/XAMtMq6LCU1DOa+0V1a+pb9YL2y+Kmw7TwIrYfNe+OhR5Clehq2gZ589daz8vPqWFn4S7T8su6UbNgVL3sPJsuR4+kKi54OZhrCMiKiU6R3sHUGc6a2jyrgpDRNTEwUBkI/g1mODD6E+9SvrxtoOjVyknDQk0YcOQwGuUx56qmnJHR/qMSAdOGGG2rIqr//lgw8rJKSkhQmm2xYHPyVla3bflJq2f2HymTLslqFtlwb8sCxmAzZsi9B3AHvog7Mq48GyZNjAhQsqTh8PrOLKzedkhu7NRW7EkDVDN3wF5gVl/64TJiBjjsXh6zyBUX9u2zpUgy8siQe11A2+u46duyEQVAN6K9slWMgISGFPQdkhJ6uWbNGTqKp3NnZRebP/0rNx2uvBqpAzGR/9dW17yg+GRAQCEKI/5Reyt69e8XDw0P69etnuFkl+rw7/JxExaTKJPS/1EYFQTPLPeDmUk/duzknG7RboWeN5yrv3Td2cRNC58rL1u84JzFns1TFb9zt/qov7J6hvmBVdCzy+DJwiku8KDv3J0jfroX3tlmyHynQ0uJ5Xrt2HfXO62DDho1gnxsvQwYPtmRR8tH/PpLQUB3L6eHDR6Rly5byG9jhtm7dppZz/MQJlSAgmxx7lPaHhQkRDhERETIY60pNSRVPTw9AZp2EFNuLFn2rrqE+fXpLLTDW7YC23gksI+bkSXVdkg3SGv16iYDL/bI2Wp65vzUSmhpkzpyDTt3FIUhwJZy/JJEnmfT8VwgFjUu6VvHjcshMl5qRq6BzppbbDlVPChAXJ5TcG73uVWD8ehBIHaMaqCmvaN9ZxQMxGMRP/jhUqAxPI5Xk4/cEyMOAyZUFrt0qG21jC/lr81mZNvdAibeK9LhNnOuBqrceBCebio8VhSbfefsd2btvr3wHAcjLOZfFHkFQgwYFg9yrV67KlatXkOnWYXd1EJ4r+TSoJd45C2ek/sfrs7bJhDGBci+yRJpVfQ/w4UiB1Y6tLK8IcnB682PrZNSwQOnRqfxEfM+gX+LDmTMVJX1Obo5cvHgJ2jWXJRGVo4ULv1HwOTZ3m2tMOHDw6uBwPd2wufMbT/fx1/sAOW0g7zzb0fgn7W8LPPDwlB0QN01Vz8W3ANehrgqNQclz41pZsKTSTUoNQEJEC6uWFrV0PuNHT9wsD9wVBGr6JkVNatZvlGMgAc/w4cPloQcflBT07Hh6elol0DBnA5iY0wsfX758Of+ZZc681prm+98Oo8E/U5Z/0o+cFZpZ6IHPIePCZHFhxqB18Yd9JKBZwbGKfp63PwtTmlv6v43fKdZqiqDBeDob/3uJnY1vYJXZPFIhzlwYkU+F2MSlLogTgks0MKkyTinBjgS11GFjSzCrmoVsUJnAq998U3OrBkOG28MbjLubjgXI8Hv9Z8IL+NIbG8Nr1izIBKP/vazeN+85owLxW26yTjazrLZTW671PFDUQ6+4tZBFbQga3tfviAW010NqIKFT1sbkwetvvildu3SRZs2aFVjdzl27pWEDywKbhg0Lf/AXWEExXxw9kSInATt9Y0JQMVNqPxfngVHDfFVARHKFqNhMaQQYeSogO+t2xJVrQMRsd0nNz9tBhuN++vv6KGkNiGn9utZjRauHBJtXHtJAv32EzpkD8rnrrhFy00036mcz+10fDHEGfQLP7JmtMOExVF73hsfL/73UWQuGSujPXEDiizLWRD4DYmk2GOkKs9cntJc0tCpsC0kwOYmeHdnkj5XoSy0gKuODxWwse4XWbDubv6Y+nd1kGii1OcDQzDIPsGSux5dbNqcAGlZDwRTH3t5KWvpYniEvbn0ZwFjTiMl2cipd4Fbcukr7e1Z2jhrYMvtKWnLNNA+Y44EnRwXIyG2b8GA8o6Bz5sxTmmmugoDk6pUrCo7jBhiPr6+P1AS0NBKZ87ADB6RPnz7CgWJFGLEVv6+Llr64n2s9eKU/AoN7esoni46ovoXfN5xSjH2EQFOfj8LAncC+Whns+XGtZRtgqStWR8m4O9uUapMJq6bp+3yMF2Zvby/3QJy1OGvRokVxk9jc75cuX5H5y8IAl6wDsorLCvpFWBbyjZqZ6QFqWn7/x/Fip2avUejhlEIZHcnOO2NisDz1zi4Jh/yCsWk9RMYe0f4u4AFioJ8GvWHoYV3TInGdzwEH+8r4tlK39rUKQYEZtS+u8wD7f0IizsuqLWdlEahNY+OyVXPndRMV80evTk1UBiT6VJbsDUuQ7h09rdagSda4T2bPlv2hIWq7oqKigDWvBZ2h5sVsVcX9vHTlURB65MgHYEviealZ9fPA7vBkVDeywESE/h3ovvCdPRBx+HwWaufUquArPvmSGpQSj07abgr+rd12Wtwa2+PzFfR55EgucOb29awfWJP4IDCwlWRmovdp8xbVR3EAgZB9PXsZNWqU9OzVq8IO3PptsRioJ8rMVzoLdWg0K50HCGPkucV7PXttg0H7fjBSN/jKyf2v0D6H0q3V+nPXg0BmM1SKvvklEgQMYGgsISw7FP1yCxYsyO8pPRd/TvX4GPaU8vrwgRhxcS8Hh8rFzoZHqixcflBp4ZBkg7o6DI5/AClASESKui+x/8XFqS76GbUIydRZrEclmfrN1Hcnz2bLnWAVLczo5/7d3GVrSKLqyTacjsQ87PWr5Kb1EJXVAfxx1UnoCx1RAwWuw8utvqLqbKPRGxfpcjbwMZA8BPKEiOhU9U6RvpIam3UnPtQm/2HKbOOYl7ZIpzZuMmq49diLsrOyr9tENpnWrm39AeJ1KynhH7v2x8kPfx6Vz0CrTEIPzaqnB17+MEQRlJR27z2bOMijo9shk1uvtIsqdv6cnJxy77UztVGxcRny8cJ9UrcW2LUAkSJDHysYWvbalLfM/44aKbdN2IiA6Kp6ZtarYyfRpzIgzFtD/vpioEmRSfOXXr5TMjs/d8lRGX9PO2nf6hqVuLlbkZOTK7k43w3N3qF6kAosQ8Ju38F4xb7LZ3ZhRnKXQL+G0gEMfx3QF9kR786OWnIiHImEx9/cqbS9CvOdqe9nvtJF+nUrOrBhYmw8qLwTzl87LgPBvjjjpWBTi6xM3y2pVKQKzB4xY8mbZlpmDuiX+Z4LushcxYRB1jb9ixmGWnY3KFYYZsDJ/sObqxPgQczmOTWAsjRe7OUxpeRb0qOYgcbjd+aFXzfQoOAlha4oeKXZ9R5gtSdCBT9pKviJjNUxolw/1bW/+GD0AMX2KcxXlPFGOeaWZoq0wlhBeR1YhKbMCpUxt7aSXsHl1xxe1PaW128xp9Pls8X7ZcxwP3n2/vJrVC6v/dPWY74H9NeB+XMUnLKxc32Z9EQ3NWAt+GvV/CYt45J88k2o/AtdljRUx/TWGNpxg3t5KHYn6jlpVjIPfDD/EFjFYtXMt/dvKn/8oxMhfereVvLwXf4lW2gp5+J44v0vw+UKqhKsR7BPlMGvqk3kfUaBiz/k/a6bJhTVrhOowrZu7iy8VjgPJ+jSzk18PKzXy1bK3bOZ2QlDXb46UrYDksvBOasORIiEAaZ14GiKHDiSqmijixIM9YZ4K0ljGBxR5L66MtMdjk6XpX/HyPqd58wWWGWlZ9n/biy2X4ssv4+9sQOC9zq6bfqZUNGk1EuSnHIZQt6X5CLG60xssNp7BX2BdWrrxuF1gI5qCFp9V9wvGwMOSc3Nph72RTI4ltMJapsBESsCHBhHncyEgFSWnI4HjCMxu0CZrj6a4+3ta4s93hn0kLVNNaiDioRsJGzOvIKmXGrw8QK6nHNFstBUz8b6q7ix6a0uStzUsfFys8eBqS/+Pg0lEIwbFAFj34m5Rmzl1E/2K3gJ5+GBZ3VixGAfcxdRpafTUV/rAh9dBShNaU0UtdO8uZFIQf9iU3jWhSsyZPy6QmfjxTkJdML+PoU3Wy9YHiXzf46Uh+8OQmap5E20hW6EDf5wLiFLZi8KVVpXWpOqDR6gct4kPqgG4zqiQJ+lxoQDyTj+BA33kN7NZHh/24WHWrpvRU2ffTFXZn8bimb5GqDwbyIbMNiIA7zQ2IgIIO0tXy2KuA8Zz6f9LQquOfK5Ter57YOBUkp6jnpuu+MZ/cdnA3RBRQU46sUZ+wptKjd3cxhI+fs4KrKFu4a0FJdypBM3dxsrajomsxf9EiFHj5+XDyZ2RjBk+rnM+1ZEdBqooFMUHXQYqiFFCYMy+c3qUYdAVJFaN5I2zZ0wXlShbEXtarmul/1Xy9fEKsghdYWKszee6iC39y+caIkFif3oN1q3Mw46YfGKuttwmQ0cUHDAq3ZtO1WUsCOJFMbmV4D+ycVgnGPx7AsoaCCZxCIHjeN1ryb20gI9Yq2bNwSCxwW0944Wjb8Nt6GEnys+IKK2BaN/ZlIOHE1FEJSuaGGZSSE9chNXB3XTIFbUxameuDjWBV1qbakPvDobvUpqHAQwMEpJvyQpUJdOTuM7o1vi6LNUxYnL5w25PTINwRhkd2rjrKoTxutkVuNb9LZ8+eOx/BJlMy8H1YRW1KDceDlV6W899I03LlKpsgp0phjoGyt2zKwy+GnLFz4XRjxx5zP/KI0hQ5+x8scerduKuJgNp//w6wj5dX2s3HtbG5WxM/ytqn0+BSasL38MlxZNG0DzqpstZGOqmosr5f68Pns/CF/iLNp2ikh/gAbbYNwPf99wWt77IlwG9/GV2wZUTPbeoo0vxcSsDH25NBz3+KtQg++NjL+ONv9gZJpQaJuZWGZGja05rjl9cNS0EOFb43mq+9+GNL8UK9+DfjcazzsiLirCVm+Nkzfm7C/1qplMeOLeDoB6WZ/Yp9QbV0ELSIDW0KIVh4D6uSSzJnVTAYy5m8LxF6VMqJWjqkgIlOKBJCrMmKhm6wJhdqwkkRCFRE1V3RhwroUW0TJUjY6euCbOarzf1KhbMaffdWMEJvs37o5H/1CCQucwgGnq0QBCwbXkGJg2G6IwMXF8V4zXalvEPnoJFaQ0jMHjk7KB/tK9OFbhuJyorvYIYgnh69/dXVWSjLfVyn9XTEB0GIKZm/cmqEY5BkAsR7s3ro+KjJM09Wwg3m4OCDwcEGFWDPEAtycpFY3F8Vl4ZUIENF1icZAY2TZxrSs92jeWm7q6SXe8s7HvlZn7hA3Kert9QFN55REQJ6DyVF3sFBTGVdUHgQ/fzYG+sdqjD4CCWjYSSwYLrMRxEEJj1u2uQU2haN5KihOXND4eny4+KsR63z7IXwb18jX+uUr8ffBYMjJvh6Rru8agLw1WpesqsWPaTpTKA6ygL1geiVfhGhXGK+B1+uHLnfODAf7+N8hO3pkXJp3busvY21qpbKDxfJX9b1ZXv0Aw5NTQToloE+ZhbByYkRCA9yUOHgjpNrbWGIgxOBrcy1PcANfWzLQHSPBxzwubAbXBMxe+Tk69rMR2mUFe+lFf0zOV8bdkjB366Lr8rHaJVoccLgeNj45uDyiXBpmjD3eExskvayIh7OmIgLeTyaSzpb5OTLmk4HVhx3RVpCggjjhWM2UcPzQHCUYHJL0ZILGS5Nmk7PshTW1LeX3H4JGB0aY9CeoaM17vCw+0kb6o0JHIYhWE71NQZeIYvW2AqwT4NhI/H6d8YrCdB87JH2Db/OAV612X55FYio4FPPJEqhyOTlYohjagsb9zoLfcDOkH4zYI4+0v4d/lFxCx+rMajt20N17O4+bmjIpPUEtXadHMCSVkJ0Tott0Ix+iaQVH0qTQ5EnVeYhAkERPZDYPMpNSLOHAZqFrZyWSold/ct2r3paSjb4tVHwY+fB0+npaPJS3sRDQFfbMEjmi83KV/xcjH3x6WVlAPn/R4kAqsjKcx9+9lf5+UWYsipF1AYzWgY/WxKti/GPCu/OeEote+c5CPTHosqFRV1argk+q+D0z2hID1Ug3ad50r9ro19Behv688EmQSbrIrLFkm/S9EVfEfGNEWvZkVQ4VtuL3W+qwfsDEY/OjVzmZlkxls7gpLkjUIjpj8IzufoXEQRhgPgyNWPKzZx2q4nsr8eQZ6iVbk9RLxPn80RpfV/t9rut6Siti3AQ+tLRbmXdh28Zi/BAg9z4d9EcnSp4u33NLfz6paRYWt2xa/JxJn+d+RSDinyf2Qf3gKAuGEVpWFEYnEscoB0LdzLMrxix6uZWp97AdkD5KeqIF036yKVDUjQQLhdL+tP6X68vX7R0Y59v0QldW9ozt6sZogICqc0GPDjlMysFfZtIbwXnosJkVCDiXi+CVILWzbLf2ayv23Nbd24Fq2ARGj9L82nZHf0RR5FhkfL1R+OrRpgoGnq/qsd35lfM+EjktEZDI42ZNVgMRGyf5g2hiLRv72AVWnFM5AUM/6xhtKBG4kFkHfAHsj/K0w6FtJjz23ieXxe4Y2s8qNijfJqSBayMW4ZcytgRDVcynpptnEfMxok1o7PjkbgVA79Ht42cR2aRtRMR4g5IGDc/a8EFNuidUGk9prj7YVVr6LMvZ+Tv54v8SczZQ7B7WQnsFeFdbvUdR2mvtbRtZlpSVzANTaD9/VQh4fHVCihAJ7HraHJipoIumD2WhsaBwEdglyVcHRAEBDHDTyHeUenqcj0EvEYJLJxouo0FBEklW272b0MXRhmX/WH8PPlx1TVPUlWSFRI6OGNVOzMuv+yaLDkgPRzGE3+uFa8QR9dBUccZtwVHomxHa3xSots9bNG8nkJ4JUz7aJScvsK/aQcwzBPiQSNbCSVNR9kedfO4xj9Gx27ZAcKaMqRZntc1EL5vn96eIj8hsg0Pr7U3BbN3kQyS3E8TZjF9DDuScM0L29ZxSs7laQrowf2cIqVUXsZNkERJEnM2TR78xMnwWzWm1AKdykWwd38XYvvMndZjxegg2hyOW+gwmyFzSRp89lAhvsKA/e6S+kIqxsWYUSQd+QPWHQoyM+sAz6VgJ3l9ks6WBMmfHVIfQCxKmMCJteqZZemYy6MKs2n5Qte05j4OCkBICrK8tOZTpuZbGtxIkTM75uR1w+0YvherzzGv/dgBn/4KuDhj/lfyae/EPo7LRu7pj/XVEfrmCA9wV6KRf/cUJhzO8eHlDp2LQ4WNq65wx0z2IAkaslb0zoIF2DrJMgIexq05546DjFKZg1IWGGRhbNXh0bIzjyUk3l1Ql2begH/edvfomWeUuPqT89ANk5l6QjsPhkcldFaqGfrizei6vyWbLOJ1H94MDN0LJBDjQf5D7L15xUvRjMsPfq5FVlGRvZg7d++ynZAdkHUmM/AZHn4pIshv4q68+EaV5js0spMvBlEoOQf8LrFKMd4HYuTpVrrKD3J5l+WY3ddygZAV9jadXCWQ5HpUgUqjKvP9MDbQi2t1+8R+8Lj5d122MlFf1GHG8/PKLFdX1P+v2z4N26ARGbS7/4MRINkEkIfhykXw8f6YJgqAZOnupisWfTkYU9LeFHE4WDiYeQWbxjgI9NBkZ66FsE6BkV8QEIEPQ0ioUdL2tD3wpbT0V+z36wGfMPgmf/Emi5vdBb5AOKdtu7KRj6iFmdLcia/LPrlJBr5FmQSxQlsmY4r/a56nggBnSorAStw4vJDWOjzMCgnp4ytI+naizm73y43PzY+gIsnl2hUfX+C51KJDpKWtYPvjyoqridgAq4uW8z8QBCwJaNcEJqn6zbFiPn8ZB94I4W8ggesoRGl4UxAbMRFTser/2A8hj3ODAYIu0wj1VPBEmlgRiXxfaXxzJJzjP6xc3XUAkcSqAVxB9EFT98VDw9sKXbaE4fGMVWyVZrrt17a3N58cHWhU7OygT7WAldYjWWyePenb3Rp1c1+liOxaSCRvusHDyWBFKsOmrgeudAH5PQ20KdVAE/8Ppkr42qIqGSdAQJJgpQF2ZMMOkrSITb+aEvyZr21+azCl5rrfsR7/vzf46C2H00oGcOcvfNARAUvpb4Yh9P9qUcm05o6ZJXp+XvzTEqyH69dMkr6wREVDdnczoz6y3QDzT0xmYS2NzZmudCpVtWMpjrNgJXuRPZEDLVvfBgG5X5q6gdKQn0jQQFOtKDRjrmN7K+QcepOhgz3b9tOCULkaEkzWR3PKR6dfYC8YNtVTlTQaO5Y/9Z2b7vLAZU/0J7qTmwtX5m9TlUh+NYHfaRULW10NZiX1A0moeNjVXOgT3cFSSL4qGmzLBfg7+PA6b/mftalTqRw6bdr36KFDY1U5yybxcvm3s2XLiUK3uRbdyyh83DF+XWft7yyMiW1sanm3J7/nckDaAuFI8hk1PG5gAWLKrEMzjqCnhdZUMeGO+PJX/v2J8kz7+/R81CP+gplic/3s5qkhaWMAXGIth/8f/2mrULdwBm+vqE9mZNy+cMIUvsm0qAGGkAWOgIW2LW3h5BWGUysoYRahqCBAMFPMkWds9QXzWgr6yU1wzOSQjGPiRWksIBsysqgUzWXLZPdATVNytJbUpBI81qZb8H16gqFM/77mBeLI1Rz3PKrP0SCVKzOwBvZj+bLUHjLN03wpuXr4pC8Ap484iWqvpYgntk6QIiRmcLV0QLy9qkxb5tYAtpF1i6A2WpI2x9+qSUi7Jy43HwticiIGoiUye0Kw/6QJUd1vX8oO8HlZ9jgDEWJWZGqEZAFYG+WfOcYCD5x8YzsmxVjJzEg7CZd0Po+HhgcOdaYaVkUlVGRJ5HNvscGFjOK1jf3UN8ZTTw6Zay7FnTV9qyys8D7M9cnzeA5vVtbKSRJVUpm/a7AO5VnERBKHQlnnhrp+rVeBMDuIFWpjZmYPTDyhhUQs4rgcruHT0wUGhcYeQLJBw5djIVjboJcuBwAvo3bkAvh7eMu6M59DAqlhCCCUYGRnwxkDQ2BrgkYuCxJVynOthrH4WCue+c2lXClThApB9+/bSf2KO/oyRGaL/Oz+fyoXiGyzGlJbXjQJL8b2GEyeqr4bz8TMj8+y8GWxy8skq1DT1nK9F/vT00QYnNk9krCGOrAD/nIpvbjbehvP7m8TgJ0qmjJ84DHZMk56Ab6QLRzcGoRt8xsGmV1eI6fjoL+6sjamAfEpNThRmrf+x/0xM1kO7b3N5q3uMfmrw9f9HDca+ixmVJEtR7D52XV8GM7Nigrjw0sq24uRZOlpC/wkryYXvoWfl1TRQS+I2E5CsW9mOWPCAiJOLNOQfAtpYpt0KU78ZutgkLs5XjePxUuixbeUQJUr0yPkiG32i9JndC33R6P3maP2ZC365RXjuh76lhtYRkWHJ+ENayAjSUW8CUSIYaP9DEBwW4qKooVcfLEhpKjv5oDOCOHE/BQydF/kMygpCmu8D8RQr44ga8luynNq1teoDCxtS64SCOJCBsMDe0ehCoppghB8pMvliSieWiJry9S16FoDEpaMvKCOkjlevqbWcVRM8DYnwk2WmJbLifl2OZSi1Q2yI6FskhXD+HokDlimZ99tkRWjqsb5lRuZbKlfSXPjgyBYF0c6kng3vrgiNz+7xKtUEVNDNhZaMnblFU5jWQyv4379x/AL0Dz6KSaa6xX0Lvz5NnswrMRnaxwb10/uTzUW/xqNjMhG7dln0J+q+KfO/RoTH0dLpadA2aWiCfMwyOWD1kKwJFyQnfZvXID/AmH2jBEIpa3nBKNrezX/oUXsfBvHsc1xVh2x4Qz+0LmCdp5TtgwF+Zqw6mjkdx37HSq9NC0kHtmNBgsGjKyDpIvUpF9Z2nicQg3JQt+TNGPvnu8HU/UXfxRdBjD7eAMIk9pW/N3a96pMfe1rrcz5vrdqCM/iB74Zc/UCKhltJcNCWRUMiqSxYQMXPxPvDhPl4NIWrZuspgXQtxktW+Jv505aYTsgl9HkPApc5MLMWnLLEC0DcEP2fiC/YKGC6zOkPfDP1grc8snZNSl1ojO5ExJEc/j6NfU0fxaNJAPNH8y4EeKSsdLKSTp2AwB27xyLDFJWWpTNtJULwTs85MaOe2LjIAmUcOfKuDmJy1jlllXQ4HQP/gPOMgbi+aXo0frsw69urEJnxP1W9iLXx5WfuL41kOHKh0vhVZcLKQKiFsaLN4UocO1w9x7eyjaGBfx6Isew4GZqkZlwE74jWUra6lk+jtpPA2B45BgLEQfkZGNzfXumW9q1ZbfnEkGU0BzeZ5wFdZBrVW2yELF2QojMpzhQgVIhuWfNi3yH4NBjPrMBDkNaSn7jZctROgTQNQcRsKvxFSajiI5/W2+M8T0OqKyqdp5kC2ewcX2XUgWU1rlJdQ8KjP3uxu9d4z9rjxHNh9MBkQz2Q5fCINCdYr6rrh9eIO7UbXRvXEFdcMXy4NIWKP5w8raiUxBjhk0z1P4XogXZIgWp+EyvQ5DDiTITVC42CTwvXd2rkAOQH24AqurpZkP8tyHjIjHowkix1EY5FQPQimXj7jCzPXRnVVIKlEYwG1I3EDz/VXZoYoMhZT8xE+N/mJdsX6nj1I0+YekAHoiSYLaFU2nYh2GJ6X/14nol3MPlsWEPHCn/sDhCx/Oy6DeuuUyQ1vHsWsTPs5zwPM8lOV2c+7gXw8qYtqBivMOacxUNBr/kTgYqLgaVGNffnQtzy6azK/sYdJs7LzADO3hBzxxhcFGEYMMo96jQM+jJjRo5Izj01tu5pih/cb8C/3ylUlisbjmYmAJx0sPAy2aJzPF8etZZ54bXAbFwjXVU0thLI7MpVzyTx3mInmAI4QHePrnfS8HHxw4NsP1UH7KkDTzMwqryE2MEfFIut8OiMfn89nDJmOiMmvU9sOQc0NuD5qqmuEMOAruVclFwPX7Is5qoKgH3BwPjKTtfBpqPD7HOzyfmhpEsoWzyI2e/P8YMUwBX2ExkYBU11w5FHsQMl4Xlv+e9LHoYo63nAb2yHA/fq9XtcFMinpqKaCXZEZ8XDcl42rqSRGYGV9CHqyOKA0VWEn6uLdeeE4FzPzV0c9pNeg58bqEUkQ2Gj/J2RF9MYB7Jdv97QUqqOf3eJ3jg+OQAeQOoisePFZFJd4QcHs9Atj5ZiBERNqfP7w2WKHxEBN3Ef+w+PmKiKtK+pZ9B+eP1dV8o2BkOF9h/cYb/f6eCY5SHMQWhD6xYqkpp+l97J57wziOUZghV9H1pCK4PJSoTPz2PGexTEgg6vCjAQsj98TgAKFn8lzmX14E2fsReDvg9YW/8IWU6W+z0YVc/a3odD5qqHuD2bA58wPiHgg35h9QAmr3ntra+nczq1KOa+8d4a9RfOXhWG1/6kbKJWRSwR9Q4m1LfCSOsprDfpW3sexsPWdxUMpHo2LSSmXIdx7CdSQOcLeH2bdyPnPBzQHZnVq11Qvlr8bA3NNyAYV7Ju626sHV2HL176vWh5g5Xd7aJIa5G4NScgPqPV7WQNZQg7omcVmhbAk2HH9sirLO2FSxOSzX4rX0XlUeC4iWOT1w+uI2fvaYICrk3cdsWLK60d/Hfl6Olg9S29rvmPVgHS5DI5Ysc7MLsh+RkkEnjdkF2zsbNtsmcX5lxXTe1/eWqDn52Vo/BCGzmrqGlCaU3zYuJrKQWPv4CaKmKJ3pyaFBsZMSFBviILdeuY/BlBPjgkooHvH6hO1khg8MPG4AIFZRQcJTFwngiE1AWMMwmwZHJKwISMrR3Jw7eTgXsPtZfKNjeesmvJZVAvV5rq4nvgscm5YR5xAje2Mz0wqVIf7TXHnXln9zrFCWJ4WEgMlwmSNA3hz103Jl6lPtrtOJoGB8v2vbgVBh7sSnjd3WVVhOlaKPvkmFEkxB2HVthgzPyB6HcHQxl3n5PEx7W2OJaiYnbTZn7NRFZj7fagq6/FhbqqB1nDjNeiboTe0z5oHKrcHmGTaA+gLB7MkHTBF5cvmUGb62dfgioBZM80DhXmAzJisKK7dfhYVxsQC0Bx9UM3ziQ3/lXWQy4rPE2/uUtV1oSI6koqqwoOPxkEQKyHs52FAyIpQcWKaHJC+DViRofj4gO4e6K1rW6jOzEyQLGzZmyAL3u1VqSCYhZ1H2vcV6wEy1/EcJ8SO5yPJsQj9MtdYARw9zE8mIIDn5wcnbRfkj0D93rlM+5zN3b7yni42LgNBUYg8PbaVIs0pYv3mBUSfL4sET360PDqqPaAH1hGoK2KjqtVPLE1/+l2oZCJ7k40mX73xRk7WN1Z+9KKnGvRN7x3tXfNA5fQAs7ck56D2DDVo0jJzCuwIYTe6IMizXKmfC2yI9kWl9QCraIRdslrCPkdCoQztOthlN8AuS8jUZrjM8vrMauqHCw4pimpT62Tgx15LXkPsEzOHeZPVElaFyISorwqxn4OBEPvNijJWMlm50gSwi/KS9ltJPfDOZ2HyJ/r2LTVW9jh+JCnHK493FVf0NFdX2wAJnJX/HJel0C4rQh+q+ICIGafnp+8BNrGV9OjkWV39Wab7TQGsGV/uVhngMcP91Emssb6Vqcu1hWseKFcPMMun7/lgY7KxkW2IAzi+tIGVsXe0v0vjATbe/7NHByUrjJijd7CuitKns5tNwgz11VQmEjYXUk2lj1gFok6Li5P51VQy5k6dFSrRp671Ct1yk7cQhmdG30FpDo02r+aBYj1w9/ObJTauICNisTPmTdAUfV9P3tsR5DS1zZ2lyk3HROTM+Xukmae9zJrcpbD9KzogYsVi5POb0ETXSMbd2aawhWjfW8ED4VBxXvDjQZkztZtSJbfCIrVFaB7QPFCBHiA5AIMgvtjobGzM4OkpkwNRFdJM80BZe6A46vb6qBTdCNpkCsD2BNSMkJuKMn01ldfPBsD1ue3GVq+OHfrKrkdWkGChDZr+zbGfQYww+7sjqieN0ztD22gKGLsYWGmmeaCiPcBzfvD4daXeDF7XI4YESDcIzFdXo9zCZ4sPKGgrtaBMWNEB0bylx+SnVSfljWd6QrDPukrJP/zwgxzYv1+SkpOlY8eO8uyzz6LBzzIKahM7VOhXu8I90bEAAEAASURBVHfvlq+++lKeeeZZ6dSpU6HTVeQP3yw/hMbhbPl5Vj+LaGYrcpu1dWse0DxwzQPF6ZwQhqMX1WwXcE3n5NoStE+aB8rHAySr0NNRHwZTmbERatYf/TMMjjqD5bIMH8/XrfpwdLqClK4DS1xx1VRSZj80Zft10hPeIBpaPLNvkTBAJnvJIMdAS2+sjr31VHtFKqD/TnvXPFCRHtiM3rSXP9xX7CZQfoGEKaRBbwJSJicw2y5fe1J6QACbL8V061C3zK7hi5cuyc6dO2Xvnj2y/8B+WbZ0WbHbzAn2IwYICQ2Vg+HhMnv2bLPmKc1EHy8MAZW5g0x/vqOpxSwpVOKZZfYf/45BBtPP6sHQ77/9JnvguPc/+EAWfv21rFmzRu68807x9fU1tZFW+S4hIUESE5OE77Zqtw3wl3c/26lu0myi1kzzgOYB2/fAObAJcvBGit9jMekFNljpnPRwV3A40qdrUgUFXKR9UQEe4ODpPtD08nUG9M36aqaeZprN3b9vOKVehKANgtjmEIjAtgfNtbXtOOBq+vUbEhro11NUNZWoikdf35FPP875P/jqkLxnetCjaN1f+18IhEV1+n1k+nzu/lYyalgz/eq0d80DNuEBarWxv0/PPtsEQszXPoNRk6y0eJEZ0NBICU+ikTugN1QXVdSytgsXsiF/kCuhISFy4aJOo6q4dZJJ78yZ07Lq77+wrTWLm9wqv/dEcLhiTSR6/oJMwmFvwEYBXVfQqCb+v28i5L2Jfazu0Mcee0xVhZ5++mk0MP4rCYmJ4uFe9qW8pKQkady4ccGdtaFvFvx0EFoB/8nnbxVLEWhDW61tiuaB6uUBNlJTA4aDuHCI7hlbvs4JeoKUzkkFQo+Mt037W/NAUR4oTXBS1HKNfzMVhBlOY0kQFgltl8ff2qmESvXLmPhQWxl7SzP9n+qd1+u7n4fn09pTzHbGxGAlgHndhNofmgdswANkjrSD7pql9ux7e0CvXkMeGtnW0llLNf1LE1+SyKhI+fPPP81ezjPPPCOJKFT89PPPZs9T0gkvAV47aeYWmf5CsEJqGC2n8ArRBiiIBwU0tnowlJOTq6o0dhDWoxEmVx7BENdl68EQt7FLkJt8A9FW6kmQilszzQOaB2zDA+nImFN6gJWgUOic6Nmo9FtHnZM+0Dmh2GNROif66bV3zQO26AF/nwYywSdQJowNFD18bf3OOKVtw+09l3RBibNToN1SMhBzYHrU2SK5iCUwPTIzzpnSTZ56Z3d+P9Cs7w6LP/RHurVzha7LNVF5vc9JPf4GIHKViWFPv+3ae/XwQEmCIZ7rByHkOuym5uXupBoQ+7XUyrJVxnhbWC3zdm+gkpiErhubyVoaHRp+LEXuGNzCePpS/f3rr7/J0aNHlehUWFiYwgw2bNhQHn74YbXcY/ht06ZNKKOdEZfGrtI5uLP07ds3f52EvG38ZwNExGpLs2bN5Pfff5fOnTvL7bffDqXlK/j7DzkcESFX/r2KeYPlxhtvFCcnHU7/NJa5GctmUDR06FDJzsqWPfv2yo4dO+SJxx+XXbt2yu7de8TV1VUeeOABadToGiwgF6XAFStWSMShQ5A9uEECAgOkZYuWallcHvfBWhbg10jdvJl1poicZpoHNA9UnAfYa0BWK2aWd0Mz6IqRHkQtCBqy+ZwDODajF6dzUnF7oq1Z84DlHmjTwhFSG47ywgOtFV08rwNDggOKPn71U6R6URSSkLrBgNZ5NL5G8VteRA7tAxvJ3De6y9Pv7lICpP9B5+uF9/fIohl95AtIh5CGnEZa7qfvDZQH7vC33CHaHJoHbNwDpwAFZctLc2/ziEUs2Z1k9PyzxSUyKgrPOvQrNWksjzwyvshFHMG4fhfG2awc1a1bT4YNGybdunUrMA8RXP9s/EcOhB0QXx8fGT1mTP74vcDEpfiimZcjtJ0KQtu5SJMBEVWOqdbs2aRBKVZbcNZu3boiiHBFELJd3N09ZNjwYSq44ZR///23zJ8/Xwiju2vESPy9Uj788EMVsLz22msShqarT2bNEh6Qpt7ekosAKCk5SVJTU2XwoEEyderr4t/CX1565WVhYPXmm2/Knr175b1330UgtFl+XrFcYk+elFGjR6kN4/p+/vkniNddkrOnT0tTH1+5jM/r16+XjPQMeePNN/J3YPr70yUjLUOmf/C+wkjOmDFD/da+fTsZPXqs8N1aRvIKJ6hEnzybjYDIWkvVlqN5QPOAuR6ghsu2EGi4YPC3Y39SfsZZPz+Zt7rk6Zz0h86JVsnVe0Z7r6oeYN9bcBtn9XplfFuTgsLsn+Pr08VHpW0LJyEcjWOJMCRXjQVT2bfTq5P1qb47gj3q8zd7yJPTdmKM8J963f/qtvxqLq/V6S900phcq+qJqu1Xfi+dI8aR1raPPpopHTp0lLenTZNQkCHMnDmzyIDoyJEjwvH7C88/L+NQaPh6wQKZPn26zJs3T7y8vPI3j+Pwl196WZo29Uavf6IcPHgQ4/c9MvfTuVKvfv386azxgX6JPnne5KJMBkRpEAmlWftBTwewZ4jm4uoiAS0D1Odz8fHy9dcLpH///jIIwQ3toYcelmPHjsm2bdukS+cuMnDQQJk4caJMmTJFLly4KF8heMrM0EV5i8FYF3vqpHww4wOpU6eOYpFrGRAgp2NPqWrUTf1uEgcHB5n29jS1bP53z6h75PTpU/IPqkZ33323DBg4UP02btw4ORiBSlCeRR+PlpB9ITJ27FgVEffu3Vv8/f3l+PHj8tLLL4tzI2f9pFZ7J198et4xsNpCtQVpHtA8UKgHiNXeGZakKkHMJF8wEEnmTDdgREiqTlaCWGpvZNTEWuiCtR80D1QxD7BZu2fHxuo15Yl/ZXtoEgRgz8pWJBGYTKBFRKepl+Gu2wEe362DqwzFNXRTGYrBslK04N3e8sgbO+QqRFz10FZWrT59vbum82V4ULTPVc4D6Zm5ap+szQxNhFZExGEJ7tRZLT8YKKzWrVoX6b/QkFA1BvcFosvOzk6Cu3SWlX/9JVHRUdcFRFzIqwic2rZtI0RkTcY4n4WN3//4Q8agUmRNs0fRIR0tKabMZEBkasKy/G7XTpa4cyUwMPC61RAyd+hQhOzYuUMFRM7OuuCjU3AnqV27FoIqVzX9WpTw2rfvoIIh/QJmfjgTWamraiDD7+wwvbHVRvBE80bFSW9NmzZV0SkPSq1atVAZ0gVdcXFn9ZNIAIItBkRZmVllEhAhGaeZ5gHNA2XsAeZm9h1KVkHQxt3xqm/PeJVt/J10QRBYH91AZ6qZ5gHNA9c8cIPcoCBorJrWuIH9A7qA6NoU1z79d8N/alpOVoMlpzK0mmhEt0dPXwYCIr2lZeZoDI96Z2jvmgcs9ADhcW5ubvL94u8VOuu+++6Tl199pciljB4zWrr16KaKCJEocKz+e7WaPikp+br5HB0dVTDELznuHjpkiAqIWGEqTzMZEDk51FbbwMZ+10bXsMBltWHnzsWpRRs3V7Vuo2PI0Acj+t/175wpMSFRLqHcZu9gf93m1URzF1+WWk0jsYU2bduqA3TgQJhaT13gJtnj1MStCSLca4GUpespavrMCznimHcMippO+03zgOYByz0QdjRVweE2gCUuJf1ygQX4N22ggiBWg7zdrVuuL7Ay7QvNA5XMA/pEAiGl/xSSSGjVvKH4KMjcZdXgTcgcX4Si8kUCEvbIUuPI2gQkByPT5Lnpe0Ctq8sCs7pLMt2Ll67K2Je2yMeTuirmx0rmdm1zNQ+Y5QHHBrrk/4WLJObSjeXNmtGMiZ4H9I0wudWrV8vmLVtkwoQJ0r9fv0LnZFXoQna2THxxovg195PgzsEKClfoDHk/tGqtqzylp5vu9Slu/qJ+z4ZfHAshLDMZEFHYqU7tGnIuMUv8vK1HGFDYRjo21DV/RRyOkMGDB+dP5uKqqwjpiRHyfzD4UBOCVDTC2q5euSo17XR/87ucnBzFYseDUlJjAPTWtGnyzttvy5TJU1SfUlpamkydMrVEAVdx23HhUq6kYpDm63V9gFfcfNrvmgc0DxTugSMn0lUliEKUCecL6iR4u9vnB0H+TR0KX5D2i+aBauoBJhJIqkC6eVOJhBa+DfOuIQ/xanItkZCSniPr83S6wiNTVXDCHmUmJPgiy1u/btDpQnCkKOoBySupkYyIwRDJUGjDb/SW2wd4g2hhDwKyfyUn91959r3d8ty41nL/bc1LuhptPs0DNusBZ0cd8ik947JVAyK2uwQGtpI5c+bIArS4sDd/1scfC5FbHdq3N+mP7du3Ky4ABk4333yz4gIwOaHRl/Xr6woxnp6eRr+U/k/6xaWQQo/JSIHV7A6BzhIZkyq9gq27QXrZI/07dy+gVYDaywjA4wwtOipa/VkUTtHF2UUaNGgomZkZsnnzpvxeIB68mR99JI+OH6/KfIbLtfRzAnqcevXsJXffc7fY29srJjpLl2Hu9PQ5/V8W4nfmboM2neaBquCB46ez1ACOg7gz8ToRRsP9coPI3WCwYrES1Lq59Rl5DNelfdY8UBk9cFQlEs4p4eH45IKJBBIn8Prhq7m36USCs2NtJXpK4dOE5Ev51+TRPBFjBi9/bT6jXkrEGD167DPq1NrZIogbg61nEQzp+/9GDvGVSY8FKbd/CvHW597fDTba/xCQicz+7oiiFH99QnupX/daErUyHiNtmzUPGHqAlVn7+nYScyZdvD2sR4yWAK0gMjs/+eSTigChA9pUGByx5aWwgGjF8hWKN6BvHx1bNInLaBBANdzkAp/Dww+q77qARdraFhuXDmZYXbuN8bJNBkSciFoAH38bIZdyrkpdMMJYy+LO6npxGGTorWuXrtKhY0cJO3BAdoKer2evXuqnEKjektJ6xN0j1d/68pnhvPzh3nvHyJdffiWzcXAOghqb82zbthU4xJvzg6ELFy6oZSSjMUxvZKijZef9xs8UiaVlIMBisMWTYO7cudK1a1f0FoWjT6kuqAPriruHu6LeVhNb8b+QQwlg8nGVhg66sqcVF60tSvNAlfcAxR7XIRu9ZlucHD+dWWB/mT0biAEXB3BkpNJM84Dmges9EHPmWiKBFL7GVppEgptrXRl3R3P1io3Lzg+OSN9NY5/PL2tj1auxc11QeOuuVbLWFWXRpzJBsb03Pxi6e6ivvPaoLhjifF3bucicqd3leQRMuXl9RbxPHIvJkP97OVhaQHtJM80DtuaBq6COJ4mJJcaEelCLRnL8VLr07Wrdto4tW7aCWXm0kqXhmJjWps01YoWUFB17G8fq7AuqkYfYWoy+o6B27WTJkiVqnsjISEjd7JIePXqov9n2QgZpyt6QT4C8ACRY0JOdqYms8B/jmTPnsqT9qJYml4ZAzXSolgUe8+FPrJchff0wgPAxObOlX/7110qhFhGDDMLYyCo3cuRIxTZBXaB5n38GJ+2WPtAeunolV2JiTsrkSZPEG0QHe/bskW+/+UaoJ8Qeop49eyr8Ip3OXVgOnaAfly2Ty5cvg3ChtmKRGzN6jNpEslr8/ttvEo8gjBR+w4fdjKqSo/zwwxIFq/Pz85NnoZa7dt06hY3kTO1w8CZPniR2Ne3kOeAmOa+xMXCbMnmy8dcl/js59aK899lOefe5YAzYCopGlXjB2oyaB6qwBxJA7bt+BwVT4+Tw8bQCe0q2zAGgx2YQ1CXIFfePApNoX2geqNYeiEu8mB+cRMVmFPBFIyQSyK5YVomEyJMZav3rcB3HJeqSl4Yb4eVWX62b6zcOXpgEeRSMcufTdP2AI1AZmpxXGTJcBj/vOZgsL87Yi+f+NbKF2tASe/HBNsIgSjPNA7bkgdnfH5HfN5xWhD5NgGhgkoDkPk3w3hjvbnzHyziB/vOaWFUFfXdiH6sVNM6dOycvTnxRHOwdpAuKGOfiz4Emu6lCYaWcPy+fgUqb43RaUFBbGf/oo5KSkiKfzvlUMrMypV1QO7BHPyTvQ74mLTVNOD4n2/PWrVtl6dKlwiCqOcbi57GsdoDgjQe6i2N5a9qu/XHy86pIWbNgkCkW7SWFBkTciM9+OCZ07BtP9xBrU/gVtpOEvsWCLpuCp2S0sMSIEU5MSpQmro2v6yWyZBnG056BRtGHaCIj5XdObo5cvHgJN9PLIHNIkIULv1F86pZup/E69H9/s/wQburZ8tOsmyzOCuiXob1rHqgOHkjNQF8C+g8YBLG3wTivUx99CRRK5QCKwqklUfyuDn7U9rH6eiA59TKqqbpr6BCU7Y3NwSCR0LUcEwkkReB1zes7OVUHsTHctuYGpCccCD48ZbuczqtkkaTh3ec6FQm14/Ip2JphRL3L+8WbT3UQfVO64Tq1z5oHKsIDmyAK/srMfcWumiQlKkhSAVM9dQ7/tOqk9ETLS49OHuLUoA4CgDpFXhfFrYTPWDI3sx2FQYu7uzuWV3z1ir39V65eyWeB5jj9Kv42DnYuXLwIKZ0Mi8f9xW234e+zFoZIy2b2Mv35ToZf6z8XHRAR2zvyuU3i7+Ms9995rSymn7uqv/NAjn/sUenapYsSjDXe39ffeEOmokJkDeGog8eSZf6P4TJ7SjclWGe8Lu1vzQPV3QNkvSSrFQdL+yLOK9YqQ5+Uldij4Tq0z5oHKrMH0rNyZSMCDTLE7T+Skq/Ro98nDqwYGDCwoNZQLbuKK6cSuxKC65zXO2nx0wGnM7b69WoCJndVfd2rUxP5+LUuIDsqfpDG/kKSKySlXB9wuTjVkalPtpe+nZsYr0r7W/NAuXsgDYm/wePXlXq99erayYghLaV7x+qLPDqG/vzPvt8v89/tVRhcfkmhPUQ8AmR/eQNNhy98sFdaNHOSHtXMmVcRCV+9ckV2oK/JDdGwr6+P1ISIQiREpdjv1KdPH6sEQynpl+S7XyPE281eziZcAOwnXQLA2KNltUt9H9AWUMk9QKpcCqVyUETh1FywRBmaHaj1yUzFSlBZij0arlP7rHmgMnngAq6hTXuQSEBf3e7wZGRrr7+GagEy1gvBz5DeXgiGmihKbFvYPyafuwS5qBf7gXYp4eRzsmlvfH6vkD4Y4vZmgmb7l/WnzBJOJpPkog/6yETA5/TkDlwGYXf87rb+TeWlh9qo5nR+r5nmgYrwgBMEwH09HSQ2TtdjV5Jt8HJzkAn3dQCsTsc+V5JlVPZ5mFz5Y300ig1NCguG1C4WCZnTO2He0mPy/R/H5bFR7aV1Cxf919Xincq8f/+9Ush6QbptUoB37NBRbr31Fgls1arUPsjMzpFPv9sPEofLcuGSjiqUC60NOvGAZg0lqKVT/os4as00D1R1D5Aad/v+RDWA2xYKnTFQ9BpaDTSZBrdxwQDOQ5G/OGoEJIbu0T5rHpDL6JHZjmuH5CK8li6jmdjQWEVhPx3Z3Pqjv84BrFSVxbhvHy2MkN82nDK5yfp9Y5KEvYNF7RvvLa/PPiCbEWQZG3szyFLHillRRlpxommaapplRblJ+62EHnjnszD5c9MZi+d2d60nbVs6yo4DyfLaY93E2amuxcuoKjNs3Hla/twYLUtm3ihFyGoUDZkzdMbUT/YruMoTYztIgF/1ZGeirpEx7tHQR5Z+pkAUg6ErqEKx8fs4mHKKMlKSts0LkMi6w5dxM11R82u/aR6wVQ9QtJHZa1aCmM3W64gYbm/7wEaqEsTmbkJbNNM8oHngmgd4DbGKQjjc5r0J+VUU/RTE+3cAsyIDBV5DjZB9rozGHqDH39qJ5+a/Yofq1sN3+cvhqPRiql+eKrAhJNCUzf85ShYsjyoAIeS09NXLj7Q1ec8hjfcnaHxnQLUAUBw2vGumeaA0HiChGSnkwwBpPYD+2HD0910xQkYUtXwmDO+5uZk8PTZQoYzGvbYd5/UN8txDwdWyN/30uQyZ9U2IPDk6UB68078o15kfEJH+b8qs/UpleuxtbaRzkIaxLcqzxf2WnHJR5v8ULlf/vSpfvd1TCdmxUfxQVJp6ReAiiAB0Lsuo8dN4ueScD2rZSFWRGCxpUDtjD1XM39TsiIfmRiIY0NgYTCHDS8hsMlNLhqN/UcOtg4c5+174cmpYS1wb6Rhj+FAlfLKqs6GxjB16OK9HYFe8otw1PlqBfo6qEjS4l6d4NNaJtRlPo/1ddTzAQT2vnfO4XtIyctU5kY77YnpWjhLVJGUyYZO5GIgS0myHHpfaeBH2xX6XehjwEmbC5JH+nULjlXXwX9yRNafPprU/ryFPUFh7VvoBezYGi/e+sjWfiY79PncObKrcpPqjdqE/ClWxkvRH7TiQJG/MBloDfVbGRoKJpzDAvBsMdoZ95I++vlMORaeqfsZmXg4y/52e6rwznr+8/05EbxT7ozimYAUrDWKU9A+fPTl51xAr8XzG6K8fsu1RYoXXTaOGdaQR9KN43fC+y4StZmXjAd7vDhxBAHRMFwCdgFzEvxhvl8RaotXidVwTbVpc09U7ARr9B17bKl3aucvoW0qPairJdlXUPBRh/eTbUPHztpd5b3a/7to1sU3mB0ScmTffOYuPyOI/TsjgPr5ya3//4lZgYp3aV8diUuRbMMoRG/rxpC4mM096L1GrQRckpar36NjMAhhw/bR8J9Qu0K+hqh7p4XYa1M7QQ9b/zL6vkMMpchBZnSgcn5jTGYA/6iAq1BBoiMFZA/va6thw0KYalfFUzQVpx1UM7HLwzsA3DbBJZj1pnMbX0x6MKLpjSYgY6WYNH8bW35PyWWJxLFIcWHAAxxd9oFnV8wAHnaRa5osDgNMJF+UsxHOTID2AOCffmNF3qF8bLKd2UqtmTbCH3qCa5muhd+wGXFsMoNgTw3eyF126fEVVF7Mu5Kjv9Avicjyb1Eeiob5QTJQDB0KSKSZqThO+fjm28l7cNWTIxFaVoFxMylI/iHbLTd4y7ZkOJg+JOQx6/bu5K/IIQwY9Dk4JoQs7mmJyuW38nZS+kWeTevLVz5Hy8+rY66ZrhQTOF9N6lFvv0VlQlB9B4pQCuifPZoNtL0v4HWGFemPCjc8finXWgi6MHeCSNXH9MJnAa42JBT531LMo94rqxeL1w+qX3ohE8XK3l2a4H/t5N5A2CLJbQcxagyvrPWTeO8fQpLUnM+oBnGN8TzhfUPBYvzTet4gEYl/5RYOWCv3v+nce40fvbqn0vUzpFhF6/tL/7ZVBvTBuH1BklUS/yEr/fgEIrDmLQpFwvkG+fq+XOUG9ZQGR3kN//HNGPvgqXPy8nGTs7a2Q2dYyt3rfFPXO7OaqTTGycVcsyvCeiuKzTm3LWHx4ozsGhW8GSRHRumqSKd0Gw+1gxocXlS5AaoTPjuacHIaL0D4beIDHkfAuMp7tOJAoySnQvkJ2rZmXo3g0cRB3DLz47grMrqVUl4RRpqZdkrikbDmXlCXxCdl40KWrQV4Dezvp3NYVfTPuYEFyKxIbb7C5NvHxmF5nZPs57FdBnREOVvVBUEtfTSTRJg6alTZCPwgIRdKArGGHcd9iBpvW0KE2AhV7ccEzRL2c+F5XHEERa4+sNEkzSmqXMIDIQqM9SWtS0i6iUovKE5rmk1KyUWHI1kGuEGBxgNexlTP60vhyEWdkxm3R9Fo9awu5horS6rHF/bF0mwinJXSfxqB2yYd9pV5d0xA4w2VbqrHEIIHwuYW/ROUH1UxE8TymEXrIZykTYKasY2tnmft6dwzESn7umlou189nP7WU9oTjOjqeKpnZV1SVx6Oxvbi54pnjDK0aXEuuzvVxHtfFM6KWqp6aWl5x3/H6yUCP83k8j5JSLqjrh8iWuMQsXE+665d9Kh1w7XSD8G03kNvwb82ueYA9ahyrhR1DAAQIHM8ZU3Bw/RwUDic0nPejjq0bCQNsJmxemRmioOT66Qzfu7ZzlSmPtxPvYnrY/sS4/Z15YTKot6/cPrBqB0XpSC5/8UMYks5X5Jv3eytKckOfFfK5ZAERF8Z+lzc/DcNgLVNug3P7dmla5SE+hTjRrK9PnEmXZX8egUBVjsIj39rPegrClkLteENn1lCD2pl16PInCsdN7Zd1pxAInVMVIL+mjujpckW22Ul8PBuWWaaZD0IGR9EnU/EQTJFIVBj5Xff2jWXEYB8VHNkivI7VTcJXmNHVK9HnOxMf2LSsF3vkAEOzquOB9MxcxQ7IXpaQiGQEJlcUa6m/j5P4ejuiUuOAjLNDhTEfEQKeeP4CqlJZyKpnoKqbLqfwzsEwYchkI+rXzQ2DEucKxd3zGmIgwFdh19DgXjrBVCa9qqoR9jX6xc0KQskg+evpvVSVwtL9jQF8SO/PU3naRYbLcIP45WCQtTA5w8TX23PDcF5kG05i1ufeOH/+ZyYFeFELVOQYIMWgJtNu9IexssokQstmjaQ5nj/eHg3VtUTIaHlaFgKl0+dQ2cUrOjYVVd50BWn1wrjiRiTrBuGcbB9Q/XrNmXBh8MMKI6s/x2IyikT0ECXUEX19DCrZ38d7jylb8meMfPLd4et+cgTy5PlxrcGIaP5YcvXWOJn22QHp1MZN7kUxozQJp+s2xob+YLLrq6VhuE7sZO4b3S2BCZc8IOL+E6YwH5mURb9FIwKrrwIjDhA1u+YBZiVXbjwhoREJagD7xlPtLTlA1xZk4Sc+PA2rSOZC7fQwOz5cNaidKNjAn5tOy7K/Y3DTzxIfrwbIhHlIh8DG4gicdUUYM3eHos7L3oPn5Eh0CrKBdYBtbyajhzWr8KoRs7EMgDjoYEbb2FitHNDDQ7FbdcJgsypAAI33sbr+zSBo1dazsgGDt/DIFCTIagC+20ha+ztDtqERKkEONu2aS+jvY2AUdTJFInB9ncODlVChvtTlwQC5BwR+y+N8JWxrHapAvIYMKaH1zmN/1AA0+nObqss1NOnjUHVe0QePjGghE9DPU1ojzIzVNt6v6HNjYxWK9yr2gK7acja/r4NN6+b0eLBna/oLRYvEGq+TfzPZtRP9TCvBLLY1JEH1nbZA0q1dQGN1PXkgmWBrRtjqyTMZgO+dl4PHkiQeKAAmvOiDO9DjRWhqVTSOs/TwNxIgnAHstzAjRLE1YIYMfFgB6oBKEJ+H5hjHchQg1tvQPl7y0sNtStQbuQfollf/F4IKYj154K62qCxWHfbiXfvjZPnqSCRLnFRCwkLSsdIFRPqDwx6KOd8fhXjaOWQunGRoX79qy0Sn9wlhGht3npLtIWcVbv2FB9tIn+CKI6IwhNrx4uLLFHRJv/18r85QO+Kn//zntCwAbCIFWZ+u7TykdxdPZHAaGrqowj+fx8N6x/6zsgPnmQDWce8tzeXeW/3KNTAqFq8P2Aaz7cy6ElZhCuNc4Y7UNqBEHlCDN2Suf99wGhWheJVxbB/YRIICXRAIuSjCkBIt2AZmIjzoYGQy2J4SVQacAzzq09w+wFuR4FhzE1kBWa8SCQwmUzEoxqjYwAh9uqkrriEIplJ3qzpdQwwOnpu+R3nDv2kDWQyonLU1+jioZQDKSgwJcIyN/TPs4eCzwBK7c5CPTH2inVmzMKHw+8ZTsnxtLILxi0AeNJJgkFe1b9VY9QCZtRAbmehcQhaILZIk5GC86gvs1NoFzGe+iga9Mvbs0a0cExw+AfhbHgECz5k0E2LB+kNAIo4OqJJ1QOKPwQ+TzCWFUbL40O/BNQhi6igqeIoml8aYuJw8K1Qhve4Y1EJ6d/Yql2RPaba5qHkpX7N8VaTsP5woD93VQp4cE1CSe6R1AiL9hhJS9PmySNl3KBkDxwbSv4ePdGzbpCQbpl9kpXsn7OIfcJ7vP5yADEk9UIK2kDsHNbVJH/AhrO9DMofVjlA7HasdKL8BceIFHojG5Mp6gyvs5Np76LzMmH8QWOmLgM6Aorb3/7N3HeBRVF30CtIhAQKEkABJKKGEXqTIT1NRxAICYkMRUbGADURRBEFBUVGKHRUEFUHsgkhv0msIoaRACmkkhARC9z/nLbNs+mazm8zuzuVbZrI78+bNnXkz77Zz6sE41DecKovJ12+LQTpfNCalIqMeaiZ3FiKUnpcu8vqeL+/VRHTCJCIvRCfWOdEI6tq2ZBnv8zoH43vbNcB0Inqw5/8aDq/oWQAUVJOOrXyQYlYT1AQF13XYfuSS2ZPpdVv3npAdmOARsatHBx+8eBtkQXMqbM/SAaSyGuiKnIjvBNoiJz2WwoLqrnCi9YERxBQs1im6m/A+G/z8euV55/uHKG70sDtKmDLJ+QuvyWrUiPIaFVUeurMBnsd5o3vxPUyex8XLo5RDoQPGESeo3l6u4bkPi0hRjuGQQ8nihWyGx+5phHcT5kSojdGz8NpzTsvIDwEQCF6Rnc/Lsv8+NSuaoz9MgyOwiT0jyqwBYnosnwv2ED5vPvvxsHqG+3lXkYF9GwPE6JrDl079MwDXqKszJ7DluXO8btgRLX+ti1QAH68DYY9OVxvFvgaR1gmGouf9EoEHSpxUAUJQuxbe8Gr5iB5DvVqfi7JkITw9IdvwOY6aqsb+njL0rkCVR+tsnjwt1U6l2yGKdBS1YtmZzS11paHamVPtYCj5okDeGYX52e98eQAvw1gUNqI+p08jpyMzYzrdn3g4bNgejRqx6vLG0y3zzEsu7DUi5O1a1IRwssCwe/b74hrjff6cH4U9rrG9fjTACeqPy47JfEzgOF46tqwtPTvXc5nJW0Ga5gt4X1iiiv5HxZ5WICcj4Y20dpKeCfRJ1lVxDP2LyJqGKqkdl2k1TM1jOhwjQtaABmj7uuLym5/DZc53YerU7uxVV14f2bLYTpMRAUJx81qt35GQL8pXQZ0iZPcwpPpZCovrv/rpqCxaFolJbhnp3bkuDCE/m0EQLNvW43oqIm//bIqSLXvigKxbHrwwjcWetdRFPWc6QBXyG2uAAIBAuOrskVrtGEybJFIlx32rIBMAQi1EkJ1RWFc37YsQRYHRplktadrQSw4cPimHIk7Ka890KrE6z/x0aXJcxMs/G6MA+pEpQ+F0eBSGtq0RuKvHcoxBpJ1IAnhYWH/xG1KPGAJm1KhlU6RTNK6hkIW07ZxxyaLCHfsT4EFIkkgAJtB7x3xZplTQO+AqYmuqHQEbiGanLe3NY0DUMnpvsvNC2Kp3tsUQMvlNiNXfDA8FZ5ZYpCx8DxAPogONG9FC+v7P16bT4fXnZICTgs27k1A4m5PxnrC1jAQ5G+O9TQpx450Yzfjw21A5mXpeunX0w/Wuq8uXZXFdoqPHTsny9ZEAOUmV3qg1GYUCZ8IxZxcakZt2gTB1Y6wQ/pbIU5bCyVW75l7KCOIYKmTeu2VTLrXOyEn/Z9coglmmH/08s4fVNRf2VgSfg7x+Uz7dn+ckuaBjjn0sWAb1qa824/P0g29CFTddn27+KiKk6BgKasQFfj91+pys2HhMNu+KleCG1WXc48EwLooXWZQTasJf0wAi+huBEMjblJcwKsO5DOd2BFqhA7gSqABcRchJ9REIhn9dddwM2d4u2FseHtBcV6fItNXt+07I+u2xeA9lSt/ufjJ8YEN7OeEdaxBZapKQq8tRcLsOkyvm4RIeslnDGqg1qipEHqpUQd/EXwwvHo87jUF0Sg6i4DYi5pTp9BD17fs/PxmHh529QpmWetPjOl9UpgiSiRuJGPmEt81Lsqfa8WFCAtmihMy//zMKL5QDqFPwVBN+8iLYKuSSeP/rA9KskReQV5rq/l609jx5z/62OhwpnMdlAAgFXx4ebBUSpMkzmggjCA+eXDyjvJ58MbCwszcgwK0tDLW238Z2+tIAPaeT5uxVHkSmxfXrGaD7FNLi1GDIkWT5fWW4giUeDi4QptJRGEVlSum6bQk5no8cQy0aV1WOBCItelUtGYCW4tRTYY/17twDKo2M+40e2lQevCOwsE3YdfvZC8OQ+RJuc5u85i892gwRwkQA4iRJl7Z1FCeM3uc+Np9wATvGxKfLT8uOSGTsKXn47obyBCJGjsqoYWR2P8juWfdDBLj9yH45iwhdXsLxqIAPGAECAAK5HR3Vt7z6UBzfJ4EyZMnfUbJ05XGkAl8wH5Loc8wAYdnHDXjmt0IgIz/whdUoE+mFCKcj5ArmMYeBZLgTAQiWonDeeBvm3EPvDLQ38FfxGUSWiuJkmmkDmwAneRRWOutHyUURUBeQrHWqSB3AsfoAU7+4oSS1PrI/RIeLxYCNgac9CshDkeCCIUM6Eb0Id+wBj9X3f0Zqu6jQ7yvAgnfHPG8qobhT7V4DozghnSn0sBJ++un7mxQaTGDOd4fkm5+PqhfTLSAbdkVhFHP+0gMqFeft59vmGlYmDPEO1E7Rc8nceRLFZhfWjDESREhVZ00PyH5Oxt/5a4BgCe/D8UBOk/vgLNAbqEj+vS++Xzl+1m2NkT/XhqtUGj6TQgCOkF2CwCtyy1VYZ4OzJbt2rv1NctGBo9apSRlrM36a2d1EaH1tk2JdI3DMXc+skQtAIiyKsGqmSuVyMuLeYKkP3jpDRNUX/bziCDh3qsrbz7fBxLvoqWeEv2bkh/U/ew+BqgIZJdlr9DTd01AlATgjPxr6W0GcPtq+zroksTORcxn1z576znOiA4IgYKQZWY7IaCqc4OS6Cg5CEAP1ogGgTtBqRbftjRdev6ljutlNHUyvPAIj6FBEKtL3kkBzcglEwFXlbiAW3oZsl4pW8I/Z0JmSMYgsO0p+CoYtdx1Ikd1Y0kBiSgF5VWqhqNDbqzKI+sqhlgNkYyAc44u5CnD4K5YvU6SCNRbHcdKXivAtQ28kHyN5H5nS42AE8Xf2wc+7kvIQEN6UxH2WUNS8Wd5DZIGGEoUkWu+OaYcbJ2fahOU5u8M6UwxYS6YiSVcJZK1BtdO4kRhFYtQnr1S7u/FyIrqhpRCBhbj8fbtblx42/asD8hMQfe67o6mqhbBsy9XWo5DW+fmifSoyN/v1jubJBQERaAStwoORD6Hs0hCRPBpBnMQ5a21Y9nMy/i5YAxy/E8HBwjrQ3l3qY0wFFimiW/ARXWOLBIAvLPz1oMQAXOcSPJsUf9/KV8dQHTXxco0zdexZvPnxPoXyyaNMeqa11c90R/WKE+ppn+9XkYWiHoN1YiPvb6W4hIralqvsHw8i8nlw2qWfOS8zXulQaA4j1vto3D+cT2afG1jqiY72Zphcq/qfq/w/noDXd3Vh5sc//8bJD8iuCQ2/muGUy0mTi2vprB5m5z4DBEwpJIjSBmSMxGDexWhZXQQvKmAefgjciJynvfRYB2XsF4YTkXNngjckJIMsG/dAPKgOjqE2k3VBTCFtAZS+Hh1Rp3qDd3GQ/pa8QZT9elD50cByP4IH0OFj6UhTy1AoRnHAtWcRryYw6lVqE3OLK6AgkcorhVAaQ32lcUWugzFzBSG/i/jQM3AZN8O5i5eAmnER6QsXFISi1hYLsWoDi53GDrkHGtarAivYQwjxWVCRFif8LwPTPfGkKf+UZFlTRreBN95mpAutWy63tFeqHdFfbh7+T576Idv8yyOC8+U++HrpUSAiHpJh9wRLaxQSuoOwrmjm/F3SCczWteF1XbUlznzfWp4/kQRNRlAdeIJckz/C8nyN9awaoHf1hXd2CElBOT4ag0vIEOs1wGjRD7+HKVQ6Qg2PRaqqIdZrgCmaA0atUe9tElf+OKO7Vam+1h/Bti37P7s2X54Za1rlHKUxOIXKlCmNNObGcPYWPRpizXGdYRtOjuctDQGZaYpMe7FdnjQlrMkLPZqmIj97AIFNmPq0fOCvWZPXMojRH1P6Gx2t7pTJw3kXHb904JNTqyAhcAkBTPISvh9YAkO+OVLtcM6uCeflHoiAch5cruz1mJdfB0daaeVMI4DMpUuXVZ32mcwLkpZxQTKvpi1yP0aCG9bzQBmEB4IPXqpOq5ivk/4MIk2xuS2JyhKflImozgWF/07oX+LAc4LMwcSBckEp/YpiHKeRRG8ALwoVW6H89YDmK4v89zLqgpHgzturfJHzt9mfV0Act/PASdVtpksQQSU7qkxu5+Tu3xHh5Br0d8GoduUA6euDwuUo7Jef0AtHPp4RyOnPXtvFgUyivyG3B0kXwJu6k4QfT5OPF+4G+uP18Mxcyxlm+g5BQRgJagLyOEPcUwMn8Hx9/I1/wQ55nTw+pJWq9XRPTRT9rMkNthjcGOSiGQcHjSHWaeCdL0NQ13BMbfzmqNZyW7eSf0azvuKTRYdJ9aYyU5hmxXX+p61zUscPgXk4J8k4cwnEuqckGRPICpgccm7yH6r5WzXzxoSzgWEMqSuc9T+CHSxefki27IqT6WPbS7d2tTBx1uCvAX6AFDjCX2cH97FshY5thfwGA6glIkAN6rqnU+8gMnQW/RUlK8BvpmUxWeopt3VGsxd9ULADgiUSj73+r9kQbQM9P/NgEwVMkYT0Uhpemcj0YqYBs60YlCiLwEM5zMM5h2NEibxuNZH9xSUdHwUFH3Lrr52/cy6DyM4nb9fm6BVk0eWC3yLM7RI2leH+Sph8GmKdBixT7UJQBBmCdDsawbYKJ/ovDmuuiEHZBpEPh7y4Tr2U7u1bdLZzW/tVkvttxstm0Z9h6qF0KyYbhPhtCeI4Q9xbAzSQh726Uf6TUjJqaFs8t1w/jcTRV3wPiAK/gdd7SN8AeR7k3IbkrwEaHv1GrlaTqDqgb2DqjrMWs3+LucCsBQezZCEQYY0O1AC/a3wv+WvEPX/9HhHWnSHxKJMoDwL5zDyR/VhgT/hrDQCByxqYZLu7MGo2AgbLFcxLCyMs+eiJFLX8JBFofMPHb5b45GvzMqJsTnuxbX67OcNvC42Zup0uEx/arF9h4fnkT/YpBBMCRwwdtxF1Re3d1ktRWPXSS2DK7eUEPUDtzhDtAYTISR7LFEVG4miAWiMctGOm71AEh0xdmYJrw5DugJsbWbO7S25DdKODR0+iVu60PPtAkxwRNJc8aeOk8tUAo+vPT92OupfrZNTDdOIYxlC+CrPyR6bj3n+pmSz4JVTq1q4kA6/CLlu5u9tttgSpPRr5JVHlnNUY2rAzUWYCxnjgrY2ypGST4FvvJN96uOkGI3sjLf0cUuMITHLtXV8RUNfBmGMRAKEVnHisMXF3rq7crldL6IVR6bc/25/bz7l+1xx12wUZQ4x8jpqyLYsxxMZYuuIKch2Ip67dba5wRjo4B6aBjZm+Ezn4prQupmy9+kQLXYT+daCeIneh58MrcsDYWtMo0ycZOh71UFtpCEQbd5Z08GhNnv0vIIIbyvB7shIGurNe3PXcCTDy+9oYeWl4BwW16q56cNR5L9+A1JUNkfLN2zcqCF9HHceZ22XRd7+Rq1BQfV5Fr//6rLdTOmsY5Rr43FqAJnjJ0P5GVNDWe5Ik4699sAmF+ter9xRrgEh6X5iifVuP7Sr7WRIbF3ROn07spPjQ8tqO2TtPv7lV1W5l3+ZBQGAzIODkshDQA4bYWwMsRJ83ratCx2DbRM2bMHOPYgOmJ9YQ2zXAQu/8OI/ya1nLo/3hrzBVuJnftq7+W5VKZeWmrv4y/5ejKkfb1c/XOL+8NUBo2h+XRcngW4MMYyhvNRXpl1tBvhngV1Uh97FOwpCcGliJ2k4aQ5T+qLvKXvuZcw99fjNzQRg6VkoG3ua+WQj2uDLlUfM9YkgLdU/UQq03a1sNY6hwmiUn2v39AgvcqXPrmvkaQ3xmvQriekKY5yZVXCSjwDCIcru6dviOLMbTkY/5LArNNAJSIn089tq/KifWDodwyyYOIGWuKFIRBMB80P6LOprjgHcsKQkPD5cFCxbI6NGj5YcffsizG7t375a5c+fK4yNG5LmNrT/06OgnzDz8a12MrU0Y++lAA0eAxkleCVuFpMRNwRjfroW3rU3Ybb+tW7fK8OGPCu/7wsr48a/KpEmTCrtbsW0/pF8T8Nmly29roovtmM50oB+XR6nuEpSoJFML1+9IFNZJ2CIsNv9jbbTcfXNDRQ1iSxuO3icuLk5mz54tTz31lLw2frzMnzdPxo17WZKTkx196EK3HxRYXdq3qC1zvgvLgmZW6IbceIeCOHsICvI0Uufzk7c/26dI2vPaJi96lLy21+v3Rg2Rg6/M0LsaAD6wmoz/cLdC3iD++4NjN8ibz7ZWdS0OPrzLNU+QhfyEyIFEoWNBbh3AOKp18EL9AoLJ8OgzMmZEh/x2L7bfzp07L9HRMRIRESGdOnXK9bhX4JZJSkqSlStXydmzZ3LdpihfklitbXNv+WV1tNx3u6leqyjtGfuWjAaI0vjWp/vUPd/nxjrS50Zfq2sWt+xNVnxhYx/Xx7hISEiQxMQk4bIwwrESFXUMiKJlVAE2X/J6E7K+d25TR75eekTuAqytDrtYYio7BJqN/VeJbIksVpJcft/9EaFghck7yPHUCwXj1vLUkKzdGxQe7YJL3rmQ28U8f/68vDLuFRkwoL/0799f3n/vfVm8ZElum+rmuz6Irr718RbZuCtRoc7ppmM67wjrrKeihuhXvN8pfN7kViBDZNkg0MzkJR9/f8jcRl7bVK7kGqaEa5xFXldJJ9/zwbrg3RsRctyNB+1Jxaf0/LQdMgzhzCfuDTLCwIW4TsdPnFEEuDR4NGPHh8aPMoAq5FlgOWHWHunV2b8QR3Lsps2bNwMG/1nZvHlTngcqhfyAW265RVb+848cOXo0z+2K8gO9bzPnxQm5P+rAkDTEeTUQl3gWk+2j6kMuNRpG5JPK77r+DG6KRv5Vxa92FV2c+J133imdO3eWmjVrFqo/HCuff/Y5nqWAQ9axpdG9Y13ZuCNWtu9PBhm0wVWnXeSfrsJs8++Bffy1r0tsydJqgvfw8+7cA+AVrCm3wjgicmxeqXysgfp7Y5xwAq9XWbdunaSkpki79h3E19dXpr/3nkyZMll27NhhghHXYcdpYAYFVpM/1sQYBpGV14dlGqQW2QQjksJ7djIg7L/7I1JIxq4JeTufxBw0L1mEVGq+UwoSI0JUkIaM37NowKtqOfnkjU4y5/sw+fbXCOXF/Ao32n6kgL31XBup5lE2y/bGH7lrYPZrHXP/IZ9vWXdEUt8G9fXFr0PCMmvkOkz2ONFzhNT39ZDrwdO1D0zU+U2cHXFso03HaeDo8XQ5ijQTppoQUv1WGEc3dfHJ8pzhBG7T7gS5+xZ91ToU1hjStFipciVtVbdLTu7qgeF9zbZ4wyC6epXOnrssy2FIUPyAxKc3UnMSSm7cmaA+nFjSKKKzoTOMJD47NdkdliLkSmzVVJ/RIfbz8OHDqruVr46V0pgQV6yof0dYy6CaMIjCFb+kUUek3XG5Lwnq8dy07UDlNWXTVMXc8qNXOkqzhp7SIbiGPDFxC2qo09TOd99UF2OuYq4Nrdh0Qt7/OjTX37J/6So1REaEKPuVdeDfHMiEOSZc5Buz94K47aLyFD4wZoNMe6GtwQXjIN0zTZFErY7wgmdkpMu6devlTMYZ6dW7l/z+++8SExMjN3brJt3/9z9JSEzE72vlcNhhadasqQwcNKjAs9y/f7+s+HsF0obipUGDBpJ+mrVO1168BTZQiA1IXuznXUUORpySW7vVKcSexqbOogEau/y8/80B9UJkGlDPG2pLRHSGIs4LCqhu11MpypiIxthZt3atihD16dMHz8gMWfjtAsDsm5AIPD09pVWr1rJ+/TrV58aNG8tNN90kFy5ckO3bt6t9x73yChwIpdSY3LZjO6Kwm+WJxx+XLVv+la1bt0mNGjVk6NChUq3aNe6tw4eQFvLbrxivSeLn5yuNGzWWgMBARJzLi7+/v13108i/muwG07shJg0s3xArmUAUowwAmIKehZ53RoH48ahcRsi/Qi63NoCBJiVE9arlpZqn/XhwjoYflU2bNqMeqQKiOu1k+fLlEhsbI71vull69exZKFV99dVc4buF8t1336GW1tTPyIjILO2wZvXkyZMqxWrMmLF4n8XKD4u+lyuXr4inh4c88eSTsgZj9GCoabLMiGzvXr1k87+bkdqdqcbekCFDpGrVqlnaLcofDepXwz1yWSJi0oXRb0Ny10AssgSeBSx2NDJpKH4gqZ352g2A/DcZPeTEnDm+I0hVNysS1cdAXJ+bbEMEe+KcPcpxn9vv2b9zFdhtwyDKfmWL4e//tfdWKXQvv7dLWepJKOB84o0t8tT9QfIQ4AsNsa8GiFzkUbms3TktOHmb9dFHcjAsTE2yNiH9rUXLlqo49YP335edO3dIxNFwaR4cLGGYcG3H5KxCxYpy++2353mCq9eskc8++UReGjtGWrVsJUuXLhUep1w5+71ksx/c06OcMBVx9db47D8ZfzuBBsLASG6NkC18y94k9Zn2RYgEAg2THm7PKva7t4oyJtatXSeLf1oix6KAeHfvYHVKlStVlnsxuXrxpRclMSERxd/jpFGjhjLlrcny4AMPSO/evSUtLU3mfvWVrF+3FozoV8x58n/99ZcsXvwjJlLnJDY6WurWqy/nsb5y5Uo5nXZaXp/wujrGwYMH5ZVXxsmLL74kbdu0BTDDRFmFuj0vGE4tMZ5feP55a9Rr9TY+NSvL+u3Rxni7qrF5v4SrNYIPecKbXdLPodTTJqS7gi4osw5+XnlcfWpWL6+gwj1Rw2ovicY9+9mnn0kY3i9e1b1kHZwADWCkh6PudP+MGWbD3drjdenSVQ4eDBOCKnSDw65MmTJq14jIKPWO0dp55JFh8tBDD0l6+mmhQUQHwbCHH5Fhjz4qXl5eyiDq2aOHpKakyNdffy316tWTkSNHyt69+2THzl0y8Y037GoMsV/eXhWVgcb3lGEQaVcq6/Ig3gPPvb1dUtJM92/TBp7yISJD1T2z3pP8ezaMpA2IejJzKbucOXtJcWlqqLzZf8/t7ypGDVFuajG+s1YDvqh5+eqtLio/+ddVx0GIeEURue2C53DiM62sLuK09njuvF1a+kWpDHQ5e0tdPz+Z+Oabcu/gwUASLC1vvTVFKleuIifi4xUq3OGwQ/LBhzNAWlZZBg0cJMMfGy5H86kF4gvos88+kw4dO0gH5HhTBgwYIEt//hneucv27r65vUrQzbG4NHn5vZ3m74wV19YAyS/5AqWMf3+jtGxSQ6E5NfavDg+v7edelDHRvUd3jJ/KMhEGiaXQ0/zc6Ofk1Vdfle+ByBgSckC6dO4i/frdoTZj1IhGCyOzR66mBPGHQYMHAbjkuPJmDxw4EBHc3mp7Tvb2HwhR6/xvyeIlyojq2rWr8m7fg22nTJmCMdhenn76afN29lqhN/Xixf+M8ZZNoTTYJ3+8N9u3zvEnnZr8UMjvxtpMAtYwRdJWqVu3rrzw4ovqXcJ00PdQ71MeEUsa+p/AaXbwQKiKZFrbfpMmTURLKw0KCpKyZU0TZU+PrBEXptHV8a0jh8KuobDSOcBxZil8N+3auVP27tun3lF//PGHvPX228o5aLmdPdZpLBMdlkaoITk1QGTE8R/uUhQv/JU1b+++1C7Pmmqmx997m3/OhvANo0hLPuwhyxC5JR3DkWPX7oNcd8CXrpIyV4RXX16qMb63VgNlQRT62pMtYAC1NhdqMlf5QaTQ7buKuGNtW8Z2JaOBihVM+df0nNEYovjUrg0DqZTUQGE4jSFKLe9aeAGVkSSk0OUl69evl7NnzkiLFi3Nm/Cl5V+/vq4Lxc2dNVacUgMsIP8PGWlXgErEf0WVooyJ6zFGcpMWLVpI3759VfRo48YN8lguMPRly+RMeCh7NbLqB+eFJpxoZp49C6PENLk6dToN535FTpw4oTbhZJGSnp6ulsZ/hgYKowEieRHhyx6c93xnUPguoTFEYUSGwshoScuzo0apfn2NCG1/INcxmmRI8WqARsuY6TvMxlC/Hn6IDHXI0xiypnflypaSu3vXle/e6yZfTO6igKzyStonMEOF8tbVQ1tz7JLcJucbpCR746bHvr27rzRDePMVoIKER6dLfHKmPDHBSKGz1+3U0bnjAABAAElEQVTgWaWMZGQWr2epTJmsYWqeS+lSpREJNOXK53Zux4+b4DG9YVBZCnO083oYWW5n6/oZ6Ka+TyV56j7TRNDWdoz9SkYDzPcmWlxhpAycMYF+VST8+Gl5+0WmzzjeN2bLmLA8p4ceelDVUBCCnuk6FYGSZYuUzhYCa926lbCGiBxI9Hoz0kRp166dLc0XuM8Z1I5yEjF5dOsCt3XlDch8//bn+1D/dQW1N+VkzKPNdXG6n/94WCJjMgrVlxrVTClz/+FZ/cKw9oXat7Absz5OL+Lt7S23oNbvt19/lWPHjjmsW4wenkOdGeu2DDFpgIb3h/MPAjkuwqySxwc3lhGDGpn/tscKa94zABZCVxmfW7wGWloe23cVyG2ei2EQUQs6kADk88+bdqO882WI/A7iPiOFzn4XpTqKXE9nXFBeu9IOQmuzR281j3UYahpat2pljyataiMt/bw0C/SSmzr7WLW9sZG+NJCBnG9rDCIiFbZv7mXmVuGk79HxmyQt47zUqKZ/pCmmjvr4+KCoPFY+Qu3eO++8Y5fI6eBBg4XOiPnz50k80l0PwTi6554BcvPNNzvkQsclZQBprpLbj7elMOJpDFEe6BegG30sAQS4NQYRoYZ7AZzkFoCUtG9eQ+b9clQWLSucY8IhN1gxNpqamiobN25UICiszWNtUtu2be3eg4STZ1Vaa12gEBoiCgxnwsw9qLczRbUJGvXaky2FznV7y7rtCRITbwJpuKOnn4wdHiyrtpwQEikTrMdVILepN8MgsvfdU4T2GKac8FRLaYdJy7Qv9qsQqJZC99bzbaRl42pFaN19d23esKoQOjUmPl3q18mbgMyeGrIlXSIgwF91Yffu3UKUHk2IsHWF7iAHyMWremk6INABrRtN6kEDzRtVlT5dyUnkk6WItkmgh/CZczgytVgMIlvGhKa/QygsX71ytcyeM0s+/PAjFc35/bff5M677tI2sXl54cJ5yUD93quvjpfaiM4OHz7coSAmR46lSsfg6jb311V2pEFE4WTujp51neK0CLt9Y9taClmuS5uaIAK+Fq0JxvuZJJapKGq3J9JcSShGi6IytZRAQBQClpQqnfU9NGvmLLn9tr7Sum1rGfPSGJk1a5bM+fhj0dJm7dX3iOOnVFpWYF1TCrq92nXGdgirTR7LkCOpqvusSZyOeqH2wV4OOZ0Fv5siUMxUuR+OCwLxmAjAUWcWeVq27Ut2yHFLotFro7kkjm4cM1cN0Mqfj2hRYF1TTYqWQvftb9dCo7nuaHyZqwbqwxvLMG/EcfvnXJ86dUod8wxeHJoQApiM4MlJSdpXCPefg1fngmQAnluTlJMm6F0tF5xQqiwgDwWcKdF7WNOwZvVqFLeGqfZ+/vkX0Y6ntVHU5bHY0zAW/zMg34uqSJ3tH4B0uJFDguSX2T3lm7e7yn23+2cxhthdTua6tPaWXQcS7Np77R61ZUycvTqOkgF/rQnr6j4AqhZBSVinNxKwv6ytmP/tt6qmyLwdIH8pScnX6vTowaZY9oVQ+JTTMIIoRKiLjIqSyMhICT0YKpvg8d65Y6dC2VIb2PG/RHi6j8emS4+OWdNi7XgIp2hq/+FTZi4URln0zMPHNKEubWrJpGdby99f3ixTQZFBLiJLY4hKb9OkulSqcL3sPXjt/ivqxTiVanq/cAxokphkaj89o3Bpfdz/7BnTe8pE5WBqkRDblAyLY7Rp20Z9NxMGDtFRp06dhndXuqRgW6aW0rlBcIfE5CREU+9R4A63wTBKTk5WKKlqZzv+tw867dyqlt2RYu3YxWJpipyKw17dZDaGfGpWlLlTujjMGAo9miZ7wa9FoQPA3zerQRoU4CEP3eU6ztTSEyHqbI3/dKUBviDuhNcsKfW8HI46rSIEW2GJ8wYlgkhebNm6OgkddSYqNkP2hJ2ULm3tF1ImfOlHM2dKAtJsTqPANQV1DUzpmTZ1qnoxZOCFxYlWoL+/zPjwQ2Xg0PjhhIzgCot+BCQwJoBxJ+LkbOZZad+uvUo3CAFXBF86f/75J6KE55G3W1qlJAQG+OPF00jKXEUHsod6/9kUhRf7f/LoAPvmHdujb0Yb1mkgDF66DTsSpHbNCjLglvoqpeHJIY2lTbPqBebcVwJc6sLfwoEyVxPb5qx7s64H17YqyphI5JgAihzHTTzWz4CDyLu2j/AVxboepuOwoJzjZTO4Wc5gAkdeocoelWXRoh/BsbJPdeTw4SMS2CBQ1qxZK//88w8825clCuOwITi9fkD7e/bsUduFh4dLRyA6ZsKQ2rRpk+wDWtb2bdvBV7RF1q5bK8v//luaNGkqtWrVunaCRVz7c22EXLx0SV58pLmCES5ic067++yFYULyYMrYx4LFB/euXuSPtTGo4z0nrcEt9PDdDVTWRn/wIzWq75HDCLLsM1NS4xIz5V+QHXfrcA3Ew3KbwqzHg4fuoxkfycmUk4oXiE618gDx+ezTT9W9HweHWVUgv5Grzhp57/33ZNeuXcqYCQ09CPj6RvIL0lA3bNiodiecN5HoiCZHYKDde/fKfoyJAwcOqPTR1BSQd9fxwTGrKojtefO+Ue+7G2/sqt5J5PuKQBt850WhnoiojYwqFFXoRFi64qg882BTZHi4b8oca0XJMZSMOSGFsNqfvNFJiFjsKJkx7yD46kzj9NXHW4C83XHHctQ5FKLd/dfB0s8aAy3E3samxaOBP9fFmlPoeEQWcE56tpV0bFGjeDrgAkfZfTBFHp/wr4x9vINDCFrtrSK+BCuUKy8VK1VSUSF7ktxpfb0A6OXXZmxUkQSGwg1xTg0cgsPkHEgLWzWxLaX2wbEbpfT118uT9xVf3ZqeNP3Fl18KEep69b5J8ROdP38O+syUFTCmPDxMkN726G9ySqa89ckWeRk5+P1vrmePJp2yDfLC3fHUaqD8XVGcMt+//z9dnQdrJpoEeAIy24TqVpjOsfbo3hfWydD+wdIu2H6GdGH6YM9tk5DlUBMIdxRmPTiSDy+vfn/7S6jEJ6UrKGgd4Unk1V2HfM/00ulzD6jach6AXJZvPdfGoY5xZibd/cwaOJT+kyCMhwXv3uiQc9NRowuv11FnjK7koQGm0DVv6CmvztitMOGTU8/JM5O3ySP9G8gT9zZ2+zByHmrL8jWZxJl//M/GYzJsYHCW3/T4B4n4NHGEMcS2122LUV7q27sX3Zup9dVYFr8GgvyLVhf34rBmylmwMyTRJSZxhbkCTPshQtbChQth/ECPFkOhEtLz9uzeVZjm8t32hz/DpL5vFbkTcLbuLAQt0EgfB+fBhVKS+mE6nK1CcKS+eJ7+uvKING1YTSqWdywqGtNTGUEtSPr3HyDduxfe8NSMIbZfEsbQIdQ3bt8XL++82K5I/GgF6Uevv6N8GEhyofL9n5HmLj5wR6CMfqipwyPM3/8ZpYwhHthdHKaGQWS+zfS9wtzNb6Z2VTCLi5dHqbD310uPyo6Qk8pToKeUA71q8lmE3J+ful26x6QBcthTr90sln5lnLkgKzcfk4fubCCEJTfEfTVAZ8GgW/1BwocJe50qUqO6ftKXHH1VLqKujzJr9mzp0qmT1ER6nIY0xzS9MWPH2KULKzZEAeL8lKrn0jPSpV1ONp9GCLW9ZIUJnrlqlbLS938WFmg++znTT5ysbkQK60/Lj8hDdzdzaNcrIYNg0KBBBR6jYcOGBW6jtw3Onb8k3/9+UDq3riW9Orlfzd1ZRP1fnbFLNu0y1YwRfGQc0kvvKgaHSjqoAX5ZdVzdEjWrlwcoTx293R4O6Y9hEDlErY5plESuY4c3lxta1pDJn+yTtPQLsh8ErveDyHX8Ey10A1vqmLMveqtEB2ofXEMWw1P7wvAO+eaDF/1o+m7hx78Ow0t7GWhA1ys0Q6MmTd/Xy9G9e/7hpupZ8tkPe+XZoW1QT1TO0YfURft9b79dFZPvArLjh4DyLo16PRK39urZU6HNaWSYRensjv0JwtqhMY8GS5NA93bE/LzyuBAli8J6N6Icupqw/nfC063kxXd2oAjdwy71RHnpqEyZMqpWJ6/fnfV7Rkbm/xwKwugr8sbTLZ31NGzuN9PV6LzV6uwICvUukOSIQFwcsnj5MdQ1X1KHuu/2ADwXi14LVhz9LuoxjBqiomqwhPZPOHlOXvtot+xBbYwmLPx8cVhzl3zJaOdY1CUfNENeXI+Cc28Z3DeoqM055f5bdsfJd7+HmfvOhy3vnXtvC5Ca1d1jImw+eWPFrAHWdjzyyia8/ErJM0PbAjHLvSKHFy5cVOh1ZoXYYWVvWKJ8vSQEEbgAeQmpie4sRLO865nVkoh3Fx0wv3/cS6rCeHBVmf9ruBA8YvigFgq0xFXP0xHn9cMfYbIzJF4+n9RFmqFcwJ2E3D5jpu80k5/WA2n6jFc6CJfFIYzissaP8PHkGPrj095I/SxdHIcu6WMsNAyikr4ERTg+vShfLjkic386IleumLAxCNXNYruG9UyQ3UVo3mV3/WfzCRWKHtKvCVDn3CMUrF3MyOg0mbNgtzSFp5roZOcBrKAJQ/IkZ33ozkBpXMS6FK1NY+lcGiBK1uNvbEa+fmkZMaSl1KjqPulz9r5S/+6KQxriIYUW+ioi+O4uJA9++/P9Sg339wsURiWdReip53uDoGn0lRM9TQNQ47KU9jeX6nduI7J2a4IcCD+F2jxv8fWuor4jMMANrXxgFBoJOtmvPyG+liw/LJt2xsj0Me0VeED2bVz5719XRcs7c0PMNXbMaHnnxbYFooXaUyeLUeP37pchqslHBzSUkfe5jePYMIjseSOVVFu7QlPk9Zm7leeNfShbprQ880ATxT1SUn3S+3FpSH6x+LACWGjVxPnRgKzR94mEDPlo3i7piJRLFqmezrggfPixyDkF3iBN+LInis2IQY2ALoNCc0OcVgPPIe2CsP2cnHHSxtkap2yclGFVzdq037ikkAQ4IekcEI0ug2CyAiLOpeU6QAr37R4gwY0NZEuTlvL+n44qAigwEjvoVhMMet5bu8cvjA7dM3otYKnPqvfTr3N6Ai3VeaLRjJ7e/uQqc5G5rVeN7+bGAVUxFktJ/1saiRcQYw0xaYAk4fOWHpCw8JPgemqHd5B7vJd59pfh0P7gm1A4UKJMysD/A4BEORaIlMWZrsZ+DHjWNE753P8NUdzqnq4bxTUr27Sy0OAhyqYRZ/yTgAr9etSVY3EZ+JxRg+vfPUmyD+R3rDeqWN7wRGW/rm2beUnq6YuYuBwBYWVFqeOdlXAs+/bO/vdxELB+8v1eaQwujfdfbq/Yppm2wpzkIX39cf6VJC7hLAwjU34/7yVCfZKJuh64H2oaL26nvAXIY7Zma7ycQT54xlntc1FYNHuanwzTh/WIp65+0tIvqmcIvbVnz12STBQ3D74tSIKDDGOooJuAnClf/rgPfHEnAXwjijdu1RbwlEHPLE72RHqqO8qiZcdkxaY4deqsHbq5i49TqYHv0N1wPMbCoCuKcMKZAuNqwK2Npa6PkcWh6TIB4+bT7/YAXjtDZo2/Qc1btN9cfZmGZ8ML03YgAmkaHyQCHoNacSIIk9uqOGXFxhPy2+podUim0TvbOC2irvYbBlERNaiX3cvDmr8FSCDkKCLy3CVgx8digkuSufp1KudgGNZLv0uyH10BspAJJJf5vxySMgCsCKxbtSS747Bj7z+ULJ+jWJ5G4Afj2ueoMSPqFSNBA/vUB7x7VYmOPytJKedUf2hgsxA6NDxNGtTzgPHoPF5dhynUiRqu7VVBvv8ryuYe18Ik/gLAN2IRXazv54EJvXH9c1Mmo0LrtkbL1z+FqGdwLa/y5jGUChABPpMXwfu7EYhRNE69a1SQyhXdw1FFQ/zl93eq9NwKMCyYAuSMTjpGudYDPa5Igvkt6zI6ImWuahVjLFGXm5FaOhdOBJJ+fgyi0Yb13cdQDI/OkJGTtsDxmKZuK9bUsV6oN1LXS0ImzNqjskUYlWLphQfuVTcSwyBytYtN9uLenXwQHUpVjMbnUCNCzxzZjTuAyJV1IoZc0wAjaB6VyyqjKDY+AyhQ1WEcuUYB4RUYxb+vjlA52Xf0qiuTR7UuEFmPhZt3wzMU3LiaMqhZAE2JPmEyjE4kZUqzBlVRcO8ek7lrd4pzrnHSvXVfsiQATKSw0udGX/l4QichT9V2sKT/vCIcUdVziBh6GPUPFspkNOirxftlV2iCDBvQSJFm39WzrgQ3qqpSDVmXxXQgCp/DW/cmg1ckSsg8zwJmTgQrIFrrqvLpD4fVufL8WJNwYzvnTIXy9a4o8wCUYKswFfmlR5oD1fCS/PT3UURsL0pAXQ88k1332uenq7jEDPl6cYhsRL3Q/eDWmYL3EwF+3EXWbkuQ56dtVxFDnnMjZG98OrFTidXvbt6dJAv/iFDqp3P97t713OVSaOe53wBV0FThYkt6sz5ZdEgW/BZhBlxgpGjy6NaqoN7FTrfIp7MnLFXGA/P/IpAmh/QLAqle8cBbFrnjeTTAeqHvgdQTn3xGxo1ogUmtbx5b5v81J9Of/3hYiHyjCVPtSA738F0NpIJ7oM9op+6US0KovotCXWuFKRujwKVy3+3+WXZhUfnMbw8qD2L3G+pKD3yqVHKb/PIsuuAf4cfT5O/1ERIWkSo9O/rI6KFNUDhfMcd2NHrIJfL3xjjZtDsxC5AJN6Y3lsXTnIT0uqG2S0WOGGG+/6UNKsrI7IWfZ/VQCHM5lKTzL7Rr+Nan+1SqqS3dHfNoc6Se+qtdl22IlQ/nhcoFvKdv+1+AdAa4D8edO0ha+nlFkE5DqGlgNXnliWApKrm0M+mNqbSfLjos5JH8j39Aet3goxwpJUl/Mfy1zeb3/MLp3UrMMCvBa2mAKpSg8ovl0DsPnJQ3Zu2VhJMmDzEjRE8MbiwP393AjJJTLB1xgoMwl3fa5yGy8t84ad20lip6rebpXGkNJLNbti5K1m+LlqaI5Ex8phVSJosO10lv1qwFB+U4IkWaMH3uKSDQ3InokyH600BYRBqiwycwEY+VxFRE+kzv3nw7yms69YW2QrLW3OTCxSuyCCl4838LV6lfTP3p1ake6mPcA42O85d9hxJl1eZoiQLBc5umXvLkkMZIR81dX9l1SLLFtdviZQWMIzobLl02RY607Zi626V1TRhHvqqovCQnSFqfirJ8cuIW4TuI8vrIlk71rLiMCPuWvUnyNzIs1m1PMPOy2KKPJ4cEyfB7GmbZ9QxSCb8AuM+Sv6PgWCojvbvUky5tfFX6dpYNXeSPU4gur9x0XDYDbISF+pyHuNu7g7WEpEthjTeFUUOCF/FTkrIdKb1PIXWPcmM7b5mB1Ho3FMMgcoeLzgLqtz/bj4n+CfPptsaEh5NlX6RrGJJVA5yoTPtiP4zIc4Dl9pWb8KLy1Hm+N+Gz12+PkTVbjgPBSOTZB5si5G1fQ4UThJ/AMs+XuEauSM3xXnr18RYS4OfawBRZ7xJ9/hUZk6FSZJkma2m8WtPb4EbVFPmfNVxUNIx+XxMj8389CuSwTAmCp5fGUWsgNnJS72qSnJIJA+aEbN8bjwjZORgrteWR/g2kRWPb6w7pgFmNZzIn3LvBJ6dRJ2i6ozFEtMc+N9aRzjCSyjhZujPvjzc/3qtOh8+IL97srJ2abpc0eGnAcfysBhgJwUayC9EaicRorRQEMU4EO3IWEe2TKHQdW9WWru38XMbJcCgyFTDasbL/UJJ4wcE4DGmTTMe6/vriBQyw9no5artDQPscC34hIi1SKqM+581nW0s3HaSQWjouvpnaVdUSO0oPOm7XMIh0fHHs3jUCLLz31QHl2WXjFVEH8tzQpoqU0+4Hc/IGmXL4y6rj8hXC2iyKvgEvqS7tfKVubX0VfJI8bfPuWNm0IxYTqisy5PZAefCOAFW466hLQM/mVz8fRR1EpJkvgZHHoeAvGj6wEV7qrjchdpQu7dEuwVNWIJ2Nk7ijx07naJJRzkAYq5qnPscG+ILIX2NA6lzYSQrBBDbvSRTyZ2zYmaAmdC2b1JQWQKRrGujl1MbRSUTV9h9Okr1hSRJ+7BQgksvJHUDzpFe7bm37OpJYW8SURF7DkCPX0lO1a8XJU8+OtZVx1AHpdYRN17Nwkn/vC+uVQUED+Tuk4Pj76tdhsh+IrNQ9nYbJjKZmE/L7MaWRnzkLDwqRA62Ru3CvvIbImDXC98wvGEd0OrHmr3FANWnb3FtaYTxVquhctTXxSWdAGp8oO/fHq+yUlkHVZRBAe8hzV9hnjDW60/s2f62Llalwsp47b+L9a4D7afrY9nZ/jtiiB5YLjHh9s9r1hlY1ZfZrHW1pxhX2MQwiV7iKhTkHwoZOnL0XD6sU825d29SS159qaSCImTVybYXF0L+tjpEflkUiRSZD/IG01bGlD5jHCcZQMul0BMo4cPik7Nh/QsH7csI7EBPae5GfXpxFqawPmAqiRcuJtl/tSkISyg7Bzl2Dde0O0OdaIlAAV16dQB84eipHJ4lk1RP1KJzAtce1oBHbZ8RKuXS1uF/bgR7pl0cEgzzUT/vK5iUndMtRG7Fyc7zsP5KiIhpBACkhUElD/2riU7PoqZs2d86KHS9gXEXGpskReLQPACiBICsEpejGKA302Ll1rWIxRBhx4+ScnyN5GLicWPLatm5SzYozK/5Nnp2yTaWb8ciPwUlCCGG9Cfm5THo+ISeScsJpsx5MM4I0ovPNSHWa8vE+ScrFaMp+fgQ3evv5toW+Zxh8IhohHZibdiUoQI7G9asp2PvGAdWltg7HEbMHomJPo57upOyDA+FE4hnlQLi5cx25C5kKmv6y68jV/6ZeFL/Q8ijzqd7cpY5MwHxLL+mwz2CsbkVqKOWLyV10+0wxK9BxK4ZB5Djd6rdlPnAX/B4hRP8hpC7Fs0pZefmxYHfDnS/URWJay0/g5lm/PV55egIA0x3c2AsP+6pSz8dDSqE42lFyAh63o1GpcjA8BS+dFPkPfBZEDewP8rbuHbyF0NklJXxxf4Riey2NjnnRLMgnObCzpfmUlA6tOS71Sw82J3H06mkFudq+hDQmmSHrT1iHkt0T+zy4LjYiiqNJbfCXvftSO4eArJDPagMgill7sSv0pIpKM8rRAGPF39dT/GpXBgBBZaSNlAwoAyNbSSlnJCaen9MSEZ0m5OoiT4wvoj9dYfx07+gt7QBVX5zEiNq10ZYFpUB6A1b95q4m46hpoKe2W4kuv/sjUmYAMIASFOAp37zdNce9WFIdpBNHMzajYjNydINcUeReoSFECgJN4hGxmT73gNWw253gaZ8xrkORz5sRBRpHjB5u25ekuMSYvs3oUYCfJ947VcQH46i4n7NnMy8CeTQdabnpABc5pSKoTNsmJyIdCJz0twqq5tZ1ygnJ5+QVADXtB+Ivhc+RZx9oCkCiAO22KvFlyJFTMuzVTaof5CQkyp0bi2EQufHFF2LgvwHceQ0Dn7pgvjrZkYsz0uBs14D1Eyy2ZY45iyNJtMc0sYC6nuJTq4rUqVkRy0qIuIFrpJATvkwQYbJGIR4etjiQ1NHTxuJtQrQS6poPrV7wPHLiyyiAXoT1EB/NP4i6EhOpG/tFr+Dk0W3c1jtoj2tDDhcSq3IStz0kWehxtBRGeLq0YRF+HVVvUq5s3rlURLaaMHOP2p3G9FR4rz2rOP4eovFxOCoNhlGKiiYeCD8lJ5EiRuHkjqTANapVlOpVy2PMlJcaGDcecNBUrlC2SE4GTtAyUD9JqPCTqZmmD1JMk05ibAHyl+OYjoT6SOVijUs7ACOQq6sGUuP0KBpIBgkcOUHPLnUBma9FNJgiWRJCIudh4zepVFp6wBe8280uoC5FORfq6h+Ai3AMhV3le7FsryrutV6IuDEKSDAR+HPMwvFG5+GXqJvU0p3o8KnjXQERxLNq2+zlRK2QHjZnwg05+N7Mjdq4wnHEe2Ar4Nq3o841NOKUivzyHub7pnbNyrh3K0gNAJzw4+VRXr1/bDXoOX7Sz1yQk6cyhTV0SSkmfroTGDvJGE8U8pS1bFJdOrbwQuZEDaMm+eq1JYQ1OX20GjRmcUwDWI214CtXm3H44rmp2xUCJg/0CTigmE3gxmIYRG588dWp84FPWGXyK2iTLXrJiAjEQl5DCtYAi9c52aMn6AjSMCLhedRennwZcdLnAcOIufRlwTlxPZbX4d/FS5cVytRFTMzSYfCkYeLGSRqF+9XHBKeRv4fyVHKiRp4CvdcO0Js5GSklKZh4Ujhhf/r+ILm/n368YqpjOv6P9w4JIDmBY4oO7w9LITwvJx+c/PZAdLAS0rqsESKc9XnsHxl8qz+uSZMSvZcY7WLKEguNI6LTwXmVKTEJZ1T9huUEswKcAJUrlIEzoAzGT2kYMAKvO5YYH5yYXgFK2yVEdZgKyOfX+QuXEI26iIncxSzpgTQUfeCo8IXxVc+nIsgfPRTUb2Dd4veuW3OtCtpmLyKEvD8YMdTGmuU+PD+TceRTbJNUXtOh4zaZ08/GP9nS7sAulueY3zojlCthOBJlkZx82aOplVGTw8j6LXAAkosutwg7U1H5LAvH/alJE0S8mGLK5zTPl887SydQYzyvP5vUudig00mifRAOhrCI00hZy1BAKiza13iv2G9GjumYo0ON7x+OHdZ8lsZg+g+PlsuwtC6pd9F/KmOEzjcaQpbPHT5j/BA5re9TGQTmVYBg6qkiy9VAJGrINQ3QaP3kh0My75dw8z3XCgYjnU/WgNVca8nxazSuH3p5ozoQjfgvp+gf9MTBWjEMIgcr2GmaZ+iU0SJLZKq7gATzPEAXrJ1wOc3JFkNHWasVDxLTpBR4pJFvnooXNGt/6HUjpwVf0IwqlStbWn3IUF0Tnmkao95gua+LWhy+uJxRWEvCiQSL7DXpBihPEsMa95KmkaxLTmA27UpSk1zqTTOota1KwQtM7zW92IwQ2hrZOXo8XdcRO+qB5L+c0HLCeQooX1wSrvY80ntp+HAyehGgJ5dhDJmcDKXUBI/jiQSnHEtMAeaS3n+OJ8KJu6JwArYDkUMaR4xY0xDMLs1BEMv75ibUczhqUkZjdCRge5lWTKExRqb74hSe+2qAHVAXO5GmqTn4tD4wYtW1bS2VBcG62bzAXzj2OKn9AfDyGvIfDSjCqw/q45/FkcDo04BRa5XxQFLrL6d0kZI2EuhQIKF2AqI6HDscS3wmn864IBfw7rmAMURjh+OIDjam21EXdDiUh+OA46a6RzmpCmjs6linI8HW542me3dYEhhl/Ie7VYowz5cOmwfB1/f0A0G5GtwlrRPL6NDM8R0NB7iIYRCV9E2pp+Nzok7ixcWA/9Q8arUwmRj/REuVlqOnvhp90b8Gfka91QeoJdAm90SZeg/IOvbgRdL/2RfcQ9arbEPqCydw5HmiZza7EAqbk0vWNeg1lSt7n42/S0YDRMZkRHHFplhEGBOF6beWohnVvJ9Y8G/PSS7BVZZivFMYRaFhkF/6pmW/irKeiagno6kkvf0XaczZQUMYCWE9Dw1CRoQKIpJmbd6k2XsQrbwGskDSzLGPNc/TqJ4O5Nb1qJX7EgXp3jXKF+V0jH2dVAPbkMb4+kd7zNFalhxMfEYfkNq5qdSydqglar3mYrwaYhhExj2QiwZI0jXlk31mvHxu0q+Hn7zwSDNd1a3k0nXjK51pgGkcL727U47FmQqYmbbxJiJF5FdxR6H3ll50cs+Qg4YRkOzCtBuTEVRH6tRyD8LT7Dow/i6aBujcMhsKMJI08Byt1SxplwCP4Li0Vb766aiKqHD/6qiVmP/OjSoqZ2t7Be2nRVNJOMyUNc3hou1Hw4+1lhxDvYC0aE09LKMljAoREEKLCtWoVl4ZQoQ7z08IMc5aP8PRk5+WXPM3OrU+W4SSA6TIafcNwThIbk2ACb3KM5O3KmJo9u9j1A4ZqLDqShkRIr3esCXdL3reZi0MU2RxWrSIL4hXQMDJgn5DDA1YqwFCPr+OYn4thY6pBIThzc7cbm17zrgdPXKMBBEtKjeeE0bPOIHjx5hYOeMV1m+fOf7WbItXUZS8gDm6tjVFUchSX5jIDslE3/kyRJ086wU/eeMGocfZ3mJNNJXH5fghJHlhUiQjQKcwHmhgTCfV5PbufvLSo82LrRZIO66xdB4NEB6fKXKWvGH39g2Q5x5qWmR0QUdqgQ65xyf8qw5hIMtl0bRhEGVRh/FHDg0QLGAy2MYtUwhu+5+vvAQSR2s8bzkaNL5wWw3QkzYX3mTNwB6E4v4xmHTAPnJJOXIsXRlBNIQ0dnLLE2VuvgaZHISokCGGBhytAdaU5AfdTrJuRm+JNtqpZU7odsv+Ld8Qp5C0OJ4ZcXrnpbZ2jfxq0VSOn1VbEE1F37MLYb1vuQo7XrtG4T3yTA8nOiZrOymMcJFHjel1hhgayEsDvCenfh5iTnPmXOj1ka2kB6Ktepcn3thirnNyc96h7JfKMIiya8T4O6cGmJIw57tDsmhZlHkySw/cWPAWMSXBEEMD1mqAtTKvz9xtTnOhR/fNZ1s7LYBE9vMuiOeEUVaNVLNF42s8J9nbMf42NOBoDZDcV4OjDgVSWXbhJK8n6mdoHJGPyRLhksbQpDl7FUomI74Tn24lfbv7Zm/Cpr9Dj6aplNKV/8YpcIDsjdgjmnom85ICfqGhpQmjY2+AMJOgAoYYGshNA8yceXduiCLN1X4nAizTwAneondhrdPTb25V3bwBtXWzX+uo9y4XZ/8Mg6g4te3sxyLU65uIFlki0fWEQUTeIqPg29mvbvH1n/fR89O2mxGxCO9OglC9MHcXVhNERSM3DCF+LTm9tHYUz0mn2iqdhy9PV42IaedrLJ1PAzGAb6bXmx9LmGntTOgAuwlkm4zGhCO1bNoXIeaaCaaW3Xubv7apTUu2qR3fMhtBa8ye0VRGbl9+fyeIRc+o5on0OerBJjK4iOeg9dVYuqYGQsPTAJyw2zz/IQrsYwMbyaMDGmVxFuj57B8dv9lMFPvN1K5ZyIf13O9i6pthEBWTol3mMCzWZfHp939eKz4lAz1fKP1vqucy52mciGM1wHz9Z6dsM9fTkAdhFrxVBaFAObZX1rfOQmrWA9EQ2nfIxERuubeZ5wQRMMVz4qQQ6pbnZKy7hwYKMk4stcCUV1sNicIYYS0b26cuiUbXZAAGaUAMJLMlYSaBTAwxNJCbBli/RuAQfi4B6p9CA33K6NYOqZfLrQ/2+I4AJM+DiJXCaOiMce3t0awrtWEYRK50NYvzXJjWMPnTfXL02GnzYen9Zv61URRuVomxko8GyNX0zORtEhNv8tTy/iEfQmGKuvNp3u4/pYELZzVSbBgJ2gWeEw1VSDsQI1w3gueEZI/58Zxo2xtLQwN614C909esSdMjzxZTabOn6RVFV6xHmv1dmMwHGpgmhB5/HSlyRUHY09oylq6pAaZATwAgkGVK6c2IlHKeU9lKQmw9aIb3//0vrVfAIUxx/fbdGxUxtR76pqM+GAaRji6G03WFxHeEm2ShvAbrSqLER+5uKMMGNFCEb053UkaHi1UDjLQQ8UZLw2T63IxxHXRTU8Rag3Woe6JneSs4gzQPoaYk3u+dkYvNCRyL0Z0lwqX131gaGihIA8wKeA2pQmuBVEchBgrmVzkkL4CDwgA5cCzZm5Ca0SCigRGGnEJY7qfvD5KhdzXIcQ7GF4YGNA2QmHcOjGgtmlgFmTBjhjeX27rZp1ZOO05xLP9cFysTwa9FYU3glNFtiuOwznYMwyBytiumx/7Si/L2Z/vNyCXsY/06lQHRHaz4IPTYZ6NP+tEAvcY0imKvkiH2/Z+fTHq2VYl1kBPAjTtpBJ2QTbsTzQhUWoc4YWt/leeENXR8URpiaMAVNUCHxQvTdpg95EyPfvs502SKToI8CYXBxeJXu5JwbO89lCJ0nlkK63a6tLEN6tuynYLW2f/nkCYUFpGmNuVYfQv9p+PFEEMDuWkg4eQ5BRiyHQAEmjDteQKAQ2pV1z9wgtZnbUnergGj1ko8al1JVLzkw+7i611R+9lYXtOAYRBd04WxVlQN/LY6WmYuCJM0C7JJQnSPBi5/YXghitoPY3/n0wDT54aj4JMTGMpwFKs+Ca6i4pJLl/5TTPec5NGTfBaRIUthmkGrJtd4TqoZSFSW6jHWXVADrI0b98EuSYJRQ6lTq6J8+EoHCfCrbD7bgkhSzRtipTQiM0S2YjSV8MSOTlWLBlAEEbVOJJ1V3fDDJPDDVzsaKd2WF8VYz6KBn/85ruYwGWcvqu+ZBv3sA84NuMF67w++CVXnQ6qLsYhyGZKrBhbaTk+da3vGl+6sgTt71VVpQx/MC5Vl62OVKrjcsDNRRmJyO7CPv9OgsbjzdSyJc/fFZOsjTFYef+NfZYzMXXJEAjHx4uTJUXIF9bE7QpJVOtzqrfFm1DvL4zVrUFX14aYuPk4Bq2rZd2Pd0ICtGmC60EffHpRL8C5TWgDU4P2X20t2R8B1SKBjCpo1aW7XYTtuS/juUg6GWjwUdVpGAbQlJc3kYGH/P0D/DUhtW+8I196P2QlTPt2v3gfamTZvVFUmPdPaqQ1opnwTDIJSofz1ChVPOz9jmVMD14FULWssO+c2xjeGBgqtge0hJ+VdMJhHxWaY920CEj3m4DqCydx8EGPFqTVAJJwX39mhAAuYVvP5m52lWQNPu54TYb//RiRo1b8nzBMmywM0qFtFGUE0xvxqG6kFlrox1l1bA5xAMf2ZkVJN7updT3mVy6JejmKNI6FJoIfUA4Jb0snzsu9Iao6UOXreuwKAhPUM9gYg2X/4lIx6a5uZNJOIWkSS0ytYi6ZnY1n8GuC9/MNfkQo5V6sVKlumtIwY1EgeuitQRTWLv1f2O+InPxyGQXRENUiI8CeKMevCfmdRbC0ZKXPFpmo3PBDTkL79PUINSO1hQzUwjW7Ug00N7iI3vCesOeVvf4uQmfBOU8g+v+DdbuJZpWh1OgdRQ8BJHokoE05m5ugG6x1oAPHToO61lKAcGxpfGBpwUQ3sO5yqELW0Wj46JJhew8g/hY4EjqGVeTgSGtb3uDqGfIQRX01S0i7Iyqs8XTxGdh8sU+d6dARPF4wjBVGPKJKtwjQ/GkM07CisR5zwdEunn9jaqg9jv7w1wHfC24gKhUWa6su4JekfiDzoCki5TD+/+5k1ChSCkdFf5/SSiuVL560Q4xfDIDLuAcdrID45U977KlTWbTehFPGIFfESHH5PQ7n/9kAU+hX8Arxw8YpoHkrH99g4QklrYOLsvfLnuhjVDRZAM52usFk24dEZagLHSZwG7W15Xt5eFeRmEE3SCGoaaN8olOVxjHVDA3rWAL3kc+FF5kcDP2C9zbQX2ynjheAi5Nviczy7kMdHcyQwxbUgYRt0SnBMWk5Etf0UiXFnH+mDMdmmafVCjXkaW+Q20+r/7rmlvowbEaw1bSwNDSgNnD13WT7+/pAsXh5lpk5gOtlT9wUpguHCvmf0qtYp4Nv6FXXdlBeHNZchff3VuvFfnhowDKI8VWP8YHcNbNmbLO9/fSBLGh0986MeaiI94SHMS5jUOWz8JnluaDNpjcJ2Q1xfA0R6e+TVTWaeK4b6GfIvSAoie6zuWU5uwoSLkziCJBhiaMCdNRAZkyGTPt4rB46cMquB8PEETliDujoNDt/8I1bs5UggOikNI34sU6u1Y9UEotfNqN3jWG0O1Lr8hETPRKpMP2Mqhh/Yp768/JhhDOWnM3f8jcb9h/NDzUAh1AHv97HDg8W7hvMhyOV1DY8cS5cHx25QBh/nWItndLfK8ZxXe27yvWEQucmF1s1p0gPJgt0vUTSvIbmwc+0AY/z8I81yJQtbviFOXp+5Wwj5+unETrluo5sTNDpiNw1wMjb05Y0q/YVF21+82QXF3TknRoRJXbnZ5HW2JNDTOkKo3V6Ax+bEqn1wDQPYQ1OMsXRbDVy+8p8iKf0Cz+GLiL5TypYtJdU9yiMSZEJls1RONQtHgiOcUocBgkDD6B+M4zggTmYXwgRrkaiG9apk+ZlOkMdev4ZQOQCRoVeMyFAWHbn7HxEw/FnTvPPASbMqanmVl5cebZ6vM9a8sZOtEF1x21XY8HfHtHPJc3TAJTEMIgco1WjSCg0wr5xh69/XRJvD1kQg6tfDT0YOCTLXF9GAGjh6rcRc5aihh/+LyZ1Vwa4VhzE2cXINcJJEUkUKPV3fTe+myE9TT6MuAbUM/J21DdnrEpiSSc8fJ1Eke7QmLdPJVWV039CAVRogJw8RtQ5Z1E7ktiMdUJojoUMxOhIIisBxzfGdnGqC/LbsX6AF6IlH5TIyDJHkaDhPKARpmDyqTaFS7SzbNtZdSwOsJfti8RFZBCesRqp9felSMvi2+gAYCHLJmhqi+r4wbbu6kEw7JTCRIVZpwDCIrFKTsZHDNEDP4Ix5B7PAXRKB6IE7AuVhMIn/BdjuaV/sz3L82jUryNwpXZySJC3LiRh/WKWBCbP2mGHc+YBnLdkOePq0egetERaBE7HqFtQFEVnKQJXSNGMsDQ0IIvKXZAbShX5HXUFe2LJ89tKRQMOCtXtlQORYUsI+0qNP44iw+Jb8dlqfKlQoLZmZl9WfXTD2Ca1tDQS4tr+xdE0N8N4hLyKdrhr0Os+0bTMvBRTSIFuU0VW0wMjvkBfWqxRUcufNm9bVqI+1/uIaBpH1ujK2dKQG1m1PULwXmqePxyKZK2tJLFPrtD74+1aWLyd3KTL6mNaesdSnBjJRAMsJ0fSvDuBeME18LHtKbx+RqRgJ6l4MZI+WxzbWDQ04gwboJf/o2zD5A9F4EqlmlzJwMHSB8XNLV18YQ7WERpHehM6PLXuT8Cw4IWsBzqMBJ1j2k3QOt3bzVTWC2fmSLLcz1l1bA7sPpqBWOTRLBLRGtfKKIP7Wbo7jtdODVn9cFqXelezL7d39ZOIzrfTQLWfpg2EQOcuVcod+8qW3ZMUxVV90CilRBUlT8NN8OrGzS4a9Czp3V/6diIKbdifKio1xQl4iS8h27bzp6ePLrVcnH/FE2owhhgYMDVzTAB1JmzB2fgSS1q7QlBwppaVg83QIphGEMYT6usoVnYejnedGB8mvq45fO2GLNUaIWCvojOdmcRrGaiE1QLj4WQvCZNWWE+Y9ySl03+3+8ug9jVx+nsAIcH/AbJ9Kv6CcGj/N7GFk0ZjvBKtWDIPIKjUZGxWrBs5gYH/242FFmJZXaofWIYIxzBzf0YDk1hTipEsaw1v3Jato0Npt8WYeEcvTqeZZVlJRe0Z5pH9Defr+IMufjXVDA26tAS2KQtLhNVsT4EgwcfFYKoWF5ExHvg2RFGeNorDG6PE3/pVLiHaxNnDYgIYSeiRNPT+0OhHtnJ0h+qX11VjapoHTGReVE3Xx38fUPaG10hOG/uihTbNwYmm/ueLyg29C5fs/I9WpGSSsNl3hhc7jFrLp/IydnFEDleCtrIC0jYKMIZ4bc8xfnbFL3nmpXbGS79Ebw9xkLpmSwhSO7MsLeGFzknIJn8uXr63zb5H/0N9S6oVeGmAS1yNXn0t6N5m3T1CASiBRU0vooxJ4EtQ6viewBIuJnZ0vgdd3V+jVGoEt8cqzlf1+DQrwhKfXBL1bEToY9Nw6pfeFf0TIXSCM9Kt9jQAy+77G3/rTADlvCI1ML+bZc5eEKZGZWJIbJPP8ZTlnsU70s8vYgePlCnLjOZaYI68tCaRBIBbTuOH4EYyfa+OIY6k86soqYBxVKIdnCpdX10lQyNQwjiPPymXV9/rTVsE94hgqqM6GrfjXqSzjHm8BNM/qBTeq4y3oLHvto93mie/Lj7WQu3ubiGPTMDFejejA34gsM22K9wzvIaZj86On+ihHqzgx5ZyClib4DAGMTp0+L9TPBUTX+F6iXhiJxytIyuJ9Q8ORtZkcLyTxrOZRTuiAotHsg5pdInXqTZj+uQgpYl/9dNQMt84+Nvb3kOcfboYooZfeuuyw/pBzb/HyY6p9wtU/fHcDhx3LlRu+Di8Vzs4MMTSgGw3wAU6GZU6UrBV75cvyJXEiKdP0AfxrHNaJdJRy6oIkp51TEYpUvFwuXco5bGjU8KXLyRcL/E1GjmmCxhdPKUzWSsGKUUW//10nV/67IlfUJI8Tv/+UAagmezCezqFe5jw8vJn4sE/Zhe1V5UurSllVa1Ud9VY1qpUTnxoVxAcs8XXwEiP4BFng9SYFoUixPozpLvxkZwxnoexkEM5RugE44YNx7fV2em7XH967ZEVPBPw5J2JcJmFJh8Gp0xclFcZPGsbMaRhC9ObmJRw75ZDiUpbjB5Mzjh8aPBwzasl1i7+vk+swZjCGMBRpOP1HwwnrV/BK42SYn/MXMY4wli7gwzHFqEJuwkgDDSNPjCd+qmMi6IlPDYyrWphgMLKiLfUwOSxoDGnnWA+G0MvDm0tH1Nm5grw6Y7ciieW55PfMT049ryC8WX8YciQ1x6kTQY/cdwSPsAVBj7Ompf8cl3tuqZej7eL8IhbvqIPhaULkwKjYM0DbyxB+x7RCTfguqlKprNDRWOZ6vJfgKKDjgOOLDgrldKDzDu+0CxcvSfrZi3D0XcjyjqPjwBcon/51KoGjqoo0Q7p6E5BZl0S6MnVPsKVPFx2SeLyfNeEYJUIt7wtndxZq52Tt8smJW8yQ4lNGt1H3tbX7GtuZNWCkzJlVYazoRgPT5x5Que+F7dCQvgFgZG5W4G58oMYlnRWSEkbCsxIRky6ReJmcwHcpmNhpwgmaV9XymCCVVxxIfKlUxsejIpaYPPGlWql8GSlHI6js9TB4rtN2teuSL63zFy6pCd25TEwq+cI6cwFesQtqyYnmGbzA0tLPo//nsoBQVKl0PTx8FYVGBskWA/Ey47IeGOaVYWbXnubd2CGNZwRF0dRzdqkDI04zghrVz8ozYrktr90jr2wSjW9ozoQbpGML15jsWZ6n3tZp6JBIk3n62icGE6/Ek5lqzPC6aMJxUbUKo5jlYJBjjFQsIxXxYZSzEsYO/6ahXqFcGcV9Ux5jhx7q4hCOpQsYS5qhdBbj6Qw+ZxF5yOA6xlGGivjyO0ayaNSdy+KUIHphTRRp18a4Ij8OP3741EW0kgYIo0+OEI2rh8ACuY0hy2PSS/z44MYqiuoqk0MaNxoEf108vxa+a4Lgtzzv3NbjEjNVKi73P3LsdI5NbOFYYl3WE0jb640axjeeblUsEUaOMUKlk19m276TeAam4h1wSUV5fGpWArFoZalRvQLuzQpYVkQmAd5bGGu2ji1Ga0/jHXMS75SklLNwDMI5mJIJnqgM9Z6hImvDAdeqSXU8g72U0c2/HSmElJ6zMEzCo9PNh2HmBBFpmQrqjsiihKd/5YNdSh8GzLb5trBlxTCIbNGasY/jNMCX18Dn1prJAgt7JE4CRgxqZN6NkZwjx09L6NE0NYmmJ+1YXIbZg+aJiRsnNt5elRBhKS/VYAB5eVaQ6lhWxGTOGYUecb7EUtMy1TLlVKYkgGwx8eRZvNhMHjUaQ37elSQowAPevqrSrCE8fkhPY0qRvYQTaKavcCJCnWcXTtpu6mxKhwtulJNwNfv22t/0jj86fpP6k30ntKgh9tEAjR1ONpiCEX48XTkLiPyoAVswraYGJlxe/FStgCglHQblVLSSRhCjKkT+czU5ew7GEdKP0mAc0fFAQ4nOB46zkxhfqYgea0Yho7VMUSOBKDlzCPHLdVuACziGOH74iYrNOYZo7GjHpc6Z7sR0mUF9/F1qcsisgXufX6dSLXl/zX2ri4pSFPY+oxNM0yeJn7OLt1cFuflqim5TREDykqlfhMhSAABReI2ng/ySTiZ7iwLHAMAMJ71bgbLHCKsHnHGN/KvhuJ7i5+OB53hlm40eW/tLh1z0iXT1OXosVSKi05TTwBdOgf8hcn9TFx9p2biarc3n2I8pkHO+OwTOuRTzb4xwDbipngwf2AjGX1nz9+60wufywNHrJAGOKb7TF8BJkJ242J30UcRzNQyiIirQ2N3OGjgUeVo9/I9jIsBJdDRYyC8g7aUw0g8hc3qN9h85JUfhEWSuMaM9frWrqBdIHRpANSvDu1URBoD+UsoKc66F3Za54/Enz0giojQnkqhf00stA1EmTq44kQvGi6xF46qKsyF7ylpBx6NB+89m0wSOHu3swgkbkeH6IB2O3ixbvdcvvbsTdQHxqvl3XmyHNmtnP5TxdwEa4GT7AMZIKJwEh/Ch95x1cBR6l70xTmrXqCS1ME7oMKgJ7zONH0NyaoCprsmpJqdDYjIcEBhj8cn4JF0zJuk9Z00c0TGZctSsYdVcU47isf8/iAJx4h6WC3kqveA8nqkW0dQXRgIeuCNA7u8X6LAIVc6zLr5vxsEDvgpGAeVRgCiMvK/ogCp0jjHaxucVdZ5dGIXSotaBiKprwhTRWx9bqYwz7Tvqf9KzrRSHk/adrUsauP/uSZI/1sbIhp0JKuWzYf2qeCbXxP1TTXxgAOlNCGYRFXMa6XsnZf+hJNz3Z/G8KC83d6kjd6HGy1J/hel7KNIBPwGXECHXNSG/Dq/LyCGNVXRW+94dl9TNV0uPqlMffKu/jEF6rCE2a8AwiGxWnbFjsWiALwfW9NCbdxwGEidxx9Q6JxyZ8I5a5OpY9Ihe7KYNvVCD4oE0lipIJ6hk8+TbolmXXaW3+3jcaegZn7h0vNxMHj8aMExJI9fPDS1rQo/lc+igwHx9TBZ6gCOILzHWMrAIvqgSAU/vfS+uV3UiDeCh/eGD/xW1SZfen57EEBg/e+Bh3X0wVQ4cTUV62CXlVfTFBMsXzgIu/byrSB1EDsujjscQ+2iAYys2IV1i4lHfkZAhMfCspyCiRKmDVDs6BlqBQ6c1Uo/oBV+/IyHHc40OHi94wU8knYMhhLy/q8J6pyF9/fEJsCkCpbWj5yWNg1FvbVNd5FinF5w1X/aUvWGpygBlJMaSyFM7RsP6HleNIx/1Dhp9tT/a71xyov7oPQ3lCWQp2OLoSUu/KL+uPq6oJ07AsdSofjVpG1xLWjapqWqALI+l9/UTuM93H0ySnfvjJQmpdm2aesmgW+sriHdrUrUZnf7kh8Nmp5d2viQLfvqBJhIE4AR3lxg4i+8FCSsdxnxPLwXMth7qG534uhgGkRNfPLftOo0iQjOvBXJQKApmgU8gVSuXV+leLKhOR1oBIWeHD26BaIdRX2LLjUKwBxpHR46dkiORKeaUiMC6leEFrS3dO3ijZqKSvPz+TjOik+VxGJEj6AGNoK5tHcN4P2HWHlmG4lrKe2Pbqz5Z9sGd1+nFZmrhNkCZs+aAheWMKjDyE4BUmwA/D6nv66EMIVdMcdP7tWf93zE4IKKQahQJ58Ox2NMq5YjRH60gnuusz+BEmbUjlkLS6gcQDRrYp75d01wtj6GHdUb3Bz+/HsbkGWVwfPFmZ+jEfqlY2c+RNWY7Qkzw/6u3xmdBL9O25eQzP568rm1qyeTRra2enDId8NvfwoESFqXSTTu08pGu7XwRlXUNBM2wiBTZtDNWQg4li1f1cvIYOIHu7Fk31xpWOrq+AOXGKqCOWjo76TR46v4mcBo47tpr19dZlqPf3i6bkU5JGf9kSzPaorP0X4f9NAwiHV4Uo0u5aICpc8s3xKr0BuaBV0EedbMGXtKskRfQbqrnSH1joTS9sHXh+Tak6BrgxOQojKPQIyjmPZqsapFq4OWWmXnZnGZ1jfOjjkodoVHkSOHLcwg8ZHxxNkf60TdT3buWiBNnRhc2ovB46/4kFQFipLShf1VpCG8z6w4Ip2uI/jRAAzYGDoite+NlDzzrLGhnJIjIWUTt06Q+Ulrv7xegkLTcoYD8m5/DETULU6d/J2D2Xx/ZUlOFw5esP92M6BRTFzmuCoN6SpCN6XDS5FfPwfRUQkYvWhaJiGwZ6d25kigWwAAAQABJREFULgwhv2KvB3K4Iq8eIBWok/9sipIte+IUWNGT9zaWfj381K98p3+55IhCBrQ0hPhcfxLIcZ1aGY5Ny+tkCaRAHX39dlebopKWbRrrYhhExk2gXw2Qm+TvjbHyy6oYTMJTVf1C66a1pFXTmigorarQdfTbe9fuGZGG9mLitmF7tOJiIl/FPSjkHggYWnqvi0vGTN+pooU83ufwHtOT6E5C7zKZ2VlzsvfQSYyJUkgnqYZ00erSBA6DWi7iZXana8pzpTEUcTxNDkaclHVbYxRcOOsdCUJyMwrW7ZV6qme98t7u/+waxfFG5MKfkRLE6ExJCKN2NIoW/h6BdNNTVnWBDiEacIySZxcaWSTSPId2+3TzVxEh8s+5gxC1ccXGY7J5V6wE4znl71dJ1UsRJl8T1to9PriRXWqytDZdZclU54Hg4yMdCNMP5029UYEjucr5leB5GAZRCSrfOHQeGmBKHBmX/1ofo9J8WjX1lo6tvEG4ZnsRfh6HMr62gwZYE7ENueI78MmE15MpdfejwLs40hv2HU6V4eM3q7PojcnitBfa2uGM9N0EeamYMvrb6hjZHpIEQsXSSA2tKa3hKGC0tCx4RwxxHQ0wWkRI8P1IOdpzMBFUAWniUaWM3Hqjr0o9IlKkK8q7oF9gGhll9NCm8iBglUtaCG9M73xhhBG9UQ81VbWT5OuaMGuvbEcEt0vbOtKvVwMFTV+Y9lxl2xgA+vy07AiQLE8hym86K5KqjhjUWNWcusp52vs8LMcF7y2S0BpiFw0YBpFd1Gg0YhcNsOh77pKjsnFXgoLC7tbeTzq0qC3l3QwJzi7KLIFGWKOyNwxRox0xEo70uiaBVWU4iowJqOBIeejljYqYkLUwv37cUxFoOvJ4JdU2gUWW/H1Mfl8TrTipmjesIR1a1lbgIe7iXS4p3evpuCSG3hkCB8S+eHDCnEEqpKcMRsH6bd18XQZqm+SiA0etU5Ey8qj9NLM7SEVLNoKSiYyFWx77xwxBX5h7ol1zL1Xv9e6XIQCEuF6G9m+KGr68Yb0L07azb7txR6wsWXZYiOo35/UbcgXucfZztFf/SR8yDJQTjKYRIn7xh91duobQXnqzsh3DILJSUcZmDtQA4ZlnLQhT0Jqsc+jdpR4gab0ceESjaUdrgFGjlZuPy+7QBKQ3esioB5tIlzY1HXJYwtNOmrNXtZ2dh8ohByzmRvcAAetrQKuygNYbENhd2voiYlrbbT3Lxax+XR+OwCebdsTJrgMJiBReBySvAIU6xxRWZ5Y3P96nDH+ew6RnWkvf7r4lfjrLN8TJ6zN3F6kfdGI8PLC5lDeiuFn0SHj6eUsPAMTivMx4pYNdOYyyHMiJ/yDgx9BxG80AK9PHtHe4s9GJ1WVL1w2DyBatGfvYRwNE6pkF1unfVkdLA/As3N4jEMXf1hN02qcXRiuO1AAJYZevj4RHO0E6BNeQsY81F39f+/JoML//1sdXCrmUfFHM/Mvsno48pWJrm2SEHwOGmVDZTRpUl56d6oHDxr1qpIpN2U5+IKbVbdoVh5q+GNT0XYBh5C+PgKC1pGpuiqJOcpkNGLVGpUsTROLHGd11US/6wrQdiheoKOfGKPbA2xqrdLmitOOK+5Ijb97SEEz4U2QauOVubFvLFU/T5nP67o9ImTEvVO1PlFciqxpiVw0YBpFd1Wk0ZrUG/loXK9O/CkG4t4zcdXNDaRnkmOiB1R0yNnSoBujJXrr8iIIafnRAI5VKZw0fhbWdmvr5fln6z3G1+WeTOoNU1nkNByIqvv91KCa4CeAgqSG3dgsAobCBlmjtveDO2xEuf8veE7Jy0zGgP17AOGuk4LntzdvjSB2/g7QypoZS3hzVWqUCOvJ41rRN7/yUT/epFL7rBP9Ig4SPWuAP/s11rmjrXDK6GxWbLo0DAHACYmOWypCviDWx9Xxcs/aLarBVqOfFy0HECuOeKH3d2hlGEXVJJ8EQ8O4R6ZDgKovhJMiNE9BWvRv7KQ0YBpFxIxSvBoiQMmnOPiD2xEvPzvXktu4BJZ4bXrwacO+jbQYfxa+rwjEZqCTvvNRWfGvZh2uD9WfDXt2klHv3TfVk/BMtnE7RrMEi6/hXS4+ATNhD7rqpkeILcroTMTpc4hrgvbQe0aIVGyKlGghdJzzVyikQGJk10G/kanAxXZY6eDYsndXDLkTOJXFBvv0tAqngB2XYPcHSupkxsS/MNfj+9zCkgcbLF5O7SNNAo9bq6Te3Kj456pAgCgRTMMTuGlhYslWKdj8fo0E9a4BF4Q+8vEH2gTBy1CPt5M7eDQxjSM8XzAF96wLCwZef6CgXL4k8MGaDIg61x2GCG1VV6XJsiwhs5HVxJuHYeBBjg7C+g28LkucwPkieaoihAVs0wOhrz051ZfxTncSnloc88ca/8tG3B1Uami3tFdc+S1YcU8YQj0dUudKlVNyluA5vt+NsABfYTOj7nj6NDGPIBq0Ovj1Icac9P3W7EJnPneWXVdFmYyi4UTW573bDGHLU/WAYRI7SrNFuFg2EhgMdBR78iuXLypgR7Y3JXhbtuNcf1T3Ly6ihbcE4X0tGvbUNXFNxdlFA704+qh16mXceOGmXNoujEfKbPDR2A1jqr5dxT94gndrk5C0pjn4Yx3A9DVSuVFYeuruZDB/cQn5ZGS2PT9giHB96FBKhaqlyVcA7dEdPE2mnHvuaX5+o30lz9kh7IKT+r2Pd/DY1fstDAzSEH+7fHHDc1wkBNtxVElPOyUfzD6rTJ/H5hKdaqpRMd9WHo8/bMIgcrWGjfTl6PF2embxFAkCm+vQDbaRihTKGVtxcA6XgwR7Sr4ncfKM/eDl2y+qt8UXWCHmINFm3LUFb1fWS3r8x03dIFzDUj8TY8KxSfKS2ulaM0Tm7aoA1mi+N6CAn087LI4DtjU/OtGv79miM/D5aNKA/0l5JbOqMMhOIqSIET2jkjN3XTZ9Jt3HfHU0UuqY93g+6ObFCdGTq5yEASbmo9hgxkCnU9gUkKkRX3GJTwyByi8tccieZln5RnkPY26+2hzyCXGpOhPUi4eHhsmDBAhk9erT88MMPeXZr9+7dMnfuXHl8xIg8t7Hlh5CQENXuyJFP2rK7S+zTt0eA9OpUXyYAzvYQ4NeLIs0aeEqNauVVE4So1rss2xArb3+2T/rf0kilj7II25Vl/PhXZdKkSbo+RVcek4zMjkYqZrkyZeSJiVskJU1fkaIfr5KwlkJ0YGCf+iV2n+wKTZHImAybjh8VmyF/rI2WuwEUVBGAQfaWk8nJsmzZMnlz0puyZPFim5qPi4uT2bNny1NPPSWvjR8v8+fNk3HjXpZktK03CQLRNCNtc74LM5O36q2PjuoP3w8bd5oce0EBnjIUqJGGOFYDhkHkWP26fetE/7oM5JhhA4N1lw9+7tx5iY6OkYiICDxsc685uQLYm6SkJFm5cpUkJNp3kp2YkKDajYmJdev75M6bGkhgvaoy/sPdqC3CzVIE0biOYhLOAtHuTBFacuyuYRFpMhmpIISa7+4GaTUcR1FRxyQyIjLPseZYjVvXuquPyfLlrpcn728lglSkce/vAsGjdXpx9FZ0huw/nKoOQ2Qxn5oVHH3IPNtfteWEDH5+napx/PbXCElIPpfnttl/+P7PSMUV1i7YMWTUp9LS5GBoqGzfsR3v1cJfvPPnz8sr416Run5+Mh7G0NmzmbJ4yRI5cMAE55z9fPTwd59u/nIcz/KNu+z7/tXDueXVBzoriDRKIVQ7U+WctZ4ur3PU4/eGQaTHq+IifaKnjS+X+5AaxRex3qR582Zy88035dutUqVKyS233IIXiC+4MOw7XHr17i2Bgf75Ht9dfrz/jqaYeGTKj8uOFemUu7S+Bt++bZ/+PJ48OQI+TJi1R+j9vAUve3cQjp3PP/tcPvnkYwU7rNdztveY3LN3ryxcuFBXp8tn8fDBLeXA0VRZtCxSF3376SrMNjszsI+/LvpEwvCZQIm746nVMmLCv/LTiuPCjIe8hDVQrIfs4sAawAYNGgjvUUppG95H69atk5TUFGnXvoP4+vrK9Pfek/btTXw2eg1Qk4w6KLCa/LEmJi/Vu9z3Uz7Zh3vNFMFlZKixvwGwUxwX2b4zvOLosXEMp9HA14AQbtqwujTyr6bbPpcubV2e+nV4+TCVw95yXSnrjm/v4+qtPdbO/K+Dn8z/LbxIXuv2IH8lzwdlZ6g+gRXIwUWuoUF9g/R2GRzan0qVK0mFivaBWXdkR+01JhP/z951wFVVtvFHUVT2BkFkoyjurWnONFflbFla2fjKcjTMhi1H2tS00izTLHNUVpp7i1tBRGUooAzZe4jre/7v9eAVES5w7+VeeJ/f79yz3/Gc897zPuv/JKfQ5zzhvHGz4pp8XfYPZTtyTpwBPTxpyZoIKrxyXdfVlVl+fuF12nwLWKWJizl1beNQ5vX6PgnvgWBOkjx3aSgNmridJs8+Spv2xhParU4nOYFyXsE1ahOgG+tQcV2Kb20lBKKIiAhRjAWPRZAJWx/MzKrPGicaocEPYuAOhiRX6dugQTUGcQniSvfdcpXz9bAixA5J0g8HDE9tr59+y1p0zIGs3KsMFZki4oZ0XBWdPXuWdu7cSSNGjKDwiHA6cvgIWVvb0PDhw8jZ2ZkOHjxIBw8dZIHGhB4aPpygZSuPQkNDaeuWrZSUdFlcn5ON+JaKC0TXrl2jDRv+pjNhYXTtxnXq0L499erVi2xsbO5oQmZmJm3fsYOCT5wgZxcXGjduXPE1uXm5tH//fkJ8Q0JcAvk386enx4/nBG2qeJmo81F04EAQ+6w3Ys1fB9q8eTPFx8dRv/4DqG+fPnfUExEeThv+3sDufynUhK1e/n7+5OXtLcry9PQU1x48dIgO8YI2+fh406gRI8nMXPUBvaMwLe90b+9GW/fHCkGmU6B9pUq3tqxP3hx4ev5SDp1kC6Uh0npOIAuXGlsr/QIolPfsMYHfuWsHmdY3JbwLGzZsoA4dGB6fx8zZc+foUFAQRURGUEN+zx588EHq3LmzYG9BYSH99OOPpbLaz8+PrbADqKioiI4ePUp7du+m6W+/Layt2n5vS22A2kHEDO7fv4/s7OzIycmZ9u3by+M7mTp36kwjR428ywJ8rzGJ2I1kdqMF4T9lzOhRZGdvT/9u3EgXY2OpvqkpDR06lGZwP1HGoYMcr5OWTg8OflCMt6Kiq3ztvxR+Lpz5coW8mNdDhg0le7vKvfNqXazQZq/OTWjbgRgCmMHQ3tWH6LaZYyWQcBI0gsEUDJmusf/3AY5PxALQh/vaO3HiZDe2CjkScqHZ2TTkvE/aHdcYO2v5nTvH37m69eqRk6PKCo4EsRWhH39cRviugX799Vcex6p2wo1VnRAvm5aWJtDM3njjTYJL9+rff6Mb3HdrK4Zwf/FF2sXjGK57ICig+vXtS0EHg4QLHqzBjz76aPH3S73sym77eNjyO3KdLsTlkG/TmpukOj45n778WcVXoMp9NKktGVNS5co+X0O5TwpEhvIkalg7wvjjABdnfy/dWofWrV8vPhb5eXkE7ZcTC0AOPOHZyBMOCEGYbOAP+tr163Ti+HE6yQLHihUriq0IpbF9565d9P2339Lrb75BbVq3oT/++IMuxcVRgwYV+9AV5Oezn/a75OPrQ9PeeJ0nQOfo/fffpyM8Mfzk44/vqBr+3B4eHpzJ/CZt3bqV0tPTaebMmeKa+fPm0+XLl2nxosWUkJhA015/nVD2lKlTOQbqEn3/3fd0jsvGhGrP3j3kwwLOeY6LCv3yy2KhBwVBcHz77ek0bdrr1L5dew5w/4B2cGyUvYMDtW7dmqZOmSJAJsJYeHvqqadE2YvZxekgT4S//noBmfJET5eEyYSjvRmdCs+gygpEaF/7lvZCIEpnRC3EEXm46l6Y05Qv0CqfPZ9Bz4zSb+LY8p59P3bD+YrfFwRWI77gKgvyKakplJGRQRBq3nrrLZrM4CPj+L1Y9sMPNGvWLFq8eLFwuyksKKBjx47R+PETqGlTd0Kcwwcz3+fxf5N69+5NWby/jAWmvXt2cx6cGyI4WtvvbXn8hzLhB243hCILCwsWiJyEoiOOxzXGTmpaKr300kt3FHOvMfnAwIE06ZVJwvUIYxTCEGgQHx/FgtXs2XOEgmHI4MH0M//X+Pn7UV+eMDo5OlFubg4LhDPIhieWExmkJZ95N3/+fNq0+T+a+f5MCggIuKMNutxBxnsfjt2DxdKsUfVNBX7+67zoJnInWVuZagVxsip8i7usWewhLGsQJrEAJtyW227Ba4SjsoygFcL78c6MGWRna8fAB29TemYGAyp8KMquaB3du/fgb8A5AqhCz549qT6Da4AuRMeI75vY4R+MYyjkcnKyCQIRFGcTnh5PE555huz5XYdA1IfHdQZ/o3766Sce803F2AkJOUXHjp/gsT9Tq8IQ2uXM3wX0F/naaqpAhPdm5sIQ/k9QKQdeHOtPfh41V/hT3jdDWlffv6AhcUG2RescSEorYItFPZ0g7ag3dtTIkZTFWti//vpLaKmGP/SQOJ3HAhK0WKNGjmBt7TBx7MuvvqKdbIXBpM/xlpZNvSxs4yPw/fffU6fOnagT+1mDYHn6488/WUN2p4uEOFnGzy+shYu9GENz5s4RwlS7du14cuRPl2IvisByxbULRbwyaRIFNG8uSsPH6AwLLyBoB6HVa9miBZnUMyF3d3dq4uomNPY4j/2p06YJBDy4JH3GLjoN2XK0adMmjtf4ls5ysCysQKB1a9eJj3WPHj2EkDhy1Cj65JNPuJ8d6eWXXxbgEtBEQkPowEISJmcHDhygEyxE7ty1kyd8g0Q5uvyxZ+0qYomqQq38bWjtZlUJZ85nGpRAhL5BUQDBT59U3rNHW6aygD2DJ18ItF6ydCnlZGeJJm7evEW8rx6sXKjHGur2bIWENSQyKlIIRPXr16Ohw4ax5bOnuH7+/HlC8Hno4YepBb+3IAjbED4ib7nsaPO9FRWU8xMYGEjPPfccKwQ4oJzHzLx588QdI/n/49VXJwmr6pjRo4VyQCnqXmPSioWZsY+OFePr8OHDxTEYGKc+3r7Fffbw8hRFuTi7UKtWKgF44cKFFBsTQzNZQFT+gyZPnsKKkxn0Ff8/LVq0SPBYaYOu14725nToZDwdPV398XbXr99koJEQXXdZJ+Xn5F0lLKD3vjzAShkn6shW4KZuVYv9+IMVflFRUfTdd98R/t+xPDDgAVqxckWF+9Gcvy+4H9SsWbNiBZe11Z2TbrjRubq5sgLvNuonlGbW1tZ31InvIpSMIadOie/jv//+S7Nmzxbfjjsu1MIOhGXAcGez50lNpRUbzlMIu12C2gbY0bjh5Xuy1FReVFe/6lZXxbLeGs4BLWrJyuOU+S13rqY8YVPIjbXcIE9PL+UQa7pUx+AadC/au3cvwdrUqlXr4ktgGfFk6426AFN8soyNrVu2sOWlzR2WJVh7lrKmumRZsGQphLrQBmgHUfci1sRD6IFVCBPReLYSqUOkmpqqNH0OLORBGAJBaweCdl6hTJ7gAu0rMTFRHMJHEZSTkyPWu3fvEueh/Z/CE1gsKYys15hd+PLz8sU1xvDTwue2O+KZqNv9N4a266qN5T171AtXMlC79u34vasvhANMhDD5/+LLL4RFBW53mzeppM2UFNUk2sLCkkY88oi4F66We/fuI1dXV3qKBXt1MmXBSZ209d6ql1nWdoNbLkJeXrf/ExBY3uO+nuK9j2V3N3W615jENf379xcTxJ07dxSPsd1sWe7Xv596EartOrcP7d+3T1hyFWEIZ1q2bCnGLTT3Jdtw+065ZSwcQMwRgFNYvqsybdr0n7BA4j1VyL+Zn2qzoiYipQAtrie9+qp4d+Ey+8iIR4Q1SYvF15qiImNz6HuO5wPBWvvBK220ZmWsNUzUQkfv/EJpoUBZhOQAOOBk31AEmMI3HK4Z+ib48Zekeiaqdly7rjJJlzyP/YsXL4nDiONRJwgwavMa9VOlbidzbEIhx1YoGjnlImjfsJRFdW8BPdSBDZ0Jmrnf2NoUwohVD7MFDIJdNLvElUVwEyxJbdu2IUxoodWGdg8aexDiREDxcQki6P1Ldp2qLkLiSCf720hxlWkHXOQszOqLhHZnGd7akMjZoZH40KWk5TO0sP5c+cp79uCR8s4oa4VvsApBQJ86ZSrHm3lR+w7t2e3ziHK6eJ2Xm0ffLv5WCPuTJ08u1kAXX6DBRsm6cYsmbdeg6HteAoEflFuG0F9yTEJRMYytYshjtpGVFLA0HWNt+fPPv3BXPco/B+KJoORoVCIeD/8HzdhyDE17fHyCEDzvKkRHB1LS86h1M1sa+YCHjmq4d7FXim7Q7CWn2Ap+g2NvGtAbz7S898V6PPP3zkt0MPjeSrPSmoL/G1ur+lSH/7unTujEY6m0qyp2DO8LPBYUK6ty981bXyLlvVKOV8caMbpwIf2b4w11KczDeljIcwkrC5Xyrzr6qqs6MQ7e+5pTTlxl1wGmaeNbkJuTfj0IdNU3YytX/zNVY+OQbG+lOBDoZysmfhEx6dSmuVOlyqiOm65eVZnkEcDatk2bSjfBpL6JuPf4seN0/dp14e6mFAY3OEz8MNEsj2DR+fTTT4WlZt68Tzn+wZK2btta3m2lnh8zeowQ+Fas+FnEJIWzcDSSXQoR9A4ybWAqrFAIooXfuDolcc4kfPx0SelZhQRBARO0qpIv+14DGQrgCoZEZg1NKMDHlgOwU6l186oJfhXpV3nPvqyy4DYJFzPE2AwaNEhM3Eu7/odlP4i4GrjKaTMWpiptL62dJY8hngnk6ta45Kky94cMGUzrOIcL3FNh8YHgVlIBggKUiStilyD8ILkmrNROTrefP2IzQCXBVsRBHf1ggnk+NpOmT2xF/btVrO/aaNIfDC4CYQj0xFCvamlDaf04yf8bmghEDUxNqEc7JxrY01WAK6z65wLDmF/UijCEdl3nOD4QXObwXVJifsRBA/lBjCEAf/D+b9++XcQmtWfgIG1TEn8XoB90ZxTCmkZfLA8r/k716uhMw/u617QuGk1/tKDHMJq+yobqkQNA++rciqEyT6rcs/RYtaqqW9aVitbr5eUpbjl58qRYKz9IgnejAmUC4MDS0oqDqHNpz57dSjHCNQe5H4DiowkBIQ9+2og/gjAEQkLZeyWSLatMIFrlssZxxox3BAoWJrkIoFUIYAyglStXKofEGghjy5cvv+OYLnaCTiSQnbUpdWBQhKqSt7uKV7ns15+cXljV4rR6/8gB7nQsNIkys/XXrvKefVkdXL9uvXhve7JrGegKWz5BigUT24gzw4SopKscLChVpaq0XZO6EfuDCZ2nh6cmlxdfg/GIHGVwS120eBH169e/+Bw2FEFIUbJAAeLt5SOuOR12+o5rI3jSC6uTt4/XHcd1ubP3aBxPsuvSgO76F4bQLwhEoHr16tKwPsYxCUQsS5c2jjTz5Ta05Yf+9Onr7alvFxdGZqxLgf62lJZZQBls5dYGwV0V7xgUaGG3EN1QrhLLelOHcO5KjiO4aSsEQJSS38CFCxbSkAcHM1DIdKHkQ4wcrKDapgsXM9nTxIS83S20XXS1lrfz0OXiceBo15ATsFZeCVutHakhlUuBqIY8SEPsxvhHfOhMZBpFsRZSlwRENlABu/UoBFcDUAprYxVSgsTzGOlJIcDhgpRYG0BVQ0t7hj9AQNBBvM0uhvQGQhyyfP/5518CSle5v6z1448/Kk5/vWABo7R9LcqbOPE58vPxLba2pDGSF0i9nUp8UCoLTY0aqXJEILYJrm5ArQJSFuC8D7GwhADtzAwVf+HWpFBySrLYzGGBTCEgfUXz9dHR0QzacIYOsGYPFiyFVwMHDWT/5UYUFHSA3n3vPfrvv/9E4PhnHCQ/kQPSdUnZuVdo75FL9CQHkmojI7fvLYEIbb5w6TYPdNkHTcsewhDHTVzMaN1/Kp9xTe+rynXlPXuUrYyBJEY0VKe6DOYB+uWXlQJkQxGOgeqImCFMgL755pu7XOWAzIjEpAoBrAGUkqp6N7X13irla7o+dSqEIXxVQh2sX3BNepLjnRSLbXljUr2eh9kaBquPjY2tUFqon1Niso4dPyYAKAD88uytcbTxn395oquyRsNaFMdWqtFjRjNKmX4mfKkZBQxxH8M5TvwFfLR6u/WxHRqRSeHRKndWCBRAaDNkgtUaLn2bvu9P37zbWcCUm5dA5mvX3I5wLOSs6v3WRn8ABw9auOBrAa6D//vff/9dHMP/f0hIsNjW9EeJBVWlkVDdpSjnctW+H4gjBC1gAefosaM0Z85cgZCYzt8kfIegkINlNJm/X3AXBXDPgywY4dsFhFZt0ynmabc2Tlr5Nmi7bZUtLzGlgD757pS4HTkOP361LUGRLKn6OGDyAVP1VS9rrskccGU/2PMXc2lHUBx14ezd9XjioG3awJMMAA1AC3uRJ2CA0z3AMNEb/v5bHIuMjBRQoadPhzIS3QYh1EAggPbtFE/Wfvt9tXATA5x1fkE+dezQkWDyP81aY/zxIz6gkAWheuwbDi2yt5cn//n7iVwj5fXFjz8SiGUC5DHaAUFmGOd0GTt2rLgV2rTgYNWEMYLPA/oaQlhwsOojh2PDhg/j2J54IcScZjjsQEbt6tKlM2vkT9JFnkS1btOWlny/hNLS04TVCXFLDVmo+Z5RiYC0l8ACnQ3HICH3EiZ+mACe4liFo0eOisnsbrZebWbwh+bNAwT6VqtWrdgidULUh7wxWVmZ9NprU8iT+61LWr4ujLXVDDvKmldoYatKefnXRPJElNOK3Tdb+tpUtUit3V+X49HgUrpsfTjVrVNXQB9rrfB7FFTes4/hiRbemWzOt5WSkiLiAfAuAKTDxtaGx8opCmMhOic7h/73v//RiZOqd6Spe1POjxVKR44cETXHxsSK/F3IYbR2zRry8vQUMRALWJMcGqr6+EdERJK1jTUt/2m5Vt5bjHlNCIqTLfyum5o2EOt9DHCwY8dORp97lpG7VG6jmoxJIFAqigoAuoQxkmPHjh3YxbbtHc2wtLQU/yEXON4PFjTkZgGEua+vr8ibBlejeAZSWMmIYcj19OjYR4VQeUchOtgpLLpO3/4aTJ4cazf9+UB+B6s+3irazG9WnaOoiyrF1JvPBXI8nUrxU9FydHF90MkUCovKFPDOjw3xpndfbE1PDvOmln42wkpxrzoxqU1ILmCviCTqyUmmtUFAhoOFCN+Jbdu2ca6fg4xk6MMul0nUkpETfX3973C9LKvOzz7/TLyHEGbOnDkr3sW/GD1137794jakakB9iFlFXN1J/j6G8rcCaRjgVp2RnsEW4Mb8PbFhQewU/fzzcmrcuDHdd18P8Z0L4u8u3nUo3WIYoARopiXBg8pq373OJbO73B9bo+iVJwMMCjH0Xu3V5DhAN6bMPUqXGEYc9MxIPxpuJFZSTfpnpNeEstdDBfyAjLSXstnVx4HM7CJ64q195GBrThPHtjYqDQ+EjEYNGoqkpJhQVta/H64GsNg4OTjeEUtUkacC14VGZmbFt5TcLz5RxgbQ7YD01Zdde7Kzslk4LGT3O9YU84fWyspaQCMrtwskOp4oKQHnynFdrP/ecV5Yh5Z90p2ae1trpQrkHxr12m5R1uMcnzDl6RZaKVebhWzcE08fLgqmkYP8qZeWJlD3al9Fnn1pZSAODmAkSi4uvNPXeR9uXrqmqrZdaR9gwgEMMZjzA41/+mmOd8IEz7VKkzZYap966mn6nCebmByWJHxekxmpEUKb+uQQsYGIXSpg65onC40KOmTJ+7W9D2Hou19DhNvtj7N6kD2DGeib0jKv0LD/7RRB5Mgp89vnvfTdhDLrO3Y6TVisfCqRADQ6LpfGTt1DTz0SyMmXNRPUy2zMrZN4z5ArCxDuUHrhXVLGoib3V/YaKEegCATBQ0IfdZZs68q/ztDllBxa91VvrcVnlaxD3/uLfwunn/6IEtUCYvv7D7rVmL7pm5darG9VPS0WJouSHLiLAzbsCvHV9M70/PtBtOKPMHr6kZZUVwsWgLsq0sEB9czx6sLQarYqIft8WWTGwstszskAgktNVQULdWEIZZbcx7GyCO4NQAJatWoVCz9WRGoKTHP2Uw9mjb86lTa5Uz+vre3Ne6PZghhLc6a015owhLa5MJqbQvFJt/3glWOGsB5yvxtd4Qnq3KWhnF+jiIb2UcVwabttFX32pdWPHFhYFMI7bWKie2GovLYf5uTLQLQrjx55ZMQdoAkYP25qCoby7i95HoIOJqVIoty8WbNShSHcg2tKAyMBqAoSMeuTENuyZHUIuzuxhWhmt2oRhtDfdVtihTCE7TEPemJlUNQxsPIxjF5NLGjw/U1ow/ZICvC11VoePrhzQhgCqQvPUNRp4uSD9//++ysueCrCEOqtDmEoPDqDjp66TJ9O61BjBIbDp1Jp+Z/nwVKBmvfJa+1qTN9Ep4z4RwpERvzwjKXpyLb8zXtdaNInh+nb34JpwqhArX0oqoMHSNjq3qTsIOD6nMfFkOgqu12AFnKsR/euXcmRNdaXOVYESHOA337jzTf02ly4DKzfHEEHGUjhw0ltqZ+WUa4amNYlW07yikkg3FgMlUYMaMpAEg3o/YXsApmQTU8+FMAfSe1q7Q3t2VfkWZTX9ilTJnMen8Byi4SbWnx8vLhOiZko96Z7XIDYuiVLlhBynSWyy9vcT+fe40rDORwankq//XOWPNws6Mvp3aotZgcQw+u2xgrG2Fia0uBeapoZw2FXlVry2rgA2n8sif/fImncw7q1TMNlczQnFC6P8P4bGxVeuSbe2W5tnahvV5UwaGx9KNnepLRCepchthXHLIAoOHOKEkmGwQHpMmcYz6FWtAJuTK/OOkxF14gtRS3446wd96hawbwqdhIxVms4puMEo+dFcWySCcdEubu7U98+fWggQymrax2rWFW5t0NI+fnPMEpKyaU5U9tTt7Yql4xyb6zgBU9PP0BnzrOrI1spty1TxYhUsAi9XY6x8ebnxzlIuZAe6u9LXdre7X5V2cYY0rOvaB+01XYgNf7ALqMAeoCbX89ePdltbnyl3GARLwUofFh/xk+YQEOHDKlot/R2fV7BVfpzWxQdCU4kuI6+ynEY2ojRq2wHVm+Koc9/ChO3I27ipUf9K1uUQd+3lwWiaZ8eo9EP+mstnsigO6zlxrFHKf2whvNyXc4WLpXV4dqp5S4xENJNeo49ZcIiM0XRjw3xoqmcc0iSwXBglRSIDOZZ1I6G5HKw+8yFIXTgRBL1696UBt3vpROwhdrBzcr1EuhWptVkwYJF6K8dUdTE2ZzmTWsv0NYq14vy73rrsxO083CiuDDotwepPsP7GjLhg/nD+kh2p4gSyoIRA3ypqRu7N2qRqvPZV7UbVWk77lWsTUo7SssZpJwrbw1BrW5ddiPUAVBMeXVrcv4GJ7LceyyOtu6LIStGrprJmuj2Lew0uVVn1+D9fuiVnZTMWvKGDUzon8V9hbJCZxVWc8ErNpwngEc8O7qVXnOOVXO3tVL96n/P0fHTl2nJh92phW/NUJzOXXqa1t+yjrZhRMLvP+harcoJrTyomlWIjCGqWc/T8HtjYVaPPn+rA/2zK44+54RkwWdT6GGe+AX6Oxh+42tIC6tDGIpLzOGPQSTFXMqi8SN86TnWDterp1t0KxfH264IqRlXDArJqrRXCfx4cSzD1/Z0Yy36Gfps2TFqE+DIiR89WXBU5VUq7b6KHKuOZ1+R9pV1bVXajnurcn/Jdhlikky0Ea6oRzjmYjtDaufkFdEzPNaeHO5tEMqAf3ZdEsIQ2jligIdRCUNwdfrl7wu3Yj04wxT/dYkF2abwN4Z9rHhHdZy3GXWuuZc1/bjuNCcndyQXR/Pie7qyBdjaSruuseCrsRMgvtaxK/Wh4ASa/0bHGiMMbWIAHUUYgrVrLntGVKel1tjfE121X8YQ6YqzstwyOTCsTxPq0d6Jvv7lLAf6niJ/T1sa3NubvJvWDG1QmZ2vRSdT0gto0+5oOhF2mdoF2NOvjCjlzYHH+iB1N4sMRjs0JGjfsvrvwXDIC97pRCfOpNOiVeE0b8lRhvy1p95d3amZV/Vq+ctqtzxXfRxAvAWSYO/hXF45DNAxaqAHTWBhyFDy+8A6tPwvVSC5aX0TGsdCmjGRo21D2noggdIrmXT15Jlk0d2Gpib0xMMBUhgq5eFfvXaDfmbgpXPn01gY6kS9OmoPpa+U6vR2CPDycxg4BwQhaDYDCDnYSmFYbw+gAhVJgagCzJKXapcDdtam9CHnnXmMkYYW/nKOvlp+nPy9bNmVzoMCfOTET7vc1m9pcZdzGD3uEp08c5k8OZD787c6Uc8O+v3A2ahpYAH/bmwEF6dls7oJwQioRItWBrNQZ07d2rtS5zYuRg1MYmzPwlDbC8vrgRPx7F6UJCZbowd60qODPasNQe5efFrLyHIJySq0x4f6uRvdhJBBAQXwy9rNMffqokbH/Vmh0dy78ih2GlVihBclca6hn9efpqycQlr8fle2qNkaYS/ubnJO3lV6Y/5xzmV4XZx85Ynm1e66encr5RGFAzKGSOGEXFc7B06FZ9CydVEUFJxMjZ3MqWfHJtSxtQtBqybJ8DmAuIVT4SmMsBRPETEZ1MzTmhPO+VYbQpAS2AzOfcRZwOGKZswE4IU1PCHbuDuOrly9Qa3YzbRjKxd2K7GX7hfG/GAr2Pas7Ct0PCyZjp1KpLikXLaqW9KjgzzpwV5uIjangsXp/HLEjT78yi6e7BZxYtN69OfC3gYnsGnChOBzGTTxvSBNLr3rmt6dXcTzmbMklHPBsYWM0094uGo3PvCuSo3kQBDHlf6xJYL8PKwZZKed0Vjyy2Mv3Fdfm3WEALMN6te1Mc3luFlJBssBCapgsI+mFjfsAie3W70xmjbtjWN4SqJ2LZyFRtzXw1blr12LeWOIXY/nSdlRnpwdC02i3Pwiuq+9Cz0xzKvaNWFwOXth5kHBsjefDaTRgzwMkX0VbhOgiwEW8c/OODoWlsoIgfWolZ8jxxs5CO1z/fqGDR5R4Q7LGyg9s1AoGxBzeeFipshf8kAPVxrG2e1b+Bi2m/GCledo5d8qd7nnx/jTxNF+RvtEh720ky6nag7jX5fjiBAXCPdFEGIZ318QLMbtfazwG9LHq9ZaehOSc2ndpgi6EJfJcW4+9L9Hm9Uoxc78H8NozX8x4rl7u1vST7N78LOWyl3BEMP8kQKRYT4X2SpwIK/gGm3eB1SyS3TuAuCTG1BbFo7acoCql7u1FI6q8TVJTMmjkHMpdDIsiRKT88jVqREN79uUhvMEzdHOMPyjI2Nz6PHX9wouvcQfW1irahqlZV6h7QcTOb4hkUIj0kXwPNxy4HIawJYjB9vbCWprWt9rcn9gbb0Ql0VnOZ7iTFQaww/nEgBp7u/kQgO6N6aubRyNYvIIq+bjr++joqvX+V1sKKxDQJgzNoIS4sCJZFr06zm6mJinUfOtLOoTkm6Wllbgv33x9NXPZzgFxU22HHkJN9h6BopYqFFnK3BRVs4V2rY/lvYfj6MAb1t6+4VA9iaoWdayP7dfpNnfq+KGkG9r+dwe5OZkVgEuyUurgQNSIKoGpssqK8EBfFg380dka1ACJ7DMI2sLU2rBgeaY9DX3tmNXERkOVwm2anzLdZ6gRV3M4BwK6XQ2MoWS0grIjtFy+rMbwMCertTa3/B8vuM5ZuHhl3eJPj79sA/Bf7smE4Aj9nH+k73HkuloaArlF15n4bQR+TJgiT9bV309bGQwt4G+AMi7EpeUTYdDEimEA/AL+NkhyNzNxYwtrs4sCDkLi6sJWxyMiV784BC796WJJr/3UmtWmpSd0NqQ+ob/vEMhKbSFwRT2HE2ifFbQaUr+PMGf90aHMifBeexKuHRdJK3bEsOuhPVFGoru7dyoplp4M7MLafuBixR0MoGTUZvSC2wtNKb3QdNnD8+Elz8+zHmHbjCSal1axEnpqxvyXtO21/LrpEBUy18Ao+w+XOr2HLksJn5hURlUl3FOPThfC5Dq/BiUwZMTvtbUj4q+Hhj8nwGMEBWTyfFA6XQ+NpO1vDdEpvv7O6omaK2bGZ4QpM6f9KwiGvjcNnFoDAN3vPFMS/XTNXobk7kQjsmD//rR0FS2MmQKSGZYjGBd9eTx4tHEiidslkZhaahpDyuXIbEvMiBCTFw2Q9FnUkxCtgi8NmV3R4wzkKlpXerNFqGB97kKK4Oh59Eq+YyQWuGjxSHicNsAO1r6UbeSlxjcPly0IcABUW7n4csi7qlkI9WfUclz2Ecs1zsvtKYG/Pw0IVh5kbNoHQNPIL4IgCk9OjQRygxN7jf0a8KjM+jA8XgK5fhSe+sGwn3w4X5NdZ52oTr4kpBcQE9P30+ZHC8HmvF8K3pkQNPqaIqss+IckAJRxXkm7zAkDmTlXBWTviOnUugQT/6S2L8b0JZNOWAVQavurpbUtLEVOTtIc3VZzy2DA7Uv8aTsIpb4bIrm5UrRdZHUsWOgA3VtzQu76RgLdDX6CgvJ/eM2i24/xJrpd1lDXVsJFofQiAxCYHjw2XSCIgH8Qe6jJs6W5Mp5jpo4W7BFgrcZ0KSBBDLR2quCsRWflEPxibm8zhWKhtQMVRyKi0MjgrDQlhM1tmVkrcW/hbOiJ+muui3M61MfDsyHcNSJxyNQzwyZMMkfO3WvECignPp1fk+BNmmobQ6NyBRCENxPUzMK72omYkAQs4Vl4544Bv+JvOsauLy99lSAQPm766QGB2DhhXs48tXgOwbE1fYtnUUOI3Oz+hqUYDiXXGaX6uCzyXQ89LLwJmjdzI5GMxR8/26Na6QgBM4DPOTZd4PowqUc8SBqmxLOcN6+SrdECkSVZp280SA5gIBXmKyBWBcamUHnOQcAtOVAN2riYiGEIyTIA3yxEy+1DcHu2vUbnBwxny6n8MJBrZfYCnSJNdXZnLsE1LSxOaOX2YqlHcM+6ytnkC5eJuQ+6fbYJlE0tLYfTWqri2qMskxowqPjc9kFMpPOXsii8AvZFBGbVQwPa2/TiBNJmolkks72ZuRoz+PFvhFZmpsaZX913WjE/KRmFlByej4l839QUloeT2rzCLF2BbdcrZzsGnIeKWuO77IWQAgtfW1KTU4KLTMsFFgiY7Pvarota9kxscTkHEKUIdKkT44IdzO07blRfvQCAwsYGkXEZN/icyI/JxUkuHob3ZzNioUgX0byUyiaPRTGTNmj7Io1cp7N4WSb7Vi4rSphbO7neKV/GU3ywIkk4ToJl9fAZg4sJNmJMVnVOrR9P76xMaxEO3chjU5xbCniSu05186Abq4EmHV1/mm7bkMoD9+aVz45XOwe2rmVAy14tzMZm4urIfCyGtsgBaJqZL6sWg8cgB8+PnxnorIo7HwmJ33LYiEgr9gtxc66ITmx9ciZJ3wOPGHBvh1PBu15DfQuY6Sr7HKTnlUgkKnSswpVE7XUfJ6g5VMKT9jwwYWG2Y0tAf5eVtTSx0ZkBG/ubU3mjYyzz/d6Tp1GbxSnMIHEhEXSvTmA9wJj4zxrOC9cyhXKBGzHJ+WxtVDlxoWAeLjdiTFi05ATfzYUYCdWDHhia9lQxPbVZQttTSMkPkUweCZbe7BgO43HVgZberBOY2EIcUAgGytT8nS1IB+eRPuwZUGseduag+wrSph8K8JRacH8ziykDuihEo4CePwaAv36bzR9yYABIAiAyxldC5ZIQyDEoir8jGGFQEly5G8AQCsgbEJgvRc9xkARUbeE1UA/W5r3egedgMkgfw2Eo21BiQQvCFghrC0bCOuRVxNrVmBZUmO27OrbnTK/4KpQpMHt8zyjHsKlGh4F8CDoyS7VA7q7Uht2qWZv9lpB7359krbsTxB9hTVx2SfdBQhKreh8zemkFIhqzrOUPdGUA5i4IOA+Ok418cOkA9ryBNYSZqkl8GzEwgEEI2ue9Fmxy4IFa8ct2XXF0rwBWfDkBtpyBMPCytTAtJ5O3VgK+WNzhSdlBYXX+KN4lXI4BgFxCMhKn8NQ10gAl31rkoZjCpkzMpULT5o8m1iwtceSvMSaLWWu5nr/iCpt0ue626P/EaxiyAMyn4OcJVWOA7C8AswkLimfBSTVEsda4OQ0Fgo4VkudAHhiw1YMCx4nELDNG9UnczNTXlTbFthmZUNDHjuIk4F7nj4RtjBxA+oZ1nk8scvn8QREy7yCIkKgO47liWNFxUKQklgR/cTk05G137CgwYqApQkv7i7mYlwBDU4XdI4teUAT3MbAMqVBP7uzdVdx66ouy254dDZNeOcAQSkD4fmXeT3ZddlcF+zQuEzwahvzDYLQueisu+4DClhfVpgMZCEIFh5NJvEiUTIjzo3g+JA3ngnUi8CH7xbegcMcE3iU3cPPMPIq3ldYIZC3z8XRQigrHBhIBYs9f7fwzYILeWUI4wPfGQj7qekFQpmWkl7I1p9cditUuXzC6tma3T07t7KnzuxWXRuR1JBUHjFgIAjUP83qwS76DSvDcnlP9XJACkTVy39Zu6FxABOfxJQCsSCzeiJ/TFPTr7CV5YrK4sICE4QmgA6UJATbNmK0uwa8QEiqX8+EoC2HNQYfrbq8UYe/tsoH6gaXIRZWzd9gSxaKRLnXeQJfWHRNuC9BAMKHqSTho23JQpkda+btWFPvYGPKH0OepLG1CxDY0NS5MsynriZnJdtjqPs9Ht8sJr9A6frszY6G2kyjbhfcZVI47gLCUTJPmJQ1BCXERagsKjxuclWCR2mdxRjBuGnAQeUYOwgur8fjCYApyOUCcLW6HKNxe7+OmLhiknjz5g0xbjCWbmIs8TGMI+wXXb0mrMEYQ7DyKIAFJduA8YnxhMmxWNjKA0uPA7tCYdLnZN9QTHZgkbG2rLilp2R9Vd0P4VgwTPAR85KedeWu4nw9rG4JR431NknN5Gf91PQDxe5n77zYmh5md6nqIACqbGfBEQLkKY6dw3uhThas4MJ/wgMck9WFJ/IVdW2CW+Ox06nVjpJ26XI+Q7Oz5wO7vMLiBSsivlvwjFAI7uIQjKCcMOVvEr4/QD8z4fHEQ4fHyg1GRLvOiiOMl+vFCjcItQpBmdGEEQ89GrNijS0gcPuERdKWx0htprWbY2nestOCBeDv0o+7c4LZ2+6VtZk3Rth3KRAZ4UOTTTYADmByl8ETEbgwqLTL1wQsK6BZlX18lPCRwYRRtb69jw80PkhwJanHkzET/kCJNX+soIE24z/XGLZc7T2eJKxCMxi1CFpf/OkihgCTNkwiJZXNgZ5PbhaC5X0dnOnL6VIgKptbuj+LsYCxk8UoTPks7APYAYAPEPwLWBkh1ryPc1fZL19REOA+CDjXeME2FtUY4rHDAwFjAeNJTPYgQPGCsQXrUyO2VJjBIiXWvM/bjThBIo7D4ouxhAmfMRKEP0zMIRwBFQ2W4pLU0s9GWD/6czyHrnKE4Xm89OEhOsmAHSBYqmZNbleyKTrdR993HroseHH8TJp4R9QrxPPv0d5JAFP0aOfEQnfN/AOF7AelRBJbdSCkQjiEYgJxokXs+lrE3yUIO1AOYNzgewNe1BfKiLpCEQBFmw1DY9uxwNOYLaGGoARQf5aGsL37SBK99flx8b8EAfPrtzsJK5khtE22oVIcWGWcX4FK9VXeJDmgPQ4gHqAyMQEVaQG0v5v3x4tbQs6lV5u2tSJtNrRrYZEDldQQG1o7a0t7ILAgBwkWSVXnACa0cFXCMn1iKwoKTmGBIF6kJIBwCQJwBpavVpwVLmEQVvpx/jBtTnKhJVeEoeYcN/T+/9pUvXMalABhGqh8iN84yDmDkPtFnTBRBTom3OFgEYIgXNMJf3lw2ZJuW7p70kdPp9E7X50UwhBqeY+toRiDkoybA1IgMu7nJ1tfgzmASYunm4Vwhdi8L4EmjvYX7nA1uMu669qdHjO6q0eWLDlQTRyARaxXRyexAASjWFBgIQmuULCwIccOlvnLwsQEDsJR787OwvJc2Wb/uD6K/th2Udxux9brz97qyJY53VlfYHk/cIITprKyCIAD6vFdaASsgx1a2gsrVd8uLmTFyitJkgPa4gAg2qd9ekyMKZT5v8ea0eD73bRVvCynGjkgBaJqZL6sWnKgLA5A0zdhhC/NXBgsgAF+/iuK3uZEb5I050CxZUhlKNL8Rnml5IARcwACCdDSsCDwfhcnsoYV5Si718G9DUAjQSeTxYJ4rR7tVVYUuJZWRJhBMtFvV4cLTqEcAJc4c7yVtgmuk0cYSGALuwbuYVclAMuUJCSKhoAHREnAYEuSHNA2ByJjc+i12UeEay/KfnK4t/hGa7seWV71cKAOTxik7rR6eC9rlRwolwOIERj56i6B7oUEh39900cEeZd7o7xAcEDGEMkXQXLgNgcQUwJXXMQcIUlvyc8/Yhd7MWwyEsB2Y1czBQDmdgm3t2C1fp+VNSgDKIGfvt5e3Hv7iqptYWYCNzy0dcehRBEPU7JEwHo/cAt2HEluJUkO6IoDgGx//v2DxSAmQBiUCkpdcbtaypWgCtXCdlmp5EAFOIDs5bO+OyXueHSwF02b0KICd9fuSxWUOUzyPmdXHkmSA5IDKg4AEVCBoz7DSGUlCa5mfbowHDULRx1a2IsAfOUaCEMfLgoRlibE6X3wchutuQ0hZxwsQdsPJghwAKVOZQ03YliCsFQ3pLfSJrmu2RwA8uzE9w5ywmUV3PignqpE37dCVGt252tP76RAVHueteypsXIAWbAfmbRL5B4BUtKGRX1lULqGD1PmIdKQUfKyWs2BOIZvhiUGC5LxliS4oPXnZJuwxpy/mENzl54uDih//ZmWNPZBz5K3VGgfZSr1I9dVSQLSmZKAtpmnVcnTcl9yQGccQB6rFz84JPKvoRKAc3zKiXgrCtWuswbKgrXFASkQaYuTshzJAV1yQD3fwVMP+dCkJ5vrsroaU3aXsZvExA0AFXOnta8x/ZIdkRzQFQfKE07U632DhaExlRSGKiKEtfa3Va9WbksO6IUDyDcFYSiRk7aDOrdyoK9mdBJQ5XppgKxEnxyQsNv65LasS3Kgshx4iBMc/vhHFGcIL6R1W2NpHAdzInGkpLI5AGQtEJCnJEkOSA6UzwGfppb0UtNm9BKjZ5XnvraWQRWQV0pT9zVN3PT6svIC5ZV00yu/5fIKyQHtcSCeLZUQhmAhAkEY+mK6FIa0x2HDK0mizBneM5Etkhy4iwNInAch6Mufz4gEsD9vOE+vjQu46zp54DYHgEylkBSIFE7IteSA5hxo4WtNEJASkvM5nueyuBGqBWVkxcTn0pI1EWJRAA4GsGtdY8fbAAfaBHLQvOXySsmBynPgYmKeSDSMBLcg5LL67E3dwslXvrXyTm1xQApE2uKkLEdyQMccGPmAB638+4LKSsSa2SeHeUt42TJ4DnhhhepxQlBJkgOSAxXjQFrmFZo69xgpoAsW5vVp9uR2ohDE/OxWg8AOj84iLAt/OUeBvjbUxMWcYBEKCU8XUN/qNTcwNaHu7SoH9a1ejtyWHNA2B4AmB8sQvDFAPdo50TyGk4dSUlLN5oAUiGr285W9q0EcQH6QZzgvEbLCIxnhT3+ep9cl4tw9n7C6QFQWfPA9C5AnJAdqMQdORWTQ9M9PUAoLNSBXJzP66u1O5NXEQux3a+tIM14oPUnq6ahMwqJOCELvwpr2gewOd38Vk8Gqlyu3JQe0xYEz57No8pyjlJF1RRQJdNJPp3UgJD2WVPM5IEXemv+MZQ9rEAce6d+UXG65o/zJ2eGTbpn0a1AXtdYVdZc5iQikNbbKgmoBB37/L4ZemHmoWBhqxaAGy+f0KBaGFBbUoToiPk8ThUMdFojgui12+NUAAEAASURBVCoWiVessFCuDYQDB4NThGVIEYZ6d3aheYwmJ4UhA3lAemiGtBDpgcmyCskBbXEAf87PjvQTeYmKrl6nH9dHyuRw92CutBDdgzHysOTAPTiQV3CNZn8fKiCwlUse6teU3ny2ZbHLEJJFHzudKq7Zefgy5eRdVS4tXjf3tqKmjc0pJe0KnYrMEC5z167doP3Hk8SC9AE92juJHEdwSZLuSMWskxvVwIGNe+Lpk29PibxaqH5YH3d658VWElq7Gp5FdVYpBaLq5L6sW3KgEhwY1qcJ/fxnFCFfx9+74uiph33Ijd1ZJN3JgWvXeeZ2i0xMpDFc4YVcSw6UxoHQiEx69+uTAkAB503rmwhBCAiXoJBzGUII2n4wkdJvuRSJE7d+fD2sBDocchWp/x+lZxXR9iDkOEokuOHdvHlTuPzu4HKwmDeqR9DGP8AJYLu0dpCTUHWmym2dc+C73yNo2brI4nrglg6ERUm1jwN1+M/pduRx7eu/7LHkgFFyYBNrtGZ+EyzaPrR3E5rJmeIl3ckBBHQPeWGHOPj4UG+a8rRE5buTQ3JPcoA4TxcxpH8k/cCTQsWq2sTZjOZMVeXtgiCzjQUaBX5YnWfubAUCRDYW71uxRernS26jjG1cHgAZzjEAQ0mysTSlvt0aizijdgF2JD3rSnJI7muLA/mF1+k9VgDsPZYkioQrJ5IMjx7ooa0qZDnGxQGZmNW4npdsreSAigOYxIyduocAewv//d+/uJ88XM0le9Q4gMnXsJd2iiPjhvvQq+NkMls19shNyQG6EJdLHy0KoTA1AIT7OZDck4WbXewOB/jhkuRs34gGsBUIQlCAt3XJ0xrvA80LghEW/I+VJEe7hjSgu6qeloxaJ0lyQFscuHQ5n6Z9eoyi43JEkbBSfvxaO+rZwUlbVchyjI8DUiAyvmcmWyw5oOLAtqBEmvHlCbHTj5MZzp2m0uhK/qg4gCzjD72sEojgVjjpCSkQyXdDcgAcAODIig0XaOnaCLp6lbUrTHCRs7M2LdUSZGvdgPqz5QZCUNvmtuJ6bf5ExGQLwQj/ach5VJLc2GKlWKJ8OS+SJMmBynJg56HL9PF3pyj3VuwbYt0+f6sjebqp0BMrW668z+g5IAUio3+EsgO1mgNPvrlf5P4AE36a3YMC/aQmVXkhkGn84Vd2id0J7Bf+P+kXrrBGrmsxBwAtPIeBE0pzWVNnC3IO9e3CsT0sBHUKdGB0OPWzuttGLBOsRohVUnLBqNfm7W5ZLBy5u8jYSXXeyO17c6CIBf8vlp+h9Vtjiy/qzoAeszivloWZDKcvZkrt3ZACUe199rLnNYEDh0+l0isfHxZdgc/9ko+61YRuaaUPcIsYMUklED3DyHwvPeqvlXJlIZIDxsiBvPxr9MWKs/TvzovEBqJSCehvyL0ykAEOkGeofj09SUGltAbRzcfD0oRwBDS7rJyiu64K8LEWwtGA7q7kbN/wrvPygOQAOAD3zLe/OEGRsdmCIYgXAnjC82P8ZZyafEUUDqySYrHCCrmWHDBCDgCVCQsEo5Nn02nf8WTpB33rOUq8GCN8oWWTtc4BQGl/zYLQxt1xVMTQ1yWpfv261J2Fnwd6uLEw5EQQigyBAKjQMdBeLG89F0iHQlJYOEqk3UcvUz73CXSWrV1YFqw8R23YlQ/WLLj22VqZGkIXZBsMgANrOKfWwlXnBLIhmuNg25A+ea0tdWhpbwCtk00wJA5IlDlDehqyLZIDleBAOPvfj2PXOQgAcCf57bNeenNvqURz9XYLtIKjXtst6nt2lB+9OFZaiPTGfFlRtXLgStENOnAimTAZPMGKkpLKAbi/dWoFIchVuMUZk8uQ0rct+xPowMlkulJ0/Q5eA2SmI7v4GWPf7uiI3KkSBxBD+tHiEGFlVAqCi9yHr7QhGykwKyyR69sckBai27yQW5IDxsmBZp5WwsVl8754unAph/7dfYmG91XlDjHOHslWSw5IDlSUA4DMhhVlC8ff7DpymQoZVrgkObFb2RMMQf9gLzejtaI0MK1Lfbu6iAXQybu5r1tZOIKVHLnHwIfDzAcsc5eG3rJ+uQpXQEOxfpV8LnJfuxz4Y9tF+nrl2WJLIp77y483p0cHe2q3IllajeKAdJmrUY9Tdqa2cuClR5vRjkOJAjHq+zURLCC5ESYOtZngJ66QzLamcKJ61uB/bv5VymZkJ8SyFF65rlpYu1/Ak1rsF6gdK2ILB5DQbvCCdfE2T3Zx7BqviW6yJbSOWOrdWmMfFgKTW/vYbmhqItzAGvGkCBMjLOrbDRvUIysGELCyqG90Y0aTOBs8cS+G0Z4+sRW1b2FXPS+Ajmo1a2hCg1m4w5KVe5V28n8gLEdwH8Z7AgS9PUeTxILnbijxUTpiR3GxGdlFAi0wg5PiIoku9hGDBesa3CbBF4AMwFJoynFicJsEyiB4BHdDoA0CWdCOt1056Xcj5rMxUDTDyM9ZEiqev9Le1s1sRZ4+oMlJkhwoiwNSICqLO/Kc5ICRcMDVqZFIKPfrv9GUnFZIv22MpvGP+BhJ63XTzNviEE+dpUSkVSZf5UkVEt+mZVyhNJ5wpWeqJl6YfGXzxDQrB0uREIBy8opYGLrGz6D0JkB4MWWhBRMy1ZonaCZ1qS4LM3U5kEQsPHGrg201wYdu1mFwgJvi2WKNCTAW1KNs37ipmvjBraroKi9ifXccjdIygAhYsmAEAcnSwpRssG1Zn6x5294GE0WeJNo04O0G5MixCJg4VgeVh8SmtAmJU998NpC6tnFQDtXYtTU/q0f6NxVLKr+XgPAGWt3pyAzRZwjdSt4jIOj16ewiLOuVRdA7GJwi4puqE3gCgs4ZziEF5EDkcopJyKU4zh0Fy5lCpizsWJo3IAvzelTPxISBMqA0qEv1+F1HPrvr/HOVxwaUDhgfOay0yOUxi32F8M43dTEXue58PayoBYNZNPOyNhgFAgS9ZesjaeXfF+jarTg5/J/ATfrJ4d4SOEF5kHJdJgdkDFGZ7JEnJQeMhwPQkAJmGvkV8MH/a2EfsubJXG0lmYeo8k8e71Lc5TzOCVNAl1MKKJGT3Kq28wUUMs6rUwMWaKwtGwghwrxRfTJrWI/MONmhWcP6rF3GtimZM7St2OZj0EorQhCsOPomlYb8Ol3hiSCEpQIO0s8ruMrWqmvsZlMkXG3ysF14lQr5XC4fEwJe7hUxiVTai7ZDOHLiZKWujo3IhZfGDrzwuglPIKGo0NaEWcnVA2CBxJS7c/UobcIayVOfH+NHQ3u7CyuA+rnato33VhGEFJQxdR5UJscShO4hL+7g596Q5r3egZw4iaw+CMmmD4ek0iF2Dzx1Ll0oJVCvox2/c04WYu3A2462ZvxeNmJBiMcaj83KUD6Ph+zcIkrNLKDUtHxKySiklLQ8/l/IFdZeWJc83SypI4MTdGZgH6wxxvVNiJX77McwiuM0CwoBMOHt51vJZOUKQ+RaEw5I2G1NuCSvkRwwFg4s//M8Lfr1nGjuiAc86O2JgcbSdK23M4ktZUN50gJ6Ypg3TX4qQOt1GHOB0KpCqxzLWuWY+DzVmreRvwlubSAgfUHQsbNpyK40DTkYuQFbSxqw1YQXtpqIhbfhllYbCBPh3HwIR1cohyeLWbxg0oj99KxCyswuZGtZoRCsFH5gsgzhyJPd1jxdWcvOCSA9XS2EsKRcc681gEGUyTyeVUnC80GbFLK2NKWnOQnx2Ac92eJWu11mFZ6or+FSpfDzIltSShIEyQE9VAloA7ytS54u3j9xJp1emHlQ7MNqOGdqe525I4axBQhC8F5G18OkH8/Vp6kN+XrYUlNXS3JvbMkKB/0qvvCOX0zI5v+MbIqMyaBLiTnivyLQ1476dHWmAd0YBt1Bt0IixgPyCsFSpxDAEiY/1YKG3O+mHJJryQFNOSAFIk05Ja+THDAGDsAvfPTkPSLbO7TXv8zrSbU1s3ta5hUaNHG7eGyPDvaiaRNaGMMj1EkboSUPj8niJL7ZFHUxh5dsSuDJFSbT0PTa25gJK4ejvRnD0jYqXqBlrg4Ljk6YoMdCYVlKzShgl8JCSk0voJR01rCnFVASa9gRSwVCvIYXo0L6N7XkCa4VuyDxwgAp0LJv3BNPq9nttbTkqbDGwZ1JcQ1CWbAEPD7Uix7j97w6tPRog7HRuQtZQtDYFpQg4m1Kth/uhkCqw+LNwqw6zVl6mv5QS/BZj13QXh0XQI8N8VS/rNLbUEr8uf2iAMiAhdaJx2Wb5o7U3MeO3xlrdn0zLGEX1tXI6AwKi0qj0+EpbG29Rq38bQV4B+K7zNlarC2CS+6StZG0fkusANFAuXCnHd6nCU3iZwDXSUmSA5XggBSIKsE0eYvkgEFzYAdneJ/OSehAgJ/9dmYXg26vrhqHD2e/CVtF8bXJWob4idORmTw5yRTrsxcyiy0+jnZm5Opszm5d5uTCLjYujubsXmUmhR5dvYSllIvJ42V2ebuckkuJyXns/pZH8Zdz2D1PZZWDm10jBno4z4iRCmFCiTiOBL7v+rXbJiEAQTw2RApCCp8quw45lyEsR9v5vxNxcCUJcTMq4agxubBL5KDntlNmKYliAWbz3kutKxVbA+VEEMOIr/kvloKCkxnUoCF1bOVMbVs4kbuLZckmGez+DQY8ORedTifCkin4TJL4bxncqwmNHuRJPu53CpYV6QSUfWs3x9JPf0YJ91XlXoAmTBvfklr43tuip1wr15IDZXBACkRlMEeekhwwWg68MPMQnTiTJto//42O1Luzs9H2pbINh0vYfU/8J24f2ruJQBqqbFmGfB/cqo6HpVEwxxScZFcexBmAXBzNqGlja3Jn9yx3FyshCAFRTZJhcgBuSHFJOcL9CG5I0ZeyVBY89kZEAPvFeHZlZGEKhOSSsEaMZLdYbWrfDZMz+msVQAaOnU4VwtHOw5cFwEDJ2pu6mfOzuNvdTrnOj4Wn+W92IDdGZ9OUdh66TN/9Hk5w6QtgK1CPjm4U6Odo9PFfiMk7HJJIQSfihRLg/k4u9AIDHfh5aC7g4ZkglcSSNZFsYVX9t4GvcG+c9GRzAYyhKZ/ldZIDZXBACkRlMEeekhwwWg4gWetTb+0XaFtNnM1ozVf3ay2421iYAo1r5zEbRXMHdHel2VPaGUvTy2wnXAEPcWD14VOca4WDq9N5H25UXk2s2J3GRrjUeLpZsUuWFH7KZKSBnwTwQxaDOFxgwQhLWESKSjPOcUP+7FrXtY0jdeOlbXM7RgzTPzCFgbOvys27xpa4II5PQczR3mNJd8SFlVc4LHcfv9ZO5EAq61qM369WnKXz7MLavqUzDezpKay2Zd1jrOdCw1Npy75ojj3K4RxSjek1dm+DNbQsgqD47epwEeuoXAewlic5JvSph3wqZYlTypFryYESHJACUQmGyF3JgRrDgVnfh9Jf7IcOgiYNH5DaRj2f3Cxy3PRo70Rfvd3JaLuP2B8koMTEDGhjgMz15VgCPy878vVUBVcDvlpSzeUAEo5C437+UgZFRGdyMDtbA9mFDjliOgU60v1sBe7VwYmBL6oHCrzmcp5E/h6Mvf/2xtN+RjXTBMYfEPEvjPGnZ0b63sUauLV+/tMZ2n4wgVo1c6Rhfb1rrCBUsvMQjP7ZEcW5kQrp2VH+NI6Fm5ICPQShH9ZFkjoqIHIljRzgIfiJXEmSJAe0zAEpEGmZobI4yQGD4QByVIyYtFsEccOtZv2C3gIi2GAaqIeGPMC+/hkcE9C+hT19/2FXPdSovSrOctA3kkxu40lTcmqhgNFt4WdPLXnxY4QpTBAk1W4OANnu7Pk0OhOZRuEX0gn5odoF2It4l35dXaRwpOXXYxcrJd6cf7xCpcJN7MNJbYpdGzexUDXvh9MCin7kID+OfbGvUHk14WLEGe08zKARe2OElWjOlPbkzQAjiOFaxoKQevwcBMsHGZgBOYUQvyVJckBHHJACkY4YK4uVHDAIDiBR3YKVZ0VbHurrTu9ywG9tohGv7uaYjDwWIKzo1896GnzXkex04+442rDrEgfa53M+G3NqE+DEgdWOPHGofECywXdcNrDKHICL3TkWioLPptBpdq9Dks0u7FKHcd+ro/NdWvgqV1gLCwBYDUBrKkoeHMc3i13o1m6JoQ07LzE0tTsN7etT69yYS/INAv3Kv85wMtlsgYYaFpVVfAkEof7dGtNzo/zYHVj+9xUzRm7oigNSINIVZ2W5kgOGwAH4wY+ZukcIBYAm/Wl2d2rpa2MITdNLG8a/fUCgrSFh5j+L++qlzspUgpig3zfF0IGTSSLvT4dAZ+rcyoUaO8uJQGX4WdvvuXb9BoVFptLRkCR+/1MFFPeI/h40ikEYdJ0fpqbyvqDwOj3w3DbhgluZPmKCjxxCT41oyYAJDpUpokbeA9CE//ZeoK37Ym6lAagjgBKeHeknE6vWyCdusJ1aJaNuDfbZyIZJDlSdA/DNnvJ0C5o696jwe5+75DT9PPc+o0cv0pQzSFQJymT3QUMjxIT8ty9ewMjCitWSJ0kTH23NAquDSHJoaO2V7TEeDiBPTZvmTmLJzSuiw8GXObdRAq3YEEX9urrS+Ed8BDCD8fSo+lu652hSpYUhtP4G544qvHKdLlzMpBY+DrXmP7i8J4c8aEN6ewto8R/XhVKX1o70/kttpEWzPMbJ81rnQB0ODryd1EDrxcsCJQckBwyBA6/PO0b4oIPeeKYljeFM9rWBPlgUIlzQ0Ne9KweJAPTq7jc0ohv3xNF3a8IpK/sqdWnbmHqzC40DJ0GVJDmgSw6cjkilXYcuMSBDBvXs4EwvP9G8SrlhdNlWQyt7KScDBbw9CNZ2NvioFBe8DTgTXonjAtrk1jbcFkMYDt+EBVTPJpxQlcFQcHWAjy117+AmypI/tzlw/mIWLV0dwvnz7OmzNzuq+Hv7tNySHNAlB6TLnC65K8uWHDAUDiRytvMxU/YIDaUFZ7VfxzDc9pzosabTwlXnaMVf50U3ASrRlLPPVydhQvUpZ7mPT86nXp3cqV/3puzOJDOrV+czqY11X+CJ56Y9F4RgNKxvU3qVBSNrS/keavNdgIvds+8GUU7+dZo8oT2ZNZT81YS/yL/1zS8nBaLctAktNLlFXiM5oA0OrDL5gEkbJckyJAckBwyXA5YsBEGreTQ0lZDxO5Vz1/Tt4mK4DdZSy2Ljczn7e4oorU9nFwYm0DxZopaaIIrJzb9Gs74L5ZwjZ8jP254mjm3NYAmOHFPAWTclSQ7omQO21g2pc5vG5MH5qnYcjKOVf0eRo10jAT6i56bU2Ore+TKYoi7l0qSn2pGVRc1XPmnrQeLddLY3o5UbIhhUxoyTEltpq2hZjuRAWRwIlbitZbFHnpMcqEEcQL4HTzdVkP5mjl1R3D9qUBfv6kpjNQEoLin/rvP6OHAqPENY546fSafXnu5ATw4PEMAJ+qhb1iE5UBYHAPk8/fku1JOtlR9/G0LTPz9B+WzZkFQ1DmwLSqRdRxLpyYdbkB1P8CVVjANtWzhRn25N6fPlYZSSfqViN8urJQcqyQEpEFWScfI2yQFj4wAAFt56LrC42XPZdQuB/TWZ1F3kLibk6b2rf++Ko+dnHiRfzhv05sTO5N3UWu9tkBVKDpTFgbomdWhQLy+aOqEjnYrIpPHT99Pl1IKybpHnyuBATt5VmrfsNN3X0Y2tG7ZlXClPlcWBoQy0YGFmSvhOSZIc0AcHpMucPrgs65AcMBAOwGXsYmI+nb+YQ5k5RdSwgQm1bW5nIK3TfjMsOT7npz+jBJwrsps/0MNV+5Xco8Q1/8XwxzyUHnnAT+QcMeGJZ02nd96ZQfv376fevXsbRFfTUlNpz5499Nuvv1FKSjK1aNlSJ+0ytH5XppNWlg2oU2sXzmOUSms3R1NvTihqZVH74l6S0gopNCJDuNfWBVJCBenHP87z/Zn0PCNGAkTB0GjTpk3084oVtG7dOoqIjKSQ4GA6fTqU2rZtZ1BNxf+lo50Zrd8SRd3bOZGTvbS0GdQDqnmNkS5zNe+Zyh5JDpTNgSlPB7DmTTXR+YGzgick11xtMD6qStxQrB4tRDsPXabPfgqjx4YF0P2d3ct+IDXk7A2Gz4uJiaXoC9EC4t0QupWZlUVnz5yho8eO0nXA++mADLHfle0mAv9feqItu3Q2pEmzjhCsHbWNktIK6FXu+4PP72BLTxjB5VVTQnzmek6+2quLOyubDC+ryd8bNtDvq3+nGTNmUL9+/WkvKws2btxI2dk5mnZRr9cF+NiRRxMrWvVPtF7rlZXVTg4Ynvqidj4H2WvJAb1xAOhyLz7qL+pDXozZS0L1Vnd1VOTjbimqvci5foD8pGuChvnDRcE04D4v6sqQ2rWF6nJCkSXfL6Fvv10sADwMod8+Pj7Ut18/0RQTJDypAAWHhNCqVavKveNe/UZGC1iOIDAZE9Vnq8bEMa0ZkfImzeG8ZbWVMrKusKUsRiDFDfvfTvqGESsjY8sWHA6cTKZsFiK7tzO8cY/38c+//qLAVi3JrFEjGjlyBM2ZM8fgH2/39m6cMuKyjG0z+Cdl/A2s2BfC+PsreyA5IDnAHBgzyJMTgdoIXhwOSaF/d8fVWL608FX1E4kRz0Vn6byfC1aeJVvrRvTg/Z46r8vQKjC3MKdGZtWD5HdPXihuTxUQiJKTU+jzzz6jGzc1E2ZK6/eKFT/TqVOhwl3znm0z0BMNG9ajxxn8Y1tQAp1gMJDaTpc5bcHPDN//+Ot7aezUvfTj+iiKLwWk5cCJFPJuYmOQqHLpGemUyi6k5uYqYB08UwsLlbLIkJ9v62YOdO36DYGQasjtlG0zfg4Ynk3X+HkqeyA5YPAcwBzxPc4G/uSb++jatRv05c9nhJ+2nbWpwbe9og1s4XMbyOBMVBa1C9BdzFRqxhXafjCBxo8MJBNkbtQzXb16ldavX09hp1mzzw/Zv5k/+fn6kaOjo1gKC6/Qzl07GO7blDw9PWkDu9B06NCBhg8fTrl5uSL+5zTfmxCXIO59evx4TmbbkC1rhfTTjz+W2hs/Pz8aMGAAFRUV0dGjR2nP7t00/e23CZaTqPNRdOBAEOdgaUQdOnagzZs3U3x8HPXrP4D69ulzR3kR4eG04e8NlMTCSJMmbuTv509e3t6ifrRVU0I71q5dS+fOnqW69eqRE/cdpEqfebuUg4cO0SFeMjMzycfHm0aNGElm5uaUePkyzeD24/ihg4coPS2dHhz8INlY25bKu0GDBt3Vb1WMxnpR2dcLviYHB3u6WnSV0tLSRLLJN954k+Li4mn177/RDZ7sWVtZ0QsvvljcuINBQbT/wH5+JvnU2MWFOnfuTO3bty8+r48NHwYAac4uS7/+G03tW+huzOijL9qs48KlHPp2dbhYAv1saeB9rjSge2OR1y00PJ28PbTLq71794kYn9L6MG7ck2RpWT4s9YkTJ2jP3r2iiLCwMPrxx2ViOycn945iz547xy50/4p38r77elD37j1oLccaRV+4IK4bxv8T7u5NaNXKX4pdUK2tralNm7a0d+8ecY2/vz/179//jnKrsmPeqD7Db5vT6chMur+Tc1WKkvdKDpTJASkQlckeeVJyoOZywMfdgiY84ktL10ZQdq4KGWnuVP1OuvTB3QDv2wJRaCTiAbx0Vu0htrbV46z0gf4OOqujrIJnzZ5F2ZnZNGvObDpx/DjNnTtXXN66dSvq2KkzIYYAWmL3Jk3o6rVrlJKaQhkZGUIgmj9vPl1mYWDxosWUkJhA015/nQry82nK1KlUWFBAx44do/HjJ1DTpu6E2JwPZr7P7mA3qXfv3pTF+8tYYNq7ZzcjF94QVpFLly7R9999T+d4kmVvZ88Tsj3kwwLOeZ5chX75ZbHQgwaeZeHl7ben07Rpr1P7du3pww8/oB3bd5C9gwO1bt2apk6ZUla3i8/lczvf4fgIO1s7mj79bUrPzKCPPvxQnK9Tp/gy+uWXXwgTw6eeeorQzsXs5gch5OuvFwgBbMjgwSLw3M/fj/r27cuADKk0Z/acu3gHXkadP39Hv1FLty5dWJgKEkLPAJ4cNjA1JR8WTMeNG0c5OdkEgQhC34Snx9OEZ54he3v7YoEI/Fqw8BtasuQ7atCgEX380Yd0iYUifQtE6Ee7ACf6Y2skbTuYWC0CPtqgb4pNuFNIKKv+0/x/ggUKpY4t7SmWAWs6tnIr65YKnwsNPSWUCxhnjdjVbe3aNSx87KN27dppJAyhwqZNm1IgA4rs3LFDjEEIOiBYQrdv3y628RPQvDlF8Pv3w7JlLPi4C4Fo9KhRtGjRIqHMQJ24Zuyjj/L/wzRKTkrmcTad/Px86ZNZH9OTTzzBsUkqF9XiQrWw4WRvTpcu6x8lVAtNl0UYEQekQGRED0s2VXJA2xx4ZoQv7TiUSNB67uBJz56jSTVOCwekLN+mlhTFyHrHT6dpm4V3lIdcR46cVBBCkb4J1pjjx47TY489Jib1PXr0YMuHD53nCTuEGwgJvryPgOr8/AJasnQp5WSrXAhhVQkNDaWWLVqQST0TMRlq4upG0BiD6tevR0OHDaNevXqK/fnz5wnB56GHH6YWfA8IQktcXBxFRkSIfUyopk6bRs9PnEhwKfuMXdAasrUJKFfffvstnQ07I6xAuHjd2nVCiEKbYVkayZOwTz75hDp17Egvv/yyKE+Tnz/YOhYVFUXfffedqBP1PjDgAVqxckXx7RdYIIMFaRlP+hxY4AoICGAr1gGCFn3nrp00aOAg8vDyFNe7OLtQq1atxLaVpWWpvIPQpt5vXOzfrBk/A5XroHI/jru6uVL4uWxsCsK90LCr086dO5kHdahuHRMyNa1Pjz3+OJ1hgbE6yMXJgq4UXacZX5yojuqNpk4oBo5w0mvQ+i2RdO5COgtGzuyW7MBjp2r/Bc5OTjR46FAR9xMbE0MHWHCHW+qrr76qMX/wnnt6eIjr7eztqDkLNaDSXObcWXgqSV5edyqRbGxsaPJrk8V4+G31arZghVH3bt1p6NBhJW/Vyr4ZgwBl5sh8RFphpizknhyQAtE9WSNPSA7UfA4gN9G7L7am594LEtr+T384LTSd5mY166+hUysHIRABajwiJpv8Pct3MzG2p5+dqRJuEhLii5sO9xUIRLk5uUIgsrNTufO0a99OTLYxIVdo0eLFjIzVQFiFduzaRfFsJbrKghIIE6cRjzwituFmBg21q6srPcUWD3UyZcFJnTChBzmw2xqEIRC01SBYlRTKZMEM4AOJiYnk5sb5W1igAOXklB3ErtyvrDdt+o/s2NqCMhTyb+an2rxlItq9e5eoa9asWcoldIVdAuGals8uaneQmlWpLN6V7LdSRh11s5RysJx1c+77f//9R69OfpWFyeepC1ub4DooyTg4cLPOTY49u8kuZdpp76jRo0VBGB+wYF6/dp2ee+lZIcxrp4bKlQJBfzBbUqHgyGL30u+XLKlcQfIuyQED4cCdXy8DaZRshuSA5ID+ONDK30aALKzeFM1ZwQvpKwYFeOcFlVZcf63QbU0dAx3ot40q6NajoWk6E4iaOJtRSlq+CALWt5UIOXbq169PwcEhVMgTfAggsFw4OTuxgNBEMBjWF5CyFju3fmCp+O3XXymE0dUefughdulqUhw7oFyXl5tH3y7+VqDITZ48mYWqiseclVZ327ZtCDFEhw8fphEjRoh2o07EN2lKiPmBO5pisVLuu8nRQyAlhiie46OgYf+S3fbKI+UeXKe0W1mXdy/OV0Yguq9nLzrFeWHgMggrGdyUprGlDbFc+qaklFx29zOh9//XWlit9F1/ddQHl7nvVqusnJrWD4seYhNDzqXTiIH+Iimrpvdqet0ff/5JkVGRwnXygQce0PQ2nV6HGCbEBebn51FGejqZqSkitFlxfsFVsrdWKVe0Wa4sS3JAnQNSIFLnhtyWHKilHHj58WbCXS4xJZ827LhEAzmBacdA+xrDjQ4t7YQbG9CK9h5LoieG3ekCoq2OdmntKIShsMhUatPcSVvFalQOBKCZH3wgYmZmvD2DY1Z8BDDAOzPeIZNyXPigff70008pJTmZ5s37VFiEtm7bele9Pyz7gYBWBVc5uJppi8aMHkMXL14iILMhjimchSPAAgOsQVO6zjFRILjMAVwCwmFpZNrAVFjBAGqAOB51SkpKImfn24Hb6gKR+nWablfCQMSWhevCHalD+w4iluPkyZMiFqw6IJJPnkkmvNP6TGisKW91dd0pTsqqqUAUwIAtg+5zY1AFV04i2oDGTtlDGVmFWm8a3tXffl0lBPlJkyZpvfzKFgghrXHjxgyUEs/Wq6/Ff0hllADl1Z+SlkdtmxkelHl57ZbnjYsDVXNuNa6+ytZKDkgO3IMDDRuY0IxbViHkq/ho8SnKK1BNMO9xi1EdNm9Uj9rdQsoKZi1uWqZu/NExKerX1ZW27ovlie1NvfMoiYUJ+PK/Nvk1Gjt2LC1mNzhvDdytDh48KEAYYI1Q4gqASId3QSHE2CAAu6SrHAAKqkpFRVfYrS+bYxLe4TiEoSyUzRMADhUpF+5/aDviocI4GatCN66rck/dvAWhDWAH0MqVK5VLxBrxUsuXLxfbiiAEwaqyhIkhACawKKTkQgJYhUI4DxcrhRBfBaCLnj170qJvFornB+S/3NyKuQ8q5VV2HX2Jk9qeT6fHh3pWtogaeZ+nmwU9P8af/ljYh1bMvY/54yWEIXS2lb8tnWe+aZMwBhcs+Jrf66s08bnnil3lEKt35OgRbVYlyqprYiLWeWrv6A0W0kHq/wfhPF52bt/JsYHzhVsngFH++ftvcZ02f2AdSkjOo5a30idos2xZluSAOgekQKTODbktOVCLOdC1jQM91NddcACWoi+W355U1gS2KJCtCIDeefiyzrr02rgASs3Ipy17VC56OquoRMGwbnzzzTeUX5DPAAmnCJaFffv2CTcb5VIlbgeCkzoBvQq0l6F54bYG2GjEHl1jq8shFpbCz4WLsjHJV3eVu8QueUhgqhDAGkApqclinZmRKdb5ebcRopJTVOdycm+jeQGhLpoDxqOjoxlA4Awd2L9fAETABa4iNHLUSHH5Qp5AAiQCQei///67OIZ+hIQE08BBA0WAelDQAXr3vfdEvA6EkM8YKAITTpASL3Ts+DHBv784oeW9eIfrS/ZblMHB66BtbGnbtm2bQLND7BZowcKFdPTYUU6MOVcIOukMxw2+Y8J5kwWkfxn6GAQBD3FgiLtSzx8jTurwp/DKNVr191kW7htTB0ZPq+3kbN+Ixg33oV/m9aS1X91PE0f7kbuLCjRDnTfd2ztR9KVMRu3UnsLl33//ESiMQBlUt5j+888/6lWXu43/BVB29m3BOi09TRzLU4udA4ocErfuYfh8vLeIEVq9WjWGAKsPZDqM5y/Y5fTZ554V7+hLDBmPeMEVrGTAmNMmnQpPZQt3HerUSr6H2uSrLOtuDph8wHT3YXlEckByoDZyoCODD2zZn0C5+VcpPDqbmnvbkIereY1ghZN9Q44jihF9KbxynYb2VsXVaLtzFgxI4d7YnJasOcu5SRqRm8vtRIjarku9PFgfIADB3ew4Q25jgg30tC1btlBMbCxDODdgGOzveEKUzTDSKRTLxxAYDVc7FwYUiIqMEgLJaYajDmTkuC5dOjPy2km6yLDUgLMODj4pqouNiaWtW7aKHEZr16whL09PEbezYMFCIYjhooiISLK2sablPy0nTLqQf0fENfFEC23I4wlVAgMo2HDcEpDwEP+Dtp46dYqOHjkq8gPt3rObNnPbmzcPICdG2tKEgJ4FCxHiqDCZC2IhyMfbhydxSdQyMJB8ff1FbBT6feL4CdFfTPKysjLptdemkKeXp6jGkhHlwD8g0sEy1rJlIP3Ck72SvAPoQ8l+e/t4C4HqJgveQQy9jfLhVtS7d28B3HCSBchQ7idgvzHBzUjPYKtbY+aFjUCnO3joIO3atZsiIiMpmIVa8O+ZZ54VeaQ04UFVr7nKecl+WHNKuO598VYnEUNU1TKN6f6ktEL6e+clsrE0pcH8HzH5qRY0bQKPB1YYOdg2KLMrbhxDuHZLLCME1iVfD1VC6DJvKOckrIIfffSxUExc4fd6P4OZAHDjt19/YwTIswx0MqJYeC+rKLzDEPpzWQmRkJAg8oqZmTXieMDFQtDH/wHcZgN5jNTj3F11uLDg4GAeA4cIhtWhw4YK+HwI5w15DCP+DvGJsGJCWAdMf9CBIDGuDx8+Qm7ubuTGKJXaoDWbIqhdc1sa3Es75WmjTbKMGsmB0Dqskbptq6+RfZSdkhyQHKgIB5CZ/sUP+EPIfw32Ng3o9y/uJ2vL0uMxKlKuIVyLfh0PSxMB4pu+7yf6p6t2Qfj6akWYCLLu1Uk3wpd62+NYcJk3fz5N5bxBRVeLqKCgkIWDK5wrJIkTMf4k3OfU42PU71W24coFwAGFSu4rx7W9XvrDD5wsth717defsrOy6cqVQhagCmgrCzVWVtYa5yFS2gXLVmpaKgE2G4IYLFsQCEsSUO34pBBUSp7D+5/MMVUQxnB/ZQgCFALuYelRJ0xAkSwXdOXKlTvaBtcoaNshCNWvV5/7rz9ExMLCaywMhVJGNsOyf9iNmpRiBVHvR03cTkotZETKbOraxlFYJirax29/C6c1m2Pp/Ve7U0NTlftZRcswhOuhBIGrnIW5hcrtk8cDIPn1SeEMX77ol2D6cVYPdkesuoCpz7bLuoyOA6vqGV2TZYMlByQHdMoBZKV/bIgXZ6i/IGJt5iwJpbnT2uu0Tn0V/mBPNyEQwW3u711xnJjWR2dVPzbEk8wamtCcpaHsA59LIxl9qn493XgpA4r33fffF3l7PNliU5IOHjpMyKNTHqkLQ7i25H5591fmPFxykDB21apVqsm/muxozoJE8MkTtPr31ey6d6jM4s1YkJs9e7a4BlpuCEMgBe5b7JT4geXmXgQhqDwB8l73KsfvJcwowhCuKymoKVDlSGarT4q7nEPL14cxml1d+uHj7uTqpHKj1GcbDKEuZ4eGhKWy9MQwb1q/LZb+2XGeRj/oX9liNLqvouNCo0JvXQS3OYXKA2VRrtPmGpbK9VsiqFdHZykMaZOxsqx7ckAKRPdkjTwhOVB7OQDUuYPBKRQdxwlbOXHrf/viCcKEsRPQspBVHoARf22/SOMf9oGBQGf0UD938uAg7Le/PE7zlx6hR4cFkHeTOxNxaqPy6+zuApS1IE7a6Mzubx4eTcmE3XYiGKY3hF1f7rvvPr0IN5Xpi5LraCHHP3Xv2pUc2SKjIM3BLeeNN98QbmXuTVTxbfeqo/6tnEf3Oi+Pl84BgH/sOBBLm/dFU8/2LjTzldYEEBJJleMAEkG/+WwgvfPVSWob4Eh+nraVK0iDuzp17EQ1dVxs3H2BcnKL6K2JgRpwQl4iOVB1DkiXuarzUJYgOVAjOXD2QhY9MyNIwEhbmten1V/0Iie7ymtODYVJSD67jv38QfPf6Ei9O9+GWdZVG3PyrtKnP4TR1gPx1LVtYxrSx5usLO524apK/WFhZzgA+l+OwwkVMTnIJt+2TVtGbRtCzW5lpq9K+bq6F0huazgW6QTHy0RFRrKbkgm5u7tT3z59GABhUJkWHl21qbaUe45dkv7aGilAAKZNaKmzuLrawk/1fr4+7ziFhGfQlAkdydZau2NdvZ6auB1yLpl+XHtaJA0ffgvopyb2U/bJoDiwSgpEBvU8ZGMkBwyLA0vXRjI4gCpJYRf2qf/m3c6G1cBKtCYmPpfGTNkrYqRaN7OlZZ90r0QplbvlSGgqC0anCXEKvTrzpL+bO2vjtR+fBWCByiRNrVyvtHeXEj+jvRJlSaVxIDoumzbvOc+w2hksBLnTq+Oak62VaWmXymOV5EB+4XV69p0gwvq18R3YFVFa3TRhJd7NRStP0MP9m9Ibz7TU5BZ5jeSANjiwSqLMaYONsgzJgRrKgbacfT3oZAqlZBRSfFI+w7HWIwgRxkw2PPE7ez6LLibmERClOgY6UGPH2/7yuuwbUKhGD/QUaFUbdzNa2/5YgvXI2cFcqxMmWFmMkYy13cbCayQMXrMpnDbuukD+nlb06esdaMSAptSI85BJ0i4HEC/Yk+Nf1m2NYUsRJ2pu5sjAIZLPZXEZua+WrA6hji0d6INX2urUnbmsdshztZIDEmWuVj522WnJgQpwIDYhj558cx8Bqroef+RhUWnBGdqNmZCNHtpbEIS+pR9103t3rl27SZv2xtHyv87/v73zgI+jOv742Oq9d9mS3GXLRZZ7wTbENth0QgkkgRQwJMCfGkxvMTWBYEiAAEmIgYSAKQZTTNx7t1xky2pW771X/2eevKd2UmxZJ+3e/d6HZff29nbf+747eWdn5jdsbFZTzCh/mjU5jKKH++FGoN9nw3ovWM2FLXcfyqOdB7KooKSWFkwPUWIiY4YZ+zdslBnLLqih3zy9i2XMB9EdN04iX2/jhx1bgv0RNhrf/+wozY4NohX3TrKYAI0l+o5zWgUBhMxZxTRiECBgYQKiyPbsX1oLcIYHu9GHL89VCmoWvqxFT/9/z+1l71drkdDXHplGs2JbZZAtetFuTi5euI+/OUU74ws4dMmZ4mKCaOqEYAoOsI4aUN0MG7stRKC5+TQlJBfR3sP5dDSpkH+r9nTlRUPpx4sj+s0baqGhGfK0xWX1dOezeyi/pI5uujyaxo7oXwVBPUNjPRj6ngU9vtuSRlfwd/ThW8ezVL2ee4y+WSkBGERWOrEYFgj0OQFRTVq3PUed9xIukvfMXZP6/Br9eUIRjbh5+XaVSxTJSnAiGmHHNWMGsklu0VebMumrjZks1V1LYUFuNDE6UC0hMI4Gcmp0f22RKT6ZVkKHEgrpSGIh1TU00bTxASRJ6SIcYinJd92D0UkH6xtalAT/2k1ZdOGsoXQpC6vY27Xd+Tc2tpCDQ9trnXTbot0orainDz5PoPSccnqART2u4vBNNBAYIAIwiAYIPC4LAoYjIFLVNz24VeUSSeclxnvpPGNLcT/5erwKW5Px3HfLOK6/FCmbumjHksuUAbpuRw4VldRTgK8LjRvpT2NH+tHwod64wdXFLA1sJ8oq6uhESgl7gYpJilg2NDZzjp8vLWZ5+YtmhpCvF4QSBnaGiB+4EJWU11Mhe4cK+He8YXcu5w5mK2PI39dVFT0tq6ynC2cMpUVzIwe6u/1y/Rb2YG7ck0nfs1coyM+Znr8vjkZG/O86af3SOVzEVgnAILLVmce4QaA3BOQm/deP7VRS3KKa9MFLc2hoiHHDugr4JuWauzep/CgRjPjPq/PUP9C9YWPJzySwCMTmPXm0eV8+pWRUqifJI9goGhXpy3VOvCk8xBNhJpacAJ2cu6aukVLSyyjpVJnyBuUUVJOT42CaGhOgvECSxA8jqP8mS/IqRZhF/o4U8lrEZ9S2vGbjR4wgCZdraua4sB6aqE1euXBkD0dYz1si7LGGi9YWl9bSL68ZSTdfMZxzUwfWM289dDGS8yAAg+g84OGjIGCTBP75ZQq9/sEJNfYxUV709+dmG/oftFVrUmnlquNqPHPjguiV5VN0Pa9FpfW0g4vm7jnMC8t4l5Y3kDOrhEUN8eLFm6K48GtkmCffKEPRStcTeRadKymro1RW3krL4iWzjL2zVcrjMJIV4qZP8KeZLIUfO9YX3sKzYGmJQ6Rg9RMrD53Xqf28nZQHKS4mmBZfEMkPZFzP63x6/bAYQt9tPsXhcRVsvAfTPT+P5pBg6xyrXucA/eqRAAyiHvHgTRAAAbME7vz9HtodX6jeu2FJFN3/i7FmjzPCzuaW0yqXKDGtXHX38TsmqLwLI/Rd+piaVUX7jxVT/IkSOni8hAr4SfUgfuAqOUdDQj1pSIgHL54UFujGtYlgJOl1XiWfIjuvkjJzKymDbxozeF1Z1aDy2kbxg4fYaB+liBg31o+L+vZ97Sq9ctFzv+Rvx9V3beJ8v5pedVOK4d6wJJJ+2JGr6r1JjbRxLLgwe0qYCo+V37HWUjPYKM4u51BI4+TZ1NU3sbBHHm3bn0257M2UB07LbhhFo9mgRwMBnRGAQaSzCUF3QMAQBCQM5MYHtqonm9LhF++P4xj4YEP03VwnT56qoFse2U6S2Cyhcx++NJfCg4359FJCeI4llZGENx7l9YnUMlUcUm6uAvnpc0iguzKWxGAKYSMpwMeVBtu1u/MyBwj7+oxAbV0T5RZVUx7fIMpNYl5RFWXlVVF1TaO6RrC/5Ip5Uwwv40Z4UzTLY4sHEE2fBD75Pp1e4mLL59IG8Y/xwV+N45pkEaaPSa7R1v0F9J9vT9Fu9v6KPPeEMQEkv8yE5GIu5lxDv/1ZLI2O0ncdODEST6aV0sGEfDp4rIB7f5qWXBBO110SSSOGIk/INOHY0BsBGER6mxH0BwSMQmD34SK6e8UeauF/AN3YiHj/hTkUEWrcfKL2oYCj+Yn831bM4kKK1qH6lJVXQ+IBS2TDLzm9kpIzKii3sFZ91URZz58FG8RYEuGGAE709vNx4eKxLuTj5TzgyntG+T2076c8GS/kHIlirvtTLHklpTWcY1LD3rsaqmCvjzTJ/YlgdcOREZ40ShZ+aj46ypM83OD9ac9S79uiHnf5bzaYHg79r/6KMbT81hhVENfcsSJD/cWGDPrgy1Q2lKvZnGhtgf6udMPSMTSMQ2L19gBDctuSTpVSQlIJHWaFQzHuo9mYX8pqpEvnhZO7q725oWIfCOiJAAwiPc0G+gICRiPw7qdJ9PbHJ1W35enfP56fo270jDYO6a88ob3z2d0qL0deS02Mx24fL5tW2WrrmjkEp4pj+qvoVHZ165rD73IKa0jekyb1QLy5LpIYRj6eTqpGkpeHI4dsOZGXhxN5uDuSF2/bklyw3OxVsCpYRXUDrxuovLqeyioaqKy8liTsraSslkSRUWt+Pk40hOXTI8Pd1QMDkXiPDHVX+RPtQ6K047E2HoH2apU99V6MoUeXjee/LUO6HCYPLb7ckElfsyx3ERvR7Zs8tJDvkYTDSm6gCKqMiPDhkFgPFrXxIGcWuOnPJt/zTA7rTM+uUIaQ5AVJix7uQxfNCKKFs0JR76o/JwTX6gsCMIj6giLOAQK2SkCMiHuebytwKqERT9810bA4SvnGVqTFRR1Kmhbjb9gB9bLjJSzUkJUvIV21ypOUW8Rr3s5jY0mUtKpq2m745RKiOKgMJTcnDjl0IBf2GErooauzg1q78dqF97vxcc6839F+MOczydpuQNTxRPVLJKobGpqpnsMka9iAqeWn3NW1jVTLixg0EtpWw9s1vK6qOWP8VNWzTLL2zL7VYPTlpHjxrIUGuKibwBAOeQsOcFUGTzgnjYsnCM36CIhnaO3mLPro6zT1MOF/jXAwGzWSn3jp/HDToXKO9btyaQ0bQpIH2F371Y9H0u3Xj6JszlXaE19Eu3iJP1nC3sd69ZEgP/7uBXqc8fCyd1e8vN4u5M7ext7Wn6rj7305ezOL2cAvLOHfvVLOq1bhnZLbJsa8eDgnc06bCHxMjfGDd7O7CcR+IxCAQWSEWUIfQUDPBMqrGumnv9vKN8utIVjLudL4NYuMk/jbme3hk6V0x1O71Q2zHefWvPLQVJoVG9D5MJt+LTdyIi8sT7JLOJ9MDKhirrVSyot8H8orxYvC3hPermKPSnuPSWdwIrnr5GCnBB9k7SALG0zyNF2ejA9ie2Iwb6uF50P2y82l5FZIeFELW+WneZF1Cxs6HMGpvH1S66SZD2hsYsOHjZ5W44cNIDaCxJA31+R67ixY4OnKC3vCvPiG0suTF/aEieHjx3V91FqMIB9nlrh2GhCDzlzfsa9/CMh3/ZPvTtGn69KVZ/Bsrip/R574zUTOpWmt2yZFob9cz3V4uNB1VXVr7lh35xFjZ/XK+WYNa8nlFEn+47ykZlWyx4bz0fJrVBkB7XySf+bp5qgeVMhvS/oivy87Lgp7mn8/8htp4ocDTfzDkd9IJRv/lez9bGpq+5F4eTrS0GDxcLqqEM/o4V4kCqMuzsht0zhjbXgCMIgMP4UYAAjogEBCcjn9+okdSpTAnv+xffupGVwgUt/Jvz1h+2ZzNj35Rqucrng/3nxyukpw7+kzeK97AmK4VPKNnzxxFuNI6rfIUntmXd9uW/Y1NrUoT4zkp8mNmqzFM6PW2jZbNWLAyA2eGEj2vMja9Jr323HMn3ho5KZQFpcza2cn9lQ5te534W0PN3sOA+SbRtzgdT+JNv5OSmYV/WttGn27JVs9LNFwOLKRcfHcUJUrIzmVYnC3b/J9fOauSTSDJdJFplsMoaT01hCz9sd1t73insm0aHZId2+b3S/S/PnFtepBhTykEM+3PKSQvjXwb0vEY+QhgYTEinEkuZIyDvmN+LDx48OGv4TISk2rsEBXckMOkFnO2GlVBGAQWdV0YjAgMIAEPvshg57/6xHVA6mtserFuRzC4TSAPTq/S7/575P0t9VJ6iTe7C346zMzucaP+/mdFJ8GARAwDAHxJIry27+/SaO9XPOrfZO/CVcviqDrLo7g8LTWv3MvvHOUVrPnSGv27IV59v8mKcEMCcWVhwDn0mKjfdXfnXP5DI4FARDoFQEYRL3Chg+BAAiYJfDc20fo8/9mqPdEMvidZ2f2Oobd7AX6eecKHs8XZ8YjNz1vPz3T0Ep6/YwPlwMBQxKo5hy5NRuzWAI7TYWgtR/E0BA3+snSKLpswZAuYWyS43PN3ZuUN1M85c/dG0sLuAiptI9ZTvsPfzvW/lQ9bou3c9WLc5Qx1eOBeBMEQKAvCHxo9xS3vjgTzgECIAACMycF0r6jXDODwzUkCbegpJ7mTQ0yLJg5k4MojZXXZBHltQ2782jO5EBWXnM07JjQcRAAAfMEMnKr6a+fJHG4bDxt2ZfPSoJt+T0iHCAiKw/8cpwKn7XnULjOzZNzztJzWLGRz/Pi/ZNp/tS22mxSV0py6qQ+2Nm0qxZG0JVm1OjO5rM4BgRA4JwJHBnEyahtmXPn/Hl8AARAAAQ6EpBE3589tM1qlNokd+XhVw/QRjaGpEmozMrHpqmCmR1HjlcgAAJGIyD5bdsO5NN/vkunPVxbrf0tkeTUiBDC9UuiuP7P2YXLpmRUcuHdWvXgpDMLudZ9L+yl7QelYGn3zZOFPT5buYCVG1GTqntKeAcE+pQAQub6FCdOBgIgoAjIU9DbnthlUmp7dflUmjnJuEptYhQ98upB9hDlqvFJIdqXHoyjaeP9MeMgAAIGJCBqcV+uzyDJfcxjA6Z9C2YJ9esWR6p6QWKc9GUTw0tC59obXp3P/yB7oa67JLLzbrwGARCwHAEYRJZjizODgG0TWMtKbU+dUWpzZxnj91bMOuunrHokJ093f//WYfpqY6bqnuQIPHzbeLp8QVtdET32G30CARBoIxB/opQ++T5dPdwQtbX2bUqMP13LIgkS6iYKbH3Zmlkd8Y9/T1CS3T2dd/gQD/rwD3OVgmJPx+E9EACBPiUAg6hPceJkIAACHQj8+aNE+sfnyWpfKMu3vv/8bMPn36xcdYJWrUkxjfPnVw6nO28cowoVmnZiAwRAQDcEpJDwN1xEVQRfkjmkrX2ThzVL5oXRtYsjKJILjVqiieT1Q3/cbyq+KupzN14axWF6p7ooz7355AyawkVO0UAABPqVAAyifsWNi4GADRL43R/2m/JvJo72pTefmm5o5TmZwtXrMujlvx1ValLyWsIBV9wTi0rtAgMNBHRC4HBiKX3GRtD6nbldDI8REZ7KCLpkbphFC4yePFVBD7y0n3ILaxQVHy7m+8J9k2nyWF/auCePHvrDAVP43IXTQ+jFBybrhB66AQI2RQAGkU1NNwYLAgNAoL6hhX79+A46wdXZpV3MNyDvK72cAAAfn0lEQVTP3j1pAHrSt5fceaiQHv3TQVVwVM4cFuRKz/ONTvQwr769EM4GAiBw1gSkALCE64o3KDWzozdIio9eOCOYfsz1gyaOsXzh6G+4iKvUZtPqD42O8qI//C6Ogv1dTONZ9WUqrfzgOEt429Enf5pHIZy/hAYCINDvBGAQ9TtyXBAEbJBAIctv3/xwm/KchJndddMYw5PIyquh+1/aZ7rxcuCK73f/NJpuWBJp+LFhACBgFAKilSuFU6V20Cb2utQ3dCyAKqFwVy8cyqFx4eTVxyIJ5hiJCMsr7ydwHaNTprcXzgqlJ387sUvtIjng928doQAfJ1p2/SjT8dgAARDoVwIwiPoVNy4GAjZMIJFDR5Y9sZOqa5sUhd/9OkaFrBgdidQnkgKu32/LNg1ldmwgPf6bCaYK9qY3sAECINBnBHIKapXIydecH5RX2FEpTjwuF80IoavYEJrUD94gbVBFpfW0/I8HKD6xRO2y43pFv+Ucw59dPkw7pMtaDKgmXpwc+1jJocuVsAMEQKAbAjCIugGD3SAAAhYgsJvrfNzz3F7+x7+FVZwG0UsPxBm6cGt7RBKiIypS2tNpKd766LIJNH+acQvTth8ftkFADwQkBHf9rlxasyGTDiSUmPJvtL5JbtAVFw6hpSyU4MGFUvuzHTxeQg+/coCkFps0yRd6/t5YihsHkYT+nAdcCwR6QQAGUS+g4SMgAALnQeDrTVn09J/j1Rmk8OFfnphB40d5n8cZ9fPRlMwqemLlQZJEaq0t5TCd+24ZS31dz0Q7P9YgYO0EJCRu/7FikpycDWwMaV5mbdzy21o8J4wuYwn8gcrhW7Umlf7CqprysEdazEgfJZAQ6OusdRNrEAAB/RKAQaTfuUHPQMB6Cby3Opne+neiGqCXhyP99ZmZhq5R1H6mmppO05sfJ9IHfIPUwrVHpPl5O9FDt8bQgmnB7Q/FNgiAQA8EUrOqlFz2d1tzKL+4Y0iceJincmHkyxcMUV5YR87fG4gmIg5PvRFPW/blmy5/NYs2PPiLcWRvP8i0DxsgAAK6JgCDSNfTg86BgBUTEPUlqRIvLYCfor777CwKDbQehSUJn3nmL4cpK6/aNIuS03A/3ygF+DqZ9mEDBECgjYCEm32/LYe+ZW/QibRWZcq2d4kiQt1pyQVhtHR+OAX5Daz3JSG5nJZziJwmqS0e7+W3jlfheu37jG0QAAHdE4BBpPspQgdBwEoJtHBkySOvHlD5ADLE8GA3NopmWpUQgeQ7vMmesH+tTTN5i9xc7JWa1PWXRHEelZVOLoYFAudAoLyqUYXCrduey3lBxabfinYKycdbxCptUkB13Ah9hNd+zApyr606To2NrSFyomT3IudEDgu3THFXjQXWIAACFiEAg8giWHFSEACBsyIg4WX3vLCXdscXquNHckK0hM+5u9qf1eeNctDRpDJa8dZhSs5oq4siY33wV+MoNtrXKMNAP0GgzwjUsDqjSGSv255DIrbS1NRqWGgXEJW4uXGBSip7Fhc+FrU2PTQJkfv9m0dow+5cU3cuYY/VI7eNJ/EQoYEACBiSAAwiQ04bOg0CVkRAihb+5pnddORkqRrVhNE+9MZj0y1aPX4g8DVzPtFHX6fRO58kUW1dq/S49EPC6O7+WbRVhQsOBF9cU/8ExAjatr+A1u/Mpe0HC0yKjFrPxeiZwopsi2aHqt+Fm84ejMSfKKXHXjtIeUWt+UxitN3/i7F01Y+GakPAGgRAwJgEYBAZc97QaxCwLgIVHDKz7MmdJg+KeE1WPjrNKp+45hfX0Sssz93+CbOjgx3dsDSSbuGCtf0tFWxd3ySMRm8EqmqaaMvefPV93xVf1MUIGjRoEE3kOkGaEeTr5ai3IbC0N9F7q5Po3U+TSGoGSZMQuRX3xNKoSE/d9RcdAgEQOGcCMIjOGRk+AAIgYBECkkx9GxduzchtFSGYEuNPf3p4qtUWKxQZYalblJTeJtEtxtDNbBTdsCTKasdtkS8PTqorAuWVjSocbsPuPNpzpGs4nHQ2eriXMoIWcm7QQIsj9ASvoKSOHn/tkMpt0o67nOscPfjLcVb5wEYbI9YgYGMEYBDZ2IRjuCCgawJy87GMjaKs/BrVzxkTA+iV5VPIwd461QfkyfMX6zPo7Y9Pmoo5ysBFdU+8RVdyKM5AyQnr+ouCzumOQGZejfIEbd6bR4c5/FXzpGgdFU/Q2BFeKhTuwhnBFBboqr2l27WE9j3/zlEqr2xQfXR3daCHb4tRhpxuO42OgQAI9IYADKLeUMNnQAAELEcgv6iObmWjSJOynT05kF5+MM5qjSIhKXlUkl8kxR2rahpNcP19nJXHSHIUnByt0yg0DRYbhiIgxryIhYgBtGVfAaVltQmGaAORWkGSEyh5cgumB+vaE6T1WdZS+PXl947R2s1Zpt3jRnqrEDkjGHKmTmMDBEDgbAnAIDpbUjgOBECg/wjkFNSqnCIteXk6e4r++LspVm8UiPzw3z9LptXr0pWRpBH39XKi6y+JpB8vjiBPdwdtN9Yg0K8EqjkfSBThRBBBxBFKyuu7XN+BC6TGjfXjYqnBqmCqFCU2UpP6YU++Hm96ICNGnYSxLrtulG6U7ozEE30FAYMQgEFkkIlCN0HA5ghkcQjO7U/tMlWoj2P1qVeXT7U69TlzE1tS3sDeohT69PuOhpGLsz1dwfkLIsCAJ9XmyGFfXxNISq+kHWwA7ThYSIcTS6mpuaM8tlxPjHTx5F4wJYhmskS21NoyWpMSAG99nKi8tC2sCCktLMiVnr5zkhJ9MNp40F8QAIFzIgCD6Jxw4WAQAIF+JSCeojue3kU5Ba05RRNH+9Jrj0415A1Xb8CVVjSoULpP2WNUxfVPtCZPrefwDah4jaZN8Nd2Yw0C501ABBH2Hi0iUYQTQ6iQ8/rMtXA2FuayATRvahBNYlVIO/5OGrUlnqqgp96Ip+R2AieXLRhCD7BwgqszagsZdV7RbxA4BwIwiM4BFg4FARAYAAIiUy1GUeYZ9TmpVC+S3LYUOib1Wz7/IYP+tTbN5DHTpkLkfy/nm7cl88LIaOFJ2hiwHjgC9Q0tFH+ihHazGtweNoLEODgtCUKdmkjDx471pdmxATQrNpAiQt06HWG8lyL88N7qZBWmqnm+vD0c6ZHbx9MCDvlDAwEQsBkCMIhsZqoxUBAwMAGR5JbwuVPZVWoUw4Z40OuPTaNAVmOzpSY3cOt35dLH355S4Uvtxy5FLUWV77L54XQBP7W3VmW+9mPG9rkTkO9QQko5iez7XjaCDnGx0YbGZrMnCglw5VC4VgNoaoyfVclMSyjgU28copNsAGpNvF0P3zYeDxY0IFiDgO0QgEFkO3ONkYKAsQlI+Nidz+423cAEB7jQG49Nt4on1b2ZmROp5cow+i9LA4tKXfsm3rPFc8LoUjaOxnK9FzTbJdDY1KLU4A4mlKhaOvGcB9T5+6LRcec6WCKIMJ3DMCUU0xq8QNrYtLXkCv3982T6G4uXNDEbafJ7uf8X42jJBWHaYViDAAjYFgEYRLY13xgtCBibgKhc3fvCXhIlKGneno4qfC56mO3e9AuTb7dmcz2jTEpMK+8ywUND3Gg+Sx7PnxpM40d5d3kfO6yLQCXnmh05WaZqAR3i34lIY9c3dDSYtRHbc32vCaN8lPEjRtDY4d402IrV3YXLircOU0pmm0T4nLggenTZePL3MZYanjaHWIMACPQJARhEfYIRJwEBEOg3Ag2NLfTIqwe4/km+uqYrK1q99ECceqrdb53Q6YUS0yromy1ZtG57LhWVdk2Gl7pGEk43n5epMf5kb2/cRHidTkG/dystq0qFTx7hYqji/UnPqTabAyQdE0lsMXriOBdoMqs2TuQaQc5O1i8aIDl4f/7ohFJt1BTkPNgbJl6hpZx7hwYCIGDzBGAQ2fxXAABAwIAEmlkWd8VbR+irjZmq9/Z2g2n5rTF0xUVDDDiavu9yC0cC7eH8kO+2ZSvDsb1CnXY1d1eRSg5gmeRAmsL5IUF+tpWPpXEw0jq3sJaOc/5PQkqZWh/nsEnxCHXXnBztKIYLiopkfSwrwY1nb5CtFfjddqCAXnznKGk1zYSVFIp98FfjkCvU3RcH+0HA9gjAILK9OceIQcB6CLz+4Qn65xcppgHdctUI+u2No02vsUGcJ3FaGUeSa7RlXz6VVzaYxTKEQ+um8I3zFPYciYHk6+Vo9jjstDwBEXnLzq+hkywDnchGj4ggSM5YWTdzp/UokI1aMXrE8yPrMVFeNusFLGC58Ff+nqBESNrzeejXMapekrYPaxAAARBgAjCI8DUAARAwNgGRo37xvaMk6lnSFs0OpafunAiVNTPTKp6jwxxatZUNIzGONNU+M4eSKPmJYSQ5JtEszCC5SGh9T0A8PKJ4JjVwkjMq1bbkuNTWNfV4MfH+jIzwVHlhYvzIPAX5w8sn3uOPvk6jdz5JMjEcNGgQXb1wKN310zE2U8Osxy8P3gQBEOhMAAZRZyJ4DQIgYDwCOw8V0sOvHKDq2tabyAn8hFzyilCXp+e5lDCivUeKad9RlmDmYpzdFeGUs0iI3RgWrxABCzGQRL0ujItzop0dAWEt+T1ihLYurdvmcr06n1Fyf0YO9TRxl3kYPtTD0MVQO4+xL16L2IqEx7UXTRjBRuPDHE4rfxPQQAAEQKAbAjCIugGD3SAAAgYjIE/Z731+r6lwqYQPvfzgFMhOn8M8yg27GEf72Djax3VqyljqvKcmcsUj+EZd5JkjQt0pIqx1HRroYnM367WcuJ/DOT45BTWUy0t2Qeu2vM7gosLdSV135iuFQeUmfmSEh1qPjvSk4eytgwBGZ1Jtr4tK6+n1D04oQRFtr4itLLtuFF2/JNLmvosaA6xBAATOmgAMorNGhQNBAAR0T0BujB54aR8dSy5TfZWwokdYUhf1RXo3deLJSEjmHBbOXznOifxSxPJsbuxFzjmcvUdiJEmonRinUkQ3gBdZi8SxFJI1SiuvaiQpDqwW/o7JuqisjvKL6pQBlM1Gz/8yHjuPVW7YxZCMCmPDh709I9gAkhA4yD93JtX9a1Gc/PCrVK4rlGIKj5OjfzQzhO67ZRx/3yCl3T09vAMCINCOAAyidjCwCQIgYAUE5CbpubeP0NrNWabR3HTZMJU/YDfYODfhps7raENykFKzKlWSvxhIJ1Ir6FROFZlTseup25LTIaINmoHkw/WkpCiou6s9iRyyhOd5uNmrteyT16KOJmqC9mxIicElc9mT10RyyqQoaROvpQBnI4tLNDW3UC0Xsa2qaeSlifvdRNWyzaGW6jVvS12nivYGUHm9qYBnT2My954YfYG+LmwUsnEY5k6RZxYxhMQwROs9gQ278ui1VceVQaqdRfg+8MtxkODXgGANAiBwtgRgEJ0tKRwHAiBgLAKSWL3yg+MmsQWRHX7u3sl4Am+BaRSPiYTbpUt+jKzZSJLXuYU1Jv4WuKw6pRgdYijJWmrMiKqeGD790cSwE49OaIArhQa5UGggrwPOrHlbRA5ghPftTIjU+Gv/PE77OaRTa2JM33btSLruYg6P4+8BGgiAAAicIwEYROcIDIeDAAgYiMDuw0WqiKs88Zfm6+VEv78nlouS+hloFMbtqhgnIiYgEsgi2GBuLWGO4sHRU3N0sCNfb0clyuHv7aTWft7OZ9baaycVkuXA3io0yxOQsMQ3/5Woig6fFl1yboPZS3gl1x6744bR5M1eRjQQAAEQ6CUBGES9BIePgQAIGISAFLMUBTotr0huom7jZOtfXTPCICOw/m6KOqCEr0koW6WEssma5ai1fRLOJqGQ4vmRUDgVBsfbmjdI1iK3LHOrQurYSyDKbO1D7BzsWz1Jzk52KjTPjcPwWsPxWkPy3Dinp314nvVTN8YIyysb6d1Pk2j1D+nUyN8BrUm9rPtuGavEJ7R9WIMACIBALwnAIOolOHwMBEDAQATkhvnV9xPoP9+dMvV62nh/rlc0CYnXJiLYAAH9EKhh1b5/rU2jD9akKgNZ65kIUNx50xiaPTlQ24U1CIAACJwvARhE50sQnwcBEDAOgR925NKKtw6b6hV5scTxo7ePpwXTgo0zCPQUBKyYgKgYfvztKVrFhlB5ZZvse5CfCy27fhRdOj+cOHULDQRAAAT6kgAMor6kiXOBAAjon4DUhHn8tUOslNYqzS09vuKiofTAL8aShFOhgQAI9D+B+oYWWr0unf7xRQqVsrKf1kR18OYrh9MNS6KU0qC2H2sQAAEQ6EMCMIj6ECZOBQIgYBACkofy1scn6Z9fpihlMun2EK6X8/gdE0jU6NBAAAT6h4CExq3+Pp0+4vC4otI600VFav0nS6PopkujyI2l19FAAARAwIIEYBBZEC5ODQIgoHMCBxJK6MnXDyklNOmqyChfe3EE3cU5CvAW6Xzy0D1DE5Bit/9mI0jy+jQVSBmQFKy9/pJI+inXDvN0dzD0GNF5EAABwxCAQWSYqUJHQQAELEJAFMxefu8YfbOlrZBrWJArPXb7BJoCeW6LMMdJbZdAflEde4NS6fP/ZlJtXZMJhKj8XbM4gn7GhhAktE1YsAECINA/BGAQ9Q9nXAUEQEDvBLYdKKDn3j6i6uVIX8VbdNmCcLr7p9Hk5YEn1XqfP/RP3wSOnCyjj75OpY178joU6/VmYZPrl0Qqr5DkC6GBAAiAwAAQgEE0ANBxSRAAAZ0SEG+RyHOv2ZBp6qEo0YnMrxSARAMBEDh7AlIbav3OPCWffTSptMMHA/2cVVjcVT8aivDUDmTwAgRAYAAIwCAaAOi4JAiAgM4J7IovohffOUJZ+TWmno4f5UPLb42hUZGepn3YAAEQ6EpAwuI+X59BX67P7CCUIEeOifJSYgmLZoeSPRfLRQMBEAABHRCAQaSDSUAXQAAEdEigobGF/vF5Cr3PMsANjc2qh4MHSxjdELrjhlHk5+2kw16jSyAwMARaWoi2Hyygz37IoB28bmHvkNbkdzM3LohuZMW4yWOh4qhxwRoEQEA3BGAQ6WYq0BEQAAFdEsjMq6GX3j1Ku+ILTf0TJayfXzFchfw4OQ427ccGCNgagdSsKvpmcxZ9uzWbCorbZLOFg4SbLp0XTtexcqMIlaCBAAiAgE4JwCDS6cSgWyAAAjojIMngK1edoKy8alPPgvxc6LbrR/FNXxjZ8VNwNBCwBQKlFQ30/bYcWsuG0InU8i5DnjjGl65ZNJQumhFCjg54YNAFEHaAAAjojQAMIr3NCPoDAiCgXwJNTadV3ZT3Vid1qJ0iRV1vvXYkXTwnjNXp9Nt/9AwEekugpLyBNvFDgfU7c2l/QnEHpTg5p0hly/f/qoVDaVi4e28vg8+BAAiAwEAQgEE0ENRxTRAAAWMTkEKS73ySRKt/SKdGzjXSWlS4hzKMfjQzBIaRBgVrwxIQcYRNe/Now648OnSipENekAzK0cGO5k4JpCUXhNPs2ACys8PTAMNONjoOArZNAAaRbc8/Rg8CIHA+BPKKaundT5Np7aYsampuM4wiw9zp5iuH0yVzOZQON4nngxif7WcCiWkVtGVfPm3Zm08n0rqGw8n3OTbajxbNDqGFs0LJ3dW+n3uIy4EACIBAnxOAQdTnSHFCEAABmyOQzfLc73yaRN9xYnlzc5u6VnCAC/388uF0+YVDCOILNve1MMSAJQx037FikxGUX1zbpd/29oNpaowfXcg5QfOnBqnwuC4HYQcIgAAIGJcADCLjzh16DgIgoDcC6TnVSqZbFLeamto8Rr5eTqr2yo8XR+CJut4mzQb7IyGfIpEtXqCdhwqpurapCwVRUpw5MYAumBKkwuI83By6HIMdIAACIGAlBGAQWclEYhggAAI6IiC5F/9ck0JrNmRSXX1rDSPpnhvfZF5x0RBlHAX7u+iox+iKtRMQL6aEwm1mI0jygdp7MrWxB/o5q3pBF7AXSDxCDuwZQgMBEAABGyAAg8gGJhlDBAEQGCACIk/80ddp9On36VRV02jqhb3dYLpoZrCqYzRmmJdpPzZAoK8InObIzaNJZa2hcGwIpWZWmj31qEhP5QUSIyga30WzjLATBEDA6gnAILL6KcYAQQAEBpxAdU0TffZDBv3727QuxSvjxvnRjZdGqSfzkOwe8KkydAfqG1po9+FCFQq3dX8BlZTXdxmP5APFjfU1GUHwVHZBhB0gAAK2RwAGke3NOUYMAiAwUAQkTEkKWn7wVSolpVd06EZooCtde3EEXcECDMjX6IAGL3ogUFxWT2L8SD7QniNFVN/QFqKpfUy+T7NiA2kee4FmTQogNyjDaWiwBgEQAAEhAIMI3wMQAAEQGAgCew4X0ao1qbQrvrDD5Z2d7LiuSxhdd0kUDR+CApcd4OCFIpCUXqlC4bZyKFxCSjmdlvi4Ti0syLXVC8SiCLHsEbIbjBpBnRDhJQiAAAhoBGAQaSSwBgEQAIGBIJCWVUX/+e4Urd2cTbV1HdW+Jo7xpasXDiUp9OrogAT3gZgfPVyzkRUL9x0tVp4gMYKk/lXnNojjLceN8CbJBRJlOBjTnQnhNQiAAAh0SwAGUbdo8AYIgAAI9CMByTNaszGTjaN0ysqr7nBlT3cHWjovnK760VCKCofXqAMcK30hoXDbDxQoI2g3exM7G8sybPEmTp/grwygOXFB5OvlaKU0MCwQAAEQsCgBGEQWxYuTgwAIgMA5EpDoJ6kRs3pdBu3gdUtLx3CoiaN96dIF4bSQvUbIBTlHuDo/PCG5XM391v35dCK1wmwoXICvSGMHcm2gIJo23h+eQ53PKboHAiBgCAIwiAwxTegkCICATRLIL66jL9dn0pcbMrqo04l3YN7UYLp0frjyEkChznhfkXIukLqbc8h2HOSFC6SWmlGFk1C4McM8lQqhGEKQaTfePKPHIAACuicAg0j3U4QOggAI2DyBlhaibRw+9cV/M2gn3zg3NfOOdk28BgtnhdLi2aE0dgTqGrVDo6vNZvb2HT1ZpoQ0drIhdJwFETp7AKXDLs72yvsjBtAcXvy8nXQ1DnQGBEAABKyMAAwiK5tQDAcEQMDKCZSUN9C3W7Lp681ZlNxJuluGHs7qYgvZMFrEy4ihHlZOQ//DS8moZDnsYtrLktgHEoqpurajcIY2gqEhbjR7cqBaJrMqnAPXC0IDARAAARDoFwIwiPoFMy4CAiAAAhYgkHiqgr7emEU/7MghScLv3OQme/60YJo3LYjGj/QhhNV1JtT3r9NzqunAMTaAWBVuP6/NFUeVq4oXSIrySl2gWbEBJDLZaCAAAiAAAgNCAAbRgGDHRUEABECgDwlISN1+9j6s255DG3fnUXllQ5ezS9iVJOKLJPPUGD+lUNblIOw4ZwIpmVV0kNkfPF7CHqASKiqtM3uOwVwHKHqYF01jVThRhhNxDHt71AYyCws7QQAEQKB/CcAg6l/euBoIgAAIWJZAc/NpEplm8Rpt3V9g1jhy4JpGk7jG0cyJATSDPRQjIxBadzazIvWAEtMq6HBiqTJ+Dp0oMctXO1dUuIcyPqeyGtwUNkLdXe21t7AGARAAARDQDwEYRPqZC/QEBEAABPqWgHiO5KZ905482rw3n3IKasxeQLxHk8f68eJLsbxGUc9WTFIA9QiLIBxNKmUjqIwST5VTY2NHQYv2QCPD3Gkyh8HFMcO4cb4QQ2gPB9sgAAIgoF8CMIj0OzfoGQiAAAj0LYFkTvCXYp+iVBfPXo4m9niYa94ejjQp2pdiRnqzah0vHOpl7TWPsvNruPZPOZ1gD9BxXifyUmYm9FDjJSFwIyM8KZY5xYohyWsfTxRG1fhgDQIgAAIGIgCDyECTha6CAAiAQJ8RqK1rpn2c9C/G0T4WAEjLquz23FILJyLUjcYO96YRHF43nNXrRMEukOW+jdYKS+rVWFMyK0nyf0QFTsbenfqbNj53Nwcax8bheDYSx4/2oQmjfBACp8HBGgRAAASMTQAGkbHnD70HARAAgb4hUFrRYBIGEJU0MRjM1chpfzUPNhKGcZ5MeIgrDQl2U5Lf4cGuFBLgSr5eA+MtqappIiloW1BcSxLylpVXQ5l51bzU8HY11dU3tx+C2W1HBztl8ElBVDGCYtj4ieJwOKj0mcWFnSAAAiBgdAIwiIw+g+g/CIAACFiCQA17kCSE7FhSGR1L4SW5jPIKa8/6UvZ2g8nfx4n8eAnwcVYGknhZPHkRQ0oWZyc7cmSBB22xP1N75zQXMOX/6PTp0yQiEWLE1MrCfaqtb6Ka2mYVziZqehLWVl7ZqOStxRCqrTNf56e7jkt4oHi8hg/xoNEcGihKcMPC3cnODgpw3THDfhAAARCwMgIwiKxsQjEcEAABELAYgfKqRhViJt4jyUeScLP07Koec20s1plzOLEYN0F+LuzFcqVw9mRFscEzjA0gMYIGypN1Dt3HoSAAAiAAApYlAIPIsnxxdhAAARCwfgISpibhaFksTCAhahKqJvV4JF+nkNdSnFQ8PZZoYux4ujsqQYMAzmkK8nNWuU2BvA7yd6GwQFe1oOaPJejjnCAAAiBgFQRgEFnFNGIQIAACIKBjAhz5RpXVjW0LG1CV7G2qa2hWMtYNrHbX0NBCUudH8nQGy//OrMXgkdA6F1mc7cjZ0Y5cXezJi0PdvD1aQ+90PHR0DQRAAARAQP8EPkSVOP1PEnoIAiAAAoYmIPaNpzvnD/GCBgIgAAIgAAJ6IzBYbx1Cf0AABEAABEAABEAABEAABECgvwjAIOov0rgOCIAACIAACIAACIAACICA7gjAINLdlKBDIAACIAACIAACIAACIAAC/UXg/wEoKCjrt0H/sQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "npzd_model.visualize()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup and run the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "in_ds = xs.create_setup(\n",
    "    model=npzd_model,\n",
    "    clocks={'clock': [0, 1]},  # not used but required\n",
    "    input_vars={\n",
    "        'time__days': ('time', np.arange(0, 366)),\n",
    "        'n__c0': 15.0,\n",
    "        'p__c0': 0.01,\n",
    "        'z__c0': 0.01,\n",
    "        'd__c0': 0.01,\n",
    "    },\n",
    "    output_vars={\n",
    "        'slab__c': None,  # None is meaningless here but required\n",
    "    }\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre>&lt;xarray.Dataset&gt;\n",
       "Dimensions:            (clock: 2, component: 4, time: 366)\n",
       "Coordinates:\n",
       "  * clock              (clock) int64 0 1\n",
       "  * time               (time) int64 0 1 2 3 4 5 6 ... 360 361 362 363 364 365\n",
       "  * component          (component) &lt;U1 &#x27;N&#x27; &#x27;P&#x27; &#x27;Z&#x27; &#x27;D&#x27;\n",
       "Data variables:\n",
       "    light__kw          float64 0.05\n",
       "    mixing__kappa      float64 0.1\n",
       "    d__label           &lt;U1 &#x27;D&#x27;\n",
       "    light__alpha       float64 0.15\n",
       "    grazing__pphyto    float64 0.67\n",
       "    z__m               float64 0.1\n",
       "    n__label           &lt;U1 &#x27;N&#x27;\n",
       "    z__kn              float64 0.75\n",
       "    z__k               float64 0.6\n",
       "    z__label           &lt;U1 &#x27;Z&#x27;\n",
       "    d__m               float64 0.6\n",
       "    z__m2              float64 0.34\n",
       "    z__beta            float64 0.69\n",
       "    p__label           &lt;U1 &#x27;P&#x27;\n",
       "    n__k               float64 0.85\n",
       "    grazing__max_rate  float64 0.8\n",
       "    n__cb              float64 15.0\n",
       "    light__kp          float64 0.03\n",
       "    p__m               float64 0.2\n",
       "    grazing__pdet      float64 0.33\n",
       "    light__v_max       float64 1.1\n",
       "    time__days         (time) int64 0 1 2 3 4 5 6 ... 360 361 362 363 364 365\n",
       "    n__c0              float64 15.0\n",
       "    p__c0              float64 0.01\n",
       "    z__c0              float64 0.01\n",
       "    d__c0              float64 0.01\n",
       "    slab__c            (time, component) float64 15.0 0.01 ... 0.002407 0.004502</pre>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:            (clock: 2, component: 4, time: 366)\n",
       "Coordinates:\n",
       "  * clock              (clock) int64 0 1\n",
       "  * time               (time) int64 0 1 2 3 4 5 6 ... 360 361 362 363 364 365\n",
       "  * component          (component) <U1 'N' 'P' 'Z' 'D'\n",
       "Data variables:\n",
       "    light__kw          float64 0.05\n",
       "    mixing__kappa      float64 0.1\n",
       "    d__label           <U1 'D'\n",
       "    light__alpha       float64 0.15\n",
       "    grazing__pphyto    float64 0.67\n",
       "    z__m               float64 0.1\n",
       "    n__label           <U1 'N'\n",
       "    z__kn              float64 0.75\n",
       "    z__k               float64 0.6\n",
       "    z__label           <U1 'Z'\n",
       "    d__m               float64 0.6\n",
       "    z__m2              float64 0.34\n",
       "    z__beta            float64 0.69\n",
       "    p__label           <U1 'P'\n",
       "    n__k               float64 0.85\n",
       "    grazing__max_rate  float64 0.8\n",
       "    n__cb              float64 15.0\n",
       "    light__kp          float64 0.03\n",
       "    p__m               float64 0.2\n",
       "    grazing__pdet      float64 0.33\n",
       "    light__v_max       float64 1.1\n",
       "    time__days         (time) int64 0 1 2 3 4 5 6 ... 360 361 362 363 364 365\n",
       "    n__c0              float64 15.0\n",
       "    p__c0              float64 0.01\n",
       "    z__c0              float64 0.01\n",
       "    d__c0              float64 0.01\n",
       "    slab__c            (time, component) float64 15.0 0.01 ... 0.002407 0.004502"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "out_ds = in_ds.xsimlab.run(model=npzd_model)\n",
    "\n",
    "out_ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "out_ds.slab__c.plot.line(x='time');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:xsimlab_dev]",
   "language": "python",
   "name": "conda-env-xsimlab_dev-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}