{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Programming your solver\n",
    "\n",
    "In this notebook, we will look at some of the more advanced capabilities Firedrake has for configuring and developing preconditioners. In particular, we will show support for geometric multigrid, as well as user-defined preconditioners.\n",
    "\n",
    "As our prototypical example, we will consider the Stokes equations. Find $(u, p) \\in V \\times Q \\subset (H^1)^d \\times L^2$ such that\n",
    "\n",
    "$$\n",
    "\\begin{align}\n",
    "  \\nu\\int_\\Omega \\nabla u : \\nabla v\\,\\mathrm{d}x - \\int_\\Omega p\n",
    "  \\nabla \\cdot v\\,\\mathrm{d}x\n",
    "  &= \\int_\\Omega f \\cdot v\\,\\mathrm{d}x, \\\\\n",
    "  -\\int_\\Omega \\nabla \\cdot u q \\,\\mathrm{d}x&= 0.\n",
    "\\end{align}\n",
    "$$\n",
    "for all $(v, q) \\in V \\times Q$. Where $\\nu$ is the viscosity.\n",
    "\n",
    "We will use the inf-sup stable Taylor-Hood element pair of piecewise quadratic velocities and piecewise linear pressures."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Code in this cell makes plots appear an appropriate size and resolution in the browser window\n",
    "%matplotlib widget\n",
    "%config InlineBackend.figure_format = 'svg'\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.rcParams['figure.figsize'] = (11, 6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from firedrake import *\n",
    "mesh = UnitSquareMesh(8, 8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now build a hierarchy of regularly refined meshes with this as the coarsest mesh, and grab the finest one to define the problem."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "meshes = MeshHierarchy(mesh, refinement_levels=3)\n",
    "# Grab the finest mesh\n",
    "mesh = meshes[-1]\n",
    "\n",
    "V = VectorFunctionSpace(mesh, \"Lagrange\", 2)\n",
    "Q = FunctionSpace(mesh, \"Lagrange\", 1)\n",
    "W = V*Q"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We set up the problem in residual form (using `TestFunction`s but no `TrialFunction`s)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "v, q = TestFunctions(W)\n",
    "w = Function(W)\n",
    "u, p = split(w)\n",
    "\n",
    "nu = Constant(0.0001)\n",
    "F = nu*inner(grad(u), grad(v))*dx - p*div(v)*dx - div(u)*q*dx"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now need to augment the problem with a forcing term and boundary conditions.  We will solve a regularised lid-driven cavity problem, and thus choose $f = 0$ and the boundary conditions:\n",
    "$$\n",
    "\\begin{align}\n",
    "u &= \\begin{pmatrix}\\frac{x^2 (2 - x)^2 y^2}{4} \\\\ 0 \\end{pmatrix} & \\text{ on $\\Gamma_1 = \\{y = 1\\}$},\\\\\n",
    "u &= 0 & \\text{ otherwise.}\\\\\n",
    "\\end{align}\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "x, y = SpatialCoordinate(mesh)\n",
    "bc_value = as_vector([0.25 * x**2 * (2-x)**2 *y**2, 0])\n",
    "\n",
    "bcs = [DirichletBC(W.sub(0), bc_value, 4),\n",
    "       DirichletBC(W.sub(0), zero(mesh.geometric_dimension), (1, 2, 3))]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This problem has a null space of constant pressures, so we'll need to inform the solver about that too."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "nullspace = MixedVectorSpaceBasis(W, [W.sub(0), VectorSpaceBasis(constant=True, comm=mesh.comm)])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since we're going to look at a bunch of different solver options, let's have a function that builds a solver with the provided options."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_solver(solver_parameters, *, pmat=None, appctx=None):\n",
    "    p = {}\n",
    "    if solver_parameters is not None:\n",
    "        p.update(solver_parameters)\n",
    "    # Default to linear SNES\n",
    "    p.setdefault(\"snes_type\", \"ksponly\")\n",
    "    p.setdefault(\"ksp_rtol\", 1e-7)\n",
    "    problem = NonlinearVariationalProblem(F, w, bcs=bcs, Jp=pmat)\n",
    "    solver = NonlinearVariationalSolver(problem, nullspace=nullspace, options_prefix=\"\", \n",
    "                                        solver_parameters=p, appctx=appctx)\n",
    "    return solver"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, let's go ahead and solve the problem using a direct solver. The solver is configured with a dictionary of PETSc options. Here we select MUMPS to perform the sparse LU factorisation. (Note that these are actually the default solver parameters that Firedrake assumes.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "solver_parameters = {\n",
    "    \"mat_type\": \"aij\",\n",
    "    \"ksp_type\": \"preonly\",\n",
    "    # Use MUMPS since it handles the null space\n",
    "    \"pc_type\": \"lu\",\n",
    "    \"pc_factor_mat_solver_type\": \"mumps\"\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Programmatically inspect convergence of solver\n",
    "def convergence(solver):\n",
    "    from firedrake.solving_utils import KSPReasons, SNESReasons\n",
    "    snes = solver.snes\n",
    "    print(\"\"\"\n",
    "SNES iterations: {snes}; SNES converged reason: {snesreason}\n",
    "   KSP iterations: {ksp}; KSP converged reason: {kspreason}\"\"\".format(snes=snes.getIterationNumber(),\n",
    "                                                                      snesreason=SNESReasons[snes.getConvergedReason()],\n",
    "                                                                      ksp=snes.ksp.getIterationNumber(),\n",
    "                                                                      kspreason=KSPReasons[snes.ksp.getConvergedReason()]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We're ready to solve."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "SNES iterations: 1; SNES converged reason: CONVERGED_ITS\n",
      "   KSP iterations: 1; KSP converged reason: CONVERGED_ITS\n"
     ]
    }
   ],
   "source": [
    "w.assign(0)\n",
    "solver = create_solver(solver_parameters)\n",
    "solver.solve()\n",
    "convergence(solver)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now have a look at the solution, using some simple builtin plotting that utilises matplotlib."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "449c533e3816477894a1baa628d51679",
       "version_major": 2,
       "version_minor": 0
      },
      "image/png": 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",
      "text/html": [
       "\n",
       "            <div style=\"display: inline-block;\">\n",
       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
       "                    Figure\n",
       "                </div>\n",
       "                <img 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epxbxs3dGt6Tt12trSHeqCfX+f/mZlDYmHFQiOnbluYWcDWwhbmZhannmdpxkbFBx9n/9lYWMPX1v/UbTMzc1iZDeZPsOvspJ+tx5yfrw++jp3GWrARdTN2YnnafSdP8Nnzhl7LT9f5dYv/PcYH9Af3aTDoMXgCzfZz8PHB17N/ezZyPxg0IKPRH/3L/lGY4I+GOljIR6xYILFfp0KfVgW5RoUejQrSvn709Ksh69dAJlOjqaMXJVWtWL+3AE7mlpgYHYwlM5JgayPmn4z8E2vXruXnEu+//z4yMjLw+uuv83OE0tJSeHl5/b/n7969GytXrsTEiRNha2uLF198EXPmzEFhYSH8/f35c1566SW8+eab+Pzzz/l5yOOPP87XWVRUxF/DXH755fychWW9azQaXHvttbjxxhuxZs0ao78HhJwLs4HBHE1CfherYeLs7Iyenh5e/2Q0jcYWHCjF4Q3HcPCXHDTXtPFpMa7eznxqjV+EDwIifRAUE4DQxED+GCHEuFiQQqnVol+j4ZdDS79WC7Vu8LpuYAAytYpfZwELmVrNr7OghqWFGTr6+6HSauFsa4sGeQ9ULPCh08LOyhKtfXKodToEO7sgv7OFBzrY4yHOzqiTdfMAR7izO1r7ZbCyMIe1uSVi3TxRr+iGlbkFrM0tYG9txQfN2XVPO3v0adX8MauTtxVa1anbjtY2PLjBb7MggQULJpifetzSfPC25cnH2Sgvv2SPscuT11kAgwdGzAeDH+w+dsrPLgkh5O/qlvfj8Ilq7N5bithIX1y1bDzEzlSOU4f281iRNxwcRzbTWC7TY2xc6196D1iQZNy4cTwbfei4mNUsZNkfDz300J++XqfTwdXVlb/+qquu4sFmPz8/3Hvvvbjvvvv4c9j+sA6dbGD30ksvRXFxMeLi4nDkyBGkpaXx52zevBkLFixAQ0MDfz0hYkMZJuS8w+qN7F57EHt/OMyn1bDpNO5+bphz1VQkTIxCQmY0fMO8aXSRkHOk0erQp9GgT61Bn0oNBbtUq3ngQaZU8ftZZF6qUPDAh7mZGaR9ffw1/ey5upMBEY0W7g4SlEml6NdokeTnjRMtLbCzskKYmys6VP2wtbTki5Pd4EgVu+7v5ASZRgVbC0s429hgwBxwsLaGu4Udv4xwZVMWLCGxsoKlBctusOSBEBbgsLa0GLxtbg4bS3ZpAWsLSx6IIISQ842Lgx3mTozjn9O78iqF3h1iIGq1Gjk5OXw6/xA2PWbWrFnIyso6p3X09fXxDBFWGmCoVACb2sPWMYQFiFhghq2TBUzYpYuLy6lgCcOez7Z9+PBhLF26dER/TkJGAgVMyHmjt0OG3z7cgS2f7eadaxyc7TF5aTomLE5F6uxE2EoGT8DIyNBotGhv6UWXVIb4McFC7w75nUCHQsVSslWQK9Xo7VdC3s+yMzToVih5sIN1emqXKfjjVlYWaOjsgVylhqujHUqa23kGSJy/F+q7eyGxtkKsnyfa5QpIrK0R7O7Cn8vu93K0h5WFBZztbOFoY40YS08eCGGP2VgNBjPYbRbIYPexQAh7PiGEEAFILKGi2cYGoR8YXEZ6nUNZLOfSyEEqlfIMEZb9cTp2u6Sk5Jy2+eCDD/KMkKEACQuWDK1j+DqHHmOXw6f7WFpa8qDL0HMIERsKmJDzoj7Jlk924ZsXf+btd33DvbFg1QzMuDQTngGsGBv5u1j6ZUdrL+qq2tHW0o2qshb0yVUozBuspdPc0AUbWyusz3qMMnYM+G+gUKrRo1CiR9GPHoWKX/arNDzQ0atQ8o5KNW2d6O1TwcPZHrnVTVBqNIgP9kFjZw8cbG0Q4evOgyf2tjbwcXXg9R3sba3h6mCLMG83/hx7WyvY29jwKSgSm8HrLEODEELI6FLV3QUHFxpIMjVsSs3pWAHWp556asS388ILL+Dbb7/ldU2GapMQMlpRwISMag3lzXj7X59B2tjJa5Kkz0vB/Ounw95JIvSumZyeTgWqSpvRVN+BshMN6FOokHOgAp6+zqitaoOPvytamrrh5CJBb08/zC3MYGtnBS9fFyhkSjg40VDVuQZAZH0qdPQo0CXrR3dvH6S9feiWsY4IQE1rJ59j7uxgh7zKJt75JNjXDWqNFm6OEjhIbOBkbwtPF3s42FrD180Jbo52mJoYBkeJDRztbOAksYWtFRW1JIQQcnYlnVKk+PkIvRujko73YRrZ79+h9dXX159Rw+Rs2SWMh4cHLCws0NrKumv9D7vt4/PH/+4vv/wyD5hs374dSUlJp+4feh1bh6+v7xnrTElJOfWctra2M9an1Wp555w/2y4hQqGACRm1Dvx8BD+9vRlatQ6x6RG46snllFFyjro75CgraEB5fgPqq9tRkFPDOyB2tMvg6umIrnYZnF0lPGjS2S5DYKgngiO8MGlOAvyD3eEX5I6AYA+4ejhQC+WTWGCjs1sBKVu65OiRKdEs7UGPXInmDhkPkHi42qOwupUHNqKCvaDV6fh9LDji6iTh2SFpsYFwcbTj88yd7W1hRRkehBBCRlB9Vzcaentxy6Szt5cl4sWCJedS9NXa2pq3Bd6xYwcuvPDCU0Vf2e3bb7/9d1/HuuA899xz2LJlyxl1SBjWFYcFPdg6hgIkbIoQq01yyy238NsTJkzg7YZZ/RS2fWbnzp1826zWCSFiRAETMipt+HA7Nn2yE+0NHbjm6Usx75ppNJr+O9iXVG15KwqPVqO2og1Hd5fwk/v25h44uzvwzBIHFwnPEgmL8YVfkBtCo30RFM6CJN7wDXSHpdX5fdKu1w+gq6cPrdLek4sMSrUGFTVStHbIeEtHFiiJjfDhLWE9XB0Q5OfKL6NDvOHh5gB3Z3u4ONrCxppaOBJCCBHO/upaeDnYI9KDBplMLcPkr2Atha+++moe+EhPT+dthRUKBW/zy7DON6xd8OrVq/lt1kb4iSee4O1/Q0JCTtUccXBw4As7zr7rrrvw7LPPIjIy8lRbYVbnZCgoExsbi3nz5uGGG27g7YxZ0VgWoGEFYalDDhErCpiQUeentzfh08e/ReTYMDyz/kFEpYYJvUuim/JRV9GG3P1laK7rwM6fjyE01hf52dWITwtBS0MnYseGwMFJgtTJUfAOcENEvB9CIn1gK7HG+UomV6KptQdt0l7UN3aiqaWH1weprJXCz8cZ0i4FfDydEBTgBmdHO4QEuGNsfBC83B3h5iKBtRV93BJCCBG/wuY23pmMBpoMQz9gxpeRXudftWLFCrS3t/MgCAt+sKwQ1uJ3qGhrXV3dGVnC7733Hu+us3z58t+tk/LAAw/woMuNN97IM0kmTZrE13l6nZOvv/6aB0lmzpzJ179s2TK8+eab/+CnJ8SwzAbY2RMho6C/PfPrB9vw89ub4R3kiTvevg4+IWdW4j5fseyQvANlOLqnGMf2lUGn06OjVYagSG/UVbYhPi0U9k52GDspEmExfohI8Ied5OzzXkczpVLDgyEtrT2orpWis0uB0ooWtEtliIzy4a1xI0K94OJkB19vZ/h4OfNLid35G0gihBAyerAW7xNe/S/eWr4Ik8NDYApM5Th1aD/3F/jBwXFkpyvLZXpMSmgS/XtAiCmiIU8yahzddgJ7vz8MjVqH29645rwPlrB2vllb8lFR2IBt67IRmRSI4mO1CIn2RUtDB6YuTkFMSjCSJ4QjOMrnvKo10t+vRm1dB6qq29He3ouikiY0NHbBxc0ejo62iI/xg7OTHRJi/XHZxRnwcHeEuTmNtBFCCBndchua+ffd2ECaHjHap+QQQs4NBUzIqMC64Hz+5DooevvwyJe3wy/c57zNJDmwKQ8HN+ejqqgR7S09CI72gVajg7uXE1bcOgvpM2IRlRR0XtQdYQl0ra09qKxsQ3l5C7+UKVTo7u5DWlooJPY2CAvxxORJ0Qjwd4W1NX0kEkIIOX8dqqlHjJcn7K0pc5IQQhg6OyCjomgpax1sZWOJi+6cj+hxETifsKBA0ZEqbPz6IO9ew2qTxIwNgbS5B5MXJiMuLRQT5iTyWiSj/X1oa+tFaUkzGhs7cexoDQ8K9fWrERXlg9BQT0yYEIngYA/Y2lJhVUIIIWS4Q3X1mB11fh1HGZsO5nwZ2XUSQgyFAibE5LFpOJ3NXfAN98bCG2bifKGQ9WP7umwc2HQC5fl10OsG4OLuwKfezF6ejsfevxbuPs4YrdRKLcrKmlGU34DSsmaUlTYjJs4frq72iE8MwKS7ouEf4EZTaQghhJBz0N3Xj+NNzXhwxmShd4UQQkSDAibEpKn61fj53S0wMzfHqudXnhcV3TtauvHTh7tRWdiAvP1lSMiIgLW1FS68fiomzk/m9UhGI6VSjaITDSgraUZ2VgV6e5UICfNEfFIALrl0PMIivGF1HkwzIoQQQgzhUF0DAl1dkOg72CWFGMaAAbrksHUSQgyDAibEpG36dBfMzM2QMS8FngHuGM3aG7uw+ZuD+O6d7QgI90ZtaRPGTI7GrOXpmLRoDKxtRtefM+vkU1bchJLCRuzfXcyDQh6ejkhKDcb9jy+Bj6/LeREgI4QQQoxhX1UNIj3cYW05uo4nCCHkn6BPRGKy1Eo1cradQGdzN5beMQ+jVU+nHOve2YYt3x6Cg5MddBodolOCcNtzFyM+PWxUBQ16uvuQd6QaB3YVQ6XWwsLSAhmZkbjvsQvg40cBEkKIadLp9VDqNFDpdFDrdVDptFDz61p+XanTQqPXQ6MffFyj0526zl7LXqcdYPfp+W3NgB5avQ4DA4BKr4We3x7gl7qBAWj1epibmfHXsfpOerDHBvjzXWzs0Kns49cHeLN0wFfihCZF76k+G+yz1l/ihJY+OcxhBgtzc3jYStCl7IeFmTks+W179KpUsLaw4Lddre34z2Jtbgkna2u+ZhsLS9hZWsLKfPBSYmkFWysrfmlnZQlHaxteXJS9ngiL/Z6caGzB1ePGCL0rox51ySHEtFDAhJis/T8fQUl2BZb/ayHs7G0x2mg0Wvz6yV4c21OMnD0lSMgIh6OrPR794HpEJAZitGhu6MD+XSWorWpHZWkLps6Ox5JL0hGbEAALSzqIJoQY/kRRqdWiT6dBn0YNhVYDhUaNPq2aBwB61Sr0aTU84MEu+zQaHuiQa9To12n4a9lj/VotrMzN0dov568bCoSwx7UDevjZO6C5T3bWfXCztUOnqv+sj7FARNewx9ipkZW5BVxt7KDQqnkQw8LMDOYngxksWCKxsOYBE3adLew/dumv1/PgyNB6GDsLK34fey9YoIP9v5WZBSq6O3gAhgVbQhxdUNolhV4/wH+eGBdPHG9r4cEZdjvZ3ZffZhLcvVDQ3save0vs0Sbr49f5eyCX8+spXr7Iax58frqvP2q7uhHu5gaVRgcXW1sEOzvznyfQxYkHXnydHOBsawcvRwd42EtgaUHfDyOpprMbpW1SjAsKEHpXCCFEVChgQkwSO6j79b1tPNti/g0zMNoUZlfgzfu/gbmlJWrLmnltkuW3zkLs2BCMBi2NXcg+UI6tv+QiMMQDkbF+uOz6KfAb5Z18CCEjHOhgAQ2VCjL14NKjUZ66LtOoeUCABSlYcEOuVvFLDzsJCjpbeFCE3WaX3hIHNJ0lmDHG0w+50qZTt1lAxM7Siq9DN6DngYbBLAorni3hbiOBt8QRthaWsLGwgC173NISNuYsm8Ka7w97PsvK4Jfmg5csyMGusyCItQXLyBi8za6bDeBkFsdgJoclC46ILCOD/VuoTmbFsOyYwQwalhmjhUqrRb9GywNK7LpCzYJRGgzoBzApKBhytRqWMIernR3/OaukXSiVSlHb0Y3qzi6k+fkhp74J4wL9cbS2EemB/sipbcK0iBD09CmR5O8LOysrhLi7INjDFaFernCyG32DKIaWVV2LKaHBCHIdvcXixUI3YM6XkV3niK6OEHIaCpgQk1R+rBplOVVYftdCOLk6YLToVyjxxYu/4eCm4+hulyFyTDDPKJk4L8nkp6Ow6TZ7NuejILcW0jYZZi5Mxr9fWwl3Tyehd40QIiC1VotulXJwUbLLfn695+RtdmljZYnyLil6eHBk8D6ZVs2ntQyJc/dEYddgVgPDsikm+gWhprcLDlbWcGDTP6ys4WxtixQPP36dL5bWfAqJreXgVBHJyfskQ1NHTi7sOgtokP+PfT/ZWlrCdoQPK1mApUPRx5euPiUuTJRBoVIjxN0VA7oB1HX04GBZLRq6eqBSapES5Ivj1c2YEBEETyd7+Do7IiXUDzEBXvBwth/RfRtt9lbUwM/ZyeSPNUyBHmbQj3BbYTbtjhBiGBQwISbpwPpsRI+LwMQL0zBalOXVYvXNn8DD1wXS5m5c9eBiXHD9NNhKrGGq9Ho9cg9XYvOPx9DW0o30SVG47s7Z8PF3FXrXCCEGwKZu9Cj70dnfjw6+9EGmUqGtT8Hv61T281oWBdI2Xg+DBURiPDxwtLXx1DrY+Ro79GcZFk42Nnx6RrKXD592EuLswgMe7H62sKwOJ2tbnr3hZG3DgyKOVtb8fhbkoJM/08ayc9hJPFv+KLulrVeO+o4eNHX2IDHQB03SHkh7FfjtUDE87O0Q5u0OnVaPSD8PTE4MRWpUAOxsTfe7daSxwGObTIFLxiYKvSuEECI6FDAhJnkSXnmiDiqFElFjwzAafp4Nn+/D+4+vQ1hcAGTdCry+4X5EJgfBVPV292HLjzk4vK8U3n4uuODSDMSPDaaTF0JMNAjS1d8PqUKBdkUfD3q0yGWQ9vVBquiDekCH6q4uHhxhQRFvB3s0ygZrZDCxnp5olsvgamsHNzs7xHp6IdbDE662tnCxteM1LlalpMLZxpY/x8WGBUBseIYHfWaQP8N+R7ydHfmCsAAsSftfIKW5U4aCmmZUNnWgprkT246W4cddxxHh64H0mCCMTwzBuPig874eSl59M4pb2pEW5C/0rpwXqOgrIaaFAibE5BQfrkD2pjxc/shSkz+YVsj68faD3yJvXykcnCVImRyNK+9fBGtbK5ii2so2/PTlAchlSsQmBeKJ1y6Dk4tE6N0ihJwFO6HsUarQIpOhVa5Aq0yONrmcF/Asbm9Hu1yONoUCga4uyG5oOPW6OB8v1PX0wEMi4UuEuztS/fzgbieBu0QCL3t7ONvYwM1OwgMk7LqlBU1lIcbFjg/83J34gtTB+6TdcmQV1GJXTjm2ZBXj0PEaBHo5Y/KYcMyeGAOJ3fmZdcK640wKC4aTLdV+IYSQ4ShgQkzO4Q3H4BfujcwLx8GUtTd14YnL3oGVrSU0ai3uffNKTJibDFNUlFeLbz/cA0dnO2TOikfG1BhYnOcjdoQIrbdfiWaZHE09vejq6+dZIC29crT0ymBtbYnsugbewYVNkenTaXmXFQ97e6QG+PLXx3t7Y5q9Pe9Scn1qKjztJfC0t+dBENYalhBT4+HigMWT4vnS2avAlgMl2LS3EO9/sw8/bz2OlYtSMWtSrMkPxvxVmwvLMCVydBSVP3+LvlINE0IMhQImxOS01knh4eeK8ORgmKqa4kY8suIt+IV6oVvai9c23IfACB+YmhNHqvHtB7thJ7HBilVTET/GdP9NCDG57JB+FRq7e9HQ3cMvWfeRwpZWNPbIeJDE3UHCW4UyCb5eUGg08HFy4PUgIjzdMCMiDD6ODnxhgRB3e4nouq8QYihuTvZYOT8Vl8wdg11ZZdi6rxg/bT6O40WNuGHlJDg72eF80N3Xh25FPyaGmu40YEIIMSQKmBCT0iPtxe61WZhz9VSTHQEqzqnGCzd9zIu5Wpib4ZVf7oOzu2l1+ikvbMSvaw7xzjfX3T0XEXF+Qu8SIaOOTq9Hc7cMjd09qO7oQn1nD9rlCpS3d6C+qwdylRqJ/t7Ib2qFh70EKYF+PPNjUngw/J2dEODiBC/HwQCJs62NyX5mEmJILEg4KzMGmalh+OTbg9h3qByNTZ24/9a58PN2wWh3uLoBXYp+JPqb3qCNaXfJGdnP45FeHyHkfyhgQkxK/v5SuPg4Y9w805y6Uny0Ch89/SNvGZy5KAV3vXKFSdUraW3swvovD6KyuAnX3j0XsSk0IkXIPzHY4UOBamknaqXdqJF2obajC+zYd39FLbQ6PW+VWtbegSBXZwS6OWNCaBAuHuuMAFcn+Ds7w8/VEXY0RYaQf4R1zbntmmmICvPCmp+O4I0Pd+ChO+bBdZS3I86tbUJasD9v3U0IIeT/o09HYlLqShp5NkZcRiRMTUV+Pb5+ZSOKsquw6NopuPmZi2FhaRqFEFVKDb7/eA+fgnPBFRNx44MLRDlarVRqcDynBhmZpvf7MVx9YyeaW3uQPjZU6F0hI0Cj0/G2p7XtXShtkaK6vZNnjxQ3t6FPreHPifbzhJWFOYLcXRHv74V5idEIdnfhgRE3e4ko/+YIGW1mT4njNbjW/nwUH329H/fePAfm5qP3b29XSRUuH58i9G6cV/Qwhw4jO/1Rz5uxE0IMgQImxKRkb8qFqk8ND383mJLmWin++/g6FByqwOLrpuKmp5ebTLAk50AZtv6Qg/BYXzzzwbW8WKTYdHXKsWH9Maz//iifJvTpt7cgIMgdpqa6th27DpShtqEDuw6Uws1Fgh8+uYUK6JoQvX4ADR09qG7rQHFTOypbOqDSarG3tIZni3g7O8DGxhKhnm5ICvLBBalxCPF0RYiHK9zs7SgoQogIzMiMQXWtFHmF9dixrxizp8ZhNGrs6uFT/TLCAoXelfMKFX0lxLSI78yHkN+h1WgxoB8wue44sm4Fnr3uAzRUtmDWivFY9eRFJhEskfX04/PXt6CjrRc3P7IY3v6uEJvmpi78sOYQDuwpQYdUjqg4P0gk1pC2y0wmYNLU3IVte4qxa18pbOysUFzWjNAgdx4kCQ/xQq+sH64uppESzlp2Himsg421JWaMi8Jop1CqUdEsRVFDG8qa2tGn0mBXUSX61VpE+XpArdMhwscdCYE+mD8mBuHe7gj2cKHUd0JMwOXLMlBY1oxt+0swY9Lo7Px2uLKetwmP9PIQelcIIUS06KiNmIza4kaUHq3CtBUTYSp0Oj1euuVT3jY4MjkENz97MaxtxF9rIGdfGbb8eBQZ02IwY8kY0Y16s0DJmk/3oaKsBZVlrUhMCYK3rwsuuWIin44j9gNbmUyJPQdKsXFrPgpLmhAa6oHqug4kxPojMz0CUydEIjMjAo4OthAzlVqLE+VNOHi8GtkFtZD1qdDaKUNcmPeoC5j0KJQobmhFcX0bzyDJLqtHnbQbwV4ukKnUfDpNQrAPHls2E5G+HgjzdqPACCEmzNbGCmOTgnAwpwrHixowNnH01ew6XFWPIHeXUT3lSKxTctgysuukDBNCDIWO5ojJqMyrgbWdFSLHhMBUrHl1E6//0dspx7Pf3A57R3G3KVSrtfjmnR28qOsdT18ETx9niIm0vRdrPtmPhjop8nJqEB7ljbSMcFx6dSaSRN7SmE3VOJZXg70HyrBpWwE0Gh2CWCaJuRkSYgNw6bJ0TMqIhIO9DcSsub0XB/OqkF/ehF1HyhHg7YKKxg7+WEq0P1ydJBgXH8yLmYot0Hau+tUalNS1oaC2hWeQHKtoRFNnLywszeHl4sADIxdkxCE20BvRfh7wcDatLleEkHMzbWIUDuRU4Vhh/agLmLDP6OKGNlw3NU3oXSGEEFGjgAkxGe2NnXB2d0SoiRy0HN9fiu3fZUHa2IXn190JrwA30XfA+fy1LQiJ8sFT718Nc3PxZGn0KVT4/ussHD9Wi/xjtXBwtMX4SVG47NrJiIn3h5h1dsqxeVsBjuXV4mhuDb/P398V9vY2WLIgBZnjI0Q95YYdVJdWt2HvkQoUVTTjcGEtvz85xp9nmPT1q3Hh9ESkxwcjNS4QLiIPCp7t52vq6EVxbSuOlDUgv7oZtjaWyK1sgp+7EzITQnBRZiIPkkQFeMBZYlo/HyHk7wvwceWB0vLaNow2rCNXZVsnUoL8hN6V845uwIwvI71OQohhUMCEmIzCA6UwtzCHvZMEplC35D93fA6FTInrn7gIyZnRELPjhyvxwfO/4ebHFiNxXBjEQq/XY+fmfPzwVRYaG7p4F5wps+KwdEU64pPFHTgrKW3GDz8dRU2NFBXVbTxZNirKB8kJAVgwNwmhIZ4Q81Sy/LIm7D9aiR0HS+Hr5YS8kkb+M/j5OsPHwwnT0yPx4HWzEeLvZlKZJDq9HmX17ciraEJpfRsOFtZA2tuH5HBfXnslMz4EKRF+iA3yhpuj+D9rCCGGJbG3RmuHDKPNkcoGTAgL4nWVCCGE/D4KmBCTYWVjheRpplGp/v3H1sHTzxWhzhJccMN0iNmWddm8C86//3sNPEQ0Bae6ohVvvbgBCpkKNZVtyJwRi9kLkzF+cpRoT9BZoOFwdhXWfJuFnp4+NDR18/unTY3G5MwoTJoYJcouQ0OZFqzA4f6cSmzcVYiObgXio335iQILJMzOjMHktHCkJwXDycHOtAIkde0oqmnFvhOVyCtv4tOIOmV9SI7ww6qFGUgO90OEvwcsRJRVRQgRB5YB2NDewwP4Ysq8/KcOV9TD3YHalQtBZ4C2wjqqYUKIwYjzyJ2QYfrlShzacAwX37sIYpezqxjVxU1oqm7He7seFW0BUp1Oh49f2sS74Tz/2SrY2IqjGC2r+fL1x3tQUtCIwrx6hEf74Ma75mDJJemwsrIQbe2XPXtK8PkX+/n1zm4FD4xcekkG5syMR2ioeLNJquqk2La3GNUNHdh3tILf5+3pBBcnOz7F5vqLJ2JsfCCsTKCz01Dgp66lC9nFdThaVIe8yiZ09PYhPS4IPu6OuG/ldIyN8oefh3iCg4QQ8WJtwFlMQavVw9panN/nf+dzslvej6Xp8ULvCiGEiB4FTIhJaK5qReLkGIQmiHsahlqpxjuPfIvmGiluf+FS+AaLs1WfVqPDKw9+h+Aob6x6cIFoRs2K8+vx5Qe7kZtdBTbmNf/Csbjypmlw93CEGKlUGuzYUYRPP9vHr7MDalab5NabZ2LWzHg4OYkzE0MmV2LHgVJs2H4CtrbWyC2sh52tFTxcHZCZFo7Zk2KQGOMPS5EG+4aT96uQU1SPg/nVOJRfC28PRzS0dSM9Lhh3r5iKlEh/+Lg7Cb2bhBATpNXrodEPwNJEgsbnoqq1E9kV9Xhq+Syhd+W8pB8w58vIrpMyTAgxFAqYEJPQWNGC/H0luPbfl0DMfnh/J2wlNkibHod5l4uz/THLgHjrsR8QEe+PZddPgVgCOF99uBt7thWiqb4T46dEYdHycRg3MRJifQ+3byvAZ5/tQ3e3As6uDnBxluDKKzMxdWqMKKfdsBHFvIIG/Lb9BHYfLEV0hA9KKloR5O+KmZNiMHdKLNKSQ2At0iye4eqau7A/rwrVTR3YsL+IdxuamBKKFXPGICM+GGH+7pRqTs57X6/Php2NFXw8neDv4wI/bxfRZuqJVW+fCtoBNh1n9HyeHK9txtTYUAS4U6adEGhKDiGmRXxH9YScRXd7LxInxcA3zBti1dHajSPbC1Bd1Ihbn7sEFiIcjWIn+h88/ysiEgJwwVWZEAMWIPn6w93YvuE4AkM9sGBpKm64aw4kImyvy1oD79pZiM8+3Yempm6EhnnCwcEW11w7GZmZUaKcfiVXqLB9TxG+33AMdQ2diIrwhlqjg6ebIx68bS6mT4yCvUR87/XZ3vviqmbsPFKBfccq0aNQQmJrjXkTY/DK3RdgTEwAbK3FMa2MELGYPC4CjS3daGrtRklFC0orWuHuYg+ZrB9hwZ6IifRBRKgXPEWaxScGta2dsBJhEPyfOFBSA3sbGwoqE0LIORhd3wBk1KopbEDZsWq4eot3NOTb17egOKcai66ZgoTxERAbnVaHVx5Yi5iUINEESw7vLcVLT/4IZZ8aMQn+uPyGaUifFAUxysutxZdf7OeXCYmB/EDziiszMXlytCgDJSw4smlHPn7ckIt+pQbhoZ5wdrLD5IwIPH73QgQHuMMUCrbmFTcgu6AWG/cUwsnRDnZ21lg0OR5TUsMR7GdaHXoIMbYgfze+DCdTKFFTK+WfEx/s2QupVIbIcG+EBLljTFIQfH2pc8rQZ1B1UyemjA3HaMGyDZVqLeYkhwq9K+ctvQHaALN1EkIMgwImxGSkz0sW7clRa30Hqosa4BfqiQtWTYMYD5DeePxHRCcFYuk1k0XRTeabj/bgy/d3IzE1mI/ePfjsRXBxc4DYtDR349tvDuHXX44hMTEQbm72mDM3EXPnJYpuTjv7dz5eUI/NOwqwaUchvy8k2B2ODrZYvjgVE9PDYW1lKfpMkoKyJuzIKsXuo+W8W8/CqQm4dul4TEmL4HVWCCH/jKO9LRLjAviycE4Sv6+tvRcFRY345PN96OqUI3VsCNLHhSE8XLyZnYbGCki7uEgQHTJ63oOati7sK67GfReIY0ouIYSInbiPnAk5qfBgKbyCxFlAlfnpg10ozK7CrEsyECDCg8tv3t0Jdy9HLL1W+GCJXNaP1Q99j5bGLt6ZJzktFJffOFU0hWeHaDQ6bPwtFx+8txM+vi6wtbVCytggrH5xBewk1hBbkCEruwIbtxVg/6Fyfl9UpA/CQjyxfPFYPnIsdtX1UmzZX4KSyhYcKajF2Pgg3HhJJiaOCYOrk0To3SNk1PPydMKMqWyJ5UHtvLxa7NxRhB/WHUFCgj+mz4gX3WefoZXWt8HX0wkhvq4YLQrqWpAc7IsQz9HzM5kaPcz5MtLrJIQYBgVMiElw9nBCcFwAxKirvRfb1mYhISMCl/5rHsRm/+YTKC9owGNvXyl4hk5rUzeevvdbaFjr3Q45nnh5BdIyxVfYtaSoCS+/+Buk7b28gGtwsAeee/ES+Pi4iC5QsvdAGX7bdBw5J2r57ZTEQCTGB2DpojFwF2HGzvBpAbuyyvDL9hM8y0il1mLu5Fg8eutcXmOFECIMNs0wNTWUL/19auzZU4w3Xt2EsHAvLLpgLCQmUPdoJFQ2daC1S4aYYPEHnc/VwZJaeDs5CH48QAghpoICJkT0lH0q5O0uxNiZCRCjXT8egUalhZuPM/zDvCAmZfkN+O6DPVj9+Q2C19moKmvB5+/uREVJMyJi/fD6Z6sQHC6u94u1Bv7s4z34fu1hhIZ5wclZgrvunY+xaaGim3qTdbgSa3/IRklZCw80pKeFInVMCBbPT4bETryjwHzaUFEDNuwswI6DpUhLDMLY+EDMmxqPMBFnkRFyvmJZJfPmJ2P27ETs2VWMN1/ejImTozB5WsyoP+nefrQcMDeH9ygJ4LLP3+bOXswZM/K1wo7tLsLYaXEjvt7RSDdgzpeRXichxDAoYEJEr7OlGy6eTnD3E1/6qFqpwdo3t8LGzhoX3TwDYiLr7sOXb2zBQ6+thL2jraD7UnyiHk/fuxadUhlmzE/ETffNh4ubPcSktLgJLz7zC1zd7flJQObkaFx6+QTY2Iir80p+QQM++Xwvauo70NXdhwkZ4UhPC8OCuUmwEXEnB0WfCtv2FOOHzbmoa+zE4tlJeObexcgYEwpLERbNJYScycLSHDNmx2P8xAh89ck+5ByuxA23zoKDk7DfL4bSKO2BXKnG0sniHKz5O5o6e5Fb2YQHLxr5Wms/vbuNAiaEkFFJvEfXhJzU2dyNbqkcHiLs6nF4ewHcfZxhY2eF6JQQiGkU6bNXN2P+ivHwCxZ21P7E0Wo8ccfX8A9xR1S8H/71+BLYiigDgk1j+W5NFvbtLkF9XQcsLc3xxrtXIzbOH2LS2NiF9z/ahbr6DtTVd2JcWigyxoVh0YIUUQdK6pu6sGH7Cfy4KY+3L7548VjMnhwLT/fRMWJLyPmGtXy/8Y5ZOJJVgTdf3ohVt86El494O9j9XUdK6uHpao/YUVTwNa+qCY521ojy9xzxdXsG/P9uTOTs9DDjy0ivkxBiGOI9yibkpK7WHn7pJsKWwr98uhfVJc24740rISYb1hyCuYU5Js6OFzxY8soTP8HJxQ7+ge64/9mLeK0KsejuUuCtVzbhxPEGfv2q66bgksvHiyqrpL9fjS/XHERBYSPPLomJ8cU1V2bikmXpvMWuGLGAXX5xIzZuz8fGXQVIjPHHo3fOR+a4cNF1FiKE/D3jJkTA2UWCt17eiLseWgR3j9EVBN18tARVLZ3IiAnCaHG0vAFp4YGwMECR9ZYa6Yivc7SiKTmEmBbxnLkQ8jvkXXIExfjBTWQFNxur29nUZt5KeNLCFIhFY40UBUercffqiwXdj5L8enz78V5e6HXe0lTc8egiWIjoZLm4sBFffLQHR7OrkJAchAceXYxx48MhpqDD3n2l2La9EAcOVcDDwwEL5yfhuqsnw02kxVx5bZWjVTxQsj+7AtMzo/Hfl65AbKSv0LtGCDGAqFg/rLxqEj57fxfufHAhrKzE8xn/T3TJ+9HSJePTcSS24gxM/x25lY1YnGGYaTNeQeLLAiaEkJFAARMieh0t3Wiuboe9s7hai+78IRsnDlZg/hWZvIaJGOj1enz04gZcevMM3rJXKPXV7XjqX2vQ3anAhZeNxw33zBVVsGTPjkJer8TaxhITJkXizvsWwMNTPKOjbW29+OKrA9i46TivpzJpYiSuuGwioqN8IEZsWtP+w+X4eVMeCkqaMG9GAta8dz38fMRXd4iQIVqtDr29Ssjlg0tfnwoyuYpndWm1evT29kOl0vIpbx2dcui0ejg42KCzU86LaGvUOrCapy4uEsjlKri7O/COMg6OtrAwM4NEYs0Lpjo42MLJyY4XkXZzs4eTs92oKpYalxSIqspW/PTtIVxyZSZGg70nKlHf3o1VIRkYLTp6Ffx3NjXC3yDB8qbK1hFf72ilgzlfRnqdhBDDoIAJEb0BACnTYkV1gMkODqqLmhCeEICZy8dBLDZ/l42AUE9EJwcKtg8dbb145JYv4OnthNikQKy6WzzBEnZi/8l/d2HtlwcRm+CPmDh/3Hj7TNFME2G/Vxs25OHLrw6ivV2GtHGhmD4tBnPnJMHcXDy//2dklBypxLc/ZKO2sZMHSh6+awE8RJoBM5q1tstwMKeSZ/hcND8F41PDcD5iv5MseNHW3ssDjyzQ0dEhh1qjQ10dK5SsgKurA3Jza6DoUyMy3AsVFYMnen5+Lmhq6ubXAwPd0N4q423Fo6N90dDQAQsLC4SGuPN6QrY2VlAqNfwLytvbidc/CgxyR0lJEw+g1Nd08Ncq+9W8LpJOrYeLix26u/oQFe2L3p4+pKSGYEA/gLAIL/j4uiAwxAO+/q6i+Tz6K+YtHoPnH/sB0naZqILPf9fPh4rg6eKAqUniyTocifol0l4F4gJHviaLRq0R5bRpQggZCRQwIaLXUNqMnnYZxKSyoAFZW07AK8ANcWniODHp7pTjeFaloFNx2MnBm8/+yq+zGioPrl4OS5GkaKtVWrzw9M+8Uw9LG194wVjMXZgMsWABkk8+3oOtW/N5wdnExEDcftssfvIlRvmFDfjoi31oae/BlIlReOqhC+DmKq7OR6M9MFBZ046cE7XYsruI/zso+jU8KOjl7jiqAyas/XdTczdfOrsUqKpqR0trD3Q6Pa/zwwMZAJyd7XiWiKurPZISA3n7bT8/VwT6uyImyodnfbAghr2dDc8CkUisILG35W25bWws/1GQnv379PdreNYKC47IepV8XzraZVD1a1Bd2cYzXI5lV+HA7mIEBnmgpKAR0bG+PGji7uGA5LQQHnR2dhH/3xUL8kydFY9dW/Nx8eUTYcpYZkmXrA9JYb5wtLPBaFHWIMW8tGhYW438ob9GqUWfTDni6x2t9ANmfBnpdRJCDIMCJkT0ejpkcHQX14h13v5SxI0LQ0JGuGgyXz5/dQsmL0iCrUSY6UHsBOHTN7fh8N5ShEZ546k3LhNNNxyFXInXVv+GvJwaPiL84huXIzFFPIX8DmVV4IUXf4XEzoaPVq+8dDwyJ0VBjBqauvDfj3ejsaUbsdG+ePS+hfDydBJ6t84L7G+stKIVuw6UIud4LcqqWpEQ64fy6jaegTRtYjQiQ70wKT0Co0F3Tx9qa6Wore3gWSLFJU2ob+iAt7czjuc38OeMSQ6CXKGCj7czQoM9kJ4WCk9PJ3h5OvIpMu5uDnz6jLGZnZySwxaPPymG2qdQoaGuA1XlraipaENvlwLbN57Aj19nITY+AL4BrgiP9sGEabHwDxZvnYiMzEg8+9j3WH7ZBNF8L/4dvx4uQk1bF+6/eORb7wppf0E1xsca5ntPrdLAkTILCSGjFAVMiOjZO0ngLXBr3OG2r8tGbWkzVj12IcSgurQZep0OmXMSBNuHH786iN++O4LkcaG4+YEFcBVJ21g2uvvaCxtwYHcJksYG4877FyAoRBy/T6w+wvvv7cCmjcfh6eWI8Ahv3H33PDiLrF4Pw0bKv/wmC0WlTbCztcYTDyxGiMj+Lkermjoptu0uxonCBhwvHgwUJMb582khvl7OWDI3BRNSw+DsZAdTxDJD6us7+dSY5tZu5Oc3oLq6HV5ejiguaYatrRUmTojkGSHz5iYhNMQTN984A36+Lvy+0dCmlxVPZcvp2Xos4+T4kWrUVbbh4M5i/Lwmi7fvnb8sDVPmJMBaZMVIWYDcxs4GrS3d8PE1zfpFOr0eBbUt8HZxGFXdcRT9KtjbWGFsRIBB1q9V66BWqg2y7tFIb4AaJmydhBDDoIAJEb2KvBp4+LtBLJpr2+HgLEFQpA+ixwZDDL58cxtW3jJDsFG9orw6HNlXxkfAF69IR2jkyM+R/jt6uvvw4B1foqW5G2PTQ3Hvo0vg6SWObIiWlm78+8mfeHFelh1wxRWZmDM3UXQjs+zfdPfeEmzdUcizS+68ZRbGpYYKvVujHptqsvdgGX7dfJxPISsub+H3s5obIYHumDklBqlJwaJqgX2uv0+sBkhlVSvyTzSgtKyFB35yjtXwWh+Tp0TzaWjLlo1DRJgXAgLdeAaTGGv4GDr4kDIulC9MY50Uh3eXIvdQBb58ZwfWfrAHiy8fj/nLxomqVXtwqCdqKttNNmCyv7AaB4trcfOC8QZpvSuUgtpW5JQ14OUbFxtk/ayGiVDZreSfeeedd/Cf//wHLS0tSE5OxltvvYX09PSzPrewsBBPPPEEcnJyUFtbi9deew133XXXGc8JCQnhjw1366238m0x06ZNw549e854/KabbsL7778/oj8bISNFPN+yhPzBAbajm3jmcOfsKkZhdiXmXDoe5iI4oCo4WsNHKCMTDDNy9Gdkvf14+Ykf0VTXictvmoZJs+IhBr09/TxYwt4bVlDxoaeWwkUkNTZYwck1Xx1EWVkLwsK88M571yA01BNiw+pCfPbFfuw7UIbLV07A048vHTVtQ8VIq9PjUHYl9meVY+vOQsTH+qOiqg32EmvMnhaLaZnRGDc2BDbWViZVb6S0tJlnjRQXNaKktBldnQoEh3jweiFRUd6Ii/XHTTdNR3CwB/1+/Q7/IA9cdJUHLrh8Ao7sLcXujSewZ8MJHNtXjuXXT0aCSIKYXt5OPNhnqrbnlfPORxeMF8f32Egpr2/HmAh/ONnbGmT9aqXWIOsdrfQD5nwZ6XX+VWvXrsU999zDAxUZGRl4/fXXMXfuXJSWlsLLy+v/Pb+vrw9hYWG4+OKLcffdd591nUeOHIFOpzt1u6CgALNnz+avOd0NN9yAp59++tRtiUR8mbWEDKGACRE11ia3vb4DziKZ3sHUlDXDzsEGadPiIAYbvz2EFTdPF2z7767ewANH4yZH4dJVUyAGrCbAu69tRk11Oz8xe/GtK0UTLNm4IY8Xd2UnjRctH4frrp8KO5HUehnCCof+8lsu1v14BIH+bvj4/evg40MdEAylta0Xv23KQ3ZONUrLW5GUGAA9CxQ72uCRexdi8vgISCQ2JhMgKSxo4EGSQ1mV/JIVMe7okCEu3h/XXjsFkZE+CAvzNMluMEJj9VjGT49FxrQY7N2Ujx8+3Yu3n1qPucvGYcmVE3gnHyGxNsqs3owpqmvvxm9HirE4Iw6+buLIRBwpBwprEOZjuPo3Go0WlgYoJjta6WDGl5Fe51/16quv8sDFtddey2+zwMmGDRvwySef4KGHHvp/zx83bhxfmLM9znh6njn488ILLyA8PBxTp049434WIPHx8fnL+0yIEOjTjYhav1zFT97sRVLTQafVYeePR6FSapCUGSn07qDkeB00ai2CI4SZAnNwVzFK8uvRKZXj6Tcvh5UIDpg0Gh3+88zPOLCnFBMmR+GuhxaJIljCfo/XfH0An368FyGhnrjoonFYebn4iiOyVqyffrEfB7LKcdedczB9Sozo9nG0ZM7lHa/Djz/noKpWisamLt7JhRUvHT8uDI/etwjeIpk+9me/11WVrTiSXYWcnGpotXoU5NcjKtqPB0iWX5yO+AR/uFFByBHF/ianLkhC8oRwfPXmNuzfmo/2lm5cf/8CQYrcDmGBPlP9vPh2Ty4GBoB5qTEYTfjfZHULFk8w3CCPVqWFs4gygcmfU6vVfGrNww8/fOo+Nvg1a9YsZGVljdg2vvrqK57FMvxz4euvv+aPsaDJ4sWL8fjjj1OWCREt4c9uCPkDfb19iEoNFU319fL8eh7EiUgMgLMI9mnnL7m45EZhKvnLevrw5jO/QN7bjzseXwK/IHdRnIS+tvpXVJS18AKKt983H64iOIhjQZyXVv/Ku+GwjJdrr5uCSZOjITYHDpbjxf/8htSxIfj0g+t5lxEy8r8Lu3YX47sfjkA/oEd1jZRnlISFeGLJwhSkjgkRfb0OhUKFnCPVKCttwpbN+ejuViA03JsHRsaNC8fTzy4fFcVYTQELBt/6+BJ8/tpWHNhWAGsbK1xz91zB9kcmU8JWYjpTxobI+1XYU1SN2WMiMT569BR7Zcoa2jCgG0BK+P+KCo80VvBV1U9FX8UwJae3t/eM+21sbPgynFQq5VNnvL3PHHBjt0tKSkZkn9avX4/u7m5cc801Z9x/2WWXITg4GH5+fjhx4gQefPBBPg3oxx9/HJHtEjLSKGBCRK1PpkRZTjUkjoaZd/tX5WdVwMXDARPmJAq9K2iq60BDtVSw2iVfvrcLASEefDRz9uIUiMG6r7NQXd4KhUyJZ1++VBQFXpVKDZ567HveEcfaygL33b+Qj7yLbRrFBx/sxm8b8vCvf83B/HlJJjtKLFb9/Wps2nIC3649zH8vK6vaMHZMMDJXRvJACWuFK2adHTIc2F+Og/tL0adQo6K8BdNmxOGqayYjLT0Mvr4uQu/ieYuNCl9zz1zeWefQjkJEJwVgwkxhanC0t/ciTqDvpH/ip0OFaJD24JqZaaIPWP5VeRVNcLSzga+7k0E7XTn/SftsYhyBgYFn3H7yySfx1FNPCbIvH3/8MebPn88DI6e78cYbT11PTEyEr68vZs6cicrKSj59hxCxoYAJEbW+3n5+aS+S0crCI1XolsoRFi/8AeGe347jgisnCrLtssJG/PLtYV6g8d11t4qi+O3RQ5X46O3tsLa2wHOvXY7gsP9fsEyIk+S339iCYzk18PR0xGtvX4mgIHG14m1q6sZT//4RPt4ueO/dq3kRWjKy7Zg3bz6BL748iJBQD0ilMoQEu+O+u+dh9sx4WIuow8lw3V0KHNhXhp1bC5B/vA5xSYE8Q2rS1GgkJQeZXIee0YwFOK+7bz7+fcvn2PRtNlImRMBOgLo3ZeWtmLcgGaZEo9PhUEktXOxtsWhcLEab+tYuzE6LNGgQXNWnhlKhMtj6RxtWEnXka5gMqq+vh5PT/4JjZ8suYTw82ICXBVpbW8+4n90eidoirFPO9u3bzylrhBWcZSoqKihgQkRJvEdqhLA0T5UaiZnRoqhhwqZ7qJUaxKeHISY1RNB9YSOJLP36kpumCVKI9+3nf0NknB8mzohFYIjw3V0a6zvx7KPrkJAchOlzE5As8L/PUGbJi8/9gv17SzEhMxJ33D0PXiLIeDnd0aPVePutrYiND8Cdd8wWXfFZU8b+/bdtLcAnn+yBm5s9env7YWlujmf+fREmjI8U7Sg2C/Jl7SvD4QPl2L2rCK6uDpg1PxHX3jANcYkBot1vAt6ifNFl4/HNezuxd9MJXgjW2N+RrOCrj49pZRttOVaG/cU1uG3hRNiZUAeqc/032XmsAtfON+zvwsCAHo40hVMUWLDk9IDJ77G2tkZqaip27NiBCy+88NTxHbt9++23/+P9+PTTT3mnnYULF/7pc/Py8vglyzQhRIwoYEJETdHdh/wDpZA4CR8waa6RIndfKfzDvOAicNeew7uKMeOCMYIU9zu4qwR1VW1wcLLD0ssnQGhqlRaff7ALet0AAoLcsfiiNKF3CWq1Fk8++j1qq9uRkBiAf907Hx4iS1de/9NR7N5djAULUnDxJek0BWcEU9N3bC/Exx/tgVyuhJW1BRwd7fDKyyuRkhIkyveZnVSVFTdj4y+5yM2phlymwpTpMfjPm1cgISmIgiQmZPzMOPz0xQEUHKkxesCE1eOJifMTtOjs3/nd/2zHUdhYWWDZROGn2o60xvYe+Lg6IDnC3+DTp9V9VMPE1NoKs2KsV199NdLS0pCens7bCisUilNdc6666ir4+/tj9erVp4q4FhUVnbre2NjIgx0ODg6IiIj4377o9TxgwtZtaXnmqSabdrNmzRosWLAA7u7uvIYJa1E8ZcoUJCUl/cN3gRDDoIAJEbU+uZJf2toL31KzsqgRjq4SRKcIXxBu09ps3P/SCkECAR++shnWNpa44Z55sBVBRsJn/92F3dsKMTYjjBd5FcMJ88sv/gbFyZPlx55aKqpgCdu/r786gO/XHcEjjy3B+PH/O8gh/0zO0Wp89+0hnrnDWul6eDhg1Y3TkJISLMpACWu/vWd7IdZ/f4RnaaVmhGPVLTMxfmIkrG3p8MAUsemR8amhKDpWwwtzOxoxOzO/oB7R0YYrLGoI+4qq4WhnjYszk+DuKPzAjCHql1Q1dSIy0LCZoFqtDlY0Rc/krFixAu3t7XjiiSfQ0tKClJQUbN68+VQh2Lq6ujOmXDc1NWHMmDGnbr/88st8YS2Dd+/efep+NhWHvfa66647a2YLe3woOMNqrixbtgyPPfaYwX9eQv4uOiIiojagH0DipGhRjFiV5FRD1tWH6DHCTvdoqe+El58L3L2NP71j96YTvG6JvZczJs82XIvCc3X0cCV2bStAdJwfbrtnHg/kCO3brw5i57ZCuHs44LW3roKHiIp5soDX229t4916Xnv9coQL1I56tGlt6cG6dYfx0w9HER3jC/8AN6xcOR4TJ0WJMlDS3NiF9WsP4+C+Mt4q/YJL0jF9TgK8vJ2F3jUyAvyD3XFweyGaajsQnWS8IMCx3FrcuEqYrm1/N7vkg82HUVTfimeumIfRqLi6BYlhvrA08DGUul8DcxEcp5kK3YA5X0Z6nX8Hm37ze1NwTg+CMCEhIfzv5s/MmTPnd5/HAiR79uz5W/tKiFCEP7sg5A/0dsjRWNECMagsaOCXYQJ3OGFz01MEyApgJ9tfvLsT0tZePP3WFYIXemUZHD+syUJHuwxXXD8FgcHCF1P95accfPbhHiSPCcJtd82Bn78rxIJ363n8ByhVGrz19lXw9qGT439Kp9Vj/U9H8NXnB3inocBAN8yYGYcLLkyFlZX4vl7LS5rx3RcHIG2X8UDOLXfPRfrECFhaWgi9a2QEsemSbMS/p1thtG02N3ejta0X/n7i+cz7M4fL6pBf24Il6XEIcB+dn4dHiuoxN8PwLew1Kg2sbCnD5FwNwAz6ES76ytZJCDEM8R3REXIaZZ8KNgJU+h+ORcotLM2RkB6O0FhhU45LT9Rj0QvGrx2yZ0s+vHxd4O7piHGTIiG0j9/ZgZzDVVhwwVi+CC3vWA1++i6bByIuuyoTYeHeoirk+dgj38HFxR4PPrIYrq72Qu+Syassb8W3aw5i144iJCYH8ayS62+YBlc38b23xfn1+OrDvejt6YNPgBtuvnsuz8oio5OljSUcXSQY0Btvm9t2FGLRAnG0lz/X7/QNR0p4IebrZxu31oux9Mj7Ud3UgYRwwxfS1OsHYCXijl+EEPJP0KcbET1vEWQOdLT04NjeUvgGe8DeUbgWx021Un4pcbAxet2Lbz/ci8a6Djz2yqWCTzMoyq9HXa2Un5xedu1kwfenrbUXX3+2Hw31nbjt7jlIHRcGsWCZD2+9vgVqlQ7/unsenETSottUsb+F9euO4NOP9vCWwDGxfrj2+ilISgmG2JQXN2HdlwdwcHcJ5i9Nxa33z4d/oJvQu0UMjBXgZPUkjPU9wbJZyitbcd8S4QPX5yq7vB6/ZBdh6YQEhHiPzr+J/MpmxId4Iz70n7eI/TOspbCTG3XJMcUpOYSQP0cBEyJq8i4FNEqt0LuB2tJmfhkSI2zLs0M7ijFhlvFrhxzZXw43L0c4ONth4vQYCIkdnL/36haUFjfxKQVeAk8t0Wh0eO7JH1Fc2IgVl43HBSLo0nP6e8WCJRXlrXj59csoWPIPtbZ04/OP9mLb5nwkjQlCXEIArrx2Mg+ciElLYxd+XnuY1ylZfsVEfLTudviIaHoYMay2lh6YW5gZrc7Vnn2lcHCwhbOJfL6w7JL3NmaBxdmvnGY6QZ6/Kq+0EVqdHg5GyNK1trWCjb3wReAJIcQQxHWUR8gwyn41bCTCfwnXVbbB099N8IKv2XtL8PBrlxl9uz+vyUL+0Rrc/sgiwWuXbNtwnE8tmDA5CkuM3DbzbL78ZC/PcImK8cOV108RPNvl9LZ+L7+4AZUVrXjxFRYsGX0dIIzp0IEyvPnyZt4qOC7BH6tunoFYgesZne3z8puP9+LArmIEhHjg/W9uQXCYl9C7RYystrIN1jZWRgmSseDDT78ewz13zIWpOFJej9yqJsxPjUa4rztGq84eBTKTQ42yre62XngHCZ8NbCr0A2Z8Gel1EkIMgwImRNRUrIaJCFrXNlS0oL2pC55+LoLtg7y3n88TdjZy/Yn6mnZo1FpExvph5qJkCKm/T41Nv+Siuakbdz+6mNeVEVJhfgO2bDzB27O+/dF1sBFRW8UvPt2H3p5+PP38xXATYV0NU8H+5n5cexj/fWs7YuL9kTw2GHfeOx92Igjkni5rdwl+WXsYnVI57nx4EZLSjHOiRMRFo9GirqodUfH+RgluHz1WAx9vJ4SFGrZt7UgGeN749QCCPF1w47wMjFZqjRZbD5fiiVXGCWRZ2VjyLBNCCBmNKGBCRE3iaAcHF+FP9rqlct4yz1/A0doT2VWYKMB0nC0/HUNBbh2WrMyAncAFeH/69jBKCxuxaGkaUlJDBS+k+tIzP0OlVOP2u+ciOEQ8o2u7dxZh355S3P/wIvj6ChfkM3UsQPfO61uw5bfjPFiy8IIxmLdIXIUtuzrleO+FjTw4xooxL7k0g7f+Pl+wtsit9R18qS1uQkdLN3qkMrQ1dkKr0fH3gn12s5bjEgdbuPu6IDQ+AGHxgQiI8hVFy/qRdOxQJZ9qEhXvZ5Tgw8df7MO/bp0NU7GnoAoFtS1YnB6LMO/Rm11SUtPK/32SI42TBdctlcHMnDIczpUO5nwZ6XUSQgyDAiZE1KSNnXD2cBR6N1CaV8sPPoQMmBw7UI5ZF441+slIVWkLImJ9Mc/I2x5OLlMiP68OFubmWLJc+Doh33yxH+4ejvALcMWcBUkQCzYF5z+rf8X9Dy3mBUnJ38PaVT/10Hfo7JAjONQDt98zF9Eiez+z95XizWd+QcLYENz95AXwFjADzljTzOpKW3B0ZwGKj1ShvqwFjZWtvMApy4LTaQfrXbETtwH9AHiXTd3AqftY4032PxZQYIESe0dbRI0NxdwrJyNjfsqoaK98YEcxbCU2GDvR8J3M9h4oQ0y0L2Kjha3t9Veyxd7ecJB3xrl53niMZqx+iY+LI7zdjXP8pFVrRdlKnRBCRgJ9uhFRU6s0/GBYSKp+Ne+S4+RmDwcBi9opZEpEGrlmAhutZAsLmIQJfFC8/rts5ByqxPwLxiAkXNi6DNVVbfj+m8P8BOvDr24UTd0SVl/jqce+x+VXTcK0mcbPRhotmhu78OAdX8LN3RGubg546sVL4OEpfOB2iFqtxZfv7MDBXcW4dNUULLw4XTS/g4YYuc7elo8tXx1AU3Ub5D39/OSMGfyRzeDh68KnA7j7OSMwwnfws9rVngdEeDBFpYW8W8E/x9vqpGisakNrTRsUPf04si0fx3YWwDvYE6ueuRgTFphuEdCOtl7UVLYhPMoHHl5OBi92/dEX+/DvRy6Aqdh4tAR21la4YtoYBHiM7uDi8dImxBuhnfDpgyus0DA5N1TDhBDTQgETImqscJ29wJX32xq74B3oBndv4bqxdEllPGBiYeQR0Lwj1YhO8Me0ecJmUCiVGhTm1cHHzwXLLp8g/Bz4/2yEl7czll2aDm8fcRx48/T4/+5CRKQPVqwU9j0yZXU1Ujx4+xfw8nWBg6MNHn1mOexE1P2hvaUHLzz4HZycJXjy9csQNAqLuipk/di57hA2fLoXHc09UMj6MKAH/y5wcpUgMiUYGfOSEZ0SAv8IL9jYWv+tv5eq/DrsWHsQu9YdQkt1G1Zf+x6W3TEPVz16kUkGoH774SgP4k4ywtTNH389hvHjwhEW4mkyNT3e2XgQnbI+vHL9IoxmLJOmR9aP6eMijDqwZCmybmFipoc5X0Z6nYQQw6BPNyJqvR1yWAg8H7+Fz4/vRMyYYMH2oSCnBglpIUYfxd70w1H0K9R44tWVENLW3/KQc7gKmdOiESRwrZD9e0v59CArawssWCye0eg9u4p57ZL/fnKD4MVwTVV9rRTPPLgONrZW8PVzwb2PLRFVmnlJfj2euWsNps5PwlW3zYStCApij6Sakkase3MLju4s5HVILK0s4ehqj6TMaMy6dALGzYyH8whNMWABkfCkYL5c/dgyfPDIN9j65T788OYWBET4YOalmTAlnVIZio7X8VlIY8eHG3Rb0g45Nmw5jjdeFPZ74a9Yd+AEmjp7sWpOOrycHTCa1TZ3oqCiGQ9eN8to23T1coYVBUwIIaMUfboRUdOIYEpOW0Mnv/QKcBNsH0qO12H8DONOsSg4VoOAYHfY2tnA3cDp3X82WrZneyESUgKx/IqJEBILIn3w9na0NHXjpTevEE1gQtrei9df2oh7HlwIL++R/7fat7sERQUNuO6m6aO2oGhbSw/eenEjpFIZpsyIxZ0PLRRVQdDsPSVY/cBaXHf3PCxaMbqm4JTl1uCDx79DRX491EoNBgb0GDMtFhfcOANp0xNgaeDfOdaJ7Y7XruadPn79cCd2rztkcgGTX9Zm84LYj6y+2OC/G+9+vAsXLBwDVxEUZD8Xsn4VDhTVwMNRgmtmCl//ytBKq1uREOGDsAB3o3YSZMWVybnRDZjxZaTXSQgxDAqYEFFjhfyEHrVgHXLYAbuXv3ABk/KCRlx5u3E7EezfXoSywibccI9x2hL+ntwjVcg/VofoOD/EJwUKui9bN+bB09MRUdG+GJNq3IyfP/LZR3t5zZLJ02JGfN3tbT349uuDKCtuhqeXEy66JB2jDcsY+uD1rcjPrcOcRcmiC5bs3ZyPVx/7Efe/uByZM+MxWkibuvDavz5HYVYFVCo1H6VedO1ULL99Dlw8jB+kXfXMCjSUt6CztQempLykCevXHkJKehjGZRq22GtOXg3a2nvxyL0LYSo+2ZqNg8W1uH/ZNDjaCdvpzRgO59fCwc6GF0g3FomjrcEDm4QQIhTxHBGSc/bOO+8gJCQEtra2yMjIQHZ29h8+//XXX0d0dDTs7OwQGBiIu+++G0qlEqaAdcixc7AVdB+aa6W8PSUrLCgEnU4PTx9n2EqMl37P5vfXV0v59QkGOAn/K7b9ehyxCQG4cEW64Nkl33+Tjfzj9Vh5lXhGnw9nVWD/nhJcee3kER9ZHqzXshlV5a2YPjseSy5KxWij0+rxydvbsW9HEabPjcdt988XV7Bk0wm88dR6PPn25aMmWMJ+r358bxtun/4McncXwcFFgmsfvwifHXseq55aLkiwhGFTgLwC3NGvUMFUaLU6vLV6Axwc7XDNrTMNml3S16fGK+9uw+03zoCliP5G/gibhvP17lz4uzvh4sxEnA8KK5qQHO1v9OCnsWusjYairyO9EEIMgzJMTMzatWtxzz334P333+fBEhYMmTt3LkpLS+Hl9f+L/61ZswYPPfQQPvnkE0ycOBFlZWW45ppr+EHVq6++CrFrq+8QfNpDx8nRRqECJg3V7Uav41Jb2Yb8nBpExfvDL8h4ab3D9XT3Ye/OIl78N3N6LIS0ffMJyHr7edAgIsoHYqBWafH2q1tw/U3TeYvjkbZzawHa23t4YVsWkBkNbVeHW/flAWz4MQeZ02Jw233zYS2iefiHd5fg3ed+xeNvXIaUDMPWpTAWRW8/XrntE+TsKOStfudeMQk3PbsCtvbiGPlvrm4XvND4X/HjmkOQyZRYeFGawbuHfbs+G1MyoxATaRpthJmvdx6DWqPDnUsmwVpE9YgMpa1Dhpb2XqQYOWCiVet4wJEQQkYj0xgiIKewIMcNN9yAa6+9FnFxcTxwIpFIeEDkbA4ePIjMzExcdtllPCtlzpw5WLly5Z9mpYiFTqMTvPL6qYCJjzBdcqpKmhEeY9wDVNZKmIkVeArM7m0F8A1ww5KL02BrayVoHZVDByvQ29uPOQuSIRYbf81FWIQXFiweY5BpKju3FaKqvA3zFyUjUMDAmaFk7y/Dp+/uwNj0UKz612zYC5zNdrqy/Aa8//yvuPPfF46aYAmb6nLv/BeQvfUEvALd8eKv9+Ffr10lmmBJe0MnaoqbkDQpGqagsqwFWXtK4ehsh4sM3D3syLFq7NlfhmtWmE4HrhPVzTy7ZO7YKMwZE4XzwYmyRgwMALFhxg3qsywxc5HU9DIFAwPm0I/wwtZJCDEM+usyIWq1Gjk5OZg163+Vz83NzfntrKyss76GZZWw1wwFSKqqqrBx40YsWLDgd7ejUqnQ29t7xiIUNhVG6FFttv3whAA4ukoE2X5jrRThsX5G3eaJnFoERXgjNdN4bQnPZs+2ItTXSDFugrD7kZ1Vgay9ZZg0JRrRRv63+D0sePP15wcwf1GKQbKw1nyxn//cGRMjsezSDIw2rKvIa8/+goTkICy+eBz8BCzqPFx3hxwfvLQBS66cgImjZBqOvLsPjy9/DU2VrUicGIU3dzyK6DGhEJN3H/gKMBvA0tuErdt0Lvr71Fjz8V6UFDTg1nvn8c5OhtLT24+vvj+Mh+9eANu/0cJZqGlf//lhN79++fSxo6pI8h+pqu/A7AkxsDVysXyWDTxaC4ITQggFTEyIVCqFTqeDt7f3Gfez2y0tLWd9DcssefrppzFp0iRYWVkhPDwc06ZNwyOPPPK721m9ejWcnZ1PLazuiVC0Gq2gGSZsykN1cRO62nphYWEhWMHXIAOnWg+vmXL8aDXqqtoRlyzcv31HuwyN9R3ImBSJ+OQgCOnwwXL4B7hi9vwkiMX6ddmIivZGxsSRDyY1N3Zh++YCxCcG4OKVGYIHLQ3hoze2wsfPFaGR3pg4TdjpXqfT6/X4/I2t8PZzxYVXiKdWzj/9mV688QM0VrQidUYcnl13l+C1qYZjNVXy9pVhwXXT4eEnnuDZ7wUD/vvGVuzbVYKb752HmIQAg27rP29vwdikIMREimMq4rnYlluG/JoWzE+LQVKo6Uwh+qf2H6uEq7PxB3cCI31gLtAxkinSwcwgCyHEMChgMsrt3r0bzz//PN59910cO3YMP/74IzZs2IBnnnnmd1/z8MMPo6en59RSX18PoVhZW8FawKkYPZ1yfuniOfL1Ic6VpaU5nFztjVq/JDzKBxOnx/BCgkI5sKcEXR0KPvJvbi7cgUBLczc2rD8GlVoreKbLEIVCheLCRlx4sWHay274+Rg/UXJ1c0DyWPF0Axoph/aVoaG2A53SXlx720yIyc5fcpF/pAq3Pr541IyKb//6AIqyyhEU44eHP71FdMUhd/+QjS+fX4/o1FBcdv9iiN36tdnYvbUQC5aOxeLl4wy6rc07C3k22xUXj4ep6Fdp8PKPe5Ec6os7Fgvbit6Y5AollCotkqKMnwVZfKRS8I6GpkQ/YIjCr0L/VISMXvTpZkI8PDx4lkNra+sZ97PbPj5nH/l5/PHHceWVV2LVqlX8dmJiIhQKBW688UY8+uijfErPcDY2NnwRg36F0ugFT08n61QgIT0MgRE+gnVmUciN27GhJL8B+cdqsUTgrjR5OdWISfRH5nRhu/Ts2VmExOQgpI0PF03K8cZfcnlB3LT0sBFfd2N9J9atOcR/1mtumIrRhv1N/frdYZQWNuK5t68UVd2SjrZerP3vbtzx1FLYO5hO4dE/yy75+b/bYG5hhrvfvpoXcBaT9R9sx5qXfkNIXACe+PI2UXVIOpujWRXYu6MI7p4OuO7WmQYNJpdVtuLDL/fhvZcuF/37crqPt2ajrVuOiyYmwM9dmNpjQsgva0ZDU5fRO+T8r96cOL4fCSFkpJnONyCBtbU1UlNTsWPHjjMORtntCRPOXoitr6/v/wVFhqaWsBFksdNpdYKORrIMk4LsKvT3CdNmsrmuA76Bxk0Pb6zrhJ3EGjGJhkvz/jNKpQaHD5SjvlaK+EThpgWxv5GNP+fiRF4db6srBmzK1O4dhbj0ikyDZCB8tyaLdwGauzAZwaGeGG22/nwMRw9WYMGyNKSJJGNoyPrP9yM6KQDJo6TIK1NX0oT2pm6EJgQgNF7YqXWn06i1ePOeL/HRE9/DJ9QLz3z3L0gcxRM8O5uqila89+oWVFe04vHVF8PJ2XBBNZlcidVvbsL9t8+Ft5cwbZ7/job2Hny5Iwe+bk64ZpZhs2/E5nhJA0L83eDiJDH69ySbOs26XpFzM9IFX4cWQohhUIaJiWEtha+++mqkpaUhPT2dtxVmGSOsaw5z1VVXwd/fn9chYRYvXsw764wZM4a3Ia6oqOBZJ+x+oWpy/BU6rV7Q+gm9XQp+6egiTMHXtqZuhAnQIYcVFIyKE664aeHxOoRFeCM2IUDQttLlpc2wsbHE7PmJ8BGorfRwuUer0dvTjwmTR77rQ0+3AvW1Hagoa8F9j4p/asJfpVJq8PUHexCXFIilIitk21QnxfYfc/D6utswmjRVt0Gn1iAmTTxBoPLjNVh93QdoqZNizspM3Pby5aKfTtDW0oPH7l4DnV6Px1YvN2gLYdYV7LnXNmJKRiQmpI18Fpsh/ef7XfBzc8KdF0yCrQj/TXefqESUvyf83Ec+CFVZJ8WUcRGCDGwpevpElz1GCCEjRXzfJuQPrVixAu3t7XjiiSd4odeUlBRs3rz5VCHYurq6MzJKHnvsMT4KzS4bGxvh6enJgyXPPfccTEFQrJ+wU3K6+wQNmLAOOe5GHN1j0xVqq9ogsbeBf7BwbWRzj1SjtKgJ85aMfLvcv2L/nlJUV7ZjvgHa9v5dv60/hqkz4gwyPWjLhhPIz6tD5pRohBqx0LCx7Nh4HH5B7ggO80RQmLh+vk3fZmPinAR4i6hbz0gYymQUQ0ajrEeBj5/6AZu/3A93H2fc8/Y1mLVC/DUuursUeOyeNfD0cca8RSlIG2/Yk+Kv1h3i3wVXmVALYWZPfiX2FlQjPSoQ05LEE6Abwv4GPt92FO/eftGIr5vV2Mo+XosZE4zfElvVr0bipGiYm9C0LaHpYcaXkV4nIcQwKGBigm6//Xa+/F6R19NZWlriySef5Ispqs6vF3Tkr0+uhL2TLZzdHATZfmtjN+LGBBtte/VV7QiP9kFQqOdZ69sYi7StFwnJgUgRuOBozuEqRMX4YuIU4x+Eng0rvqhQKDF3QbJBDuYPHShHYnIg5iwQTzegkfz58g5Xob66Hbc/tBBiwqb8bf3hCF786iaMNhFJwbCyscKJ/SWC7YOyT4X1/92BdW9tgULWj0mLx+KW1ZfC3UccWWN/RNbbj7df3oSaqnZcfeM0zL9grEG3tzerDPsOl+OlJ5eZVN0SVuj1/Q1ZsDQ3x4MrpouyYHJhbQvCfN1gZ4BMjNKqVkSFeCJJgPolWrUORYcqRPmeE0LISKCACRH1CY5epxd01ELe0wdFrxJ29jaCtVX29DVe0bqayjaUFTYhIsZP0GkTe3cWwclFAj8j1285XVtrD5+Sw6YGefuIo3Dg/t0lUMjVCDRA9k9FaQsa6jr49YyJkRhtSgsacXhfOaLj/XgrYTE5uqcUgeFeCDGhtq3nyjvIA+HJQSjJrsRvH+3ColXTjbbt7vZeHiQ5uDEXLaweVIgnHv/8FqRMFraQ9F8Jljxy19coK27CtTfPwKVXG7bNdEV1G159bxte/vfFcHU2Xme2kfDp1iO8fsnNCycgzEe47Mg/8v2+fFww0TC1sHKLGiDtlMNPgHozGrUGFlamE1wTA92AGV9Gep2EEMOggAkRLTaPmhEyYMKCJYxQxQBrylrhYsTsFpbZ4eJmj6AwDwilsryV108JCHQXdMQqJ7sKjk52mDozDmKRm1ONuQsNk/2xc2sBujvlWHn1JEHrxhjK9l9yMaDTYd5Sw47Q/x2HdhRi/Azx/J6NtJteuAyPLX0FXzz7A9QqNZbeOsdgf9usnsKx3UX47q0tKMgqh4OTHZw9HPHA+9dh2lLDtOE2BFan6OF/fQUbGyssWTaOB0sMue/sZJvVLbn31jmICBXXdLU/U9vahc+2HYWbgx1WTkuBGMn7VSiub8OTYbMNsv4TxQ1IjgkQ5Pdbo9KKvgYQIYT8E/QJR0SLZZcIHTBhB98su0TI7gnGPAAqL2pCd6cCgSHCdUcpLWpE0YkGTJws7DSYwhMN/KQlRsDit6fr61PzKUJXXTfVINlcTQ2diIn1M2h2CQuCGrIN6u9vV4/iE/WITgjA+Kniyy4oPdGAJVdNwmgVFOWHBz64AS/d9CG+eu5H7F9/BNc8uQyJmTEj8vnGMgGP7ijEljX70VDegt5OBdRKDTLmJeGiW2YjcUKkyQRKmK5OOd55ZTPKS5qxeFkabr13nkH3X6nS4JFnf8LMKbGYPN60ssvYZ9enW45Ao9Hh/ounQWJrDTHalVeBBWnRBvl31Or0KK1owc2XT4FQHafY9G9y7gzR1Ya65BBiOPQJR0R9IMQKiRmiuOW56ulU8I4x9o6Ga9/4Ryd5bp6ORm8pzASGCJdh0trcA1s7K0QauTvQ2brRBAS68U49YpCfWwtnZ4lBpuM01HUia18Z3NwdEBNvmDnwvd19uO7id5CSFooHn15q1L9r1obV3tGWF1BmBY3FhBWWZoHZ0OjRNx3ndAmZMXhl8yN4667PUXi4Ak9c/DpcvV2QOjMe4xeORXRKCBzPIZuuT9aPxso2lORU8UySlhopOlt7eAt4G4kVIhKCsOi6aZh35SQ4uxn383MkNDd24eG71qClqYvXLLns2skGDZawk+3Vr2/C2KQgXL4sHaZmQ3YxfskqxLJJiZiRIq424acfy3y9Ixev3rLEIOuvqGlDd28/4qOF+c5kNUyiTaybEiGE/BUUMCGixQ4S8/eXwkLAtsLKPjW/tJUYf9RK3quEVqsz6kGds6sESakh8PA2/jzoIceP1UDZr0FohHB1JqRSGTqkckTH+sHWVhytEivKW5CRaZjR35zsSri6O2DG3ASDZYAUnqhHQLA72tt6jR4ELStoQnVZqyizS5pqpfANdDsvWnJ6Brjj6e/vQVF2BX56ZwtKjlRh+5qD2P7NQf7z2zrYwsFZAjsHW1jbWsHM3JxninRLZbxtKWu0Y2ZuxoMmqn4NrGwseV2SyORgZC4eiwkLkuHiIdxn1z9VWdaCJx/8Dvb2NrjiuikGD5awz/zX/7sd8j4VVl0xyaSycJhueT9e/X4vbK0sce3ccaLd/8KaVrg7SQzSSpgprWxFenIIQgKEqd3CptnVFDUIsm2T7pIzwjVHqEsOIYZDARMiWgP6wSk57KBZKMp+Fb+0lRh/VLq7Q27U+iVymRK5h6t4oVWhuiPotHrU1bTDzcMBLq7CFR0sK2rigYOUVON1KPozB/eWYdmlGQZZd0tTN6ytLQwapDqWXcWnWl200jA/wx85nl3FC2jGpgRBbFjmjbO7aRXY/Kfi0iP4woIhxUcqkburEJUn6iBt7oKitx+9HXLoWdFv/QBP91crtZA42MAv3BteAW7wCfFETFo4osYEm0Snm3NxJKsC77+xFe2tPVhx33wsvijN4Nv8fG0Wunv78MyDF8BSwIGJv4tNxWHBkqtmp8LfQxyFuc9my5ESLJucaLD1HzpWDRtrS8ECRizDxN5ZIsi2TdWAAdoKs3USQgyDAiZE/EVfBah5MISNYgqVYcJOpIzZIaetuZtfehlxm8O1tnbzaScpacK2E2aFZ9mc+MBg4aYmnU6l0sBOYo24RMNMD9qzvQgdUhmSxhguoFBd3orgUA8kjzV+EKqjrQeRsb4IixJXdxyGBQRYYc/zEcsiSZ4cw5ffy4AQa9bASPrtpxy8/com+Pm74bFnl2PyjFiDb/OH345hy85CvP3iSkgE+H77pw6X1OHLbTmIC/bGZTPGQKx6FEpszSnD7UsnGew4qaahAxfOHflW8+dKpVTzwq+EEDJaUcCEiNbAyYAJS8EWilajg42dFaxtjf+nIu/ph40Rp4N0SmW8ZkpwmHAdEprqOyFtl8HWzkbw6S9u7vYIjxBHt4iqijbUVLUbpL0xm3rk4+eCsEgveHkbJlim0+n5CDYr5Bxu5Hn2rBaQpZUF3B1sRRmYsByFHYlGymgPlrCg7HuvbUFpUROcXSS4//ElRqmZxAIl+w+V45WnL4a7q/GyGEdKv0qD577eDnMzMzx06QxYi7jg6KbDxZg1Ngo2VobZx5p6KRoau3iHHKFo1axLjullKAmJTccZ8Sk51FaYEIOhIzUiXmZASHwAzAWtYaIanCtvbfwTLVlPH2+JaSyd7XJ08mCFcCeVnR0KJI0JRmi4sIGK+poOdHf3IUAkGSb1NVKMzzRMp4/a6jZeX4St21AnqG3NPcjNrkJ3lwKeRq6Pw7o+Ndd3ncpYExt7JzsolYOZbOT80dkhxxsvbuDZJRqNFm98eJ1RgiV7DpTi3U924/ZVM+BnotOZ3v81C57ODrhydioSQsVbLJl95ny36zgWTzBcy/DjRQ2Q2FkjQsCBDja4xY7VCCFktKKACREtdvJWU9ggWD2NoXR5C0tzQfaBTcNwcjHevGCFXMVrh7h6CNdZor6mHSdya+HqJlxNB5YNIZMpkTwmWNAOTaerrGg1WHeXuhopHJ3seIFbQ2lt7kZAkBtS08ONnjXQ06VAS2MXHJ2N3+nqXHj4uPBsMnL+YAHK2675EFt/y8OSZWl4/YNr4eNr+ODFgcMV+OHXY3jhiYsQLmDr+H+ioLoFX28/htZOGW5YYPx6SH/F0bJ6SGytEBNkuGBGbX0HZk+JgaWAx0l9ciWaq9sF274ptxUe6YUQYhj010XEi7VE4IETCBowsbK2FKzdKJvjbyztLT3olMoFLbba36+Bi6sE7kZup3w61sWFZUKwaRxi0d7aiyADtXpmgTJ3Twd4ehku84O9nza21rCzN36tBJVSi6AwT6O36D5XrE7RgE4PactgDSEyevGMg68O4qkH1/LA4bU3T8et98wzSqvrrCOVvH3wqisnIzZK2Jbtf5dao8Xb6/fzgsCPXzkLEltx117Zm1eFZVOTDLZ+VuNn18FSeAvcGUqn0cLOQVzt2gkhZCRRwISIPV4i6Dz2iIRAxKWFGnWb7CDoize3IedAhVHrG/T29PFLJxfhRuJrq9vR3dUHLwPU6vgrwQlvX2eECTwtaHgQx9PL0WCtTGsq2w36nre19PDt2DvYQohuW86u9qKsX8KYm5vDO8ANJXl1Qu8KMaCuTgVeefZnfPzudn77vseXYOU1k41S1PxgdgW+/v4wnn3kQiSZ8NSJD387jOMVTbh+fjoyYsXTwexsWAbMLwcKMWusYVrBM/WNnbAwt0CKwP+mbNoy63BH/noNk5FeCCGGId5KWeS8N1SsUcjKA4VHquBoxGkxQ1kt37y/i183ZrFZWztr3klEyKkLXR1ynlHk4mIvaHCitbnHKKO+56qhvtNgGSCsQ0ZCcqBhs3oGBhAV5wcPb+NnebCi0TUVrYiIFe+oemRCAIqP1WLSPMONRhPh5ByuxEv/Xs+DJvOWpODqG6YbLYuOFXd94Y3NeOaRC5CSGAhTVVTTgs+3HIG7kz2umpMKsVu/vwAzUyPhKDFckDi3oB4yWT+iw4Wt48KLvoooI5MQQkYaBUyIaFlaWfIuNUJNiWFYVw9zI88NHmplzDg4GS9YU1fVjvLiZkGyAIY4u9jDzd2B140RSp9ChcgYX/gImOVyOrVSyztosPfFEIoLGlFfK+XbMORUKxYMszA3/r+rncQGsp5+yHuVEKuMmXHYc88aXgtAIuDfHxlZfX0qfPTWdmxYn4OIaF8sWT4Ol149yWg1sXbsKcb6jbl47rGlSDbhzBKVRouPN2TDSWKLx66cZdAgxEhNHTpcUIO7Vkwz6HaOFzYgMdYf1gIeIzHs+9o3THwt28VMDzO+jPQ6CSGGQQETQv4gw4XNOTf2ybtKqUFAqAekrb38ZM+YB/eMUJkVbCpScUE9vI1Q/PDPMkzKS5pFc+Iqk/ejqaETTs6GCWhIHKzhH+Bm0H93FoRiU50sBOh4xWryRMf7QavTQazcvJzg7e+GfRuPY+4l4i5kSc7N8ZwavP/GVrS39sDX3w233TsfcYnGC1ps2HoC73+yG6ufXIaEWH+Ysvd/PojdeZVYNiURmYnGnSL7d+w8VgF5vxqJYT4G/b5s75Bh6oQoCK23U45OqsH0l1BbYUJMCwVMCPkdet3AqRoDxu7S0lAt5aOQllbG27avvyuflmMnUFthVpwzJNwLHp7CFrBj73tichCcjNjS+Y/0KdT8RMtQtQ765GrI5UqDjlK6utsjISXIqDV5hji5Sni2mkalhZjNW5GOb9/dgekXpMLahr6aTZVCrsSaT/fh+zVZvA7XZddOxoqrMmFnZ7wCpd/+kI3DOVV45bkViAo37ZH/4xWN+HLLUfi4OeHO5ZNhCrYcLsGyaUkGrb/W0NTFM0xuuEL494RNybG0pik5hJDRi47KCPmDDBMLK3Ojp7uyaUicGWBtbbzgRWlBIy/OaczOPKdTKtWoKG2Bo6OwgYrmpm7kH6+DnUQcHRjY+9LcaLjRu65OOa+bY0jKPjUKcmsxYWo0jI2dtChkSjTVdUCn08HCQpwH9rFjQ/h0uK3rDmPRFZlC7w75GyP+WXvL8NZLG9HRLkPmtGgsuSQdY4xYNJztwwef7cX2PUV4+ZlLEBzoDlOmUKrxxEebkRTmh5sumAgHO/HUlfo9RdUtOFJUhyevm2vQ7RSWNsHPxwUxkcLWL2HY94e1yDsWiQ1lmBBiWqhLDhGtAQwgcmwovxQCO9HSafTQ6Y1b/V2rHQyYmMEM5kYckddodLC0tDB6Rs3pU5EYoUfXHZ1sEZvgz7NtxIBlHEXFGK5gKavXEmHA9TNOLhJezFfZr4YQYpIDeA2VxtoOiNnl/5qDvRuOo6VB3PtJztRY14lH71qDfz+wlmemXbQyAw88tdSowRL2vbH61Y28c8o7/7nc5IMlzGtrd6NR2oO4EG9kxAXBFPyw6zhmpUXBxcGwgf/DOdXw9XKCtZXw455qpUbQboaEEGJown/SEvI7rKwsUX6smp/En0/YKGHcmGDUV7fBCB0nTwmP9hG0zgML2IRHecPT21nwDBNWCNVaJCnGrI5OTVWbYVs5dyr4752hDnrdPRwRlxzEgz9CYNuuLmtFyYl6BIWJp130cP6hnsiYEYdv3t6BO59bbrTioOTv6e9T45vP9mHLL3l8lD0yxg93PrQAUbF+Rt0PhUKJJ1f/wosrP//kRXAWyXTCf2J3bgUqG6QI8XHD7csmwRR09vahuUOGm5dOMOh22Gc165CzdMEYiIHE0Q52Iqn5ZSoow4QQ00IBE0JEhs17L8qt5S2VjVkks/hEA88CEAo7ma4sa0VQiKdwO8Hb7NrA28dZNCerrO5HqAHrEMQnBfIpBGq1DjYGyu5xdpWgMK8OLq7GbdE9JCLWj2e31FQYLvA0Ui68bgpe/NdX+OGDXbjklplC7w75nc+q7RuOY/MvuSg6UQ8HJzvcfM88zJyfaPTPjTapDA89+T2S4gNwy/XTYGMjzJTKkSTtluOZz7bywqmfP7oStkacmvpPrN+Tj84eBRLDDRswq63vhJuTBKlJ4si6aW/ooIAJIWRUE8cZASFnc/LsnQUQhMBGcUJj/eAjYGoz2wdjbsvMmCktZ8mk8A90g4OjsAdecpkSrS09okkxZhlWJUWNBls/615TlN/AW9oaio+/K0IjvNDT1QchhER6o7e7Dwe2F56a8iZW7IT79meW4cThKuz8+ZjQu0OGfUZmHyjH6ke/x6vP/IKS/AZcdv0UfPr97ZizKNnowZKyihbcfNcXGJMUhDtumjkqgiXse+Dlb3ajR67ELRdOREywaRStVWu1fDrOJTPHGPy7Ize/Do3N3YiOEL5+CaNV63hhbfLXM0xGevk73nnnHYSEhMDW1hYZGRnIzs7+3ecWFhZi2bJl/Pns9/z111//f8956qmn+GOnLzExMWc8R6lU4rbbboO7uzscHBz4OltbW//W/hNiDBQwIaI1dMxhzKDB6djBb3VxE1rqzo96AjGJAYiKM24q+XCN9Z3oF6jOxZChbjTswF0MbG2tEBZuuGkkYZHevENSV5fCYNvw8nGGUqnhI/OsmLKxsbo8cy8cy1u8FubWQuycXO1x8xMXYs+vuTi8o0jo3SEA8nNreZDk8bvWYN+OYsxckIT3vr4ZV980ndfoMbY9+0tx1wPf4vorJ/FgiVgy4v6pb7Ydw6H8GiyaGIcr56XBVOzMLoejnQ3mZBi+sHV9QycyUkNgZSWOaaPWdlawdzb9aWDno7Vr1+Kee+7Bk08+iWPHjiE5ORlz585FW9vZszH7+voQFhaGF154AT4+vx+wi4+PR3Nz86ll//79Zzx+991349dff8W6deuwZ88eNDU14aKLLhrxn4+QkUIhYSJaLCqdOPnMqLQxmZ88ADV23QUWKDK3MOMn7uzk0lhFWFmXHCEzTE4FKk62cxaKk5Mtz3JhNVXEgHVOyTlSzX8PDXVSxLojdUrlCIswzGgu+x12cpagOL8BzY1d8Bcgayt1YiSfFrR/WyGSx4VB7ALCPHHVPfPw+SubeXefiXMShd6l8xL7ndnyax62/JLLC0Enp4bg0msnY2yGML9DLJC7bv1RfLnmIJ58eAkyTOB3+VyV1Lbi7e/3wc7GCjdfOBEWAhUg/zu+256LlCh/OEhsDP7vv313EVYsHQex6Gzpgau3i9C7YVLYUY6etUIc4XX+Va+++ipuuOEGXHvttfz2+++/jw0bNuCTTz7BQw899P+eP27cOL4wZ3t8iKWl5e8GVHp6evDxxx9jzZo1mDFjBr/v008/RWxsLA4dOoTx48f/jZ+EEMOigAkRdcCkKKv81Im0ENsfCloYewoGCxrozQZgzE2zII1OK0xRzqGfOzEliNe7EJJOP8Cn5agN3Gr3XNnb2yB5TBBkvf1wcbUf8fUHBLrzYAwLmhhSemYk/3uqLGkRJGASPzaYTwmqq2qHQq7kgSixC4/zx3UPLMCHz/+Knk4F5l9KB5LGwLIa845UY9tvedix8QQ8vZ34lLLLb5iKzGmxgn0n9fWr8cIrG1FR1Ya3X7kcIcEeGC0U/Wq89d0+eLrY418rpsLH3QmmoqCiCT0KJS6emWLwbVXXtqOnt59PwxILZw9HOAqQZWXKDFn0tbe394z7bWxs+DKcWq1GTk4OHn744TMGN2bNmoWsrKx/tC/l5eXw8/Pj03wmTJiA1atXIyho8HeWbVOj0fDtDGFTdtjjbLsUMCFiZDrhe3JeYtkWQk6NYFkmxs54GMpsYe2UdUbMcohNCkBUvHBTclh6cX5eHVqbuyEkXz9XxMT7Q9mvghiw4r911VJ0dRpmyoyPvwsCgtzR3SWHIfkFuaGkoAFlxU0QAjsQnL1kDI5nV2HXhuOC7MPfERLti7tWX8Kn5nzy0gbejYUYhk6rw+4t+Xjynm/w0K1foLq8FUFhnrj+jtl4+8sbMXlGnGDBkrr6Dvz7+Z+hUmvw/htXjqpgCQtQvfjlDmQX1WFiYihmpkXBlHy9+RhcHewQHmD4f5PcE3VIigtAVKQ46pcwTVWtosnIJEBgYCCcnZ1PLSxYcTZSqZRnL3p7n5lZym63tLT87e2zOiifffYZNm/ejPfeew/V1dWYPHkyZDIZf5yt29raGi4uLiO6XUIMiTJMiKiZsZRcoaq+AkgcH87bGxu9K0q0D9qbe/hBiLFmBteUt0Ha1mvQ9rJ/xPpkhxa1StgTQvaelxQ2QiEXR8CEGZMWgq5OOUINUMskMNgDDXUdvJWyIbGpDPaOtjh6sByr7pwNIcy9KBV7t+Tju0/28uvG/tv+uzz9XPDQG5fjk5c24oU7v8KqRxbDN0i4YtSjTW9PH7b8fAx7txeirKgZcUmBiIz1xYprJmPitGhYWAhbK+JAVjme/89vWLJwDK6/ZgosR0m9kiFbD5Vg59EyhPm7464VU2FKWjtk2J1TjqdvXmCU7R3NreVZh2L6HdCqtaKpp2IqDJlhUl9fDyen/2VonS27xJDmz59/6npSUhIPoAQHB+O7777D9ddfb9R9IWSkmMbRIjlvCZ1hUna8zqitfRkra0tUl7YM/uxGrJ9ibWvF32s2LYdlNRgbqw9g72ADlVIDIbH0+7gEf/T1CVt89nQqlRZNDV0Ya4Bp66wzEWstbOggmbunE2ISAqDsU/PAnIeX8VPuWR2V8dNikHOwHFt/ysHCSzJgKmwlNrjlyQt555yX7/0G05aMwcLLJxitxtFoVFrYiA0/HMHRgxW8tXZSagjGTYzARZdPwJj0MME7ZbGOTh98sgdZ2ZW4/+75mCZgTS9DqWyQ4tlPt8Hfwxmrb1kIWxPr9PP99jxMSAjB9NQIg29Lq9Pz76WpkyIhJl6B7pA4UdFXsWDBktMDJr/Hw8ODB4OHd6dht/+ooOtfxTJJoqKiUFFRwW+zdbPpQN3d3WdkmYz0dgkZSXSkRUTNP8JH0EKkLHihNXKqKQtcMCyxpk9hvCyHsChvxCUH8m4mQrCxseTBEqEzO1hXmqKCRl4EVSxi4vx5DRND1Y5hJ2aH9peh24CdcpjwKB8U5NXh8L4yCGXRigw01nbg0O4S9PeJJ4voXLAT+JkXpuKB1y5D0dEaPHTZ+6gSaIqTKWeT/PztYdx19Ye4/8ZPseXnXASHeWLR8nG4/aGFePbNKzA2I1zwYElLaw9eeHkjjhyrxnNPXjQqgyWKfhUefuc3qNRaXDE/DWH+pjXNSN6vwvfbjyM21Jt/jhpaaVkzTuTXIyVRPPVLmKqCekHrn5kiMbQVZtNiUlNTsWPHjv/tl17Pb7O6IyNFLpejsrISvr6+/DbbppWV1RnbLS0tRV1d3Yhul5CRRBkmRNTaGzr+XunvEWJpZYk+mXFPqqyt//dnqZAZ5iT5bDrb5Sg6Xg9Vv5p3iTE2doISmxAAm5MBI6G4utnzbjQ93X0QC1YIN2tfqcHWn5EZyX/msqJGpGcarn7AlFlxyDlUgYO7S7BwmTAtQ51d7XHJtVPw4Sub8PPXWbj0hmkwNd4Bbnjg9ctwYEs+XnlgLcZOisKSKzP51B3y/7ECzrmHKrHxxxyU5DdA1tPPCx1PnhOHxDHBmLkwGQ6O4hkh33egFC+9ugkzpsXhvdev4kHc0YZN/Vz92XZIbKywdFoiFk2Kh6nZerAEWq0WF81INsr2cnJr4eIsQWiwJ8SEFYynKTmmibUUvvrqq5GWlob09HS8/vrrUCgUp7rmXHXVVfD39z9VB4VlhhQVFZ263tjYiLy8PDg4OCAiYjDL6r777sPixYv5NBzWLpi1LGaZLCtXruSPs7oqbGoO27abmxvPhrnjjjt4sIQKvhKxooAJETVWAHXAyF1qTsempmg1WqNvMyk9FK0NXVD0Ko22XYnD4DxXhUIFoaojdHf1Qdp+ZoV3Y/P0chzsGtMm7H6cLjTck2eAGEpQiCe++HAPL7pryIBJeLQvlP0a9ECBrg4ZXN0dIYTFl41HQW4Nvnp3JzKmxiA0yvTSgFmAcdK8JKRPj8WW77Lxwl1fISzOHxffOA1efq4432k0LEhShbzsKvz2/VH+N82yBW3trLDsqomYMDWGF7oWOpPkdP1KNd797040t/TgzttmY/YM0wsinKsfdh7H1kOlg3VLVk4T1b/DuWBZeZ/9fBjzJsbB3WXku5edTXllK2ZPF65L0++RdSpgYSL1oM6HGiZ/xYoVK9De3o4nnniCF1xNSUnhxVqHCsGyrI/Tp32yAMiYMWNO3X755Zf5MnXqVOzevZvf19DQwIMjHR0d8PT0xKRJk3i7YHZ9yGuvvcbXu2zZMqhUKsydOxfvvvvuP3wHCDEc+oQjosam4whZwyQowvvktBwtzzYxBnbgWFncDIVMiV4jZjl4eDkiIMQD/Qq1oJkU9bVSXvh1qAissXl5O/PLtlbDttn9K4JDvXD4QDkPZrGCfyMtcUwQYuL8UHSiAYb+3Z6zeAw+eXs7dmzKx/IrJkIILIvrgssm8L+xN57+Ga98tsrotYpGirWNFRZfmYmZS1Ox6dtDeOaWzxGXGoJpi8cgJiXI5E5E/wlZTx9yD1diz5ZCNNZKUVPRNhj01umRnBaKhRePQ/qkSF4vSWxKSpvx6pubYWFugUcfWoQAfzeMVgWVzdiZXQ4HO2u8eMdi2JlY3RJm15FytHXKceGMRKMF07IOV+Cu2+ZAbFjRV0tr0/z8JMDtt9/Ol7MZCoIMCQkJ4dlhf+Tbb7/9022ydsPvvPMOXwgxBRQwIaLm4uHERwaF0tnWi/qKVn4Cb6yACWMrsTZ6wIRNyWiokUIuN15Wy3BRMb4Y0A+gq0sBb5/BwIWx2TvYInVcKAYE/L0bzk5ijcxpMagqb0FiSvCIr9/VzQEatR41Fc3o6VLwaSuGMmN+Io5mVeDwvlIsu3yCYCf0KRnh2L+tACUnGrD247247KbpMGUSB1ssWzUNS66ahEM7B1sQMzMuGIsJsxPg4u6A0YZljLBpZMeyKlCa34CjB8rh4GQHWa8STi4SJIwdnG4zcUasQX+n/wn2/fb1N1n46pssXHfNZCxfmmaUehhC6eztw0Nv/or2LjleuHMxgn1NLzDEThh3Hi7DzPRIxIcP1mUwtKLiJvh4O2NsirjqlzCRY0N5UWpy7gYGzPgy0uskhBgGBUyIqPXLlTAXcIR0qACrWqnhJyTGIrG3QQcrUNhlvICJo7OEX8oErN2h0w2g8EQ9ujrkggVMGLmsH9UVbfxkhgWSxIAXo81vMEjAhJk+Nx6fvb8LRw9XYeY8w42aeno7w9nZDof2lSI3u4oX2BTKqnvm4Y6V72HDd9mISwniQRRTxzLiJs9L4ktteSsO7SjE3cveQmRyIMZNiUHa1Gi4ejjyYLCrp6NJZaCwIr0VRU0ozKtDYW4tjh+p5jWPWE0SV3cHnm3DuiCNyQjHuMlRcBR5546aWilef2MLtHo9XnnpUiTGB2A0Y5k+7323D+2dcly2IBXT08TV7eVcZRfU8gyTp241Tith5nB2FQZ0A/DzFdd0O1Yk9PieYlx4i/gyX8RMDzO+jPQ6CSGGQQETIvoaJkJmmAwVIDV2q9vIBH8+itVnxC4ejs52p7pICMXdc3AUvEMqg5DiEgP4KG9rSzf8RJIan5IWipKCRoOtPzk1hBdmK86vN2jAhLn4qkzkHa3B918cEDRgwkZFH33lUjx28+d46eF1eP2rm0ZV/Y/gSG++LL9hKkqP1+PE4So8ePl/ERTljaxthaceT50cjfBYPwRGeIkmu4EFR2or2/j0xMqSJh4caa7v5N8JLLOEZZJo1DqMnRCBUPYzZEYiNjHAJKZWsfoXv/6Wi/c/2IV5c5Nw0w3TIDkPRujf+mYvftlTiEVT4nHbJZNhqr7fkgsvNwfMyjBcvafhcnJrkDomBGKcjjMUqCWEkNGKPuGIqLGDY72AARM2NcbGzop3jjGmLqkcDdVSRMQbr/Com4cDr3bf2228zjzDseCEh4cj2puFrR/i4mrPsznqazpEEzBhNUbee20Lbr1nrkGyXiJj/Hj9li2/Hsf1t8+CnQHrPEQnBCA6zo+3sC4tbEC0gCPrIRE+WHXvfKz9aA/+/a+v8dKnq/i0rNGEBcLixobw5dJbZuDg1gJkbS3gWSbVJc08eMKCwo4uEvgGuyMwzAtRSYHw9HGGb5A7vP1dYWeA2jls2mF7aw+kLT1oqu3gba1Z5gibFlZT3nqy6PZgW3cbW2teJHDcpCgEhXkiOT0M0YkBJvdvVV7ejP+8uplPfXzhuYsxRoQnwYawLasE3245xgMNLFhiKZLMvb+quKoFRwvqcOvKyUYLLnZ0yPkASkZ6GMSGHRv5h3nDkgImJln0lRBybugTjogaGy3UC5xhoupT8xomxjSUSm7M6THOLvbQ6wb4dBihuHs6QtomQ2uLsAGTkDAvfllb3c5b7opBQJA7z3ypLG9BVIzfiK+fdV6YNCMGOYerkH2gHFNnGbZDx7IrJ+LhW7/A2k/344mXL4WQps1PQk15C4py67D6vm/x1FtX8pP10WrinAR8f+xplJ5gWSeVqChoQGVxEzrbZLzIdtmJBuzbfAJqpRZuno7obJfBP9QDWq0ePix44mADZzd7uLg58PfJ/mRLXhZc1qgHCzYr5Cp+QtndqeDPaa7rgIWFGapKW/nzyoua+DpYcJR16OqTq2BlbQGNVscDgmzE2j/YHSnpYQiO8EZUvD+/NNV/l/5+NT77fD9OnKhHYkIAVl031aBBSTEpqmzBMx9swbi4INy6YhLcTk7/NEVf/3YErOTlnImxRs0uqa3rEOWULY1Ki8aKFlhYmmYAjBBCzgUFTIiohcQHQP8nFbkNyfbkqKpSYbypMQxLN2cUvcbL9nD1GJwOI2TAxMfXBQ6OtpDLhCs8y4RHekNib43KshaIBas1wTJfcg5VGSRgwsyYm4hvPt2PzT/nGjxgMiY9DPMvTMWuLSdQeLwO8cnCFjO86vZZePXxH1Fyoh5vPr0e/3pqqWjq1xgCyxhJmRDBF4aNYLNW5vXV7agqakJvtwJ5WRU8gMICJt1SOQ+CsOewkyOdVj8Y4FDrYGVjCY1GxwMlvMOVrRWv+8Sz85Tak1l6Gtg72vKsElaAlj3Pwtwc3v4uvK2zh7cz79LlHzKY4eLl63xGO0tTduBgOX5en4OOTgXuuXse4uP9cb5o65Lhwdd/hlqjw4JJcYgLM70W3kPqmjrR0aXABdMT4XwySGgMVdXtGJMcBCcR1uTRqjUIiQswalH80YCKvhJiWugTjohac3UbzISLl8Du5LxyNp/emHwCXRES5W3U2ilsSk5knB8v4iYUtg/sxKu8uBlC8vJx5idz5aXiCZgwEyZH4eDeMoOtPzjME5NnxKKzQ47W5m54+7oYNAA0/6JU7NlWgN++P4K4pEBBC5Cyk/O7nlqKlx5Zh10bj8PBSYLrDTT9SYzYe+8T6MaXcVOiz3hMrdaio7UX3VIZujvkPBuEBVRY0IR18mKBkp6uPh4QYa19WeaJQq7kxWVZvQ5WkJVlC7KMEhYQdXZ34JkrrLi1KRWd/asaG7rw7vs7cOxYDa67bgqWXpgqmhoxxqBUa/Do67/Cz9MZMzOisWByHEzZ178dRV5JIx64frbRtskCmVu3F2LR/CSIkbJfg5qiBpPN/CKEkHNBARMiahYCF30VKsOEHVTXlLXC3dvJqNOA6qraeQq9UNjJk3+gGxrrOvjINpsmItR+TJkZx6e/sBM/sdRJYIVfVz/xE1bdNoO3AjaEjEmRePnpX7Dhpxxcd+tMGFJ0vD+mzU3Axh9zkJoRjlmLUiAkdtB/33PL8e7zv+LHL/az0xVcd9fc8/5kwNraEr6Bbnwhf44V6/7mm0NY991hLFyUgk8/vYG3hD2fsBP95z/YivyKZoxPCsHtK023yCvT3iHDpn1FmJIWjtAAd6Nml3R1KZCWGgox0qgGB3WohslfQzVMCDEt58fQGTHxGiaDRf+EwFLHWTvOocKDxuLsas8vtWqdUYMEnt5O6GiXCRqkik8ORESMLy8GKSQ2/aWksAklRU2C7sfp7O1tMGNeIo4eqjTYNthUnHETI3BwT5lRMpyuvGk6UieE44v/7obMiFPQ/ig4cOsji7H40vH48YsDePmx76E0ctFnYppYkHfL5hO49ebPcCynGk8/swx33DHnvAuWMJ/+dAjN7T0I9nPDs3csgqWFaQcdv/rlCEL93HD5wnFG3W5ubg1SkgIRF2uYaZj/FDtGSciMpoAJIWRUo4AJEX9bYa1wJ+9srn758Xr0dhk368LpZMDE2NNjElNDeMCiU8C2vq5u9ig8Xo/6GimExAqs+vq7orqyDWKSkBSI7ZvzDVroODLGF3XV7di1pQCG5ubhiInTYtHdKce6Lw5ADFjQ5KYHFmDBxeNQmt+AB6/7WNC/CSJ+ucdqsPq5X/DO29uxaPEYvP7GlUhPF65ltpC2HCjGh98fRH1zF16590I4GKBlckFFE9Qa4xRj7+7twy8782FlaYHEaOMGLg4droS9xFq0U7nUSjUKDpSe91l4f7eGyUgvhBDDoIAJETX/SB8IWPP1VPcHRa9xi5Cyuf8s/Z3N/zdm0GRAP4D8Y7VobeqGUIJPdqipqWqH0AETVsfjyMEKiEnm1Gie8cQygQxlyfI0xCb449vP9xsl22jBRak8q2Xtp/uQkyWO95udANzx+AWYviCZ1+i467L3USGibCMiDhXlrXjpxd/w4ANr4exihy++ugnLL06H1Xl6Anm8pAE/bsvjbYNX330BAnxcR3wbzdJefPRDltHqC/22qwAOEmtceUG6UWvusM5KcrkK49LE1054iPrklBwrayuhd8WksOCGfoQXCpgQYjgUMCGi1tXSLeiUHInjYO2KPrmxAyb2aK7v5J0leoyY3cI6VjAtjcIFTEIjvJA0NtigAYFzweqWhEf5oCi/nhe9FAu2XywLZ+fWfINmfUTF+aOzQ4EDu0tgjIKrq/41B8lpoXjv5U3o7TFeO+0/wk6OWPec+cvH8ekWz9z9NbauzxF6t4gI1NdJ8dwzP+PmGz+GrY0VPv38Btx+xxy4uAxmB56PWEbJA6/8jPyyJjx52wKMiR35NrhsAOH/2DsL6Kiu9uvvQtzd3V0IhEBwAsHd3d2luBV3l0Jxd3cnEBIg7u7urtBvnUPp1/bfvgWamXsnOb+17prI5J47k5k79+zzPHtvP/kMc0e2p8bcgqa4tBInrvpAVkoSrVyEWzEUGJiM6KhMfgsmFdWQUZDGDxz5jTEYDIYwYIIJg/ceJly25HwRTMpKhOutoKgiRw1PySQtPSlfaOPq6qvCwFhdqCLNX9HRU0F0RDoC3iWAa1q3t4acvDQiw9LAJzx7ONFjIsaKgmLQqJY0WvnEwedCeQ9q66mgUw8npCbl4szPLwT62L6VDj2csGrvcKKg4PENf2xfflXoyVkMfpCWlo/NG29j3JgjkJYRx4FDYzFrjid0dRu2IW5+UTl2n35BBYZx/VrAw+3PSUt1xfVnwXCzN6TeKMLgyn1/lFfWYEjPpkJPzHr3Ph56usrQ0RFcWtl/paaqFuXFFTRKnPH1kE838hFXpxvXD4rBqMcwwYQhAoIJdxUmcorSsG1mQkvyhQm5MCOiCfFQSU0QnoeGtp4y9a6Ij+YuTpcIRaSyIykhh/PKDuLlkZdbAv938eATTVxNEBWejvDgFIGNoa6hgHadbNG48Q94/lBw1Sx/xKOHI/oMa4GbF31x47wP+IS5tS72XZxGo3F9XkRg0fijiAhK5vqwGEIiOTkXm9bfwrSJx1FYWI7tO4dj3vxusLDURkOnorIGCzdfh49/Akb0aIbx/VsIZJzUrEJ4+cWhr4cjhEFJWSUevopAS2djdGsr/Ehk8hno0dEWfIYI25ZNTZmHCYPBqNcwwYTBa4hgwGWFiayCDMLexyONAz8NHQMVfKz5iNQE4Zmf6hp8jktMSxZeVcvf4dLcBEoqskiIyeL0OOwdDaiXSQaHni7/JKj16t8Ur18Ktl1m6JjWyM0pwS/7n9J+emG0wIyZ2gEubqa4dekdwgL5JUjIK8pg2fahmLyoO5LisnFs1yMc3fFAKGlCDG6IikzHmpVXsXXjHfpeWLN+ADZsGgQHRwOuD40XEJ+tPaeeIyIuEy52+pg02F0gPh8fP33C2TvvsWBMR6G04hCuPghEamYhHKx0ISEu3EWT1NR8BAUlw9JSC3ymrKgcUR/iIC7JKky+hU/4QSAbg8EQDEwwYfAa0hv7q5CTYv6IvOJn09fSIuHHnSooydDbjJQ8oY0pKy9FhYrMtAJwCWnPIB4mpIqCS0hlkbyCNJ4/CqWVJnyiUzdH3LvhL9DjUlSWwfDxbaCrp4Lr54RT8SElLYHpi7ujuLAcJw8+Q3Ymt/HSf4VMBj16OuPAlRkQF2+Em+feYumk4/DnmTkw4/shrZC+b2OxYPYZHNjzGNXVHzFpakds2zUcTs6GQjX+5Ht1wabDj3H7aSg8Wlphw7xeNElGEBCxxNxQA3qaSkKrLvkQnETNXvt7OkHYBAclQ1NTEY5OhuAzpCWHwCpMGAxGfYYJJgzew2WFiaS0BL0QKCkUvqeHmqYCvc3JEO6E0dpBn0a8FuZz52NiZatL7CKQGM99pK9bKwuIiTXiXVuOsqocPLo64JkAI4YJvQY2Q0FeCc4f90JGWr7QvHRWbhuCMP8krJx1FmVCNl3+GnQN1LDu0BjMWd0XSbFZOLH3MdbNO49MIT1HjLqHVFHduuGHqROOYvOG21DTUMDseV2xftMg2Dvoc314vOPUjXe49yIMmmrymDWqLWQFEB9MiEnKRnhsFvp2dICwuHIvAAFhKRjSw0Vgj+t/8epVJAwMVCDFc2+QHxo3gp27JRMRvxEWK8xgiBZMMGHwGiJWkJJfriAXAapaitRLRdho6CpDS09ZKLGuf0RFTY4aiKUkcBfrq6OnDG1dZfj7ci9StGhtAUkpcbx6GgG+0bN/U1w49QYV5YJrl5GQEMP0Bd1gbqmNg9seCM2MlSTmkEoTGTlJrF90GTU1/Ekq+mO6DzGEPXJrLqwd9fH6cSg2LryIYzsfooQnST+MfyclOQ8H9zzGsoUXcPzICzR1NcGho+OxeFkvmJh9jjln/Jlzt97jyPnXcHM0ws5l/aGmLCeQcSqra7DjxHPMG91eaJPy0rJKXLzrB0kJMfTpLBy/lD9SWVlDE3Jcmws3led7KMkvRWxgIteHwWAwGAKFCSYMXtNYTAy1HE+UiPt7WkIOjTMUdoVJZmoBcoXckmBook5vkzjwbfkCuTA2MddEemoB560wKqpyMDbVgN+7ON5VOhiZaKCpmymePgwW6DguLUzp6/Hdmxh4v4iCsOjWvymcmhkjIigFR3Y8Evp78GtRVpPD1MU9sPfiNIiLi+HpnQBM7L0Ll4+9QqUAxSzG91NdVYuXT8OxcPZZ7NxyF4F+iejYyQ7nrszExCkdoKHxucKP8X95+Coc+0+/onHKE4e4w0CAiTUHzr3C4K5NoKEqD2Fx4bYfjHRUMLRXUygrCj8mOiAgEaYmGnB15b9gQha0pGSFX4Ej6nz69QeBbAwGQzAI18WKwfhG5JVlOc9KU1SWQ8rHLJQVV0L+N18RYaCho0xvqytr6Kq+sFbXTCy1YG2vhyyOWwvsmxgiLSUfkaFpcG9nxemxdOrqQCt9fF7HoGMXe/CJvoObY9XCC/Do6ijQ8u0p87ogJ6sYezbdgZ2zARSF9F4YOaU9aj9+wsWjXtQEetI8T96Wf5vb6GLriQnwexODozsfwvdVFK6c8MKQSe3QubcL9QhicAc5j8bHZuHh3WA8exRKPaqsbHTRo7czNXfm6+uKTzx5E4n1+x+gia0exgxoAWtTwZmSvvGPh6S4GNq5mkNYFJdU4PJdP+pbs2ZeD3CBr08cSkoroafH/6jqyhJ+LSKICl+igOt6nwwGQzCwChMGryEryrU13LXkEBRUPq8wlRQI19NDU/ezYFJVVYsiIY5taKKByOAURASngkvsnQyQEJvNC+8QV3dzRIWl4ZmQ4nW/1e/F3tkQT+8HCbxVq+fAZqisqMH5o68gLGhyzrQO6DvcDU/vBuHQ1vtCawv63uNt2soC+y9PR58RLaGqoYAnNwMwstMWnD/8HFnp3BoqN0Sys4pw48p7TB59BCsXXUJQQCJGjG2NXQdHY9GKXrB10GdiyVfg4x+Pn/bcozOzQd1c4GInuKSgvMIynLruizH9BBNR/E/cfhJCTX97eNhDU034VUbk3JaWmo8WLcwgCtRU10Bcgt8+KwwGg/FfYRUmDF4jJiGGjxy35CiQKheAihY6xp/bVYRV5m/tbEBNb4UZW0rSeYjZYUJ0llArW/6KibkW5BWkEPiB+/5oVTV5NHE1QWlJBQryy6D8m4jGFwaPdMfSuWfRrrM9ZAVYHt3e0w6BHxJw/YIvLGx00KGrg9C8QkhlSVlpFUIDkrF/8z1M+7Er/TlfIcfWysMW7h1t8O5VFG5f8MX5Iy9w5uAztO/mgA7dneHY3BiNG7N0CUFAIoC9XkTA+1UUAv2S0KyFKZo0NYZHF3uYmmsygeQbeR+UhKVbbsHBUhc9PezR2lVwE3oiWBy78hazRrWDrLQEhEV+YRmOX/KGpIQ4hvZqBi6Ij8+Bv18iBg91gyggLS8NUwcWsf2tCMKklZm+MhiCgwkmDF4jJtYYNdXcVphoGajA2sUI5cUVQp9wkTjjlPgc5GeXQPO3Fh1h0Ky1BTV9zUwvpOarXNCo0Q9o18kOiXHZyMkuhjrHngLtO9th60838fxRCPoN4dfFrLmVNlxbmuP+DX8MGC64FVkyyZwwsxNiItJx68p7WNvrQ1tPWWjvh7kre+HQ1ge4ecGXth+NndGRE0Pmb33Omre1oltcZDqe3w3GrfNv4ecdC2kZCXj0aoKOPZ2E+v6ur6Sl5OGtVzSiIzPw4mk4ZOWk0K23M4aOcoejsxFt6WJ8O8ERqVi86Tqqa2rRubU1PNvYCHS8c7ffQ0dDEbbm2hAmZ675orKqFp1aW0NHUxFc4PM2BtLSEnB05Hec8Beyk/OQFpfF9WEwGAyGQGFXDwxew4cKEwlJcUT4JSI/u1joY2vrf+5hzkjJE+q4yqqyCPVPQlxkBriErASHBiQhgAdpOa3aW9MJLmnL4WNLyPCxrXHuhBdyBfw6VVCUxtR5XRAVmoYtq66hulp4708imkz9sSvGz+mEyyffYM38CwJNCKprTK10MGF+F5x9uhhjZnWCpJQEfF9GYrTnNvw05yzuXPRFQW4p14cpMtTUfETAhwQc2fcY44ccwI4Nd3DxjDdkZCWxdutgXLozFxOndUSTZiZMLPlOgsJTMX/dVVibaWHu+A7o1UmwVWVhMRkIiUrH0B5NIUwys4vg45cAeysdjBrAnSAeHZGBLl0dIC7ObyH4CzVVNRCXZGuv3wqLFWYwRAt2lmPw3vS1EccryIpqn935C/OEP5HRNlCltxnJwhVMzK116G1seDpadRTsauK/pbMQ/Hzi0LmnE7iErPr1GeSKG5feIToiHZY2uuATOnoq6N7XBdcvvsPEmR4CHcvBxQgTZnrg7jU/HN75EDMWdYcwKzYGjW5FIz9fP4vAosknsHrnUKj89j4VBYjZqGffpujcxwUJ0Zl4ejsQaUm52LfuFh7f8IeEpBhae9qhqbsFdH47BzA+t2okxmcjyC8RSQk5VLwknjouzU1p/LdbKwtY2+mhcWMmjtQFIZFpmL/uCq26aNnUFP27NRHoeMWlldh1/Dk2LuhFKwyFyYnLPkhJL4C9lS60NbipLikoKIP3m2gsWMSN2ez3IKcsC31x4VYCMRgMhrBhggmD91SWcevCrqQqR2+LOBBMjK20Ye1kgOKCcqGOa2ajQ31MCvK4jfTV1FZCGw8bJCfmUi8XrleJW7axxPkTr3H3uj/vBBPCkFHuGD/wAFq2sYCto2D7yvsOa4Fg/yQ8vhsMG0cDdBByelDvoW5Q11LCluVXsWP1TYyb5QETC8EldggsPttSm27E4Do8MBnB7xLw8IYfvB6F4cDGu2jqbk4rzVxamMHOxQhyCtJoKJDI0tioTIQHpyIkIBEhAckwMFFHQlwWmrubY9Kszmja3BRaOkpcH2q9IygiFQvWXoWVqRbcmhhjWG/BenqQqr3Nhx5h8lB3qKl8/swVFgnJuYiNz4KYWCOMGSxck9k/EuCXCCUlGbi68T9O+AuZCdmcG/OLIiQC+Ic6rghhscIMhuBgggmD14iJi6GyrIrTY1BSk6dxoJXlwj8ONU0FRAQm08mUUMfVUKAXsG+eRWLOKu6MX7+IJq+ehCMiNBV2Ttyay1lY61DRhHgllJZUQo5nMbHEs4FUfty8/B5WtnoCFZjICvDCNX2xdtEl7PjpJvW6IXHUwqRleyvsOD4eK2efw+IpJzFrWU9OK6L+a7uRXRMjug2d3A5JsVl45xWNtOQ83L/yAXERGVgz59zvAoqNkz6s7PVpmlZ9MDCtrf2E9JQ8xERk0PeXv288fY2FBqdCVk6SVo8MGuUOJxdDmFpqsyoSQbfhrP1cWeLmbIwRfZsLfMwLtz/AxkwLTe2F791x5KwXouKyMLSvK2fVJYRXLyKgraMM5d+M5kUBcu6RFqDROIPBYPABJpgweO9hUiNEj4S/Q0VDAWUllcgUclsMQc/ocypPakKuUBNryDiW9nrwfhZBJ2x6hmrgiuatLXD5tDfee8dwLpiQ56VFG0tsX3cLD+8Eoj8Pkww6drXHw9sBuH7RV6AGsAQiGM1c1B1LZpzGsf1PsGBVHypwCRNSVbLv7CQc3fMEaxdcxLAJbTBicjvem8H+2+vMyFyLboSpP3ZHRHAyAn3jkZ9bgvtXPyDoXTyS4rJpbLGBqToVEUyttKFvrA49IzVICTFd5Fv4WPsRmRmFSE8toMdfXFiOgHcJSEvJR3l5JfAr0LqjDfSN1GDrqI+Zi7vTqHNht2g0VN4HJmLJxhuwMtNCi6bGGC4EsSQ4Mg0fgpOwZUk/CJuwyDR4+cZCXKwx+nXlru2zuqoWH94nYPgod4gSJQVlUOLYkF0UITZodW2FxkNrNQaj3sAEEwavEZdojFqOBRPqo9K4EQpyhN+eoq6tSP0MSKwwMYNUUReeTwOpFvB5EYno0DROBRMbe33a/uL1NBxjpnbgfDW9fSc7/LLvCfzfxVNPE76tdJPnZ/biHti79R7c21kJPOVIz1AVC1b1xtIZp7Fq3nlsOTQaCooyECZKKnKYs7wnlJRl4P0iknpcLN7QHxpa9aNVQ0pGAs5uZnQjzFzRG4kxWYgMSaH+RkHvE6hhLDHg/fTxVxiYaqCyohrObqafxRczTeqbQirWyDlEWUUOMnKSdR7LTCrhSoo+R28TYacwvwxZGYX0/BUdnk7FkfiYLHz8+AmWtjpIScxD05ZmMLPSQqcejlTwMTHTpObKDOHj4x+P5Ztvoqr6I9q4mWNQTxeBj1lQVIbrDwKwfGZXoZ9LySLEgRMvYWOuDRdHA2hxWF3i758AYxN1tGhpDlGCmL4SY3zG9wgmdR0rXKe7YzAYf4AJJgxeIyUryXkiCbmIIz4mBTnCT8khExobZ0ME+sQhNSFHqIKJXRNDiIk3RlhgMjp0dwRXkLYSY3MNPLgZgKT4HBiZaoBLiIDVd3BznPj5Od6+iqLpOXyDiBgkGWTHutvYvH+kwFfnHZsaY+6K3rhxwQdrF17E+r0jhH4RTSpKxs/uDAcXY+zdeAcrZp7DyCntRLZF538hISEGC1tdun2hproGacn5SI7PQXpyHo0Fr6ysQfD7BPp98IdEGJppICk2+/M+JMVoq6G5jQ4VOYzMNVFcWAFVdXn6d8TPQVxCDL9++hVyClL0d+T+eTkl9JxMWtJIQhFpA0uMzYKOvgr838XR+5D9EcystGk0ubOrMSSlxGHfxBCevZvQ16eeoQrUNBQ5F0AZn3n6OhJrd96FjYU2PNpYo19XZ4GPWfvxE9buvo/RA92grCj8NhTv93EIiUij76f1i/uAS954RaOosByGRtwtTnwP2iYa0GDG1AwGo57DBBMGrxGXEKcrGFyjrKGAhIh0akIoJuRS/y+ms8lx2XBwNRHauGS1l0yWwgKSwDXu7a3h+zoGAe/iORdMCF17OePs8Ve4esGHl4IJgbTjkDSf+zf80b2f4FeKPbo7oiCvFEf3PsbhXY8wdX4XTtpimrUyx84TE7B99Q3s3XAHH7xjMXFOZzqRr+/nSlJJQra/UlVVg/ycEroRwaMwvxRFBeUoKSz/vdWPvNdzs4roOS4tKY+2zpBzXVVVLYzMNJCamAsjC01kphXS2F7yvyW32npK0NBSoNVwrm0saJWPsqo8VNTkoKomz/wNRID7z0Kw7eBj+lro2ckBXTvYCWXc8zffUUNZR2vheh99gQgl+jrKaO1mDrXfPme5gKQ/pSTloWUrC5ETEKM/EJG04RhR1xWCiAFmscIMhuBgggmD9x4mpLeXa3SN1ZEQkYbivFKoaAq3bNfgtwkQEUyECakQMLfVpckdxYVlUFDizojOqakxampqaZVJXx74hiirymHQ8Ja4e92PJsU4NBG+UeHXVOaMntIe8yYeh6OLEV3VFzQDR7nTtoxj+57SCgRiCstFyxKpkli/bwQe3wrA/s33qVfG8IltqWFqQ0RSUhzaeip0YzC+QASSs1ff4fDpV7Cz1sXAXi5o39JSKGM/845CbGIuVs8VXiT5X2nmZIST+8ZRkZBLIsLSEBaUgnET20HUIAta4pJsKsFgMOo3/Gq+ZzD+AvkgruGBYCIjL0W9AfKyhN+WY2yhBXM7XeoDIGxatLOEho4SbcvhEtI+QPw4EuOy6cYHOnV3pG0K5497ga8YGKlh7LQOOLb/KWqEFP04aHQr9BzYDHk5xdi9/pbQE56+QFZqO/dugkOXpkJbVwnLp5/GoS33qK8Gg9HQIVUNx869walL3pCSFMeYQS2EJpbEJ+Xgyl1/LJ7WmfOKCrHGjejj5xJvrygaJ2wr5JSxukDXTBtKQl5Eqg/8KqCNwWAIBiaYMHiNup4q3HsJvp3g31D97YIgL7NQ6GOTBIyY0DT4PIsQup+LiYU2stMLEeLHfVtOO0972DoawNcrGnxAR08FXXs3QVV1LY085ivd+jTBx0+fcPzgM6GMRyZA0xZ2pUbBD28F4sSBp9TkkytIVcXCdf0xb01fhPgnYUKfPXhyO4DTY2IwuISIp+t23sXJi29haaqFnWsHwbWJsVDGLi6pwMZ9D7B0RhdISzFzX/KZHhOdia49nXhnIP5vEDE86kMcqsqquD4UBoPBECiidXZmNDhIQo2du3BWvf4XX9pwcjOLhD62lr4KJKXFUVxQRpNyhImtswFt7SApHFxD2koy0vJpGwxZHeUD/YY1R3hQCk4feQm+QgSM+ct7ITUpDz5CEpuIWTGJgx0+sQ0unniDbauvU08MrvhcbeKMjYdGo62nLY7vfYI5Iw8jIiiFs2NiMLigrLyKiiXv/BOhriaP+VM7wdZSRyhjE5PX9bvvY+Lw1tDTFmx6l6iQGJ+DgA+JIlldQtpxiDG8GGvJ+W4Pk7reGAyGYGCCCYPxFZC2FDkFaU4qTMjk0/A3H5OE6Ayhji0lLYEO3Rzx6SOJCy0Hl5DVt/ae9shKL0RYIPcVLwR9QzV06GJPxYiwYP5OvhWUZDB0TCvsWH8b6an5QnvdjpzUHv1HtEBCTDY2LruKao4NnMnzMHNZL6zZM4J+vXLWGaxfeBHpKXmcHheDIQxyckswY/F5PH8dBWsLLRzYPAxGBsJLZdl//DmaORvB1clIaGPyHf8PCVBRlYOzi3AqfOqSqvJq1NZ8hJg4E0wYDEb9hgkmDMZXtuSUFpUjj4MKE0KTFmYwMFFHYnSm0MfW0FZEQnQmAt/Fg2s69XCEpa0Ont4PBl8YObEt8nJLqE8I1xHY/wtrez2Mm9aBChckNlZYlR0TZ3dG+y528Hoajs0rrqG05HPkLJeQuNt1+0di7qo+yM4oxJwRh7Fvwx3kcxAdzmAIg9iEbPy45gqKSyrhZKePlfN7QFNdQWjj33oUhNKyKvTvJvi4YlHi4d0gODgZUJ8uUaP6t88RYUfI1wuYiQmDIVIwwYTB+ArUdD6XD+dmCL/ChKCioYDk2GzEhqYLfewmbqb0NsCHe8HE2EyTVru8eBiKyopq8AFtXWXqZRISkIwPPnHgM549nWBorI4DW+8LTdwhosngMa0xd0UvRIWnYd74Y8jmoFLr746rZQdr7DgxAePmdKbx2VMGHsCRHQ+Qn1vC9eExGHXGO/94zFh0DvGJuXB3NcW2NQMgLye8mG3/4GQEhaZgwVTuTV75RGpKPhListGqLfdtx98rmFg3N6Ot04xvRBDtOKwlh8EQGEwwYTC+AjlFaUhKS6AoX7geIl8wtf7cYx4XIXzBxNJOD07NTehKPB8qKDx7OVPDTu+XkeALw8a2go2DHg7vfoyPtfw1EyWTlVlLeqAgrxQXTrwW6thdejfB7CU9UVhQhs3LryIqLA18oLFYY3Tp64JdZyZh1LQO8POOw5QB+3Bg811eCDsMxvdCztdXbvlh9abbUFGSxchBbpgz2QMSQmyhSEkvoK0408e1h6SE6FVRCBLiKeXUxBCubmYQRaorqxHhG8t5LDODwWAIGiaYMBhfOdHUM9VAekIOJ6KBsaUW9VGRlZdCRblwHemJqZuMrCQ+vIlBWhL3Xg/tu9hDVk4Kty69A19QUZOHa0tzJMXn4MHtAPAZUvo9d3kvPLjhL3TRqZm7OTYdHIWs9CIsnHQcr5+GgS9ISoqjxyBX7Dk3CRPmdkFkcCqmDtiPzUuvIC5KuN5BDMZ/hUxij572wt6fn6KqqgajBrfAhJGt0aiR8Fahi4orcOS0F5bO7kYFG8afefYkjH6WScuIZlrQ7y05Uqwl51shl5GC2BgMhmBgggmD8ZUoqsqhsrwaJYXCNz+VlpWkFS6RgclIjBK+j0mzVhb0logmXCMnLw1Xd3NEBKcijgNPl3+i/zA3NGthittXPqC0pBJ8RkVNDqt3DMGeDXcQJuSkGBMzLew5NRFtPGyx9sdLOLrnMa9WKCUkxGmizq7TE7FwfX8UF5RjwbijdPN+HklNDhkMPlNYVI75yy7h/JV3cLDRxY71g9Glo51Qj6G6phYrN99ENw87mBqpC3VsUSAjrYD6ObVpbw1RpaqyBhZNTSAjJ831oTAYDIZAYYIJg/GVaOiq0NvsNOGkjPwVi99iB6NDU4U+tou7Gazs9ZAYmwU+0L1/U5hbaePVYx5VKEiJo1MPJ8THZOHML/yNGf6CsakmFq3th9OHXyA5MVfoFTmzl/ek/8fQwCQsm3maturwCZLy49bWCusPjsL24xOgb6yGQ1vuYmTX7bhy8jUy0wq4PkQG4/8QE5eFLbsfIDAkBfq6Klgyrzsc7fSFegwk9v3g8Zfo0MoKbi4mQh1bVHj1PAK5OSVwbSma7TiEqvIqRH/g3ttMFGGxwgyGaMEEEwbjK9EyUIG2oRryMrhJyjG306W3MaHC937Q0FJCTc1HPL0diPIy4bYE/R2WtrpoLNYI18/78CJ15QttPWzQxNUEcVGZSIzNBt9xdjWBZy8nLJ99FjlZwk2IERcXw6ylPdGtX1OEB6Vg45LLCA3gR1z0XzGx0MLsFb1x8PJ0DJ/UDoHvEjCm+07sWHUdT3jynmAwHj0Lw/QFZ+HtG4ve3ZxwYPtw6GgrCf04fj71CuLijdG7q5PQxxYVXj6LQFNXU8jVsfludnohPn36JDQPE4I4a8lhMBj1HCaYMBhfibq2MjKScpGZzI2Ph5WjASwc9OgxcIFbW0sqmvi/jQUf6D24OS0JfngzgFdeN1PmeSIsMBl7Nt+lK618p72nPQaObImd62+hMF/4VR6kKmf3yYmoqa6lviaXTr6mpr58hPgNEJ8TEkl84NI0aOoq4fiex1g27RQ2LrqE10/DqV8EgyFMSEvb3kNPced+EKqqajF6aEvMmdYJMjKSQj+W2w+CUFBYhimj2wp9bFFqx/n140d07FL3bVJ7V14TWhJRZVkV9cRpLN5YKOPVK76k2tT19h3s378fRkZGkJKSQvPmzfHu3T/7w4WFhaF///70/uR1tmvXrv9zn40bN6JZs2aQl5eHhoYG+vTpg6ioqD/dp127dvTv/7hNmTLlu46fwRAGTDARQb7l5EYoLCzE9OnToa2tDUlJSVhYWODevXtCO976gqaBKr3NSuVGMCHGr/ERGQgPSEZpsfCrKtzaWcHcRoeaYfKB1h42aN7aAg9v+fNqgm1koo4BI1ogJSEHzx+GQBToOaAZHJwNsWTGaRQXCd+jx8RcCxv2j0LnXs5UACPCSWY6v1teSNXJ8EntcerBfIyd2YkaMl8+7oUh7Tf/Jp6EoYznXjYM0ScntxgLl13G1Zt+iIvPxuY1/TF2RCuhmrt+4fnrKDzzisT8aZ05GV9UePEkjLZBNm1uWqf7Jf5KJPVLWIJJTWUNXRQghtkM0eTixYuYN28eVq1aBX9/fzg6OsLT0xPZ2X9fIVteXg4TExNs2rQJWlpaf3ufly9f0jmHj48PHj9+jJqaGnTu3BllZX9ekJk4cSIyMjJ+37Zs2SKQx8hg1AVMMKnnJ7fq6mp06tQJiYmJuHLlClV5jxw5Al3dz+0djK9HU++zh0lmMjceJhKS4jCx0qZfRwUL16iTYGatQysQHt7ww0cemHSSlg5rez0kxefC+3kE+MTQ8W2grqWIA1vvIz+Xmyjqb2XI2Nbw6O6AJdNPoYQDQU5KWgJzV/TG2Bkdqdi0ffUNPLjhx4so6/9F48aN4NDUCLOW98KOkxPx094RUFGXx4m9TzCo/SbsXHMDV0+9of4/fH8sDNHig38CJk4/iZqaWhgZquLnPaPg1qxuJ+FfS0BwMm7c9cfapX1YfPC/EBOVAbdW5pCVrdsKoOTYLNi4GEJo/PADrJqbUUN6hmim5OzYsYMKF2PHjoWNjQ0OHToEGRkZHDt27G/vTypHtm7diiFDhtAF2L/jwYMHGDNmDGxtbekc5cSJE0hOToafn9+f7kfGIaLLl01BQeHbHwCDISSYYCJifOvJjfw8Pz8fN27cgLu7O61Madu2LT2J1We87wZgivtq7J5zqs72qaKpAFtXE0hIcXcx6NjcFJaO+kjmwB+DrFq17GCNkqIKhPjxw2uCmIaSSMbXzyN4NRmVlpbAxFmdaFrOqZ+fQ1ToN6wFmrU0x5YV1zipNCG06mCDQxen0yjrnWtvYc/GO8jOKIQoQMQTexcjTF7QFUeuz8Khy9NhaqUNf584HNx8D8M7b8PWZVfx8Lo/0lPyefWaZYgOpKLu9DlvrFp/gybiaGsp4eDOkdD7zZhc2ETFZGLv4adYPr8H5OpYBOASQVQuJifk4PWzCLRqV/fpOFEhqdD5rRJWGBTnlSDSN5a15HwPvwpo+wbIgioRMTw8PP5kdk6+f/v2bZ091KKiz75/Kip/Pj+dPXsWampqsLOzw5IlS2j1CoPBV5hgIkJ8z8nt1q1baNGiBS2P09TUpCemDRs24OPHf64QqKqqQnFx8Z82UaO6sgaJEWlIi687YYE810V5pXj/LJyziY6JtTaiglLg/zqak/FbdrCBmbU2Qj4kgA8oKMrQdpIXD0IQ4s8PEeePhqpDx7XGg5v+eMmjNJ9/E8VGT+0AGwc9LJp6krPkGlV1eazeMRSL1vXHm2fhmDRoP6024VPr1dc8l/rG6ug1uDnWHxiFtftHYvHGATAw1cCze0GYMmAflkw5iXULLuL2pXfU96ay4rOJIoPxT+TklGD+4gs4dtILpkYaWDDbE0sXdqciLRckp+Zj54HH+GlJb6iryaO+EB2diWNHXwqkHYckqrm1sqjzfceEpsLc7nOanrCuswgSzPSVV/z1+p1c0/8dubm5dC5A5gZ/hHyfmZlZJ8dCDIjnzJlDF2zJ/OMLw4YNw5kzZ/D8+XMqlpw+fRojRoyokzEZDEHA6iZFiP91couMjPzbv4mPj8ezZ88wfPhw6lsSGxuLadOm0Z5C0tbzdxDDpjVr1kCUUVCRo7fF+XXbDkFSclLjslFcUAbF38YQJtbOn8ttIwKT6QcREXGEiV0TQ+RlFePe1fcYNqU9XVHnmp4Dm+HqGW9cOO4FBxcj8AlipvrkbhD2bbkHe2dDqKgJ/zXzPRP9oePb0l74fZvuYvI8T6hrKnJyHB26OqBJcxMc3fsYR3Y9xt1rfpi1pDvMrUWvpVBCQgwOTY3pNnhca9pGER+ViYjgVGSmFuDGeV/k55RAQ1sRppZasHEygJaOMgxN1aGmoSA0XwIGf3ntHYPDR1+gqKgC+noqmDWtE8xMNTg7nqzsYqxcfwNL5nfnrLpFUFw4/xaTJ3eo032ShZaA9wno0ddFIAJXSWEFNHSEl4okJt4YNm7mEJNggsm3IogY4C/709f/c4w4udZfvXo1uIAs1oaGhuL169d/+vmkSZN+/9re3p56LHbs2BFxcXEwNeWmrZDB+F8wwaSeQybVxKX68OHDaNy4MVxcXJCWlkZ7EP9JMCFqL/FJ+QJRqP96AuY78sqyf1oBqUvBhJCZlMeJYEIuhlQ1FVBaVIGkmGxqBCtMiEDSqpMtbl/wRVhAEp38cY2GthJNzIkISUF0eBosbHR5laoyf2VvHN37BLs33Mbq7UNEZuI7aHQr3L36AQsmHce63cOhb6TOyXEoqchh/qq+aNc5Fge23sPm5dfg1MwYo6Z0gIKSDEQV4sFjaadHN8LEeZ60hYt4ncRHZyInowjP74UQmwDERWdCz0AVVna6kFeSof8LNQ15qGsrQlVdgUa4cgmZCJYUV0JSUoyunjPqlsrKGpy58BZnz3+uJB08wBWjR7hzVlVCyMsrwU+bb2Hu9E6wNPvzIo6oExOdCR0dZWhqKda5d0lYcAoGjWyJuoYkxtXW1Ar186UguwjhPjHUX43BH1JSUv7kB/JPXiOkHYbMC7Kysv70c/L9Pxm6fgszZszAnTt38OrVK+jp/e/KJxJgQSCLukwwYfARJpiIEN9zciOqrbi4OP27L1hbW9NyO9LiIyHxfy+4yMn1n06wooKiqhx16c9N++wTUFcXEUbWOjCx0UVmSi4sf6v2ECbkcbi1t8a9i74I908UumBCaN3ZDpHBKQh4G8sLwYTQfUBT3Ljgg/NHX2HV9qHgW2uOrZMBblzwxb3rfujerylEBeIRo6AkjVXzzmPhmr6wtudOOHVpYYaD56fh2llvnDv6CmlJeXBrY4nuA5rRlc76gJy8FOycDen2R4ifTGpiHk0OykjJR0JMJh7e9Ed2ZhHKSyup2R95nRUXV0BJWYYKSaRdTU1TAY3FGlGfHxIxKy4pBikpcfp8EfHzy/NGzo5kH6TlqfbjR9RWf6RRtRUVNagsr6aTMSLmkArHnKxiFBeWgyRmZ2cUQVVDnppByytKY+iY1jAy467ioT4SFZ2B9ZvuID+/FAb6Khg5zB0eHWw4PSbim7J41VVMGtsWjnaitZjyNZw79xaTp7Sv8/1+8I6DnqEqmrqZ1fm+E6MyYOlkAGFS9VsLIWvJ+U4E1NlNxJKvMVAl1/9kEfXp06c0+vfLIiv5nogd3wu55p45cyauX7+OFy9ewNj4368TAwMDf5+zMBh8hAkmIsT3nNxI3+C5c+f+1L4RHR1NT0p/J5bUFxRU5fDp4ydUf/yEyrIqSMtJ1cl+VTUVER9OvFFywBVGFlr49dOvCH2fgO5D3YQ+PmnLKcgrxb0r7zF8SgdeTFb1DNXQztMe3i8i6Wq8qYXwhaT/xbjpHZGZVoArp97AvokhDDiq1vgeWne0pZPvDUuuYPqP3ahIwRUSkmIYMq4NOnZ3xIl9T3Fgy30Ef0hCxx4OaNHOSmSqd74V8vzbOJLt7yenFeVVKMgrQ1HB5620pAolxeUoK61EXm4Jqipr6X2qq2qhpCKLrPRC+plAIkEJWtrKyM0ppiIKMbUWExeDuqYCaqo/UrFFQUEaUjISUFRWgJmVDhSJIKMsCzk5yXr7nHMNEazOnX8Lv4AkpKTmw85WF0t/7AFtbeG1XPwdRJRbs+EmRg1zRzMXfgjmdUlUVAbk5aWgpVW3zzMRI29deY/mrSwEUhFG2nRJ1LkwIddWRIRtJMZ9ay7j+yDV5KNHj0bTpk3h6uqKXbt20fhfEixBGDVqFE3VJK36BLLQGh4e/vvXpGKdiB1ycnIwMzP7vQ2HzDtu3rwJeXn53/1QFBUVIS0tTdtuyO+7desGVVVVBAcHY+7cuWjTpg0cHBw4ey4YjP8FE0zq+clt6tSp2LdvH2bPnk0V35iYGGr6OmvWLNRnpGQkISkjgaryahTll9aZYKJj/Hmim56YC66wbfrZpyPkfXydVs98LUR4a9fVAVdOvIa/TyxcW3M3gf4jwye2pWkkpw89p4ahfIK0KYyd1gEzRx+hwsOeExNEqozZsakx1u8djpVzziM1OQ/9h7fgdKJMPFUWru2H3kOa4+yRl1gz7wLadLZFz4GuNOK3oSEtI0k3Hf365SPRUElMzMWWbXcRGZVBo2cnT2iHgf2bce4ZVVJaiUUrr2BQv2Zo3dIc9ZFzZ7wxbfr/N9avKwI/JNCI+Y5d7CEIooJT0K6HE4RJdWU1aqpq6fUWgz8eJt/C4MGDkZOTg5UrV1Jhw8nJicYCf/FKJHHAf/TKS09Ph7Oz8+/fb9u2jW4kfZNUkxAOHjxIb9u1a/ensY4fP07jhsli7ZMnT36fv5CW//79+2P58uXf/dgZDEHDBBMR41tPbuRE9PDhQ6reEuWWiClEPFm0aBHqO5ZNjFFZXoXi/DJoGXz2HvmvaOqpQE5RBhWlleAKQ3NNNG1jiez0AqQn50H3N18VYdK+mwNC/RIR8DaON4IJqTJp1socj28HIiosDZa2/PEyIRiZaWLKvC54/iAEh3c/wowfu0OUMDDWwK7j43Fg2wPsWncb0xd1o0amXGJhq4s1u4bBzycOdy6/w8KJx9Gqow36jWhB26AYDFGCVCFcvOSLk6dfw9pKB8ZG6li8qDvMzbivmCstq8KaDbfQt1cTtG9jhfpIWFgqVFTl6ty7hPDycTiau5vDTkDnJSLKK6nKCd1c36KpCcQkuK8yZXw/pEL9n6rUv4ggXzAyMvrXlMh/+z2Zl7x8WfcJVAyGIGF1dCIIObElJSXRqDBfX9/fzZK+nNxOnDjxp/uTWGEfHx9UVlbSUrilS5f+ydOkvlJRVoVo/0QU55bU2T7FJcSo6WLY+3hwBRHESGtCcmw2Qt9xcxwmltrUiJC05ZCyf74wYmI7KpTcvOADPtKtnwuNzL196T2ePQiGqEEMWBes7oOPnz5h+awzyMvhR+S4i5spVm4bgtU7h9IWsXljj2LR5JMIfJfAWQQ4Q3Qgr5HQkBRsXHsTWVlFnBxDbGwWVq66hmMnXlHhxN5eDwf3j+aFWEIrS5ZfQmcPW3TuYIv6yuNHoRg2vO4NWSsqqvHySRiMzTWot1pdU5hfSq93hE1qTCZi/BJoCx/jG/lVQBuDwRAITDBh1Fu+rLYU5tbtpE7PVANFeaUoKSwHVzg0/+wiHuTLjWBC2jE8ejpTM8jXj8PAF7R0lWFpp4un94IRwJGY9G/P2+xlPWls7L1rfkiMy4aoQapKSPJP+y72mDPmF4QFJoMvzy3xMVm8oT/W7B4GZVVZLJp8ArNHHcHbl1F0Espg/JGysircuuGHbZvvwvt1NMaMbwtNIUdoV1XV4OxZb0yZegJv38aiTStL7NszCuPHtuW8gotQUlJJPUt6dnOq12KJv18CTXlSV5ev832/946Bmro8OnYRjD9DVFAKLB2Eb75bU1VDDV+ZjxGDwajvMMGEUW9RVPt84VNUhxUmBF2TzykQ6QncGb86upnSCyQSL8zVCnr77o6wa2IEP+9Y8InhE9pSo8orp99QY0u+ISMriUVr+yM2MgM/LbyA0pIKiBrkArlrHxcsWtcf546+xLVzb3lTyUGTpNpYYtH6/thyZAw0tZXw0/zzGN9nDx5c90M5ByuxDJ5VkwSnYNuG29i28Q5NDZo11xOTpnaEto5wDVX9/BIwYfwvuHc3kIojo0a5Y/GiHrCy5EdSRFFxBX5cegldPOzQpZNgvDf48pq4dvUDhg5rIZD9P7gRAGlpcRiaCMbsOyooGZb/YAgtSFR1VWDb0kLo49YPfhDQxmAwBAETTBj1Fn0Lbdg0N0NNdW2d7tfUVg+2ribISM7l1MeEeJi8fxmJFI6qFEhriay8JF49CEZaEnfPxd+1jYye2gH+b+Pw4mEo+Ai5cCatLbW1n3Bg632RrX4g8bfzV/WBz8sobF52FSXF/BF/iHBCzGqXbRmEgxenwbGZMa6e9sZwz+04vvcJkjkUPBnCJzurGOdOvcG2DXfg9SIS/Yc0x6p1/dG5iwMkhWzAnJdXinXrbuLnQ8+Qnl4IFRU5HNg/GmNGt+ZFVQkhv6AMa9bdwMD+TeFRjytLCK+9omBupgklJdk63zeJ4SbVjp26O0JQxIWlwcxWT2D7/8dxA5OQGJYq9HHrBawlh8EQKZhgwqi3NBZrjHDf2DqvBNHQVUaYbxwSI9LB5WTQ6beUggAOKzy69HWhtw+v+4FPdO3rAhVVeRzb84S2DfGRVh1s0KmHE20fOr7/KUQVFTV5bDwwChZ2Opg54mfetOj8ESNTDcxd2Rvbj43HsIltkByfg4n99uHHicfp88/X1wjjv1FaUol7twKwdP55HNr7GPoGqpi9sCumzuoE498qBYUdFXz7lj/GjP4Zz5+F08qG2bM7Y/eekTAyEr559z+RlV2MBYsuoF8fF3RoZ4P6zMfaT7h2+T0GDP7/XnB1ydN7QTA200CbTnYC2T+polRQkaWmr8Kmpqoa4iKU9sZgMBjfCz+WMhgMAaD0Wy9yYXbdepjom39OJEqJ/ZwtzxXOLc0Q9iEBKfHc+WA0a20J1zaWNC2ndvpHarjJB6SkJTB2Rkc8uOGP6+d9MGRsa/CR4RPaICEmE8F+iXh0OxCdewo3FrKuIHGn/Ya1hIW1Li6e8IKZlTaGjW/Lm9fDFxSUZDBwdCt8GvkJAb7xePsiCjtW3cD+jXfRe4grXFtbwMpej/XkizClpZXweRODl0/DoaQsAzMLbfy4vBeUlOu+euBbCAhIxL49j2lksLm5JgyN1DB5SgdaXcInUlLzsGXbfUyZ2B6uzUxQ33n0MBgennY0vrmuIYLY4zuBMDBWh7KKYF5/xPxdWgDH/jVUV9RATa/uPV8aBIKoCGEVJgyGwGAVJox6i5K6Ap34kGjhOt2vmjyU1eVRXsJtOoyzuwWy0wvx9IZ/nbcdfS1kQkwSc2LC0+DzMhJ8okN3B9TUfMS5wy+Rw1HyxdckHi1c0w+fPn7E7nW3EPQhAaIMadEh/iyZ6YVYMu0UkjgU8/7teXdpYYYZS7rj7MP5GDmlHQLfJ2DO6F+was45nDr4jFahMESDgvwyPLofjOULL2D9imvIzy3F9LmemL+kJ3r3b8qpWJKeXoBVK6/ilyMvqVhibKyOqdM6YsnSXrwTS2Jis7B0xVVMGNe2QYglJL3mwd0gdOosGH+WyJBUyMtLC1QIjwhIgrWTIbigorSSicsMBqNBwCpMGPUWZXUFKrln1rG/BrlA0DJUQ4hPLGpruKuqUNNUgJGlFhKjMhHun0SNYLmg64CmuPjLS9y/8h6tPGx5NSmetqgbZo88jCunvDF1YVfwEWJQu2bnMMwe8wsuHPeCsqocXZEUVWTlpfDjT/3w9mUkls04Qys3+g5rATExflWbfEFJRRZ9h7egW1JcNny9ommC0bUzb6FroEqTd1p5WMPQRINNDngCWblPjM9BcGAyjWv9odEP6NDZFjPmdYGWtnCNW/9XOxBJv3n5MgJZmcWQk5PEvPld0KWrI63I4huBQUk4cuwlViztBQtz7qOMhcG924Ho1ddFYL4xD24FICO9AM3cP7fPCgLiYdZrZN1HIX9Ne5mxvT60jITf2lYv+PWHz1td75PBYAgEJpgw6i3KmoogwR0F2UX0ArsuJzuGFlqI+JCAtIRsGFpwl2jg0soC6Ym5CPdP5Eww0dRRRvdBrnhxL4iav+oa8qcX39JWFwNGu+PyiTdo0dYSTq78XDVVVVfA+j0jMHfcUSybcRq7TkygPxNlWrS1grWDPs78/Bzzxh3FnBW9YMLziZihqQbdBo52pylGXk/C8fx+MBVRiKFt6442cGlpBlsnfYiLs49PYVJcVI5Av0T4+cYjLjYbGpoKaNnGEis3DOC83eaPVFfX4smjUBw+/Jy+Zmzt9NC8uRnGjG0NRUUZ8JFXXlE4dvwV1v7UD/p6qmgI5OeV4s3rKGzbNUIg+y8vr4LX0zCaJiZIsTg2NBWaeioQNtUV1Yh6Hw9JaW7agRgMBkOYsCs+Rr1FUVWOiiQ1VbUoL66AbB1erBr8JpIkR2dyKpi4trPGzZOv4XU/GEOndeTsONzaWeHORV/cueCLyYu6g08MHOWOB9f9sXfjXRy8OAUSEvw0qSMT9VXbh2Dj0ivYvf42Fv7UD/IK0hBlyER2xuIe8H4RiXU/XkJ7T3sqRhCPGT5Dzhvm1jp0I144SfE58H0VRW+XTDlJj79DN3sYm2vBubkJrURh1C0k/jkiLA2BHxKoUKKrrwp1DXm072yH6fP1eJMm84VPn37F82dhOH70FU3BkZGVQHM3U0ya3AFGPK4Yu3U7AF6vI7F961CoqvKrRUiQnDr+CqPGtkGjRoJZlX/9JBziYo0F2o5TlF8KFQ15Tirfqiqq6a0Ez8/lfIUs5pGtrvfJYDAEA7+uOBiMOk7JadrJDpWllcjPLq5TwcTYRheWzkZIT+TW58Da2QDiEmJIiMxATkYh1DkqR2/S0gy6hqp45xWFkdM9ICPHn1UnRWVZTJrXGXcvf8CVE28wbFI78BUSgztzcXesW3SJemls2D+S9+LC19CynRUcmxrh1IFnmDr4IKYv7o6mLc0gCpDJCEnZIRuBtHb5+8Qh+EMiLh7zwuPbgSjILYVDUyO4tDCFha0udPRVWPvON5KbU4yIkDSEBicjJTEXuTklcG5qDBt7PfQf6sarKpI/QqoXvd/E4MTRl5CTl0JGRiHMLbQwZVpHOHHkLfG1As8vv7xAVHQm1qzuCzk5KTQUEoi30q+Ak7Pg/j/EcFxbT4W28gmKCP8kmNkJP06YUFlWBZsWFtDmIG2KwWAwhA0TTBj1mrz0QsSHptC2HP06bAcwMNNEVEAijRjmEiKWNHE3x5tHofB/HQ3Pga6c+YUMHNsGu3+6gad3AtBziBv4hEcPJzy6EYDzv7xCG0876PGobeivuHewwcwlPXDt7FtsWXkNi9cP4N1q+vcgKyeFqT92g0fPdBza+gC3Lvpi8vwuIledQap+2na2oxuZLKen5FPxhCQdXTz+GgkxWXBsZkwnzyRxx8ZBHyaWWpDhKMmCjxQXVSA2KoP6kIQGJSMuOhPWdnpQVJaBrYM+Bo9y550h6l8h//t3vnE0kjYoMAm1tZ9gaqaJZSt6o117G4FVLtRV29Ce3Q/xQ+NG2LRxEMR5lmYl6P/bLwefYcrMTgIbIzk+G40a/4CuvZpAkIT5J6KlgOKKv0YwCX8bDXUO2oHqBSwlh8EQKUT/KpzB+B+oaCpSwSQ/s25TUpQ1FCCvJIPEyHRwTauuDgj9EA/vx2GcCSaENl3scXjbPdw44009TYiIwhfIscxa3gsLxx/FxaNemLu6N6+O769069cUFeXVOLzzITYuuYylmwbWG88M0uay9ZcxeHInCPs33aViwtBxbahZrKhBqkiI4EO2rv1c6M+yM4sQE5GOUL8kvH0RifvX/JCRWoAmbiZUbDGx0IKZtTb0jdSgrqVYrytRqqtqkZaaj8S4bORmFyMkIBkJsdnQM1ShlV+WNjoYMMyNRv9KSIqJzITb1ycWTx+F4vmzCPqzFu7mcGthBs+uDrw1N/5CQUEZTe1xdjbEmLFt6vXr7+/44BtHhS19AQq192/4Uw+ktbuHQ5CUFlXAzFYXXFBd+bklR0qGCcHfBTN9ZTBECtG4QmEwvhMVLUWISYihKK+kTvdLLjKd21ghP6sIVZXVkJTirm3CqYUZigsqEOgdg8ryakjJcHMsZAW9S7+m8H8bC783MWjW2hJ8wsBEHf1GtsTxPU9g5aCH7gOagc/0H9ESlRXV8POJw9aV12hcL2kzqw8QsapzL2e06miDi8e9sHnZVTRrZY6ufV04S52qKzS0FOnm3t6afv+x9iOSE3KpaBAdlgZ/33i8ex2N0IBkKrTIKUhBz1AVFja6VETQ1leGlo4yrbYQhclsZWUNstILkZNdjLTkPFpxU1hYjqjwdEhJidNKG9LORMSRoWNa0a9JMpSo8fHjJ3i/icaZE17UdFZbRwlqanIYMrwlunV3EgnBJzEhB9u330OPns7w9HTg7DhI3HtUZAbs7PWEPu7pY15Yt22IwMaoqqqh7+/2XewhLUAxobqqBlmp+bTKlKtIYXKulhTB9zKDwWB8K/z/hGcw/gOkXLS2uha5aQV1vm8lNXm8uuWP1NhsmHLUR0yPQ1UO1k0MEe6XiADvGLTgMNq39/AWuHH6LS4d9eKdYELoP9Idrx6G4vSBZ2jqbg5NnkSQ/hPDJrSl8Y3nfnlFZDr8+FNfiNWTSpMvItvYGR7ISi/A6UMvqDnvoDGt0LqTDa8rgL4FInIZm2vSjUyivlBcWE69OlIScpCanIe0lDw8IjGkaQU0rpyIJU6uxrRKQ1VdHvrG6nRSTrw8lFRlabWKvKI0FSTIxKyuomqJMECEOhKLW1ZSSdNpCgvKUVlZjay0QpouUlZSRY+XHA85XvI+snXUh6SUOEzMNWnLm76hKhSU+JkK863tKy+fh+PsqTdITcmHmZkm1NXl0X+gK7p2d6KPWRR4/y4Ou3c/woKF3Tj1ViEVOnv3PEKzZsJPLLt59QM8uthDQYBm2m+eRiA9OR9LNgyEIIkOToGFvT64gqTkkPMUWZBifDs//Pp5q+t9MhgMwcDOdIx6jZLa52hWUglS1xhb69DbhIg0TgUTQodezshOK4D3o1BOBRMSMdy2iz2e3w1CVHAKLB24u6D7O8iK2Lyf+mLdgovYufoGNhwcxeuJOZk0j5rSgX4d+D4Rm5ZexeINA0S+CuPvXjcLfuqLhNhMnNz/nFadjJ3pQY1hRaHK4nsgYoKtkwHd/sinT59oQkx2RhHyc0t+vy0vrUR0WB5t1crNLUZJUQUMjNQQ5JdI/87STpdWeiiryOHjp0/Ul4IkLxGR44dGP0BdU4G2xZCWESLCECFOS1cZCbFZqKqqha6+CsKDU1BVWfO7CGNmpU3FE3KsBsbqtHrNTt8AapoKUFNXoPusD6bEf0dRUTnu3QrAjSvvoa6hQMUSXV1l9B/kirYdbETGV4gIFJcu+uKtdww2bR4MPY49J+7cCYSmpgJatxGuoJ6XW4K3r6OxeecwgY5z58p7mFpo0/eOICGGr3auxuAKIpaYNzGGopo8Z8fAYDAYwkI0PvEZjO9E9bcKgrzMwjrfN0nKIaTEZoFrnFqaYd+q6/B9HkFbALhs3eg/thWyMwpx/8oH3gkmBFNLbdoOcnL/U9y++A69h/LLoPavEMFg9NSOEGv8AqcOPkejxteouCAqE7ZvwdhMC6t3DkVEcArOHXlFU3VGTGkH11YW9VY4+StEwJOTJ5Ujn/1OvqYCgggsleVVVPgglSC11Z9QU12LX/Erams+0aqRxo1/wKdPAPEibSzemAon5DUkLilGb6VkxCEjI0kFkLqqVhFFkhJzqUjy9GEorYDKzyuDqpo8lq/ui9ZtrUTquSHtUkd/eYG01AKs2zCQ8yQcItqEhaRg0ZKeQh+bGL2OGtdaoGJzQkwmPtZ+Qt9hgv9MCfGNQ1cOP7tKC8sQ45+AFj0+ezcxvhFm+spgiBT174qbwfgDajrKsGthDmkBJFQYWWpDVkEK0YFJ4BpdI3UYWWghMToTIe/j4dTCnLNjMbPWoRONh9f80H9MK+ibqINvDBrbCnFRGbhz6R2auJnSdge+M3xSO1r+//CGP1bMPIvVO4cItEeeS6wd9LF273AqnDy8EYCT+59h8NhWcO9ow3tTTWFDxA4qnvE0dlcUIIKSr3csbl59T30uQoJS6M87drFD2/bWcHQ2FDnBLiuzCKtWXoNbC1OsXT+Ac6EnLjYLN6/74ad1A4T+XIYEJkNRSQaOTYwEOs7dKx+QkpAL946f/YsEBVkUIdc0cgJsLfo3qio+m74yDxMGg9EQEJ2lEgbjO1NyQt/GIPh1VJ3vW1JaAqpaSogLTaVlz1zTeUAzaOoq4c2DEK4PBYMntqXPydUTXuAjpAJn7MxOyMksxualV1BTUwtRYMAod/QZ5oag9wnYsvwa9cGozxDhZM7KXpi/pg81SZ08YD9uX3qHivIqrg+NUQ8gbRpnT3hh0qjDWL/yKvzfJyA9NR+9+rng2LkpmD2/K5yaGImcWPLhQwK2bb2HIUPdaBIO12IJMQTet/cxFi3tKXRzXFLxQapL+g9uLtBxykoraTKOR09HgbepxYWnUw81LiGf79bNzSHPhNr/lpJT1xuDwRAITDBh1GtI/G+jRj+grLgClWV1P8kysdVDSWE5ctPrvuXnW3F2N0dWagHePAyhK6ZcYudihM59m1CD1UwBGO7WBbqGqpjyY1dUlFXj4lFiqioakHSfpZsHIDI0FfPGHaXtT/Ud0kY1fXF3rNs3AsnxOVg67TSO73tCPTkYjG+dQPu8icHyhRexctElnDjyEsmJuXBtYYapszrhlzNTMHNeF4HGzgqKT59+xdnTb7B/zyNMnd4R7X5LauKS0tJKbNp4G/Pmd4UyB5Prh3cD4d7GkvrQCJKnd4IQGZSCHgMFn74W8i4O9q7CN839I0U5JYjwjQEP1ooYDAZD4LCWHEa9hlQSmDsbUYMy4mOia6pZp/u3a26KpIg0xIWmQF1XGVxiSNIpTNSRGp+DsA8JcGhuyunxtO/hhEfX/XHx8AvMXtMXfMSzTxME+MTizMHnsHYwgEtLM4gCbTrZQV5BBj/Nv4A9629Tg1QiKtR3tPVUqHBSUlyBB9f9cGDzPTRq3Ai9BrnC3kX02iYYwiMxPhuP7gXjvU8cEuNz6M80iPloOyv06NMETi7GVFwXVYoKy7Ft810oq8hi38ExkBVAG+q3QtqbNq69ibHj2nAiQJHKlge3A7H94GiBjkOMmm9f9IVzcxNqjixo0hNz0KEPt94hv7fk1FPTZ4HDPEwYDJGCCSaMBkFccDJyMwrqXDDRM9FAQngaYkNS4ObpAC4hk8WuQ9xw84QXvO4FcS6YODU3gY2zIaJCUpGVmg9NjtMZ/uk5m7msFyKDU3H64DMYW2hCRURc/8nF+bajY7F6znnMH3cMy7YMQjN37rxrhAmJ1B04uhWtpHrnFY3Lp17jl50P0b6rPTx6OkFeUfTjbBn/nbycYjx/Eo5Hd4No6g0xcCXY2OuhZWsLdOrqABVVOYg64aGp2Lj+Fnr3bYr+A5vxQjgkIsL2LXfRydMedhzF3x7e+xgTpnekiVGChLRI1lR/RN/hLSBoyDkvMSoTyhx/TpWXVFCxmphGM74DJpgwGCIFa8lhNAjjV0KuAFpDvsQJE8GED7i0tkR2eiG87gdTYzguIRftY2Z1RHxkBi4cfgG+Qozzlm0bgqTYLGxadJnz5+1bIFUl24+Ph6aOEi4d96Imtg0J4s3Qop0V1u4ZgSWbB6KooBw719zAhh8v4cObGBqdy2hYFBaU4e4Nf8ybcgLzp53C4T2PkRiXTdtBuvRwws5Do7Hr0GgMHtFS5MUSMnk+d/oNtm25h8XLemHAIFdeiCWEwwefwcFRH+062HAyvs/raPo/d3A2FPhYN8/70nYoYVQoJkSkw0TAkcVfQ3VFNT59/ATJemo8zmAwGH+EScOMBiOY5KTl1/m+5RRl0LKrA2qq+DExI205RpZadAUqyCcOTVpZcHo89s1M4NDMmLbmDBzfFjqG/PQFsLDVxZiZnXBw811cPOaFYZPaQVTQ0FLEjmPjsXHRJezbcAfpKfkYP7sTp9HSXLXrjJnpQQUvf584PLsXhAOb71JBpX1XB5haafNmMsmoW/JySuDrHYOnD0IQFpxCW2tqaz9Rg1G3Vubw6OKA5u7mNGWqvpCbU4wTx16hpLgSu/aNhAKHiSl/5eL5t1TM7NbDmZPxK8qrcfroK2zYOUzgY2Wk5lOPtN5DmwvlnBvsE0tbgblGVlEGNi0sICXLbVS1yMIqTBgMkYIJJox6j46xOq0iyBWAYEIQlxSH9/1g5GcV0VQeruk2xA3n9j/Fi9sBnAsmZII6aqYHTu55jCvHX2HWan56mRB6DW2OzPQCnDv8grbmtGjHvWHi1yIrL4U1e0fQtpTg9wlYNu0Ulm4ZDAWlhteaQiYtzVpZ0K20uAKvn4bj1kVf2nbV2sMWbTztYGCizsQTEYYkdJCqkSD/RDy8E4S46EyoqMnRlhvyf23nYQt7Z0O4t7WCgiJ/hIS64o1XFPbteoihI93Rs3cTXr2W798NpB5DEya35+wYLp/xxoBhbjRKWNDcOu+D6LA0rNw5FMIg6G0s5m4eDK5JjclA+Nto1pLDYDAaBOxMx6j3KKkpoLSwDDkCSmuxcDTEyxt+iA5Khltne3CNawcbHFhzA68fhGDa6r4Cjzj8N+yaGkNGVhIPLn9AjyFuvCgn/kdxZ1oH+L2JwdZlV7H7zGToC8HAr64gK7qTF3TFo5sB2Lv+NlbPOUcNUkllRUOFCKVd+rrQLT+3hIon+zbcpq07Hbs7wsnVBOa2OmjUiHWn8p3ysioEfEjAB584+L6JRm52CSSlxFBVWUtf+45NDOHU1AQt21gKZaLMBeXlVTi47wlKSyqxcdtQGPHs/PTqRQTe+cRh+aq+nIk4kWFpiAxPw8iJbQU+FokS9nocJjTfJFI9J68kw3mkMKGq/DfTVylm+vpdCCIGmMUKMxgCgwkmjHqPuv5ns9HcVMFUmJg7GcLMXh/p8dngA5q6yrBrZozIwGR8eBmFVl24F3HGzOmM96+icfnoKyzayv3q2D8hLSOJlTuH4cDGO1i/8AK2n5gIWTnRKjnu3NsZhibqOLTtPuaOPoKZy3qiUy9uSuP5BDHz7TW4Od0K80tp2865X14iOS4bLTtYw9bJkBoVE3GPAV4krMREpsPfNx7v3sSipLQCaSmfz+FEEJGRkYCruzlad7CGi6tpvf+/EWPXzetvoWUrC8yc7UnbjfjEu7ex8P+QgCXLe6OxWCPOXjNXzr7FzB+7CUWweXjdn4pXvYVg9kqICUml1YR8QFxCDLpmWhCvR21uDAaD8U/w6xOXwRAAGroqsG1hDkFdPpk76CM+LBWKqnLoN6Uj+ABJy4mPSMfDy+94IZgYW2qj/9jWuHr8FboMaApHHvRg/xN6RmroO7IlVs44g82LL2PV7uF0BVuUsLTXw6qdw7Bp8SVcP+ON8KBkTFnYrV55OPwXlFTk0KGbI90qK6oR4BtPK4vuX/uAqsoaNHEzRZMWpjC10hG5/72oQp73qPB0RISm4sPbOISHpNA0pPzcUvp7XQMVKgSSChLiR2JprcvZxFyYVFfV4viRF3jzKgrzl/aAo5PgTUy/lSD/JFy54Is1GwdyKuScP+EFa3s9aP/mWyboao8b597C3sVIKFHChEDvGDi25EcSWnZKHtJiMyFdz4VKQfHDr5+3ut4ng8EQDEwwYdR7VLSVEPUhHrU1H1FZVgWpOv6Al5KRhKGVDqICk2hvPR/6yZt3sMbupR/h7xWF/OxiqGgocH1I6DnMDTfPeOOXrfex+9I0XrdBEP+LcXM6wfdlFI7veYwJcz0haiipyGL9wdG48MtLnDn0ApEhqdTXRN9IjetD4xWkZY0Yw5KNvH/Tk/Ph9zYWD2/4w8/7MozMNKhxsa2jAW1vEhNwRGlDgDzPWRlFiI3KQGhAEoL9k1BcVIHsrGL6ezGxRtS0lcTBevZwpAKWk4sRlEU81eZ7qkquXvSlVW4Hj0+ALA8np6HBKXh0Pwgr1/WHtAx37RnxMZnU02bLvlFCGe/N03Coayig/yh3CIuY4GR0HyGcapZ/Q9tEA9JyUhCXYtMIBoNR/2FnOka9h0zMSVJOZlIuTcrRt6h7Twfrpib46BOD9IRs6Jpogmtk5aXRsrMd/Lyi8eZRCHqOEN5F3T+hoaOEvqPdEfIhAS/uBaNDDyfwmQGjWyElPgdXjntBz1AVXfo1hahBqiOGT24PK3t9XDvjjRlDDmL6kh7o1MuJF8Ie3yDPia6hKt0Inz59Qnx0JkL9k3H55GsquhIvDWsHfdg6G1IBRVWdez8BPkOew/TUAiTEZFGBJDujCL6vo1FWWkUFkIK80t9TjtQ1FeDU1BhNW5jC2k4PWjpKaIgQr5LTx7zw+mUkZs7vAlc3wcfVfg8RYWk4evAZ1mwaCDkOW0VIK87ujXcxb0UvoVSEEcHv8onXNFbXsZkxhAGphCvKL4W8oiz4ADF8LS0sh5QMP1qERA6WksNgiBRMMGE0COxaWkBNV0Vggom5oz7unfJCxIdEXggmBM9Brnh1JxC3T75Bj+EteTFBHjSxLR5d+4Bj2x+gZQcbSHG4IvlvkOdr5opeyEwtwLO7QdDSVYYTj1uJ/hcuLc1o8s+25dewY9V1xEakYeS0jrTlgfG/xVYzKx269Rnm9rkyIr0QEcEpdCPVO8WF5TAw1YCxuSbMrXVgaKYBTR0lXldQCapFITOjkIojibFZSI7PQW5OCUL8E1FT/ZG2z3z8+CutGiETXHJrYa1N2xlIGwXZiM9MQ+eDbxz2bLuPDp52OEQ8lHhYVUIgxqo3r37Ayg0DoCAEw9P/xfnjXnBvbw1DIbXGhPonoaKsCsMmtxPa52qkfyKatLYCXyCmr+SxS7A2TwaD0QBgggmjQfBDox8Q6h2NzMQcgezfpunniXSEXzw8BjUHH3Bobgo1TUWkxGUjwj8JNi5GXB8SLS0fPbszdq+8juun3mDoFO6iJ78GcXExLN85FPNGHsbaeeepCayROT8EsW+FTEbXHRiJB9f8cHDzPXg/j8SCn/qKrAjEBWSCQIQzslGmd0RNTS3SkvIRF5VBqyhIhDERVSzt9Ohqt76xGvXe0NRVgY6+MiQkxUW2UoSkC+VkFiIrsxh52cXUhDUtJY/6bIQFpdAV9z9WjRBzViKWSEqKoYmbGTS0FWFmqQ1zIpQYqTcID5KvpSC/FBdOe1Pz1AXLesKBh14lfxRLDu5+jFUbBkBZmduKh5iIdGRnFGLu8l5CG/PiLy9RXVWD1p3thDbmh5eRaMqTqPua6lqo6alARl6aFwsxDAaDIWiYYMJoEGgaqP1uVCYI9Mw0oKAii2wBJfF8D2SFu9OAZnhxOwA+T8N5IZgQOvV1QbBvPC4efo72PRyhpfc5xYivkNXTdQdGYe6ow9TPZMaynlDXUoQoQl4T3QY0g5WDPravvI79G+/CubkJxs3uzOtqH76LasTnhGyEofgcZ0oqT1KTcpGSkIuosDQ8uhWIooIy+nN5RWkqqBATXnVNRWjpKkFRWRYq6vJQUJJG48aNhVoZEh6SirKSSlSUV6Egv4weJyEhNgu5WcWQlBSnca1EECHtfiRO9Y/IyknS3xHDT1NLLSgoSsPIVAMm5lpUMNLUVmITq3/g06df8eBOAE4cfonufZrQqhI+mzOHhaTg1tUPVCxR4dhThhgF79l4F4vW9hOaOXN8VAY+vI7GlEXd6XtfWIR/SMCo+d3AByrLKpERnw2FBuYpVJeQs2Gdm77W7e4YDMYfYIIJo0GgbawOfUtt2gcsqIko8TF5/yQU5aWVkOFJFG3nwa44t/cxbp30wuAp7SHLgxYMcmHbY5gbnt8JxOFNd7Fy30jwHSLqrN0/EgvHHsXyqSex7cQEyHNchv5fMLHQws5TE3D20AtcPu6FlMRcDJ3YFg5NhdOP3xBQUJKBjZIBbBwN/vRz0tZDRBPi5ZGVUYjczCJEh6UhKT4HBbklEBNrTFtZyPuEJFAQceVLRQsRJkgFBxEfyKSaROmS6jkS8Sku1ph+3ajRny+biZBBDFQ//foJVRW1qK6upcdQUlyOivJqfPr4K04ceErvS/ZHPFoIxMCT/J5AfFrIfojmoWuoQnvldfRUYGCiDjUNBWjrKUPXQJVOoJkw8vVERaRj3/YHMDJWx7Z9I2HAc0Pm4IAkHD30HKs3DoSyCvdeGlfOeMOztxP1mBIWF4++grWjvlA9rYhxu6GFFm+ipCtKq2DhYkIrTBgMBqMhwI+zL4MhYDT0VJESlQF5JcFd5Fk1MYLvoxBE+SfCuQ0/eo01tJXRrJ013j2PwPNbAegxoiX4gI2zITz6NEFGch4+vIpG0zYW4Dtm1rpYsWsYVs88g0Nb7mHm8l40YUVUkZAQx9hZnWg6zMl9T/Dj+GPoPrAZxs3uRKsIGIKBCAqkmoRs5jY6/3i/jx+JwWw1SooqUFZSgdKSKipm1NbUoqS4EoX5ZSguKkdxYQU1oxUTb0Tv/+snklf52QCQiCq1tR+pCEOEF/K1hIQY/VpaRpK2acnKS1HBQ0pKHJq6n2+VlGWhqaMMKWlxKtAQwYTcl0ySG4uxlKD/SmFBGc6fegPvV1EYO7k92ney5b3QRLxVnjwIwdotgzj3LCH4+8bTNrB1u4cLbcz05Dx4PQzBsCnthVqRF/A6CgZm/GkFrSytRLRfPPTM694PrsHw6w+ft7reJ4PBEAhMMGE0CLQMP5vBZSYJxsOEYNPMFI0aN6LRf3wRTAhdh7rREvsPryLRfXgL3lyYj1vQFZO6bcf+dTdx6NYcXpehf4H4MCzePAjr5p1HYV4ZVu8dLtSybEFA2nPW7BuJC0de4sObGEzquxdTF3eHe0cb3rxWGiKkLYeY8grDmPfEzTkCH4MBKljdvxWAU0e90Kmr/WdTVw7TZb6WNy8jcf3ye1pZwmUazhdIhdbZoy+xdP2A/1NRJUjuXPSFXVMj9Boq3GjfCL9E9BjdCnyhsvxzFZoUTw2JGQwGo65hjmuMBoGKthIkZSQgJi6GyvI/99/XFRZOhpCWlUDgq0iB7P97adbeGgXZxfB9Eo5wv0TwBbJyPWZuF2Sm5OPGqTcQFUi6z6yVvRH0Lg6Ht96nHhCiDqk6GDW9I+as7kOrDNbNv4B962/ThCAGg/HfIC1Qb72iMGn4IcREZWLnwVGYNMNDJMSSR3eD4PM6Bmu3DOaFWEKey13rb6PP4OZCjfQmZse3zvnA3EaXttsJ02w5KiAJhuZa4Auk7VjPQhvapvypehHZWOG63hgMhkBgggmjQUD8ANR0lKnpa46AJoGkRNfQQhvh7+NpiTyfHnvX31bE7pzhlzDRdVAzdOjljNN7HyMpNguiAulfn76kB26f98G2ZVfx8eMn1AeIt8m24+Mxf20/vHwYgkn99uDSsVc0EYLBYHw70RHpWD7/PC6e9sa0eV0wb0kP6BkIz3Pjv3D9oi8VemYu6Eo9bfjAg5v+1Bi5dUcboY778JofXRDpL+RKj9iQVJjZ6fGq2q+ipAKp0RkoKyrn+lAYDAZDKDDBhNHg2nIyBBQtTLBzM6MeAnEhKeATnoNc4djCDCHvEpCfUwy+QMxyB4xvQ7/es/I6XU0TFboOdMW4OZ54/SQUx3c9FKlj/9d0pV7OOHprDjr2cIL38whM7LMHr5+E0dVdBoPx75DI5QM7HmDVj5fQqp01th8YjaYiEuFN3ue/7HuCnKxiLF83gDdmo4lx2Xh6LxiT53oKdVwSoX3xyCt49HCmXj7CJOxdHJp72IJPkAUhYvqqZchvk2JewypMGAyRggkmjAaVlEPIEqCPiWMrS2rWGPI2BnxCWV0ealqKyMsswr2zb8EnjC20MGhiW+r/cveCL0SJQePbYNxsT1w58Rq7VomW4PNvEFPS2St7Y9riHlDVUMD+DXewbOpJxESkc31oDAZvyc0pxu7NdzF7wlFoaCri2KVp6NrLGY3FGomMz8ruTXfRqPEPmDjTgzfHXVlZjW2rr2Pq/C7UsFiYXD35mqhI6DdG+D4ibx6EwN7NDHyiOK+Emr6StBzG90EihQWxMRgMwcCPT0IGQwgYWGpDTKIx0hMEJ5hYuRiT6yoEe0eDb/Qe05reej8KRVUlv1oshkxuT41pj21/gKw00fLN6DvKHaNmeODVwxAc3na/3rTnfMHCVhfbT0zA9KU9kJqYi1lDD+Hozoe0p5/BYHymIK8UB3c9xLTRRyAvL4WjF6djwPAWkBahJK2ysiqsXHABFtbaGDe1I6/aQH7Z9Qg9BzaDqaW20KtLHl/3x6AJbaGupSjUsQvzSiEu0RiyQjB+/hYqf4sel5Jjpq8MBqNhwAQTRoNBXVcVtdUfkR6fLbAxZOSkYO5ggNS4bLpSxyfM7fXQsU8TJEam49XdQPAJCUlxzF3XH1UVNTh34KnItX4Mm9weY2d74sZpb2xZfIlXHjZ1AZk4tfKwxS83Z9PY4ef3gzGuxy78sv0BFboYjIZKfm4pft71CJtX38DH2k84eHISxk3rCEUhGoPWBTlZRViz6CK69W6Cbn1cwCee3guiMdqdezoLfeyLv7xCZXk1ugxoKvSx/V5EoElb/iTufaGmuhb6VjpQ1hCugFSvYC05DIZIwQQTRoNB20SD3qbHC9Zc1KW9DfVJ4ZuPCaFFZzsqRlz/5QXvRAlrJwOMmuWBR1c+4J6IteYQeg9vgSmLuuPD6xjsWXujXhqlEmFr4NjWOHh5OnoNdUNMRBrGdtuB0weeoqxEMOlTDAYfyc4qwukjLzFh8AFUVdVi7tIemLGgq1CTW+qK2KgMrJh3AaMntUer9tbgE8S3xNcrGnNX9BZ6xUtORiHiI9LQd7S70KtLCO+ehcO1vXDNbb+2JSclMh2f6lk1JYPBYPwT/HDyYjCEADEo0zHVREFWEfWaIOaWgsDG1YTeBr2OhqWzEfiEWyc7aBuqQUJKHEHeMXBytwCf6De2NZ7fDsQvW+7BuaUZdETMVK7PiJaQU5DCjhXXkJ1eiJW7R0BGtv6VLcsrymDi/C7IySzChSMv6Sqsn3csXNtYoPewFpCV4z5+lMEQBClJubh23hcvHoWiU3dHHDo7GRocTKbrCh+vaFw6/QYrNg2Err4K+ER5WRW2rLiKeSv7cBLBfP7Qc8SEpmHJ9qFCH5tUKTZq9AMMLPgTJ/xH4dyymSmU1BW4PhTRRRAVIfxaA2Mw6hWswoTRYJCSkURNVQ1KC8uRK0CfDNtmplSQIK0vfINEDPeb0BZRgcm4dOgZ+HghtmDzIFjY62Hbj5fwkWdtTV+DR68mWLJ1CJJis7FpwQXah15fIauuM1f0wi+35sDITANnDz3Hj+OO4szBZyhhkZOMekRESArW/HgJc8Yfg7auMo5dmYFp87uIrFhCKgwvnfam0cGrtgzmnVhCju/UoWfUt8TMSri+JYS0pFxEh6aix1A3anotbMLex0NeSYZXPjJfyEjIRtT7ODY/ryfs378fRkZGkJKSQvPmzfHu3bt/vG9YWBj69+9P709em7t27fqufVZWVmL69OlQVVWFnJwc3WdWlmCrvxmM/wITTBgNCl0TTXqbFie4E7OUrCTsmpvB+14Aqnlmrkrw6NcUCsqyCHgdjdjQVPANczs9OLc0R0RgMi7+/AKiSOvOdli0aRBCPiRi3ojDSE/OQ31GS08Zc1b3xdHbc+HaxhIXf3mJUZ7bcOGXl8jN4k+MNYPxLRBPkldPwjBn3FGcPvwSNg56OHVjNgaNbAllFVmIKtVVtTiw/QGyM4qwftcwXvqtXD3tjYrSanThyE/lzL6nSE/Kw8DfYu+Fje/jUDT3sAMfqfwtHUeaVRKKfErOxYsXMW/ePKxatQr+/v5wdHSEp6cnsrP/3uuvvLwcJiYm2LRpE7S0tL57n3PnzsXt27dx+fJlvHz5Eunp6ejXr9+3PwAGQ0gwwYTRoLBqZgI1HWWkC1AwIdi1MKMGphEf4sHHSpv+E9vB3tUE14++BB8ZOLEt7JsZI9AnjgonooiTmym2nJhAYznXzztPVyvrO1q6yhg9wwMn7s9Hj8GuuHXOB6O7bsP2FVcRH5XB9eExGF9FcWE5Lp58jRmjDuPKmbfoNbAZ1uwYioEjWkJWxJNBcrOLsWjaKegbqGL6wi4QE2sMvhHsl4g3zyIwfXE3Tios4qMy8fJ+MI0RVlKV46S6JvxDAuzdTMFHZu4di4PvN6JlL+Eb4TLqlh07dmDixIkYO3YsbGxscOjQIcjIyODYsWN/e/9mzZph69atGDJkCCQlJb9rn0VFRTh69Ci9X4cOHeDi4oLjx4/D29sbPj4+An28DMb3wgQTRoNCUVUeuWn5SI3NFOg4zm2soKyugKiAJPCRLkPcEBOaiuc3/ZEqwNSg/9I6NH/TQMRHpGPL/AsiayhqbquLNQdG0RjnhWN+gc/zCDQESAn7+LldcPjGLCqgkISLaQP3Y/HEY3jvFUU9hBgMPkEmqZGhqdi25gaWzDiNmIgMTFvYFbuPj0eHrg4QF+efsPCthAQkYdf62xgzrQN6DXLlZbtHZloB9q6/g8UbBtAWTS44seMBzKx10Ge0OyfjJ0VlQENPmbPH/2+o6arA1NGQeZj8F379QTAbEXyLi/+0VVV9rgj6K9XV1fDz84OHh8fvPyPefuT7t2/fftfD+pp9kt/X1NT86T5WVlYwMDD47nEZDEHDBBNGg0LX7LeWnFjBVpiYOxqgpqYWr2/7g4+Qlpxuw1rSScK9c/z8gNLUU8Hstf2QlVaAs/ue8C7V52vR0VfBzjOT4exmin1rb+HayTci+1i+FTkFaQwa1wYrdw3DjxsHUOHr1P6nGN9jJ66efM18ThicU1ZaibtXP2DzimtYPf881NTlsWrbYCzfNBD2zoa8FBW+FXK+uX3lPY7ufYKZi7vD0YVfZuRfqCivwsYlVzBrWQ9o6ihxcgzB7+Lx/lUUOvR0gpy8NCfHQAzZW3Vz4mRshujHCuvr60NRUfH3bePGjX97CLm5ufj48SM0NT9fF3+BfJ+Z+X2Lil+zT3IrISEBJSWlOhuXwRA0LCWH0aDQt9CGvqU2stME6ynRuHFjOLe2wus7ASjMLYGSGv+iJvtOaINg31jcOumFXqNbQUtfFXyjdVcHxIan4dLPL6BnrIZuQ9wgihDzvqU7hmLP6hs4vOUe8rKLMWZ2J4hLNIxTsLi4GDp0d0L7bo4ID0rGnQu+uHLiNc7+/Bwt21uj64BmsHEyqBeTU4ZoCAihgcnweRVFxRJLO1306N8M81f1pq/V+gQRIXatuw0VNTlsPjAKklL8rFogVWdbl19D1z5NYM+RoEOO4cKh53DrYINuQ5qDK17e8seaE5M5G58h2qSkpEBB4f9X//xT6wyDwfh66teVAYPxL2gYqCEzMYea+VVX1Qi05LVpBxtkJuUi5G0MWvdsAr6hpqkEmyZGiA1JxcUDTzF74yDwkeEzPPDhVTQOb7gDSwd9mNroQhSRkBDD/PX9YWSuieunvKmnybKdw6AkwuaR3woRRGydDOlWkFuKx7f8ce/ye6Sn5KO0pBKefZqgQ3dHKPNQYGSIPpnpBXh2LxjPH4aivKwSHt2dcODsZOjwUCyuCxLjsnFw6z209bSnQgSfBckzB59DRVUOXfpxY/JKeHU/GAFvY7Fo22B6vuaC7LR86jNGRHZG/eV7TVr/bZ8EIpb8UTD5J9TU1Oji3l/Tacj3/2ToWhf7JLekdaewsPBPVSb/ZVwGQ9CwlhxGg4J4Y+iaauLTx09IjxdsW45LexvEBCXB50EQ+MrAKR2grC6P9MRcZKXkg48QUWvZnuEwt9PF+llnUVpcAVGFTFgGjG2NWat7Iy4yA+vnnkNMeBoaIspqcrRd59jduRg1vSNMLLRwYu8TrJl9Bqtmnsbrx6Goqqrm+jAZIk5RQRnuXHmP9YsvYdLA/UiKz8bUBV1w6vZcjJ3esd6KJY9uB2DTsiuYMLszuvV14bVY8uxuEBVNp/zYjbNjIAsoL+8Fw6G5Cdp0deDsON7cD0Lr7o6cjc9oOJC2GGK4+vTp0z9VWZHvW7RoIbB9kt+Li4v/6T5RUVFITk7+7nEZDEHDBBNGg8OupQUUVOWQEi3Y1A5VLSWY2Onhw/NwfPzIT5NLNS0ltO3hjOC3MTi39xH4io6hGvqMaY2M5DxsX3RJ5E1Dm7e1wu7zU0GsTOaPOIzHN/jpdSMMiCGcU3NTLN48COee/ohOvZugKL8M25ZfxZgu27Fz1TUE+MTy9j3E4B8lxRV4dCsAezbcxvBuO+D1NBwt21nh/MMFWLJhIJo0N6XieX2kvKwKezbehv+7eGz/ZRzMrXXAZ0L9E3H55GtMX9oDYhwa69449QY+zyIwYnpHek7iircPQuDasW7jhAtzinFi/fU63SeDvx4m3wKJ/z1y5AhOnjyJiIgITJ06FWVlZTThhjBq1CgsWbLk9/uTypDAwEC6ka/T0tLo17GxsV+9T+KrMn78eHq/58+fUxNY8jsilri5iWbbNaP+w1pyGA0yKac4rxSp0YI3l2rTqwk+PI9AbFASLJsYg48MnNIe985648nV9xg0tSN0jdXBR9w722HotI54/zISFw4+w7Dp/99hXRTRN1HHmgMjsX3ZVVw4/AJRQcmYtLg7b5MRhIG8ogy6D2pOt9TEXBrt+fxeEBKiM5GTUQR3D1u06WIPW2fDejvhZXwfhfml8H4RiejwNDy5GwwLax107O6IU5PaQaWBtHiRx75j7S30HNAM3frxu6qEQN7jp/Y/w/KtgyGvwI3BKqEgtwTP7wTSzxj7ZiacHUd+dhFNx1HVUqzT/caHpkJBWfjxyAz+M3jwYOTk5GDlypXUcNXJyQkPHjz43bSVVH38UUBMT0+Hs7Pz799v27aNbm3btsWLFy++ap+EnTt30v3279+fpvh4enriwIEDQn3sDMa38MOvDSWugfHdkFgyogiT7PSv6YvkOy+u+GLTuEPoOLQlFv48UaBjhb+Pw/we2zB0XjeMWtQTfOX83kc0YtjUVheLdo8EX/lY+xErJh5HwJsYrDo4Gm4dbSDqkFPwzTPeOLLlPowttLB019B62ybwvc9PfFQGXj0MxauHIVBRl6cTLbd2VmjT2R72TY0atMjUkF8XKYm58HkZhRC/RPj5xsHWyQCtO9qgRTsrqGvW7aSTz5DqK1KlERGcgrEzPGD0WxocnyFR44smHMOsFb2oAMolu5Zdwcu7wThwaza0Dbg799456YWa6lr0ndi+Tvd7+9hz6JtpwamNNeoronKd+uU4TVZsQGMpqTrd98fKSsSvXcr754DBEEVYhQmjwWFoowslDQUkhqcKfCxSVaKgIot3j0N4LZh0G94Sl39+hpTYLAyc3AEmPDVWbSzWGEt2DsPi0Ydx+cgLaBuqwlAEJgf/C7IK3GekO8xsdHF0230sGP4zpi7tidZd7Lk+NN48P6ZWOnQbM6sT4qMz4f00nG7+3rEoLipHEzcztGhvjSYtzaDWgCbKDY3KimoE+yfhvVc0PryNRU1VLSxsddG6ky3mr+nboAyU/2hke2j7A2hqK2HZ5kEiIR6S/+P2ldcwYmoHzsWSmNBUPLrqhwET2nAqlhBe3wvE/J0j6ny/Eb5xaN2TOzNdBoPBEHWYYMJocBDTV9KSU1FSSb0wBNmvTNoGOg9zR8DLCOSk5UNdVwV8RFFFDgMmdcDtU164e+YNZm7gZ2IOgaQHLNw2BPMG7ceaKSex6/J0KCiL/kTJzsUIq/aPxI6lV7Fh3nkMjkjHkCntISUtwfWh8Us8sdSm28hpHZGZVgDfF5EI/pCAPT/dQG3tJ9g2MYS1owGatDCDXRNDkZhAMv65ciI+KpNGAPu+ikJoQDIcmxlBV18V037sBnsXQ0hJSTTYCpuHN/1x5bQ3pi3shiZuphAFSJXgxh8vwbm5KRW6uIR8/h9cewtOLU0xZEoHTo8lP7uYpouo6yjX+b6L8kqgpM4qDnjFd3qO/Os+GQyGQGCCCaPBQSZQOiYaKMguphHDOiaCrVCwamKEK/sewedhMHqOawe+0ntcGzy65It7596iTQ9nOLY0B18xMtfCoh3DqAHsofW3MHfDQIhzFANZlyipyGHNwVG4dfYtzh18hjdPwrBo62BafcL4v2jpKqP38BZ0qyivQtC7eESHpuHlg2BcOe4FKwd9Kjg5uhpTY1kzGx2Ii4v+66Q+CyQJMVkI+ZCAtJR8PH8Qil8//YqOPRzg3sEas5b1hI4+P0VnYZKXXYzDOx+hsfgP2HFsPBQUZURG5Dm46S59H/Yb2ZLrw8HTmwGICEzGgs2DICMnyemxvL4biKbtrQhRDeQAAMQXSURBVAWS/sPXhZoGDRNMGAyRgl05MhokFs5GeHbJB8lRGQIXTJq0s4a4pBh8H4XwWjCRlZOiMcP7ll/B8S13sOPabE7TAv6N5u2tMXxGRxxadxtiYo0xd+NA3pscfg3kMfQe0ZJWnGxeeBF7V99ESw8bDBjXmrYkMf4eaRlJuLWzptuoGR60+iQ84HP7xu0LvvB/G0vFFEt7PTRvYwVDc01Y2etBjkOzyYZOWWklYiMzEOqfhPDAZDqhjghKga2zAVxbW2LDgZEws9JmBr+/QZ6fZ/eCce7IS4yZ3pHzCo1v5fjuRygrq8K0pT24PhQaT3/7tDfadndEh97/38SSK2JDUzBiXtc6329SRBoUVJjhK4PBYPwXmGDCaJDomH4WSYiPiVtXJ4GOJS0rhU5DWuDljQ8oLSqHHI9XA7sMcYP/qyhEhyTj+Q0/dOzXDHym10h3JMfl4NW9IBge80L/8W1QXzC11sGey9Nx/uAznNj1iLaezN8wALpGalwfmshUn5CtQw8nOtFMT86jEaahfkkIeBeHI9vvU3GqRXsryMpLUyGF+GEYmWuyNh4BUFVVg8ToLMREpCM7o4i22CTH58DJ1Zg+/83czWk7lYm5JhMG/8GrZM+629DWV8H2Y+NFzq/l8nEvFBWUY/5P/XghxJ/a9Qhx4WmYs74/50J7dlo+MhJzoSGASpCkqAyYOXHrE8P4v/zw6+etrvfJYDAEAxNMGA0SQ2tdqGopoTC7WCjjWTob4d5JL7x7HIoOA1zBV8hExXNIc3g/CsHxLXfh3tWR1x4a5EJ32opeqCipwC+b7kBBWQad+jVFfYE892PndYFzS3OcP/QMMwfuw6hZndBreAteTDpEBfI60TVUo5tn36a/p3REBicjLjIDYf5JOLH7EWTlpZCbXQxDUw3YNzWmggtJLiKpI0qqbJX2ayDiVF52CRJjs5CRmk8rRkjKEWmZS0vOh5m1NhyaGmPElPa0wkdNU4HzCSvfPT9uXnyHFw9CMXR8a5oAJGrcvfwOvi8jse7gaIiJcy+GRYekIDE6kwruJtY6XB8OvG4Hom1PwVS5RL6LQ5+pHgLZN4PBYDQUmGDCaJAY2eghL7MQga8ihDJe884OtC0n7F0srwUTQrP2NnBuZYHstAIaczhgSkfwGSLyzFzbH6kJOTi5/QFUNRTQpJUF6hNObqYwt9PF0a338fPGu4gNS8fgSW2hb6LB9aGJLGSF/ksLzxcDyMzUAiqgxIanISk+G2+ehCM3qwjSMhKQkBKHgYkGTCy1oK2nAl1DVegYqtF0Ej5MAoVNZWU1fb5I5U5OVjHiIjKQnJBDfxcZnEKFEHsXI6hrKcKtrSUVnshzxoS+rycmPA171t+BuY0ONuwfIZLtY09vByD4fQLW7B3JC/Gd+OTsW3Wd+sCsPDAKfMDrjj9WHZ8kkH2nJ2RTzzYGg8FgfD9MMGE02JYcSWkJpERloKa6VuCGoYqqcnBqZYmnl3wwcVV/SMlyazD3vyCrvZNX9sH0LltxevsDtO7hDE09fpvGSctKYs2RcVg3/TTWTj+FTacmwdLRAPUJ4jEza01ftO5qj8Mb7mJa370YOqU9BoxvA4l6YHjLNWQir2OgSrfWne1+/3lRYRlS4nOQFJuN1MQcWjnx9nkECvNKUV1Vi0aNG0FLVwnaeqrQ0FaEpq4yFQnUtBTprYqaHKSk+ft+/6cqkdKSSuRlFSM/rxRZ6QXITi+kIklMWDoVSkg1TkpCDjR1lGg7k7KqPDz7NIG+iRoMTTUhL4KTe75A/DVO7n+KjNQCTFnYFbZOonkue/UwBFdOvMbGI+Po64UPPL76gb5+Z6zuwwsBKik6A/LKslAWQIoNqU4ihq9MpGQwGIz/BrvKZjRIiImgU1trFOeXIiU6HSZ2gr8gbe7pgPdPw/D+aSha93IBnzG00Eb3Ee64dcILp7ffw4KdI8B3lNXkMX/LYCwYsh87Fl/G8n0joP+bV019wtnNDDsvTMWZ/U/h+zwCL+4EUiHFrqkx14dWL1FUkoViE1nYNTH6088rK6qRlV6IjJQ8eksqLYioEB+dSb8uKaqgYsqnj5/oZFFZVQ5qWgqQk5eBkooMFVHl5KTopE1eUQYyspI0qUNGVoKKuWQ1XlJKDGJiYt8tehAxuKqyhh5rRXk1KsqqqehRXFBOJ+W1tR+Rk1mEwoIymh5EqmsKckugoq6AiKBkuh8bJwMa10zEIH0TdbTxtIe2HvGHUYKWngpLHapDSJXTk9tBuHf1PVp72GLygq4iW7309lk4Xj0KwbpDo3njt5KdXoCfN9yGS2sLtO7qAD7g+zgUnQY3F8i+iam9tBw/hCrGX2ApOQyGSMGudBgNFmVNRfg+CEJ8SKpQBBP3bk64f+o1wnzjeC+YEIhjf0ZSLrzuBqFjf1fapsN3dAxVsfboeGyaew5LRv+C7Rem8r465nuQkpHAhIVdERuWhr1rbmDhyMMYNLEt+o5pRaOJGYKHCBrE64RsfwcRKYgfSkFOCQpyS5GfV0KrUoryy5CfU0LNiosLPwsX5DqXtAgQVNXlkZdTQr8mEbok7YdUwJGWFuIJQkQY4qlCqjsIxKQ2PiqTCjOmVtrUM4QIJSRSOcQvkd6HtMaQr0n1mLObCVKT8mgFiImVNqoqqqGgJEsFEGPzz14tKupyUFVXgIq6PBNEhER0WBoObL5LW7yWbRlMq5NEFZ8XETQRZ/3PY+nriA8QAfHMnif0duLiHrzwzSEC2dMr77DrzgKB7D8xPA0WzszwlcFgMP4r7EqI0WAxsf8sksSHJANDWwp8PCV1BcgryeDBaS+MXtKL9ys/5Fjduzni/fMIHFhxBfsf/AgJSf6fMkxtdGnE8NIxR7B1wQUs3TMCKhr8uGiva8xsdbHj/FQ8vPoBp3Y/wr2Lvhg5qxO6DW5Oo5YZ3AoqeoZqdPtab4XK8mqUl1WiqrKWCi7V1bWoqaqlAsinT7+ipuYjFUYaNfqBVn0QSAXCr59+RaPGP9B0n36jQAUW0qYlKSUOSWlx+nNSzUK+5sNEkfH/IULZib1PUJBXirEzO8GxmWhXipHKkqd3grD+5zG8En1e3A7Ek2sfMHNtP9o2xwfC3sXDwtGAtpQKgvB3segxvr1A9s34b7CUHAZDtOD/7IfBEBBmjoawc7ekUb/Com2fpgh8FQnfRyFox/PIXkKnga54eMGHfn3j2EsMmspvA9gv2DQxwqpDY7Bz8WUsHnkYm89Opi079bW9rNsgV7T0sMGJHQ/x8l4wHlx+j8lLesCxuSnXh8f4hv8jad3hi9cDQ7AQcezKqdd49yoann1d0KWfC30NiDKvn4Th1N7Hv4klSuALBXklOLTuJiwc9NF5AH8+dx9f9EGH/oI7npSYTOiZawls/4z/CBM4GAyRQbQ/nRmM/4CxrR7CvKPx9o4/LdMVBi27O8GhlQV87gdBFCBmcTM3DEJUYDLO7LiP9MTPbQCiAIninfFTX+oxsW3hRdoKUZ8hrThz1vXH5MU9IC0jicVjfsGBdbeQnpTL9aExGIw/GHE+vROIqQP3oaqiBhsOjUb3gc1EXix5fjcIr+4FY8PhsbwSSwgH19yEho4yZq8fwJvnuaKsCpEBSXB0F0yrK6lKI+2ofHm8DAaDIcqwMymjwUJaYnTNNFFSUIac1HyhjKmgIgcZOSm8vuNPDWdFAWNrHfSb2I62Bpzf+1ho4lJd4NrOGsv2jURCZDoWjzxEPSTqOxb2eth2djIW7xiKdy8jMbnnLvy88Q4KReT1xmDUR8h5883TcEwduB+RIanYdGQcxs/15EVSy3/lwdUPuPTLK0xe0h1qmvxpwyG8uhsEr/vBcPe0h7Elf6otvO8HovPg5gJLsEkITYGsoui/tuq96WtdbwwGQyAwwYTRoGnexRFGNrqIDU4S2pjt+7uCaA4+D4MhKgyf44nmHrZ4cvkdnlx5B1HCrYMNZq3tj9T4HGz/8QJNAanvEJ+Ktl0dcPjOXIye3RnJsVkY77kN5w8+o60ADAZDeEKJn3csFo47iuf3g7B400BMX9KDxjHXB66dfIMg33hsOjaONwavX8jPKcapXQ/RppsjBk5sCz7x4Kw3WnYRXFJPQlgqrJqylkwGg8GoC5hgwkBDT8ohKzEx/p/TJISBa2d7qOsq4+GZNxAVpGQk0WtMG/r14Z9uIj+rCKKEm4ctVhwYjbjwNCwafgi5maJ1/N8LMfscML4NFm0bgq6DXHH+0HOsn3MWN894U0NRBoMhOILex2Pzksu48MtLjJnpgeXbhsLEUhv1RQgifiXeT8MxY0UvKCrzIzr4j8e3Z9lVZKcVYNgMDzTmkQl2WnwWjRXXMf77hK26IORNFMydWEIO301f63pjMBiCgQkmjAaNuZMRvU0MTxGq+ODU2grh7+KQ/ls0qCjQpI0luo9wh4G5JvYvvyxSrTkE1/bWmL9lCL2A3vHjRWQJqQ2LDygoy2LCwm44+mA+NLSVcGTzXUzqth0PLr+jve4MBqNuIOfF4A/xWDjuF5zY8xiefVyw5eg42DX5/FlTHyCJTvvW3ERxQTnW/Tyal0bFDy+/Q1lJBcYs6ApDc03wiQfn3sKts+CqSwiZSbnQMlIX6BgMBoPRUGCCCaNBY+ZkBDVdZYS9jRGqANBxcHNYOBvh7b1AiBKjf+yOrOQ8eD8Iwcub/hA1XFpbYu2x8UiJz8L8wQeQFJOFhoS6thJmrumLw3fnoXUXB+xdcxMTuhDh5D2rOGEw/gPk8+OdVxTmjTqMy8dfY8iEtthxahKc3UzrVZRzVWU1Ns47j6qqGkxZ0oPGZ/ON9MRc/LzuFvUH6T26FfgEEajfPghCqx7OAhuD+LIZWGrXq9ddvYN5mDAYIgUTTBgNGlkFaUjLSqEot0Roxq8E2+ZmKC4oxa2jz/Hp0yeICvJKMpi1eTAkpcXx6LKvyLXmEOxdTbHi4BjUVNdg74oriApKRkNDx0AV4xd0xZG7c+HYwhTPbgdgXKetuH7iNfM4YTC+MfWGJMTMHHIAD659wJhZnfDTvpFwaWle7yasJYXlWDbhOMztdDF/wwCIifOnzeULtTUfcXzbPahpK2L+1sG8S4kh3mXu3Z0gJSM4oSnKLwHquioC2z+DwWA0NPj1ScJgcIBdSwsY2+kjLlh4E2ey8tVpSAtkp+Qj6HUURAnXjrboPNgNAa+isOvHCyLXmkOwsNfH1vPTUFxYjkUjDuHDy0g0RHQM1TBv/QDM3zgQLTra4PjOh1g6/ijO7H3MUnUYjP9BeVkVbpzxxsqZZ2j6zcwVvbFy53A4NjOpd0IJgcSz/zjmCNp0dcDgie14+xjP7HmE1w9CMHxGJxolzDfunX6Ndr2bCnSMpIhUWLsyw1c+wzxMGAzRggkmIsj+/fthZGQEKSkpNG/eHO/efV1qyYULF+hFTp8+fQR+jKKEib0BNX4N84kW6rgeg91oW87j8z4QNUb/2AMaeioIfB2Np1ffQxQxMNPE+uMToaWngpM7HuDxNdF8HHWBpq4ypq/sjRNPfoSDqwlunnqDyV13YP9PN5EqQj47DIagyUovoCauE3ruREpCDqb+2A3LdwyFpZ0e6isRgclYPPYoxszxRK/hLcBXgt7GwP91NDz6uaBdT8G1vHwvaXFZUNFQhLGNrkDHCXgRDnNnY4GOwWAwGA0JJpiIGBcvXsS8efOwatUq+Pv7w9HREZ6ensjOzv6ff5eYmIgFCxagdevWQjtWUcGyqQm9jXofL9RxNfRUoaAii9e3/VBSWAZRgpj8Ldg1Auo6Sti39DJS47JE1tNj64VpUFaTx44fL+Hs3sciWTFTV6ioy2PMXE+cfL4EY+d74v2LSEzssh3bF12C/xvh+vwwGHyBvO5D/ROxbt45zB91BGKNG+Hg1Zm0qkTPuH4ba75+GIItP17E0p3D0LydFfhKYW4Jtsw9j/LiSkxdyc9FIVJd4tzGSuCGvKStkrQbM3gM8zBhMEQKJpiIGDt27MDEiRMxduxY2NjY4NChQ5CRkcGxY8f+8W8+fvyI4cOHY82aNTAx+SwOMP5cYeLYxhr44XM/ujDpMqIVTTB5cycAooZ9c1O07u6EqopqbJ97FtVVNRBF5BVlsPzAaLTr6YQ3D0Kwa/GlBp8cIyMniS6DmuPoowVYtmc4/dmycUcxudsOPLzyHmWllVwfIoMhcEjKyu0LPpjSbw9O7n2Clh1scPz+PAwY25p3Mbp1DfHWOr33MY0i33h8PCzt9Xh9rKd2PkRpcTkW7x4OGTn+pfZUlFUh0CsKbXo1Eeg4ieGpsHEzE+gYjDqACSYMhkjBBBMRorq6Gn5+fvDw8PiTFwb5/u3bt//4dz/99BM0NDQwfvx4IR2paCEuIUYNQIO9IpEYkSbUsV097fHrr59w/dATkVy9Hz6vK9w626EgrxQnN9+BqCIhKYaF24fC3dMOj65+wNLRR1DEPDzQWKwxWnnaY/7mQdh3YxZsmhji7nkfjGi9AXtWXkNcuHDfLwyGoCHn4ajQFOxafR3je3xuu1mwbgC2Hp+ADj2cIC4uhvoOmdwfWHsLCdFZ2HZmMrR4biB68eAz3D/vgykr+sCMp61RL66/h0t7a0hIiQt0nDCfGBhZC7blh8FgMBoaTDARIXJzc2m1iKam5p9+Tr7PzMz82795/fo1jh49iiNHjnz1OFVVVSguLv7TVt+xdv28IhPhGyPUccnFd6dh7kiKTEeYTyxEUWyauLwPivNLce3IC/g+CYWoQsTH4bM602SFyvIqLBi0H0nRf/++aoiYWutgzvoB2HB8Am3bifBPwrLxxzCz317cOedDV3cZDFGFGEDfOO2Naf324eKRVzCx0MIvt+di2pKeMLdtOBPQjJR8zB9+CHKK0li2axikZSXBZ4J8YnH3jDdad3NAlyHNwVcR7u5JL3Qf3UbgY2Uk5MDmt+sZBn9hpq8MhmjBBJN6TElJCUaOHEnFEjU1ta/+u40bN0JRUfH3TV9fH/Udh1ZW0DHRRKSQfUy+tOU4uFvi8QVviCI6xuqYtXEwdIzUcHrbPeSkF0CU8ejbFFNX9kZZcSXm9t+Ld88juD4kXiGnII3eo9xx4PYcrNg3EsaWWrj483OMarcJm+dfgN/raCrsMhh8h7TevX0Wjp9mn8XmHy8hKS4bc9b0wYpdw9BrWAv6Wm9IfPCKwv6fbmDI5PbU4JVvkbx/JSejEBtnnoaqpiJmbRjI2+Se4LcxsGlmAk19VYELMxG+sdA0/PrrPQaDwWD8O/W/trQeQUSPxo0bIyvrzwab5HstLa3/c/+4uDhq9tqzZ88/9foSxMTEEBUVBVPT/xs9t2TJEmos+wVSYVLfRRMLF2Okx2dRHxNho2WgBlkFKTy95IPRS/tARVMRoka7Pi4IfReHu6ffYMPUE9hyeSatPhFVbFyMsfvGbBz66QbWTT2BgZM7YPjsTrQKhfEZMjmxdTGiW1lxBV4/CsWT6344uPYWNR1s18MJ7Xs5wcRKm7cTGUbDg3wGhgck4/ndIORkFFGTTI9ezmjR0RpS0hJoqM/J5V9e4fF1PyzbPRzGFv/3eoJvVFfV4ujG26gsq8acTYN4LW7dPPwMfSZ2EPg4GQnZVCxh51sRQBCeI6zChMEQGKI7o2mASEhIwMXFBU+fPv09Gphc6JDvZ8yY8X/ub2VlhZCQkD/9bPny5bTyZPfu3f8ogkhKStKtIaGiqQQjWz00FmuEgqxCKGsqCXX8nhPaI+BlBF5ef4++U/6/R40oMWllX0T4J9LEnzM77mHs4l4QZUgC0I+7hmPv8is4t/cxCnJLMPbHbtQklvFnSCKD54BmdEtPysWLO0F4disAIe/jUVFejbbdHNC2u2O9TxRh8BOy8h4dmoZXD0KQlpiLvJxitOvmiKGT20FNBAXquqS4oAzbFl2iiWG7Lk7jtfDwRw6tuYZXdwKx9MBoGFtpg6+kJ+RQAcO+pbnAxwp5EwUnAafwMBgMRkOECSYiBqn8GD16NJo2bQpXV1fs2rULZWVlNDWHMGrUKOjq6tK2GikpKdjZ2f3p75WUPgsBf/05AzBzMsSTs2+ol0ir3k2FOrZzG2voW+jg0p4H6DGunUhWZxAzu+WHxmF+v524tO8JjK10aeWJKCMpJY75W4fApokRjm25i4DX0Vi2fxRvjQX5gI6hGoZN74ih0zogNjwdL+8E0WSdAO9YlJdWUhPZVp52MDD7sxcTg1GXkMWEyKAUvHkSTo1bs9IK0KaLPcYv6AJ9JtxRIgOTsX3JZXTq64IBE9qITAXdk6vv8eCCD/qOa4tWXRzAZ24ceQa3Lo5CqfoIeRONgXO6CnwcRh3AKkwYDJFC9GZlDZzBgwcjJycHK1eupEavTk5OePDgwe9GsMnJySJz0cM37FtaUsEk1DtK6IIJuZjqMsIdexechddNP3QYyE/zun9D20gNs7cMxeoxh3H152cwMNeEia1oiwvkf9NtWAuY2Ohgw/TT2L/qGjr2bYruw1uw0uf/AXluiFkm2cYt7ILo4FR4PQzBw6vvEfg2FgW5pWjhYYOWHrawcNBj5y3Gf6aqshpBvgnweRaOtOQ8lBRVwL2TLcbP84QhE+j+JCZdO+aF22ffYuHWwbBragxRIcQ3DrsWX0THfk0xbnEP8JniglIEvIrExNX9hfZ/NbDUEcpYDAaD0ZD44VdRzDJlCBXiYULMX4uKiqCgoID6SlpcJnZOOwZ5FVmsOj9b6OOTZJa1ow/RRYL1l2aJ9GT8+pHnOLrhJtS0lLD77gIoqsihPlCUX4YDq0kpeBBadXXA7A0DIMdadL4J8pETF54O7ydh8HkaTlsAUuJz0KytJVp0tIFDc1PIyktxfZgMEYEYf773isb7l1HIzS6GtIwEWnSwRvP21tAxEKzJpiiSn1OCEzseoDCvFPM2DoSSquicmzNTcrFy7C/UrHf3zblQUJYFnzm/8z7EJMQwcHongY+VmZSLX1ZcxPJT09EQEZXr1C/HaTVrAxpL1u3n3MeqSkTuWcr754DBEEVYhQmD8RskJSctLguFb4tQVlQOWSFPhKVkJGHuZIALO+8jxDuaJueIKr3Ht0WITyzePgzBz6uuYd6O4RATbwxRR1FFFot2DYeFgz6eXffHrN67sXDHUFg3MeL60EQGIgSa2erSbdTszshMLcD7l5E0jej6yTdYN+ssbJwN4O5pDxtnQ5ja6PA+rYMhPCorqmmrzftXUfB/E0N9p/RN1NGmqz1cWllAQYkJmP8EeZ/tWXkdfUa7Y876/iJV1VVWUonVE47hY+0nrDoynvdiSVVFNR5f9MGeh4uEMl7Y22g4tmb+JSIDa8lhMEQKJpgwGH+YyDm0soTXjQ8I84mBq6ej0I+h5/j21FH/5Y0PIi2YkAvxBbtHYvfC83hx0w9SMhKYuWmwSFfN/PGx9Z/QDnZNTbBl7jksGHwAE5b0QK9R7mgsJvqikLDR0lNGz+Et6EbSdYLfx8PPKxoh7+Lx8/rbkFOURoeeTtA31YRDcxM6Oa4PryPG10GqCYhha2x4Gt4+DUdUcCqc3ExhZqOD2T/1g7mdLhPUvkJkOrrlHvKyS7DqwCgqVooSH2s/4vC6G0iJzcKqX8bDyJK/Jq9feHLJB52HthBaBeKHJyEYsoDfLUoMBoMhqjDBhMH4Ay6dHOB9xx9BryI4EUxUtZTQcXAL3Dn2goonRlai248sIyeFsUt6IfBNNO6f9YaZnT66jXRHfcHSyQB7b8/Bia33cHL7fby6G4SF24dCx0iN60MTWYiw5trWim6E7PRC6neSFJOF8weeYv+aG2jSyhxy8tKwbWpEvRcMzTXZhLmeTe6jglMQ6peI3MwivLgbDC19FTRvZ4k+I91h38yYtWx9A5FBydi19Crsmhnjx62D6XtM1Fr4Dqy+jidXP2Dm+oFwbW8DvlNb8xHXf36KzdfmCu05So3JZP4lIsQPv37e6nqfDAZDMDDBhMH4A7bNzVBdWUMFE67oPbED7h5/iSt7H2LB/s/pR6KKloEqVh6diDPb7mH/8ktQ1pBHC09+pxp8qyg0bU0/OLiZ0fjhrfPPo2PfJtQkVpTK3fmKho4SOvf/bMA8YVE3GglLJtMBb2Jw9dgrPL8VgKTYbFg5GdCJNDGYtbDX4325PuP/T/RISxapHgn9kIiIoGT6niorroCtixGatbHC6DmdoVRPPJCESXV1Lc7tfYJnt/wxd8MAOLtbQBS5e8Yb9868Qa8xreE5WDTM0N/cCYBbZwe6ACIMkqMyYOduwSrvGAwGQ0AwwYTB+AM6pOy/jRXtBS3OL4UCBxfq+uZa6DWhPXweBiErJQ+a+qJtXGjbzASdh7jRSpPN009iy9VZsHA0RH2CGMDauBjh6Mbb2L/iKt48CMacTYOgqSfa/zs+QSYDesbqdOvYuwn9WXZGISIDkhERmITE6Eyc2/+Uru4amGrAyFKLCiik/YD4oMgzc17OxRES7UsMf0ncNDVrfRVFf9e6iz1UNBQwdq4nLB30ISMryfXhijREVLx0+DkkJMWx/+YcyIuor8vzm37Yv+IKeoxyx6QVfURCEPj48RPO77yHlaemCm3MgGehMLbVF9p4jDqAeZgwGCIFE0wYjD9ALsg0DdTw+MxrhLyOhHsv4cYLf8FjcAvcPPIMl/c+xIwtwyDqtO/blIo//q+isHLUz9hxYy50jNVRnyATvgU7hsG5lQWu/fISM3vtxMg5XdB9REtWbSIgNLSV6Namm8PvfhcJUZlIiMpAuH8Snt8JxMldjyAhKUbTeIwttWnlgoauEozMtaBrpFYvzIj5aNCZHJtF04/iItMRH5mJxOgMqKgrQFlNjopYJFJ6+AwP6mEjChNhUaCqsgbn9j3Gs1uBmLayN1p42EJUIQL7z2tuwMrZEOOX9BKZtru39wNh5mgAHSPhfb6lRGdgMPMvYTAYDIHBBBMG4y80aW9LBZPAl+GcCSbmToZo1skOyZHpyM0ogJq2MkSdwTM7IzutgKbnLB9xENuvz4ayhiLqE2Ti59G/GRVN9q+8igOrriHcLxFDZ3aCgZkm14dX7xGXEKMtOWTzHNCM/qy6qgbJsdmIj8ygQkpMWCqun/BCQW4pNZEtyCmBvqkG/RsieukaqkHHUIVGYbOJ/P9eSSdVImlJucjLKqFtNanxObSSJPh9ArT1VWBkoQUjc030He0OEyttaOoycURQkDa1vauu02qdA7fniHRFVXRwMtZMPApLZ0Ms2TsKUtISIvOeOLv1LpYdnSi0Mcn5LTogERqsmlG0YBUmDIZIwQQTBuMvOLWzgZGNLgKeh3F6HMPm9cBcz424suchpmwcAlGHTJSmrx+IwtwSlBSWY8Won7H50kzIKkijvqGqqYgVh8bC614QLu5/gundtmHglA4YPK0jJKVE4+K/vkDaEr7EGP+RwvxSpMbnIjkui070icHswysfaNuIorIsqiqroaWnQg1HDc00qICirqMETR0l+v9VUJap15VDpFonP7cEORlFVBghGzFhTUvKQ2ZKPhRVZZEYlQkdQzXqH0PifInfDEkxIht53hmCpzCvlLbf+DwNx6y1/eHUwgyiDKlMOrj6OmTlpTF38xD6vhMVXt34AFN7feiZaQltzDCfWNi6mQttPAaDwWiIMMGEwfgLKppKdCKUGJWKzMQcaAmxtPaPWDczgUsHWzw88xoDZnpCTUf0q0xI7O6P+0ZhxYhDiAtNxZpxR7Dm1GRIy9Q/zwIiELXp7kSrTY5vvoMnV97D+2EwJi7tDZffUmAY3EGMRMlm19ToTz8nHihENCGiQEZKPrJS86mY4u8di5z0QmjqqSAiIIm28pDJaVlJBZTV5KGqoUBFFDLBI7dKqnI0zYWs9MspSFEzUy6rK2prP6K8pBIlRRUoKSpHaVEFivLLUFRQhqqqWmQk56Egt4S2LgX6xNGJuI6BKn7Fr9DQUaatT3omarByNKAiEmmlIY+bVYxwV83w8PI7nNn9CJ4DXXHg9lyRqcT4J7JS8rF0xEH6mlp3ajJ9r4lS9PGtYy+wYO9ooY7r9yQETT3shTom479Dzpp1feZkZ2IGQ3AwwYTB+BvcujtDQkocIW+iOBNMCCOX9MLa0Qdxac8DTNs0FPUBKWlJrD4+CUuG7MOnjx+xYfIxmqRD2inqI2TCPGvDIET4JWLv8stYPuYw9TUZNKUjNHRFXwSrbxAhhHibkO3vIIJKfk4J8rKKqNhAKjBIW09pcQU1NC0uKKc/J5VTsaGp+PTpVxrpSlqzpGUloWeijoqyKjq5lZaRoG1AJNFEQkKMpgIRQYMcAxFZqitr8UOjH+gEkvyMjE32JycvRStkPtZ+ojGxRNwgFSHVVbX096VF5aisqKH7iwxMRllpJQzNNBEdkkrfZ6QahIg65JYIPOSYtA1UYdvEEGpaihgwvg1UNRVopQ0TRPgH+Z8e+OkGTKx0sPnMFNpSJurkZhbi57XXqai38exUGFlqQ5R4duUdDMy1oWsi3NbLkNdRGLm0j1DHZNQBrCWHwRAp6ucMhcH4jzi2sca5TTehoqWETsNbcXYcVi4mMLXTx/0TrzBgRud606dMJpM/nZqCH/vvRti7eGyZcRKL9o+GmHj9PSVZuxhh7+15uH36DR5ffodJHpswcGoH9J/YXuRXhhsSRLggQgTZ/o1Pnz6hoqyaVnOUl1airLQKlRVVKC8ht9V0IyvTleXVVOyQlBanQgf5GRFLiAhDBBDiCyIpJYaa6o/4oVEjiEs0pmJJY7FG9LVDRA7SAkPuIyktAUlJcSqkyMlLY+DEtrS6hSTPkLQUSSnWKiOqEKHuxgkvGqc9YXF3tOnmWC8ELSI4Lhl2EDnpBbSyxNJJtFLUiI/I2/tBmPTTAKGOm52aBzMnQ/qeZzAYDIbgqL+zEwbjP2DbwgLSclIIeBFGL4a47McftbQPnWxd2fsI0zbXjyoTgpKaPDZcmIHFg/ZQY9tts09j4Z5RtG2nvkIeW5+xbdCupzNObL2Hm8e94HU3iJrCkvad+jD5Yfx/SGsfacshG4PxvRA/nZsn3+Dq0VfoMsgVP9+fT0Ww+gDxtNq//DKy0wuw8udxsHM1hahx78QrqOsoQ8vw76vSBMX7R8EwsNQR6piMuuGHXz9vdb1PBoMhGOqvYx2D8R8gZeudR7amq8lh3tGcHouZgwFUNZVw5+hzJEdloD6hpq2EDeenIz+rGC9v+mPn/HO0N7++Q8SiOZsHY/3pydQzYtPM01g39Tgi/BO5PjQGg8ETSGXRi9sBmNx1O/Kyi7HrygyMXdC1/ogleaVYMvQAfJ+EYcXBMSLp7UQ8jPyeh2PI3K5CHzs2KAnNOjP/EsZ/Y//+/TAyMoKUlBSaN2+Od+/e/c/7X758GVZWVvT+9vb2uHfv3p9+TxZ+/m7bunXr7/ch4/3195s2bRLYY2Qw/itMMGEw/gFTewOU5Jfh3YNArg8Fo5b2hqSMJO4ef4H6Bmkz2nxlFnSM1JEUnYFts07RloSGgLmdPrZemoFlB0bTdot5/fdQ4SQlLpvrQ2MwGBwS5BOLbQsv4vqJ11iwZQimruhNfWbqC6QNZ+us0yguKMOyg2PRtL0NRJHLex7CsokRlDUUhDpuZXkVYvwToCNkzxRGHXuY1PX2jVy8eBHz5s3DqlWr4O/vD0dHR3h6eiI7+++vQby9vTF06FCMHz8eAQEB6NOnD91CQ0N/v09GRsaftmPHjlFBpH///n/a108//fSn+82cOfPbHwCDISSYYMJg/APNPB1hbKePrOQ8utLHJbqmmugyqjVuHn5GYwTrG1oGalh3fhqK80rx4oYfts85Qw0uGwLkQqJVV0caQzx1dV+EfUjA6v/X3l2AR3U1fQD/AyHu7iQkISEkEAjB3d0dihQoUNyKa4FSrGiBAgVKoWhxdw1ugRhxIR7iLt8zJw2FfPRtabNZyfye3m5W2L3Zu9l7z9w5M6N2YuPcw6IQImOs/AjyeYsFI3dh/dyj8GjuiB+OjBdFgxUJTcGc1X8TXj4IwKTv+6FBOxfIo9jId7h37jl6f922zF/76bXXcGshn0EmJjvWrVuH0aNHY8SIEXB2dsa2bdugrq4ughyfsmHDBnTo0AEzZ85E9erV8e2336JOnTrYvHnz+8eYmpp+tJw8eRItW7ZE1apVP3ouLS2tjx6noaEh8d+XsX+LAyaM/QUq+Eqdcu6eeoxwf+lPhek/uYNIxd638qTUAziSYGZtiFW/T4G1gylC/KKwbPRO5GTlojxNA+s2rCl2XpuDlt3r4MapZxjfaS12fndKzPNnjCmusIAYrJi4DwtH7kLDNs746cIMUeuI6uAokpjwBCwcvgOVKyth4Y6RqN9aPoMl5JcVJ9FzbGtR76ys3T/3DA071ynz12WlSMrZJTk5OXjy5AnatGnz/jb6vqHrnp6en/w3dPuHjyeUkfJXj4+JicHZs2dFRkpJNAXHwMAAtWvXFtN18vLyPv+XYKyMKNaemJVr1JEiKji2VGtgNOxSdEDy6OILSJuukTaGL+gJ/2chuHHsf88xlVcmlvpYfuBr5Gbn4sHlV1g+ZpdowVqeaGipYciUDvj55jz0Gtkcp/fewfBmy7Hvhwtizj9jTHFEBMdh9fTfsHXpCdjXsMDOK7PQaWBDhWyzHh4Qgxl9NiEqNB6jFvSAh5xOwyF+z0IQ/DoC7QY1LvPXpmMcv8dBcPKQvwK5TPJSUlI+WrKzP30MFR8fj/z8fJiYfDyti65HR0d/8t/Q7Z/z+L1794pMkl69en10+6RJk3Dw4EFcv34dY8aMwYoVK/DNN9985m/KWNnhgAlTCJRx0d9yHIY5TkF0UEypPW/jru4wtjLA7ROPIAs6fNEUWnoa2LX4mJjDrIgMzfWw+vcpcHK3QWx4ouiik5xY/gIFugaa6D++DXbfmo8O/evj1cNADG+6DDuWn0JCbLK0V48x9h+EvonBzpVnML3fZpjbGGLe5qHoN7aVaAetiPxfhGFm302wtDPGiv3jULtJNcjz8caOhUfx1bK+qFSp7A+jve+/QcPOblJ5bVa6XXJKeyFWVlbQ0dF5v3z33XdS+z1pas/gwYNFgdgPUd2UFi1aoGbNmhg7dizWrl2LTZs2/WVwhzFp429bpjB1IIwsiwrihfpEltrzWjmai2k5Pg8CkBAl/XoStC6jv+0r2hce2XQRitxF5tt946Cpqw7/52FY+MVWRIcnoDzSN9bG2EU9MWvDF+g0qCHO/noXK8bvxY8LjyEmIlHaq8cY+wyB3pFYPmEfvhn0I3T0NbDr6mwMnthWdMtSVC/uvcGsAZtFuv9XC3qgurt812S5fuwhtPU1UKuJo1Re/87Jx3BuIL8BJybZoq/h4eFITk5+v8yZM+eTq2BoaIhKlSqJaTMfoutUU+RT6PZ/+vjbt2/Dz88Po0aN+tu3g7rz0JSckBDuFMhkEwdMmMKo3doFNRo7IjokrlQDMa0HNoZrE0eZ6JZDGnepAyVlJRxefx5Rpfi7yhpNHXUs2/81mnRxQ1ZGDqZ3X4eg1xEoryhw8tX87thzez5c69nh2okn+LLFCvy46HcxCGOMyW5GwnPPAMwbvgPLJ+6DQ01L7L4+B32/aqnQgRJy7fhjzB+yFXWaOmHNsUmwrW4OeZaRloXLv3li1OKPO36U5dTjuIgELvjK/pK2tvZHi4qKyicfp6ysDHd3d1y9evWjzxddb9iw4Sf/Dd3+4ePJ5cuXP/n4Xbt2ieenzjt/5/nz5yKgamxs/A9+Q8bKHgdMmMLQ1tfE67t+8HscWKrP69GuJrzu+OH64U8XtSprFMQZ990A6Bnr4MimC1BkKmrKmL1lOGp42CExJgVLvtyBJze8UZ5R9s3wmZ2x9+5CEUC5d/ElJnRei7lfbMOjmz7igIcxJn3U6ev6qWeY2mcT9qw5j8btXbDt3HT0+6qlKOCt6EGikz/fxPoZv8HGyQwTlvcRU4/k3f7VZ+Bczw7mttIZ2FHtEtovKqtUlsrrM9mfkvM5aGrMjh07RK0RHx8fjBs3Dunp6aJrDhk6dOhHGSqTJ0/GhQsXxBQaX19fLF68GI8fP8aECRM+el6qnXLkyJFPZpdQgdj169fjxYsXCAoKwv79+zF16lQMGTIEenp6/+btZEziFK+qGCu3bGpYQcdIG1mlXCTU3s0GpjZGiH/7DokxydA30YG0VXEyR7OeHji68QI82riiYUc3KKpKSpUw8fv+MLLQw82Tj7Fw6HaMX9EPnYaUfbE9WaKhpYruw5uKaTo3Tj/DgyuvsHDYT7CyM0b3L5ujdU93qKp/+swSY0xyUpMycP7QA3heeSXS5HuPaoFG7VzKTc2J/Lx8/LjgGM79ehcdBjXE6AU9FCJAFOwdgejQeHyz9UuprYPnmado2sNDaq/PFEv//v0RFxeHhQsXisKtbm5uIiBSXNg1LCzso05djRo1woEDBzB//nzMnTsXDg4OOHHiBFxcPu52RQVdKWg6cODA//ealPFC91OwhWqW2NraioAJBW8Yk1UVChWxPykrVRQppsJRNBeS0vtkVWx4PIbYTRLdBU6++xlKlUsvHrhv+e/4dcUJTFg/DF1Ht4YsyEzLwlcNF8HCzhiL90+AqobiD46vHn2I9TMOQE1TBZ0GN8YX33QpN4OQv0Nf5S88A3Di55tIeZeOMP9otOtXH12+aAxzGyNprx5jCi/Y5y2unXqKswfuo24zR/T8shmq166C8iQ9JRMbZx/C09t+aNKpFsYv6wulypUg7yhzb0aX1RgwtRPqtXWV2jqMqTcPm24vgaqaYhYHVvTj1OL1dB25ApWUSzeImJ+TBa9dc2X+PWBMHnGGCVMYVPRVQ0cd6ckZiPCPhk0Ny1J77kZd3EXA5OaR+zITMFHTVMWkdUOwoN8G/PLdCXy1rD8UXes+9WBsqY+9K0/h0MaLCPaOxDc/DheteMs7mqrl1shBLFEh8Tiz/y4uHXoAvxdhUFGrjM6DG6N+6xoKMXhhTFbkZOfh/tXXOLn7tmgR3Ht0C2w/PwNG5roob6hd8OIvdyD8TQzGLOmFbsObiu8lRXD18H1YOphKLVhCXnu+gUNtGw6WMMZYGeNTs0xh0IFZ4x51YV3dAsFeYaX63FVrWqNpTw9Eh8UhLlJ2OpN4tHVFk27uOLHtKt48Lx/VxV0b2GP6hqGwrmaK1w8DsWTYNkQEll4raUVgZmOI0fO6Y9+Dxeg8uCEyUrKwbOxuLB61E3tWncVbBS4WzFhZiAiKxY4Vp/FFo6XwvPRK/J3tuzsf/ca0LJfBkud3/TFn4BaRBTFv+wh0H9FMYYIl1CFv38pTGDq7m1TX497pp2g1oJFU14EpVg0Txtg/wwETplDUNNUQ5hOJoJehpfq8dOBHNVLiwhNxQ0aKvxYb9/1AONWtinUT9iA3Jw/lgVkVQ6w7MwPNurvDyzMAUzqtxsPLXtJeLZlDZyJb9fTA+pNTsenMdNjVsMDpfXcwssUKrJvxGy4ffYjMUq75w5iior+VS0ceYka/Ldg0/xhys3Px3a9jMWv9YLTq4V4uC3HSVMALv3li3uCtKCwoxKxNQ9G44993xZCn3+/ghvPoN7kDDM2kV5AyLzcPjy69gFuz6lJbB8YYK694Sg5TKNXq2MKxblWkJWeU+nO36t8Qt449wJtnspXJYWCqiw5Dm2Ld+N04vP4cBn9TtmfBiruyfFgYrCzQNJwJK/uLrjEnfrqGrfOPwvdZKAZP78R1TT7B3sVSLAMntsPd8y9EjQEKmvy48Jioc1KnmRNqNrDn946xEt9vL+8H4urxJ4iNfCdu6zSoARq1dy33UyOyMnOwec5h3Dz1FE0618K4Jb3F97EiuX3yCUJeR2Ds8n5SXY+n116jWa96pVqbjUkRZYOUdkYIZ5gwJjH8zcsUiq2LtWi7FxMWj0mbvyzVlGDzqiaiRsrNYw8wYGZXVHW1hqxoO7ARvB8E4Pze22jQ0Q12ZbRu1A1hw9RfUVm1MiasGljmKdgUpBn6TRc4udvg+3G78dsP5xEZGIOvV/SHjoFmma6LvKBBXuteHmIZOr0Trh1/DJ+noTi6/ToMTHTQaXAjeLSsLoIripJSz9jnZhUE+0bh+smnCAuIQVhALNr0dMegiW1hZm0g7dWTCTSt75c1Z3Hz5DN0G9EMo+Z3FwXXFcm7uBT8tu4sFuwZh0qVpFv76dKvt9F7YgeprgMrRRwwYUyuKNbejZV7VWpYiqKW2Rk5SHj7DoYW+qX6/G0HNxEBmXtnnspUwIQGtkNmdcOdk0+weuwubLw2v0zSw30eBeHKoftigKGhrYYvF/SENNRr7YJNF2dh5bjdCPaJxPi232H21hFwqW8vlfWRFyaW+iLjhLaf/4swXD/xBK8fBWHfuvMwtzEUXXbqtXSGjZMZB0+YwgsPjMWtM88R4h8lskqadqqFfmNbwtndlj//H6AMtR+mH0BBfgHmbBmGZt3qQNHQd+LmmfvR4YsmMK9qLNV1SUlMRU5WLpw87KS6HowxVl5x7jVTKHSGq1ZzZ2SmZiDwRelPnaGUWH0jbZzedlnMKZYlhuZ6GLdqILR0NbDvu5Nl8pouDR0wae1g0dL48dVXOPjDeUiLua0xVv0+Bc4edqJI3/JRO3F40yXk5xdNGWJ/jQaDjm5VMHZxLyzd8xW+O/A1ajV0gNeDAHzdcTVGtVyBAxsvio47xVOwGFOEQXGIXxT2b7iE7ybuw5SeGxEVnoB2fetj//1FmPBtb9SoW5WDJX/Iyc7FtkXHcHLXDTH1Zu2JqQoZLCG3Tj5GSmI6uo5sKe1Vwc1jD0WwhD+HioOLvjImXzjDhCkckyqG4tL/SRDqdyrdgzlNXQ24NHHE9UOeeHD+ORp3qwtZ0qpvAzy48AJHN15E3TYuqNXESeKvSWfgaOCxcfp+BL+OFC1se45tA2lQVVfBlLWDUbOhA87tu4PdK07iyQ1vzNg0DEbm0ivYJ0+ohklxe2KacvXyQSDunHsBv+dh2LfuAgxMddCkYy0xbce1vj2UVXg3wuQHBVDps+x5+RWiQhPwwjMA9ds4i6Kt09cMKJeFW/+JyOBYrJm8D75PQ9GihzsW7f5KYdu5x0YkYOeio1h9ekaZ1+b6lLunHmPa1lHSXg3GGCu3+EiXKRyXxk4IfR2OqKBYiTx/py9biue+vO+2zAVM6AzUxLVDEOYXhYNrz8LW2RLa+pKv5dFxaFPRQeLIpos48dN1cZDZ/atWkJZWfeqhWu0qWPX1Hvg+CRGth/tPbo+mXRTzbKikVFKqhNqNq4mFMkv8noXh3iUvMXiaP3Q7VNWVUaepI+q3qQH3Zk6iBgpjsiY9JRNP7/jj4XUfJMQkI+xNDOq3riFaAc/aMFjham+UJgqGXz32CD/OO4JqbtaYvGoA2g9sqLDZDhRQ2zjtVwyd0x2m1kbSXh0EeYWJ/amxJdfOUShcw4QxucJHCUzh2LpY4bWnP94GxYiDvdI+sKMMk5TENJHBEheRCCPL0q2T8l9p6Wni6+8HYXb3Nfhh4h4s/HV8mRzc9hrXRlTw3zrnILbNO4yKShXR9csWkBZLOxOsPT0dx3+6hj0rTmHF6F3oPNwfI+Z0F/VW2Oehg/bq7jZiIZHBcXh49TV8noVg09wjyMvNh62TOZp2doNTnSqo4W4LZVU+W8/KHgX3qGjrk5t+eHLbD6lJGVBSqiSyoroNbSzaa8tC5oCsS32Xjt0rT+PS4fuwsjPFuKV9UMXRDIrsyEbKotNFm/4NIQvO77mBDsOaS3s1GGOsXOOACVPIwq8UNKGONrHhCTCxLpqiU1roQLvLV61x79QTXDt4F/1ndIWsqdnEEQNndMHdM09xbs9NdB5RNoGLbqNaiGkcZ3bfxC/fnRKF6np/3RbSQmeO+01oB6c6tlg7+ReRbTK25TJMWTsE7i2qS229FIGFrRF6jmoBKvObkZaF53ff4MlNH1w78Ri/rD0HFdXKaNWrLsyrGKJWIwdUdbbglsVMYmLfvhOfwWd334iCrUqVK8Kptg1a93SHezNHzn76TM9u+WHttF+REJ2ML2Z0Qp+xrRU+AOr7JAgXf72DjVfnyUQGTVZGNvyfBGP0ioHSXhVWyirQybzC0k0JKe3nY4z9iQMmTOFQlgMFS17d9YP/48BSD5iQ1gMaYfeCw2JqTu/JHcVrypqBMzrj9f032Db7IBzcbFCtdlFmgKT1HNtaFIHdNH0/di46Jg48KftEmmo2csCPV+Zi++KjuHzwPpaO2I6OQxrji5ldONukFKhrqqJRe1exUFZXVGg8nt72R5B3JA79eBW7vjsNl3pVoamtDpf6VeFSzw52NcyhpCR7fzdMPsRGvoPvi1A8vemHF/cDQONbA1M91G7sgG7DmsDB1VJklbDPQ1Mrdy0/iRd3/UXHueUHvkadZpKvhSUL2TSrx/6Mb7aNFIXTZcGtYw/h2sSJ6+owxpiU8dEqU0hO9exF+9/gV2Fo2qt+qT+/rrEOmvaqJ6blPLr4Eg1lsDYGBXFmbh+FCc2X4rc1ZzBt83AxXacsdPyiiSgGemL7VexedhzvYpPx5cJeUj1rp6Gjhmk/fIHGndxEQdiTO2/g7rnnmPj9QNRr4yK19VI0tI3NbYzEQiYs74Mg77fwfxmGx9d9cGjLFexff1EMcKkzD7VsdalvBwcXSw5esb+sKxHmHw3vpyEI9n2LR3/UImnW2Q1G5rqY8G0f1KhrI4o+s3+PgiS/rDkH70dBaNmzLsYt7Q0tPdkIHkgSBXl/mLwX7QY3RnUZat17/9xTjFw+oNSf9+CqU+g0siW0DbRK/bnZP8Q1TBiTKxUKaU/B2P+QkpICHR0dJCcnQ1tbG/Lg3unH+LbfOjjVc8APN5dI5DX8HgdiUtPFqNmsOlZfnAtZ5XXPD7O6roF7qxpYcmhSmc7d97zwAt+N2oHc7Dz0m9xeFNKThWkZVNNg59Lfcek3TzjUsoa5rRHGLO0DPSP5+HzLe30JKrr5+lEQXj8OhvfjYKhrqSLENwpW9sao27y6mO5TrZYVqlQz44Kc5VBibAr8X4TB93kYQt9EiU42NBhwcquCui2cxPQuJzdrDpCUktTkdOxecRrn998Twcseo1qgccdaKC+ObLyIML+3mLppmMzUtvF9HIhflx/HsuMzSv2553ZZieWnZ8nEtKPydpxavJ61By9HJWXVUn3u/JwsPNs/T+bfA8bkER+JMoXkWNcO+XkFIgMkNydPIoOuau5VxUKBE6pkX9XVGrLItZEjRizshYM/nMexTRfRd3LHMnvthh1qYdmhSTiw5gwOb7iAcP8ozNo+CipqypAmLV11TF03BK16e2D9tP24+SIMz2/7Yficbmg3sKHMHDQrInpvbRzNxNJ5SGNxG2ULiAHys1AkJ6Zh53enkJmWLf5ua3jYijoodjUsYe9qiSrVTKGiKt3PDysddL6Gtn3g60iE+EfD71ko/L3CYWplgJR36XCsZS26Lw2Z3B42TuYyEWxVtPf/7rkX2LPyNNJSMtG6twe+WtwL2uUgq6TYy9u+OLf3JjZcnitT3/s0HafbuHal/ryZ6VmwdDBTqGAJY4xJGmeYMIWJ3Je0qPcaBL0MxZxfJsK5YTWJvMbt4w/xw9idaNzDA9O3j4asoj/zDZN/wYV9tzH/l3Fo0tW9TF8/2DsCC/pvRHxUEtoMaIgx3/aTmVTvrIwc7F93Dm+eh4qUdMc6NpjwXX/Y15TNAFh5yUJ5GxyPN17hYioPDagDvSNR1dkc3o+CRQaKbXVz0bGjioOpCKKYVTHkAbWM18aICIxFkO9bhPhFiZoRj276IiUxHRY2RqjTzBHG5rpwqGkFO2cLaOqoS3uVFVpMRCIObbqE87/ehbGlPiavHoA6zcpXIWzqcje75zrM2jEK1dzKpsbXP5EYnYTZXb7HtofLSz2I8+z6a/g+DMDAWd2hSOQuw2SQhDJMDnCGCWOSwBkmTGFpaKsjJiQOrz39JBYwadC5DtQ0VXH94D0MX9wbBmay1WK4GJ1NGvvdAPg/C8GhH87BzMYIdmWYEWPrbIl152dh56KjuHLQE76Pg/DtwUliPaRNVV0ZI+f3QIjvW2yedRCvHwZi28Kj4oz20G+6QFu/bOq+sD/RIMHSzlgsLXu4vw/6RYcnINgnStSxoJ9vnHiCyJB4FOQXiE48NPi2on9nbyyCKha2xmIwrqnDtVHKAm2jxJhkRATHISIoDvHRSQjwihBTsKiLjVtjB9E5i7KLnOvaotPgRuJnKhrMygZlXFKr9QM/XICOgSZ6j22NIdM7lrvpTdSBZumwH9F/akeZCpaQa4c90W1MG4lkvIS8CoNrY8dSf17GGFNknGHCFCZyX9LlfbdwYc91WFUzx5Stksv+OLX9Ms78dFUUfh2xpB9kWUx4PKa3/04ciG24Nh96xmXbajM9JQPLv9yOpzd8ULdVDQz+piuq160KWUFfh9d/f4SdS6lQbQo0ddXx5bweaDegASpxxw2ZlJOdh8igWESGxCHULwphAbFITkgV9S+yM3PEY+q2qC7q1phZG8DU2kBko1CxUBMLPRia6YkCxeyfoRbScW/fISbinQhaJSemiwBWVGgCosISRBZQdFhCURaQoxnMbAxhbW8KawcTGJrq8FQAKXpywwc/LfkdOdm50DPUxvgV/WDnYonyhr7nV43ZCTNbY1FXS9YCOeMazMeWu0uhrlX6gd4FPVZj/m+TpT4ttrwepxavZ52BkskwefobZ5gwJgkcMGEKsyMqKcL/Lb6sMU1M/TgSvUNi85PTkzMwxGEyKlaqiH3+6yVykFOavB8EYFbXVWjawwOTNwwr8wOnvNw8HFh7TtQ1qayihGkbh6Fl79LvZPRfpKdk4sAP5+HzOAg+j4Nh5WCK0Yt7waNVDWmvGvucbIfYFEQGx4mpYGEBMWIgT+1oqe1xUkKaeBzVSHkbkgBDc10xoLe2NxFn2/WNtaFvog19Qy3oGmqJug7UZlVR0SA6OSEN7+LT8C4uBUnxqYiPThYL8Xseiri3SaLWhVPtKngXlwpTK31Y2hUFQsyqGIhaMxSU4uk0suVtcBx+33EdZ/feFlklXy3qhRY93WWqZkdZOrrpAh5f88ayw5NENzlZcmn/bUQGxGDEoj4S2feu/Wo7Zu0eD0UjL8epHDBhTD7J1p6CsVJk4WAGW1drqGqoiPbCdjUlk3aroaOOXpM64vmN1zj/8w30LsOiqv+Gc317zN41Bt8O2YLsjBzM3TuuTGs/0AHqF7O6QtdQE9vmHcbJHddEZsDQ2d1k5gCe2tuOXtQL4QHRItvk4eVXOLjhAo5vv4qRC3rCzsVK2qvI/gZlMhiY6IjlU7KzchAXmYT4mGQRRKGgCk0hocyJ8MAYEWyhIEmof/T7f+PezBHR4YnidloMzXRFdoqGtqoIqqioVBYdf9Q0VKChpQYV1cpiyhcFJVXVK0NZpbJEPuPUdpemumRl5ohLqheSmZGNnMxcEeBIT81Cemom8nLyRZHVtORM5OXlizoWVGSXskSobojXg0AoKVeCvqE2bJyoQ1El8f5RRk7NBnYwMteDkZkuDEy0ZW6gyf6/1KR0HNx4Cad+vik+r9T9ZvC0juU6oHX75BPcOf0MSw9OlLnPMP0dH1l3Ft+d/kYizx/wPASGMjptuNzhtsKMyRXZ2lswVsoDpirOlrhx6B5e3vCWWMCEdB7VCofWnMHbwBh0HdMayjLexaNxV3cMX9ALh9efw/6VJ/HF3B5lmipPr9VtVCvYVLfEshFb4fs4GBFvojF14zAx0JQVVvamWPLLODy75Yvf1p/Hs5u+mHhrJXqPa43Ow5rBtIqhtFeR/UvUaae4Tspfyc/PR2pSpsi2oIwUyjyizIrkd2lIfZchghI0PYWCElo6aqK+Ck1ZocXZ3RavHgW9fy5dIy0kxaWKLBWanhIflYxKShXFdQdXKxGYqVixAipULAr0UMCGsmRocEsFUgvyC8X6UKHbNy/DRS2KvNx8MfUl4FWEeA01TRXRXYjQc9dsYC8ybDS0VMXfFbVtpn+jo68BbQMNEQTR0dcUg2ldA02xjpraajxtRs7RZ+PsL3dwcONFEfx1a+qIUQt6iDbd5Znf02Bsn3cI35+cLpO1qe6ffQqnevYwNJdMUMP7/hu4lrPCvowxVho4YMIUmluLGrh74hFCvIsGFJKib6qLTiNbIuBZCK7svyt+lnX9p3dGWnIGDqw6DTUNVfSdUvaZMTUbV8PGy/OwdOiPiI1IxOS2KzB/zzhRcFWW1G7mJNb1+rGHOLHjOk7vviUuOw5pgv6T28PAVFfaq8gkoFKlSkWBBIPPG1xRoKM424O6MNHPuVm5yMnJEzVX8vPykZ2Vi/zcfJHpQdP56LEFBYUoLCgUwQ5qi160DhWBCkWX9DjKVunQr77IBKG2yxScpUwQVVVlKKsqielEtHBdlvLZXerWqaeiGPKDy69EltDoRT3LXfebT3kbHItlQ7dizq6vYGFnAllD3xkHVp7ErJ/HSuw1nl31QofhLST2/Oyfq1BYtJT2czLGJIOPqJhCq9PGVRyI3Dp6H5M2j5Ro4U6aijPSdSZiQuPQZkgTkX4vy+gs8ojFffA2KBaXDtyBjpEW2g1uUubrQVka1EFn8zcHRAedub1/wLgVA9C0e9m2Pv47NGBt068Bmnatg3O/3MbBDRfhefElLh++j05fNEHf8e3EGXrG6G9LTV1FLDqcAc8kjPZxT276YvfykwjyjoS9qxWmrBmENv3qc6ttAO/ikrF82FaMWtoHNerbQxY9vPgCplWNYe1kIZHnz83JRU5WjszXWCs3eEoOY3KF96RMoZnaGMPIUl8UZvV/8md6vCSYWBuiw4jmUNdWw9Xf7kIe0ME01TOhdV8/YTfunHoilfWgM+LTNw3HxDWDRQBl+cjt+HH2b6IQpayhM/w9x7TG7odLxdQceg9/33YVc/ptwK6lv4uCmYwxVhYZJTRdcGrXdVgyYruYbjVyQQ+sOT4F7Qc25GAJdXVKzcLC/pvQfmhTNO9VD7Ia8Pp1+XEMniW5jj2+DwNQoyG3E2aMsX+D96ZM4bXo3xiaehp4cuWlxF+r/4yuImPjlyXHRHtAeaCsWhlzd4+FXa0qOP3TVTy+4iW1s/KdhzfH+O8HwdzWGJcP3hNTdSiVWhapa6qix+hW2P3wWwyc2hFauuo4+uMVjKi3APtWnRa1LRhjTBID7Ke3fDGz1wb8tPQ4wt5Eo9/4tlh7cir6jG2tcC1j/y2a5rZl5n4xpZJqZsmqhxeew8jKAFVdrSX2Gs+uvkKtFs4Se37276fllNbCGJMcDpgwhedQ2xZp79Lx+OILib+WkaUBun7VWrTvu7zvNuQFpekuPzYNSQmpWDp4M57f9JHauti5WmHTtXnoNLQZHl99jQktl+Hm8UeQVRQoGTqrKxbsHovB0zuJuhLUsvLL+gvxw9R9CH/zZ5cVxhj7L4GS+5e8sHriL5g3cAuCvSPRors79nguxhczOpfr7jcl0T74u5HboaKujBGLekOWs4T2LTuOIXN6SPR1gr3C4FTfQaKvwRhjiooDJkzhubWsgVrNnREbnoDUd2kSf73+M7tBSUkJe5YcFd0t5IW2gSZWnpwhCuL9svw4Xtz2ldq6UEeP0Uv7YsaPI1BZtTJ2f3sca8bvFq1RZTlwMmRmF+x59C0ad3aDlq4GLv3midXjd2PpiO14/TBQDHgYY+xzUJHgO2ef4+u2K7FkxE8IfB2BAZPaY8/9Jeg/sR209TWkvYoy1553z9LjIng9fvVgme76dOfEI5hXNZZodklKYpp4T6gzGJMRdCwgiYUxJhEcMGEKT1O36GAyPiIBT8tguomesTa6jGmNjJRMnNlxFfJEz1gHy3+fJg6wFvZdjxe3pJdpQtr0a4j1F2ZD30wHVw55Yn6/jXj9IACyTENbHf0mtheBk8lrByM9NQue519gRre1WDlmF26eeCxqDTDG2N/V3zi56wa+bLwUP0zfj8z0LAyf3RXrTk7DsFldOFDyFxkb6yftQURgDGZuHynTdVwoC+bgqtMYulCyGTDPrnmhQec6En0NxhhTZLK7J2GsFDXt3QCuTZ3gdbtsAgC9JnaAQx0b7F9xAtGhcZAn1CL5+zPfoFodW3Hg+eTqK6muj5mNEVafmonh83ogOysHM7qsxo6FR5CdmQNZrw3TYXBj/HRnEeb//BUadqwlWn6uHPszRtRfINoSJ8WnSns1GWMyJi7yHXYtO4EvGy/BwU2XRDtpCpRsvTJHZJRoaHOnk78KluxYcARZ6dmYu3sMKivLdqe6i3tuopq7LSwdzCT6Og/OPkOtFjUk+hpMuvVLuI4JY5LFARNWLtRu5QKv2764cdhTpKZKmpqmKjqPao3c7Fwc/eEc5I2BqS7m7hkLFXUVLB6wUbQ8lCY6SzhgaifM3DICVV0scem3e5jdcy18Jdz5qLTWvXEnNyzcPQbrz32DFr08oKRUCdsXHMEXdeZhzYQ98H0azNN1GCvH6O//1f0ALP9qJ2b324ijW6/C1NoQE1cOwM5b89F1eDOoaahCVsja9xXt13+YuAcR/lGYuW0UlFVkO1iSkZqJcz9fx5C5PSX+vtCULgt7U4m+DmOMKTIOmLBywcrRHA271RUHDa/vlk1tjjaDm6BZ73o4ve0SvO5Irx7If5mes+rMN2jUtQ6WDtmMq4c8pb1KsHW2xIZLczB4ZmcEvAzHtI7f49dVp2U+26SYYx0bzPpxBNacnI5B0zqJuiePb/hgWpc1mND2O5z75bY4kGaMlQ80zYb+7hcP3YqZPX/AvXMvUK+Ni5h2s/7MdDTqUBOVlCpBVryLTcHKr3bC72kIZAUFBChYQoHoBb+OF9l9su7w2jOo36k2DC30Jfo6fg8DoGeiI9HXYP9CoYQWxphEcMCElRtW1czg8+ANPE8/KbPMgu7j2omfD3x3vEwyW0qbtr4mJq0fBuf6Dlg7bhfO7bkh7VWCUmUl9PiqDTZfnw/n+va4ctATY5suxtMb3pAXBma6+OKbLtj7eBkmrRoE1wb2CHoVgR2Lj2Fkg8VYN2UfF4llTIEFvgrHnhUnMdhtHjbNOoi8vAIMnNIBex99izFLeqN6XVvI2nSXa0ceYNmI7eg6sgWc3GVj/XKyc7Bs+FZkZWTj69WDZT6zhNA03Ze3fNFnSieJv9bz66/RuHtdib8O+zwVCiSzMMYko0IhH5Gzv5GSkgIdHR0kJydDW1sb8srn4RvsnH0AFStVwKpLC8qscv6OuQdwYtMFjFk9BN3GFgVQ5E1OVi52LjyCU9uvoM+kDvhySR9UrFhRJg7iz++9jV1Lj0FVXQXurWpg5MJe0DWSv88ptR++c/YZTvx0DSmJRd2VatS3E2ebW/WpD0MzXWmvImPsP0hPycS9889xevctvHkRhuoeVUVWRKehTcS0PerqIotCfCKxdc4hOLrbiuw+Wem2QkVxl36xBQ5uVTB8fk+ZysT5X74dtFEUYW07uInEX2tK0wVYe32x3Lw3in6cWryeHj2XQaly6U6xy8vNwqPj82X+PWBMHsnm3pkxCXCsa4eo4BjERyQi4FkIHOqUzRmyXhM74tzOazi46hSa9KgniqrKG0pxHvPdAOTn5uHSr3eQnpKBcaukfzaPgjadRzRHgw61cPTHSzi+9Qo8zz3H6G/7ok3/hjLdIaEkKwdTDJzSEb3HtcGDi154dO01rh6+j9cPArFnxSm06u0Bt2ZOaNTJDeqaslPLgDH21yiz8OU9f9Fi/N75F7B3tUJMWAJ6jWmFdgMbooqjOWQ5wLNv5SlkpGVh7Ir+sHW2gKxIik/B8uHbYF/TGiMW9pKJAP4/8ez6a3HZemAjib9WqE8EajRxUvhgiVySxBQaPv3NmMRwhglTmMj9P/HLkiN4fuM13FrWwNCFfcvsdS/suYHdCw/BrXkNzNk3AfKKvi5ObL2M7XMOwqVRNcz/dTx0DbQgK17c9sXWeYeQlZYNLT0NjPtuAJzr2UFeJUQn4drRh3h45ZUoCElU1Cqj0xdN4drIAe4tnaUetGKM/f/vyWDvSFw79hA3jj+GaRVD+DwKEn+vFCShrDFZ/rulIM+N3x/i8IaL6P5VK7Qf3Fimgs9RIXGY33c9unzZEj3Gti6zbNHSmD40odFCzNw5Fg61bST+er8uP4aqrlXQqJviT8mRuwyTHhLKMDnBGSaMSQIHTJjC7Ij+CW9Pf0xpthCmtsbY67ehzA606AB0SvNFePMkGMtOzkTddrUgz24cfYCjmy4gJzMXC/aPh5WE2yJ+jrzcPJzedQP7vj8lUtzrt6+FoXO6wdBMD/KKvqYDvcLFAOzO6WfIysxG6rsM0V60w5DGqNnQQWSfyPIgjDFFFxkci1snnuDGicfQ0deE1/0A2FQ3R7sBDdGsWx3RfUzWeXm+Ea15bWtYYsSCntA1lJ2AOPF7EiTWr92QJmg3qDHkyW/fnxTTW4ct6lMmr7dqxI+Y/OMoqKjJxhQqSZKX49Ti9azXXTIBk4cnOWDCmCRwwIQpzI7on6CP+8Keq5HwNhHjN4xAjYaOZfbagS9DsWbUNqQmpmP705XQ0FaHPHv94A2+HbxFtE6mTJPazZ0hS5LiUnBq53Uc/OEcKqtWxpBvuqLrly1ErRN5lp+fj1eeAbh58gnunHkGEysDBLwME8GTlr09ULuZE+o0d4aquuIfJDMmC0GSu2ee4/bpp1BWU4b3w0AYmeuh/eBGaNi+JqrWsIQ8iAiIxs7Fv0NTRw3dRrdCNbcqkDV3Tj3Blpn7MX3rl6jbygXyJDIgCvO6rcEWz2+hoSP5fX+YTyT2rziGOfsmoTyQl+NUDpgwJp84YMIUZkf0T+2a9xsOrTqJ7l+3F0GTsrR3yVHRMaff9C4YuXwg5F1MWDx2Lz6KW8cf4cslfdF7YnuZS48O8X2LHQsOIzEmWRRT/WJ2N7Qd0FAh5nXn5ebjxV0/kXVCxSSrOJnD694bMW2ndrPqaNSpFjxau8jcWWLG5BUdMtF3yr1zz0VNEhL0OhJ6RlpoP6gx3FtWh7NHVbmpqUHT/k7/fBM3jz/CF7O6okUvD5lbd3rPD/9wDj6PAjFsfi+R/SJPaP1nd/4e3ce1RaOu7mXymkfWnoZ1dQvU71QH5YHcBUy6fSuZgMmpBTL/HjAmjzhgwhRmR/RPhXqHY9PEn5Ecn4qtj1eKNrVlJTcnD6tHbsPNI55YfnoW6ratCXmXlZ6NdeN/xr0zT9FhWDOMXNIXajJYlPT5LV/sWnIMwd4RqFHfHj3GtBbFYmUtwPNv5efl49WDANw79wKeF16INOyIwFjx+zm526B5d3e4NLAXZ7wV5XdmrKy+t6nN94NLXnhw2QuZadlIik8V7cE7DGqEmo2qiY5WslTn4+8kJ6Ti961XcfnAPXT5sjl6fd1GJrPvqF3wpmm/IDIgFgv3T4C+iQ7kzfk9N/Dg7DMsOjylTL576bB+UqP5WHtjcbmZpikvx6kcMGFMPnHAhCnMjuhzjK8/F2+eBmHRselo3M2jTF878EUIJjVZCFtXaxE00ZGhoqn/Fn2NnP35Bn6c8SusHM0x75evYV1NduqafNiG+MGFl6INcURADOq0qI4BUzuhZuOym5pVVtsj6FUEPC++FIO8AK9w6BtrIzE2RQw4WvSsC6c6NnBr4iiK4zLG/n/mxZMbPqLgctDrCESFxIvbrexN0KZ/A9Rs5CCmrchaNsbfSU/NFN3ETu28gW4jW6DLyBYym4EWG56Ab4dshltzZ3wxt4fo1iZvYkLjMa/7Knx/bg4MzMumjpbvwzfwPPMUI5b2R3khL8epxetZv6tkAiYPTnPAhDFJ4IAJU5gd0ee4sOc6zmy7DCNrAyw6PL3MX//UtkvYMec31O/ohnn7JynMGf8Xt3ywado+FOTl44t5PdGybwPIajYGteylTAxqQ1yriaOYquPSwAGKKP7tOzy+7o1HV1/j2W0/2LtawsszQHzuHNys4dGyhui6U93dVi4HJYz9V1SM0+dxEB7f8MHTG97izLzv0xBUUqoIl/r2YnqbewtnWFQ1hjxKS87Aie1X4fc0RATpB8/sDDMbI8iq57d8sGXGr+j8ZQt0H9NGLveRFKBf0nc9mvauhzaDmpTZ626csAstBzSCa5PqKC/k5TiVAyaMyScOmDCF2RF9jtR3aRhgNU6cid8XsAkGZdxBhbrmzGq/HG+eBmPS5i/RugwPpiQtPuodVo36CS9v+6L/tM4Y+E1XmUz1Lu6oc/mgJ35bdw6GZrpiGsug6Z3h2qgaFHl6ge+TYDy57o2nN32RmpSO6LAEcR8FS1r1rgdjS/33Z9Cp0xBjioaCppR59eKuP57f9hNTbhxqWuH1wyAx1YYKttZqXA21mztBQ0sN8oqKX1/6zRNHNl4UnbQGTu8k04VoKchwZP15nPrpKmbt/Ao1mzhBXp3YchF+T4Lxza4xZRbwycnKwbqvtmPW3glyGWQqNwGTLhIKmJzhgAljksABE6YwO6LPtWP2r7iy/zZ6TuyEAd90L/PXjw2Lx8Lea/A2MBab7i5FleqyexD7bwJCh9edxdGN52FgqofZP49BVVdryHIQ4fqxBzj0w3lEBsWiVb8GaNOvAWo3r67wB51JCal4edcfL+744/kdPzE4fPMyTNynqaMO+5pWqFHPTixUC0VNQ/bq0zD2TwokB3iFiSDJoyuvRb2fjNQsaOtrIDc7D64NHdCgvSucPexgXc1U7v/uo0LjcXL7VVzYfxfuLZ0xeGYXmQ6UkJTENGyYtBeFKMT41YPL/ERGaQr1jsCS/uux7tpC6BqV3XHT9YN38TYoBoPn9kJ5Ii/HqcXr2aCzZAIm989ywIQxSeCACVOYHdHnennbBzNaLYGjhz023Fkqlbno9049xpJ+P6Bpr/qYseMrqCrYYPTlHV+sGv0TLKqaoF5HN/T8uq1Mz/mnQM/d009xaMN5BHqFw76mtTgj26CDm1wVdPwvYiMS8ep+AF56vhF1HB5f835/H03byUzNQvW6tnBytxWXptaGcj+4ZIqHpqBQJpX342CE+Ebi6Q1fZGfmwM7FEuEBMeKzW6tRNbg1dYRDrSpQqiz/XbPImxehOLblMu6eeYYOQ5qg0/BmsHW2gKzzvv8GK0f9hBZ96mPovB5lWoy9tNHnbHKzxRi1YkCZF3ZfNeJHDF/aD8ZWhihP5OU4lQMmjMknDpgwhdkRfS766H/b/wfcOf4Qy07NQr2OtaWyHr+uOI59S4+KaTkzd41VuMFnSkIqts06gGuH78OthTOmbh4OE2vZnTtf/NmgOgZXDnni+tGHMLMxRI8xbdBuUKNyl2FB7ZhfPwrE6/uBiI9KEoVkC/ILULFiBSgpK4npVo61q8C1ob1oa+xQ0xp6ZXhGlTHKHgnxiYTfs1D4PQtBWnKmqE9UjGoUVa5cSXSyoXok1WpXUajuIRTofXDxpSjmmpeXD5vqFug9vi0s7UwgD1OjDqw6jYv7bmPqlhFwb+UCebd3yVFkpmdj7KrBZfq6Ef5v8dOs/Vh6fCbKG7kLmHRaKpmAybmFn/0ebNmyBatXr0Z0dDRq1aqFTZs2oV69en/5+CNHjmDBggUICQmBg4MDvv/+e3Tq1On9/cOHD8fevXs/+jft27fHhQsX3l9PTEzExIkTcfr0aXESrXfv3tiwYQM0NTU/+/dmrCxwwIQpzI7o3zi97RK2TNmD9sOaY+r2MVKbDjKz3TJkpWWh69i26DyqNRQNfc1cP3wfVw/dg8+DAIxePkC0IJaH4FDQq3D8vvWKOEsdE5aAjl80RdeRLWBkoY/yiNp8+j8PQ6BXGLzuB4oBKgVVnOtVhffDIPEYqgHRqENN6BhoimkAdi5WMLLQk4vtzWRbTnYuwvyjEf4mGt6PgkRGRZB3JMyqGIrbiWNtG1jaGaN63apw9qgKa0czhcwQS32Xjov778LL8w1e3w9Ax2FN0W1USxiVUTeW/+ptYAz2rzot6qxM3zpSLlsGl3T9kCeOb76ANVfmQVlFuUxfe/fCQ3Bt4oS67WqhvJGX41RZC5gcOnQIQ4cOxbZt21C/fn2sX79eBET8/PxgbPz/C1zfu3cPzZo1w3fffYcuXbrgwIEDImDy9OlTuLi4vA+YxMTEYPfu3e//nYqKCvT0/vxe6tixI6KiorB9+3bk5uZixIgR8PDwEM/HmCzigAlTmB3Rv5GZlilaDIf7vsWu1+tg7SSd1OW4t4mY3GQhkuNS8P2FeXBRsDa3xeIiE7Fx0h48u+GNxt3dMWJRX5hWkY/U4cToJJz75TbO7rkpDuzp7G3XkS1Ro4F9uQ4E0C6EMk+o9arP42C8eRGGgJdhMDTXRdDryPePs7Q3hp6RDmyqm8O2ujmqONJiBg1t+S2oyST7uXoXm4IQ37cI9olEqG8UAl9FIMw/SmSUONSyFp81PWNt8TO1yKaWv1SoWFtfU6HfF+p0c+/sc5zaeQ16xjroM6EdWvWpBzVNVbn5Hc7vvYW9S49h4Dfd0O2rVjI9VfOfCvWJwNYZv2LKjyNhWqVssygz07MwvcVibLq/QiGDg4pynFq8ng07SiZg4nn+8wImFCShQMXmzZvfF122srIS2R+zZ8/+f4/v378/0tPTcebMmfe3NWjQAG5ubiLoUhwwSUpKwokTJz75mj4+PnB2dsajR49Qt25dcRtln1CWSkREBMzNzf/V78+YJMnvJFHGSoGaphoadHZHVlo2rh24g+FL+0tlPYzM9TF330QsH7wRuxcdwqyfv4axtXwEEj4HZWUsPToVN44+wJbp+/Dg/AuMWtYfHYc3l/mDPH1TXQz5piv6Te4g0t9P/HQVM7quhkdbVzTsUAstetWDupZ8DFhKEwWL6Iw2LfXbur4fEFEtlGDvSAS+Chdn/ikThc6E02Jua4S3wXHvs1FosKulqy4GvJb2prC0N4GekVa5DkSVt4BbREAMYiMS4P8iDGF+UQj1jxZZIhSEK+ZUxwYerV1g52oJe1crkblkYKpTLj4nVJPF8/wLnPzpmqiv1LxnXczeMRoebVxk/rvzQ7HhCVg/aQ+09DWw+vxsWDsqxuAoPSUDywZuwKjvBpV5sIRcPXAHHUe2lqvPApNcUOZDlN1BS0k5OTl48uQJ5syZ8/42Cly2adMGnp6en3xuun3atGn/b7pNyeDIjRs3RIYKZZW0atUKy5Ytg4GBwfvn0NXVfR8sIfSa9NoPHjxAz549/+VvzpjkcMCElXs9J3XE8U3ncWTtaXSf0EGcsZMGyioZtrgPfhi7E4v6rMXaqwuhLsftLP8KDW5a9m2Amk2dRG2T3zdfwMVfbmHShmFwcLOBrKPaB027uYuFpuvcOf0UOxcfxY6FR9B5RAu06OUhisWWZ7SNTawMxNKg/Z9FDzPSshDq+xaRwXEIeBGGUP8ohPpFISY8AVePPHj/OJcG9iKbwMLWCOZVjVGtlrXoZmJWxUhkJOmbaCvEGelylS0Sl4KokHhEh8aLTlT0GUiKT4X/sxBkZeSIx9m5WolggKauOqpUM0OtJtVEm2vKSrJxMhddm8oTOtvr/TAQF/bdxZ3TT0RwoUZ9e8zcOkJkaMnb73Jh7y0c3nAe7Qc3Qd8pHeW6sGvJ341qh7Qe3AT1pVALjV7//M6rWHlhXpm/NvuXKLe/tPP7/3g+yhD50KJFi7B48eL/9/D4+Hjk5+fDxOTjWkd03dfX95MvQXVOPvV4ur1Yhw4d0KtXL9ja2iIwMBBz584VU3AoUFKpUiXx2JLTfZSUlKCvr//R8zAmSxRjb8XYf2BkaYBWAxsjwj8K53ddw6A50otudxjeEiGvIxDwPATfDd2MxUemoZKSYnRvKMnAVBfz9n4Nz7NP8ePM/fh+5HbUaVUDw+b3goacDIyquliJpd+kDrh18rFozzuh1TIx8Os0rBma9/Qod4O8/0VdU1XUlaClTd/6729PeZeGyMBY0b0kIjBGFEykFqMUUKE2sPFR797XRyG1GjsgMSZFBGSMrfTF4FFTR60o08VCT3y2Kivz7q0si45SHYq4t+/ENJq3IXGICU8UgTA6iH9xxw/ZWbnisbTNxO0i40wPzvXsYFHVWGQXWTuYwsrBVEyzKQ9ZI38lKiQWVw8/wJVD98WUNergM2Z5P/F9Qn9D8obqzWycvAc5WblY/NtE2FSX7fbGn2vf0mNISUjD5C0jpfL6D849Q502NaGlp7hT0dg/Fx4e/tGUnE9ll0jSgAED3v/s6uqKmjVrws7OTmSdtG6teDX6WPnAR5SMAeg7vStG15yBMJ9IdB/fHhra0hvkUkrvt/3X4/7Zp/h5wSGMWjFQoQcPDTvXgVtzZxzfcgn7vz+F28cfYcz3g9Csp4fcZBGoaqig3aDGYhkwtRMuHbiLh5e9sG3eITTs4IY2AxqI7g+cLv1p2nqa0K6rKQIpJQfi8W/fISo0XkzhocuokDiRsRDs/VYEWAhNz6DASjFqGUvZDBQ4MTDTgV0NS1RSqii69+gaaYsMFR0DLegaaoouP4r89/VfUK2Q5IRUJMWniWyQxNgUEcyiYp0JMcmi7TQV9n109TXy8wreZwdRW2pCA32P1jVEUIQ6TVFhVppyZWKlL7KFVNXLtiimLKP39/bJJ+8zrSIDY9CylwfaDGyIanKQefcpFCA5tO4sbv7+EJ2+bI7uY9oq3Hfgtd/uIuJNlOhwJ6391cktFzB5yyipvDb7dyoUFi2l/ZyEgiX/pIaJoaGhyPigAq0fouumpqaf/Dd0++c8nlStWlW8VkBAgAiY0GNjY2M/ekxeXp7onPO/nocxaeKACWMAbGpYocOIlgj3eysOPgbN6SW1dVFSqoQ5v4zHmtHbcW7nNWjpaWDAN92hyKhY4aBZ3UQh2L1Lf8e6cbtw4sfLGPf9IDiWGETLOqq/8eXCXmKw/+KOL64c9MTRLZfxw+RfxBniFr3riS4ePEj/ezS4Kp7aQ3VOSqIpPlQrhTIbYsOLLqmwcEEBkJKYLrJVqIYKdRP5sBaGawN7eP0xqFdWrQy3po5IiEoW037o783USl9kdmnoqIkMIU1tdahrq4opchqadKkKFXUVqGkoy3xQj4IemWlZyEjPRkZqFrLSs5CanIn0lEykJ2eIzym9h6lJGUh5ly6mw/g+DkZyYppoz/vhe0Vc6tvhlecb8TMFS4wt9eHe0vl9HRuqTzNsdlexzag4sqINkEu7LgkVb71x/BHycvJEp5vazaujdf8GaNy5tvhsyqvHV7xwavsVVKqshBXHp8PYqqh+gSJ5dc9PdKZZc2WB1KbPvrrjC30zPZhVlf0W0uwDBYVFS2k/52dQVlaGu7s7rl69ih49ehQ9RUGBuD5hwoRP/puGDRuK+6dMmfL+tsuXL4vb/woVck1ISICZmdn756CisFQ/hV6fXLt2Tbw2FaFlTBZxlxw59Dk903fs2IFffvkFr169Etfpy2nFihX/s8e6vFYf/68CngVjXN3Z0DbUwi/+G6ChoyHV9UmKTcbUlksQF5GAKT+OQpvBTVEe0FfS3dNPsGPeIeib6MLUxhAjFvWR6wNuasV7/8ILXD/6UAzoqZV0sx51RQDFulrRQQSTzGeJggQJMUl4F5sq6mjQlJHc7DxEBMUgOSFNLAYmOnhx118EEYh9TSsEvPwzY4UCA/S4D+kaaSEpLhUqaspQ01CBg1sVxEQkQkW1shjo0vSShJgUVFauJKYH6Rpqic9BxUqVRLYLLRRsqVixAhV9EZeqasrIyswR6017Zk1tVRH4oaBGQX6ByHxLjE1GXk4+8nLzxGtTZgKdyc/JzhMtd7Mzc0RNkKw/Lg1NdBD9xxQYQvVgqLDqRwGQB4HiZwriubdwQnRYgvidaaFaIhUrFWfnaInno0uaNkP1fNjnoalmnude4PapJ2J7ed17I1pvtx7QAC16eEDfVL5b60aHxuGnuQcR+DIc41YNQoOOblBEYX5vsaD7Ksz9dSIc69pJbT1WDN6IAbO6o2rNKijP5OU4tXg9G7VdIpEuOfcuL/rstsLDhg0T7X1pXEBthQ8fPixqmFBtEmo5bGFhIdoIF7cVbt68OVauXInOnTvj4MGDYkxR3FY4LS0NS5YsQe/evUW2CNUw+eabb5CamgovL6/304OopgllplBnneK2wlQEltsKM1nFGSZyhr7cqEL1hz3TqUL1X/VMpzmDAwcORKNGjaCqqir6pbdr1w6vX78WX4LsT/a1bcV0HM8zT/H7xvP4YkEfqa6PrrEOVpyejR++3oG1o7dDWVUZzXorfvSdBm1NutVFvXa1cGbnNfz63QncPfUEg2d1Q+eRrcQZcHlD0z6oiw4tVK/j/vkX4qzyyR3XREeFxp3d0KRrHVg5cPCktD9LVAOCFut/8N5SNgad9adBLQVPUpMzkJ6cKQIdFLig4AtltWSkZorHUlYG3UeBicKCQvFvaNoKBTAo1fnlH5kYpGoNi4/aLCspVxKBjw9ZOZgg/E3MJ4MZpLq7LXye/JkpQ9kdsX8EaSqrVhaX9Ltq62mIbA+a8kKBxho5eVDXVBH1L6jbU7sBDYuyZ7TVRHciyqLR0tUQt3FGSOmjbXTv7DPcPftcZPhQMI4CwR0GN8b4VQPlroDrp9DfxKF153Bu9w30mdQBs3aOEQE9RRT/NhGrR27DmNVDpBos8bn/BtmZ2eU+WCKXJFj09XNQm+C4uDgsXLhQnISl9sDU4re4sGtYWNhHWZQ0lqCgxvz580UxVwcHB9Ehh4IlROz3Xr7E3r17RRYJtQimMce33377US2V/fv3iywWmqJDz08Blo0bN5bGu8CYRHCGiZz53J7pJVFFbGrzRf+eIseKFLkvDcGvwjDG7RtYOZlj7Y3F0DWU/u8b8jocM9ouQ5XqFug7vQsadKqD8oSKSR7ddAEntlyCqoYqBs7sgi6jWinEwTjVh6BaJ3dOPcXTG96o1cQRjnVs0ahzbVR1seRpO3KMdq1U14OyiSijJTcnFwX5heI7mG7Py8sXQRaxBy4sRMEH6dS02WnbU2ZHxUqUfUKXRQtlq1SuXAlKykoiS4Wu8+dEttB+2f9ZqMgqe3j5lWilS8E0E2sDdPiiCeq2rAG7mlYKsd0o++nSr3dw/+wzqGqqYOSSvnKdDfh3aHrfzLbfipMrHUe0lOq6rBuzHV3HtINDHVuUd3KXYdJGQhkmVz4vw4Qx9s9wwESOUM90dXV1HD169P18Q0LpdBTJPXny5N8+B6XFUSbKkSNH0KVLF4XaEZWWnXP24/Ca0+g1qRPGrv1nQSVJ838ajNkdlyMnMxeLjkyDR/taKG8iA6Kxd9nveBeTgrdBMRg4syvaD22mMN1Q0lIy8PS6NzzPv8DDSy9F8IQGHvXaucK1YTWF+T0ZU0Q03evZTR88uvxKFCimdsmEuv60G9RI1CZRpCAoHTo+vuyFnQsPQ01TDaOX9UONBg5QZFQLaPOUPTCvaoLBc6XXTY+8vO2D4xvPYdGR6VJdD1khL8epxevZmAImSqUcMMnLwl0OmDAmEXwELkf+Tc/0kmbNmiVS5Nq0afOXj8nOzhbLh1/w5UnXse1wYvNF+D8NQmRAFCzspT9NolodWyw7OQurRvyIrTP2ARUAj3blK2hiYW+KuXu+xpvnIdi3/AQ2T9uHR5deon5HN7Qd3ETuAwpUWLRZ97piofoU3g8D8eDiS2z55jdRiNTEUh9127igbisXUT+CMSY9NA3L90kQnlz3xtNr3mI6E9UjoVo0TnWrYtSSPqjf3hWWdopXjPPV/TfYs+QY3sUkY+S3/dCwk5vCBIL+CtUGWtxnHezcqmDQnD9PWEkrWHVp7w0MXdRXquvBGGPlhXyPMNhnoSJNVKCJ6ppQPZO/QsWdqGhTeWVSxQj9v+mGXxYfwc7ZB7DoqGycwXFu4ICZP4/D/G7fY0mfdVh4eCrqdVDMgnr/i4ObDZYemQKfhwHYv/IkNk7ei0Nrz6L/9M5oM7CxXHeWKKZUWQk1GzuKZfTSvngbHIvH116LtqM/zvoN1erYwLmePdxbOMOprq14PGNMcqhAa+DLMLy444/nt3zx+mGgaFkdE5ogasA096iLjkObio5B2vqaUER+T4Kxb8VxMTWsVb8GaDekSbn47snJysGKIZvgVN8Owxf3k3pw6O6JR6Jeha2LtVTXg/0HlNxf2gn+PGGAMYnhKTnlZErOmjVrsGzZMly5ckVUov5fPpVhQnVSylOaX3pKBqY1XwQVNRUM/7Y/6rR2hazwfRSIX5YexfPrr/HN7nFo0fev27mVB94PAnB5/x2c33NTFLTsO6UT2n/RRGptHiUtOytHtH2lmic0hUfbQBPKKkpwa1Ydbs2cxBQAWW91y5isy0rPhs/jIAR6heHxNW/4PAqCaRVDhPlFiSwS+1rWotOVs4cdqtWuItpQKyoKlJzeeQ2PL79Ev6md0fnLFgpRQ+qfZpasHL4ZOgbamLT5S6l/t1JNpO+GbBTThY2tDKW6LrJE7qbktF4smSk5VxfL/HvAmDzigIkcFn2l1l/USri4uJy1tbWoNv1XRV9XrVqF5cuX4+LFi2jQoIHC7ohK24Xd17F21DaRgrvx3nKZaqEZ+CIEc7p8Lzpi9JnaBZ1HtUJ5R1N1Dq07i5T4NAS9CkPnkS3RbUwbGJjqQpFR151Xnm/EWe83z0MR9zYRro0oO6UaajV2hJmtkdTPiDImy+gwKC7yHbwfBSLCP1pkc715ESZaOTvXsxPT4yyqGqNWU0fUblFd1BeirkKK7vX9N/j9x0t4fe+N6HzT6csWotNSeUEdjRb1WYdq7lUxYmk/qQdLyOG1p5CVls3TceT0OLV4PZu0kkzA5M41DpgwJgkcMJEzn9szndoIU7swagPWuHHj98+jqakpFkXaEZU2CkatGr4FL657o/uE9hgwS7rzlksK84vEqhFb8eZpMIYs6I0hc3vywBhAuH8Uft9yEVcO3IWje1WY2Rqj5/h2qOpihfKAOu94eb6B1z1/0WHI53EwqtetCpeGDnBtVA3W1TgDhZVvKe/SxfQa3yfB8HsaIv5mKGuL6BpqISk+FWY2RnBpYA/3Vs5wrm8v2jSXB3RI+OTqK/y25gxiQuMxeHY3tOzbQLRGL0/SktKx7ZtfoaGlhrFrvpCJfWtC1DtxTLLk+Mxytz0ULmDSUkIBk+scMGFMEjhgIoeoJfDq1avf90yn3uWUeUJatGgBGxsb7NmzR1ynn0NDQ//fcyxatAiLFy9WmB1RXEQCbhy6h9aDm0DftPQObIO9wjCu7mxYOpqJgxQLO1PI2u89v/sqaOiow8rRHBM3jigXc8r/icSYZJzdeQ2nd1xDSmIaWvVriKY964q6L5UqlZ+AQXpqpphOQGfJfR4HIjIwFlUczVCnhTOq1rAUNWHUtcrPWWNWviRGJyPAKwwhPm9FBtabF6HQM9IWwZJirg0dRNvf6vWqiik2lJ1lWE4CJB/WaLlx9AGObboIE2tDNOpSG636N5SpzMqyQsVs53ZdifqdamPYor4yESwha0dtRb2OddC0d9HxHpOv41TCARPG5BMHTJhC7IgmNZoLn/tv8PX6Eeg5qVOpPveh1Sexa+5vqNWiBlZdmi8zB08fnglb2n89Xtz0RtshTTFu7VARQGF/zkG/9ftDHN10AaHekaIOQdfRrdB6UGPoGsrm51mS8vMLEOb3FkGvI/Dq3hv4Py9qf1q3tYs4q17NrQqsHc048MbkbsAf7h8tPtfBryOQmpSOJ1e9RbtfQpkCFDwkVOfHxNoATu62cKxjC0d3m3IxxeZTKAvt7O4beHjhBSoqVUTfSR1F57HyFFT+UGRgNFZ/uQ3N+zVAz/EdICueXH6JU1svYfGx6TJ3DCIL5OE49cP1bNpikUQCJrdvLJH594AxecQBE6YQO6Iz2y/h2m93oKWviSW/f1PqA+6x7rOga6QtMli6fNUWsoYKwf0061dcPXAXBma6WHJsBswVsJ3lf0Ffda/u+Yvgydmfb4gBQZMeddFxeHO4NnYs1wehmWlZCPKOQMCLMHEWPsw/ShTNta5mBvua1rCraQUbJ3OFLmzJ5NvRzZewa8nv76/TdJpX9wOgolYZNs6WcG9ZHRZ2JqhW2wbmtkblflqa/9Ng8T344MJzuDV3Ro9xbeBU1w7lmd+jACzp/wNGrRiEVgP+nMIsbVkZ2Vg+cL04IURTTJl8HqcSDpgwJp84YMIUYkeUkpCK/uajkZebjz3+G2Fhb1aqz+/z4A2mNFkAZTVlbHu2Suam5hD6Uz646hR+WXIENZtXx4CZ3VG7lYu0V0sm0Vzwi/tui45DDy++FAOpDsObo/WARtA30ZH26skEChSG+L4VtR6iQ+MR4BWOgrwCGFvqo2GnWmjYsfy1tGay68l1b6z8aqeYZmbrbAGnulXFpaW9CQf6Phh43zr+CDePPRR/1x2HN0OnES1gZKGP8u728Yf4cepezP5lAmo1qw5Zsm36LzCw0EPfaV2lvSoySx6OUz8KmDSTUMDkFgdMGJMEDpgwhdkRbZu+B7ePPUCHL1vhi4WlX0F+z6JDeHb1FQpRiB9uLJHZg/CHF55hxZDNyM7Ixtfrh6HL6DblOnvi76anPL36Chd+uYWk2BT4PAqER1tXtBnUWKSll8f5+/8L7S5iwxOQnZUrsk8Yk6Ui3fQ9x991/1+QVxhuHnuEsz9fh00NS3Qf2wb1O9Ti77c/vtMOrT6FJ1e9MGH9cFSpbglZ8uqOL878dBkzd48vt9OkFOk4lQMmjMknDpgwhdkRPbn8ArPbL4N7u1pYcW5uqadc0xz5SY3mI9Q7AiOXD0SfaV0gq0J9IrB50h68eR6Muu1qYdq20WKKBftr7+KScf3QfVzafwcqqsqIDIpFs54eaNW/AWo0cOCBGGNMbqQmpuHOmac4t+u6yBDr+XU7NO5WB1WcLKS9ajIjMz0LG77eidjwRCw8OBm6xrKVXZiRmonJjedjweFpsObtphDHqcXr2azpQokETG7dXirz7wFj8ogDJkxhdkR0hnF6i0XijMzKi/Ph3rZWqb9GiHc4vhu8CSGvwrDqykLUau4MWZWenIE1o7fj3qnHaNClDoYu7AO7mlWkvVoyj74Sg16F4+rBe7h+5IFoM0rFIlv0qocWferD1sWKgyeMMZmsZfXsujcu/Xobjy57iS43tZtXR4POtTmbpISooFhRr6RBp9oYPK8XKivLXpFr6opTo7ETOoxoKe1VkXnycpzKARPG5BMHTJjC7IjI0XWncf23O3CqZ4+JW0ZL5DUu7L6On+cfhKWDKeYfnAp9U13IKvrzPvfzNWyZvBcVK1XEhPXD0H54Cx7w/0P5efnwuuePa4c8cff0E2hoq6OyipLIPGnawwO2NSz5vZQxq7/eDVUNFRHoosXYQh9a+hrQFoum6CDFqe1MUdCJAu8Hgbh+2BO3TzxGraZOqFbHFi37NSh3rZH/qfvnnuLourNoNbAxOo1sBVl0+/cHuLj7Br499Q3vYxQxYNJEQgGTOxwwYUwSOGDCFGZHRNKS0jDIepwoWLk3YBNMq5R+RXn6k9kwfhfObr+M+p3riDZ/st6ClYrW/jjtF0QGxoiOMFO3jRZdf9jnTcl6cdMH147cx4Pzz1GQXwDjKkZo0L4mGnWtIwYp5b3zhiycYe9mMeGj21waOuCV55v315WUlYqCJ3oa0NRRh6auOqo4mYt6NtR6VkO7aNE10oKapirUNFTEpbqWqni8Ig5e6Henz3dmahbSkjOQlpSBzIxsZCRnIuVdOlIS05D6Lh10sBATFo/UdxkoLChEq3710WloU2mvfrlD+yDfx0GigOvt449gV8saVg5maNWvAWfA/Q95uXn4Zekx3DjsiTn7JqB6PXvIoog3UVjYYxXWXl8MPRmbJiSr5C5g0niBZAImd7+V+feAMXnEAROmMDuiYr8sPoz7Z5+I2h1fLh8ksW4D3w3ZCO/7b9CiXyOMXz8csi71XRo2T94jDhbrtquJbuPaoX7H2tJeLbmUk5WLl7d9cPP4I9w/91wMJqu528KhVhU06OSGWk2rQ1mVU+ClsV0u/HoHyQlpSIpPRXJ8qgiIBL4MR8q7NKQmpqOyamWkJKR99O9cGzrA64OgCqFMFJrWVsy+ljUCX0VARU1ZtKpVUVeGY20bJEQnQ1lFSUx5oOfW0lUXBaGVKhctlNVCkYaKShVRqWJFcUmPoSAFDWwrVqTBbQXxfCgsFINh2isrKVUCjXvzCwpFcI4Weg0KbFDmE3UEo6WSUkVkZ+aKYBH9/rQu6SmZImhMxXnpkgJA8W/fITM9G1np2aKeUUx4gqiRkJGahYKCQtGGNzkuFRo6aiIwZGZjJD7DFFgSGTp6mqJluZ6Rlmjfzlk6ZYs+L94P3uDuqSe4c+oJqtawgqmNEZr18oBzfXsO1v6N6JA4rBy2GfZuNhi6qG/R36UMomOLH8b8hM6jW6NmM9md8itr5OU4lQMmjMknDpgwhdkRFYsJjcNQ+wmo4myJDfeWQ02jdHdKxaJDYjG+3lwxmBgwq4fczDOmVN91Y3cgIyUTvad0wuA5PcXgkP2HaTt3/eF57plYYsMSRFq8uraa6Ljj0a4mt+2UIRRwoMACZVHQkpqcgbycXKQlZ4pAAwUQ6G+DtmtGWpZYKMigbaCJ6NAEMaARwYjMHDEtL9ArHLn0nNlFAQsKcny4V63mVgX+z0M/Woca9ezw+mHgR7dp6amLzI1ilPUS6vtW/ExBFZpS59qoGkK8I1Hpj2AMLYZmOsjLKxQ1GChYYmJtgLyc/D8DO2rK0DPWRmXlyiJbhqYrqWuqQsdAU2TNUPCEnofJZlHS5zd94Hn2GR5ceAFHd1uYVzVGk251Ub2+PQet/qFrB+/izI4raNarPrp/3V5mM3DocPz7oZth52aDvtO5hbAiHqcWr2fzRpIJmNy8xwETxiSBAyZMYXZEH9ox+1ccXnUS434Yjl6TO0vsdbzu+GJBt+/FgGrlhXlwa1kD8iAuIgGbJu1G8KtwFOTnY9LmkZxtUgro65QGtI+veolBju/DQDh52IlBt3srF9Rt4wLnBvaiCw9TzO1PgzHKBsjLyRMZIBR4oWAHZYjk5xWImhMUValUsQIKaPdL/xUWiqlCFBgpbo1boSKgoqby/jZWPtCUp8eXvfDg4gsRLKEplNXq2KBhp9pwqG3Dn4XPkJKQiq3T9yHwZShm7R4Hu1o2kGVH1p1BfEQCxq4dyttZQY9TOWDCmHzigAlTmB3RhyL832JW+29RxdkKi3+fKdEOARd+vo5TWy8iMSYJK8/Pg00NK8gD+tO/uOcmts/6VXTPMbTQx9jVQ2SutaK8t/Z8cccX98+/wJOrr8QgmjIcXBpWQ+0WzqjT0hk2NSw5nZ6xcoqykrwfBeDRhZd4dMULidHJcKpbFfXa10S9djXFtBv27wq7bpzwM7qMbo3eUzqLTCtZ9vjSC+xecBBrri2SWFasIpO7gEnD+ZIJmHguk/n3gDF5xAETpjA7opLWfLkFF/fcwKQfR6Hr2PYSfa3dCw/hwPLfUbuVC2bu/hpGlgaQF7Hh8dg55zfcPHof5nYm6Du1Czp82YIH8ZLIPnkdgafXX+PpdW+8uucPK0czMYWHziJT8KRGo2qwdjTj954xBUXZR0Fe4fC+748HF17ilac/ajZxhJmtMeq2dUWtJk5i2hT791klP80+gLeBMRixpC9cm1aHrAt4HoLlA9dj1aUFMLKSn2MHWSIvx6kcMGFMPnHAhCnMjqikqKAY/PDVNsSExeGnF2tFeruk0J/RrrkHcOyHszC3N8W6G4uhYyg/7xW5e/IRzu++gUcXnsPRww4TN46AQ21baa+WwqJME99HgXhx2xcvb/uK6RreDwJEMUKXRg5wb+0K+1pVYFfTSua7MDHGPo2mYFGglP62KVD68o6fqHPTun9DWDmai0ApBUvYf98HU30u6gbXsn8jDFvUB6pykKlBNde2z9yHgbN7wKFOVWmvjtySl+PU4vVsUV8yAZMbDzhgwpgkcMCE/S152RF9ytape3Bu5xV8tXoouo5tJ9HXoloFS/qsQ8LbRFGUccXZOdDU1YA8yUzLwv4Vv+PRxRcI832LdkObYcSSfjxNpwxQMVG/J8Ei88Trrh/i3r5D5Jto0T2F6p9YO5mjukdVURNFx0BL2qvLGPsE6lb05nkIXnu+QWx4Aq4feYDCwgIxDa9W8+oig8TWhafhlaYI/yhsnrIHSXEpmLp1FBzr2kEeJMUlY2abb/HV90Pg0cFN2qsj1+TlOJUDJozJJw6YMIXZEX1KckIKJjeah5T4VOx5swna+pIdaGZnZmNRr7V4cukFmvdriKnbv4KGtvx1oAnzjcS2Gfvw5IoXajR2FAVhe05oD2UuVlpm8nLzROr+6/tvEB0ah7unnyI+8p24r3HXOlBWUxZdM6rVsYWdq5VEM6gYY5+WGJ0En0eB8HkYCL8nQQh4EQpVdRXUaOCAOq1d4OBmg6quVtzRRgKy0rPw2/en8ODcU7Qe3AS9JnYULb3lQXpKBma1Wya64TTv21DaqyP35C5gUm+eZAImD5fL/HvAmDzigAlTmB3RXzmx+TxuHr4H54aOGP39kDJpBblp/C5cP3gX9nVs8d25uXKXaULoq+HxxRfY9s2v4gwedQBqO6QpWg5ozAf/UhIXmQjfR0EI9Y3Ei1s+8H8WgpzMXGjoqMHYykBM4XGsW1UEUGycLcXAjTFWOqgNdeCrMPg9ChLZYP7PgmFmayRud6prh+r17OBc3160/uUuJ5LdN10/dA83j9wX33GjvhsoV3XDKJNz44RdcKpnL9ocs3IYMPGQUMDkEQdMGJMEDpgwhdkR/a+pMuPrzUbo63BsfboKNjWsy+SAaF7XlUhPzhAHdN+e/AbacjqNgjIdzv98HdcO3oW35xvYuFhh5Lf9RQoxDwqk/9kO9XuLgGchePM8VCy0ReiMN7WjtXQwhXsbV+gb64hpALY1LKFvqsvbjbG/8S42GYFe4SLLK8zvrZhiExUcC7ua1qLmCGV3ObpXhX0ta6hrqUl7dcuN157+2LfsmMgaHbN6CGo1c4Y8yUjLxIJuq9Cgizv6Tusi7dVRGPJynMoBE8bkEwdMmMLsiP4Xnwf+WDd6G7T0NLHm+uIymT9OmSYrBm7A/bNPUb9THUzZPhqG5vqQVxmpmTi2/hyeXX+F1/f84VTPDsMX9xOZJzwAl60AV0RADAJfhiHoZRjSUzPhefYZkuNTxf3UiSMnOw9VnMxhU91CtDW2cjAVxWYZK49tfSMCohH8OqJo8Y4QGXQPL3mJ7BE7V2vUqG8Hq2rmcKhdhesHSQllOf688BB8HwZg5PKBaNGvodxlOtI0nCV91qJRNw/0mNBB2qujUOTlOLV4PVvWnSuRgMn1xytk/j1gTB5xwIQpzI7o72yetAv+j4PQaVQrdPiydZm8Zk5WDjZP3o0bh+6J+ikrzs6GdXVLyPuZ16M/nMWprZdgWc1MZNAMntdLzNfnQoayKzEmGcGvwvE2OFZM5Qn1fYswvyhYVTPDm2ch0DHUgnU1M7g0qgZtfQ1Y2pvCwt4EJtaGcjcwYaykrPRshL+JQrh/NOKj3sH3MU1te4uooFgxjSYvLx+2zkVZWGJKW3ULaOjIX/0pRUOFc/cv/x0PLzxH9/Ht0OPr9nLR/aaklMQ0rBu9HTWbOaHX5M4Se52TWy7AuWG1ctdxR16OUzlgwph84oAJU5gd0T/J+BhTa4aYLvPj4+/LbM4zTZtYP24Hnl17JTIxZu0dD5fGTpB3CVHvRODk7I6rMLY2hKqGCgZ80x2Nurlz4ESOWp7GhiUg/E00wvxpMBklppFR69PkhDTxGNfGjkiOTxHTECzsjMWgkqb10Nl3Y0t9bnnMZKpVd0xYAmLD4sVnmjKtIgOjUZBfgBe3/aCmqQJLBzNUr1sVhuZ6IsuqirOF+Bzzd5ZsiYtIwKHVpxAZEANbVyv0n9FNBHXlUXxkIuZ1+Q7dJ3RAp5GSO1kT8joM22fsw7Izs1GpknwUvy23ARP3OZIJmDz5TubfA8bkEQdMmMLsiP6JV3d9sXr4Zpjbm2LFuXllNpWE/swOrTqBXXMPwqSKkUgpbjmwMRTBu5hkXNx7QxzcZqRmoXF3D3i0r4XWgxpzVx05lvouDeFvYsTgk4IpkYGxIjtFS1cdT697i8dQnRS35s4oyM8XQTMTawOYWBnA2MpQXBqY6XBAhZVqgC8pLhWxEYmIDolDdGi86CBF6DNJA1NUqADXRtWgrKIEC8qSsjOBpYOJyJiiIAlPH5RtMaFxOLLujOjQVrddLfSf0RWGFvI7lTXUJ0IUge86rp1Eu+FQW/qZrZdg9r6JsLA3Q3kjL8epHDBhTD5xwIQpzI7on/p5/m/wuuWNFv0bo/v4sp1HfHHPdRxecxphPpEYurivmMqiKGc2U9+l49yuazix5QISo5Jg5WiO5n0boOtXbaBrrCPt1WOlHEyJCokXg9ak+FRR+yEmPAExYfFIT85EUlzK+4AKZaPUbFIN+bkFMLTQE4NWIwt96JvoQN9MV1wqq1SW9q/EpIwORdKS0pEQnSzaZ1NHKFri374T3y00fSw2IgG52Xmo09JZBE5MbQxhWsUQ1tXMxWfLnLKerAw4SCengYWrB+7g9PYraDe0OfpM7Sy+J+TZy1ve2DB+JyZtGolaLWpI9G/nxym74VTfAa0HNUV5JHcBkzpzoFSplAMm+Vm4/pQDJoxJAgdMmMLsiP6pvLw8zGy1BKHeEVhzbTGq1qxS5gdRKwZthJa+JkxtjMQZIUWaK091W64euIsnV71w+9gDVFZWQo+JHdC8dwM41LGV9uqxMsgCoAKzlAVA9QfoMiMlU2Sp0ECYBsAa2moI8Yl8/2+o4GzNJo6isLCesQ50jbShb6oNPSMdkYava6Qlim3Sz/R5YvJVVJU+D0nxKUhJTBdT+RJjUkQtJKqrQ/VxqEVvQnSSeCzVEKE2vSKwZlkUXKPpYAamOiIYQoumjjpniigAOvx8dddP1MN6eu2VaLFLAXY9E/kPsJ//+RoOrz6Fxcemo4qzlWRfa9dVeN32wczd48vt34W8HKdywIQx+cQBE6YwO6LPERMWh6V91oiB3OaHK6GhXbYBCzozv7j3GgQ+D0H9zrXx5bKBZR64kTT6anl6xQsnfryIYK9wMSfdsa4duo1rhyY9PUSxWFY+5efnIyUhTWQTJEQlITE6CempWeLvggbS72JTkJuTK+oX0DSvYjUaOCDEOwLaBpoigKKlp4Eq1c2Rn1cATV11aOlqiNvofnVNVTGwpmAkBWiUVSuX28FEacjNyUN6SiYyUjLEJWV9fLjkFxQgOjhO1L6hApe0fWkbxr9NEv+eAl3O9e2QmZ4tgmKUWaRnrA3TKkZimxmY6YqFgmVcZFjxP0uep5/g8NrTSIpNwYBZ3dBqQGOFaM9MNcu2z9yHtHfpGL1qiPisSxIFSrbP2ItVVxdBXVP+3z9FP04tXs9WtWdLJGBy7dlKmX8PGJNHHDBhCrMj+lzPrnlh76JDMLIyxNz9k8t8MEVzjn/99hgOrTopBnNTt3+F1oObKuSgjgovUnFYaglJLYlpcNt5dBvRGrKqq7W0V4/JsOzMHJGhkJyQipR36WKARQNyGpinJqZBSbmSmK5RNHDPEJcG5roI94v66HmodTL9OxqUqWupikuazkEtllU1lKGmoSoKF9MgnqioKYtFVV0ZapqqUKpcCZVVKou/VeU/LikIoETLH/dJa6BPu/G83Hzk5eSJSyp+mpudi5ysPBG0oGks9D5mZeYgJzMX2ZnZQCHEe5qVkSMKYVMXGfo9I4OKglSU7UOXFlWN8fy2L7Izct6/Xs2mjgjzfQtNClD9EaSydjQT2UXUDUzHQLMoqGWoBT0jbegaakFdW00hv9vYPxcXkYhzu67i/M/XRfC85YBGaNLDQ2GmUFGw9+d5B+kvEhM3j5T4VMO3gdHYMetXjPxusChmXJ7JXcDEjQImpXvSKC8/G9eec8CEMUnggAlTmB3Rv3Fi03lsnbYHY9YMlWirv79Cf37nd17DtYN38OIG1VVphIlbRkJbTxOKiAZtt39/gHM7r6FipYrwuuMrpul0HdMWDbu6i6kZjP1X+fkFYsBPHX8oGyItOUMM/qm+CgUHioMBlBWRHJciggZZaVki+4EG+hFvosVt9HmlxcGtCrzuvfnoNRzdbcVUkg9RgIX+pispVRRdKuiyolIl2Fa3QERgjKjpUqFiBXFp7WguMmhKouALnYEvoF1zYSEKCgpFN5cgr3Dxe1HHF8qooUKmgV7hyMvJF2e1CQUkPrVLpza5VLCXijCLIJCaMuxqWonpUZTpRd1jqFUrFe2l5ysOKFGWjpaeugikUBaeho6ayNqhwBIHP9g/QUG059dfi9okT696ocOIlmg1oJEImCgSb09/rBy6Gf1mdEXnr9pI/O+DOpdRkdfR3w+BR4faKO/k5TiVAyaMyScOmDCF2RH9G/Tx373gIH5b8TvmH56G5n0kV8X+fwl6GYrlgzaIs9Z01vybPeNRs5kzFFmobyQu/3ITVw7cgZJSJdFtp36n2mg7pBnc29XkQqBMpr4nKChBGRsieyMrF9lZucjLzfsgsyMPubn5yKcARj4FMYoCG/TvKlaqIDJZKNhRWFAUBKHsFKrZURKNsyioQgOu4uAKfS8U5FMgpigIQwv9+woV6ZJuq/RnpouyElRUlVH5j0yYyipFt3OAg5UlKgB9ed8tXNp7E3ZuVVCjkaP4bpfX1sD/KyBEtUquH7yHaT99BUcP+zLJTp3Vdinaj2iFTqMk16ZYnshdwKTWLMkETF58L/PvAWPyiAMmTGF2RP8WDWjWfbUNz6+9wrzfpsC5oaNU1iMzPQt7Fx/BsXVnYOFgioZd62L40v7ijLAiy83NE7VOruy/jYCnIXgbFCPqTrTs30i0KK7VvLoYEDLGGJNdlLl17+RjXN5/G5EB0XBt4oSOI1rCtamTQgbsqI31lim7oaKmggkbR4gpapJGAdsN436CnokuRn03WOKvJy/k5TiVAyaMyScOmDCF2RH91zM237RZAnUtdXy9YQSsnSykti4Pzj7FbytP4PU9PzjVt8dXq4bAtUl1lAepSenigPv6oXuiBgN1UKAzku2Ht0Dtli6o2cxJYea7M8aYvKPMquc3vHF1/x3cO/0Y9TvWRu1WLmjWu75CdX8r6dax+9g55wAGfNMdHUe2KpOAEL3Xa0dtQ0F+Pmb9MhEVK3JxZLkNmLhKKGDixQETxiSBAyZMYXZE/1VKYqqYE5yWlI5115fAxMZYiuuShh+n7EFkQBR8HwSIdosjlg1Q6APQkhKj3+Huyce4dfQBClEIr9u+ohVzxxEtUL2BA+q0duVOO4wxJoWsTK+7vrhxyBN3Tz5CrWbOqFrTGi37N4ZZVentN8tCUlwyNk/8WRSwnbn76zIrtkrBkhWD1kNZTRkzdn3NJw7k9DiVAyaMyScOmDCF2RGVhncxSdg0YRcCngVj7Y0lMLI0kOr6PDj3DBvH74SpjREi/KMwZs0XojBseTuzlBD1Dp5nnuDuiUcimBTwLETUdWg/rIU4UK/XwQ2GFvrSXk3GGFNIOVk5eHnbVxTtvnfqMexq2aCae1U079tAdDpTxCk3H6JD5RuH7mHH7P0Y8E0PdB7Tpsy6YtF7v3bUVtHZavqucaJ4M5PP49Ti9Wzt8o1EAiZXX62S+feAMXnEAROmMDui0gyaLO61WpzJ+WbPBKkHTairx/7lx3Bk7RnY17YVHS3Grx8B23Lajpfaxj66+FwEUIJfhSPc9624vWnverCwM0PddjXh3MCB654wxth/kJKQiidXvHD31GM8vvgCltXMRGYfTbexq1VF4YMkxaKCYrB58m7RLWr4kn6wrGZepnVhqGYJtVZf/PtMzqqU8+NUDpgwJp84YMIUZkdU2mm3s9svE0XcZvz8NUylOD2nWLBXGPYuPiyyLExsjFCvY218sbAP9Ix1UF5RmjLVOaFMnFd3feH/R5vXGg2rQcdIG3Vau6BOK1eY25uUm4N7xhj7N+hwkDq2Pbr0Ek8uvxStcimLpHEPD9HBzKoMAwWygDI7jqw7g5uHPdFzYke0H9GiTLM738UmYX6XlTCraiI653FmiQIFTGrMlEzA5PVqmX8PGJNHHDBhCrMjKm1Uy+T7YZvx5kkgVl5cAJsaVjJxQEsHb9SO9+GF59A21EK/6V3QfUJHPvNErSxD4/Do0gu8uOGNp1e9kJaUAZsalkhPzoRbC2fUbV8L1evZw6SKkbRXlTHGpI7auT+79gpPrnrB/3EgEmOS4d7GFY261hXFWxWtDfA/3c96nnqMHXP2i4LrXy4fAF2jsj0xEeYbiW/7rUWt5jUwdt0wrlmiaAET5xmSCZh4r5H594AxecQBE6YwOyJJnWHaNn0vbh+7j4VHZsC1qWx0q8nOzMHvG87ixU1vPLn0EvpmehjxbX+0HtwUlZX5wIrk5xfgzZMg8R49u/5adB2iVGo6g0oBk2Z96sPS3gwuTRxhYW/KGSiMsXJxIsDrjq/4Xnx+/TXSUzJhYK6HOq1c4NG+lsgoKc/TGQOeB2Pb9F+Qm52H8RtGiPejrD25/AJ7Fx1CjUZOGL1qSLmrWabIx6kcMGFMPnHAhCnMjkhSCgoKsOObX/HmaRC6jm2H5v0aQVYkxaXgt++OizOENGWHisMOX9ofzfs15DNSnwh++T4OgtctH7y87YO8nKLpPMStZQ0xP51qn1AHnmp1qkJFjdOfGWPyjfYR3vf9RZcx+t4LehEKlyZOsHWxgluLGuIkgJaeBsq7t0Ex2LvwEHwfBmDkioFo2rtBmQfR6XD83I4r2DLpZ3y15gv0mNCpTF9fnsldwKT6dMkETHzWfvZ7sGXLFqxevRrR0dGoVasWNm3ahHr16v3l448cOYIFCxYgJCQEDg4O+P7779GpU9FnNTc3F/Pnz8e5c+cQFBQkftc2bdpg5cqVMDf/c0qfjY0NQkNDP3re7777DrNnz/5XvztjksYBE6YwOyJJO7/zKjZN2IlR3w9Bz0mdZCojgaaiHFhxHFcP3IGGtproIDN4fm+0GtQEyiqVpb16Mlv/JOB5qMg8iQ2Nw81jD0R6unlVE8SExcPW1QpOHvao0agaqtasAitH8zLrisAYY/8muB/u9xY+9wMQ6h2OhxdfIPJNNKrUsBTTa5zrO8C1iRP0TMpv3auSYiPicWD5cby86Y0uY9ugy5h2UtlnZqZnYf2Y7Xh1xxeTfhyN+p3qlPk6yDN5OU6VtYDJoUOHMHToUGzbtg3169fH+vXrRUDEz88Pxsb/v3bfvXv30KxZMxHc6NKlCw4cOCACJk+fPoWLi4t47T59+mD06NEi+PLu3TtMnjwZ+fn5ePz48UcBk5EjR4rHFdPS0oKGBgdvmWzigAlTmB1RWfD29MPi3mtEUdEZeyZAQ0sNsiQqOAYHV57Epb034FjPHlGBMeg5uSO6fNVWFLBlf42+CmNC4uD/PBiv7/jB52EAQl6FUydH5GTmQFVDRXSH0NBRh72bDezcbGBNQZRynL7OGJNuu3WaYujt+Qa+jwJQkF8An4eBcHSvCudG1UTGnHODapxB8gmx4fE4+P1JkZlJndXoJIi6lPbnFNxa/eWP4kTH5B9Ho4qz9OulyRu5C5g4Sihg4vd5ARMKknh4eGDz5s3vg65WVlaYOHHiJ7M9+vfvj/T0dJw5c+b9bQ0aNICbm5sIunzKo0ePRMYKZZRYW1u/D5hMmTJFLIzJAw6YMIXZEZWV+LeJ2LPgIHwfvMGCw9Nk8uAmNiwev288h7Pbr8C6uoU469hxZCv0mNgRZrbS7/gjL3KycxD6OhJ+TwLx5kkwUpPScf/MU+Tn5YuME8rssXGxQlVXazGVx8LOVBQH5gEKY6y00GFawtt3CHgegoAXoYjwi8TL275IjEpCkx4eIphLAXInDzsx1YanY/61CP+3OLT6lHjvnOrbi+430jqZQNv17E9X8PuGM6K+1szd46GlpymVdZF38nKcWhYBk/Dw8I/eAxUVFbGUlJOTA3V1dRw9ehQ9evR4f/uwYcOQlJSEkydP/r9/QwGPadOmfRToWLRoEU6cOIEXL158cr2uXLmCdu3aiecsXi8KmGRlZYkpPPScgwYNwtSpU6GkxN9dTDbxJ5Oxz2Roro+p28dgz8JD2Dp1L1oPaYq2XzSHLDG2NsTYNUMxaE5P0VHn4KpTOL3tMq7svw2XRo4icEJ1O2RpWpEsUlZRhkMdW7Fg9J+1UEK9I0UXA8pCoZoAN4/cx/MbPogOiROPqdnUCcpqyqhS3QJVnC1hW8MSltXMpHYGkzEmH6igd5jvW0S8iYL/4yAEeYUhKz0bfo+DxHeIXa0qqNHIEZ1GtoadWxVoaKtLe5XlAk29PLL2tHgv67Rxxdi1Q6X63r2LTcauOfvF/pnWpcdE2ZrmyySrQmGhWEr7OQlliHyIAhqLFy/+f4+Pj48XU2VMTEw+up2u+/r6fvI1qM7Jpx5Pt38KBUVmzZqFgQMHfhTEmTRpEurUqQN9fX0xzWfOnDmIiorCunXrPuM3ZqzscMCEsX+BpmGMXDEIz6+/wsovNiLkVRgGzestcwev2gZa6D21C7qOa4c7Jx7h8OpTuHfqMVIS07Bx/E50/qoN2gxpWuYtE+WZsuqfQZTWg5q8T2OlbJPgVxEIeR2O1MR00abz2bXXIhuFWhuHvI6AoYU+7N2qwMTaUAx+ihdDcz3uhMBYOQuMRAZEI8wnEqE+kaJuEgVF3gZEi2Brk+4eUFZXRpOeHiKDjRYqTM3+udycPNw+eh9nfrqC3JxcdB7VBq0GS7+u172Tj/DDV9tQpYYVfrj9rZg2xVhp+VSGiTRQ9ki/fv1EJtXWrVs/uo+yVIrVrFkTysrKGDNmjKiNIq31Zex/4YAJY/+BW0sX/PRiLTZP2oUxbjMw+9dJcGnkBFkc5Lca0Bgt+zcSZ9ou/Hwdfg8DRDrwrjkH0LhHPXQa3Rq1WtTgwqb/AgU7zGxNxNKoq/tHB+xiUOT3FuG+b8WZ48zUTFzYexPZGTniMS6NHfHmaTDM7UzEQm0stQ00i67bGsPAQg+VKnGdFMbkDQVS4yIS8TYwRmSMJMWmwO9xIML9ohATGo+azZyQlpQhMtGsnczRtKeHmFJDGYIcQP1v9UnO7bgKb09/KCkrYeCcHnBvW1Pq72liTBI2T9wlamPV61gHX28YDg0dnr5ZLlE2SGlXRPjj+ShY8k+mJRkaGopji5iYmI9up+umpqaf/Dd0+z95fHGwhOqWXLt27W/Xh2qp5OXlic47jo6Of7vujJU1DpgwVgpZHHP3T8Hdkw/xbd91aN63IUasGAQ1ddmLklPKr0tjJ7GMWjkYd44/wPGN53HziCeCX4chIzkTbb9oJrrrUC0O9t9UVlaCjbOlWEoOpBLeJolBVFxEAhxq2yAyMAah3hGICo5F0Muw9481qWIoahJQy2hTW2OYWBvApIqRyFIxtjaAvqkup3IzJiXZmdmICUtAdHAsooLjkPouDW+ehYggCd2mrq2OihUrwNLBVEynqd3SBd3GthU1kCgwwgHq0pGTk4dH557h/K5r4vuVpkJO+nEULB3MpL1qYn2oy96Fn6+JTKKJm0aKtsWsHCsopDk0pf+cn4GyOtzd3XH16tX3NUzos0rXJ0yY8Ml/07BhQ3H/hzVMLl++LG4vGSx58+YNrl+/DgMDg79dl+fPn4uA5qc68zAmC7joK1OYYlqyICUhFQe/P4G7Jx5i8tavUKe1K2QdfQV43/fHxd03cOvofVg7WcDnwRtUrVUFnUa2Qr1OdbhQbBnKzy9APJ2VDopBVFAMokPjxcCL6qNkpmWL2inFtPQ1kZ2RLab6GFsZwMjKoOhnS31xWbToiUKCHFRh7PO/G9OS0kWWSGx4AuLCE5CRlomgF2EiQyQmLA4WDmZ4dccPRpb6IqDpXN8ealqqoj05ZYmZVTWWuamaiiTIKxSXf7mFa7/dgaOHPZr3aYCmveuLrEpZQBlFB7/7HXeOPxQnIsatHwFdQz6OKq/HqcXr2cZuikSKvl4JXP/ZbYWpyOv27dtFJxtqK3z48GFRw4Rqk1DLYQsLCzFVhlC9kebNm2PlypXo3LkzDh48iBUrVrxvK0zBEmorTNepk86H9U6oXgkFaTw9PfHgwQO0bNlStBKm61TwtWPHjti7d2+pvieMlRYOmDCF2RHJEqptcmLzeahrq2H0yiHQM9GFvMyrf3zpOS7vvYWH559Bz0wPsaFxcGnihPqda6NprwawsP90qiYrG1npWWLwRgO2hKgkkZFCAznKVMlIzRS1UvJy898/nqb3UKFDAzM96JvpohoVsKWDF1Nd6JnqwshCD7pG2tA11hGPlXbaOmNlISc7F++ik0XxTepAE//2nWjTW1hQCP+nwYiPTBSLXU1r+DwIgIF5UVCyWl1bMRg3rWIoMr1MbQxhZGUo9boY5QnVi7r9+0Nc2ntDZHi6NnUSmZHmdrKzb0qMfidqhv2+/oz4nEz5aSzc29SU9mopLLkLmFSdLJmASdCGz34PqKXw6tWrReFWag+8ceNGMUWGtGjRQnS02bNnz/vHHzlyBPPnzxfTZxwcHLBq1Sp06tRJ3Ee32doWHWOURNkm9HwUTPn6669FUCY7O1s8/osvvhB1Tbh+CZNVHDBhCrMjkjVUZO7ExvN4ds1LZGl0GdNWrto9prxLg+fJx2K6TmZaFl7dKaqaTlOOqFBpw67ucHCvygNsGUMptSnxqYiLfCeCKNQ+kwaCxQNCGth5P3iD5LhU8Xjnhg7w9nwjfq5YqaIInljYm4hBIf2sY6gFE1sjqKopQ8dQWwRV6HbKbtHUpekGvP2Z9NGhDH1PJcenIjkuBUnxqchIyRTBRAqKvItNQVJsMlIS0hAXmYjUxDTx71waV0NceCIMzPVgYKYraoioqKuI7CwjC31RQ4guqdA3k57YsHgRxL+87xYi30SJ/WnddrXg3KiaTH0HZaZl4tTWS9j/7VHk5eRh+LIB6PZ1B6jK4BRdRSIvx6myGDBhjP09DpgwhdkRyap3MUnYu/AQkuJS0GVsO3GQJ29oXv6Ds89EzZOoIKqxESrm5Zvbm4oWl/U71xHTjzR0OPVcXuTl5uFdTIr4XCZGJ4nBpBhYxqQgOSEV72KSxcAzOT5FTDvwuv1xm0H72jai5ammnga09TVFAIUGlpVVKkNLT+P9Qvdr6mpAU0ddfD4oyEJ1HdQ0VHiaEPvLwAdNhaGCqLRkpmeJgqmp79LFdxFdUuAv+FW4mAZJAcLkhDTx+aLPMX036Rhpo0ZDB6QlZ0LPWBt6xjoii4qmqOkaa0PfRFdkWWnpa8jUgJv9+TmgDkJ3Tz5C4PMQPL/+Gk161UPj7h6iLbCsnXygjKXLe29g76JD4ruz29ftRYc6Wcp6UWTyFzCZBKWKpRwwKaCAyUaZfw8Yk0ccMGEKsyOSdSGvw/DzvN/E4LHXlM6wd/t02qI8TNt5dtULXrd9cO23uyJtvXoDB/g9ChSXzXo3gKOHnVhk7aCW/fuslfTkTDE4pTP4dJmemimCK9Sims7W00A2LTlDDF5pkJualI6crFxxlrUkCqLQ2X+asqahpSYuqW0qPY+6lpqoAUEtVPVMdERRTPpZVUNFXKppqIrHK6tWFmdtaVFRVxYLD3ylg1pn07SvrIxsEeygDlAU5KCaO3SdFjrUoIAcbXeaOpaRkgUNHTXRUjc9OUN8dtJTMmFpbwrfR4Hvn5syO1ybOIrsKKrFUxyIo6mBFHDTMdAU0zIoE0rHSEvUh6DPF38W5A8FHV7d9sH9s0/h/yRI1G6iDm5Ne9UT00JlcX9C63z111v49dujIguGMjDpxAh10GNlR16OUzlgwph84oAJU5gdkbzwue+PPYsOiTNPvad0hmU1c8gr+voI9gqD1x1f3Pn9gZi2Y+tqLdrk0sC3Ufe6onhsreY1xKUSp7WXK3l5+WKALAbESRl/DIzTRfCFBs00QC4eQCurKIkuIxlpWeI2GmRTIU2/J0HvWzAXo8Ka3g8C/t/rVa1pLaYhqagqQ1lN+Y/LyqJTBgV6KMhSWUVJZCdUVq0sLmmgTe2fqaMRLUp/LBSIoQG5UuVKqFS5krikzy/9TP+uQsUKYgpTJaWKYnBOPxddr1T0c8UKRY8Rlx9cr/DHQP6D5BqRaVP03x/dJul/RZdiEVcocFWIgvwCUWeDigMXFvz5c35egbgvPz+/6Oe8fHGdfjeqaUMZRXSZn5sv1oXe59zsPORm5yI3N19c0u9F7xMNAinYlZuVK36m94jaY2dl5CAnM0cETWmp4mwhCp7SaxSjoBZtO0LvtwhyaarCsW5VMS1MBMj+CJIZW+mL35cyQ6gwKgVQtHQ1iq7rqoufKVDGmUiKiT7b4X5vRav7u8cf4eUtb7HPoIy2Bp3rwM7NRmYDXzT15ur+2ziw/HfxnePR0Q1tv2iB5v0ayuw6KzK5C5jYTpRMwCR4k8y/B4zJIw6YMIXZEckbOkjcv/yYaC3Za3Jn0Z1G3tFAiTJPnl71gs/9N6LbDg3oqLgoFSt1aeSE2m1cUc29qshA4Xnd7J+ggAB1A6KMBfoc0cCdshnotqLLouuU6UCZCzlZRQP6nMxcZGfliM9ZQvS7j4IAFCyg6/qmOqKlM2XC5OXkIze3KIhAbZvDfN9+cn1oqsfrP+q+fNZ9jarh9T3/T97n0qgaXv2L+yj7wuuO3yfvo5ocVBhYBH4oEERBH+VKqO5hjxDvCBE8qqxcWdxHP1M3LMryKQosVRaXKmrKYjoLBVxEEOqPhWrc0PQqOutPQQ2R7aNRlO1DQRH6met+sE8VbKXpNS9uvEbw63BRY6lhN3e4NnaCe7taoj6SLKNMp6v7b+HgyhNIe5eOGk2c0G5oc7Qd2lwmM2DKC3k5TuWACWPyiQMmTGF2RPLK/0kgjqw5BTVNNUzbMRaKhKZuUO2L4FehuHfysZiLXr1hNby+6ycGU836NBBFRJ3qOcCpvh0s7M34bDKTGbR7pCyNogwNys4oytKg22iaUkF+oQjSUECnII+yOwpEioi4r6BQBAvp56LLQvHZFlkhH+x1i3fBFSvRfZRtIlJOxGVR5kkFMS2pKFvlg0yVP7JYKMOlkpISKtJlpUriseJnkQ1TUQRE6DbGyhp99qkOCU3XpM5xL2/5wMrRXATXarWsgdqtXVGluoVcfOf7PnyDW8fu48SGcyLLzLq6BbqObY/WQ5qKvzEmXXIXMKkyQTIBk9DNMv8eMCaPOGDCFGZHJO/Sk9OhoaMBRZaekiEOnmnqDp1pp8Hjs2uvxH00Rz3kVbjIPqnT1hVmtiZwqGMLU1tjuTigZoyx8ozqGAU8C8aru34iw5DqW9EUL7cWznCsZ49azZ1h42ItNwE8mnZz/eA9PL3yEjcP3xOdkwwt9dFnalc06VWfM6hkiNwFTKy/lkzAJOxHmX8PGJNHnD/ImIxQ9GAJoToF1E2HluIzkOG+b8XUnQj/t2IqBE3poQNvqoNCardyEbUw7GrZwKmePSwdTFGlhhVP52GMMSlJS0pD4PNQ+D8NQsLbd7h/5iliQ+NE1iAV3W09qAmcGznC1MZIrgLedA7R29MfF3ZdFYVnqSMcBe6b9q6P7hM6omYzZ7n6fRhjjP13HDBhjEkNTTGo4mwplmJUXyL0dYQ4U/nmWbA4Sym68tzygX1tW3E7HbA26uEBFBTC2tlStLi1sDOFpaO5qK/AGGPsv6OgdlRQDIK9whH8KkxMqwx8ESqKB1NghIIJzg2rie4wdrWqiNo28ijcLxLXDtzB7WP3ER0SK+oieXSojfqd66DT6DYwtTGW9ioyRVJU3bv0n5MxJhEcMGGMyRTq1EEH4bR0/OigPRYhr8LEGT86eKcin08uv8Tdk4/g2qy6mOZDgRSPDm6ixoRlNTMRiKEpPdSC1MjKUG5SwRljrCxR/R1q4xvmG4lw30hEh8bB72GguG5kZQAdAy3RAa1epzoYOLuHmFoj78HpUJ8IPLn4Ahf3XBf7k7cB0WIf0ml0a9RpUxMNu9Xl+iSMMcY4YMIYk49MFAp60NK4R72PslEi/aMQGRgtpu7QwX3FChVw79RjPL70AlVrVhEBFkItY62cLESrWlNbE1R1tYauiY44c2hqYyiK7jLGmKKi6SaJ0UmICoxBxJsoRL6JQmJ0sig8TsEC25rWImBAHduq1bVDg051YFPDSgRMFGEaCgXeqbbKvZOP4HXHBz6e/qjR2EnsI6iI69i1w9Csb0MYWRpIe1WZoiugbJBCCTwnY0wSOGDCGJPrbBQ660lLkw8CKXRgTPPq3wbGINzvLd4GRCEuIhGh3hGipWX2uWeoWtMaQS/DxOMppZwGD8bWRjCxMYR5VRMYmOnByNoQJlWMYGiuBx0jbRG4YYwxWZWRmomY0HjEhceL77+o4BjEhMSJdr4Rb6JFnSiXxo5QUVcRAWjXpo5o80VTWFUzh4G5nkIERkq2Afa65Q3P048R8jocQS+KAugujZ1E9qFbyxqYuHmkCK4r2u/OGGOsdHDAhDGmcCiwQWcJaaHODJ86y0rp59EhcWK+ek5mjhhAxIbF4cUNb9z5/eH7x1P3HpruQxkqdIBNgRPqlkABFQsHM2joqMPATFdc1zfT5UwVxphE5GTliJpO8W/fIS48AfGRCchMy0awVyhiwxKgY6SFp1e8oKWnIYLIWvqaMLM1FtNLzO1NRCDY2NoQSpWVFDpg9PzaKzy75iWKifs9DHj/HU7cWrrAqb4DmvSuj2p1qnKQhEkH1zBhTK4o7l6TMcY+gQ6QKbhBS41Gjv/vfspOSYpNEQOS2PB4pCSkiu48NFDJSstC8OtwcRCem5MH16ZO8LpddCBOdAy1kJOVCz1TXTHlBxUAXSMd6JnoiEwVdS016Bppi6ALLTSw4QN2xsovCuCmJ2cgKTYZ72iJThYB3ZTENNF1hjIkNHXV8fTqK6QmpolWtiZVDKFnoiumylBgxL1NzT+y4QxhYm0kgrjl1et7fljUc5X4maYV0fula6yDMWuGigKultXM+TuXMcbYZ+GACWOMlchO0TfVFYujh91fDnIokEKDmcSoJCRGvRM/U6AlMSYJ76KTREDl9V0/pCWli39DhWmp00+xanWrIuBZiAiyaBtqoUp1SxGs0dbXgraBpgjoqGmqirPEWnqa0BSXGmKR104UjCm6/Lx8EexITUhDalKa+E5IiU9FckIq8nLyxDSZpLhkEfygwAjdL6bJNHES3WgoEELfPRbVqFC1gQjWUjCk77SuMLQ0EIFWLl791+h9pLordNmgqztcmzqLQDVjMkWUMCntDJPSfTrG2J84YMIYY5+JzlDqGGqLpaprlf/5WAqc0NnjpLgUJMel/PFzKnKysmFXy0YEXpLjU8XgKcI/SlynLj+UiUL/phil0seGxYuflVUri+wY+jd0NllTV0MULaRWmBo6auI2GlipqCpDXVutaNFSF/fR4EFNS01MMWKM/f9gaGZaFjJSMpGZmom0pAykJaeLy9zsHFEkNe1dmrhesVJFRAZEi+BH6rs0GFsZvM84o+8I97Y1ERMWJzrMaBtqw9rJXARBHGrbiKwHXWNt6Brrigw0CpJyjaT/Tk1DFTu81kl7NRhjjCkQPmJmTI5RRsLv68/CtWl12NW2hZJSJWmvEiuBAhPF9VT+6YCNBmvJCSlIe5chBmI0ICu6LRVp79LFUlGpIipUrID0pAxEBkSJf0cFbelsNfmwQ1AxOzcbBD4Peb9edrVtxBluGmSoaamKjBZqx0yvpaqhClUNFXGbpp6GqHugqq4ibqN6L6qaKuI6BWVU1Gmh25VFq1Ee+LGy+v7LzsxBdkY2stL/XOi2rPQsEfighW6j4EbC20RkpmaJOhd0G13SZ11cpmaKIqgvb3qj4I9uExSUDHgeIqbEUBCS2pRTUJIClJTpZWChB6tqZkVZYLQYaEJbTxPaBlrQ0NXgTBDG2KdxDRPG5AoHTBiTY6Gvw7F9xi/ibCa1y6WgiUcHN1Sraw+7WlUUurifoqJtSYOzf1uHgFotU00EMRAUg8Gin9NTMkUwhTJYMlKzxNlzJWUl0U1DXE/LEhkwFPDwexwoBpR0G2W76Jvri6lHf6eyihJys/NEMEZZrSh4Qpkv72KSRVZM8ULFcum1aGoR/Ru6rbJKZTEwLSwoeh66Tuun/MfP9Jx0XalypaJLZbpdCZUrV0Yluq14UVb643rRY6mGAV3SgJlrF/w3FJTLy81HXm4eCvIKxGU+XebkiUwquhQ/0/35BaKeD30e6HNHt+dk07/LR3ZWjri96P5csX1oGgsFOnKzcsXt9DNtu+T4FPEzFWamS/ocJMYkiyAJPY6oqlcWn9diZlVNRACEgn0U1KNLGxcrEXikn2nRN9ODhsi8KsrAoqwrCoKorRwirtPfH11SJy7GGCtVBQX0Pwk8J2NMEng0xZgcy88vQKPuHqJ+BhUipe4JTy+/FAML50aOYiBKARRrJ0tU87CDvomutFeZSRgN8JSNdaBnrFMqA2QaCNPZejqzTosIpKRniYGuuJ5RdEZfDGAzc8VgWAxuaeD7xwCXpheJwXB20WCYgjVUy0EMmrP/HDzT46h7Eb1mMRrQ0uD3c1CbaG9P//93Ow3ADS30kJJAxTMriuu0UDCFrlOx3rTEdHH9/VKxgrik6RMUiKpQseg2yu4puqwoplNQUKo4ICMybCoUBb+09DU+uu990KYCxKCc7vsUuo9ej7YBzU2ny6IFYiCfkZwhMiHEbX9cUsaFurY60pPSxX0UtKD76HuikO7TUhOBCbqdAh10Wbyoa6kiOTFN1OCgYAhdFi0FIpsoOa5oqtiHqJAxtav9lOL76L0rCnhVEpdUo4O6vdDnVAS8VJVFsdKM5Ewoq1FATVlMHdM10YGmjrp4nynwJhZ1Zahqqv6R1UQZTnQ7ZTZVFt2pirOfOLODMcYYY6WFAyaMyTF7N1ssOf6NGChRtgm1UXx5y1sMFqnVZHH6uf+jQDFthwYwbq1cYG5nKupn2NexgYGZPp95Z59EnwsRgKGz7Ppl97rFgRqRlZCV8z6LoSiTIf+Py1wxoC/KeMhH/h+ZD3RJg+wuo1sjP/+P+ygA8Ec2BAUqKEAjAgPFgQMKDBQUiOAJBXnE7X8EG4oDCpXVKiMrLbvotoKi+4p/psBkblbe+6AGBTiKgwvFwSBx+x+/W3HmNAUT/ioYRFkyFJwSf5t/BF+KFvq5KPij9Efghm6n4A3dUZQ1oSqCNnQfBQ9EcKdSRTHNit4fCg4VB4Qq/XFJwQh6IbqPsnOKHkMBpYpFGT0UXHqfyfNHFg8FQSpTJlClPzOAKPPnjywfCn5w8IIxxkrgKTmMyRUOmDCmAGhwZOtaRSydRrURt1Equ+/DAIS8CoOhub4YGFLRUCosemnPDTHnnlLU6ZKKE1IBU8pCMbM1gZWTuWiHy5i0AzXc4YIxxhhjjEkLB0wYU1AUAKnfqY5YiqUkpiLUOxyBz0MRF5EA73t+CHkdDs9Tj4um8fwxlaFaXTvEhMSKWhNUg8KsqqmoAWBsZQhTW2NR9JAxxhhjjH0mzjBhTK5wwISxckRbXwuuTZzFUoymFFBb2zDvCDFlx7mh4/upEFQbpbgWhH1tGwQ8C4Gjhz2ig2NgYmMMmxqWIhPF2pkutWFgoS8KLlJWAE/zYYwxxhhjjMkzDpgwVs7RdB69vygSSsGUpNhkUaAzITIBkQExoqjnmydBiAtPwP0zT0QBTddm1eF1y0dknqQlpYvii1RsNi0pA/pmurB0MBf36ZvrwtDCQLwWFXWkdraMMcYYY+WGaF1eyhkhf7RDZ4yVPg6YMMb+ZzBF31RPLH8lMy1TdOdJpCX6HRLevkN8ZKK4Ly0pFAFPg+HzIADRgdEwrWqCtwHR4j6XJk4IehEKHSNt8TO1sNXW14S5vakoFqltoCWCLVp6mqLOCgVYqDsGY4wxxhhjjJUFDpgwxv4TaudpVY0W8//5OOpYkpKQitTEdJG1kpGSgcToZCTHpaCwsABRQbFIiksRrVTD/d8iNSENFZUqIiYkTkzvoe4i1I1Ex0gHmnrqIpBCrVepVgtdUgYLtX6l6UDUWlXbUKvoZy1VaOppiPXkaUKMMcYYkyY65qGltJ+TMSYZHDBhjJWJSpUqQc9YVyzWThb/+N9RoIWm9mSnZyH1XbqY8kPtVimwkp5MP+eIS+oKFB+RgPTUTBGMofamCZGJyEjNgnEVQwQ9DxHPV71BNUQFxUBNU1UUtc3NzoWqugpUNVVFtgt1ZlFRV0HN5s6wdbGW4DvCGGOMsXKHCrSW9hQaLvrKmMRwwIQxJvOBFh0DLcBAC8bWRv/5+aguCwVJaAoQBV6owC1dZqVnIZeK3WbmiOsVK1UslfVnjDHGGGOMyScOmDDGyl1dFhU1FbHgv8dfGGOMMcY+MxuEM0wYkxd8CpUxxhhjjDHGGGOsBA6YyKEtW7bAxsYGqqqqqF+/Ph4+fPg/H3/kyBE4OTmJx7u6uuLcuXNltq6MMcYYY4yxPxQUSGZhjEkEB0zkzKFDhzBt2jQsWrQIT58+Ra1atdC+fXvExsZ+8vH37t3DwIEDMXLkSDx79gw9evQQy6tXr8p83RljjDHGGGOMMXlRoZB6dTK5QRklHh4e2Lx58/sCllZWVpg4cSJmz579/x7fv39/pKen48yZM+9va9CgAdzc3LBt27Z/9JopKSnQ0dFBcnIytLW1S/G3YYwxxhhj7N+Tl+PU4vVsrTkIShWUS/W58wpzcDXtgMy/B4zJI84wkSM5OTl48uQJ2rRp81EBS7ru6en5yX9Dt3/4eEIZKX/1eJKdnS2+1D9cGGOMMcYYY4yx8oQDJnIkPj4e+fn5MDEx+eh2uh4dHf3Jf0O3f87jyXfffSci4MULZbAwxhhjjDHG/pvCggKJLIwxyeCACft/5syZI1L6ipfw8HBprxJjjDHGGGPyj6ohSGJhjEmEkmSelkmCoaEhKlWqhJiYmI9up+umpqaf/Dd0++c8nqioqIiFMcYYY4wxxhgrrzjDRI4oKyvD3d0dV69efX8bFX2l6w0bNvzkv6HbP3w8uXz58l8+njHGGGOMMSYhBYWSWRhjEsEZJnKGWgoPGzYMdevWRb169bB+/XrRBWfEiBHi/qFDh8LCwkLUISGTJ09G8+bNsXbtWnTu3BkHDx7E48eP8dNPP0n5N2GMMcYYY4wxxmQXB0zkDLUJjouLw8KFC0XhVmoPfOHChfeFXcPCwkTnnGKNGjXCgQMHMH/+fMydOxcODg44ceIEXFxcpPhbMMYYY4wxVg6JeiOlXKSVa5gwJjEVCgv5L4wpRn97xhhjjDFWvsjLcWrxerZS7gulCpVL9bnzCnNxLeeIzL8HjMkjzjBhjDHGGGOMsTJQWFCIwgqle76az38zJjlc9JUxxhhjjDHGGGOsBM4wYYwxxhhjjLGyUFgggRompfx8jLH3OGDCGGOMMcYYY2WAp+QwJl94Sg5jjDHGGGOMMcZYCZxhwhhjjDHGGGNlgafkMCZXOGDC/nGaH7VDY4wxxhhjTFYUH5/Ky7SUPOQChRJ4TsaYRHDAhP2t1NRUcWllZSXtVWGMMcYYY+yTx6s6OjqQVcrKyjA1NcWd6HMSeX56bnoNxljpqlAoL+FYJjUFBQV4+/YttLS0UKFChf98FoACL+Hh4dDW1i61dWSyj7d9+cTbvfzibV9+8bYvv6Sx7WkoQ8ESc3NzVKwo2+UZs7KykJOTI5HnpmCJqqqqRJ6bsfKMM0zY36Kdj6WlZak+J+1E+SCqfOJtXz7xdi+/eNuXX7zty6+y3vaynFnyIQpocFCDMfki22FYxhhjjDHGGGOMMSnggAljjDHGGGOMMcZYCRwwYWVKRUUFixYtEpesfOFtXz7xdi+/eNuXX7ztyy/e9owxRcNFXxljjDHGGGOMMcZK4AwTxhhjjDHGGGOMsRI4YMIYY4wxxhhjjDFWAgdMGGOMMcYYY4wxxkrggAljjDHGGGOMMcZYCRwwYaVuy5YtsLGxgaqqKurXr4+HDx/+z8cfOXIETk5O4vGurq44d+5cma0rk96237FjB5o2bQo9PT2xtGnT5m8/K0wx/uaLHTx4EBUqVECPHj0kvo5MNrZ9UlISxo8fDzMzM9FFo1q1avydX062/fr16+Ho6Ag1NTVYWVlh6tSpyMrKKrP1Zf/drVu30LVrV5ibm4vv7hMnTvztv7lx4wbq1Kkj/t7t7e2xZ8+eMllXxhgrLRwwYaXq0KFDmDZtmmgp9/TpU9SqVQvt27dHbGzsJx9/7949DBw4ECNHjsSzZ8/EwImWV69elfm6s7Ld9nQQRdv++vXr8PT0FAfQ7dq1Q2RkZJmvOyu77V4sJCQEM2bMEEEzVj62fU5ODtq2bSu2/dGjR+Hn5ycCpxYWFmW+7qxst/2BAwcwe/Zs8XgfHx/s2rVLPMfcuXPLfN3Zv5eeni62NQXL/ong4GB07twZLVu2xPPnzzFlyhSMGjUKFy9elPi6MsZYaeG2wqxU0VkmDw8PbN68WVwvKCgQA+GJEyeKg6WS+vfvL3bAZ86ceX9bgwYN4Obmhm3btpXpurOy3fYl5efni0wT+vdDhw4tgzVm0trutK2bNWuGL7/8Erdv3xZZB//kTCWT721P3+mrV6+Gr68vKleuLIU1ZtLa9hMmTBCBkqtXr76/bfr06Xjw4AHu3LlTpuvOSgdlmBw/fvx/ZgjOmjULZ8+e/egk2IABA8R3/oULF8poTRlj7L/hDBNWaujs4ZMnT8TUimIVK1YU1ymD4FPo9g8fT+gs1V89ninOti8pIyMDubm50NfXl+CaMlnY7kuXLoWxsbHILGPlZ9ufOnUKDRs2FFNyTExM4OLighUrVogAGlPsbd+oUSPxb4qn7QQFBYmpWJ06dSqz9WZlj4/xGGOKQEnaK8AUR3x8vDjwpQPhD9F1OqP4KdHR0Z98PN3OFHvbf+pMFM2LLnlwxRRru9PZZErHp/RsVr62PQ2Sr127hsGDB4vBckBAAL7++msRKKWpGkxxt/2gQYPEv2vSpAkosTkvLw9jx47lKTkK7q+O8VJSUpCZmSnq2TDGmKzjDBPGmNStXLlSFACl9F4qIMgUU2pqKr744gtRt8LQ0FDaq8PKGE3boMyin376Ce7u7mJK5rx583j6ZTlANasom+jHH38UNU9+//13MVXj22+/lfaqMcYYY/8TZ5iwUkMDoEqVKiEmJuaj2+m6qanpJ/8N3f45j2eKs+2LrVmzRgRMrly5gpo1a0p4TZk0t3tgYKAo+EldFj4cRBMlJSVRBNTOzq4M1pxJ42+eOuNQ7RL6d8WqV68uzkLTNA9lZWWJrzeTzrZfsGCBCJZSwU9CHfGoftlXX30lgmY0pYcpnr86xtPW1ubsEsaY3OA9FCs1dLBLZw0/LOpGgyG6TvPWP4Vu//Dx5PLly3/5eKY4256sWrVKnGGk4m9169Yto7Vl0tru1D7cy8tLTMcpXrp16/a+gwIVjWSK+zffuHFjMQ2nOEhG/P39RSCFgyWKve2pRlXJoEhx4Ix7DyguPsZjjCkE6pLDWGk5ePBgoYqKSuGePXsKvb29C7/66qtCXV3dwujoaHH/F198UTh79uz3j797926hkpJS4Zo1awp9fHwKFy1aVFi5cuVCLy8vKf4WrCy2/cqVKwuVlZULjx49WhgVFfV+SU1NleJvwSS93UsaNmxYYffu3ctwjZm0tn1YWFihlpZW4YQJEwr9/PwKz5w5U2hsbFy4bNkyKf4WrCy2Pe3badv/9ttvhUFBQYWXLl0qtLOzK+zXr58Ufwv2uWj//OzZM7HQEGLdunXi59DQUHE/bXPa9sVoW6urqxfOnDlTHONt2bKlsFKlSoUXLlyQ4m/BGGOfh6fksFJFc9Lj4uKwcOFCkWZN7YEpe6C46FdYWNhHZ5mocv6BAwcwf/58UfzNwcFBtBel7glMsbf91q1bRRp+nz59PnoeKv64ePHiMl9/VjbbnZXfbU8ZRBcvXsTUqVPF9DsLCwtMnjxZFHxmir3taR9PbWjpMjIyEkZGRmJq3vLly6X4W7DP9fjxY5ERWGzatGnictiwYdizZw+ioqLEti9ma2sratXQ3/yGDRtgaWmJnTt3ik45jDEmLypQ1ETaK8EYY4wxxhhjjDEmS/i0H2OMMcYYY4wxxlgJHDBhjDHGGGOMMcYYK4EDJowxxhhjjDHGGGMlcMCEMcYYY4wxxhhjrAQOmDDGGGOMMcYYY4yVwAETxhhjjDHGGGOMsRI4YMIYY4wxxhhjjDFWAgdMGGOMMcYYY4wxxkrggAljjDHGGGOMMcZYCRwwYYwxxhhjjDHGGCuBAyaMMcYYY4wxxhhjJXDAhDHGGGOMMcYYY6wEDpgwxhhjjDHGGGOMlcABE8YYY4wxxhhjjLESOGDCGGOMMcYYY4wxVgIHTBhjjDHGGGOMMcZK4IAJY4wxxhhjjDHGWAkcMGGMMcYYY4wxxhgrgQMmjDHGGGOMMcYYYyVwwIQxxhhjjDHGGGOsBA6YMMYYY4wxxhhjjJXAARPGGGOMMcYYY4yxEjhgwhhjjDHGGGOMMVYCB0wYY4wxxhhjjDHGSuCACWOMMcYYY4wxxlgJHDBhjDHGGGOMMcYYK4EDJowxxhhjjDHGGGMlcMCEMcYYY4wxxhhjrAQOmDDGGGOMMcYYY4yVwAETxhhjjDHGGGOMsRI4YMIYY4wxxhhjjDFWAgdMGGOMMcYYY4wxxkrggAljjDHGGGOMMcZYCRwwYYwxxhhjjDHGGCuBAyaMMcYYY4wxxhhjJXDAhDHGGGOMMcYYY6wEDpgwxhhjjDHGGGOMlcABE8YYY4wxxhhjjLESOGDCGGOMMcYYY4wxVgIHTBhjjDHGGGOMMcZK4IAJY4wxxhhjjDHGWAkcMGGMMcYYY4wxxhgrgQMmjDHGGGOMMcYYYyVwwIQxxhhjjDHGGGMMH/s/0CWHwaG/h7MAAAAASUVORK5CYII=' width=1100.0/>\n",
       "            </div>\n",
       "        "
      ],
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from firedrake.pyplot import streamplot\n",
    "\n",
    "u_h, p_h = w.subfunctions\n",
    "fig, axes = plt.subplots()\n",
    "streamlines = streamplot(u_h, resolution=1/30, seed=0, axes=axes)\n",
    "fig.colorbar(streamlines);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Configuring a better preconditioner\n",
    "\n",
    "For this small problem, we can (and probably should) use a direct factorisation method. But what if the problem is too big? Then we need an iterative method, and an appropriate preconditioner.\n",
    "\n",
    "Let's try everyone's favourite, ILU(0)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "solver_parameters = {\n",
    "    \"mat_type\": \"aij\",\n",
    "    \"ksp_type\": \"gmres\",\n",
    "    \"ksp_gmres_modifiedgramschmidt\": None,\n",
    "    \"ksp_max_it\": 2000,\n",
    "    \"ksp_converged_reason\": None,\n",
    "    \"pc_type\": \"ilu\"\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Linear solve converged due to CONVERGED_RTOL iterations 960\n",
      "\n",
      "SNES iterations: 1; SNES converged reason: CONVERGED_ITS\n",
      "   KSP iterations: 960; KSP converged reason: CONVERGED_RTOL\n"
     ]
    }
   ],
   "source": [
    "w.assign(0)\n",
    "solver = create_solver(solver_parameters)\n",
    "solver.solve()\n",
    "convergence(solver)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is, unsurprisingly, bad. Fortunately, better options are available."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Block preconditioning\n",
    "\n",
    "Firedrake hooks up all the necessary machinery to access PETSc's [`PCFIELDSPLIT`](https://petsc.org/release/manualpages/PC/PCFIELDSPLIT/) preconditioner. This provides mechanisms for building preconditioners based on block factorisations. The Stokes problem \n",
    "$$\n",
    "\\begin{align}\n",
    "  \\nu\\int_\\Omega \\color{#800020}{\\nabla u : \\nabla v}\\,\\mathrm{d}x - \\int_\\Omega\n",
    "  \\color{#2A52BE}{p \\nabla \\cdot v}\\,\\mathrm{d}x\n",
    "  &= \\int_\\Omega f \\cdot v\\,\\mathrm{d}x, \\\\\n",
    "  -\\int_\\Omega \\color{#2A52BE}{\\nabla \\cdot u q} \\,\\mathrm{d}x&= 0\n",
    "\\end{align}\n",
    "$$\n",
    "is a block system with matrix\n",
    "$$\n",
    "\\mathcal{A} = \\begin{bmatrix} \\color{#800020}{A} & \\color{#2A52BE}{B^T} \\\\ \\color{#2A52BE}{B} & 0 \\end{bmatrix},\n",
    "$$\n",
    "\n",
    "admitting a factorisation\n",
    "\n",
    "$$\n",
    "\\begin{bmatrix} I & 0 \\\\ \\color{#2A52BE}{B} \\color{#800020}{A}^{-1} & I\\end{bmatrix}\n",
    "\\begin{bmatrix}\\color{#800020}{A} & 0 \\\\ 0 & S\\end{bmatrix}\n",
    "\\begin{bmatrix} I & \\color{#800020}{A}^{-1} \\color{#2A52BE}{B^T} \\\\ 0 & I\\end{bmatrix},\n",
    "$$\n",
    "\n",
    "with $S = -\\color{#2A52BE}{B} \\color{#800020}{A}^{-1} \\color{#2A52BE}{B^T}$ the *Schur complement*.  This has an inverse\n",
    "\n",
    "$$\n",
    "\\begin{bmatrix} I & -\\color{#800020}{A}^{-1}\\color{#2A52BE}{B^T} \\\\ 0 & I \\end{bmatrix}\n",
    "\\begin{bmatrix} \\color{#800020}{A}^{-1} & 0 \\\\ 0 & S^{-1}\\end{bmatrix}\n",
    "\\begin{bmatrix} I & 0 \\\\ -\\color{#2A52BE}{B}\\color{#800020}{A}^{-1} & I\\end{bmatrix}.\n",
    "$$\n",
    "\n",
    "$S$ is never formed, so it's inverse is approximated using an iterative method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "exact_inverse_parameters = {\n",
    "    \"ksp_type\": \"fgmres\",\n",
    "    \"pc_type\": \"fieldsplit\",\n",
    "    \"pc_fieldsplit_type\": \"schur\",\n",
    "    \"fieldsplit_0\": {\n",
    "        \"ksp_type\": \"preonly\",\n",
    "        \"pc_type\": \"lu\",\n",
    "    },\n",
    "    \"fieldsplit_1\": {\n",
    "        \"ksp_type\": \"cg\",\n",
    "        \"ksp_rtol\": 1e-8,\n",
    "        \"pc_type\": \"none\",\n",
    "    }\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "SNES iterations: 1; SNES converged reason: CONVERGED_ITS\n",
      "   KSP iterations: 1; KSP converged reason: CONVERGED_RTOL\n"
     ]
    }
   ],
   "source": [
    "w.assign(0)\n",
    "solver = create_solver(exact_inverse_parameters)\n",
    "solver.solve()\n",
    "convergence(solver)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This looks good, but we had to use an unpreconditioned Krylov method to invert $S$. To do better we need to provide either an approximation to $S$ or $S^{-1}$.\n",
    "\n",
    "For the Stokes equations, [Silvester and Wathen (1993)](https://epubs.siam.org/doi/10.1137/0730031) show that $S \\approx -\\nu^{-1} Q$ is a good approximation, where $Q$ is the pressure mass matrix.\n",
    "\n",
    "Problem: $Q$ is not available as one of the blocks of $\\mathcal{A}$."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "PETSc's approach is to allow us to supply a _separate_ matrix to the solver which will be used to construct the preconditioner. So, we just need to additionally supply\n",
    "\n",
    "$$\n",
    "\\mathcal{P} = \\mathcal{A} + \\begin{bmatrix} 0 & 0 \\\\ 0 & -\\nu^{-1}Q\\end{bmatrix} = \\begin{bmatrix} \\color{#800020}{A} & \\color{#2A52BE}{B^T} \\\\ \\color{#2A52BE}{B} & -\\nu^{-1} Q \\end{bmatrix},\n",
    "$$\n",
    "where $Q = \\int_\\Omega p q \\,\\mathrm{d}x$.\n",
    "\n",
    "We will construct P by symbolically computing the derivative of the residual to get $\\mathcal{A}$ and then subtracting $\\nu^{-1} Q$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "w_t = TrialFunction(W)\n",
    "_, p_t = split(w_t)\n",
    "\n",
    "pmat = lhs(derivative(F, w, w_t)) - 1/nu * p_t * q*dx"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now pass this pmat form to `create_solver` and can configure an appropriate preconditioner."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "pmat_parameters = {\n",
    "    \"mat_type\": \"nest\", # We only need the blocks\n",
    "    \"snes_type\": \"ksponly\",\n",
    "    \"ksp_view\": None,\n",
    "    \"ksp_monitor_true_residual\": None,\n",
    "    \"ksp_max_it\": 100,\n",
    "    \"pc_type\": \"fieldsplit\",\n",
    "    \"pc_fieldsplit_type\": \"schur\",\n",
    "    \"fieldsplit_0\": {\n",
    "        \"ksp_type\": \"preonly\",\n",
    "        \"pc_type\": \"lu\",\n",
    "    },\n",
    "    \"fieldsplit_1\": {\n",
    "        \"ksp_type\": \"preonly\",\n",
    "        \"pc_type\": \"lu\",\n",
    "    }\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    0 KSP preconditioned resid norm 5.496162170027e+00 true resid norm 7.005934058591e-04 ||r(i)||/||b|| 1.000000000000e+00\n",
      "    1 KSP preconditioned resid norm 9.288239850614e-01 true resid norm 2.453830661658e-03 ||r(i)||/||b|| 3.502503222463e+00\n",
      "    2 KSP preconditioned resid norm 4.322571804187e-01 true resid norm 1.513282272808e-03 ||r(i)||/||b|| 2.160000736736e+00\n",
      "    3 KSP preconditioned resid norm 9.747752360340e-02 true resid norm 4.889677087758e-04 ||r(i)||/||b|| 6.979336440888e-01\n",
      "    4 KSP preconditioned resid norm 2.168769655068e-02 true resid norm 2.367419610200e-04 ||r(i)||/||b|| 3.379163421182e-01\n",
      "    5 KSP preconditioned resid norm 6.602391994106e-03 true resid norm 1.323183601894e-04 ||r(i)||/||b|| 1.888661227509e-01\n",
      "    6 KSP preconditioned resid norm 3.201127617659e-03 true resid norm 5.559951302511e-05 ||r(i)||/||b|| 7.936059997159e-02\n",
      "    7 KSP preconditioned resid norm 1.583402976189e-03 true resid norm 1.881712632717e-05 ||r(i)||/||b|| 2.685884019148e-02\n",
      "    8 KSP preconditioned resid norm 5.459453639346e-04 true resid norm 3.548481385676e-06 ||r(i)||/||b|| 5.064965436443e-03\n",
      "    9 KSP preconditioned resid norm 2.435745734653e-04 true resid norm 1.708889316321e-06 ||r(i)||/||b|| 2.439202684509e-03\n",
      "   10 KSP preconditioned resid norm 8.264338654907e-05 true resid norm 4.816892094520e-07 ||r(i)||/||b|| 6.875445949442e-04\n",
      "   11 KSP preconditioned resid norm 2.869421013434e-05 true resid norm 2.297141940561e-07 ||r(i)||/||b|| 3.278851786714e-04\n",
      "   12 KSP preconditioned resid norm 1.126260479121e-05 true resid norm 1.157469223178e-07 ||r(i)||/||b|| 1.652126916266e-04\n",
      "   13 KSP preconditioned resid norm 3.183234318532e-06 true resid norm 2.964758722405e-08 ||r(i)||/||b|| 4.231782225768e-05\n",
      "   14 KSP preconditioned resid norm 1.315553337784e-06 true resid norm 1.114853946562e-08 ||r(i)||/||b|| 1.591299514438e-05\n",
      "   15 KSP preconditioned resid norm 6.727350493786e-07 true resid norm 4.697218905430e-09 ||r(i)||/||b|| 6.704629056093e-06\n",
      "   16 KSP preconditioned resid norm 2.943828879120e-07 true resid norm 2.160101611906e-09 ||r(i)||/||b|| 3.083245708339e-06\n",
      "KSP Object: 1 MPI process\n",
      "  type: gmres\n",
      "    restart=30, using Classical (unmodified) Gram-Schmidt Orthogonalization with no iterative refinement\n",
      "    happy breakdown tolerance 1e-30\n",
      "  maximum iterations=100, initial guess is zero\n",
      "  tolerances: relative=1e-07, absolute=1e-50, divergence=10000.\n",
      "  left preconditioning\n",
      "  using PRECONDITIONED norm type for convergence test\n",
      "PC Object: 1 MPI process\n",
      "  type: fieldsplit\n",
      "    FieldSplit with Schur preconditioner, factorization FULL\n",
      "    Preconditioner for the Schur complement formed from A11\n",
      "    Split info:\n",
      "    Split number 0 Defined by IS\n",
      "    Split number 1 Defined by IS\n",
      "    KSP solver for A00 block\n",
      "      KSP Object: (fieldsplit_0_) 1 MPI process\n",
      "        type: preonly\n",
      "        maximum iterations=10000, initial guess is zero\n",
      "        tolerances: relative=1e-05, absolute=1e-50, divergence=10000.\n",
      "        left preconditioning\n",
      "        using NONE norm type for convergence test\n",
      "      PC Object: (fieldsplit_0_) 1 MPI process\n",
      "        type: lu\n",
      "          out-of-place factorization\n",
      "          tolerance for zero pivot 2.22045e-14\n",
      "          matrix ordering: nd\n",
      "          factor fill ratio given 5., needed 5.8857\n",
      "            Factored matrix follows:\n",
      "              Mat Object: (fieldsplit_0_) 1 MPI process\n",
      "                type: seqbaij\n",
      "                rows=33282, cols=33282, bs=2\n",
      "                package used to perform factorization: petsc\n",
      "                total: nonzeros=4459972, allocated nonzeros=4459972\n",
      "                    block size is 2\n",
      "        linear system matrix = precond matrix:\n",
      "        Mat Object: (fieldsplit_0_) 1 MPI process\n",
      "          type: seqbaij\n",
      "          rows=33282, cols=33282, bs=2\n",
      "          total: nonzeros=757764, allocated nonzeros=757764\n",
      "          total number of mallocs used during MatSetValues calls=0\n",
      "              block size is 2\n",
      "    KSP solver for S = A11 - A10 inv(A00) A01\n",
      "      KSP Object: (fieldsplit_1_) 1 MPI process\n",
      "        type: preonly\n",
      "        maximum iterations=10000, initial guess is zero\n",
      "        tolerances: relative=1e-05, absolute=1e-50, divergence=10000.\n",
      "        left preconditioning\n",
      "        using NONE norm type for convergence test\n",
      "      PC Object: (fieldsplit_1_) 1 MPI process\n",
      "        type: lu\n",
      "          out-of-place factorization\n",
      "          tolerance for zero pivot 2.22045e-14\n",
      "          matrix ordering: nd\n",
      "          factor fill ratio given 5., needed 6.96187\n",
      "            Factored matrix follows:\n",
      "              Mat Object: (fieldsplit_1_) 1 MPI process\n",
      "                type: seqaij\n",
      "                rows=4225, cols=4225\n",
      "                package used to perform factorization: petsc\n",
      "                total: nonzeros=202291, allocated nonzeros=202291\n",
      "                  not using I-node routines\n",
      "        linear system matrix followed by preconditioner matrix:\n",
      "        Mat Object: (fieldsplit_1_) 1 MPI process\n",
      "          type: schurcomplement\n",
      "          rows=4225, cols=4225\n",
      "            has attached null space\n",
      "            Schur complement A11 - A10 inv(A00) A01\n",
      "            A11\n",
      "              Mat Object: (fieldsplit_1_) 1 MPI process\n",
      "                type: seqaij\n",
      "                rows=4225, cols=4225\n",
      "                total: nonzeros=29057, allocated nonzeros=29057\n",
      "                total number of mallocs used during MatSetValues calls=0\n",
      "                  has attached null space\n",
      "                  not using I-node routines\n",
      "            A10\n",
      "              Mat Object: 1 MPI process\n",
      "                type: seqaij\n",
      "                rows=4225, cols=33282, rbs=1, cbs=2\n",
      "                total: nonzeros=156930, allocated nonzeros=156930\n",
      "                total number of mallocs used during MatSetValues calls=0\n",
      "                  not using I-node routines\n",
      "            KSP solver for A00 block viewable with the additional option -fieldsplit_0_ksp_view\n",
      "            A01\n",
      "              Mat Object: 1 MPI process\n",
      "                type: seqaij\n",
      "                rows=33282, cols=4225, rbs=2, cbs=1\n",
      "                total: nonzeros=156930, allocated nonzeros=156930\n",
      "                total number of mallocs used during MatSetValues calls=0\n",
      "                  using I-node routines: found 16638 nodes, limit used is 5\n",
      "        Mat Object: (fieldsplit_1_) 1 MPI process\n",
      "          type: seqaij\n",
      "          rows=4225, cols=4225\n",
      "          total: nonzeros=29057, allocated nonzeros=29057\n",
      "          total number of mallocs used during MatSetValues calls=0\n",
      "            has attached null space\n",
      "            not using I-node routines\n",
      "  linear system matrix followed by preconditioner matrix:\n",
      "  Mat Object: 1 MPI process\n",
      "    type: nest\n",
      "    rows=37507, cols=37507\n",
      "      has attached null space\n",
      "      Matrix object:\n",
      "        type=nest, rows=2, cols=2\n",
      "        MatNest structure:\n",
      "        (0,0) : type=seqbaij, rows=33282, cols=33282\n",
      "        (0,1) : type=seqaij, rows=33282, cols=4225\n",
      "        (1,0) : type=seqaij, rows=4225, cols=33282\n",
      "        (1,1) : type=seqaij, rows=4225, cols=4225\n",
      "  Mat Object: 1 MPI process\n",
      "    type: nest\n",
      "    rows=37507, cols=37507\n",
      "      has attached null space\n",
      "      Matrix object:\n",
      "        type=nest, rows=2, cols=2\n",
      "        MatNest structure:\n",
      "        (0,0) : prefix=\"fieldsplit_0_\", type=seqbaij, rows=33282, cols=33282\n",
      "        (0,1) : type=seqaij, rows=33282, cols=4225\n",
      "        (1,0) : type=seqaij, rows=4225, cols=33282\n",
      "        (1,1) : prefix=\"fieldsplit_1_\", type=seqaij, rows=4225, cols=4225\n",
      "\n",
      "SNES iterations: 1; SNES converged reason: CONVERGED_ITS\n",
      "   KSP iterations: 16; KSP converged reason: CONVERGED_RTOL\n"
     ]
    }
   ],
   "source": [
    "w.assign(0)\n",
    "solver = create_solver(pmat_parameters, pmat=pmat)\n",
    "solver.solve()\n",
    "convergence(solver)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Providing auxiliary operators\n",
    "\n",
    "An inconvenience here is that we must build $\\mathcal{P}$, even though we only need $-\\nu^{-1} Q$ in additional to $\\mathcal{A}$ in the preconditioner.\n",
    "\n",
    "Firedrake offers a facilities to build Python preconditioning objects, utilising petsc4py.\n",
    "\n",
    "In this case, we can subclass the \n",
    "[`AuxiliaryOperatorPC`](https://www.firedrakeproject.org/firedrake.preconditioners.html#firedrake.preconditioners.assembled.AuxiliaryOperatorPC) to provide the mass matrix."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "class MassMatrix(AuxiliaryOperatorPC):\n",
    "    _prefix = \"mass_\"\n",
    "    def form(self, pc, test, trial):\n",
    "        # Grab the definition of nu from the user application context (a dict)\n",
    "        nu = self.get_appctx(pc)[\"nu\"]\n",
    "        return (-1/nu * test*trial*dx, None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we just need to select parameters such that this Python preconditioner is used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "mass_parameters = {\n",
    "    \"mat_type\": \"nest\", # We only need the blocks\n",
    "    \"ksp_view\": None,\n",
    "    \"pc_type\": \"fieldsplit\",\n",
    "    \"pc_fieldsplit_type\": \"schur\",\n",
    "    \"fieldsplit_0\": {\n",
    "        \"ksp_type\": \"preonly\",\n",
    "        \"pc_type\": \"lu\",\n",
    "    },\n",
    "    \"fieldsplit_1\": {\n",
    "        \"ksp_type\": \"preonly\",\n",
    "        \"pc_type\": \"python\",\n",
    "        \"pc_python_type\": \"__main__.MassMatrix\",\n",
    "        \"mass_pc_type\": \"lu\",\n",
    "    }\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KSP Object: 1 MPI process\n",
      "  type: gmres\n",
      "    restart=30, using Classical (unmodified) Gram-Schmidt Orthogonalization with no iterative refinement\n",
      "    happy breakdown tolerance 1e-30\n",
      "  maximum iterations=10000, initial guess is zero\n",
      "  tolerances: relative=1e-07, absolute=1e-50, divergence=10000.\n",
      "  left preconditioning\n",
      "  using PRECONDITIONED norm type for convergence test\n",
      "PC Object: 1 MPI process\n",
      "  type: fieldsplit\n",
      "    FieldSplit with Schur preconditioner, factorization FULL\n",
      "    Preconditioner for the Schur complement formed from A11\n",
      "    Split info:\n",
      "    Split number 0 Defined by IS\n",
      "    Split number 1 Defined by IS\n",
      "    KSP solver for A00 block\n",
      "      KSP Object: (fieldsplit_0_) 1 MPI process\n",
      "        type: preonly\n",
      "        maximum iterations=10000, initial guess is zero\n",
      "        tolerances: relative=1e-05, absolute=1e-50, divergence=10000.\n",
      "        left preconditioning\n",
      "        using NONE norm type for convergence test\n",
      "      PC Object: (fieldsplit_0_) 1 MPI process\n",
      "        type: lu\n",
      "          out-of-place factorization\n",
      "          tolerance for zero pivot 2.22045e-14\n",
      "          matrix ordering: nd\n",
      "          factor fill ratio given 5., needed 5.8857\n",
      "            Factored matrix follows:\n",
      "              Mat Object: (fieldsplit_0_) 1 MPI process\n",
      "                type: seqbaij\n",
      "                rows=33282, cols=33282, bs=2\n",
      "                package used to perform factorization: petsc\n",
      "                total: nonzeros=4459972, allocated nonzeros=4459972\n",
      "                    block size is 2\n",
      "        linear system matrix = precond matrix:\n",
      "        Mat Object: (fieldsplit_0_) 1 MPI process\n",
      "          type: seqbaij\n",
      "          rows=33282, cols=33282, bs=2\n",
      "          total: nonzeros=757764, allocated nonzeros=757764\n",
      "          total number of mallocs used during MatSetValues calls=0\n",
      "              block size is 2\n",
      "    KSP solver for S = A11 - A10 inv(A00) A01\n",
      "      KSP Object: (fieldsplit_1_) 1 MPI process\n",
      "        type: preonly\n",
      "        maximum iterations=10000, initial guess is zero\n",
      "        tolerances: relative=1e-05, absolute=1e-50, divergence=10000.\n",
      "        left preconditioning\n",
      "        using NONE norm type for convergence test\n",
      "      PC Object: (fieldsplit_1_) 1 MPI process\n",
      "        type: python\n",
      "          Python: __main__.MassMatrix\n",
      "        Firedrake custom preconditioner MassMatrix\n",
      "        PC to apply inverse\n",
      "        PC Object: (fieldsplit_1_mass_) 1 MPI process\n",
      "          type: lu\n",
      "            out-of-place factorization\n",
      "            tolerance for zero pivot 2.22045e-14\n",
      "            matrix ordering: nd\n",
      "            factor fill ratio given 5., needed 6.96187\n",
      "              Factored matrix follows:\n",
      "                Mat Object: (fieldsplit_1_mass_) 1 MPI process\n",
      "                  type: seqaij\n",
      "                  rows=4225, cols=4225\n",
      "                  package used to perform factorization: petsc\n",
      "                  total: nonzeros=202291, allocated nonzeros=202291\n",
      "                    not using I-node routines\n",
      "          linear system matrix followed by preconditioner matrix:\n",
      "          Mat Object: (fieldsplit_1_) 1 MPI process\n",
      "            type: schurcomplement\n",
      "            rows=4225, cols=4225\n",
      "              has attached null space\n",
      "              Schur complement A11 - A10 inv(A00) A01\n",
      "              A11\n",
      "                Mat Object: (fieldsplit_1_) 1 MPI process\n",
      "                  type: seqaij\n",
      "                  rows=4225, cols=4225\n",
      "                  total: nonzeros=4225, allocated nonzeros=4225\n",
      "                  total number of mallocs used during MatSetValues calls=0\n",
      "                    has attached null space\n",
      "                    not using I-node routines\n",
      "              A10\n",
      "                Mat Object: 1 MPI process\n",
      "                  type: seqaij\n",
      "                  rows=4225, cols=33282, rbs=1, cbs=2\n",
      "                  total: nonzeros=156930, allocated nonzeros=156930\n",
      "                  total number of mallocs used during MatSetValues calls=0\n",
      "                    not using I-node routines\n",
      "              KSP solver for A00 block viewable with the additional option -fieldsplit_0_ksp_view\n",
      "              A01\n",
      "                Mat Object: 1 MPI process\n",
      "                  type: seqaij\n",
      "                  rows=33282, cols=4225, rbs=2, cbs=1\n",
      "                  total: nonzeros=156930, allocated nonzeros=156930\n",
      "                  total number of mallocs used during MatSetValues calls=0\n",
      "                    using I-node routines: found 16638 nodes, limit used is 5\n",
      "          Mat Object: (fieldsplit_1_mass_) 1 MPI process\n",
      "            type: seqaij\n",
      "            rows=4225, cols=4225\n",
      "            total: nonzeros=29057, allocated nonzeros=29057\n",
      "            total number of mallocs used during MatSetValues calls=0\n",
      "              has attached null space\n",
      "              not using I-node routines\n",
      "        linear system matrix followed by preconditioner matrix:\n",
      "        Mat Object: (fieldsplit_1_) 1 MPI process\n",
      "          type: schurcomplement\n",
      "          rows=4225, cols=4225\n",
      "            has attached null space\n",
      "            Schur complement A11 - A10 inv(A00) A01\n",
      "            A11\n",
      "              Mat Object: (fieldsplit_1_) 1 MPI process\n",
      "                type: seqaij\n",
      "                rows=4225, cols=4225\n",
      "                total: nonzeros=4225, allocated nonzeros=4225\n",
      "                total number of mallocs used during MatSetValues calls=0\n",
      "                  has attached null space\n",
      "                  not using I-node routines\n",
      "            A10\n",
      "              Mat Object: 1 MPI process\n",
      "                type: seqaij\n",
      "                rows=4225, cols=33282, rbs=1, cbs=2\n",
      "                total: nonzeros=156930, allocated nonzeros=156930\n",
      "                total number of mallocs used during MatSetValues calls=0\n",
      "                  not using I-node routines\n",
      "            KSP solver for A00 block viewable with the additional option -fieldsplit_0_ksp_view\n",
      "            A01\n",
      "              Mat Object: 1 MPI process\n",
      "                type: seqaij\n",
      "                rows=33282, cols=4225, rbs=2, cbs=1\n",
      "                total: nonzeros=156930, allocated nonzeros=156930\n",
      "                total number of mallocs used during MatSetValues calls=0\n",
      "                  using I-node routines: found 16638 nodes, limit used is 5\n",
      "        Mat Object: (fieldsplit_1_) 1 MPI process\n",
      "          type: seqaij\n",
      "          rows=4225, cols=4225\n",
      "          total: nonzeros=4225, allocated nonzeros=4225\n",
      "          total number of mallocs used during MatSetValues calls=0\n",
      "            has attached null space\n",
      "            not using I-node routines\n",
      "  linear system matrix = precond matrix:\n",
      "  Mat Object: 1 MPI process\n",
      "    type: nest\n",
      "    rows=37507, cols=37507\n",
      "      has attached null space\n",
      "      Matrix object:\n",
      "        type=nest, rows=2, cols=2\n",
      "        MatNest structure:\n",
      "        (0,0) : prefix=\"fieldsplit_0_\", type=seqbaij, rows=33282, cols=33282\n",
      "        (0,1) : type=seqaij, rows=33282, cols=4225\n",
      "        (1,0) : type=seqaij, rows=4225, cols=33282\n",
      "        (1,1) : prefix=\"fieldsplit_1_\", type=seqaij, rows=4225, cols=4225\n",
      "\n",
      "SNES iterations: 1; SNES converged reason: CONVERGED_ITS\n",
      "   KSP iterations: 16; KSP converged reason: CONVERGED_RTOL\n"
     ]
    }
   ],
   "source": [
    "appctx = {\"nu\": nu} # arbitrary user data that is available inside the user PC object\n",
    "w.assign(0)\n",
    "solver = create_solver(mass_parameters, appctx=appctx)\n",
    "solver.solve()\n",
    "convergence(solver)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This performs identically to the previous approach, except that the preconditioning matrix is only built for the pressure space, and constructed \"on demand\"."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Multigrid preconditioners and smoothers\n",
    "\n",
    "So far, we've only used direct solvers for the blocks. We can also use iterative methods. Here we'll use geometric multigrid to solve\n",
    "\n",
    "In the same way that Firedrake hooks up solvers such that [`PCFIELDSPLIT`](https://petsc.org/release/manualpages/PC/PCFIELDSPLIT/) is enabled, if a problem was defined on a mesh from a `MeshHierarchy`, [`PCMG`](https://petsc.org/release/manualpages/PC/PCMG/) and [`SNESFAS`](https://petsc.org/release/manualpages/SNESFAS/SNESFAS/) are also available."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "fieldsplit_mg_parameters = {\n",
    "    \"mat_type\": \"nest\",\n",
    "    \"ksp_view\": None,\n",
    "    \"pc_type\": \"fieldsplit\",\n",
    "    \"pc_fieldsplit_type\": \"schur\",\n",
    "    \"fieldsplit_0\": {\n",
    "        \"ksp_type\": \"preonly\",\n",
    "        \"pc_type\": \"mg\",\n",
    "        \"mg_levels\": {\n",
    "            \"ksp_type\": \"chebyshev\",\n",
    "            \"ksp_max_it\": 2,\n",
    "        }\n",
    "    },\n",
    "    \"fieldsplit_1\": {\n",
    "        \"ksp_type\": \"chebyshev\",\n",
    "        \"ksp_max_it\": 2,\n",
    "        \"pc_type\": \"python\",\n",
    "        \"pc_python_type\": \"__main__.MassMatrix\",\n",
    "        \"mass_pc_type\": \"sor\",\n",
    "    }\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, when the solver runs, PETSc will call back in to Firedrake for restriction and prolongation, as well as rediscretising $A$ on the coarser levels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KSP Object: 1 MPI process\n",
      "  type: gmres\n",
      "    restart=30, using Classical (unmodified) Gram-Schmidt Orthogonalization with no iterative refinement\n",
      "    happy breakdown tolerance 1e-30\n",
      "  maximum iterations=10000, initial guess is zero\n",
      "  tolerances: relative=1e-07, absolute=1e-50, divergence=10000.\n",
      "  left preconditioning\n",
      "  using PRECONDITIONED norm type for convergence test\n",
      "PC Object: 1 MPI process\n",
      "  type: fieldsplit\n",
      "    FieldSplit with Schur preconditioner, factorization FULL\n",
      "    Preconditioner for the Schur complement formed from A11\n",
      "    Split info:\n",
      "    Split number 0 Defined by IS\n",
      "    Split number 1 Defined by IS\n",
      "    KSP solver for A00 block\n",
      "      KSP Object: (fieldsplit_0_) 1 MPI process\n",
      "        type: preonly\n",
      "        maximum iterations=10000, initial guess is zero\n",
      "        tolerances: relative=1e-05, absolute=1e-50, divergence=10000.\n",
      "        left preconditioning\n",
      "        using NONE norm type for convergence test\n",
      "      PC Object: (fieldsplit_0_) 1 MPI process\n",
      "        type: mg\n",
      "          type is MULTIPLICATIVE, levels=4 cycles=v\n",
      "            Cycles per PCApply=1\n",
      "            Not using Galerkin computed coarse grid matrices\n",
      "        Coarse grid solver -- level 0 -------------------------------\n",
      "          KSP Object: (fieldsplit_0_mg_coarse_) 1 MPI process\n",
      "            type: preonly\n",
      "            maximum iterations=10000, initial guess is zero\n",
      "            tolerances: relative=1e-05, absolute=1e-50, divergence=10000.\n",
      "            left preconditioning\n",
      "            using NONE norm type for convergence test\n",
      "          PC Object: (fieldsplit_0_mg_coarse_) 1 MPI process\n",
      "            type: lu\n",
      "              out-of-place factorization\n",
      "              tolerance for zero pivot 2.22045e-14\n",
      "              using diagonal shift on blocks to prevent zero pivot [INBLOCKS]\n",
      "              matrix ordering: nd\n",
      "              factor fill ratio given 5., needed 2.49235\n",
      "                Factored matrix follows:\n",
      "                  Mat Object: (fieldsplit_0_mg_coarse_) 1 MPI process\n",
      "                    type: seqbaij\n",
      "                    rows=578, cols=578, bs=2\n",
      "                    package used to perform factorization: petsc\n",
      "                    total: nonzeros=30636, allocated nonzeros=30636\n",
      "                        block size is 2\n",
      "            linear system matrix = precond matrix:\n",
      "            Mat Object: 1 MPI process\n",
      "              type: seqbaij\n",
      "              rows=578, cols=578, bs=2\n",
      "              total: nonzeros=12292, allocated nonzeros=12292\n",
      "              total number of mallocs used during MatSetValues calls=0\n",
      "                  block size is 2\n",
      "        Down solver (pre-smoother) on level 1 -------------------------------\n",
      "          KSP Object: (fieldsplit_0_mg_levels_1_) 1 MPI process\n",
      "            type: chebyshev\n",
      "              Chebyshev polynomial of first kind\n",
      "              eigenvalue targets used: min 0.0993901, max 1.09329\n",
      "              eigenvalues estimated via gmres: min 0.0274474, max 0.993901\n",
      "              eigenvalues estimated using gmres with transform: [0. 0.1; 0. 1.1]              KSP Object: (fieldsplit_0_mg_levels_1_esteig_) 1 MPI process\n",
      "                type: gmres\n",
      "                  restart=30, using Classical (unmodified) Gram-Schmidt Orthogonalization with no iterative refinement\n",
      "                  happy breakdown tolerance 1e-30\n",
      "                maximum iterations=10, initial guess is zero\n",
      "                tolerances: relative=1e-12, absolute=1e-50, divergence=10000.\n",
      "                left preconditioning\n",
      "                using PRECONDITIONED norm type for convergence test\n",
      "              estimating eigenvalues using a noisy random number generated right-hand side\n",
      "            maximum iterations=2, nonzero initial guess\n",
      "            tolerances: relative=1e-05, absolute=1e-50, divergence=10000.\n",
      "            left preconditioning\n",
      "            using NONE norm type for convergence test\n",
      "          PC Object: (fieldsplit_0_mg_levels_1_) 1 MPI process\n",
      "            type: sor\n",
      "              type = local_symmetric, iterations = 1, local iterations = 1, omega = 1.\n",
      "            linear system matrix = precond matrix:\n",
      "            Mat Object: 1 MPI process\n",
      "              type: seqbaij\n",
      "              rows=2178, cols=2178, bs=2\n",
      "              total: nonzeros=48132, allocated nonzeros=48132\n",
      "              total number of mallocs used during MatSetValues calls=0\n",
      "                  block size is 2\n",
      "        Up solver (post-smoother) same as down solver (pre-smoother)\n",
      "        Down solver (pre-smoother) on level 2 -------------------------------\n",
      "          KSP Object: (fieldsplit_0_mg_levels_2_) 1 MPI process\n",
      "            type: chebyshev\n",
      "              Chebyshev polynomial of first kind\n",
      "              eigenvalue targets used: min 0.0993111, max 1.09242\n",
      "              eigenvalues estimated via gmres: min 0.0197314, max 0.993111\n",
      "              eigenvalues estimated using gmres with transform: [0. 0.1; 0. 1.1]              KSP Object: (fieldsplit_0_mg_levels_2_esteig_) 1 MPI process\n",
      "                type: gmres\n",
      "                  restart=30, using Classical (unmodified) Gram-Schmidt Orthogonalization with no iterative refinement\n",
      "                  happy breakdown tolerance 1e-30\n",
      "                maximum iterations=10, initial guess is zero\n",
      "                tolerances: relative=1e-12, absolute=1e-50, divergence=10000.\n",
      "                left preconditioning\n",
      "                using PRECONDITIONED norm type for convergence test\n",
      "              estimating eigenvalues using a noisy random number generated right-hand side\n",
      "            maximum iterations=2, nonzero initial guess\n",
      "            tolerances: relative=1e-05, absolute=1e-50, divergence=10000.\n",
      "            left preconditioning\n",
      "            using NONE norm type for convergence test\n",
      "          PC Object: (fieldsplit_0_mg_levels_2_) 1 MPI process\n",
      "            type: sor\n",
      "              type = local_symmetric, iterations = 1, local iterations = 1, omega = 1.\n",
      "            linear system matrix = precond matrix:\n",
      "            Mat Object: 1 MPI process\n",
      "              type: seqbaij\n",
      "              rows=8450, cols=8450, bs=2\n",
      "              total: nonzeros=190468, allocated nonzeros=190468\n",
      "              total number of mallocs used during MatSetValues calls=0\n",
      "                  block size is 2\n",
      "        Up solver (post-smoother) same as down solver (pre-smoother)\n",
      "        Down solver (pre-smoother) on level 3 -------------------------------\n",
      "          KSP Object: (fieldsplit_0_mg_levels_3_) 1 MPI process\n",
      "            type: chebyshev\n",
      "              Chebyshev polynomial of first kind\n",
      "              eigenvalue targets used: min 0.099288, max 1.09217\n",
      "              eigenvalues estimated via gmres: min 0.0130154, max 0.99288\n",
      "              eigenvalues estimated using gmres with transform: [0. 0.1; 0. 1.1]              KSP Object: (fieldsplit_0_mg_levels_3_esteig_) 1 MPI process\n",
      "                type: gmres\n",
      "                  restart=30, using Classical (unmodified) Gram-Schmidt Orthogonalization with no iterative refinement\n",
      "                  happy breakdown tolerance 1e-30\n",
      "                maximum iterations=10, initial guess is zero\n",
      "                tolerances: relative=1e-12, absolute=1e-50, divergence=10000.\n",
      "                left preconditioning\n",
      "                using PRECONDITIONED norm type for convergence test\n",
      "              estimating eigenvalues using a noisy random number generated right-hand side\n",
      "            maximum iterations=2, nonzero initial guess\n",
      "            tolerances: relative=1e-05, absolute=1e-50, divergence=10000.\n",
      "            left preconditioning\n",
      "            using NONE norm type for convergence test\n",
      "          PC Object: (fieldsplit_0_mg_levels_3_) 1 MPI process\n",
      "            type: sor\n",
      "              type = local_symmetric, iterations = 1, local iterations = 1, omega = 1.\n",
      "            linear system matrix = precond matrix:\n",
      "            Mat Object: (fieldsplit_0_) 1 MPI process\n",
      "              type: seqbaij\n",
      "              rows=33282, cols=33282, bs=2\n",
      "              total: nonzeros=757764, allocated nonzeros=757764\n",
      "              total number of mallocs used during MatSetValues calls=0\n",
      "                  block size is 2\n",
      "        Up solver (post-smoother) same as down solver (pre-smoother)\n",
      "        linear system matrix = precond matrix:\n",
      "        Mat Object: (fieldsplit_0_) 1 MPI process\n",
      "          type: seqbaij\n",
      "          rows=33282, cols=33282, bs=2\n",
      "          total: nonzeros=757764, allocated nonzeros=757764\n",
      "          total number of mallocs used during MatSetValues calls=0\n",
      "              block size is 2\n",
      "    KSP solver for S = A11 - A10 inv(A00) A01\n",
      "      KSP Object: (fieldsplit_1_) 1 MPI process\n",
      "        type: chebyshev\n",
      "          Chebyshev polynomial of first kind\n",
      "          eigenvalue targets used: min 0.0958699, max 1.05457\n",
      "          eigenvalues estimated via gmres: min 0.128792, max 0.958699\n",
      "          eigenvalues estimated using gmres with transform: [0. 0.1; 0. 1.1]          KSP Object: (fieldsplit_1_esteig_) 1 MPI process\n",
      "            type: gmres\n",
      "              restart=30, using Classical (unmodified) Gram-Schmidt Orthogonalization with no iterative refinement\n",
      "              happy breakdown tolerance 1e-30\n",
      "            maximum iterations=10, initial guess is zero\n",
      "            tolerances: relative=1e-12, absolute=1e-50, divergence=10000.\n",
      "            left preconditioning\n",
      "            using PRECONDITIONED norm type for convergence test\n",
      "          estimating eigenvalues using a noisy random number generated right-hand side\n",
      "        maximum iterations=2, initial guess is zero\n",
      "        tolerances: relative=1e-05, absolute=1e-50, divergence=10000.\n",
      "        left preconditioning\n",
      "        using PRECONDITIONED norm type for convergence test\n",
      "      PC Object: (fieldsplit_1_) 1 MPI process\n",
      "        type: python\n",
      "          Python: __main__.MassMatrix\n",
      "        Firedrake custom preconditioner MassMatrix\n",
      "        PC to apply inverse\n",
      "        PC Object: (fieldsplit_1_mass_) 1 MPI process\n",
      "          type: sor\n",
      "            type = local_symmetric, iterations = 1, local iterations = 1, omega = 1.\n",
      "          linear system matrix followed by preconditioner matrix:\n",
      "          Mat Object: (fieldsplit_1_) 1 MPI process\n",
      "            type: schurcomplement\n",
      "            rows=4225, cols=4225\n",
      "              has attached null space\n",
      "              Schur complement A11 - A10 inv(A00) A01\n",
      "              A11\n",
      "                Mat Object: (fieldsplit_1_) 1 MPI process\n",
      "                  type: seqaij\n",
      "                  rows=4225, cols=4225\n",
      "                  total: nonzeros=4225, allocated nonzeros=4225\n",
      "                  total number of mallocs used during MatSetValues calls=0\n",
      "                    has attached null space\n",
      "                    not using I-node routines\n",
      "              A10\n",
      "                Mat Object: 1 MPI process\n",
      "                  type: seqaij\n",
      "                  rows=4225, cols=33282, rbs=1, cbs=2\n",
      "                  total: nonzeros=156930, allocated nonzeros=156930\n",
      "                  total number of mallocs used during MatSetValues calls=0\n",
      "                    not using I-node routines\n",
      "              KSP solver for A00 block viewable with the additional option -fieldsplit_0_ksp_view\n",
      "              A01\n",
      "                Mat Object: 1 MPI process\n",
      "                  type: seqaij\n",
      "                  rows=33282, cols=4225, rbs=2, cbs=1\n",
      "                  total: nonzeros=156930, allocated nonzeros=156930\n",
      "                  total number of mallocs used during MatSetValues calls=0\n",
      "                    using I-node routines: found 16638 nodes, limit used is 5\n",
      "          Mat Object: (fieldsplit_1_mass_) 1 MPI process\n",
      "            type: seqaij\n",
      "            rows=4225, cols=4225\n",
      "            total: nonzeros=29057, allocated nonzeros=29057\n",
      "            total number of mallocs used during MatSetValues calls=0\n",
      "              has attached null space\n",
      "              not using I-node routines\n",
      "        linear system matrix followed by preconditioner matrix:\n",
      "        Mat Object: (fieldsplit_1_) 1 MPI process\n",
      "          type: schurcomplement\n",
      "          rows=4225, cols=4225\n",
      "            has attached null space\n",
      "            Schur complement A11 - A10 inv(A00) A01\n",
      "            A11\n",
      "              Mat Object: (fieldsplit_1_) 1 MPI process\n",
      "                type: seqaij\n",
      "                rows=4225, cols=4225\n",
      "                total: nonzeros=4225, allocated nonzeros=4225\n",
      "                total number of mallocs used during MatSetValues calls=0\n",
      "                  has attached null space\n",
      "                  not using I-node routines\n",
      "            A10\n",
      "              Mat Object: 1 MPI process\n",
      "                type: seqaij\n",
      "                rows=4225, cols=33282, rbs=1, cbs=2\n",
      "                total: nonzeros=156930, allocated nonzeros=156930\n",
      "                total number of mallocs used during MatSetValues calls=0\n",
      "                  not using I-node routines\n",
      "            KSP solver for A00 block viewable with the additional option -fieldsplit_0_ksp_view\n",
      "            A01\n",
      "              Mat Object: 1 MPI process\n",
      "                type: seqaij\n",
      "                rows=33282, cols=4225, rbs=2, cbs=1\n",
      "                total: nonzeros=156930, allocated nonzeros=156930\n",
      "                total number of mallocs used during MatSetValues calls=0\n",
      "                  using I-node routines: found 16638 nodes, limit used is 5\n",
      "        Mat Object: (fieldsplit_1_) 1 MPI process\n",
      "          type: seqaij\n",
      "          rows=4225, cols=4225\n",
      "          total: nonzeros=4225, allocated nonzeros=4225\n",
      "          total number of mallocs used during MatSetValues calls=0\n",
      "            has attached null space\n",
      "            not using I-node routines\n",
      "  linear system matrix = precond matrix:\n",
      "  Mat Object: 1 MPI process\n",
      "    type: nest\n",
      "    rows=37507, cols=37507\n",
      "      has attached null space\n",
      "      Matrix object:\n",
      "        type=nest, rows=2, cols=2\n",
      "        MatNest structure:\n",
      "        (0,0) : prefix=\"fieldsplit_0_\", type=seqbaij, rows=33282, cols=33282\n",
      "        (0,1) : type=seqaij, rows=33282, cols=4225\n",
      "        (1,0) : type=seqaij, rows=4225, cols=33282\n",
      "        (1,1) : prefix=\"fieldsplit_1_\", type=seqaij, rows=4225, cols=4225\n",
      "\n",
      "SNES iterations: 1; SNES converged reason: CONVERGED_ITS\n",
      "   KSP iterations: 10; KSP converged reason: CONVERGED_RTOL\n"
     ]
    }
   ],
   "source": [
    "appctx = {\"nu\": nu} # arbitrary user data that is available inside the user PC object\n",
    "w.assign(0)\n",
    "solver = create_solver(fieldsplit_mg_parameters, appctx=appctx)\n",
    "solver.solve()\n",
    "convergence(solver)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also do monolithic, or \"all at once\" multigrid. Here we're using Vanka smoothing. This is supported by a new preconditioner in PETSc `PCPATCH`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "vanka_parameters = {\n",
    "    \"mat_type\": \"matfree\", # We only need the action\n",
    "    \"ksp_type\": \"fgmres\",\n",
    "    \"ksp_max_it\": 25,\n",
    "    \"pc_type\": \"mg\",\n",
    "    \"mg_levels\": {\n",
    "        \"ksp_type\": \"chebyshev\",\n",
    "        \"ksp_convergence_test\": \"skip\",\n",
    "        \"ksp_max_it\": 2,\n",
    "        \"pc_type\": \"python\",\n",
    "        \"pc_python_type\": \"firedrake.PatchPC\",\n",
    "        \"patch\": {\n",
    "            \"pc_patch_save_operators\": 1,\n",
    "            \"pc_patch_partition_of_unity\": False,\n",
    "            \"pc_patch_construct_dim\": 0,\n",
    "            # Topological decomposition\n",
    "            \"pc_patch_construct_type\": \"vanka\",\n",
    "            # Pressure space is constraint space\n",
    "            \"pc_patch_exclude_subspaces\": 1,\n",
    "            # Configure the solver on each patch\n",
    "            \"pc_patch_sub\": {\n",
    "                \"mat_type\": \"dense\",\n",
    "                \"ksp_type\": \"preonly\",\n",
    "                \"pc_type\": \"lu\",\n",
    "                \"pc_factor_shift_type\": \"nonzero\",\n",
    "            }\n",
    "        }\n",
    "    },\n",
    "    \"mg_coarse\": {\n",
    "        \"ksp_type\": \"preonly\",\n",
    "        \"pc_type\": \"python\",\n",
    "        \"pc_python_type\": \"firedrake.AssembledPC\",\n",
    "        \"assembled\": {\n",
    "            \"pc_type\": \"lu\",\n",
    "            \"pc_factor_mat_solver_type\": \"mumps\",\n",
    "        }\n",
    "    }\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The solver can be invoked as below, but frequently crashes Jupyter notebooks:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#w.assign(0)\n",
    "#solver = create_solver(vanka_parameters)\n",
    "#solver.solve()\n",
    "#convergence(solver)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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