{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "90142464",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using backend: tensorflow\n",
      "Other supported backends: tensorflow.compat.v1, pytorch, jax, paddle.\n",
      "paddle supports more examples now and is recommended.\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "import deepxde as dde\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2a0d93ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "rho = 1\n",
    "mu = 1\n",
    "u_in = 1\n",
    "D = 1\n",
    "L = 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "260f0675",
   "metadata": {},
   "outputs": [],
   "source": [
    "geom = dde.geometry.Rectangle(xmin=[-L/2, -D/2], xmax=[L/2, D/2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9b291fe2",
   "metadata": {},
   "outputs": [],
   "source": [
    "def boundary_wall(X, on_boundary):\n",
    "    print(\"X\",X)\n",
    "    print(\"on_boundary\",on_boundary)\n",
    "    on_wall = np.logical_and(np.logical_or(np.isclose(X[1],-D/2,rtol=1e-05,atol=1e-08),np.isclose(X[1],D/2,rtol=1e-05,atol=1e-08)),on_boundary)\n",
    "    return on_wall\n",
    "\n",
    "def boundary_inlet(X,on_boundary):\n",
    "    on_inlet = np.logical_and(np.isclose(X[0],-L/2,rtol=1e-05,atol=1e-08),on_boundary)\n",
    "    return on_inlet\n",
    "\n",
    "def boundary_outlet(X,on_boundary):\n",
    "    on_outlet = np.logical_and(np.isclose(X[0],L/2,rtol=1e-05,atol=1e-08),on_boundary)\n",
    "    return on_outlet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9bee3881",
   "metadata": {},
   "outputs": [],
   "source": [
    "bc_wall_u = dde.DirichletBC(geom, lambda X:0., boundary_wall, component= 0)\n",
    "bc_wall_v = dde.DirichletBC(geom, lambda X:0., boundary_wall, component= 1)\n",
    "\n",
    "bc_inlet_u = dde.DirichletBC(geom, lambda X:u_in, boundary_inlet, component= 0)\n",
    "bc_inlet_v = dde.DirichletBC(geom, lambda X:0.   , boundary_inlet, component= 1)\n",
    "\n",
    "bc_outlet_p = dde.DirichletBC(geom, lambda X:0.  , boundary_outlet, component= 2)\n",
    "bc_outlet_v = dde.DirichletBC(geom, lambda X:0.  , boundary_outlet, component= 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b0eca8ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "def pde(X,Y):\n",
    "    du_x =dde.grad.jacobian(Y, X, i=0, j=0)\n",
    "    du_y =dde.grad.jacobian(Y, X, i=0, j=1)\n",
    "    dv_x =dde.grad.jacobian(Y, X, i=1, j=0)\n",
    "    dv_y =dde.grad.jacobian(Y, X, i=1, j=1)\n",
    "    dp_x =dde.grad.jacobian(Y, X, i=2, j=0)\n",
    "    dp_y =dde.grad.jacobian(Y, X, i=2, j=1)\n",
    "    \n",
    "    du_xx = dde.grad.hessian(Y, X, component=0, i=0, j=0)\n",
    "    du_yy = dde.grad.hessian(Y, X, component=0, i=1, j=1)\n",
    "    dv_xx = dde.grad.hessian(Y, X, component=1, i=0, j=0)\n",
    "    dv_yy = dde.grad.hessian(Y, X, component=1, i=1, j=1)\n",
    "    \n",
    "    pde_u    = Y[:,0:1]*du_x +  Y[:,1:2]*du_y + 1/rho * dp_x - (mu/rho) * (du_xx+ du_yy)\n",
    "    pde_v    = Y[:,0:1]*dv_x +  Y[:,1:2]*dv_y + 1/rho * dp_y - (mu/rho) * (dv_xx+ dv_yy)\n",
    "    pde_cont = du_x + dv_y\n",
    "    \n",
    "    return [pde_u,pde_v,pde_cont]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "13127bbf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X [1.  0.5]\n",
      "on_boundary True\n",
      "X [ 0.5 -0.5]\n",
      "on_boundary True\n",
      "X [-0.5  0.5]\n",
      "on_boundary True\n",
      "X [-0.25 -0.5 ]\n",
      "on_boundary True\n",
      "X [0.25 0.5 ]\n",
      "on_boundary True\n",
      "X [ 1.   -0.25]\n",
      "on_boundary True\n",
      "X [-1.    0.25]\n",
      "on_boundary True\n",
      "X [-0.625 -0.5  ]\n",
      "on_boundary True\n",
      "X [0.625 0.5  ]\n",
      "on_boundary True\n",
      "X [ 0.875 -0.5  ]\n",
      "on_boundary True\n",
      "X [-0.875  0.5  ]\n",
      "on_boundary True\n",
      "X [ 0.125 -0.5  ]\n",
      "on_boundary True\n",
      "X [-0.125  0.5  ]\n",
      "on_boundary True\n",
      "X [1.    0.125]\n",
      "on_boundary True\n",
      "X [-1.    -0.125]\n",
      "on_boundary True\n",
      "X [-0.8125 -0.5   ]\n",
      "on_boundary True\n",
      "X [0.8125 0.5   ]\n",
      "on_boundary True\n",
      "X [ 0.6875 -0.5   ]\n",
      "on_boundary True\n",
      "X [-0.6875  0.5   ]\n",
      "on_boundary True\n",
      "X [-0.0625 -0.5   ]\n",
      "on_boundary True\n",
      "X [0.0625 0.5   ]\n",
      "on_boundary True\n",
      "X [ 1.     -0.0625]\n",
      "on_boundary True\n",
      "X [-1.      0.0625]\n",
      "on_boundary True\n",
      "X [-0.4375 -0.5   ]\n",
      "on_boundary True\n",
      "X [0.4375 0.5   ]\n",
      "on_boundary True\n",
      "X [ 1.     -0.4375]\n",
      "on_boundary True\n",
      "X [-1.      0.4375]\n",
      "on_boundary True\n",
      "X [ 0.3125 -0.5   ]\n",
      "on_boundary True\n",
      "X [-0.3125  0.5   ]\n",
      "on_boundary True\n",
      "X [1.     0.3125]\n",
      "on_boundary True\n",
      "X [-1.     -0.3125]\n",
      "on_boundary True\n",
      "X [-0.90625 -0.5    ]\n",
      "on_boundary True\n",
      "X [0.90625 0.5    ]\n",
      "on_boundary True\n",
      "X [ 0.59375 -0.5    ]\n",
      "on_boundary True\n",
      "X [-0.59375  0.5    ]\n",
      "on_boundary True\n",
      "X [-0.15625 -0.5    ]\n",
      "on_boundary True\n",
      "X [0.15625 0.5    ]\n",
      "on_boundary True\n",
      "X [ 1.      -0.15625]\n",
      "on_boundary True\n",
      "X [-1.       0.15625]\n",
      "on_boundary True\n",
      "X [-0.53125 -0.5    ]\n",
      "on_boundary True\n",
      "X [0.53125 0.5    ]\n",
      "on_boundary True\n",
      "X [ 0.96875 -0.5    ]\n",
      "on_boundary True\n",
      "X [-0.96875  0.5    ]\n",
      "on_boundary True\n",
      "X [ 0.21875 -0.5    ]\n",
      "on_boundary True\n",
      "X [-0.21875  0.5    ]\n",
      "on_boundary True\n",
      "X [1.      0.21875]\n",
      "on_boundary True\n",
      "X [-1.      -0.21875]\n",
      "on_boundary True\n",
      "X [-0.71875 -0.5    ]\n",
      "on_boundary True\n",
      "X [0.71875 0.5    ]\n",
      "on_boundary True\n",
      "X [ 0.78125 -0.5    ]\n",
      "on_boundary True\n",
      "X [-0.78125  0.5    ]\n",
      "on_boundary True\n",
      "X [ 0.03125 -0.5    ]\n",
      "on_boundary True\n",
      "X [-0.03125  0.5    ]\n",
      "on_boundary True\n",
      "X [1.      0.03125]\n",
      "on_boundary True\n",
      "X [-1.      -0.03125]\n",
      "on_boundary True\n",
      "X [-0.34375 -0.5    ]\n",
      "on_boundary True\n",
      "X [0.34375 0.5    ]\n",
      "on_boundary True\n",
      "X [ 1.      -0.34375]\n",
      "on_boundary True\n",
      "X [-1.       0.34375]\n",
      "on_boundary True\n",
      "X [ 0.40625 -0.5    ]\n",
      "on_boundary True\n",
      "X [-0.40625  0.5    ]\n",
      "on_boundary True\n",
      "X [1.      0.40625]\n",
      "on_boundary True\n",
      "X [-1.      -0.40625]\n",
      "on_boundary True\n",
      "X [-0.953125 -0.5     ]\n",
      "on_boundary True\n",
      "X [0.953125 0.5     ]\n",
      "on_boundary True\n",
      "X [ 0.546875 -0.5     ]\n",
      "on_boundary True\n",
      "X [-0.546875  0.5     ]\n",
      "on_boundary True\n",
      "X [-0.203125 -0.5     ]\n",
      "on_boundary True\n",
      "X [0.203125 0.5     ]\n",
      "on_boundary True\n",
      "X [ 1.       -0.203125]\n",
      "on_boundary True\n",
      "X [-1.        0.203125]\n",
      "on_boundary True\n",
      "X [-0.578125 -0.5     ]\n",
      "on_boundary True\n",
      "X [0.578125 0.5     ]\n",
      "on_boundary True\n",
      "X [ 0.921875 -0.5     ]\n",
      "on_boundary True\n",
      "X [-0.921875  0.5     ]\n",
      "on_boundary True\n",
      "X [ 0.171875 -0.5     ]\n",
      "on_boundary True\n",
      "X [-0.171875  0.5     ]\n",
      "on_boundary True\n",
      "X [1.       0.171875]\n",
      "on_boundary True\n",
      "X [-1.       -0.171875]\n",
      "on_boundary True\n",
      "X [-0.765625 -0.5     ]\n",
      "on_boundary True\n",
      "X [0.765625 0.5     ]\n",
      "on_boundary True\n",
      "X [ 0.734375 -0.5     ]\n",
      "on_boundary True\n",
      "X [-0.734375  0.5     ]\n",
      "on_boundary True\n",
      "X [-0.015625 -0.5     ]\n",
      "on_boundary True\n",
      "X [0.015625 0.5     ]\n",
      "on_boundary True\n",
      "X [ 1.       -0.015625]\n",
      "on_boundary True\n",
      "X [-1.        0.015625]\n",
      "on_boundary True\n",
      "X [-0.390625 -0.5     ]\n",
      "on_boundary True\n",
      "X [0.390625 0.5     ]\n",
      "on_boundary True\n",
      "X [ 1.       -0.390625]\n",
      "on_boundary True\n",
      "X [-1.        0.390625]\n",
      "on_boundary True\n",
      "X [ 0.359375 -0.5     ]\n",
      "on_boundary True\n",
      "X [-0.359375  0.5     ]\n",
      "on_boundary True\n",
      "X [1.       0.359375]\n",
      "on_boundary True\n",
      "X [-1.       -0.359375]\n",
      "on_boundary True\n",
      "X [-0.859375 -0.5     ]\n",
      "on_boundary True\n",
      "X [0.859375 0.5     ]\n",
      "on_boundary True\n",
      "X [ 0.640625 -0.5     ]\n",
      "on_boundary True\n",
      "X [-0.640625  0.5     ]\n",
      "on_boundary True\n",
      "X [-0.109375 -0.5     ]\n",
      "on_boundary True\n",
      "X [0.109375 0.5     ]\n",
      "on_boundary True\n",
      "X [ 1.       -0.109375]\n",
      "on_boundary True\n",
      "X [-1.        0.109375]\n",
      "on_boundary True\n",
      "X [-0.484375 -0.5     ]\n",
      "on_boundary True\n",
      "X [0.484375 0.5     ]\n",
      "on_boundary True\n",
      "X [ 1.       -0.484375]\n",
      "on_boundary True\n",
      "X [-1.        0.484375]\n",
      "on_boundary True\n",
      "X [ 0.265625 -0.5     ]\n",
      "on_boundary True\n",
      "X [-0.265625  0.5     ]\n",
      "on_boundary True\n",
      "X [1.       0.265625]\n",
      "on_boundary True\n",
      "X [-1.       -0.265625]\n",
      "on_boundary True\n",
      "X [-0.671875 -0.5     ]\n",
      "on_boundary True\n",
      "X [0.671875 0.5     ]\n",
      "on_boundary True\n",
      "X [ 0.828125 -0.5     ]\n",
      "on_boundary True\n",
      "X [-0.828125  0.5     ]\n",
      "on_boundary True\n",
      "X [ 0.078125 -0.5     ]\n",
      "on_boundary True\n",
      "X [-0.078125  0.5     ]\n",
      "on_boundary True\n",
      "X [1.       0.078125]\n",
      "on_boundary True\n",
      "X [-1.       -0.078125]\n",
      "on_boundary True\n",
      "X [-0.296875 -0.5     ]\n",
      "on_boundary True\n",
      "X [0.296875 0.5     ]\n",
      "on_boundary True\n",
      "X [ 1.       -0.296875]\n",
      "on_boundary True\n",
      "X [-1.        0.296875]\n",
      "on_boundary True\n",
      "X [ 0.453125 -0.5     ]\n",
      "on_boundary True\n",
      "X [-0.453125  0.5     ]\n",
      "on_boundary True\n",
      "X [1.       0.453125]\n",
      "on_boundary True\n",
      "X [-1.       -0.453125]\n",
      "on_boundary True\n",
      "X [-0.9765625 -0.5      ]\n",
      "on_boundary True\n",
      "X [0.9765625 0.5      ]\n",
      "on_boundary True\n",
      "X [ 0.5234375 -0.5      ]\n",
      "on_boundary True\n",
      "X [-0.5234375  0.5      ]\n",
      "on_boundary True\n",
      "X [-0.2265625 -0.5      ]\n",
      "on_boundary True\n",
      "X [0.2265625 0.5      ]\n",
      "on_boundary True\n",
      "X [ 1.        -0.2265625]\n",
      "on_boundary True\n",
      "X [-1.         0.2265625]\n",
      "on_boundary True\n",
      "X [-0.6015625 -0.5      ]\n",
      "on_boundary True\n",
      "X [0.6015625 0.5      ]\n",
      "on_boundary True\n",
      "X [ 0.8984375 -0.5      ]\n",
      "on_boundary True\n",
      "X [-0.8984375  0.5      ]\n",
      "on_boundary True\n",
      "X [ 0.1484375 -0.5      ]\n",
      "on_boundary True\n",
      "X [-0.1484375  0.5      ]\n",
      "on_boundary True\n",
      "X [1.        0.1484375]\n",
      "on_boundary True\n",
      "X [-1.        -0.1484375]\n",
      "on_boundary True\n",
      "X [-0.7890625 -0.5      ]\n",
      "on_boundary True\n",
      "X [0.7890625 0.5      ]\n",
      "on_boundary True\n",
      "X [ 0.7109375 -0.5      ]\n",
      "on_boundary True\n",
      "X [-0.7109375  0.5      ]\n",
      "on_boundary True\n",
      "X [-0.0390625 -0.5      ]\n",
      "on_boundary True\n",
      "X [0.0390625 0.5      ]\n",
      "on_boundary True\n",
      "X [ 1.        -0.0390625]\n",
      "on_boundary True\n",
      "X [-1.         0.0390625]\n",
      "on_boundary True\n",
      "X [-0.4140625 -0.5      ]\n",
      "on_boundary True\n",
      "X [0.4140625 0.5      ]\n",
      "on_boundary True\n",
      "X [ 1.        -0.4140625]\n",
      "on_boundary True\n",
      "X [-1.         0.4140625]\n",
      "on_boundary True\n",
      "X [ 0.3359375 -0.5      ]\n",
      "on_boundary True\n",
      "X [-0.3359375  0.5      ]\n",
      "on_boundary True\n",
      "X [1.        0.3359375]\n",
      "on_boundary True\n",
      "X [-1.        -0.3359375]\n",
      "on_boundary True\n",
      "X [-0.8828125 -0.5      ]\n",
      "on_boundary True\n",
      "X [0.8828125 0.5      ]\n",
      "on_boundary True\n",
      "X [ 0.6171875 -0.5      ]\n",
      "on_boundary True\n",
      "X [-0.6171875  0.5      ]\n",
      "on_boundary True\n",
      "X [-0.1328125 -0.5      ]\n",
      "on_boundary True\n",
      "X [0.1328125 0.5      ]\n",
      "on_boundary True\n",
      "X [ 1.        -0.1328125]\n",
      "on_boundary True\n",
      "X [-1.         0.1328125]\n",
      "on_boundary True\n",
      "X [-0.5078125 -0.5      ]\n",
      "on_boundary True\n",
      "X [0.5078125 0.5      ]\n",
      "on_boundary True\n",
      "X [ 0.9921875 -0.5      ]\n",
      "on_boundary True\n",
      "X [-0.9921875  0.5      ]\n",
      "on_boundary True\n",
      "X [ 0.2421875 -0.5      ]\n",
      "on_boundary True\n",
      "X [-0.2421875  0.5      ]\n",
      "on_boundary True\n",
      "X [1.        0.2421875]\n",
      "on_boundary True\n",
      "X [-1.        -0.2421875]\n",
      "on_boundary True\n",
      "X [-0.6953125 -0.5      ]\n",
      "on_boundary True\n",
      "X [0.6953125 0.5      ]\n",
      "on_boundary True\n",
      "X [ 0.8046875 -0.5      ]\n",
      "on_boundary True\n",
      "X [-0.8046875  0.5      ]\n",
      "on_boundary True\n",
      "X [ 0.0546875 -0.5      ]\n",
      "on_boundary True\n",
      "X [-0.0546875  0.5      ]\n",
      "on_boundary True\n",
      "X [1.        0.0546875]\n",
      "on_boundary True\n",
      "X [-1.        -0.0546875]\n",
      "on_boundary True\n",
      "X [-0.3203125 -0.5      ]\n",
      "on_boundary True\n",
      "X [0.3203125 0.5      ]\n",
      "on_boundary True\n",
      "X [ 1.        -0.3203125]\n",
      "on_boundary True\n",
      "X [-1.         0.3203125]\n",
      "on_boundary True\n",
      "X [ 0.4296875 -0.5      ]\n",
      "on_boundary True\n",
      "X [-0.4296875  0.5      ]\n",
      "on_boundary True\n",
      "X [1.        0.4296875]\n",
      "on_boundary True\n",
      "X [-1.        -0.4296875]\n",
      "on_boundary True\n",
      "X [-0.9296875 -0.5      ]\n",
      "on_boundary True\n",
      "X [0.9296875 0.5      ]\n",
      "on_boundary True\n",
      "X [ 0.5703125 -0.5      ]\n",
      "on_boundary True\n",
      "X [-0.5703125  0.5      ]\n",
      "on_boundary True\n",
      "X [-0.1796875 -0.5      ]\n",
      "on_boundary True\n",
      "X [0.1796875 0.5      ]\n",
      "on_boundary True\n",
      "X [ 1.        -0.1796875]\n",
      "on_boundary True\n",
      "X [-1.         0.1796875]\n",
      "on_boundary True\n",
      "X [-0.5546875 -0.5      ]\n",
      "on_boundary True\n",
      "X [ 0.         -0.49950024]\n",
      "on_boundary False\n",
      "X [-0.5       -0.4990005]\n",
      "on_boundary False\n",
      "X [ 0.5        -0.49850076]\n",
      "on_boundary False\n",
      "X [-0.75     -0.498001]\n",
      "on_boundary False\n",
      "X [ 0.25       -0.49750125]\n",
      "on_boundary False\n",
      "X [-0.25      -0.4970015]\n",
      "on_boundary False\n",
      "X [ 0.75       -0.49650174]\n",
      "on_boundary False\n",
      "X [-0.875    -0.496002]\n",
      "on_boundary False\n",
      "X [ 0.125      -0.49550226]\n",
      "on_boundary False\n",
      "X [-0.375     -0.4950025]\n",
      "on_boundary False\n",
      "X [ 0.625      -0.49450275]\n",
      "on_boundary False\n",
      "X [-0.625    -0.494003]\n",
      "on_boundary False\n",
      "X [ 0.375      -0.49350324]\n",
      "on_boundary False\n",
      "X [-0.125     -0.4930035]\n",
      "on_boundary False\n",
      "X [ 0.875      -0.49250376]\n",
      "on_boundary False\n",
      "X [-0.9375   -0.492004]\n",
      "on_boundary False\n",
      "X [ 0.0625     -0.49150425]\n",
      "on_boundary False\n",
      "X [-0.4375    -0.4910045]\n",
      "on_boundary False\n",
      "X [ 0.5625     -0.49050474]\n",
      "on_boundary False\n",
      "X [-0.6875   -0.490005]\n",
      "on_boundary False\n",
      "X [ 0.3125     -0.48950526]\n",
      "on_boundary False\n",
      "X [-0.1875    -0.4890055]\n",
      "on_boundary False\n",
      "X [ 0.8125     -0.48850575]\n",
      "on_boundary False\n",
      "X [-0.8125   -0.488006]\n",
      "on_boundary False\n",
      "X [ 0.1875     -0.48750624]\n",
      "on_boundary False\n",
      "X [-0.3125    -0.4870065]\n",
      "on_boundary False\n",
      "X [ 0.6875     -0.48650676]\n",
      "on_boundary False\n",
      "X [-0.5625   -0.486007]\n",
      "on_boundary False\n",
      "X [ 0.4375     -0.48550725]\n",
      "on_boundary False\n",
      "X [-0.0625    -0.4850075]\n",
      "on_boundary False\n",
      "X [ 0.9375     -0.48450774]\n",
      "on_boundary False\n",
      "X [-0.96875    -0.48400798]\n",
      "on_boundary False\n",
      "X [ 0.03125    -0.48350826]\n",
      "on_boundary False\n",
      "X [-0.46875   -0.4830085]\n",
      "on_boundary False\n",
      "X [ 0.53125    -0.48250875]\n",
      "on_boundary False\n",
      "X [-0.71875  -0.482009]\n",
      "on_boundary False\n",
      "X [ 0.28125    -0.48150924]\n",
      "on_boundary False\n",
      "X [-0.21875    -0.48100948]\n",
      "on_boundary False\n",
      "X [ 0.78125    -0.48050976]\n",
      "on_boundary False\n",
      "X [-0.84375 -0.48001]\n",
      "on_boundary False\n",
      "X [ 0.15625    -0.47951025]\n",
      "on_boundary False\n",
      "X [-0.34375   -0.4790105]\n",
      "on_boundary False\n",
      "X [ 0.65625    -0.47851074]\n",
      "on_boundary False\n",
      "X [-0.59375    -0.47801098]\n",
      "on_boundary False\n",
      "X [ 0.40625    -0.47751126]\n",
      "on_boundary False\n",
      "X [-0.09375   -0.4770115]\n",
      "on_boundary False\n",
      "X [ 0.90625    -0.47651175]\n",
      "on_boundary False\n",
      "X [-0.90625  -0.476012]\n",
      "on_boundary False\n",
      "X [ 0.09375    -0.47551224]\n",
      "on_boundary False\n",
      "X [-0.40625    -0.47501248]\n",
      "on_boundary False\n",
      "X [ 0.59375    -0.47451276]\n",
      "on_boundary False\n",
      "X [-0.65625  -0.474013]\n",
      "on_boundary False\n",
      "X [ 0.34375    -0.47351325]\n",
      "on_boundary False\n",
      "X [-0.15625   -0.4730135]\n",
      "on_boundary False\n",
      "X [ 0.84375    -0.47251374]\n",
      "on_boundary False\n",
      "X [-0.78125    -0.47201398]\n",
      "on_boundary False\n",
      "X [ 0.21875    -0.47151425]\n",
      "on_boundary False\n",
      "X [-0.28125   -0.4710145]\n",
      "on_boundary False\n",
      "X [ 0.71875    -0.47051474]\n",
      "on_boundary False\n",
      "X [-0.53125  -0.470015]\n",
      "on_boundary False\n",
      "X [ 0.46875    -0.46951523]\n",
      "on_boundary False\n",
      "X [-0.03125    -0.46901548]\n",
      "on_boundary False\n",
      "X [ 0.96875    -0.46851575]\n",
      "on_boundary False\n",
      "X [-0.984375 -0.468016]\n",
      "on_boundary False\n",
      "X [ 0.015625   -0.46751624]\n",
      "on_boundary False\n",
      "X [-0.484375  -0.4670165]\n",
      "on_boundary False\n",
      "X [ 0.515625   -0.46651673]\n",
      "on_boundary False\n",
      "X [-0.734375 -0.466017]\n",
      "on_boundary False\n",
      "X [ 0.265625   -0.46551725]\n",
      "on_boundary False\n",
      "X [-0.234375  -0.4650175]\n",
      "on_boundary False\n",
      "X [ 0.765625   -0.46451774]\n",
      "on_boundary False\n",
      "X [-0.859375 -0.464018]\n",
      "on_boundary False\n",
      "X [ 0.140625   -0.46351823]\n",
      "on_boundary False\n",
      "X [-0.359375   -0.46301848]\n",
      "on_boundary False\n",
      "X [ 0.640625   -0.46251875]\n",
      "on_boundary False\n",
      "X [-0.609375 -0.462019]\n",
      "on_boundary False\n",
      "X [ 0.390625   -0.46151924]\n",
      "on_boundary False\n",
      "X [-0.109375  -0.4610195]\n",
      "on_boundary False\n",
      "X [ 0.890625   -0.46051973]\n",
      "on_boundary False\n",
      "X [-0.921875 -0.46002 ]\n",
      "on_boundary False\n",
      "X [ 0.078125   -0.45952025]\n",
      "on_boundary False\n",
      "X [-0.421875  -0.4590205]\n",
      "on_boundary False\n",
      "X [ 0.578125   -0.45852074]\n",
      "on_boundary False\n",
      "X [-0.671875 -0.458021]\n",
      "on_boundary False\n",
      "X [ 0.328125   -0.45752123]\n",
      "on_boundary False\n",
      "X [-0.171875   -0.45702147]\n",
      "on_boundary False\n",
      "X [ 0.828125   -0.45652175]\n",
      "on_boundary False\n",
      "X [-0.796875 -0.456022]\n",
      "on_boundary False\n",
      "X [ 0.203125   -0.45552224]\n",
      "on_boundary False\n",
      "X [-0.296875   -0.45502248]\n",
      "on_boundary False\n",
      "X [ 0.703125   -0.45452273]\n",
      "on_boundary False\n",
      "X [-0.546875 -0.454023]\n",
      "on_boundary False\n",
      "X [ 0.453125   -0.45352325]\n",
      "on_boundary False\n",
      "X [-0.046875  -0.4530235]\n",
      "on_boundary False\n",
      "X [ 0.953125   -0.45252374]\n",
      "on_boundary False\n",
      "X [-0.953125   -0.45202398]\n",
      "on_boundary False\n",
      "X [ 0.046875   -0.45152423]\n",
      "on_boundary False\n",
      "X [-0.453125   -0.45102447]\n",
      "on_boundary False\n",
      "X [ 0.546875   -0.45052475]\n",
      "on_boundary False\n",
      "X [-0.703125 -0.450025]\n",
      "on_boundary False\n",
      "X [ 0.296875   -0.44952524]\n",
      "on_boundary False\n",
      "X [-0.203125   -0.44902548]\n",
      "on_boundary False\n",
      "X [ 0.796875   -0.44852573]\n",
      "on_boundary False\n",
      "X [-0.828125 -0.448026]\n",
      "on_boundary False\n",
      "X [ 0.171875   -0.44752625]\n",
      "on_boundary False\n",
      "X [-0.328125  -0.4470265]\n",
      "on_boundary False\n",
      "X [ 0.671875   -0.44652674]\n",
      "on_boundary False\n",
      "X [-0.578125   -0.44602698]\n",
      "on_boundary False\n",
      "X [ 0.421875   -0.44552723]\n",
      "on_boundary False\n",
      "X [-0.078125   -0.44502747]\n",
      "on_boundary False\n",
      "X [ 0.921875   -0.44452775]\n",
      "on_boundary False\n",
      "X [-0.890625 -0.444028]\n",
      "on_boundary False\n",
      "X [ 0.109375   -0.44352823]\n",
      "on_boundary False\n",
      "X [-0.390625   -0.44302848]\n",
      "on_boundary False\n",
      "X [ 0.609375   -0.44252872]\n",
      "on_boundary False\n",
      "X [-0.640625 -0.442029]\n",
      "on_boundary False\n",
      "X [ 0.359375   -0.44152924]\n",
      "on_boundary False\n",
      "X [-0.140625  -0.4410295]\n",
      "on_boundary False\n",
      "X [ 0.859375   -0.44052973]\n",
      "on_boundary False\n",
      "X [-0.765625   -0.44002998]\n",
      "on_boundary False\n",
      "X [ 0.234375   -0.43953022]\n",
      "on_boundary False\n",
      "X [-0.265625   -0.43903047]\n",
      "on_boundary False\n",
      "X [ 0.734375   -0.43853074]\n",
      "on_boundary False\n",
      "X [-0.515625 -0.438031]\n",
      "on_boundary False\n",
      "X [ 0.484375   -0.43753123]\n",
      "on_boundary False\n",
      "X [-0.015625   -0.43703148]\n",
      "on_boundary False\n",
      "X [ 0.984375   -0.43653172]\n",
      "on_boundary False\n",
      "X [-0.9921875 -0.436032 ]\n",
      "on_boundary False\n",
      "X [ 0.0078125  -0.43553224]\n",
      "on_boundary False\n",
      "X [-0.4921875 -0.4350325]\n",
      "on_boundary False\n",
      "X [ 0.5078125  -0.43453273]\n",
      "on_boundary False\n",
      "X [-0.7421875  -0.43403298]\n",
      "on_boundary False\n",
      "X [ 0.2578125  -0.43353325]\n",
      "on_boundary False\n",
      "X [-0.2421875  -0.43303347]\n",
      "on_boundary False\n",
      "X [ 0.7578125  -0.43253374]\n",
      "on_boundary False\n",
      "X [-0.8671875 -0.432034 ]\n",
      "on_boundary False\n",
      "X [ 0.1328125  -0.43153423]\n",
      "on_boundary False\n",
      "X [-0.3671875  -0.43103448]\n",
      "on_boundary False\n",
      "X [ 0.6328125  -0.43053472]\n",
      "on_boundary False\n",
      "X [-0.6171875 -0.430035 ]\n",
      "on_boundary False\n",
      "X [ 0.3828125  -0.42953524]\n",
      "on_boundary False\n",
      "X [-0.1171875  -0.42903548]\n",
      "on_boundary False\n",
      "X [ 0.8828125  -0.42853573]\n",
      "on_boundary False\n",
      "X [-0.9296875  -0.42803597]\n",
      "on_boundary False\n",
      "X [ 0.0703125  -0.42753625]\n",
      "on_boundary False\n",
      "X [-0.4296875  -0.42703646]\n",
      "on_boundary False\n",
      "X [ 0.5703125  -0.42653674]\n",
      "on_boundary False\n",
      "X [-0.6796875  -0.42603698]\n",
      "on_boundary False\n",
      "X [ 0.3203125  -0.42553723]\n",
      "on_boundary False\n",
      "X [-0.1796875  -0.42503747]\n",
      "on_boundary False\n",
      "X [ 0.8203125  -0.42453772]\n",
      "on_boundary False\n",
      "X [-0.8046875 -0.424038 ]\n",
      "on_boundary False\n",
      "X [ 0.1953125  -0.42353824]\n",
      "on_boundary False\n",
      "X [-0.3046875  -0.42303848]\n",
      "on_boundary False\n",
      "X [ 0.6953125  -0.42253873]\n",
      "on_boundary False\n",
      "X [-0.5546875  -0.42203897]\n",
      "on_boundary False\n",
      "X [ 0.4453125  -0.42153925]\n",
      "on_boundary False\n",
      "X [-0.0546875  -0.42103946]\n",
      "on_boundary False\n",
      "X [ 0.9453125  -0.42053974]\n",
      "on_boundary False\n",
      "X [-0.9609375  -0.42003998]\n",
      "on_boundary False\n",
      "X [ 0.0390625  -0.41954023]\n",
      "on_boundary False\n",
      "X [-0.4609375  -0.41904047]\n",
      "on_boundary False\n",
      "X [ 0.5390625  -0.41854072]\n",
      "on_boundary False\n",
      "X [-0.7109375 -0.418041 ]\n",
      "on_boundary False\n",
      "X [ 0.2890625  -0.41754124]\n",
      "on_boundary False\n",
      "X [-0.2109375  -0.41704148]\n",
      "on_boundary False\n",
      "X [ 0.7890625  -0.41654173]\n",
      "on_boundary False\n",
      "X [-0.8359375  -0.41604197]\n",
      "on_boundary False\n",
      "X [ 0.1640625  -0.41554224]\n",
      "on_boundary False\n",
      "X [-0.3359375 -0.4150425]\n",
      "on_boundary False\n",
      "X [ 0.6640625  -0.41454273]\n",
      "on_boundary False\n",
      "X [-0.5859375  -0.41404298]\n",
      "on_boundary False\n",
      "X [ 0.4140625  -0.41354322]\n",
      "on_boundary False\n",
      "X [-0.0859375  -0.41304347]\n",
      "on_boundary False\n",
      "X [ 0.9140625 -0.4125437]\n",
      "on_boundary False\n",
      "X [-0.8984375 -0.412044 ]\n",
      "on_boundary False\n",
      "X [ 0.1015625  -0.41154423]\n",
      "on_boundary False\n",
      "X [-0.3984375  -0.41104448]\n",
      "on_boundary False\n",
      "X [ 0.6015625  -0.41054472]\n",
      "on_boundary False\n",
      "X [-0.6484375  -0.41004497]\n",
      "on_boundary False\n",
      "X [ 0.3515625  -0.40954524]\n",
      "on_boundary False\n",
      "X [-0.1484375 -0.4090455]\n",
      "on_boundary False\n",
      "X [ 0.8515625  -0.40854573]\n",
      "on_boundary False\n",
      "X [-0.7734375  -0.40804598]\n",
      "on_boundary False\n",
      "X [ 0.2265625  -0.40754622]\n",
      "on_boundary False\n",
      "X [-0.2734375  -0.40704647]\n",
      "on_boundary False\n",
      "X [ 0.7265625 -0.4065467]\n",
      "on_boundary False\n",
      "X [-0.5234375 -0.406047 ]\n",
      "on_boundary False\n",
      "X [ 0.4765625  -0.40554723]\n",
      "on_boundary False\n",
      "X [-0.0234375  -0.40504748]\n",
      "on_boundary False\n",
      "X [ 0.9765625  -0.40454772]\n",
      "on_boundary False\n",
      "X [-0.9765625  -0.40404797]\n",
      "on_boundary False\n",
      "X [ 0.0234375  -0.40354824]\n",
      "on_boundary False\n",
      "X [-0.4765625 -0.4030485]\n",
      "on_boundary False\n",
      "X [ 0.5234375  -0.40254873]\n",
      "on_boundary False\n",
      "X [-0.7265625  -0.40204898]\n",
      "on_boundary False\n",
      "X [ 0.2734375  -0.40154922]\n",
      "on_boundary False\n",
      "X [-0.2265625  -0.40104946]\n",
      "on_boundary False\n",
      "X [ 0.7734375 -0.4005497]\n",
      "on_boundary False\n",
      "X [-0.8515625  -0.40004998]\n",
      "on_boundary False\n",
      "X [ 0.1484375  -0.39955023]\n",
      "on_boundary False\n",
      "X [-0.3515625  -0.39905047]\n",
      "on_boundary False\n",
      "X [ 0.6484375  -0.39855072]\n",
      "on_boundary False\n",
      "X [-0.6015625  -0.39805096]\n",
      "on_boundary False\n",
      "X [ 0.3984375  -0.39755124]\n",
      "on_boundary False\n",
      "X [-0.1015625  -0.39705148]\n",
      "on_boundary False\n",
      "X [ 0.8984375  -0.39655173]\n",
      "on_boundary False\n",
      "X [-0.9140625  -0.39605197]\n",
      "on_boundary False\n",
      "X [ 0.0859375  -0.39555222]\n",
      "on_boundary False\n",
      "X [-0.4140625  -0.39505246]\n",
      "on_boundary False\n",
      "X [ 0.5859375 -0.3945527]\n",
      "on_boundary False\n",
      "X [-0.6640625  -0.39405298]\n",
      "on_boundary False\n",
      "X [ 0.3359375  -0.39355323]\n",
      "on_boundary False\n",
      "X [-0.1640625  -0.39305347]\n",
      "on_boundary False\n",
      "X [ 0.8359375  -0.39255372]\n",
      "on_boundary False\n",
      "X [-0.7890625  -0.39205396]\n",
      "on_boundary False\n",
      "X [ 0.2109375  -0.39155424]\n",
      "on_boundary False\n",
      "X [-0.2890625  -0.39105448]\n",
      "on_boundary False\n",
      "X [ 0.7109375  -0.39055473]\n",
      "on_boundary False\n",
      "X [-0.5390625  -0.39005497]\n",
      "on_boundary False\n",
      "X [ 0.4609375  -0.38955522]\n",
      "on_boundary False\n",
      "X [-0.0390625 -0.3890555]\n",
      "on_boundary False\n",
      "X [ 0.9609375 -0.3885557]\n",
      "on_boundary False\n",
      "X [-0.9453125  -0.38805598]\n",
      "on_boundary False\n",
      "X [ 0.0546875  -0.38755623]\n",
      "on_boundary False\n",
      "X [-0.4453125  -0.38705647]\n",
      "on_boundary False\n",
      "X [ 0.5546875 -0.3865567]\n",
      "on_boundary False\n",
      "X [-0.6953125  -0.38605696]\n",
      "on_boundary False\n",
      "X [ 0.3046875  -0.38555723]\n",
      "on_boundary False\n",
      "X [-0.1953125  -0.38505748]\n",
      "on_boundary False\n",
      "X [ 0.8046875  -0.38455772]\n",
      "on_boundary False\n",
      "X [-0.8203125  -0.38405797]\n",
      "on_boundary False\n",
      "X [ 0.1796875 -0.3835582]\n",
      "on_boundary False\n",
      "X [-0.3203125 -0.3830585]\n",
      "on_boundary False\n",
      "X [ 0.6796875 -0.3825587]\n",
      "on_boundary False\n",
      "X [-0.5703125  -0.38205898]\n",
      "on_boundary False\n",
      "X [ 0.4296875  -0.38155922]\n",
      "on_boundary False\n",
      "X [-0.0703125  -0.38105947]\n",
      "on_boundary False\n",
      "X [ 0.9296875 -0.3805597]\n",
      "on_boundary False\n",
      "X [-0.8828125  -0.38005996]\n",
      "on_boundary False\n",
      "X [ 0.1171875  -0.37956023]\n",
      "on_boundary False\n",
      "X [-0.3828125  -0.37906048]\n",
      "on_boundary False\n",
      "X [ 0.6171875  -0.37856072]\n",
      "on_boundary False\n",
      "X [-0.6328125  -0.37806097]\n",
      "on_boundary False\n",
      "X [ 0.3671875 -0.3775612]\n",
      "on_boundary False\n",
      "X [-0.1328125 -0.3770615]\n",
      "on_boundary False\n",
      "X [ 0.8671875 -0.3765617]\n",
      "on_boundary False\n",
      "X [-0.7578125  -0.37606198]\n",
      "on_boundary False\n",
      "X [ 0.2421875  -0.37556222]\n",
      "on_boundary False\n",
      "X [-0.2578125  -0.37506247]\n",
      "on_boundary False\n",
      "X [ 0.7421875  -0.37456274]\n",
      "on_boundary False\n",
      "X [-0.5078125  -0.37406296]\n",
      "on_boundary False\n",
      "X [ 0.4921875  -0.37356323]\n",
      "on_boundary False\n",
      "X [-0.0078125  -0.37306347]\n",
      "on_boundary False\n",
      "X [ 0.9921875  -0.37256372]\n",
      "on_boundary False\n",
      "X [-0.99609375 -0.37206396]\n",
      "on_boundary False\n",
      "X [ 0.00390625 -0.3715642 ]\n",
      "on_boundary False\n",
      "X [-0.49609375 -0.37106448]\n",
      "on_boundary False\n",
      "X [ 0.50390625 -0.3705647 ]\n",
      "on_boundary False\n",
      "X [-0.74609375 -0.37006497]\n",
      "on_boundary False\n",
      "X [ 0.25390625 -0.36956522]\n",
      "on_boundary False\n",
      "X [-0.24609375 -0.36906546]\n",
      "on_boundary False\n",
      "X [ 0.75390625 -0.36856574]\n",
      "on_boundary False\n",
      "X [-0.87109375 -0.36806595]\n",
      "on_boundary False\n",
      "X [ 0.12890625 -0.36756623]\n",
      "on_boundary False\n",
      "X [-0.37109375 -0.36706647]\n",
      "on_boundary False\n",
      "X [ 0.62890625 -0.36656672]\n",
      "on_boundary False\n",
      "X [-0.62109375 -0.36606696]\n",
      "on_boundary False\n",
      "X [ 0.37890625 -0.3655672 ]\n",
      "on_boundary False\n",
      "X [-0.12109375 -0.36506748]\n",
      "on_boundary False\n",
      "X [ 0.87890625 -0.3645677 ]\n",
      "on_boundary False\n",
      "X [-0.93359375 -0.36406797]\n",
      "on_boundary False\n",
      "X [ 0.06640625 -0.36356822]\n",
      "on_boundary False\n",
      "X [-0.43359375 -0.36306846]\n",
      "on_boundary False\n",
      "X [ 0.56640625 -0.36256874]\n",
      "on_boundary False\n",
      "X [-0.68359375 -0.36206895]\n",
      "on_boundary False\n",
      "X [ 0.31640625 -0.36156923]\n",
      "on_boundary False\n",
      "X [-0.18359375 -0.36106947]\n",
      "on_boundary False\n",
      "X [ 0.81640625 -0.36056972]\n",
      "on_boundary False\n",
      "X [-0.80859375 -0.36006996]\n",
      "on_boundary False\n",
      "X [ 0.19140625 -0.3595702 ]\n",
      "on_boundary False\n",
      "X [-0.30859375 -0.35907048]\n",
      "on_boundary False\n",
      "X [ 0.69140625 -0.3585707 ]\n",
      "on_boundary False\n",
      "X [-0.55859375 -0.35807097]\n",
      "on_boundary False\n",
      "X [ 0.44140625 -0.3575712 ]\n",
      "on_boundary False\n",
      "X [-0.05859375 -0.35707146]\n",
      "on_boundary False\n",
      "X [ 0.94140625 -0.35657173]\n",
      "on_boundary False\n",
      "X [-0.96484375 -0.35607195]\n",
      "on_boundary False\n",
      "X [ 0.03515625 -0.35557222]\n",
      "on_boundary False\n",
      "X [-0.46484375 -0.35507247]\n",
      "on_boundary False\n",
      "X [ 0.53515625 -0.3545727 ]\n",
      "on_boundary False\n",
      "X [-0.71484375 -0.35407296]\n",
      "on_boundary False\n",
      "X [ 0.28515625 -0.3535732 ]\n",
      "on_boundary False\n",
      "X [-0.21484375 -0.35307348]\n",
      "on_boundary False\n",
      "X [ 0.78515625 -0.3525737 ]\n",
      "on_boundary False\n",
      "X [-0.83984375 -0.35207397]\n",
      "on_boundary False\n",
      "X [ 0.16015625 -0.3515742 ]\n",
      "on_boundary False\n",
      "X [-0.33984375 -0.35107446]\n",
      "on_boundary False\n",
      "X [ 0.66015625 -0.35057473]\n",
      "on_boundary False\n",
      "X [-0.58984375 -0.35007495]\n",
      "on_boundary False\n",
      "X [ 0.41015625 -0.34957522]\n",
      "on_boundary False\n",
      "X [-0.08984375 -0.34907547]\n",
      "on_boundary False\n",
      "X [ 0.91015625 -0.3485757 ]\n",
      "on_boundary False\n",
      "X [-0.90234375 -0.34807596]\n",
      "on_boundary False\n",
      "X [ 0.09765625 -0.3475762 ]\n",
      "on_boundary False\n",
      "X [-0.40234375 -0.34707648]\n",
      "on_boundary False\n",
      "X [ 0.59765625 -0.3465767 ]\n",
      "on_boundary False\n",
      "X [-0.65234375 -0.34607697]\n",
      "on_boundary False\n",
      "X [ 0.34765625 -0.3455772 ]\n",
      "on_boundary False\n",
      "X [-0.15234375 -0.34507746]\n",
      "on_boundary False\n",
      "X [ 0.84765625 -0.34457773]\n",
      "on_boundary False\n",
      "X [-0.77734375 -0.34407794]\n",
      "on_boundary False\n",
      "X [ 0.22265625 -0.34357822]\n",
      "on_boundary False\n",
      "X [-0.27734375 -0.34307846]\n",
      "on_boundary False\n",
      "X [ 0.72265625 -0.3425787 ]\n",
      "on_boundary False\n",
      "X [-0.52734375 -0.34207895]\n",
      "on_boundary False\n",
      "X [ 0.47265625 -0.3415792 ]\n",
      "on_boundary False\n",
      "X [-0.02734375 -0.34107947]\n",
      "on_boundary False\n",
      "X [ 0.97265625 -0.3405797 ]\n",
      "on_boundary False\n",
      "X [-0.98046875 -0.34007996]\n",
      "on_boundary False\n",
      "X [ 0.01953125 -0.3395802 ]\n",
      "on_boundary False\n",
      "X [-0.48046875 -0.33908045]\n",
      "on_boundary False\n",
      "X [ 0.51953125 -0.33858073]\n",
      "on_boundary False\n",
      "X [-0.73046875 -0.33808094]\n",
      "on_boundary False\n",
      "X [ 0.26953125 -0.33758122]\n",
      "on_boundary False\n",
      "X [-0.23046875 -0.33708146]\n",
      "on_boundary False\n",
      "X [ 0.76953125 -0.3365817 ]\n",
      "on_boundary False\n",
      "X [-0.85546875 -0.33608195]\n",
      "on_boundary False\n",
      "X [ 0.14453125 -0.3355822 ]\n",
      "on_boundary False\n",
      "X [-0.35546875 -0.33508247]\n",
      "on_boundary False\n",
      "X [ 0.64453125 -0.3345827 ]\n",
      "on_boundary False\n",
      "X [-0.60546875 -0.33408296]\n",
      "on_boundary False\n",
      "X [ 0.39453125 -0.3335832 ]\n",
      "on_boundary False\n",
      "X [-0.10546875 -0.33308345]\n",
      "on_boundary False\n",
      "X [ 0.89453125 -0.33258373]\n",
      "on_boundary False\n",
      "X [-0.91796875 -0.33208394]\n",
      "on_boundary False\n",
      "X [ 0.08203125 -0.33158422]\n",
      "on_boundary False\n",
      "X [-0.41796875 -0.33108446]\n",
      "on_boundary False\n",
      "X [ 0.58203125 -0.3305847 ]\n",
      "on_boundary False\n",
      "X [-0.66796875 -0.33008498]\n",
      "on_boundary False\n",
      "X [ 0.33203125 -0.3295852 ]\n",
      "on_boundary False\n",
      "X [-0.16796875 -0.32908547]\n",
      "on_boundary False\n",
      "X [ 0.83203125 -0.3285857 ]\n",
      "on_boundary False\n",
      "X [-0.79296875 -0.32808596]\n",
      "on_boundary False\n",
      "X [ 0.20703125 -0.3275862 ]\n",
      "on_boundary False\n",
      "X [-0.29296875 -0.32708645]\n",
      "on_boundary False\n",
      "X [ 0.70703125 -0.32658672]\n",
      "on_boundary False\n",
      "X [-0.54296875 -0.32608694]\n",
      "on_boundary False\n",
      "X [ 0.45703125 -0.3255872 ]\n",
      "on_boundary False\n",
      "X [-0.04296875 -0.32508746]\n",
      "on_boundary False\n",
      "X [ 0.95703125 -0.3245877 ]\n",
      "on_boundary False\n",
      "X [-0.94921875 -0.32408798]\n",
      "on_boundary False\n",
      "X [ 0.05078125 -0.3235882 ]\n",
      "on_boundary False\n",
      "X [-0.44921875 -0.32308847]\n",
      "on_boundary False\n",
      "X [ 0.55078125 -0.3225887 ]\n",
      "on_boundary False\n",
      "X [-0.69921875 -0.32208896]\n",
      "on_boundary False\n",
      "X [ 0.30078125 -0.3215892 ]\n",
      "on_boundary False\n",
      "X [-0.19921875 -0.32108945]\n",
      "on_boundary False\n",
      "X [ 0.80078125 -0.32058972]\n",
      "on_boundary False\n",
      "X [-0.82421875 -0.32008994]\n",
      "on_boundary False\n",
      "X [ 0.17578125 -0.3195902 ]\n",
      "on_boundary False\n",
      "X [-0.32421875 -0.31909046]\n",
      "on_boundary False\n",
      "X [ 0.67578125 -0.3185907 ]\n",
      "on_boundary False\n",
      "X [-0.57421875 -0.31809098]\n",
      "on_boundary False\n",
      "X [ 0.42578125 -0.3175912 ]\n",
      "on_boundary False\n",
      "X [-0.07421875 -0.31709146]\n",
      "on_boundary False\n",
      "X [ 0.92578125 -0.3165917 ]\n",
      "on_boundary False\n",
      "X [-0.88671875 -0.31609195]\n",
      "on_boundary False\n",
      "X [ 0.11328125 -0.3155922 ]\n",
      "on_boundary False\n",
      "X [-0.38671875 -0.31509244]\n",
      "on_boundary False\n",
      "X [ 0.61328125 -0.31459272]\n",
      "on_boundary False\n",
      "X [-0.63671875 -0.31409293]\n",
      "on_boundary False\n",
      "X [ 0.36328125 -0.3135932 ]\n",
      "on_boundary False\n",
      "X [-0.13671875 -0.31309345]\n",
      "on_boundary False\n",
      "X [ 0.86328125 -0.3125937 ]\n",
      "on_boundary False\n",
      "X [-0.76171875 -0.31209397]\n",
      "on_boundary False\n",
      "X [ 0.23828125 -0.3115942 ]\n",
      "on_boundary False\n",
      "X [-0.26171875 -0.31109446]\n",
      "on_boundary False\n",
      "X [ 0.73828125 -0.3105947 ]\n",
      "on_boundary False\n",
      "X [-0.51171875 -0.31009495]\n",
      "on_boundary False\n",
      "X [ 0.48828125 -0.3095952 ]\n",
      "on_boundary False\n",
      "X [-0.01171875 -0.30909544]\n",
      "on_boundary False\n",
      "X [ 0.98828125 -0.30859572]\n",
      "on_boundary False\n",
      "X [-0.98828125 -0.30809593]\n",
      "on_boundary False\n",
      "X [ 0.01171875 -0.3075962 ]\n",
      "on_boundary False\n",
      "X [-0.48828125 -0.30709645]\n",
      "on_boundary False\n",
      "X [ 0.51171875 -0.3065967 ]\n",
      "on_boundary False\n",
      "X [-0.73828125 -0.30609697]\n",
      "on_boundary False\n",
      "X [ 0.26171875 -0.3055972 ]\n",
      "on_boundary False\n",
      "X [-0.23828125 -0.30509746]\n",
      "on_boundary False\n",
      "X [ 0.76171875 -0.3045977 ]\n",
      "on_boundary False\n",
      "X [-0.86328125 -0.30409795]\n",
      "on_boundary False\n",
      "X [ 0.13671875 -0.3035982 ]\n",
      "on_boundary False\n",
      "X [-0.36328125 -0.30309844]\n",
      "on_boundary False\n",
      "X [ 0.63671875 -0.3025987 ]\n",
      "on_boundary False\n",
      "X [-0.61328125 -0.30209893]\n",
      "on_boundary False\n",
      "X [ 0.38671875 -0.3015992 ]\n",
      "on_boundary False\n",
      "X [-0.11328125 -0.30109945]\n",
      "on_boundary False\n",
      "X [ 0.88671875 -0.3005997 ]\n",
      "on_boundary False\n",
      "X [-0.92578125 -0.30009997]\n",
      "on_boundary False\n",
      "X [ 0.07421875 -0.29960018]\n",
      "on_boundary False\n",
      "X [-0.42578125 -0.29910046]\n",
      "on_boundary False\n",
      "X [ 0.57421875 -0.2986007 ]\n",
      "on_boundary False\n",
      "X [-0.67578125 -0.29810095]\n",
      "on_boundary False\n",
      "X [ 0.32421875 -0.2976012 ]\n",
      "on_boundary False\n",
      "X [-0.17578125 -0.29710144]\n",
      "on_boundary False\n",
      "X [ 0.82421875 -0.2966017 ]\n",
      "on_boundary False\n",
      "X [-0.80078125 -0.29610193]\n",
      "on_boundary False\n",
      "X [ 0.19921875 -0.2956022 ]\n",
      "on_boundary False\n",
      "X [-0.30078125 -0.29510245]\n",
      "on_boundary False\n",
      "X [ 0.69921875 -0.2946027 ]\n",
      "on_boundary False\n",
      "X [-0.55078125 -0.29410297]\n",
      "on_boundary False\n",
      "X [ 0.44921875 -0.29360318]\n",
      "on_boundary False\n",
      "X [-0.05078125 -0.29310346]\n",
      "on_boundary False\n",
      "X [ 0.94921875 -0.2926037 ]\n",
      "on_boundary False\n",
      "X [-0.95703125 -0.29210395]\n",
      "on_boundary False\n",
      "X [ 0.04296875 -0.2916042 ]\n",
      "on_boundary False\n",
      "X [-0.45703125 -0.29110444]\n",
      "on_boundary False\n",
      "X [ 0.54296875 -0.2906047 ]\n",
      "on_boundary False\n",
      "X [-0.70703125 -0.29010493]\n",
      "on_boundary False\n",
      "X [ 0.29296875 -0.2896052 ]\n",
      "on_boundary False\n",
      "X [-0.20703125 -0.28910545]\n",
      "on_boundary False\n",
      "X [ 0.79296875 -0.2886057 ]\n",
      "on_boundary False\n",
      "X [-0.83203125 -0.28810596]\n",
      "on_boundary False\n",
      "X [ 0.16796875 -0.28760618]\n",
      "on_boundary False\n",
      "X [-0.33203125 -0.28710645]\n",
      "on_boundary False\n",
      "X [ 0.66796875 -0.2866067 ]\n",
      "on_boundary False\n",
      "X [-0.58203125 -0.28610694]\n",
      "on_boundary False\n",
      "X [ 0.41796875 -0.28560722]\n",
      "on_boundary False\n",
      "X [-0.08203125 -0.28510743]\n",
      "on_boundary False\n",
      "X [ 0.91796875 -0.2846077 ]\n",
      "on_boundary False\n",
      "X [-0.89453125 -0.28410795]\n",
      "on_boundary False\n",
      "X [ 0.10546875 -0.2836082 ]\n",
      "on_boundary False\n",
      "X [-0.39453125 -0.28310844]\n",
      "on_boundary False\n",
      "X [ 0.60546875 -0.2826087 ]\n",
      "on_boundary False\n",
      "X [-0.64453125 -0.28210896]\n",
      "on_boundary False\n",
      "X [ 0.35546875 -0.28160918]\n",
      "on_boundary False\n",
      "X [-0.14453125 -0.28110945]\n",
      "on_boundary False\n",
      "X [ 0.85546875 -0.2806097 ]\n",
      "on_boundary False\n",
      "X [-0.76953125 -0.28010994]\n",
      "on_boundary False\n",
      "X [ 0.23046875 -0.27961022]\n",
      "on_boundary False\n",
      "X [-0.26953125 -0.27911043]\n",
      "on_boundary False\n",
      "X [ 0.73046875 -0.2786107 ]\n",
      "on_boundary False\n",
      "X [-0.51953125 -0.27811095]\n",
      "on_boundary False\n",
      "X [ 0.48046875 -0.2776112 ]\n",
      "on_boundary False\n",
      "X [-0.01953125 -0.27711144]\n",
      "on_boundary False\n",
      "X [ 0.98046875 -0.2766117 ]\n",
      "on_boundary False\n",
      "X [-0.97265625 -0.27611196]\n",
      "on_boundary False\n",
      "X [ 0.02734375 -0.27561218]\n",
      "on_boundary False\n",
      "X [-0.47265625 -0.27511245]\n",
      "on_boundary False\n",
      "X [ 0.52734375 -0.2746127 ]\n",
      "on_boundary False\n",
      "X [-0.72265625 -0.27411294]\n",
      "on_boundary False\n",
      "X [ 0.27734375 -0.2736132 ]\n",
      "on_boundary False\n",
      "X [-0.22265625 -0.27311343]\n",
      "on_boundary False\n",
      "X [ 0.77734375 -0.2726137 ]\n",
      "on_boundary False\n",
      "X [-0.84765625 -0.27211395]\n",
      "on_boundary False\n",
      "X [ 0.15234375 -0.2716142 ]\n",
      "on_boundary False\n",
      "X [-0.34765625 -0.27111444]\n",
      "on_boundary False\n",
      "X [ 0.65234375 -0.27061468]\n",
      "on_boundary False\n",
      "X [-0.59765625 -0.27011496]\n",
      "on_boundary False\n",
      "X [ 0.40234375 -0.26961517]\n",
      "on_boundary False\n",
      "X [-0.09765625 -0.26911545]\n",
      "on_boundary False\n",
      "X [ 0.90234375 -0.2686157 ]\n",
      "on_boundary False\n",
      "X [-0.91015625 -0.26811594]\n",
      "on_boundary False\n",
      "X [ 0.08984375 -0.2676162 ]\n",
      "on_boundary False\n",
      "X [-0.41015625 -0.26711643]\n",
      "on_boundary False\n",
      "X [ 0.58984375 -0.2666167 ]\n",
      "on_boundary False\n",
      "X [-0.66015625 -0.26611695]\n",
      "on_boundary False\n",
      "X [ 0.33984375 -0.2656172 ]\n",
      "on_boundary False\n",
      "X [-0.16015625 -0.26511744]\n",
      "on_boundary False\n",
      "X [ 0.83984375 -0.26461768]\n",
      "on_boundary False\n",
      "X [-0.78515625 -0.26411796]\n",
      "on_boundary False\n",
      "X [ 0.21484375 -0.26361817]\n",
      "on_boundary False\n",
      "X [-0.28515625 -0.26311845]\n",
      "on_boundary False\n",
      "X [ 0.71484375 -0.2626187 ]\n",
      "on_boundary False\n",
      "X [-0.53515625 -0.26211894]\n",
      "on_boundary False\n",
      "X [ 0.46484375 -0.2616192 ]\n",
      "on_boundary False\n",
      "X [-0.03515625 -0.26111943]\n",
      "on_boundary False\n",
      "X [ 0.96484375 -0.2606197 ]\n",
      "on_boundary False\n",
      "X [-0.94140625 -0.26011994]\n",
      "on_boundary False\n",
      "X [ 0.05859375 -0.2596202 ]\n",
      "on_boundary False\n",
      "X [-0.44140625 -0.25912043]\n",
      "on_boundary False\n",
      "X [ 0.55859375 -0.25862068]\n",
      "on_boundary False\n",
      "X [-0.69140625 -0.25812095]\n",
      "on_boundary False\n",
      "X [ 0.30859375 -0.25762117]\n",
      "on_boundary False\n",
      "X [-0.19140625 -0.25712144]\n",
      "on_boundary False\n",
      "X [ 0.80859375 -0.2566217 ]\n",
      "on_boundary False\n",
      "X [-0.81640625 -0.25612193]\n",
      "on_boundary False\n",
      "X [ 0.18359375 -0.2556222 ]\n",
      "on_boundary False\n",
      "X [-0.31640625 -0.25512242]\n",
      "on_boundary False\n",
      "X [ 0.68359375 -0.2546227 ]\n",
      "on_boundary False\n",
      "X [-0.56640625 -0.25412294]\n",
      "on_boundary False\n",
      "X [ 0.43359375 -0.2536232 ]\n",
      "on_boundary False\n",
      "X [-0.06640625 -0.25312343]\n",
      "on_boundary False\n",
      "X [ 0.93359375 -0.25262368]\n",
      "on_boundary False\n",
      "X [-0.87890625 -0.25212395]\n",
      "on_boundary False\n",
      "X [ 0.12109375 -0.25162417]\n",
      "on_boundary False\n",
      "X [-0.37890625 -0.25112444]\n",
      "on_boundary False\n",
      "X [ 0.62109375 -0.2506247 ]\n",
      "on_boundary False\n",
      "X [-0.62890625 -0.25012493]\n",
      "on_boundary False\n",
      "X [ 0.37109375 -0.24962518]\n",
      "on_boundary False\n",
      "X [-0.12890625 -0.24912545]\n",
      "on_boundary False\n",
      "X [ 0.87109375 -0.2486257 ]\n",
      "on_boundary False\n",
      "X [-0.75390625 -0.24812594]\n",
      "on_boundary False\n",
      "X [ 0.24609375 -0.24762619]\n",
      "on_boundary False\n",
      "X [-0.25390625 -0.24712643]\n",
      "on_boundary False\n",
      "X [ 0.74609375 -0.24662668]\n",
      "on_boundary False\n",
      "X [-0.50390625 -0.24612695]\n",
      "on_boundary False\n",
      "X [ 0.49609375 -0.2456272 ]\n",
      "on_boundary False\n",
      "X [-0.00390625 -0.24512744]\n",
      "on_boundary False\n",
      "X [ 0.99609375 -0.24462768]\n",
      "on_boundary False\n",
      "X [-0.9980469  -0.24412793]\n",
      "on_boundary False\n",
      "X [ 0.00195312 -0.24362817]\n",
      "on_boundary False\n",
      "X [-0.49804688 -0.24312845]\n",
      "on_boundary False\n",
      "X [ 0.5019531 -0.2426287]\n",
      "on_boundary False\n",
      "X [-0.7480469  -0.24212894]\n",
      "on_boundary False\n",
      "X [ 0.25195312 -0.24162918]\n",
      "on_boundary False\n",
      "X [-0.24804688 -0.24112943]\n",
      "on_boundary False\n",
      "X [ 0.7519531  -0.24062967]\n",
      "on_boundary False\n",
      "X [-0.8730469  -0.24012995]\n",
      "on_boundary False\n",
      "X [ 0.12695312 -0.23963019]\n",
      "on_boundary False\n",
      "X [-0.37304688 -0.23913044]\n",
      "on_boundary False\n",
      "X [ 0.6269531  -0.23863068]\n",
      "on_boundary False\n",
      "X [-0.6230469  -0.23813093]\n",
      "on_boundary False\n",
      "X [ 0.37695312 -0.23763117]\n",
      "on_boundary False\n",
      "X [-0.12304688 -0.23713145]\n",
      "on_boundary False\n",
      "X [ 0.8769531  -0.23663169]\n",
      "on_boundary False\n",
      "X [-0.9355469  -0.23613194]\n",
      "on_boundary False\n",
      "X [ 0.06445312 -0.23563218]\n",
      "on_boundary False\n",
      "X [-0.43554688 -0.23513243]\n",
      "on_boundary False\n",
      "X [ 0.5644531  -0.23463267]\n",
      "on_boundary False\n",
      "X [-0.6855469  -0.23413295]\n",
      "on_boundary False\n",
      "X [ 0.31445312 -0.23363319]\n",
      "on_boundary False\n",
      "X [-0.18554688 -0.23313344]\n",
      "on_boundary False\n",
      "X [ 0.8144531  -0.23263368]\n",
      "on_boundary False\n",
      "X [-0.8105469  -0.23213392]\n",
      "on_boundary False\n",
      "X [ 0.18945312 -0.23163417]\n",
      "on_boundary False\n",
      "X [-0.31054688 -0.23113444]\n",
      "on_boundary False\n",
      "X [ 0.6894531  -0.23063469]\n",
      "on_boundary False\n",
      "X [-0.5605469  -0.23013493]\n",
      "on_boundary False\n",
      "X [ 0.43945312 -0.22963518]\n",
      "on_boundary False\n",
      "X [-0.06054688 -0.22913542]\n",
      "on_boundary False\n",
      "X [ 0.9394531  -0.22863567]\n",
      "on_boundary False\n",
      "X [-0.9667969  -0.22813594]\n",
      "on_boundary False\n",
      "X [ 0.03320312 -0.22763619]\n",
      "on_boundary False\n",
      "X [-0.46679688 -0.22713643]\n",
      "on_boundary False\n",
      "X [ 0.5332031  -0.22663668]\n",
      "on_boundary False\n",
      "X [-0.7167969  -0.22613692]\n",
      "on_boundary False\n",
      "X [ 0.28320312 -0.22563717]\n",
      "on_boundary False\n",
      "X [-0.21679688 -0.22513744]\n",
      "on_boundary False\n",
      "X [ 0.7832031  -0.22463769]\n",
      "on_boundary False\n",
      "X [-0.8417969  -0.22413793]\n",
      "on_boundary False\n",
      "X [ 0.15820312 -0.22363818]\n",
      "on_boundary False\n",
      "X [-0.34179688 -0.22313842]\n",
      "on_boundary False\n",
      "X [ 0.6582031  -0.22263867]\n",
      "on_boundary False\n",
      "X [-0.5917969  -0.22213894]\n",
      "on_boundary False\n",
      "X [ 0.40820312 -0.22163919]\n",
      "on_boundary False\n",
      "X [-0.09179688 -0.22113943]\n",
      "on_boundary False\n",
      "X [ 0.9082031  -0.22063968]\n",
      "on_boundary False\n",
      "X [-0.9042969  -0.22013992]\n",
      "on_boundary False\n",
      "X [ 0.09570312 -0.21964017]\n",
      "on_boundary False\n",
      "X [-0.40429688 -0.21914044]\n",
      "on_boundary False\n",
      "X [ 0.5957031  -0.21864069]\n",
      "on_boundary False\n",
      "X [-0.6542969  -0.21814093]\n",
      "on_boundary False\n",
      "X [ 0.34570312 -0.21764117]\n",
      "on_boundary False\n",
      "X [-0.15429688 -0.21714142]\n",
      "on_boundary False\n",
      "X [ 0.8457031  -0.21664166]\n",
      "on_boundary False\n",
      "X [-0.7792969  -0.21614194]\n",
      "on_boundary False\n",
      "X [ 0.22070312 -0.21564218]\n",
      "on_boundary False\n",
      "X [-0.27929688 -0.21514243]\n",
      "on_boundary False\n",
      "X [ 0.7207031  -0.21464267]\n",
      "on_boundary False\n",
      "X [-0.5292969  -0.21414292]\n",
      "on_boundary False\n",
      "X [ 0.47070312 -0.2136432 ]\n",
      "on_boundary False\n",
      "X [-0.02929688 -0.21314344]\n",
      "on_boundary False\n",
      "X [ 0.9707031  -0.21264368]\n",
      "on_boundary False\n",
      "X [-0.9824219  -0.21214393]\n",
      "on_boundary False\n",
      "X [ 0.01757812 -0.21164417]\n",
      "on_boundary False\n",
      "X [-0.48242188 -0.21114442]\n",
      "on_boundary False\n",
      "X [ 0.5175781  -0.21064469]\n",
      "on_boundary False\n",
      "X [-0.7324219  -0.21014494]\n",
      "on_boundary False\n",
      "X [ 0.26757812 -0.20964518]\n",
      "on_boundary False\n",
      "X [-0.23242188 -0.20914543]\n",
      "on_boundary False\n",
      "X [ 0.7675781  -0.20864567]\n",
      "on_boundary False\n",
      "X [-0.8574219  -0.20814592]\n",
      "on_boundary False\n",
      "X [ 0.14257812 -0.20764619]\n",
      "on_boundary False\n",
      "X [-0.35742188 -0.20714644]\n",
      "on_boundary False\n",
      "X [ 0.6425781  -0.20664668]\n",
      "on_boundary False\n",
      "X [-0.6074219  -0.20614693]\n",
      "on_boundary False\n",
      "X [ 0.39257812 -0.20564717]\n",
      "on_boundary False\n",
      "X [-0.10742188 -0.20514742]\n",
      "on_boundary False\n",
      "X [ 0.8925781  -0.20464769]\n",
      "on_boundary False\n",
      "X [-0.9199219  -0.20414793]\n",
      "on_boundary False\n",
      "X [ 0.08007812 -0.20364818]\n",
      "on_boundary False\n",
      "X [-0.41992188 -0.20314842]\n",
      "on_boundary False\n",
      "X [ 0.5800781  -0.20264867]\n",
      "on_boundary False\n",
      "X [-0.6699219  -0.20214891]\n",
      "on_boundary False\n",
      "X [ 0.33007812 -0.20164919]\n",
      "on_boundary False\n",
      "X [-0.16992188 -0.20114943]\n",
      "on_boundary False\n",
      "X [ 0.8300781  -0.20064968]\n",
      "on_boundary False\n",
      "X [-0.7949219  -0.20014992]\n",
      "on_boundary False\n",
      "X [ 0.20507812 -0.19965017]\n",
      "on_boundary False\n",
      "X [-0.29492188 -0.19915041]\n",
      "on_boundary False\n",
      "X [ 0.7050781  -0.19865069]\n",
      "on_boundary False\n",
      "X [-0.5449219  -0.19815093]\n",
      "on_boundary False\n",
      "X [ 0.45507812 -0.19765118]\n",
      "on_boundary False\n",
      "X [-0.04492188 -0.19715142]\n",
      "on_boundary False\n",
      "X [ 0.9550781  -0.19665167]\n",
      "on_boundary False\n",
      "X [-0.9511719  -0.19615191]\n",
      "on_boundary False\n",
      "X [ 0.04882812 -0.19565219]\n",
      "on_boundary False\n",
      "X [-0.45117188 -0.19515243]\n",
      "on_boundary False\n",
      "X [ 0.5488281  -0.19465268]\n",
      "on_boundary False\n",
      "X [-0.7011719  -0.19415292]\n",
      "on_boundary False\n",
      "X [ 0.29882812 -0.19365317]\n",
      "on_boundary False\n",
      "X [-0.20117188 -0.19315341]\n",
      "on_boundary False\n",
      "X [ 0.7988281  -0.19265369]\n",
      "on_boundary False\n",
      "X [-0.8261719  -0.19215393]\n",
      "on_boundary False\n",
      "X [ 0.17382812 -0.19165418]\n",
      "on_boundary False\n",
      "X [-0.32617188 -0.19115442]\n",
      "on_boundary False\n",
      "X [ 0.6738281  -0.19065467]\n",
      "on_boundary False\n",
      "X [-0.5761719  -0.19015491]\n",
      "on_boundary False\n",
      "X [ 0.42382812 -0.18965518]\n",
      "on_boundary False\n",
      "X [-0.07617188 -0.18915543]\n",
      "on_boundary False\n",
      "X [ 0.9238281  -0.18865567]\n",
      "on_boundary False\n",
      "X [-0.8886719  -0.18815592]\n",
      "on_boundary False\n",
      "X [ 0.11132812 -0.18765616]\n",
      "on_boundary False\n",
      "X [-0.38867188 -0.18715641]\n",
      "on_boundary False\n",
      "X [ 0.6113281  -0.18665668]\n",
      "on_boundary False\n",
      "X [-0.6386719  -0.18615693]\n",
      "on_boundary False\n",
      "X [ 0.36132812 -0.18565717]\n",
      "on_boundary False\n",
      "X [-0.13867188 -0.18515742]\n",
      "on_boundary False\n",
      "X [ 0.8613281  -0.18465766]\n",
      "on_boundary False\n",
      "X [-0.7636719  -0.18415791]\n",
      "on_boundary False\n",
      "X [ 0.23632812 -0.18365818]\n",
      "on_boundary False\n",
      "X [-0.26367188 -0.18315843]\n",
      "on_boundary False\n",
      "X [ 0.7363281  -0.18265867]\n",
      "on_boundary False\n",
      "X [-0.5136719  -0.18215892]\n",
      "on_boundary False\n",
      "X [ 0.48632812 -0.18165916]\n",
      "on_boundary False\n",
      "X [-0.01367188 -0.1811594 ]\n",
      "on_boundary False\n",
      "X [ 0.9863281  -0.18065968]\n",
      "on_boundary False\n",
      "X [-0.9902344  -0.18015993]\n",
      "on_boundary False\n",
      "X [ 0.00976562 -0.17966017]\n",
      "on_boundary False\n",
      "X [-0.49023438 -0.17916042]\n",
      "on_boundary False\n",
      "X [ 0.5097656  -0.17866066]\n",
      "on_boundary False\n",
      "X [-0.7402344 -0.1781609]\n",
      "on_boundary False\n",
      "X [ 0.25976562 -0.17766118]\n",
      "on_boundary False\n",
      "X [-0.24023438 -0.17716143]\n",
      "on_boundary False\n",
      "X [ 0.7597656  -0.17666167]\n",
      "on_boundary False\n",
      "X [-0.8652344  -0.17616192]\n",
      "on_boundary False\n",
      "X [ 0.13476562 -0.17566216]\n",
      "on_boundary False\n",
      "X [-0.36523438 -0.1751624 ]\n",
      "on_boundary False\n",
      "X [ 0.6347656  -0.17466268]\n",
      "on_boundary False\n",
      "X [-0.6152344  -0.17416292]\n",
      "on_boundary False\n",
      "X [ 0.38476562 -0.17366317]\n",
      "on_boundary False\n",
      "X [-0.11523438 -0.17316341]\n",
      "on_boundary False\n",
      "X [ 0.8847656  -0.17266366]\n",
      "on_boundary False\n",
      "X [-0.9277344 -0.1721639]\n",
      "on_boundary False\n",
      "X [ 0.07226562 -0.17166418]\n",
      "on_boundary False\n",
      "X [-0.42773438 -0.17116442]\n",
      "on_boundary False\n",
      "X [ 0.5722656  -0.17066467]\n",
      "on_boundary False\n",
      "X [-0.6777344  -0.17016491]\n",
      "on_boundary False\n",
      "X [ 0.32226562 -0.16966516]\n",
      "on_boundary False\n",
      "X [-0.17773438 -0.1691654 ]\n",
      "on_boundary False\n",
      "X [ 0.8222656  -0.16866568]\n",
      "on_boundary False\n",
      "X [-0.8027344  -0.16816592]\n",
      "on_boundary False\n",
      "X [ 0.19726562 -0.16766617]\n",
      "on_boundary False\n",
      "X [-0.30273438 -0.16716641]\n",
      "on_boundary False\n",
      "X [ 0.6972656  -0.16666666]\n",
      "on_boundary False\n",
      "X [-0.5527344  -0.16616693]\n",
      "on_boundary False\n",
      "X [ 0.44726562 -0.16566718]\n",
      "on_boundary False\n",
      "X [-0.05273438 -0.16516742]\n",
      "on_boundary False\n",
      "X [ 0.9472656  -0.16466767]\n",
      "on_boundary False\n",
      "X [-0.9589844  -0.16416791]\n",
      "on_boundary False\n",
      "X [ 0.04101562 -0.16366816]\n",
      "on_boundary False\n",
      "X [-0.45898438 -0.16316843]\n",
      "on_boundary False\n",
      "X [ 0.5410156  -0.16266868]\n",
      "on_boundary False\n",
      "X [-0.7089844  -0.16216892]\n",
      "on_boundary False\n",
      "X [ 0.29101562 -0.16166916]\n",
      "on_boundary False\n",
      "X [-0.20898438 -0.16116941]\n",
      "on_boundary False\n",
      "X [ 0.7910156  -0.16066965]\n",
      "on_boundary False\n",
      "X [-0.8339844  -0.16016993]\n",
      "on_boundary False\n",
      "X [ 0.16601562 -0.15967017]\n",
      "on_boundary False\n",
      "X [-0.33398438 -0.15917042]\n",
      "on_boundary False\n",
      "X [ 0.6660156  -0.15867066]\n",
      "on_boundary False\n",
      "X [-0.5839844  -0.15817091]\n",
      "on_boundary False\n",
      "X [ 0.41601562 -0.15767115]\n",
      "on_boundary False\n",
      "X [-0.08398438 -0.15717143]\n",
      "on_boundary False\n",
      "X [ 0.9160156  -0.15667167]\n",
      "on_boundary False\n",
      "X [-0.8964844  -0.15617192]\n",
      "on_boundary False\n",
      "X [ 0.10351562 -0.15567216]\n",
      "on_boundary False\n",
      "X [-0.39648438 -0.15517241]\n",
      "on_boundary False\n",
      "X [ 0.6035156  -0.15467265]\n",
      "on_boundary False\n",
      "X [-0.6464844  -0.15417293]\n",
      "on_boundary False\n",
      "X [ 0.35351562 -0.15367317]\n",
      "on_boundary False\n",
      "X [-0.14648438 -0.15317342]\n",
      "on_boundary False\n",
      "X [ 0.8535156  -0.15267366]\n",
      "on_boundary False\n",
      "X [-0.7714844 -0.1521739]\n",
      "on_boundary False\n",
      "X [ 0.22851562 -0.15167415]\n",
      "on_boundary False\n",
      "X [-0.27148438 -0.15117443]\n",
      "on_boundary False\n",
      "X [ 0.7285156  -0.15067467]\n",
      "on_boundary False\n",
      "X [-0.5214844  -0.15017492]\n",
      "on_boundary False\n",
      "X [ 0.47851562 -0.14967516]\n",
      "on_boundary False\n",
      "X [-0.02148438 -0.1491754 ]\n",
      "on_boundary False\n",
      "X [ 0.9785156  -0.14867565]\n",
      "on_boundary False\n",
      "X [-0.9746094  -0.14817593]\n",
      "on_boundary False\n",
      "X [ 0.02539062 -0.14767617]\n",
      "on_boundary False\n",
      "X [-0.47460938 -0.14717641]\n",
      "on_boundary False\n",
      "X [ 0.5253906  -0.14667666]\n",
      "on_boundary False\n",
      "X [-0.7246094 -0.1461769]\n",
      "on_boundary False\n",
      "X [ 0.27539062 -0.14567715]\n",
      "on_boundary False\n",
      "X [-0.22460938 -0.14517742]\n",
      "on_boundary False\n",
      "X [ 0.7753906  -0.14467767]\n",
      "on_boundary False\n",
      "X [-0.8496094  -0.14417791]\n",
      "on_boundary False\n",
      "X [ 0.15039062 -0.14367816]\n",
      "on_boundary False\n",
      "X [-0.34960938 -0.1431784 ]\n",
      "on_boundary False\n",
      "X [ 0.6503906  -0.14267865]\n",
      "on_boundary False\n",
      "X [-0.5996094  -0.14217892]\n",
      "on_boundary False\n",
      "X [ 0.40039062 -0.14167917]\n",
      "on_boundary False\n",
      "X [-0.09960938 -0.14117941]\n",
      "on_boundary False\n",
      "X [ 0.9003906  -0.14067966]\n",
      "on_boundary False\n",
      "X [-0.9121094 -0.1401799]\n",
      "on_boundary False\n",
      "X [ 0.08789062 -0.13968015]\n",
      "on_boundary False\n",
      "X [-0.41210938 -0.13918042]\n",
      "on_boundary False\n",
      "X [ 0.5878906  -0.13868067]\n",
      "on_boundary False\n",
      "X [-0.6621094  -0.13818091]\n",
      "on_boundary False\n",
      "X [ 0.33789062 -0.13768116]\n",
      "on_boundary False\n",
      "X [-0.16210938 -0.1371814 ]\n",
      "on_boundary False\n",
      "X [ 0.8378906  -0.13668165]\n",
      "on_boundary False\n",
      "X [-0.7871094  -0.13618192]\n",
      "on_boundary False\n",
      "X [ 0.21289062 -0.13568217]\n",
      "on_boundary False\n",
      "X [-0.28710938 -0.13518241]\n",
      "on_boundary False\n",
      "X [ 0.7128906  -0.13468266]\n",
      "on_boundary False\n",
      "X [-0.5371094 -0.1341829]\n",
      "on_boundary False\n",
      "X [ 0.46289062 -0.13368315]\n",
      "on_boundary False\n",
      "X [-0.03710938 -0.13318342]\n",
      "on_boundary False\n",
      "X [ 0.9628906  -0.13268366]\n",
      "on_boundary False\n",
      "X [-0.9433594  -0.13218391]\n",
      "on_boundary False\n",
      "X [ 0.05664062 -0.13168415]\n",
      "on_boundary False\n",
      "X [-0.44335938 -0.1311844 ]\n",
      "on_boundary False\n",
      "X [ 0.5566406  -0.13068464]\n",
      "on_boundary False\n",
      "X [-0.6933594  -0.13018492]\n",
      "on_boundary False\n",
      "X [ 0.30664062 -0.12968516]\n",
      "on_boundary False\n",
      "X [-0.19335938 -0.12918541]\n",
      "on_boundary False\n",
      "X [ 0.8066406  -0.12868565]\n",
      "on_boundary False\n",
      "X [-0.8183594 -0.1281859]\n",
      "on_boundary False\n",
      "X [ 0.18164062 -0.12768614]\n",
      "on_boundary False\n",
      "X [-0.31835938 -0.12718642]\n",
      "on_boundary False\n",
      "X [ 0.6816406  -0.12668666]\n",
      "on_boundary False\n",
      "X [-0.5683594 -0.1261869]\n",
      "on_boundary False\n",
      "X [ 0.43164062 -0.12568715]\n",
      "on_boundary False\n",
      "X [-0.06835938 -0.1251874 ]\n",
      "on_boundary False\n",
      "X [ 0.9316406  -0.12468764]\n",
      "on_boundary False\n",
      "X [-0.8808594  -0.12418792]\n",
      "on_boundary False\n",
      "X [ 0.11914062 -0.12368816]\n",
      "on_boundary False\n",
      "X [-0.38085938 -0.12318841]\n",
      "on_boundary False\n",
      "X [ 0.6191406  -0.12268865]\n",
      "on_boundary False\n",
      "X [-0.6308594 -0.1221889]\n",
      "on_boundary False\n",
      "X [ 0.36914062 -0.12168914]\n",
      "on_boundary False\n",
      "X [-0.13085938 -0.12118942]\n",
      "on_boundary False\n",
      "X [ 0.8691406  -0.12068966]\n",
      "on_boundary False\n",
      "X [-0.7558594  -0.12018991]\n",
      "on_boundary False\n",
      "X [ 0.24414062 -0.11969015]\n",
      "on_boundary False\n",
      "X [-0.25585938 -0.11919039]\n",
      "on_boundary False\n",
      "X [ 0.7441406  -0.11869067]\n",
      "on_boundary False\n",
      "X [-0.5058594  -0.11819091]\n",
      "on_boundary False\n",
      "X [ 0.49414062 -0.11769116]\n",
      "on_boundary False\n",
      "X [-0.00585938 -0.1171914 ]\n",
      "on_boundary False\n",
      "X [ 0.9941406  -0.11669165]\n",
      "on_boundary False\n",
      "X [-0.9941406  -0.11619189]\n",
      "on_boundary False\n",
      "X [ 0.00585938 -0.11569217]\n",
      "on_boundary False\n",
      "X [-0.49414062 -0.11519241]\n",
      "on_boundary False\n",
      "X [ 0.5058594  -0.11469266]\n",
      "on_boundary False\n",
      "X [-0.7441406 -0.1141929]\n",
      "on_boundary False\n",
      "X [ 0.25585938 -0.11369315]\n",
      "on_boundary False\n",
      "X [-0.24414062 -0.11319339]\n",
      "on_boundary False\n",
      "X [ 0.7558594  -0.11269367]\n",
      "on_boundary False\n",
      "X [-0.8691406  -0.11219391]\n",
      "on_boundary False\n",
      "X [ 0.13085938 -0.11169416]\n",
      "on_boundary False\n",
      "X [-0.36914062 -0.1111944 ]\n",
      "on_boundary False\n",
      "X [ 0.6308594  -0.11069465]\n",
      "on_boundary False\n",
      "X [-0.6191406  -0.11019489]\n",
      "on_boundary False\n",
      "X [ 0.38085938 -0.10969517]\n",
      "on_boundary False\n",
      "X [-0.11914062 -0.10919541]\n",
      "on_boundary False\n",
      "X [ 0.8808594  -0.10869566]\n",
      "on_boundary False\n",
      "X [-0.9316406 -0.1081959]\n",
      "on_boundary False\n",
      "X [ 0.06835938 -0.10769615]\n",
      "on_boundary False\n",
      "X [-0.43164062 -0.10719639]\n",
      "on_boundary False\n",
      "X [ 0.5683594  -0.10669667]\n",
      "on_boundary False\n",
      "X [-0.6816406  -0.10619691]\n",
      "on_boundary False\n",
      "X [ 0.31835938 -0.10569715]\n",
      "on_boundary False\n",
      "X [-0.18164062 -0.1051974 ]\n",
      "on_boundary False\n",
      "X [ 0.8183594  -0.10469764]\n",
      "on_boundary False\n",
      "X [-0.8066406  -0.10419789]\n",
      "on_boundary False\n",
      "X [ 0.19335938 -0.10369816]\n",
      "on_boundary False\n",
      "X [-0.30664062 -0.10319841]\n",
      "on_boundary False\n",
      "X [ 0.6933594  -0.10269865]\n",
      "on_boundary False\n",
      "X [-0.5566406 -0.1021989]\n",
      "on_boundary False\n",
      "X [ 0.44335938 -0.10169914]\n",
      "on_boundary False\n",
      "X [-0.05664062 -0.10119939]\n",
      "on_boundary False\n",
      "X [ 0.9433594  -0.10069966]\n",
      "on_boundary False\n",
      "X [-0.9628906  -0.10019991]\n",
      "on_boundary False\n",
      "X [ 0.03710938 -0.09970015]\n",
      "on_boundary False\n",
      "X [-0.46289062 -0.0992004 ]\n",
      "on_boundary False\n",
      "X [ 0.5371094  -0.09870064]\n",
      "on_boundary False\n",
      "X [-0.7128906  -0.09820089]\n",
      "on_boundary False\n",
      "X [ 0.28710938 -0.09770116]\n",
      "on_boundary False\n",
      "X [-0.21289062 -0.09720141]\n",
      "on_boundary False\n",
      "X [ 0.7871094  -0.09670165]\n",
      "on_boundary False\n",
      "X [-0.8378906 -0.0962019]\n",
      "on_boundary False\n",
      "X [ 0.16210938 -0.09570214]\n",
      "on_boundary False\n",
      "X [-0.33789062 -0.09520239]\n",
      "on_boundary False\n",
      "X [ 0.6621094  -0.09470266]\n",
      "on_boundary False\n",
      "X [-0.5878906  -0.09420291]\n",
      "on_boundary False\n",
      "X [ 0.41210938 -0.09370315]\n",
      "on_boundary False\n",
      "X [-0.08789062 -0.0932034 ]\n",
      "on_boundary False\n",
      "X [ 0.9121094  -0.09270364]\n",
      "on_boundary False\n",
      "X [-0.9003906  -0.09220389]\n",
      "on_boundary False\n",
      "X [ 0.09960938 -0.09170416]\n",
      "on_boundary False\n",
      "X [-0.40039062 -0.0912044 ]\n",
      "on_boundary False\n",
      "X [ 0.5996094  -0.09070465]\n",
      "on_boundary False\n",
      "X [-0.6503906  -0.09020489]\n",
      "on_boundary False\n",
      "X [ 0.34960938 -0.08970514]\n",
      "on_boundary False\n",
      "X [-0.15039062 -0.08920538]\n",
      "on_boundary False\n",
      "X [ 0.8496094  -0.08870566]\n",
      "on_boundary False\n",
      "X [-0.7753906 -0.0882059]\n",
      "on_boundary False\n",
      "X [ 0.22460938 -0.08770615]\n",
      "on_boundary False\n",
      "X [-0.27539062 -0.08720639]\n",
      "on_boundary False\n",
      "X [ 0.7246094  -0.08670664]\n",
      "on_boundary False\n",
      "X [-0.5253906  -0.08620688]\n",
      "on_boundary False\n",
      "X [ 0.47460938 -0.08570716]\n",
      "on_boundary False\n",
      "X [-0.02539062 -0.0852074 ]\n",
      "on_boundary False\n",
      "X [ 0.9746094  -0.08470765]\n",
      "on_boundary False\n",
      "X [-0.9785156  -0.08420789]\n",
      "on_boundary False\n",
      "X [ 0.02148438 -0.08370814]\n",
      "on_boundary False\n",
      "X [-0.47851562 -0.08320838]\n",
      "on_boundary False\n",
      "X [ 0.5214844  -0.08270866]\n",
      "on_boundary False\n",
      "X [-0.7285156 -0.0822089]\n",
      "on_boundary False\n",
      "X [ 0.27148438 -0.08170915]\n",
      "on_boundary False\n",
      "X [-0.22851562 -0.08120939]\n",
      "on_boundary False\n",
      "X [ 0.7714844  -0.08070964]\n",
      "on_boundary False\n",
      "X [-0.8535156  -0.08020988]\n",
      "on_boundary False\n",
      "X [ 0.14648438 -0.07971016]\n",
      "on_boundary False\n",
      "X [-0.35351562 -0.0792104 ]\n",
      "on_boundary False\n",
      "X [ 0.6464844  -0.07871065]\n",
      "on_boundary False\n",
      "X [-0.6035156  -0.07821089]\n",
      "on_boundary False\n",
      "X [ 0.39648438 -0.07771114]\n",
      "on_boundary False\n",
      "X [-0.10351562 -0.07721138]\n",
      "on_boundary False\n",
      "X [ 0.8964844  -0.07671165]\n",
      "on_boundary False\n",
      "X [-0.9160156 -0.0762119]\n",
      "on_boundary False\n",
      "X [ 0.08398438 -0.07571214]\n",
      "on_boundary False\n",
      "X [-0.41601562 -0.07521239]\n",
      "on_boundary False\n",
      "X [ 0.5839844  -0.07471263]\n",
      "on_boundary False\n",
      "X [-0.6660156  -0.07421288]\n",
      "on_boundary False\n",
      "X [ 0.33398438 -0.07371315]\n",
      "on_boundary False\n",
      "X [-0.16601562 -0.0732134 ]\n",
      "on_boundary False\n",
      "X [ 0.8339844  -0.07271364]\n",
      "on_boundary False\n",
      "X [-0.7910156  -0.07221389]\n",
      "on_boundary False\n",
      "X [ 0.20898438 -0.07171413]\n",
      "on_boundary False\n",
      "X [-0.29101562 -0.07121441]\n",
      "on_boundary False\n",
      "X [ 0.7089844  -0.07071465]\n",
      "on_boundary False\n",
      "X [-0.5410156 -0.0702149]\n",
      "on_boundary False\n",
      "X [ 0.45898438 -0.06971514]\n",
      "on_boundary False\n",
      "X [-0.04101562 -0.06921539]\n",
      "on_boundary False\n",
      "X [ 0.9589844  -0.06871563]\n",
      "on_boundary False\n",
      "X [-0.9472656  -0.06821591]\n",
      "on_boundary False\n",
      "X [ 0.05273438 -0.06771615]\n",
      "on_boundary False\n",
      "X [-0.44726562 -0.0672164 ]\n",
      "on_boundary False\n",
      "X [ 0.5527344  -0.06671664]\n",
      "on_boundary False\n",
      "X [-0.6972656  -0.06621689]\n",
      "on_boundary False\n",
      "X [ 0.30273438 -0.06571713]\n",
      "on_boundary False\n",
      "X [-0.19726562 -0.06521741]\n",
      "on_boundary False\n",
      "X [ 0.8027344  -0.06471765]\n",
      "on_boundary False\n",
      "X [-0.8222656 -0.0642179]\n",
      "on_boundary False\n",
      "X [ 0.17773438 -0.06371814]\n",
      "on_boundary False\n",
      "X [-0.32226562 -0.06321838]\n",
      "on_boundary False\n",
      "X [ 0.6777344  -0.06271863]\n",
      "on_boundary False\n",
      "X [-0.5722656 -0.0622189]\n",
      "on_boundary False\n",
      "X [ 0.42773438 -0.06171915]\n",
      "on_boundary False\n",
      "X [-0.07226562 -0.06121939]\n",
      "on_boundary False\n",
      "X [ 0.9277344  -0.06071964]\n",
      "on_boundary False\n",
      "X [-0.8847656  -0.06021988]\n",
      "on_boundary False\n",
      "X [ 0.11523438 -0.05972013]\n",
      "on_boundary False\n",
      "X [-0.38476562 -0.0592204 ]\n",
      "on_boundary False\n",
      "X [ 0.6152344  -0.05872065]\n",
      "on_boundary False\n",
      "X [-0.6347656  -0.05822089]\n",
      "on_boundary False\n",
      "X [ 0.36523438 -0.05772114]\n",
      "on_boundary False\n",
      "X [-0.13476562 -0.05722138]\n",
      "on_boundary False\n",
      "X [ 0.8652344  -0.05672163]\n",
      "on_boundary False\n",
      "X [-0.7597656 -0.0562219]\n",
      "on_boundary False\n",
      "X [ 0.24023438 -0.05572215]\n",
      "on_boundary False\n",
      "X [-0.25976562 -0.05522239]\n",
      "on_boundary False\n",
      "X [ 0.7402344  -0.05472264]\n",
      "on_boundary False\n",
      "X [-0.5097656  -0.05422288]\n",
      "on_boundary False\n",
      "X [ 0.49023438 -0.05372313]\n",
      "on_boundary False\n",
      "X [-0.00976562 -0.0532234 ]\n",
      "on_boundary False\n",
      "X [ 0.9902344  -0.05272365]\n",
      "on_boundary False\n",
      "X [-0.9863281  -0.05222389]\n",
      "on_boundary False\n",
      "X [ 0.01367188 -0.05172414]\n",
      "on_boundary False\n",
      "X [-0.48632812 -0.05122438]\n",
      "on_boundary False\n",
      "X [ 0.5136719  -0.05072463]\n",
      "on_boundary False\n",
      "X [-0.7363281 -0.0502249]\n",
      "on_boundary False\n",
      "X [ 0.26367188 -0.04972515]\n",
      "on_boundary False\n",
      "X [-0.23632812 -0.04922539]\n",
      "on_boundary False\n",
      "X [ 0.7636719  -0.04872563]\n",
      "on_boundary False\n",
      "X [-0.8613281  -0.04822588]\n",
      "on_boundary False\n",
      "X [ 0.13867188 -0.04772612]\n",
      "on_boundary False\n",
      "X [-0.36132812 -0.0472264 ]\n",
      "on_boundary False\n",
      "X [ 0.6386719  -0.04672664]\n",
      "on_boundary False\n",
      "X [-0.6113281  -0.04622689]\n",
      "on_boundary False\n",
      "X [ 0.38867188 -0.04572713]\n",
      "on_boundary False\n",
      "X [-0.11132812 -0.04522738]\n",
      "on_boundary False\n",
      "X [ 0.8886719  -0.04472762]\n",
      "on_boundary False\n",
      "X [-0.9238281 -0.0442279]\n",
      "on_boundary False\n",
      "X [ 0.07617188 -0.04372814]\n",
      "on_boundary False\n",
      "X [-0.42382812 -0.04322839]\n",
      "on_boundary False\n",
      "X [ 0.5761719  -0.04272863]\n",
      "on_boundary False\n",
      "X [-0.6738281  -0.04222888]\n",
      "on_boundary False\n",
      "X [ 0.32617188 -0.04172912]\n",
      "on_boundary False\n",
      "X [-0.17382812 -0.0412294 ]\n",
      "on_boundary False\n",
      "X [ 0.8261719  -0.04072964]\n",
      "on_boundary False\n",
      "X [-0.7988281  -0.04022989]\n",
      "on_boundary False\n",
      "X [ 0.20117188 -0.03973013]\n",
      "on_boundary False\n",
      "X [-0.29882812 -0.03923038]\n",
      "on_boundary False\n",
      "X [ 0.7011719  -0.03873062]\n",
      "on_boundary False\n",
      "X [-0.5488281 -0.0382309]\n",
      "on_boundary False\n",
      "X [ 0.45117188 -0.03773114]\n",
      "on_boundary False\n",
      "X [-0.04882812 -0.03723139]\n",
      "on_boundary False\n",
      "X [ 0.9511719  -0.03673163]\n",
      "on_boundary False\n",
      "X [-0.9550781  -0.03623188]\n",
      "on_boundary False\n",
      "X [ 0.04492188 -0.03573212]\n",
      "on_boundary False\n",
      "X [-0.45507812 -0.03523239]\n",
      "on_boundary False\n",
      "X [ 0.5449219  -0.03473264]\n",
      "on_boundary False\n",
      "X [-0.7050781  -0.03423288]\n",
      "on_boundary False\n",
      "X [ 0.29492188 -0.03373313]\n",
      "on_boundary False\n",
      "X [-0.20507812 -0.03323337]\n",
      "on_boundary False\n",
      "X [ 0.7949219  -0.03273362]\n",
      "on_boundary False\n",
      "X [-0.8300781  -0.03223389]\n",
      "on_boundary False\n",
      "X [ 0.16992188 -0.03173414]\n",
      "on_boundary False\n",
      "X [-0.33007812 -0.03123438]\n",
      "on_boundary False\n",
      "X [ 0.6699219  -0.03073463]\n",
      "on_boundary False\n",
      "X [-0.5800781  -0.03023487]\n",
      "on_boundary False\n",
      "X [ 0.41992188 -0.02973512]\n",
      "on_boundary False\n",
      "X [-0.08007812 -0.02923539]\n",
      "on_boundary False\n",
      "X [ 0.9199219  -0.02873564]\n",
      "on_boundary False\n",
      "X [-0.8925781  -0.02823588]\n",
      "on_boundary False\n",
      "X [ 0.10742188 -0.02773613]\n",
      "on_boundary False\n",
      "X [-0.39257812 -0.02723637]\n",
      "on_boundary False\n",
      "X [ 0.6074219  -0.02673662]\n",
      "on_boundary False\n",
      "X [-0.6425781  -0.02623689]\n",
      "on_boundary False\n",
      "X [ 0.35742188 -0.02573714]\n",
      "on_boundary False\n",
      "X [-0.14257812 -0.02523738]\n",
      "on_boundary False\n",
      "X [ 0.8574219  -0.02473763]\n",
      "on_boundary False\n",
      "X [-0.7675781  -0.02423787]\n",
      "on_boundary False\n",
      "X [ 0.23242188 -0.02373815]\n",
      "on_boundary False\n",
      "X [-0.26757812 -0.02323839]\n",
      "on_boundary False\n",
      "X [ 0.7324219  -0.02273864]\n",
      "on_boundary False\n",
      "X [-0.5175781  -0.02223888]\n",
      "on_boundary False\n",
      "X [ 0.48242188 -0.02173913]\n",
      "on_boundary False\n",
      "X [-0.01757812 -0.02123937]\n",
      "on_boundary False\n",
      "X [ 0.9824219  -0.02073964]\n",
      "on_boundary False\n",
      "X [-0.9707031  -0.02023989]\n",
      "on_boundary False\n",
      "X [ 0.02929688 -0.01974013]\n",
      "on_boundary False\n",
      "X [-0.47070312 -0.01924038]\n",
      "on_boundary False\n",
      "X [ 0.5292969  -0.01874062]\n",
      "on_boundary False\n",
      "X [-0.7207031  -0.01824087]\n",
      "on_boundary False\n",
      "X [ 0.27929688 -0.01774114]\n",
      "on_boundary False\n",
      "X [-0.22070312 -0.01724139]\n",
      "on_boundary False\n",
      "X [ 0.7792969  -0.01674163]\n",
      "on_boundary False\n",
      "X [-0.8457031  -0.01624188]\n",
      "on_boundary False\n",
      "X [ 0.15429688 -0.01574212]\n",
      "on_boundary False\n",
      "X [-0.34570312 -0.01524237]\n",
      "on_boundary False\n",
      "X [ 0.6542969  -0.01474264]\n",
      "on_boundary False\n",
      "X [-0.5957031  -0.01424289]\n",
      "on_boundary False\n",
      "X [ 0.40429688 -0.01374313]\n",
      "on_boundary False\n",
      "X [-0.09570312 -0.01324338]\n",
      "on_boundary False\n",
      "X [ 0.9042969  -0.01274362]\n",
      "on_boundary False\n",
      "X [-0.9082031  -0.01224387]\n",
      "on_boundary False\n",
      "X [ 0.09179688 -0.01174414]\n",
      "on_boundary False\n",
      "X [-0.40820312 -0.01124439]\n",
      "on_boundary False\n",
      "X [ 0.5917969  -0.01074463]\n",
      "on_boundary False\n",
      "X [-0.6582031  -0.01024488]\n",
      "on_boundary False\n",
      "X [ 0.34179688 -0.00974512]\n",
      "on_boundary False\n",
      "X [-0.15820312 -0.00924537]\n",
      "on_boundary False\n",
      "X [ 0.8417969  -0.00874564]\n",
      "on_boundary False\n",
      "X [-0.7832031  -0.00824589]\n",
      "on_boundary False\n",
      "X [ 0.21679688 -0.00774613]\n",
      "on_boundary False\n",
      "X [-0.28320312 -0.00724638]\n",
      "on_boundary False\n",
      "X [ 0.7167969  -0.00674662]\n",
      "on_boundary False\n",
      "X [-0.5332031  -0.00624686]\n",
      "on_boundary False\n",
      "X [ 0.46679688 -0.00574714]\n",
      "on_boundary False\n",
      "X [-0.03320312 -0.00524738]\n",
      "on_boundary False\n",
      "X [ 0.9667969  -0.00474763]\n",
      "on_boundary False\n",
      "X [-0.9394531  -0.00424787]\n",
      "on_boundary False\n",
      "X [ 0.06054688 -0.00374812]\n",
      "on_boundary False\n",
      "X [-0.43945312 -0.00324836]\n",
      "on_boundary False\n",
      "X [ 0.5605469  -0.00274864]\n",
      "on_boundary False\n",
      "X [-0.6894531  -0.00224888]\n",
      "on_boundary False\n",
      "X [ 0.31054688 -0.00174913]\n",
      "on_boundary False\n",
      "X [-0.18945312 -0.00124937]\n",
      "on_boundary False\n",
      "X [ 8.105469e-01 -7.496178e-04]\n",
      "on_boundary False\n",
      "X [-8.1445312e-01 -2.4986267e-04]\n",
      "on_boundary False\n",
      "X [0.18554688 0.00024986]\n",
      "on_boundary False\n",
      "X [-0.31445312  0.00074965]\n",
      "on_boundary False\n",
      "X [0.6855469  0.00124937]\n",
      "on_boundary False\n",
      "X [-0.5644531  0.0017491]\n",
      "on_boundary False\n",
      "X [0.43554688 0.00224888]\n",
      "on_boundary False\n",
      "X [-0.06445312  0.00274861]\n",
      "on_boundary False\n",
      "X [0.9355469  0.00324839]\n",
      "on_boundary False\n",
      "X [-0.8769531   0.00374812]\n",
      "on_boundary False\n",
      "X [0.12304688 0.0042479 ]\n",
      "on_boundary False\n",
      "X [-0.37695312  0.00474763]\n",
      "on_boundary False\n",
      "X [0.6230469  0.00524735]\n",
      "on_boundary False\n",
      "X [-0.6269531   0.00574714]\n",
      "on_boundary False\n",
      "X [0.37304688 0.00624686]\n",
      "on_boundary False\n",
      "X [-0.12695312  0.00674665]\n",
      "on_boundary False\n",
      "X [0.8730469  0.00724638]\n",
      "on_boundary False\n",
      "X [-0.7519531  0.0077461]\n",
      "on_boundary False\n",
      "X [0.24804688 0.00824589]\n",
      "on_boundary False\n",
      "X [-0.25195312  0.00874561]\n",
      "on_boundary False\n",
      "X [0.7480469 0.0092454]\n",
      "on_boundary False\n",
      "X [-0.5019531   0.00974512]\n",
      "on_boundary False\n",
      "X [0.49804688 0.01024491]\n",
      "on_boundary False\n",
      "X [-0.00195312  0.01074463]\n",
      "on_boundary False\n",
      "X [0.9980469  0.01124436]\n",
      "on_boundary False\n",
      "X [-0.99902344  0.01174414]\n",
      "on_boundary False\n",
      "X [0.00097656 0.01224387]\n",
      "on_boundary False\n",
      "X [-0.49902344  0.01274365]\n",
      "on_boundary False\n",
      "X [0.50097656 0.01324338]\n",
      "on_boundary False\n",
      "X [-0.74902344  0.0137431 ]\n",
      "on_boundary False\n",
      "X [0.25097656 0.01424289]\n",
      "on_boundary False\n",
      "X [-0.24902344  0.01474261]\n",
      "on_boundary False\n",
      "X [0.75097656 0.0152424 ]\n",
      "on_boundary False\n",
      "X [-0.87402344  0.01574212]\n",
      "on_boundary False\n",
      "X [0.12597656 0.01624191]\n",
      "on_boundary False\n",
      "X [-0.37402344  0.01674163]\n",
      "on_boundary False\n",
      "X [0.62597656 0.01724136]\n",
      "on_boundary False\n",
      "X [-0.62402344  0.01774114]\n",
      "on_boundary False\n",
      "X [0.37597656 0.01824087]\n",
      "on_boundary False\n",
      "X [-0.12402344  0.01874065]\n",
      "on_boundary False\n",
      "X [0.87597656 0.01924038]\n",
      "on_boundary False\n",
      "X [-0.93652344  0.0197401 ]\n",
      "on_boundary False\n",
      "X [0.06347656 0.02023989]\n",
      "on_boundary False\n",
      "X [-0.43652344  0.02073961]\n",
      "on_boundary False\n",
      "X [0.56347656 0.0212394 ]\n",
      "on_boundary False\n",
      "X [-0.68652344  0.02173913]\n",
      "on_boundary False\n",
      "X [0.31347656 0.02223891]\n",
      "on_boundary False\n",
      "X [-0.18652344  0.02273864]\n",
      "on_boundary False\n",
      "X [0.81347656 0.02323836]\n",
      "on_boundary False\n",
      "X [-0.81152344  0.02373815]\n",
      "on_boundary False\n",
      "X [0.18847656 0.02423787]\n",
      "on_boundary False\n",
      "X [-0.31152344  0.02473766]\n",
      "on_boundary False\n",
      "X [0.68847656 0.02523738]\n",
      "on_boundary False\n",
      "X [-0.56152344  0.02573711]\n",
      "on_boundary False\n",
      "X [0.43847656 0.02623689]\n",
      "on_boundary False\n",
      "X [-0.06152344  0.02673662]\n",
      "on_boundary False\n",
      "X [0.93847656 0.0272364 ]\n",
      "on_boundary False\n",
      "X [-0.96777344  0.02773613]\n",
      "on_boundary False\n",
      "X [0.03222656 0.02823585]\n",
      "on_boundary False\n",
      "X [-0.46777344  0.02873564]\n",
      "on_boundary False\n",
      "X [0.53222656 0.02923536]\n",
      "on_boundary False\n",
      "X [-0.71777344  0.02973515]\n",
      "on_boundary False\n",
      "X [0.28222656 0.03023487]\n",
      "on_boundary False\n",
      "X [-0.21777344  0.03073466]\n",
      "on_boundary False\n",
      "X [0.78222656 0.03123438]\n",
      "on_boundary False\n",
      "X [-0.84277344  0.03173411]\n",
      "on_boundary False\n",
      "X [0.15722656 0.03223389]\n",
      "on_boundary False\n",
      "X [-0.34277344  0.03273362]\n",
      "on_boundary False\n",
      "X [0.65722656 0.0332334 ]\n",
      "on_boundary False\n",
      "X [-0.59277344  0.03373313]\n",
      "on_boundary False\n",
      "X [0.40722656 0.03423285]\n",
      "on_boundary False\n",
      "X [-0.09277344  0.03473264]\n",
      "on_boundary False\n",
      "X [0.90722656 0.03523237]\n",
      "on_boundary False\n",
      "X [-0.90527344  0.03573215]\n",
      "on_boundary False\n",
      "X [0.09472656 0.03623188]\n",
      "on_boundary False\n",
      "X [-0.40527344  0.03673166]\n",
      "on_boundary False\n",
      "X [0.59472656 0.03723139]\n",
      "on_boundary False\n",
      "X [-0.65527344  0.03773111]\n",
      "on_boundary False\n",
      "X [0.34472656 0.0382309 ]\n",
      "on_boundary False\n",
      "X [-0.15527344  0.03873062]\n",
      "on_boundary False\n",
      "X [0.84472656 0.03923041]\n",
      "on_boundary False\n",
      "X [-0.78027344  0.03973013]\n",
      "on_boundary False\n",
      "X [0.21972656 0.04022986]\n",
      "on_boundary False\n",
      "X [-0.28027344  0.04072964]\n",
      "on_boundary False\n",
      "X [0.71972656 0.04122937]\n",
      "on_boundary False\n",
      "X [-0.53027344  0.04172915]\n",
      "on_boundary False\n",
      "X [0.46972656 0.04222888]\n",
      "on_boundary False\n",
      "X [-0.03027344  0.04272866]\n",
      "on_boundary False\n",
      "X [0.96972656 0.04322839]\n",
      "on_boundary False\n",
      "X [-0.98339844  0.04372811]\n",
      "on_boundary False\n",
      "X [0.01660156 0.0442279 ]\n",
      "on_boundary False\n",
      "X [-0.48339844  0.04472762]\n",
      "on_boundary False\n",
      "X [0.51660156 0.04522741]\n",
      "on_boundary False\n",
      "X [-0.73339844  0.04572713]\n",
      "on_boundary False\n",
      "X [0.26660156 0.04622686]\n",
      "on_boundary False\n",
      "X [-0.23339844  0.04672664]\n",
      "on_boundary False\n",
      "X [0.76660156 0.04722637]\n",
      "on_boundary False\n",
      "X [-0.85839844  0.04772615]\n",
      "on_boundary False\n",
      "X [0.14160156 0.04822588]\n",
      "on_boundary False\n",
      "X [-0.35839844  0.04872566]\n",
      "on_boundary False\n",
      "X [0.64160156 0.04922539]\n",
      "on_boundary False\n",
      "X [-0.60839844  0.04972512]\n",
      "on_boundary False\n",
      "X [0.39160156 0.0502249 ]\n",
      "on_boundary False\n",
      "X [-0.10839844  0.05072463]\n",
      "on_boundary False\n",
      "X [0.89160156 0.05122441]\n",
      "on_boundary False\n",
      "X [-0.92089844  0.05172414]\n",
      "on_boundary False\n",
      "X [0.07910156 0.05222386]\n",
      "on_boundary False\n",
      "X [-0.42089844  0.05272365]\n",
      "on_boundary False\n",
      "X [0.57910156 0.05322337]\n",
      "on_boundary False\n",
      "X [-0.67089844  0.05372316]\n",
      "on_boundary False\n",
      "X [0.32910156 0.05422288]\n",
      "on_boundary False\n",
      "X [-0.17089844  0.05472267]\n",
      "on_boundary False\n",
      "X [0.82910156 0.05522239]\n",
      "on_boundary False\n",
      "X [-0.79589844  0.05572212]\n",
      "on_boundary False\n",
      "X [0.20410156 0.0562219 ]\n",
      "on_boundary False\n",
      "X [-0.29589844  0.05672163]\n",
      "on_boundary False\n",
      "X [0.70410156 0.05722141]\n",
      "on_boundary False\n",
      "X [-0.54589844  0.05772114]\n",
      "on_boundary False\n",
      "X [0.45410156 0.05822086]\n",
      "on_boundary False\n",
      "X [-0.04589844  0.05872065]\n",
      "on_boundary False\n",
      "X [0.95410156 0.05922037]\n",
      "on_boundary False\n",
      "X [-0.95214844  0.05972016]\n",
      "on_boundary False\n",
      "X [0.04785156 0.06021988]\n",
      "on_boundary False\n",
      "X [-0.45214844  0.06071967]\n",
      "on_boundary False\n",
      "X [0.54785156 0.06121939]\n",
      "on_boundary False\n",
      "X [-0.70214844  0.06171912]\n",
      "on_boundary False\n",
      "X [0.29785156 0.0622189 ]\n",
      "on_boundary False\n",
      "X [-0.20214844  0.06271863]\n",
      "on_boundary False\n",
      "X [0.79785156 0.06321841]\n",
      "on_boundary False\n",
      "X [-0.82714844  0.06371814]\n",
      "on_boundary False\n",
      "X [0.17285156 0.06421787]\n",
      "on_boundary False\n",
      "X [-0.32714844  0.06471765]\n",
      "on_boundary False\n",
      "X [0.67285156 0.06521738]\n",
      "on_boundary False\n",
      "X [-0.57714844  0.06571716]\n",
      "on_boundary False\n",
      "X [0.42285156 0.06621689]\n",
      "on_boundary False\n",
      "X [-0.07714844  0.06671667]\n",
      "on_boundary False\n",
      "X [0.92285156 0.0672164 ]\n",
      "on_boundary False\n",
      "X [-0.88964844  0.06771612]\n",
      "on_boundary False\n",
      "X [0.11035156 0.06821591]\n",
      "on_boundary False\n",
      "X [-0.38964844  0.06871563]\n",
      "on_boundary False\n",
      "X [0.61035156 0.06921542]\n",
      "on_boundary False\n",
      "X [-0.63964844  0.06971514]\n",
      "on_boundary False\n",
      "X [0.36035156 0.07021487]\n",
      "on_boundary False\n",
      "X [-0.13964844  0.07071465]\n",
      "on_boundary False\n",
      "X [0.86035156 0.07121438]\n",
      "on_boundary False\n",
      "X [-0.76464844  0.07171416]\n",
      "on_boundary False\n",
      "X [0.23535156 0.07221389]\n",
      "on_boundary False\n",
      "X [-0.26464844  0.07271361]\n",
      "on_boundary False\n",
      "X [0.73535156 0.0732134 ]\n",
      "on_boundary False\n",
      "X [-0.51464844  0.07371312]\n",
      "on_boundary False\n",
      "X [0.48535156 0.07421291]\n",
      "on_boundary False\n",
      "X [-0.01464844  0.07471263]\n",
      "on_boundary False\n",
      "X [0.98535156 0.07521242]\n",
      "on_boundary False\n",
      "X [-0.99121094  0.07571214]\n",
      "on_boundary False\n",
      "X [0.00878906 0.07621187]\n",
      "on_boundary False\n",
      "X [-0.49121094  0.07671165]\n",
      "on_boundary False\n",
      "X [0.50878906 0.07721138]\n",
      "on_boundary False\n",
      "X [-0.74121094  0.07771116]\n",
      "on_boundary False\n",
      "X [0.25878906 0.07821089]\n",
      "on_boundary False\n",
      "X [-0.24121094  0.07871062]\n",
      "on_boundary False\n",
      "X [0.75878906 0.0792104 ]\n",
      "on_boundary False\n",
      "X [-0.86621094  0.07971013]\n",
      "on_boundary False\n",
      "X [0.13378906 0.08020991]\n",
      "on_boundary False\n",
      "X [-0.36621094  0.08070964]\n",
      "on_boundary False\n",
      "X [0.63378906 0.08120942]\n",
      "on_boundary False\n",
      "X [-0.61621094  0.08170915]\n",
      "on_boundary False\n",
      "X [0.38378906 0.08220887]\n",
      "on_boundary False\n",
      "X [-0.11621094  0.08270866]\n",
      "on_boundary False\n",
      "X [0.88378906 0.08320838]\n",
      "on_boundary False\n",
      "X [-0.92871094  0.08370817]\n",
      "on_boundary False\n",
      "X [0.07128906 0.08420789]\n",
      "on_boundary False\n",
      "X [-0.42871094  0.08470762]\n",
      "on_boundary False\n",
      "X [0.57128906 0.0852074 ]\n",
      "on_boundary False\n",
      "X [-0.67871094  0.08570713]\n",
      "on_boundary False\n",
      "X [0.32128906 0.08620691]\n",
      "on_boundary False\n",
      "X [-0.17871094  0.08670664]\n",
      "on_boundary False\n",
      "X [0.82128906 0.08720642]\n",
      "on_boundary False\n",
      "X [-0.80371094  0.08770615]\n",
      "on_boundary False\n",
      "X [0.19628906 0.08820587]\n",
      "on_boundary False\n",
      "X [-0.30371094  0.08870566]\n",
      "on_boundary False\n",
      "X [0.69628906 0.08920538]\n",
      "on_boundary False\n",
      "X [-0.55371094  0.08970517]\n",
      "on_boundary False\n",
      "X [0.44628906 0.09020489]\n",
      "on_boundary False\n",
      "X [-0.05371094  0.09070462]\n",
      "on_boundary False\n",
      "X [0.94628906 0.0912044 ]\n",
      "on_boundary False\n",
      "X [-0.95996094  0.09170413]\n",
      "on_boundary False\n",
      "X [0.04003906 0.09220392]\n",
      "on_boundary False\n",
      "X [-0.45996094  0.09270364]\n",
      "on_boundary False\n",
      "X [0.54003906 0.09320343]\n",
      "on_boundary False\n",
      "X [-0.70996094  0.09370315]\n",
      "on_boundary False\n",
      "X [0.29003906 0.09420288]\n",
      "on_boundary False\n",
      "X [-0.20996094  0.09470266]\n",
      "on_boundary False\n",
      "X [0.79003906 0.09520239]\n",
      "on_boundary False\n",
      "X [-0.83496094  0.09570217]\n",
      "on_boundary False\n",
      "X [0.16503906 0.0962019 ]\n",
      "on_boundary False\n",
      "X [-0.33496094  0.09670162]\n",
      "on_boundary False\n",
      "X [0.66503906 0.09720141]\n",
      "on_boundary False\n",
      "X [-0.58496094  0.09770113]\n",
      "on_boundary False\n",
      "X [0.41503906 0.09820092]\n",
      "on_boundary False\n",
      "X [-0.08496094  0.09870064]\n",
      "on_boundary False\n",
      "X [0.91503906 0.09920043]\n",
      "on_boundary False\n",
      "X [-0.89746094  0.09970015]\n",
      "on_boundary False\n",
      "X [0.10253906 0.10019988]\n",
      "on_boundary False\n",
      "X [-0.39746094  0.10069966]\n",
      "on_boundary False\n",
      "X [0.60253906 0.10119939]\n",
      "on_boundary False\n",
      "X [-0.64746094  0.10169917]\n",
      "on_boundary False\n",
      "X [0.35253906 0.1021989 ]\n",
      "on_boundary False\n",
      "X [-0.14746094  0.10269862]\n",
      "on_boundary False\n",
      "X [0.85253906 0.10319841]\n",
      "on_boundary False\n",
      "X [-0.77246094  0.10369813]\n",
      "on_boundary False\n",
      "X [0.22753906 0.10419792]\n",
      "on_boundary False\n",
      "X [-0.27246094  0.10469764]\n",
      "on_boundary False\n",
      "X [0.72753906 0.10519743]\n",
      "on_boundary False\n",
      "X [-0.52246094  0.10569715]\n",
      "on_boundary False\n",
      "X [0.47753906 0.10619688]\n",
      "on_boundary False\n",
      "X [-0.02246094  0.10669667]\n",
      "on_boundary False\n",
      "X [0.97753906 0.10719639]\n",
      "on_boundary False\n",
      "X [-0.97558594  0.10769618]\n",
      "on_boundary False\n",
      "X [0.02441406 0.1081959 ]\n",
      "on_boundary False\n",
      "X [-0.47558594  0.10869563]\n",
      "on_boundary False\n",
      "X [0.52441406 0.10919541]\n",
      "on_boundary False\n",
      "X [-0.72558594  0.10969514]\n",
      "on_boundary False\n",
      "X [0.27441406 0.11019492]\n",
      "on_boundary False\n",
      "X [-0.22558594  0.11069465]\n",
      "on_boundary False\n",
      "X [0.77441406 0.11119443]\n",
      "on_boundary False\n",
      "X [-0.85058594  0.11169416]\n",
      "on_boundary False\n",
      "X [0.14941406 0.11219388]\n",
      "on_boundary False\n",
      "X [-0.35058594  0.11269367]\n",
      "on_boundary False\n",
      "X [0.64941406 0.11319339]\n",
      "on_boundary False\n",
      "X [-0.60058594  0.11369318]\n",
      "on_boundary False\n",
      "X [0.39941406 0.1141929 ]\n",
      "on_boundary False\n",
      "X [-0.10058594  0.11469263]\n",
      "on_boundary False\n",
      "X [0.89941406 0.11519241]\n",
      "on_boundary False\n",
      "X [-0.91308594  0.11569214]\n",
      "on_boundary False\n",
      "X [0.08691406 0.11619192]\n",
      "on_boundary False\n",
      "X [-0.41308594  0.11669165]\n",
      "on_boundary False\n",
      "X [0.58691406 0.11719143]\n",
      "on_boundary False\n",
      "X [-0.66308594  0.11769116]\n",
      "on_boundary False\n",
      "X [0.33691406 0.11819088]\n",
      "on_boundary False\n",
      "X [-0.16308594  0.11869067]\n",
      "on_boundary False\n",
      "X [0.83691406 0.11919039]\n",
      "on_boundary False\n",
      "X [-0.78808594  0.11969018]\n",
      "on_boundary False\n",
      "X [0.21191406 0.12018991]\n",
      "on_boundary False\n",
      "X [-0.28808594  0.12068963]\n",
      "on_boundary False\n",
      "X [0.71191406 0.12118942]\n",
      "on_boundary False\n",
      "X [-0.53808594  0.12168914]\n",
      "on_boundary False\n",
      "X [0.46191406 0.12218893]\n",
      "on_boundary False\n",
      "X [-0.03808594  0.12268865]\n",
      "on_boundary False\n",
      "X [0.96191406 0.12318838]\n",
      "on_boundary False\n",
      "X [-0.94433594  0.12368816]\n",
      "on_boundary False\n",
      "X [0.05566406 0.12418789]\n",
      "on_boundary False\n",
      "X [-0.44433594  0.12468767]\n",
      "on_boundary False\n",
      "X [0.55566406 0.1251874 ]\n",
      "on_boundary False\n",
      "X [-0.69433594  0.12568718]\n",
      "on_boundary False\n",
      "X [0.30566406 0.1261869 ]\n",
      "on_boundary False\n",
      "X [-0.19433594  0.12668663]\n",
      "on_boundary False\n",
      "X [0.80566406 0.12718642]\n",
      "on_boundary False\n",
      "X [-0.81933594  0.12768614]\n",
      "on_boundary False\n",
      "X [0.18066406 0.12818593]\n",
      "on_boundary False\n",
      "X [-0.31933594  0.12868565]\n",
      "on_boundary False\n",
      "X [0.68066406 0.12918538]\n",
      "on_boundary False\n",
      "X [-0.56933594  0.12968516]\n",
      "on_boundary False\n",
      "X [0.43066406 0.13018489]\n",
      "on_boundary False\n",
      "X [-0.06933594  0.13068467]\n",
      "on_boundary False\n",
      "X [0.93066406 0.1311844 ]\n",
      "on_boundary False\n",
      "X [-0.88183594  0.13168418]\n",
      "on_boundary False\n",
      "X [0.11816406 0.13218391]\n",
      "on_boundary False\n",
      "X [-0.38183594  0.13268363]\n",
      "on_boundary False\n",
      "X [0.61816406 0.13318342]\n",
      "on_boundary False\n",
      "X [-0.63183594  0.13368315]\n",
      "on_boundary False\n",
      "X [0.36816406 0.13418293]\n",
      "on_boundary False\n",
      "X [-0.13183594  0.13468266]\n",
      "on_boundary False\n",
      "X [0.86816406 0.13518238]\n",
      "on_boundary False\n",
      "X [-0.75683594  0.13568217]\n",
      "on_boundary False\n",
      "X [0.24316406 0.13618189]\n",
      "on_boundary False\n",
      "X [-0.25683594  0.13668168]\n",
      "on_boundary False\n",
      "X [0.74316406 0.1371814 ]\n",
      "on_boundary False\n",
      "X [-0.50683594  0.13768119]\n",
      "on_boundary False\n",
      "X [0.49316406 0.13818091]\n",
      "on_boundary False\n",
      "X [-0.00683594  0.13868064]\n",
      "on_boundary False\n",
      "X [0.99316406 0.13918042]\n",
      "on_boundary False\n",
      "X [-0.9951172   0.13968015]\n",
      "on_boundary False\n",
      "X [0.00488281 0.14017993]\n",
      "on_boundary False\n",
      "X [-0.4951172   0.14067966]\n",
      "on_boundary False\n",
      "X [0.5048828  0.14117938]\n",
      "on_boundary False\n",
      "X [-0.7451172   0.14167917]\n",
      "on_boundary False\n",
      "X [0.2548828 0.1421789]\n",
      "on_boundary False\n",
      "X [-0.24511719  0.14267868]\n",
      "on_boundary False\n",
      "X [0.7548828 0.1431784]\n",
      "on_boundary False\n",
      "X [-0.8701172   0.14367819]\n",
      "on_boundary False\n",
      "X [0.12988281 0.14417791]\n",
      "on_boundary False\n",
      "X [-0.3701172   0.14467764]\n",
      "on_boundary False\n",
      "X [0.6298828  0.14517742]\n",
      "on_boundary False\n",
      "X [-0.6201172   0.14567715]\n",
      "on_boundary False\n",
      "X [0.3798828  0.14617693]\n",
      "on_boundary False\n",
      "X [-0.12011719  0.14667666]\n",
      "on_boundary False\n",
      "X [0.8798828  0.14717638]\n",
      "on_boundary False\n",
      "X [-0.9326172   0.14767617]\n",
      "on_boundary False\n",
      "X [0.06738281 0.1481759 ]\n",
      "on_boundary False\n",
      "X [-0.4326172   0.14867568]\n",
      "on_boundary False\n",
      "X [0.5673828 0.1491754]\n",
      "on_boundary False\n",
      "X [-0.6826172   0.14967519]\n",
      "on_boundary False\n",
      "X [0.3173828  0.15017492]\n",
      "on_boundary False\n",
      "X [-0.18261719  0.15067464]\n",
      "on_boundary False\n",
      "X [0.8173828  0.15117443]\n",
      "on_boundary False\n",
      "X [-0.8076172   0.15167415]\n",
      "on_boundary False\n",
      "X [0.19238281 0.15217394]\n",
      "on_boundary False\n",
      "X [-0.3076172   0.15267366]\n",
      "on_boundary False\n",
      "X [0.6923828  0.15317339]\n",
      "on_boundary False\n",
      "X [-0.5576172   0.15367317]\n",
      "on_boundary False\n",
      "X [0.4423828 0.1541729]\n",
      "on_boundary False\n",
      "X [-0.05761719  0.15467268]\n",
      "on_boundary False\n",
      "X [0.9423828  0.15517241]\n",
      "on_boundary False\n",
      "X [-0.9638672  0.1556722]\n",
      "on_boundary False\n",
      "X [0.03613281 0.15617192]\n",
      "on_boundary False\n",
      "X [-0.4638672   0.15667164]\n",
      "on_boundary False\n",
      "X [0.5361328  0.15717143]\n",
      "on_boundary False\n",
      "X [-0.7138672   0.15767115]\n",
      "on_boundary False\n",
      "X [0.2861328  0.15817094]\n",
      "on_boundary False\n",
      "X [-0.21386719  0.15867066]\n",
      "on_boundary False\n",
      "X [0.7861328  0.15917039]\n",
      "on_boundary False\n",
      "X [-0.8388672   0.15967017]\n",
      "on_boundary False\n",
      "X [0.16113281 0.1601699 ]\n",
      "on_boundary False\n",
      "X [-0.3388672   0.16066968]\n",
      "on_boundary False\n",
      "X [0.6611328  0.16116941]\n",
      "on_boundary False\n",
      "X [-0.5888672  0.1616692]\n",
      "on_boundary False\n",
      "X [0.4111328  0.16216892]\n",
      "on_boundary False\n",
      "X [-0.08886719  0.16266865]\n",
      "on_boundary False\n",
      "X [0.9111328  0.16316843]\n",
      "on_boundary False\n",
      "X [-0.9013672   0.16366816]\n",
      "on_boundary False\n",
      "X [0.09863281 0.16416794]\n",
      "on_boundary False\n",
      "X [-0.4013672   0.16466767]\n",
      "on_boundary False\n",
      "X [0.5986328  0.16516739]\n",
      "on_boundary False\n",
      "X [-0.6513672   0.16566718]\n",
      "on_boundary False\n",
      "X [0.3486328 0.1661669]\n",
      "on_boundary False\n",
      "X [-0.15136719  0.16666669]\n",
      "on_boundary False\n",
      "X [0.8486328  0.16716641]\n",
      "on_boundary False\n",
      "X [-0.7763672   0.16766614]\n",
      "on_boundary False\n",
      "X [0.22363281 0.16816592]\n",
      "on_boundary False\n",
      "X [-0.2763672   0.16866565]\n",
      "on_boundary False\n",
      "X [0.7236328  0.16916543]\n",
      "on_boundary False\n",
      "X [-0.5263672   0.16966516]\n",
      "on_boundary False\n",
      "X [0.4736328  0.17016494]\n",
      "on_boundary False\n",
      "X [-0.02636719  0.17066467]\n",
      "on_boundary False\n",
      "X [0.9736328 0.1711644]\n",
      "on_boundary False\n",
      "X [-0.9794922   0.17166418]\n",
      "on_boundary False\n",
      "X [0.02050781 0.1721639 ]\n",
      "on_boundary False\n",
      "X [-0.4794922   0.17266369]\n",
      "on_boundary False\n",
      "X [0.5205078  0.17316341]\n",
      "on_boundary False\n",
      "X [-0.7294922   0.17366314]\n",
      "on_boundary False\n",
      "X [0.2705078  0.17416292]\n",
      "on_boundary False\n",
      "X [-0.22949219  0.17466265]\n",
      "on_boundary False\n",
      "X [0.7705078  0.17516243]\n",
      "on_boundary False\n",
      "X [-0.8544922   0.17566216]\n",
      "on_boundary False\n",
      "X [0.14550781 0.17616194]\n",
      "on_boundary False\n",
      "X [-0.3544922   0.17666167]\n",
      "on_boundary False\n",
      "X [0.6455078 0.1771614]\n",
      "on_boundary False\n",
      "X [-0.6044922   0.17766118]\n",
      "on_boundary False\n",
      "X [0.3955078 0.1781609]\n",
      "on_boundary False\n",
      "X [-0.10449219  0.17866069]\n",
      "on_boundary False\n",
      "X [0.8955078  0.17916042]\n",
      "on_boundary False\n",
      "X [-0.9169922   0.17966014]\n",
      "on_boundary False\n",
      "X [0.08300781 0.18015993]\n",
      "on_boundary False\n",
      "X [-0.4169922   0.18065965]\n",
      "on_boundary False\n",
      "X [0.5830078  0.18115944]\n",
      "on_boundary False\n",
      "X [-0.6669922   0.18165916]\n",
      "on_boundary False\n",
      "X [0.3330078  0.18215895]\n",
      "on_boundary False\n",
      "X [-0.16699219  0.18265867]\n",
      "on_boundary False\n",
      "X [0.8330078 0.1831584]\n",
      "on_boundary False\n",
      "X [-0.7919922   0.18365818]\n",
      "on_boundary False\n",
      "X [0.20800781 0.18415791]\n",
      "on_boundary False\n",
      "X [-0.2919922  0.1846577]\n",
      "on_boundary False\n",
      "X [0.7080078  0.18515742]\n",
      "on_boundary False\n",
      "X [-0.5419922   0.18565714]\n",
      "on_boundary False\n",
      "X [0.4580078  0.18615693]\n",
      "on_boundary False\n",
      "X [-0.04199219  0.18665665]\n",
      "on_boundary False\n",
      "X [0.9580078  0.18715644]\n",
      "on_boundary False\n",
      "X [-0.9482422   0.18765616]\n",
      "on_boundary False\n",
      "X [0.05175781 0.18815595]\n",
      "on_boundary False\n",
      "X [-0.4482422   0.18865567]\n",
      "on_boundary False\n",
      "X [0.5517578 0.1891554]\n",
      "on_boundary False\n",
      "X [-0.6982422   0.18965518]\n",
      "on_boundary False\n",
      "X [0.3017578  0.19015491]\n",
      "on_boundary False\n",
      "X [-0.19824219  0.1906547 ]\n",
      "on_boundary False\n",
      "X [0.8017578  0.19115442]\n",
      "on_boundary False\n",
      "X [-0.8232422   0.19165415]\n",
      "on_boundary False\n",
      "X [0.17675781 0.19215393]\n",
      "on_boundary False\n",
      "X [-0.3232422   0.19265366]\n",
      "on_boundary False\n",
      "X [0.6767578  0.19315344]\n",
      "on_boundary False\n",
      "X [-0.5732422   0.19365317]\n",
      "on_boundary False\n",
      "X [0.4267578  0.19415295]\n",
      "on_boundary False\n",
      "X [-0.07324219  0.19465268]\n",
      "on_boundary False\n",
      "X [0.9267578 0.1951524]\n",
      "on_boundary False\n",
      "X [-0.8857422   0.19565219]\n",
      "on_boundary False\n",
      "X [0.11425781 0.19615191]\n",
      "on_boundary False\n",
      "X [-0.3857422  0.1966517]\n",
      "on_boundary False\n",
      "X [0.6142578  0.19715142]\n",
      "on_boundary False\n",
      "X [-0.6357422   0.19765115]\n",
      "on_boundary False\n",
      "X [0.3642578  0.19815093]\n",
      "on_boundary False\n",
      "X [-0.13574219  0.19865066]\n",
      "on_boundary False\n",
      "X [0.8642578  0.19915044]\n",
      "on_boundary False\n",
      "X [-0.7607422   0.19965017]\n",
      "on_boundary False\n",
      "X [0.23925781 0.20014995]\n",
      "on_boundary False\n",
      "X [-0.2607422   0.20064968]\n",
      "on_boundary False\n",
      "X [0.7392578 0.2011494]\n",
      "on_boundary False\n",
      "X [-0.5107422   0.20164919]\n",
      "on_boundary False\n",
      "X [0.4892578  0.20214891]\n",
      "on_boundary False\n",
      "X [-0.01074219  0.2026487 ]\n",
      "on_boundary False\n",
      "X [0.9892578  0.20314842]\n",
      "on_boundary False\n",
      "X [-0.9873047   0.20364815]\n",
      "on_boundary False\n",
      "X [0.01269531 0.20414793]\n",
      "on_boundary False\n",
      "X [-0.4873047   0.20464766]\n",
      "on_boundary False\n",
      "X [0.5126953  0.20514745]\n",
      "on_boundary False\n",
      "X [-0.7373047   0.20564717]\n",
      "on_boundary False\n",
      "X [0.2626953  0.20614696]\n",
      "on_boundary False\n",
      "X [-0.23730469  0.20664668]\n",
      "on_boundary False\n",
      "X [0.7626953 0.2071464]\n",
      "on_boundary False\n",
      "X [-0.8623047   0.20764619]\n",
      "on_boundary False\n",
      "X [0.13769531 0.20814592]\n",
      "on_boundary False\n",
      "X [-0.3623047  0.2086457]\n",
      "on_boundary False\n",
      "X [0.6376953  0.20914543]\n",
      "on_boundary False\n",
      "X [-0.6123047   0.20964515]\n",
      "on_boundary False\n",
      "X [0.3876953  0.21014494]\n",
      "on_boundary False\n",
      "X [-0.11230469  0.21064466]\n",
      "on_boundary False\n",
      "X [0.8876953  0.21114445]\n",
      "on_boundary False\n",
      "X [-0.9248047   0.21164417]\n",
      "on_boundary False\n",
      "X [0.07519531 0.21214396]\n",
      "on_boundary False\n",
      "X [-0.4248047   0.21264368]\n",
      "on_boundary False\n",
      "X [0.5751953  0.21314341]\n",
      "on_boundary False\n",
      "X [-0.6748047  0.2136432]\n",
      "on_boundary False\n",
      "X [0.3251953  0.21414292]\n",
      "on_boundary False\n",
      "X [-0.17480469  0.2146427 ]\n",
      "on_boundary False\n",
      "X [0.8251953  0.21514243]\n",
      "on_boundary False\n",
      "X [-0.7998047   0.21564215]\n",
      "on_boundary False\n",
      "X [0.20019531 0.21614194]\n",
      "on_boundary False\n",
      "X [-0.2998047   0.21664166]\n",
      "on_boundary False\n",
      "X [0.7001953  0.21714145]\n",
      "on_boundary False\n",
      "X [-0.5498047   0.21764117]\n",
      "on_boundary False\n",
      "X [0.4501953 0.2181409]\n",
      "on_boundary False\n",
      "X [-0.04980469  0.21864069]\n",
      "on_boundary False\n",
      "X [0.9501953  0.21914041]\n",
      "on_boundary False\n",
      "X [-0.9560547  0.2196402]\n",
      "on_boundary False\n",
      "X [0.04394531 0.22013992]\n",
      "on_boundary False\n",
      "X [-0.4560547  0.2206397]\n",
      "on_boundary False\n",
      "X [0.5439453  0.22113943]\n",
      "on_boundary False\n",
      "X [-0.7060547   0.22163916]\n",
      "on_boundary False\n",
      "X [0.2939453  0.22213894]\n",
      "on_boundary False\n",
      "X [-0.20605469  0.22263867]\n",
      "on_boundary False\n",
      "X [0.7939453  0.22313845]\n",
      "on_boundary False\n",
      "X [-0.8310547   0.22363818]\n",
      "on_boundary False\n",
      "X [0.16894531 0.2241379 ]\n",
      "on_boundary False\n",
      "X [-0.3310547   0.22463769]\n",
      "on_boundary False\n",
      "X [0.6689453  0.22513741]\n",
      "on_boundary False\n",
      "X [-0.5810547  0.2256372]\n",
      "on_boundary False\n",
      "X [0.4189453  0.22613692]\n",
      "on_boundary False\n",
      "X [-0.08105469  0.22663671]\n",
      "on_boundary False\n",
      "X [0.9189453  0.22713643]\n",
      "on_boundary False\n",
      "X [-0.8935547   0.22763616]\n",
      "on_boundary False\n",
      "X [0.10644531 0.22813594]\n",
      "on_boundary False\n",
      "X [-0.3935547   0.22863567]\n",
      "on_boundary False\n",
      "X [0.6064453  0.22913545]\n",
      "on_boundary False\n",
      "X [-0.6435547   0.22963518]\n",
      "on_boundary False\n",
      "X [0.3564453 0.2301349]\n",
      "on_boundary False\n",
      "X [-0.14355469  0.23063469]\n",
      "on_boundary False\n",
      "X [0.8564453  0.23113441]\n",
      "on_boundary False\n",
      "X [-0.7685547  0.2316342]\n",
      "on_boundary False\n",
      "X [0.23144531 0.23213392]\n",
      "on_boundary False\n",
      "X [-0.2685547   0.23263371]\n",
      "on_boundary False\n",
      "X [0.7314453  0.23313344]\n",
      "on_boundary False\n",
      "X [-0.5185547   0.23363316]\n",
      "on_boundary False\n",
      "X [0.4814453  0.23413295]\n",
      "on_boundary False\n",
      "X [-0.01855469  0.23463267]\n",
      "on_boundary False\n",
      "X [0.9814453  0.23513246]\n",
      "on_boundary False\n",
      "X [-0.9716797   0.23563218]\n",
      "on_boundary False\n",
      "X [0.02832031 0.2361319 ]\n",
      "on_boundary False\n",
      "X [-0.4716797   0.23663169]\n",
      "on_boundary False\n",
      "X [0.5283203  0.23713142]\n",
      "on_boundary False\n",
      "X [-0.7216797  0.2376312]\n",
      "on_boundary False\n",
      "X [0.2783203  0.23813093]\n",
      "on_boundary False\n",
      "X [-0.22167969  0.23863071]\n",
      "on_boundary False\n",
      "X [0.7783203  0.23913044]\n",
      "on_boundary False\n",
      "X [-0.8466797   0.23963016]\n",
      "on_boundary False\n",
      "X [0.15332031 0.24012995]\n",
      "on_boundary False\n",
      "X [-0.3466797   0.24062967]\n",
      "on_boundary False\n",
      "X [0.6533203  0.24112946]\n",
      "on_boundary False\n",
      "X [-0.5966797   0.24162918]\n",
      "on_boundary False\n",
      "X [0.4033203  0.24212891]\n",
      "on_boundary False\n",
      "X [-0.09667969  0.2426287 ]\n",
      "on_boundary False\n",
      "X [0.9033203  0.24312842]\n",
      "on_boundary False\n",
      "X [-0.9091797  0.2436282]\n",
      "on_boundary False\n",
      "X [0.09082031 0.24412793]\n",
      "on_boundary False\n",
      "X [-0.4091797   0.24462771]\n",
      "on_boundary False\n",
      "X [0.5908203  0.24512744]\n",
      "on_boundary False\n",
      "X [-0.6591797   0.24562716]\n",
      "on_boundary False\n",
      "X [0.3408203  0.24612695]\n",
      "on_boundary False\n",
      "X [-0.15917969  0.24662668]\n",
      "on_boundary False\n",
      "X [0.8408203  0.24712646]\n",
      "on_boundary False\n",
      "X [-0.7841797   0.24762619]\n",
      "on_boundary False\n",
      "X [0.21582031 0.24812591]\n",
      "on_boundary False\n",
      "X [-0.2841797  0.2486257]\n",
      "on_boundary False\n",
      "X [0.7158203  0.24912542]\n",
      "on_boundary False\n",
      "X [-0.5341797  0.2496252]\n",
      "on_boundary False\n",
      "X [0.4658203  0.25012493]\n",
      "on_boundary False\n",
      "X [-0.03417969  0.25062472]\n",
      "on_boundary False\n",
      "X [0.9658203  0.25112444]\n",
      "on_boundary False\n",
      "X [-0.9404297   0.25162417]\n",
      "on_boundary False\n",
      "X [0.05957031 0.25212395]\n",
      "on_boundary False\n",
      "X [-0.4404297   0.25262368]\n",
      "on_boundary False\n",
      "X [0.5595703  0.25312346]\n",
      "on_boundary False\n",
      "X [-0.6904297  0.2536232]\n",
      "on_boundary False\n",
      "X [0.3095703 0.2541229]\n",
      "on_boundary False\n",
      "X [-0.19042969  0.2546227 ]\n",
      "on_boundary False\n",
      "X [0.8095703  0.25512242]\n",
      "on_boundary False\n",
      "X [-0.8154297  0.2556222]\n",
      "on_boundary False\n",
      "X [0.18457031 0.25612193]\n",
      "on_boundary False\n",
      "X [-0.3154297   0.25662172]\n",
      "on_boundary False\n",
      "X [0.6845703  0.25712144]\n",
      "on_boundary False\n",
      "X [-0.5654297   0.25762117]\n",
      "on_boundary False\n",
      "X [0.4345703  0.25812095]\n",
      "on_boundary False\n",
      "X [-0.06542969  0.25862068]\n",
      "on_boundary False\n",
      "X [0.9345703  0.25912046]\n",
      "on_boundary False\n",
      "X [-0.8779297  0.2596202]\n",
      "on_boundary False\n",
      "X [0.12207031 0.26011992]\n",
      "on_boundary False\n",
      "X [-0.3779297  0.2606197]\n",
      "on_boundary False\n",
      "X [0.6220703  0.26111943]\n",
      "on_boundary False\n",
      "X [-0.6279297  0.2616192]\n",
      "on_boundary False\n",
      "X [0.3720703  0.26211894]\n",
      "on_boundary False\n",
      "X [-0.12792969  0.26261866]\n",
      "on_boundary False\n",
      "X [0.8720703  0.26311845]\n",
      "on_boundary False\n",
      "X [-0.7529297   0.26361817]\n",
      "on_boundary False\n",
      "X [0.24707031 0.26411796]\n",
      "on_boundary False\n",
      "X [-0.2529297   0.26461768]\n",
      "on_boundary False\n",
      "X [0.7470703  0.26511747]\n",
      "on_boundary False\n",
      "X [-0.5029297  0.2656172]\n",
      "on_boundary False\n",
      "X [0.4970703  0.26611692]\n",
      "on_boundary False\n",
      "X [-0.00292969  0.2666167 ]\n",
      "on_boundary False\n",
      "X [0.9970703  0.26711643]\n",
      "on_boundary False\n",
      "X [-0.9970703  0.2676162]\n",
      "on_boundary False\n",
      "X [0.00292969 0.26811594]\n",
      "on_boundary False\n",
      "X [-0.4970703   0.26861566]\n",
      "on_boundary False\n",
      "X [0.5029297  0.26911545]\n",
      "on_boundary False\n",
      "X [-0.7470703   0.26961517]\n",
      "on_boundary False\n",
      "X [0.2529297  0.27011496]\n",
      "on_boundary False\n",
      "X [-0.24707031  0.27061468]\n",
      "on_boundary False\n",
      "X [0.7529297  0.27111447]\n",
      "on_boundary False\n",
      "X [-0.8720703  0.2716142]\n",
      "on_boundary False\n",
      "X [0.12792969 0.27211392]\n",
      "on_boundary False\n",
      "X [-0.3720703  0.2726137]\n",
      "on_boundary False\n",
      "X [0.6279297  0.27311343]\n",
      "on_boundary False\n",
      "X [-0.6220703  0.2736132]\n",
      "on_boundary False\n",
      "X [0.3779297  0.27411294]\n",
      "on_boundary False\n",
      "X [-0.12207031  0.27461267]\n",
      "on_boundary False\n",
      "X [0.8779297  0.27511245]\n",
      "on_boundary False\n",
      "X [-0.9345703   0.27561218]\n",
      "on_boundary False\n",
      "X [0.06542969 0.27611196]\n",
      "on_boundary False\n",
      "X [-0.4345703  0.2766117]\n",
      "on_boundary False\n",
      "X [0.5654297  0.27711147]\n",
      "on_boundary False\n",
      "X [-0.6845703  0.2776112]\n",
      "on_boundary False\n",
      "X [0.3154297  0.27811092]\n",
      "on_boundary False\n",
      "X [-0.18457031  0.2786107 ]\n",
      "on_boundary False\n",
      "X [0.8154297  0.27911043]\n",
      "on_boundary False\n",
      "X [-0.8095703   0.27961022]\n",
      "on_boundary False\n",
      "X [0.19042969 0.28010994]\n",
      "on_boundary False\n",
      "X [-0.3095703   0.28060967]\n",
      "on_boundary False\n",
      "X [0.6904297  0.28110945]\n",
      "on_boundary False\n",
      "X [-0.5595703   0.28160918]\n",
      "on_boundary False\n",
      "X [0.4404297  0.28210896]\n",
      "on_boundary False\n",
      "X [-0.05957031  0.2826087 ]\n",
      "on_boundary False\n",
      "X [0.9404297  0.28310847]\n",
      "on_boundary False\n",
      "X [-0.9658203  0.2836082]\n",
      "on_boundary False\n",
      "X [0.03417969 0.28410792]\n",
      "on_boundary False\n",
      "X [-0.4658203  0.2846077]\n",
      "on_boundary False\n",
      "X [0.5341797  0.28510743]\n",
      "on_boundary False\n",
      "X [-0.7158203   0.28560722]\n",
      "on_boundary False\n",
      "X [0.2841797  0.28610694]\n",
      "on_boundary False\n",
      "X [-0.21582031  0.28660667]\n",
      "on_boundary False\n",
      "X [0.7841797  0.28710645]\n",
      "on_boundary False\n",
      "X [-0.8408203   0.28760618]\n",
      "on_boundary False\n",
      "X [0.15917969 0.28810596]\n",
      "on_boundary False\n",
      "X [-0.3408203  0.2886057]\n",
      "on_boundary False\n",
      "X [0.6591797  0.28910547]\n",
      "on_boundary False\n",
      "X [-0.5908203  0.2896052]\n",
      "on_boundary False\n",
      "X [0.4091797  0.29010493]\n",
      "on_boundary False\n",
      "X [-0.09082031  0.2906047 ]\n",
      "on_boundary False\n",
      "X [0.9091797  0.29110444]\n",
      "on_boundary False\n",
      "X [-0.9033203   0.29160422]\n",
      "on_boundary False\n",
      "X [0.09667969 0.29210395]\n",
      "on_boundary False\n",
      "X [-0.4033203   0.29260367]\n",
      "on_boundary False\n",
      "X [0.5966797  0.29310346]\n",
      "on_boundary False\n",
      "X [-0.6533203   0.29360318]\n",
      "on_boundary False\n",
      "X [0.3466797  0.29410297]\n",
      "on_boundary False\n",
      "X [-0.15332031  0.2946027 ]\n",
      "on_boundary False\n",
      "X [0.8466797  0.29510248]\n",
      "on_boundary False\n",
      "X [-0.7783203  0.2956022]\n",
      "on_boundary False\n",
      "X [0.22167969 0.29610193]\n",
      "on_boundary False\n",
      "X [-0.2783203  0.2966017]\n",
      "on_boundary False\n",
      "X [0.7216797  0.29710144]\n",
      "on_boundary False\n",
      "X [-0.5283203   0.29760122]\n",
      "on_boundary False\n",
      "X [0.4716797  0.29810095]\n",
      "on_boundary False\n",
      "X [-0.02832031  0.29860067]\n",
      "on_boundary False\n",
      "X [0.9716797  0.29910046]\n",
      "on_boundary False\n",
      "X [-0.9814453   0.29960018]\n",
      "on_boundary False\n",
      "X [0.01855469 0.30009997]\n",
      "on_boundary False\n",
      "X [-0.4814453  0.3005997]\n",
      "on_boundary False\n",
      "X [0.5185547  0.30109948]\n",
      "on_boundary False\n",
      "X [-0.7314453  0.3015992]\n",
      "on_boundary False\n",
      "X [0.2685547  0.30209893]\n",
      "on_boundary False\n",
      "X [-0.23144531  0.3025987 ]\n",
      "on_boundary False\n",
      "X [0.7685547  0.30309844]\n",
      "on_boundary False\n",
      "X [-0.8564453   0.30359823]\n",
      "on_boundary False\n",
      "X [0.14355469 0.30409795]\n",
      "on_boundary False\n",
      "X [-0.3564453   0.30459768]\n",
      "on_boundary False\n",
      "X [0.6435547  0.30509746]\n",
      "on_boundary False\n",
      "X [-0.6064453  0.3055972]\n",
      "on_boundary False\n",
      "X [0.3935547  0.30609697]\n",
      "on_boundary False\n",
      "X [-0.10644531  0.3065967 ]\n",
      "on_boundary False\n",
      "X [0.8935547  0.30709648]\n",
      "on_boundary False\n",
      "X [-0.9189453  0.3075962]\n",
      "on_boundary False\n",
      "X [0.08105469 0.30809593]\n",
      "on_boundary False\n",
      "X [-0.4189453   0.30859572]\n",
      "on_boundary False\n",
      "X [0.5810547  0.30909544]\n",
      "on_boundary False\n",
      "X [-0.6689453   0.30959523]\n",
      "on_boundary False\n",
      "X [0.3310547  0.31009495]\n",
      "on_boundary False\n",
      "X [-0.16894531  0.31059468]\n",
      "on_boundary False\n",
      "X [0.8310547  0.31109446]\n",
      "on_boundary False\n",
      "X [-0.7939453  0.3115942]\n",
      "on_boundary False\n",
      "X [0.20605469 0.31209397]\n",
      "on_boundary False\n",
      "X [-0.2939453  0.3125937]\n",
      "on_boundary False\n",
      "X [0.7060547  0.31309342]\n",
      "on_boundary False\n",
      "X [-0.5439453  0.3135932]\n",
      "on_boundary False\n",
      "X [0.4560547  0.31409293]\n",
      "on_boundary False\n",
      "X [-0.04394531  0.31459272]\n",
      "on_boundary False\n",
      "X [0.9560547  0.31509244]\n",
      "on_boundary False\n",
      "X [-0.9501953   0.31559223]\n",
      "on_boundary False\n",
      "X [0.04980469 0.31609195]\n",
      "on_boundary False\n",
      "X [-0.4501953   0.31659168]\n",
      "on_boundary False\n",
      "X [0.5498047  0.31709146]\n",
      "on_boundary False\n",
      "X [-0.7001953  0.3175912]\n",
      "on_boundary False\n",
      "X [0.2998047  0.31809098]\n",
      "on_boundary False\n",
      "X [-0.20019531  0.3185907 ]\n",
      "on_boundary False\n",
      "X [0.7998047  0.31909043]\n",
      "on_boundary False\n",
      "X [-0.8251953  0.3195902]\n",
      "on_boundary False\n",
      "X [0.17480469 0.32008994]\n",
      "on_boundary False\n",
      "X [-0.3251953   0.32058972]\n",
      "on_boundary False\n",
      "X [0.6748047  0.32108945]\n",
      "on_boundary False\n",
      "X [-0.5751953   0.32158923]\n",
      "on_boundary False\n",
      "X [0.4248047  0.32208896]\n",
      "on_boundary False\n",
      "X [-0.07519531  0.32258868]\n",
      "on_boundary False\n",
      "X [0.9248047  0.32308847]\n",
      "on_boundary False\n",
      "X [-0.8876953  0.3235882]\n",
      "on_boundary False\n",
      "X [0.11230469 0.32408798]\n",
      "on_boundary False\n",
      "X [-0.3876953  0.3245877]\n",
      "on_boundary False\n",
      "X [0.6123047  0.32508743]\n",
      "on_boundary False\n",
      "X [-0.6376953  0.3255872]\n",
      "on_boundary False\n",
      "X [0.3623047  0.32608694]\n",
      "on_boundary False\n",
      "X [-0.13769531  0.32658672]\n",
      "on_boundary False\n",
      "X [0.8623047  0.32708645]\n",
      "on_boundary False\n",
      "X [-0.7626953   0.32758623]\n",
      "on_boundary False\n",
      "X [0.23730469 0.32808596]\n",
      "on_boundary False\n",
      "X [-0.2626953   0.32858568]\n",
      "on_boundary False\n",
      "X [0.7373047  0.32908547]\n",
      "on_boundary False\n",
      "X [-0.5126953  0.3295852]\n",
      "on_boundary False\n",
      "X [0.4873047  0.33008498]\n",
      "on_boundary False\n",
      "X [-0.01269531  0.3305847 ]\n",
      "on_boundary False\n",
      "X [0.9873047  0.33108443]\n",
      "on_boundary False\n",
      "X [-0.9892578   0.33158422]\n",
      "on_boundary False\n",
      "X [0.01074219 0.33208394]\n",
      "on_boundary False\n",
      "X [-0.4892578   0.33258373]\n",
      "on_boundary False\n",
      "X [0.5107422  0.33308345]\n",
      "on_boundary False\n",
      "X [-0.7392578   0.33358324]\n",
      "on_boundary False\n",
      "X [0.2607422  0.33408296]\n",
      "on_boundary False\n",
      "X [-0.23925781  0.3345827 ]\n",
      "on_boundary False\n",
      "X [0.7607422  0.33508247]\n",
      "on_boundary False\n",
      "X [-0.8642578  0.3355822]\n",
      "on_boundary False\n",
      "X [0.13574219 0.33608198]\n",
      "on_boundary False\n",
      "X [-0.3642578  0.3365817]\n",
      "on_boundary False\n",
      "X [0.6357422  0.33708143]\n",
      "on_boundary False\n",
      "X [-0.6142578   0.33758122]\n",
      "on_boundary False\n",
      "X [0.3857422  0.33808094]\n",
      "on_boundary False\n",
      "X [-0.11425781  0.33858073]\n",
      "on_boundary False\n",
      "X [0.8857422  0.33908045]\n",
      "on_boundary False\n",
      "X [-0.9267578   0.33958024]\n",
      "on_boundary False\n",
      "X [0.07324219 0.34007996]\n",
      "on_boundary False\n",
      "X [-0.4267578  0.3405797]\n",
      "on_boundary False\n",
      "X [0.5732422  0.34107947]\n",
      "on_boundary False\n",
      "X [-0.6767578  0.3415792]\n",
      "on_boundary False\n",
      "X [0.3232422  0.34207898]\n",
      "on_boundary False\n",
      "X [-0.17675781  0.3425787 ]\n",
      "on_boundary False\n",
      "X [0.8232422  0.34307843]\n",
      "on_boundary False\n",
      "X [-0.8017578   0.34357822]\n",
      "on_boundary False\n",
      "X [0.19824219 0.34407794]\n",
      "on_boundary False\n",
      "X [-0.3017578   0.34457773]\n",
      "on_boundary False\n",
      "X [0.6982422  0.34507746]\n",
      "on_boundary False\n",
      "X [-0.5517578   0.34557724]\n",
      "on_boundary False\n",
      "X [0.4482422  0.34607697]\n",
      "on_boundary False\n",
      "X [-0.05175781  0.3465767 ]\n",
      "on_boundary False\n",
      "X [0.9482422  0.34707648]\n",
      "on_boundary False\n",
      "X [-0.9580078  0.3475762]\n",
      "on_boundary False\n",
      "X [0.04199219 0.348076  ]\n",
      "on_boundary False\n",
      "X [-0.4580078  0.3485757]\n",
      "on_boundary False\n",
      "X [0.5419922  0.34907544]\n",
      "on_boundary False\n",
      "X [-0.7080078   0.34957522]\n",
      "on_boundary False\n",
      "X [0.2919922  0.35007495]\n",
      "on_boundary False\n",
      "X [-0.20800781  0.35057473]\n",
      "on_boundary False\n",
      "X [0.7919922  0.35107446]\n",
      "on_boundary False\n",
      "X [-0.8330078   0.35157424]\n",
      "on_boundary False\n",
      "X [0.16699219 0.35207397]\n",
      "on_boundary False\n",
      "X [-0.3330078  0.3525737]\n",
      "on_boundary False\n",
      "X [0.6669922  0.35307348]\n",
      "on_boundary False\n",
      "X [-0.5830078  0.3535732]\n",
      "on_boundary False\n",
      "X [0.4169922 0.354073 ]\n",
      "on_boundary False\n",
      "X [-0.08300781  0.3545727 ]\n",
      "on_boundary False\n",
      "X [0.9169922  0.35507244]\n",
      "on_boundary False\n",
      "X [-0.8955078   0.35557222]\n",
      "on_boundary False\n",
      "X [0.10449219 0.35607195]\n",
      "on_boundary False\n",
      "X [-0.3955078   0.35657173]\n",
      "on_boundary False\n",
      "X [0.6044922  0.35707146]\n",
      "on_boundary False\n",
      "X [-0.6455078   0.35757118]\n",
      "on_boundary False\n",
      "X [0.3544922  0.35807097]\n",
      "on_boundary False\n",
      "X [-0.14550781  0.3585707 ]\n",
      "on_boundary False\n",
      "X [0.8544922  0.35907048]\n",
      "on_boundary False\n",
      "X [-0.7705078  0.3595702]\n",
      "on_boundary False\n",
      "X [0.22949219 0.36007   ]\n",
      "on_boundary False\n",
      "X [-0.2705078   0.36056972]\n",
      "on_boundary False\n",
      "X [0.7294922  0.36106944]\n",
      "on_boundary False\n",
      "X [-0.5205078   0.36156923]\n",
      "on_boundary False\n",
      "X [0.4794922  0.36206895]\n",
      "on_boundary False\n",
      "X [-0.02050781  0.36256874]\n",
      "on_boundary False\n",
      "X [0.9794922  0.36306846]\n",
      "on_boundary False\n",
      "X [-0.9736328  0.3635682]\n",
      "on_boundary False\n",
      "X [0.02636719 0.36406797]\n",
      "on_boundary False\n",
      "X [-0.4736328  0.3645677]\n",
      "on_boundary False\n",
      "X [0.5263672  0.36506748]\n",
      "on_boundary False\n",
      "X [-0.7236328  0.3655672]\n",
      "on_boundary False\n",
      "X [0.2763672 0.366067 ]\n",
      "on_boundary False\n",
      "X [-0.22363281  0.36656672]\n",
      "on_boundary False\n",
      "X [0.7763672  0.36706644]\n",
      "on_boundary False\n",
      "X [-0.8486328   0.36756623]\n",
      "on_boundary False\n",
      "X [0.15136719 0.36806595]\n",
      "on_boundary False\n",
      "X [-0.3486328   0.36856574]\n",
      "on_boundary False\n",
      "X [0.6513672  0.36906546]\n",
      "on_boundary False\n",
      "X [-0.5986328  0.3695652]\n",
      "on_boundary False\n",
      "X [0.4013672  0.37006497]\n",
      "on_boundary False\n",
      "X [-0.09863281  0.3705647 ]\n",
      "on_boundary False\n",
      "X [0.9013672  0.37106448]\n",
      "on_boundary False\n",
      "X [-0.9111328  0.3715642]\n",
      "on_boundary False\n",
      "X [0.08886719 0.372064  ]\n",
      "on_boundary False\n",
      "X [-0.4111328   0.37256372]\n",
      "on_boundary False\n",
      "X [0.5888672  0.37306345]\n",
      "on_boundary False\n",
      "X [-0.6611328   0.37356323]\n",
      "on_boundary False\n",
      "X [0.3388672  0.37406296]\n",
      "on_boundary False\n",
      "X [-0.16113281  0.37456274]\n",
      "on_boundary False\n",
      "X [0.8388672  0.37506247]\n",
      "on_boundary False\n",
      "X [-0.7861328  0.3755622]\n",
      "on_boundary False\n",
      "X [0.21386719 0.37606198]\n",
      "on_boundary False\n",
      "X [-0.2861328  0.3765617]\n",
      "on_boundary False\n",
      "X [0.7138672 0.3770615]\n",
      "on_boundary False\n",
      "X [-0.5361328  0.3775612]\n",
      "on_boundary False\n",
      "X [0.4638672 0.378061 ]\n",
      "on_boundary False\n",
      "X [-0.03613281  0.37856072]\n",
      "on_boundary False\n",
      "X [0.9638672  0.37906045]\n",
      "on_boundary False\n",
      "X [-0.9423828   0.37956023]\n",
      "on_boundary False\n",
      "X [0.05761719 0.38005996]\n",
      "on_boundary False\n",
      "X [-0.4423828   0.38055974]\n",
      "on_boundary False\n",
      "X [0.5576172  0.38105947]\n",
      "on_boundary False\n",
      "X [-0.6923828  0.3815592]\n",
      "on_boundary False\n",
      "X [0.3076172  0.38205898]\n",
      "on_boundary False\n",
      "X [-0.19238281  0.3825587 ]\n",
      "on_boundary False\n",
      "X [0.8076172 0.3830585]\n",
      "on_boundary False\n",
      "X [-0.8173828  0.3835582]\n",
      "on_boundary False\n",
      "X [0.18261719 0.384058  ]\n",
      "on_boundary False\n",
      "X [-0.3173828   0.38455772]\n",
      "on_boundary False\n",
      "X [0.6826172  0.38505745]\n",
      "on_boundary False\n",
      "X [-0.5673828   0.38555723]\n",
      "on_boundary False\n",
      "X [0.4326172  0.38605696]\n",
      "on_boundary False\n",
      "X [-0.06738281  0.38655674]\n",
      "on_boundary False\n",
      "X [0.9326172  0.38705647]\n",
      "on_boundary False\n",
      "X [-0.8798828  0.3875562]\n",
      "on_boundary False\n",
      "X [0.12011719 0.38805598]\n",
      "on_boundary False\n",
      "X [-0.3798828  0.3885557]\n",
      "on_boundary False\n",
      "X [0.6201172 0.3890555]\n",
      "on_boundary False\n",
      "X [-0.6298828   0.38955522]\n",
      "on_boundary False\n",
      "X [0.3701172 0.390055 ]\n",
      "on_boundary False\n",
      "X [-0.12988281  0.39055473]\n",
      "on_boundary False\n",
      "X [0.8701172  0.39105445]\n",
      "on_boundary False\n",
      "X [-0.7548828   0.39155424]\n",
      "on_boundary False\n",
      "X [0.24511719 0.39205396]\n",
      "on_boundary False\n",
      "X [-0.2548828   0.39255375]\n",
      "on_boundary False\n",
      "X [0.7451172  0.39305347]\n",
      "on_boundary False\n",
      "X [-0.5048828  0.3935532]\n",
      "on_boundary False\n",
      "X [0.4951172  0.39405298]\n",
      "on_boundary False\n",
      "X [-0.00488281  0.3945527 ]\n",
      "on_boundary False\n",
      "X [0.9951172 0.3950525]\n",
      "on_boundary False\n",
      "X [-0.99316406  0.39555222]\n",
      "on_boundary False\n",
      "X [0.00683594 0.396052  ]\n",
      "on_boundary False\n",
      "X [-0.49316406  0.39655173]\n",
      "on_boundary False\n",
      "X [0.50683594 0.39705145]\n",
      "on_boundary False\n",
      "X [-0.74316406  0.39755124]\n",
      "on_boundary False\n",
      "X [0.25683594 0.39805096]\n",
      "on_boundary False\n",
      "X [-0.24316406  0.39855075]\n",
      "on_boundary False\n",
      "X [0.75683594 0.39905047]\n",
      "on_boundary False\n",
      "X [-0.86816406  0.3995502 ]\n",
      "on_boundary False\n",
      "X [0.13183594 0.40004998]\n",
      "on_boundary False\n",
      "X [-0.36816406  0.4005497 ]\n",
      "on_boundary False\n",
      "X [0.63183594 0.4010495 ]\n",
      "on_boundary False\n",
      "X [-0.61816406  0.40154922]\n",
      "on_boundary False\n",
      "X [0.38183594 0.402049  ]\n",
      "on_boundary False\n",
      "X [-0.11816406  0.40254873]\n",
      "on_boundary False\n",
      "X [0.88183594 0.40304846]\n",
      "on_boundary False\n",
      "X [-0.93066406  0.40354824]\n",
      "on_boundary False\n",
      "X [0.06933594 0.40404797]\n",
      "on_boundary False\n",
      "X [-0.43066406  0.40454775]\n",
      "on_boundary False\n",
      "X [0.56933594 0.40504748]\n",
      "on_boundary False\n",
      "X [-0.68066406  0.4055472 ]\n",
      "on_boundary False\n",
      "X [0.31933594 0.406047  ]\n",
      "on_boundary False\n",
      "X [-0.18066406  0.4065467 ]\n",
      "on_boundary False\n",
      "X [0.81933594 0.4070465 ]\n",
      "on_boundary False\n",
      "X [-0.80566406  0.40754622]\n",
      "on_boundary False\n",
      "X [0.19433594 0.40804595]\n",
      "on_boundary False\n",
      "X [-0.30566406  0.40854573]\n",
      "on_boundary False\n",
      "X [0.69433594 0.40904546]\n",
      "on_boundary False\n",
      "X [-0.55566406  0.40954524]\n",
      "on_boundary False\n",
      "X [0.44433594 0.41004497]\n",
      "on_boundary False\n",
      "X [-0.05566406  0.41054475]\n",
      "on_boundary False\n",
      "X [0.94433594 0.41104448]\n",
      "on_boundary False\n",
      "X [-0.96191406  0.4115442 ]\n",
      "on_boundary False\n",
      "X [0.03808594 0.412044  ]\n",
      "on_boundary False\n",
      "X [-0.46191406  0.4125437 ]\n",
      "on_boundary False\n",
      "X [0.53808594 0.4130435 ]\n",
      "on_boundary False\n",
      "X [-0.71191406  0.41354322]\n",
      "on_boundary False\n",
      "X [0.28808594 0.41404295]\n",
      "on_boundary False\n",
      "X [-0.21191406  0.41454273]\n",
      "on_boundary False\n",
      "X [0.78808594 0.41504246]\n",
      "on_boundary False\n",
      "X [-0.83691406  0.41554224]\n",
      "on_boundary False\n",
      "X [0.16308594 0.41604197]\n",
      "on_boundary False\n",
      "X [-0.33691406  0.41654176]\n",
      "on_boundary False\n",
      "X [0.66308594 0.41704148]\n",
      "on_boundary False\n",
      "X [-0.58691406  0.4175412 ]\n",
      "on_boundary False\n",
      "X [0.41308594 0.418041  ]\n",
      "on_boundary False\n",
      "X [-0.08691406  0.41854072]\n",
      "on_boundary False\n",
      "X [0.91308594 0.4190405 ]\n",
      "on_boundary False\n",
      "X [-0.89941406  0.41954023]\n",
      "on_boundary False\n",
      "X [0.10058594 0.42003995]\n",
      "on_boundary False\n",
      "X [-0.39941406  0.42053974]\n",
      "on_boundary False\n",
      "X [0.60058594 0.42103946]\n",
      "on_boundary False\n",
      "X [-0.64941406  0.42153925]\n",
      "on_boundary False\n",
      "X [0.35058594 0.42203897]\n",
      "on_boundary False\n",
      "X [-0.14941406  0.42253876]\n",
      "on_boundary False\n",
      "X [0.85058594 0.42303848]\n",
      "on_boundary False\n",
      "X [-0.77441406  0.4235382 ]\n",
      "on_boundary False\n",
      "X [0.22558594 0.424038  ]\n",
      "on_boundary False\n",
      "X [-0.27441406  0.42453772]\n",
      "on_boundary False\n",
      "X [0.72558594 0.4250375 ]\n",
      "on_boundary False\n",
      "X [-0.52441406  0.42553723]\n",
      "on_boundary False\n",
      "X [0.47558594 0.42603695]\n",
      "on_boundary False\n",
      "X [-0.02441406  0.42653674]\n",
      "on_boundary False\n",
      "X [0.97558594 0.42703646]\n",
      "on_boundary False\n",
      "X [-0.97753906  0.42753625]\n",
      "on_boundary False\n",
      "X [0.02246094 0.42803597]\n",
      "on_boundary False\n",
      "X [-0.47753906  0.42853576]\n",
      "on_boundary False\n",
      "X [0.52246094 0.42903548]\n",
      "on_boundary False\n",
      "X [-0.72753906  0.4295352 ]\n",
      "on_boundary False\n",
      "X [0.27246094 0.430035  ]\n",
      "on_boundary False\n",
      "X [-0.22753906  0.43053472]\n",
      "on_boundary False\n",
      "X [0.77246094 0.4310345 ]\n",
      "on_boundary False\n",
      "X [-0.85253906  0.43153423]\n",
      "on_boundary False\n",
      "X [0.14746094 0.43203396]\n",
      "on_boundary False\n",
      "X [-0.35253906  0.43253374]\n",
      "on_boundary False\n",
      "X [0.64746094 0.43303347]\n",
      "on_boundary False\n",
      "X [-0.60253906  0.43353325]\n",
      "on_boundary False\n",
      "X [0.39746094 0.43403298]\n",
      "on_boundary False\n",
      "X [-0.10253906  0.43453276]\n",
      "on_boundary False\n",
      "X [0.89746094 0.4350325 ]\n",
      "on_boundary False\n",
      "X [-0.91503906  0.4355322 ]\n",
      "on_boundary False\n",
      "X [0.08496094 0.436032  ]\n",
      "on_boundary False\n",
      "X [-0.41503906  0.43653172]\n",
      "on_boundary False\n",
      "X [0.58496094 0.4370315 ]\n",
      "on_boundary False\n",
      "X [-0.66503906  0.43753123]\n",
      "on_boundary False\n",
      "X [0.33496094 0.43803096]\n",
      "on_boundary False\n",
      "X [-0.16503906  0.43853074]\n",
      "on_boundary False\n",
      "X [0.83496094 0.43903047]\n",
      "on_boundary False\n",
      "X [-0.79003906  0.43953025]\n",
      "on_boundary False\n",
      "X [0.20996094 0.44002998]\n",
      "on_boundary False\n",
      "X [-0.29003906  0.44052976]\n",
      "on_boundary False\n",
      "X [0.70996094 0.4410295 ]\n",
      "on_boundary False\n",
      "X [-0.54003906  0.4415292 ]\n",
      "on_boundary False\n",
      "X [0.45996094 0.442029  ]\n",
      "on_boundary False\n",
      "X [-0.04003906  0.44252872]\n",
      "on_boundary False\n",
      "X [0.95996094 0.4430285 ]\n",
      "on_boundary False\n",
      "X [-0.94628906  0.44352823]\n",
      "on_boundary False\n",
      "X [0.05371094 0.44402796]\n",
      "on_boundary False\n",
      "X [-0.44628906  0.44452775]\n",
      "on_boundary False\n",
      "X [0.55371094 0.44502747]\n",
      "on_boundary False\n",
      "X [-0.69628906  0.44552726]\n",
      "on_boundary False\n",
      "X [0.30371094 0.44602698]\n",
      "on_boundary False\n",
      "X [-0.19628906  0.44652677]\n",
      "on_boundary False\n",
      "X [0.80371094 0.4470265 ]\n",
      "on_boundary False\n",
      "X [-0.82128906  0.44752622]\n",
      "on_boundary False\n",
      "X [0.17871094 0.448026  ]\n",
      "on_boundary False\n",
      "X [-0.32128906  0.44852573]\n",
      "on_boundary False\n",
      "X [0.67871094 0.4490255 ]\n",
      "on_boundary False\n",
      "X [-0.57128906  0.44952524]\n",
      "on_boundary False\n",
      "X [0.42871094 0.45002496]\n",
      "on_boundary False\n",
      "X [-0.07128906  0.45052475]\n",
      "on_boundary False\n",
      "X [0.92871094 0.45102447]\n",
      "on_boundary False\n",
      "X [-0.88378906  0.45152426]\n",
      "on_boundary False\n",
      "X [0.11621094 0.45202398]\n",
      "on_boundary False\n",
      "X [-0.38378906  0.4525237 ]\n",
      "on_boundary False\n",
      "X [0.61621094 0.4530235 ]\n",
      "on_boundary False\n",
      "X [-0.63378906  0.45352322]\n",
      "on_boundary False\n",
      "X [0.36621094 0.454023  ]\n",
      "on_boundary False\n",
      "X [-0.13378906  0.45452273]\n",
      "on_boundary False\n",
      "X [0.86621094 0.4550225 ]\n",
      "on_boundary False\n",
      "X [-0.75878906  0.45552224]\n",
      "on_boundary False\n",
      "X [0.24121094 0.45602196]\n",
      "on_boundary False\n",
      "X [-0.25878906  0.45652175]\n",
      "on_boundary False\n",
      "X [0.74121094 0.45702147]\n",
      "on_boundary False\n",
      "X [-0.50878906  0.45752126]\n",
      "on_boundary False\n",
      "X [0.49121094 0.458021  ]\n",
      "on_boundary False\n",
      "X [-0.00878906  0.4585207 ]\n",
      "on_boundary False\n",
      "X [0.99121094 0.4590205 ]\n",
      "on_boundary False\n",
      "X [-0.98535156  0.45952022]\n",
      "on_boundary False\n",
      "X [0.01464844 0.46002   ]\n",
      "on_boundary False\n",
      "X [-0.48535156  0.46051973]\n",
      "on_boundary False\n",
      "X [0.51464844 0.46101952]\n",
      "on_boundary False\n",
      "X [-0.73535156  0.46151924]\n",
      "on_boundary False\n",
      "X [0.26464844 0.46201897]\n",
      "on_boundary False\n",
      "X [-0.23535156  0.46251875]\n",
      "on_boundary False\n",
      "X [0.76464844 0.46301848]\n",
      "on_boundary False\n",
      "X [-0.86035156  0.46351826]\n",
      "on_boundary False\n",
      "X [0.13964844 0.464018  ]\n",
      "on_boundary False\n",
      "X [-0.36035156  0.4645177 ]\n",
      "on_boundary False\n",
      "X [0.63964844 0.4650175 ]\n",
      "on_boundary False\n",
      "X [-0.61035156  0.46551722]\n",
      "on_boundary False\n",
      "X [0.38964844 0.466017  ]\n",
      "on_boundary False\n",
      "X [-0.11035156  0.46651673]\n",
      "on_boundary False\n",
      "X [0.88964844 0.46701652]\n",
      "on_boundary False\n",
      "X [-0.92285156  0.46751624]\n",
      "on_boundary False\n",
      "X [0.07714844 0.46801597]\n",
      "on_boundary False\n",
      "X [-0.42285156  0.46851575]\n",
      "on_boundary False\n",
      "X [0.57714844 0.46901548]\n",
      "on_boundary False\n",
      "X [-0.67285156  0.46951526]\n",
      "on_boundary False\n",
      "X [0.32714844 0.470015  ]\n",
      "on_boundary False\n",
      "X [-0.17285156  0.4705147 ]\n",
      "on_boundary False\n",
      "X [0.82714844 0.4710145 ]\n",
      "on_boundary False\n",
      "X [-0.79785156  0.47151423]\n",
      "on_boundary False\n",
      "X [0.20214844 0.472014  ]\n",
      "on_boundary False\n",
      "X [-0.29785156  0.47251374]\n",
      "on_boundary False\n",
      "X [0.70214844 0.47301352]\n",
      "on_boundary False\n",
      "X [-0.54785156  0.47351325]\n",
      "on_boundary False\n",
      "X [0.45214844 0.47401297]\n",
      "on_boundary False\n",
      "X [-0.04785156  0.47451276]\n",
      "on_boundary False\n",
      "X [0.95214844 0.47501248]\n",
      "on_boundary False\n",
      "X [-0.95410156  0.47551227]\n",
      "on_boundary False\n",
      "X [0.04589844 0.476012  ]\n",
      "on_boundary False\n",
      "X [-0.45410156  0.47651172]\n",
      "on_boundary False\n",
      "X [0.54589844 0.4770115 ]\n",
      "on_boundary False\n",
      "X [-0.70410156  0.47751123]\n",
      "on_boundary False\n",
      "X [0.29589844 0.478011  ]\n",
      "on_boundary False\n",
      "X [-0.20410156  0.47851074]\n",
      "on_boundary False\n",
      "X [0.79589844 0.47901052]\n",
      "on_boundary False\n",
      "X [-0.82910156  0.47951025]\n",
      "on_boundary False\n",
      "X [0.17089844 0.48000997]\n",
      "on_boundary False\n",
      "X [-0.32910156  0.48050976]\n",
      "on_boundary False\n",
      "X [0.67089844 0.48100948]\n",
      "on_boundary False\n",
      "X [-0.57910156  0.48150927]\n",
      "on_boundary False\n",
      "X [0.42089844 0.482009  ]\n",
      "on_boundary False\n",
      "X [-0.07910156  0.48250872]\n",
      "on_boundary False\n",
      "X [0.92089844 0.4830085 ]\n",
      "on_boundary False\n",
      "X [-0.89160156  0.48350823]\n",
      "on_boundary False\n",
      "X [0.10839844 0.484008  ]\n",
      "on_boundary False\n",
      "X [-0.39160156  0.48450774]\n",
      "on_boundary False\n",
      "X [0.60839844 0.48500752]\n",
      "on_boundary False\n",
      "X [-0.64160156  0.48550725]\n",
      "on_boundary False\n",
      "X [0.35839844 0.48600698]\n",
      "on_boundary False\n",
      "X [-0.14160156  0.48650676]\n",
      "on_boundary False\n",
      "X [0.85839844 0.4870065 ]\n",
      "on_boundary False\n",
      "X [-0.76660156  0.48750627]\n",
      "on_boundary False\n",
      "X [0.23339844 0.488006  ]\n",
      "on_boundary False\n",
      "X [-0.26660156  0.48850572]\n",
      "on_boundary False\n",
      "X [0.73339844 0.4890055 ]\n",
      "on_boundary False\n",
      "X [-0.51660156  0.48950523]\n",
      "on_boundary False\n",
      "X [0.48339844 0.49000502]\n",
      "on_boundary False\n",
      "X [-0.01660156  0.49050474]\n",
      "on_boundary False\n",
      "X [0.98339844 0.49100453]\n",
      "on_boundary False\n",
      "X [-0.96972656  0.49150425]\n",
      "on_boundary False\n",
      "X [0.03027344 0.49200398]\n",
      "on_boundary False\n",
      "X [-0.46972656  0.49250376]\n",
      "on_boundary False\n",
      "X [0.53027344 0.4930035 ]\n",
      "on_boundary False\n",
      "X [-0.71972656  0.49350327]\n",
      "on_boundary False\n",
      "X [0.28027344 0.494003  ]\n",
      "on_boundary False\n",
      "X [-0.21972656  0.49450272]\n",
      "on_boundary False\n",
      "X [0.78027344 0.4950025 ]\n",
      "on_boundary False\n",
      "X [-0.84472656  0.49550223]\n",
      "on_boundary False\n",
      "X [0.15527344 0.49600202]\n",
      "on_boundary False\n",
      "X [-0.34472656  0.49650174]\n",
      "on_boundary False\n",
      "X [0.65527344 0.49700153]\n",
      "on_boundary False\n",
      "X [-0.59472656  0.49750125]\n",
      "on_boundary False\n",
      "X [0.40527344 0.49800098]\n",
      "on_boundary False\n",
      "X [-0.09472656  0.49850076]\n",
      "on_boundary False\n",
      "X [0.90527344 0.4990005 ]\n",
      "on_boundary False\n",
      "X [-0.90722656  0.49950027]\n",
      "on_boundary False\n",
      "X [1.  0.5]\n",
      "on_boundary True\n",
      "X [ 0.5 -0.5]\n",
      "on_boundary True\n",
      "X [-0.5  0.5]\n",
      "on_boundary True\n",
      "X [-0.25 -0.5 ]\n",
      "on_boundary True\n",
      "X [0.25 0.5 ]\n",
      "on_boundary True\n",
      "X [ 1.   -0.25]\n",
      "on_boundary True\n",
      "X [-1.    0.25]\n",
      "on_boundary True\n",
      "X [-0.625 -0.5  ]\n",
      "on_boundary True\n",
      "X [0.625 0.5  ]\n",
      "on_boundary True\n",
      "X [ 0.875 -0.5  ]\n",
      "on_boundary True\n",
      "X [-0.875  0.5  ]\n",
      "on_boundary True\n",
      "X [ 0.125 -0.5  ]\n",
      "on_boundary True\n",
      "X [-0.125  0.5  ]\n",
      "on_boundary True\n",
      "X [1.    0.125]\n",
      "on_boundary True\n",
      "X [-1.    -0.125]\n",
      "on_boundary True\n",
      "X [-0.8125 -0.5   ]\n",
      "on_boundary True\n",
      "X [0.8125 0.5   ]\n",
      "on_boundary True\n",
      "X [ 0.6875 -0.5   ]\n",
      "on_boundary True\n",
      "X [-0.6875  0.5   ]\n",
      "on_boundary True\n",
      "X [-0.0625 -0.5   ]\n",
      "on_boundary True\n",
      "X [0.0625 0.5   ]\n",
      "on_boundary True\n",
      "X [ 1.     -0.0625]\n",
      "on_boundary True\n",
      "X [-1.      0.0625]\n",
      "on_boundary True\n",
      "X [-0.4375 -0.5   ]\n",
      "on_boundary True\n",
      "X [0.4375 0.5   ]\n",
      "on_boundary True\n",
      "X [ 1.     -0.4375]\n",
      "on_boundary True\n",
      "X [-1.      0.4375]\n",
      "on_boundary True\n",
      "X [ 0.3125 -0.5   ]\n",
      "on_boundary True\n",
      "X [-0.3125  0.5   ]\n",
      "on_boundary True\n",
      "X [1.     0.3125]\n",
      "on_boundary True\n",
      "X [-1.     -0.3125]\n",
      "on_boundary True\n",
      "X [-0.90625 -0.5    ]\n",
      "on_boundary True\n",
      "X [0.90625 0.5    ]\n",
      "on_boundary True\n",
      "X [ 0.59375 -0.5    ]\n",
      "on_boundary True\n",
      "X [-0.59375  0.5    ]\n",
      "on_boundary True\n",
      "X [-0.15625 -0.5    ]\n",
      "on_boundary True\n",
      "X [0.15625 0.5    ]\n",
      "on_boundary True\n",
      "X [ 1.      -0.15625]\n",
      "on_boundary True\n",
      "X [-1.       0.15625]\n",
      "on_boundary True\n",
      "X [-0.53125 -0.5    ]\n",
      "on_boundary True\n",
      "X [0.53125 0.5    ]\n",
      "on_boundary True\n",
      "X [ 0.96875 -0.5    ]\n",
      "on_boundary True\n",
      "X [-0.96875  0.5    ]\n",
      "on_boundary True\n",
      "X [ 0.21875 -0.5    ]\n",
      "on_boundary True\n",
      "X [-0.21875  0.5    ]\n",
      "on_boundary True\n",
      "X [1.      0.21875]\n",
      "on_boundary True\n",
      "X [-1.      -0.21875]\n",
      "on_boundary True\n",
      "X [-0.71875 -0.5    ]\n",
      "on_boundary True\n",
      "X [0.71875 0.5    ]\n",
      "on_boundary True\n",
      "X [ 0.78125 -0.5    ]\n",
      "on_boundary True\n",
      "X [-0.78125  0.5    ]\n",
      "on_boundary True\n",
      "X [ 0.03125 -0.5    ]\n",
      "on_boundary True\n",
      "X [-0.03125  0.5    ]\n",
      "on_boundary True\n",
      "X [1.      0.03125]\n",
      "on_boundary True\n",
      "X [-1.      -0.03125]\n",
      "on_boundary True\n",
      "X [-0.34375 -0.5    ]\n",
      "on_boundary True\n",
      "X [0.34375 0.5    ]\n",
      "on_boundary True\n",
      "X [ 1.      -0.34375]\n",
      "on_boundary True\n",
      "X [-1.       0.34375]\n",
      "on_boundary True\n",
      "X [ 0.40625 -0.5    ]\n",
      "on_boundary True\n",
      "X [-0.40625  0.5    ]\n",
      "on_boundary True\n",
      "X [1.      0.40625]\n",
      "on_boundary True\n",
      "X [-1.      -0.40625]\n",
      "on_boundary True\n",
      "X [-0.953125 -0.5     ]\n",
      "on_boundary True\n",
      "X [0.953125 0.5     ]\n",
      "on_boundary True\n",
      "X [ 0.546875 -0.5     ]\n",
      "on_boundary True\n",
      "X [-0.546875  0.5     ]\n",
      "on_boundary True\n",
      "X [-0.203125 -0.5     ]\n",
      "on_boundary True\n",
      "X [0.203125 0.5     ]\n",
      "on_boundary True\n",
      "X [ 1.       -0.203125]\n",
      "on_boundary True\n",
      "X [-1.        0.203125]\n",
      "on_boundary True\n",
      "X [-0.578125 -0.5     ]\n",
      "on_boundary True\n",
      "X [0.578125 0.5     ]\n",
      "on_boundary True\n",
      "X [ 0.921875 -0.5     ]\n",
      "on_boundary True\n",
      "X [-0.921875  0.5     ]\n",
      "on_boundary True\n",
      "X [ 0.171875 -0.5     ]\n",
      "on_boundary True\n",
      "X [-0.171875  0.5     ]\n",
      "on_boundary True\n",
      "X [1.       0.171875]\n",
      "on_boundary True\n",
      "X [-1.       -0.171875]\n",
      "on_boundary True\n",
      "X [-0.765625 -0.5     ]\n",
      "on_boundary True\n",
      "X [0.765625 0.5     ]\n",
      "on_boundary True\n",
      "X [ 0.734375 -0.5     ]\n",
      "on_boundary True\n",
      "X [-0.734375  0.5     ]\n",
      "on_boundary True\n",
      "X [-0.015625 -0.5     ]\n",
      "on_boundary True\n",
      "X [0.015625 0.5     ]\n",
      "on_boundary True\n",
      "X [ 1.       -0.015625]\n",
      "on_boundary True\n",
      "X [-1.        0.015625]\n",
      "on_boundary True\n",
      "X [-0.390625 -0.5     ]\n",
      "on_boundary True\n",
      "X [0.390625 0.5     ]\n",
      "on_boundary True\n",
      "X [ 1.       -0.390625]\n",
      "on_boundary True\n",
      "X [-1.        0.390625]\n",
      "on_boundary True\n",
      "X [ 0.359375 -0.5     ]\n",
      "on_boundary True\n",
      "X [-0.359375  0.5     ]\n",
      "on_boundary True\n",
      "X [1.       0.359375]\n",
      "on_boundary True\n",
      "X [-1.       -0.359375]\n",
      "on_boundary True\n",
      "X [-0.859375 -0.5     ]\n",
      "on_boundary True\n",
      "X [0.859375 0.5     ]\n",
      "on_boundary True\n",
      "X [ 0.640625 -0.5     ]\n",
      "on_boundary True\n",
      "X [-0.640625  0.5     ]\n",
      "on_boundary True\n",
      "X [-0.109375 -0.5     ]\n",
      "on_boundary True\n",
      "X [0.109375 0.5     ]\n",
      "on_boundary True\n",
      "X [ 1.       -0.109375]\n",
      "on_boundary True\n",
      "X [-1.        0.109375]\n",
      "on_boundary True\n",
      "X [-0.484375 -0.5     ]\n",
      "on_boundary True\n",
      "X [0.484375 0.5     ]\n",
      "on_boundary True\n",
      "X [ 1.       -0.484375]\n",
      "on_boundary True\n",
      "X [-1.        0.484375]\n",
      "on_boundary True\n",
      "X [ 0.265625 -0.5     ]\n",
      "on_boundary True\n",
      "X [-0.265625  0.5     ]\n",
      "on_boundary True\n",
      "X [1.       0.265625]\n",
      "on_boundary True\n",
      "X [-1.       -0.265625]\n",
      "on_boundary True\n",
      "X [-0.671875 -0.5     ]\n",
      "on_boundary True\n",
      "X [0.671875 0.5     ]\n",
      "on_boundary True\n",
      "X [ 0.828125 -0.5     ]\n",
      "on_boundary True\n",
      "X [-0.828125  0.5     ]\n",
      "on_boundary True\n",
      "X [ 0.078125 -0.5     ]\n",
      "on_boundary True\n",
      "X [-0.078125  0.5     ]\n",
      "on_boundary True\n",
      "X [1.       0.078125]\n",
      "on_boundary True\n",
      "X [-1.       -0.078125]\n",
      "on_boundary True\n",
      "X [-0.296875 -0.5     ]\n",
      "on_boundary True\n",
      "X [0.296875 0.5     ]\n",
      "on_boundary True\n",
      "X [ 1.       -0.296875]\n",
      "on_boundary True\n",
      "X [-1.        0.296875]\n",
      "on_boundary True\n",
      "X [ 0.453125 -0.5     ]\n",
      "on_boundary True\n",
      "X [-0.453125  0.5     ]\n",
      "on_boundary True\n",
      "X [1.       0.453125]\n",
      "on_boundary True\n",
      "X [-1.       -0.453125]\n",
      "on_boundary True\n",
      "X [-0.9765625 -0.5      ]\n",
      "on_boundary True\n",
      "X [0.9765625 0.5      ]\n",
      "on_boundary True\n",
      "X [ 0.5234375 -0.5      ]\n",
      "on_boundary True\n",
      "X [-0.5234375  0.5      ]\n",
      "on_boundary True\n",
      "X [-0.2265625 -0.5      ]\n",
      "on_boundary True\n",
      "X [0.2265625 0.5      ]\n",
      "on_boundary True\n",
      "X [ 1.        -0.2265625]\n",
      "on_boundary True\n",
      "X [-1.         0.2265625]\n",
      "on_boundary True\n",
      "X [-0.6015625 -0.5      ]\n",
      "on_boundary True\n",
      "X [0.6015625 0.5      ]\n",
      "on_boundary True\n",
      "X [ 0.8984375 -0.5      ]\n",
      "on_boundary True\n",
      "X [-0.8984375  0.5      ]\n",
      "on_boundary True\n",
      "X [ 0.1484375 -0.5      ]\n",
      "on_boundary True\n",
      "X [-0.1484375  0.5      ]\n",
      "on_boundary True\n",
      "X [1.        0.1484375]\n",
      "on_boundary True\n",
      "X [-1.        -0.1484375]\n",
      "on_boundary True\n",
      "X [-0.7890625 -0.5      ]\n",
      "on_boundary True\n",
      "X [0.7890625 0.5      ]\n",
      "on_boundary True\n",
      "X [ 0.7109375 -0.5      ]\n",
      "on_boundary True\n",
      "X [-0.7109375  0.5      ]\n",
      "on_boundary True\n",
      "X [-0.0390625 -0.5      ]\n",
      "on_boundary True\n",
      "X [0.0390625 0.5      ]\n",
      "on_boundary True\n",
      "X [ 1.        -0.0390625]\n",
      "on_boundary True\n",
      "X [-1.         0.0390625]\n",
      "on_boundary True\n",
      "X [-0.4140625 -0.5      ]\n",
      "on_boundary True\n",
      "X [0.4140625 0.5      ]\n",
      "on_boundary True\n",
      "X [ 1.        -0.4140625]\n",
      "on_boundary True\n",
      "X [-1.         0.4140625]\n",
      "on_boundary True\n",
      "X [ 0.3359375 -0.5      ]\n",
      "on_boundary True\n",
      "X [-0.3359375  0.5      ]\n",
      "on_boundary True\n",
      "X [1.        0.3359375]\n",
      "on_boundary True\n",
      "X [-1.        -0.3359375]\n",
      "on_boundary True\n",
      "X [-0.8828125 -0.5      ]\n",
      "on_boundary True\n",
      "X [0.8828125 0.5      ]\n",
      "on_boundary True\n",
      "X [ 0.6171875 -0.5      ]\n",
      "on_boundary True\n",
      "X [-0.6171875  0.5      ]\n",
      "on_boundary True\n",
      "X [-0.1328125 -0.5      ]\n",
      "on_boundary True\n",
      "X [0.1328125 0.5      ]\n",
      "on_boundary True\n",
      "X [ 1.        -0.1328125]\n",
      "on_boundary True\n",
      "X [-1.         0.1328125]\n",
      "on_boundary True\n",
      "X [-0.5078125 -0.5      ]\n",
      "on_boundary True\n",
      "X [0.5078125 0.5      ]\n",
      "on_boundary True\n",
      "X [ 0.9921875 -0.5      ]\n",
      "on_boundary True\n",
      "X [-0.9921875  0.5      ]\n",
      "on_boundary True\n",
      "X [ 0.2421875 -0.5      ]\n",
      "on_boundary True\n",
      "X [-0.2421875  0.5      ]\n",
      "on_boundary True\n",
      "X [1.        0.2421875]\n",
      "on_boundary True\n",
      "X [-1.        -0.2421875]\n",
      "on_boundary True\n",
      "X [-0.6953125 -0.5      ]\n",
      "on_boundary True\n",
      "X [0.6953125 0.5      ]\n",
      "on_boundary True\n",
      "X [ 0.8046875 -0.5      ]\n",
      "on_boundary True\n",
      "X [-0.8046875  0.5      ]\n",
      "on_boundary True\n",
      "X [ 0.0546875 -0.5      ]\n",
      "on_boundary True\n",
      "X [-0.0546875  0.5      ]\n",
      "on_boundary True\n",
      "X [1.        0.0546875]\n",
      "on_boundary True\n",
      "X [-1.        -0.0546875]\n",
      "on_boundary True\n",
      "X [-0.3203125 -0.5      ]\n",
      "on_boundary True\n",
      "X [0.3203125 0.5      ]\n",
      "on_boundary True\n",
      "X [ 1.        -0.3203125]\n",
      "on_boundary True\n",
      "X [-1.         0.3203125]\n",
      "on_boundary True\n",
      "X [ 0.4296875 -0.5      ]\n",
      "on_boundary True\n",
      "X [-0.4296875  0.5      ]\n",
      "on_boundary True\n",
      "X [1.        0.4296875]\n",
      "on_boundary True\n",
      "X [-1.        -0.4296875]\n",
      "on_boundary True\n",
      "X [-0.9296875 -0.5      ]\n",
      "on_boundary True\n",
      "X [0.9296875 0.5      ]\n",
      "on_boundary True\n",
      "X [ 0.5703125 -0.5      ]\n",
      "on_boundary True\n",
      "X [-0.5703125  0.5      ]\n",
      "on_boundary True\n",
      "X [-0.1796875 -0.5      ]\n",
      "on_boundary True\n",
      "X [0.1796875 0.5      ]\n",
      "on_boundary True\n",
      "X [ 1.        -0.1796875]\n",
      "on_boundary True\n",
      "X [-1.         0.1796875]\n",
      "on_boundary True\n",
      "X [-0.5546875 -0.5      ]\n",
      "on_boundary True\n",
      "X [ 0.         -0.49950024]\n",
      "on_boundary False\n",
      "X [-0.5       -0.4990005]\n",
      "on_boundary False\n",
      "X [ 0.5        -0.49850076]\n",
      "on_boundary False\n",
      "X [-0.75     -0.498001]\n",
      "on_boundary False\n",
      "X [ 0.25       -0.49750125]\n",
      "on_boundary False\n",
      "X [-0.25      -0.4970015]\n",
      "on_boundary False\n",
      "X [ 0.75       -0.49650174]\n",
      "on_boundary False\n",
      "X [-0.875    -0.496002]\n",
      "on_boundary False\n",
      "X [ 0.125      -0.49550226]\n",
      "on_boundary False\n",
      "X [-0.375     -0.4950025]\n",
      "on_boundary False\n",
      "X [ 0.625      -0.49450275]\n",
      "on_boundary False\n",
      "X [-0.625    -0.494003]\n",
      "on_boundary False\n",
      "X [ 0.375      -0.49350324]\n",
      "on_boundary False\n",
      "X [-0.125     -0.4930035]\n",
      "on_boundary False\n",
      "X [ 0.875      -0.49250376]\n",
      "on_boundary False\n",
      "X [-0.9375   -0.492004]\n",
      "on_boundary False\n",
      "X [ 0.0625     -0.49150425]\n",
      "on_boundary False\n",
      "X [-0.4375    -0.4910045]\n",
      "on_boundary False\n",
      "X [ 0.5625     -0.49050474]\n",
      "on_boundary False\n",
      "X [-0.6875   -0.490005]\n",
      "on_boundary False\n",
      "X [ 0.3125     -0.48950526]\n",
      "on_boundary False\n",
      "X [-0.1875    -0.4890055]\n",
      "on_boundary False\n",
      "X [ 0.8125     -0.48850575]\n",
      "on_boundary False\n",
      "X [-0.8125   -0.488006]\n",
      "on_boundary False\n",
      "X [ 0.1875     -0.48750624]\n",
      "on_boundary False\n",
      "X [-0.3125    -0.4870065]\n",
      "on_boundary False\n",
      "X [ 0.6875     -0.48650676]\n",
      "on_boundary False\n",
      "X [-0.5625   -0.486007]\n",
      "on_boundary False\n",
      "X [ 0.4375     -0.48550725]\n",
      "on_boundary False\n",
      "X [-0.0625    -0.4850075]\n",
      "on_boundary False\n",
      "X [ 0.9375     -0.48450774]\n",
      "on_boundary False\n",
      "X [-0.96875    -0.48400798]\n",
      "on_boundary False\n",
      "X [ 0.03125    -0.48350826]\n",
      "on_boundary False\n",
      "X [-0.46875   -0.4830085]\n",
      "on_boundary False\n",
      "X [ 0.53125    -0.48250875]\n",
      "on_boundary False\n",
      "X [-0.71875  -0.482009]\n",
      "on_boundary False\n",
      "X [ 0.28125    -0.48150924]\n",
      "on_boundary False\n",
      "X [-0.21875    -0.48100948]\n",
      "on_boundary False\n",
      "X [ 0.78125    -0.48050976]\n",
      "on_boundary False\n",
      "X [-0.84375 -0.48001]\n",
      "on_boundary False\n",
      "X [ 0.15625    -0.47951025]\n",
      "on_boundary False\n",
      "X [-0.34375   -0.4790105]\n",
      "on_boundary False\n",
      "X [ 0.65625    -0.47851074]\n",
      "on_boundary False\n",
      "X [-0.59375    -0.47801098]\n",
      "on_boundary False\n",
      "X [ 0.40625    -0.47751126]\n",
      "on_boundary False\n",
      "X [-0.09375   -0.4770115]\n",
      "on_boundary False\n",
      "X [ 0.90625    -0.47651175]\n",
      "on_boundary False\n",
      "X [-0.90625  -0.476012]\n",
      "on_boundary False\n",
      "X [ 0.09375    -0.47551224]\n",
      "on_boundary False\n",
      "X [-0.40625    -0.47501248]\n",
      "on_boundary False\n",
      "X [ 0.59375    -0.47451276]\n",
      "on_boundary False\n",
      "X [-0.65625  -0.474013]\n",
      "on_boundary False\n",
      "X [ 0.34375    -0.47351325]\n",
      "on_boundary False\n",
      "X [-0.15625   -0.4730135]\n",
      "on_boundary False\n",
      "X [ 0.84375    -0.47251374]\n",
      "on_boundary False\n",
      "X [-0.78125    -0.47201398]\n",
      "on_boundary False\n",
      "X [ 0.21875    -0.47151425]\n",
      "on_boundary False\n",
      "X [-0.28125   -0.4710145]\n",
      "on_boundary False\n",
      "X [ 0.71875    -0.47051474]\n",
      "on_boundary False\n",
      "X [-0.53125  -0.470015]\n",
      "on_boundary False\n",
      "X [ 0.46875    -0.46951523]\n",
      "on_boundary False\n",
      "X [-0.03125    -0.46901548]\n",
      "on_boundary False\n",
      "X [ 0.96875    -0.46851575]\n",
      "on_boundary False\n",
      "X [-0.984375 -0.468016]\n",
      "on_boundary False\n",
      "X [ 0.015625   -0.46751624]\n",
      "on_boundary False\n",
      "X [-0.484375  -0.4670165]\n",
      "on_boundary False\n",
      "X [ 0.515625   -0.46651673]\n",
      "on_boundary False\n",
      "X [-0.734375 -0.466017]\n",
      "on_boundary False\n",
      "X [ 0.265625   -0.46551725]\n",
      "on_boundary False\n",
      "X [-0.234375  -0.4650175]\n",
      "on_boundary False\n",
      "X [ 0.765625   -0.46451774]\n",
      "on_boundary False\n",
      "X [-0.859375 -0.464018]\n",
      "on_boundary False\n",
      "X [ 0.140625   -0.46351823]\n",
      "on_boundary False\n",
      "X [-0.359375   -0.46301848]\n",
      "on_boundary False\n",
      "X [ 0.640625   -0.46251875]\n",
      "on_boundary False\n",
      "X [-0.609375 -0.462019]\n",
      "on_boundary False\n",
      "X [ 0.390625   -0.46151924]\n",
      "on_boundary False\n",
      "X [-0.109375  -0.4610195]\n",
      "on_boundary False\n",
      "X [ 0.890625   -0.46051973]\n",
      "on_boundary False\n",
      "X [-0.921875 -0.46002 ]\n",
      "on_boundary False\n",
      "X [ 0.078125   -0.45952025]\n",
      "on_boundary False\n",
      "X [-0.421875  -0.4590205]\n",
      "on_boundary False\n",
      "X [ 0.578125   -0.45852074]\n",
      "on_boundary False\n",
      "X [-0.671875 -0.458021]\n",
      "on_boundary False\n",
      "X [ 0.328125   -0.45752123]\n",
      "on_boundary False\n",
      "X [-0.171875   -0.45702147]\n",
      "on_boundary False\n",
      "X [ 0.828125   -0.45652175]\n",
      "on_boundary False\n",
      "X [-0.796875 -0.456022]\n",
      "on_boundary False\n",
      "X [ 0.203125   -0.45552224]\n",
      "on_boundary False\n",
      "X [-0.296875   -0.45502248]\n",
      "on_boundary False\n",
      "X [ 0.703125   -0.45452273]\n",
      "on_boundary False\n",
      "X [-0.546875 -0.454023]\n",
      "on_boundary False\n",
      "X [ 0.453125   -0.45352325]\n",
      "on_boundary False\n",
      "X [-0.046875  -0.4530235]\n",
      "on_boundary False\n",
      "X [ 0.953125   -0.45252374]\n",
      "on_boundary False\n",
      "X [-0.953125   -0.45202398]\n",
      "on_boundary False\n",
      "X [ 0.046875   -0.45152423]\n",
      "on_boundary False\n",
      "X [-0.453125   -0.45102447]\n",
      "on_boundary False\n",
      "X [ 0.546875   -0.45052475]\n",
      "on_boundary False\n",
      "X [-0.703125 -0.450025]\n",
      "on_boundary False\n",
      "X [ 0.296875   -0.44952524]\n",
      "on_boundary False\n",
      "X [-0.203125   -0.44902548]\n",
      "on_boundary False\n",
      "X [ 0.796875   -0.44852573]\n",
      "on_boundary False\n",
      "X [-0.828125 -0.448026]\n",
      "on_boundary False\n",
      "X [ 0.171875   -0.44752625]\n",
      "on_boundary False\n",
      "X [-0.328125  -0.4470265]\n",
      "on_boundary False\n",
      "X [ 0.671875   -0.44652674]\n",
      "on_boundary False\n",
      "X [-0.578125   -0.44602698]\n",
      "on_boundary False\n",
      "X [ 0.421875   -0.44552723]\n",
      "on_boundary False\n",
      "X [-0.078125   -0.44502747]\n",
      "on_boundary False\n",
      "X [ 0.921875   -0.44452775]\n",
      "on_boundary False\n",
      "X [-0.890625 -0.444028]\n",
      "on_boundary False\n",
      "X [ 0.109375   -0.44352823]\n",
      "on_boundary False\n",
      "X [-0.390625   -0.44302848]\n",
      "on_boundary False\n",
      "X [ 0.609375   -0.44252872]\n",
      "on_boundary False\n",
      "X [-0.640625 -0.442029]\n",
      "on_boundary False\n",
      "X [ 0.359375   -0.44152924]\n",
      "on_boundary False\n",
      "X [-0.140625  -0.4410295]\n",
      "on_boundary False\n",
      "X [ 0.859375   -0.44052973]\n",
      "on_boundary False\n",
      "X [-0.765625   -0.44002998]\n",
      "on_boundary False\n",
      "X [ 0.234375   -0.43953022]\n",
      "on_boundary False\n",
      "X [-0.265625   -0.43903047]\n",
      "on_boundary False\n",
      "X [ 0.734375   -0.43853074]\n",
      "on_boundary False\n",
      "X [-0.515625 -0.438031]\n",
      "on_boundary False\n",
      "X [ 0.484375   -0.43753123]\n",
      "on_boundary False\n",
      "X [-0.015625   -0.43703148]\n",
      "on_boundary False\n",
      "X [ 0.984375   -0.43653172]\n",
      "on_boundary False\n",
      "X [-0.9921875 -0.436032 ]\n",
      "on_boundary False\n",
      "X [ 0.0078125  -0.43553224]\n",
      "on_boundary False\n",
      "X [-0.4921875 -0.4350325]\n",
      "on_boundary False\n",
      "X [ 0.5078125  -0.43453273]\n",
      "on_boundary False\n",
      "X [-0.7421875  -0.43403298]\n",
      "on_boundary False\n",
      "X [ 0.2578125  -0.43353325]\n",
      "on_boundary False\n",
      "X [-0.2421875  -0.43303347]\n",
      "on_boundary False\n",
      "X [ 0.7578125  -0.43253374]\n",
      "on_boundary False\n",
      "X [-0.8671875 -0.432034 ]\n",
      "on_boundary False\n",
      "X [ 0.1328125  -0.43153423]\n",
      "on_boundary False\n",
      "X [-0.3671875  -0.43103448]\n",
      "on_boundary False\n",
      "X [ 0.6328125  -0.43053472]\n",
      "on_boundary False\n",
      "X [-0.6171875 -0.430035 ]\n",
      "on_boundary False\n",
      "X [ 0.3828125  -0.42953524]\n",
      "on_boundary False\n",
      "X [-0.1171875  -0.42903548]\n",
      "on_boundary False\n",
      "X [ 0.8828125  -0.42853573]\n",
      "on_boundary False\n",
      "X [-0.9296875  -0.42803597]\n",
      "on_boundary False\n",
      "X [ 0.0703125  -0.42753625]\n",
      "on_boundary False\n",
      "X [-0.4296875  -0.42703646]\n",
      "on_boundary False\n",
      "X [ 0.5703125  -0.42653674]\n",
      "on_boundary False\n",
      "X [-0.6796875  -0.42603698]\n",
      "on_boundary False\n",
      "X [ 0.3203125  -0.42553723]\n",
      "on_boundary False\n",
      "X [-0.1796875  -0.42503747]\n",
      "on_boundary False\n",
      "X [ 0.8203125  -0.42453772]\n",
      "on_boundary False\n",
      "X [-0.8046875 -0.424038 ]\n",
      "on_boundary False\n",
      "X [ 0.1953125  -0.42353824]\n",
      "on_boundary False\n",
      "X [-0.3046875  -0.42303848]\n",
      "on_boundary False\n",
      "X [ 0.6953125  -0.42253873]\n",
      "on_boundary False\n",
      "X [-0.5546875  -0.42203897]\n",
      "on_boundary False\n",
      "X [ 0.4453125  -0.42153925]\n",
      "on_boundary False\n",
      "X [-0.0546875  -0.42103946]\n",
      "on_boundary False\n",
      "X [ 0.9453125  -0.42053974]\n",
      "on_boundary False\n",
      "X [-0.9609375  -0.42003998]\n",
      "on_boundary False\n",
      "X [ 0.0390625  -0.41954023]\n",
      "on_boundary False\n",
      "X [-0.4609375  -0.41904047]\n",
      "on_boundary False\n",
      "X [ 0.5390625  -0.41854072]\n",
      "on_boundary False\n",
      "X [-0.7109375 -0.418041 ]\n",
      "on_boundary False\n",
      "X [ 0.2890625  -0.41754124]\n",
      "on_boundary False\n",
      "X [-0.2109375  -0.41704148]\n",
      "on_boundary False\n",
      "X [ 0.7890625  -0.41654173]\n",
      "on_boundary False\n",
      "X [-0.8359375  -0.41604197]\n",
      "on_boundary False\n",
      "X [ 0.1640625  -0.41554224]\n",
      "on_boundary False\n",
      "X [-0.3359375 -0.4150425]\n",
      "on_boundary False\n",
      "X [ 0.6640625  -0.41454273]\n",
      "on_boundary False\n",
      "X [-0.5859375  -0.41404298]\n",
      "on_boundary False\n",
      "X [ 0.4140625  -0.41354322]\n",
      "on_boundary False\n",
      "X [-0.0859375  -0.41304347]\n",
      "on_boundary False\n",
      "X [ 0.9140625 -0.4125437]\n",
      "on_boundary False\n",
      "X [-0.8984375 -0.412044 ]\n",
      "on_boundary False\n",
      "X [ 0.1015625  -0.41154423]\n",
      "on_boundary False\n",
      "X [-0.3984375  -0.41104448]\n",
      "on_boundary False\n",
      "X [ 0.6015625  -0.41054472]\n",
      "on_boundary False\n",
      "X [-0.6484375  -0.41004497]\n",
      "on_boundary False\n",
      "X [ 0.3515625  -0.40954524]\n",
      "on_boundary False\n",
      "X [-0.1484375 -0.4090455]\n",
      "on_boundary False\n",
      "X [ 0.8515625  -0.40854573]\n",
      "on_boundary False\n",
      "X [-0.7734375  -0.40804598]\n",
      "on_boundary False\n",
      "X [ 0.2265625  -0.40754622]\n",
      "on_boundary False\n",
      "X [-0.2734375  -0.40704647]\n",
      "on_boundary False\n",
      "X [ 0.7265625 -0.4065467]\n",
      "on_boundary False\n",
      "X [-0.5234375 -0.406047 ]\n",
      "on_boundary False\n",
      "X [ 0.4765625  -0.40554723]\n",
      "on_boundary False\n",
      "X [-0.0234375  -0.40504748]\n",
      "on_boundary False\n",
      "X [ 0.9765625  -0.40454772]\n",
      "on_boundary False\n",
      "X [-0.9765625  -0.40404797]\n",
      "on_boundary False\n",
      "X [ 0.0234375  -0.40354824]\n",
      "on_boundary False\n",
      "X [-0.4765625 -0.4030485]\n",
      "on_boundary False\n",
      "X [ 0.5234375  -0.40254873]\n",
      "on_boundary False\n",
      "X [-0.7265625  -0.40204898]\n",
      "on_boundary False\n",
      "X [ 0.2734375  -0.40154922]\n",
      "on_boundary False\n",
      "X [-0.2265625  -0.40104946]\n",
      "on_boundary False\n",
      "X [ 0.7734375 -0.4005497]\n",
      "on_boundary False\n",
      "X [-0.8515625  -0.40004998]\n",
      "on_boundary False\n",
      "X [ 0.1484375  -0.39955023]\n",
      "on_boundary False\n",
      "X [-0.3515625  -0.39905047]\n",
      "on_boundary False\n",
      "X [ 0.6484375  -0.39855072]\n",
      "on_boundary False\n",
      "X [-0.6015625  -0.39805096]\n",
      "on_boundary False\n",
      "X [ 0.3984375  -0.39755124]\n",
      "on_boundary False\n",
      "X [-0.1015625  -0.39705148]\n",
      "on_boundary False\n",
      "X [ 0.8984375  -0.39655173]\n",
      "on_boundary False\n",
      "X [-0.9140625  -0.39605197]\n",
      "on_boundary False\n",
      "X [ 0.0859375  -0.39555222]\n",
      "on_boundary False\n",
      "X [-0.4140625  -0.39505246]\n",
      "on_boundary False\n",
      "X [ 0.5859375 -0.3945527]\n",
      "on_boundary False\n",
      "X [-0.6640625  -0.39405298]\n",
      "on_boundary False\n",
      "X [ 0.3359375  -0.39355323]\n",
      "on_boundary False\n",
      "X [-0.1640625  -0.39305347]\n",
      "on_boundary False\n",
      "X [ 0.8359375  -0.39255372]\n",
      "on_boundary False\n",
      "X [-0.7890625  -0.39205396]\n",
      "on_boundary False\n",
      "X [ 0.2109375  -0.39155424]\n",
      "on_boundary False\n",
      "X [-0.2890625  -0.39105448]\n",
      "on_boundary False\n",
      "X [ 0.7109375  -0.39055473]\n",
      "on_boundary False\n",
      "X [-0.5390625  -0.39005497]\n",
      "on_boundary False\n",
      "X [ 0.4609375  -0.38955522]\n",
      "on_boundary False\n",
      "X [-0.0390625 -0.3890555]\n",
      "on_boundary False\n",
      "X [ 0.9609375 -0.3885557]\n",
      "on_boundary False\n",
      "X [-0.9453125  -0.38805598]\n",
      "on_boundary False\n",
      "X [ 0.0546875  -0.38755623]\n",
      "on_boundary False\n",
      "X [-0.4453125  -0.38705647]\n",
      "on_boundary False\n",
      "X [ 0.5546875 -0.3865567]\n",
      "on_boundary False\n",
      "X [-0.6953125  -0.38605696]\n",
      "on_boundary False\n",
      "X [ 0.3046875  -0.38555723]\n",
      "on_boundary False\n",
      "X [-0.1953125  -0.38505748]\n",
      "on_boundary False\n",
      "X [ 0.8046875  -0.38455772]\n",
      "on_boundary False\n",
      "X [-0.8203125  -0.38405797]\n",
      "on_boundary False\n",
      "X [ 0.1796875 -0.3835582]\n",
      "on_boundary False\n",
      "X [-0.3203125 -0.3830585]\n",
      "on_boundary False\n",
      "X [ 0.6796875 -0.3825587]\n",
      "on_boundary False\n",
      "X [-0.5703125  -0.38205898]\n",
      "on_boundary False\n",
      "X [ 0.4296875  -0.38155922]\n",
      "on_boundary False\n",
      "X [-0.0703125  -0.38105947]\n",
      "on_boundary False\n",
      "X [ 0.9296875 -0.3805597]\n",
      "on_boundary False\n",
      "X [-0.8828125  -0.38005996]\n",
      "on_boundary False\n",
      "X [ 0.1171875  -0.37956023]\n",
      "on_boundary False\n",
      "X [-0.3828125  -0.37906048]\n",
      "on_boundary False\n",
      "X [ 0.6171875  -0.37856072]\n",
      "on_boundary False\n",
      "X [-0.6328125  -0.37806097]\n",
      "on_boundary False\n",
      "X [ 0.3671875 -0.3775612]\n",
      "on_boundary False\n",
      "X [-0.1328125 -0.3770615]\n",
      "on_boundary False\n",
      "X [ 0.8671875 -0.3765617]\n",
      "on_boundary False\n",
      "X [-0.7578125  -0.37606198]\n",
      "on_boundary False\n",
      "X [ 0.2421875  -0.37556222]\n",
      "on_boundary False\n",
      "X [-0.2578125  -0.37506247]\n",
      "on_boundary False\n",
      "X [ 0.7421875  -0.37456274]\n",
      "on_boundary False\n",
      "X [-0.5078125  -0.37406296]\n",
      "on_boundary False\n",
      "X [ 0.4921875  -0.37356323]\n",
      "on_boundary False\n",
      "X [-0.0078125  -0.37306347]\n",
      "on_boundary False\n",
      "X [ 0.9921875  -0.37256372]\n",
      "on_boundary False\n",
      "X [-0.99609375 -0.37206396]\n",
      "on_boundary False\n",
      "X [ 0.00390625 -0.3715642 ]\n",
      "on_boundary False\n",
      "X [-0.49609375 -0.37106448]\n",
      "on_boundary False\n",
      "X [ 0.50390625 -0.3705647 ]\n",
      "on_boundary False\n",
      "X [-0.74609375 -0.37006497]\n",
      "on_boundary False\n",
      "X [ 0.25390625 -0.36956522]\n",
      "on_boundary False\n",
      "X [-0.24609375 -0.36906546]\n",
      "on_boundary False\n",
      "X [ 0.75390625 -0.36856574]\n",
      "on_boundary False\n",
      "X [-0.87109375 -0.36806595]\n",
      "on_boundary False\n",
      "X [ 0.12890625 -0.36756623]\n",
      "on_boundary False\n",
      "X [-0.37109375 -0.36706647]\n",
      "on_boundary False\n",
      "X [ 0.62890625 -0.36656672]\n",
      "on_boundary False\n",
      "X [-0.62109375 -0.36606696]\n",
      "on_boundary False\n",
      "X [ 0.37890625 -0.3655672 ]\n",
      "on_boundary False\n",
      "X [-0.12109375 -0.36506748]\n",
      "on_boundary False\n",
      "X [ 0.87890625 -0.3645677 ]\n",
      "on_boundary False\n",
      "X [-0.93359375 -0.36406797]\n",
      "on_boundary False\n",
      "X [ 0.06640625 -0.36356822]\n",
      "on_boundary False\n",
      "X [-0.43359375 -0.36306846]\n",
      "on_boundary False\n",
      "X [ 0.56640625 -0.36256874]\n",
      "on_boundary False\n",
      "X [-0.68359375 -0.36206895]\n",
      "on_boundary False\n",
      "X [ 0.31640625 -0.36156923]\n",
      "on_boundary False\n",
      "X [-0.18359375 -0.36106947]\n",
      "on_boundary False\n",
      "X [ 0.81640625 -0.36056972]\n",
      "on_boundary False\n",
      "X [-0.80859375 -0.36006996]\n",
      "on_boundary False\n",
      "X [ 0.19140625 -0.3595702 ]\n",
      "on_boundary False\n",
      "X [-0.30859375 -0.35907048]\n",
      "on_boundary False\n",
      "X [ 0.69140625 -0.3585707 ]\n",
      "on_boundary False\n",
      "X [-0.55859375 -0.35807097]\n",
      "on_boundary False\n",
      "X [ 0.44140625 -0.3575712 ]\n",
      "on_boundary False\n",
      "X [-0.05859375 -0.35707146]\n",
      "on_boundary False\n",
      "X [ 0.94140625 -0.35657173]\n",
      "on_boundary False\n",
      "X [-0.96484375 -0.35607195]\n",
      "on_boundary False\n",
      "X [ 0.03515625 -0.35557222]\n",
      "on_boundary False\n",
      "X [-0.46484375 -0.35507247]\n",
      "on_boundary False\n",
      "X [ 0.53515625 -0.3545727 ]\n",
      "on_boundary False\n",
      "X [-0.71484375 -0.35407296]\n",
      "on_boundary False\n",
      "X [ 0.28515625 -0.3535732 ]\n",
      "on_boundary False\n",
      "X [-0.21484375 -0.35307348]\n",
      "on_boundary False\n",
      "X [ 0.78515625 -0.3525737 ]\n",
      "on_boundary False\n",
      "X [-0.83984375 -0.35207397]\n",
      "on_boundary False\n",
      "X [ 0.16015625 -0.3515742 ]\n",
      "on_boundary False\n",
      "X [-0.33984375 -0.35107446]\n",
      "on_boundary False\n",
      "X [ 0.66015625 -0.35057473]\n",
      "on_boundary False\n",
      "X [-0.58984375 -0.35007495]\n",
      "on_boundary False\n",
      "X [ 0.41015625 -0.34957522]\n",
      "on_boundary False\n",
      "X [-0.08984375 -0.34907547]\n",
      "on_boundary False\n",
      "X [ 0.91015625 -0.3485757 ]\n",
      "on_boundary False\n",
      "X [-0.90234375 -0.34807596]\n",
      "on_boundary False\n",
      "X [ 0.09765625 -0.3475762 ]\n",
      "on_boundary False\n",
      "X [-0.40234375 -0.34707648]\n",
      "on_boundary False\n",
      "X [ 0.59765625 -0.3465767 ]\n",
      "on_boundary False\n",
      "X [-0.65234375 -0.34607697]\n",
      "on_boundary False\n",
      "X [ 0.34765625 -0.3455772 ]\n",
      "on_boundary False\n",
      "X [-0.15234375 -0.34507746]\n",
      "on_boundary False\n",
      "X [ 0.84765625 -0.34457773]\n",
      "on_boundary False\n",
      "X [-0.77734375 -0.34407794]\n",
      "on_boundary False\n",
      "X [ 0.22265625 -0.34357822]\n",
      "on_boundary False\n",
      "X [-0.27734375 -0.34307846]\n",
      "on_boundary False\n",
      "X [ 0.72265625 -0.3425787 ]\n",
      "on_boundary False\n",
      "X [-0.52734375 -0.34207895]\n",
      "on_boundary False\n",
      "X [ 0.47265625 -0.3415792 ]\n",
      "on_boundary False\n",
      "X [-0.02734375 -0.34107947]\n",
      "on_boundary False\n",
      "X [ 0.97265625 -0.3405797 ]\n",
      "on_boundary False\n",
      "X [-0.98046875 -0.34007996]\n",
      "on_boundary False\n",
      "X [ 0.01953125 -0.3395802 ]\n",
      "on_boundary False\n",
      "X [-0.48046875 -0.33908045]\n",
      "on_boundary False\n",
      "X [ 0.51953125 -0.33858073]\n",
      "on_boundary False\n",
      "X [-0.73046875 -0.33808094]\n",
      "on_boundary False\n",
      "X [ 0.26953125 -0.33758122]\n",
      "on_boundary False\n",
      "X [-0.23046875 -0.33708146]\n",
      "on_boundary False\n",
      "X [ 0.76953125 -0.3365817 ]\n",
      "on_boundary False\n",
      "X [-0.85546875 -0.33608195]\n",
      "on_boundary False\n",
      "X [ 0.14453125 -0.3355822 ]\n",
      "on_boundary False\n",
      "X [-0.35546875 -0.33508247]\n",
      "on_boundary False\n",
      "X [ 0.64453125 -0.3345827 ]\n",
      "on_boundary False\n",
      "X [-0.60546875 -0.33408296]\n",
      "on_boundary False\n",
      "X [ 0.39453125 -0.3335832 ]\n",
      "on_boundary False\n",
      "X [-0.10546875 -0.33308345]\n",
      "on_boundary False\n",
      "X [ 0.89453125 -0.33258373]\n",
      "on_boundary False\n",
      "X [-0.91796875 -0.33208394]\n",
      "on_boundary False\n",
      "X [ 0.08203125 -0.33158422]\n",
      "on_boundary False\n",
      "X [-0.41796875 -0.33108446]\n",
      "on_boundary False\n",
      "X [ 0.58203125 -0.3305847 ]\n",
      "on_boundary False\n",
      "X [-0.66796875 -0.33008498]\n",
      "on_boundary False\n",
      "X [ 0.33203125 -0.3295852 ]\n",
      "on_boundary False\n",
      "X [-0.16796875 -0.32908547]\n",
      "on_boundary False\n",
      "X [ 0.83203125 -0.3285857 ]\n",
      "on_boundary False\n",
      "X [-0.79296875 -0.32808596]\n",
      "on_boundary False\n",
      "X [ 0.20703125 -0.3275862 ]\n",
      "on_boundary False\n",
      "X [-0.29296875 -0.32708645]\n",
      "on_boundary False\n",
      "X [ 0.70703125 -0.32658672]\n",
      "on_boundary False\n",
      "X [-0.54296875 -0.32608694]\n",
      "on_boundary False\n",
      "X [ 0.45703125 -0.3255872 ]\n",
      "on_boundary False\n",
      "X [-0.04296875 -0.32508746]\n",
      "on_boundary False\n",
      "X [ 0.95703125 -0.3245877 ]\n",
      "on_boundary False\n",
      "X [-0.94921875 -0.32408798]\n",
      "on_boundary False\n",
      "X [ 0.05078125 -0.3235882 ]\n",
      "on_boundary False\n",
      "X [-0.44921875 -0.32308847]\n",
      "on_boundary False\n",
      "X [ 0.55078125 -0.3225887 ]\n",
      "on_boundary False\n",
      "X [-0.69921875 -0.32208896]\n",
      "on_boundary False\n",
      "X [ 0.30078125 -0.3215892 ]\n",
      "on_boundary False\n",
      "X [-0.19921875 -0.32108945]\n",
      "on_boundary False\n",
      "X [ 0.80078125 -0.32058972]\n",
      "on_boundary False\n",
      "X [-0.82421875 -0.32008994]\n",
      "on_boundary False\n",
      "X [ 0.17578125 -0.3195902 ]\n",
      "on_boundary False\n",
      "X [-0.32421875 -0.31909046]\n",
      "on_boundary False\n",
      "X [ 0.67578125 -0.3185907 ]\n",
      "on_boundary False\n",
      "X [-0.57421875 -0.31809098]\n",
      "on_boundary False\n",
      "X [ 0.42578125 -0.3175912 ]\n",
      "on_boundary False\n",
      "X [-0.07421875 -0.31709146]\n",
      "on_boundary False\n",
      "X [ 0.92578125 -0.3165917 ]\n",
      "on_boundary False\n",
      "X [-0.88671875 -0.31609195]\n",
      "on_boundary False\n",
      "X [ 0.11328125 -0.3155922 ]\n",
      "on_boundary False\n",
      "X [-0.38671875 -0.31509244]\n",
      "on_boundary False\n",
      "X [ 0.61328125 -0.31459272]\n",
      "on_boundary False\n",
      "X [-0.63671875 -0.31409293]\n",
      "on_boundary False\n",
      "X [ 0.36328125 -0.3135932 ]\n",
      "on_boundary False\n",
      "X [-0.13671875 -0.31309345]\n",
      "on_boundary False\n",
      "X [ 0.86328125 -0.3125937 ]\n",
      "on_boundary False\n",
      "X [-0.76171875 -0.31209397]\n",
      "on_boundary False\n",
      "X [ 0.23828125 -0.3115942 ]\n",
      "on_boundary False\n",
      "X [-0.26171875 -0.31109446]\n",
      "on_boundary False\n",
      "X [ 0.73828125 -0.3105947 ]\n",
      "on_boundary False\n",
      "X [-0.51171875 -0.31009495]\n",
      "on_boundary False\n",
      "X [ 0.48828125 -0.3095952 ]\n",
      "on_boundary False\n",
      "X [-0.01171875 -0.30909544]\n",
      "on_boundary False\n",
      "X [ 0.98828125 -0.30859572]\n",
      "on_boundary False\n",
      "X [-0.98828125 -0.30809593]\n",
      "on_boundary False\n",
      "X [ 0.01171875 -0.3075962 ]\n",
      "on_boundary False\n",
      "X [-0.48828125 -0.30709645]\n",
      "on_boundary False\n",
      "X [ 0.51171875 -0.3065967 ]\n",
      "on_boundary False\n",
      "X [-0.73828125 -0.30609697]\n",
      "on_boundary False\n",
      "X [ 0.26171875 -0.3055972 ]\n",
      "on_boundary False\n",
      "X [-0.23828125 -0.30509746]\n",
      "on_boundary False\n",
      "X [ 0.76171875 -0.3045977 ]\n",
      "on_boundary False\n",
      "X [-0.86328125 -0.30409795]\n",
      "on_boundary False\n",
      "X [ 0.13671875 -0.3035982 ]\n",
      "on_boundary False\n",
      "X [-0.36328125 -0.30309844]\n",
      "on_boundary False\n",
      "X [ 0.63671875 -0.3025987 ]\n",
      "on_boundary False\n",
      "X [-0.61328125 -0.30209893]\n",
      "on_boundary False\n",
      "X [ 0.38671875 -0.3015992 ]\n",
      "on_boundary False\n",
      "X [-0.11328125 -0.30109945]\n",
      "on_boundary False\n",
      "X [ 0.88671875 -0.3005997 ]\n",
      "on_boundary False\n",
      "X [-0.92578125 -0.30009997]\n",
      "on_boundary False\n",
      "X [ 0.07421875 -0.29960018]\n",
      "on_boundary False\n",
      "X [-0.42578125 -0.29910046]\n",
      "on_boundary False\n",
      "X [ 0.57421875 -0.2986007 ]\n",
      "on_boundary False\n",
      "X [-0.67578125 -0.29810095]\n",
      "on_boundary False\n",
      "X [ 0.32421875 -0.2976012 ]\n",
      "on_boundary False\n",
      "X [-0.17578125 -0.29710144]\n",
      "on_boundary False\n",
      "X [ 0.82421875 -0.2966017 ]\n",
      "on_boundary False\n",
      "X [-0.80078125 -0.29610193]\n",
      "on_boundary False\n",
      "X [ 0.19921875 -0.2956022 ]\n",
      "on_boundary False\n",
      "X [-0.30078125 -0.29510245]\n",
      "on_boundary False\n",
      "X [ 0.69921875 -0.2946027 ]\n",
      "on_boundary False\n",
      "X [-0.55078125 -0.29410297]\n",
      "on_boundary False\n",
      "X [ 0.44921875 -0.29360318]\n",
      "on_boundary False\n",
      "X [-0.05078125 -0.29310346]\n",
      "on_boundary False\n",
      "X [ 0.94921875 -0.2926037 ]\n",
      "on_boundary False\n",
      "X [-0.95703125 -0.29210395]\n",
      "on_boundary False\n",
      "X [ 0.04296875 -0.2916042 ]\n",
      "on_boundary False\n",
      "X [-0.45703125 -0.29110444]\n",
      "on_boundary False\n",
      "X [ 0.54296875 -0.2906047 ]\n",
      "on_boundary False\n",
      "X [-0.70703125 -0.29010493]\n",
      "on_boundary False\n",
      "X [ 0.29296875 -0.2896052 ]\n",
      "on_boundary False\n",
      "X [-0.20703125 -0.28910545]\n",
      "on_boundary False\n",
      "X [ 0.79296875 -0.2886057 ]\n",
      "on_boundary False\n",
      "X [-0.83203125 -0.28810596]\n",
      "on_boundary False\n",
      "X [ 0.16796875 -0.28760618]\n",
      "on_boundary False\n",
      "X [-0.33203125 -0.28710645]\n",
      "on_boundary False\n",
      "X [ 0.66796875 -0.2866067 ]\n",
      "on_boundary False\n",
      "X [-0.58203125 -0.28610694]\n",
      "on_boundary False\n",
      "X [ 0.41796875 -0.28560722]\n",
      "on_boundary False\n",
      "X [-0.08203125 -0.28510743]\n",
      "on_boundary False\n",
      "X [ 0.91796875 -0.2846077 ]\n",
      "on_boundary False\n",
      "X [-0.89453125 -0.28410795]\n",
      "on_boundary False\n",
      "X [ 0.10546875 -0.2836082 ]\n",
      "on_boundary False\n",
      "X [-0.39453125 -0.28310844]\n",
      "on_boundary False\n",
      "X [ 0.60546875 -0.2826087 ]\n",
      "on_boundary False\n",
      "X [-0.64453125 -0.28210896]\n",
      "on_boundary False\n",
      "X [ 0.35546875 -0.28160918]\n",
      "on_boundary False\n",
      "X [-0.14453125 -0.28110945]\n",
      "on_boundary False\n",
      "X [ 0.85546875 -0.2806097 ]\n",
      "on_boundary False\n",
      "X [-0.76953125 -0.28010994]\n",
      "on_boundary False\n",
      "X [ 0.23046875 -0.27961022]\n",
      "on_boundary False\n",
      "X [-0.26953125 -0.27911043]\n",
      "on_boundary False\n",
      "X [ 0.73046875 -0.2786107 ]\n",
      "on_boundary False\n",
      "X [-0.51953125 -0.27811095]\n",
      "on_boundary False\n",
      "X [ 0.48046875 -0.2776112 ]\n",
      "on_boundary False\n",
      "X [-0.01953125 -0.27711144]\n",
      "on_boundary False\n",
      "X [ 0.98046875 -0.2766117 ]\n",
      "on_boundary False\n",
      "X [-0.97265625 -0.27611196]\n",
      "on_boundary False\n",
      "X [ 0.02734375 -0.27561218]\n",
      "on_boundary False\n",
      "X [-0.47265625 -0.27511245]\n",
      "on_boundary False\n",
      "X [ 0.52734375 -0.2746127 ]\n",
      "on_boundary False\n",
      "X [-0.72265625 -0.27411294]\n",
      "on_boundary False\n",
      "X [ 0.27734375 -0.2736132 ]\n",
      "on_boundary False\n",
      "X [-0.22265625 -0.27311343]\n",
      "on_boundary False\n",
      "X [ 0.77734375 -0.2726137 ]\n",
      "on_boundary False\n",
      "X [-0.84765625 -0.27211395]\n",
      "on_boundary False\n",
      "X [ 0.15234375 -0.2716142 ]\n",
      "on_boundary False\n",
      "X [-0.34765625 -0.27111444]\n",
      "on_boundary False\n",
      "X [ 0.65234375 -0.27061468]\n",
      "on_boundary False\n",
      "X [-0.59765625 -0.27011496]\n",
      "on_boundary False\n",
      "X [ 0.40234375 -0.26961517]\n",
      "on_boundary False\n",
      "X [-0.09765625 -0.26911545]\n",
      "on_boundary False\n",
      "X [ 0.90234375 -0.2686157 ]\n",
      "on_boundary False\n",
      "X [-0.91015625 -0.26811594]\n",
      "on_boundary False\n",
      "X [ 0.08984375 -0.2676162 ]\n",
      "on_boundary False\n",
      "X [-0.41015625 -0.26711643]\n",
      "on_boundary False\n",
      "X [ 0.58984375 -0.2666167 ]\n",
      "on_boundary False\n",
      "X [-0.66015625 -0.26611695]\n",
      "on_boundary False\n",
      "X [ 0.33984375 -0.2656172 ]\n",
      "on_boundary False\n",
      "X [-0.16015625 -0.26511744]\n",
      "on_boundary False\n",
      "X [ 0.83984375 -0.26461768]\n",
      "on_boundary False\n",
      "X [-0.78515625 -0.26411796]\n",
      "on_boundary False\n",
      "X [ 0.21484375 -0.26361817]\n",
      "on_boundary False\n",
      "X [-0.28515625 -0.26311845]\n",
      "on_boundary False\n",
      "X [ 0.71484375 -0.2626187 ]\n",
      "on_boundary False\n",
      "X [-0.53515625 -0.26211894]\n",
      "on_boundary False\n",
      "X [ 0.46484375 -0.2616192 ]\n",
      "on_boundary False\n",
      "X [-0.03515625 -0.26111943]\n",
      "on_boundary False\n",
      "X [ 0.96484375 -0.2606197 ]\n",
      "on_boundary False\n",
      "X [-0.94140625 -0.26011994]\n",
      "on_boundary False\n",
      "X [ 0.05859375 -0.2596202 ]\n",
      "on_boundary False\n",
      "X [-0.44140625 -0.25912043]\n",
      "on_boundary False\n",
      "X [ 0.55859375 -0.25862068]\n",
      "on_boundary False\n",
      "X [-0.69140625 -0.25812095]\n",
      "on_boundary False\n",
      "X [ 0.30859375 -0.25762117]\n",
      "on_boundary False\n",
      "X [-0.19140625 -0.25712144]\n",
      "on_boundary False\n",
      "X [ 0.80859375 -0.2566217 ]\n",
      "on_boundary False\n",
      "X [-0.81640625 -0.25612193]\n",
      "on_boundary False\n",
      "X [ 0.18359375 -0.2556222 ]\n",
      "on_boundary False\n",
      "X [-0.31640625 -0.25512242]\n",
      "on_boundary False\n",
      "X [ 0.68359375 -0.2546227 ]\n",
      "on_boundary False\n",
      "X [-0.56640625 -0.25412294]\n",
      "on_boundary False\n",
      "X [ 0.43359375 -0.2536232 ]\n",
      "on_boundary False\n",
      "X [-0.06640625 -0.25312343]\n",
      "on_boundary False\n",
      "X [ 0.93359375 -0.25262368]\n",
      "on_boundary False\n",
      "X [-0.87890625 -0.25212395]\n",
      "on_boundary False\n",
      "X [ 0.12109375 -0.25162417]\n",
      "on_boundary False\n",
      "X [-0.37890625 -0.25112444]\n",
      "on_boundary False\n",
      "X [ 0.62109375 -0.2506247 ]\n",
      "on_boundary False\n",
      "X [-0.62890625 -0.25012493]\n",
      "on_boundary False\n",
      "X [ 0.37109375 -0.24962518]\n",
      "on_boundary False\n",
      "X [-0.12890625 -0.24912545]\n",
      "on_boundary False\n",
      "X [ 0.87109375 -0.2486257 ]\n",
      "on_boundary False\n",
      "X [-0.75390625 -0.24812594]\n",
      "on_boundary False\n",
      "X [ 0.24609375 -0.24762619]\n",
      "on_boundary False\n",
      "X [-0.25390625 -0.24712643]\n",
      "on_boundary False\n",
      "X [ 0.74609375 -0.24662668]\n",
      "on_boundary False\n",
      "X [-0.50390625 -0.24612695]\n",
      "on_boundary False\n",
      "X [ 0.49609375 -0.2456272 ]\n",
      "on_boundary False\n",
      "X [-0.00390625 -0.24512744]\n",
      "on_boundary False\n",
      "X [ 0.99609375 -0.24462768]\n",
      "on_boundary False\n",
      "X [-0.9980469  -0.24412793]\n",
      "on_boundary False\n",
      "X [ 0.00195312 -0.24362817]\n",
      "on_boundary False\n",
      "X [-0.49804688 -0.24312845]\n",
      "on_boundary False\n",
      "X [ 0.5019531 -0.2426287]\n",
      "on_boundary False\n",
      "X [-0.7480469  -0.24212894]\n",
      "on_boundary False\n",
      "X [ 0.25195312 -0.24162918]\n",
      "on_boundary False\n",
      "X [-0.24804688 -0.24112943]\n",
      "on_boundary False\n",
      "X [ 0.7519531  -0.24062967]\n",
      "on_boundary False\n",
      "X [-0.8730469  -0.24012995]\n",
      "on_boundary False\n",
      "X [ 0.12695312 -0.23963019]\n",
      "on_boundary False\n",
      "X [-0.37304688 -0.23913044]\n",
      "on_boundary False\n",
      "X [ 0.6269531  -0.23863068]\n",
      "on_boundary False\n",
      "X [-0.6230469  -0.23813093]\n",
      "on_boundary False\n",
      "X [ 0.37695312 -0.23763117]\n",
      "on_boundary False\n",
      "X [-0.12304688 -0.23713145]\n",
      "on_boundary False\n",
      "X [ 0.8769531  -0.23663169]\n",
      "on_boundary False\n",
      "X [-0.9355469  -0.23613194]\n",
      "on_boundary False\n",
      "X [ 0.06445312 -0.23563218]\n",
      "on_boundary False\n",
      "X [-0.43554688 -0.23513243]\n",
      "on_boundary False\n",
      "X [ 0.5644531  -0.23463267]\n",
      "on_boundary False\n",
      "X [-0.6855469  -0.23413295]\n",
      "on_boundary False\n",
      "X [ 0.31445312 -0.23363319]\n",
      "on_boundary False\n",
      "X [-0.18554688 -0.23313344]\n",
      "on_boundary False\n",
      "X [ 0.8144531  -0.23263368]\n",
      "on_boundary False\n",
      "X [-0.8105469  -0.23213392]\n",
      "on_boundary False\n",
      "X [ 0.18945312 -0.23163417]\n",
      "on_boundary False\n",
      "X [-0.31054688 -0.23113444]\n",
      "on_boundary False\n",
      "X [ 0.6894531  -0.23063469]\n",
      "on_boundary False\n",
      "X [-0.5605469  -0.23013493]\n",
      "on_boundary False\n",
      "X [ 0.43945312 -0.22963518]\n",
      "on_boundary False\n",
      "X [-0.06054688 -0.22913542]\n",
      "on_boundary False\n",
      "X [ 0.9394531  -0.22863567]\n",
      "on_boundary False\n",
      "X [-0.9667969  -0.22813594]\n",
      "on_boundary False\n",
      "X [ 0.03320312 -0.22763619]\n",
      "on_boundary False\n",
      "X [-0.46679688 -0.22713643]\n",
      "on_boundary False\n",
      "X [ 0.5332031  -0.22663668]\n",
      "on_boundary False\n",
      "X [-0.7167969  -0.22613692]\n",
      "on_boundary False\n",
      "X [ 0.28320312 -0.22563717]\n",
      "on_boundary False\n",
      "X [-0.21679688 -0.22513744]\n",
      "on_boundary False\n",
      "X [ 0.7832031  -0.22463769]\n",
      "on_boundary False\n",
      "X [-0.8417969  -0.22413793]\n",
      "on_boundary False\n",
      "X [ 0.15820312 -0.22363818]\n",
      "on_boundary False\n",
      "X [-0.34179688 -0.22313842]\n",
      "on_boundary False\n",
      "X [ 0.6582031  -0.22263867]\n",
      "on_boundary False\n",
      "X [-0.5917969  -0.22213894]\n",
      "on_boundary False\n",
      "X [ 0.40820312 -0.22163919]\n",
      "on_boundary False\n",
      "X [-0.09179688 -0.22113943]\n",
      "on_boundary False\n",
      "X [ 0.9082031  -0.22063968]\n",
      "on_boundary False\n",
      "X [-0.9042969  -0.22013992]\n",
      "on_boundary False\n",
      "X [ 0.09570312 -0.21964017]\n",
      "on_boundary False\n",
      "X [-0.40429688 -0.21914044]\n",
      "on_boundary False\n",
      "X [ 0.5957031  -0.21864069]\n",
      "on_boundary False\n",
      "X [-0.6542969  -0.21814093]\n",
      "on_boundary False\n",
      "X [ 0.34570312 -0.21764117]\n",
      "on_boundary False\n",
      "X [-0.15429688 -0.21714142]\n",
      "on_boundary False\n",
      "X [ 0.8457031  -0.21664166]\n",
      "on_boundary False\n",
      "X [-0.7792969  -0.21614194]\n",
      "on_boundary False\n",
      "X [ 0.22070312 -0.21564218]\n",
      "on_boundary False\n",
      "X [-0.27929688 -0.21514243]\n",
      "on_boundary False\n",
      "X [ 0.7207031  -0.21464267]\n",
      "on_boundary False\n",
      "X [-0.5292969  -0.21414292]\n",
      "on_boundary False\n",
      "X [ 0.47070312 -0.2136432 ]\n",
      "on_boundary False\n",
      "X [-0.02929688 -0.21314344]\n",
      "on_boundary False\n",
      "X [ 0.9707031  -0.21264368]\n",
      "on_boundary False\n",
      "X [-0.9824219  -0.21214393]\n",
      "on_boundary False\n",
      "X [ 0.01757812 -0.21164417]\n",
      "on_boundary False\n",
      "X [-0.48242188 -0.21114442]\n",
      "on_boundary False\n",
      "X [ 0.5175781  -0.21064469]\n",
      "on_boundary False\n",
      "X [-0.7324219  -0.21014494]\n",
      "on_boundary False\n",
      "X [ 0.26757812 -0.20964518]\n",
      "on_boundary False\n",
      "X [-0.23242188 -0.20914543]\n",
      "on_boundary False\n",
      "X [ 0.7675781  -0.20864567]\n",
      "on_boundary False\n",
      "X [-0.8574219  -0.20814592]\n",
      "on_boundary False\n",
      "X [ 0.14257812 -0.20764619]\n",
      "on_boundary False\n",
      "X [-0.35742188 -0.20714644]\n",
      "on_boundary False\n",
      "X [ 0.6425781  -0.20664668]\n",
      "on_boundary False\n",
      "X [-0.6074219  -0.20614693]\n",
      "on_boundary False\n",
      "X [ 0.39257812 -0.20564717]\n",
      "on_boundary False\n",
      "X [-0.10742188 -0.20514742]\n",
      "on_boundary False\n",
      "X [ 0.8925781  -0.20464769]\n",
      "on_boundary False\n",
      "X [-0.9199219  -0.20414793]\n",
      "on_boundary False\n",
      "X [ 0.08007812 -0.20364818]\n",
      "on_boundary False\n",
      "X [-0.41992188 -0.20314842]\n",
      "on_boundary False\n",
      "X [ 0.5800781  -0.20264867]\n",
      "on_boundary False\n",
      "X [-0.6699219  -0.20214891]\n",
      "on_boundary False\n",
      "X [ 0.33007812 -0.20164919]\n",
      "on_boundary False\n",
      "X [-0.16992188 -0.20114943]\n",
      "on_boundary False\n",
      "X [ 0.8300781  -0.20064968]\n",
      "on_boundary False\n",
      "X [-0.7949219  -0.20014992]\n",
      "on_boundary False\n",
      "X [ 0.20507812 -0.19965017]\n",
      "on_boundary False\n",
      "X [-0.29492188 -0.19915041]\n",
      "on_boundary False\n",
      "X [ 0.7050781  -0.19865069]\n",
      "on_boundary False\n",
      "X [-0.5449219  -0.19815093]\n",
      "on_boundary False\n",
      "X [ 0.45507812 -0.19765118]\n",
      "on_boundary False\n",
      "X [-0.04492188 -0.19715142]\n",
      "on_boundary False\n",
      "X [ 0.9550781  -0.19665167]\n",
      "on_boundary False\n",
      "X [-0.9511719  -0.19615191]\n",
      "on_boundary False\n",
      "X [ 0.04882812 -0.19565219]\n",
      "on_boundary False\n",
      "X [-0.45117188 -0.19515243]\n",
      "on_boundary False\n",
      "X [ 0.5488281  -0.19465268]\n",
      "on_boundary False\n",
      "X [-0.7011719  -0.19415292]\n",
      "on_boundary False\n",
      "X [ 0.29882812 -0.19365317]\n",
      "on_boundary False\n",
      "X [-0.20117188 -0.19315341]\n",
      "on_boundary False\n",
      "X [ 0.7988281  -0.19265369]\n",
      "on_boundary False\n",
      "X [-0.8261719  -0.19215393]\n",
      "on_boundary False\n",
      "X [ 0.17382812 -0.19165418]\n",
      "on_boundary False\n",
      "X [-0.32617188 -0.19115442]\n",
      "on_boundary False\n",
      "X [ 0.6738281  -0.19065467]\n",
      "on_boundary False\n",
      "X [-0.5761719  -0.19015491]\n",
      "on_boundary False\n",
      "X [ 0.42382812 -0.18965518]\n",
      "on_boundary False\n",
      "X [-0.07617188 -0.18915543]\n",
      "on_boundary False\n",
      "X [ 0.9238281  -0.18865567]\n",
      "on_boundary False\n",
      "X [-0.8886719  -0.18815592]\n",
      "on_boundary False\n",
      "X [ 0.11132812 -0.18765616]\n",
      "on_boundary False\n",
      "X [-0.38867188 -0.18715641]\n",
      "on_boundary False\n",
      "X [ 0.6113281  -0.18665668]\n",
      "on_boundary False\n",
      "X [-0.6386719  -0.18615693]\n",
      "on_boundary False\n",
      "X [ 0.36132812 -0.18565717]\n",
      "on_boundary False\n",
      "X [-0.13867188 -0.18515742]\n",
      "on_boundary False\n",
      "X [ 0.8613281  -0.18465766]\n",
      "on_boundary False\n",
      "X [-0.7636719  -0.18415791]\n",
      "on_boundary False\n",
      "X [ 0.23632812 -0.18365818]\n",
      "on_boundary False\n",
      "X [-0.26367188 -0.18315843]\n",
      "on_boundary False\n",
      "X [ 0.7363281  -0.18265867]\n",
      "on_boundary False\n",
      "X [-0.5136719  -0.18215892]\n",
      "on_boundary False\n",
      "X [ 0.48632812 -0.18165916]\n",
      "on_boundary False\n",
      "X [-0.01367188 -0.1811594 ]\n",
      "on_boundary False\n",
      "X [ 0.9863281  -0.18065968]\n",
      "on_boundary False\n",
      "X [-0.9902344  -0.18015993]\n",
      "on_boundary False\n",
      "X [ 0.00976562 -0.17966017]\n",
      "on_boundary False\n",
      "X [-0.49023438 -0.17916042]\n",
      "on_boundary False\n",
      "X [ 0.5097656  -0.17866066]\n",
      "on_boundary False\n",
      "X [-0.7402344 -0.1781609]\n",
      "on_boundary False\n",
      "X [ 0.25976562 -0.17766118]\n",
      "on_boundary False\n",
      "X [-0.24023438 -0.17716143]\n",
      "on_boundary False\n",
      "X [ 0.7597656  -0.17666167]\n",
      "on_boundary False\n",
      "X [-0.8652344  -0.17616192]\n",
      "on_boundary False\n",
      "X [ 0.13476562 -0.17566216]\n",
      "on_boundary False\n",
      "X [-0.36523438 -0.1751624 ]\n",
      "on_boundary False\n",
      "X [ 0.6347656  -0.17466268]\n",
      "on_boundary False\n",
      "X [-0.6152344  -0.17416292]\n",
      "on_boundary False\n",
      "X [ 0.38476562 -0.17366317]\n",
      "on_boundary False\n",
      "X [-0.11523438 -0.17316341]\n",
      "on_boundary False\n",
      "X [ 0.8847656  -0.17266366]\n",
      "on_boundary False\n",
      "X [-0.9277344 -0.1721639]\n",
      "on_boundary False\n",
      "X [ 0.07226562 -0.17166418]\n",
      "on_boundary False\n",
      "X [-0.42773438 -0.17116442]\n",
      "on_boundary False\n",
      "X [ 0.5722656  -0.17066467]\n",
      "on_boundary False\n",
      "X [-0.6777344  -0.17016491]\n",
      "on_boundary False\n",
      "X [ 0.32226562 -0.16966516]\n",
      "on_boundary False\n",
      "X [-0.17773438 -0.1691654 ]\n",
      "on_boundary False\n",
      "X [ 0.8222656  -0.16866568]\n",
      "on_boundary False\n",
      "X [-0.8027344  -0.16816592]\n",
      "on_boundary False\n",
      "X [ 0.19726562 -0.16766617]\n",
      "on_boundary False\n",
      "X [-0.30273438 -0.16716641]\n",
      "on_boundary False\n",
      "X [ 0.6972656  -0.16666666]\n",
      "on_boundary False\n",
      "X [-0.5527344  -0.16616693]\n",
      "on_boundary False\n",
      "X [ 0.44726562 -0.16566718]\n",
      "on_boundary False\n",
      "X [-0.05273438 -0.16516742]\n",
      "on_boundary False\n",
      "X [ 0.9472656  -0.16466767]\n",
      "on_boundary False\n",
      "X [-0.9589844  -0.16416791]\n",
      "on_boundary False\n",
      "X [ 0.04101562 -0.16366816]\n",
      "on_boundary False\n",
      "X [-0.45898438 -0.16316843]\n",
      "on_boundary False\n",
      "X [ 0.5410156  -0.16266868]\n",
      "on_boundary False\n",
      "X [-0.7089844  -0.16216892]\n",
      "on_boundary False\n",
      "X [ 0.29101562 -0.16166916]\n",
      "on_boundary False\n",
      "X [-0.20898438 -0.16116941]\n",
      "on_boundary False\n",
      "X [ 0.7910156  -0.16066965]\n",
      "on_boundary False\n",
      "X [-0.8339844  -0.16016993]\n",
      "on_boundary False\n",
      "X [ 0.16601562 -0.15967017]\n",
      "on_boundary False\n",
      "X [-0.33398438 -0.15917042]\n",
      "on_boundary False\n",
      "X [ 0.6660156  -0.15867066]\n",
      "on_boundary False\n",
      "X [-0.5839844  -0.15817091]\n",
      "on_boundary False\n",
      "X [ 0.41601562 -0.15767115]\n",
      "on_boundary False\n",
      "X [-0.08398438 -0.15717143]\n",
      "on_boundary False\n",
      "X [ 0.9160156  -0.15667167]\n",
      "on_boundary False\n",
      "X [-0.8964844  -0.15617192]\n",
      "on_boundary False\n",
      "X [ 0.10351562 -0.15567216]\n",
      "on_boundary False\n",
      "X [-0.39648438 -0.15517241]\n",
      "on_boundary False\n",
      "X [ 0.6035156  -0.15467265]\n",
      "on_boundary False\n",
      "X [-0.6464844  -0.15417293]\n",
      "on_boundary False\n",
      "X [ 0.35351562 -0.15367317]\n",
      "on_boundary False\n",
      "X [-0.14648438 -0.15317342]\n",
      "on_boundary False\n",
      "X [ 0.8535156  -0.15267366]\n",
      "on_boundary False\n",
      "X [-0.7714844 -0.1521739]\n",
      "on_boundary False\n",
      "X [ 0.22851562 -0.15167415]\n",
      "on_boundary False\n",
      "X [-0.27148438 -0.15117443]\n",
      "on_boundary False\n",
      "X [ 0.7285156  -0.15067467]\n",
      "on_boundary False\n",
      "X [-0.5214844  -0.15017492]\n",
      "on_boundary False\n",
      "X [ 0.47851562 -0.14967516]\n",
      "on_boundary False\n",
      "X [-0.02148438 -0.1491754 ]\n",
      "on_boundary False\n",
      "X [ 0.9785156  -0.14867565]\n",
      "on_boundary False\n",
      "X [-0.9746094  -0.14817593]\n",
      "on_boundary False\n",
      "X [ 0.02539062 -0.14767617]\n",
      "on_boundary False\n",
      "X [-0.47460938 -0.14717641]\n",
      "on_boundary False\n",
      "X [ 0.5253906  -0.14667666]\n",
      "on_boundary False\n",
      "X [-0.7246094 -0.1461769]\n",
      "on_boundary False\n",
      "X [ 0.27539062 -0.14567715]\n",
      "on_boundary False\n",
      "X [-0.22460938 -0.14517742]\n",
      "on_boundary False\n",
      "X [ 0.7753906  -0.14467767]\n",
      "on_boundary False\n",
      "X [-0.8496094  -0.14417791]\n",
      "on_boundary False\n",
      "X [ 0.15039062 -0.14367816]\n",
      "on_boundary False\n",
      "X [-0.34960938 -0.1431784 ]\n",
      "on_boundary False\n",
      "X [ 0.6503906  -0.14267865]\n",
      "on_boundary False\n",
      "X [-0.5996094  -0.14217892]\n",
      "on_boundary False\n",
      "X [ 0.40039062 -0.14167917]\n",
      "on_boundary False\n",
      "X [-0.09960938 -0.14117941]\n",
      "on_boundary False\n",
      "X [ 0.9003906  -0.14067966]\n",
      "on_boundary False\n",
      "X [-0.9121094 -0.1401799]\n",
      "on_boundary False\n",
      "X [ 0.08789062 -0.13968015]\n",
      "on_boundary False\n",
      "X [-0.41210938 -0.13918042]\n",
      "on_boundary False\n",
      "X [ 0.5878906  -0.13868067]\n",
      "on_boundary False\n",
      "X [-0.6621094  -0.13818091]\n",
      "on_boundary False\n",
      "X [ 0.33789062 -0.13768116]\n",
      "on_boundary False\n",
      "X [-0.16210938 -0.1371814 ]\n",
      "on_boundary False\n",
      "X [ 0.8378906  -0.13668165]\n",
      "on_boundary False\n",
      "X [-0.7871094  -0.13618192]\n",
      "on_boundary False\n",
      "X [ 0.21289062 -0.13568217]\n",
      "on_boundary False\n",
      "X [-0.28710938 -0.13518241]\n",
      "on_boundary False\n",
      "X [ 0.7128906  -0.13468266]\n",
      "on_boundary False\n",
      "X [-0.5371094 -0.1341829]\n",
      "on_boundary False\n",
      "X [ 0.46289062 -0.13368315]\n",
      "on_boundary False\n",
      "X [-0.03710938 -0.13318342]\n",
      "on_boundary False\n",
      "X [ 0.9628906  -0.13268366]\n",
      "on_boundary False\n",
      "X [-0.9433594  -0.13218391]\n",
      "on_boundary False\n",
      "X [ 0.05664062 -0.13168415]\n",
      "on_boundary False\n",
      "X [-0.44335938 -0.1311844 ]\n",
      "on_boundary False\n",
      "X [ 0.5566406  -0.13068464]\n",
      "on_boundary False\n",
      "X [-0.6933594  -0.13018492]\n",
      "on_boundary False\n",
      "X [ 0.30664062 -0.12968516]\n",
      "on_boundary False\n",
      "X [-0.19335938 -0.12918541]\n",
      "on_boundary False\n",
      "X [ 0.8066406  -0.12868565]\n",
      "on_boundary False\n",
      "X [-0.8183594 -0.1281859]\n",
      "on_boundary False\n",
      "X [ 0.18164062 -0.12768614]\n",
      "on_boundary False\n",
      "X [-0.31835938 -0.12718642]\n",
      "on_boundary False\n",
      "X [ 0.6816406  -0.12668666]\n",
      "on_boundary False\n",
      "X [-0.5683594 -0.1261869]\n",
      "on_boundary False\n",
      "X [ 0.43164062 -0.12568715]\n",
      "on_boundary False\n",
      "X [-0.06835938 -0.1251874 ]\n",
      "on_boundary False\n",
      "X [ 0.9316406  -0.12468764]\n",
      "on_boundary False\n",
      "X [-0.8808594  -0.12418792]\n",
      "on_boundary False\n",
      "X [ 0.11914062 -0.12368816]\n",
      "on_boundary False\n",
      "X [-0.38085938 -0.12318841]\n",
      "on_boundary False\n",
      "X [ 0.6191406  -0.12268865]\n",
      "on_boundary False\n",
      "X [-0.6308594 -0.1221889]\n",
      "on_boundary False\n",
      "X [ 0.36914062 -0.12168914]\n",
      "on_boundary False\n",
      "X [-0.13085938 -0.12118942]\n",
      "on_boundary False\n",
      "X [ 0.8691406  -0.12068966]\n",
      "on_boundary False\n",
      "X [-0.7558594  -0.12018991]\n",
      "on_boundary False\n",
      "X [ 0.24414062 -0.11969015]\n",
      "on_boundary False\n",
      "X [-0.25585938 -0.11919039]\n",
      "on_boundary False\n",
      "X [ 0.7441406  -0.11869067]\n",
      "on_boundary False\n",
      "X [-0.5058594  -0.11819091]\n",
      "on_boundary False\n",
      "X [ 0.49414062 -0.11769116]\n",
      "on_boundary False\n",
      "X [-0.00585938 -0.1171914 ]\n",
      "on_boundary False\n",
      "X [ 0.9941406  -0.11669165]\n",
      "on_boundary False\n",
      "X [-0.9941406  -0.11619189]\n",
      "on_boundary False\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X [ 0.00585938 -0.11569217]\n",
      "on_boundary False\n",
      "X [-0.49414062 -0.11519241]\n",
      "on_boundary False\n",
      "X [ 0.5058594  -0.11469266]\n",
      "on_boundary False\n",
      "X [-0.7441406 -0.1141929]\n",
      "on_boundary False\n",
      "X [ 0.25585938 -0.11369315]\n",
      "on_boundary False\n",
      "X [-0.24414062 -0.11319339]\n",
      "on_boundary False\n",
      "X [ 0.7558594  -0.11269367]\n",
      "on_boundary False\n",
      "X [-0.8691406  -0.11219391]\n",
      "on_boundary False\n",
      "X [ 0.13085938 -0.11169416]\n",
      "on_boundary False\n",
      "X [-0.36914062 -0.1111944 ]\n",
      "on_boundary False\n",
      "X [ 0.6308594  -0.11069465]\n",
      "on_boundary False\n",
      "X [-0.6191406  -0.11019489]\n",
      "on_boundary False\n",
      "X [ 0.38085938 -0.10969517]\n",
      "on_boundary False\n",
      "X [-0.11914062 -0.10919541]\n",
      "on_boundary False\n",
      "X [ 0.8808594  -0.10869566]\n",
      "on_boundary False\n",
      "X [-0.9316406 -0.1081959]\n",
      "on_boundary False\n",
      "X [ 0.06835938 -0.10769615]\n",
      "on_boundary False\n",
      "X [-0.43164062 -0.10719639]\n",
      "on_boundary False\n",
      "X [ 0.5683594  -0.10669667]\n",
      "on_boundary False\n",
      "X [-0.6816406  -0.10619691]\n",
      "on_boundary False\n",
      "X [ 0.31835938 -0.10569715]\n",
      "on_boundary False\n",
      "X [-0.18164062 -0.1051974 ]\n",
      "on_boundary False\n",
      "X [ 0.8183594  -0.10469764]\n",
      "on_boundary False\n",
      "X [-0.8066406  -0.10419789]\n",
      "on_boundary False\n",
      "X [ 0.19335938 -0.10369816]\n",
      "on_boundary False\n",
      "X [-0.30664062 -0.10319841]\n",
      "on_boundary False\n",
      "X [ 0.6933594  -0.10269865]\n",
      "on_boundary False\n",
      "X [-0.5566406 -0.1021989]\n",
      "on_boundary False\n",
      "X [ 0.44335938 -0.10169914]\n",
      "on_boundary False\n",
      "X [-0.05664062 -0.10119939]\n",
      "on_boundary False\n",
      "X [ 0.9433594  -0.10069966]\n",
      "on_boundary False\n",
      "X [-0.9628906  -0.10019991]\n",
      "on_boundary False\n",
      "X [ 0.03710938 -0.09970015]\n",
      "on_boundary False\n",
      "X [-0.46289062 -0.0992004 ]\n",
      "on_boundary False\n",
      "X [ 0.5371094  -0.09870064]\n",
      "on_boundary False\n",
      "X [-0.7128906  -0.09820089]\n",
      "on_boundary False\n",
      "X [ 0.28710938 -0.09770116]\n",
      "on_boundary False\n",
      "X [-0.21289062 -0.09720141]\n",
      "on_boundary False\n",
      "X [ 0.7871094  -0.09670165]\n",
      "on_boundary False\n",
      "X [-0.8378906 -0.0962019]\n",
      "on_boundary False\n",
      "X [ 0.16210938 -0.09570214]\n",
      "on_boundary False\n",
      "X [-0.33789062 -0.09520239]\n",
      "on_boundary False\n",
      "X [ 0.6621094  -0.09470266]\n",
      "on_boundary False\n",
      "X [-0.5878906  -0.09420291]\n",
      "on_boundary False\n",
      "X [ 0.41210938 -0.09370315]\n",
      "on_boundary False\n",
      "X [-0.08789062 -0.0932034 ]\n",
      "on_boundary False\n",
      "X [ 0.9121094  -0.09270364]\n",
      "on_boundary False\n",
      "X [-0.9003906  -0.09220389]\n",
      "on_boundary False\n",
      "X [ 0.09960938 -0.09170416]\n",
      "on_boundary False\n",
      "X [-0.40039062 -0.0912044 ]\n",
      "on_boundary False\n",
      "X [ 0.5996094  -0.09070465]\n",
      "on_boundary False\n",
      "X [-0.6503906  -0.09020489]\n",
      "on_boundary False\n",
      "X [ 0.34960938 -0.08970514]\n",
      "on_boundary False\n",
      "X [-0.15039062 -0.08920538]\n",
      "on_boundary False\n",
      "X [ 0.8496094  -0.08870566]\n",
      "on_boundary False\n",
      "X [-0.7753906 -0.0882059]\n",
      "on_boundary False\n",
      "X [ 0.22460938 -0.08770615]\n",
      "on_boundary False\n",
      "X [-0.27539062 -0.08720639]\n",
      "on_boundary False\n",
      "X [ 0.7246094  -0.08670664]\n",
      "on_boundary False\n",
      "X [-0.5253906  -0.08620688]\n",
      "on_boundary False\n",
      "X [ 0.47460938 -0.08570716]\n",
      "on_boundary False\n",
      "X [-0.02539062 -0.0852074 ]\n",
      "on_boundary False\n",
      "X [ 0.9746094  -0.08470765]\n",
      "on_boundary False\n",
      "X [-0.9785156  -0.08420789]\n",
      "on_boundary False\n",
      "X [ 0.02148438 -0.08370814]\n",
      "on_boundary False\n",
      "X [-0.47851562 -0.08320838]\n",
      "on_boundary False\n",
      "X [ 0.5214844  -0.08270866]\n",
      "on_boundary False\n",
      "X [-0.7285156 -0.0822089]\n",
      "on_boundary False\n",
      "X [ 0.27148438 -0.08170915]\n",
      "on_boundary False\n",
      "X [-0.22851562 -0.08120939]\n",
      "on_boundary False\n",
      "X [ 0.7714844  -0.08070964]\n",
      "on_boundary False\n",
      "X [-0.8535156  -0.08020988]\n",
      "on_boundary False\n",
      "X [ 0.14648438 -0.07971016]\n",
      "on_boundary False\n",
      "X [-0.35351562 -0.0792104 ]\n",
      "on_boundary False\n",
      "X [ 0.6464844  -0.07871065]\n",
      "on_boundary False\n",
      "X [-0.6035156  -0.07821089]\n",
      "on_boundary False\n",
      "X [ 0.39648438 -0.07771114]\n",
      "on_boundary False\n",
      "X [-0.10351562 -0.07721138]\n",
      "on_boundary False\n",
      "X [ 0.8964844  -0.07671165]\n",
      "on_boundary False\n",
      "X [-0.9160156 -0.0762119]\n",
      "on_boundary False\n",
      "X [ 0.08398438 -0.07571214]\n",
      "on_boundary False\n",
      "X [-0.41601562 -0.07521239]\n",
      "on_boundary False\n",
      "X [ 0.5839844  -0.07471263]\n",
      "on_boundary False\n",
      "X [-0.6660156  -0.07421288]\n",
      "on_boundary False\n",
      "X [ 0.33398438 -0.07371315]\n",
      "on_boundary False\n",
      "X [-0.16601562 -0.0732134 ]\n",
      "on_boundary False\n",
      "X [ 0.8339844  -0.07271364]\n",
      "on_boundary False\n",
      "X [-0.7910156  -0.07221389]\n",
      "on_boundary False\n",
      "X [ 0.20898438 -0.07171413]\n",
      "on_boundary False\n",
      "X [-0.29101562 -0.07121441]\n",
      "on_boundary False\n",
      "X [ 0.7089844  -0.07071465]\n",
      "on_boundary False\n",
      "X [-0.5410156 -0.0702149]\n",
      "on_boundary False\n",
      "X [ 0.45898438 -0.06971514]\n",
      "on_boundary False\n",
      "X [-0.04101562 -0.06921539]\n",
      "on_boundary False\n",
      "X [ 0.9589844  -0.06871563]\n",
      "on_boundary False\n",
      "X [-0.9472656  -0.06821591]\n",
      "on_boundary False\n",
      "X [ 0.05273438 -0.06771615]\n",
      "on_boundary False\n",
      "X [-0.44726562 -0.0672164 ]\n",
      "on_boundary False\n",
      "X [ 0.5527344  -0.06671664]\n",
      "on_boundary False\n",
      "X [-0.6972656  -0.06621689]\n",
      "on_boundary False\n",
      "X [ 0.30273438 -0.06571713]\n",
      "on_boundary False\n",
      "X [-0.19726562 -0.06521741]\n",
      "on_boundary False\n",
      "X [ 0.8027344  -0.06471765]\n",
      "on_boundary False\n",
      "X [-0.8222656 -0.0642179]\n",
      "on_boundary False\n",
      "X [ 0.17773438 -0.06371814]\n",
      "on_boundary False\n",
      "X [-0.32226562 -0.06321838]\n",
      "on_boundary False\n",
      "X [ 0.6777344  -0.06271863]\n",
      "on_boundary False\n",
      "X [-0.5722656 -0.0622189]\n",
      "on_boundary False\n",
      "X [ 0.42773438 -0.06171915]\n",
      "on_boundary False\n",
      "X [-0.07226562 -0.06121939]\n",
      "on_boundary False\n",
      "X [ 0.9277344  -0.06071964]\n",
      "on_boundary False\n",
      "X [-0.8847656  -0.06021988]\n",
      "on_boundary False\n",
      "X [ 0.11523438 -0.05972013]\n",
      "on_boundary False\n",
      "X [-0.38476562 -0.0592204 ]\n",
      "on_boundary False\n",
      "X [ 0.6152344  -0.05872065]\n",
      "on_boundary False\n",
      "X [-0.6347656  -0.05822089]\n",
      "on_boundary False\n",
      "X [ 0.36523438 -0.05772114]\n",
      "on_boundary False\n",
      "X [-0.13476562 -0.05722138]\n",
      "on_boundary False\n",
      "X [ 0.8652344  -0.05672163]\n",
      "on_boundary False\n",
      "X [-0.7597656 -0.0562219]\n",
      "on_boundary False\n",
      "X [ 0.24023438 -0.05572215]\n",
      "on_boundary False\n",
      "X [-0.25976562 -0.05522239]\n",
      "on_boundary False\n",
      "X [ 0.7402344  -0.05472264]\n",
      "on_boundary False\n",
      "X [-0.5097656  -0.05422288]\n",
      "on_boundary False\n",
      "X [ 0.49023438 -0.05372313]\n",
      "on_boundary False\n",
      "X [-0.00976562 -0.0532234 ]\n",
      "on_boundary False\n",
      "X [ 0.9902344  -0.05272365]\n",
      "on_boundary False\n",
      "X [-0.9863281  -0.05222389]\n",
      "on_boundary False\n",
      "X [ 0.01367188 -0.05172414]\n",
      "on_boundary False\n",
      "X [-0.48632812 -0.05122438]\n",
      "on_boundary False\n",
      "X [ 0.5136719  -0.05072463]\n",
      "on_boundary False\n",
      "X [-0.7363281 -0.0502249]\n",
      "on_boundary False\n",
      "X [ 0.26367188 -0.04972515]\n",
      "on_boundary False\n",
      "X [-0.23632812 -0.04922539]\n",
      "on_boundary False\n",
      "X [ 0.7636719  -0.04872563]\n",
      "on_boundary False\n",
      "X [-0.8613281  -0.04822588]\n",
      "on_boundary False\n",
      "X [ 0.13867188 -0.04772612]\n",
      "on_boundary False\n",
      "X [-0.36132812 -0.0472264 ]\n",
      "on_boundary False\n",
      "X [ 0.6386719  -0.04672664]\n",
      "on_boundary False\n",
      "X [-0.6113281  -0.04622689]\n",
      "on_boundary False\n",
      "X [ 0.38867188 -0.04572713]\n",
      "on_boundary False\n",
      "X [-0.11132812 -0.04522738]\n",
      "on_boundary False\n",
      "X [ 0.8886719  -0.04472762]\n",
      "on_boundary False\n",
      "X [-0.9238281 -0.0442279]\n",
      "on_boundary False\n",
      "X [ 0.07617188 -0.04372814]\n",
      "on_boundary False\n",
      "X [-0.42382812 -0.04322839]\n",
      "on_boundary False\n",
      "X [ 0.5761719  -0.04272863]\n",
      "on_boundary False\n",
      "X [-0.6738281  -0.04222888]\n",
      "on_boundary False\n",
      "X [ 0.32617188 -0.04172912]\n",
      "on_boundary False\n",
      "X [-0.17382812 -0.0412294 ]\n",
      "on_boundary False\n",
      "X [ 0.8261719  -0.04072964]\n",
      "on_boundary False\n",
      "X [-0.7988281  -0.04022989]\n",
      "on_boundary False\n",
      "X [ 0.20117188 -0.03973013]\n",
      "on_boundary False\n",
      "X [-0.29882812 -0.03923038]\n",
      "on_boundary False\n",
      "X [ 0.7011719  -0.03873062]\n",
      "on_boundary False\n",
      "X [-0.5488281 -0.0382309]\n",
      "on_boundary False\n",
      "X [ 0.45117188 -0.03773114]\n",
      "on_boundary False\n",
      "X [-0.04882812 -0.03723139]\n",
      "on_boundary False\n",
      "X [ 0.9511719  -0.03673163]\n",
      "on_boundary False\n",
      "X [-0.9550781  -0.03623188]\n",
      "on_boundary False\n",
      "X [ 0.04492188 -0.03573212]\n",
      "on_boundary False\n",
      "X [-0.45507812 -0.03523239]\n",
      "on_boundary False\n",
      "X [ 0.5449219  -0.03473264]\n",
      "on_boundary False\n",
      "X [-0.7050781  -0.03423288]\n",
      "on_boundary False\n",
      "X [ 0.29492188 -0.03373313]\n",
      "on_boundary False\n",
      "X [-0.20507812 -0.03323337]\n",
      "on_boundary False\n",
      "X [ 0.7949219  -0.03273362]\n",
      "on_boundary False\n",
      "X [-0.8300781  -0.03223389]\n",
      "on_boundary False\n",
      "X [ 0.16992188 -0.03173414]\n",
      "on_boundary False\n",
      "X [-0.33007812 -0.03123438]\n",
      "on_boundary False\n",
      "X [ 0.6699219  -0.03073463]\n",
      "on_boundary False\n",
      "X [-0.5800781  -0.03023487]\n",
      "on_boundary False\n",
      "X [ 0.41992188 -0.02973512]\n",
      "on_boundary False\n",
      "X [-0.08007812 -0.02923539]\n",
      "on_boundary False\n",
      "X [ 0.9199219  -0.02873564]\n",
      "on_boundary False\n",
      "X [-0.8925781  -0.02823588]\n",
      "on_boundary False\n",
      "X [ 0.10742188 -0.02773613]\n",
      "on_boundary False\n",
      "X [-0.39257812 -0.02723637]\n",
      "on_boundary False\n",
      "X [ 0.6074219  -0.02673662]\n",
      "on_boundary False\n",
      "X [-0.6425781  -0.02623689]\n",
      "on_boundary False\n",
      "X [ 0.35742188 -0.02573714]\n",
      "on_boundary False\n",
      "X [-0.14257812 -0.02523738]\n",
      "on_boundary False\n",
      "X [ 0.8574219  -0.02473763]\n",
      "on_boundary False\n",
      "X [-0.7675781  -0.02423787]\n",
      "on_boundary False\n",
      "X [ 0.23242188 -0.02373815]\n",
      "on_boundary False\n",
      "X [-0.26757812 -0.02323839]\n",
      "on_boundary False\n",
      "X [ 0.7324219  -0.02273864]\n",
      "on_boundary False\n",
      "X [-0.5175781  -0.02223888]\n",
      "on_boundary False\n",
      "X [ 0.48242188 -0.02173913]\n",
      "on_boundary False\n",
      "X [-0.01757812 -0.02123937]\n",
      "on_boundary False\n",
      "X [ 0.9824219  -0.02073964]\n",
      "on_boundary False\n",
      "X [-0.9707031  -0.02023989]\n",
      "on_boundary False\n",
      "X [ 0.02929688 -0.01974013]\n",
      "on_boundary False\n",
      "X [-0.47070312 -0.01924038]\n",
      "on_boundary False\n",
      "X [ 0.5292969  -0.01874062]\n",
      "on_boundary False\n",
      "X [-0.7207031  -0.01824087]\n",
      "on_boundary False\n",
      "X [ 0.27929688 -0.01774114]\n",
      "on_boundary False\n",
      "X [-0.22070312 -0.01724139]\n",
      "on_boundary False\n",
      "X [ 0.7792969  -0.01674163]\n",
      "on_boundary False\n",
      "X [-0.8457031  -0.01624188]\n",
      "on_boundary False\n",
      "X [ 0.15429688 -0.01574212]\n",
      "on_boundary False\n",
      "X [-0.34570312 -0.01524237]\n",
      "on_boundary False\n",
      "X [ 0.6542969  -0.01474264]\n",
      "on_boundary False\n",
      "X [-0.5957031  -0.01424289]\n",
      "on_boundary False\n",
      "X [ 0.40429688 -0.01374313]\n",
      "on_boundary False\n",
      "X [-0.09570312 -0.01324338]\n",
      "on_boundary False\n",
      "X [ 0.9042969  -0.01274362]\n",
      "on_boundary False\n",
      "X [-0.9082031  -0.01224387]\n",
      "on_boundary False\n",
      "X [ 0.09179688 -0.01174414]\n",
      "on_boundary False\n",
      "X [-0.40820312 -0.01124439]\n",
      "on_boundary False\n",
      "X [ 0.5917969  -0.01074463]\n",
      "on_boundary False\n",
      "X [-0.6582031  -0.01024488]\n",
      "on_boundary False\n",
      "X [ 0.34179688 -0.00974512]\n",
      "on_boundary False\n",
      "X [-0.15820312 -0.00924537]\n",
      "on_boundary False\n",
      "X [ 0.8417969  -0.00874564]\n",
      "on_boundary False\n",
      "X [-0.7832031  -0.00824589]\n",
      "on_boundary False\n",
      "X [ 0.21679688 -0.00774613]\n",
      "on_boundary False\n",
      "X [-0.28320312 -0.00724638]\n",
      "on_boundary False\n",
      "X [ 0.7167969  -0.00674662]\n",
      "on_boundary False\n",
      "X [-0.5332031  -0.00624686]\n",
      "on_boundary False\n",
      "X [ 0.46679688 -0.00574714]\n",
      "on_boundary False\n",
      "X [-0.03320312 -0.00524738]\n",
      "on_boundary False\n",
      "X [ 0.9667969  -0.00474763]\n",
      "on_boundary False\n",
      "X [-0.9394531  -0.00424787]\n",
      "on_boundary False\n",
      "X [ 0.06054688 -0.00374812]\n",
      "on_boundary False\n",
      "X [-0.43945312 -0.00324836]\n",
      "on_boundary False\n",
      "X [ 0.5605469  -0.00274864]\n",
      "on_boundary False\n",
      "X [-0.6894531  -0.00224888]\n",
      "on_boundary False\n",
      "X [ 0.31054688 -0.00174913]\n",
      "on_boundary False\n",
      "X [-0.18945312 -0.00124937]\n",
      "on_boundary False\n",
      "X [ 8.105469e-01 -7.496178e-04]\n",
      "on_boundary False\n",
      "X [-8.1445312e-01 -2.4986267e-04]\n",
      "on_boundary False\n",
      "X [0.18554688 0.00024986]\n",
      "on_boundary False\n",
      "X [-0.31445312  0.00074965]\n",
      "on_boundary False\n",
      "X [0.6855469  0.00124937]\n",
      "on_boundary False\n",
      "X [-0.5644531  0.0017491]\n",
      "on_boundary False\n",
      "X [0.43554688 0.00224888]\n",
      "on_boundary False\n",
      "X [-0.06445312  0.00274861]\n",
      "on_boundary False\n",
      "X [0.9355469  0.00324839]\n",
      "on_boundary False\n",
      "X [-0.8769531   0.00374812]\n",
      "on_boundary False\n",
      "X [0.12304688 0.0042479 ]\n",
      "on_boundary False\n",
      "X [-0.37695312  0.00474763]\n",
      "on_boundary False\n",
      "X [0.6230469  0.00524735]\n",
      "on_boundary False\n",
      "X [-0.6269531   0.00574714]\n",
      "on_boundary False\n",
      "X [0.37304688 0.00624686]\n",
      "on_boundary False\n",
      "X [-0.12695312  0.00674665]\n",
      "on_boundary False\n",
      "X [0.8730469  0.00724638]\n",
      "on_boundary False\n",
      "X [-0.7519531  0.0077461]\n",
      "on_boundary False\n",
      "X [0.24804688 0.00824589]\n",
      "on_boundary False\n",
      "X [-0.25195312  0.00874561]\n",
      "on_boundary False\n",
      "X [0.7480469 0.0092454]\n",
      "on_boundary False\n",
      "X [-0.5019531   0.00974512]\n",
      "on_boundary False\n",
      "X [0.49804688 0.01024491]\n",
      "on_boundary False\n",
      "X [-0.00195312  0.01074463]\n",
      "on_boundary False\n",
      "X [0.9980469  0.01124436]\n",
      "on_boundary False\n",
      "X [-0.99902344  0.01174414]\n",
      "on_boundary False\n",
      "X [0.00097656 0.01224387]\n",
      "on_boundary False\n",
      "X [-0.49902344  0.01274365]\n",
      "on_boundary False\n",
      "X [0.50097656 0.01324338]\n",
      "on_boundary False\n",
      "X [-0.74902344  0.0137431 ]\n",
      "on_boundary False\n",
      "X [0.25097656 0.01424289]\n",
      "on_boundary False\n",
      "X [-0.24902344  0.01474261]\n",
      "on_boundary False\n",
      "X [0.75097656 0.0152424 ]\n",
      "on_boundary False\n",
      "X [-0.87402344  0.01574212]\n",
      "on_boundary False\n",
      "X [0.12597656 0.01624191]\n",
      "on_boundary False\n",
      "X [-0.37402344  0.01674163]\n",
      "on_boundary False\n",
      "X [0.62597656 0.01724136]\n",
      "on_boundary False\n",
      "X [-0.62402344  0.01774114]\n",
      "on_boundary False\n",
      "X [0.37597656 0.01824087]\n",
      "on_boundary False\n",
      "X [-0.12402344  0.01874065]\n",
      "on_boundary False\n",
      "X [0.87597656 0.01924038]\n",
      "on_boundary False\n",
      "X [-0.93652344  0.0197401 ]\n",
      "on_boundary False\n",
      "X [0.06347656 0.02023989]\n",
      "on_boundary False\n",
      "X [-0.43652344  0.02073961]\n",
      "on_boundary False\n",
      "X [0.56347656 0.0212394 ]\n",
      "on_boundary False\n",
      "X [-0.68652344  0.02173913]\n",
      "on_boundary False\n",
      "X [0.31347656 0.02223891]\n",
      "on_boundary False\n",
      "X [-0.18652344  0.02273864]\n",
      "on_boundary False\n",
      "X [0.81347656 0.02323836]\n",
      "on_boundary False\n",
      "X [-0.81152344  0.02373815]\n",
      "on_boundary False\n",
      "X [0.18847656 0.02423787]\n",
      "on_boundary False\n",
      "X [-0.31152344  0.02473766]\n",
      "on_boundary False\n",
      "X [0.68847656 0.02523738]\n",
      "on_boundary False\n",
      "X [-0.56152344  0.02573711]\n",
      "on_boundary False\n",
      "X [0.43847656 0.02623689]\n",
      "on_boundary False\n",
      "X [-0.06152344  0.02673662]\n",
      "on_boundary False\n",
      "X [0.93847656 0.0272364 ]\n",
      "on_boundary False\n",
      "X [-0.96777344  0.02773613]\n",
      "on_boundary False\n",
      "X [0.03222656 0.02823585]\n",
      "on_boundary False\n",
      "X [-0.46777344  0.02873564]\n",
      "on_boundary False\n",
      "X [0.53222656 0.02923536]\n",
      "on_boundary False\n",
      "X [-0.71777344  0.02973515]\n",
      "on_boundary False\n",
      "X [0.28222656 0.03023487]\n",
      "on_boundary False\n",
      "X [-0.21777344  0.03073466]\n",
      "on_boundary False\n",
      "X [0.78222656 0.03123438]\n",
      "on_boundary False\n",
      "X [-0.84277344  0.03173411]\n",
      "on_boundary False\n",
      "X [0.15722656 0.03223389]\n",
      "on_boundary False\n",
      "X [-0.34277344  0.03273362]\n",
      "on_boundary False\n",
      "X [0.65722656 0.0332334 ]\n",
      "on_boundary False\n",
      "X [-0.59277344  0.03373313]\n",
      "on_boundary False\n",
      "X [0.40722656 0.03423285]\n",
      "on_boundary False\n",
      "X [-0.09277344  0.03473264]\n",
      "on_boundary False\n",
      "X [0.90722656 0.03523237]\n",
      "on_boundary False\n",
      "X [-0.90527344  0.03573215]\n",
      "on_boundary False\n",
      "X [0.09472656 0.03623188]\n",
      "on_boundary False\n",
      "X [-0.40527344  0.03673166]\n",
      "on_boundary False\n",
      "X [0.59472656 0.03723139]\n",
      "on_boundary False\n",
      "X [-0.65527344  0.03773111]\n",
      "on_boundary False\n",
      "X [0.34472656 0.0382309 ]\n",
      "on_boundary False\n",
      "X [-0.15527344  0.03873062]\n",
      "on_boundary False\n",
      "X [0.84472656 0.03923041]\n",
      "on_boundary False\n",
      "X [-0.78027344  0.03973013]\n",
      "on_boundary False\n",
      "X [0.21972656 0.04022986]\n",
      "on_boundary False\n",
      "X [-0.28027344  0.04072964]\n",
      "on_boundary False\n",
      "X [0.71972656 0.04122937]\n",
      "on_boundary False\n",
      "X [-0.53027344  0.04172915]\n",
      "on_boundary False\n",
      "X [0.46972656 0.04222888]\n",
      "on_boundary False\n",
      "X [-0.03027344  0.04272866]\n",
      "on_boundary False\n",
      "X [0.96972656 0.04322839]\n",
      "on_boundary False\n",
      "X [-0.98339844  0.04372811]\n",
      "on_boundary False\n",
      "X [0.01660156 0.0442279 ]\n",
      "on_boundary False\n",
      "X [-0.48339844  0.04472762]\n",
      "on_boundary False\n",
      "X [0.51660156 0.04522741]\n",
      "on_boundary False\n",
      "X [-0.73339844  0.04572713]\n",
      "on_boundary False\n",
      "X [0.26660156 0.04622686]\n",
      "on_boundary False\n",
      "X [-0.23339844  0.04672664]\n",
      "on_boundary False\n",
      "X [0.76660156 0.04722637]\n",
      "on_boundary False\n",
      "X [-0.85839844  0.04772615]\n",
      "on_boundary False\n",
      "X [0.14160156 0.04822588]\n",
      "on_boundary False\n",
      "X [-0.35839844  0.04872566]\n",
      "on_boundary False\n",
      "X [0.64160156 0.04922539]\n",
      "on_boundary False\n",
      "X [-0.60839844  0.04972512]\n",
      "on_boundary False\n",
      "X [0.39160156 0.0502249 ]\n",
      "on_boundary False\n",
      "X [-0.10839844  0.05072463]\n",
      "on_boundary False\n",
      "X [0.89160156 0.05122441]\n",
      "on_boundary False\n",
      "X [-0.92089844  0.05172414]\n",
      "on_boundary False\n",
      "X [0.07910156 0.05222386]\n",
      "on_boundary False\n",
      "X [-0.42089844  0.05272365]\n",
      "on_boundary False\n",
      "X [0.57910156 0.05322337]\n",
      "on_boundary False\n",
      "X [-0.67089844  0.05372316]\n",
      "on_boundary False\n",
      "X [0.32910156 0.05422288]\n",
      "on_boundary False\n",
      "X [-0.17089844  0.05472267]\n",
      "on_boundary False\n",
      "X [0.82910156 0.05522239]\n",
      "on_boundary False\n",
      "X [-0.79589844  0.05572212]\n",
      "on_boundary False\n",
      "X [0.20410156 0.0562219 ]\n",
      "on_boundary False\n",
      "X [-0.29589844  0.05672163]\n",
      "on_boundary False\n",
      "X [0.70410156 0.05722141]\n",
      "on_boundary False\n",
      "X [-0.54589844  0.05772114]\n",
      "on_boundary False\n",
      "X [0.45410156 0.05822086]\n",
      "on_boundary False\n",
      "X [-0.04589844  0.05872065]\n",
      "on_boundary False\n",
      "X [0.95410156 0.05922037]\n",
      "on_boundary False\n",
      "X [-0.95214844  0.05972016]\n",
      "on_boundary False\n",
      "X [0.04785156 0.06021988]\n",
      "on_boundary False\n",
      "X [-0.45214844  0.06071967]\n",
      "on_boundary False\n",
      "X [0.54785156 0.06121939]\n",
      "on_boundary False\n",
      "X [-0.70214844  0.06171912]\n",
      "on_boundary False\n",
      "X [0.29785156 0.0622189 ]\n",
      "on_boundary False\n",
      "X [-0.20214844  0.06271863]\n",
      "on_boundary False\n",
      "X [0.79785156 0.06321841]\n",
      "on_boundary False\n",
      "X [-0.82714844  0.06371814]\n",
      "on_boundary False\n",
      "X [0.17285156 0.06421787]\n",
      "on_boundary False\n",
      "X [-0.32714844  0.06471765]\n",
      "on_boundary False\n",
      "X [0.67285156 0.06521738]\n",
      "on_boundary False\n",
      "X [-0.57714844  0.06571716]\n",
      "on_boundary False\n",
      "X [0.42285156 0.06621689]\n",
      "on_boundary False\n",
      "X [-0.07714844  0.06671667]\n",
      "on_boundary False\n",
      "X [0.92285156 0.0672164 ]\n",
      "on_boundary False\n",
      "X [-0.88964844  0.06771612]\n",
      "on_boundary False\n",
      "X [0.11035156 0.06821591]\n",
      "on_boundary False\n",
      "X [-0.38964844  0.06871563]\n",
      "on_boundary False\n",
      "X [0.61035156 0.06921542]\n",
      "on_boundary False\n",
      "X [-0.63964844  0.06971514]\n",
      "on_boundary False\n",
      "X [0.36035156 0.07021487]\n",
      "on_boundary False\n",
      "X [-0.13964844  0.07071465]\n",
      "on_boundary False\n",
      "X [0.86035156 0.07121438]\n",
      "on_boundary False\n",
      "X [-0.76464844  0.07171416]\n",
      "on_boundary False\n",
      "X [0.23535156 0.07221389]\n",
      "on_boundary False\n",
      "X [-0.26464844  0.07271361]\n",
      "on_boundary False\n",
      "X [0.73535156 0.0732134 ]\n",
      "on_boundary False\n",
      "X [-0.51464844  0.07371312]\n",
      "on_boundary False\n",
      "X [0.48535156 0.07421291]\n",
      "on_boundary False\n",
      "X [-0.01464844  0.07471263]\n",
      "on_boundary False\n",
      "X [0.98535156 0.07521242]\n",
      "on_boundary False\n",
      "X [-0.99121094  0.07571214]\n",
      "on_boundary False\n",
      "X [0.00878906 0.07621187]\n",
      "on_boundary False\n",
      "X [-0.49121094  0.07671165]\n",
      "on_boundary False\n",
      "X [0.50878906 0.07721138]\n",
      "on_boundary False\n",
      "X [-0.74121094  0.07771116]\n",
      "on_boundary False\n",
      "X [0.25878906 0.07821089]\n",
      "on_boundary False\n",
      "X [-0.24121094  0.07871062]\n",
      "on_boundary False\n",
      "X [0.75878906 0.0792104 ]\n",
      "on_boundary False\n",
      "X [-0.86621094  0.07971013]\n",
      "on_boundary False\n",
      "X [0.13378906 0.08020991]\n",
      "on_boundary False\n",
      "X [-0.36621094  0.08070964]\n",
      "on_boundary False\n",
      "X [0.63378906 0.08120942]\n",
      "on_boundary False\n",
      "X [-0.61621094  0.08170915]\n",
      "on_boundary False\n",
      "X [0.38378906 0.08220887]\n",
      "on_boundary False\n",
      "X [-0.11621094  0.08270866]\n",
      "on_boundary False\n",
      "X [0.88378906 0.08320838]\n",
      "on_boundary False\n",
      "X [-0.92871094  0.08370817]\n",
      "on_boundary False\n",
      "X [0.07128906 0.08420789]\n",
      "on_boundary False\n",
      "X [-0.42871094  0.08470762]\n",
      "on_boundary False\n",
      "X [0.57128906 0.0852074 ]\n",
      "on_boundary False\n",
      "X [-0.67871094  0.08570713]\n",
      "on_boundary False\n",
      "X [0.32128906 0.08620691]\n",
      "on_boundary False\n",
      "X [-0.17871094  0.08670664]\n",
      "on_boundary False\n",
      "X [0.82128906 0.08720642]\n",
      "on_boundary False\n",
      "X [-0.80371094  0.08770615]\n",
      "on_boundary False\n",
      "X [0.19628906 0.08820587]\n",
      "on_boundary False\n",
      "X [-0.30371094  0.08870566]\n",
      "on_boundary False\n",
      "X [0.69628906 0.08920538]\n",
      "on_boundary False\n",
      "X [-0.55371094  0.08970517]\n",
      "on_boundary False\n",
      "X [0.44628906 0.09020489]\n",
      "on_boundary False\n",
      "X [-0.05371094  0.09070462]\n",
      "on_boundary False\n",
      "X [0.94628906 0.0912044 ]\n",
      "on_boundary False\n",
      "X [-0.95996094  0.09170413]\n",
      "on_boundary False\n",
      "X [0.04003906 0.09220392]\n",
      "on_boundary False\n",
      "X [-0.45996094  0.09270364]\n",
      "on_boundary False\n",
      "X [0.54003906 0.09320343]\n",
      "on_boundary False\n",
      "X [-0.70996094  0.09370315]\n",
      "on_boundary False\n",
      "X [0.29003906 0.09420288]\n",
      "on_boundary False\n",
      "X [-0.20996094  0.09470266]\n",
      "on_boundary False\n",
      "X [0.79003906 0.09520239]\n",
      "on_boundary False\n",
      "X [-0.83496094  0.09570217]\n",
      "on_boundary False\n",
      "X [0.16503906 0.0962019 ]\n",
      "on_boundary False\n",
      "X [-0.33496094  0.09670162]\n",
      "on_boundary False\n",
      "X [0.66503906 0.09720141]\n",
      "on_boundary False\n",
      "X [-0.58496094  0.09770113]\n",
      "on_boundary False\n",
      "X [0.41503906 0.09820092]\n",
      "on_boundary False\n",
      "X [-0.08496094  0.09870064]\n",
      "on_boundary False\n",
      "X [0.91503906 0.09920043]\n",
      "on_boundary False\n",
      "X [-0.89746094  0.09970015]\n",
      "on_boundary False\n",
      "X [0.10253906 0.10019988]\n",
      "on_boundary False\n",
      "X [-0.39746094  0.10069966]\n",
      "on_boundary False\n",
      "X [0.60253906 0.10119939]\n",
      "on_boundary False\n",
      "X [-0.64746094  0.10169917]\n",
      "on_boundary False\n",
      "X [0.35253906 0.1021989 ]\n",
      "on_boundary False\n",
      "X [-0.14746094  0.10269862]\n",
      "on_boundary False\n",
      "X [0.85253906 0.10319841]\n",
      "on_boundary False\n",
      "X [-0.77246094  0.10369813]\n",
      "on_boundary False\n",
      "X [0.22753906 0.10419792]\n",
      "on_boundary False\n",
      "X [-0.27246094  0.10469764]\n",
      "on_boundary False\n",
      "X [0.72753906 0.10519743]\n",
      "on_boundary False\n",
      "X [-0.52246094  0.10569715]\n",
      "on_boundary False\n",
      "X [0.47753906 0.10619688]\n",
      "on_boundary False\n",
      "X [-0.02246094  0.10669667]\n",
      "on_boundary False\n",
      "X [0.97753906 0.10719639]\n",
      "on_boundary False\n",
      "X [-0.97558594  0.10769618]\n",
      "on_boundary False\n",
      "X [0.02441406 0.1081959 ]\n",
      "on_boundary False\n",
      "X [-0.47558594  0.10869563]\n",
      "on_boundary False\n",
      "X [0.52441406 0.10919541]\n",
      "on_boundary False\n",
      "X [-0.72558594  0.10969514]\n",
      "on_boundary False\n",
      "X [0.27441406 0.11019492]\n",
      "on_boundary False\n",
      "X [-0.22558594  0.11069465]\n",
      "on_boundary False\n",
      "X [0.77441406 0.11119443]\n",
      "on_boundary False\n",
      "X [-0.85058594  0.11169416]\n",
      "on_boundary False\n",
      "X [0.14941406 0.11219388]\n",
      "on_boundary False\n",
      "X [-0.35058594  0.11269367]\n",
      "on_boundary False\n",
      "X [0.64941406 0.11319339]\n",
      "on_boundary False\n",
      "X [-0.60058594  0.11369318]\n",
      "on_boundary False\n",
      "X [0.39941406 0.1141929 ]\n",
      "on_boundary False\n",
      "X [-0.10058594  0.11469263]\n",
      "on_boundary False\n",
      "X [0.89941406 0.11519241]\n",
      "on_boundary False\n",
      "X [-0.91308594  0.11569214]\n",
      "on_boundary False\n",
      "X [0.08691406 0.11619192]\n",
      "on_boundary False\n",
      "X [-0.41308594  0.11669165]\n",
      "on_boundary False\n",
      "X [0.58691406 0.11719143]\n",
      "on_boundary False\n",
      "X [-0.66308594  0.11769116]\n",
      "on_boundary False\n",
      "X [0.33691406 0.11819088]\n",
      "on_boundary False\n",
      "X [-0.16308594  0.11869067]\n",
      "on_boundary False\n",
      "X [0.83691406 0.11919039]\n",
      "on_boundary False\n",
      "X [-0.78808594  0.11969018]\n",
      "on_boundary False\n",
      "X [0.21191406 0.12018991]\n",
      "on_boundary False\n",
      "X [-0.28808594  0.12068963]\n",
      "on_boundary False\n",
      "X [0.71191406 0.12118942]\n",
      "on_boundary False\n",
      "X [-0.53808594  0.12168914]\n",
      "on_boundary False\n",
      "X [0.46191406 0.12218893]\n",
      "on_boundary False\n",
      "X [-0.03808594  0.12268865]\n",
      "on_boundary False\n",
      "X [0.96191406 0.12318838]\n",
      "on_boundary False\n",
      "X [-0.94433594  0.12368816]\n",
      "on_boundary False\n",
      "X [0.05566406 0.12418789]\n",
      "on_boundary False\n",
      "X [-0.44433594  0.12468767]\n",
      "on_boundary False\n",
      "X [0.55566406 0.1251874 ]\n",
      "on_boundary False\n",
      "X [-0.69433594  0.12568718]\n",
      "on_boundary False\n",
      "X [0.30566406 0.1261869 ]\n",
      "on_boundary False\n",
      "X [-0.19433594  0.12668663]\n",
      "on_boundary False\n",
      "X [0.80566406 0.12718642]\n",
      "on_boundary False\n",
      "X [-0.81933594  0.12768614]\n",
      "on_boundary False\n",
      "X [0.18066406 0.12818593]\n",
      "on_boundary False\n",
      "X [-0.31933594  0.12868565]\n",
      "on_boundary False\n",
      "X [0.68066406 0.12918538]\n",
      "on_boundary False\n",
      "X [-0.56933594  0.12968516]\n",
      "on_boundary False\n",
      "X [0.43066406 0.13018489]\n",
      "on_boundary False\n",
      "X [-0.06933594  0.13068467]\n",
      "on_boundary False\n",
      "X [0.93066406 0.1311844 ]\n",
      "on_boundary False\n",
      "X [-0.88183594  0.13168418]\n",
      "on_boundary False\n",
      "X [0.11816406 0.13218391]\n",
      "on_boundary False\n",
      "X [-0.38183594  0.13268363]\n",
      "on_boundary False\n",
      "X [0.61816406 0.13318342]\n",
      "on_boundary False\n",
      "X [-0.63183594  0.13368315]\n",
      "on_boundary False\n",
      "X [0.36816406 0.13418293]\n",
      "on_boundary False\n",
      "X [-0.13183594  0.13468266]\n",
      "on_boundary False\n",
      "X [0.86816406 0.13518238]\n",
      "on_boundary False\n",
      "X [-0.75683594  0.13568217]\n",
      "on_boundary False\n",
      "X [0.24316406 0.13618189]\n",
      "on_boundary False\n",
      "X [-0.25683594  0.13668168]\n",
      "on_boundary False\n",
      "X [0.74316406 0.1371814 ]\n",
      "on_boundary False\n",
      "X [-0.50683594  0.13768119]\n",
      "on_boundary False\n",
      "X [0.49316406 0.13818091]\n",
      "on_boundary False\n",
      "X [-0.00683594  0.13868064]\n",
      "on_boundary False\n",
      "X [0.99316406 0.13918042]\n",
      "on_boundary False\n",
      "X [-0.9951172   0.13968015]\n",
      "on_boundary False\n",
      "X [0.00488281 0.14017993]\n",
      "on_boundary False\n",
      "X [-0.4951172   0.14067966]\n",
      "on_boundary False\n",
      "X [0.5048828  0.14117938]\n",
      "on_boundary False\n",
      "X [-0.7451172   0.14167917]\n",
      "on_boundary False\n",
      "X [0.2548828 0.1421789]\n",
      "on_boundary False\n",
      "X [-0.24511719  0.14267868]\n",
      "on_boundary False\n",
      "X [0.7548828 0.1431784]\n",
      "on_boundary False\n",
      "X [-0.8701172   0.14367819]\n",
      "on_boundary False\n",
      "X [0.12988281 0.14417791]\n",
      "on_boundary False\n",
      "X [-0.3701172   0.14467764]\n",
      "on_boundary False\n",
      "X [0.6298828  0.14517742]\n",
      "on_boundary False\n",
      "X [-0.6201172   0.14567715]\n",
      "on_boundary False\n",
      "X [0.3798828  0.14617693]\n",
      "on_boundary False\n",
      "X [-0.12011719  0.14667666]\n",
      "on_boundary False\n",
      "X [0.8798828  0.14717638]\n",
      "on_boundary False\n",
      "X [-0.9326172   0.14767617]\n",
      "on_boundary False\n",
      "X [0.06738281 0.1481759 ]\n",
      "on_boundary False\n",
      "X [-0.4326172   0.14867568]\n",
      "on_boundary False\n",
      "X [0.5673828 0.1491754]\n",
      "on_boundary False\n",
      "X [-0.6826172   0.14967519]\n",
      "on_boundary False\n",
      "X [0.3173828  0.15017492]\n",
      "on_boundary False\n",
      "X [-0.18261719  0.15067464]\n",
      "on_boundary False\n",
      "X [0.8173828  0.15117443]\n",
      "on_boundary False\n",
      "X [-0.8076172   0.15167415]\n",
      "on_boundary False\n",
      "X [0.19238281 0.15217394]\n",
      "on_boundary False\n",
      "X [-0.3076172   0.15267366]\n",
      "on_boundary False\n",
      "X [0.6923828  0.15317339]\n",
      "on_boundary False\n",
      "X [-0.5576172   0.15367317]\n",
      "on_boundary False\n",
      "X [0.4423828 0.1541729]\n",
      "on_boundary False\n",
      "X [-0.05761719  0.15467268]\n",
      "on_boundary False\n",
      "X [0.9423828  0.15517241]\n",
      "on_boundary False\n",
      "X [-0.9638672  0.1556722]\n",
      "on_boundary False\n",
      "X [0.03613281 0.15617192]\n",
      "on_boundary False\n",
      "X [-0.4638672   0.15667164]\n",
      "on_boundary False\n",
      "X [0.5361328  0.15717143]\n",
      "on_boundary False\n",
      "X [-0.7138672   0.15767115]\n",
      "on_boundary False\n",
      "X [0.2861328  0.15817094]\n",
      "on_boundary False\n",
      "X [-0.21386719  0.15867066]\n",
      "on_boundary False\n",
      "X [0.7861328  0.15917039]\n",
      "on_boundary False\n",
      "X [-0.8388672   0.15967017]\n",
      "on_boundary False\n",
      "X [0.16113281 0.1601699 ]\n",
      "on_boundary False\n",
      "X [-0.3388672   0.16066968]\n",
      "on_boundary False\n",
      "X [0.6611328  0.16116941]\n",
      "on_boundary False\n",
      "X [-0.5888672  0.1616692]\n",
      "on_boundary False\n",
      "X [0.4111328  0.16216892]\n",
      "on_boundary False\n",
      "X [-0.08886719  0.16266865]\n",
      "on_boundary False\n",
      "X [0.9111328  0.16316843]\n",
      "on_boundary False\n",
      "X [-0.9013672   0.16366816]\n",
      "on_boundary False\n",
      "X [0.09863281 0.16416794]\n",
      "on_boundary False\n",
      "X [-0.4013672   0.16466767]\n",
      "on_boundary False\n",
      "X [0.5986328  0.16516739]\n",
      "on_boundary False\n",
      "X [-0.6513672   0.16566718]\n",
      "on_boundary False\n",
      "X [0.3486328 0.1661669]\n",
      "on_boundary False\n",
      "X [-0.15136719  0.16666669]\n",
      "on_boundary False\n",
      "X [0.8486328  0.16716641]\n",
      "on_boundary False\n",
      "X [-0.7763672   0.16766614]\n",
      "on_boundary False\n",
      "X [0.22363281 0.16816592]\n",
      "on_boundary False\n",
      "X [-0.2763672   0.16866565]\n",
      "on_boundary False\n",
      "X [0.7236328  0.16916543]\n",
      "on_boundary False\n",
      "X [-0.5263672   0.16966516]\n",
      "on_boundary False\n",
      "X [0.4736328  0.17016494]\n",
      "on_boundary False\n",
      "X [-0.02636719  0.17066467]\n",
      "on_boundary False\n",
      "X [0.9736328 0.1711644]\n",
      "on_boundary False\n",
      "X [-0.9794922   0.17166418]\n",
      "on_boundary False\n",
      "X [0.02050781 0.1721639 ]\n",
      "on_boundary False\n",
      "X [-0.4794922   0.17266369]\n",
      "on_boundary False\n",
      "X [0.5205078  0.17316341]\n",
      "on_boundary False\n",
      "X [-0.7294922   0.17366314]\n",
      "on_boundary False\n",
      "X [0.2705078  0.17416292]\n",
      "on_boundary False\n",
      "X [-0.22949219  0.17466265]\n",
      "on_boundary False\n",
      "X [0.7705078  0.17516243]\n",
      "on_boundary False\n",
      "X [-0.8544922   0.17566216]\n",
      "on_boundary False\n",
      "X [0.14550781 0.17616194]\n",
      "on_boundary False\n",
      "X [-0.3544922   0.17666167]\n",
      "on_boundary False\n",
      "X [0.6455078 0.1771614]\n",
      "on_boundary False\n",
      "X [-0.6044922   0.17766118]\n",
      "on_boundary False\n",
      "X [0.3955078 0.1781609]\n",
      "on_boundary False\n",
      "X [-0.10449219  0.17866069]\n",
      "on_boundary False\n",
      "X [0.8955078  0.17916042]\n",
      "on_boundary False\n",
      "X [-0.9169922   0.17966014]\n",
      "on_boundary False\n",
      "X [0.08300781 0.18015993]\n",
      "on_boundary False\n",
      "X [-0.4169922   0.18065965]\n",
      "on_boundary False\n",
      "X [0.5830078  0.18115944]\n",
      "on_boundary False\n",
      "X [-0.6669922   0.18165916]\n",
      "on_boundary False\n",
      "X [0.3330078  0.18215895]\n",
      "on_boundary False\n",
      "X [-0.16699219  0.18265867]\n",
      "on_boundary False\n",
      "X [0.8330078 0.1831584]\n",
      "on_boundary False\n",
      "X [-0.7919922   0.18365818]\n",
      "on_boundary False\n",
      "X [0.20800781 0.18415791]\n",
      "on_boundary False\n",
      "X [-0.2919922  0.1846577]\n",
      "on_boundary False\n",
      "X [0.7080078  0.18515742]\n",
      "on_boundary False\n",
      "X [-0.5419922   0.18565714]\n",
      "on_boundary False\n",
      "X [0.4580078  0.18615693]\n",
      "on_boundary False\n",
      "X [-0.04199219  0.18665665]\n",
      "on_boundary False\n",
      "X [0.9580078  0.18715644]\n",
      "on_boundary False\n",
      "X [-0.9482422   0.18765616]\n",
      "on_boundary False\n",
      "X [0.05175781 0.18815595]\n",
      "on_boundary False\n",
      "X [-0.4482422   0.18865567]\n",
      "on_boundary False\n",
      "X [0.5517578 0.1891554]\n",
      "on_boundary False\n",
      "X [-0.6982422   0.18965518]\n",
      "on_boundary False\n",
      "X [0.3017578  0.19015491]\n",
      "on_boundary False\n",
      "X [-0.19824219  0.1906547 ]\n",
      "on_boundary False\n",
      "X [0.8017578  0.19115442]\n",
      "on_boundary False\n",
      "X [-0.8232422   0.19165415]\n",
      "on_boundary False\n",
      "X [0.17675781 0.19215393]\n",
      "on_boundary False\n",
      "X [-0.3232422   0.19265366]\n",
      "on_boundary False\n",
      "X [0.6767578  0.19315344]\n",
      "on_boundary False\n",
      "X [-0.5732422   0.19365317]\n",
      "on_boundary False\n",
      "X [0.4267578  0.19415295]\n",
      "on_boundary False\n",
      "X [-0.07324219  0.19465268]\n",
      "on_boundary False\n",
      "X [0.9267578 0.1951524]\n",
      "on_boundary False\n",
      "X [-0.8857422   0.19565219]\n",
      "on_boundary False\n",
      "X [0.11425781 0.19615191]\n",
      "on_boundary False\n",
      "X [-0.3857422  0.1966517]\n",
      "on_boundary False\n",
      "X [0.6142578  0.19715142]\n",
      "on_boundary False\n",
      "X [-0.6357422   0.19765115]\n",
      "on_boundary False\n",
      "X [0.3642578  0.19815093]\n",
      "on_boundary False\n",
      "X [-0.13574219  0.19865066]\n",
      "on_boundary False\n",
      "X [0.8642578  0.19915044]\n",
      "on_boundary False\n",
      "X [-0.7607422   0.19965017]\n",
      "on_boundary False\n",
      "X [0.23925781 0.20014995]\n",
      "on_boundary False\n",
      "X [-0.2607422   0.20064968]\n",
      "on_boundary False\n",
      "X [0.7392578 0.2011494]\n",
      "on_boundary False\n",
      "X [-0.5107422   0.20164919]\n",
      "on_boundary False\n",
      "X [0.4892578  0.20214891]\n",
      "on_boundary False\n",
      "X [-0.01074219  0.2026487 ]\n",
      "on_boundary False\n",
      "X [0.9892578  0.20314842]\n",
      "on_boundary False\n",
      "X [-0.9873047   0.20364815]\n",
      "on_boundary False\n",
      "X [0.01269531 0.20414793]\n",
      "on_boundary False\n",
      "X [-0.4873047   0.20464766]\n",
      "on_boundary False\n",
      "X [0.5126953  0.20514745]\n",
      "on_boundary False\n",
      "X [-0.7373047   0.20564717]\n",
      "on_boundary False\n",
      "X [0.2626953  0.20614696]\n",
      "on_boundary False\n",
      "X [-0.23730469  0.20664668]\n",
      "on_boundary False\n",
      "X [0.7626953 0.2071464]\n",
      "on_boundary False\n",
      "X [-0.8623047   0.20764619]\n",
      "on_boundary False\n",
      "X [0.13769531 0.20814592]\n",
      "on_boundary False\n",
      "X [-0.3623047  0.2086457]\n",
      "on_boundary False\n",
      "X [0.6376953  0.20914543]\n",
      "on_boundary False\n",
      "X [-0.6123047   0.20964515]\n",
      "on_boundary False\n",
      "X [0.3876953  0.21014494]\n",
      "on_boundary False\n",
      "X [-0.11230469  0.21064466]\n",
      "on_boundary False\n",
      "X [0.8876953  0.21114445]\n",
      "on_boundary False\n",
      "X [-0.9248047   0.21164417]\n",
      "on_boundary False\n",
      "X [0.07519531 0.21214396]\n",
      "on_boundary False\n",
      "X [-0.4248047   0.21264368]\n",
      "on_boundary False\n",
      "X [0.5751953  0.21314341]\n",
      "on_boundary False\n",
      "X [-0.6748047  0.2136432]\n",
      "on_boundary False\n",
      "X [0.3251953  0.21414292]\n",
      "on_boundary False\n",
      "X [-0.17480469  0.2146427 ]\n",
      "on_boundary False\n",
      "X [0.8251953  0.21514243]\n",
      "on_boundary False\n",
      "X [-0.7998047   0.21564215]\n",
      "on_boundary False\n",
      "X [0.20019531 0.21614194]\n",
      "on_boundary False\n",
      "X [-0.2998047   0.21664166]\n",
      "on_boundary False\n",
      "X [0.7001953  0.21714145]\n",
      "on_boundary False\n",
      "X [-0.5498047   0.21764117]\n",
      "on_boundary False\n",
      "X [0.4501953 0.2181409]\n",
      "on_boundary False\n",
      "X [-0.04980469  0.21864069]\n",
      "on_boundary False\n",
      "X [0.9501953  0.21914041]\n",
      "on_boundary False\n",
      "X [-0.9560547  0.2196402]\n",
      "on_boundary False\n",
      "X [0.04394531 0.22013992]\n",
      "on_boundary False\n",
      "X [-0.4560547  0.2206397]\n",
      "on_boundary False\n",
      "X [0.5439453  0.22113943]\n",
      "on_boundary False\n",
      "X [-0.7060547   0.22163916]\n",
      "on_boundary False\n",
      "X [0.2939453  0.22213894]\n",
      "on_boundary False\n",
      "X [-0.20605469  0.22263867]\n",
      "on_boundary False\n",
      "X [0.7939453  0.22313845]\n",
      "on_boundary False\n",
      "X [-0.8310547   0.22363818]\n",
      "on_boundary False\n",
      "X [0.16894531 0.2241379 ]\n",
      "on_boundary False\n",
      "X [-0.3310547   0.22463769]\n",
      "on_boundary False\n",
      "X [0.6689453  0.22513741]\n",
      "on_boundary False\n",
      "X [-0.5810547  0.2256372]\n",
      "on_boundary False\n",
      "X [0.4189453  0.22613692]\n",
      "on_boundary False\n",
      "X [-0.08105469  0.22663671]\n",
      "on_boundary False\n",
      "X [0.9189453  0.22713643]\n",
      "on_boundary False\n",
      "X [-0.8935547   0.22763616]\n",
      "on_boundary False\n",
      "X [0.10644531 0.22813594]\n",
      "on_boundary False\n",
      "X [-0.3935547   0.22863567]\n",
      "on_boundary False\n",
      "X [0.6064453  0.22913545]\n",
      "on_boundary False\n",
      "X [-0.6435547   0.22963518]\n",
      "on_boundary False\n",
      "X [0.3564453 0.2301349]\n",
      "on_boundary False\n",
      "X [-0.14355469  0.23063469]\n",
      "on_boundary False\n",
      "X [0.8564453  0.23113441]\n",
      "on_boundary False\n",
      "X [-0.7685547  0.2316342]\n",
      "on_boundary False\n",
      "X [0.23144531 0.23213392]\n",
      "on_boundary False\n",
      "X [-0.2685547   0.23263371]\n",
      "on_boundary False\n",
      "X [0.7314453  0.23313344]\n",
      "on_boundary False\n",
      "X [-0.5185547   0.23363316]\n",
      "on_boundary False\n",
      "X [0.4814453  0.23413295]\n",
      "on_boundary False\n",
      "X [-0.01855469  0.23463267]\n",
      "on_boundary False\n",
      "X [0.9814453  0.23513246]\n",
      "on_boundary False\n",
      "X [-0.9716797   0.23563218]\n",
      "on_boundary False\n",
      "X [0.02832031 0.2361319 ]\n",
      "on_boundary False\n",
      "X [-0.4716797   0.23663169]\n",
      "on_boundary False\n",
      "X [0.5283203  0.23713142]\n",
      "on_boundary False\n",
      "X [-0.7216797  0.2376312]\n",
      "on_boundary False\n",
      "X [0.2783203  0.23813093]\n",
      "on_boundary False\n",
      "X [-0.22167969  0.23863071]\n",
      "on_boundary False\n",
      "X [0.7783203  0.23913044]\n",
      "on_boundary False\n",
      "X [-0.8466797   0.23963016]\n",
      "on_boundary False\n",
      "X [0.15332031 0.24012995]\n",
      "on_boundary False\n",
      "X [-0.3466797   0.24062967]\n",
      "on_boundary False\n",
      "X [0.6533203  0.24112946]\n",
      "on_boundary False\n",
      "X [-0.5966797   0.24162918]\n",
      "on_boundary False\n",
      "X [0.4033203  0.24212891]\n",
      "on_boundary False\n",
      "X [-0.09667969  0.2426287 ]\n",
      "on_boundary False\n",
      "X [0.9033203  0.24312842]\n",
      "on_boundary False\n",
      "X [-0.9091797  0.2436282]\n",
      "on_boundary False\n",
      "X [0.09082031 0.24412793]\n",
      "on_boundary False\n",
      "X [-0.4091797   0.24462771]\n",
      "on_boundary False\n",
      "X [0.5908203  0.24512744]\n",
      "on_boundary False\n",
      "X [-0.6591797   0.24562716]\n",
      "on_boundary False\n",
      "X [0.3408203  0.24612695]\n",
      "on_boundary False\n",
      "X [-0.15917969  0.24662668]\n",
      "on_boundary False\n",
      "X [0.8408203  0.24712646]\n",
      "on_boundary False\n",
      "X [-0.7841797   0.24762619]\n",
      "on_boundary False\n",
      "X [0.21582031 0.24812591]\n",
      "on_boundary False\n",
      "X [-0.2841797  0.2486257]\n",
      "on_boundary False\n",
      "X [0.7158203  0.24912542]\n",
      "on_boundary False\n",
      "X [-0.5341797  0.2496252]\n",
      "on_boundary False\n",
      "X [0.4658203  0.25012493]\n",
      "on_boundary False\n",
      "X [-0.03417969  0.25062472]\n",
      "on_boundary False\n",
      "X [0.9658203  0.25112444]\n",
      "on_boundary False\n",
      "X [-0.9404297   0.25162417]\n",
      "on_boundary False\n",
      "X [0.05957031 0.25212395]\n",
      "on_boundary False\n",
      "X [-0.4404297   0.25262368]\n",
      "on_boundary False\n",
      "X [0.5595703  0.25312346]\n",
      "on_boundary False\n",
      "X [-0.6904297  0.2536232]\n",
      "on_boundary False\n",
      "X [0.3095703 0.2541229]\n",
      "on_boundary False\n",
      "X [-0.19042969  0.2546227 ]\n",
      "on_boundary False\n",
      "X [0.8095703  0.25512242]\n",
      "on_boundary False\n",
      "X [-0.8154297  0.2556222]\n",
      "on_boundary False\n",
      "X [0.18457031 0.25612193]\n",
      "on_boundary False\n",
      "X [-0.3154297   0.25662172]\n",
      "on_boundary False\n",
      "X [0.6845703  0.25712144]\n",
      "on_boundary False\n",
      "X [-0.5654297   0.25762117]\n",
      "on_boundary False\n",
      "X [0.4345703  0.25812095]\n",
      "on_boundary False\n",
      "X [-0.06542969  0.25862068]\n",
      "on_boundary False\n",
      "X [0.9345703  0.25912046]\n",
      "on_boundary False\n",
      "X [-0.8779297  0.2596202]\n",
      "on_boundary False\n",
      "X [0.12207031 0.26011992]\n",
      "on_boundary False\n",
      "X [-0.3779297  0.2606197]\n",
      "on_boundary False\n",
      "X [0.6220703  0.26111943]\n",
      "on_boundary False\n",
      "X [-0.6279297  0.2616192]\n",
      "on_boundary False\n",
      "X [0.3720703  0.26211894]\n",
      "on_boundary False\n",
      "X [-0.12792969  0.26261866]\n",
      "on_boundary False\n",
      "X [0.8720703  0.26311845]\n",
      "on_boundary False\n",
      "X [-0.7529297   0.26361817]\n",
      "on_boundary False\n",
      "X [0.24707031 0.26411796]\n",
      "on_boundary False\n",
      "X [-0.2529297   0.26461768]\n",
      "on_boundary False\n",
      "X [0.7470703  0.26511747]\n",
      "on_boundary False\n",
      "X [-0.5029297  0.2656172]\n",
      "on_boundary False\n",
      "X [0.4970703  0.26611692]\n",
      "on_boundary False\n",
      "X [-0.00292969  0.2666167 ]\n",
      "on_boundary False\n",
      "X [0.9970703  0.26711643]\n",
      "on_boundary False\n",
      "X [-0.9970703  0.2676162]\n",
      "on_boundary False\n",
      "X [0.00292969 0.26811594]\n",
      "on_boundary False\n",
      "X [-0.4970703   0.26861566]\n",
      "on_boundary False\n",
      "X [0.5029297  0.26911545]\n",
      "on_boundary False\n",
      "X [-0.7470703   0.26961517]\n",
      "on_boundary False\n",
      "X [0.2529297  0.27011496]\n",
      "on_boundary False\n",
      "X [-0.24707031  0.27061468]\n",
      "on_boundary False\n",
      "X [0.7529297  0.27111447]\n",
      "on_boundary False\n",
      "X [-0.8720703  0.2716142]\n",
      "on_boundary False\n",
      "X [0.12792969 0.27211392]\n",
      "on_boundary False\n",
      "X [-0.3720703  0.2726137]\n",
      "on_boundary False\n",
      "X [0.6279297  0.27311343]\n",
      "on_boundary False\n",
      "X [-0.6220703  0.2736132]\n",
      "on_boundary False\n",
      "X [0.3779297  0.27411294]\n",
      "on_boundary False\n",
      "X [-0.12207031  0.27461267]\n",
      "on_boundary False\n",
      "X [0.8779297  0.27511245]\n",
      "on_boundary False\n",
      "X [-0.9345703   0.27561218]\n",
      "on_boundary False\n",
      "X [0.06542969 0.27611196]\n",
      "on_boundary False\n",
      "X [-0.4345703  0.2766117]\n",
      "on_boundary False\n",
      "X [0.5654297  0.27711147]\n",
      "on_boundary False\n",
      "X [-0.6845703  0.2776112]\n",
      "on_boundary False\n",
      "X [0.3154297  0.27811092]\n",
      "on_boundary False\n",
      "X [-0.18457031  0.2786107 ]\n",
      "on_boundary False\n",
      "X [0.8154297  0.27911043]\n",
      "on_boundary False\n",
      "X [-0.8095703   0.27961022]\n",
      "on_boundary False\n",
      "X [0.19042969 0.28010994]\n",
      "on_boundary False\n",
      "X [-0.3095703   0.28060967]\n",
      "on_boundary False\n",
      "X [0.6904297  0.28110945]\n",
      "on_boundary False\n",
      "X [-0.5595703   0.28160918]\n",
      "on_boundary False\n",
      "X [0.4404297  0.28210896]\n",
      "on_boundary False\n",
      "X [-0.05957031  0.2826087 ]\n",
      "on_boundary False\n",
      "X [0.9404297  0.28310847]\n",
      "on_boundary False\n",
      "X [-0.9658203  0.2836082]\n",
      "on_boundary False\n",
      "X [0.03417969 0.28410792]\n",
      "on_boundary False\n",
      "X [-0.4658203  0.2846077]\n",
      "on_boundary False\n",
      "X [0.5341797  0.28510743]\n",
      "on_boundary False\n",
      "X [-0.7158203   0.28560722]\n",
      "on_boundary False\n",
      "X [0.2841797  0.28610694]\n",
      "on_boundary False\n",
      "X [-0.21582031  0.28660667]\n",
      "on_boundary False\n",
      "X [0.7841797  0.28710645]\n",
      "on_boundary False\n",
      "X [-0.8408203   0.28760618]\n",
      "on_boundary False\n",
      "X [0.15917969 0.28810596]\n",
      "on_boundary False\n",
      "X [-0.3408203  0.2886057]\n",
      "on_boundary False\n",
      "X [0.6591797  0.28910547]\n",
      "on_boundary False\n",
      "X [-0.5908203  0.2896052]\n",
      "on_boundary False\n",
      "X [0.4091797  0.29010493]\n",
      "on_boundary False\n",
      "X [-0.09082031  0.2906047 ]\n",
      "on_boundary False\n",
      "X [0.9091797  0.29110444]\n",
      "on_boundary False\n",
      "X [-0.9033203   0.29160422]\n",
      "on_boundary False\n",
      "X [0.09667969 0.29210395]\n",
      "on_boundary False\n",
      "X [-0.4033203   0.29260367]\n",
      "on_boundary False\n",
      "X [0.5966797  0.29310346]\n",
      "on_boundary False\n",
      "X [-0.6533203   0.29360318]\n",
      "on_boundary False\n",
      "X [0.3466797  0.29410297]\n",
      "on_boundary False\n",
      "X [-0.15332031  0.2946027 ]\n",
      "on_boundary False\n",
      "X [0.8466797  0.29510248]\n",
      "on_boundary False\n",
      "X [-0.7783203  0.2956022]\n",
      "on_boundary False\n",
      "X [0.22167969 0.29610193]\n",
      "on_boundary False\n",
      "X [-0.2783203  0.2966017]\n",
      "on_boundary False\n",
      "X [0.7216797  0.29710144]\n",
      "on_boundary False\n",
      "X [-0.5283203   0.29760122]\n",
      "on_boundary False\n",
      "X [0.4716797  0.29810095]\n",
      "on_boundary False\n",
      "X [-0.02832031  0.29860067]\n",
      "on_boundary False\n",
      "X [0.9716797  0.29910046]\n",
      "on_boundary False\n",
      "X [-0.9814453   0.29960018]\n",
      "on_boundary False\n",
      "X [0.01855469 0.30009997]\n",
      "on_boundary False\n",
      "X [-0.4814453  0.3005997]\n",
      "on_boundary False\n",
      "X [0.5185547  0.30109948]\n",
      "on_boundary False\n",
      "X [-0.7314453  0.3015992]\n",
      "on_boundary False\n",
      "X [0.2685547  0.30209893]\n",
      "on_boundary False\n",
      "X [-0.23144531  0.3025987 ]\n",
      "on_boundary False\n",
      "X [0.7685547  0.30309844]\n",
      "on_boundary False\n",
      "X [-0.8564453   0.30359823]\n",
      "on_boundary False\n",
      "X [0.14355469 0.30409795]\n",
      "on_boundary False\n",
      "X [-0.3564453   0.30459768]\n",
      "on_boundary False\n",
      "X [0.6435547  0.30509746]\n",
      "on_boundary False\n",
      "X [-0.6064453  0.3055972]\n",
      "on_boundary False\n",
      "X [0.3935547  0.30609697]\n",
      "on_boundary False\n",
      "X [-0.10644531  0.3065967 ]\n",
      "on_boundary False\n",
      "X [0.8935547  0.30709648]\n",
      "on_boundary False\n",
      "X [-0.9189453  0.3075962]\n",
      "on_boundary False\n",
      "X [0.08105469 0.30809593]\n",
      "on_boundary False\n",
      "X [-0.4189453   0.30859572]\n",
      "on_boundary False\n",
      "X [0.5810547  0.30909544]\n",
      "on_boundary False\n",
      "X [-0.6689453   0.30959523]\n",
      "on_boundary False\n",
      "X [0.3310547  0.31009495]\n",
      "on_boundary False\n",
      "X [-0.16894531  0.31059468]\n",
      "on_boundary False\n",
      "X [0.8310547  0.31109446]\n",
      "on_boundary False\n",
      "X [-0.7939453  0.3115942]\n",
      "on_boundary False\n",
      "X [0.20605469 0.31209397]\n",
      "on_boundary False\n",
      "X [-0.2939453  0.3125937]\n",
      "on_boundary False\n",
      "X [0.7060547  0.31309342]\n",
      "on_boundary False\n",
      "X [-0.5439453  0.3135932]\n",
      "on_boundary False\n",
      "X [0.4560547  0.31409293]\n",
      "on_boundary False\n",
      "X [-0.04394531  0.31459272]\n",
      "on_boundary False\n",
      "X [0.9560547  0.31509244]\n",
      "on_boundary False\n",
      "X [-0.9501953   0.31559223]\n",
      "on_boundary False\n",
      "X [0.04980469 0.31609195]\n",
      "on_boundary False\n",
      "X [-0.4501953   0.31659168]\n",
      "on_boundary False\n",
      "X [0.5498047  0.31709146]\n",
      "on_boundary False\n",
      "X [-0.7001953  0.3175912]\n",
      "on_boundary False\n",
      "X [0.2998047  0.31809098]\n",
      "on_boundary False\n",
      "X [-0.20019531  0.3185907 ]\n",
      "on_boundary False\n",
      "X [0.7998047  0.31909043]\n",
      "on_boundary False\n",
      "X [-0.8251953  0.3195902]\n",
      "on_boundary False\n",
      "X [0.17480469 0.32008994]\n",
      "on_boundary False\n",
      "X [-0.3251953   0.32058972]\n",
      "on_boundary False\n",
      "X [0.6748047  0.32108945]\n",
      "on_boundary False\n",
      "X [-0.5751953   0.32158923]\n",
      "on_boundary False\n",
      "X [0.4248047  0.32208896]\n",
      "on_boundary False\n",
      "X [-0.07519531  0.32258868]\n",
      "on_boundary False\n",
      "X [0.9248047  0.32308847]\n",
      "on_boundary False\n",
      "X [-0.8876953  0.3235882]\n",
      "on_boundary False\n",
      "X [0.11230469 0.32408798]\n",
      "on_boundary False\n",
      "X [-0.3876953  0.3245877]\n",
      "on_boundary False\n",
      "X [0.6123047  0.32508743]\n",
      "on_boundary False\n",
      "X [-0.6376953  0.3255872]\n",
      "on_boundary False\n",
      "X [0.3623047  0.32608694]\n",
      "on_boundary False\n",
      "X [-0.13769531  0.32658672]\n",
      "on_boundary False\n",
      "X [0.8623047  0.32708645]\n",
      "on_boundary False\n",
      "X [-0.7626953   0.32758623]\n",
      "on_boundary False\n",
      "X [0.23730469 0.32808596]\n",
      "on_boundary False\n",
      "X [-0.2626953   0.32858568]\n",
      "on_boundary False\n",
      "X [0.7373047  0.32908547]\n",
      "on_boundary False\n",
      "X [-0.5126953  0.3295852]\n",
      "on_boundary False\n",
      "X [0.4873047  0.33008498]\n",
      "on_boundary False\n",
      "X [-0.01269531  0.3305847 ]\n",
      "on_boundary False\n",
      "X [0.9873047  0.33108443]\n",
      "on_boundary False\n",
      "X [-0.9892578   0.33158422]\n",
      "on_boundary False\n",
      "X [0.01074219 0.33208394]\n",
      "on_boundary False\n",
      "X [-0.4892578   0.33258373]\n",
      "on_boundary False\n",
      "X [0.5107422  0.33308345]\n",
      "on_boundary False\n",
      "X [-0.7392578   0.33358324]\n",
      "on_boundary False\n",
      "X [0.2607422  0.33408296]\n",
      "on_boundary False\n",
      "X [-0.23925781  0.3345827 ]\n",
      "on_boundary False\n",
      "X [0.7607422  0.33508247]\n",
      "on_boundary False\n",
      "X [-0.8642578  0.3355822]\n",
      "on_boundary False\n",
      "X [0.13574219 0.33608198]\n",
      "on_boundary False\n",
      "X [-0.3642578  0.3365817]\n",
      "on_boundary False\n",
      "X [0.6357422  0.33708143]\n",
      "on_boundary False\n",
      "X [-0.6142578   0.33758122]\n",
      "on_boundary False\n",
      "X [0.3857422  0.33808094]\n",
      "on_boundary False\n",
      "X [-0.11425781  0.33858073]\n",
      "on_boundary False\n",
      "X [0.8857422  0.33908045]\n",
      "on_boundary False\n",
      "X [-0.9267578   0.33958024]\n",
      "on_boundary False\n",
      "X [0.07324219 0.34007996]\n",
      "on_boundary False\n",
      "X [-0.4267578  0.3405797]\n",
      "on_boundary False\n",
      "X [0.5732422  0.34107947]\n",
      "on_boundary False\n",
      "X [-0.6767578  0.3415792]\n",
      "on_boundary False\n",
      "X [0.3232422  0.34207898]\n",
      "on_boundary False\n",
      "X [-0.17675781  0.3425787 ]\n",
      "on_boundary False\n",
      "X [0.8232422  0.34307843]\n",
      "on_boundary False\n",
      "X [-0.8017578   0.34357822]\n",
      "on_boundary False\n",
      "X [0.19824219 0.34407794]\n",
      "on_boundary False\n",
      "X [-0.3017578   0.34457773]\n",
      "on_boundary False\n",
      "X [0.6982422  0.34507746]\n",
      "on_boundary False\n",
      "X [-0.5517578   0.34557724]\n",
      "on_boundary False\n",
      "X [0.4482422  0.34607697]\n",
      "on_boundary False\n",
      "X [-0.05175781  0.3465767 ]\n",
      "on_boundary False\n",
      "X [0.9482422  0.34707648]\n",
      "on_boundary False\n",
      "X [-0.9580078  0.3475762]\n",
      "on_boundary False\n",
      "X [0.04199219 0.348076  ]\n",
      "on_boundary False\n",
      "X [-0.4580078  0.3485757]\n",
      "on_boundary False\n",
      "X [0.5419922  0.34907544]\n",
      "on_boundary False\n",
      "X [-0.7080078   0.34957522]\n",
      "on_boundary False\n",
      "X [0.2919922  0.35007495]\n",
      "on_boundary False\n",
      "X [-0.20800781  0.35057473]\n",
      "on_boundary False\n",
      "X [0.7919922  0.35107446]\n",
      "on_boundary False\n",
      "X [-0.8330078   0.35157424]\n",
      "on_boundary False\n",
      "X [0.16699219 0.35207397]\n",
      "on_boundary False\n",
      "X [-0.3330078  0.3525737]\n",
      "on_boundary False\n",
      "X [0.6669922  0.35307348]\n",
      "on_boundary False\n",
      "X [-0.5830078  0.3535732]\n",
      "on_boundary False\n",
      "X [0.4169922 0.354073 ]\n",
      "on_boundary False\n",
      "X [-0.08300781  0.3545727 ]\n",
      "on_boundary False\n",
      "X [0.9169922  0.35507244]\n",
      "on_boundary False\n",
      "X [-0.8955078   0.35557222]\n",
      "on_boundary False\n",
      "X [0.10449219 0.35607195]\n",
      "on_boundary False\n",
      "X [-0.3955078   0.35657173]\n",
      "on_boundary False\n",
      "X [0.6044922  0.35707146]\n",
      "on_boundary False\n",
      "X [-0.6455078   0.35757118]\n",
      "on_boundary False\n",
      "X [0.3544922  0.35807097]\n",
      "on_boundary False\n",
      "X [-0.14550781  0.3585707 ]\n",
      "on_boundary False\n",
      "X [0.8544922  0.35907048]\n",
      "on_boundary False\n",
      "X [-0.7705078  0.3595702]\n",
      "on_boundary False\n",
      "X [0.22949219 0.36007   ]\n",
      "on_boundary False\n",
      "X [-0.2705078   0.36056972]\n",
      "on_boundary False\n",
      "X [0.7294922  0.36106944]\n",
      "on_boundary False\n",
      "X [-0.5205078   0.36156923]\n",
      "on_boundary False\n",
      "X [0.4794922  0.36206895]\n",
      "on_boundary False\n",
      "X [-0.02050781  0.36256874]\n",
      "on_boundary False\n",
      "X [0.9794922  0.36306846]\n",
      "on_boundary False\n",
      "X [-0.9736328  0.3635682]\n",
      "on_boundary False\n",
      "X [0.02636719 0.36406797]\n",
      "on_boundary False\n",
      "X [-0.4736328  0.3645677]\n",
      "on_boundary False\n",
      "X [0.5263672  0.36506748]\n",
      "on_boundary False\n",
      "X [-0.7236328  0.3655672]\n",
      "on_boundary False\n",
      "X [0.2763672 0.366067 ]\n",
      "on_boundary False\n",
      "X [-0.22363281  0.36656672]\n",
      "on_boundary False\n",
      "X [0.7763672  0.36706644]\n",
      "on_boundary False\n",
      "X [-0.8486328   0.36756623]\n",
      "on_boundary False\n",
      "X [0.15136719 0.36806595]\n",
      "on_boundary False\n",
      "X [-0.3486328   0.36856574]\n",
      "on_boundary False\n",
      "X [0.6513672  0.36906546]\n",
      "on_boundary False\n",
      "X [-0.5986328  0.3695652]\n",
      "on_boundary False\n",
      "X [0.4013672  0.37006497]\n",
      "on_boundary False\n",
      "X [-0.09863281  0.3705647 ]\n",
      "on_boundary False\n",
      "X [0.9013672  0.37106448]\n",
      "on_boundary False\n",
      "X [-0.9111328  0.3715642]\n",
      "on_boundary False\n",
      "X [0.08886719 0.372064  ]\n",
      "on_boundary False\n",
      "X [-0.4111328   0.37256372]\n",
      "on_boundary False\n",
      "X [0.5888672  0.37306345]\n",
      "on_boundary False\n",
      "X [-0.6611328   0.37356323]\n",
      "on_boundary False\n",
      "X [0.3388672  0.37406296]\n",
      "on_boundary False\n",
      "X [-0.16113281  0.37456274]\n",
      "on_boundary False\n",
      "X [0.8388672  0.37506247]\n",
      "on_boundary False\n",
      "X [-0.7861328  0.3755622]\n",
      "on_boundary False\n",
      "X [0.21386719 0.37606198]\n",
      "on_boundary False\n",
      "X [-0.2861328  0.3765617]\n",
      "on_boundary False\n",
      "X [0.7138672 0.3770615]\n",
      "on_boundary False\n",
      "X [-0.5361328  0.3775612]\n",
      "on_boundary False\n",
      "X [0.4638672 0.378061 ]\n",
      "on_boundary False\n",
      "X [-0.03613281  0.37856072]\n",
      "on_boundary False\n",
      "X [0.9638672  0.37906045]\n",
      "on_boundary False\n",
      "X [-0.9423828   0.37956023]\n",
      "on_boundary False\n",
      "X [0.05761719 0.38005996]\n",
      "on_boundary False\n",
      "X [-0.4423828   0.38055974]\n",
      "on_boundary False\n",
      "X [0.5576172  0.38105947]\n",
      "on_boundary False\n",
      "X [-0.6923828  0.3815592]\n",
      "on_boundary False\n",
      "X [0.3076172  0.38205898]\n",
      "on_boundary False\n",
      "X [-0.19238281  0.3825587 ]\n",
      "on_boundary False\n",
      "X [0.8076172 0.3830585]\n",
      "on_boundary False\n",
      "X [-0.8173828  0.3835582]\n",
      "on_boundary False\n",
      "X [0.18261719 0.384058  ]\n",
      "on_boundary False\n",
      "X [-0.3173828   0.38455772]\n",
      "on_boundary False\n",
      "X [0.6826172  0.38505745]\n",
      "on_boundary False\n",
      "X [-0.5673828   0.38555723]\n",
      "on_boundary False\n",
      "X [0.4326172  0.38605696]\n",
      "on_boundary False\n",
      "X [-0.06738281  0.38655674]\n",
      "on_boundary False\n",
      "X [0.9326172  0.38705647]\n",
      "on_boundary False\n",
      "X [-0.8798828  0.3875562]\n",
      "on_boundary False\n",
      "X [0.12011719 0.38805598]\n",
      "on_boundary False\n",
      "X [-0.3798828  0.3885557]\n",
      "on_boundary False\n",
      "X [0.6201172 0.3890555]\n",
      "on_boundary False\n",
      "X [-0.6298828   0.38955522]\n",
      "on_boundary False\n",
      "X [0.3701172 0.390055 ]\n",
      "on_boundary False\n",
      "X [-0.12988281  0.39055473]\n",
      "on_boundary False\n",
      "X [0.8701172  0.39105445]\n",
      "on_boundary False\n",
      "X [-0.7548828   0.39155424]\n",
      "on_boundary False\n",
      "X [0.24511719 0.39205396]\n",
      "on_boundary False\n",
      "X [-0.2548828   0.39255375]\n",
      "on_boundary False\n",
      "X [0.7451172  0.39305347]\n",
      "on_boundary False\n",
      "X [-0.5048828  0.3935532]\n",
      "on_boundary False\n",
      "X [0.4951172  0.39405298]\n",
      "on_boundary False\n",
      "X [-0.00488281  0.3945527 ]\n",
      "on_boundary False\n",
      "X [0.9951172 0.3950525]\n",
      "on_boundary False\n",
      "X [-0.99316406  0.39555222]\n",
      "on_boundary False\n",
      "X [0.00683594 0.396052  ]\n",
      "on_boundary False\n",
      "X [-0.49316406  0.39655173]\n",
      "on_boundary False\n",
      "X [0.50683594 0.39705145]\n",
      "on_boundary False\n",
      "X [-0.74316406  0.39755124]\n",
      "on_boundary False\n",
      "X [0.25683594 0.39805096]\n",
      "on_boundary False\n",
      "X [-0.24316406  0.39855075]\n",
      "on_boundary False\n",
      "X [0.75683594 0.39905047]\n",
      "on_boundary False\n",
      "X [-0.86816406  0.3995502 ]\n",
      "on_boundary False\n",
      "X [0.13183594 0.40004998]\n",
      "on_boundary False\n",
      "X [-0.36816406  0.4005497 ]\n",
      "on_boundary False\n",
      "X [0.63183594 0.4010495 ]\n",
      "on_boundary False\n",
      "X [-0.61816406  0.40154922]\n",
      "on_boundary False\n",
      "X [0.38183594 0.402049  ]\n",
      "on_boundary False\n",
      "X [-0.11816406  0.40254873]\n",
      "on_boundary False\n",
      "X [0.88183594 0.40304846]\n",
      "on_boundary False\n",
      "X [-0.93066406  0.40354824]\n",
      "on_boundary False\n",
      "X [0.06933594 0.40404797]\n",
      "on_boundary False\n",
      "X [-0.43066406  0.40454775]\n",
      "on_boundary False\n",
      "X [0.56933594 0.40504748]\n",
      "on_boundary False\n",
      "X [-0.68066406  0.4055472 ]\n",
      "on_boundary False\n",
      "X [0.31933594 0.406047  ]\n",
      "on_boundary False\n",
      "X [-0.18066406  0.4065467 ]\n",
      "on_boundary False\n",
      "X [0.81933594 0.4070465 ]\n",
      "on_boundary False\n",
      "X [-0.80566406  0.40754622]\n",
      "on_boundary False\n",
      "X [0.19433594 0.40804595]\n",
      "on_boundary False\n",
      "X [-0.30566406  0.40854573]\n",
      "on_boundary False\n",
      "X [0.69433594 0.40904546]\n",
      "on_boundary False\n",
      "X [-0.55566406  0.40954524]\n",
      "on_boundary False\n",
      "X [0.44433594 0.41004497]\n",
      "on_boundary False\n",
      "X [-0.05566406  0.41054475]\n",
      "on_boundary False\n",
      "X [0.94433594 0.41104448]\n",
      "on_boundary False\n",
      "X [-0.96191406  0.4115442 ]\n",
      "on_boundary False\n",
      "X [0.03808594 0.412044  ]\n",
      "on_boundary False\n",
      "X [-0.46191406  0.4125437 ]\n",
      "on_boundary False\n",
      "X [0.53808594 0.4130435 ]\n",
      "on_boundary False\n",
      "X [-0.71191406  0.41354322]\n",
      "on_boundary False\n",
      "X [0.28808594 0.41404295]\n",
      "on_boundary False\n",
      "X [-0.21191406  0.41454273]\n",
      "on_boundary False\n",
      "X [0.78808594 0.41504246]\n",
      "on_boundary False\n",
      "X [-0.83691406  0.41554224]\n",
      "on_boundary False\n",
      "X [0.16308594 0.41604197]\n",
      "on_boundary False\n",
      "X [-0.33691406  0.41654176]\n",
      "on_boundary False\n",
      "X [0.66308594 0.41704148]\n",
      "on_boundary False\n",
      "X [-0.58691406  0.4175412 ]\n",
      "on_boundary False\n",
      "X [0.41308594 0.418041  ]\n",
      "on_boundary False\n",
      "X [-0.08691406  0.41854072]\n",
      "on_boundary False\n",
      "X [0.91308594 0.4190405 ]\n",
      "on_boundary False\n",
      "X [-0.89941406  0.41954023]\n",
      "on_boundary False\n",
      "X [0.10058594 0.42003995]\n",
      "on_boundary False\n",
      "X [-0.39941406  0.42053974]\n",
      "on_boundary False\n",
      "X [0.60058594 0.42103946]\n",
      "on_boundary False\n",
      "X [-0.64941406  0.42153925]\n",
      "on_boundary False\n",
      "X [0.35058594 0.42203897]\n",
      "on_boundary False\n",
      "X [-0.14941406  0.42253876]\n",
      "on_boundary False\n",
      "X [0.85058594 0.42303848]\n",
      "on_boundary False\n",
      "X [-0.77441406  0.4235382 ]\n",
      "on_boundary False\n",
      "X [0.22558594 0.424038  ]\n",
      "on_boundary False\n",
      "X [-0.27441406  0.42453772]\n",
      "on_boundary False\n",
      "X [0.72558594 0.4250375 ]\n",
      "on_boundary False\n",
      "X [-0.52441406  0.42553723]\n",
      "on_boundary False\n",
      "X [0.47558594 0.42603695]\n",
      "on_boundary False\n",
      "X [-0.02441406  0.42653674]\n",
      "on_boundary False\n",
      "X [0.97558594 0.42703646]\n",
      "on_boundary False\n",
      "X [-0.97753906  0.42753625]\n",
      "on_boundary False\n",
      "X [0.02246094 0.42803597]\n",
      "on_boundary False\n",
      "X [-0.47753906  0.42853576]\n",
      "on_boundary False\n",
      "X [0.52246094 0.42903548]\n",
      "on_boundary False\n",
      "X [-0.72753906  0.4295352 ]\n",
      "on_boundary False\n",
      "X [0.27246094 0.430035  ]\n",
      "on_boundary False\n",
      "X [-0.22753906  0.43053472]\n",
      "on_boundary False\n",
      "X [0.77246094 0.4310345 ]\n",
      "on_boundary False\n",
      "X [-0.85253906  0.43153423]\n",
      "on_boundary False\n",
      "X [0.14746094 0.43203396]\n",
      "on_boundary False\n",
      "X [-0.35253906  0.43253374]\n",
      "on_boundary False\n",
      "X [0.64746094 0.43303347]\n",
      "on_boundary False\n",
      "X [-0.60253906  0.43353325]\n",
      "on_boundary False\n",
      "X [0.39746094 0.43403298]\n",
      "on_boundary False\n",
      "X [-0.10253906  0.43453276]\n",
      "on_boundary False\n",
      "X [0.89746094 0.4350325 ]\n",
      "on_boundary False\n",
      "X [-0.91503906  0.4355322 ]\n",
      "on_boundary False\n",
      "X [0.08496094 0.436032  ]\n",
      "on_boundary False\n",
      "X [-0.41503906  0.43653172]\n",
      "on_boundary False\n",
      "X [0.58496094 0.4370315 ]\n",
      "on_boundary False\n",
      "X [-0.66503906  0.43753123]\n",
      "on_boundary False\n",
      "X [0.33496094 0.43803096]\n",
      "on_boundary False\n",
      "X [-0.16503906  0.43853074]\n",
      "on_boundary False\n",
      "X [0.83496094 0.43903047]\n",
      "on_boundary False\n",
      "X [-0.79003906  0.43953025]\n",
      "on_boundary False\n",
      "X [0.20996094 0.44002998]\n",
      "on_boundary False\n",
      "X [-0.29003906  0.44052976]\n",
      "on_boundary False\n",
      "X [0.70996094 0.4410295 ]\n",
      "on_boundary False\n",
      "X [-0.54003906  0.4415292 ]\n",
      "on_boundary False\n",
      "X [0.45996094 0.442029  ]\n",
      "on_boundary False\n",
      "X [-0.04003906  0.44252872]\n",
      "on_boundary False\n",
      "X [0.95996094 0.4430285 ]\n",
      "on_boundary False\n",
      "X [-0.94628906  0.44352823]\n",
      "on_boundary False\n",
      "X [0.05371094 0.44402796]\n",
      "on_boundary False\n",
      "X [-0.44628906  0.44452775]\n",
      "on_boundary False\n",
      "X [0.55371094 0.44502747]\n",
      "on_boundary False\n",
      "X [-0.69628906  0.44552726]\n",
      "on_boundary False\n",
      "X [0.30371094 0.44602698]\n",
      "on_boundary False\n",
      "X [-0.19628906  0.44652677]\n",
      "on_boundary False\n",
      "X [0.80371094 0.4470265 ]\n",
      "on_boundary False\n",
      "X [-0.82128906  0.44752622]\n",
      "on_boundary False\n",
      "X [0.17871094 0.448026  ]\n",
      "on_boundary False\n",
      "X [-0.32128906  0.44852573]\n",
      "on_boundary False\n",
      "X [0.67871094 0.4490255 ]\n",
      "on_boundary False\n",
      "X [-0.57128906  0.44952524]\n",
      "on_boundary False\n",
      "X [0.42871094 0.45002496]\n",
      "on_boundary False\n",
      "X [-0.07128906  0.45052475]\n",
      "on_boundary False\n",
      "X [0.92871094 0.45102447]\n",
      "on_boundary False\n",
      "X [-0.88378906  0.45152426]\n",
      "on_boundary False\n",
      "X [0.11621094 0.45202398]\n",
      "on_boundary False\n",
      "X [-0.38378906  0.4525237 ]\n",
      "on_boundary False\n",
      "X [0.61621094 0.4530235 ]\n",
      "on_boundary False\n",
      "X [-0.63378906  0.45352322]\n",
      "on_boundary False\n",
      "X [0.36621094 0.454023  ]\n",
      "on_boundary False\n",
      "X [-0.13378906  0.45452273]\n",
      "on_boundary False\n",
      "X [0.86621094 0.4550225 ]\n",
      "on_boundary False\n",
      "X [-0.75878906  0.45552224]\n",
      "on_boundary False\n",
      "X [0.24121094 0.45602196]\n",
      "on_boundary False\n",
      "X [-0.25878906  0.45652175]\n",
      "on_boundary False\n",
      "X [0.74121094 0.45702147]\n",
      "on_boundary False\n",
      "X [-0.50878906  0.45752126]\n",
      "on_boundary False\n",
      "X [0.49121094 0.458021  ]\n",
      "on_boundary False\n",
      "X [-0.00878906  0.4585207 ]\n",
      "on_boundary False\n",
      "X [0.99121094 0.4590205 ]\n",
      "on_boundary False\n",
      "X [-0.98535156  0.45952022]\n",
      "on_boundary False\n",
      "X [0.01464844 0.46002   ]\n",
      "on_boundary False\n",
      "X [-0.48535156  0.46051973]\n",
      "on_boundary False\n",
      "X [0.51464844 0.46101952]\n",
      "on_boundary False\n",
      "X [-0.73535156  0.46151924]\n",
      "on_boundary False\n",
      "X [0.26464844 0.46201897]\n",
      "on_boundary False\n",
      "X [-0.23535156  0.46251875]\n",
      "on_boundary False\n",
      "X [0.76464844 0.46301848]\n",
      "on_boundary False\n",
      "X [-0.86035156  0.46351826]\n",
      "on_boundary False\n",
      "X [0.13964844 0.464018  ]\n",
      "on_boundary False\n",
      "X [-0.36035156  0.4645177 ]\n",
      "on_boundary False\n",
      "X [0.63964844 0.4650175 ]\n",
      "on_boundary False\n",
      "X [-0.61035156  0.46551722]\n",
      "on_boundary False\n",
      "X [0.38964844 0.466017  ]\n",
      "on_boundary False\n",
      "X [-0.11035156  0.46651673]\n",
      "on_boundary False\n",
      "X [0.88964844 0.46701652]\n",
      "on_boundary False\n",
      "X [-0.92285156  0.46751624]\n",
      "on_boundary False\n",
      "X [0.07714844 0.46801597]\n",
      "on_boundary False\n",
      "X [-0.42285156  0.46851575]\n",
      "on_boundary False\n",
      "X [0.57714844 0.46901548]\n",
      "on_boundary False\n",
      "X [-0.67285156  0.46951526]\n",
      "on_boundary False\n",
      "X [0.32714844 0.470015  ]\n",
      "on_boundary False\n",
      "X [-0.17285156  0.4705147 ]\n",
      "on_boundary False\n",
      "X [0.82714844 0.4710145 ]\n",
      "on_boundary False\n",
      "X [-0.79785156  0.47151423]\n",
      "on_boundary False\n",
      "X [0.20214844 0.472014  ]\n",
      "on_boundary False\n",
      "X [-0.29785156  0.47251374]\n",
      "on_boundary False\n",
      "X [0.70214844 0.47301352]\n",
      "on_boundary False\n",
      "X [-0.54785156  0.47351325]\n",
      "on_boundary False\n",
      "X [0.45214844 0.47401297]\n",
      "on_boundary False\n",
      "X [-0.04785156  0.47451276]\n",
      "on_boundary False\n",
      "X [0.95214844 0.47501248]\n",
      "on_boundary False\n",
      "X [-0.95410156  0.47551227]\n",
      "on_boundary False\n",
      "X [0.04589844 0.476012  ]\n",
      "on_boundary False\n",
      "X [-0.45410156  0.47651172]\n",
      "on_boundary False\n",
      "X [0.54589844 0.4770115 ]\n",
      "on_boundary False\n",
      "X [-0.70410156  0.47751123]\n",
      "on_boundary False\n",
      "X [0.29589844 0.478011  ]\n",
      "on_boundary False\n",
      "X [-0.20410156  0.47851074]\n",
      "on_boundary False\n",
      "X [0.79589844 0.47901052]\n",
      "on_boundary False\n",
      "X [-0.82910156  0.47951025]\n",
      "on_boundary False\n",
      "X [0.17089844 0.48000997]\n",
      "on_boundary False\n",
      "X [-0.32910156  0.48050976]\n",
      "on_boundary False\n",
      "X [0.67089844 0.48100948]\n",
      "on_boundary False\n",
      "X [-0.57910156  0.48150927]\n",
      "on_boundary False\n",
      "X [0.42089844 0.482009  ]\n",
      "on_boundary False\n",
      "X [-0.07910156  0.48250872]\n",
      "on_boundary False\n",
      "X [0.92089844 0.4830085 ]\n",
      "on_boundary False\n",
      "X [-0.89160156  0.48350823]\n",
      "on_boundary False\n",
      "X [0.10839844 0.484008  ]\n",
      "on_boundary False\n",
      "X [-0.39160156  0.48450774]\n",
      "on_boundary False\n",
      "X [0.60839844 0.48500752]\n",
      "on_boundary False\n",
      "X [-0.64160156  0.48550725]\n",
      "on_boundary False\n",
      "X [0.35839844 0.48600698]\n",
      "on_boundary False\n",
      "X [-0.14160156  0.48650676]\n",
      "on_boundary False\n",
      "X [0.85839844 0.4870065 ]\n",
      "on_boundary False\n",
      "X [-0.76660156  0.48750627]\n",
      "on_boundary False\n",
      "X [0.23339844 0.488006  ]\n",
      "on_boundary False\n",
      "X [-0.26660156  0.48850572]\n",
      "on_boundary False\n",
      "X [0.73339844 0.4890055 ]\n",
      "on_boundary False\n",
      "X [-0.51660156  0.48950523]\n",
      "on_boundary False\n",
      "X [0.48339844 0.49000502]\n",
      "on_boundary False\n",
      "X [-0.01660156  0.49050474]\n",
      "on_boundary False\n",
      "X [0.98339844 0.49100453]\n",
      "on_boundary False\n",
      "X [-0.96972656  0.49150425]\n",
      "on_boundary False\n",
      "X [0.03027344 0.49200398]\n",
      "on_boundary False\n",
      "X [-0.46972656  0.49250376]\n",
      "on_boundary False\n",
      "X [0.53027344 0.4930035 ]\n",
      "on_boundary False\n",
      "X [-0.71972656  0.49350327]\n",
      "on_boundary False\n",
      "X [0.28027344 0.494003  ]\n",
      "on_boundary False\n",
      "X [-0.21972656  0.49450272]\n",
      "on_boundary False\n",
      "X [0.78027344 0.4950025 ]\n",
      "on_boundary False\n",
      "X [-0.84472656  0.49550223]\n",
      "on_boundary False\n",
      "X [0.15527344 0.49600202]\n",
      "on_boundary False\n",
      "X [-0.34472656  0.49650174]\n",
      "on_boundary False\n",
      "X [0.65527344 0.49700153]\n",
      "on_boundary False\n",
      "X [-0.59472656  0.49750125]\n",
      "on_boundary False\n",
      "X [0.40527344 0.49800098]\n",
      "on_boundary False\n",
      "X [-0.09472656  0.49850076]\n",
      "on_boundary False\n",
      "X [0.90527344 0.4990005 ]\n",
      "on_boundary False\n",
      "X [-0.90722656  0.49950027]\n",
      "on_boundary False\n"
     ]
    }
   ],
   "source": [
    "data = dde.data.PDE(geom,\n",
    "                   pde,\n",
    "                   [bc_wall_u,bc_wall_v,bc_inlet_u,bc_inlet_v,bc_outlet_p,bc_outlet_v],\n",
    "                   num_domain  = 2000,\n",
    "                   num_boundary= 200,\n",
    "                   num_test = 200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3e6f10c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,8))\n",
    "plt.scatter(data.train_x_all[:,0], data.train_x_all[:,1], s= 0.5)\n",
    "plt.xlabel(\"x\")\n",
    "plt.ylabel(\"y\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8e36b718",
   "metadata": {},
   "outputs": [],
   "source": [
    "net = dde.maps.FNN([2] + [64]*5 + [3] , \"tanh\", \"Glorot uniform\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "30375579",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Compiling model...\n",
      "'compile' took 0.008789 s\n",
      "\n"
     ]
    }
   ],
   "source": [
    "model = dde.Model(data, net)\n",
    "model.compile(\"adam\",lr=1e-3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f0068ae7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Warning: epochs is deprecated and will be removed in a future version. Use iterations instead.\n",
      "Training model...\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\mecha\\anaconda3\\Lib\\site-packages\\keras\\src\\initializers\\initializers.py:120: UserWarning: The initializer GlorotUniform is unseeded and being called multiple times, which will return identical values each time (even if the initializer is unseeded). Please update your code to provide a seed to the initializer, or avoid using the same initializer instance more than once.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:AutoGraph could not transform <function <lambda> at 0x0000026388DC39C0> and will run it as-is.\n",
      "Cause: could not parse the source code of <function <lambda> at 0x0000026388DC39C0>: no matching AST found among candidates:\n",
      "# coding=utf-8\n",
      "lambda x, on: np.array([on_boundary(x[i], on[i]) for i in range(len(x))])\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "WARNING: AutoGraph could not transform <function <lambda> at 0x0000026388DC39C0> and will run it as-is.\n",
      "Cause: could not parse the source code of <function <lambda> at 0x0000026388DC39C0>: no matching AST found among candidates:\n",
      "# coding=utf-8\n",
      "lambda x, on: np.array([on_boundary(x[i], on[i]) for i in range(len(x))])\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "WARNING:tensorflow:AutoGraph could not transform <function <lambda> at 0x0000026388DC3C40> and will run it as-is.\n",
      "Cause: could not parse the source code of <function <lambda> at 0x0000026388DC3C40>: no matching AST found among candidates:\n",
      "# coding=utf-8\n",
      "lambda x, on: np.array([on_boundary(x[i], on[i]) for i in range(len(x))])\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "WARNING: AutoGraph could not transform <function <lambda> at 0x0000026388DC3C40> and will run it as-is.\n",
      "Cause: could not parse the source code of <function <lambda> at 0x0000026388DC3C40>: no matching AST found among candidates:\n",
      "# coding=utf-8\n",
      "lambda x, on: np.array([on_boundary(x[i], on[i]) for i in range(len(x))])\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "WARNING:tensorflow:AutoGraph could not transform <function <lambda> at 0x0000026388E1C180> and will run it as-is.\n",
      "Cause: could not parse the source code of <function <lambda> at 0x0000026388E1C180>: no matching AST found among candidates:\n",
      "# coding=utf-8\n",
      "lambda x, on: np.array([on_boundary(x[i], on[i]) for i in range(len(x))])\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "WARNING: AutoGraph could not transform <function <lambda> at 0x0000026388E1C180> and will run it as-is.\n",
      "Cause: could not parse the source code of <function <lambda> at 0x0000026388E1C180>: no matching AST found among candidates:\n",
      "# coding=utf-8\n",
      "lambda x, on: np.array([on_boundary(x[i], on[i]) for i in range(len(x))])\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "WARNING:tensorflow:AutoGraph could not transform <function <lambda> at 0x0000026388E1C400> and will run it as-is.\n",
      "Cause: could not parse the source code of <function <lambda> at 0x0000026388E1C400>: no matching AST found among candidates:\n",
      "# coding=utf-8\n",
      "lambda x, on: np.array([on_boundary(x[i], on[i]) for i in range(len(x))])\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "WARNING: AutoGraph could not transform <function <lambda> at 0x0000026388E1C400> and will run it as-is.\n",
      "Cause: could not parse the source code of <function <lambda> at 0x0000026388E1C400>: no matching AST found among candidates:\n",
      "# coding=utf-8\n",
      "lambda x, on: np.array([on_boundary(x[i], on[i]) for i in range(len(x))])\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "WARNING:tensorflow:AutoGraph could not transform <function <lambda> at 0x0000026388E1C7C0> and will run it as-is.\n",
      "Cause: could not parse the source code of <function <lambda> at 0x0000026388E1C7C0>: no matching AST found among candidates:\n",
      "# coding=utf-8\n",
      "lambda x, on: np.array([on_boundary(x[i], on[i]) for i in range(len(x))])\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "WARNING: AutoGraph could not transform <function <lambda> at 0x0000026388E1C7C0> and will run it as-is.\n",
      "Cause: could not parse the source code of <function <lambda> at 0x0000026388E1C7C0>: no matching AST found among candidates:\n",
      "# coding=utf-8\n",
      "lambda x, on: np.array([on_boundary(x[i], on[i]) for i in range(len(x))])\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "WARNING:tensorflow:AutoGraph could not transform <function <lambda> at 0x0000026388E1CB80> and will run it as-is.\n",
      "Cause: could not parse the source code of <function <lambda> at 0x0000026388E1CB80>: no matching AST found among candidates:\n",
      "# coding=utf-8\n",
      "lambda x, on: np.array([on_boundary(x[i], on[i]) for i in range(len(x))])\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "WARNING: AutoGraph could not transform <function <lambda> at 0x0000026388E1CB80> and will run it as-is.\n",
      "Cause: could not parse the source code of <function <lambda> at 0x0000026388E1CB80>: no matching AST found among candidates:\n",
      "# coding=utf-8\n",
      "lambda x, on: np.array([on_boundary(x[i], on[i]) for i in range(len(x))])\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "Step      Train loss                                                                                    Test loss                                                                                     Test metric\n",
      "0         [1.91e-02, 1.89e-03, 3.66e-01, 1.07e-01, 1.21e-02, 2.11e-01, 2.95e-02, 1.58e-02, 2.98e-02]    [1.97e-02, 1.97e-03, 3.77e-01, 1.07e-01, 1.21e-02, 2.11e-01, 2.95e-02, 1.58e-02, 2.98e-02]    []  \n",
      "1000      [8.62e-04, 7.69e-04, 7.53e-03, 2.59e-02, 1.45e-02, 3.66e-02, 1.05e-03, 5.71e-04, 1.74e-05]    [7.12e-04, 4.61e-04, 4.43e-03, 2.59e-02, 1.45e-02, 3.66e-02, 1.05e-03, 5.71e-04, 1.74e-05]    []  \n",
      "2000      [4.86e-03, 1.15e-03, 3.91e-03, 2.08e-02, 9.88e-03, 3.16e-02, 6.19e-04, 2.43e-03, 3.97e-05]    [4.37e-03, 8.51e-04, 1.68e-03, 2.08e-02, 9.88e-03, 3.16e-02, 6.19e-04, 2.43e-03, 3.97e-05]    []  \n",
      "3000      [6.17e-03, 1.30e-03, 4.33e-03, 1.85e-02, 8.74e-03, 2.35e-02, 1.72e-03, 1.26e-03, 1.75e-05]    [6.02e-03, 9.07e-04, 2.11e-03, 1.85e-02, 8.74e-03, 2.35e-02, 1.72e-03, 1.26e-03, 1.75e-05]    []  \n",
      "4000      [1.13e-02, 1.71e-03, 2.52e-03, 1.72e-02, 8.28e-03, 2.00e-02, 3.18e-03, 7.09e-03, 8.67e-06]    [1.10e-02, 1.04e-03, 8.36e-04, 1.72e-02, 8.28e-03, 2.00e-02, 3.18e-03, 7.09e-03, 8.67e-06]    []  \n",
      "5000      [8.58e-03, 1.43e-03, 3.03e-03, 1.60e-02, 7.78e-03, 1.79e-02, 4.11e-03, 3.27e-03, 1.31e-05]    [8.50e-03, 7.03e-04, 1.19e-03, 1.60e-02, 7.78e-03, 1.79e-02, 4.11e-03, 3.27e-03, 1.31e-05]    []  \n",
      "6000      [2.29e-03, 1.04e-03, 2.68e-03, 1.51e-02, 7.37e-03, 1.69e-02, 4.90e-03, 5.69e-05, 4.38e-05]    [1.69e-03, 9.14e-04, 9.74e-04, 1.51e-02, 7.37e-03, 1.69e-02, 4.90e-03, 5.69e-05, 4.38e-05]    []  \n",
      "7000      [1.82e-03, 4.91e-04, 2.69e-03, 1.49e-02, 6.46e-03, 1.70e-02, 4.91e-03, 4.17e-04, 1.39e-05]    [1.72e-03, 2.98e-04, 1.03e-03, 1.49e-02, 6.46e-03, 1.70e-02, 4.91e-03, 4.17e-04, 1.39e-05]    []  \n",
      "8000      [8.69e-03, 2.98e-03, 2.61e-03, 1.54e-02, 6.45e-03, 1.52e-02, 5.30e-03, 1.40e-03, 2.61e-05]    [7.45e-03, 1.86e-03, 7.85e-04, 1.54e-02, 6.45e-03, 1.52e-02, 5.30e-03, 1.40e-03, 2.61e-05]    []  \n",
      "9000      [1.06e-02, 1.76e-03, 2.11e-03, 1.45e-02, 6.18e-03, 1.55e-02, 5.44e-03, 6.43e-03, 1.16e-05]    [1.05e-02, 8.71e-04, 6.63e-04, 1.45e-02, 6.18e-03, 1.55e-02, 5.44e-03, 6.43e-03, 1.16e-05]    []  \n",
      "10000     [2.40e-03, 6.83e-04, 2.53e-03, 1.47e-02, 5.95e-03, 1.48e-02, 5.38e-03, 2.29e-04, 1.79e-05]    [2.06e-03, 5.48e-04, 9.62e-04, 1.47e-02, 5.95e-03, 1.48e-02, 5.38e-03, 2.29e-04, 1.79e-05]    []  \n",
      "\n",
      "Best model at step 10000:\n",
      "  train loss: 4.66e-02\n",
      "  test loss: 4.46e-02\n",
      "  test metric: []\n",
      "\n",
      "'train' took 445.064119 s\n",
      "\n"
     ]
    }
   ],
   "source": [
    "losshistory, train_state = model.train(epochs = 10000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ef05f688",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Compiling model...\n",
      "'compile' took 0.076881 s\n",
      "\n",
      "Training model...\n",
      "\n",
      "Step      Train loss                                                                                    Test loss                                                                                     Test metric\n",
      "10000     [2.40e-03, 6.83e-04, 2.53e-03, 1.47e-02, 5.95e-03, 1.48e-02, 5.38e-03, 2.29e-04, 1.79e-05]    [2.06e-03, 5.48e-04, 9.62e-04, 1.47e-02, 5.95e-03, 1.48e-02, 5.38e-03, 2.29e-04, 1.79e-05]    []  \n",
      "13000     [3.55e-04, 3.28e-04, 4.73e-04, 3.37e-03, 9.81e-04, 2.89e-03, 1.01e-03, 4.93e-06, 3.22e-06]    [2.13e-04, 1.76e-04, 1.31e-04, 3.37e-03, 9.81e-04, 2.89e-03, 1.01e-03, 4.93e-06, 3.22e-06]    []  \n",
      "\n",
      "Best model at step 13000:\n",
      "  train loss: 9.40e-03\n",
      "  test loss: 8.77e-03\n",
      "  test metric: []\n",
      "\n",
      "'train' took 766.946313 s\n",
      "\n"
     ]
    }
   ],
   "source": [
    "dde.optimizers.config.set_LBFGS_options(maxiter= 3000)\n",
    "model.compile(\"L-BFGS\")\n",
    "losshistory, train_state = model.train()\n",
    "dde.saveplot(losshistory, train_state, issave=False, isplot=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "beedc68a",
   "metadata": {},
   "outputs": [],
   "source": [
    "samples = geom.random_points(500000)\n",
    "result = model.predict(samples)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "c74e1c35",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 2000x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABuEAAAGGCAYAAABlgCxvAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOz9a6xlW3bXCf7mXGvtvc8jIm5m3ps3/chMJRjKbidU43TJJG5k8bJkoRKGD5hCbaokEFgCJMtSq51CgsSi5GqDVYXUpGV/AZlqLItSNXyxRGWrWlXpSqEW2UmJbndX0cYi7cx78+Z9RJw4j733WmvO/jDnWGOstfd5xomIE/vM/1Xcs/fac833eszxn/8xXIwxUlBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQcGvwL7sCBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQW7hkLCFRQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBTcMgoJV1BQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBwyygkXEFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUHBLaOQcAUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFt4xCwhUUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQU3DIKCVdQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQcMsoJFxBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBwS2jkHAFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBbeMQsIVFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFNwyCglXUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQ8MrhC1/4Ap/61KdYLBZ85jOf4Utf+tK5aX/913+dH/zBH+QjH/kIe3t7fPd3fzf/5X/5Xz7X+tXPNfeCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCglvGr/7qr/KTP/mTfOELX+AHf/AH+cVf/EV+5Ed+hN/4jd/gE5/4xEb6g4MD/tpf+2v8/t//+zk4OODXf/3X+St/5a9wcHDAX/7Lf/m51NHFGONzybmgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCg4DngB37gB/i+7/s+fuEXfmE49j3f8z386I/+KD/7sz97pTz+zJ/5MxwcHPCP//E/fi51LO4oCwoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCl4qVqsVR0dHo3+r1Wpr2vV6zVe+8hV++Id/eHT8h3/4h/nyl798pfK++tWv8uUvf5kf+qEfeua6n4edc0cZQuAb3/gGDx48wDn3sqtTUFBQUFBQUFBQUFBQUFBQUFBQUFBQUPAcEWPk6dOnfPu3fzveF+3Ry8ByuWS9Xj9THj/3cz/Hf/6f/+ejY3/rb/0tPv/5z2+kfffdd+n7njfffHN0/M033+Ttt9++sJzv/M7v5Fvf+hZd1/H5z3+ev/SX/tIz1fsi7BwJ941vfIOPf/zjL7saBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUvEL/927/Nd37nd77satw7LJdL3tjb4/gZ8/nYxz7GN7/5TRaLxXBsPp9feM5UjBVjvFSg9aUvfYnj42P+5b/8l/z0T/803/Vd38V/8p/8Jzev+AXYORLuwYMHAPz25+BhA7RAyD9GYA58X/7+FiBKxic53UPgdwENUOV/3wJOc7oPAx8zxx3gITqgA/fx9Nk56BqgBv8OdB+DuJ+rsYbONXzw6CEALjj6Chp6iJH99Sk+9jyZPWLlF4Cjw3PMg9yIVOwhx6xoaJkRcUQcPTMi8AGv0VGzxwlVPqelATynzBFPpBHocQQaINJT0dIQ8Hg6luwRgTUL5iyJONbMCHgC9VCfljp/T53t6emY55pChxtq7omQ/w+RgGdNQ0WgpwIikYqYWwUQhnpVBCDiCLjc/cHUBzwtFSGn8XTUhJxvRU+fP6Wa+FxnyU36KZUc6fFUZoYFAlXOp6LH55aEPCkS+tzDbvgu+fmhTamMSD3UNY2hG86LBBjOcLm3w5DztnCOctzeZGRMoslD0rr8zeXfokxpU5bUfLMEqXM6HoZSp7e4aY3Gv4zzD4z95F5Hz+roh/rFPOZSt6vuf6noh3LPr7dFGr3r1VfnQOrluHGeBz7JG3zAKY85wee2XReO/hp+h2MuOw5X6FXP82ZWXQ29metXLwe4Zl/IbNB7501RmXG+OkK+m41x/XqE0ZjodXfdsK6SPs3zanL05vWb5r/tCopU5j7x7NB7h9xJEvzwu4z71a7l54lwzl3b3ofZuO6m9S76/oICeWth8tQ574ny8q/+uwi504RyhxmQ1kT27b0AyOuuitIvN8N43Sb3o9KXdw3R/NU1ZBmvgoKCglcRq6MV/+eP//2BHyh4sViv1xwD/wcSBXMTrIC/+/bbLBYLHj58eGn6119/naqqNlRv77zzzoY6bopPfepTAPy+3/f7+OY3v8nnP//5QsJdFcJwPvgEPHxKIuFq0sh/J/BxYA9YQ/wOcA+BJxA/APK4uN4YyhbAHwCeAu+Ae5RINBqIDbgjCA8gzmD9JuwJWQe89e2vUfnA4pOnPFh1RA+tr/nG4tuIzrNgTR17gvO4GGlczMTHQ77Oxwaj9hmHdHh6ahyBQ47pqWmZsWbOAWdEoKMGPEccUnGYyZuGSM+KWSL5gJ5DDlnigDPm9JncWjNnTY3LL55dpmNmrHDMCeyxYkbEU9EyA5TkmBNxrJhnc3zLPJNLCUps+YFci7nOjhkVZLqrp6dnjs+koEMMxY6emo4GYDCuerps5vYkLvSAloqKNR0zAhU+mxnEzOmA2hjUO2P6bMyLdpfT+rwETZScmJFdrp3QpHqeww+plNaohl/1pV6NQ/JN+keot4pIR0VNn49eTqnY3IU4THn3eFPvi3OIVMaYjSFmtpFwUmIiFcdwJheBEHxKQEmZbkjhBtJ2s3bb8j//12BSbSe9xucLMenwVyLwhHaNl+R9/vlCflp8Pe8dmbHHtt6+DImAuw5JE25IVsVrElRpBl2PIJTrMODyPeAq5bgrXzXbz5d5fRUi1+ctAXKu39Inz0JuXZ/o3IYu34nq52SajpPPet3djglvXGtnyFzb8zeZX89erzDUJY6u1jGpne7FCVLnq9yV7w8skWuPXX1rxIvAnH1WnF6esOCFYPyEhG2blXx+L73pE2GXoAZn6Y+7c229LBQSbjvStVVdnvAlwOG2bHO6HB/j23mbbzyHGo2hBKagXGd3FfoWB4V4KygoKNgNlBBVLxeHJErlJriqxU8wm834zGc+wxe/+EX+9J/+08PxL37xi/ypP/WnrpxPjPHcuHO3gZ0j4QTLPwKzR476W1CvI+6UpGA7zAneBSef3wC+g2F2xK+Da/NnEY29Bv3M8Xj/AT2eR0+e8t6nXuNsPuND7fvMaXERzvYj3WrO8d4Bp/M9GlZ8K77OojrlYXfCSbVPGMiimsb1rGiYu441NSvmvMPrPOU1APazkq3PL/ARzzf5KGccZgNjx++wR6BizpqHHHHKwWCIOGOfD3iNOWscPcc8wBM5Junmlhxs9J2nx754djQEHCccoAo6R0dShpyxyOROalNLg6chssYN5IcbvdxGPGeZuPPEQV0WcbTM6KnomJFMkx0z1lmxNzWqpGNLZgMJIMaVNfNs9oXe5J9aXuFZDzo4UQN6U0rA0WXSkVwzuWASlaWGnJhpvpowaLGExumysbsyijO7c9vSQ6rDItfXsyISaeiIWdenijY1b2OOjrUVn+B1/j1PENWjy2dUjM2aFi5TDiGPj9S5H35P/LYq6AQ+90oc8lYzuKjs4miRY5WBqgFMIx+YmrJ153tadjusik7znMKSiR5Hv7EMHtOh1ej45VCjTUDVLNJ/wJY6jc9PFORFKqHUq+NxDqbXJJUb0aDXUzi5POuuklZy367vuRg3eRkKKGVx1VKuq+ib4qbE1yYBd/0W671Tvz+rSWA8Xs+DgBNlMtxEUXl5/lOlne0h/RxeoJlL+7SaHLe9bK8S+/l+UAHjZ/bm8U1yLd2JhLbt0XvyRfeb6ZPC3vftU1Gp0osNotueEHq+EnDT9km+92N07wrkCQkyAs48i3U00jtIz4wFv4//Hf+K//tLqe/Lht34FUZP/vuOQpII0p30bs+LmxBwwHMl4Oyb1qYWv8ytl43pVrHxDL/b872goKCgoOBVQs3NSaebnPdTP/VT/PiP/zjf//3fz2c/+1l+6Zd+ia997Wv8xE/8BACf+9zn+PrXv84v//IvA/AP/sE/4BOf+ATf/d3fDcCv//qv8/f+3t/jr//1v37DWl+OnSXh+g/B0UcO6T42w6963vw3j/FziPLmlWRcg0ll+O7gg29fsDgK7Mc1UVSPEb7+0Tc4m+0BkQ8+9JCYWfVvNR/lO9qv8757xNt7b/LhwyPk9buj5pvuo8TKc1gdc8jxQLN01HyTh7zDx6hZDuq2loaKFodjTcNeJrPEsdZZJs4iFScsOOWAPc5YsuCYhzh65qzoqXifj0AmnToa+ux2csYZax4YQ7EoU9TEJJqelhkdNT31kL6jziRZwjITcetMnCX3kRWO5G5SKIaKnlPmnHA4LPhrWhasWDKjpaYnu+bMC5WOilMe4khKtDntUM+OhjWHQKTNZFlDm40xYzKmoxqUd+A4ZX9Q2ykaKtbGlKd0RGRGS0dPTcyaFyGbQu7jYNQMcVACJpqpy/0ghJy0QRxhdoPBUVRwQvDJHgBPoMoeVD1JfRdyf4vSUFqtS79/x1Pjci6pCVPtQiZclWRTPZ/2XqpfP9SMPKpxICJ1/sict6oo205d2qhyzC5WQ6aQtNflm6We5Dw1bsvcGOek5Ss0F2t+daNfpqZXN5j2hCKrJySXzjSXx0P1LclR6WVGAulrIS/OI+NsOTGPwZjsGRNw4xFKc+/ieoj68DLz9Lg+8Qppzz/7KtimKrtq/s9i8rjOuTLudvxuZs6bkk3PujdfZ/r13VdelKeYtuWJkdpd32CsrlKebsPYrgg97/Nt1kHuCOm+Zu8c28khOxd2w/Q2vUtuoxbHZi2PuglWt1wObzZDpF5Sok23NKT84jDXYj5PNsH0w+zTeogL2LFrYbkfWopM6yVl67XWj0ZOnkM6nrKtJpE9IR8bEz+2ndG0fdyfF32/m7ip+uR5Y3yt6bPPbjoC8qaxJf+G/4FNB+J3AReRzbdbynnfXjxeTJsvq4HPb7gvvz/uEl4eMTFjhqdiydlLq8N1YUl/eUYU3A3E4Z9dkd6vbVEFBQUFBQW7ih/7sR/jvffe42d+5md46623+PSnP82v/dqv8clPfhKAt956i6997WtD+hACn/vc5/it3/ot6rrmd//u381/8V/8F/yVv/JXnlsdXYzx7q2inwFHR0c8evSI3/mmg49+WH84njNrT3ht/RQXIt+af4i3HrzJXrckVDCPHZ84+zrrqubfHX4yGXiC48QfMIst33X6b/nm4ZsEHO/wUVZxwcwt+Sjv0FHxzfhtBJdioh1wzJu8Q8DzDd6kH7ygRl7jA07Y5wHH9FQ84RFPechTkq/aip41CxjIocicJRUdB5wRcBzxiAVrAo53+XA2STnO2B9eKl/jfcDnvJIJqzXeWCORNXP28qJGYpsFYMleJskStRGpiUQWtNS0gOMp+8zoqPI5EpdOHP05YMkc6wc/ZionDFquVG7AU3NGmMSQ22ONJ7KmYZnbkQxlqvvypkVCQkWUEqizE5UIrGjomNNkEm+bgzkxvHWZrExOQFWBsh5462TCq+mIRFYscm0CNevB1B1xQzyojhp1+NNnnRf5W537QykSn0k2cdCYzJBiqJQzp0sJhRgQhSwkny9UmBqype/stzByp6mlWGWBjKD2neQUhpHyJlc/nKeR/dQkKrW1I7O5m3RcAylP+kmhbgilFEsXWUO+z+NgZ9TYjV2fR3YcZ2rbklrNNtsM1UJEbyf7NB2j9m9PJwiDKfp8jI3kl7vou9il6PllhA1D58UIVyAnbfnhBkRcOHesLoca3K97/k3j99mydbxuVodxXunv7bizlLGQuXqVefos0Gvi2frh2WHbPTXkeHO/TGlfdWwjh7T9dsOATS2bLmzcV3udW5rOjqdGKHQ5d52r2+5FlkgTWFX5xffnTfTmOTEl0C2BN817Wy/pZ83Tbmqw7hBV8Xdeay0s4Vtwm0jz764RcS8OusnIXsUv+477chApLinB3qvlfnW/++M6SCsc8UsxXq0WvDjYp+X02VyUvwUFBQW7jdXRip9/9HM8efLkSvHECm4Xwsv8n0jRwG6CM+D/CDs3hjurhPvXsz/Afxj/Pc6lF6//3+G3ccJDPJGaNXtxiXNw1uzTUhGp+MbsTV6L7/Pb7jtYM+d1/x7eRdZuxr86/I+Yc5KVanOiS6TX27zJ+7zOA3eSS/acsM9v8rvoqZhlwifB8Rt8N8ccsmCNI/Ih3uc4E16OmJVkKa2oyN7jI8w4Y8UeLQ3v8WH2OUNIp0SVVSMS4h0+hqfngDM8gVUmxOqs1VizYMWc90hE5YIzanoCFT2eNQ2OyJJDejw1Patspliyl8mVnoc8QeKdOSIrZrlWfjCxq9HJccI+s6xZc6iry5YDFmYvWqDmJNdhvFvN09FkzVmHdR0pdRNqKkXD8lR0BBL5l6itigUrNDKdo6Wioc+kYJ3r4OlpaHNuiZKr8tG0c7plNhgcZfwDPsdxS/msB/Ud1HT5eAMEZnSDcc6OYVq41XQIPSYEnI08p0YT66hTzXiq0kim/NRW615SDYOiOxCCrspKOXEtpzv9GfJOLdJ0kkdqQzcYU3skXp+U0+e5aNtQmbyTIi+1KjnilEVsGJa0Wtf0eRyLTcdWrpOxUkE1QeqiU8+O2RmoxB/rcYZYcYSJfjLlu2mY3nRxEhG11HYz1zZaziEuLjd/dfn3sdvOaZrxZ3XkeR7EIB0YE5jnn5PGR/QeV40cch1zcrr+wpXzTkhaV6nh1aFXxU3MTuNd0DeB9vizE3DjO8KzQ+6cits1zY1JY0tuvVgT4HYaekqyPbvb0ZeJOPl83mzbjC25rd1u9FmJ/Dj5zf4VJAJ/+7W6rUbbanqZSe2isdm2IWDcnu3k2EV1teeM+ycOz5ek5VM6Ue7mtn81lm16iouj19vGXVW3vQikeMTb2v5qXdE3hYO8qS5OnhaF9L0pZsxZ8/ziSbwoxHOfCwWC8zbf3NU4evcB9j3cbvs8/2leUFBwFXyID/EBH7zsahQUFLwieNHuKF8F7Gq7WPp9/j/uu3mDd3iXNzjlYFBA9cxYu2AIsvT/zjX8L+4/GBQoZ27BAWdE4G3eoOfb2OOMRzzlhH3e5Q0ADnjKPmeDumfNjPf5CKcsOGDJjBXgOOaQD/gQ4Fjl5f77vM6CMx7kKG1PecAhJ4j+4S0+xpoFETjkCac8IOI55YBHPKHNseBWOY01t56xl005kcc8YC9Tb0JQnbEY2n7GPi47rHyPN0huFcNAZbQ0WR0m1Eh6vX2f14HIgnUm2Q5oaTjghJqOQMWSGS7XJ+BZAXOWJLIl7XxPSr1EdPRUrGmY0eeSXDale1ZZzdfjaIiD9ibgWDGjyWTQOivzUutnOY3Phvw5ifJKrTjjgAisM7Gnyhk1mQWabA5O2jjHaiBFhKiQ3m+ZcZZdSPrBwWOeY+aSi1Sssk5O6STRDqT4a4FIY0bCjm+KaFcN7k0dEtsuxWUTl4kSx01cVvaETO+JrsHu8LdLSD8husbxzbS2PpM22xbpEnVvvPRJMQU1b+llufZCnhcuzz0//CZEH8PvTD6rGsLWKM01mU1hqL0SmOO66I5w0TGIq0yX29UNtKj0rxpFNfbhdAmuO4qj6RFV2KRvB+xzxhKNAecGA7X0v4U6MN10C7nNKKC61G1pHEqzCM3vhjpeTMRpKZcjmZ+vDmnh9dRciRS/roLOErI3MT+NIx9eH+Or/eZ5qFvf2zY7PGvttkPGaZNgePEEnNzr9D4/3VV9e3V6Pr05xpjctPD5OlSKW+7s3dDO67R1el+5asvuk5l37LJZyDXrwFr7I6nK7f1E02vcPNvLN+vJ+0rAwfO/+l4VyJ13/GQuRNxN8CoTcLohshBwV4FVOqfvRTn4MmA3/ogvFJAn6+1GKC4ouK84HeIhFxQUFFyOJv+7CbrbrMgdws6ScADv8jr/nk/gcXyI9/BZfROBb/E6j3jCWRZHPuCEJzxkzYIFS97lDb7Bd/CAo6x+m3HAMUsWdHhOs/tIgJY5T3nACQtO2OcDXs86sJ4lawD2eUrHjEBNlQ1bkAzEH/Bh1sxYM2PFgse8hssmlza7YXTAE14biJVIxVt8G5GKMxrmrHG07LGmJpCizx3w3qBIcyxZDcqmYw455CmzPLVXzHjMm8xwmSYTVxrJiKuqtkSGneaYbslN5Yoz9jljHzEGxXyO6IWm5vk1MwKeNTNqOjw9xyyo6HP8uYo1PXOWnFGz5jBr4NRwdcaCOhMDHRUBT0c/kG3Sv5AUd9ZF3CkPR0SjqAmrTPylPflucK8ncduECFgzy/H1Er0zNy4uW+qhnI4ZNT2iaoy4HPVO+yJQZVpI9R5tJv0g5BmUSJA6u7Hs8rgmcrLO7bQuGR2ONpdZTYggn+MOijJMzYE6YmOVlu7ZB6uEERO/dbE4NtpGREmndI5AlXiJ4KyHVEpKpSN9NhNXhOwmSGMDqZFcyvdDHtH0qqjxpGytoRI7m6amMSkpmkQyAZqO9ozjslkCx2Uq2tK6mybqRImlWnvgOBP3mm68dJR+G+siRXsitG68gHxyQz+Jfm3a2m1tkRl2Oya5MWF4eZ6ir0mjfTVXljJP/eBe9TqwO2mvB4kTdZMlvxjg+2c0f7n83+1HI9lG3i7YZ/lMCzMb5Usjk74YTLc4jN3BwlXiOj5r+c8zP5lJwfy19/MEjbW2PXrZTVFMkRdjvB3lojQKdeQ8jWeWtuOkc2RjSkUZhYshJlp5Zwijt5r7B3Xnfb+UUGnryv1p7zbImkefife3Ly6DXcOk76WvXgbsGsa6kj1vZVdQUHBzrF7hDSYFBQUFdwE7S8J9g4+x5GOAY84pJ3wHVVYl9ThqWv5Xfm8mHQJzVpnISq7vVjk+2RGvDeRNm4mc3+R/wx5nfIjHROCIA97io/TUHPMgkyM9hzxlxYyKnv8v38WcjkccQ3bd2GcirWXGoxzDbZbjiT1lnyMe8RpHgDj1gxbHgjUtNRJpZc2CUw4yKZSWzQccZzKoGhYJQoZIHLZ3+QiHWbGW4sDtc4zjIT0zuqxUclQE1pnIicCSxYjs+YDXqEfuqlLEsz67tlyywAH7nA1KmiVzViwy5ePwdNSEHDctELOC7YgDIjVCgXV5nCJJWddlndg+p0Qca+a4kYE+GvWZVUuJUUocMKX8H/OaIZFibkFyPxmNoqtlZhRQDU9p8tyqstauHRayZ1mlWGVdn7gW7KgyORiIrHM/N0jEMT+kFNN0REi7ROv0ppWJeLTxiloaHPBt7PFNjgcyNpHAyZWmaBEl/+RCsqNDovOoeTYRamM9mBA5gYASaHFQdYU8umrQVoLToWZFGS+hRCWVxpETUtW6gXSDckOVA5ux4KxCzZradVmW3JEmI9w45pM1yyuBNlaLiPtSJXS1ftJ3AdGfjX9hlDbpHcbOoNSwKqS3dVu23diuuJhEUlr5aiYnib43jte3Lc+eq+0DtjtVpa4NFd2FCjkZqcti22l90mxKqa9HxiW3rNcjYHQu3gxp1t2GszmdLbeJ7f33rAScbbPQ4s+X+LJQ4kK+yd+7b76ZUtnTY4lSEIpe3Rz2o3ucG/7dX+LhVYTcxUW7qP+PJk244h35fmNMO8jGKHgV7gK3Dfvml95KLuuDF7lp4vnBXkf3GfoW8uqP6fNAWovBszoeL3g26CaBeMEdqIxNQUFBQUHBy0JxR7mJXW0XH/ARFtmc9A4fGxYTTSa5TpkbJVPgo7zDGfscccgey0yiJbeQLXPe5SOsaZjneGbHPGDJgm9lFducNT0Vh9kQGag45oD3eSNrdZKR/1usM4mU4os12fB3xGucseCUfZLDw44Fa97hdZ7yCHAc8JQ5K47yS39Dx5IZJxwyz8oZeSH9Ot+JEJCPOKalYZVVWZEUt66l4YQDVtkt5YIlDjjiIRUtHTPmrOipOctqQInDts86m/Z8JpNmmXxaAZEjDjnlQXbvmNp4SmRFgzgAbJkPJImQcCvmA3nicgkaN81xwiFP8QN51FMT8axZAJE5S+pscI9UnGZlY0NPNyikFEmBlGiXJQuE9BIXk6luNWvEHaOkTnVO5GydibH0+t/hWWfiUI1hjhaPODQbu0CsBneONoB3ILmNtEuLpBbr8nmiVEyEm9Q5Ke0SZdMD/54ex2KgcJRC8SRXneKaMtFuPbNcWjsoqXrE5WQkUlPTDkcYShsvgoQ8FJKOgc5Q91nW5akliiMStw2UiAMGHaAzC2DVuCXnXOpsElSJKX0vzjuj+b+Q1/KfjJp1xeOwe4OVKmTUBwxp1TXkeGkoi3dLJmLKPJ84cZO05y05LXG3aT7UOmq9zyNWtuUtZNn55slEKAoxexkJOO7VcAkBp+fGQbd6PcIq4K5J7lweQ2+aXvbSP4vpNik/nxWiHX1WQ5pe1c8rwok6aE2lXTRGD/gwT3n/lkpWAnDbWN1t8802Er43mxoS/JYz7rfWYzdg75yM/unvIM+rdH9N8MOZ+vzVd4/7DnkjCOZq2ezV3cb1W1nIml3B+M3yfsz3m6HEe3tZmCoQxb39fVLtFhQUFBQkJF9m1wlxUvAiUXNzd5TtbVbkDmFnSbgWzwKy+VF3qmlMKX15jnje4Q32WNLR8IQ5Ec+7vJHjfyU85ZAex7fxNjWBd3id9/lILm+JJzJnTUNHj+OUh4DLrgsrOmpWfDibqFvmrLOaznHC3kACBSqe8oAFq4HAisAxh6yy2m1GyxMesmJBZcgaR+SIB8hL6JL9TPFEvsF30NJQ09Jn0mzGEnEmKOcnJeCCRFYe4Ehx01JMuEido5FJnLYle3Q5VtsexzSsWbGf6619fpLVesndi6iHUqqehpZ+MPGK5sZl0sRDVokl41Ei/drR2Kb27tHQ4UiEX8uclnqIggUwY53Jmn6gb1oayCSZuKIRgnGWY+6JgarJ4xJzf/TZjaLOp0RkCYEodIcSSgx9LWPbMkNjriWKqUZUXGOiJ6n/hIJJup4+q9wcjjXNQBCOd3KT574b1JounysRxUS9psRmN3IoKq49UpytcZ59dpaaHoMpTWOMfjYnl3teFlEd4gLNqjfUyaNEnEtzZ4aq7Vweg5gVHo51VlOOSbLUUzNqVqOHtBt9jkNfKn2mPSg6zt7Qn34YC2dSJpK9H9VB2hKGtqmSS6MQSj8Hk+8UciSMSAuHOpuTkfFITD4tWzYkiMs9VVv2jGeLkk/jOmzGO9skm5TitHGGzm+LpPfGIexlJg6XU/RcnSRTk/X2+mw/5/pk2nWIze1lYlSHN0MwqqfbgGh0nwfG8+yyNt8uAcdwZd99bJuJ4/ucJfTH969CvO0qdFzHRIg+B2D8vOgHNb2cG865299nyNvXdR0n7xLuGxmj19H9aO82WHXRfe6H87D5lL19fIJP8jX+/XPJexeQ3mzHXkleZbX3Hnuccfayq1FQUFDwSqIQcHcbRQm3iV1tF/+O7+INlvRU1PTsseSMOZEFS+bsZ9IMkmbhm3wnjsCbfHMgL9aZ8KqzuTypjTz/b76XjppHPB7Ka2kyMfaISMxqt4o1Dd/kzUx6ALkGZHXVO3w0k0Ced3gTgI/wLi0zTjnE0fOQYwCOeMBTviMbTlxW8kUecMRjPoTPai8hxyoCDWv+V/6DUayvpNIKrFnQ4gfC7ICn2SXigo6aA45xRN7lI0MfJleZD3mPD/MR3s/6NaW4Emm3h+yp7geSqGZNTUPIlODmq3JHRY1dAKa/p+xlMkyJiaQ0nDNnNfRrR5XJMrJiL0F0Z/J9zTzr+Rp6XHYBmgzmNeJyUs3BLTOsWfiUfSpaHMmtaY3sL09lCNnUU3FGzYzVoIpiaLeq7RIdUpPcGgodlahOGatEbFWZCE1uC+vcxiY/ehJ55LMi0RJYQgymby0VkZpEEq6QGHqzka5I2l8h+8FjJgeFIlyNFGqq+esQd4lxIGviUBNRnAllVI1ohjrrFRn6VKk2GX353Xr7ByETZfSmc8yxpmIN+Up0uQ/VxKSRdYTcETpNjJc6J2S8JY1jkxAjUxaij0wkK8M5Hc7EeJwSThKdJgy1teZVl8cjjI5qrwvE+anqHzUniYWn81GNsuquVSLyjfuyz+Sj9L034yz5MeRrzXjB/DKut/71Qz17MzvOW1pbRej5sARpcuR6nYffNkXhZeUF0+83Mws8izEhmjvA7eD5mjaet0lre3ljretdR9qCgLk2leiNG9fVNqpuNzG9OxYIzpsDac6PI1/Je0LBGNpHwbx1vDhj7z4POc1u6V8WNlX3uztPxmT27rZzG/QdVmb9/Wr/VaBP3eerGi4E3BhiF9DV+dRV6qtNGBcCrqCgoKCg4P5gZ0k4cDzhw8O3x0Q+4DXUzV1ysFfTglHLHfGAt/hYJpCS272nPKSi4w3eY8WMJftA5JQD9vOL0yl7tDQsmfMer/Ma77PHkiNeY8WCGeuhXj0VTzlkzpI5a57ygFPmrNkn4uioecARgZr3+CjHPKWh4ykPBjXawpCIpxwwY837fAhPR6TKdYSaJTEb162h8yTHrpMlRZ+jus2yoq6l4ZQ95KV2TcOSBYccc8aCgOObfISQVUlCFK6YsaaiIbLgFEci/cCzYk5gnWmuZKQ+ZQ+fxyLgecAJomKrEL0UrNhnBexxlsm2hjkhx5tLdOeS/UwRCaGX6KYzFixYIVFRWmpaGhpCVicm4s7Tsc+KFQ1NdoEp+iwxewpJ1TLParCami6ThO2gDkvm0iS8XeKzzi8p+JKLxxRnTmgQiTqliijomBklnNBRQnskGjARQm2mOhKNE6g5HZRxqkeSWFiiTvR41gPB6FgNTidlweMzDdQM6kKpT6JfmtzWMJQzNgAnFaiQNd6UPybZ1DGizG9IatExMZVKEXJDdqd7QycJhZoIOdEI2l2TakYTgki0Yn0mBG3MMIejG/pRFWTJTakST+q+FIQAlHtKSlfnXuvRMWiyllPiNVlqT/rJapl0wVkNvah9t2l+j6ZnrJNOS5AJnUfuOVF1js21m0Zu1WICAzW7XSswrY+2R75dRMgFhEY/39CuRpE4jMt2KFUYhnZfBeLWcVzDi+EQJ7M3Mw88C7EwVnHeBkSlePtquMuJ1tuHzvtXxWyjV2uPrf99R9pmtavOKp4fts8d0VLL00U2upS4RwJRFab/P/87yMsm4HTrlGC37zppQ1QcNq3dR1zXAfd9wZiEL3gRmPZ5zOsBjSJd5mlBQUFBQcFdRsPN3VHe9Ly7jh0m4YRsqVmwYsmCM/bZy6RZom1qjnjAQY54dsqCt/m9tNm1YiKHQibGGn6LTwCOQ85ILiT3eZc3iDga1jzgmPf4ME95xGM+xCEfMMu6r3VWykUcj3nIEQ9ZM6OlwRM44HRw0RjwHPEgK+aSS8gFS07ZI1IxYzUQNhHHaZxxEt8g+kSkPeApYsA9jg9YM2fOKa9loqx3EKIHl8packAEVnHBIUccuweZQAoccIKL4FwiKVJsvL0RWUOEM3dIRcc+p5zEB+zHE55mZV/NKpMxjmMOmcez1G6f6h9czSrUiSbwFcRUYIiRs3jI3C1xLhnsjzkk4OljhWdJDRyzTxtrZq4luBoxHp3FfRyBmWs5MZdwjNDH5MwSXw1taJnxBJ9a7tJYdJnUm7HC5bFJLj27YV6scrw1D3RE2uwOUbVUFSc0A9ERcs5WPbbOsfBquiGmWz/87nPZY9d06VvFGk9NO6JFemr84MpU9727gehyVDkWnNwEktKuykYIa4yTeHhKRMrsSwukGjHaVbTDOUKvqapRyDtxXekyCa5kkhItjjZr8+oRcaW5S2+kpVhnekbKq/NyTR0wjuNOJVWgaOtEu9nnHN2Qez2UKKZvWQbqmCrpIUSgjpPLVFiKA1llF6V2HCti1gBKxDwhPK0mTUoPE1Pp2L2uupl0Q/5+VENQbZe4ILWKuyl9lvplO5TU6g0xaZfFm+o4RerD89xDxuHvRSHXhawj1zWgjkPPTy+Obq+DKj8NrudmMk7+Xu1c2QJwU5eUt60MGutlbjfnbUTs88bLKPO6sNpasHUeG8XvNwoBdztwMGzVkKeFxjUVJ8H32+g5vfuNr8Pd7Rfd9rT7MbD0SXd/77GFgBtDn8C3vbWp4CLoxtTxZsA4bKgsY1FQUFBQUHDXUUi4TewsCXfEA1yOyfYtXqcmRVk6ZZ99Tgk43ubbCHjeQwz8FXPOsuHBsWJOipuWDPxnHOZlaDKNnrIY1E4rZqzjjBN3ADm/x3yYNQsqWro443X3Lg5HFz3vhdfBR6KrIEIfa/bdGY5I7yr62PBu2Oc1/xRcxTfCt7P2SbG1iCecsuDUPSDGRKhFHFUM4KCPFQ84TmTe+hGrfo5zDzmbHfHAn9CFajCwrGM9eHWIOL5+9p0c7p/mXvS8v/wQB/UxfWyIDlw9NkM4MqG1rmlqR+8PCK7mKL5GiFm51O/jfHqdblczTqp9vId9d4J3MbehoqemikmNdbbaJ9YVuMgy7uNCx9y1QCS4iug8p/GALlTgHH3v8VWk7ZLL0FnVUvnAqq9Z9jPAMatOmflIG1OsuOS2cT00pKeB2OBcl+LmRc8qzInRs6qSg8cQK5yDtYs0cYkTwtB5VizoY6bfHOzFU7wL9NHTOyVYAtCyzykuq+c6JEZboKJFYtMJlRdynMGajopFJmEtVdLjqemzAc0P5NdYd5RUlqKSSaRJzFdGgsYrm9I1dY5PGAdXhA3iaFDNFitmiNNFP9Q4ZqJxlktWMktcWkqNpcSGNpNXPivRwkCibSMkulw/66DEIa5BXXZzKbWSiHI2r9TKdB+oEeeQidBKUfKmKjmhKuV866TSup5UEimd2Q8x6zS1Na8nnYs6euwRwlM0vOKYc6wrU/Jx01Q43TNqFZdKYokBVulWIVJFNVvnnrCLY6mL9JYfUij5uv1BI3Nt7BpwO+EUB3ehcThzmhfIPvaQx+2iJfqU4kjOUS+DG0ZjWvL2MqzhwE3G6+JytDw/XKNXNzm4LfPgNkwWt+viEsbqz+cPq+e47bbcJuz8UjfMFsX8VPA8IO+G247F4W59f8k4e1d15t5+P7D7hu8x4XI+ahq6HdoAYN+H7s98vhpKn7w42HloPVxs3nvu7ngccsAJp/eYxi8oKCgoKFCUmHCb2NV20VHR4OhIbhDPgFMeZGNWoKUBfI4HluiRHs8xhzzIirFT9lgx5yCeJgLDNfjYs3QLUsy4ijomA/sRj5jHFZBUY0R43H2Is3afGByz+Zp1M2cWl/TBs+rnzKqsGnJwGvcAiZDVs4ozPjj5CI95He87ZouWyiWj4dP2ES62VLOsdAmeEB2+jrgYaZnx9bNvp/I9bTsjBMd6OePM73H28CmBitBnksEFDhZJ7bZsF4ToWbc1s6ajbWvO1gccnz2AmEzji71jFnvd8DocIjx58ogYPbjAweFTmibSh5S+72pizM7cesdquWDvMJF8Z90eTbVmvZ7T9TNmiyVnYZ+jx/uEtmFxsGSxvyIGWC0POYnJ8LOYH9M0gafHh8TocS7gqx43i4SYyIJ11zCrW9arBSFUeB/oY01bdziX1XB42lBTuT5TRak/E0nXECOsuxkxeHzfUzc9MTr6zuEcrOIMgFm1Zla1WcOWFVQRTjjAhUjlEoGWdpcns01Nh4vQ0dC5mjq74xStTYe6aUzuL6tM+HrOsjG/oUWcQ6boZn4wnimBpVHFxGVgivKWDEgdNR1JSVXlCH/JbWaVXVAmRWlShvVoJDtogf+IPf41R9l9j0N3KKY6rbNbSHFopWSU6JXEiKV6uaSCS2V2mYJSrVyKj6cklCzWhDoXR6ei3ZI4ffWg/EuLOlGAiatDhn6rhnrYpV+qg8RatNAF43i5qEo50dO6Ub6qKPSTnIRyE5dX06iGSiImJ6Sb5gEtm1xelYkcOw56njoes1SPVcCl491wfEzuqjJ2qhMb7hRb6EJG6arJGWNiTcZL89u+BJczbf22L9dD7hNL6/UjTeE2yPWlI3sxxrSxnW/XI9T8MEJXRcz0t+c2gxXbOIK3l59GF3yeEPrgNuv/PCCOdT0aCbOg4MXhPFOn3M364X4vz9L7BUvEpRjM018LXlWkDWrnbwdKadxOEXCgb1WvlpPm5wslZEufPG9odFt9359ueXhVcMzJy67CreIhDzl6yW6RCwoKnj++jz/Ab/KbPCnXe0HBc8fOknAr5hxzyCkPBzd74hZOTPniUnLBksfxET01zsFpjqfWx4ql2+OUAx7yFGKkDQ2dr+hCxTrM6WLNmgV13XLUP6KKLT5ETroFrvb0XYpxtTzbo657nixfY3W6oJp3zJo2KbA6x+p0j36vgRiJHSyfPmC9XlDvL6kaz+mThv1Hp3gXOTneZ328T+V79j90RNvNaGZr+rM5vuqTGqz3nB7vUc1aQpVeYvu24sk3Xmf+6IRqnvU2seK9dz5KvVji60QgPnn/w/hmTfd0PxFQeyuqWTIznDx9QHvW0nc165N9qvmKapFNENFzdrrHuoqcnuzh+oqwbiDA7OFT+lDRL/fwwPzwlL6vOfrWawSXiJuurfBVR7deQPScPm3oVjXr9YL5/hnOpfodH71GPVsTcaxO58TgcVWPe3hCVUsME8eqbei7GSCkYAuVI0RYr2Z4Fzhd77O3f4rzkeXpHs5H9g/OiNFxdjbDuezyMHpCiISQFwrREfPaYNXO6fqarq9omg4XI1XVZ0LCsw4pLlofUpS7uk6EW4wQnafvcvw2F6ndmsa1iL7IucgqzsG5YRaL8m3FLNNs6g4SoGGNOLKUOR8Rl5U+u8xshnPqgeypCKwg57caKbbSQrTLx4Sq+FecIbHNHKqK0lhlQsxZQ/tm9LgORzNowEQz5DLxaOnIpGzz9IOST0px1Dk3PxBwSq8npaMoWS3Jo3Re+qaR3VIaMbXUA7nnsuFNlGBJQyU6PI+4s4yItk51fqLqU6onIqrEcbkacS7VT8aiztpJ7b/pbkuX+27sxlDoN411omOSSqkYRwKJgzrMUlXW1SWj8yVX62g1taQztNOUxLSKuWmLxmScTatRS7aRbOMeHy/nNxV00wDvmyTmOL2lLq8C21c3jePltrbzKueJivF2lF9Kw9+OSUQUlJvqm9vHq7Cf3c6Ru6zUK7hvsPdldQYWuK8xkqbPqV3XPKR3n92PwzTdfrU9zW6OdiGbFBH7xlD65HnAeqHY9HNR+vyuoBBwBQX3A/9Pvvqyq1Cwo6i5uVvJXSWrdrVdLJnTM+eMBcnBocbBmrFCjf813wwfpWMGEXzskqrKQXQpTe8a3ms/xLI9GPLvOwhdzWq5ABz7B09xPnLywZuAw7mew9c/wC7N1+uKJ+9+BKKDE4gdxOA4e//DEDz4AF0E5/NMjayPDuDYQRVZd3OIOcfg6FcNT48/Sv2Rpyy/8WEIDdQR+gidgxmE5Rx/eEz0jvhkD6JjeToD19O8sSR0FX3b0H5rD/b6FDBu5uC0gTZdLn3w+Cbt7ApdzdnZAvrsRnO5B2GJ3+sIpw3rJ/v4eUfcg9jXcFZBdKyXr8FBC33F6skh65M9qsWK0NdQpRfv1ZN9fJWIMum39SopBNvlnHrWEruKdjmjWzXU85bYJpItBs/Z0z3quoPoWR/v42ZrZgdr8jBydrTPcd8kws7YxbuuoqoCMavoHq9mxJAW2qL6W5/sEULF/oNjfBUHQjBGWK8bCDN83dO1yfWl9z1V09K3FSEkIqlqWqoq0HWBs/YQ8DjXUdUxl+9o44JVv4fP7jsr1w8Wnp5E4jnnWcdUToieyts92bBmMSxfPG1W1Xm6rPyMhkqBRACJm8qGSDOYIRxnLPAEZqwzSZfUQ2ImXuVYh0oaicvLjiaTQIlkqpBFVk1LZUzuotRb5ys00YGdqaMo5pLDzp4GT4WN6pWi81nVlqiyKgI1PsdhqwjZneNmDDSrhhMCJ+Qap53PSZPoB5JPCMEun++MhqXPhkpHT3LdKXl7UUtykZkn5WjJyh5xIyq9wqie3pyX8hY6MEzaJqk8IROfDKSikIFCB3oz3i5TlGFSllJcQiH0WGXb2LBz3v5WzVXdbI77x5blcj3OI+LENaa2XVSqY0WZPcsqKC8bm2j+rwabi1V0Y1PtdUm1NJ9vYhLScm9nT/HzMDmnubXPIWdZiX77sC5v7xqs8angrkKjit5PnKeQu6+wTzXZJnHXaf5ngTqthl2dAfo2uJvtm0J9CxRYpD4Zr5UKbg/jp+j9uNYKCgoKCgruI4o7yk3sart4zIeJPCAM5u9MusWKx/GjLFhS+cA6NgRqYky/r/sFfV9R+Y62n9HMeo7e/RCr431mD0+pm47lBweEtoImwqwDImdn+1ROzaoxVDz9+keoFitYV4TOs35Uw8rBUQUeTp++DlUAn1/yg4fHUWz7aXT2fWYCPOFxgzvsiG0N71fQJlKj++CBrhMcEDJDtIzwCMI7B9D0Q92SL8Wa9oMFLIAlsPRwko3ldYTGw8Iln4Odoz2aw3t1LhM4iImsa6B/f4++d9kSHQmrGj6I8KBnCOSEg1VFDipF7B3dySLVlT6d+7ROpFwTYBYTKbh24CEczVn7OcwjOIitpz1rYB4yy+Ppjw/pxd5fR+JqwboKVDj603laUFURqn5QscW2JpylqH++6XBNBy6TiwTOQkvdtISQzOtnx/ss9pdJGdc52nWTXHES8T7iK6jqjhAq2pMGX4VM+Dn6tqGq1qyWe/hsrQ+hwkdxk5hIvTQX01xadw2zJrnQ7LqKs3aPqurAueyKs2fh1gMhKH8HgsuJG8sUuy8p6lQLlVxfpgnngJYmE02RNh8PVCxZDHWszfIpULPMJJfoqJKqK5XSsGYce82xYpHpNM8sa6Qy5ZhnqKejydrVTZUXqbeH38TJXI+NniUR+FKKkG91YahZjRvKdoOOTTQ+QrK5fByEBJmhEdxEueZMXYS4ImsEhPYRRVw05W5SQJpSWqvOWdzouBCTMUfqI5OE5JqLw1HNV/rNKqJSLEKBEHopTTrXZcITk0pprJhHQtWHMkZhUAqKq0ot09JgEmfOmTRuOFtqpSYxm5OlzKZqN+n3cU/H3Gub/S9tkLO1/uL8c0rySV/p95jJx4uM9G5wsCp9sZn3NqRZEPOoTHvicow1rY6xSvK6uFr8vKtgTME+PwIulXWZgvLl4j7pal5N3GcCbhuEdormWXcfIS2PoyfM7mH8vNtNvAp66duCVSLFYeF5fyHvywlyT9vd6/llQN5g7dNi/MZT+rqgoKCgoGCX0HBzJdxNz7vr2FkSLqlvavM6F1n3DSdhP8UG8w3zHF1r3VbUyRMkXVex7mb0fVqQrL+5gL6BCOvHB6yrLivEHHQBagdrR3wn0wkf7VOvroB3avrQJFtxBf1pDadevbkEB4vs2Mc7WMWkRFvGTFx5aCPMcytOZsTTGRxFaFz2CTR5Ye2cWkejg5O8gFjWKc8a9S238vAkQudT+YJT4MwlwqvPhN5sbjo3wuOscXpEzh+Y589Sj2/VmqYGnmaFnweOq0QANqQ+ac25K5f+Pa5z+ZmY9Jmwa4AnuR/Jv618am9MJB29gy4STvcJjThPAlxLbD2uDqn/l7Pch5HQNrlvYiICa0d4usc67OEenOE8hK5idbpIw3+yl+rsO/CpyL5LdfK+z4QahiCLrJYNoatofI5Nl+P19U2Hd8klZwgVMXiqZp0ojK7GuZjJOUfbzajrPg9FxVk7y8OX3GB6l5VEzvjZj2k+rUMDzElOFjNt5tOErEgqwlM3Rwz1tVF5yXXUUxmiQYibRG311NR0w6/rNCloWA2qOHUzCS0SuSpRJut8vM7R4NTt1Xgx7LLZT2JlhRxN0bpBVEWVUmQSOS59q4dfOjw1XSaIUqmVMTFuEjBCLbrBReeUSkqkSYWY5qqswVtngtEPNI/OfSEOpbU9bJCRQjoGRO8Vhz7qcUONJf5lhUQCzJfGQOGNdR3OtNXqBLUcKdsun6Vubqibfj+PTBg7yRSHZtoLwDDDZMZsN35b14jbU0wNw25oz+ZS35pSVb0Xc69dvv9fiLmLXE46GGa8tkFp6IvzhmcxCaVybsMtpbugx2+W34uCHyko7w60P68b+6+g4GUh3TFlM8j9dWV3P1u9m7hPNPt4C0yZxeO3wtvZZlSQkFas6nJSNhGCVQ/f7Tl4wD4nnL7sahQUFBQUFBS84thZEk7MjR0VFT3L0HDcP2QwlIaKd598hG61YLZYUzctMSTT3PrpHAKE0wZCncilpySixzfwgCzC8PBbM1V6OeAtjz+AsHJZ5YX6intaje3PPfAN4KmDPeCBgw+APaf24KwEIwJPSCPW53zl+BnpfAeckNYOCxKZFlyqv8QJnpNIrCVKCEqHfTifc+JSGtkY2bl0/iEwQ9uFS/0yB9YMZCMdxqdbbtMZiTjcl+O5T4XebjRLolMiTQ6Kou7Ip7p7klLvSR6fgWdziaRcAlWV8q1JTFgHHDfALC0AZi6pGZ2RjnW5/N6T3IJmQ/zTPaKL0Do6R1LphVxoaGDWDu1qjxfU+2uci/QtxDqRCO26ToRX8MmlZ9UROo+rI84pcVQ3mRhaJzKsa2t8FZgtVngPsXfEXL0QwLmKdVvhXGR55tnbP8NXgb51tG0iDGdNi/MxKe+E4M3Ndl3EVzERUU6phkhFC8Tombl1vqZUTeJHZEOkzy4tVW2WFrUdFT2OOjvGlDQaWy7tjdTYdmRdXYVEJKto8TAotFrqEV2laqs4WtCFTBJJ3bvBLaYYutPnBV0uU2g8j8sOQIVmEtVSNXLqKO2pcYMmTWgSG2tM8qmG3pLYaj5/7rLiTIhPce8pDqCUqnGmns6UyXBM9VZ+yI2hNXbUlJQRfeTYHCOk1DjSn8KPVGt6jn4WomzcGzpSMgecaXFKY+mmOPmm5euvDP8XmmusopMUaeaKXnGTXBuPrdW/jKngaVul1kqonE+YbZ4HKebOZUq6RMvGkbbtOnh2+izpNSVyo+T5LBjPYCnleUCu14sIz5cBodan11JBwd3HeIvI/ZvBdkuSbmzZvX5wl2wxedVxnwg4+065i3P1qtjsh/vbF88P9m1Levv5RwG+bewCAScr77uK++7yu6CgoGAXUdxRbmJX20UgqULWcZ68L/YphlY0z/Z+vQA867M568f7iVxau6QMs6bKJdAKoebgmNRzZ6T0c4zd1hN+h/TOuY++069IRNaDnK4F3jcVPiWRbD1wBLyWz3sP4SHEHg8fIZFhbU57nPNtMUqznO8J45XlQS4j5HY9RC2g76Lk2Qol4h7n9CckVZsntbnLdRZuc53PPSaRgPLefZrrdpY/v2bKqfPnx7m/Qm7bOp8j/SX5nDr1hbbK5wsRSD5PVH3SZye5LVZth0tpnUsknM9En7ShysfbfMrcwZnPbYrQ1qnsWf59VcOszwReRfc0qSdTGTEd94CPZj0ygyoQQyQ22aXk2Yy2jlTzFlf19F0mtvqKbtVQNYEQ3OACs+8dfZfdSTrwPnB89ABcTzNLseuIkfV6xmy+pl031E1LVam2qAuJqHbe0dMnlUg+r2VGjJ4u1lS0yQjja2L0eNdlwihpN+qBzPEjKqLPNFOgoqbLv7nBTWJLijrnCdTZvK9R1cTpYJOpqpDJB3GBlcYyqct6muzqUdVeajCqsq5MvgkN5KhIkd16bDSzNh+b5V4h5wstFaIQSxd4HPKUOGS9URy5DeNV0sVJHYRalP5Sskz7T40D0n7JOxGaQgAy5JNmiMuplFwUDaGQeR6hsuTaUBePmONhGDUtw6GaQXFIuUlqSW9ZJ5TSd/pNI8Ep3evNXJFeEiJSCFTV+CkCqmaLpr+8Safz9DwHYpbMBZUwqzvH8wg2jdt33cXkVU24aYZUQ/uuav5WUvTZIG5b+1t6ibAUbDDq2NuG5CjXwV0xuaW71v2kMAp2AWLa0y0X92cm65Nsut1kF7G74yrvjrvcRguJA3xf2rsNaYMd3Pd+eB6Q9xl9s9ncFlfwYnGXCTiwWx4LCgoKCnYFNTd3K7mrZNWutosz9lhxSB+SGqgPDudJbv2C4+x4jxiyOfksxzpr1egKqCJrnf9F0gw6y3+PSASPJ6nIIBFFHYlgEweoq3wM0tt+k/M7QkkmIfakCl/P5c1y/uuct5BPByi5F0lqsz7X7WMokfU0n9Ojij6fyxa7uKjkatMOcp5TvJfrfGbyEGKtzf9OJufWaPimNtdZSLO3zOdTU0/prxPUheYctW20Oc+TfM5xbueM1L/Sjx1j6YewEy1ZDUhS+lmyT8jO3hlS0qV8Y15ERJLb0DbPkdbBode8m1x2JM2jYU6h5FwkqfFmQF8R2wpiRWwdXTsD3+JmHa5KJ81O9ziJjvrgjJBdivZdBdHhfMBXMZNzEKOj73rqJgwx5s5O5vgK2vWcvuqpqj7lg6NtG3BQVz19CFTeEaKjriW2WEUnVE1MpqYuNnSxHkIaypBVtAPh1g+KthxekAZPz4w1PRWBZogxl1Rz1k2JGrOE2kqToc28tDP5C6kkDhhjJvxUk5RIC6tTUqd6p7iB9BKiJv0mNKMluDwdqgTSXHwux+eFZ5PzEXppu3pJCSbJKWaKR0gmG5MtDumVYrOqOaH6qkxECe3mMpklpKE9txoIw5h7tx4oT1kyu6HPzlskjYmy8b1Uv6Wz1dmkuv8c941ScpKuH/phHKtj7LZUe5xJbuT+sP2vEdzCiMxidDaMDTRXobBkLmhtLobWPgxk87Z6jMsIjGO7XYXE0RnxLHu/x+N61bIvzm+qN3yecOY6uguQO8+UAC8ouPtQahtkK8l5MaZm7LHm7IXVreD2sKvm0Tj6u/sEgcaAg/vQ3vOgLhLvbx88D6QI9LJhDQr59uqiKNMKCgoKCp4VRQm3iV1tF33cS9xIO6cPabHRd44YKmKAbj1L74NHNazzS7iVySXBSyJqVvmf4KkUgpI8oqCqUYXbavJbRSKLoskbVLl2QHb3iBI4YmF9jK4UZyTV2l7+9xh5601pfgtVrEWyYg9VxzmU+JJjUt93UWXfST52mP91ue1CXomLyEPgt03eVpQjJOIeqno7QcnADlXvkc8TNofcF2tURHOU/y5yu4QYlXNlnHI4utE7v7SrMflJ+vdQd5vkNia2QOtRkZRzgxzHpboGMrkoKjcHIRfuoxJ8Xf43z+dCcnG6BGIFtZyTy+oa4rIh1j1UjuOsfmuP93HzNKliBNdXxFjj/Tqp7kix69ZdTdd2NLOO1XKe4szVHYv9ltBXhD7pTXyONxeDS9cF0FcdIaTJ65wjBKjrHueSmjQa0qcPyZ2mqN+Cq4nZSaEXInsYouS6cTXQOjHTT6nfuhwtrRrcsiUVWDQETdLeJI2aN+eS85bdvckVbYeo3lLMt0idXTUKFaWGF3WNWeXoaumXmtaQQsm7rBJAfqC4BGMSSDVuqm/TnfPk49Xw3S54fFYLKqwG0A+tti4m+9xrPretReKmaV+FidvDaM4FT4u444QxOeWzpk++WYeYnqmzS+2DaqD10qxLo6QUkkS2s+Np1YE63+oN2imRUcKdy/l2POT/200AMjaWylM3ptP6TGnJTbjRZxlTpQ0vOkfN2WE4J/JhXuMxj7fU3Jm807xzpu7n10+1ZjGrS6c1vwps+XZO38zUomdZmvUm9bpqeULEvXzT0MuvQUHBs8Le4867O+42ASe6mt28nuV5tGvtS6OW3ht3HbKsFPfj9xnWMX3B7UDml1xP/Q7fD+8DCglXUFBwl5F8eZUNrAWvHnaWhOuA2Dv6UGduzRF6z3o5I/Q+Kd+WdVIwRUiyovx5SSJ4AmmlImSTkC85+UCWQSJhJL0QbGKZrEjEjyi2jlBiLqJqqw4l4cSi/Hb+e5DTOBIBJsTUU1QVNrhGJJFKDyf5k88TZdl0vSltP8npZvnYEYmck/Si2hNYl5eyqpFypZ1HJHeTc3OeuMV8QiINQZV1kk76sM/pWnOuuMu0CkIZo6Vpo3VHL24pW3OutOsJiYg7zr/Xuc5CpjamnYcoiTjMCacrOiHZeqdzoSO5uRTCFJJ7U5l/rTMx/6JR4dWJoBvEUZ7YVzgfoK2JXapY21dUeyv6zuPrVJHQ1yyPamKSgdKtK7qmo6ojoXf0XU0IPpFoPgyqtuTiEtbLxTAQfdex2FsTgsP7aI57qrpP8eoi9MHjqCA4mjoOFImLcfAUmoi1JFlssxtJMelLvmLq6XFZQWdjvzV5uMNgGOpwmVhSl5N9zsllgq8i0uKoJ+TfGFWmsAKOwCor6/qBfHIDiWSJtXSbEOpQ6y9TMn2WlHma5HQa4U3ixkmrerpMFtQEU76SdlNSTxx2NnRG+TZt5ZjiaAfyTFNF6txOW4LUKt2khPCTVidnJ9qvm2WmRXnMfarHpZ80PlyiIKcUj6PL/eBMCaJo264kGi62jaXk5vjLZ/EBHEzfjdNdHh1HTdLB1OHq+661hz7g8bnkXZjUwu7duCx/hvl0UzOJz66Gkv5zIOJ5tn3PMc8lacuLMPu+fNPytviEBQWvDmT+3iVHry8S8hy37pB3BbLRZpfaJNCn8m7fgePk733Hbo7yi8f2dyfdUHbXUVPTjXb/FgjuuvvKgoKC+43tBNxuv8u9ihDngDc9dxexsyRcRUebXQfG4AjB0a6aRMg9nSUFkrUYRJcIIyFvIJEm4uZQVFRLU4i97i0pd8rYAilxxWqSau0p6hzV5nFi8peYbWvSO6yo2YREkvfaIdYZiTASzqRHXUIKWXWay5C6vYa6rRTVG/mYkH6S1zrnPV3FTTe5iSpsG8H3JP/9CLoVU/rtKLe3z33T5b+HqIKuN3m1qOtKUcNZ67OMhZCgkPrw2NTX5Tbt5fQSs06u9g5V3oGq4/ZyOhmjBdrvIryyOJ58PyH174gMddo2Ys7f5brE5DIzRmhyOlcR+8w+DvZ9T3+ygC7rWuoOqhwHrdYX6eXRAc3+EucjIXhCLwqgitkiTaZB8RZJ4r8Ioa9Yryu6dUPddNRNUsv1XUPXNczmqxyvTjrAU7Gmz/JC7xLRFnGE4PFeF2ktc5IppM3DnRQqPRonLDkMTANc0yHOI9cDBSSOHIXMItNywgiLXsjTDkRRyDdBISNU/7ZN4xZy/qm3+kyiNYOuq8+UQdLOqVknZGItZvrJqtCSFkn1XEojijpL3HlWNKyH/hC9XT+0yxqTXHba6XLp0z3sSvCkNqTId7Zeim0vMeosMYlElYVPxrrExG+a7Bwaj0sptPHFq8ShkldjFZrEE/SjfpTRVYem49/GqkIxFI8Vb4o4/LpNVeYy2ZlSWTXVZnstkm9gW7ftpJk1CG62ZTPtTaEkps6k6+aaxjRM6uyI58ynq+RnyVd1lXr7r9N3SQ0nFHt4Tm0tKHi+sPdAeardR6jSaNew63el3W2fajTPe+O5P7hMrVtwdWzTud2Ft6nroBBwBQUF10VNRbejb3qvPhy6KbtsJLgLKO4oN7Gr7SI4j8vvgetVQ+h9io21qqHLpIMkWKHx1ES5tSYRKnJc1F+iippaefv8m8Rgs1iSyJwFSow5EsFkrW1naPy2CnVBKSST1MvaNVbm+xnJFaaQVB8mEWqnbCriIkktt83aZ8moPVOHM9MHB6gSz5EIQJEshNxWu7m0Rfv27VyXvXyekICOcTw3aZ+4q7QqurOc58LkYdsm50IaxwVK+tn85Txxe+kZu7e0aeU9/ZikMpRzl4wJPyFLK9RdpeUXAmM1oXBg1lWltKEDFi6Pnctz1cHS5fkb0zwCWLt0buOySq9O/xb9eNXZVbTHe1D1+Fk2cGfl3vJkj6pZ51hwkRAcVRXBkZSkZ3Och/VyzuosMY/NvMU5WJ7NqaoeN8xPz7qtqaqI99D1FZUPhOgIscKHntrbTnYEGgIdEc8qE3N1jkZnqZCWhpoOdfLniYMDykQr1UMnukyOieIrInRNMhXKQPYDl5uGSRRn0Zwj5sUqE0Fj8k7UZF32hyqUU6qJqo7SVEw0n6jnxjHPtO5KArmBJos5f0+XKaD0u88tVWrH5fPS7vxmqImjp2asgUoqNTfUe1OZo24wpT8YStOcUt9aVdo4H6EPs0LS5OhM/RxVVtwJmTfOZdOt52YbQA0C4gxTISUm5V0YchCIykzdZ+pZUg815Ux/P49IEaLQ1jJeYraQOG4w1SVug3VReVVTW5rrQg9eP1baJgWZ5sRldb08PyHJrLrudk08Ms5Cud+0zoqahm/nd/E1/pdrnqlkZlXIuIJXEOm63x4P7j5An4y72P7dvCPJ03UX2wayLLNvUbvZzqtAN3dt3zZVcD3Yt3C9993PzRcFBQUvHnPmrEYxg14M7goB97Laf/dgbUT6Fm43axe8PIj26Kbn7iJ2tV3JBO0hdGTXeYHQO0LnsxXPEB3i2rE1GQj5Nj0mJMoaJclEJbciETIzVKEmbi1hrIiKwDfy54Ocn7iyhERQCaw1VUguIXvO0Jhjq/xd3n+/Zcr2JJeP1lL8JJcpyjeJfydx3FYk8tDlNlqV2VMSsSWuHaVtj3Lbz1AS0zF2GSmqtqNcB+k/leZoP4uqUAi0h4w3NfQ5zVR9eJA/d7kMiR/3AN26J0pDIdSEZBPxkajahFy0dTtBXV3avqxRElYIvQaNNYdpm+QlsQVjboe4FrVkor1SI0kZV5Pm8TFQm4Vky5iwDED0uRwPfTVkFJY9bn8FHmJIi/TQVayW83QOkX6+JvSJ9Gnmayof6FrRj0VCP6NuWkKo6X3FbCHx6hyh36NtA7hIVfUjh4DJe2uHz3x4H3yuaiLpZDDWzjEf3FVKzX0munRQEsUU0Nheq+x20tGJGm8gfvrhEd1SD24s02tMZx4UQovJQ936EpUhEqLGZ6WcnOfyGQHRwOkACQHoB1otnZEYW4nrpUq8kKmg2qjLUgw8b6J7jIkPa2RIdVnTUQ0EjRsp1eziWdV7EUeb09kd1DETNRoVTEk06aUq1016RC/cRLJ506u23Ta1z/0vLLeSUOftZrZOQxVCs4mCcPybjZgn8eVUATkd7/HuX4do8tJ/dlZOYwBK/VQL54a0m6agafnjCH1CZW7CuqhU0+LlZhFLfLnRvLoetDVhy3hdHWMiLuXshivt9k1nlp5/tvw72hsQcAIh+t0wT19V03fDgnbkPmB3cZ/aejlexdn67Ng08Be8Ctjl0Sqkk0L3zpZr9LZw2dtoQUHBq4OGhnZk+Lz7uO8E1H1vf4KGKZmGwyhP+oK7iudOwn3hC1/g7/7dv8tbb73F937v9/Jf/Vf/FX/4D//hS8/7n/6n/4kf+qEf4tOf/jT/+l//62uXu2DF2kW6LpnTfSZSwro2cbsYky9N/p7s92MCTuQrx4zJMnLaClXSiWJsiZJXLYl0eoCSQ0IsPUZVW/Ieu87nirtIiT92YupLzkvim9m/LRojTkivD/K5ErdNRl/6wKoAZ/l3q/Z6kOt7Yo4JuSRKrrdz/kJ5S2ilBUosPWFsVQyomu1JPucQJbsweT5FXWVKTDnpR6k7uS4PUfee0sZlPi4Eq5wvbi0lJp64qKzzb8IlzEw/gyoXZU6J61E7xqIeFP7GuvcUN5YRHQ9xtTmbtG2P7ayDKCftby2kAGwRTit1z+mBJqbfooNQE5942OuhytRFN9f2RUdom8wZOdpVTbeGGFNlnesIPrJeKZMa+grnI94H6iY3OubfQk9VQYzQrWs+y4IveWEgU5roO7wRLIVQs3bZ/aBTc0LMuq9qODdkZ5apQ1csDHmT0gRENya0RCQav7A+Bxvsh4tMSIRUakdSlGW6EqVqct0Rd4j62J9qawLVQBNNNWIxu8+0IShjroVAXD1K3snZZMg0VoVVbVXmrFRGQ08Y3ASKQ83K1GP8ApM0hT1uoswRuqwi6fMw+UlfCPEo7kGVzRadnpZaDf0nvTUmHlRZkYjOQI/SetYE0A+1D+YBJyWm2qSzxWnpWLlg66b7HqrhE7ndom90OYKgvP6F4ZMf6rPprnK860CpzOmMmEJzsbEFz08nsQMjl6ntpvnbODky2ld7mdVWpGu0H3rPlnA9KFUMDD11ey/X4/yDmbEvB+Jc1zqbvc32vhjcJ1LqPrX1ctxfV5TnOTB+lbHr+4hvut3kVUDM7yv3FdM37d1Vqb4YbG56276WeRWxxx5nnL3sahQUvDS8agRcQYEl4OC+rjzuPkpMuE08VxLuV3/1V/nJn/xJvvCFL/CDP/iD/OIv/iI/8iM/wm/8xm/wiU984tzznjx5wl/4C3+BP/bH/hjf/OY3b1S26jmSIsc5CGdzVS9JfC953oh9dZX/WbJKyBn5zaqiTkmEiijTBKKkOyOROKIMe0wiU6ax3yykjJjPI9dT+ASLSCKm5D14wTiOmRBpotiT7xWJ5BEXmOI+kpzPEermUI59AKP1nJB10h9C5Ml345HIn0AQ5Zx19wipD3uUcBMytEHJyXk+d5XTg47NjNSnNmbcGUpkSf3J57+HkpcuHxO3lpKH1EXKlfasTBsd8CE23Q2LUlE+v8f4qeBI4yBzokEJPyFRJSaeKCJFZXmAzjUhf3uUEPX5t2Ge5A/ijFdIx0WEVhZOPinr5iERdJ0HHwxJ6tKxUIGriLNMUDkhTcAuwGJ0VC7QtRWzKvDIOb7VQ99VOcZcxLlI31f831zEuYpmni4e5xxdmOHpqas+u8ms6GIiyNLfjtoLFRZHj157eQRqWhw1HVbV1GV5Y4PGmuqoM3GSBk5cQ0rbGtqcsiJAJlp8fvCrPkduIz6nkLo1tBMzv/z1WXApqrJErAmtI4ou2y5xTTh2z6i0oLqhBFgP8eX0fEtwCbFF7ktxWRQHOq6hHgW+1VJF4SY1JZ8rRyW1JXZ0JEJuc+pVTBrJZeycUlugUfkk7fYYPNJPtv1qQhgft3CDUc6Nzlf61+agriLJdJv9XUgdGZMwKBHH+7Qc1kHleIws7FFt3+Xmj/SLtOtq6jRLRY5Ny5crs+yvfjR7Km7DRB2v0OabQHsl4njZTsosPe4vJF0Lnj/slVZwHvRucf8gStpdhNWxv8qGdovx28BNtNp3f6ZfZWvPriIMf20fFBPdsyDd42ysCdiVeVUIuIKCgoJXAXbzx66+de8WSky4TbgY43NbQfzAD/wA3/d938cv/MIvDMe+53u+hx/90R/lZ3/2Z88978/9uT/H7/k9v4eqqvhn/+yfXUsJd3R0xKNHj/jEk/8Hpwevc3JySN/NCMHRvS/yKqekmijLxHWkkDdyfZ+ghJ0o5Mif1yghBJtW4BN0Q/Bxzk8UVXPzfT05T+KszUjW6SPSDBRlm1WjCZFGLmsPXWPIyE7rFVFVlsRbi6i7Sonf5nI9hRSTfpEylvk3UXEJ8Shkj0PVW+S0oqiz0gipuzAY0h81qhTzqBrOElxCTIpizebbo64gxbWkuB7dtg6zYUyE1LOMifwuf6VOQm5Kn8hnZRs2neGK+EpUdYco+bsw6YRYO2+zoagNpb8Eos6T+jb592l7KvPdATHqnHBR58ncPOJcD7McZ653ED1uvsL5dLrzqn4bVHFVj/eRmFfF6ZhqvWbzFufjEKYRkpJOvscYiTFT6zFQVx2NS6o0XAUuUVfNlsnuWA/eUFXVlKK62QgZSoBoTDRtterDkiNMoTPSxJ1OZ1BKa2gPcXKsR2LdJTVaR21MXW5IFWnOIVvckIdtr80hjAhA/SUy3a/vJvWVKSFuJ4VilFSVkRTLZ43gliiipFWMw2/puJJs2jdTlVaY/K7jtK2nbbpxm1T1N6YJlSLdNL+NLzbr3HETEXFGOS5jmpd+r85VpCmhq/W/DEISCaV7fj01R22XEs1XQXpwKAV4HWifjpWXN8VYnXe7ZmFbN6nzRXPgRSBde/5ac6PgNuGps7K64DLIbL1PBu/UZnkx3B2iSpDeeHZLUdVvPG13a8xkSaXjtlvtuwib16McvT/3pOcBia9dUFCwm3jIQ45Gu/lvhldhk0rBqw1rAzvvqbQ6WvFzj36eJ0+e8PDhwxdYuwJQXua3Knh4w1eHowif6tm5MXxu5OJ6veYrX/kKP/3TPz06/sM//MN8+ctfPve8f/gP/yG/+Zu/yX/9X//X/J2/83duXP4q1qzbOXUTqJsly6MDjZvVxqT8WaIkg6jexIbv0Jhh2zZ7iWpOIMSW5HOGxvJ6PDkvSW8SwSJxz8T14BGqfhKSb6rQEyWbxG2TUXQksm8fjWm2bcJLDDKpzyKf9x5KFO2b9kn/yHFp71mut8SDs3HfavNdCLwD1KWkrAmFRHT5d6sqEy+F0hdC9gmxJ7Ho5HgDfDinP0ZDb9myhCDbNqaWvxGFoIyVJdDEXaQQpTKW4jLUqvmFOJSxEnWekLjSTjtW0gfr/G8PdevZMibpzlCCTfKeqibFXaXUWURdMCZtIaveJD+X00UlPyMpptxQ1+xG8GxB9H0i7nI8uhjSxIkh0gcP9ZoQ8mD0kcaJ3NSxXkEza/E+ZjLPEUJFjJGqCoTgidETI3jv6fqa3gWqKlC5rOOKnrUDHzPR4ZL5vGNBR0eFjQcmThQdVXZsOCavpjuIvRmeijA4iqszudZm0kmHMFENQpKISi0Ov8YRjZXM/D3dQF6pE8MweKRNOfWGOxVqL2nFJNqYtsXnmHchmx8SCSI5uOFciXcnrVYlXiC5Au3ROG4YvVfI9Gc1mnSiaBNlmKUzw8Yyfho7LLVTEUd/pb5alvwaJiaC1K9KkKlzTFX7heGYEFlTanKT9tNSZUb1Q93dllTjVpyHsf5vHHfuvBdM0XzH/P/zdoS5yec4avPVUed5pGTE1czN2pJ0S9UdbHbvw9WhdKXoIm/PLDQdszTGGu/xZSDtptB7CPkesYvm/ruJQsBdDXq/vI8uKafPpN3AZVs8XkXok2+32iXQ7WGO+3gtKgEHu3pdvgjI/dyuhspbR0HB7uI2CDiQdWlBwW1hOp/CxhO+oOBVwnMj4d599136vufNN98cHX/zzTd5++23t57zb//tv+Wnf/qn+dKXvkRdX61qq9WK1UrZsKMjeXh4ah9Zd8nYHLosA4pOyZOnJiNLTIG6iZT3eImrBkrwCBFlZTDLLfnKP+vBYZn/CWEi1Y4k0i6gccqGxqIEj/xu85VyTknEjMRAE1LuGCVYMlFCl4+Lmk7euJ+i8cxE7eby8X2UKHqKklD2LijKsyf53CaXc4i6dTxFlVoyHkIkiapM1IRCOoISW7Z/1iZPO3WkH4QMlGPialMIPLtGFYJMxl2IOImJZ2MKnqLx7Cw5JggoiSpjJ3NGyDPpL+m/E8Z9KXHsbNw5q2pboy5NF+hmT6mrKO486pRXxnM1KUvyHUm7HJxFVd1F4KhOMeT2SNcUDtaZkOt9IuOqmAnQVEC/mpnvgRgTK+gchL6iy+4xY4S66XEuVbLLqkznwPlA6MFXWXkVIj7/hnPEmJy2BVdlsznIhOlJarNqUPMIueTzUKm6JpVstWuRJru1TNOjHkixHnEfGajpzDRKv9WjpasQBmOSiaGeVfaE63Fmf0+FjX+WLnaJhZfitWmZU1WWxjMTd5BCRCplo6SbpXH6oY1xYIElflmd478JHQI9dXaZ50wOZEefFlKPsavHMExYhxJtdjedjowQaJvkVNoFIbyz6tjEEGX7xuW+STdR+SUM+UrO1TCT3KgeOh7ax7Ls0NnhTcopxi22eQGjsdC6b89pGnlw+z4Dmz61Rl2CXsdMJQTezUwxY0Jw7Lj1OvnpdSv/v31SapzTXTI9hTzX75dpteCuI91pRQV3l66Y5wt5kp7f3vFz41WCPC93CedtrtkFjLe33D/11y7O15cFuZ9f9PZZUFBQUFDw/JDeaqwHo4JXC3WtWqhrnytCpx3Dc3ez6dzEiBXjxjGAvu/583/+z/O3//bf5vf+3t975fx/9md/lr/9t//2toIJUSVP9d4ykQCgZNs+ifhZowRJZ/7a9bSQVKJOAnVFKQRKYOyeEsbknlXZrdB1UT9JL2VJbLgFiWSx7gMl7QpVvwnZI8Rck9slrjeFRJFYaEJ0SWw3u06TdqxQBZsQWZU5V9KdAa8xdsMoccyEaIskUm6OkkPWxiyuKJfmuFx4QtR5NDbcGYkYcya/GWP3nhFVntlV9wf5r+RZ5fyEsLSkluR/YvpBCDqJEScx3hyqXJOyhBgVF6Sgarp91AIv6YXAlOMBjcfnc5tlzMbcgg2CsPmUkvkh+QiRbAk9mTsSy1AIu94p+TcwLA7WcbK+d9Bl2WAbk9vKuk8Ku+Ahk2dET7ecUS/Shdd3Nc614BztuiL0nnrWKcEXs8m/Tx3jY0td93TdjHbtcS5QVS11HfDZLWYAcGPNVU+TCSeGzqtp6XLwwETShZFYUpAum5iNG+Q4cQx5BSrWVJmsEyqnwmfdTxqCNHGseUTzsHGu1AGjNymsMk8vn5CJjLT7d1ML5bKKzWqHJE6eUhdpythzfc5Zbjup7tEYV+WSUo2Ox0ZyS7SnBs3VfgdR8Wkdks5OW6rEyiaRJVTiVFVnSazeGGMSbSR9ZgW3nmqU/1QhJoSskmJqblXl4fQilnM1XpzUpM+jqxSikIpTN43O5GNfPQPb3DlKZEJ79mVGRh0Dl2fJRbD1Seq/Z4PO+kiVdZ83MYum60xJ3HTMEsTPDqWu/WQ8XzzGdKUbzeaCgruA61PquwCfN3FsJz1eTcrn1az15bDvIrsJfa+6T9Doys9nW859w/j6f/X7saamK6r2goKCglcKzx7GouBloqmgueErRLOjQ//cSLjXX3+dqqo2VG/vvPPOhjoO4OnTp/yrf/Wv+OpXv8pf+2t/DYAQAjFG6rrmv/vv/jv+6B/9oxvnfe5zn+Onfuqnhu9HR0d8/OMfByKzZk0fPO26Zr1uYL6E4wXgNeaZuHcEdTkoGweFsJLvosyyk0He763aSdY9LYl8setxUVYFkvpN4oXVuS5nqFrNkmQCe1zK8LmcKdHE5DwhWGBMFp7lMg5QF52WHXh/S3vlQpLYaX3um9kknXyW2HuiiBPCrCIRURLjTtol5JS4YBTF4CLXfZF/O0YVcZLeElA2RltHGqND87uQlDIOq1yvmfm9Qcf3wBy36ztxuSnHOsbkqozvnmmLEFySvxCCQnTOzPmWrBSy7yz3g+Qt5KEwPF3um6k9SPpEYOeMzDUh6abxCleoC0sh54QktXY36Ze2AvE6GT2ESIo154jO0y4zQ+ocHRACECvafka7CtTNmnomseEk40joatZd6jhfRWKsCKGi6wLNbD0QcbWPQ71ibmfvKmpxYYmjGyYI2SVkz5yWgKPLNwVPi8dTmUVoh6cxRn8hVWRqkL/1meRRU3kis4T+kImbWieTQhV1AZ8pt1TfgB9+dXT4/LvP+QghJznLp5hrkdxRqltJNUaNJ4on4DOh1WeCRtxRjslDofeUqpJxEposEXVj93lxONveUKw7TaFR1Anf+PmdyBclg6xZLYxKU1J1SvNENl1muGxGjcOtH4TGk5qQS5lGyZm+YURCZrmV5PJbWyQk2vZ3FDf6vD2dJSDHcequshd+0yXoRSaXpPt0xphhb/fXe89SEthqZ67/riZzOOWnbbittzepp6oA/eTXF4+xNnNKvL76JrOCVwnTrQsFrzbGEUd3A3HydxeRnoTWE8R9g3VrUu5D14V9l7hu5OC7jkLAFRTcHkrct4IXhfI0f7XxzEq4HcRzI+Fmsxmf+cxn+OIXv8if/tN/ejj+xS9+kT/1p/7URvqHDx/yb/7Nvxkd+8IXvsB//9//9/w3/81/w6c+9amt5cznc+bz+cbxhjWtD+ztnbE8+RAArorEeQfrWWr5MeN41VZRNBAHJLJDiBMhhaxLwiNzno2jtmRMjFly68jkYYlAQYu6bRQ3h46kiBLCyE3yAFXACYQY6idpVmicMSGNvoWSWA8ZK9YsaWTfycWFpLR9z3wX5ZeQStLn5Hykr4SslLhj0nf7jNePLaoShERSPUHj1DmU0LJk2buMybcjxmo+saZKvwkJKv1mXZE+Nn0o5Qr6nN8xqoYTyHxaMXYrKUSiuMS0qx+J9+cZk5uiaoz53IYxAWZjE4q7TCHjZP4JUSnkonVPCjr2226YUzetQvA6NBahqEOrfLACnIO2yXfTmJRxDvAR6j4rWexj1tO1C2JsaWYtfXBUuU9FTBujI/QyISO+Sqq64APeO1br5O6yqrp8kocIXYxULhKdTyRJDPn3RG+sRvQVBBpSHLikpUtDUWVxYFK9JeO8y2SGoKFFlEad0W5VVLR5SPSCUtOBxosD0a31+XXXXhRVvkQlPxvrLRFf0gYhvuJADKWWVXmg44goYkI2iEtGbZslpGShLvouKVG0bBEhsVTtZEkbeRAlwlGdP0pevWn19EVMY+851D2fKvJSPeT3aBRX0oIxRWrJNokpJ8c2zZFa2/TNEli2pi6PqdUqjok1UK+4llQ5DxcTZRKLT1NeTvBpbDu5jTjzb3sdxNlqGCnErgdprY0fGKlvlJ+9Eer4bfb0TSEzQ+Y3wzXzchYI6grVKuLG10dZuBQ8fySDbWW+3b+Zt2utjcOL3m5gl8mFKXa3Zdsxfdu56VaeAnmTLo6/CgoKLsbzIuD22APgbGT0K7hfmCpeCgp2C8/VHeVP/dRP8eM//uN8//d/P5/97Gf5pV/6Jb72ta/xEz/xE0BSsX3961/nl3/5l/He8+lPf3p0/kc/+lEWi8XG8avgIU95j9cBx2tV5L1MosS+SteyxPwS5ZAQMT1KPAnhtkSJMkfqNSFvLNEm7iMtmecmv61RMk8gBJ2sdy1RJ+cKuSFkkHWhKYSSWE5FwScEi5QhbRQ1lmfsblFcJHoSudUwJmNEzeZILhFhHEdP3C7KrLKxzaQdEsvOnvOYsfU5ixU5yt+FPLPKspXJT9LJuqsmkYNi3Rd3i0IYSbw/UY8JiRRJZFdPUv/Jb6BjL3Vr0NhutfndkrH7jO1QDu136XNpt6j0hA8Q8q01eYibyIjGlBPiT4ha+Q3zXeLlWamJcDB27shvEXX5KTHk7FpMPts6W+JX+iy6PK8dQ+gtUc41XusH0NWwqmHeJpLOzJt+1dCvZjgf6OsO73vqWdYSBY+rDO0Roe9rwtqBi1SVLMk93vd4nymDWBMQB3MA1kWlqF2s+Sl1bDpjPaSN1MNlqDSHGOejiT+XcguEQUvW0xjzveijhLTqs1vMlGdHjaMy+UmdlJTrqQc9nTigFNpN9VEuE12YsoRgEgJRDaj90B5l/kN2p6kqOv0kpOKYwpQxSCRlHGqmJrFx9DPJxdJZQgKOVX76WiakW8U0xpj8btsr58uNJQ55OXOZVKN6plvIOAaXy+nkbNErphm3GfcvHT3fjWMc5sg4TKXWV/pDXWZOw1m6LWekdOO+22ZaiaiDTclPVZoXmWOUDBpTaVc14bjJ3/Er983MQEqYyTViSeCbY1wbdc8qpb1oKD081umNH7/FlFbwPDG+M75I4mb8JHhZuA/EzquO8VNmN8dJ7vX3dS6mpcbFW5QKLoZsmrsKDjnkeNhdW1BQUPDsuAr5ZrfsFuwa1LLhLw2VUfAqoKnU9Hvtc3f0Mn+uJNyP/diP8d577/EzP/MzvPXWW3z605/m137t1/jkJz8JwFtvvcXXvva151J2j+MhTzntF3zQdLDOZujZGk5n0PpECGyz6Do09psl1dRer8dkQnXmXHEpKOonsblG1G2kkEWRRFb15pgQHFY9dcZYESWkmZQvdsCVyc+qp4T4q/Lvcp4o8BYowSbnCJHnTTohj47QGHM2L+kTicMmdXhq6iuuPoXoVOt7wokpd05S6DW5b/bQManMZ+sXTvpN6iREZE9S+AmZ6UxaO6bOfLYqOFvHfdQNpPzeMCbDxHXpDD52CG8LEbaapBM3lPadw5PIRynzMeP1pFy5csy6Pa1N/kzyFVJSzpO6W2WnHXdxsdrk70JiTslXOx424JnkY4np3pk6RC0XIHro+3FMvZgGNvae6HpiP8P5NTFCDI6aHl9BDNB3DZFEvoXOE/pA3XQpRlyfYsc5B31IFfT1hBCJiRCKzg+XrhuIs9Thyb1kT8RRYaNPSBo/0EqiOUvdlJjTFDnODqZQPZaA8HSjeHIaaa3O2iyNpSXDKZSiklip5kJeyc0poLvXlGCRugSTh/rgtnSkxDlzuXbqiLIjUI/oNdsvmN7SdruhN6NpiyMO+YRJ+4R6DIOxKQ6UmZw71oEJyail2r/VUL6QNYmwCrnHxvSXve2PTT1jE58oO8XFoyW2NutgS/CD+s6StM7kNY6nF0eehjcJFzsvZa4p1aj1k7RTkqmiJwyOP/1GinHbLTX7LD7cpXXVZQkvzcU+1DaPPjv02gR4g4/zLr99a7lfHd7URcdZrpp0p1OitCxqCm4Lsk3h5SgnXj4Bl66t7XfGVxVjGn83IPFYX/6MuX3YbSDy5nJ/cT8JyGeFrgoEl/dhIeCeHwrJ8Pxx3lqgbFy7+yjXxi5Cr7zL49QXvFKwfMF1saMTwcUYd2o9cnR0xKNHj/jfPvkXnD78COuw4O2jb6Pvkyk8Bkf3zj6ssiLuCRoTbAm8h8Y3s24WJTabEDOnKKEihJgQE5ZYk/TichGUeBISyLqiFKJqmb87VA0FY/WTWIMXKEkn50iee6hiSX4T8kwmtcRcWzC+SCxRFhmryASvTdown+Qtn4X0EpWhxIMTt5szlJiRPrP2f8ln7AtPfxfiydYdlAiyaR1KcDmUBBMVobTbxj+TvMQqLHVbmPrKdyEMYUxeigLQ5lOZ32RsJaiYkHqWdBx7IkwQotMzbq+4oJxasWvUVWiPuvCcKugak17aIL8B2VvAOF85Tz6v0bGeil0qoItafwe4CPOQz83uKueGIXQO5JZV94gLSVet8XJNxUzZVAHnHFXdUdUajNH5SOhrfNVT1112bZnMh31I94WqXlMTM40QmLuULuUgqpremL07ZltNO/0w5ay+SF4uquxiUtqnpJ6awKxCzuU8U/dZGieMyB5nLiDJozLl2F0HqneTC1RfgOpzXe3F0dFqZNRP9fXZ2G/PERWgxribPpPD4OBQy0nHN81KY+rQml3EKKwlSDQ9oexc/j7ObexCMgy9ommlz8Jwy7jYfWQc+kd/7zONmVh8qff2d4wpiaRzZDPNeEfDxSbhcR97dK6cn16Z9mnfbYf0t639Vc1j4/qlsb8Ns7DuBng2cu+yMm6rvreBbT3fT8jXgoLrweqS9Wny/K6qu4h0ZckTe3dIuF10Raev6bs7R9M1aV+27wfsE062Pt23Prgpxlvitr9pFxS86piupHSzaTKEWNPS5gZORTCpputH+VuunoIXgTd4g2/xrZddjVvE1DZ1FTvD5Vgdrfi5Rz/PkydPePjw4S3kWHAdCC/z5GPw8IYDehTg0dvs3Bg+VyXcy0bHnOgrDhbHHJ08YtglvsqzQMgYiWvWkxROomASyFN4RiKgRM1m43tZ8kOIJjlXSCpZKfTmfEkjLgmFXLMkkpU5TDedSdwvaxGOqJpJ4rHZt4/jnHYxqYONdedIcdWswk9IG5vfB6at4rZQiCfpw8N8zhlqxw1oXDMhMSXd1AZ/hhJ04vpR3GqKykqOzfLnPv8+tcwLGbhEScWVSSu/i5vF09wPukJRYhLTDilDlGNW+SXx4ST2mpw7xE0jzUEhIqWu4nJz6hYUxvNO3Gzac505d8oSdKZuQjZKP9X5nM4cl3wr83eGEqhy7Yhr05UpQ+oncfZEdSj16B3Gn2P6ofdm/sekYvVBCb4+T8BaJ2vsZ8kBo+tw2eVk33u8h7D24CKx9zivEvfQV/SdZzbvjDoudXAINd3w1bOMnhlt/i5UmlBJjqSmWo5eGBItkpRo4xdzR6AeyKCabiDiJD9rJpLlQapOxI8koOmXniqbAcUEkUoUEizgWOBos8YrU5cIu67KMnGAmXJJQ6zkz7h1ti/UPaLUNLm+7Aey0A0XhRscWmJikCX4HP9NY8uprm7qUlHPioPizy6GNLabpFFjjcYLs9z12IWk0G5xyF8daap6b7pA22a61DEMaHy9mHtXYumpKtHOlfHyUeO26VH9TVEN6bbDLjUV24lEwSYFerGZy+f+EOesQqWl3jufuGRyNI1Wz20QWzradrbeLqyaUl2tvjyT4LbSfZ7v/Wgm3GezpRsUvgWXYapbjub/9wW6IefiO9mrBiUWd6M9AjWe7ibUfLW7bbwIsg1pl0nW24aSbvYedj/nT8FuY0qujZXrNoyBvseox5H0TcIpkM+Pw4p9TP4HYwCcxkAvKLgt7BYBB2O7UuSGfE1BwSuDnSXhlsyS+TfCvFlT1T0xONqjQyVqhJACsUWnv6IgskQaaHw4OWaJFlGviTVXyCsheSzR4Bi785N6dJM0QswJ8SP/hMggf1+buktdLEmzYhxjTH6zCjzdJqr5HLGJPcZBiKw3P+tCU9oQSWrDmrG7SCHibHufmPodogotm7eoyaSPhZwT8k9cbfaoG8Y9VFEmdbCx+VamjvJetm/yXObP09ho0s/ym9SrZXxlSby4LtfpwORjSSohsiQvq6S04wlpDu+ZY2cmP1HQSV0CqvaLjN2GSr4SY06IYGmrJUU7U4YcFxWltF3GXtxhCqkpn0XNF81naYMlGKX/OpfnWB6YPkCTE/Y+EXERPdmluHDORYiO0KcLaXXmqapIPXqqO2KoWZ3V+KqlquOgigvB4Ud3x4o+RiqnkzZGP6jjwLFmjick95iZZklaoFR3T6Ax0SqElGhp8HQjBVg77EdNjhaFxPIErCNCXVi44ZdUdo2QhEKHHdDwLivEFaFEP6sGckny1BchJZt6HP3osneIE8oIpu5yLkPadJYfai5MvMv16EZtkiCNEjEv7cqvBhVbql8/MbVoLLcxTz0m4iRumZBh6fKyATm7c82pmr/tH+sQQ2jQcRQyO7mFlhk7uUy9I4s4Rz+Qg+O+3KyVnQNu8ouUdTERJ+1Qwm67y8kpKaaRxy4j4jZfpW3swatCiN1UQxvG9fpIYy2Upx9mym1CaAmhzkFp4Je9IB+Pmqgn/Ohhfv9QCLirI258un/G2/GVvGttv3ukYkVDP9pBdnXIs+K8bSnpLejVv/9JPN27NnZXgY1l+iy5FFwdY/8RxeRZ8OrCroXksw1koOursGVNInm4YaPn2JeIXUXqp+1xN8XTirzxT9eqBQUFiqmlQq6++7Wp715AbOw3wY4uz3eWhBODLUDvKg4fHNP1FeuTBXGvSi1furHSyxITlmATtY+QM09Qsk2e+OLS0uYlrgWnBJz9i8kLlNSxxAyo+g6TToNWbVfVSRlClkzQ1NCuN49fiCmBJsQKjBVV4u6QXLZVpkFSfok7xjnaVzIjhRx9wPjNSsqLpD6xZJa405yupc9Qd53znLYjjSWmLZYQEjWd/C5qRnFjaSGuSsX1p1ULWhJU+usEJclk/snvK5OHEI6gakchx1a5jBrtOyHvxJ2lHfcZY6WdbhvVNrPluxBiklfI9bIuUmVM16bOUr+Q2+ty+WJDsdfBIeMbs00j16TLJ3ROXWx2PinporKrsQJXB6ILuIpMyAHR5RhyKd+uTel9Vs2FkDq1qlOlYvTEOL7r99ENRg7nIrXwfqa6gYqWSG0uPtlfp7HLdLddor8cgRkhu6ZsCESanKpH3FGKfsjns1oqo07TRUKf2U35TUr8BklF56hxmWqREmr6wUFdPdrdl+quxJ5VcMVMKcqypssluaFPhKpTlZoQb5YAqgaeWRcrNS2q5Et9lb73ud5WOSaMcRz61jFWxrnJ/9NYpBySKtHn8fOGKJUWSD/2uXe13G0s8jhG2PhG7HNfTd8oZKKncfZDz26a1IRs1VHYLEm/RUNdKgl0HmThebGKLuU9XAuoPvHqLg5ViSW4uulQ9VsBdVdxPRpgPFbTx/Gzm/I2F+ZCPupV/7I0Czofg7niAhXqmlZSjM8ouN9Qd347uiK6Nl5NwuM8SGs8sr3k7hjme1rm7LEaBTm+HbzKBJx9Vb9L43VdXJeAS8tmj7gdl3fLgqshXesvX6dfUHBTbFthybp1HCDBmXPOc1Wr67y45fi2tOP7TTS/anxxeZq6UYpyxRXcTXwXv5vf5N9d+3l8U9hQH2LOLk+kHUUh4TawsyRcReQhT3jKQ7wLBCoqH4hdnQmBADE/jOckImVFIjs6klrpiM2nvLqQVhJM1F8NSlR05h+MFV02zlpnjnUoaWLJFlFESV0sOQTjJ3ucHh/vC7Jm7na97UQ5ZuV25nY4JRXZ8l3aWU++CxnVoqSV7QNxbyikTEBVaOLOUdxICgG1rV/mjAks27wzk/4xiQybmTTypvQ0Hztg3C2nJPJOXEDaLlyj8faWqK1KOCLpRmmXuD+VtgqxVZt01n2lxG+T+bI2aT3jmHqnkzLXJv0hm/Na1JYy7ELseZO39I+46rSKNSb52Tlujwms68ynqDtNUWtO3YPO5LuDZcxt9en8GeByg/qG6AKESGw68IHYpo4JPtKtA84HnIv4KhCjw7lIDJ52tUfoO+pZRwwV7TpS1R1ERwgRXyUKK8ZI6Dy9D8yqVqegE9N6gwPWg5tJ0YylndhVVltZ77FimvfErIJTl5DyMmRVXOLiMQx0hJBfuqzWrleCJ1BRZabf7j5aU2eHHJ4WN8Sqk9/7ge1VZ5j2VSlRXnVuhSjrwpDeTo3p65XSKJGxcistXqSP4pA2EXcdbutyyUbG2763WolRPSIuIbWf5ZdxGWni29uspHaTdtlxkNpZKlDT2XM3Xz2nCj9M3XvzZjKOpScjb2P7jPtCZoW2UtscM1l4fqQj3RlhjTiqLruMYNKRiqMjV3n5tn3lTC2nTvKuBluL6WL+9mBzE9WjG5kKXvyiY7O1McfAxByBNK7Ta63gfiGN/tQ39f2FfdbsCl7evehquAkBpyTVdZ8Mdx/yHJGnyX25Q0fzqZBv14MumYujvIK7i8vuZbqq0XWHzOmXQyzb9WQ1Oi6bBex6ypqbxmcXFLw8/A5f53/Pn+cf8395IeXZa2D33tAKRhABRcGAnSXhILJiD+9gnyVnwPH6AIIxCdfAh0hkmxj/hTRrgIckgkDUaZGxUskqkYSIcyjBYi15lhgTN4hCXlkCTu5GolDrzHli176k3ePHu8j6YDPg2rY0AisHrCbnbu472nrrFOuxZDVdP6/YdJso6UQdJ3H3HBpzTFSBQihJVSyZJ4ST8I5CGLWT76ek8TpA48oJ+SR1tATUGh03IYeECKoZuxSVNJGkBhOCzsaCO83p7I1J8vDoPBBl4ZP8XWK9WeJRFHa2XFsPyfs4p+tRAlLaJXNYjssUsJws6BhZstWhcf+kTfJPxlaIQevzThipOMnH7poQpSdA5fL1FlPaFqhd+k7MceYcuIYRYgSfqQgfISYez87dvkuNqepIjI6uVYY29BG/aOn7NFh9qFjFmNR0ztHHTEW5nlMHPpNxiZQQssybq0UHKZjvLhu8hZwZh6Z1dMzos+osbTfwNFk+aMmxgLi0DNg2xuGfN4ZER6BFdvB1SLQyOQOEyOvN8ThSq/mhRJfN9qLiU1pxGoFiTF2F0fIl6Qb7oVWqbgahXMbklKjMpKQ49D+j8xidJ2VNyTAlOoUITP3vR73ZDz05htCHm+qzOPq0SbLBNBxxSrPNbeKYwJz+Hk3NrZkumBHcJEdtr10WV+68X6P5//mp+qHlYVTb6xBy1o2FLsOvAyHFpH+j6dXnQcxZg4HMxau49nxecJPP6VmvIyiEryoXnx9Z+bwwfSAWXIZpb+3oRsRrYduGjlflCrgMYryMw/341Sc2xCV4wm6Mk0A3JdltSvcNL8PY/upjTAUUFNxNyDrVrqM0crlNc9eJeH3XV58wNpCBpCooeLlYsnxBBJyu1i/a/HU7bqoLCu4mdpaEm7Gk4pRjDglU+Njh3RpyrChw6WkoLhS3udL3aPw4cTW4bc1tFW9iw5qSMZhze8bkmo3TZtPCpivLc+9Fut9z7NtSFmfCSI2jOm3KlWxFLREnMqwpJBCeMEyCPhGeYWzYHZUjhNC0P5do7D0bW+wYjSMmLjefoPHbZHxEJSbjJ+sN66bRVqsHPsh57zN2EWlJM4HMF5k/Umch4qTcaRw7icU2tWBK/af9YOOpnaJqMNi0kazMOfuodd+2Q8rrSOTyzPSJnCvuPG2cQSEvZyiJ5kwamQLTXQ5WQWj7XaaWxKGTa0BgyUrb/4MaTvLKZBwxqxBdIuNiLixkhnKok8tz0kPd0kcIMV0fVUX2eOnou4a+66mbgPOR0IOvUsVXS0/daH4hNsTQU2XXl5GKdUyx59wQPy4RaaIOWjMf6CVxOyganojLnKWeW+WYaTr0SWtn6ZMeNyihxMO2z8uWNKxKbaRUnjH5o37sUxn6WuToR3lbykC1PJZAEecC6jO3GvKrBt44XUbjwNYOTz/K0UbvqjP5qATeNOaZ3O2EDJScw3DeZnrtsentVf36B6xDE3HAKFq7pEgbK4UsuajkWD/8MiZWg6GjyK0eQ1wHjt0F2jr1mRCd3s0tsTiOGXOejmN89qay8nLY+lmPxZupdF6k0UnUqfSGRDq8uCRbs7DxpLl6vXXejqmmsJHXsy+WJQd1LSol3I3dgNMZtp1+2EYg302URdxVsHnvKD0nUNLDvjfvBtSgKc+dV3+7qr4H7CbkPS7hfpFR96eltwu7LEwoPVlwd2Df3eUpK9/sm/2rvyFI1+8S7qFciQX3B3It95e+uRQCbocgtuibYEdvkDtLwjlgkRm2JzykcoHQ1dSHp4TVjLCs4cQn470lVRYkskfc81mFmnBNhyTSJjJWRkXzD/NXlHNiEbfb7DvzF3N8SkzY/IAxQWYNzjaxJeGEAepQ1sShfgDH8vkxpCzpLGGVRPYkLI01t0aT3kqatrAy26ymAuv2U6rSmLRLVDUlxTQmreUkJV8hyETJKL8JOSXEX0uaCza+nXSjjbEmZQhxKK4znclX3FoKbBcvUbeUtmumGHsTVVjLWWvaIGyFuAIVIktUm0K4WbVahxJjQmTW5jtoXwsRZ4nBbSTcdMjlcw8TRkbbIJ8tWb1mTMQN5eU51EZzGThY+6R4E8VcBHFbGbs0iWLX45qOtk9uKau6x3uAaogbB5G+S4X6yhP67KbSpVdpPxmTEGqi74ZhSd1Q05MiuTWII7o6N1UZVSV8xL2edQU5dt9nuzJQmVuIG3YLVjmKXDeQGUlnp+cJQdTlKTBlS1PdWyIN7Yj0sZ8TmdATB7ovxZ8LWZsm9E+PpRlirnmc5OgzyaWEkU5xn8k9N7rDueGflOSGmjGcpySc0GgayywM/SG0otQw0ZmqREtlNrnWXS5N6VSP1Z9pPDZVn4lbUnuRV6ZGStpp6+VInUsJQy8pmeawLkvlkhob1scUXr9FgTa++0ur1QTNRppNKM0n5VyFXBIiWhj9RGqH3McXnTeubxj1LHkmXT3Q81g3qZH3pI63HWjd9npEFHlweT+/OKSaBCSwvcxKiVDZbd3HVPBqQRwJp3kosTeLyzfQDSgJu9UfavCcvoy/mkjP2Fdje8BNoK/V9++OO363KLg+dve6KHg1Yde01pww3ay4W/BMAyPcv7v5/cWH+RDv88HLrsZLQLraqy0bhgt2HIWE28DOknDz7OuwoWWfU57yAO/AVxG/v6KjJ5zsa3wsq4h7gJJrT1ACYCowW6Pu+KZxzYQksK4Fp/HhxP1lNHlY8u3c7T7C5mESCiMhDEuYpLeBxewrz9S/Yof6fZR8hH0Rt3bWN6Dkaf0XWmZL6mXLlIBqEx+FcctVJkSQtX1YN56SrcSqswRRYBzbbUqkbVMgSpUk7Sr/22McA07mw5xNW4yQS0J0yTyRbpX6SdpAcok6R7vKm3Okq6wyTiDdvpf/yhxb5/yEsJTjLRpnTshlmUozc9xyqcpGaB3sd0kj7imVgdD00kdCotpybew6yXvFmPyM5vgMnUKYNDgTqy8mt5S9S8SbjxBiUsqNGJaK2EbwjugghkiIAefJyjgtwPlsnozQd6mzfBXwFfR9j3OOkJWf67bG+0BTZ8IopvwiNSGTWUKwOboR9TWN8WF1V/LJRkoTAqbL7LPEZAv5Fb8y5jV57U/mVjGpg6NGNW1poCz9AtDmgavpB6rEUl3W6WNEglEnd5k9PYGkcFLyKfWC3ekk+akqL332G2nk0q1MHYUomkZnI9dBaSY36ATjJJ0QinHoK597bhwlT87TGHG2xlJPrZGWIL/0G60Uk7c677Q5jsslE25uaJ0l2qK54DS3RJmMb1dCSaZ8kkJzm3nLPg8uWyxuulCVnrj8pdtenAp73mVlxw3XyTFfERe51ryoPvYvMMzt5wGHxOcTVaNt78t/D/VEpiRlun/EHOuS0a9umH1lwXX3ML5K0qdxDMnxnf5+w16Nu9Ef9npNm2R0A0TB3YTMwLTMvH/Xpry96ltWwVUxdoBX+q7g7kDWtPLO+HLiur1oSKS48apn11tdwD0l4BJEy1rm+T2DCD4KBuwsCVcRqFlxwn5Wg0S86wkh0q1n4Dw87OCsTkRNNZyYnoQNavCX+FfCWZ0yVhLJeZZw20cVdWN7rX7uGZNAltg4F5HtsjnxjWgf49Ko83xd2u+W5TlF/RSK9EhYrXpLHtIx4rsTUgeKD0MJfCa+IUEZR2vUOMdPvbi0nHKL2xBIsdEkVptASB0h3yyhA2Pl2Bkac01whJJje2jsuB5Vbh1OzhFCT44JQSgx7aRNUtYZSmbJUCwZK/uExJM8pbulq+W4z7+Jy0hLqm3zDyfE4dTSHU0dLLEs4kfrvUhIzW0BlSzR1jCOswfCSOlUtE9nZ8qXfrfEop+cEwBvMljL/InqvnIRcpke+jwxZi19mzvb9TgficGDCzSzHheZxJDzhB5CFfDeE2P6LcaIc54QPF3fJ4Wd1+tSVFCi2Gpx1LS52ySwtFBA6dpoMlEnL+1K7CTTt1AsYjjVGHTSzVV+2ZeXH58NGWLyd9lpZFJRJaqtRX3vK93W5cGrMhlnVV9TsknJKdVz6bCH3B8p+lxtyMjx3UWVVIGKnnqgxPzohqCmKUHI5Wvtha5Lyj05LnSkJb2UmNL81dWn9omSYQHVMW4SWZt95IZWjV9GNZadtEFLs3SGG6XfzCPmHtW0IZfnTTomn637yXEKcauqikCLTXJvs01XN+/qjShOxvR65ytuz+yko3M1YvAmsHrMRCPaW7imetmQeaWbbdwwP8Ya0YK7BdnCMb5X2av07lC/Lwv6zNpNo38YqR1hF9oXR592l/7flfG6Kuxo3re2Pyv0fbb0WcHLhfreSN/GawLxgHGf5qknDOvyOLxJFxTsKnSTdkHB/cbOknCgD/LjeAjOcbh3wnK9oFsvMr/Uwzp3gbjVkxXcCYmkCYzdF0Ii2M5Qd4aizJIYW5ZgsRv7rQdJGFvTbJy4S1o1/iwZi/GkN8dFgiVG4jVjf5iWERQWQ9gbqbio3sTAvW1BK+zM0uQhndKiMi1hwlpTRmPqYRlQawDKZVqrrxBG1gpqrdU25J3YcJdovDbpMlFAiuLuMH8/zulENRZMfmeMXVYKsfUYeMimENCq01w+V4ZASDw5XqPkoJQpKj/QeGm23Zg6Yo7b4bWhTEQJWKOeReWcqeVd+nLq+bTbcq7UQdpv7y6WnJu6WbVuQ+3bp111C9loSW+ph62zMEJCSsrxDnBO8z+toIomzKGD1kMdE1lHRRzcmHr6LhBcBV3Eu4jPbitjdKyXM7zvqWdpoJ3TG0nfVzgXIXbZzWUmwJw0PbmHFHeRyRxW4VlTD4PgB+1ZytlljZksq93QvdplMV+xSoaNo5hZOmhsIhfHgT01YahRn39zw7kpol0Y6DxnSvfDS5bQfrrUEupN71fptzYzraJkEw1XHFop+SRKwmrbZPoll5pTx3jBcL5qxtE+IfeKuAC10fHG/SN9IzHcZERlnKQnpO5kI3dA6SQ9Z3yP07a4fNmNjeC9iZQ2huimpmaW7UaXcO4v6RwdSeuEUX8Po+eA/A2XmnGF3j2/Ztvrk86tiUNEveu+vAtRpO16Nq1H0qiJUk17XV2A3g7GakLZMZvUrXfNU74SN2OzitwFfE6lZGpZgL042FdOS7NZ+iUMLwoF9tq6uhPeVwtyJ1aCcTdgn0q7SNao0+r7R6r0w7tduU9dB+M3w/s1ZwquhgULlkNMjecD2WCqd2dxSrebGyWuDjdsbtzdbSMF9xt283Ah4O4lijvKDewsCefpWdGkR7z6lePBwTGnpw8Al1zVdTGRCUunajYhHdwow0TiCOkia4HphBIyoWfsSlFcT1qXlqK2s8SIlLWh+JIbmPXBKD4wrf9FGLuPtD4wbWA1C3vcspHWD6QwTdGkEzbJsiSW+QK7FE51OUHZG2FLpCzpoJbEdE4N4W789TzLgWQrkDhn4tbRuhYVUk66QVSRNYlsW6JuES1xNnXLKOUem7Jq87vtRnEJaX8TQrA2n2VY7I2rQ0klESla96YCIfXssEv5VoxoeVOJc2f7tUbdR8pxIcC8+U3yWZg0062rtv/sncd6Q7UktZ1G8Zw0lpCETU+p4rpS8ogw+JnsXSLdhrHxWTnYJ4LOrFlj9EMeA81dBYipgn3w9F2Nr3vqpsf7MBQTo6PtGpwD5wLEmqruBjMKPhEbibpJHRfNTSAi7vTc0AQxLnkz+cSUmncYZNqu3Yg2lsg5MWe4HKtOShWSTegKUTCpU8PxlPaDOsqa8kKmBYVCCUO9NEfQ2Gjq5rE21IbVaCjJpMSMRLKzDtTC8E32VE7dKArlFocUNpJdItjUZCGkk56tFOf2pVIc6iTkj9R53EcKpZU8cdTGcaox/LCDUvpDzx67p5zmcvEuNCEjx443be7T2sTcBv28neiys1VJzKu/W4nr0auRfpvweaYl8vfZzHh2Xmj94o3qdRWoc8pt9PDdXbiPZ4LcK6bkrn1ERFRF5whsU5UWXI6eTUWubAPQ+4Y4qL2bs+dlYRwVFHaxb/Tp9HIJjQMecDL4jX82xMm/54VP8xn+X3zlOZZwPu53fMbduw5fLEr/FWzH8ybgYLopwv4t89Ki9EbBq4R99jkdPJ6dB7X/7Na2r4IrQ2zrBQN2tjtqOmYc01Oz4IwTDpMxvXa89vr7PH38kOgg1DU4nyzrD4GTZIPvDlCVkvBLD0gkjVUFCZkWSG4GG3OexHlz+bjco5z5J14k5filq8dpsDlQv4DR/I6pqFRCMDXdiQwvMvYTKISbGLGnQdRELmYrLOVYxuV4Uu42VorJb0uUyYko29NwI0gXrRnfCKQbhdCyVQRVvomLSOkO4QttfDfLUwq3OGfcpQ5V3wmJa+vyFB2GmTkupF+Nzscl6hpT5pJ1BSnDGkjkmBzb5pdXyMkOFS5KPtJ3tp0yb9cmbZX7yqMKQg9vNPAtGz9RhJLSL9I2GWLBGdq/UudBnWbaYOvWolNkPUk3rb9NH4DgM8/toenTP5c6J0aSm0khj2JNDD3VcAdNgxs6R+8iwZHjykW8j4S+wvuAyOFCqJKSjoiPgRirFBLRWYN1IsT7rPpKujOXVWBddgspOjmpg8YHC3nCiBYtGbnd8JvQXaoxE7IsUk2WASFr20SNM3VhFjKR5DNZo0SPkDnqJ9VN/J4quTa+j2jtJMadkH0p7TjfmPNSM76kccRMNFqyTv6vuWlrqtzL02hI0z5RQmScRmIpjUlD/U0vVT0vzwvTvs28FRH4JN/Ob/E7oz7Tumm0v00S4yrm1+nzQtVf7tx0egPV2HbnQXWfFyvzttdN5tr1X+Wl7XYWyD/HIXssL11MXJa/JaP12np2pF4aU9rbb/2S+m5gPEdEWRSHmz+oGyK5quS7dRtLPnaXSccXC9sXgvFrTHp3093n0mPTF4D73pMK3TQhf3evb6bU7Mts420ScP0wz59vm56VgHM4HvCIIx5f+Ry7OeG+wT5R7zIBecghx8N69+VjfJ3fv3lTcLfgzok4XaBrmnKVFrxquJiAk03Vu/o2XXBlPEtMuB19cNzdt9lnRMRxygENHY84YsEZS/aIeCof8FVIape618GtgIMkjhu4K0uKCSEgqiGHelMT5ZPkY70+wmZIG+G0ppsapxaVSau2/yhMScfYP6KwRr35XZiZtfl7NjlPGJbT/NtTkoKtZ0zYxfy7rZOcK2WJL8VgzjlFJYDTNjlUgnaCxqdbm7yOGQdWm/bFORCy04oJxYvmCo3xZqslTV6bf0vgifksgkT5XULzSVPOTJdIV0jzJW9pmhwXclDqYYfR4sycb9OucvdJmaKsk3lt3VdKX8gUmZYdGSs15bv0mSUYpU/Pcvkn8K3Hpm+lj2T+S3/IdLR1sv0h9W4ZK1KtHRdTRmfykPGVKW3PXTs4ddC6pIyLuRFtBesGVg10FYQK+poYffoXUsy30LscC85Uoa0JfUPoGvq2oe+SXE93ATpUnOsIwdHHFMVSEKhYsaCjITmDnNFS0dOQaLY6a0TcxDAz3meUui5RY+O9/fpKFLOsUeKtTb30K8nms/rH58/WYJlJyHxcXrv6nLZHo7eNr/rk7DLkuHAMx6UMn2PGybl+yNtqs8ZLFzcqR9polUt5FEx/u9weh2j++nzMXgbjXnGTPmUj1fQSS3dFn8d0234woTAZ5Z3+yXhDzZzfx+8x9bOvuDKuVe7bKeIwd6Y13n73HNOB030K29My6c/NdDImqki09OnF0PHRuXXRWG3Czss0HuA4eyYCDqT9Om56Pcqj5NnfJb25ZqT+3rRd9WO3V+bzgOwqkZ0Z0pbxCIY8h/W6V8h8mV5nd7O9N8d0TtvPPepAd0w/eMSZ8A6/6t8apI+rwfUB2PvZrkHdRO9GG/W9xv69m4jEaxFw9xmv0j19PdqV+HIx3qBR7v+7guoVHEu1LL0KV/HLQqHgCnYNaXVmI+IUFLxIfOELX+BTn/oUi8WCz3zmM3zpS186N+1/+9/+t/yJP/EneOONN3j48CGf/exn+Rf/4l881/q9ek/zK6Izfuoqeh5wig8tbVtx9PgwHa8iLsaU7CMop/QA9bRoyQVLRDiSgkZUN0ISwFgpN0MVQ2ID7lE1lLgutATGubAuH615VipgWaSpXM+aqeSV6IyxlMkyPdtM5tacZ8uxHSPk27Gpz5QpWeXfhdiz5U3rL8eEXTlDWarTSTrLMJ3zsmdJJSFjhDRqzW+nJPLGEkBCAnXmnzPHnSnDWj9t13QoGSTeH6YuI6X5lmzClGUZlnbyeb3lWMe4vY4xDzvttjOUJ7UE4ilKYkla28aL3q+n7bMEnEDKEILPEogyrWzbu8m5U8+r1u1oZPMSiFu+h3zQkQi54FKsuC4biqMbzouhomsb2lVDCGS1nJysn0NIN5IY/HA8BDek7/qKEDwxSN6mjAnBpd/VAC9NkKanHMRYrWSPNWAHUtS57U75HB2ebiChHOLAUoi19LnOxN2kC015frgohHhiqLeQINsImunfNKRKQo01WUq6CZkm5aUyqyGttslqhmUnhN7whfxLBIA627TkoRBmY8JTydYwEGG2XXYbkM9bGtxkj4aO05iUEuKxYk3H/5X/ganTx3FZQpopSSUGe8mry+3scl0ufkmOQ10uXkpL2UICTOOEbaa3mrkxoXRRGeoXV/oljVc9kMEXY3OeMSnzJiYDIR4tOR6G+upcvB1zhL0/yD1CVJW2j14NI2aq66YP4pjvNdGQtgoh8sn3o2rjbUXn//Re9WJxVZJ4W5rxPcEPmwRk/st9GTDXm32BfbYl8D6Hz3T+XYXOhbQpoBteJHbLZDB9puzS8k82H7wqd7mbQN9y7h/kXequX5N3jYRL/8Q7xG5iM0bybqO/0pvt3ekT3RQEd/36fXl4lbYaFBRcBvHI9Co8tQteGOpn/HdN/Oqv/io/+ZM/yd/4G3+Dr371q/zhP/yH+ZEf+RG+9rWvbU3/P/6P/yN/4k/8CX7t136Nr3zlK/yRP/JH+I//4/+Yr371q9cv/IpwMU51HK82jo6OePToEX/0yT9m/fCjkI3GHQt+J76JBx4/fY112wCOsKzpn+ynk98i8UKgxMwHJCLgCBVfSXw4EWSJC0FZ3R6hBEOfv2PyPWIcp8qSMxeOxpKxeUbYFqtuk0Lsi7cwLXLMsigWYpa2O6dtGvnNokUDc2172Rem0aa1v8k5EbbuQJY08t36SiTn1zD2VSgB066w92K6bpe4aMINymZ9UDu9FCuuJOemKPGgadNKHtbqZ59M+/l4Y47LEETGN6DONFm6TVxNir3NdiOmPlNi2U/OkzbJuUIOT29+No20V4bYqkQlD+mDBiUAJW85buuyTa7s2AwTaIU/ErtOjkteliSHcR/b8dGwYfn3mMcwk9iLfL14swAy0935lmaefgshuaB0PhIzaVc1mX7yEecizgV8ZSeew7meqsoaLy9Hlf6RyG3WoVuqrjCoaXArupH+KZ21qYnSIeyoRzcku2QSmkyX80IhSH7R1DMp7lSboWZhQTC0lLY/0Vyd9MRAFY3TOfTlztZyTMe5URk2D21bohilXNsnkmZMe0keQhdt1k3rqP2i7i7H9Ng26jMyjjIWUVeeaQ6e58jFb8isp3e9OPklTNpl00XqKy0ExWx9XpmbZUsI9ovTC/prei2w4yb9tRl77Cr5yJzo803h2RYR4+tJj41pkucPja326i+IlOqOo6tx27zdPuMcPRIf7Tyt5viM8UhOcz1vlKe/nzf/t+Vnv8sVu73U68/y+45p//ajF4LdhZLSu7UvWQj47VfibkCfGrs/T6fQiMS7ObbPCy/jTaOgQDC2Hu3WM+d2oNtHgMm6t6DgVYQ+de7KXF4drfi5Rz/PkydPePjw4cuuzr2D8DJPfgge3jAI2lEHj/4HrjWGP/ADP8D3fd/38Qu/8AvDse/5nu/hR3/0R/nZn/3ZK+Xxvd/7vfzYj/0Yf/Nv/s0b1fsy7GxMOIdjRjvwA2/ziCrfEeqqpe1SwC0/65IJc4WSBGKvFfeFNSle3Bl6V/GoEs7aQyoSWfA0pxG3lNb+Ld/tNm3rvnLTpsp4X7cz360aTWCP2woIWTfFVHkmsj2H9x0hVOa8GmUxRI4kzJAwTdZ8KnvS7WcrCZTjkEjGhckb81lYmzPGBJuQj8LQ2DbMJ/WwJKEbFy0Qd4mW3FG7ro6/KN3ks6z/ZyYvqaI9NuUYIc2zKpc7y5+nIf+EWJPulTnao4o6q6i0RJykOzN5HqDTROq3hWNtArSnJi+Z37ZuclympcSQw/wN5vi2OHodSpjJlJ1aKe2QSl1lbGTcJB6dlLtmHEbQuoCV8bPTTfLzbiwgbYHaQVszuKxs2vzZE3tP61d4Fwm9J9ZKqMQY6VtPVUdCrw2vaKkqLTi5qEzu2PqsyPPO4ZyN0SZu/FLUr0ikx+fuShOkz79Ys61EVlMSKgwUT48b6trh8vCIkdvTo3oma6weG6XVJVoi4jIhiVBzAqWWJF5bRGi0FHgxxWLbpp8Suq9GHe6Ra2/rpnUeE026sSBQUeVe1Fqlfnb5CKhjPDekErI/mnSaXmpp6Y5N47z4K7ZwjHfy2+WYGtslb9svEitP1A1K2mremo/k7QlbdYgyIueTC5qn0FXqRHCzvVp2n79XXBbLLbVQYspdzax6cYqrm2aFCpUaVrmfrlrSefUak4RCNF+F7nx26Lz0k6N2D8arg/NMFdtosG0PXNlOIHmljQNCQXvz8JUcesYxCPV+J3cz1dTKeVO1kd0EEbB3Ixkhe9+0sa0k52n7tret4GKMTQT3g8CMo8+v3hV/EfT9AXapXRbpjeN+7SmPG9/uH/n4rNh8Bzwfc2as7pCSr2A3oKuR+3PvuhyyDphu6iy472hoaDfizrxKiCMTZZnbBQNuqGizODo6Gn2fz+fM5/ONdOv1mq985Sv89E//9Oj4D//wD/PlL3/5SmWFEHj69Ckf/vCHb17hS7CzJFzDihZVEwQjtWnqlrMV6bt3mduJycj+IYZYViNUwCNU9Sbu7iD1on13XZnPVnkjRILYYOUO1TD2NDkl7YyZZrurSWGBpmRXb86xfy16xgyE/EuGqBBg0++fTSOvEJZlEabIEnXbjk07SOowxZTpWaPsmDA2wqJaSGe3Ob1ItK5o+pxyhlLFqQfMfbSrlyiqXOQyf55yglIV6TZRXwqfKSSg5DtVw0koHfEjZ/MTUku6CZMGlFC2ZKN1b5kJ5Vbmp1ojx4Tx1IuoHBMSUNpm+26ah+WTpU2Wy7XnilrUKgHjJI20oZ78FaxI42IvB1EeyttDb8/zsHI6nWQatjXUhthoK0IdUry4noFgcy65pPRRvudmtw0utCk2ZUzpkjDZDeeJy8rk2jHgXSLrYiYpZKDS/a3LdFzMOq/kmC8hIs4hle4R2sqzNuxmoM2kn5JacaB6+oFa6lGnlzJoejcQg7LPU1QWHWmyyhCnvzEfYaiX9EEgEHGGFhHTtze/nUcmOUOgyJNA7zNKowlNGHOfSg6SktGxOPTHlDazRFl2QToo2OyraBjle95L6jhfhpZsOlq0xJGMbj/JN278TVrGbkSQptqlsZPd59Oog0zS2x6UEranrXL+Qs3qL+NzbD0i0fSzPB4vJgdB9IFSzvV3mGpq6wJVFInXX1iMjcQRN5CS9vlu6b/bW7zIVR/piaN5K4TwbtIRF7VkSkdKX+i8S8fkfUUJZ/uw1VkvFJ3ddjDVAIPedxyq09R0Y4NVMTzfJsZPiN0mbabQebV7rummuvhdglD7dlPPfUHAblQp98Gb4DpXeiHgCm4buj3p4rf2+wW5m4fSGwU7h/v5tlLwovDxj3989P1v/a2/xec///mNdO+++y593/Pmm2+Ojr/55pu8/fbbVyrr53/+5zk5OeHP/tk/e+P6XoadJeEOOeMpXTY5RfY4I+I4jodUdYevWkKfLPlub0U8makCSricOeqCUkgDsZK1JM+ILcktZc1YrAZKui1QsZqcYzG1Qm51+W1NwpaUs2yeMDnWNaPF1AWllfpIIy+DkG/TxlrGyKOdafMURsvenoVRkt+F/RTSTBgb8WEo/g7bybkS1E2kZFYFKP0lskXbXlnAn7PIE1JHihb+UKVDfPsj+MYHpju2jaMlbUXpZl1DnuSq1yi3KWllfqzQLpb8GtI8lTlpiStbhgxXw7g7q5zvCu1aGNtqptv2TxmrzaQ+znwWdR/okAikT2XK2GHozDnRtNeSltJWgQyfDHfLmI+1nK09ZvtKLhs7Na3CEZfbG8f20dF1WxPXEaoe56DvHd7nQqMj9CkzX4WBYGu7Gh8izkNskxG3aTp8lX4P0eFclfLD4eggJteVATe4KBTnj6K/c0PqNAFSBLZ0PxQKS5ohqrAEP5BrQoGM1W+iY1Im1A1mMEkp95kq1yTk/KMxdIt5W0iJzQVJov18Hgq7cIlYY3ZSqWy/gi3ZoYueRAgJkWYdfcZMNerEkNLCQHumofe5DWrejCZvhRsoDlUmSp0SAWKdBaniRjeOaOQ/S5WMp15NRbfl/j0l8sa/xEka7bNo2pDi6glls52AiohTTKMAhXPTKgV3UbQ4pc60ptcJ7S7lCAGl5trrEnLRfBZy79mcPNlaaM/3uT/k/xc8mW5c5rQHhWR0eZZLubtr2p7CT2bhlNL353ye9o7cV7bP/G2x7LbV5eLfb4LxfeO+worn75NBUDcQwH1q925g6ob2PkE2M5T5el3oSv/+PMULXhwqKvor2IvGBHqZh7odZrzVqjyVCwSvtgquzOeCCyAcyk2QzXK//du/PXJHuU0FZ+HceCbGGDeObcOv/Mqv8PnPf55//s//OR/96EevX98rYmdJuOTUTE2XDthjReUCJ/4gG1TTrcLvdfSHNTyu1PAuqiRRqbn83RJy5N8XqAJqhRJ524gCcf8X2CTj4BweTB7VgwQnH7f+EUFvf5bVCOb4lCEMk7/TfIRpmEq/tkHykTpZ/4LTfKcQ9khiW8nn6a1c8t7my1PYK4kHNzUtC8E3Q5ko28ZJWbaadkykiWZt+I3fQeOawZjwkWytYNH+Zm0ja5N/RyK6rGtHUAJQqiqEkzfnQ5qTDWNV5vRdeCpQFHJuepOU9kp3BRIR16Bz2RJmkrcMxdqks0ICIf2USRiXKdegJQttWyLK09qpL220G7+FSJ22yRLgdnpOxabkvKUdDugczOL4fJeulxgcITiij8QIdaPETruukqLNh0y2eWIv5JynbRtcF6iqZM6tKmmUp4tNpmcc3oWBzJDFzljBISUmikt1cNBl4m07z5ocNUouaSgSEVPTIbo1IbSmcc/EcKLUhY2Clu5Nm1NMCCfRxE3JHiFjfCb1BOkiakku5ZrJ5gNLPsURcZZ+DcP9TftB6Ecbg03OVapMetbh0PvR5sunEi3OTE51fFnnfJNTUaEXtW2pbwIhT1/rwET1kOOb1Kabw+31GvfxxS/OMndUv7XZRhBHp0J/xeHMbflJeiUJpjH6poim3raNF9db/laINj7NooTL3wndxmdx03pZfa8PJQ4Z5sPY1eGzY5qTRn3U7+pDwI7f9KlasA13rXfuLwGnb7KymUPo1slL3I5h/Da/e+0cz+jdJBpk+4ibvPHcF0wjAxdcDekN7b4StwUvAlch4ASyqTH9//7dx8aQDa72SMGrjBcZWODuwxrDCgomeBZ3lHlKPXz48Eox4V5//XWqqtpQvb3zzjsb6rgpfvVXf5W/+Bf/Iv/0n/5T/vgf/+M3rPDVsLNvahU9VSarWrOz2cX0aW9xOgxq6D3s9fAwpneEE5JiTUQNe6SJI5ySlV1EEllygNpBheepzd8pEeNMPpd5Yxz9YKlka0gQRsUSVNXkmJrUVc02VbSBLmojSozFLf+msKo1zjlHfpfvLYldsQyS9U9oyxGDuUjKpv4T5Zh1nxkmn5f5HMvcbJUeKqTaIr4Tt4hrUz3pzrVpnvB+y3y8nTRL8rIeRnvzV+KcLcGtTR5T8laUbLYZyy3Nkjzt8MkxGTppY4t29dr8ZqdLm9vXTc61QyZ1lX6Ra8Dyv53JQ9ChrifbLe2RfFbms/yTvKXu8rtlnNpJX0ibpyLPqYeWzpl0DlZV+tcD0eV6N8T1HNYzwmpGbGtCl6mlCDE4+r6i7+yF7wgB+i6TcrGm62rS7Wr8mu59JgFizTpWtFT0w01GtVnqMFGO6stRIhH8YKBLxJukJZt+PF2+k8qO7D7744zGcK/kjZQ/jh6nbrDccG6XKakOTzeoqDyi+xIKbPNOk/IaO42UcmvW1LnOfuiHcb/40S5hGzkumvym5Trz/ymkztLfaVorHcror+Q3JXYqs6XCbfmX+kldu0g/VFjljra5oh/G4rzHSnJjGYb6j29F01ZeDTJPpA+rTOBuOgzVtmubLza6CTElddb6X6125lob/PnWQ12vjzTP7ZPk2aAEn363sRZf1NLG3ieqoY19vqdcvb8LCl48tpPzch+t8rW/u/G1fj8/mK9TcVO9W+2047vLrih3s1XXwYt74u0WrIuVgoKXh/H2PLif1/TYBnbT1UbB3UQh4MY2jvLkKTgX9TP+uwZmsxmf+cxn+OIXvzg6/sUvfpE/9If+0Lnn/cqv/Ar/2X/2n/FP/sk/4U/+yT95vUJvgJ1VwtWsSY6XApGGRzzliEOW7hAXI4fNMU/iI7p1luK0VbqHPCDFfbOuI0/z333UYH+Yjz9FbVYHwGM0bpQogYR8sHyRdV/Zk5RL4qLvXDWyle1MJT/bTA9TbUMkvQpZEkxgJUryOU7OtWkjY0OGZUjk2JpNnlemnDRW0nYmrbTP1kmOiUG/N/lZklCYmLlJZ30NCuuzzdBt2zp5jEjWtnnWk6c0AdS1oyVqa5TEki4Qr5qSTlyX2i7J6qs4trKnNI1JZ33AkfMUN6lCKEeTdpsKDPgPvx3+52+iJJXkJXyv3AwHVpuxV1NxZykQwtKG4xPyzZnzZN6L2kymqh0KK9cS9Oa47YPe/C7KVcnb1s2ShlI3uX4tby11CbmyEXBRf2sr7Ssf028yr6KnX3n6rsXXSrXE6GhXFbNFnyTSeLwfX0chePrcFucilbfXIYRYJZO4S64na0I2MqbOFmeLKZqbknLVUPFNAlqirqUusu4ZlSJJxFW6HpM7S6GeZIhc1nZZR28up9dYcAo5Igb+5BhSlDh2WgtpuF02rGo3KU1HQmpfESf3T6toizmfPlOOmF5Kl02FxJrTy0B7Rgyf02ikSY2obR7fYURReN4LvRvyHseEi7mVqZTxLssxObfdHaP9JlStUHeb5Vsl2sWw0lklNs+jMe3Nweq+zqflxvW2rlmvhrF5IAz7dK+Sx/jc5OpSYufFUYrz23yVvLefbZ+Kzw9T84m9CTKaB3HLGQUFLwvT15r7pqb5n/kyO7y/8sKn5PNERU0/2uD3/GCfnPEF3O3vLu5ru2+GOPp0n+dNwV3B5pv27j6bNmHtbLuoSS8oACXgSpzDgruDn/qpn+LHf/zH+f7v/34++9nP8ku/9Et87Wtf4yd+4icA+NznPsfXv/51fvmXfxlIBNxf+At/gb//9/8+f/AP/sFBRbe3t8ejR4+eSx13loQ75IwTepbsDceabCqrXQAXebR/zAd9RQyO6D04r/yNuAKsSC7xZOUnrv8ciaR4QCI7hGixXhGt0k2+iwVNyIAzk2+Fqo62irOsKdoaoe33bUtU6wpOML1VCnOx7fypBMl+vmw5LKzGtuP2t4HhMHUVdc+UXZFj3hw7RdvpUSZMSDrxaSjyLmuqkUG1L4eXmE/lZ+EZJWacwBJG1g2jFfLZOWPVcdJsSWP/ymeZJ9aV5JwxjwpjIo2cfpn/CjEmfGgD//Nv5d+sJ9Gpas3WaerjV8qzRBqo28xt08dOOSGrpR21OS59J/29jaDbNmSbnEWC5WKlrlJOb/rAxruzZUVn8ovjdth2OdIJsSb0Aed7YvQ4l8i6rg04B1WdTw8uEXmAw9N3Dl+lRnR9Mvp7p4TIQOpR0+Z7nNBKVvlll+SWClLCTX5NijTbtY5AY1LEXDfJQ3hOS94kYmTzDuGyikn0b9KKkOssrZHrPA7f1Z2YOHccUzGKSCIl01QJo2HXNm4STUL6pD6rBopLSuqJ2Y1nhcTLG+uCxo5GJGKeaO7CMCK2H+Vzokqnudj8IOZ22Thx8jeVIPH2Ni8FJTgl2sq2/NMdVWILStw8nSl25omzQumHKbFoL4jz76aWBJR7upCyMlsux7Y79tVJsPT8nN6OrnauzFiXb8njGS+36evD5ptIPlWV9qN6Xr2uN4XdETGNFKlXk86q++hAreBlwt5p7iOBYe/Hit1t+21HzLwMC/Y4GRZ7zxfpOXteBNb7gZdBtL5MPOAhTzl6pjzS1R+2vt3dVRR3bvcHOj/tLtdXHXZbnNi17NZTvYvvQmsLChTWknR3nzpzLo4bVvCCIAKHm+ASh3Xb8GM/9mO89957/MzP/AxvvfUWn/70p/m1X/s1PvnJTwLw1ltv8bWvfW1I/4u/+It0Xcdf/at/lb/6V//qcPw//U//U/7RP/pHN6z4xdhZEi4C+5zS0rDkAIDVwAykRfqsOYP4GjjwB2vCysNxpZNEjPIPURJCCLIzVPk0R2PCHQBHjCeM8DvS23KecEJXekLbH9fmmLzIzHKlhImwTN70BcFWLEw+b3sZPm/2X+XFWUgyy7JIWbVJc54CwNZZzOOy+BY2StiaaNKESV5WZjZtj7BfUzLOskCmTlNizIrtziPQ5HfbFHEtuTDNku6yxZ6RhteSuFJNa4mVuGdTO4yQTUIqSZcuGc9LGRphVKpJHkLaTefpVKEmyk8pU+Z5N0kXUdWnVaiJ1dp6QrUcswyv9In0gVX5Tesk31fQzKCVPrBTYY1OASnbEnEW0jdSnyp3Sp/nSXSqirPtxRNDJkxixFeJVYwxkXFV7XHOEfsUM24cQDQTQb0juIhzEe8jIYKLichzTt0w+my676nz//uhGqLjqon0I9Ody90l8a7kflmxpsr/l6thPBECQv8JTTNWf02XJmP10lgpZdV42vbU8RLJzudrXspKTi4lrdBnicDY5kBvrNETkkzpJQYSblxvzc8qx9QIqmSUtjhpy/qs7bPsfKq50k8VFzmQxPSrJbZ0JKSlYcO0Yfs2tSOMLh83SiUtEMWjzaXKR/QiS73XM54RNr8pgbv5WdNvzqvLFWqyx3ZstrwOiUZWOEq/Sb9cfWEhJgZ1RipXlp1ZV6/XuH7TfbS2R+x19vwW+tPZNn5Gj+u3XWVbUPA8YTXQ982sq66Kdw/2ftyP7jEvDic8fe5lxOHf7rpLvQpEl36f8KwEHOjmsVcJhYC7X0j3cVnjiAePBFnZ3W3IXTq1JG1AG68l1Z/H3SUmCgqeHWMPMHcV/3/2/ibWliYtDwWfiMy11t7nnO/7iqJwta8vLujbFpIv3RZdDIyvkNotg4Qsy221ZCRL2JZggIoJYgTNwJiBGRnBBGQGFmIAQm5ZTVtCQjWhTTfyoMsg3ZatK9924+IWhaGqoL7vO+fsvVZmRA8innyfiIxce+199j5n/+RztM5eKzP+IzIy4n3ifd9LXL7rIqwAbsUn3HXxuc99Dp/73Oea92pi7bd/+7dvlskb4NGScB/jGQCHDUZ0+BgDPD6BgI/xLAuUtxi6DZwP6LqQ/MLBGVESkcz4kWzjDpAKVV+pMuwkDEn31/lvTcLQROUhf6dvqiVFNACmLuVgjF+9SVMbl5AwNfm2mMkdoRQXWrlVFam2T6gbiZoBYWNpWmRfatTGu5Rwu6q8ZIkohj+yKWayJLA4fsiHajVI1hGq+XUBCFdsxacmGfNSbpNDg9WtNdkg9xiWPO4OpaxUrcixe3x1vZacHySMydxtmKk8gV3E8vM7+eOzfI1EXM0ja120DXifJidZV42Xy3TYY5n/NZWukiSsFTC1PwrOxOX0XNn3SoKKEDuMHWJg5A79RmzW5ssxOoQR8J3Gd/DeVPQiMqkHlzTsMgHl4PMCyU/GI6kl5ybiRENoE7PxbFs/FhTcCNJlHHgjSFKFnLotz+r0URE8XMolyi8WQ2jpyabhR7aB/k9qLk2/1NSy+zUZpAc0XDFo6/mSBCJ1BFVLiIRIQMu8ZEle2gOTtJ3MnKS2S1nG+aNk6dovpqeDea5RZtqAcyLMoCYwy3zqErRINQs7Fu1vsUi4LhN4yBqIV8FngtbeC3PtvKvQFWVLI8cItFPSUiKAV3RaeVMyLqXoQd3RdOddboJavefQYYdRNj83q/OKFcehBM1TEnnNn6fH/WSVs/Djq2t6cz1t8i2N6X4lZm6Ip6w5ueJhIG2jY35f254kbZfr9/epq+67gr5lQ7GyT9t+vpPSnsNIt3X+WtHGGc4w5H8PH+bCY33zrLgStez7OnikZ3ofLQmXRI5pQ+MRscGIEQM+wJ/jq/gmDOgAB2zPDogR6PqAsIuIFxEYsgCd8iOuCzhnOgCfAvBfMScfNjCNnPP8veUajeHZAyQGeiQipvkOp7B3mwtHVoBqRi4nwIKTQSFLwA/Vik4Z1S2/S2+C8vR8KmtrGHKZY35o5mwJ6wuUTJaK7alWFmGqX2SYWhtesliaB9tLhdoLUDJVCZiavNL+Vc03anlpVzG8Nocq+2l8/lZlPq0OrXQqD6raXiwXCSzlO/dIQ08n0trkpmq0aVvk3x2AUck1JcuUUOO9mrSrNd94T/Pl86bWWjfyne3X6k6aw9T613yw1knDOQ3jqrgOnxw8vtYFkSHpuE4JHPYe/SY1Thi77A8uwvs8hl2E9wEhIJmw7IAYgXHs0HUB0ZH8SZWlhhIzjejQ5Yarpxgd+bWgq9QyUrGfEk/WwCRuzJhgLD40pRgmUo/xRjEC2UmTjuAmh/Qh0zAhbMuwDekJ8y7nC3qv7DYndSLmvLPL4VMafUW4pPbwUN1AbQFM7VwPHJp7ZLswpZZRNfqcWy5fObhJ9pXhIkotwfliOuUQsoaeaomViMWjilkYjr8IPTdq2lOmPTZHeo/phn0ZOuGlccvr1Ha87mbBVX+v3nDMCSnT4IwTzXcdYq+Vh5n0MTIzIcidt7M5apEfexyaY6Lu5XXztqKFklzD9Ks8D6RP59MbSeUK+e3V/22ZkGMOJpzV/cDjwtyU6NMD101PvR1ujsf5bKx4fNA3d+uoYvlXYyiWTunWWBK+1Nfst5MZmabWS5/ethfqit3dihXLuJiUKB465pZnVqxYcT08WhJui0t0+BiX2AGiAxDhsMWAHQZcYoOP8R5o8W2yVU3toGcwjTX6iFNi5Xn+3sH8wqnvNzXp52HcWXnUPl0/yG81b6nhpozp/OsCJUmkhePfetPKRc0GmKnomkC0FBfH6tqbgGyKorYdyHKyTktabrH6TiKOpJsJ961eyv6QJWGH0R7hgHLRFqp0TqwmidV6LIwwLTkyH5rsASX5VleBUAJYrW/qelD5RlZF16v1WpfpsAwR1pxIdfKbHIwmI1XuTZ92mrZJUvDJcwBb4E+pJcr2UCaE7aFDrpbmKnfDemk76ZqaxJpqBzLPg6Sh+da++WqwXdleJAGn8A4YopmpBPC1kDVVvXVmRET0qfDORYSxwwCXTUuyHiFZuWTW3QjngCGkxo3RtgoxeoRsmhJIaZgSbhosAzx6HEBNtS5XIhmsNAN6DkaR0C+Vz78oAFUiLD1NRnAZEQdJEVN8Ui+lIY8Oo5wqtK70+YoO9KrJQa20kqxxUw0tXiLHdIDpXFmTUsroKlxuz1TvmqDkec9kqK/0kBemdl4aYEakRSG/7I7Lxi1Ta87v12WJU9v4Kox5FzMBdxnb51y83KvPrCZNtzj11tLinISUlUtrsDy7UrPzurouNnGk8rc0Ao/HH6XmOk0B1zWYlXqchPbtiBprihAwjcDaa+SbkH43K1vL5CvLoSIOPTMCzF9TK54u9NhGmnkwfX/IOMM5LvD66oAVOAfT/OR1ZrPbwtvUVBqnmeBtHit4Fyjf14+3nstwaHurfUy4KwI7pfiwTFGueNqoV680Kt8yp9+yvOGnNS6P6KmgYGn1q9dsH1O/R2sxiR544z3zl71ixVOAyVWfnsH3FW+MN/EJ90iXNo92tdsjYIsDPomv4AIdLrHDARsc0E8i4607YLt9BecixgEIl73xWyQhXuTPMwAfwAaR+sjaAfiG/J3C+z7f38De2pv82eY4JGIAM8On/uKABd6rdeqG5NMSYQWUajskoDR+zXTo97scKi2NvEE+o3xaDUL2SG0UDvJ3j5JBZRymfynpX1ZxQ5X2YaG8C8VilnWUi1ysmhPUorG6NamGKg6rf5nTrIuuVYTcH9BOlwQiy1c1fSCpVbv6Y9qMxyEmBN+ffgx82wcSvl7B7qXuCja7llfbtNZQq4eJtkVNWNZDSzln7fKWpVctX52Oc2bOMgKIMbeFS9/hgdEDsQNihxg8wuimqJqRdy5FARADaZQNhsEnk5YRmYxLoncj5lI+Y0GLOAzClAZ02cykRzZomYmnDmGil0ggbDBgg4geIRv7pZGOCDd5ZbPw1iEUvEd0CPDSXXWn+UyVeAwF/ZEGCw1Lhumvdkgqdyofy1+2ZYAaV9RHjLpe9SPnENDjM/hGoEqLn+XlKMvkpB5uql9ZNhJ0JcUY4XM7l57RTK/LT2HGWZp1WX1RN71e98Y8vv2NOS99DKOkEySf40t12+qyPZbDeygReD1Y+a9v1aDLZePY8eBLvuUF7ypYf1rcxjT7BtBnxBV9oKLstwN9RtI8NE7knGm3aFvwWYyYP4srHi/09ZvmlTRWSvqbKH9/Ep++6+LdOm5CwAFcytQmOB4f5sdsHntd11nuKcz2d0VgrzoJKx46eESvQ5zOLVOixR1TueO0ePNd21ilna75vANNH4vvijfOfJfQItzWg2Irnhq6vJNbx/6Ka6N/w88jxCOtFsCXcZ/1BS6xwR5bJMt0ySHWiB7n2z36rcPlRYc9X7EUqNNXG31sfT3dLpSzeiR/Vh0SSXdAabZSZZy6JiApZ0eADDTX1/QRFyUQnc+RUWHCVK1Twq2Oz7PmtenFevFRnrC/O8OsLW04gqJeNj7L5WGqWLX4VhtfGc36ZBT1hNRsZXlSqlRt5DXN40ix+bd1KAvym93HlacQV1OYS5gpyLoKbBrKZajUV68YtSkYh5py9EVHcJzX5Bab/iDXNyi7T619Vl3z//wvKJtUyDofADckbqrgiEurhWU+Xu6rCoVaHdSZbmkIa5nroa6PXX0ig0NmrkiVNOFizP3igOiBLtuSRL5eTAIew75Dv9mn9sj3U7NzqyGZxOQ/I449Oh8QQiqI90lDjpSMc/T/xi5MRICfdLJMm6vsajV6yLLacxjzxGZd7zHmfGieMk5UBQkUmsz0s80OaTvqwlH3zcw3arPTOKWdg0QzXJkPh1vIp7lKD15GYCBv1hjn/4uvT/p45eMcM0GDHEdLEUFSClCNqnJi1/7V86A1LVZrjMVGmIAlH2tWPx3cFs58qrUPHemWl6ODfaRmLdP9Medz2gEma4953cpwqtHXTuGqzUF7VB+Hz2WqBWgeIde0Xd7jaY5VCUhDXj+tdvoA6zgW9a1fSbeT3ymo35kp96StOsj0TgLODgapXt2xV+mKhwEjWO15Lq8BpTn15R7+Gv7rnZb1XWJOSzx+Uorg8YzHTjgmtGa3p4O0olxFezdHRLkJXLHicaAezS19Nzo0ACDr8VC8PVozq75NuYs7FmbFiqeL8ERXJytuBW9Cpt0V9fCO8Wg14bp8hvaAHb4RX8UIhxE91NSFx4jgPLwb0W8Czj/95wAisI1z8otacbvquoNpuyiRovwXCbedXCdB0MNIBJ/DnOXPRu4VGZZC+9LOHlDaOOwkTo++r5+AmgU8Nr3etTZcSwWqDlP/Do3rLVEjP8e16T71qfq6tkvNUJ0AZsfqaXa8x2qreXOthnaJVlf51FGuMcwBJUi4Kb94kOvUEtOqUj0jNK5psx5gpHXNYdYac6y7csc5bBiB8VBddxJXra5qm1BzTxUnj8nuaP61Hjpj40MwfMvn37EXRHTJR1wAEDxw6NJn9OleA8NhgxAwab+F0SMGhzCmDzXdogzJw6HHOPYIwWMcSQE5hOgwBiAElzWlqNXDs4Am+tYuZXygVAwsB2opNElhqDlk1zRclHtMN4AEUzeJ2t3sf81D5yI3aUyUXZrbaPa8u9y1HYZ8TEM1t5SOq2eMgA4ePZ5hNxFvpgHm4WflLQmHIPUjiUVtpZKr7ha0wqxt62lC62zttASjActwqS6hutdOy8bYnPJjObr87nVXpGVl0jot52tnWcPU6gnHtemQ60etwetqxs2FhDW5dT3Mnx+9crwPgc10EOc4qIUWpK/q6e/mdXhT8Enr8nNY0tgWxuEMz4tXVP3slvPUivuENOf6aT7Vgw/W96334dMVKqcxnZ6JOD0bj78tAtJaZZjerY+zzjZvab++27r+FXwbHN6uaGB+/GbFdcFjPCtWPBXUM6baV+D6wjfCLaW1HgFYsaKFtNtan48VK24Xj1YT7gyvccCzSazbY8Ahiy8DuixD77DDHhc4g3cR3VkW4excahn6g4swE3sbJF9wNPFHv18Azi+B15RpqzYTfcE5JCKPZMVHMGU2+qwicaJH65t8D21iAur1yZYRh5wxSbrkP24Y1FxlkHSWzpO32MgTCagboWZxgJLtqE1mmp5MeU81+2qzm7XIlumO+MpXGJ9h+uL+jQzTKhuhnCjH1UZ+18SRErZKbDGcrjqBedXqtBi3a1zXQ5R6j82o5DKq+0r+1RpxgDWjmmfVMtZkV0u1pM5XP1ruOediZCd5aRJ3DubPrZUPYO2i5aXS5CBxqJW3tOqPcjFGIHpg8Il57McqjkMMHcYY4VzSaos+wncRzjmEAHQdstlK3YKkwRKjwzAAXefgXIR3Ec4p2Ubyx+U5ElMa1M6hkTgzeWgNVP5qzR4ukyO5PJOYJaUYp3w6uOkXNdJofCSZDkm5haJ5qBdDTTXT+WM3jbkOMdeJcces/6ZEWGo3anNFyYMlirnUQMjKzkMjdUztCdhjr5SilQ9TXa0leRacYTsExKJmFLSUOdTtb7Wca+0h18RNS2qmZSJwl4W+1EcsNSQtJ+ux2vRLidSqIYdmaqVBRraLPfi+qt/y4t9oVuoPst2ObxisVuNE5aDRXsfTaGla3nSjEnPZy7G+nOZh5td1uZxWXm2xdLYxoqS+3qQOb4Zj4hKHl3gN5PlACRvzDOLzM8ha2ROzbh7vHkvjhm8UrqXmMxdgc9G61S9RL2oeb9vYEljXNE8B96dPdzhHLFZTdw+zRXB/2uGhYW25FU8ZS+N/fS5WvAt8gA/wIT7EXZkffpsovXuvWHFDrJpwMzza5+oSZ9jjHPS+0yHiDBfTC3mPLQI67NwlnAPG0ePVRzu4T74Gnl1iMiFHzbQdTJvJIZmePEciFc4A9MBIC5FbGMnxLN/XPQbljEoc0F8cC3jACcppJNCUSOJfqtgBc6YGEsblv8xcC1PbGGRh7nrYLD1trZeZXqt1ILilVwdoGiYuxFPUmnOq2nWDWYEEbJ0FCSwSv6qZRrd2qi1HaRevx+oecdkoPmDEWt18/KtN1mo21WLjdfVFxzJrmSLm2nl6r24X1VbjfaZ/yHVTk5hKBFJjTdNdUrYcMM+/LremqZp2dTto/vUQicCkuqaPUPCJkEOuS6Ed5xBjeb4vhg7j0GMcfNaK0wyI8pk3H3M2EUWZL+hPjJ8oRJUr4gE0XBky9cJjDkZjaDOa/zNrYgpb/VQO+rghuUYNlzGnfai05+oa190XC500V1y3cEY5mXBYtaNqoZCFUQOWyomHTG7R71Xya1cjTtRaSWVS40HhpnDla0D7qSW+YrsmP31snx020u/acpZPlDwwUXmt2c7eOW4Wdx6WnhiOazZYmjGn2+LVS9BTRMl+t6bD42mY+car8zSYfz9/7bhzuGkM1tNUfe7gzWAjJk6k943fam8JVmYT1JuwXrWEbP4iKWvP/VLbvct6fzf+T+8w9+vj2PjT507nwjbB2lpfrmIzQJ/z+RGXxwbOO2k98Wi3pTPYIaf7I6j7/+D333URVtwQtf2JFU8H7hG/H1aseGj4Or7+KAg4PRK1YsUbgQpKN/k80m3Bo9WEqzesPQ5wcDjHK4x4DwA3fh59vMRHF+cAfBJWn42IXwuA70xgTyKNsgSSDJLVfge4HogHlGb9aIZS5Qu1T3beOwOlF3PzloCQIYywywXZonTepWwPVZ8YeYfEXtT2D1W7DhK3hj9y7zawNOVf9RTWalg1RpgqFqFsKMOQmOT9WtOQeSn5eQIYfYD5X1NZODnRltSdBBaLodU4wDhXVb9hsQaUFktJOjmUyn0cd6rxNsj9PWx8LqlLkKSuNeq0npcozbCyyVXJkOVg3XRoazc7+W2KXiUxyDav25N1IFlX8wNq4rPWHKTmKqqwG5Sai4w7ISdC335Mb3CAzxE2KVwi3zId5NInxkyoRY9xcPDdCBcjfAc4FxEC4L2Rbvw7xatAAZCbhNbUIxnhsgCVtJCRWkEEZVHoDmqoaPV91jJKZN2m0PayTtAciKTpFvP9HiNG0FeaEUdlpy/TO9Z5ZW3qBSYJytrPHPO0Wpsj7x7UknMTmVamaTo5nG+AUqvYaCxqENrjkzTJRsRyKIF6e2Mx5FCFYZpAxEuM0kZ1zbVOdflJOMYcsjwZl8x00k/gMXFxXPxVxzFtPaXUjk0/dQqlBuXxcjGXDkbhXEcEznHjMOZDPzWuzt/SsvYOsJFho8FJ2d5sg2QTtBHAHMtl3e8PFVC/LFolml/TkVArdbN91WsmZwHOVXdJD/2/8H+/o5RvB8eeU9434jOi9nU4j/XuR9F9hD15xFL7PS6kpWhrwfgU8BgEdW+G0tPtu4NHspXzkHFf2nLF28XjEPivWLFixYpHiTfRhLtLA3zvEI+UWwSSsck9PAIu0eMDfB1n+Bgv8DHOskiTYsQz7BGDLVqdA3A+mm82IFlzfIZEntC/23m+xyOcPRCpUKYyoq18OhTac9M1wLgfatSd548SGrMeY2a1ttsWNuJbVrK3wGyzXxcIWN4Mt+wgklkgdX2TjcASqdW6zvx08XnVBqolTqrj1LoGVL2ai4nnDtOOQKXBqiVWE1YM0zrUyCxr7TmSTEvH/UmojZIGm1O1ulrx6vy12Q6NOHX8Vnn4zOwljdpPXF0/1Wxj/JoTrcusGnm1KpWGq7X/6vtL5W+5GFR1FdXI0/4tfNa5pA03btJnKqdDDB4xeIQAhEIFLJNQY/IVM5mljB3C2E3+5FKc5BOOGnMxynd4kFAzcio9y6novlAUTGEsfD1M275NqIlCDTrkvz5Tdto0DvOHweUmL33N0W+MabyU80P7TDC1zUp/bIqlLm+VTU+xq8ZYnaf5RCsf0mWdXy9l49xXknt2r0fMmoiqhFmW28qvbVjm6Rbqrn2h2otlmKQNeZW5Xmu/EfRR2Kb+tL23OMf/gP8hPzZL4a/O+bR4JNOQx1fC8bhlG9dTyE3FI6ZFai8Jrf/diNoSITdirk2Ypmlqe75Z3d4edN2TTn6otm2iTPWoHec3085V7ZxWnUmenLgSmOFdCH7rV9fSa2yc5soE1Zi1FZAeZ2Bbt+byFYpYfcKs3Z5O+5lO99PAw5g77x7zddG7w8Mn4HT1sWLFihUrVrwJHvY7ccWK+4xHrAmXhCiX2GQDXMMkBj7D1+HxTQj5jDMc8Jc3Ef/zPm8CI5KvJvXvdIlSM8YhkXIXMDIhpzXJodW+EzWXuhxHNWE2KMmCDsaRnbwv9ZizElrYGi5nvK+udfJ3DyP4dMtYC2VbaUcp06nlX+KEa1azLoM7MRwRUDKfvNYSeOg1qqJRhQvy+wSwK9ictZaWyq6IAeVTymLWSn96jX+ZX+1vjXlFzIuu6ShJx2Gh5dG0VcZJJoCEs9aj1R0jSq0yEoZaPipyqlJnC2zTenhw+LLM2n59FUa1WBlfCVP+5X22D0lJpnkUDjhE04iMLg8pl+eeaPV2SORaSJn5HM7ldMLoEDvAOYcYY9KYC9m04xjQ9SO6rkNQ9g0RXVaNS0OxywYpbQMf0WXRa4cRAX1xj81CIXWa7PwkBogTIWMGHuOUD6bSq/pmnTryHG3aKPRPN05hIkY49LnT2K1uCpfSK8Xo6gUv5OFAOtIINeYz9yel+jKAacWpmcqhiuMxYsy51iQl06pZ+Hr+TARM7Y9PCcw4HUqgxmEL1PiqDYhS/44ljkXaqMIulSH5XB2LWs7fQvaQcvykkdY+w32JPf4t/h2oChxQaii14WT8lNPe1a9Uhw5bjLkfGf60uOqNjvTM7Z54iqCe1l2epEoph5yjweoW8izQ0vy731jqxSVhsFEErTFg5msB1aBkSvq6qMeSEpytvtRXFfOhl81yVmNP0Yyribb1ta81Va1bLbPl56c0rb/TlVjUroWnQ6hcF3xbJtQj6mm2W3rbPR0iLj1/ahb86YEE3Kq5dXuoj7WtWLFixYoV18fdWgBZ8cSwasLN8GhJOJ9FEn0W13k4XGIHoMMWl3iBV3iFiD22cAD+9e4Fvst/iGF0OLx6hrBzWaAeAedKZTNKKmiqzyNpttH/1icAfIi5BgwlIiOSptslTOAPJFJvRDJV6VESGBq3kBM5lN2oDAMD00Yebfv1+TfNWAKlShMrSTuAzEdx1emIJWEW02+Fr1Fr09VC6jr+khaGsl+t+GRZ+oU4mm/NGLGTT9xIc9wwaWqkRfleh6/NLypRxCxpulFJPpqu1Opqt7SIPIatSagRQhjJPW1ytdoJ2PCrzU7W8epm065SQpH3NA29r89mPRzqOmnaSjJC0qjbhyRga9bUdmF71cN39ji49AhuYtWOHeBGiZsSTZptPpmW9AHdZEbUIYyJzHLOwZHMQ4TzqWHGMYWP0cO5kE1bRtOggxJeNSEEAB4DYtaPAxJdksslDRUyQdJJw6lZybKjTAiTSKow5W2kloVgbnPCSlGbeGQLukn3uaSPjA6kCNuE0C5frw1ldZMGXzeJpUtiLWkwqcnGVOoRbqJorJ1j0TJlOnVNVANtrKYLrZmHOzpHLxmvKfumbiONf4z8ipOJTr1al7e8m0KYjt1xcqkDjQUyfLs8pETGqoeuJtTGYsIqH++roblRC7Ikb28Ol18XHCOhOY3fLuqS63c/9dvt1O9do375pd6LE/nPXxrSftkZDjPPGArKKn2zmVT7siTO+Mte49REnVN6drCAqYySVodY6auZBnRN6yOvmlvtot8fdi+/S5hoo94gPB1QI37+vD1e2HPMZ/gqzfHHD5t7Hnffv108/LfwihUrVqx426CNoFI8vWLFG4NG8m4a9xHi0ZJwSQw4TMLaAzpsMWAPn7U1RjzDa2yxxys8xw/Hj+C6iE0X0XWv8HIE4sfPLUHyLspLPQfwKn8/Q5LDvIRp1uzytUsks5Ik3TiYnuXwQdJUR4SHHF81dfTw7MQMc5pUx2DMmDiD+Y0jQ6CMyVYKEiW9vrpGW4E1U0KoZgsJQo1firOXNwt1hVvQeLVZSr2vTBVhOjMJtYh1rO4fy1/FaddAzQvWJBGbqiZ/tIhsYnXl1yKJ6+LWpJiDaZ+Rs60PagcJp81Sc5s1Uc1hqfXTUw0clkpyK/T3KO1Q20Pb57KTnGP7Ktm2JK1mW3XVtS3m4LOnypS1phy1YElARrnGci3K3hwwZm04P8KcuXkgZnFuBGIA4CKcc5aoC5OyW9KMSw0Qo8M4GvXTdSFrfwXzG5fF6DQemarFGdTUIl3WfHFSAfNC4UDNOWtKmx9ITinRls6/O7iJ2AtQki0tCZlf0rLStip/m/aGk29kpcMk9rM8lJ8N0oE2G5hnOkz1mhOXJUi+aLtY+eiBK5GDqR3MB5XV3P5vzUQetUFLyytK+duzrK/ClKlcreeiC/T2LFlfVfKgvmsphUkDs00T1jFrrrwFmpfkuNazCVdtMkKVurb5dQg5HZf65ruZ+FlHiZFDQKmPWb9O3gw1CVPC2skMm96dlt5dY6mu+hxTlK5rGl+8khgnVvEsrVjMXiWdXMc5RtjMn3QbFzpPt2ar+dpsXocVb4LyHQP5dWwN/Phg25Z6Tf406k94xKNHUp4KbLZZ2+K2UL/3V6xYsWLFiquRVv56jv8+Y4cdLgs594p7jVUTboZHvPJNmxz6nRnQwwHYYo89epxhn4VhASM8/sTZgxyjg+si0OXNcYdEmFGBLCWfWu+ZZEkTkvrhTPYcwAcoW7yHkQZW7JIPUtOUFN5vYORcVWdjZXS014nyOv3GuSreFsYg1ukxPNkFL+FYYPqFqyvK6yq0qhsNEu6UV4DaPKxRMy6aB1CSZ5vqnjJQ+ncJN9z2qOxMNSc5dlpS1FqKvCRpNYlPWcRWOkrQHWD+2cjZttJkN4VGGlomcrt6XeMwjyHlPSlyafyDxFHfbeqgBjkcy695jmi3h8YtFV/S9QNKDlnLxvS1nK081ERmlGsMt6/S5+QSumSakteiQ4zd5CduHDuMg09kXEwFiyEL+qdx5TMpV4/9HL74TfGsakbwOdcQ5uNMS6zpIvuUIoWmYag71talKgXBLKWT+/SJVQ99+21C6DJdplaS6/Q1p8L09gkwV/DLWof500+/aW2fZ6UmSwdUdYqSRpzKN88jkZBWfxt6Kd6Itv83oLSuPAdJRL+w9nE5j7mxzjZiHgldJtmuCn2KZxN9KTLeEtJ7KuSxyz5xV8bTfGzctvr16vKmMiT/fXNdzpsLztgGTD+lTJ92Y34W6+nqdqFtRL+LfjZ1Pi5c15RcTaRZOtZKupaqw1x3uc44+sI/PueuuBvwaEmQ+d7w1Npe9wwrVqzj4DbBlduKFStWrFhxNdIxVSeShofwVl4JuBUPHY9YEy6gxx5jVmXZYoDLuh8bDBiwyeLJDTxGvOwvsRsDLvY7XLzeIAwe6JxJKgcgW7ME/jz/fYVSS41hA4D3UBIASlz0MOE+Zdw7GNmg5J1q1lAzThXdimNvKlLskLTfRiQndBTR1ufkyOiFKkxqtTmDQsJKK6Zx9HvtLy2iVNnyEq4+yb0ksNK4dfp6uraOX9s05DV+atRsTAuaHtO/5qurLlKWWvoOCEtyipbMjFnXGmxEy2wjqvuA8S21OcWasKvLsMRVLnWlaqoxv9ylIyW32r01UV2XXe9TW06Hc50XtdNqOWV9OJ3DflvdA+Z9UJv/dEjPNRbiOPmyjxYWDsnmpEtEXMyVkPBOtOPG0SOEAZtd9os2OvguTiYn+ZtJAhEhODgX4T11OSI6p1U08sVPlEHMpEsyqjjmBRv1e0otLl6jUD6AooGkuxYqvTc1vOaybhymErD5OtB7GxuQBJ9Rfn0uiUMsLKIqrDuMRLOuTQMmYO61h49Pec1JPMA08KzsPPeudSq17TSsmWQsH8C67EpxkhB0U1uYOMZBzTFa7C4/CiPaQ9oehIBSu8oVoSPqx2Y+E5b10Ono2Ix51SPHq9Qu1B5sp2214PQQi9BX+W9TSljb+nowLSWjMUPWEgXcG/lYs2e3fP+lFN+m16U0xuJU03TtIW3ybhetyV/XIredx4q3jfoQhWFpIfd0kNrl6RK+5Xmt+eL+Bd7DJ/AN+F/wxbdarneJY4akV9wcpc2BFStWrFixYglhtiNZseLWQR2dm2C4OshDxCMm4Sj2pBDRtBb2mZgbs6jLI2KDA/a+x3Y3wHWXeBU8hj4AsUtJkHDzSNwWkLioryIRbhcwvmoP8+vGg68dzBwlrVx+nO8f8u8II+k+mipivUQzgZRgbnM+SiBMkfhXmYA9SpWiOoMg9yhuJtm1l3SVUSSxpowG49c6FLxfMzDHhFP8zvSVNSnFuF0HjGPXiI8qPmAMU4shqtmnug61XcPNQtgjYNa1v7ecRGi54qvJI6Atu9Oit/LQbq6VDbWJVIOL5hrZ7OrXTrtHyTvNe0CpVKXkMvOo/cvRBKfHvJxAScYtKRiwHlomhq/L4JGGOfNSRdIlm3e1tL8uh7aLthfqcA4IUdKTBhw6oBuLOCEA3lsjxOgRAk1LeoTRSBznHZwfTDsOSTsuxkwCdNksYnI2ByDOuqIrGs4qZpt9GjcsxUsprpm55PPmG3FJHKS8kg8jcrHMc8zzR5d/mx8qy3WAGda0+UYRQWOQXhafdX2S4c/SgBtNY6rhvXoQxNmgAGgKs5uVpY3WTLJBh6Exp9YGFlsEWNKmswfaSb1bvL22SYofprYh5235+PwIjUVJ2rXK43VG8s5ReuIrqddW2mwHNyM3l8qiZTUDHO4aRFXNuZ8Oq1uYJiCALTsntG+CeQqx+TzcLcy4SapzqHQI7QkLs9fbihUPCVd7s3x6I7t8p8TZlaeA9hGPsv4f4yP8Rfy3b7lk7w5pJNx3g1cPE2lkneJSoUSXw4+P1fbTihUrVqwQ1P6tV6y4I7yJOcpHylY9WnOUZ7hAj4gdLtBhwCuco8cBHiNe4GPsscGIHpc4w4geHgFnbg84h+HQIcQO7hMXwPMDsIsmddRZysGsGJ6h9AWnZvoYloQCcY62hccN2hYWqbS2hOZaW8knmpncwkxRUqsNMNuXTEw3yy32oG4MpqFsTQ1WsMfVmwM1bcmPiotVXOcqAo4Nrg7JUN1fyq+lfkWwPAzzBo+QkkREbbJRu0CVFUkstbInwaTVbBFUrTGtaaC6V0tHD0jkmirtkHttpRlgfG1d7qUuad0jQVebyazzrMkwxlGN1bqeteKm5kmoL7qAFsfTLhPbhuFrV4mjqzT3gg3B4NInIhF2sU8Kcix2F4HJVCUrYg0XQ5eJNy1oJgGiQ4iuEIiUVTJTjRSelwYidZB1ImJ3gFAniUbpsw6cDScLS8IHclcpmuVBYiWb30mGFnw2zajhaq2mZLovFPc9Sp9gHtRTC6C5SZZN47WgZjxdQb2U4SPqYQkAl4jZYES7ni0xZ1mmLn8c1GNfzOYKGS+Zb67NWHrQbCdyW9Z51LQRza+V4abBPX1qn2sWrm73U4RJRi9evaFwKMdUGo3X24h007iyz9xK7nHos0qiysbqXVBmp5ftNjBfP9DEqurSojGmVqy4r4jVJ6E1p9TXnh74njfD1E+1LY6Phf+E//hWS/Pusc74d4vrte+IsBJwK1asWPEkYNKWp7wqW/GW0L3h5xHi0ZJwGwS8wMfwiNhhjy0uMWZfNB4BL/AKHQ6FSJMeTA6X/SSsdn1IM9MnYikr447yORJvpQfMqcFDvqtWOdA92BmAF5gTImdIJB3TJLGnLtW2OW5rzz/9qH2wkZwiGaasH9XrlMxioZjOprqvwlLNa5MrsZNy8Pc2f9cp38NIM5JhrrqnUNt/DKPQ+pLdVOEv02C66htP49d56gBgWy6FX4AmUUPfhLUFTXVb15KiKnlXX6/9lNWcJlCaotSyHiPiNHwdbkl7DDDyTqVYx6TNLYJN27HFs9YkWk1o1pLyVr2ifKcvupo9MfZi7leuJgq1bC2mRcMW7SkVyiptMZp6n2m5OcTJD1yOGvPNWBPYESF0iDE9HyGycPT1ZWHrpm0VcR4ifSehUE9S9NlZxy3DJ6Ks3U3lPb1e1tIGYSxSiPnfVYh5uKg/ujLNOo02sQSoTqEReDXou8xn3rgkNal5ttz2c39jdm8JJBS1Xq3w2i81aWLxw9Q3p3snafHZdZnp2+y4rzEvupLXJZzcrN6nkXnmHy/K95vB2jegR0Q/88F4c9Dyfzu967fXzcpQ/uX3q9trFdnef/jHuluqkOarLn+oB6wLj1WkwXahb+64eHLs8cIOG9UHS66Pb8Anb6NI9wI8bvK2NbOfAtIMxL3u+tZcsWLFihU13PT/ulpdseLt45Eq+CXssMcrDHiFFzhgW5y3DnA4xyUiIg7YAM6jiwE7vMbufINx6BGjQzxkgUIHYBMB74BPAvhTlG7SDki80iuUpMAnkExGDvneBsnkHfce5JI2MMkir/t8/ULCkyejcL/P+cYcDqjW3Mr6MQMmrM63dDO0hRlgVaaA4XuUmVCbrvb3xhbfyXeNdyaNWKsN1nXgX5Zfibkg1xlfh7ar7jGt+tWj37vGb+av6krXfHUpIUtZVZ1VqK7pONE0RpTpKNQEY4sEHmDdVstE57zI3Mqodi8kPc2Lw6TVTEoYLvmh0/y0jkpisSw14VeTYcqbajhtax3qS/wD4wxYVubUstT90GpbJSyVUYoOcNLp/C3li8HBeQgJl8LGALguXfQu+2OLQBxTeO+NSRxHj75PDTGGAO9KUi9Vw4FGB+ndzWXBGiknM6Fn9B2XeFGu60C26a6mc+om9RNllYgOGh/s0KKz5o9FTdERyWdVuq6Mrpo0NF9tKZyflbcldm0PI/rVo4HOZPZyTtnw15IKdjJvqTRYWcO5n7Yy9VAMNVelYGU9PsMFuKrs1LKzvq7PEszhMJrKJ+amGDmq9K9DmPnZq8vmQb24q/ulvluP4VNhLW7/3wR1rqm/gWXznafDT30f8xjSZQdzPzZ+bg9lyhxPrVZrlaEOt24k7xL6klxGeISaFPX5GR51iMUMuo4+oHxbcfa9+k3yOKErntswMPxn+Nobp3GfwNGx0nB3gaUN3e2hR4/hsTprWbFixYongKe3MlvxTrCao5zhkVYrIcnKN9hP7EDaEr3EcwAuny8XP0AuaYZ0fcCLT3yMl19/htE5219T6D4i8VQXsNlrh1JDBih37B7ANwJ4CdOq+bqEY3ya6xvk3gZJ4y4gkX0vUWrxUNuuy/dqiWMh0GXL8DeddWnhz1Da7CO7p2lRw61Wm6pVf2pCrBbQePnQoZ4646rLX7MoWi82uIrfa1NfTLclFFD2FI0w6XfXhWz68oYebNQAsxJXhJJMLHZNMtXWQQszhlWcJaKOJBbHmOavyoNaJsarq60ksZJVLOtVZlRbTaj5k+Sry648LPNaKg+7tx5WKm1u1Uv7S+PU5Szl9waWs+ZRNJ0oN8aY7+fO60i8eWDID35u0xg7IAaEELOPuJxcdIgxwrmYvo8RvkuTRAwDovNwmeBLYZB/e8QYMnGnhU3ieqOEYqaQqCGmQy9RPOaJcz7AS4Iu+aDQITp/qsqJlRSWknza9A4tn2NtkX0rn9rro90riS/18lLTckuPXZknCaU6XUuHZFjM7yqNS8KqbuGQw1oboIrXTYLRiJavOt6L+Iv4FAYc8JXphVWWfT6n896pBJQ9DMf1x8qHhu2xPAOznqFI9zixlK5So091ME+f5S0HnbbfnNCaiyzfNM1kgtWemhHmUw+ZxNS34t1v1mo6PTau1ELtVNqnoX/1LnFTQvlhoX5Np9nNFlU+P4HlzLuKMfhMml+8+TGPp4rrvkGeEp7GrPJ2wVWl+Qu+G+3TlYBbsWLFioeEUlqyZI9nxYpbx0rCzfBIq0WzVTtscECHETtcosOI/eTzzKaiDQ4T93UxpvvORWy2B8TnrxEGD3x9B2zzQvYCwLOcyEuUpihJiJEEoXXFl1gmXSJMSE8lNfV7xeuUAxxy+NcwAuCQ453n8ikZUShSUKNrkIutTXIPI+jOcwaQOLVokYWkmF7Fvx3mWnQquFXzkCT3IH+jxA1VPCXlanFkrSdBgk7VpJQh0vK3mLFEYoyjknrX3Fi3iJ9WVkpAddVvHQtaLUh4plPLQMiW6JPP7qjTaZVr6Z6ON/5W7S4lCVU7TNkL/dt6RpR0a5WhVVbmqwpFNdeq4ephqxICDhu6DWQ9lG1p1SE/x94nt25FHlreUX70Mefv02PoQrrGSNHGcAweYXSIIZnO9T7AOVtkxeiSz7gJHjEEuC5pvHk/F4PMCY40oZU8pM/aSEZxmeYPfczR4GQohgC1lHyOSdE/qY+5hpWSWnVZXEPryepfEl5J8601dMq6erjZ4rQlSvNwkyYd03DTL3ap1VXbqkxnQMyGzRQMTwImCqmYTBa6ql/S9VIHKxTxyrTb5SH91OGP8JWjouarCS3gNI0wV4yR4zOrpXf1DJzE5kuE5VJZUi4keNtj5qr4tsVJNXPXKvdSmbqpTLcjXisndvUNyScyPXvvYrOm/gsDvhn/Hf4Q/zPmJ1KSPxvVUK2tBq9UwIpjsOUBX/yt2ailW7sibVf0tBLwlJ+4NHrq03BPsy2WYEvltV3uAkmjPOatBY+VrW29YsWKFU8TaWXCVdp6cHHFWwXlqjeN+wjxSKsFAA47DHiBl+gwokfADnsAEed4jQ4HDOhxyE3QYYBHxK7bw7kAxIj95Q4xeGDcAM8j4CPMWRwS6fUeEke1RZrVnmGuRaQ8lJqRfA5La5fjbvPf92Cu2krJd7q2hVl5dPk3UPqJY96NtjEyjgmdo1Q10sjM4FwqSLZQfaIpKUbfcVQfVO+Krkpbib3akR4a8Wv7frrpJzTvOq1e6trSlGuxTzUzdkP9g1ZRVT69xXKTKQHEoi/5ddOi19AmYzosB2VP6nipFZ6yBaanZZCwHctXl7NeAZBTWton1kSdxltCq62PpVmTcmhcZ1uMVdgBZXthHidom9btsdRP+iOinH+mDkv3YvSIwWEckhAxBW0Jwhxi7DAOPbxX05OmvUbhd23o8RjZQhLFEEHTkYn8Kitc+hSrKSLTnzPqxMzocVCV+k1GcBlswRmnMmkJy2Fcls/ClClilkYr3HzglGnMYe0Xq+vH5ib20djUpdOat+610izD0C/d0n2gbJO67In+88X9OAtHdIjom7p5miY/y21fl9JnijONs7oshF9YEmn44/7oWmVNKZMc5dXT02il6RDRT8+QjuPbQylM13zezdlJjz/Gl7Bs39fmKmoqkRwgBZ0sOLtr9uGKxwqdi9IzqevSJf3PVZBdo+019ukiFuNnFXXVsHfJSgzdJUoxwNrOK1asWPE0Ub5xH7Hwf8WKB4NHqwmnZEmUJWiHcfoEAOc4oEPER3gPER69HxG7ESEAzkWEIW+gXAR2EXgtC1nu3LdImm4kSFomIR1MmYw4Q+Kc9khabeSeYv6+h5FuA4ys8/leramj2kdbAJcoNeUKkEjjdohszzYH5r2XOTxZgzEXqNYgY8EPmNsGJNgo1Hgj86Ji8ChhlOlQso/3l5gL6rxST4AmMzxKYq6T6y1hC4m8W3pdzW3WWZGZXU1UsXlqv2U1d8k01P5ZKy0A23Ngf4BVuUWO1d2ifgrr9FWKVeU7xvm1qZx1Wyzxqcf26RGlP7pY/a3D6n0tv1po1bI4tP3rKRmnypOxut7igJU7XpLRUOvPScTRA35MH4ULQsoRHjFERBcmki2ZnCwrN2YNPeeQNOZ8kLAkb0zV17qHhFeXTUmmNFV3YCoHktE7u0ZxZ01/qXnFFnGUCCelBalB56aBbIbClMhTlD7lAIcu65IFvI8eL/NkS99mavbSiDJSS8i1A1T100PplromfBOZSU8rW9KI6xcpggAdOppean9fpVm2wDweU/VTK8xjqap3jZR3GvYxX2n5LHNQilfF3fP09PsxqsRlg9KkZw2tx1/LAUSMU//p9LlUT1+8jWgS8jSxVlmnIBpbmvdymY+nrbUYc920D24uequJ4/LgTKufT6F13wQH7I/ej0U/tV4ERoSG6vmrp/DWimDFw4OOgLF4+eoioLYlD7Tnn3VE1DBCe20bwzpujiE9fTc057/ihji2OVqxYsWKFY8Tdgx2nflXvDOs5ihneKTVAuip6DXO0WHAiB4X2CFggx4jzvEKH+N9OETsMGCPAw7YYHA9ECOcc3jvgw/x9a9+AyKJuE0E9jG12s4lkqvepzsAnwDwNRjpQg6oh+1Y9xLeA64DIoXxB5h8gIRay+JiOtZt1ygr5Xcqe3X5+6tWO7UED8pEnGEurDig1AdQ4dwuX+9zJRkuVuFJlimbUZdDRaS1vUOFOuRr6e3sUDKWNcNT2x5UNqwWFfvGtSugxdes9elTHrOWnta+3WoSR6GcauttuwP26vCKBJSSQ3V6WpZjb/Cay6SpRq2DQsvh5LdKpWsia4ncUh+KdVtq/Zb2n/X+tEWSuSp8PRTqNo/V/XoYH2vL6BKDObVHBFwu0JSOkFfRNNp8N9enCqNLZiclT+dSp4SQSTEfEKIQT2LSMqcs9DpJNXrVTF7I5nxiKjOJvOUql6REzZcuo+wsUjuxoCOihCaRFkE6ztLx+Aimd8dBuUQW0e+aUX06Z5SqknNzjA4kBGKDoDxGZrTbwwZ2PLHVYvELk1c+NysPALTMc7bLk+jJ0Gg3pcpiHg2lCcE6pavFRYnkpSnT00VM6cHmOLiqveoHO+Z82a/XeyM4eXrCNPUxnettkuqJME066f/b1lcrySw+GSSjB3ln+2b/vw201jOt70YFj/KCoP5lGpmuGJt3TTCuuD2kZbEtcmzmaVlg4PVWz669vQSbOXVhs8Kwkh1LSE8fF/Mr3gZsVbYScStWrFjxNGCHgq/a5a5YcadYSbgZHu3OqcsCshd4lcUsHoeJlXKgESeeio4IOKCbBFcOEdE5vPeNHwI+S/g9gGcxkWJbJH6Ke4jnsNYk6aXExvMc5z0A78NMUWYSJJKk2Ejc5zl8LUciMUdrkFxX72CWFs8kbzUHSFKh2Pu4xqfOkPDyaSUWYZpyJNk2MBW+Hub3TZkdhmXlzmF2PjW9XsJoWnS+x0ao66f1VDBdNpL5n5mHVa24EzcwdVPWZNZS0di0rJarrms3APZ2bVngxEJYgtVm3JZtubbNPkt7qQ7EAMx8eCsBqIqWeq+F2sWGdpvyqK2yzHwkVmFaz4u2gcZvuSes09Nwmi9NfgIlUVnHL37HUnmVicZuslLZbwK8twTM1KRDCKIf5QOcbzG+VlR+K4X5VqGyCZcEmKXpPyV3Ql4aajpm1mpZl8tN33i/9ioSZQj7yQRhi3iqy5tqVzKrx0ivZROQCjO1eQrdc3xuKV/Z8/qQjGyTY62ha/EzGbtQJnoRXa6Dlfvqetq4WyqPluTYY5vCzs1XntrW9Xg6vX9IorbGwFXx+Wz4TGalSSxcO62lspXTzt2AhHb9TnT5CNR9RD0q9OUzx5g16zgv0PSrjsd1Y3t/wLnEKPn0QlcNXMPSmnfFEurxHqaF5foUEKmNlk7BrSAerfDhHmJpdb9ixYoVKx4rbssqyooVt4Daq9R1P48Qj5RbpGgoIMBhgwNGdOhwloWmEQEdtrjEHucAgGe4wIAd9ojwLtluHJ3HAJ9NteWEYxaE01QkBekkzj7M198HcAHgz6cCJXdqjEf+KKA0PQmkwXaQ788kXcC0onb592W+R1kS456hNENJ05dE08qYkm4kty6q+7ucqYajY6xao4xkGVD6nOtyGkSPuU+6CMw2+fxNc5M1s8N8DlX4uvws86kCmJY61BEsHbQOKE8DkJlQ0sdJfMpngbZ/95YmVh1GCWGaOdT4x9Qv9D7He1fFUQ29Y9BJlPGXDmbWaet9atiR33XVvZKPWC6XplkPkVpWp3F0SAaUfaVhtezMS9tfSVHOI63+n5XLA6404ZrItigmJ9O9EJBJOQfn52LxwuxksTf3iDGgczXBVXvxUBotYoRH7d2NZEXSMzEtKx6FSDo0JKkihtxE1DSrm4VNZrSWEnJKcJi+EQCMWTSm5dL4JS1kYdqmGutBUrbHfMj5bLazJDhLrTPLc8ytszR0W4+MlthyKIlPanC1qR7zANhOOx1nWdIprq8cnw6YAzXiWlpvHElh6lF9lOYpJp13fyRMuyQO4yL5uIT5tuY6ec7Tsd83S6eGn+pVvjhi4/l8UzgMReulGqSpcXkMvxscKw3Nm0YZDUbi2Ixj6sytcy3zQwL2+361xcNCq/2UOD8+0tQYz9oT10FtsMDODOkbeW1PADJPrO2x4n6BR2biOj5XrFix4hGDcpl47f3wihUr3h4eMQnHSchNHot2eI0BG3yM5xiwxTkuENBhjx0CPM7xChfYYUSPGB06P2J/2GL34hUuvv4CGDsguqyoFYHgEglwkT/khkioUaHrFYxs2wD4GCaMJ49EXgv5Ook15DjPYKTZSxiRx2s14XOAkR7Mq+atFo+q1wI1ZQ2UPTmgVJ9iofkhW6PiKGWdzlDaIFSRFm13HiQuqu8exkTSTKb6q5v7nDLHe4xfi8wYrmbErrFxqYkUZfFJqtXkj3KFSkCxjyPmJwFqAk7TVgKq7s6lNLT8TKeXvxq/borWKYWaYHPVPVT3NU5dXg1XE2EtTbxjlm7qsptM1VwRdtU9jatDQ/uSQ1mHsZaZca9aEWkfTmY8HTA6oI/V9Qjnx0y8eYxD8gHnXITvgBjSttv7kniJAXBT+2ThcuwQ4ziReCE6OOeLxVzS1qEOWwtqpnHKTb51iJPhQ15T84WpgRI14ydqJk4Elgn32YxBJroupzgntxxoOFOvtYw0RNQPyXJnaQokAGOmPlokViwm3aTBNYqnIidhA2JBmNm0YHNqqS/G8nf5sWgTfAw1h89mHefEpMX1mahQ33st+OzzbomI0Qf+mC5ZeshocjKVqyaCFZ2QP8cIxXm9WPd6+jll1ueYrx/362986EHxNgxHMVapSk3a83bRej87MfXIUTkPfb/QGont0sbpGXU5lLVrLMKVevWn1P2pbJpJnpkvUEOr/rbUTXf5vIbi5dqKfVXKK1pIWqD2jkxj+Zrr4ScA2/WsGnCnoBxRa3u9LaRZMk4rbMO700t8jud4OfmeX7FixYoVN4fZZ1k+LLtixTvAao5yhkdaLcMhq8oc0GPEJi/+TbS1wQGv8Qwxi3zP8Bov8R6ci+jdmCzA+YD+2WsMHz0H4EpSAkg80GukHesOieSitOAMVOsAPsrhz2Ec1gWSjOw9lPzVBqW5OiVrtjBZg3JZzBc5PsvgJewzGNkXMVdyq12zwaHUUFNmUNGj6yLGkQTaJUoSrLXZUu23UhfF7lPtaY82AxIlrD7hSu61zqlrfXTjXKtdnShsaJFDvF6Ta/X3pf1Pi0RS2bVqydXEn1YHcs1XaQDzrlFzjJq23m+lV6dTd2crH4IktV6rJ+xWV3CMk3s9RvgBc8upSzITfea03LV0tSXrq8Pr9xE2fyyp9Tj9kkk3+oNDRFLNleCujBwjEGPSfHPOZU03hzEA3qXwyRRlLlbEFCYEh66bE1VuEjfHqmpJU8wXYua6kWI1LObPJH3GLcMjwnTjTFRuabmi8ediniWaJ8rfpSddiUULR1OKNRHm81UroaXDtlLizkizEinHxAmPxXUrQU34WR8cqw9rMr9PPSkSr3OtRsY/bjiR9BtJs2NaffWYOh4qpXycYEvtVjLex/2UMdVSzHw6fDGOEq4iKpfKAdBfnZPe0LvXT8+eAiU9bpJuhx5jUwO9BXuJjNOEB/QwrcaHZ5bMWqs8atDBSHa+XCD3uSm2l4ua+m3NXleRuaf031KYN9mg189d6zm0QxLWTnpowJ40n39byFJ3k+kwrI3luebRKm54U5A01rcaly2rFk0baXweMymxosZ905F+CtBZUg9bneJ9966wEnArVqxYcRsoD5+uWHGvQLdHN437CPGISbiYdbN22OISF9hOd3bY41WuukNAhwExk0H95AHEwbmA3eY1Xr/6huRPqQtA7JLUepAFq0Mitz6CcVaUmVJW5fP1ff5LBzuaRp+v72FCeqZRS8yU6KFy1x5Jy45hz2AE3SWMl1ITeWcoybslSU1RUIJqQ6ktx3EnYVgZmqkcc8VjLgz9t0UkNlLt+pEZYSN26LozjKOqD2p5lJEEjL2pnZuxfEy3Vh1rNcA1cEwWpMRXLWlTMo2gvz8SN74Kj+pvbbbQVWkyTMtnnJqT1CZpSdn0eu1Kp9zhlURWC6yLk7+MX5vg1HqoZE7JrLoMdXn1e0uTTlGTqXp9rO5rmnW/6/PG9l8aaoyvdWbfwAEuj1uV70aIyUkmaoVWzS8ScjECiB7OhWxql2HCRMYBAd7Zs1PS0iS9lpA029rKhHFaJvqqISJItdTaZZYrf82HViqziWdLzSQrV4uaqT1xkfCztCM6OAzVcDAxco0A0yAsCbKWuKMse4njwhHqC7ZAYep8eJvPuDk55orvAW7BK54Jy4Fa81FLUP5aJsyc/DpuGrL2I7gMfeBr2ulYHLboTTXGOJLTZHHzN4o9aW/qKa6enMNE7rGNjhOlitMJuBbS0zcUk/9YTLVxcTw9BLTUoAl9caYwJOX5LJWklSvSYvvwFVQvNkjY1sSVaurVBJjNvBYv5Je3LjNrMrBeMbVWUJaW1n+cws0pcV/UNCJUJJvWTK+vuC0kf4f1winhqrf+U8Qt7BqeJOZz3Iq3iVh8f3jHYFasWLFixRzrbL7i3mLVhJvhkVYL2GIP4AzP8Bp7bLBBwIgBIzbwCOgx4BXOEdHhDK/xIbY4YJs154BEwiWBgO9G+C5g9AHjfkQcPfCsAy77UvMMMH5nCzNRSS5IhedU8KL7MgfghcRJxU9xPpT4gOXJeJx1twCe5+sHCU/2ea5oYQRiQNLmI3emYYBy1T6hZh3qwGQfyChRpNNLeP5WJ3gqrErEXd9fYhzJzoxyj2Yoe5iKYC2uoj3IWvVKy/yG5FsLbB4q9FF+pBpmNVGm4yMuhGGTqUStLkcvv2ulxbq8LJPmb2xGQsvvW03SqZKilovlYdk5HpUfVUlfKSlsE336fakL6z2+fme6S0zFEoFYE6BKbNcyQrXGqhJNYomVCPnTRenHmMIPAESLLU1SyX8b/cIxceeALRwuA+AljnMAokMYPeCSqcq+b/uLm8JXBEZLHGq/aQxhWaMo3R/R9oAVJ99M9RBjbjTZ9yl4/Nl08kvNTdaGJxMCkrZT7RtubqayjJfi1v7u0sOp5jmVOIxZ6Nyqe0BN8Jj2XLu95uWxsi6Ze+yyQLuVbuqZUJkGnafBfprfG4oXWkA/e0lYi8SifVr52Jwds9Yl72yxwb5wbuoQpkM0wxTuOFIZTjMLaP13Kt03j2/xWuPwdKhvN+B29C04NhT0emjadzfJ41vwV/AH+E9H8rX8CT1+k57k9N6uz2U8LJxS4lRr896wZB5XKdjSq2YZrtTEmxsF5jgqFw7M0Y4U2NxBs6gsm+rJAuUhgNLQLvMuZ/C5xnNrVuDzd8wUwIrbhpGmrTZe272GHrHhe3Ylhlc8BNh6GDA6NODqI1ArVqxYseJ+oNwr3Gy/umLFineFR0vCXeIMW3RwCBjg4TFgh4g9gMvMSpzjEnv0uMA3AKCQNYl3gYgxOrhuixfvvcLFxQ6brcPr6DBiC2xCUqwaI3Dp0mqWWmXUdOuQTE9SGWxAItmUXGFYgBLHUhPOAdtPAvs/lzBjTnMncVWOEXOYUX53SATdaxh3BbnvpfzkuTSvuWxICqQJLTEftY1Gvd9JxnrfhHWXl8Bu53F5SSEO7yk7xN+q6baRtGKV7nyz8Zf+EvClL7XqWUE1tRR10dW0Y4skaqXJdFU7TbnLmlSr4VB2g4431/jNMLVmXR1OCbKakKu18RS0OhrlN8upcTjmtA1bbXaMvFKts1qGtyQNb8k0l+QoLXmhDvuaZFMysyYy5zLKErP6e8CHxIoFl8g47beiQOnPpUMi6UKA6+pnAHAuPUvj6CaiLmnDZS2eRjtQy6smvGqSR6teElfUdGAVzfMMCTabfJaQBuZXChKJQm0KgTuYwUiWwVemHaemqrqi7EyWTcVs9eN1GkzwXi+YU7ua7ofmTPeQLVxlynPEMU0ql3nkJY240sdfWS4V5i+9IFhfP9Fx7Y1C2ZoapiTgFCldd6TuJaiheaqWVSIg6xKehlTvVpzrjxf9203anMemwutB5wQ+t6e2aQl/IwFe64ns8jmIcapna3Xx8NHS+Tv28il1BudhfRG6fDZ15kppkZi28PWJl9JgZqlLx2sMW87tSwueObn2uHr0oUDPH1nPtdbzK1qIeY1hs9NKXpyKtLpsraZWvE2kt4m9gZJH42MOtR8vevQY3kjLf8WKFSveFsxdhO6PVqy4t6A+zE3jPkI8WhJug0sAZ9nfWz8JYLc44HXeeibOa0CPC3Tw6LBHwBl2uERAh0v3Ai7G5BOuHzCOGyB4IGYh+CYCo7M9xDPQk7lprQwwbbgdEgnn8veDhKWkg+7ULjGRLfsDjHCLSMQe82lpru3yX7plo8YdCRWarKSWDfOmNpxafXRIpCDTLuV1ley3JXRZMh9J9UBlecgOsqKqztXh8rJl4C41rvcOIUDS00LW5Vt+VZ1EwNXZA/M3ICcbyqqXsjR52Px67Q+N6epvva9kWeu+a4TVekT5znBXHYxm2Ja2XU0iHpNIK1GpYecWqwzcw/NarW2n6WudXOMaw9YEnta/brOaNNXwtdyT0Ov1wemj40oePoc0B40ecDQhWcJ3Efrc8ds4Ap2U2zlkk5Qe4xjRdS3he02w0E/cOP2ea4lRX8NN2hJl98ciLd+YI2qPSvNhVKbpZ8RBauQlekihvtr4roiZ7CjT6xGzPp3mTLJmPsRr05ZlavOwPlNLtfaZn7RSyrhX1S4NMPo6m69jUkrJ7GSr7ymsd41y2WC+mrShAP4q7yOpz1QT8nhYl7nt00ij64r7qF10MyfXNvaJWgfoZjDStZ4ub4YWkRNmT+Qp+M/4n96oJC3U/ql0nnlam86b1Hopjopdj6W3HK8d5mn1yENFOqeXnqlxusK/T/PpOsM5LvD65PDxibbTbSG9u1by8l2jta1302rz6YztlYBbsWLFw0CS7+gK5OnM1CseLFZzlDM82tVvhxFuOvcODBML5XA2qaMl4WOPES/wcRaZRfT5hAEAwDnRBgmAC0CMiYzbAdhGZjhX9nqer1/KNbqm03D14ePnSKQGtYu2KMmXDYBPAHgPifirFUe2SKYtP5E/L1AK+t/PcVXziUTIM5TKYyzzFqm+Z1LmlrbRDBrYy2+qR5Foo6oUv9cJ10PVhAbOecSoDaR5vSG0nrVWGz80r7jJn3OkdlIirgbbNcfdsl01nSqep1lLLdsuf9ikWk6Wq47XemMrgXelRlZ1r6/us1wsQ8kHzWV2+gFKImyJ9IKEZZ4tUk1XKXq/r64xjZoUq8k7zVev8Xs9zLXeqgGocTiGtJ6NmfkvdbG6HgCftbymskU4n0xR5v+QSGpqx/lsZjKZ2dUKea8mWuekhhqpMwLOThHUQ8DJr3JIuVn4q4myFiFDs5fpfoskMeN6hhF+NiRJ5hjmHossxXlJKdCc3+FbpbwGuEYZNM6cVoroFks1wudPK01jolvlI5Y9oB0TNlKUZmkv55Ee7NCgS0v4xdyWyjfmN/5VZCv9EUbMp6Vj5dF2b7fxsbhdrrPPpJ5frNu34FtPSDOR3knZ3U3teXqZrkaYnhGSp9dtszdFysl8mpVP/9JYe3vlW3FbOMOzW0vrZpqY7xb9laYNbh/zt+8trpkfKK5HwAEr+fxmsOX1OmO/a9TbnvT3tlcVK1asWLHiNrCanlzx4NC/4ecR4uHtWK+BDgE9Dlmeb+c96eViRDJbmWTgES/wSmIncq7DAV08ICIijB22ZyO255dAH4F+BJ4F891G5S2iRyK8zmHEDMmxZ/nD+yqnBOY9cwYIj2ifDYAPcl51GOW5znI+3O/TVGZNAEYkAoXlPcvl3Eq6JIwYZyP3gRvsR5dYiZrtaseJE7Fwy2ARWIyaNySUMKoJnrrYSpDRHGkP7EcYKUfyrCJ0vu1/k9NQclRJQNX6WpKpLJFbdRNrunVdtHw1kcX611ALogrtaiUNlRhWMrPuZ9XE03tzBuM4tH5MU+10qXRXCbZambNWS6HSZ6vsS1a5RiSiv4gT8aXAHwHoQpqDnANihzDCfLg1RNTm180V10jeeRJ3Upi5KUoVgtPzmT2v9C9xjM5ppcUS13FUj491anWjEgWtfGLzjtOWuKJ8dXptLGkkLematPTSdKDFxuTcpq90/mwZW7yK4mT7HSfpltNIYa4W2Vg5X+D50ZAEFbmvTtfqflXYgA6jtORprw43EWnUor8e9NmbP6H8+wf4/10jvfIdWS8h3gwe6TiSPeV1297MYOWpYHvpSQgenOpyH7qCeOVUayFXPAT8r/DNxWz8Jgh3PCrvAsOiud03hz4H88MDrXX1imPgHLN0GGbF9XBs5bbi7aN1gOvY8a4VK1asWPE2QNlKgFtXICtWPArc+XP8C7/wC/jWb/1WnJ2d4bOf/Sx+53d+ZzHsv/7X/xrf8z3fg2/6pm/C+++/j+/6ru/Cb/3Wb90o36QYNOAZXmGEwxaX8Nhjjz4rfL0sRI482egRQCfbHkMi70IEYocYRS1mOi4Wge1oGj9nMG03h2Ryklpk1GrrUUoWqWHGuCTTWLA+h6F23Cb/JgmjxA41owjK6qjNpoduO8C9D7j3pFwk7J6hJER0pLA8JPWUOBJ5cM+8WsTVlXhLAgKV9SnpVsvHa06wh7Unr7GPlvYqqiXH8QBYe7Y050i27YD/+AeSV8syJ9tZy6plVgKu1par694qB69zfNdaZNTGqzXAIHFRxdH8W2RWzcEqEcj27iS8oi6/todaQ10yOcly1OO/Ze1UJcGj3CeOyQZrQrdHJtdc2Y6TPcY4q6tzRrQYeVY3mhbEIYxGXsdYknK8ppUrCaAWOeNmV5Z0U5YJt1aYlqhGCMaq88twoamMSrOIZX5zYrBV+rlpzbK0rTvtVliq9U3mPcY5NtCMJm2Xo01XlmGWpzcaqzxFXPNRNgq9DJ97LmmPnULrpvKf2namRXm69pT1zZuKo0Jup+TB9E3ec0bYntr214OfSMuYPwCyhtp12+9NUT8b6Xeqt88mTA1pKnYYcFMPdyveBv4A/9PRmWfF9UGyaJyeD5sjBlxlgnRFC7okTVjb8E2xtCJZ8W5Q7xj02M0x2w0rVqxYseKuEPLx0biu3lY8XNRKKNf5PFLW+U6r9eu//uv40R/9UfzkT/4kfu/3fg/f/d3fje/7vu/DF7/4xWb4f/tv/y2+53u+B7/5m7+JL3zhC/ibf/Nv4u/8nb+D3/u937t23jy5fcjMlYPDFgNcrvImn4Xf4QJAwACPA3pscYkOIwZ00xlw7yO822MScndZ1BUcEDrgzAMfxGRGkqYgtWVJvHAHp5pjJG9IXlDb6gXM3OQ233ue01HNLJJuH8B8wQHz/aH+JiHWJUF7JDl4htJkHh8YEoSqSeertICSaHHAwLg0r9gidmptrZp0aaEmmK4LElstP2Yts4ZaLu5BahOUJNHUjKSSWnWfa5hWXqjuq7aYlr0mq1DFVZKrpfEGlNpuNelaX1eCrL6uhBTvk2BWTT3m2TC5WbRBvefTsDop13WqSTOG1/GnpHGrvet6tOqomnC8VpdTy166EbNytEhGJhg1M17L5FzMYmafxWoOcC7CeTS02LSAliZNU8aI7FNxKc584afbc9OTSZUpz2nVjRyL9LiorLf3V/vP4smwpVJHlGIEu9vWQYtVKVvkz/XFxcv6Y0v+6tjfcx93avayFU8NdM6RlvDHCMTjmmRX1dwellPaqF33pbKdMtGn/E83ouQms4s4OY7FoznIU8xgztE+7XH9dOYTX8xluj1jUvqypVnN9DsUT/HbFtHZi48mK82MJnK/2H0+UYP03SpSXPGYoOSbHWIw8vwhmZ28Le3I2wZnlBVvjts71rLitsG3vqHe7Kx9tmLFVfgEPnFv32UrHgLidPB3fhBoxYoHhNUc5Qx3Wq2f/dmfxQ/+4A/ih37ohwAAP/dzP4ff+q3fwi/+4i/iZ37mZ2bhf+7nfq74/c/+2T/Db/zGb+Df/Jt/g+/4ju+4Vt4DgC0CDuizyMX8nHAy2+ISl9hiRA8Ph2e4wCsE7LHFFge8FpbA98DWXSIEh/HQAX4Ahh0moTgJBx5v3yL5glOtmR4AubzaLKBKJBl2gGlW+ZzuGZB5w9K+DDWDDjCSKUpadHfQS3plYyA3UiojkEi3IPc3AF5JHQlNY5vvdSi1jWKOR22+A0xjiM0c8nVNl+BuYMScYBmlXhqX5azfWCQ1kct5kN8sL0kztYcWYeSR5sNPL3/5ZF2iJEe1PtSIbNVVZbP6lDr5TYlLrMJrmeq2UhKK9azL1UmYVl11DNXQFUJNeiGXt1UmJ/eVYNN9n8qCa+23WN2/ipRTQlHLoWhp7l1FntZ5KWHcVeFqFCR1Lhh9uU3abznAGIEe8B3vM07MWm1tSsvl9JwL0/f0e07cRemUOa2i5NzYWBjG3CVu+kVKrPTSRi9dJRXEXO1/V2mgtSgcmsSkJ1A31bzsSjcj3BI84ox2czl+lLhtLF/3mOviJLpszKJSHcoluVDrSaXW6xZyaz02ZVlOo77mOH3ZT6ONS2VgegEeV3mHmw6+TOPnKqRJOmI4scQp1JgfVDeNnVPi6agy/4qnlZNpxOmbXnmzTRbHTmyMu9tAOemGYjIdi2ny7aAkakPxsmmDNPyIgH4VJq54oEjLv/Tszc0X48jv+w8edznDOS7Ej/e7RlplPdxjuWc4wyUub3Cc6PZRr/xWnCNfJQABAABJREFU3D+UK8aQty9mL2jtvxUrlvF1fP1ezLUrHiLMK/aKFQ8eb0KmrSTc9bDf7/GFL3wBP/7jP15c/97v/V787u/+7klphBDw0Ucf4ZOf/OS183fwmYfppkVkj4AtLnCBZ6AwtccIj5fYY4cxqwwlQZzHGS7wKvuucc6j74OZaOtHAHtgn9mu0SXNlI1LpI4OtgNsFUsyzcO04waUBB7DuSqez2HUBx1JK0runuXvryUOtZUuc1iaQnyJuaMI1YYbYIQZcvrP8/Vc/dQWUkaiNsnnYGYyY/7+GiWZ0+W0tTy1Zp5+BqmPygUZvh7dLfkziSiVXjNNtt0A65dNFU60Cqe+VdnsWSM/arMpQamaYtpmWjagln+aHzktj0r19e1N0s+j1ELT8DXxpBLhyVRilW4dRq/XBKGSZJqfjvdWv/mF663veo1jX9Eiv51cj1W8+m+dp6s+qK4vQUm5VtmntortOgCTGUn+9R3JtJivO/guAA7wQsDF6OCdPSwxAuMI9P1ccL1EO1HkXVbTSJO2GHBZfNUiH5SYmpeDORshaNc1bIexIAeSRhnJojJuCyVpp6nXj8FS/DkRaOVvE4Jx+r++F+Hy1Dxvy2NlAI5RiJZLQJS2KsOkN+PY5NLrMpyyceAZieVHxVr7NHLq+tsVra0JtK+TUurBm2lHpJqHPD6TYCvlfBoZ2EqvLh3yWui2dGBc9Vevd4j55f/uN476Um8JDXksoKSml15tK1bcN4Ri9nycI/ViOkH4bhCnT33S7WEiEZr3A2ne5ZtvnW3vK2x2ifL/8up1xYoVCSsBt+JmSO/EdXZdseLx4s5IuK985SsYxxGf/vSni+uf/vSn8cd//McnpfHP//k/x8uXL/H3//7fXwxzeXmJy8vL6feHH34IADjAwwsB55A2rBuMuMjisi1GXCBin208umye8oAuCyIHBDgc3BZ93OMQNhhDB++BFx+8xMuvvwD618DoEQ+7RMT1EdjLtHmOUlvtFYykI4lEP2wbua8EjUrQCAcj8fZynWGe5+tKHm1gpFvMZQOSZl1LA40r7z7H43WSXs9gmnEkdVjeS5imlqalmmnnOW/de5H82ErYPUqirpfvA8p929IbS/2kjTCSUH2RAUY8qkxDNb+YzlwJJ8FLWP5WYlU16ZRYdXKdv3eSnpJqTuIyTi2H0bGiphdbJim1vkw3YE4MKnFXt7mSo8yT5J62B8dTyZGU93U8qM83zXfEnMCr69byQ8exU0v/tY1YDxKIJh0v2wmY56f1OnZYWgk/rVeMQsBFwId5f4lWXMxsSCLePEII6LrUgL4Tg3TRIYwxa86lDp5rzJWNTwNz2n05MdGM40Oo1W45DbYHoRS1zDWiXG7o4yfAjm1sguQR4ZsmK92kXcffba0hoxpJisSs5jvXzvIIzbofOzdfE4ZXXbcyzbUT3Ww4zUviMKJtjtNidoi5fq37AT1c1ed1HqeJZtJAd83+KcPFPHHXr8XlMvg8ijgBHQvfvnMdkaDW+ZTyzeN3FbVpXgtvvglLz5abUiIRlXA3Z9gdomgiRpgG63XI2dsvU01p2kgi/anlrMeZ0vxXa3muWHE70HHH0anPbUn9r6PytmHtv3TwYMWbII3oVUj9UNF+p65YseI+wMnqf8Xt4Aw7XODy6oA3gq44HrK+/YoVDbRksteJ+whx5wp+rrJxFmOcXWvh137t1/BTP/VT+I3f+A38hb/wFxbD/czP/Az+6T/9p7PraaNqnkAG9AjocDnZXUwLxiDMgQcFwN0Un2HP/CVcDHidNeO8j1mAnUWIzwLw2gODKwXsJMuARHTtkGTWHUoiYYdESJ0j9cpLmHYcYKYpB7lGMoNV+hBmjpL3laAjlHgYYRpqLdOLKrEDSjIOSGTfiFJbrkNJsEVJg2Y0+TBuYCY6HUrNLidhlQAByjrWzm+Yzggj1WrHjvy+lWuUsGkeyjUoycPv1NyrSSUNy/5q2Yoj2aZPompLajmULALmpiQZtv7dV9e1P1ozANuvJryU4GvlQ/Ko8g1YmMzUOs2VErDtgH1NOLakt6yXatBpmjVxVceXND2AUPf7Un51mpw+asn7Ut5sHw1Xk3q1+dEp7ZDMU05pp+9Oflu2YdKOm0gI5xq+4qyBu0zadd7mPcmpSFuvllpALRJKQ7sjmlYpVDn31vHnSOcZjGxTAu64Ob5YfKeJnfn92s9aXWe7bsYztaxLm6DTNkfterfjmg8sLJKPbbObZbpKRLTCkHRCM4wvzPzdlmjGicjuFIIv1YH9cdzbnSFOU+/pW1eX47GELl9dfhbaaId+s/YrqWwb4S4Txnd10rKfWjJMo+UUM6VvC/PJPjSe59SvSn+k9eS6OV5x10jLcYrRbKEQsoniU44XrLg50pNen5ZacZtIo/r4inDF/YOtsyPqw1FzXwErVqx42+jQ4W/h/4jfwuffdVEeFW6XgOPeyFZ4tShpxYpHg9Uc5Qx3Vq1PfepT6LpupvX2J3/yJzPtuBq//uu/jh/8wR/Ev/pX/wp/62/9raNhf+InfgI/9mM/Nv3+8MMP8c3f/M3oEKaz8AAFkgExM2LUe/A4wKPLIsO0ETjHK7zCOWLW0NhiwAE9Oke7hA7ORXSbAcPrHTD6TODELM2HaZm9REkikHQhEcd7PZJm2QGm/UMCLQL48/x9l69zhlYNrg9gZBjJOgcU7wzdT6qW2pncG5FMRZIQuASd7KX7LyUeYMTZBYxkoNlM1e6KMK02JSXYHjUBoRpZ6l+OPt1Gua/+5VhOEpc0XankEwk3BUmu7OoPlzCzoYyjbU8CVLXqVBOMeC7hSUopSCYpmbeR8EpK1aShkjhKyg0wrTv2KUG5whKpxL8qiVYtMS1XnUaL/PKNcLwuK49PPwf+9FCFa9nx6+VaXV6FpsMySNt/agf8Hz4N/D8+BP6URDBQroDqurVQWymqJfhKGEb56DhSTPccEBzQkXiLVftGeI8ikeTnbYSbtONyEXwoCbnpt4Rx5VZaK2CaTWrQjZRD2XAlOTM/S15UUX4fF2yb5pqTa0DEAOA9dHg18zBCAiJW3WimKVVRdS5mc/Azn2xW2jD9XyLAoy+u+8ncYJnWvGxlHu3HKRlZXoJSpX7yknd9pDK386EwOB5J33Tlkh7SEnhMxk9E2bHNhwqfj29Syh43OvAquEzZtXz1XZ1fKRQ/RlReDaWs/GxsvwmsfDczoXn9fNgK91tvp1Ua7ddU4pDHPvJ85Iu7878rVixBiTSd+8zXW30eOiLiNOq8Q4/xiMbyimUY1d5aCN5//EX8N7jABf4MXzs5TpfNdr9tcF1ZiiFX3Hcs9RT9GI/TCtvurH27YsXbwYjxSRNw918LkNpu84Ol6yy54lFClUhuEvcR4s4O9G63W3z2s5/F5z9fvgQ+//nP42/8jb+xGO/Xfu3X8I//8T/Gr/7qr+Jv/+2/fWU+u90O77//fvFJiJkPSqxP4mw2cPDosM/8jcMz7LMQhWQdBYEBAR02wt50LuDZ5gJ9n0y+bXcDNmf7vBoNcLsB//gbRhtoLa0Xaol9ACO+nNxTrS2SW9QYQ772Acw0INNXs4Qk3/qc1zZ/38HMUm4AvI9EEG3zPWpkOSRCkGTXOYAXMFLng3yfxA/Tfg4jrFhm1q2v0tI2OQfwHuZEjc/XdznOi5w+y7mTtFl+anCpRlZfXWd7tA5fajue5Tp1VVyC7XmW2+MZSp96KunXfqX5Ua2LjoMt5qYflXiCXNexo9dIRvLkgfYxx2Bd953EU3JLxzJJU5afhLFOrkmCUZKKOtNoeRmvB/7rAQgtn3SqWcg61TtAbSumz7IpAdbZ5ysO+L/+qRB/SmgS5BhaL4DWLrQux5Lmm8arzWiqiciYGrHDWJUhAE4JkIiuH+F9gO9QkHPORSHcHJwfC+05IKLrsh8nF83vJeODYvQ2oaCkV1m9NBBc1rhREsTiJEKLv0k2lEvnAI9xpo1m3rOAl/k0bhnXqD27ForuKI1g1pREXKCYkqniuaZZ0sabE3PHRaZL24RlLT53JL2a2myH+Wv4zBXxr9b2uZoESxPgVenEqY+OC5fj1EduNr1cXdJTN2Mm+B5BgnB+buJYXJaWgtyAueHQ09OiiNLK8mbbyrIt7naLOp8p6me0df7g/kIncdpN8NP4GOGmMaNWv08fpyueAnROGacxlI6GBAADku/NWMwnxOkkxUrA3Ry20nmYT+6X8UfXIuAAvBMCDtClMp+MFQ8Z5dGb1snGFQ8VH+CDd12EFStOwn0n4HR9sc6MK1Y8Tdypgt+P/diP4Qd+4Afwnd/5nfiu7/ou/NIv/RK++MUv4od/+IcBJC22L33pS/iVX/kVAImA+4f/8B/i53/+5/HX//pfn7Tozs/P8cEH13v5xywQ0TPJe2ynKW+LESMcBrh8XjQtGfeZ2TnDAR9n5sZjRIceDhGHnEIIHmH06DYjzj94iYtXO8Sxwy+/jsAmAtElrTQSVernDShJsY9hkho1CUk4JDJqQNJCU8LO5fDUoKNGGeOTUFGzlLrPIdlDk4Me2HjgEJH8vWkZ9Hj3Nn9ocpJp0UTmAeZ7rSZiSPIBEHUJM4tJf3lKmlCpkX7t2I70HdcqoxIqJKEIXg+SF4k69aMHKT/zr8vAewxXq9zWe3klJ1Wtg/1Koo9aYCbzK01bMg79Dmq/1qSRMg8cO/zQ36ASf1pv7bfapCTrw9+1vTGOFU0nf392BrzSdiFnQvWkCCNaJV7zu6pSLWneKdTsqZa9zp8EKNNln3VV/BaohVmHmZGprURy5l0EuoBRx3WOk4i2CDgj2pLPN00D+b5cDR18f4D3EYgaJ5tRdBbXzuGb16ukAcb7y3pkQM1zu5yODvzyHn1IKWmmvppc4zqwtOBOD0xNlB0TB7jZr7k2E0tYU3bLWmHHbbvPNfwYZ2lwJW+l/eJ9pThbtFbE7+G/NM8gXJ13uk9K7BSMcJNIebm8p2yYnIwEM3N4tTbXTTZjZcpJi/46phtrQu6mWmfalz6ndaouzFVpmvnU9OsqP4xvAgcS3Wp+RZ/c+2uQ7HipgrwI0pPBGdPmuO6KeWfF40Pd37bELwl2xUPWvnro4FstPbl6qm3FXeI+i0pX3BxptcJVGlfAuuFa34gPDV/H1991EVaseKDgsUzDOvuteFJYzVHOcKfV+v7v/3589atfxU//9E/jy1/+Mr79278dv/mbv4nPfCadwv/yl7+ML37xi1P4f/Ev/gWGYcCP/MiP4Ed+5Eem6//oH/0j/PIv//K18h7h0cNjhz0iPC5wBp45DbnaHSIOAHoMAAYEkfjrNjiRdAHOeZyFgD1GjEPZdM6NaaEZZLFJyTcT09mXJA7NH+YoiEimIHuYaUmmQa2rPRKJQGKNGlo0jbiHkVaAkSAHzAkRknbc+/fAQdfLF/k7SQmSNZcws4+XVZqsF+UMrQO5GlbbRzWeaqj5Sfp6o4aY+lBjebiPjijNMio5l8k/50Clo3KfQFJOSR6g1Ppa8jenIOnDflHCjuQa21ZJTS1HZb5xInn0WkRby0/bWdub6bRkQpqnkoM1yVaTcqoVp4SThttUBJzmyXitduT40PGt4duyreW0+Z11Ug09VPcYRwm+VpvVxOmxlZavGsFrxUKZh9MBSrO4iUTjVBNl2iFiYLqAczH7s1RtuZy1s+fAOYrMAT2tpdREqTtGgs3q0yZpnKQLGJkTp1+jbNmVbKhJqlLA6fP/pb+6lIISccuET5t4moen968AJ8TbcZJpiQxjrRLfXhKox0UUdb3aWEpD+eT5/atIMR0Vp2wkTisr0zxNNMPxckra6aEc8wvgdKsGZRvcXGyU+n+EB82P3AxKV92W1oJNVKTG1LciQ9wejOarn6oxzwU0C8554aop9P6g9TJIL6uxqEUolg96Bqc+lgDcdMytuGssPR98O0e4aU7nWz0UC4kW1p5+F0h9pifV1qfubYHzXnpS7sw4z4o7xAu8wMf4eHbd3vSjXJkfqguzDeuKFStWPHSUe3oVSa6z3dvDf49vf9dFWAGY8sJN4z5C3Dm3+LnPfQ6f+9znmvdqYu23f/u3bzFnlze9NBDWwWFEEGdbEXHym0PRUIchGz5TYTLFiBEXzmPjRvizC+y7AYfLM4yjQ9dHeDdg2Hog9imLfQScN3KFJBJn4FpG7WDmIz+GkTN7GCFDTTmSW0yDPfkixwVM+w453Q1M80vjeRjZxmsRZqrS5fsk7Jge180k40j+AUZe0NddhGnWqVYbUPpzKy3szUGelMQRyTGVSVLLLsL8wilBpXlmIi/Wkn5tOw98wzcCf/ZnVdlrTSi2lbTv5jyTmlpWJdWUmOJ3aiW6Kk3Gj3K9JtqUmNM3fa1NSKgGnLahk3uar7YRYO1ZX1MSqy6j5s2/XurIeqm2mbYJ4+ikTOLPoWwXJddqgrX1t14d1emdIhFWwraV3lTmmH9L5puYWTC5nAlm5wJKrbYIuFCYmgwj0PVyHw7dJqDryskmRocYszjcZS03km8uxfWSRpovS8N61ForK1eKkpVsm/9tESJO/l8iK5Y0y1oibFd1WctP13I+KjCwaz4TcWWb6jmAGrxXDp1jA+kqIuyUhfzxdlqObxpLx4lFj2S2+TZFZ1fXuwxzqoYRw3cnE4I3y6eVrzm2TDTTeIP20pytLLezmdPJzcZGRNkCd79pdNjiHJf55UsSo9zG3q/Na48NttjhVUMAaZirh5OUT8vBtDZd2mOc4QUiDtg3HMLft/Z4rNB2bs1O9TXOvNnIc762Ejz3Ee23zdo/bxP2LgtZVLk+Iw8JLQKOKHcF5dq7vQbXDdSKFaehR49hNcG84h6hdk2h4q2nhr+Mv4yv4WtH3xV3hT/EF68OtOLusWrCzfBIq4VMvY3ocImIM3Bh102qUC6Lxkbs0eMMl3iJTR4jIy6xhceYfTMAHpe4wHvoXEjCaxcxuB6uGxEPfRJe9wHoI+DHnJ0vTVHuUZIdXf7+qiw7PBKRdAEj4Dh775DIijGHOaDUNFPzjWcwrbCa9GFZSOjQpCVg62FVyjnL9/coCQ/V7HoGIxmV6CM+gBF6DuYb7QAjSS6kDpcoybIzqSvrcYbSaU/NA+xQShFJbF3CyBn+ZRj+pTnRHfDRgLlfNrPkZe2rRJ9qFWranIhiFVe19PhREq3W2oLca0lL9a/mqfsdNUHp5RokvI4DrQOJZSK3VVEeJbGUBKxXIXqdJlRj456GV7JO26pucyUKg/z1KJkTPpu1Jpv2uUqnW3lp2fQav0/adFE0NXNFaZZytv/MLGRj5dY1JLcxhkymRTgf4V3IWZDASwmNI9D3IWnHOYvrXYu8qQku1XqzAaLXrLil2qJ53GqLWjRee7HaFoMaIVhu6C2Pq8idVhPXWmzW8SUJRx3BJeKKxF0bc+0oHgRZSu8qs4HHzDQ62DGUZSIupbBc5pIwXQaNHl4VKuF65hBPS5vpX93/7XjIIzZMU+LpZdSHfk5w3Qyl/inTvK2NHcVkNHzJtls2t3obSKW/LNT/5z123zavHborCDiifBGM1fWkwxvyqyld4wzwclogetR+KMc8D7VeZSndmr69f234rqDPkLYL27FF/upyiGHGqQ8pPK5TXH7DrXj3sF4KMt+tOBXv4QN8dAtm6tIcdtP39Ir7jqVZkGtRs7CB7CwE0531mVxxFVYCbsX9QLlvv+2Z66GSzX+IP6yOVL49fIgP30m+K1ZchUd73Mjn7VQPhw1G9NkFeocRyfcJuaoOI3o4ODzHK3jsAYzY4BI9RmxwwBZDVhjLi0QX4WJACID3AS6begsBQMhT7pRBNNJqByNt4lRQzHzdcpdPrTjVAKrJNJXx8feL/HmORFJtqjA9TDOO2m6bfE013GoTi89yWfsqLebLcDSxqZpJSqLsUEo5tvJ5IfV+kcv/POdN7TNIHiRZmB/90m1znLOqvM/yvedSPta/1hA7z/E9MIT8m3FYT8YjSXYm7ajpKWrCb5fLcAbrL+5Fo8Spfa/18qG5yHPJh3XTNBmP7VSTREqEMRzDsl2lXbCT32doj1UlOreYr0pcFbaWJELKre2ukrBa0lijJg91bDP+BuW4rU19QspXm5pkeB0LvrrfJHwzad+PgGuogboAeAfnUWipAUDXjxI2a5b1Ab6LyUSlT3NViKmyk3VcN6LrArrONr0xRiBmQq7RfHNSx353WUDfi/lKC5v0irnJ9rLR1r9zHCNXriJqLHW+B/Q6vdKV6dVkE6/VfWIkY0v7YYmymqdf59W6evz1fGw564+2n8tlvVq//7guXJBwy6FO94WW2ve00CSCr4MUOr3zr2sUMj3g4zVqU+fLv7cj5rVy3C6x4hDR5bFXTmAtivvukCZX6t4q3T8KiVWfKXnbuCxMCJwKXcQpBdBhzG0f8xEyM5Fn7TFOn3Qt5t86CtI5KJfbyh1tJxpHGOAxwIwDa3+/63a+Lpa9c87JNdaX4yqgw5Dbl20d5Hssfrupv+aLH/274r5B+x7AFU/JiiV8C/7Xt5bWzfymrnjo0O2UHmXwqH0Pr8/oihUr7hPitDJ3Wc7sUfrtvs0Za8Awk2A8BLwrAm7FPQK5kJt8bmiO8hd+4Rfwrd/6rTg7O8NnP/tZ/M7v/M5i2C9/+cv4B//gH+Dbvu3b4L3Hj/7oj94s02vg0ZJwQNpUH9CjwwEdBvTYZwFsyPdVeJEmtS0OeStNMcgIIGJAl0V3STDrXUDfJWH29uwC/fYSMTjZcztMmi2EB/A+ElkhwbBBIodapvuewwT3/JA0IvHBAery72cwjaTnOU8SJSSnSFCR3NhKPkreMc655EuSYpfDngN+C3znt8MIPpJQH8CIPaIHzr4BJdnB+ioc8Kn/BkaysewvGu3F8pxL+bcSRwlG5L8kmuo2531Nn+G0/UnUMX22GduIchmNw3ITSkop0UOyyEl6DM9yt+zrkhjjRwkf/bDf67h6n99ZHpUxsZw1GVWjJohb5a1Jr1rpSuWQGp71V4KXZT2moab9uJXwWoeaqNM6a1nQ+K110+eWL5OaPHSurBdiIt+6MZmsLJBJLB/hMqvmfEDXh2QSt4vwJOx8gPcjvEvkmnMhk28k9KzQ3gV0BQFXimF1QYlpHpTyFGUsCSdupknGLWmy2ULW0jvGp1q8UUiuWN2tf83bc2520ei0OWGSxNslyXVMTB2yuePWI6Ii8jaOiRxuvgw/hU459lAztptCHkvpVF01IxpOFcW9yUbkdB06jRNzz7/JduJ2tk80s+0mRfDbJ0u0pLF59W7BmaOfyKcoE2oiTPz0hD0N8Vz9ErdrPOLgAJlxPGI1XjleRuhMZi9y2pBIs7yhHl9LbX3VOGzP/sv3Yy5rHaY+C6zlCnCZhCzLBGCacVM7TSt68JhIQr0IctUTwDfio95CPVqkZ4DHh2wMrP15ffyP+L1bSys9UWraZMVjR2Pb37hO7ZKQx8exN8aKFStWvA3wsK6tBluHLG97z7QSWiseJLo3/FwTv/7rv44f/dEfxU/+5E/i937v9/Dd3/3d+L7v+z588YtfbIa/vLzEN33TN+Enf/In8df+2l+7foY3wKM1R3mAR8AZAIdk5qJDDyDks74jHF7hvUmEZWLXNF2O6ODzxrvHmE3UBOxwgYAOl9ii8wGdHxB7YOgGHC42GP0IxGwPcgfgEIFLUYkiURYBvJQCU0Prdf49wGb0FzAp22uU5MdZvk+fbYDtX9Q+EQk39S02SDrnKMkBXqcZRr3+AqX/t11SAPx//5dcB+Jc0tbybYCLUdrhEqbxN0obdMBXPkKpbUe7S/n+pDVIv3R1+RmPhNRB4tK2GE12kghRE5skeBiX777aJ1xNrjAPJTeR0+DvJZUIxmM9PEq/1gRNbe4lHsO0iEV+p8YWJVNKaCkJWZNhrEtt+pPfNb0aDtZGG4nbknlsqngRNq6OqX3UGmo6cddtUCoYlHWvyUZf/VVwDOgLQs3Han5TBarvzTEQ4ZwZLaO/NsABmUzz3bHz/g4+a+g6lzV2HcR/nIZtmU6gKFd9w6XrRlq1NsJXG3QqzSCaFpqKOid+clZWvWZ0SFnusvzpl/nhKpevbnrMDKFKU9Nm/VUkG6ZWmtMzSyYq1dBfGw5L5J2VZgkBriJO65gt8rSVyrHFvlGxvrBLO4e+ao7nmSaeAHeC+cOSSFNNy2NxDDffyHA0dTim4bgUl1NMGjk336DxSYygKVQlwG9342d53X7ap6B+enXLm54mj7EgW95+Gd89KKKsax/gp2cj3W+pkNfxUp9bPJvpuEQaZbTVOdf9EHN4Ulit3Em4WX+rk+Fa89ZM/M7jMZzDMHsZx2q2qhc7LTzF0fQ4QT+MCTXBuuI+ID2R4wmryRWPEfMeL9fCuuZO4Bx/bIO4YsWKFbeL1iy0YsWKBt6yT7if/dmfxQ/+4A/ih37ohwAAP/dzP4ff+q3fwi/+4i/iZ37mZ2bhv+VbvgU///M/DwD4l//yX96woNfDoyXhOtlWpU1XxIA+cwD0ddKhQ8CIMAnVKG4YctPwzOwZ9hgmgW3ABXaTRNu55Fvpr5xF/OfNBQ77HuMhswbPR+DQA9EZCRWRiKkOyR+ckkrPkKQJJB1IGnEFeo75sWDG5XXVqnkt8bdIZVANOL2v/rA9zF+bSVzmYUnoMH2tjxJi78EIo0HSUS0v4oV8p/SW9eNvwMxR8hpJsqW3oJo6pGZXyH8HqRd/b4BCDtwj9Q8lfWzvcxi5yXqPqLSbMuh/r9bUUiKIWliaXi2tVzJLyTHmy7r5Kg2SncpyOImjkjOVU/gqLSVmaxZD04fEYZrqWKlu35YGmu6+avJQSUV9Lni9JhUnP2yST00m1ljSqlPTqErGXXmYOgI+5LQcksZsZtldBLoRLqcxkWYRAMak5eaTr7d0P8J3wdzKMU3YfcA1CLg4fXytsSv3U5Qk2uUVJeD8RNSZMNQVaZTCU4ptmQb1MMp4vDsnKGpHx1pbJfXSdXqTCM1ubxMKEW3izGiO+eTr0Oc8YxUHjTpouef5l/kdW9AfX/D76a3WIgBPw+nhjpclPSwB1/MrdtWGJvV7OdqWffK14t/0tL3SwNc9oJXaQuh1ALWm6U3Lw0kpTOP0NjeEUVr8LkVe53iGiIiLaXGiqF8q+uTPQxBPZ3O8PNsYPXsqTZmepjGHtRnf7pbp8yXIuVKJU8vT0qTH5YRE0HGG5uiyxcQIN4VnXcZ8VK4c6fpdFwpa72Pk44rHirTKOv7mXfFuob0TsPrpe+qot1MmoggzUYPtJhhy1WxdsWLFm8AOCpt9nHS9k73v09ljrFjx7vDhhx8Wv3e7HXa73Szcfr/HF77wBfz4j/94cf17v/d78bu/+7t3Wsbr4BGTcCOAA0ZsM39A4WomzrLRnYAe9PCQeJUN6I3D4TCJiWsOZIs9gnMYxRbeH2TWo98MGA+5ab0DPrEHDh545YCYr5PkeYZETHEGv4SRFMhhkps6Iz5Ing0w8ojmEF/BiLtNvk+JX0Ay1Yic/jaHFQ21SavqPIehttUg5UAu94BSvs5y7GEkEeuhq+UL2Pq41t+uNcVIrESYhtQA0xRjuV0uK8tEjbYLmGaZkm29xCU7f4CZCiUBp/Kj92DE0QAjlFgvtZJBUmvOIhiRx77ZVOGUEFOZFPviUKWtRJOasWSbk3ylCgaquDVRSIKS+SqZpyQt4yshxf5UbTD2XSe/tY7Muy4HoQQX89Hx5CWcPju1Zhx/R8zbtibtIGmgulePWdYFmPcjCWydPfqQyxmNgPMAfARcItpiNHOTQCLTuj6g38SCcKM/Su8jnB8RQy6Yi5P/OJdJNtOmSwV1GOH9XLPLFpck1yh+ZT2MTDkuzjLTv0Z4cQDEidibxzettTI1NTnpcmolkeYnkbHVpdS8s+spfIuIoaA4SLea1l1JTYVJq2Nej2WND61FG1cRYE40qpbjLvWLkpVL+V8t+qIp0NM3H6eK/9niy172AJc1YW4KpSRuunlS7bCblyLp5vdwuA0zI6anmv4ea8Prgf7iyrmCeDMi0fAar/BN+PQCCTcvE8sQ0ME8mhk1U1Pht1XOh4vTaWrglGME9bV6xl2a/ai7qLN1K149u5ezyPHeXCLnVjxN1Au9VXR2n/Gmb9cVjw/1k9t+y9ibf36tTunxwt3SmnLFiqcJswaUVqwRUew/AFfJQFasWDHhFjThvvmbv7m4/E/+yT/BT/3UT82Cf+UrX8E4jvj0pz9dXP/0pz+NP/7jP75hIW4fj5aE84BoswHncLjI9wZ0OGCLHQ64gIPP7MwIB48NaOKmy6YqL7AFfcQl6m6DAIctBkQM2KNHiP3kdykJz8kEwQTsm/w7wMguJUxIeu1gZJqSS3wTkIAikUNtNcA0rVy+voURCYOEJ0i2saEiEsHnchmZnhJylExTRqZyUC9pUk2gPiryQvKkKUjeowlLlr8+YuKQiLI6TxKO+ibc5ryoQQeYlt2ltAtJJG1XptPye6Z7eBJclIazznyy2HZewpPEUdKIROwu39vKdRJlSjaVh8utXDu5rpp46tzSwcjNlqaezgq1jKK10qjbg2nqLknVRXQXpURsC6ppWRNckOutT62i4qo4TIMEYYtIrWU2dbra11rvkCvfR6tfiEAXhLBzlo/PicdMkBRqaxGb3QG+0I6LU/ikFZdIN9+bVhwQ0GWfchaHlQjY+CD6axbHhlmL0FLizAZHuSGOEn5Ouzgh8cpuX9KUspNo8ztl+eZ359MQS+Fm9SvjqcacipU1P2pmtBfgx8TDLh/uWPJMVmsQtsro0Dak5Y6ka2GOi7hOJ7dO23zUVMibp2jnoE2/4bobodsQwdaKzdcHN3m3JSzRCTZtI7tbS1vTL/OKR5+F6+Gr+NMblccMkjopU1mi1F9jI/aK28NVrbokujilN9YeW3F9tGfAdSzdV9iKod4ErlgxHw1phJgvCT1cmMQRHjRZfr2jYw8XKwG3YsUp0PdLKb9QUZP+XbFixTVxCyTcH/7hH+L999+fLre04BSu8sFTKzi8azxaEu6QT9FTKHOJAR16REQcJnWnJEYlyKEAXSbcuswTBGxxQARwgV0h7nMAtnHAZXRwfkSfzcX5bsD+YoNhv8v5CNvhGhuKeqZ/BuBjJLKEJIr6KlMNrAgj1kiSRRgxpRWcCAGYnNVXf5UI2ue8xhyeJFJE0qpTM4wkjPS7rgGpHXWQcCSjeE39vzGsWphovQk3ElfbgZpzJLpUbYRmPdXEJtuY/CmHyUHuscwkErU9R5iJzNovGCQch9xW7qsmFYeKy+mpBhvD1D7M1MQj66+aYzqOeI9l1DGi+TC9roqv/dFLHG1zlhMox4De0w+vsU1rIo1jV9PihE7CVyXhbNuI0jQoyUu9ryYkRVvuEz3w50pgA6XvvJZWIeSei+njc0N2+bsLQBSTWw6ASyy7mypjwgfXjRMBN2tQZ6wx3yv823Vx5gPOYUTnAjzDThtV39Aiq1EvSO0BT81v5h8tvyjfr/Ix1iI0lKyLk+ZaaayRW+y5cUKg1hbjoG5r2ykJ6eRa+dtwrK2AJX9wV8U3z07LaR/zf3Zs6x3l77G+6KCeUttwR+vWCq9G6pZhBONp6Zsxousg5Pe85XgTpFF9vL9OSeUuUE7AdyvGdHktVZ4SvWme4cbt2SII6xKVs8rVhPWKFSseA1bThg8Lqx7PilMxPwRY3tPDdMmGAjeG9XwQ0F47rFix4nGhJTwy9wCP/d2zasqueJuIHojX899RxAWA999/vyDhlvCpT30KXdfNtN7+5E/+ZKYd9y7xaEm4AI8tRhyycCj5g3MY0cm5uvKkdBJL9tMVn1WrEk/hscUhi2tGvMQ5AjXtHOC7gOe7V7gYzgEA/SbAZcH6cOiBzZB8wwUHxGg+rPayyNvCzCc6AJ9AIoCoLfYSphFH8oiEAk1JqhZdn8NfwhhGmkPsUvhvdMBXX0pagBFOJBxCTn+HkvSgNp+CGlbUoGM5VbJNUo9pIodVDT0SjUqOkPAztrQ0cUgSSPNgfXYSbg8jXnyVHs05st5Dbi9tHydhReFxIqACStOZQerZajMlmDqUZBM14th3Tv6q77go8VXbLeMvfACMDvjqK8zllCy/+pbjPfoPZL7qd41aaqdACb161uHuSc06KrnpJa6rwkPuCa81Iz+38r0m/uqw+dqfazqsp5LQukprvVh8SJpwDjAbkjGPrSD5Z80rn7TYuj5iHEOaQ3KYdHpDCuwCvAtJC86NOQue8Ijwvq1R1nkVONPDWzVY8j37m9OUa7Z0M3OWqUuUsOJZZvXRZjSdNRnzqIXlpa8s5hGn73avJKSWfM0RLWIpTOnV945tw9vpX7WoJcnUJriuNpd3TNMtVn/n90/fYMSqn1r3E5F1qqjitJw5CV3la65O+yrtwXk+IY/fNyVh3lQbrp5OgNsU/2Qy/9bSW0YyUBpn0+y7w1IJ7CWUZhI7gFC/5lesWPFwwVXIzXSlV7wr2LpvLFaBK1bcBKojz79xmh2Aej9jK+x6VXa3x5keEtJR9zc7gLbi4eJh9X97ZW/zQC3nePxP+VMh4Pw1PbevePjYbrf47Gc/i89//vP4e3/v703XP//5z+Pv/t2/+w5LVuLRknDU7zBOgRNsyBpyKoAbM8/R57gq8OXSjN5n0sLsGV7jYzyHiZ27wkwcZe6+C8AhCdDjeWaZXm8wqaIARqyRVCFRo8QDkDTPSMjRLxyzBBJBMOZwJJsAk6IOEj5rbX3NwXzDvZL7Si5QwqjkEDWL1HzfFkZ2vMr5qVYXy07TjyR1QpWeloHrYJJjSlSxc6n59zJ/38HIMfrSI0g6RflOTS/ktmO61P46JpU7k/gsM4mrUdqDYL2V6CJJWEtidbegGm1AqeVWE1Pad7kOf8K2fA7TUnQShunVRKYSjgyv/vBaCihe0qr5ldr8pZKJSl4SrKP6lFMwPR5eZLm0fZXYO0UOsyQRV83Fom8iUJmPRB/y9So8E/UBcCHNCxHZb5ub/Ldttuleqckmi0M3wnmXzN96iP+3AO/jROgxY4cBndOqlxvJWouMvpWMWLP7nRixNM0mJ2RMzL+p+aYflidROKUulZaB8fVeTcqUByiUBJlrszEUyb1j1/TeTbbcDL2kRXZcw0yH37z8ZQ7znGv/fu2SnYLTyKlEB7bNiLZyPnXRT8r2utpt1yV/OkSMWevv5n1N06I327SRUjWCVKev2wDXOne/qaRhTV1EzPHuNretnNlzJoDTV3CUb499U75ixWOAHftJf9/UYPCKdweu8NbZd8WboB49tK5BxOq3bt6cHDRrGdK/yyNU9xkPh4BZcRe4v/1fC59qoU4pi3CTUPSpPLlPC2F2yHzFu8DYp89N414XP/ZjP4Yf+IEfwHd+53fiu77ru/BLv/RL+OIXv4gf/uEfBgD8xE/8BL70pS/hV37lV6Y4v//7vw8A+Pjjj/Gnf/qn+P3f/31st1v81b/6V29W8CvwaEm4tGgKOCD5cNtijz02cPBwuMSIZ4iI2GGPC2wR0MMD2OESl9hMRN0BG0R08IjYIGIPajFEeOzh0IPnvp0DzjaXeHW5QwypaccR8F1EDA5xcmEjLwU1kaeS5JYUVueRDklD6zVMg4zacA6JKKHmGrXrSBSRUBgSATARIc9gpBrTJJnkczpahg3MrOPHMPIGSGQPclok/zxK04eAaYghp0+SjWQPpao0kbnP+QS5x7TPUZq19JJGnecB5Tv6PSkvJA1q9F0gkWvcz6tMWc2EKpnVyT3VtOslbsvnXL0KUO0x3iO5V5N5lCTXxK6SqGriUjX2crrOwcYqoWUiIap5aXtxDKvJU/qKG+Ua68W61wScr+4DZZ9pGWoST/sBmO+PNM2WvzrWkyABV++1QKItX3SZgFvUyguFDzdAibZ0b349F7MP8N0I55IinRFvqeDeIxFwLm8Tfb5/hAwqB2dKx2GcCDRtqloMkq7XA5xb1Nr0pM8+5igWG3MTUeCd2qRDy/ca61GXnZjXr02mtLTPzK/bMgGz5HuttC5r5Vxq72VtO8tpLESHmi7Adj2GVtw6j9NwdbjZw3AEecyfHD7FuR4pRtrwumQYJ483ORX4piJCPk1p/OgZ7DffFJIKj9VYvSsolZXGSFpPpW83JyvvEunFbtThMD1xkL9ens8VK1bcH5SrGffWZrsVdwt7V/CozP17e6x4+JiPKt3Yl9vFubjezOHbudT2UcAVK1Ys42badbpTskOuEXE6YGd70DDFWN8mK1a8PbxtEu77v//78dWvfhU//dM/jS9/+cv49m//dvzmb/4mPvOZzwAAvvzlL+OLX/xiEec7vuM7pu9f+MIX8Ku/+qv4zGc+gz/4gz+4WcGvwKMl4Ti9+syBU6cDcDjHAR9jxJgl62c44AIeARs4xGx2socZNXNTihvsMaLHCJdDB3QIGDAgood3AefbSwxxRIwO4+gRw4AYgMvggLFLpimHriRmBqR8qLml2mvvI5FA9XuJhIOu9c5hkmEHI9Bo/vGA0m/ZK0mD5jAdTJtrL2nRx5wSfYARgkrQKQFBGecZ7M1HLT19C2YTmVP5GKaX9M4aYTW/EWaKk230Ot/bwsg8arqp5hhXz/x9LuU9z3nU+3pqfe3le6zuK3FH0pLEFdsZEu8gcUjksRwks1hXbT/9q6YdWW6Nx98caxtLKwJGlvV2vWkSk3VSv2pAaSZUy0L/f5C863HN8qiMX8lWNVPKe0v24GozqEPjer0KO5N7qMqifRWj/abWmxuBThJ0Mrh8ALpxMhlpmUR0/QG+y+Sn8/B+BFw23ugDnB8LzTjv46Q1F6P+VlVSO8FJ07s2k5kuMP2jUfPIy0EDV6TF71MDTOJ2G3pJm6c+Xzo1x/TN1EE1Lu8YKLqP1TVCfcGVp91ikWZoUnWnmJ5ws7zLWpRhbcnfSmd5wW/tukwEHjOxeSxuGSKcFPbUTVDAsm+6Et2J4Zju9USoNT18PaIkTiPkpgSL+f97M+LM5T6y1DjdvhnSSyDk+cDo0FKs2aHDeGunBu3Jtt7UVrpPqFtY1eWNmqtL3XpFrVix4u0grXhcIQCPTZMJKx4yuKYKKxG34i2hk/f+HBSycFfRMpjaWueM0xr+tlZ2K1Y8JlxNwB3bYdlBV0yhbFWwYsWKd4ehcxi6m63dhu7Y+3gZn/vc5/C5z32uee+Xf/mXZ9difLuyiUdMwqVl+wYDRoRJt4OLHif/Aw4dBuyxzSe1Hc5wgUv06NFjKGwKMib/xhx/zGRfh85H+HjAIXbo+wOGww7wwHZ3iXHsEfoOcQjAqw2wdSbv2cfkuEv9bpGkOM9FP0Myu8iin8G0ugCTF7WkgerTjVU5g5mNZH4kgUgI1qYimY/K6l6g9F8HGPF3BiOSWCaa09S3pWqJsbxLzx1JwVoKprIzgqYM1d8a4xNqzpC/FfTNVpeFRBLbEiiJzWf53gGllpj6pWNYbYsRRm6RW2F7KmlJ7S9qvrFubHOmp4RcL+myvet9rWrQaRmWTJPWpJh+V0KOaWt/AEZwqXlJTUt93bXm8J2E07g6dkliElHShITl96nsuVFJqLn8vR+BmAvUBylXAHwOGzJR5yJcP+R5QwaRC3AI6FlvH+H9IYV3NrAdXEG6OZeINxYHGjKHczGbtESap0iCUDXfC4Gii1WAVEBpojLF4Skzu6eEmz7iJiJb9n9mKbjit1EEqTQmejETl/OhkB6cWgRHg8G1TwiWF2gNKZsA2sRMnMxylvU4Jh4yUmUOJRPboHbREgGnuRwDh/3yXaNtr0rrav91N4M7Mf8WbmbnvjS5en1QtzPcwqKqprXbm9ItNtgXjlSvRpoFOBmSXLf0b4+AWwJnhrKtr29G9E1KcKozcn0hR2yxRQeP19kOuOr7RZxiknXFihW3j246YrP6fXvcSOdJ6xN3a38/JpzjHD16fISP3nVRFpFGX+lHVncx6Zpq0tneBNMvoLX3MazjesVTRqxW6vpur9fvpY90farWIxsrVqy4r3jEJFxCmrYDNhhwmZ2FRTh02GNAD9MHcdjhAq/wfJrqO4yZxOvyOYqIDS4RsMtczx4DNoiguSWHMTepc0n4e3a2R9ge8OrlDsAOXZd8NsV+wIARuKSNQ5jGVYBpb9WEApAIhwFGyHwDEjGnZBxlaRElgeSBaW3L6weYhpD6+aoJkVpr7X0k0o1kBUkVmoA8g2lr0U8btdtImoxSVjVjyHyovVSvZ7U96jcsyRaSi89yvP1CePXhxvqyHWvya5DyqvlHau9peqwjyS5dN7BtSXaxXCTT2KZ6X+sAlCYSXc6Pq3zKZumXzhSPyvy0PRXUlKu16WIVl+VlnJZ/ODXNGFC2FVD29ZJmHDUYhyoeGRfV2NP7Ol61TKpNp+2g7YQo6UXLb4zAdsz3QtJwcy6bmXVAx3sRrg9wLiZNNR8RRiDGlHHSfos570xy+UTmOReN/Isxm6nMQnMpk5t+2IAlUeWc0Qq6mLXFaq1Bhimsl5jUjouyicQsjfS91Epjs6uHTt5jONPcs/KVZguNEBynLm2dhaOmWHk9FLlq2agFVNcHOZ+5Rh7zGWdT4TiJ4luIQNUCc1TkbCONq/yjXe3zqyQjl8vBv8fKdHV5alAB9xTc3APMdTXoAD7YqQffxNxgGjVjcxxeNx1FMSlNuC4BV6ZnePtiH1c9lYC9SO/e88/NSNrUt69xCZY1eRPmosGebz3KUM5jK1asuE3YUZ234/FyxbtHmn2DrLZ0w/j2x8Bfwn+LL+F/eev5nn6Y5GHh9eTs/v5ivvcowZH5HOf4CC9nYcpe05UDUBq5r/u3lco67624XbybuaV8Dnh4ljvRAHqgtzBqun8J69OxYsX9wNj3GPubPZFjT7LiceERk3Axy/w9egy4wFk+qewmPRA/TfYRlwiI2KDDiIAuE3VpW7fFJQ7YZvFehx1GHGC8zIgeZ7jEa5wjYMwvigjnUvN6H7HdHXBITpow7FPM/mzA6C4Rhy4J8eEA1xsBdoCZLqQ23CXmx8aVVNB13KWE4fttAPABSpJONZyouTbIJ0rYMyR/b4yj/s8gcZH/XqD000W/YEDyw+aA/jVw5oGPVR63lzKRLOTbtibLGI9+8LrqOtugJqnUfxxQakmpNiLLQeLzmYThfV0zsw15r6VcsMVcO081ElmW2l8ZfdSp9hk17hSq9ahznvh+K8YP25zfWb6aoKu/a5vqfSXvdH9cS8hZJyXztG4tk6vsF7bdRu7VYHimWc94WjZqXoZo7VazPg7AJgjxmIXjLseJQp4Bk7+2EIDOh2RyMgZ4PyYCLsd1fkTXBcBFhOABF+F9gHMOMcZM5AEhpDh9NwjJ5uBdIt6+2Tn8kVTP5/kowMP8iS0RRhEeA3zRKAEdhgURBxfC1HgjCWe6Nm6aYDzmC2fTzlsCxdu+OnWq9aAfOYrGddKbn5fmW0HrYuXSOJqPEoP1MBtBjaJWXUoTh632s3rWSGXVR6mNU0xCsn+ugm2EjpFR2h+nIZEYpxN3XdZfv/4m6jjZeSzWTeMqks6pPXVvDndrZZunzENJmk+N9/DeHZxKL+z6Tt/u84Z5X6j564sulT/Ai+iCxw9G6ILEy/i/z3VdseI+QucIe1uvT9JTgq2VzLePzcEOS6upu8K7IOCAmx4mWfH2EPESL5trQB77opMA6tPbzqU+tFgfWuJ+pXX4YGlcLIV7u/Ong8N7eA8f4sO3mu+K03F7c8tVR/zqfMx/dH3Y1rSg7ff65l+x4uFg7DqMNzRHOXYrCffgECYazQwBJD2HHrVRSoo6ewzYo4OD6n14bDBiP4mOkymyA/zkYYUCmA0OSEPF7AYmX04R/WbI+UXsL7dJi6ULgPNJeN8B+NjDTNxFoHeJHHiVC09C6gzmJ65HIrQu8gf52g4oZGcR1uMksEiMqEwMMGIjyF81FzlIXNUcg1zbwAgoPneq2ZXl8sM58O2fAf7df4KRQyRELmCkWy9pKfHFcqsvL8r89ZnV0b6ReGousXzjWzySYiTVOiQyErmMrD/LoO0RcliPpOF4JmUn2cd9IwkwX91jmWuznvRbVxN9SlqpFp6az6wJMfZva2zUeTCsli/X4dMb4L8ynGqubaUOkHKwbkyDY0APuJLcJGHHMixJudmWNH1JLkjJXO0jPq7Mr0ci0wpNszxIfEUkTPLYAb4fEcbUcA5hMh2JPAcA6S8JOOcinAvwXYDL5iW7LiAEP2m5OefQdaZR513WcotZQ8ylxvII+KOqgokSOMg2zzrdhO4xE21RdG4szWUigRUfMynjJ0KstvJJYT+12uYdoJRXamfLt3xpt3TKLCY7WrtzPkhrwkG17DSfU/yw6d+SILQtsnLH87ilScCyXEvknmFuZnOez/XMRlI/fBkhE2p3uQm6vigtjf3rauhZbukJuR0yyN1a29xcM+8UeBmFY7Pud3syvXzigjwvD2GDvcMZLqdFl/YUW7JU149Hnuf7TkKuWPGuoaux1ffbCsLWmjwEdtqRoxUr3iV4yLBGbYLcDCLpITmuVkfZt5GaCBLeQtvKyg7uGf1Xh7mbFUlEfOsE3Af4AF/H199qnk8DSpfpb7vvs3y0jGNQs+66S9e/xOrXbcWKh4uALkvabhL3cR46esQknIPHiAM2GLNAXEWiSbictN50udMhYJM13xLFloTLSbyXnKRRVbvPamIjNghw6LOjryTCDCbSikmN2iECDvD9CLcHhqF8WQEuadnsu0TK6W2ac9Rx+D4SOcdw6nuNBMUHSERcTUDsYJpySkiRtPI5PZJ+tRxuC/P/poRV/fakSUaSdsyPGnaZcPt3f4LSr5eD+UW7gMmz+L6nBlaX09JD6kq8xHyfph1Zv07+UrtMTT2qLzw1yxhzOCWASMaRnGNdlSjjk6Yaa/WcwnxoypIknKkVWb22MJOWyGUaJZz280bKXcsGgXlfAaZxqeSYau756jpJui3QPUcalyTBasJPiWBFTYTVBDHj0YIr2y9IfK2XmsIkGal5sh18zPFyohsyZ9VJwf4AiPYa3AhEZhLh+xFdl01PYpyWlc4BXR8yn1cST86P6DdZqyhNFTnHkoEt/L45/nVwGNFVWklRfqcqGkkzTuFN07ckaSJ6jBOd5VBTXnyosrZv9rZJok2bVr85Sd9E7fWS3Rbyc/OVmrdOZPVCPZEwpWZYmU69BLCjFAqGdVPZ52XlZpdHOszEpm5wlzYUfEctkXtKWF5NAh7DqaSU5Xl8mZRKw+3V1UuqFPK6pyuvv1RLMXQavC5uT2x3ewtGJW7uSiyCaYVUP13AUJykuAtojdJIaWl23keSygi4Gq2n3p6sun61KOPuRGArVtx/pGVvWjTWZqjTZz0HvyKhtUp0UB2jh3KkY8WKhDn5UB9YnG/l0zY8FPd0v6Lio9LrcLrPJ0VX6umv7W+sFBr73ZqDVWywwaGhLbEScHcF7e+Qz9+bM4Hy8CclCkbJtfbH67p3xYoVTwWPmITjAmXEiB49RgygdltaSmxxiUvsMj9zwB4bDNhgyOyGg8M2MzMHbNAjoMNrvM4+4Wj6IvFJDi6LVhwiehwABIyxR3AdNt2Iw5gYja4L2GwP8J1HGD0OlzzVGYHdkIi4l5ssO83LHp9fSySgePRpByOWqClEEotro5rQQQ4j7uimsK9hZhr1bfgJJBLnNYzQoPlEc5Ni4cd8r/alxpUhiTWSLPyuZB+vP895k8DjqO3kGrXv1Kyhy3UkQXSORIIp0ahaVSQcSaLV+fAeCVHmQe01wPzyhepexecU5VPtwgGlqUqmxfxUK09XyiQZmWeXy8I02E56UM5Vaaqs/kX+q2Y5aeKy1tAjIZn5iD+6xFxLUaHEmpKxpbnvOVkIqaOaU2XdFUoUEg5Ju7RYAbo8dvMg6XyuVy5El4k2P6a4VSbOp85xPqDP95PGW0DXpXSThpt2fMqr6yO6PhTXvU/3+k0yPzkGIEYSWRSV81cK26qmkkQm4I2TRdiaEKGgn+bSPKg5x5AUZSSfWV3uOJrttRqa5thcbKY6diXJlAgpJXRsIikX8szPNOusTfVvWTtAN50sU0m9lQt/nRhb9wHTkgvFfV+lubypaGse2dZ3qWwljAZs4Triyjb5MUeq1amacN00pq6HOOVzvVg314a7qi2vU4bb0xJMT86Ynxt3Ta3GUzAfueGGfXY7ZXEYhZy2WSS9ZFqvhYcDercsnx+dKfl6jkf6unXEYMWKx4A0e3r5zdWIk8+KFSVUiJu2BekdNk6bhBUrHg9as+BVVJjPOydda7TS0GeJezVfXSvzS79tdcb75rtRD5LZHsJ2loaWsOQ0HLKkbpgOwT5VtPbFuhOkYKm1O9S9sWuEVUFi6jsldekWojwcUeppQmLXWN/uK1Y8TgyFc5vrxn2c8/kjJuEoeE1sAxcASZzl4UQEEuEzSddl4UhaPCTLgzvQywvNW5JqS8LgAQ6baXGj4qEeAd6NOMDB+QPGyAVKQLcZANej3yQzdONhg+HQ2zvubEwacXDAGAEXjfiiTVUSPCRUSBZRo4tjnZpaByTCjHsS1R4jWfMMc79zJD2ewSREgJF4JNnU5xtJLBJXNai1xzJqPerD5RsJx/xqGSu1nPiXJEzIaR9gXXMGI8tYXiXuSJqR3CGJxzqxvjW5xXqoXzdXhVetQSXoIswMpT6V7NdNdY9x2GYblFpnHqnfSX6SuCM5yvjA3Keal2tnOc6IkuRTf3ocsyQPA4zoPJfwSgATqoXJ9mB6eqCNZj7VlClNkqqp0Hr9Xmv8qZbkiESukZxDSL+jA8YuPXMOgMsJT7YlmVjMP0nAJW03mpUE8ncX0W/2iNFhHBy6LkoYLbCDcwF9F6dsOg8gDPAud5lT8w7uyASeHtpaA81lAstVYmzf9Ptmmy9qp5T3tPwk+fS6bvNKYfIWcRL0kyDUtI2ICpMITkvmp3DmGYaUWEnM2YRR1nicnRuY590m9NSvUxmfWn5XkQQmZlxejpTE3jw+31N1y5RpnG7K8LgPuNPLdhtIrXozEi719TLxeTyuGUZ5k/qxZ26PLDPviTpx3zY48ab8kOcK3nm7MGo/FguFNAtxNfbwoGIr3+jTUoc25BeVzddhmuuYws3G+ooV7w49NjjDOTp0+Dr+DIA9AfZmbInr7u9IL03TrniXUBFyGjH01X5/x8+K28EWW+wLszYrgFJj7lRwJVvPxHaUvc7DDGnaoUvbE9kzaeuWkHeX5Ryvwg3+bu2s5qUwTTgt9SnH61pHm26yuqqJr2PhbM1dE5E9+myFYil/24sGuGq3ZHvYmjar481RHvRVCy+QO5bPfA3aGi8rVrwrvIf333URVgDZDtfNjtCqP8jHhEdMwvGFxFPxHg4DInbgy8VDX1rABgEeB7zOzILdd+gRsIeDQ4ct9hjQI6KDR8QWB4zYYINxEiyzaTsMyWCb67DxA8Z8PYwjnOuyXD/CdyO23YDD3iP2HTD2SQLvB+AsJlIg+kQcvJZXHkmjFsGhhNIBRjgRHijGNUkKDUctLJJHak5xnz/UwFOzgbWmmPGP5h+NaZfS+ZTHANNsY1lpphEoCUVqk5FYaa19aIqSBBXrSBKIJi25YiBJSPC7moFU844E4zMM77GN1Owlw5Dgi7l+2lZBwqlJTCXnaB6TcQlq06k2m/pxo8ZckO9evqvvOF0LOhi5Nsp11RyMkk6EtT8JM4WaBtXxqxqG9VpV+7FJuGXCbOqDaKYnp/pH4GwAQh6g0+4iAt0hZRmBbnuA72JSlosOITrEkAvkxqTxxgWrlIFEWt8Pk1/IbpcJGB/N9KRzCIHf4+Q7bpqnclxqiplQgbDtjMb7BkR8lDt/Uy23Y47PNH1xr9x4kbxTL1dWDhJCKf8yRCqzae+EYjulOfpqm5B6xLxyZn1DuCI2D0Sw66g/YqZVStGdbY662fVUBj3tCYnPUiz7X7P+qenHGr4yH1qnA5Q+9eZg2x4XwLuqDdphUptdz3/KdXzMXZfQsrF8fa8uPHRzc1Hb1f71ToWbxH63beLybrThiPJlpt4V1YzM20FrYrd7t0lyvlu0nlKduZRu9HkzQnGXUnYlTIxVnlReseJdY8ABH+IAh428M528mVsC0fuNh0vA6eL64aMeLSb8D1cefVrx8LEScDfD8n7lePg4+17611YCTuPp6i5MB+dUNmcad3YEqXYUb2lxt5BWSJojD64umc3kvpoaghqX+dSEYL1jtlbgPrD2HjMn57Qs5b42lWpf5WOHKk0ZADnP0pRoyzqOEZ+l9R3NX/cV1zmMuM6o9wMOdoh0heGjt+yDckUbb0bCPc5Z5tGScBH0DOTRYcQAhw5JK618GcYsQm6/2NV0mfpHsjMqicXwOCCih8/p8Pw2cr4RHp0PiDGxFrsdcLk/IIYOYYzwXRLG+y6pLo3jAYdOvNt0A7D3wCb/vkQmylypnQUkX3EBSaOMxd8ikTU0peiRiKYBcy0lriBGmAaXVdwImWc5XZqWrFdZ6oeOe+szzLWZIkrtLJKBgJFuWj+ST+coCSYllZRsJGqzkM+lrkoAkUQDjMBU8oh1IiF5AfPndom5aUfmx/bTump5O5ip0Xr9pSYyuSZkGc9RmvXUcgLWltSmizl9ap0xbaYxVGmQpdE1I+vLdlHNQJKJaqITMPKVedLnnK5xdby05JK6cq/XylskYi245LvN5e8hAt4lbbcuWLztmNs4h6vyct2Avg9wLhFm3qfB4gHAB4QR8C5VyHcDuj4gBocYs65CP+Y2yYtZl7RjY5eINi6GI3zy7+YiOhwKK6RTWQoCTn2FlYabUtekmK9gHiohYXylKTRvZjZqAB3dmx836hCnFLppljMxGpfxFPz6KZ0yP9Piqsto4UvelfmWZICWX4mwVt18c2LQ0rQ2HtwItu5xAnPF9eXlwrGlRGrDDksC87rXjgnWdbwspQUh8k45ZWSterrW3M02aDchmU41j7mM29680BPebREglgpr6t+4zsfAF1T6zjnp7RJxS3BZG8zG+TFC+mGjnllUrFwa/+HcrlR++h1mY2VpjjwFW5xh/2BJhxVvGzwew7FLM2UUGdr64k1G5Yqb4fEL7WzbEBaPna1YseL6qFcnx44TLcEOP5byt9qrhUd5wDJO14FY7O2MijIrWLYSmudvezvOELQ2gCk/ruaBLTa4zIINhqF7hJpaK+dX+k0bpRa6/7Jale1aWzVZJshUklmmYXF0PqzjLf1ecf+xEnArVjwsPFoSzk+bO2pRRAzYgH6PklaFQ48DDjjLoUeM8Fl7rUeEwwYHDOjzK7AvXnDEBiMc9jgg4JBZI48BSf8EU9rp1XueXrER2PTJbvVmC8R4wDB4jEOPGDycAw4u5oxikuDvRuAQgI1LmnLOpftKsuxhxA7JHcrStkg+18jteYkHJDJJbRZschqXMC0xoDSLuMvpKwlzlsvBawHAe9Joe5TEn6ZH04W1Sgpy+ZUg4l5KtbFI8uiaht/p08wGhcVRGbSa2SQBt835vJayMc/nMMLpHGaOkaYfmceZ3HPyoe88JZ6e5d/UtqM5RiXw6CeP5WX7B5QrMBJm2m47+U2fbjQ3SaJVJa7PYX7mepT9Qy08tqlqAgJGfrpGHIJ1IUmpmoAKJQN7ZKINNp59BHwAttWCdQQmzTi2ATXOHIB+b4V3gO+GSUPVe2QtOOE1t2PWZEsN5bsIRI/oPGI8oOtHuMl0ZZg04aLDpCHncxvGaIJTun5MQ5vkzlg0HUmtORz8zHyFmzYQ6b7ZZi/JARWADdhWp/lSs5umGYr4Ka6ZH4kydELhM26+MbDNhT3CpQZUeSLPwY5DWAhN1X5pGFf91tAtnBaXOioBZjRvvnlh+Y8TcN1RQmWcWjmltVQXB5raPL6JKut0PCw3t10Oe7pZgOuQdYZ3Y3ZACdo3F8txBKb+uF1Y3wX4iQi/fdTilQ7mUeQ+bNKTyOP2tBcfEnR8pW9GyrVmIiehMJs9y1BXYyXgVlwHtbmxWCxI6zf+ineNx2xac64VA7xN8tfMzK1YcTN4eDzDM3yMj991UW4Vx56+FpnnAHwH/vf4ffz74noi6+zoR3nPHCHY3jjBCD1AfZhrfkksdFGcAU7XbR9ve9mxiGtlYG41TVeu6PTA7Hw1N8exMHVdj6WzYsWKFbeNVRNujkdLwqmbrzETaJuskTaZhMybQQqok+B7xBYH7BFxwA4OwAYDQhahHrCFGR4LcHkzucEAnwXOAR4DOngEDNjC/Mw4bNwlDjGlUQg/spC+60eEMfmO2p0NuHytzIwDNvnFvd0DFx3Qd8Ao90kGqToN1/sRiUyh+UKuOLhOeI7SHxvf/tTQIql2BiN19jDCSH14bWBaYbG6t4URPg5m0hIwP3Xcs7M8z3KZ91UZabLwIOVQ7Thq6SlxxOYi7+KQSEIH4EOUGlYsB4mlXS4jTTSyPgSJwV7CkxhlepSTkXjjYFXTkKoJaIe6Sn9mZzATlOxntkdtZvQYSMppnUmasn1YfvXPx7j8zXFSy5xVs41tpiSdpksyj2mzvXpk8jLm8kRkO43Zn1sE+lD2HWBE21mmkwZRofMBiA7d9jBpu3Vd1k4jP+fsr5FkcnrMRXR9Ni3pAuII+G7M8fK84gDvMxGXZ6CUnwOcn559P71mkpiUJ+y6gt3ENJvYgt2W7WoW0pqh7RtM/a2lQwmq+WZdpyhNBKrmW6kJ1uWyc5YrH700gB10M5LK2SKPLOVS687umpZeScBxPmY49VfnQI07apPMiU0K9414VH9zavihtUG0Mseq3bTOJQ06h7Vf6tPjPudqUzBtmMnOq8kmbZdxunYsvI7H6/vturnpPJ5EvSk49d7OUq9Fd9wmmPZdpd8CiR40npV3BXr5XabNH/rSPdGsVxG5cxFPDZ7sttc8iVxX/AbKkVW/7uuR/dDbd8XNUc8C9fiYz4At4mPFfcNjJeBsXZtmODXtnFaMd2+uciXgVrwpAsKjI+Buit/Hv59daxFOei/tpOf3l/dxp4U7hSg7lt5NyrJixYoVDwErCTfHoyXhErmW4OGyslREjzF7Gkp6B0teTjpEDFnATKFnP7ES6eV9gb4QY1LAmzifMYvNB/QYsMcGI3r0GOHcfso3wuEQtwiRajH5qgf67oCuP+DVx+dAJPsi52R2IZMTPn3GYKwBtxXeJXJthJFXz2FEzoiS9DiH+VojXlhyOACFyXWSRSRaLmEEH327cdWj/t3UlCTDswwsZ/bNtuuAS5ot3Ek+DmYSUgkezYvaVpTSab1IPKnm1ifztUu5pzxoh0RQMT1qjAHWPWdybwfTjmMe6jfYNa5t5TrJtlrq1UkYXvNS39ofHMNRa5B+++irLsL6VeurJkOPrRQV2tYdyjb0cl2xy2UZYRV1SOOZYc9DItpY/4hkotUB6MdkajJesYnejHDuMj8mDs6HSSNNCRrzy0bRvJA+PtM6DsX3GIGuPySizgEhpHidH9H5ESE69C7YI4qIEcNEVJiA1Eghh0E2DMN0ncRRn31OAkCHcTJlyP9pvqPLrzAaw1BSKDXnMPGds3kmf1obG4aPjUEyNyk4TmmUZJibCEij0WoRHttDmWErf2qTcSL/tKTtDZmdkOwRiuu++G1pACQh5+RbwtznQOsNY+ZL7Ep7wzjXeJwTbGWbHyfgOLpC0cKnwPrrKpgO5PWIGuvvmxNpNvJvBvqPvQ3Y2L1rH24Bb1uPxENN/7zrpXGa3UqfHWpGqAz77st7XVxNwJ2CehZUnUbmU5tSSofU0iqY7w8uS+w9S23mMD33d2kidcW7Bg/tpPeWvd/0nUivp2lNo2Y3FOsoaeEZniNgxMUjJcLuA5SMK1dsdthtxYoVK1asWLFixZtjnCSNN4n7OPcLj5aEq22+myZDmPRCzF8cAJhhsaT11qHDgJBZJop1S+0Hl1XbPcYseojTUt7l+yk2tetMcBERXAcXI3buEhdhC+cSqbfd7XHYbxCjg+uAF5/4GPvXOxwOPeJI1ksYpU1IGnJDBEaftH18SARcFxNDQNOStRUOEj7UxqJpRBIyLfKHGlu8Bvlea5sRSvaQUKNsSc1Bqs+0TMxc0jedkzAOpgUGqVctv6XWHsknarGxrtRSU0KO8ajJp2VkfGpuUTuQf+l3ra7LzIxi/j6mOJ96AXzlI2lD1kPrqOQZy6Bkofq8Y5mVwGPZmRbbgLLLHco9KMNQu45pkrxl+TaYK8mw3poWhy5/D9HaZivxdnnMRmTzq7lc/ZjTlcFFLbhNyA9pSM8ARYYua8dRrOgHbDapkDEGOB8QQzbx16WnllptXT/C+/TQhODgnEO/2aPrRoQAxHyNeXV+hO8Ceh8Qo8PoOzgE9C4RVJ3XhyKVgQZrPUbEaToORRhFh5A12NKAT0OZIq/SPKJyvPxY2BE065hokzDNVqyPEnW0eM9fNSljQg0xrYkBZobKZscWIVYKzEm02VRTxnM5H6abPhzKnKvN417tRYBljSipunadWgRRmRLfJkF+9Wj1n8vaecQ4I1D07RIbpSrDKQVzGnllAvKrlzRJ+H46aGRsbsblaqSX0HEScTnf2xChXZeYPDXFOE2at43WC+8uYfmV3h/vgzhdnyJ7cnT81mdw3n2Z3yXm+qYxk6tGqOnRg27q+dpbSZwWR2nhEtChy+2+9DxxhjCflCvuG+bHYPiXG4E4vfXiFGKu877ieniFl++6CE8G9Yo8jXe1w7/OTitWPBWklctdrNVXrFixYsWKEo+WhHMY4LDJ4upuItCGbNwnZnED4LHBgAEBEZvcIMMkMnbw6EAPSsmZ6pDZhGTZvc/m4ugvbsjLdoeA7SSWdIjYYsAAD4+IAX0SbOT1/Vl/icswwvdJ+Dq4DjEm9SHngO35BWI8g9sMGINDGDr0fcA4dAj7Hn4zIvQOGDrgbExExc4BrzvbCZ9HI6IGZ3sLEkeUu+j+w8NMRAJJ8+w5gFcwbTTbhRuZQg2uF0haXgPMbKR1khF71NiKOX1q1rF8r2GE4SuYdh/vs9yU2FMjjOQPy0gCkCBJp/ID3qcfNBJsfFo6CUdSkvkNEp95qSSd5SS5luv/lQC4TY5Kn3qxajPGVc29vro3wMyNqs81h6SRxzAjzNcd+1DrtZcyjigl25o/NdyipKEEq6vCOWmQjUskmpKwQNZ2iymNZzkRF5OmG5DMr/LB8SPc2R5x3GIyPekPQHRw/YizPuBizJpi/Yg+k3UhOHSbLHBkUt2IrkvEoBNfcGH06DqHzfZi0mLruoDOR4xjhHNuCusQssXMCOcGcUNX0ylGcJjdeNW6S/od1HSL8Hkmq00yxulaTSmZ6KAWIlDEShf1FL8aQ88jBWXpqTtMmmsO5tRhyGUwQtHNSIMIalH4KS83lY+oNaqoaeFADQ0jxsq/5mfN5nuX2yyJEj+JM3wNF1O5ynZTQ8Jz7Y6yfcv4Yao/YCRjrHQqa/OHWk/Lb1kMxHY4RnqVB0euPoOkJOSI00VRKd6Y36/XE1vFZvueDmv/m59hDxOBfPu4W1H029WCM7oduKlhibcHJRLoDZPk/fVNpT4FLLXK8uxXEnMUY3PWtSWB9YPNeuM0P0V5ittzze2T5E8TdRu22pyrC75hGMLNTvHVRkzrlO43VqHrCkLXayHPTCU4V929ycoVK1a8fazvghX3HR7m1XDFioeEEf0bmKN8nGP+EZNwCbWN9xEb0B68mQVz2ZxZQocxcx+9+BRCJu/GSdyXtC8GvM5sh8/sQ9KwSzp1AQdEuKxx1yFig4CIDoc8GL0JOx0QXI8YgfPta7zan6e6OKDrRnQvLhCCR4we+4sk9O+3I2I/wmUS4JBLghiTRtBmBILP8v08+M/yZjlG4ODS3vkVymOvnXyn6UiOlohErr2AEToOpbRLtdyocaYgwcTwuxzuEtn3F0pC8Llc26LUjGMYkk5KGqqfMkpoX8FIn2c5P/4ubfTVUvlEkHmpD7XdAONQ6JvNV9cZXrUJpQ7xeZUPYAQpy691JuHpYOY3NxKn9tPHR6GHkY/sM50JXG4Xtgm1GJHzUY02SmuYLvMlIUYSj+2h5k+Bsp0izKykj1nDjoRbLqgD3DZV1gfA7xJ7G/0+mX90ySSlcwP67Qh4YBcP2bxkbgCHZCIya9H5mDTbuu6QCu9i4Quu7w9wjiYqS9OByYdcN4Ulx2h+xLRysZh7zPFzzIZtw3RIgAQSgCwm4kOmhJMZtU0G2dxE1gE8se7wDMCFxKv9vrFOfurMOP0mNeKyrm/iS3nN9GFYHoYtRbK1yCKgh5laNOpRz9yrX7oynCtSXDLzNyd21OedzyH+HK9ynWptsjj91qmtlQ/TVvhcYg/tLxz9XhJg8Yowpd5iG+a9lALV42JSJYjrtluGk3qO04g8VUxVmv+8vmjrdsTydylSu1vzfHMDrm8b95kYSTOjvqTKmek+l/1+Yoke0yMDqd05j5PaTssdbXEHm+mSxnmUGV7T43zE5YQe6KjHfk3m6bz9FPra3uCxWMYD9TI9rVt4CKh8+/oqNO+prfEaD691V6Hriha4Hk1+NMMkI0hQOxPAux73NPq6YsWKFSseN1YCbsVDxQif3YHdJO7jxKMl4UJmBNSH0FCwDuXSlcJWbkATd5SE3/RGRN8uaTGemJYxa8B5IedM1EAdPHI6Afu8YE9b3AN81smIwOSHKmnRAM92F9gPHeCy2Ts/YrykwF/EDi7C+bwtoHk9h0RmbAD4MZFtlxsUGwb62yKpBiRyiUFI/HhAlEVqGUopBVGtpg2MpKMZRSXQdjBiiE1KIq7WvtrAtNICEjGIqlyAEUw0kUh5gaZF4o2E2hmSph3LVRNvbIMtTAPsLKfRUmshweaRyMND/pB0ZFlqK2VsD7qC2OT8qA3Htq3NOtYggRerdmBYEnXH3uWal5J/myoctRin8kRMTtGIPpbahoeYSbsR2GWtzUMiv7AZEgEXnYUfY043aT11/QjngHHwcG7Mz0eA75K643BIWm2Trzfn4bsRYSpnzGFTOZ0L2GySllwIIWugGjW02aSIyW9jMj7bOYoOVcAbU3kyse4y3R7zPETDTV3VIfo4bbJmV63dlbjKpF2V5iHTVDONOtP3SnFTB/+f8Rfw6/jyFDrFIzNK045qnpHzIrVFQvGYkpwjaUcyKJUvTvmU9UsmN2mwTI9GGCnIGnGOLYd3P5lK04dMIeSokE9Mu0fpv4Zl9Tl8qa9huZeilppBThMVS890KeI8jRwxsWk99bTCURR9WppWdjUv2g6fSntd0khF5jaWroebi7HaPvyun8rcUvObg0+Rx5tp+h2H6o2+XX9cHUIeh8fJ3XeN9lPF2Qgw/bgVN8GxIwalxrT5Dq1nCC+z03zGNDLVTSGMSFVClW9kL2tyO/5SE/58u9mBmJKM4shRj4NluerfZka4fiau+l0jnhh2lDwdOB+Y9Q6tr+mlW/vPyTgl4Fq5rk/KisePtKUz/WlA5wSzMEB7Eu8KKwG34r5j1d5ZsWLFiqeNEd1KwlV4tCQcl6YUFB9AH20DRvQinKXQ2cHhgAFbUK8jZgE1sMc+Mxvc8CYxMzVVQr6eiLVDZhx6xKwNZ96KaH4NiHiWxaIX2E1L/B57wEWM8PCxw1kfMKBHiB4xukwm9Oi3B4Tg4H3AOBgz0m8GHC47+H5AANBvI5yLGH3S40PogIOI1GPKFy5vItSEIYDsIMuIKuSoWxhRkxWIJvLIwUgykkAkl0ikXaI0gUkMAD6Q33SPwHL1MEIKSIQaib4oYfict57cDokco+Q15nRoXpP+1V5J2Z+hJJyYPrX8SBqS56T2XYRp+QFGIFKbTuMQzyQPygmVxHRSZ2InYcgNsC4kGy9yXucotf8czHRmQCLBnAPey2mOSMQZkAi2iQuIKe0R6cKY7xdkhgO63Ih9Jty2o2haBqCLcP0hhXZ5yKltzD4ALvln813AZhsQI7LPtohxMBNavh/x/PwCcMCwN1ubMQLOj8mMZD8iBo9xzMKnbsB2k0xHegc4NwARCDEpj7qsFufjAZ0bxQ8csgbbPlNtycytbccdIg4TITTCTd/pyYyhrFFDtZ23MDTZSNqeKdhcZiJJ+qrsMOL/hi+JhzLLtaQdouRBMWZpytBKlMxkxukBTte6XHYzEJkGaJc1pUpTjCZi9NDDEOn/UiONLUZvNBExq37aTB2m9LX9lJCciw7ZV0ZYGZlW+mSz9MI07Wl7WLiUlvLWyySF1W1Zq62M2U39cxoqZdsG5ulFlNqeV4FtrkLr02OeQlIuYZy9Pm4ONxs7t5EmazfCo78zIcTbF4rTjFvMT+NtEKFvFzW1bur8eghgxW3jlCfsGKkHlDNG+TfKBi9Mh/A4u9dvARWpp3cdPbRycWuGNakRs1xi6gPXY0fnRd5Rwws6w9dzob0FLba2QrqSnsGIegZ2UxtYzGWiMzzm7eCKFTdAi36ezz71WnXFimWc4xyvJ2HK08BKwK1YsWLFihUlHvGui8IV09dwSNoUER4HbOCyQBugXkHEgADzFkJBNNBjj6Gw/2f/91mkHnIeHq9xwC5v6kNWmEre5c5wOZ08pVj5HBd4ncNzU75BQHQpzTF2cBgB16HvhsnrXJ8JizDaNn6zPaDfDIjRIfRJmEDCIhV4QLjYAEOf7PlRo2ufyRUfs4Qgb+Zpl48+xhiG19WcIDXfakks5SI0a0mSqzZRyTAk7hyA95HIP5U8qByHJJODaabRBKbPae1h/sooh3ASn3m9l/MiKfYM2EZgH1ASXPzLelCzjlwoyUjVdNtI+zHvHcp2oklJh0QSXspvn9sm5A+JNf4m3xRhZCfjqUlPhtuyMXO/75FINZKnQzRGjGTgkMM4l8YAuWUfgX4Auph+j0gmUBGB4IBdgMMBLvt0C2MqlPMB/faQrznEmMZq4rhG+C41yDh4eB9x9mwPOIeYnbj5Lj9B3iceGQGb7SGZhXQRZ89eYxw9um5Mz8Po0fUhpe9HOD8iBIfdZg/nPWKMU9zORcRodBQQsfXjVD1FBzUdaWTQCJebn75xJp3XPK8YXdVlPV16x0lDR6kgc7LnMWCDEUMeyBQaUxPONLCSNh59XzqoaUd9oEjixUmgXoryDC5bdXZIhnkjzHSmaYKN0yOkw70k4NIVEpBu+r/UVjDxpqVPzTWjLN3s7L4h5jRLwWgdzgg8lkIFK4yhJjKBtiqpg5kDbbehimJZZhXQtpHaqaK3m2FSfldp1aXwXjSZSn3EY+Xg39KI6M3ETzfTQLpdzTK2Zk2v3gau7tnbQ5xec3eN0oyby2ua0tr7wxJHWqulecXMlNvz8S71HVYYTh1Z9SxYv43sXvmWMN1uS8PMabbT5OgIxQzJkIncoxFNTdNIQifvODvKAUlbT5qVGm9W3hptOvkz+O/wX/CfG3dWrFhxDLr+mj/1PLVZh1yxIuGv4r/Hf8B/wGu8etdFeRJ4hmdwcHg5nehesWLFihVvG8Mk6bxJ3MeJR0zCRSHTgJqUU0ddEcZl0L8NTUwkjRIHjx7bPAwGERZ32GNEn0Xctix303beYZv9wh2ALJ42QWkiBx122GedmQ3s5GoyEdO7C4xuixgDXD/CxR5DTIRg143ozgbsD9kvXZdM9R0uO/gu1bPrA8bRw4cB49ABu4jgTDzgtgGxD8Amsw6jyySKVGlEYkcOPpsyjCkcMvECZ77O2LzUMiOUlPIAPoFEelFdYyPxySR0SKNUzVoCpg1HU40OiUDqYOQVSS+SY2z6AWZ2M+Y0lDUQ6cl+i1IjjWHMQVUq15ncUz9zJOtImpEorOsFmNbZWf5OcoxlZ3ucwyQ7aJT7PAJnmUCjxuGQyzX1AQnW3H8kLR3SNRKqRBeB3gFhTH4GASCICl8vHd0hmUKNyIl0gA9Zwy2RXEkzLZuAcoDvIsYxaalh9PBdgO+zb7bdgG4zwrmYCDzn8R6A/e4ym5zcY3/ZwXdxGoreBzgXsdlkczJuBPoR4wg4lwZZ343o+3GyfMm40/OXv/tq+i81tyK6aUZomf+jH7JEudCcIkkvFfeleEkYRyI+9RB9U6QHkRo1pc+2MOXvJC3OcEYLkW4IMIqPRIwZkVTvYak9kqabzqhdPnjQg2Y2M8k6lcEE2GYGc5lAcZO3H33Q2IY2c1s/AJA2ckWqSkiZ7705NVX6x3NF/CBh6vstGKG4fJ+k4jwtNUNal1PNpR0rh46pq0m9Or3aV+Ac9AuYYo/yFr0uSD5fl6DiaLpNYuu03n2T9GufgXeVT1oxhLdCxM3RFSeeH66JRycEyCjaUWZ+eCXjHgLmM+zyvdZorQXofPO30rHjGbW2nb0V5rOW5hmPps/8W2+Y6z9lKwG3QrH6Fbse9KlPO3577yUvmHpacwkrQfcU8e/xhfVZe4t4tZKdK1asWPHOEdDf2Bzl23S08TbxaEm4DiMcDojYiCjVhLBdFizHSezdwaHDBhGHyTlZLATZES5ruiTtnQE0DTdgRJcX30kTYpu9v41IpqjCZMDJbKJuMCBiwAFbbBDRY8AlqMuScvXZ0GWPC8Al8fVmk8wnDdgkbR0fceYvMITNVNPtbo8IhzAmR2SbzYAYgX4zJs2iFxfYv9rCdxFwDjECw75P7eMdys1DJmB8AM5DJnW6RMS5mDShRg/sI9D7dC1E4DKTdpSHb2AacOf52ntIe5ULya4lY6AfM/5+AZOTkwsaJJ+apDqT76aqZOQTkTUD//IO+L/8ZeCH/7AqB48+blFCDyNHGAHIfRhJtW3OV81rvkZJYPKpVHKQPNA2Wvt0LFTGEMuyOZf6q3Pq4szIPm4CN0Mi0YIDnE/mI7sR2GaaJpttRIxwz81pYBxiImt9AGIuh4vw3ZhMPsbUCV1/iTA6jEOfxkYcsNl481+INP422wG+i+i6AeNgtFTXj9hsDpnE8/C9w6Ub0ctcvtmmU+LeZa0znzW7YnarnovXdUAXE0sbnUuabwAixkxMp3axJj7kuSK9BKjNNsBhI77YmIrRKEl7tpuoFZ50BzY4TKHNDwsZ1phnI9ugOzhsMnvL+Yr5OuwzoeazPzlq9ab86KPLzvEbAdhNZhT1FL4KoCM89jIb8aqBfncsH9UjICnnQb3fZZOI1BukTzwjBa2ulrdd198q1IgFwUPRqBrtQhFX07DDGEoHtsvNmCGPjfI+29p03pYIyCD1Ro7Dd8Cx5YcRqLUYuR2HZVbNxWPhy7i12czrmYMsW/NmxFRqQ9b3dkQZb1Keq9HNHGfeNZZH7N2DI2Ru+A94eCLHUlfVjhWYmcKEh1avh44OPcZbPRvZnpVPu7Yc5no0dH1ApE5vHWUr7gbv4wN8HX/+rovxINGmxGmpgY4qbCawYxy22jsVG2xwyPKHFW3cd0L5PpdtxYoVK1asuAusPuHmeLQkXBIMU+Sa1KqSmHucPMn4LHQOkwCcwsyklTHCnJa7bNotZDOWCpcJNBpmo2AbUxlMsLrFAWESwndZ/BzApSN54oADaEIzgFomFHd3MHN2LmkWZdnXEHp4P6LPJvguxg1c1gryLpkCjF2E90C/OyCMidlyDtg9S2TJcPAIl+LViBpTfTBfXj4Ahz5pSPG3zyyPCynsOZKZy0OfCKLnmcA7eCD6zAvkBanPveZ8Jp2yFpdzJWnmIrB1pmEWkUg9dvOARHBR60xNPLIjPJJWGOPuYOYqM+n1RQf88J8j1eECRppdAqLMmNJ9DiMAmZZq4w0w9SAnYfKIS4QdWSJNOCYCjXX1uf1JUPlMdhLPY9JAG3wi1BAzoZrDbg6pLwBgm+2GBp98sm0v4fL48F1qKJqGdJz+QkS3HRFDMu0YPeA2e2y2Bwz7HtvdALiAcejhXBqXcIkA6zcB/fYCXT8iDB4heDgPxNz/XR/Rb5J5SIC+Dzv0m0v0m0PWeEs+EB0SEZnK5zI5F9F3plnHNvQYsRVyanQ9ekd9hoiY2dFu2iz7iRABSCTFbHKWswqHg51Lt16jQH/EBnYKnhpzSqJRO8w27ZYefbopcWNacnZ9M5mAozqoy8cBgpRZPeVQ/9a0nnRLSG271HZD4dvMwqS6exFEm7lIZbmRZ1Y7BrEkdEiP8mHqg1pHgLFYH9MCNMGltQ3vWD+qGHMu+jAPfargWsZjO9oW2nTTohBw+qCbXhvbe44Uvk0otTXm2CYlKRanq8sinTbBeB0ol78kMj6Wt+qi30ykTOOqeIM06nKVZhRvFxEjhre40HpbWndXIR9+QHk+5eFBzf0ZaoKxPoOz0iV3i9sl4FaseNpYCbjbh0oV6uu0TUEj9iV4erNc3wJYCbgrsMUW+6afixUrHib+t/jf4T/iP2BY1zwrVqxY8ajwaEm4hCzQR8j6GknE2+OAMatAHbLIigJcasqRsKMhNAqQ0jm0PqcL0HwkkPzNjUgG40oNg+RHKemN7NDNxGSqIRBz+AFjFvVvcCi0Sy6xgceAMxxwcD1i7OAcsO0HdOGQiCw4wAHb3QXGYZNSzmQFOTXfJa2kEGwT0PURXT8A5x/j9UfJxqLrBvSbROoN+10iwhzgzi4RD1ntyoWkUeUzkcZqncd03WX2KwLYBuAyZo2svMk4DynuRSadzpC03caYSKw+Zv9tDhjzBiWyIki8wbNcCa7BtwA2zsi7mMeEh5Fq1CzbuJIYI/qcLvdCk0nLnO5Zzp9+7Jj+s5wGzUfuc122OS5cqSWHaOY4A5JW4fkIHFwi1XwEnoVUhkPIGmi5LKPLPLPLZQyAvwS6AOcjEBzi0MNvB6AfEB0md2+IgHMR/TZVfnQRMTO6RmalumzOs7+1HjjsE9l69mwP75Mm2uRCrj9gOPTJd1tkOg7eR3Rdor17l+yJUjtut/v/s/dvsbYtW1Yg1iJijDHX2ufcezPJRAmoACUFBqqwZZOoEOLbieDDBeKDH/OZchr/QCIV8GMeP2mVVFIJ8Zb4oZAQvyClZbKkEkKQpmxcpsokj6SMnRgyofJ1zz1nrzXHGBHhj95b9B5jzrXPfu+1145+de5ec84xYsR7RPQWrfUzkoJo6yp9epo2nE57AwNLqda3AaSwoiJiDhmIBpgbQlcRgzEyBHgQndBFgfBdgxhKTyJULn1VALydKXYwk3SNHRIbzY0fbBp3UiCSoPNPwIpFU94wo48/FZRJJudEIioyYoscSYAwI+icRLHc0nJOkId8KovrVg9SOcaJIxePMd3gygjEq8wu5jfAA0sWy9Kf8/RwMuO68RnMm4E5RQG/ity57wmG5vYM+5dlCPq7zJ0e6mP+mTcDQK1MXurxEvSy0kwH8Ch2v3twywAxL/F5TNNix13WGXAttpd39x/L8ZAxF7W10hGYfPi+/ork2E0v81wDNnU+aX+9rpUrdfkmxj7zLu0Ic79rI7/2RQDu+zDOEPbZjj99bHaZY4OUPSTM+JtyxcPM32HDhg0b9qmaX0PWtt61d4fsDq69PcYRjxfZAOCGPTUbANywYcOegg0m3KU9aRCuj4wifDKAzlZxfc4oWJGwYEcFsDZuCZ2cDLRcwfhwSdETWShT0lJc6AkVERs2pObypKBlQsGqMeRqk54jO06guRkbViwIqJhxp+77U3MVRlTc6vfiKF+xhYgdCyIyQqwomFGURTfFjGnacM43QBVXbIoFy7Ji2xeUFDHPz5GzACJZmVAxVnzje87Ie0TJCWmqqAUoOaHWgJQyKipyjkANmG/PCBHY1gm1KOBFtG/eEWJG3SegJPnts0xkQ4CmuQBlEoZWNRARcwWWrPHHID12KQJkrUmuYSy4AAFflgycte1P2fJSAJyVaUfnKAG2WoRpdn8cEsxbEAAtQO6rxQBEmgJeDVCbdeGUE3AKkn6AsNO2CJx26Vt7lOuj1lsMAg6minBagSisNOwzwrRLnWjW6paAfZI6U3AUqAhzQUoZMQG1VsTbFSEC+5ZQckQIBfMpO6ANKDkgxChx3xAwnXaUDORN6j5GAwDmZcO07KglIk2Szr5PDRidlxUh5jbe2NRAwDQJKFtKwLRsiLE0AA4AlmWHUQuLxnCrmGIBasZeReQxhYoUyCqVcgsTNSMGiZ0WkXW8RSRknHScG5BTFFwwd3FGxA2KQuoGzgXHA/KgkGezCdheNZ5bRkbAjohTY5dVJNVe5SlYRnwLCrhFWMw2c2PToSusPIOTKhhLDTBga1YwCq1urB2SsuQEnrEIbrxfQES/2a/tP3tmHweD9zKGHsFAk87swVAP5sTOdW1lSE6a8iHmk5QpX0T/PEIfdl9pvxIkChfXsT5DAye/HrCSf/vDFxRO9Nfwt1cDtHgloY2Kiu/CM3z7wXgHjBFo7p0CG48PP4egbjnUzquwq45gZWlj7k3Ml+Vt2Mcbs+xFxp7/viUwH8pL//ntxvL7UMYjA5ffm8koZXw+A+SfYp8bNuzTtscugfciY+TzYe/f7DDatb7jD+fa1YwZbd/52HPj7TJs2GO2z/AZCgrucPdS1w8AbtiwYU/BMuIbgHAf5/r66+zJgnA7JhWGlIVsdOewPRfguGRNKPBSEUHv5SJ30nNrXB5HddhW3cpQ6i1ix47FCbaF9sSIghsFA4QjNyE0hz+l6yQP4ii8x9oFNYNLT2LLRefq3dV5DUCYQAlY0nN5Xo0IIaCUijJlzEHYTdNUAGUd3d8vDbyZlwIgo5SIGhLm0xlVmXO1AtPnd8qoku9SythLNEAsADFlTHNBnc9SVwXYN6WiRSA8W2VrsQaEz2XBUe9PAtjNq4BL5wltQxIgINW0q788osk5RghoNvu6YsyyIJKMMSOUiLonNGZVrMK2O2UD0nIEpizp3Uc0hh8A3O4CAhYg3ChQtiZgXRzaUezfdVaQTQG2mxVh2gUEK0BMBfn+JEBl3Jv8ZFBpyBCAOu1N4rG1/7IjPVuRcxRgNCdIrMPYshtjbX+nKaPkeAHA1SrMyDitmOaCGBRgTQE5bVhuhAmZtwkhFiwnkUUJwRy987RhU8BumjNiLNKHpoJagVICSolIaUcKBQU79jpjSgoOBMuLSE4ympiANBMyECpSIM+jHw0TdiTVJo0wdlbE1salcBekzQMY47Gf3AVIU4BU4TEyuSZNPym8smPSMSqjPYMsNAPNb7BqOY4gVoCBjT1LKTqnjgeQCDYVBRwTtgumGxlsNiNwriCwZwwgmYPsoEDUmq0ddxhXnOecYwziCTBwiSw1PhcuR/1ngmH2OaJihsGqfpauXdk8/Irum+vObko/5sNv1gcIX9Qu/w+ZB62uX2t9yxwlPn7eMX9ss8voa6wre84lAEdXfw+f8VkvBsF6yFbAWnMOfT2Adgk/evfRmzOC2BPedCHm++S7Ntbd+148Ev59fC65a27Fp2PXjgikJjdOd+uLYzwO+xTtP8b/Av8E//2Hzsaw17QjiDJh+micpwOA+/B27X3Ag3gWqxnoD8PZ2oKRmdGuGW+YYU/XPuaDAxs2/K/wW/EP8X/90FkZNmzYsPdmO5IGuXmdewcI91GZLEfN8SkMGXMoU6ypdNcF5z6XOyJ23Ktb+pqbdVbwbNX0edaZMaCKY72JS3vHDGO4rOr+rqjYNQ832LA32pXEeHqGO+wICsZ5YE8APzrVhYFHTcPe5RVQMQVhBqUgp7RFwdA56gOwLCv2vCAEc6pO045tDUhTRcmyKUghIMSMNO1Y725Qa0RMBQkSW+7mtGMrEv+t1oo0WcyuUiQeXYgZMVbUXDHdnFGKgis3ZwH3QpX7T6vGL9uATesmVGAKQNiBPQnYNSnwVQvSvCNOGSECpQAoETkDIQYAGaHuKM9vpX7IxJqN9WP/Aem7v4P8/HPNg7CdMK+Ik2MhnTIKNmH0JZXmBBDnjOn2HnmPmCLw3XnCzxaCThXTSUCq6bN7BT4zagW2c0KtGs8wFMQpa/30wG6twDxvmE8ZwKayo8B2FsnVWi00XK3AtFCyw9z0QdHA+ZQxzwLGTiq9WSpQq4B8IilZWt6bjKuCeDfpDgEFMQC5JtRQBDcNQIrCsJywI4UKTMBUd8xhV8an6HEyL5OyTE8aB6HqdOVhdIPKM2asonDaARgyLuYWM03ynBvUwiiQZhILTRhybN25gfjoAHJgc4BWaPKVBAlj+0uEHwuEyUaoywCLXWO4kb8SXL4EPEx6GIB3JZw7sIUl9lKUfsNODpW/R8BLmX0MaqtNerC2Gjb2oABVufvswUV/uIFx2cohF6HVYWk1wKdZa/h2Du7/AQP9+HzmUmDQvo94mNODTZaml7pES7cevu3zxLQu435cloXQqa/33qrWjS+Pv/frWDR9ffp2uX5Pv6DxdXVkKr6ceUDVcvTw8x8ye1dJv5EcHfP15vYuF3R819evabN39/zHyfLz8LMxxICn7jL0ceVCm/sfZxsN+xA2ALinYxNm/K/xu/H38N/gS3znQ2dn2Edqtobn8Y3L90Xtrsu6lo6HKzxTjt/ZE4YN+9jsYwXgAJFMHQDcsGHDhg17siAcHZqMtEQ3ublGzd05AaiNxZIVvjLpyhm7orcUotxbOlzCLhoN7owJBbOmKyyV3Fzx8qy9xYxjRCcoO07Os+1ICPrMqnw+ACqv9xUKphaXzpbQ4rpPyLiXIGqN9km3+4wdGRERwn5L2EHRvsbRqQEpFNQA5Er2mdRIDBtub4WNte0Rez4hxIx5yjgtK/YsLDunaIhpT1jvTxIfDBAmFwKmOaOkghDE4Z+eZeQ9Yb3Xeo9kr1VMy4YQC8qeUCuwYwNqQFpWnG5FjjBnYDufkHcB9uabTQE8SS9GoIaCFCtK1m4fKsKzO6RYUGpEWWftEhKfLoSKmDLitGOaK8ryHZQcsW8zAoDl9qyfbRil046YzqioyLugWPNylnpREPLbYcMtKs53J61z67VpKohJZEVDrCh7Ra1Bv8sKJCaUEhCTRvCqCTEVl46AatOyoZaI5XQHAeMipnkHqkhHbuusdVSFBZeysNKqSD0WDR43xYIQGPMty/eISGFHqMoMDQEhKPMSVXDNsCuQKv0vhF1BawOSpsB+W/AM9zhjUcZZbpKJFIss2BsoR7m8qI56k/zqjfKKBMLac1U2MiJjVxiG8od+Yyqjfm8j1cAKuS650eNhEwGxCEDZBtiiTpY2QyUd6XxugswxRcEkH2es6KwhwDvT8NySfludXJmz3i3zl+cJEywsDfwxwFMYhlX1Xg1Y4AxGwMg7mg3yik0qMivjjgBLn9eevUUGEQElX4dkCRNAI2RZ3VMvmSYEJO1NYDCeZ5eRMWnzc3U1WMA4Tx7gYjsbjIZ23XUzsNLnMHafjn+/CDQqDRi+dv91C/DMype/72gG0/oyH6HLl08Lmpa0joxrGQn/W/xv8F/hb71yqj7toI6qfsS8XTsyET8ECPd4XWvMmcx977IdHqextBL582h+Th729bbghBXnD52NYcOa7djwf37t99SwYdft2jvBv039YbjLQ1GELULbg4i3wY7C2G7qoacNGzZs2LBhw4a9ulH57/XufZr2hEE4vyAtMB6BuYCru0oc0RUZFIIrzd0s8dmKLlpndS7u2BG1Q9FFX7Fgx9bSDCpGFB3ToqpQCaE/O+lm+RCxOQHNintGVfhuQ0LGiqWxhbIFRcOtAoL2RIl1J3yh0mC3PUiEqqRsHgBN1THMKwqAvc7tN1GhlCX8MmekdEYuESnJ70ssyMpkm6eMEGQTUGa5J4aKkhNC3DEvu8QBCxB5yv2Ead4RYsG+CauwViBNO9KkeUsF+zbjWVoxpYwy24ZhihVpukcIwqArOaIUoFbHeAkVMYq8Zq0BMWTkPCFMBalWxJgxnTaEUJG31CQbSwFqCYhRGGlpOkvZgrrdK1D2CMSCaREgJ6UdaX6OvCcF/bQMKosZYsHNZ/eoJQhLT8sbQkVSmcqAgl2vT9Om9ZBR64a8JYQIib2GKmBgnuA3T/O8Y1k2/ZQRahWZyRoRloB5lthsc9icBKS5j6cIbGUSMC0oKBOAWAtiEBAIIahjuyIHA66hbS86mNIGC1ZMCkrvmLQ/7w2gDqg4YUUFFDgTxli6gJqqOuhLu784lhxlXOS6vdtO9qdJiz4zN+CFXI1dgXTvIj5h1QhuaPm39CS2o4zFqaUtZdyU91GUHbvomMoIjfHngf/S/iWQQQ7bCef2t46uVrLQwIsKOztrlhrXFhrvTliAAYxxZwCVAU98ksW+lPCqxtWihK+HtOpB/VnalCn3EJQAX8Fdie53zokGglq7RFdGMuzsYIPl/sg8Yf/xz+E17LGl/W0AVw+sVESXBoHOa+afgQYb87MdEOmv9VYR0Pde72h5dcd9wZGJ82pgSO7G5LWcvY55Jh3zxNr5r/EPXjNV5shYoO8O6LBnfEg3lh8lj9WdlmEAeA9qf7zxlR62S0iWhzDk/zkb9/Vh1x7vHgYA34Pvxc/g33zobAwbNmzYBzUD5HSfiP4gFNesPXBH9lxA1b2SXyXLu/jamna8iR6b/Ub8Jvxz/LMPnY1hw4YNGzbswkTP6/XkKB8K4vKx25MF4Qp45l3cnZu6d+g6NCFKW0yKs1cYG8asCY0jkmDBAc1dTavKokuNwUNYbcaKFcp6gjgaFz29O2PF7phtvGdSqblJnfoC2pmc3IQdCzZl1VUULKDsZVSunDyzIiHjVllwwNyE+GYFJyks1xypgfJfQAomtyhgiACIE3aUGLHFhFonIAQFvCqmsDeHWgwFy7KiFOGgpCkjxQ2LSjqWGlFTwDTdIQRg3xPyFLDti9w/rZhSQIwZOSc8e/YV9hyBEFC2GV5ATuKTRUxTRo0V+z6jhIJpzpiXHXlL2PeEyDkg7LiZ75H3gBQlnl2BMLrqvKMUGR4pZSzPdo1rBmznWRiDcwb2CUusSJ9tTaoRgAJqQC0RqEXBt6oyjsIWlErOWKYdp0XTzwUxxQb+3ZxWYb3FjHWXPhQCcLq50zqUXlVSbABWqRG1BmHTBfbNokw2Ak0ZCAEpZMSg4EbQXlCtTpdwRgo7dtQGLi3xjAi0Ew2UXp2xqXyj3L/g3ECrFUsDPRJ2nLT/70gNYKHEoRdPEdDOXNr+/yV9GWsr9vZcGbMJEzYFspPmteIz3AMQ+VgocB2xY27zgUATMzbIOVGy2VIDXCJCA78tohpBqKzAu9QK449Fjf0mG+QdFKecVL5SNrvG6oKO8R4osfhooTEBjwwxYzdlzS3TtFosTfrSn461GUvqJLnvjmI2BOZ4rMGAKyjceI3ZxRnVoBsDxygMynz1MdHIhfQsOpt9Lc10+M2AUh9bzrv8+ZlQsFmbJnDNTFLT10tseX0IAqHTnb38WNK+ZAYe5a6Oj2y8h83nwfJ1ZMD1z/+6tGpXV28OOPUQw0Ng3s/g594w/Zets9e3tyEx+D34bvw8fvENUjCe7eM1zhy00gD168LfT9F4/MGMHN96MW5tnu/n8ndvN7jFPe7e09NezQYAN2zYsGFml8c97LNflfZrXa/3YP4NHq6zFT5wbX16/YnD3qc9BgAuIekBz2HDhg17v/Y0D3A+HctvAMLlJ9quTxaEE8HJqlwTkY3bO4e0OO8zSnPdlubStrNflIILej8lHIUdJ/AYwS7GoaOgGzl1CVWlKResIGghV2VEZf1kWFy6nrch34rDm2Ac3WzCYgtYcIcVqXPoJtzjrAw4AEoElfzSCVsVvCgIuIXEClsxIWPWdCSKFVrOgBNjUQWp3YyMGQLmbPqUqvHkQqiYpoIJZwEcggzEUsXxP8WCXCNylGfM845lrpg2+X6ZVoQoMcemlJFCxhQDcgVKCcoyC0AoSHFDjAHzzEXgGSi1lWW6kZba9ySgWBBm2unZKnKVFSglYs8JMWVUBbOmeW9lSQlYPr9HUW94Wu6QS8IuNEFhjSEik8GWhHUXUDFNK+ZJGEe5RGFypSxwRxXgLU22kRGgriJFldGbVuQSMccVc5C63DEBVWQdCY7sYZbCVO1TwcXoCkAMO25wr1HIlKUXzOmXAqUQ5e8AKMtLegYBjx25uePpTpV4hrP2d/ZD4DN8iQ2LjkuL2jYrPJ6BJrxqAAH7f3ZMNwJha5MWDABuNKZZ1tG94L5tDyUG29Y5pRfsiFi7DSuZcEXBs+hc2RPWdr+M2bVtXBtTtIFZQcuwgpA4wHmn6vix6GyEl2ReyK38PSRR3HMiyBYk3zW1k6+2mU5aT8ay43bas8WysvT8FloiWXoXsAk0Wo4tRatDa3MPF6FdxXuZVx58OB5pSApy+lqy+mBeyIyriOiBwktnw0MOAqsPX5KjLKkZZ2d7jpWtT9eDe/zk68dAV3+P/4Z3Zfh++i3c4Evt3w9bPfxNMO8a6Pd16RhbJ8DX6esay3UEQt+FjKIHly9lSt++vflC8c0AOIAj8RqD8/GarDQ85/kosfo07dJVetmD6uH3iOC2JO+6bV8HgLvBM9zj+TvIzbCnbsORMuyp26R6OO/TroFz1//2B8w4Di+VD3rO/3G8DmDuU7QBwA0bNuwWt7j7AAf3ZixX5enjawI/w4a9a3uyIBxwPLNvsnnHeG8bQnNEy9LyksUQ4KG6reNNiNzcpPfL/3vpsABKsZ2xAE74DW27aRFSoCyj3hZ9ZkJRN31Up6gtfpOCBn6xfcKGtUnryTW3eI6AgA2zOoTvmuQln7VBmE9S5rOCMpfubAENDCCYg8kLJmSc4qp5jK7OdlTMiJpQDJT6DIIHIeOz6R4VEXuQ1EX6UV1QoWAJO26WM851BsIkYE4QABEKsAWmrVWkuCDmOSOXAJQqTL9oE3QIBXPKWOKGMp2x1+QADJGjlDwDIe6YQsEchcVmkqAFcyxY64KYgNt4j5skgE8JIniXosUpm7Bj6+QPtZ5rximcURCwY0aKBSkKkMp2T8goIWof25BQsGDFGhLmkLFjBllXBRGzssMqAiasuNd+mnRDmFS1tyIoO9O4aeYylzzOOhYEkJsxN/bmjnp46UVU3Ojv2Y2d4PpQdnUwYW/53J3LMSJjVtmU4raFsQFDnkNg4CPBP8I2E8jwZM+1snl5VuGbsX/bePdAGXv2ES7hM/cGmBdQopHzBAEyMuh6uMx4drExRfq4bIxtRzDJADdKOPpeJXKY8lTLRVIwMrnce5irB7/QATlWb3JXuPiW7Va666C169lk10Csyd3Xc88Yd+84U1s9eJbsEYgxaOZo13410MjinlH2E602M6w3Wp9Ey0m9kpZP4xLeYvv033ypbM7ejk6P4PqDtdnXA3cysiwta+vXX8Zeq+ne1fpu2D29xOr7AOCuObo+jNmKxc9KtARGv30sxpFlOc1uHH34+vzQ1s+nJg3cg+OPxQYA97TtXQJlA4Ab9tTtfQNwr2Lcl9KOx6XQPlen6GDS7NQ7eZg55797TG+tYcPejv0q/Cr8W/zbD52NYcM+iN3hDp/hM3yFr97rcx+KD13G4YBHYTtEY+v17n1M/oq3Z08ahDuerRdH6aW7gnJ4wnCjBnoCgTRx/YoUn0mC2QmwCQW7xn1KiEhYsTvXvGeRzLpY3R3XRM7EcdHqncBBc7Irn0aAlAkFKyZE7ChIDUCMKoPnF7ryzK25UsU1J3lasGLF3OJvbThBnLc7FgDApkDcrr+nBhYKuCMgH/QzF9af40sHYsjyXOoyY0XCDTJyoFym/DYrFHoKlCkUeEEkDgXEaHKfVWOmBeA2nLEpjyig4IQzzuFGyyoSjAvOyDUpOBeAKnG45rQjIeMOzyQfoSKGjKnKfREFNQTM9R57ZbSoqM7Nog59FSvVZitIQAiYsOFZeI4cEnZyvCqZVh7YkL9v8ZX2idTaLQUKKVbMOGPDDYyTRDPgidKJCTsWBbxWEDDbnFNeeF4RFZ/jjB1J4wTS8ckoaxs8vJUUxN60rQMyZkjMuUX7AaHngqyszaD9xzZdjNkmwM+qgqq8W+p2bvKNUOHIDQYKyH8JaHVrkpBMiXVhTkr2f8+82nBqzkzGbBMOibR1QsHcZF8p08oYXAF+hiGkZ4KKMtYXCLDMMWngIGeXXRmGHja0jS5J3AYlCfMUrfylzTUCWuWuzGQCe9CqNiDWWIlmBAcNmORRAQOhWBbADjhwA26QpPTNiMuDDbVJcRoA5UvO+uzzZXXAIwi9zGR0ORAI1a7wM2MPHNLtF2Ex9fyhCL5J7HTwNak/i9/noUK2IfNgsnvMh6/n4pwXR7DzmO/+ezrnS1fWl5MkJDP8CHq8qTP00gnj4VAPQr65K8Y/q5dgfTvpv9gMqH8sbiVbS1zLz+MC4GjHnLIXf+obqMu50UaOfBd0m9nHTn0sfXHYU7MBlA0b9mnYtbeyAXNVd0v9gbPSrce8X8ODdnYI0a4FelF7rmDGm+zrbDCIH4/9LH72Q2dh2CdoCal5PT60vW8Abtjjt6xKea9374fv0+/CniwIR1e9OcXFju5TLiQDKCFX1Y1tv8t9xjETYIDEe/l2RkXBri78hAlZ3SIm3kfgKgI4aRQrivAlZJyb1OSOCQG7Tqm18WBsqStsIwlzuGGBMUB2dcbIUzfc6J0rCqaD81nyMbWSVGXHSXcn+Dbr50nja6HVlXeob811PatMobB/cnPzB6A9K6HihPsGqNCNyaX6jBUTAjYsCjgQisstrhog4N3S4t3J9xO+whlLgzhiEMYbHeKncEYM3DoEfI6vcMZJZQY3MCZehQB4cj9ZRWQiAjmk5pivEOnGhF0YX4Hgh9TNhgUIwdXRWWt4QtRpaca9csJSixU4u6nnhK+01FVFSENjxS2oOCtw5M8a3uDcADf2XRFgzQqc1RY3LWk7EPAV9ycZk3sDgqY2OnaAcpZatwRWIipWkJlpv7PXLE7eka05IyucJCAdSxJAydiMG5VMZRkpM3vJvCvw49U76f12bsIZBMMIkiTdWMqoTYioykTdYLEhMybEBqqiPau6NNirizpNjZnieUWzMu8IkLIEPnabz7OX4SSIVtWtHt01rFkBHVmHVe9hCX30yx60IIAedR7gb4xfGdoTMyixy3iTZA5WrcvaXjW7AqhHcICwqUlq9mAn2jxJkMgixNlcdExzwjH2p69NSSG2TxQzDVqHR+f/Jfh2dHgfYzmF7goDyx5ylDNC1LEc/R219S+773Uc732Mvag14OeP13d90PliZbZ6ftV8vugZ/Wfpk5dj5t2ZPOvxsbX6fvfx2mV0NOB9te1jtr6Hk+fKNw1HXmxHfo6Hd4YNGzZs2LBXN1szAtfWGbZ2p9XubdWnZQoltg+yyDGyg0gX99J34xVRPuU33GNwvA8TK3iMh9yGPXV7VTnYb+Ab+A6+845yM2xYb28WE+5pzqlPFoQT6x1RsshjfCnjKJgDPYNx4QRwEtcy2TO2EJTlX1T3si0FRaJv1fQJAtDZHRtjhU7o4KiZAmAwtpE4sj3boyhTr5d+ENba1mAEPsvAuq8UzNkVGJna07l45bVJwTPhbq0Qhs2K3bGFksIP3jEckXGrnCtCHxZzT+pvx6LghPEzEkoDkVieXSPtTcrcmjWuVlKwIWudST0Vt9hfOrBvwYapReCryu0Lre6NWSjPvMG91lzR6yckbMiY4ZmGAkDKNd/Ed3CHE4rGnFsU0PSu96r95ISzll4YZAJ4ZQBnCCNJmIAB0JaakXCGj8c3Ydf+WXBSMIrSjjtsA2O9n+cMGbuNPU3Yct5uXQy11YG1FtvMPhcE7U8Fe9su1cbmYhvc4NyAjIKo0DLQC7VaP5pVFpEn+jICGCNNXOtTu8sYZVUjLRYUbSvKLkJbWvro3n4jNyqgaCw5GaObxnq0DaMAqASdPYgwO1iHXEG2E9uB8p/M8waeGBXxTQO4DFQrKE1ikkATQPjHJMj6NgbIcDUuhFnUOgwKshhzUijeDzGGJKUeLAUknp61mjH8CPR6yMVcxRlkr10+j1vw0D3f2F3WT7zr2QB8NOjFy2eGizYyTl9w39HI2kNLkU+O7f+PdeTnaYBQrk+3dACmPx986S7oWX3XnuZlG4/3X3c/HDfmvKqP8UHzMQWP6Z2Uj3vdLqESizva54TOk9ezY/83Bt/7jyFWG5j8mN0fH6drymYIrr742eaMx2UfIs6Pma8vG4fk0vp+amsTu/Ox1eWwYcOGAcANbnB/VYL78dl/gF+L/x/+vx86Gx/cXvad4t9a3o4HNCNy99m/x+Jh3deDcg+tZocNGzbs07bHLIs8bNinYE8chKPLxhy4ymFqyzOLn+SdhRZvaFPmlyzjdvTO5aBARFBwiUKWqwJmuV2FBhFRxpALQ0k/KjBCQKeXpatK4twd92RSoKiCsEZpuTDoLigIw4XqhL0BMFVztalIZlLGDGNZTVpPEVUZW4QQN9QGZkFTtzh75iiW+FpSHxLj7U4BPZNkZCuI83fBis3x2iZsKgRIgGluznPvyA8KTFEWU6A8qWuJxLVhxwxyCvl0QlgESQHgBiuSBhXdVJIxomDVKGhkfEVUfBPPlcEo4MSu7Wc9hOAEGWeMsebbtyiDLGodZJwRcIOsNR2bvOR0OA/gmZ6UBjG5D4I5xt9LWicG/gb4WGCy4TG5yICMG8ey6s8bCkMs4ax14DlLpQPlBIhYtddYP6naawmYzDp2pMw76GydtBWlTWZUVJywdoBXwdpA36SwH4FPaXcBEAoIthqcQXZqhYF9ftz4/i3SqYTe6fpP2l9zy5ONMqmFk4LlFrlxg8SpS+2a2QFwZPMJuJRdm+yIsPYxaFRyJNdRnIx1UbS+CfMXEFwM8G76fov70Nb1GojGMcyTrFH/n4w022j3jmIvp2qMDRNX4eEIcjy8BKX1L4JoBeRzmtlzDXxj27OeeJ2Bu7F9MkCuB5o88ONP67LuevHjvpY9NBVhzy7tfvvWH9x4WWM5ysXzi9bOscV96a5ZD8BxDvbjyGSJvPvj69J9eeulZJnu+9V7N2eRr9vH6eLp2//jNa6TAK51CH4/prI9vg2t1U5286mYnwEux9SwYS9jovnx2Pp9bze4xb2u59+lJZg+yrC3Zx8LAAdgAHBvwY4g29cdOLv8jQezeODPi2P69x5XEgOkGzZs2Kdnd+9hXTRsGG0w4S7tyYNw9fA3XZ6UWjvK2FFijdclZR/JZ1kWEuTiInGGiSeIM1+kKM1xXMA4UxHBAQXirJbln+SoKJhBZlFWdo4HSgA6nCusCb1Tl6wceQbZWV6qkNdPKCrJJ7Y6sIFpLFhBWcmAgGc4K8gwA6Ak4oaMhK05+imPaTITBcA38CV2JI35JbHfRDrSHNNR2yCp1CSA9uykT1lx03IIALd4juKANH4/oYDcralFSBO+EZlInys4ctaYeARfCDDQsbxgwxlzAysZJ+2zBi5FBZGE/3VSUG7RuGcs4QYRpLRIVBkzdo21Jnk/4R5J6zY34MSDI+w1xnaL2MH4W8w/GWW32BrAC1Tc4l4hhH4KCKi4VZYdxQUDgkpFcmPjTcpwwg5gw4pbMI7Ztb424wyR05zBXkxQOKpAq8h3zgiuvBNyawnGDzS+J+PhpQY8cuR5UIPwwAxKiBb4U5OzSqpu2mMNrK1tHqAcIk3yTKlHqTVKtERs2hK2kRS4em/pErpnjYWLWiP/tTQYywtbyhUEqU1SM2gfBQiAkp1UNLfG9vNyLwaq2fa0t6OrlkCUSaAJSE1o8vLFGdysZ2COcXOlFj2fzse75D2We9Yt55mk7e4jql0yzCQNOxjB1LIrmwfLBLDzIJN/bjjkkfOQfy4BeQGOPKidD+kd6/2hxceRfdYDhD1g1NfRi5waDz/j+Jmyj7W5Pfyzvj7tlzV7pge9WCsvF/fubZjBmEc50MdpPETzFC1c9MxhL7Kep+yhd7opOesfZxTfz0d9D+vt8c+C7wOAA4AFJ9zh+Xt51rBhT9le9nDTtd/lYDDANbBfjxaYcoKFaOBhwWNq2e1HAp7qSmrYsGHDhg1715aRHAHkVe8dINxHZj3vQv4nYMZRcItMB3JPxMgkoHicjx5EtpDFMDsu3xihw4csjm0pVxS0i5gaQFNhkdHEMmZElWWkEJl3kMzIClsEFUKqICg4qdCcRcaiCzkgKwCVQGlOMwHcBCTxDmUBzXZUBXKEuZJhscQEkOBvNxqhbEXCriBbwoYJWSX6BIycseKEe4UmJkCFK9cWC451J06iBSsEZDpjUyjht2LB/4CKpGKbIrspcMeMvTmXMiQ+1oLN1cHemDEnnFucPvYfgjmUIxWJxapAByUchXfk47R9rsy1gnMDXScFsoR3uGKFxX8jkCGSjUXlHsV2RJVKFIbTgk35gQHPsCv4RsalSfQxLbI6K3accdK0d2xImFUkUZiEkudNJUtFxtJ6/aRsQmj+CP2IeOYZCcIi3DFhU4nPAHKiegbpCWesmLU/UEQ1N+c6p1vpPQIAZ2V6mvOQ6VHiMyMgK5gVWwzDDJGF3DV2ogE/UNg4gXyzADLWepBeehMBvgJjPbGn5AYsG8fTg4CE53gWs7a6Yx0zd7sC7z0wFZTv1oNgPbeMbDn2T+9SpfE3m8tmFJA/ZqAkGX2cJw2otLpLKvgqpbPYQ7X95eMR9cDdDjJioTV8ycbwIFJQtqgvE4859ByyHsY0aDB010pP83Cel7zhqMfht36+N6CQ3yWU1tt3+HeJpUhg5CFHw8PA5xEAPUJdBJ4vY9c9nPZDz7LPFvfSgGNfzx7oeTuAG82Xsx/z7x9cOp7NfvzOZ4ARGh8XY+zr7DN89oKg3n1f8P9yxhuushfZdXjctAsYXRSQOdmrELwYkh/2bi3qTuQx2WB+mT0eAO5jeT8NG/b2jZ6ca+8mO6R4XGn6Q1Xya6/8Ut0VXAdzF8Gjh8dV8XgzDhs2bNiwYYBQCvJrwk75ia5pnzAI17vwTOTNu4NN3kpMnA8UkTQpQVlc7R0TyBZlsTmw5ZPPgWfz0CUt8Mne2HPmhqZDnMCaObzNOS/5JJixdIs9iWM3K6AgjmDPJhJ5PoF09nYCbFXxRg/YROdUl2cxt5tzawv7yC9Xb/C8uW4AAQpn3DXnsF+osr68/GVUwGVRwMcHZE4KhQIC9rAm/kcUfAZxyWdlEi7IqDBJzQpheHlI8ozJDQBhD80oEInOWwBQ0c+ttUFW4My3TVZAM4On5YrrNQQx5XoyCoGIWfM4K3x6o9dQ/pNln7DjFs9ba+yYGnApYkAJ3FoEraeKqPnO4DAPEHCUY+BW28VAH9lszCrCOWNvAOuGhAWMYdfH3xI5TTsryDiFUHCPaVj7VExYNS8iLxkbiCZpfoY7N848sy8q2GscS4OOSnPOT1pGqR9xX816IrrAQ1tVmaAEwoVHtahzaUfQsRFUyrW0J7G+AYJ03ll5FJTlPEGHeMEMipNyvJh8bEVoMfIqTA4zuucRQD/hDrH1TcsTn+zjLcTG5hIAnflK6OFCzzKklFl/VrSiaPRAslwp9nvtYIK/z7M0k5Y/KRvV5krKlDLapmerWYp+jk/tV2MM+kiIvk4o42v1cgQqpI4oRct50DbgNra5Ea8IXR/wsSqOMS6O9vB2naW9ZAke3QTynFdZqFwy6cyxYOPKv8EAe+LX5/1VzLiBAZQRpkwrn9vL5r4/6+v0GpPwcVo/9j8WexiAo9mKqbT+KiPy1fr/MLHjTH1t1hS7POjQ2/GYwLC3Zwtm3A/Qa9jX2pgDh3269qI14vU9CXcMXoOilxqXNbA/Ahna2ji1OxmD9RoYdxyT4w35sdqCE1anHjVs2LBhw4a9jj1pEM4vfwq8E5Zu6kvRNTpO6b415kJtYIyIFUbHhanNoQzlZ1UAGxb93eIGcYGWUDFj1asJxtHZGF265vqN6maX+88oCNhwanldlJfSI81HB63EP6OTfUJBwRnPcAeJmnVCQMFn+LI5t+5xau59q8OMW5VhXBUwkloRd3RWqcrLha4taRdllWXlj/jz2ALt3Tc2DlM0RhQZHwZ0TsroIbPJO+RuNSadr48TVk3V5zHgBitusLUT4EVBHAELVpzxWStPQMWN1gPjoPXlNyaJtwkromMuUR6wtnRzk9xcNPaZ3UuZsdKul/iAIt8ZNAdk7wjENAGtzkoDXioKnuEMiaAmbb9gww3uFXROrm/J3wk7TpqHs8b4s/pDK3MvB1iVdbg34IKgym1jFJrsKAGOSWHkrcUCzDjhvj3Jx7bzTKekTFC0vgIQGiOrTVy4cPKU0q99jLcZBRPuITHqShMoiTCAQuqUgq8WE461bdBiVSnNfhzYKUxCrDxbSaidgclrm5+k7dauH7K9ZZTPLXcBjJLWx26LWkf+EACFXPzJ0LkxFD3owxoySN5GFuOhSYRMAoN+3oG7dnbPJODHfgR4SUsPAwlofnQJky1qsRKPvBjmhXPqNYdVRWglyhf3Wm35fmP57cvXA5D2qY/siO6a/vpjXDd7O1w/7ftio0wwn+Sf7celjd5rTo03dyFU9y+fxXiGNpZ4xYsc/2/f+EQCgzRjOeK95ud1zOaKt53PX4lfiZ/Bz7zlVF/H7JgU2+uay+txt9NjtN6BaLMvZzljVx7HJr+rQDeWh725fUyxsYYNGzbsYzG/H70GYh93EdmtOi5N1rTHe/lOtH1EPawmXwWoe+i447B3b+OQw7Bhw4a9qtF//3r3Ps0DiE8ehON/xzPS4k7OMFYP3YDC5TFngsnoJbeMMn4VIQ9hHHmXEFlIoT0Fyj5hOlUl8wi00TleGkxiC0OodOCs1wZlDt21spXmFt/1XkJDsTmcBezZ4GPPCQMrQaT8CJJUlX+TfPl4ZWRLBUABg6oso9KeBZyxI+GMBSK1d4ZEyZohsfEYOS2ohKTFiMvK+JuV+WZO4YIJAWfMgOZ/akAaXcq7OuF3VOVnST6z8qeMTxK1Lgy6MQ5fcO1owIdc+Rm+UgggNYAs6PRSlWmYFTQpypwLrZcV3GIFgdbacmOsyICMG2Q8wz0KAnYsrl6tbwcngciUNgXuDFip+BzPIeBSQsbUQLwdUwO6rI/ktiGh3Cha/ckVCSbNJ+1qZwG5wZiwquAgWtlPWFsfNxjFysV2JiyUWv+DAngC10peJjdW6OyrWp7cWrQoJO1hx9Q4Y5fOWo4TunMZw83apzTmouUYILfKxG5NGjAoeDcr+7QcXkIGoEm8Ot4vbDsBexaF/wQWIgtRajNdAWNiY6wyZhv5RYzzZwBXdW1AEJ1wm/81KEjiN5FWc7bpnLWniGUYtMxZkwwnawEPQHMm9r0CWhZjpRnAbWBbaGXjfF0V6Caf0nqC5deESAMslqEx/DyfzvLHcvHbS0nG6+Ab3ypW7gyLW+hHkgGz1+0SmPLt4f82MyYlDi1oAJwvy5vbsWf2wNZRNugI6Pt/361Zu3gggcCgF6r+OOzdOQq+g++8s7Rf3Y6AUW19O1z8OuzVzI5UXJrxQQnK+Xq3yKjGYpfP9u+18TRjwaaHSz5G87zwYcOGDRv29OzoRrQjW6V7tx1X4RaG4CG4rz+oJ2vRPjUeqvSsPbOPa5X6Mdr6Ea9Phg0bNuxDWVbv6+ve+xTtSYNwgLmTxSFT3PKHrAnK/8lvJnB0dN+YBnhuqZgbQgAjL0kozn5CJKk9cVfoKen/F5CdRncHGQCM+0UnO9Cf9Sa8Q8flqsu7E3qGz4qErKw88nKKSt0RdNm0Hm4au8RYIoxPJjmNndMeAG6UmXR0Nc/IWPAlbGFYwBhYE3ZletHVzLhYIlMYAJwxtxqOIKdIZBJZN0Gd6hTQOzmo4j9Aws/hC63LGRM2BfCARduZ8oaURjwrszCCJ8D9QpgggPCpCKqw5FH7wAlnUNKPUb22JmOZXarWs8w5XVUc1BRwCSjyaom4xrh2BkBJX7rHhpN7AutOmJdcPoq8ojDnooIVm4KIrPMZmzIIAzZMuFEWF+MZSl6K9vuIb2LGX8EP4kfxd/FPsCLq05KLjsUyy/dVe56kFxV8JNd0w4271uQIBVTdsSMpU02kS48OPwKJIs1ZmnPMM9GCy1Ft7VgUXDROztSYkVJDu6unBLKy2Ivt+4yEiIoF9yAsZAynrPKzZFtaDthuCZv2O8tv0L5FsJpl8hHYCLdBe1vUHrS32csDHlYfFnfNmMCWXwOPAM6nkjeKsF4yHwx0MZBnbyl5aT+yV+lGN65Zgmf2cD40ORiCbRL18TiyrL0z/HEM3w8zPIuSZYYeYijt1+sAWy8+aldk9Nvxh6AAO+7RA1KXFtr1RwiZ3xsUSKbgcRPv0zJO+PEZb2IvKoWBq++KqfXqZvn1fYOgcM+V/1jsyPF/e/YlvnwHqb6p+TeFWNX5KbXPj6GvPQW7NtdF93blGpIwdg+IemHh/ogF8P349fgX+Ml3lfF3bgOAGzZs2LBPz64pJFz729aWvWS9We2OqvlP/i3LQ43m0cKV1LxaiB1SPO5Vx+po2KdsN7gZSgPDhr1jGyDcpT1ZEM7ckQa29G5Uu+4IuUVlDQlIZywFW8qYe85ONjFmExdHUZ3v4tQOCnCIqzq1J5JxxihX3imdnMOSORU2nMVo8ufzRTIwYgfjd0luRaJQmGi7whbi4KeTtuJWgS+6is9YWh7ldNeKGQU3MOFIY8dVVxqCGQLOBVSsWBokMCnTDqg44R6bi1cm/1JEMOMWO+5xAiDxyyTGnYBZC/YGgU0qa7kjIWpdn5DxBb5UrlfCCfdIKPhMn3DGSaG2TRmFkvtb3CGh4Ffhm/h/44wFZwi3SuQ2d0SVz2MstQzob7fYQMDXADSpkVuNpbZhQUTGghUVAStmJAAJawMdSotoxvNuBSfcY9K8xpbbHWecwPhNQMVnuEfFPYCKFROqgkBBc5awNeZgQO90Zoy6ovckVzczVtQ2XazYAb2OYBzwR/Gf4L/C/wP/Ej+LqGxFsrwmHYW7AoezMugmhTisl1d9ZkRowBUlRgWAFDYf4+zJKFyw6hMsjhTjDFZA67loLwF27VepMRmFNZexKNvOtjMC6UYY0Bkwa/8riDjp2DKYvzigaGuwUFZma9JeTMBwArTWKWM7a97IFjUwV66XGYCx8cjfiTrXJAWzOE/48TUhw2/UKLlYEBSoJCjOuwrIgAtaB9Le0i9y4275uGrHGdWDnoTfCHH3W0Rptw1kzCWtOYNEOCbkiX7emFoLALnLQ1/ia9wOi4Hpt8hVfzvyNuyt0TMegeyuI8jl4w9ek2d7eGnh8+Hr8xrP5xJQ5SxBGr+/A4e/v958Xqx2e8nPhwBGtKt97MYPt+W/ZCYef/k6MPRxmx0Y+vjy/jZN+mZGuTruPkaL7SjIu2M6vp752vVuQfnMlbJdS9eiZ/3iowbghg0bNmzYU7NrB97ePEX/79H8noBhGK6lcUzH1EoAW2/3KwZbu9MX5Ve9R4/YsGFP3wYAN2zYsA9hTxaE65dN9eL/zVFp8JH9Lu5UwPg+vIPpxebYTOoWNQ6LLGVkMZTU6S1gBdPcsLVTSOIgntWBTnE9kb0TkZ+tAQsJjGllIKCwSgQOEH7OgqBMsaiykbxewJ8Mi2fnIxzt6sYOCAqQGXvuRtOgJCHlBbMygmoDH1bMYEwmcW0LA+tG85tbDDDfThssutuqMegqgM9wp9w9tLxQ3vEZnusCtQe+6Myn037DpOCfOI13zPgMz2GcG5F8BCzm08/gl/CstWVtsd4+x1fN2S6ylFGZZ76vGSOpujRnBV8MbK0NwMmtXoUtyVIsWPFZS0mMJwICgAX3CgZGZX1J7LUZWcMHzwrvGCCbla0XIZG7tlZ2kRlNra3ZHzJmCM9IwJodNwrYkKkJAP8l/i9Iyl5bsKEqc07AM4tDZmKKkkbGrqBYAGO1VR0xJpq3axsERJVCtfOAMj4n7Nq6Ak7tLi7hDe61ForWqwHQZNlJqe+RdVTLuBaolvn2fKUTNm1xc+gL48wYPmSSFZDRGFWu0YB8AA7KAmZlZRL08iAPFOCz7RVgbCbGYjtyopg7AThZj8ZXiEjtsIC0i5drZN0QmCMjFa19EujuJniGVnvcQBIYCK4OK2aQLZHc07gNtI0nRRQNOK6obZwwcqSlYKAaXF7ZjgZl8UmpbVQNgvE55dxN+Iu/+zpCqwOyZqVFBUj1jECya70MJtpz7W//L2dB+95LuzIPfO8EV5I3Pz/Ub9I9s0jKyGcKzH4E+q4BWu9/i+/LQBDbOyZ6WOPjdkF4MPlTNutttkbpDyV8bFa6AwWP1R6q3ePMIOtdHyfUHy/w8PgQ2TJLSHiq8RGGvX0LF2+3YcOGvbx92LHzKmsV2xv19379ilDWxQkMtRIPd4TDtddydpm66TF8rCuuYcOGDRv2NiwrBeF1732K9mRBOB/vxsd1q22bb67B3vkm3/nm/l58F/4dfgF0OUZ3TVHWTlRBM8ZTIzhFKUDv8I0AbnDX5XfFrA76rPJosghK7c4Ciy9GxzbZGD1PJKBiwQaRuDNxJuaHbBjKLvJ+iU+XlKUFnJRZxzraMXeuZwHUzpqbhBM2ZOUfAWTQCUvsM3ylgKRx/byLzAMGJ9yjNKm9is9whzOeARAga9L4YJ5RSHZO1Lovrs1FCpB5qg1QObU4dIR7uFg0qTQBWu4aY4/5tPhkOxIy7nGjIJCAflEZRBMKkkKu7HErljahJIXxyIrz4n4zzlgUyNowaZ0I0EKgY0ZWZp4AMzfKsKuuH+RWlhVQN3lUhpnAd2dQttGPioRd+0Vt/WNDUhYUnZkG+ghwd4eIqQGBQFUQWfKeUJVFllp7Sk5XbNofBVhjLEOR9OtlEM2k/tY29jx4JK4qYStKuxfnjqjwYAXTlUh/BnJO2mcDKOPK+wgssfdxLHLm8b+U1qqe1dWXhWAl48YRBEWXDuM0knVGUFm+27vyCDfMPyt3z7MxZyAZn23lgaYtPYcx5Xgn4yoay03K6CE6nwZgMiqhpSusS4LbESajaPdWFG1HX2upsTf5BMKKR7Ne6p9NgNOuMInhyzvJ3DhuZysIurHUl3CawWI8NOE3y3Z1LwfcO6EJWNqm+ri1Da1Mfao41FtvLwM/Vc1HubjS2ODXXX0vu/3+Fr6Bb7+leGPPcIvnh3csdA7wUtJHWOBp2HXG5adrvqfmK3PDsA9nAbZClc88uGQjtML4xMMGADfsVWwAcMOGfVr2MsdgZB2cL/bBssPzMbMtnIjfG9muyYfq4OqT4SZ4fPEaiPeinIr9ZvxH+Of4Zx/J4aNhw4YNG/aQCQXg9WCn/ETfAU8WhBOXoHcY945Xnofn94AJFx1dcz+H/wkTagOh+AQCauYUNaw2HNKi/FzWpUnPXkADW0pzJMM9x9wTAjIUlWCEgjMRJwXQmIPYnE0JHirgs8nEMOBOyrFoHC/WU3GlI9MKyjRaQK6bsJkmBSRL61YFi7K/dmXLEJjaG4BGp3lpDn4Rf9yQkVSCsSLgeQfMJWw448aVpKqcHjTenKQ/NxArggyVb+CrBnICdAPZOfkA4Lsw4YyvwDP0i6Yp7qGgQKWkKWDTc1Cmc1aGFFldBghKXmesCFqWSYGuioQbrFpPJqVoIOyOs4KqAvoSiBT+462yp+xcedBUs17L5wcsyK09WN8E1lZlUJIRJaAPrwVOOIOxFPmUAJObZAuyRxH8Iicu6ZI8uWcagEhWp0B3qzIYCd4VBKw4NTiAwBFlKaWfLQ0mkTF3xqzsT89oDQp8kqsltZNbP6wN8s4w1lSETJnGapp1vKyY9TuJD1db/1vbGKf8FmssKIRGFmBUAA4wsBhdy5FBZ+0rsf7Ornf5uYWxJksDuSwOgI1+AK6fHeFBplgUQGWEtCNnzEN2aOnzcEJw/0+mozHrDMAhJzRrv7G7eM0lvMB5nXOVAcRVR4vBEtYGdmjB5z3A16CV3xhuvk48V6PP1eXWsrbce6O4HOA3wpdOM8upF+E82mX8B/LVLBWfwwL2SgaU93KuvOZa3LijEV58E3tbABwA3ONLPdUL2Purjxf5lE34qcOOVkG+rthDhzteBFkPe5t2HS7uIXIePZJf4H65JhI8bNjX2WCHDRs27FO346E9/z3A3cPD63/RwShdGn5vCFjMbf8793u9nH1/sDGi4p/jf3BPH2/7YcOGDftYbcSEu7QnC8LhAXfb5SdzwXNBIs5bngAyMb+IFQWC5hq7js5iSlQml15111msswzPP/NuU8ZJihr7LSrIlZTlYyeQuBxJLX4S9Plzuy+qW1qYahEePJE4dHT+irOfjnK4sjFnAhhtOCmgU0HGF9MjIFIQ8bwbMF5C8xb3bVH3HCd9FplGUkenFi9sbylMyngxwA1I+pyqbLAdNyCz5UYBFdZZUh6NOGJ7KTKJFfcVACgsJGkGLJ0r6EahPLZ1gMTvypixYGusqRULPPOq73kCCiwXUobAjHvcYMeGm9YXLM6WyH/KCfEEA76kx4iTSgCXAIm55qUgsnOuC0y6tjZirLmErNKhIkN6i7OCz5MCnsJ8lBh/U2uLpGAhe84GkyhkGaYmHegBB5N1NfAlgGBzUiDbYMWCZ/gKEdBIbrP2GWvnGWeNqSY9c1EZSkmrNCZfv+wneBPAyHU3WEHWpo21HVFL55mbcuovK1uyqOQnx//crpsUkJL+vLX+Mavkajmkuuj1IheZFWIxIItR56RsSX810FzAYS8dKUcDPPhO8G9CbYcGRJrR4iTKiGKUyyOsLz3quJHjTBPbvWhPEBNw9xhKnH3doEIPcvmnwpVVSkZ2p99YJvevh6Rs9vW5un5C0+Jr+SMJdhXn2Ie2hX4c+DIG2Ni26Eh9+Y7P8rXhuYyhtTN7tEGPBhr7eKTGGi0dOOVd3Q+9Rd+X1cO/LwOTEGDmmOO1T93h2sPvgzX0kPkRZWOfY4fje9Tfh7bLIyX9rC2Wu3eKfe+j0gVcn0GGfbo2ALhhw4a9jn03vge/hF9EPexdnqJ93ftSfFo2lx7fs/Y+tlh1dkjSX9EfGDzK6RPss2tepAcybNiwYcOGPX57576Gv/AX/gK+//u/Hzc3N/iBH/gB/L2/9/deeP3f/bt/Fz/wAz+Am5sb/Lpf9+vwl/7SX3rNJ794k8WtfDgsAvwJ24TSuUb5/YwdC1Z8N55B3J1ZGUdkP4mYnrhJdwUYiksjI+GMSUEVWXDsTdowKSOG0nQ3uG9Sh9fODfmlyKzO/Ulht0kBqhOe4wZfKdBzhwUbnuEOn+E5brHiGZ7jMzxX5s4OyiF+ji9wg+c44Q7PcNdYR0Hrhy7fCRsW7AqIGIuLseo+x3fwDN9BVHBsQsE3cIdn+Aon3GPGjhkbnuErTFgx4azgwIoFZ8zYNM/3uFX2UULBjB23GoduwT1mrPgcX+KEe5c/aZeTspKkDXZEfeYtnrsy1VaHURl/rG1johhIMGNT2chV/zvjM3yJz/FlkxyNjY22I2HD5/gOPsd38Bm+RMKGBXf4Jn4Rn+EeN8haBzuSi6+UIDHwTrjDZ/gStzh3MEHSup+w4xZ3Wp88d5A11pv0sQk7PsdzfBPfwTfxHTzDPW40FpzIdEofOSm78YQVN3iOG9xhQsYzPMeMe+2n2QGFsnhesGLGGYSyF2zaxmdMuMeMO0y4w4wVJ6xI6GUSPfNG+pPVRdTvbrHihHMD6/z4lf7yHAvulCdKGcyMRUHkiKJtxzJuYBTDW9x1bCnZCGwdoONZS+zvEwpuNBrjCaUB09Ax4sFFSmQmHQsTCk5YWzvZfFLa2E9aDpZp0j7P+k3YcIOIgA0Sf84kLCdsMFlOgWKSjtsZPGTAuI4CajJuodRb0fa+JgvmBcX8fxS97dtzgsS7nBQG7mPXeZYa69kkIHnogXNq0L+jjjMC02xzlhcg3H906zK90sorbSYAZtQ6Yz5S9xmtfPIsxn7zdYD2HUHn0D4b9GZl4njqz4gewbCoack9JkNq7wjmieUmoMd3k82N/hn+HRPxtra4x/rw37Os1+pN2mfS/2z+Jeju36nGXPWMt0/HegHuYS9j1rvJikabNcxGbb4do8zzy1u48nc4/Me3YnT/kW9vxkN1T99tOmzYsGHD3pX9In7+kwDgXseO+wX/1ubb2QN03LdRk8b2NPWB++mX40rNr3uZ6rXPD63iXvX7d2sn3HyQ5w4bNmzYu7bc0JLX+e/14KoPh0G9nL1TJtzf/Jt/E3/4D/9h/IW/8BfwO3/n78Rf/st/Gb/7d/9u/ORP/iR+za/5NRfX/6t/9a/we37P78EP/dAP4a//9b+Ov//3/z7+0B/6Q/jlv/yX4/f//t//ik9/sRuOzAwxz1ij7Jg4fvuzOz7Viuf4ReUM0Z1osaRE/s0YV4xmJHJu5gqOykfzacvCZO2eFZGbqKB83lAwgyyISc8ES0y3DYzTI9GoChZ17gofKHVPpNNWQKU77FoqfhdVepEn7EsDJsQBTFCtAlhQcEZq4IYttaqCCVFjWHnOnThOF5yxqwQh06NsZICwn2YFJgNKu1a4S7MCdrIoS1pWsrqEpZdxwrm1t7n1TZpUADjhHN0o60mYbbu6eSf4mE8BGTdYW8sYg1LyfIPnWu/RnRizev9cGXh29osny1Z9njxFyra1mi+gtKn8NmNFgUhhTqjqzp8VaBKW1oZF6+ayz/m/pwYEmGBfUllRToM3OINn1USqc4Zo/QYFpXY3Nsg+20AenD/lPiMrpCgMMcISxv6zUch2kzGyN7Yc669CQEAK0W5tigvaB4VhujR5RkZsg4LI5IlRHDW0cWDxwqy2zK1XsagkqP0mcpRRn8D8sD6j6ysE5CSlrPHgPLPNt5j0sgSbSSqAE3ZU7FjAmHXQfMjvkwJLfUr2YgsX/99vNI/sAgKLxpYzJiav4cxJDlkv6EgpRkvRcmWx+0L7y0sxWvyB0OW5wrtabcPHnmcnKENjZ7K3eGHNo/yklbc/91m7WvR5NV4x68ADjUfRlhdvYPGC76+96foRdkz/XTgQXsQ1YW7IyOPn2liV8sniNOJKaqldxc9AeCdl+ZjMs60v+/1TNuH7v432v6wtjh3ygXko4ChIO+zl7d3GMju2BtUMvJQ0ZS25hh5xE4cNGzZs2LD3bdf2Oi97T78jLm2v1b/RTW3EYLijBpW/5niU0a8tj7uzd7MC/L34ffib+BtvPd1hw4YN+9C2qyf5de99VfuwGNTLWai1vrMjH7/9t/92/Nbf+lvxF//iX2zf/ebf/Jvxe3/v78WP/uiPXlz/x/7YH8Pf+lt/C//0n/7T9t0P//AP4x//43+Mn/iJn3ipZ37xxRf41re+hT/67f8Mp2+eXjqv1ypBXsMWPcDLfx3FbgQwupT6ooOYLmMKL/bu/OqWAhLvy5MUea53U1c9QYGCgoLJsRyA3KWTD2d/5Zm7AgEUyCRDhkCZABeEDSR3xQ0AYTQVbMrVEzBhanUSFQii42pHwoIdEgFraWg4UHGDO3WMRJwU7NtUBhIgeGm1SEcsAKyYYEGE6Sg7ouXWshmpxWuTkgvQuTVZQzmVlZVDRvx9R1T4ICMrkEWn8tzAz6U9TaQIGZFv79qAPEDmLTZHEeXlBABgLLusNUm2iImmMg4ZcIu7tmSkWzsAOGPChA091l7ds1gT5nKMLV8WI0/6xepgNclPQdS4blZukUK1JW7Cjqx9hNZDR1z+mrtz1dPtZFUadEHAjJHcDBIhWGb9g31dAC4GA6XEqYEzHkIxaFU+kaFHfhThP4JPXtYRrQzFtSOfR7nIHZRE5fMMNDLhW+thZF4ZFGfzhnx/PEfBOI38i2DzNYdjOPQFzjux3c2+ZLMY65IylpzZ+qdL/D+zDLK+DJTvzzoaTAZ3XdT6M35ccp8Ye8/K08/kvpVtPuoZYKHBrfzEJ5VWF2xbD7/1rdH3ZnvqtU2jr6/j1u7V7Os2gsccXbv2WF/Ha68946HnPryU8GxZwnF8Wz5k/ikD9LhuAflqH/wU7N3HdfLjx4DkhE+rnp+icQ3Rr+Iv5+xhw4YNGzZs2OM3D7Z5jx1w3H9xB0/hfjHvSfI7Y+5Z0NLuU7SnXdsr4cr31+0ZnuE5nr/UtcOGfYqxbV/n8OX5izP+82/9F/j2t7+Nb37zm+8oZ8MeMuIyf/zbfxg3r4DLeLv/4oz/07f+y1dqww+BQb2qvTMm3Lqu+Ef/6B/hj//xP959/4M/+IP4B//gH1y95yd+4ifwgz/4g913v+t3/S781b/6V7FtG+Z5flfZvers44sYoFu66Iu2dFcZld5k147npoXZVRUsMBZEUCd5hkjYCfAioFZ1y4MAqJTe1kCsiIIT7jWik4ANizJ9hPlik/PWROaEQbQoOESX/6asKcoJZgSccatlF14Vp79ZAaEbZZXtEBBqx4yAHSdsyDDWy0mBr+icWEkl8ihHwJIWVBTsDtSjTOSmZWOsKyDghIyEW9xhxwzG6yMASQCCUmcnvW5WmcmobBeW25ZJOwqgoKAAISuW5sT30glBy1WVKbco52zT6HgG0Aj76YSMtfHEKm6UA3aPG5C/Rud0cJAGgarSmH3QuG8WKUtgQvbZ0GLrkYWUVKIQmDDhHhOyAmSMYCYAmohM2tRA1lbEpiByajHNSgPMhBFHYVWCUMKK2zoAj2OCAMuEHRk2tiUWGk/NG5hNKdRde6CBcWQakYm3gzEAoU8IGveOwIqNc2nPquUUoUT221Vj4tlIKSoGKfV9365mmQHGaZR6pRG0TAoL8tvjNRZT0voXYFwtltd6K9lFBjQR1DfQln3Coh3yHulnKygV4keBxOMzsFWeK3KXVYF8gy2Nj2gAbmzPkbiDXgiRcSIlV5ZHQrEyE3jgy8N9vDfqWPUboKgMNwPR7FCEtbr9xX4I+LhzjLFZ4echg0Jrl5al7P/NDc703/d/X27orn9/bRN3hPaOKdfW78RkDmf98hq+ixgvjidLLW3ed81YP9cPP1g8xz6HL7MlvV5fw8Rs3vL1+SnV07vffF6OaLQZu2dS2cga9njNwNTqZr6CI1vO5nvTyBhtO2zYsGHDhj1W80dor/1Gq2795q+0Q8iXKdgO58i3O157PLz1dWZ7vAHAfRr26/Eb8C/xU2+czqcGwAFUVRo27MX2sWBQ7wyE+7mf+znknPF93/d93fff933fh5/92Z+9es/P/uzPXr1+33f83M/9HH7lr/yVF/ecz2ecz+f2+YsvvniDXPdO2NBNcGSEGLuMrBw6yAkJAHQ9b/BMjeQcZ4Ty6LyXOEm53SuOAePbkDHDuDelSfuJ9GNGwNJADQIQ5sgVmcVVAbPp4FyouFUpPeZf9LafgwyjGRmrRo/ykmEBItsYsCpdNCChatkWCFtMnPxZwZqiLCdzghd19gtUJVKBd1hxQtSafIb75jYhZ4ogoDj0VwRUhc0oR0n+msgPMp4fpS4lLcAc69C22DWm2dxcMlBWU1T2jcmgSf1MWCF8OQG2RK4RTTJyxgou455hRcWKoqCmAGpfYgOwQ04JnHCGMJHOjR+1Ieon6SECevUcs4SMqu2bsGPBHVbMKtsZ1Ol0bjDUpABccQDMjIKEDTuSW9BmfObknSguWBQsYb+ZseFGwc4EkQyVEbBDREe5+GUPZey0VQG9oq1GCKd/4UqMslXZiQI8k22WsaksaWnjQNymd4ACjIw+VbRsBDnZJ+5gABUBhElBWxm3K+zsnC35Z302e9WCs5Y3aVrCCCS70S/pzcUrsRgJ9xkTMeg84wEpyacAnZsDsQFjsTL+ZO5y7I8RVB2vcE+mJQWE/RnAqsAre0xUEJAbGj5X4F72FIN0E6qDvJiHjKr9wJiANnNyO0POZT9Lox0K4DWM2WehvI88zL68xj8km9kOU0AZcCwduYMmkkkQ/ggZGSzomco+nLiVyQBVlpl9hXnNOleYZArfIsc8GZ/Dx5vr2y+0VODaDA1esMU9YwCWQxlYSjtwwv7Qbz8f2oYOh/abmPXha86CYe/SbNQcOcD+0xEcHfYYrAdVL6Ob+hnLhJT9rGrHZAYbctiwYcOGDfvYzL/5H3qHXzsEeO2db/si2d3Vizt8HLzLQ5P9ftTv1I82VhxPxd4GADds2MdmxXkVX+de4BLjOZ1OOJ0u2XXvC4N6U3unMeEAIIT+pVFrvfju666/9j3tR3/0R/Gn//Sffo2ceYaBdxnadwa2yWs2Opep8Il6x0sEGTUWASk3BtmO1DbzSa8XB3+FifgF919SgCN2eZTXPJ3jWcEExkaz/NNdYM7UAInZJrkSV+yO1MClonAYFxKTAh6AOKcXjW+VAWy4aTVB2v7kXLXyWQCLqS1LMii1uSrzTkA6AUeS1tikMctucMaGqZWRZTcOj7m8ZpwxY0dQkGDGpswm1kLQeFx91CYgIWHHjA1Z2T0G820qzRmUuSeCiytmBEDjlVV1kBs3pgLtWRPOWp6IDR5Ft7pjGW60Zs3lT+6TfJqRUXGHgIgJZ22DogBjD6GsmEC23qL1uWKGROSTfrhrHi031eWLfapoOaEgExTM3BQkBQgnC5hIAGxFRW2swNT6oAmmLgj4zfgt+O/x/8LsgDzpKcKfYl+OoDygpPMZztiQ4JmABPNM6K5qnxDoFzDAhX2CgC0/3eDc6o5zQM/zse/8eXlhAt6BbEKpP2H0cTxzjFZsjXPKVKYGQRLklaiH5EUa60JqaYbEPJT8G9MqKo/22nECfo76vABCTASYLH2yI+HKSBBT2tGDur3UYAClMg0QD64GyUBNmgbB3uie77nGgM18HItMiZASf00Xn4VlTCDOZIL77ZKHN9lHyDEEGAfT4Dw5VOBBJ4JlUHjYUvQbvqPbd9J68u8V5tNv144SpvY0X092ByP/XQPDwuF51zaVR/D7RWXwdem/6/M27M2MLXUpijPq+EMbRwzfFDb2LJ7oaKfHZy86HmCrcbrWavt8+b4bNmzYsGHDhj1de9G7nvtXUSPK3bX82+8pq/t/uaaql4MHKi/XIv0RyH5XN+zjsu/Cd+GX8EsfOhvDhr03y28AwvG+X/2rf3X3/Z/8k38Sf+pP/akH73vXGNSb2jsD4b73e78XKaULxPHf//t/f4E00n7Fr/gVV6+fpgnf8z3fc/WeP/En/gR+5Ed+pH3+4osvLhrp0shI82eV+crzLAixqM7x0H1vjAx7Hdbm5J2AxkkyV3dod/KVm5SZtTm5vogVEYwdVVscKWFVWTwxEQIUtloCWWLCUpkUjMoKMoX2m5znXdSBvzTJRcnnhqKyktD75FkGQJKhdI8NS0tb6omRnwTimBqzwuopKQg4YcPsFiq78nWEVWcswJNCRXegFF5VNtuGM25QIIDQoml9A181uIbAG0GMGc9RIBHrTvqcHXeICNgxIyqYNWNreT5hxYqpsQwzKItoDBvyu7zz/xZfoSJhRsZZY9xNyoYiT2Vxcd8mrB2oyzogzAKt+2e4b31gwlcQWUDrkzQB/jx4CY2/R/aMMJ0Ym08AtBVk+Uk7b2A0N4GCCRBJf15w1tbJOCngWpQNJnlg7rO2elB4ydr9n+K/Q2rls4Wp8JEEhF5UDpNA4tyixhUVnpy0/5H3lCEMTsZTC5qLCALEHP2cC0w2j+1LyU243HpYizCRgD6pXcWZgsCYwSCUfLQWvYPJYHnJwqyAKyAMQpHBDO5+iyBIcJTPILjmpQVl3Ew6nrPrq6wfMvjQ6sjiYAbYgQSZgYrmUnJsRwqMFZIA1xOA4upB5iaKtZocpHGtKmKXXgAFelmPrOteirK0gw40S8ngRpMis82NAbml1Zm1iNW7F53zzESmwDxW990ld8+flbSTkD4dwL8vrgNlL7LjMsf34Gubw2Ef2i5PyPrfTLpVfqtXrhr29bbocY+9AflvatecIP5N1gPywz4W86uqPjqMF7IKuARZPdt52LBhw4YNG/Zp2DUXsx1ONfMHPf3hVfmtXuxk/V92PffAVHDp96jDHq8NAG7Yp2YMpfW69wLAv/7X/7qLCXeNBQe8PwzqTe2dgXDLsuAHfuAH8OM//uP4fb/v97Xvf/zHfxz/6X/6n16953f8jt+Bv/23/3b33d/5O38Hv+23/bYHtTgfoiI+bOaoNsGynhVhrk+7x2ACuL+MrxGc44V/CdgkrursUpA4RhZfDQAmbAqYZczKXCLzh8BURsaGDJFJFAlEct1y5yyX0iRkZa+JSTSqoo770HhGvqSTgxwENJIuwlhzlNebsOOEe0gMMZGdJAAIjQ02Q6Q7GTMuqIO7hz7FIkzODKBsHev2ORImBZ8KnuFOQbUNlLm8lP2U1HNbyhBU27V+o5arKIBSsWNq0pnFtdcJK8gfISOLMbZYTwkZZ5xQtA0JvlYIWBVRsWLG57hHhsiPUor0WCsGkBRYDVFCriKqdOOCFTsqcgd3SLkltaztIcu8GVlhP2OULQr+GVNua30ygyJ/1pcAiR9IxteMO10IRkzY1cE5YdK4YazDGWecUBsQxvYyRqNFMpPnbUg4a71YWyw4d2ymWdmDmwJRM87aS+QKAzh2bCo0GlC0b0qvpfgqpVdvG/NvaWkQhCa866UtCdfUVlciwcjyGQjdx41EyycBdmmVqbumKqC/XyzgWbJJxyaBeZtjCCCRC0ZupR1AkGuztkgFY/GRT+bj49iTsz7XNhB7q2u2L0AWnG9jAxRETLQvp8ktFj1PaJsStBLbyOPze3ZQP7dIC7H+pQ17KMM/wcZgcaU2WcgKxgXtg3pTtNGYc0e50Yqe32dOXMuDFzoDGOHRgDl/jtLnuRzK8qJt4IvsoWsG5PNuzfMV4f6Vv33sinjx+7BXsRWbzjz2bnt7dglv24xqo/I4igeb6jHbteMP3iEWwDi8PJLDqKgeMn8oouawx2kBJkQ/bNiwYcOGva49tL7z8WY9uPaie2xf7j+bazs7n48/THvp7fv4Vp2xwY7Dhg37FO2b3/xmB8I9ZO8Lg3pTe6dylD/yIz+CP/gH/yB+22/7bfgdv+N34K/8lb+Cn/7pn8YP//APAxAW27/5N/8Gf+2v/TUAwA//8A/jz/25P4cf+ZEfwQ/90A/hJ37iJ/BX/+pfxd/4G3/jreXJYpHZyZPLV9GlE0yur4554YGjoowZSjky5tXxlKy8EhMqTtgc/GeO/kUBugCTwWH+JPXcODL+BW4QBuM/mXwmHe4ieWfcG8+q8zCigIOrlsvDQxZJalYAMCM2ZlhUMIElpfP4GVYAd5ClwoS9RX8yp/OCe1SEBnxQynDWuHoLgB0COJDtMqEgavQpouS+vgABFS32Ex37zGdtoCddJwtWBRONNyOv/ak5WRIYu08E6aQuikIHlBgUm128LmFW5SZKGSCAFmOf7cp2Ym9L2PE57lEA3OGZAl0C7O7YNb8bAs4NiFmxtIUX2VQEqLzT3zh2BkHc4L7rWQkFNzgjIzp5y4KEVeU20WLhrUhaj1uDbbKCcwlbkyQtyNiwoCjIEZFxgw0FCRMiztozJs37DDRhy6DnKI7ShoR9gvYQlsBDmNIXdmUIMpIjtCfu2LEgaJ/jeJ+wYsei48ADbOTWUdZT+qyBSVvrg5uySgna0HpYo+KEcxt/nhXLdks4OoQMmuHYPC7g42EuoMxl0LlGZqhJOYI9M5hlo7CuPbUH0fksa1/A1O3Zm3iqLzYQUr6zzYIBWlX7jK8hEwPjN1ZWzzHzNcBxSwanB/gp+MHzhv70oEnEchx5RprM80APTHI8+fov7oleXMSkRlhm1lvtSlVcyau7kiCoB9qOUe+8W9hDksc33fG7FwFw177r4c4eCKS9qvv5Y94kvhzQafXEN8PxwI+dgv261hn26nY8Y/xuzc45Z9Adcv35A+r+WMy/iegS83F1j++u40pk2Ova+wLHBgA3bNiwYcPel73Kuu947WW4gNJ9Nn+LrVCu7+SBx3wsbABww4Z9vLYjNeWz17n3Ve0xYlBHe6cg3B/4A38AP//zP48/82f+DH7mZ34Gv+W3/Bb82I/9GH7tr/21AICf+ZmfwU//9E+367//+78fP/ZjP4Y/8kf+CP78n//z+FW/6lfhz/7ZP4vf//t//1vK0SXgJp8pwybXmDScMQx4n/yyQZhAubuLjKqEnpkhr7QdPBPLuGMJPvJPVVYPwSyJmSQgkyGwUdOaNV4UpR+FAXSL4kAfNEezxCgjCAVA5S3lxTwhN7jupKwu1oM/QXNqcdHk3E1uUoTCr9r1fHlCwT1uINGqsgJPTJOyjlDm2Q7GO9oa+CjAzdxkCDfcY1H23R023DSQ4YQNCzY8hwA1bDfWupfPswhSEQnPcYMzCFsm7MpYC8jYcdaYd1HzHbUvLApQylOkNhbl7E3IytGTp0zYmtRo79gnA0nAEGGQTQo8VCSckTE3cDAh4DM8hwE+wAnnBvgRBBUpvXtddJE9FrCoVCT7RNV2mpVRRwnJpNy3HQsqjBUpDKWs90nJTxorkMb4eEUlMAOA1L4LbkwJQzBqHgTUE1lWALhxwAclSSWm3a591kNLBk4t2LSnGLCTHBuvB1PIDiswVtYKYw1C08yIysYzyIQwvM0LM85uXHu4SCRdi3Lzri1rg4KqtdWPyILuys5jGgaPW4xA9vVJ21/4d4v+ZvKNBOYJgEZXixn+UIKINpK3Z2f6jenX87CKc4mRHZbbeLa8ErC2+dCAOYPAWH4CEwTlfNsdeWuX7DEDSyssBpy/01qIfY8ioqErI+N6Mj8E5j13FS1vvndxhHuBTV9vtgWikdFnTzlabdfwKQYaWvlCS6u28XidaeWfYDKk9rv18SO8eAQ75dvrZS3du9VqwJfLW2x9x5/evGZHJynzdMwfS9BD1rynB0svW6p/Vp8u3yd+LF6eOD2mybWCSVz7UUCX/XF7POxjtku43sYWI5baO439f/SBj8XsrWWf+3kkX5nNfHSX0c4vZwMcGzZs2LBhw8Su+zR7sz2h/64edm3HA4HXUhtHxYYNG/Z6lpVG8rr3vqo9Pgzq0kJl1LknYl988QW+9a1v4Y9++z/D6ZuXMpV0SJvxxWPQlWc1XLIsxKltjtLeqce4S3Tc02ITJbL4Qtk52iVfPFXLtDLo+t4VKBF5QwJ6dN1JnLSCpGyqo0kKEwruNT6ZOMWnVmYBtFbndjTGxopJWWd9re1grClzUhqsKPKL8rs4Ky0mlmfMRJffjHssuGlynKnVucgTCpsnI2lssK1rn0lZhKxXRuwQwJS5JfDF2FmEw6A5Ipiwgcyve9yiArjB2bVgUIgtNPlGlotApcBNE8gnjM4VI71JItTFBsclCDdJ2H27yj6yPQi/RmV07QrORgUQ+DefZ7HjBKjcgeaSJ1hWAC2pfJ40Lp71dUn3Xhl7k4vPVgGsWEBQb1FZSE6WJuEoLSjfMS8E66Qdzg08MlZSxKb1lxBbjwFszNjUZSe8MijPyX4peS2t9ZOyCLOWCEDjQnK8sUcw7hla/RYFzRhvkLm6BhhwPqigFjIB4oKpge7V9buHzbtl2bcKOMNZxEap8eLEZj0QRdlIW3ZzjElOQstTBOVxyZKTljfQy7OIM2z+JDBqbWWsQc4pRwDDm2cMAoxH6OO4sZ18vZFNJw5tH1EO7ikFBltZi1/CJgDbxVqWbwUPkvVgEefScmjPY048qBMfuOZF374IVroG+/RvOUvDZHVZE3ZY4PJ+nw/P4vbimw/Ba/25zH7cFhj86fvq8U19BA79PGTzGtz3/jDNdRnAni14zLWV1mrH8m9x2qQe+O7zefL5Oabi7QgbDnvqdm1OCKC4oXzbwzrDPmaz+HHyqX9vXGOYv2sbkovDhg0bNmzYp2HXjmPS62nHtC/3tJcpyN/0ZF3fNQ4bdmn/e/wf8JP4Sfxd/Dfv7ZnnL874z7/1X+Db3/72S0kZDnu7Rlzmf/ftP4Plmzevlcb6xT3+8rf+j0+uDd8pE+4x2jfwTdzhSzCcKeDhLHnpXL5K+FoS8OsaMBfVWU1nIBlPflvN87KUIxJZQbHJSYhFZZOYZZCVYqRyAeM8o4QRxhj3ii9G2/xT9nHSZ+0QCcwVFICkRZSunPxdwEIBFWYAEp/M5A7F4S6O/M+xoiAoMDh1W/6gz0gKAa0q2/cNPG8uqIwdK2bcKDhYtfwLViy4x4pTa42k+UrKussIWLEo1JNVIrAHWCzGm3d4SVve4B6EyT7D8w6ClEiCSaUgGc+LwBEXJlWZfJSGlJY+KysuKZeQ0AIBy1kZWVGfUUFJR2G8rWA8rg0Ju/5XFLATt4pIUgIBd5ozxh089kXpp5/jHt4sHpjZZ52Mo40BYcSVJoMpcp5ZIc+gdSJxAkXmVNzRC/pIObdOjnHVVpQ4dALICo1Z6nDT31iOXVmDEbldtyjHa8WpAWdHoCUiY9PnUwbWWliAvxtloe16goNylmQYEjAyR3tQuBuYO3CCeZCRKaxPy4uPNead/GTGEDTeQQlJwvIEB6vmmlytXWMOlgb+klVozMR+3gsuCqO0GfmU0L65a6/k7Ml5hCB51bsiQgPCjvHaCJUEnTMp6OtFRvtNgtWT779SoqnlxcBFAaIBY7HFw319ytfALH6Oh8/MWc9zurZZKSA0ZPEjBdDxbLcKyojWrg8dZTqBHnCMF7nty3CtLEfr3zEvlvkwmEDM6vBhJ65/pkGSpas7+c2OQPhNYoSP2uWhv/4Zx+fY933f6wU8+60kQLCutuvZLnYt+67Bj2jX2Lstuit9ftIL6niALJ+aXW9xKht4eLgc7hqA7cdo5MnbQZr+sEVyb8L3YwOAGzbs4zE5Epe//sJhw4YNu2JcW8x6jPv6cU+Ae3QemBUvWh+T2vZPtfv32mHksWIdRvvL+IsYsqLDhok9WRDOokf1FOwv8IVzCFJaDOBLJ3QvIzpDjFslaZtTr7YnHdkUBNvophV3ipeEDIBKINqpf8tXn9airC8BJCQmkEni8UUpInYL1gbO7R2TiNKJuwI7BSbuByflZ07HAEoNFoU4WCKTUNo1vze4w+Rc4AI0AgtWMHLeqtwdkbE8txq/wbnFRGNNTsiYsaE0mUnWoYAxz/AVNkwKjppkZ0DFDTaVcAy4w6nFRzOolI7wDIoFLSorCFg8KIJh3pm/qERhUmAFMGHDM06ICApCQaUiWWMiIzkrlEKWXkFQoPGua/tbfIWKiBM2SNy7gNur7nWRMiXocqux3dg+HAO7buMAkSfNmOFZdOzxFsuLXEVbZnmGTwBjwhUsOAOtdxSFiqD9r+BW48ix38qi7hIUlDEhseUKKH8JZT0Ku21xgFpBULaZ9JzZAeXynHNj3R2XiAYIRe2dGZQopEve6ndXeVC0+uMzfH0IwLohOJgR3RVFAbWMotKiZGYSap9QsCMgaQ6NDwq9blfWDQGZHqogKL+ATB3o+CWwR+BM4MBd4/0RfOQdJpPJ8ch4eJS+3HWRTsCDf+cGVbAWJYcJlHK0vMr1BtPYtyb+yP5nhxMM/DL+sO9DCQYAVieFaYBWaP+PlhZLZtCJvQ/6mH4GKfH76nJOwOfoMLHNigFNl6AeJVuP75RLhl2f7jFv/fdvvjF6k23UETi79tu1ax7eJL74GQ9ZD+oSQDNA/0XpWdt8/bOP4OCwT8NkLRZfw1l62fv7Y1A8hGV9+EVjathjtOORjmNL2m7keqzqYcOGfao2IPNhw4a9Ddvb8f9LsxWJHbLsj7JemvzGA7H0ITA1phAO//rfh30qNgC4T9ey0k9e996naE8WhBOLCl3Q6PZktCkf14eOZIJxRR3iWxdVh85t/xoxkK3fTgPAhAUV9weXPqMGVZcmwRGLLTWBMZqKMoKEZXODTXNPyTlj9REwkV8rJqyNe5GclOCm7DVf/rlJEdLlLd2D8n1Tk/ojWLKpiOaGW9w3t3bUOhZGmoBeZH/d4jkYy4uinnRsnvAcEyo2jXV2whlr4xNFZf8I847MnJMylSq2di1j1Ek+Kz7Dc6xYMGNDQsQZk6bI2pN4d6y7XWuP7SBAjwASCYydBkQF08iQSii4wYrneIaIiAm71k/V9IAZ943FElEd/8qWPWIFzxT04dP43B0mNcnWm5QVR0hG2msH44gBBlBVwIGxQTlVHizueT7s4wKyFJCZmWBShwZPo8mC8m86lCZs2CAyqAI85SZn6Z/HGIIbpvYbWYPydwUlCslM46mto6gTRUkvAXZ5ngCvBg6Rv3dkPMVWJnmOyUxS0pXjn+PwXl8afoSVJqGaAOwaW5Cny0LLK7T3XS5mg2tXu6dvswCCbQVZGabS/gTcjd1joCdA7ps/sy+il2SPUmSXXEArL+MZMh3jTHK+MeGKftnN+mJfZTn7NvQR5qr7nj16aoKXvq74iVKkBpsayG75yGB8OB4i4CZE2qF/vnEQ7ViGfwvw3l5+sV656ppD/do2xQ4F9Hf4KHd9Sh5Q9LHZ/P1HoO5pn1p8EeD3dfcMG/Z19uvxH+Lf43/Ct/HtN0zpEtb1W9fazXbl6mi+lsqwx2TXDkYYf/zIm7t2x7Bhwz4NK4MFN2zYk7IfwG/HT+Gf4Tm+eiEw9qHtReuOy71rPqxIjyEXZG98GZNuHF0cNuwp246E+Jpg2j5AuI/PvHOi//74W20vD0+39lFpjuCaf6Fcivf5Zzzv2DLyG+ODCZvKsy0WZAVTJAUBi3hSvyrYktVdb4yR0py95Jz1Dn1KaTJvN7hHRoCXkiQIxxPXPt8Wk0vAqKAMsBlrk9KU64AdaEDYjIxzAysAL8VI8TzJ467MvIqkgJ6ARZKOAQUBWUFID5bMWHHCCoHjGCmLaa844R5cDNwCGh9vRsaCBXfIGpNMgLKsEAuUVSi2YoJfUKRWv7trc+BzfEdBTuCm1Rud/wTS5HcCbVXrfNM4cF6WiDKklLpcUHHuAB55hgCbRaErSgNeOtat3oTlNqtsKPtcgLD/olsiyeesQI7FMzRAhKwpSZtx8SQOmj17VhiOgMYNVmza8kyHYOOCgk1BpAlbW85JibICnPKpKFiSsCtQbIDeNxQsFkacCS2yLYAzZhQFjCfXq6D1IUw16VsTKBcqQNSmNRK7Z4p4JZlcaEBWcH0hNQiJzEyL9EUuBecKxjlLbRyQwyot3wM/XvpuVzCwZ8vxWRU9ED+1jT7hNAPhWRts49rqsZ9nDaq0+WaG8HK9pKL0EwM7GY8ut2sk5/bZcsbSkFk8I+Dc6s/39+zqnYcf5JqgjGLCWMnNYRYRU/oaXIr9bM+eS96dXSUztB3qsFIHGOP4kq12DSjyn6NrZ2/9mUWOxp4NaeKpzI0/UHIZn876ic/F2CgNG3bNfgr/8j09qT8OUN3ot7WjybsO+xisn/ntCJjfZVyTyx82bNiwYcOGfUz2j/APP3QW3qr1e1QxH5KCO/HkrjPJf+60n6azfdiwT93Eu/t6sNNgwn105id3gKJml2JeD8m+UCjQ6NMmo2axG8TpUUDWijBAcotBRTM3roj8EVwL8I4UActCk1f0z5bUkrKv7BVnUZX4/FnjrCW9am8OX8adItOOtHR5rsT0IvxUAZyxYkFBgsRuE8lJxn7aMDnGGOOYBZzwFSi1BwTc4g4COGZkpOZEYJy2SQGVDYur09oACAElxVmesAEaWYzlITAhTwuYcda4eBVJBRFNdBMNPnqGFQWrgiJ7WxpILLB7VCQFB1dMKLhFwJf4DBZnz7vjvfN7xw12zMi4x60+syBgwwwK4W240/KyZwgkmEHYISM1sG7GhoqEGavW3YqA0KLC8dmpQS6UciRYKG2Utb1K6wcSP40sMgKvQfuNB1gzghOc3LBBYrcJ625refWMS7LiCElN2LFgb2AjUHGLOwQFZb10KhlL1tPJVowK7vnfoL0IECnLpHnJ2seKAm3srdLvpwaCUpzSgG0CPFEhR4KopYFuxopkhDJhuxlQBu0tFrsxtPIl5AZvMRakj0/lGWo8BcL+kkABVCC7RWxofdDH3OsZWQLiE6Yx8FXaawPhNi+V6ONcVQU/53Y29zijErzx0qUyRyWtP2u7I/ATWrl6Z6MH6wDGShOoLiLrGJX6SG0+9uy13m2ddSbupSo9t7CH5riR6IEyY04au86zFsgq9Of8BHAv7Ru+V+xN1LO0KyySnV3V13lw9QNXpqMZ04959nXDPsq+ZgBjv6UyEM9Aumuu4QEBDBv27sxWD32kRc7A/ehObhQPe+zGdkxtZrdjMDOKxtAdNmzYsGHDhg17LBa6v3287Ms9sjfZk/t949AAGDZs2NO1JwzCURCNHKPrmsYeSPEMIXPg9vq1l8w4ujuEo5W6tIxRMbUNtQFvZnSDbw0EKyplKMy0XV21UWEPYV4VjZvFl1pFwKIifsb/MEfwhFVRaGORLVgxawy5olwbQJz8FRKrLToQzNfDgh3AigqBxaz+JF6UACXCIoqN12POoQjgGe61LFAQJoKMP4E6pDx7A3J6CbmeASQMvQUbbrBpuuIgtnhKZPXRMSWsrwkFC+4UhBDWUoaAXDPIeKv4Fr6DgIozZgh8SKBOKPjMd1ag7jN8B/e4UVCUgncAwUcCGgYDGSAQ9NxAViAgOMiGXLlJayZjxqyCnACwYsaOCRO+QsCCCsJyZO1JPDuDma1t9wZ4ZpDtJMw0kQIVBlNRxqD0zYyo/dIWUARehEXGKymNSSlGi6cF7Ciap9IYdAUn1z+rPo0jk/V5wrmBwJvWadXxUpz7kfWXQOlU60sEE9kG5AEG7ZnSr9k3mB7h3YwVxQGaHCWWvq9jsj5P2JAhnFSB8xJCdyV0HOTWf9lfkubQx/mT7/dWo0nhTjs2YGAQwT3ODVV7ueSWQFVtNWYHAUrXagY+UxaRMy/zbL+zztjbCU95YKmHburF2L3GTvZnZKTfZscA9CwCiyfoW8hAMhN6Mye2MYFZb+S2+ZYyqU1jNALH0WXPsd8JFBaXhi9fhcWYSu2e/irWKPtsb31/uvyNgLekzHmpXr2fdWTsSOYvHlK08tvfx9SO1wwbNuzV7TjD8F8/S1qcSR6eGLHHHrNda1M5OPfd+C58gV967zkaNmzYsGHDhg17Wbu+knnxd3bs147H9kEkxsp12LCPyUZMuEt7wiCcWM+4ME11H4tMrmPkLS8qdnSIeplHc2eQ+eKfSderpEmnLt22hIb8mQ+LoRSa61rAvRl7cwz7qEQRZJPxntpKQXCDbDNCgFVZQgURM1ZYzC35djs4wpPKRAJF5RgNmDjhXllsCXuTgLQ6TK6+yQyccMYZNyiIWBTwgNbRLb5q3BxAAI9JWVWT5pylnHBGBHDGgoCCW5ybAzsg4KzCnnS+M5Yb5T+re44AhBUnrNoK8plSkrtyGyuM+XSDM3ZUbRsp86K1VxC0PuU5n+ErUOZQYKqIiIwTBGSUaHWrglqTgilcePCcu8UKlN7i6+2utTbbc8GKgB23OKNg1XqKjQpssqZsU2lrAUFNvpDxpggyA4xoJ/VqUpwBs/aVeyzYsYAwFvQJM/bGiIsKU8KlRacgQa6CiKmD0NhmQcFSgZdm7eNB+7gAyAJZCuy3w6RPDXYnO80gi4IbnBX2nrQfT238sm9LrMjc7mH8tQmh1a88eQdZc0HrryhrMWpfZP8LrY9L67BWbKwR6AutDY5gCvsicG2ZSgamvciS9jaOVulDzFM5pF/AeHHSTpI/H/cwtis9w0pcvwTpq9YY+WZF50AfOa0vXVWGW26lOAoAk4N8uST3Qm3o5m0P+slfHjy266urrx5ypORrAfst0MNJxhjz7wtLA65fUf73GnxFblrp7uivsPcWgTQ+7+sAsP5bu+N68GRjXPYms6ZtkCyeZjrczTdvDxD0wOXYXH2KlpDw6/Ef4jv4Dv4tfuZDZ+fJGWeXemVeuIT0xyh8zHan7+2vc24NGzZs2LBhw56OfTd+GX4Rv/Chs/HWjf4uwPyzsjf2IS8KCM95tZ1hw4Y9bhsg3KV9EiCcOUXJ86KzUMAWk4U0hljPkPOuz9KYIGQ0XDLsDGgLLg0CXd7hMWnMsQooB44ygrmBFbWVgcwtk5dEK4/IIDKGUsbUpAsNVpOcCZtO5CX3rtwCztBJ8w38apzxP7bfJP+b1mFpTBzJ44bs4mVVENwKrh4CZmQ8w7dRAJxxA7q0Z2w4NShG8ku2zQwqRe84Y8Ez3LU6u8EZIpNIl7u4oRec27OFHbUgYUNREGhusbwSPsOd6ydZwUbJk69n75hivKWEggVnnSA844dgkQC0q/YAKcuOE+4AMDbc1oQpCbgKpCOx+yatzw1R492FBpjNOIPSoxsKVtxo/gpusUGYgRULNuyYsGu6BA0mZA2WKe1GAIssohkbbrFjx6TQrTh/7nGj7blhbow6Kf+NynwGLd/e+kWFyInKM0oDAIu2h4GIZBRuIPuNLWBO/lucsbbxgiZ12APLlObbtQ6nBv7uCnXTOcn+LMC68NMCdo0Dx5mj6tis+owIH7MxqrzphknhTJEXBSiCGUEh1yPEHzTPuS1CKZ5pwNGN8jMl/1ObLzxjzEObbOeMgAhhipoUaQF5mLvWRNJxwXxV91fQVGwkFO03TDNo37MjCL6GraQG0xkr1cNwfrz1bKyoYKClJdcbLMjFe3XPlcMSUa8MDXbnM73YpuUlwjgjrE/mxAD8gF5uwyzqe4KpH9nEPYhmJTpuKfycQ9AyuLryDFr+a/zvPt9v6l5/0Z1Rn9rnpz/JyOt6QJHvEZvzrS566eWx4Xq6lpHxz/EvPnQ2nrCR63o8mmHv1djm3tDmmXc54qK+8Ya9mt1rjGOv8uHfnuOs+LBhw4YNG/b07CkCcLTjAdWI3v/mD9/m7tux2hk27DFbeQMQrrzmfY/dnjgI10t+Ga/HvjUnA12XBrAQZKNFMA6YndiIzZ3Y8x/Q7qVDmiyq2hyL3oE+Y1cQSoCHCODUnO3C4kkax8qARMkzZQs9WBYUBLHr7PXl8zxhBd27QMRJo4xVBHwTwC9gV7Ck4gb3bdtPMJC1M2nZWKczvJydwFiLi9uUANziDnfK8TFpM8Y7M34E2SgTKiY87xwMAZQW9E74vn4iKj7Hd/SXewg7J+Iet5gV0GOPkLhlW2szQICxRTlsZ8zImJCw43PcQc7vZ1TsOOMExr5L+gyyuIQ5J5CFiFiSUbRhbQCe5PdzfKWQkOVMAJqKihWUBmQsPNap9Jd7ZAX7hHlmrBOy1hYFZ1csiEgKSln/Fy6e9CmeQIoag65A5Bg/w5cK3pmzzmDeqiCTpD1ja0AfJTAZxy7C5FUjdseolBo4IWOD8MZO2LHhBmSWzVix6FNXRCTtJ8K82hzAY4uz5M6PTyggA5Yx5vxsQBbehIx7LJpfNAC7AlgdA9TYowWlAXWTprdremTlAb0YIsFHxp8zYHCHxTjz81HC3mLFSZxDKc+ucKoFPQ6aZtG/NxyPDoQ2mm3OMu4E2jMkDRnVnqFo4JjFMxPmXGklYUpyH+cggX0rPEsY4PGACoJiABRCryjKFvUQKh25Hsiq7UkcXQGM4GllJcxjvxNU9HHgbAmQu3FZuice82CCmHKwgmZ1ZfKYFZTufNg8KxNaxocsgUxPK0kfnc7n2TPf/Pvi5Tc2x/el5cE4cqx1i5HYP4EsQ/+e9hCp2dh0DRv26nYN4j+CcoCNRPvtW/gGvsS332puBgD3JsY3DI81Mep11VVOAY+RjOMLw4YNGzbsda0/bjhs2Puxhw6lAsA38S18gW/D+1DsqrFHHDZs2OO2JwvCeZGdgHrFeUmmWoGXpDMXhJfS83JfZDdU5zCX9GSZIpwSKBhjG2MCRntjdtENKa5oc0ie1MlPAGtW3gaZSRUBGwJu9Dq+bFbMMGYR5dvIJKrNKU8gTcCys8axAoDgygw8xz/GBGHuVFBcszbZwRVLe3rUSFwEeiQel7CbChKe4R7k9tDxI2yt5zhhw4oTMmYYsGncEBP3LJhxj6xsL2tHMgYDTrhDRFW4L+EGZwDGGmLpJHbaXWtdY/Hk1v7miDKT+GTPG/C1Y1IGoICKO+bWryQG36xPzPgce2N6ZMytzoVJxqhtBEN2zdfknl/wOc7O7V8P5YLCL3sDDEyiVNhls4sXdsI9NswweUDCRglBQayieVocWJBd/846Esh85LgT8IjX7WBsPxpBRe/2W1AaQ1A4V5KfRa8ThuNz7AitfB7oznquPil/DiBsRTG/fmEW4aOGFZhkZZ92RMBNk27tf18aqMvSSZ9ZGsN1a3dk7amhAdtFW1XmDcsZAVrGIswooCQpuucL69AzhypmlAaz2T0G/RXwGIDMBAEBz3ALoGLHc4gsLVlbxh8OrafLHEh4ls/lIQGWK6n7z7OGfftXTSk2wKs/HmFjv3fUCkDK8cFWtkMQlg7buf8mutJXbRl/lfxlHBHro57jZawufuOFjM08S8+XwWZCu5/sSIPlrP6Pm4o+taP5dH2/sjqlg93Yu5aysejsu9d34x7L+XCuL6+//EVyI+ZB9rHhGjbs1e1FLo7+87fxJUz89yhXPOzD2jGaqN8B8R18bR80bNiwYcOGvdgGADfssdlvwm/Ef4f/OzIyard/Lod/gbE/HDbsw9qO5Ly3r37vU7QnC8KZMxs4nt/hNB0bp4FcDUppHSV4DJjh74xVZs8iQEV2jcly8S8CLB6ouJ53k8ZjbsisCprObXNBG9tvafKT5sQ3tyXPyk4IDTqROGaefUAoYcOMiIJnCsZUAPdY2n2ASCXuCLhRRhY3+hYTraBgxarAlNShxSQJKHiGewABt7hDxj2qsu5Ku6Y2yU7GzopYsSNhwo6TllnYgz7eV27sJkAAgb21NsGNFQSTijK1WHcrJgURAigPCQAzVlcGKOgoQIZIPu7YIWw3zyeaWj0HZWBRhLKCsfPIMmMbz8g4t5hrwAxK0ZmsnbDYCJ5lzPgGCqqWqCpYAZVHrM11RhYde6WUTQCjDCgcFRQ0NJCuaP4pe8j6p7SncPVOCpQIey4oMBj1uoKAk7LvJG83rt9C+65nyRhonFAwoWBXEJN9PKHijDO8LKGNDmN+zrgHxUuLpkiAiHAJRw37W0FsfYSsUGn7zf0uAOveGGheFMrKVDT/hDkYuy41EDBq22R42FzmiqywIecrylxK3ft4lhIzj0CfOU3J5aXUIl13Fd/RfiFXW0Q/xj2r2juhdcM6M+iRorEGsAIml2WAI2dg1nttaex6jbFCzZ3oewM/GdPPz+89K6+/1//F8UnuQO2ey/7n25D5zN1Y5W/SM6qCrT1LL+tI8OPoGlhY9TABYS+ChD0r8BKI4/vG6u5aye0zQcjsAE4PYxm86kHq68++/pR3YT3Q1vcNf837ys+wYU/dbBVznK+ORwLGiHtcRnn2/vO7lRcdNuxd2oQZu4sl/vHZ0Sl8nEWPM2lxq0F/PfcYb3ZAqp/fadfyM2aNYcOGPS77R/iHAPwMxQOm8rffs9Kb9Wbz5bBhw17XMhLia8JOIybcR2icZsVhSge1AXPXz/xajDiLGNQLA6bGBQJ6p58BKJ4zER34llo+DICLKv23a4ysqblxCb6sTVosXjhbzdkf9XeywgjaAQK6UN6SuWb8N8rVSQkI86wNLGI+hM1WsSsDjkwcxpkj3yjBJBInoMkRbg2cEbAkNdYcAa2MoMBXwh0yEs4thpnFDYrYFTCyNpxhsaLI+hE2ltWpyTNGLFg17pekvIMA2Y4FGQvI3SJTZNN6zFrDBGMKTg68m/Su3BhxxoTym5tJZT8jskbDC5rqhqz1C1R8Q2Ooke1TAZXw9Myd0kC/k8pFFVAOMGi+n+PI9iSQJjHPyJmqWLCjNAYfly3Wf9kWBD65IbTecg8DkIL2bzI5CacZ2w3Y2pjhRMv+LeNvd2Mitnba0TPDJlg8wl3h5EmlQWXMmKAgeUu7jiVJf0dWYFTAXzLwBPKSeWHVb61uCfpQqpJAdkbWfhJB0MyiRXJ7W/WK2sYD255zyN4gEZmbfD8iMGlQLfthQVHpS7K6+rv6+RDtTrY4T81b5L6MquXxQLY59qK2IO8kd5Yii9J/+nhgSQEqz3y1OJiezSh385rQ5j4ySMlaNKDZzPcS78QILj3y+6Btii6fYsVBoATGLNf+Scc4eKnVDMstd/uYcRyPHsA9ur35XOaZXE6Oz9j6a4CJYR7rwD5dW9a82LFurPD3yYXhO+AyNx40tQMptSv70ZE1bNiwr7fwwN+965ZvEh6qGCPsMVj/xucbju11FG4aNuxjsPcLwHnA7GjHsdWDaZQe9/AW5fx6QO3ycIOZKRRU+LjLpmgjd+fucOBlOg8Bf4xv/ZAMOv0NXAsfrzsCiNfqhfXhWSpjxhk2bNi7M3/0yM+13F9zBh02bNj7MwHhXg9MGyDcR2i1cT/o4BSGGt37vZORrnXKStKpm7trKJfYAyvCZ5lQrqRJV7QI1qXGZvHLYlnsLlhRIUBFBmUkLfJadH8nZJWmlBJSKC4haz6M82bcHuMHTa4cWVMlwCfxt1aQucSFM4EjqDNeYmNtzXVsJe5DwwsAuCmvS5gyu24aLIIFQQrvyN7xOb7CGXOTUVyxtFIJG8oDn1Jft3gOkaRckLBjwoYVN6jKnluwI2ncuYITIiIW7Oq6zcp2S1j0mgxgxY3Wr/DcIlYFzXZNm27vooy4TZl4UheEZk7qRE8wvlXUWHuTbtwyNmyYG7NRviUoVnHCucleEtgwrmcPLKD1F0I+ZmzvBav2yaiMvYqMvTEJY2vL2v3/5MA6S3PHrYoZFgRsmLBjgjD7GONQYCeOtRlrc97NrQ6C3kfgSfoxo3xN+td9AyQJsSVNZ0fRPsvcC2fRAOugLcMxm/SqXRlpjHMISOwassgo0clYhQYNcP6QFkjaXzwE4IFxtmdsaR1hD/lM+Vign1vscEBFxIrSjgdU2AaavEYDLAh/Hp9l0RTlOrYXuWxkauHiTiuLfCYgy3JX+M27AHE9IJXdN0GfTTlT3wZ2l82FBE2ZL5EH5S8ZaDAinQEWi9M7JSL6+pe/cpcHX27LCQUcGdfvyM+Sup26bzx052Oh0ZmdW70f89Q7dujc6PMlOaI0suSyh9xezF05ut/Z/zwQFttmxufIsx7fLuh1LSWfH/7OeEj2/rT30tvO07BhT91ucYt73Ls1BtALtovRpRzRO6Bf5GYe9j7M7zTMDXU81DDYjMOerj3Uu68xwOx6zmU9o4KH8/xaorjf+5nRVr5V95O27pQ1lAeoeIet2Jkz23MYC85yntt6mAoftt7l+rsHCy2fRxm3Y12YWoQcVDuuZP3fGQQbeZDN1rhUH/ErXyA0VRaDF3u7vtsYNmzYsBeZHFSgkpkYPWHHXfq7tBFPcdiwYdfsSYNw3gieJfeNOGOFaSQQhD8hdgTgBGCYm1sBsEVtVuaXscmMx1BUmjHD5BS5FK0X15p8oizqo4IixvZgXszNHSGx4qDPZmrymwCPk7LpDF60TXjUhfNJ06DInTje+Ryeygu4xQooQGQQWmj5poBjAaUIJU+TMv4KAm7wXGNdyR0rZo2NFtuTZmxI2DUmmKR/wn1zVItsX1G+WsaGGQTCIoBbnJsjf8ZzbCrQOGNVgEG+v8MNChImbCrvaEyhpLy/W6wqzDmhtnajDOV9k3KkdOgCgpsBG06IOGPBWcHB2QFDEQuEpcW+xph7wdXbWTk4SZl6m7IdCTIVEJCSDZfkZ9U6Me6TcP5S60ML1gZ2Ter4P+NGmY0CuEYUbBpvUGKXzXr91sC6qbW7ubwnFGWsTfCsmYjSOGGASYaaVVDOFGAMM2i/rg2AC9rG7O8AFBZeFDDOkNh7jCeYdRTatOe3vszH3HiRhDgpz8m/i25g2VfYGyL606IEz49bfaZtW+DLmGh+LkKrgajb4ATjqnF8TjrWeUaWsocikCiwqJfA3Ro4ZdKaAFSK1eojufHAOSxrjbIO/ZEGzkmEj33kO3NuGIDCmuWMm3B9sRpdamZMtweqYht9QFGAEi0HxhbzzoxrrLbQ2sULZ/YQnoFex3Zkfh6WwLgOLDHfPmfHNO1am/V7Zzdj2/Hu7Mri3zX2xIcdG77ue+iyB0DZa7yr95pz623ZMSWDe6/XC/uLubdYJ+NU5LBh1+xO47T29pBD20D54uaao/P4xbPNsHdj/dGKoux+/+twcQ/7uO1yL9FbD0T1ZkeNLg8++UNiPNhrqhS2n78OGfnP3IHbmp6/8IjhpfB3aP9ZbG6mbfsBW6kGp6rBtdvRc9HXAMvCtWdoq0JfDwlcR/Jwb72oFztc22sx+HqykveH+qilAcg+ixL3cKWWPdDRdeXhPr9C9bkH+hrw6+Fh79u+G78MvwG/Cf8t/sGHzsqwJ2p+dB+9tsYCfvfjf4Bww4YNJtw1e/IgnD9hxTO6doaXzmCemIC7unc4ktFBd3t1i0Iv2wiIEz+iaqwnuW9SYIVLZcJq5qjnM01SLSjbjS8SeToBFLJ0uJiMmpdzc47PuEHAl4jYkNwLR+T3yM0yMI4sHVnyCqglgFaGLad7gAAg20fyNWEFIZQJIq/I+EmzMssAuEX6joKIUwOx5NnQ0oeuPqoy9ArucIKwS+wEIJlrXsiOsbwAiTsn7ChK1snTTjgjNgFDbpFEbtBEEYEFu4pv7g2wXJFwqyf5aqtDsq0Iy3jhRmg/IPhhIOk9JtxqPayYsCvwFVFxgzv4uHUBpQPzRApxxY2ClEWhywkZm8IoqfUZkyKklCUdZjMKFnylXDZjYVlsN2HJVQVoZwC1k4Y0oMcDHVK7k5Z5w4RV+VjkiaYu8CZZkQQIbcN6ZCT1294ZGTPu2riQ+HEmtjq3nnFcfAVNX+qCvf1a1Mag+bMtn2wdY4sLN2ndyWwjXFGBligVa9vbqozSihUJcMAWN8a1bYgJ+Cf91Z++hdbt3tLmZlrYp+Zsoy3YG8/LgzdkwNW2+TVjmSdt9aLgnvFeC4qC4WKEl23O4P8sQKtns5oLpMB4TFJvCR7kpexsf/zBoKV+rjEmmC3MPbRr74Di8srvqqujoxmUZePqmIdj7V/rv7E9SSx191kd9+Ki+JrcwaVXXIn9ZqQgX4yHhy0c/u2tP73N0Yb2r4cu374d83TcgNHh5YFDHhm5dOIMGzbsZcy4wqZNYDNAdFfZihUYo+zD2vXDGy8S4Rs27PEZD8VxlWRrRK8kYywsGj/Z2io3YCi2NP2hIn+3gWdmD60/PsPn+ApfXll7Xl5/dDVdW2/5v9MLrjumc+03+97K4g/CHVdDshcrL1UW/ns5z4gv5Aj01e53Qn1UQuGz5UA09+n++iP4FtoeylrYfBnH45He/Fp1zIbvwn4RvzAAuA9gERHlgTH77p9t0Pr7tmvzp6mjAP1a9e3ah6rvp2QDyPz4TXyRrwem7a9532O3JwvC2Smxh4TXRKotu6HNU2ahXVPbwptOd2OG9ae+DJDaNO6VLBQJeiQF5rjBndXZbzwOxgSTKFWhMY+48AyNgUPHqUXdMq7QpNHX5JrnIEsvI7a4YVlBsYrecSzgItlvFZ/rpoHxxUpbCEv8KkpgRmQs+kxCeozZNmnsNePmhAbY2QbHO25NBs+k44RP5pfSz/BcS02Hv+jbLxp/TgA8YXptmBQElJhwjPvFNj7hDEI2tAUZxlO0DcAt7l1OgRuV7RRHrrEVKwpODpzbVJwQWjdry3dRnljFM5zbs0RqVFrf4gDWVlMS427VeHIJE1bcar1lRKwth8DneA6gqjSkROxjeWi+PgRcq3pqIcPiWRkwlw/O9uju9xtXboAnrKBIoUmqskWF8UnA1mATsiGNUbVhgUk3ViRsIBfSAC4C1TL2TroEoszkDe5AiAUQRlhWyczUwBqWjhtxAV8l/uF+0S9MGlTiKZInGTXi3urymLU/Ti4mYoDEtLsHuvyblIzFg6NLQUAaa0urJw9mVgXrJv1c3KwGLNpufiFqQri7fm8sTb+ZTtgxK8vMnLAA2n2lSesSrLPSGdh1dGwYoJh1iVxdupZDzqVyCljmckoCe1iQ86U80aQprSTcahNYJaPRp5C1/H4h4CFM3+uProAemPPf8fva4OjjNX1cTubuuqyiPxxxHZY7uhQsTUoNeYDMu19e3RHBd5eXGRUXV4A5TTyo+u7sRanb/OXlTo9uo2HDhj1k1zinlw7OcvHt5Zw47P2ZrJXIl7eVeAU1QEL3JjXR5GHD3r/59Y0HUaL7Dt2//YGo/vjbkQfF/bB87hmib2pf4cu3kMqHsWP5X8dNfT2Ny3lE1mI9+HkU/ffQWO3ayeLX9Svy2o6ZPXSw8jIftu+wA2o8qHXcP/Qr/WHDHrN9SEDoQwFw18wDcfZO8FHax1h+TDbWnR+/FfW1vu69T9GeZqmumLlsbYFuDlfbftrZKMIydoY+uthVTCOpW3jGGemwEUhuc0sujgfwUrvf4tSJvKBI3QV3jy0eEwhlTc0JnHFSWT6A3BPZcCwNvDIZPsZJy1haSQGJzZUb+GdSnAI37HovAYfQ2HW3Cmrs6kjeYXKSjPElZeDm3mo6YdN8ERQ4t0X4pE73DSfMGmONAMwJZ0xVQMVzWJCqMf3msOOkXLKtTjiFqm0k0pb3FdjDJABT3aUVAkUdBQwVqUYp850CXbEIhBciFOQzhtLimHECjlQAc0tLIjTFtsl4hnvtFyuyQmoEKehANyDOQFL2lIQNjD+XFSihCbhzr5ypXdOJmFBQUBUWqgo5za1PZMTG4qwAzlgacPtM4/ntWCCsu7O2B+PIRQU70fLLcUQJSYmGWFpfSUCDKedWDopNChBECVWmKWXJMMYdnedWfkI3sY0/1gu01AQC+D3BF3nuor10Q9IWY7xBs6r9NiO1U6NyJZ1WhLqj9tfdLe9y5zpgmwYAN9hQsR+kIldte2GHGpwNhMYutdz5OHAAcAKAri9Jvfcytz2rzgAT9mWCfVycZjen5Q6IE7jeFrY2Z0TA5cLO49nGlkcirLaN48acsS3INCSo6FmSrC/oFZTr2bDBi+QY289O9h7PS1u+fD54jcnx2Ead+fSgXA+o8js+kxI85jiK2v99LuwpSctnQBbrNLuSXNpD2wqD3gRwtHeOtM334pfh5/ALD9z9sHkpJHsWIVrAl/woA/U+7QgyA+xDHy5Pr2qLHssYNuxx2HHc9Md+jDXHQ2Zjk/1hLHbvNX8IgzoU8ss4zT3sQ9glwO/Xd3C/+nc21R8eensfwaSP4y3/9O3rjn1dO0TmXebxyu9+hS5rZM+C4zvI7jiCt1yXM5CC7TRkZpR+5+N6+39fVJphw4Z9SIvd37L/5RHvYcOGDXuX9gmAcAYomfzB7tgVdH9zmcTlmUnXEb+1yVoWZCKrR3aHdy7vTU6PsJxfWIrzNTt3M53ORR3GFjcD7j5huuyKJcv9ElNs764V0Mdk7gj88dTYgjMYiU1cwwJPxOY8l7LtmDArgDQp+0dYbvws9/oTJUXzM2nMtYrQ1a8ADc9hMb8CUGsrZNXPsajIX4hIoaKGiIAdtWZMZcWcaqug23KPWCv2MGGPCiqVjBgBhNDyFlERSsFn9Tl2BGxhwVR3BGzYo0QwC6EiFYn1NdUdOSbc4IxagZsqcfBqBnKIqEEYPs1lH4ClWhDYPWTM2FErcIuMWs9Yw4wYKkpN8ixkLFhxxoITVtzjBtS+nXSxf8Kd68WEjn1UNWDDLWxDkHGLXePBeVBAGG7CXgKgMd3M2W5u/QCJv5dd+wq4cqcSj5QKNVh1UrDPXOwJlKIEsoJBQdPKyrTbQLVfidmWsSG2/nFWAFTAsa2VRhheJha6AwpKA6n1ZGNwSc0ZSM6a4njxIoacK5YmISvyoN45Jb1l1e+nlu7cYEVyyaQkCZte58E8L3fopRIpo0qZUUqziGwey0nJVJNYMbem1Iix9dBqiqCx/NU7RT1sxH+rgrcmwUsh3eN1Vio/p/XbUJP5FZDJpCIN2CIwJ33KYDrpp9KnrL08o6qHIxMKfG0HEH6z59lJWsuvsQdtTqZxlEDzfwnMsY7RfQtQCfshNohcbUCi8UYfYrZFzb2xzXzftvfVywJJly5zgAdGfhH/Xst+KVH6Mta/n/rvLJ/1UHM4/P3uLWg/72WSjo6Vx2nxwq34fu1/ht+Af4Gf+qB5GPZmRo2Ed3PqtD8cYGNd5qfiDn4dR9njHnlPwXzN9ywP7ogGSDrs3RrX514a+vKdZrLlPHgpe3De5eMeD/s07FXeDTzEBnCm63dB3np+MN9UfDsyEnRsa2W/DrcUX9Qfj16i8ZYbNuxDmR9919Udhg0b9rqW8fpylCMm3EdmdP/SwS4LKPnLmtLYB1yM2QK+dBv/6CbkqOCbSTkyLTpL68GZQIYT08pNcpFu4hPOuv14yP1A9lRxJQg4KTuvZ5OILGYPNApAkBQWoAta2GYBBGBEuK7ghA03HcuGm6PagAlhyni5MeAWz9VpL86cDQtq1Y1UKPisrgjBOThrAUoBYgQqUAMQiznZU80I+wqUirv5hBCCAltRALZaEarkYK67gHc5Yyo79nlGCjtSLbg5i+TkPiXUGAWM2p8jVS6nV+xpQk4TprIhZXUI1RUlBNQYgSg1GmtFqBklOUZKsPhWUlbhBEq9yHdLFfA3BgBYUWsAgm4KqjDjPsdzoFbcYQZiEoZdBRCiDtbcnrEjQbhjGQF32LBI+toHZtyD8fls20BAUlqa2von/M9R8RvxFf5rVDyHSHnuAHacsSCiYEdQlpi2AYLGepPWnBToNbk8ApLCBsyYXf+mpGk/JQtQWDBrv5R/pT9E1/OP25wJJnNAJtbmxgQlNAUMkch9IhvJGG5RYZeK0sCt2qVpcI8B5XOTSCSDsmrqUtPyLMqxGqXa5GTlmRTGLTo6UxPPJNhWtY5FspPjl+OdUCjlMqF9hew5O63pXfW15TPqdvLI95vgJXrtiIKk7qVZDFwVh4gBk3Kl8B4JXpX2NMbt4AwaFTrlmdPePTg11qOfnTlf2Ousn7fsm3RwMB+3vyzjURLHp0tpWPksULKfA5m6r+cjKHaE1AyMPOaf745LIC2A8K+c3rPn18N1Bs2aff0G45pbIHSs0x5efVXzdUVnxyUz7v2Z9QGbKy+veLx279jw79ue4dkA4J6AvX+pIlsPGMxj70Y7NoLuu8c9Ej92O77xzFX9lN3EJ9zgjDOuzfzD3rVxbc09tfxLqIQj32Ij94e6hg17Hfu6uew4E17/28crzzA/DhmZDPXBd5mPON4rMHEPdIQD7N/354j8PvwK/Dv87Ht73rBhj8G4vkwvPBY2bNiwVzV6Xl/33qdoTxaEE4YAl0Cy6LnmvuX2Xpz/E2pzZPVOeH8a3lhclg6BrXKxgRMZxNk5F+moBmThNmMDl16U2SAz7oR7CLtsa8/KXV529JEaCgg3RmXLUZ6Sn/1rRRz9KxaNi7arC3xCPjjZ5Z+IosBR1c+UPwPmsApzpQIxiMP/FmcgALVUTNuOPClo03wvFdO+o4SIOWfkKSFHN9hKwbKLg3k+32GdJiBF1F3KPa87YgH2KWJfJkxlV9lIIOwZ0xww7xtiqYgFiHnH+UbYcrF6kTtgyhrDrzqXdQiYcsEOoEZl5+wZsRRsqaCkhCUL222dhelUUwJqwWfrhhoC8jRpnirmsiGHo7QfEBRQZLs8K2fUICy7AvvNW1JwKwZgxgrUM3KNOIcFIbCfFSTcowBYVUqS0plnhT+EdfWPsODn8cvwG/El/m+wEYTWP2cUUL6SYMSE3AJmWozCDOGRWt+RGGhnjZhIQETkQTOSAkUCMhlIzFE3g4AEY8/xzD7T6jlcBTMykso6JpQmcrliaWlBR9mi5bMzj1I/exPDDG2sHIOzC635mCgAAQAASURBVOD8kM4575b7FhQUBS/57KotNIEihDuOJ7AYwhwQBtuqDksrc1VJUElvb0BbUHhoBbSuDMQKmj85TRwQcQxi7sEx5iaitPr3J46Tsivt+r3lWWYRbk6h/UFyJOClpc9ZMrSeRojN2sFDaHTaCHuNseg4ok1iE2Bw9x7+Ojp15S6LJHrcCpsT0iLp5e469vrQXW8CmEBfo/2z+SvBcsujP7nbg5/H7cHldoEgXXSfj3FULu+6tu1gj5Rc8I3jxZxfzY5l9+1R4eVKX5Srt2v+CSZv5e3rAcxPzZ7j+YfOwkdpSTnhw46jjm8Lm3FlXfwwa2HYuzJ74z1l0OPc4j0Pe7dWD/+KeQlze8P2a403taiu1WHD3oY9tEaWSJvZvb1sFrUDc36HbNfRByR7Ix+vitfa4cY+fvnbtQHAvR874aSHP4Y9ButXorJH/pjCEgwb9lhNfMWvB8Ltr3nfY7cnC8KZMFi/CKKz2kS67Kxd1ThdgJ3IE0fv5pzvtXM+V/SbBx8DhzmILR1j5qFdL0urCYwdRrAhK6OnqnPcgCGCLwQrzDEqUoPiXv0mEn7ROfrFyb9gVegjImFtzDi0tEtb6MWaURzDK6LiFFaUGlBqQMo7YgRCvRcGW60o0wQEIO47TvuOdVmACsy7gIhhW5FjxLIVxFKRdQ055x2oQNp2bCGhhooQyB/SdqzAzd0OFClVnYGoWV+2glJX1HRCqBlpK7g5FwA78gyqUiJU4PR8w6Shc843QE2hXRBKxfw8o85ATUCoFWkHlucF+7yiTEAMkp9ly4AChKjA7XNhDt7fJORYsWQBJMu2YZ+VGVQU2EgJYd+1vgJiLkh7xj6bhCL7UCwFJepQrRXCsssINbRytfaLFZ/n5ziHBaUWLLFquwIpnJVBKNeKLKUAttIHfgoZP4UFEjWQbKYFIsO5YwLl2ggZRdQWl448Lvlubz191fh5AqetDazzzm0Lmk6AQLIp4PMZQMCO1GI0bhDBV+FNLSjIbUtDQcyoY9enJ+AyJVhZy/7UE1lKwhKl5OCswFzR1Gsb6dZSfVpSH2To2SIuXoBAlO5kPaR2j3xi3EjOBrOCqxl2Njg1QKaqrCfLLPPJhAqBmD1DkcCXfKZoKDmDocFqlpKU1vMT+F1o4p86OsE4kp6lJ/3Hyurb26Kr9YyH2CAoAw5ZVl+XMh+Sz8hcm/vmUgiS6VlLQFvCH3S4dp2X5iKQKXXK+dM2xx6M83eh+xUXv1y7guBUf1rWjpxc2yjYuVsDVe2wiE/9xeCSBxr7cnE8X8v965sHPKvLeZ/+u90YEXju4+zxkMBRWHPYsFezjIzvwffg5/HzHzorj8x4dAHojy8keOHay3fpsHdjslIYdTzs9c0O/XCf6VdY3t5VPxsA3LD3ZdeOpHG9fPwOwGFFbqEK/HX+s8VVfsgeVtEY9nhsAHDDhg0b9mnakwXhbCnjFz0Uf+wiecHHGJrgATbGR8vqOs/dAsrO51a3kShIyJhQmuSkiLBdBokmiMa/0fJaVfbPcpKaQ1tYLnO3mWBg+6wylADwc/CO/VOTlqyYlBmTlIO0u0VarAVTWTGhIoeAUiNCldhzN+WMOs+Yyo55FxlDFCDUIACSglahAtMusbFu7s/ISdJACJi2gmdbQY1Q4A7YJgHHaLf3GaEAIRTUDOwnABGIKzBlraMKbDuAk94bgJuvgLSLrGdIumjNQNyAkoAqQcmwbHJPBXDzHNiWipAr0iZSmEhAOAM5AHUCUpbr41k4UnXS2rIAZYjZFt039xk53SLgDikrYHfeUaULoaYd+7yjRiDt1o5xB+Y9o4SAOVdsacc2T0g5I0/ieFrOO/IsvK5QgTwllCmhMeX039vtOWqMiFllEGMEQhL8MkTUavEII+VBKxSMlHh4FTtSEDgn1oISVjzHM+2vEnMOCJiUXxYh8qMErCcdQTcqobjDMwABaugnlWsEgIgdlIPMMOlIYwJJLc/ImtoO4B4bFmRMSFg74MU2KuLEn7FiBnBGQVUpzePYg37y8b2gf5lo5d7lBwowEQRilIAjcC9RBCjVSMC/KkTm7/HgE2EICzoPUIqwdOWzsjBQeGvYlp45Pgwu4idhtPkTJ8X9JQDlhOzYYvzNGGBFwVmenbfNowGtHtoiqGebzh6slCFW1YESW5168+3tQTwTtYyu/ux4xqVUo+TZy1YSgGG/7iPYSQkJvtEx7EUVg/vLQEUPJnkQzR8d6fvDtfJa/7wmOWnmXdqSamlP6rnRx6Mr19M65oNgVS+38zDQ+CLzVxrL+yhR146KvFLar2Psf/ZUguq0VyvfsGHeBgD3dXbNRW8Cz0dH/hiN78qS6l74t09s78VR58N6u7aW6eNO898xZod96nat/1+++RgKhHNur2zCQ6jeuG/iajq0tfOYtYcNe5F5X0n/1ho2bNirWsaE8JqwU36icNXTLBXM3SvLltzAtQ0zFtw7R7dnq/QuWZGxk9hZ0BhrQa+bVTbPtqO7iurl9lQ68W9U2M5cBpQZqFhwRsKG2uJQCRDhAbhJOSzQdD0bQGTbLHabyPCJs51MmdSpqTrWTVVGTBCHfagZp7q1vE5Vc1wrbnJW4Ka3UIHTqtG2KrDFgmktjX6RMjCfC9YJiKFi3lrWESoQCnCzAmWCgFJZ0mEFBAALDwpluYZrx3kDyqbgWhaQLEAAOASg7voSrQJwFXqii3t+Bk73rGm9NwPYgaTXVudvnb4yMC9mARJrFaCvTkCYJZ3p+R2qKh+2vqahusIOTBuwRU1L66pWIK0qEVqBm3PFKWwoE4C1oGha83lvQADWjAJgnRPKJCy3WArmraCkiqigXAkFU8wqqzmhxIBUKpCitr2CRCEi1h21BkyhADUgBpEgTah4Vp5jVznNFFYcLTVG2vHUnjDmBEoU+CgoY8wYTLLYiU0Okqwzxnmr2NxI8MuhBSsq7lEwI6h0KwGT4thRBM0W5cPZliTDYC1zD/RsF7l61lh24oASkIdUaWlKOh9M1pHsL5GvlFLsEFiQQL9wvCbnkPBykf5zQtIy7oiYFMhhXQVwy2UAb2k8NZPhRGsl5pOQjAfhPOvJnG3HmSC2a0T21oMjEiePMxQBQUqLyswa2+8EXSxFAxGlLS8ZSL7HGT+O9VDcNT0TWa6X3Hr2XdUzpBEEL309GIeydlKUDy3P+S5gnAiCfL5+WWrfLkcQEK5coeulOOTw693RfvtdUNsI8UCt/PZikCtc/F2BNgd4yPf1wTK63H15LX8+Ruq7t2vj5ggID3t5e6iH8m1wZLv6e/y/eCCdh+4Z9rGbORL5Hrk28432ftt2rG17awy+xbBL8+oH/pDb8aphT8ESEj7HN/Ft/BLGuujtGtf93mSXJOvt/nCjmYw8hjmxfZdPtXZrWQ+cDxv2aRtHSHV7+GHDhr26FaX+vO69T9GeMAgncnWpcxzzNztHz5NF3M4HkMJfENvf5EfwL4mPRYvYEFEaAGcsi9wYa/xeooYJGjNhc7Gcsrq/d3XIc6JnHsQZNenvzHV0i6aoYJ98L2DejFWFLQVgOGFFrQQUJFepVEw1I4YiTDCncdgYbBlIe0UOO1IG8hSQExCLW9AVYH5eMJ+1BmcBqkIBbu8hpKUZAk5l+S+cBQyLAMpJAbgEoZzxegXMUPRfEomKXn+v1zAfQj2UYrjipK8AKIAGhuU723cBAJ7DEooAbiT/CPJvWEFkBzWrg3hSl6yEZZMyz5oP5nvWf7VZQwCWFShn+47MQB5sq1ovtQoQ2BxMVQHEqMAdgM+es0KBPIlM515rAy2DMt+mUoGyYt4ljX2K2BaZ3EoISPuOuO+YqvTXdZolxl2Up891x1R3rGFSqVLCHAouF5VtDJMCkAIiV9B9E3Qylf58BtmpEhuO8RIJBRm/StISIA/YsbR+r7UCqfLSfSvF31Ew6YgEGMvLR7MKeu+Kk2Nxlcbu2THr+KNopcwxfNaE3Y0zkX8kv8nAEXQAzKSl9nEgY+NQMeUCgcWlJBN2AFtzPc6oYMS7BItf5ePFERgjP9EYXYxWZzPkhIoNZ6372GYJf4AAbabsN9mEiAgJ+hhsExiP0gAusuRmUGoT7buqcqbyZHv5MpacMZG8Yye056H7xuCtAIt0KcM4ddvNHti6BLP8N/XwDVmQUj8EC+04iLSYP7kKoNXT5ZbXSl8veNTXpW48QPnyFrWlWa4edn4xw+4yD+x7PciY3zB+xcMAJ7RHvd+NkdRXhgf2h73YeuhbLBx+s89HTqu5cY9p+fQ5rm1Wt75d3O/9c3ndsI/D+tkgt1nnKFV7Gc1x2Nuwo4M2gFoecUhWDgMgM7DFoY26Bxiu/XdvHzLuXUbGt/GLH+TZn6rZmvtyXQXYGulz3OIrfHVYa8n+xA5JZvf2TN2VL3fcadiwp2myypE9vKkPjbHwLu2X4ZfhF/ALHzobw96SCQD3emDa64J3j92eLAjHmFXiCJcYSpQNM1cgpdN2df723JjYbSIu46ZxEROVaWcAHO1yYcSTSAkrFl0oizN9a4ChOF7PSt0U1h2dURM2BfaqXn+EFIO5r8oZUxDsuYYdqBUpKIJTCmqMCDkj5YLTvYhSrgmYSkDKVZQNowBuKSsIuAtQlNaKEgUcgiaZzlpbWgVpBQKBqKpfr0DY5PsAA7J4PWYIQEawCwAW/T5BgK3sfiPQBqDtO84QMI33eEDs+eHeXa+Z3PdswgzgDgbcMQ0NHUhgEMm1MfOwAuFefmO5sfR5Rm7Ylnx98CGFDNQi/4YgkpocsfGsdZ8EcAv0exf5HAMwV2BXgDAnIGwFsTqwD8C0F+QI1Bgw1drkRMmgO20bcskiZ1krECNSLgipImuGQwhAKUCOiGFHCAGlnrGHqY0bxhasQc4TEYi6wRmMD5cb2CTyqwYZATtuAB2jMn55X2oAFiBMPMI9FtutYIcIE07YAFjQ69KmwIAJBdGxZNkVZEzVBhwB7IoGUXEjJHKYBphvGg+vgvJ/jMEnd+9aGpM7iG3+oc2NGUu5P4piHrdFfTw5uqPJKRMJ3g0+1o6UoI/mtejsZqHFYwORPORBPpuJ/lopUgMpCLryfsK2lnNKDhqfjnUpV5cGXBEcTToHl5Y3AwnZaua8pxAly9jPx9x0RvTQJeDzYvnz9W3XmLSwHYrwIsX+2b1UpSzqry3ofS+IoBznUeqxT5snXgGejfWQw9Fxet0uc+0DyL8uiGZ94+ue/+pp10Pabzf96880exWQ8lMzA8t42trWKmjQO1yf5Yx17KsFFjFXZiN7ggHdfbRLGbOljQHrg/ylumsLrrFLCUVfdyCPlv/Q5lvgCLlFfUP0b4bjXcPeltW38KYY9nEb11ylzarAAN/ep71vAO5/if8E/0/8t+/1mcNe3jju7vBl94bsV+N2zC8cvuuVJkxJ6anYhwKtb3CLgowVl4pCwx6vyRgJbicx3mzvygYAN+yp25MF4cToEKWjWGTv6FQH6oXDJyNhkchfkKk2N7lKQMAyE0rLyoDjJj837gNd0t7Iwphx7mLPRd20UAaSEjsJq4rTyXPpVpe0jOHinb+zMkgKIuYqgAgd+ILoCJgybxtySEjrjlOxrfOyAZNnt2Ug7hVVQaqo9J4QBDSKm76UvEoms1OAuhm4FAIEiLpHA8yC74FFf6NkJG3V7yeYf82/9+4gQNsMtKYr+j3Np0kwjCTD3f1X3e8E7Z7rcxcYCMgKigC+AnBzKLsjgzSw7gu9nuAgr2WHIiCospW4d0Vl3LxNrg8utFXl4QL1QqZNWIUIwLxK/S2wayqAQqnMAtx+WVASkGe5dz+hW1sEADELEjidN8QClJhxfxMakDvve6tSukgjdmCakIPSEitBGBsXsW3SCaiUzmmWUBWa+wo7TigqxZh0BJHVVhyYRbaascICZpxxal2AcLgX96h6h/EtCLEJeJUdpGVxzHw5ZCnfzyezSmuWNvpTK7OMe45XNnzVGvBHBfgM5jNjh8WW42ZpUngnI+m1vpNZGkXrugfDON951phnYTmwvN3BQR+1Zsh0o1QnHdh7V3f+efxbWHylPTsCKvVYFGYzHVsPLHkWn20abZlMR1DEcdKw+jAXP8CobfL/1jtymyE5C0etnx7AtJKxLvy2l2WzeH0ewLwOf9m30l4E7K4v/tlmkg+DEAy8ePGG4XgFxxfzYjzQVzOrj6PU6ZttYMxh0McufF/G9/44Fdlbz0bzduxdHt46rpj6+y5XVNZ/7Lcj3MLvLoHoY8xM6+VcyaH7roJvDR7puszNsYTDPrTxDVK6Fh32LswOnrxoJA97ivbwu3yMt94mFaUvKF9/8UdgA4D7OO1Fx1f8d8Vpusjap98z9il+fKugD8Uave8cVMM+JruMJT9s2LCvs8GEu7QnC8KZM7eoHB6jNQXlUGQFCuhEtRcx+XMV5tgWN3lW7ofFmIsuXlvS+8jtmJTBQjfRhKx8n6y5CJq/TV3uRaUmgZOCbtk5k+V5NFsUHT+LOF3GnHfUDJQknXfeMtKuJ54icLMWBII4zOeBDRYAYbOtDkdRQCt+pcAagaVN/5sUJDpreuxlBQJoEew6+pJXGJttct8zwF3W58Ceg12vW/V54XBvdb9HvS/pZwJ2EcJ2qy7tqNfwdwKEPk3mrUKAuMndz7Ru9X4CayxHgAB3BN94GCpo/qK7TnGhUND7FtlGBAXdHBXuNY1Je4YCp7wvsj40Bl/aJE4dFJjLCuJVAGGvQARiLkjaPikD8/2KEgEoS66GgKhMuhq02mpFqBmpalXmDalW5Dhhn2YtWFR4akPVCGe+kBR5POFeR5TAGLvSJmMDr5JWscB2jE/n+aEGZEnDJuwNokED6MkmY3w2kZ+Vp5BrIUDMrjChgPvkXJlDvgcTN4iUgcl8EA6MzkUYVbxPuHZRf/VnzCW23Ka6q5xP+MSEHTticySbTCKFQDN2RFRMoCwn58FdW8J3sKRSln1ULqmXnhXMO2LXhtaFyTUJSArJ2IEItDISxEsORLISGOsR2gLktPjw5D2ISbmV6q7AIR1oTXgBRnPOCxPNQ2WW4vEgR+/O999KmiJJ2m/8avdXf61PQZ5UdNp58aLkEv7oeUAvY0cIUdqoB6Rfxny9UZizZzNdy/HL2REAfRvg3qvYsW9/TA6Id2E95H/81rc1Z4aXsWt1egmsXb/+69qDs4z8fQke2nj0AO8xcmVxaT3N7cLHZtY/eA7qcl7uIdinaBMm7G0B+q6Nb/P+jTvsqZqNpuBimb9oZv7U7f2NxWHD3txszyzGvYs/IAgAVEWp6jl7sY03w7CP1+i3uX7QcNiwYddsV3rR61h+SZ/Vx2ZPGISjDE1wTUfXalJX4AoyN8hvIeOMknEJpTmH6XTuz2/bJFyQNGZTQcKOU3OO072TNb3SnmWsoKKsmYrYLXjke3Ps5yZPGbFDgoUFhFqR8oYlr5K3aqBL3HegAvO5IKl0YSazyq+kisZNS0DwcbR3IExAPaOTl2y77B0CgGUI5SqjB5Z8ECZ/v6fY8Fqmt6OP37bBgLCCHqyb9V9+RyCRICBBLab7S7D3Jst5r3kP7nm9z/5y3ei/32Hgnbc7TZf3EoSDXk8gzqdZXX2w/CcYyHjNfH6Vnagos8h1OhW7oHUZAqTuiv6UpTslAOEOqBOQAhC3CkzAHlXqcgdKBG5WAVv3WLCdAkKIiGVHDRBgrVTEdUNJEeEAOsSyI647aojY5gVT2XEqBTlu2OIMidWVMdeMGnQkB+gZUhndM4g0SsVNCoAl1CbzmBQwEGnXqmzToECdAGPknQozbXPdVeIpEsybDptnwvBBgXjCQwlbE4xlNDBKrzFuio8xyW/Y2ciQA2TO2EFmnHzDLiAQIeU1ja9EcL+2NC5djxMKKladVww0ozAHIyRx7iKuvSl4JwCRiV6yDXoIqh8wUjc+/gBBtNQ+E/Q0Li7rlHVuYpgGUvVwUA+gseTG2CMoeZSYBIhLkyloMewE7PQ14p9XWmk9IypofRZXH9ciT5HRTADJBEKBo/zkEaDuc3IdzPKu6NrK83rAF68mo9LK8/Xp+Del1EbRvvT2zLf4+7ao9fsq4OSHttd10D8EZBgofbTH7pblCu+aPQTTmOjaEVS2pU3p7rq2fLj227C3bSambAcu5HvOjKH993btUor3/dqHcPpfvv2HPT2zVY9nmg4bNuzp23FFROHvBMaAfmgd7GM4j7fEsI/TGEajjD48bNhLWcaE14Wd8hOFq55mqQAACTsWzFiRERE1UhQAkM0WFAma2ve99CRZB/xMd7k5ixO+ie/Dc/w0OAmLw77gpBtfc9+Y270gYlEA0ExcrwmerRBg0nUSCE0YcgGoxulDAU7buS2KQimYN4kDVqLEcVvO+hRd+6RN2FA1CPBSAxCfayl4bPggCdkYV8AlOEW1OIYt2t1vZ7kfzzTNDV5drjG2QDCOAN0OW8fxesDYZwSrdgjQVdy/d7B1XtY80I7UnQnGpFNQCmT3UQYTMGDQ4wzMC/O3XPn9K/edX7nyvntAQ54ZAHhk833h8s1yLehj2R1VFQqA70Dag1Kamq/AuHo7TOoS0j/qJEAbFMitWvZJwdgAIBXpM6jAvAPLJsuRdT6hTGfEXDBpvkou0hcB+CB4KQTUWpDO9wgByCkh1YpUNmRsEpcuBKAWbEEqJAYBLIQjFlTokNHJxMkmYpUccwKxRz2l66GZpHCVROnasGHW3w3w4HwgDvZ+YxFRcMIKCsNWrIrDihSjySP66EcE62zwCJMto2BGACPasQkl/lnSDQ5fRAS0TJU0IyNBQDGpm4SsTL3rm6EeJIKWSYCxCh9rDaDIoTACV8jsx0WogWJ0S/eynN5FL2KZuQF5aNeYKCNhqCOsVhuDziKeBddicq1npED7QWr56HNkkkA2MMkO9NeR6din3OeQ8qg+/pWw3mxGz/Dt4ME1yxvz+yL6PfNI2T8PIbC9rhkBTUJx5oj2V7zYWH6m8zonlKxn+HZ+O7KOJrnqJ+J3u0ny/eDDiNu8nr0uAOdHts1/L7ryY7aHyvAQsNjPaiZLewnycE6JYyv/jo01m9yc389bPdf87bXFhwTgPpwlXYfYe3SANE/JGPRg2LBhw2xvAUD9WNnB9LKu72Np8V8PyjGlYcMev3HfPaQpH7d9L375h87CMAw5ymv2hEE4gOfzBITblGFCBlqBcSzOYFSjHakx1GTjSPYMcJTbCNhxh/+PylTW5rBP3ZRsUzTj/wRYRJFwWIwEd23C7pzRASdszius7LdakXLvBk27OtMzEM7ybweqTc59VGHShd4f7a/PgPrdBZiyI+9iZ3ff2f3GY1FM5zkeBtQIuPFfVsnZ5e0aIAf3W9ZnFFiMuN6vYuDe7O4hGEUJTQ+uxcP9X+m/BL8ob8m8rxAJStbhqtd6ph4lNc/6fYWAhh5kq+46D376vHj5ToKHrHN/H8E31t3k0vG+ar0n7C7v0N9OIlvJPNZ6SDMAoRYs9QxsQF4UpIP2sQrMBUCuyAEoWs60VyAAZdZG1/485YJ9nlA1pmGqwpoTABoS77ACIQTsYW5gIYEzwkjynS3yvSuOVRTafVLgXTsO76OjWYCRftQSoCEYdYuCioytBRaE3i0VnZAdQBXac4TxmmER8QjOWA4TAkKTz5SO7WOoeYlEMuckzYy9obpV5zYyAICgwH5V9pfUi4FbxyGUWq2wDrYGAAa9exIYVa/kXMvfCyJWLXXPQmB9Ja217EQmg3PqkZ9srQP9nhy23K7zsd3g6ibAhBEtDt5xQJh5qdHjAQq2AfNvgO3R/DuggpH0LJVrUGH/rc/bUSqGv9Z273XXvgfSyC19mcXRNdjhKMH6ssaxgzZS66GlXs+sXWv7XNrE+m5NXhnso+/nme/LuELhp0v4+Gif0sb0YaDO6iyCguhAH6Ox6NvlOIr8KP66Jw17WXuIqck3kb0XRl2/ibGn88z4cFV9/EaXun8TjHEybNiw3vpdVK+Xwj2Y7c3yAdJ/8b5n2LDHYMcIMcMep6UntA8f9rTsSYNwJifJyEN0lnvBJAHqUmPGmWvY81cErqvdXcJmk0/UyTaXMhkvBRPWzt08ISNp1CYAmLBjOZxIF/dzds/aEGpFyAU5TghVdCVDrai1CioSAsJeMXkgyxIUN4NnbREoIjBFIyh1/N4BLg1o88f+few3gkpkjgECkBG0AgwI4/XQz5RovIfJVxIvJdXg6N8kEOhBvc3d52UneR3ZZwTPDMHon7XrtfybwF1AD2gZiVK+Z57IoOPvq7vfA4JweaaEpQcD2V5GPLLrmbdyeBZZhqwPln1yaTAPLIPRhABAZCvvXTouzhz0UtZBqJrdFSgTEIoAbpMD8GIQoI+OrhyBLQrDDRWYioACy7ZjnaVTpRCQyYwL8pyIKmw5bEAwMCOiYEZtwJBkz+QIJc8eBJeKYJzHqh1MwIWsQFB0zrn+XHcCZRlzA8giMopje5FBC/1mV6ieUdAI3BMmMxDO8m2R4zJqY8R5NpWZPD8oxCPgndgJFTu8SCK5gAaBybzFWVIgLQP+jtsjgh59bfJTdWU4bqIoHtpDL7Z5kwFQQRFKX07WV58mcXUKgCUcATi719IyAUgOpYeAq6Twoj1Nyuijt3FqOgJKweXH6qg0wPPYjlFRde/k6llul3k0HL+02n/I9WnP4b02mb8sgOTzWl4CxDsae7bVR4ZF93t9O8Ko8aKXvRvzY4MrDp+TDy1N9yZmThKDeT81x8iMGdvFKaAX2bF+wmGcHMHy2r7lTMF3Q25Hxfo5+NNqgYftP8Z/hH+Cn3yDFOwNLO/afNE6w17VrK+/CK4f9uFsxoKt079/kbmNgftm2LBP0RgFfNjLm+yDLvUiLlVUjgCcd8743eewYR/GeklKYPTHx2n/Dv/uQ2dhGHj44vUYba/jX/oY7MmCcAJ+ZXfqhiy2gA0LpoaQiBxcD4JRVs0LtXlEpGLG2lgYAGMbiZNKAD0BzhhDLjUnfMWCc1tGFFTMip54F2wiGlKNy5MAzHnHnCWvpQKhFCwrkKq8DGJWR7KuWaIm0xzEBH7IjiLLjTKSlC2kLKI3XguYXKO3L2DMt939e6t/M6YZ11+MdXYNAPTXe8CJBBOOR89AIzstwYAywIAq/34ka85/9uZHxg4DCftuYgDbfriez2TaZ9ja0ddrdNd6nMIDqWT2cf9LIJPdMejvTJd5PZbFg6THMjBWHYFTtgPzSHQDkLqegUCgkHnQdgqKQEz3djsCUJP8F9x6JUCkLcNasU/q2A1WDad1R4kBOUWgFGHG1Yoa1RUZlK26nhGqOtoniXhWA1BDwBQkPtru2Fqem0q2FcfnjLMCaFFBfFZF1LtEcpb8UwMi5FsoqD8pozZjanA+BTkCMubmXJVZiE7tBEbuMhkzNlZAxaRwiUEXxhZjPii0yc8E/HfVaRXuUXTXSMfI+ovF8OPWSNFXvQ4NlNN5R0EYYzQxQl7pug6NtW8ROa1TWGll5iOOzM1ZdSmY1ZYiIZAA3zntuQSoimt3wmiXbsI+X6GbAP0vfhB4ELF34R75zwAPd1QYDxqw/ngEkwDWZsElWBUu/q7aX3tw95r5be+rgFbXocCHf33o/uO2++3bUfbm/dvHBMD1IO61fH96G85XA+BexnzvZ3RJ/xtBfruG45+zjsnkXhcF/VTszQA4b9ImXmBhnKN9UwuPYPYdds1eHoAD/PGqT2/2HzastwHAvT3zB/q4+7AVUGhrm36NNGaiYR/OrPeNfjhs2NfZjoQ4QLjOniwIZ/CYmXfFZkSVj6yweBy1u7u2zUZBxI4ZjA+VO7ma2Nyb4jhPyJixtlhzCZ5R158nv8VZfydgOCHWjJtwRi0VUy0CbOQdsVTUqM7ZUnHaapOe7LK+KwBSYS1MoIcyg7yegBcg3oY7d92xQn2cNQI3BI9WWKy3Y5yyLx9I7//P3t/F2rJc56HYV91zrrXOOeTZFMVLSorl0Li4oXQT2dJNQIkykisE99JCLBAQIgsEgSMIQcgnQfCb5AfB1BPzFASQIFhXebOYRxmQ/EBEhmjDgETRNCPoIlehFUeySEuHpkVyn3P23mvN2V2Vh6qvx1eju+f632vOufrbWHvO2V3/VV1dNb4aYwCZnFIfaB2yyUcdmSR7nsEkI2flXpTPFxKPxJSvi5JcKGG8rJ4EJEk/ko1KAiaJfw4e78rX2b4sI/NV90dEh1pTj1CNwYSakGT7Mr1Wwk+tBXpJi58XMHKPYYjnQCXf99IT1pHE4grWjoU8DWsgBaElSns2RTMyhVJUEm4RWBdZQFf6re2yllwTEsK2R9+GHKFohKamQYhxINsCcoIxJbQhokkRiEAMAanJfuFSoHcwancpxZ0LskJEh+1AR6WBxApC5KWBGEpCgq1lPqHJxDU6bAFEsY+6gulOZc6S9EtXafDlbV5u/LUQiW2hDun1LtcpE1qhpEcdPEWek7JJTNvmGHFErT6jj7JGXKY1zUxvN5jbTMOcRi0mI39MgNyWepi+H6m3XFLTvmuH0niarYH5gsv/dJDm0jYIJVyCGem1B9xrzkFia2vU+ihhKI/llMeMzueqQUcaMPefkhiqP1QLszJhGyTcuKyaEtvrMpjo/vKNgm16bwKaQ/U9d3XkZ6GXctxeKw6w97SZvbm/DVNyn4cGI98Amz98ey0bzvvBuF179/KNg55tTbmTbuec9NjEAnevZWp61FHaW03xLbg6VPN/ab9DRVrG/4IFC+4NfvdgOypCDzxOCXeS+71gwf3AZAKP0zLIggXXQY8W6Ya000LCHSB60AglLfBl0m01GCzLWggqql0Xb1LU5YiIOMN20GTjaWMKkDPhlk9Hh+ILif5+moHZ4PSsGxiKGC1cTneLE2zRxIgm2gIjIOB0G4vpyeL3zdQWDDR1yHUKtcTUv5fX2lJFGiWU4K6ruyQl77YTYb3slGGVwOrlHstH/3TnFnUwSamE2zvlO00vUouLGnArSesFTEOulTQ0P2r2sfz0RYfy/Rx1OytBRVOQSj4yHstPzTWVjrEdA8wE50lpC5YJqKXiSsh5KGmn61NeV+KU/MzWlcmO11s8Enysp/qsU41HXT1vMhE3pBnLrfMSrwVCU6qVssZmKPVdC+nX9EA/kIiFEmLYlM1Xdk0YfMKBRUjZVCsAtDGhDys0fUS3aoAQEGKPJiTEsCp0VG4YElHUJ6OpRtNypbc5alBlTVc2OxutJmyoyRWhhgs4/eausIHTIJZHaoUG9GmWynyThpcRCSnOH2YmkyWkty/TWmM524EcMtrEjhXUhFMW6ppxyYCAFTboS/mbIc08u0ZsR3xwpsz6cpyBV43QZOvFoRT8ZhORhUWpWxxMhdaDL7dLi9oYoJFkJDy9mcw8uBsnzFVdFCMPE1QjBeBCnGQf06dWmWmr8ICHtrPh8kW8talSctNxvZhe63E5vA7IfJwAs8yb4DU4r4cgf1chDi+DT+GmpjOviloUcHhUSG3CdPf4WnDfmCM/vUlLm7t5DIQHy7gU8Eaejq0371fL1BZj6mf02NrwftFUS2Bgab/DAcn9Qz1asmDBgmMAD2MCth+0A3v5l+6nYyVMWbDg7pHlRMtqZsGCBdfD0ZJwqneWBecrBEemZf9sWwRk42tn2A6v6kyu9UXA7j0vmVendjinTL2TTMCtoIwR/dJh8COXlZB6p2RUfBPF4vcjRqQ2LyxWPQW6QOizCT8EZGuVLFyf7wEwoo2yYuUDVRJDjS8erlZTlWcAzlsg9maXR8m8ADNlmRvNrpF4opZUMWE4pEH5uq6NWGamx3caNfBU7sQ0Nqh38lu57w9LaTt4iRTNXyrx5duLdfLXSttXcYNLz/eL1i/BiK7nkhbLr1puTEuJ0uTikJDUOrDtmS+ZIeUfTsv3Tfmu8akdx7ZiOGC8viWhyXJoXUteoUjsA0o5imQmBaAoqw3NuOpyUqsINNHuIQD9qpAcAQghAH1EE5HJ6pDQpmKasb9AaBq0mxd5SBTtuB4dYpuJOdLx9AfJ5uGCPmvO2QxCX25K41Mn1swihmEIci5pCgEWRSSlc1LuroCIDn35ZnQZjxTQMmmLBmbqUk/s5yFCi+Wk2ewhaocDBw14IAFVbqGKQU951FtrxESl6QySrSZJOe7+FtR764d52rSOe5ghSyUGDcphr8tD3aHWSKR2o5bBm1jM9a9NebLuoSo5STPDnEadGUvh/UbeC+PznErSMJx5MFUqtAZLOyYg5zFlFnQKNtZyHvbm2Y3VoPPJsnEyuh7hNaZ/bkdk+VZ8GeQYCevD3/wvG8v9x5iM53yVZD6q/V7m+81oZluwG3wfZOi7aMHVkI/0LH7FDgN5pNOn8V29Na/nh27BggULMrgz9deinDCu93164HTBgrvHoR64XLDgZaIvrMlNsGjCHRjovl799WyxKiJ0E7CatkSAkmHtcPIvDKJ2FM23FbpBXJ8Fplm0ucYFTkTMmcP1RcDdD+YsAZqoLL7gUp99tSEU7Z2iv5IS2k3RsotFUN8DjZAfgdpM2d4di59BBR/eM/WCDNVu07jUmLoAsCpsCtNSIkh/A6Z1l+Q3SbcI07LrXLwT1JpvVKugZhm121gv/jb1G6uDyjxJgu06CEXSUMsdYIQUUL9bL1C7xlrDTEOyPUgYavn8AVIvN3uG2s8e258E24mrg8Zl29DkqJKigBGrBM17mltEIwLJPJ2XcFp+T+CyHt7UKMlcLbNnH1R7kmUOkvwrpclL/dttbpvUlCiFgF5tS/emhNhkP3BoesQApFL/BglowtA1fLZC36NFjy6sEJsGIfVIIQAhFKXITHAwTjZTaT7FyFtnX2iZguqlMUhL5ZRyJZX77Qo5xlN8vE6tqUyN0dmhNZdtLoB1deIvzzMdVtBTgRTHkvrK6aTStTR3GcpLLslsF6rycgbkFqdFGI4RUEhj3ZkJCJ2B01AerR+FmsyTMFOhsXrIuQGrzVU2pfUTbAPWFM07JcxCaXP2IYZcWaZeXvaWn23kpmAkmk29Ss6OiaBcZ518rU3MdDHNU6ZRCkYDx2GcXobcJlffkFo7sl+mKFVDN2iE0+wpKWOdvK++SbH+uRsiSwXmJLzvCzURexgwYmERWBwe5vpLNbjsiAKv00gwSeOw9P4VUB/YmfIkumAXuPoAUA4tLuKrfQQN3I7XMbfFQsAtWLDgLkELJUneJ/Nv5sf8xtndMguuj8Uz5IIFl2Mh4cY4WhIuwMzGZP9vZChMHBzEMBqQzaSRfFPhNVMEElboZNOYcIItKG5fiVm4LOYwwTS36tzOrIo3qTNcoCmEQkTAqt+i7Xq0fURqQiYLSpFTNAIuJSBsYQSXSWWNUFKNKCXmPGk3MBPlupJkL86NTAKMnKJPMSWctCwEZdwkfWjWUOWq70gYxt26MA2At2Hacywfy0ACkMRRJ/e0fdguqjWmZCI/SVC9ABDOgHVhxEgesUzkSFqMibwWZn6RRCCJLm8FT9uJ8allF5E10U5Rm4g8n0iLdVQtw0JgDWGeS/m8yUsqrpCIW6EeQyRGTyQvXdNp31NRakqG7tubiECIyH3Nvtxa0HBWxw0A2kLgNRGIhcQMySVdTFOmYCQGP0PfYxUjUgj5r2kQQhgIKjN9gTI3GLETEOQ7EIYGpJnIWMiUthreFIOGQtJ3oj3WYIuIdlAk7ZGK6cdQ0o1IUP9qNESZU89mH5VsMVOOmRqymS3nR99isfzmSf+mqlv+3JZDCSxLNyGk4ZVcBqXLVENQwRD2uJr24XjCqOMbuafEEedbanLlYxYNjBrNYWtyzfpQhzbjqcae5aXtrOlcZi6ykXdRrCYpC90O92stRkuDRBzjjQk7n3vWstM2nd+MWhzzTTg+7TCPBGo+kpC7+kKKm+oInWRuvnHW3nh5+j+HsdlX8v8QyrvgusjER22yMY9NNR2b3ExydxT4scHeoerBknf0/bA8TfPQ+diozQW78F34HryFb+M5nt9zTmbdZcGCBQv2Hf4Ake0H857O1jpc2TzGI0cLAXfXoIMQOxS7YMECjz62SPGGJNwN4+07jpiEy9uHgA6v4GJwsd5hVbTVTCTB7XSPFitcYDWYSdOXVUSDHs3wGi8EGqiVkUSERVNtRDZbmYrQOvMxcSAREIAQgNOLc5xeFLNlAcA2BwibQjTQHB/XEedD8kaKcb/E7NVkIQk2SrpZeRJYKn1BCa/ft8Cw79O4wFjjjOucZ/Ldy5npg415a1m1nj3MJxu5CBJB1LAjWEeCBNt5+SSxJSYQh/KrH7QemQiKyJHpp03nAba1EpdB4iuhZ2pT+forMKKQxKaWj1qEK0mLxORK0o7lN8lB7QOWiWtN1SxkeqeopUXUMqQ5yR6mncc4gJkIpVZdlPS1DVhm5u8lVCzPmdSHZdmgWieHHkjsx+JTTsOHBmi2QDzNRF6zwaANl823JsSQkNoGKSSQ/W5TKqQb0LdtRdQ1iIip6FcFc4NnZifjEBKF9mcDk1ZTg7ZGc5n4M1fbSI7cvaaR1SIiFDO2fdFE64ueWiiLPt1GtCUlM5RLM5dtRV3VfuI4H9q5/rYcYAgw4imfY+nQVQRdXwz3KhlH+sp82DE+aS8zjlZvhHQJm/XzGqgvvRzXNN6oW5fJTtZL+zDXMLo6My8vYuK0oAQUQLJt7PNvbiMXxEp8go0Iu6/TD81PpskwuT+nRbq5frV/ul1klz5+VrrdG1EVxuW6XE28rC1zk61uzrdHbU715pvml7X9tHz2c4Ovz1JNJO8H1lhjW514WXA38Is8fud1egutT5Pvz8jYN+jslluKZ/HVs+KCOZjuZUJCrFYRC6bwJv7yJeQSF/LtQPAE34Gn+NZDF2PBgr1Evc+Kcp0rncaFXrDg6hjvKZcV84IFC66GoyXhMrLAuIHpJGQh3kURbCds0RRTkVussR0E0T1anBSTGaGksy6mLBmC4vUcpgMNvlH7BQBabNGU1/xqIAPzi//V+LxIKRsgJpxs7ZxOiNkXVkhZiSeoNtmU/IRSbS9do2lCEllKypAE2Uo6JE5WLj3NDxKX32l2Ug8aTfkm03fTFqZx1qI2jammEUmqXUj4E5j5RSXvCOZD04heg40KS2RVtH61hNpAk+Mkp1iGFUzri+12gUy0aV21b1Rr7gVMU4315T0tm9mTqsu7cb/5R19yTMebDw0ws5MJNcF4IXXdlHs0L8k2IgFKUpBkqjf/yXHCsbhCrWHHNvBacxxjUq5AknKqHWjScgs0Kf+lLZCE+4grAF1EH4DUJMQGQFNMS6WEk80W500DkIiL0XjGpkEKoZD0ObMeZtowAGUGMYONbGYeAqD/SdNbS+UXNef0AYlQo40AtdwatGUL0RSqLA0kIIY8s/ZSVj2N5f9MxFEfwspHM42epPKaf8pZ6wOdPbLRGCc19egPrZNaBtHYywOgHx5iE2KS+GuG9GxWTaIdF0qbUmRs1Kj6BzCtOIA+aayeJAZVl87INUhNE2yOt+tTE08YXbFNn/V9PU2FgWicI+vUr9y0CUojDtkX06QVNeiY+lVMWtYC+auZc+Q0wbfiTaAjz/6/WVpGut7fJmnfCTjAyOua5t0f9FfyXrjg5tg1Nk0wle1JXIeuf8zQWZ2Lp+ll6QJCF3LmWZZ3HhPei+/EN/HXD12MBXuGthyMqLWYDQsBt2DBbvh3Sf3GURcECxbcHIsx9wUL5tF3LWJ3s7k23TDevuPISTigQ4tYTEjmqTFiNQirw2AWMouBTY/gbGAeEgK2OBvMULZo0eMUWzQiFm4LKUeSLacZJU3hJlKPVSHnkIBms8G68ybQMgEHFD7AkzMEr1PLippuvXySuMssQU2eUcupQyZCeJ3kCt8nlInpnllJJf4maaOV1rBqIlK17/hbTVo2MFOOvu4kmDS8En+MTykw5Dfz7GAahEDWxiKZRqKNpBo1vTYSntp5NIMZUWsknsPMKdK2INuCpKBq0wFGOAXJW/uBaZMMI7nHdmL6qhXJ9JTUUi1A9gP/VOuPbctxxEFMLUb+cdwFmN88mqz04Bj046uTePxds0oG7Rvp05CAlo9uqLNP/I9hkTVLeyQgIJt+BbDaZK0zmqZUki6FkP0zhvxcNkVM6cmoHo14gKOeSSY7VkjoyoNXaxc18kiFciCgH2LnvNRNX04/D5kOSqQoNw9od2t+Fof3SSeSbGrkGtvMyBTfuqaDxrm2niJimY9zKvo49kLq0GecJ7JYfytPLPUgiWAEGds7i5FblxbzsjK3SGKcWB8W65P5M/oqmtYerMtveY/B9ifp2EtfErUXnd3QVpiDEamkaa9GxOXPNKuZNxVeScybblLiFcp3FZB2vK/Nkqdu9wH2yg7yvEw9x/uBWL28Frxc1DNXXz6VIuGyar+o2/1Cbq08s5s537uaxY4J2hr0Wvr4tAjfi/ftCQlHlwzLON0HLAdSFiy4W/iVb1t2M3EQcOzP2n3BfkOlBcc0ZtoiyVqw4K7QdyuE7ma0U7phvH3HcdZqADUjqAlBH012nwLYrLWRRVVn2FZhVk4glA2v5T9quDRIxcwlgCKCX6Mr2iEBbTFlSZzELVJSDb0s1A89EDogNMgacC9gxInOh0rA0KQftZeoSTalyaZVoV+xBjU5F+T+BpkMOYFpL5HsY8FZNpJgJACZN81dMj7j0pcakAlAajlB0iYxB2TixyTR2TQmCSUSjv6dMWJh3Kf6niMJeSFxWG4SmJq/b8szjMmtJPcDjEFhOCWi2H6NxIGE1zLzmvqIY9okyHhdiUUdD56U0/HE+porMusfvdeUdmO6a9SEKs1nkpDTcbKBmeIkqLGnZkFJwLHOHH8XMHOaXoMOOd/EsAEI62KuclUPi3WXstnKkJ+5wrmh7bMouC8XUkoIfaZHYqNmCc04YlsKn8VvRqxbd/foscIK20pvrS1i5yx+IvnP5rYGyUqX1FlTRnx84n5aayg3KPXR0kDKsRGp+carwaXlc6BoMaCeT9UGv5FGoVBiWip7ZFIR/9QLWQ3LNqlLkgZRMf3VBYmX/e61VXidKNRwKDVATGsOVVpJasL2aUs7msnE+U1cTQCNw2g71WWu719lsW9GPHeHtSn86hvP6bE1De2zm+o5GIntT35cH/e9TdJpal9ghED+dUybxQX3CXt6aVyYpnP9gQK1RL2Qc4C+qWLVMvs0M+wjAsbrjeNDixV6MXXx/8VXHrA0RN547T6+s2DBggXHhTznRffWGR+uXLDAw6QnKrw6bCwE3IK7Rt81CDfWhDvOXeWRk3CAmdXJ1BkFxlkfo6/MxBF62jcU1oBcxQle4GTQrEtus5iG+CeDGcpu8EEXSpB1NNuITZdNXCIBJxfI5AaJB2q4KXHFbDg/djAtNiXJNvKdpMxz1BpO1NpqZtLeyHdqdQFGUpFQIfGi+QZkgmQDI5O2qMkrakOR/CJJRSQYscj4pjpjZeVvlpUkJJBNQuqzq+QNyzIl2VeQ4BJCp/LBRo2sZ1IHhmcYb0qSZWX76T1PyjEsLcupOVGz4mcEHjXROIZYh+DCsXxr1GQg41GLT7XRWA6mSVuNhNdY5Ji8kN+KDUwbU6E8xUXJh9dVhsVysi0YpjwXQcZT2mZyu93m60HTKW0cG6CNCT2JuBTR9Bv0TcBJStiu10AICH2fteSQNdZ0bR7Rw8w32hGAIJk1pdCcnzgLNUhI2CKWzqantQhv/i83RKiMhWW21YYRGflMuq0GoioMTah6Mdl4pXptCyVMBAkyepYbG2Y06OOUjWW2MrxrjTUM1xN4XIKmMW0+rUVCTWnhqXsY/SLsQfcx6Ikm1yvKdQxajaH0BfvJO18mSQTU9N4UyWZeAtk/teaficHGyI+/akjOL0w4AndtHEM1EaRLw2uo+mTA1ci4OpXrbWgbXE377jJQLD5m76+H/xY/hn+FfzmR9r7quxy3UHvBfcJIpFpbWs34cmafNp22b0/D/SO477WfuIUKn4KtXmhk+1jbSNcad4lX8CpeDM67r4/FF9yCBQseI/Lu1NwWZBzrG2jBXWIZJQsWLLgujpaEi2gGUfgFWpwVrTQiC1iNDAuDQbSELdri/y37iiPptsYGJ4XVSEWBfVXEwWFghYA1NsOEvBJx8brfDIJwAFh1ESfnEU3xYQUA6IGgmljcqUcAb8s1kmhJfpOQUPOTCUZyRYmnRFVCJkPOUJNmerSZZWBY3aeRKCMZwjSpZcd4JHe4ttEyaJgAIwBbuZZKG5BYollEak9RppokzXfKtQ/+d8BX/4Xlw7KZbb+xLzMSVr6tlDTifaZLrUDV3FKCbSXXlGDzmlzsT31ClZxl3Wl6ke3MvqgVduoj6v4wNolfNVOp8bzdKZWvad+zTjpm4OKp2UrmoeYwlRTW+FvU6SqZqYePpojMkl4Q91gNnxXJIyGbp4wA2pL/QGO1mWhbbXNB+rYBui53/ckJFCGlQpQACE3pThNctuU3T05ZUUlEAQmdkCzU9KKx23znXfjv8Q5+HwnPhyaJ2KAXVncFGn/MRw5IdE3rVNkhgrakFsugUAOInCcBVGVUXScl2cw/WYNQ6luTFKHQWl50G0Cq0Q8I+tnspAZaQrtKU5u5fLuJq2la0XzrZX1pb2SzToHha4IqFCOkTRUySe9rra3mU2fSrdXmvIRYDvQYOHc6TwWBpmx9GXHHuDchdeSBvIEY2kyJ3g6kwONocrs6/hX+Jb4L34038VfDtVyr/TkJmYY/fbkuWHAb6AzkFzj1U620UzPScX5sqA3yPu62mEOovh0zYXlfJndvTsDZ8Z/luMaCyzDWhV5wLHjMpuj4nuEBfRNNcS2zvL0X1Mhj5Dg1dRYsuCv0XXsLTbj9kavcJY6WhMsI6LEqAtsXw9ULnOA1PHOWAAPWg5CgQYMLnBQijkLRtVuUJARks5P5+hoXWMmy1AsdWhG5hs0W6/OEVed4Ek/AcQXwwv2mzywFTfwpeUaCCnI9AVVVSPjQ9GVdyZqAI4HH9NXkooYn4aJEkzGTRrRdwMgklDIE2MhUIqoHhDc1M5fMl3m/AtMQBLKM9Sv/wspAObxqtjHPANP+8hpfxDnMvCL7hNB2pTackj29xGM/MD3Ks0iKJYyfUCUYqXVGEkz94VHLUOXvmq9fQ3rzmp5QpSlQjgO2n5KIjXxXko0mJ1X7j+VZwTQJtSxKwDL8uYSHu8f+5j0lBVU7r9QhAEg9Kr91w+I7An3IxHgKxTxlLFpyyALtpusRm4AmAdu+R2pbIAQgJbTRTNSm1sw+psE3WULApmjD5YajSUM6iDZaKfuTyYRTKLq8qTTH55C3BhYnp7YtzWmmGZuSOvXYVoUNzToLjRBnpqaa0JSDCz260WzJmZKmJ031M5S8lY7zvKyZe2zK/TDcp65FKhpoGtPzsDS5WIuPxixwTVwZ8VLTkTXqR8TiZMu39qDR1x+ktPUGrkGSyVrzyyUeP5AsXa7bePFhVGI/vKHmyLpmqLOVczcu32DmKScO7XdVnYXcdvoCeTjUI+pm+Ab+08RVf4rhYXCCE7yoVJkXgdmCu8Z4ruURAtOxzqAm9fwRhscAfbuN31iY+P14YQu8pT1eBnhQaWnvBZdjIeCOF4+VgJuCukvItlIocFmwQPe/wPLmXLBgHl3XImwXEk5xxCQcTblldsHMwgHqqchOpdbn6ykkTsJuUCsjCzUTTgpRl6fgHutyvSnC2QYdsmO3AKQIPSexSqniZqqpW2V45zAyiXLWC9SEhq6FSdZRW0vXUjRTCBcnyn0lfkhgUMMKMHJDNcgImptUMmmLOk+Wn/epCUUoscL0z2Fk30raQ4kiEjcB2SykN2updWUZWS5qWXmykOY1+Z0mLDERdi4vpsuVXAvTwiMJR+1FphcljmqnAda2NKuppi+9dJ/XaG7yQn5TWYrhSNopWajahSwfTZvquNLjypB4bIPOhWtRk6wkz7TP6C/Ok5fUSuTDxHRUG5H+77zGH8f/yoqTtKzlYmpMMzUUkrXtgcS4ZRy3sYgZtx22KQExAiubUlsk9H0PhEzr9w2NPGa/aFk3rccGLWw2MHJnhdpgUTbDyEEfh+HRi9qlNrH6A2M31uYWqd9Fw0+ZqDNTlKbxRrOMwKi5BkKmFrhmc4k662r9jESLiO415IeTmTyrVSSVWDNtvVj+95MO2xBQDShLg9prYxForl8qOmW5HE1pKVt69zCn3jZ1TZ2O0ymuGeix8eLdXgPTYtmaWJxf/HPM1RpR0zAtvqttJtj/VzUTaW15U/GqvnRuvuFJ1bebp+OFFbXm4svfkNVLB6/lt2wQF7wMTBtdTKITDWAgoR4fbEY2c9CPtS3mkWBa5xxFy1nz+0QmhoHlTbFgwYJjgu66bh47v4O4fxwffFzwOHGCE2wqDYEFCxZ4pH6F1N+QdrppvD3HcdYKABCxLiow9OhDIfKrhf3pscIKHRISVthWhFyHFVbYFnLtYjDrRgYkm6jM4sd1YRl6FP9QyFpxp9iiSREhpkzGpQiEgKYvwttUlgUdEKj9RIIhIZMGJKrU6o+SVPykBpxqrG1gZBPJNKZHLTL1D0eSgyYDqcmm2lUk2iDp0C9YlHwa1IQJf7+AkS5+DcP6mWXPmgwqbTWARAskDsuhO/ZX/mfAf/6Pdk1JpxPURA3LofVsYIQiCSQ1q6lEkZJhLbJWnpKMjLuWcBFGimn9tP3YLmpCk3mSJ1YiUeHNWpLEVQKLcdewtqSWmGoUal1YZt5T2b2OGRJXes3LvjUPNRmqaTN/1eyjpqG2Jc1b0vQnkVCPxxKMPDk6IAUglHoN10va664Uk2Us+QYktF2H1DSIQyQgpIRV6BESEENEmxrEUBMcAcAJemxK47UDnWEsphlqbNCggxpNzE3Ri8CKtI1OFiTCanOLZlLSkDndhM51UDaB2VfNPu7wVP3OJYgwXTidsIJcSe4+SxaGXFJ5AGKJaSLLfCiiryYD1mp6g2TzeN3SoWo3C8lv1ILsy0w/NsQWRt+YnvnVM405f4ZhCqZbOE8WMYerEUqXbxrN/OZ14Pt/dwlyn91UpHr7zW+8gzQ8bMzeniS8Key5tBItWPBw8M+Ajc38ZvCGex8T7D0WZTZ8XG2wC9kw9njhuOB+kPXUl/G34DZ4Fe/Cd+Nv4N/j//PQRVmwoOD27w2dFxtEvAfvxTNscDGYb3p8K5gFlOE0+Bv4L/H/w79/6OIsWLDggHDEBwv1rHsof1kAuhq03FD8v21Bb0UN+nLSJQuCX8N3lFj0Mkd1JqDotwzivA6rklMmAFtEhJDjrhAREnB6scV606PdAKEDQkSt7daV3+cw0mWLTCzwuu5JaYKSxJuuNUhIPJcwm/L3rHy+gJEyJAG35bondHivh5F8PbLftbfL34tSxndKvkzrolyjacDOpfWOKwPTey5tk1BLrjtJW/fnJBWfl7+v/0cj3joJy7ZhGz4rf1p2JSw7iXMu4d+G9SHTZrznE2VWzUJt23OMQR99F9J2mgfjqtlPT2SyTRQcZy/cdZK5JKw0be//rpMw2ge71qK8r2nrPU0f8lu/64Ejb6JU0/LaojoGpBxBtPUCy3ZRrruqtH02V9mU9EKJ06RMujV9D/Q9kBJC+UsAUkNTht7uZ05jjS1abNEW7Th2YDPMOxHU7F0NYYxKyOIqC1drR1F/a5w3hvh+wNRmJHlan8RTO6SX4/jfAWYGk6K0FrGkYeXIYWj215dBSTvThWsGUsvomNroGfPqy2+vrQSMHyIMZWtGg7BuKy8snTtjSX2HpvRHM+RbI7lyaPysH6knDMbIb6S+av85hEvC1HW5Ck14M9zU2M1dbHFrvw53s2nOI8bo6Ze9GU8AVjjDeHQuuEs0x7xkfgBQ11pf9/2gj/OY0CIOb8jxMu/xtYeCB4sWA3j3i0Ubc8HtcIpX8Bzv7C0Btzrmc+cLXgq4a3iKb2KDd2Bva+Cxv6kfK87xAn+J//jQxViwYL/Rtbf7uyd861vfwhtvvIEnT57gyZMneOONN/Dtb397Z5zf+q3fwt/7e38P73vf+xBCwB/90R/dKO+jXpGY1UFK6RNW2CCblKyNUzbYokE3CHcbBDR4BeuiZEyxAE1RtoVksy0zCbwsBF+nPivElF1NiBGrTY/Vi6I/QhKjc+IyldHq4RpWgcQFCThWgiQRtXx4T0mKIPFJQgGm0OIlo+cwjS0SUnCfV5EYKPEWYAQUtdBI4HxbyqOyrimTmpR3RtR1Vt9oU2VopE5sH2qReZms+jsDaq0rL0tXM5z+upZL/ZMlmOlE5sN0lDRSU5DUPGR8VQCiRcPgrishpWOKf1uY+VLI/SnznFNKTZoH21W10JQM85pprCP7U33xMYxqyfFTLa2xL1RDjvmO1ZWsDxhe5eahPJsNkMp4TQFUbgMSBg25JgKx9FUTAMSEuCrF6ns7u93SZ1sCClkXGlIvtS8xhstXe9D+vBFaZpoxaxPlCoQyMJtBVywT/3nInZR0Q4mvlVcyyZsiTKDeGc0mcpAliUOfZVaHJL9VC8/yy+llE45ad3okyfNzHGZj6gKaL7ppi6ha7hyOBy5MMy4ggEYjjd6r08mP6/RDPSWo8uWx6/VENKUnlYfzVTTYpsNwSvLWV6dS4INaG0OeCqeae1fF1UV4tTbjdbGL5b9K3rvNd96kNPdB7F01b+JisKu84L4Qdz5hC64Ofev4GTGV9xDfZbjVbHFYUN3AvIJ4PHW/DNkLqfe1vWDBgv3AZvIk6f6gq0zNLADMuP6C64NOHLivW/zFPT5Q9NUNGpELFiyYxG3ItHsk4T7xiU/ga1/7Gj73uc8BAD71qU/hjTfewO/8zu/Mxnn27Bn+7t/9u/gH/+Af4JOf/OSN8z5iEq5FhxOssSn+iij8pVA2b+ZOcYEGESfYYoWIVSHiaOLsAs+cIDVhjXOcFfOV6yJ6pTbCSTFdGcvSJqSEVdej7Xqst/J6pgaY/vZSZdXAAkwrCqi1kSBxVWvpQu4DteaRvisixoQTybtzCXMq5WBYzVd9tXFV4iXTJNwA840W5Y8gEcWmJ8FIQosaUV0pFwk+kotr9ztJPK9sE3Z8eqt2bFslcKgJdgojyFTarlp/ukbT/iSxSFOKfq/Aujel3UgIqsnMAOsjEk2egFJOgeQlSda1XGO/eXOPqgrkfb+p6UyajVxh3K+87pkD5gWM+98Tn+xPJdxYnrWE4fhVf27Mi2glvNwPawknfuR4LSSgFeKvBdBvErpVAlrznxYAhL6YckwJoWkQI9A0mfZKaEFdXYrbbAjy9Hca5q48N+W7a/ToQNuxTGUo6UDmea2tXhrZyLI84BOaQkKlarj0yMYY40BjMS8+EFnrzBM3weWiZeP2LxZDvuZDzsK1iENbMFxiJ4HtnDWcO2Ge1R9dA3rgGROGc37S2AZTJFwud024mDh5erGwW6PBC6J9jrvD2JW50wA+JbVHOxUmjeo3Bx1vV0HdDtcj1Gqi92Ybnnrs3M2myabZl7sJq/VBb0dOPmYsLfeQ8C3PscyjEnoi6PD76Qme4Cme7gihsxw9nj52Ii5Un8vzeh+IS7suuBUWXdXDw0LA3Q76Zsr79eiuLrPq8WPp4wULLkUfgO6Gz0l/P8/Xn/zJn+Bzn/scvvCFL+CHf/iHAQC/8Ru/gY985CP4yle+gg996EOT8d544w0AwJ//+Z/fKv+jJeFUU4MaHg0iOqxxUhidNTaDacot1jjBBRokrNANm94N1jjDReEHeqyxwUkRzVIwnorexhr9QPghBKQ+4WyzxarLfqHazrRoRhpBakryHBjkDiStVA6h61wSRar9Rn9mqiHPcEoMKYGxcWH1GsMzPk0YtjA5pvdlRjn0oJX2KhCfjwmwC2TSJLr49B1Xy1+MtFEikCYfNf4WmRBjuZhOW/JULTF+V41BkkAdsl83pgsJ48tGU5/UtGO/rVCTdiozJVTTcEr5hHVSH30qk2c56G+O9/3aQPuG/al1VaKylevEOTLRp9p6cGloXmxz1VpTstH7beukHEyT6SvhB0lbtfioOci0NV2tu4Jpe8LVt1UpT1vK1DdAiFlTDkGarAP6pjzoxSRlk1k3AJmIQzDtJxJXTdFcw3CdVe0KLaZDJs9o1BQgzLShNZhqfVGvrS1pWPNmH11qhHKsK5Xk0WCZayOIzIvoS57NoGVnA3KKUjLSMZdk3F2ZXLOtTSz5tyX/KMOE2yLTpvCkWgDJszEhM0WqWJnN21td9nnNMXvsx2Ww9pmKZ7TkroV+vr9bcy1dI5xuID+M/z2+iN+bDW/9djlyfdO1RH5BYlgq10Odm54suAu8/E2Y9dFiItFDeyOP+TyW/bILLpy+YujNspVxsosqX3CX8AukDL/0PUTsJuAU9dtZxXkPJfIJ15rp7wNzR2YW3A7X03lfsGDBggU16HKgr3avy9vqmMEd2F1T2Q+/1hrjFKe4GPnSWbDgcPEHf/AHePLkyUDAAcCP/MiP4MmTJ/j93//9WRLurnC0JFz2y7YB0OACp1gX8esa2VfQGh1UO4N+42pkM2xrdIMA9aSwDF3xJZfFrj1W2GCljExKWHcdTrZ9tXFumAXnMdVYUy03wDSK1FyeavJ0MPJOrUCodplqyulbgtphzEcL2WH6jaLabvxN316q3UQipdJcOgXefp7jvCplnpKEqbac7xKSUQy7QW2+ksSPaqypb3cSPL58PTJpx/zo7y4g+30jUcgn5m2YdtVQR6m7EmE0oUmFJZUinEi7eXKTrI5qanlCcmrnzP5IME00bQP/Xuc19iUH+xnG7c9wQK1xBpimnubDcahtq1qXnaRDTTqmofVTrTugfkaoRaj8k5KKycVhPLatEtIErycglPRSLLwaix9KtgmIwe61ANAnZLuWQIwdUtsYh16Y+JASQghICaANzLYSt2WzuJlkMmLO+NEoTR2G8Cj3zLQk6Tc2eyxxG9RmAak9FktKNlBN48bCZYFYKmWM5aqZp8w1SqABTWq0xYGam8pbTxOOTWbqwxPkHkVzyttyXtepZYqJtfh1TkbQTYWvCbg6vJbMUD9+00Tcu/EBPMV/nkg3DY/TVNpjTIsqrWRjTT4FzayQGPz6Dnv31r73R1HQGGo9Dq+HqfFyuzL5Hrw/eOF7Hgvz/Xdo8OSK/s7zn56aYStwzrOnUV8DFteIynzUwNqvdU+jxeMBh74qi1p51rL6Mk8Re8dAID0MYtXDNssc0xMwBX3rhfLWvOwoxv3i4YVCfF5NQLWQR7fF9O53wYLHhBXW6CYdjC9YYGjQDsdl59AiIaErKxQViiyz7DHiPnr1FbyK53h2DynfHAsBt+BWoHz8pnEBvPXWW9Xl09NTnJ6eTkS4Gt588028//3vH11///vfjzfffPPG6V4VR7t/UaWWOAhg6G+BG7kW5zgFkLAu7ENEQFfEytmv0laEKM3wGm0Q8QrOixx/ixN0RZMlZKk8EtYdTdDlLAOJISWAXqA2OcnrNHGoxMEWWevrKYC3ALxT4tI8If+el3vnMJKPWlT8nSTucynHufwx766EOUetsaeknIYhAXIB06h79i0jtt4pYbW8z8rncxjxGOU7Cajo7pEITBNhaK6SYZ5JPS9g2nTsYGoQsp1IOF1I3LdL+aOEZ97ahxv5ZBoXki7L+qKkN1cH70NOZSBMU4laDevzZ1tomSOsXzXffiIO45GsU9+E2nbMX1cmPcZ5c/wwPZWcMiwfONaFfzomOF61nJqvshe6Jmb+U8Sk1qukG6Z8+mk1k31veyDE/Ow3AJASmr4HUhEJp5TniUQBSAJSj4CEFaLol1H8bL+VTKBJR+97jAXKBhgjgjQKm7SVTjXRFk1f9VU6uoGgJloYwmW/NdQKnmZ5bTC0Qx7m7yaUBg/o0aAvwsZxnZqhM+v7Ybjmhd6etpoWIrZIqHX6LM4H8XddGkaMaU65fEBT6uBPMhhZqnRBff8pvjFZvnGe0/cbl+ccrkJmaX7/AV+5QqpX34pcp6x3iSny9KbQcXafegR+Ks1PLU8oHO6mvia3Anq0w5PPabtHA9NStRlP55RY1nWcOXr5bXFzLyW0xV4BDfW2Q/g4zC6Wto6Vfpht2yqvvsyCLHcuE9CV2UxnlR5NdU3b4KHpjf2D7/N6vHM9/jiQ67+I8fIzrX8Lbo/0AO/iBQuui3Vl9uRusRBwC66Cywg4gvunZlhxDmahFhwJVGpx19g3Am7Bgluju+UfgO/93u/FkydPhr/PfOYzk1l9+tOfRghh59+XvvQlAEAI411VKkoS940j1oQzEVlbUXIUYOUXYt7i9zgtjEz2z9QNopYOLRK2RTi+HcQ5bcWmyIs15RCrvsN6kwbtmFCE8gjIg+kcY4KAGmwkyNTkol7T9zg1lxTKNDN8gBFfgGkI6d6LpAuJFmp+8ZqSb8zb3+N19cXVoDZtmYBKNsZrnuxhOdUMI8MG1PbypspIYkxlNwkY/Kdq/uqnjf7M+F717cv2YXgga3rRDCXbQLX0tK29nT+mN/U0JuR+o7acavaRQGI5/RF9aoipphfbDRKH8VTjjnVTU42qTRdQm5QkacXfKwmraXq1gSnOhiDRST9yc/DurWg6Un33ad4+D7aPtqUnM2FhgvolLOVvIpBW+fFn9ZqYv/eBJ+My6ZZCQGoaNE2DWCb5kBLakLXQsqHJbICvKwVu0ZfHrt6EknYiCRbF51nWQjPCLA0F9nHtkaQ+W+5y1epig1gDBnc1p04PA6QOa9204FLxj18q+drgrTGdO7tGtf8YYso/nQ7CukxTmm9/jn8NYLzIzlqLcz7w5kxMcpK6/svdSj0flzSB6W7PweoPAN+BD+Bb+Poor8BxuyOt+jVytXrddGlTa09eD/Zauf2GyTSstCz3t2Crz2Ecjih+bsrV67U+UzPontViYY0x3+Zp4to0ajKHcf2zn6Q843zNUGySYxC1yduEOJydtrmJc6QS0b2k20zMHI8bU33uZ/Kpt9RxIRYNdRo+nn87HzN0HrhP/evHgrC03YKDwHYhyhYcELxIxmwpLKu7Q0e9X5jeI7dFENZfkbhdsOCocQeacF/96lfx+uuvD5fntOB+7ud+Dh//+Md3JvnBD34Qf/zHf4yvf/3ro3vf+MY38IEPfOCGhb06jpaEo0NzFe2YSCUUgXFCi20lCCHBthpIOv71aNCB2htrbEu4WETdmYFoU4eTboPTrs8C+6INEyjsp+YOUGtcMXNKNFUzqHd/tWslI7ACao0qSJ78g1yvG8y02NRc4wVqQgowiTvgJWUZai5S09Q0pjS32C7e/5fK8aP7TjORGxjhRyKPaSg5xN+8z7xIUJJQoilDNXUI1BIfBbWzWJ5ewnvyTc0xkv2gNiT92BkrksH+0r7mb/aT96Gm5WDeaubSy3I9mQVYn5B0YpwQgC4ZyefNcgJGxLEcJxhzEEqCESoH1bG7duE0HRJj7B+Srxw/7Id+5joJRratH6uetC0EG3+HAPQdEFoglbQG05WhzAEBCDGha4AmRsTiJy7RVxyr1SSkYPbkmyK+bQH0uECPU1coFizPU/ZLCSGj5YxmI8mipiejxDHNkKY0MudVExZnMXMc8uA1S0G9tKnwemrqsHl7ysE13PcwuqMhprjduev1fSMrLxNOzZFCls8cgTV9PRN32q91mrVnoF2Yr6VpwVkaSsAB062+O6frbSr1FXW9eDeJ5cmrm5GgCjsvc9mIuh34nBj5uv8EHF/RfnvK6ZUT7/zzcZv63UfbXD4LXBZeCbgMHlJoEaqXNAnITNDdfqQeM4K8W3jQ5djby7yR5nmh9oHaTL6Njhm0bpLfnY+r7ncFUro8GnC5ubUFCxYsWHB12Jo44gSv4cXgf2TBISJV36ZXHu/Gu/BtfPvlFGjBLM5w9tBFWHBHeP311ysSbg7ve9/78L73ve/ScB/5yEfw9OlTfPGLX8SHP/xhAMAf/uEf4unTp/jRH/3RW5f3MhwtCQcAPVq0hb2ICFjjAmfYDMRaK6LjOAiiU+XbLYu8spm1k8GQELDFCq+Wl2goApYTdGhih7PzIipNRZhNSVqHWiOMMpcL1NpNL2BkWotMhFCjTbVySHZRs47fmYe3kaTSsAQM2s4NajOTapKQ6SjJR5IoIhMrdaObFp2GY33X5ZOmhekXzBNLLOcWRmz591xCNg9JkKmgDJ1KQ0yfJJ22gRKiTJNpsUwtxuTN1Hv3HLXmXZI/jUNCTUlBfnJdlgCQ4FfClOVRUlNleLxGIpcyW9bfy3GjS4dEoicPNSwHN8vpZdBT5iQB06xj3Y33sXTZV0DdhxxPSjSSJGU5qMFIaN7MS/3OjWWftaUzbSNN05N/JV7TAjEha8oJJxGKlpwmwawSTVIGMzTYxojYAAgBa2zA01bkkRO2g0A+DRXh/IWBYqu5TjMXSd8y5tnNHr52mAWNhFrju7HF14Vco1DZ6tMUco9+5nLInAN9wRkRYjynis3yVNEModpC7qWqg3zN7LuRg9a+YRAntSXdMOi2eK0XVDEVnjnWO/P3cko9orxmc6godRoTbVnDTmkMK0U9OU6Vp85pCjn21Uizq9BL1KC8rvj3uro+vn+vF1cf6JuLanUUmleiuxf91r27v6L1poxwTuPqh1JNnNk9YJpQP1ZcXr/p+a1e6nEZ11YhrpPLscLmkH44WhdHT8zxkXN85zVuDnqM5AlJyabs6RZcD7Z+yvP4YxxDCxYsWHB/0PXHC7yD3eZ9Xg5CWS8tuD64y9mFhYB7WLB3Fl92ewK6H7pp3HvA93//9+PHf/zH8clPfhK//uu/DgD41Kc+hZ/4iZ/Ahz70oSHc933f9+Ezn/kMfvInfxIA8M1vfhN/8Rd/gb/8y78EAHzlK9lly3d913fhu77ru66c/9HqRNM82xm2w8bsFWyLeUlgVRiCVBiKBhussMUreA5acQYSznA+CEaVVTgbVLwAboUTsgm6QdTUZN9QiEAgMeU1tHiNZhDfgfmCIwlFUo3kBn2a0dfbc7lGsm5ODYRkBv2aKVkGmLRnivBiWViGLczH2nOYv7Rz1ERclHTfBvDXMM0vpqMagfRHxnag3zT6rtP0lOiilmGU72+XOPRRx/aiH7EXMH9oJBhVZVYnDbaX+k+D3CMRRNKUhCrzV4JJ21XLzz/62GMbqAagJzo9aappqtw5uTT4W8vFNDVdjZfkvpK2LA/BNLUsbEuv/anxfF7MX8lFrUN4rY6rEzXbUc2+qkRTSeooYXx7ExF1nfy9UsxGiO+Bz+uBFEswZ2eYdocHmqDYK64rZt+MI6SXS/p9q41HjvUucgVbpPJHiov+0KYqDXT4ayvrEGKqTLUvuVbSY5mZj8U2ldipKYuem4IMcPqsC9JxVt96Q6HUC7/V12qkiW9zL0kjFa8u3iX5uEtYOresZ9zg6nhVNIUMrGnP3bgKyZTb/ip0nSFcs90MN12y6JNz8xTq6el+xPr5KTXfZvsIe2VF1/P2wtHXkZFzx0WF3By+Lfz3rEFsvu7yyZG+GEK3UBn+lTS3BDxu1Ha52Qb5jXPTWXPfUZ80mlqyLVhwFcx5Fl6wYMGCBXeJ/ZhpFwLu5rjKgdcFLx/cP3PNH4vf7wV7AG/V77p/94TPfvaz+IEf+AF89KMfxUc/+lH87b/9t/FP/+k/rcJ85StfwdOnT4ffv/3bv40f+qEfwt//+38fAPDxj38cP/RDP4R/8k/+ybXyPlpNuC3W8L1GoiwWfZLsd2NbdElOkdBjhS0SsibCu/FcDCcl9GixwgVewQusQN9NsQjAs1AuBRGA6/vtAkZOKTFCoT61qBLGBAeh5JCSJ3MMsdceI8HyDLZ3Z2FVe4cD/hxZ043mEsk7enJlCp4Q8VpbNB/ZYqzBRP9oJLW0TKoJRS05ZR7UlKTmT5ODqo2lcbyPOhKNmvaphNV6axj+Zn78fQEzdakmM6d8l2m6/ti7DmnmoQTVBcw3ncZjmUmwJtSacZqOmpcM5TfT1LZif2h9qG2ohJaa3eR3NR+q9QpyjcSvajeyrBy3FxvTgPQqA0yDGnS7Dp6p1h/TVyYrotaY03v8Ln79GqBos2XOLTC9BuhTyhdTQig+4hACECNS01TDKCfvKabah4eZluSiuhmuadzcjMkNXYvbIiGiR0I7aK9lDeFzqAcaloTN2YvewVhLw9OBdfnzOTydqJQpDUNbZr0602bTFDmk57YT+RFTlU9/9vsqGD/w1mbTWmC5y8ckShpKNY2aJAsz98a+63g/upAWw0+Iu+uehbnX0Zi7enu2OEV3zSNRVyEEp+LYVOifrOshVhTqdcbO5TCiymM/Nw8cZyxzHP5nuyRACKMFlyObtuUYtbHgl1ipeAjNrWym+ABqKPNlyzXu4wPX5oAdlMhHVmoPhMcDPnfUJN8PId/Lw64VwILLcXxPxIIFCxbsI2yvbsLAVjy/L9hf1LvI6X34gofFWFawrG/2AnfgE+4+8N73vhe/+Zu/uTNMSvWe6md/9mfxsz/7s7fO+2hJuCCnQlWmTzRFYHRSJPY0mBaxwile4BQX6HGCiB6v4Z2iCZe1EFbDhjcgoMMKHZASmthlcWyXhe7UfgnPUJMSSkyQXNpIIbWgHHgbjAfhlElpKoeoBpeasvRmF+mPS7WB9EF5ATMZCYy1lnbBk1K+/DSjSfKIZh9JuiiYBttL7zM8TQ2u5L7OvUpgerJHTU9C4vM6iTTNc43azCTjKKHH+0ooruW+9teJlJd1JdGo+ULCEErsehKXJJm2hRKJSq6p3zol13hf68P0WT7m7f3DaTnVNQ7JUsZVQq6V8FNk7yBZ3I5PSNR8y5gUVYwZr7rf+sKgsU3gwgt5F9aWRADQ9ECUtkjISbUxIaWEGICmbYAYh/sxJTQhIMUIpAS0bRkqpNNyZWrSyxNW2ezk2F8VDUXSdGRNdjE9JeaoP9CAWjk0FalDPJajCICaWawXQpqPz9eTbc0oTnJhtU217Km6o/UYn/Ku/a9pGnVIinDnCCmaK/b0Gdy1qTLPY7eoeEx2GuJE+/m47LNduI6W31U3ItRKWeME17VLsKvOu/Ibeye7Dezdf5eovRnt14ZBz+kQeRby7eBtAS+4Dq7n77Ctwtd+JO1JifKkP64eCZPzW5IFxXG1h/W56aOnaln1GHC3RyMeE2iofMGCBQsW3Cd0b03nOPlgVbjmOnDBQ0DXGEtv7Q/GfbGsBhfsN+51f/atb30Lb7zxBp48eYInT57gjTfewLe//e3Z8NvtFr/wC7+AH/iBH8Brr72G7/me78HP/MzPDDY3r4O2aHRYBRt0gyjZhNjmQ4CC4L5s37mhbUHNi1DuV0g92n6LVb/FybbHyaZHswXaWESw1B5LGJMV1GKjLJLhovtNrTQlI2imUM0G0o/cBYxwUtOWagaQpiBJ9tB04rmksy3x6aNOoTZvtFwsD81MMv1nMBOTDKsmAUlS0WzkRvLQttA24p+a4VRTlp2EZ3tTo49mKJWc1D7hdU9eEmoWlHGUaFSCTdVptd00L0j9vSlHb+rRk1KadufCqplNhY4xvebHU4dxur4sntj1JB1ceP1U85v81H5Tk60aR0lFz8TA1Zf3t8h9z2dC7/uxNRDRqR4LhCeCJZ1QwoUINGy3PpupDLGYrA1Am4DQx6wBR424UoWQlHaq6SwFzTUCPejrkiRLVsi3zkhDHIZJZSOgwlxUv2sayQaAp9NqqKaahRino2FMr6OtjK7VlJ7C87us09SJ+CljZLk8OimO7xLWznXYnNuYbLNNlh845LKnHsC5K+N7Ec1syDA8HJctQOdzuurG4mYbkIAXg0PS3WnfZoNjo46p3AUBd7eo63j35N51y9JXM0ZGXq5kA9/5r4X6fnuZCA/YPvsHJd38HKRjqZ3s18eJPI7bQtAdV3v48dBWb/jjqusUaJdkwU3wsG+fBQsWLHgcqEkc28stBNxhwIuxFjwsahGermSWFc1eobvl3xHiXkm4T3ziE/ijP/ojfO5zn8PnPvc5/NEf/RHeeOON2fDPnz/Hl7/8ZfzSL/0SvvzlL+O3fuu38O/+3b/Dxz72sWvnnZV4WnRyCr4tWmstOnRF+ds2bBGnOMcam2HjyutBRNMr0KBbAlJEGxqs+g6nmy1ONz1WfUJqYP7HlFBQTaoEI6ZIJJHYIUFGoug5jEiKcl0JB5JQzJc+zzwxEt2nEmBA/VZR4knzZzmj/CZRqKQdNd3OJb76SVP/df5t1kl6JPSUmOGnT0NJoR41QRcxT24x7DOYjz1fJi0n45OoZHsA5jeP/s88kdS5NIgeYwJSCbBpfsHyYptPHWjV/p4qh9aRxLAnqtRXXJq5DneP5dP+miIFWUbC+47TOjDt6L77ejANj6nxP82XzGOqL+RZCAlAkx/RNhYCLuXPVOIPorIErGJC20cEasBJ8npqjlpZXj+LftKUejCzjQ0ovs5ic1uYZA2JU6lErTes9AX9yLVSOlv0aDl1KaQP3Jygh3EC1K8dtZV3L6O89tl0LjQYfBXCbY48S9KCdbpTbLCSgnM1mL4eLr2nY2AuzO6BvCuPy8o3HeJqok9Px149fdXwuw5uJ1qsp7JpzcabpEnaPA4GA18+oaXl6RCKWVn6ILM1kM489ebm5YttFyHFdWH91ZfjDXo0I7+ywkDSPRZskdCXZ++4CUp7ox5vHWv4ZfqCq2CZWRcsWLDgZSLv4pZjI4cErqj7cqBrwcNhLKJtYVaJFgJu77CQcCPcmznKP/mTP8HnPvc5fOELX8AP//APAwB+4zd+Ax/5yEfwla98BR/60IdGcZ48eYLf/d3fra79yq/8Cj784Q/jL/7iL/A3/+bfvHL+FPa9wBlex1sIyH7izgrr0RSfGh1avIIXeAUXyCYsW5zivGxct1gjokeDFhc4QVedtWzTBidpi4SAkBJiEit/JFdIwimp0COTUBxUNMnI36Hc9xo/TI8aXOeSJn3NTZEkXoNJTVtq2VQW6AkN1W4Dxv7OIsZmCH0aBOvawHyNsTzmaMrMElLezE/1Xabyb4bxddH6AaaRGJHbEBjLQElKnpV7U+skJZlYHprDZNno+0zjeG21Cymb+kxrXbzT14DNMwtLn25sax0nU+8hlo9h2Z8tsilMyD3W2fvPI5TcojnQgJpwPME02DbaLr6sKiX0HAfzi/JbFYNqyf207Jxl8Bar9LnxcZgf02MZaMZT2id5SVDIGnARWQsuhnFRm5QGASlSQghhsEPcAOhDREAL0+BisdQHXJChHOEfoJoEIUX3okwZ9YKSZi49DaZp2HfLoy0esyLaontMf27Mtfahlv3C1WXSIVjnbPE8qchSUMtMfXiZychxamrOc5pO8+RRXYbLhVfjwbQrTt13U/fYhnNx6zLO5zF/76rG2uz1crXwvr0vS5txruvJSWngmyANeRJ3t6jXcTk90bxc1CQbkc0Y+udywaHCZraxHnP+nV9l9YyuMY8L40MaDaYWeYeO/FxH2KEaGpZ+GfgB/G38j/jjl5RbxviozN2gKXPiMUIPmh7n875gwYIF+wN7Ty0HIA4BKs7ZdaR4wctD3qerUHMap9VB8wUPBlo7u2ncI8S9kXB/8Ad/gCdPngwEHAD8yI/8CJ48eYLf//3fnyThpvD06VOEEPCe97xn8v7FxQUuLkzF7K233gKQN9UtejToijEewLy5JZwWQi1rkKSyCc96JWt0ZaJtcYJnWBfi7bSkBSCbiwsBq67DaVfCrwBcAO07UsAVao00ooVprQVkYojkGjWhKKtT04CqJUVtpSkzgZBrnfv9HCbfPZd7qv2kxBrzUDONLJcSaEomKfxDR602xmf7kMwAxuQiYKOV+WwlXnBpXLh0KNGn3zmgJq/YP8yD7XCOmrxrYH7iiCRh2E68rxyIanatXLk92VTLxwph+MzaHTAfbZ4sI7nYShjm699FJC0ZnvXERBkgdfcKEWoBj/foH67maCzsRsrHMjaSrubJduwnwrBdOVbVBRSQx8lqIt524hq12djPgDlAI1Q6qf0unEkAoH48Q/mvydYnEVal2RogBSCE0kQpIfQ90ObEQhQdrqZBDLkhfXdrsbQ6+awWze+ygXXRHwYuMg9bI6vyd204Jc/Gix5PWdRh6vBWpnlxoJJRAXM+3BKSDL5a+Kb5oeg9t9XdeaLG5+OJLxKYCQm9I1as/HPp57z9g+sXlfOIE2mm4d7li9LLtg5XIeF0Gpoqz3zZrr5xiVjhOqJBy+d2G6Tcmwm1r63bod68aU4Ph1ymuT5ZTnkeHzypavOznXvhWjiUN8Zt6OxDAElI83xKHA8pYW9GWxHcv1+8l03AAfnd1ctisrn07XQ1HCsBh7Lv5f8LFixYsOBlgkKO41htHCOMhHtYyyWPGSp6qw8L796rHu/abcGh495IuDfffBPvf//7R9ff//73480337xSGufn5/jFX/xFfOITn8Drr78+GeYzn/kMfvmXf3l0fYUOAe2gnApw8qRPuIQVtmiL0Lkv/hNWYjMyAELbNYOA4gQbBCS0sUegpL3MCu3b7jWqEvJNCURThySRGJ/hlcyiWUb1lUUtMcYjsUQyhFpr1Eqi3Jm+0mi+kPmR6NpgzDarmUUlmJ7DyB/ud0nwJdh6glpfnDW9Vtgz91vbw69H1Hee+lJTUpB/SvhA2oDlZ9nmNL2SxDFreeMyMpxPh/lqX6mss4ORXRqP/ejz0fz4nVp2LJvyHYTXpNwga6h58lGlTTR1ukKtIdeXMlN7jnHZjiRtlZhjfVQ7UevnSVLm1aIeK9oHbE+9r8SbKoCxrVQ7VNvsovzW9a9qoKqGpm9fveaJRgCFLxuTY43da2LRimtrUqOPEaGtNaFCSpmtE+Rk8hwWUJsUywqD+V4moRopvg0YxmkGAo6fJOP6ijBTwsmbaKybKFRNw9JaDUzwa2JBe5CYSxzm4Bqcj/0CbI5SU/7arvMc4uUk0tSSey6WaRZOLw6N5KnzMBHwOF6d/3x5M1k51qJjGvlxmBarXzUPq9/ljsTzY+7VrC/fbBpxfL2NqR/f14trhl+b0eR5MyTo+NF2eHkbbp2a+NnPEoyLIODxIY8D6sMobcHnob0jMmN/EZANLvfVa13PFx029E3sVwvHhPoQS0SzeIq7FLnNYnkCFtwe4QprowULFizI4go9zHn4q41jQ57JdV/9+Prood9peR+dBd7hGqvybeX3Z8GD4TZmJY/UHOW1jzp/+tOfRghh59+XvvQlAEAIE0K+okF2GbbbLT7+8Y8jxohf+7Vfmw33j/7RP8LTp0+Hv69+9avMCVmoYGf7GkSsi7j6vDAgFEb2ZXJtkMrjnXA6qImlIZ01uuIXKfuKC32PmLJQvXmBek4gKfUcwNsAtsmIJJo7JElFX17PYOQEfbsBRiqotppatEoS/h3U5FlX8n8h4S7kjxp2noBTgobkBokVlntb4p7DzD7y+jnM9CXDq1ag9/mlmn7Mlw9edPG8ZETj0LfaFMkJqb/6zmNZlehjWXqJoz7+lGhTX34+DLUYGZ5E0ouJNHsA3/W/sDaeyg9yjf3GP2UL0sRvkrxaTk0T7remz7om1GOFvyn7YD78VBOVit6FVVLQ+x1kmClNOf5W8s+b/NTx41kxJQP1uq9fnLmvaTKsD5/sI6RS1HKtLW2m82KTEqhKNyQdApriwUeNyKllWE8X1cv5KcbVXyURZg9X/qaN08i3MamSu6ut0la7936YWdfVdyzVZgh3FcIgD7nxAq2mHBX9KFxwA7bWiKrbbloLbnytvj/dDpdhVxijQ3eTZ311NOWmULr1cgKzZquvQsChjERn5/Xa5bsetKxX0Si8Snpx4pl6GRs4P911ZfbInySIH99GcsFl8M9qHic6S3KOnTuccMhQfSB+xiOsq18eHhf0XZ1Gy7sFiuUdcB9YCLgF+4xX8a6HLsICqAhrWZPvM8YSk8eHh3qnmYjODiknjOUxC/Yci0+4Ea6tCfdzP/dz+PjHP74zzAc/+EH88R//Mb7+9a+P7n3jG9/ABz7wgZ3xt9stfvqnfxp/9md/ht/7vd+b1YIDgNPTU5yeju299kUPJAE4xynW6PA63h5EaqfYoh+qv8Ua/aDhBgAnRT0sFLanRV/SNNWWJgFn2ywrb54DrQr6XyCTCDQxqXMXSR8lKQKMrGlghBlBMsenlZCJO5rWU5KLvrOUXCK5M0VeKUhyvQMzYUgNqbAjjpbLE2cEtd/m5k8Skt60Y4CZHNT8OUOvJb53uaMmBmMJ6xUdpkhOPUDhiSDVwOtgRGmEaZBNEWeaJwkb+oALAP7i31mYFUzzjOPEa96ppp22g5px9CDBqX7blNhknEbqdhlYtyifkPTYD37WUVKvlp3U5i9ZP29uMqBuP5Zb8/H2G1XhiHE0bf2u8cJMeE9M+nqXMEPXRyA1dTWalNAnAG0549P3SE2D0GShYAihSi7rjoXqMRiTI+awj5pXdprJKC4O675KY/x41GkbYTVeENV00BTvqYa/UhXa0EtNWes5LbxdNJ6/ZsLVeU015rkrTcu9vp7bep7AyfUd5x2quk6RiPNLTptyp0PEYTzMY64+47yuTk5ZK14tvI1n+jq8frzbwU+A10c91c+dHLhb6HQI1JZ7zQzosmFZcBVMjZOmmPTlM5Z1pNXc3+0o631BKHMPjdjkusZS18NGLUryy+TjA2upm5EFY+S2OfzxvWDBgsuwwgofwPfgz/DvHrooC2Cz74L9BNdHaVIwtOA+sMZ60GCzffTLO8S64B6wBW6slHikyozXJuHe97734X3ve9+l4T7ykY/g6dOn+OIXv4gPf/jDAIA//MM/xNOnT/GjP/qjs/FIwP3pn/4pPv/5z+M7v/M7r1tEAEDCChuc4RTPi0C0hfpAyBbwKcbNWm9tkfjzHGw2T7mV30UEHDu0KeL0RVZnC12ZlkkSncO0sXidZvy2yJpxTJCC+wtk4g2w/SJnfmqbkayhOb0XqBlif9yT90nEUFNL/ZQxHkk+SuSpKcV0PFHizTvyO99RWha/96V00LtEUrknyUleDyV9fRDV6kxAbnclf04knuYTSp3aibYgUcT68AlRaQX7Cah9jTEeyZopEmcYRPD8wTTYf1NaZEo4Mf0GNkbUnCblr0qCsW28eUr9Pqd1CdTtBgDhNaAX+6JzaxXf7yTYNG0+M8HFU59+JFMJNXk5RfYRzMdLoOYsDbCtyVIpgacagDqGPHvFcdmWpi3XEoBUwg+UWYyZfAMQYhEENg0QI0JjhSYBl+TKeCBZga14PFxgwtRxA9Vi1XqY6gBmM9SUQyziWU1xOh8lu2rW0k8bRvjlXMyUpJVTU5iKXwsh2anjUk2brRzXIUmIcV67SbgpMN4UkZSGv/nTklP1tvhXE5X7dpoqx658fNhxzN3Ij2/umzhitqdBvdDpcXY5bBRNnZS4Xjr2Cmpcevcn6mYu5s8LwIxhsRVadFds1wULDNlco78WQTPvx4RQtLnrZ7Z+8x06zOvl8cIWydlI83ERcXa06vYpjVc9CxYsOEZ06BYCbu/gBSML9gX10eIFLwNbbPE38DfwVXztoYuyYMG94d5m/O///u/Hj//4j+OTn/wkvvCFL+ALX/gCPvnJT+InfuIn8KEPfWgI933f9334Z//snwEAuq7DT/3UT+FLX/oSPvvZz6Lve7z55pt48803sdls5rKaQRZLZWfrAMoWLJZplFvrTMxllqJHi5XYVDzBedVALXogJawQ0QRge7bOsnwvz3oGIwyIgEy0ncs1Wjx7B7Xmm5rv25T0zmF7JGrMkdTTtwLJr07u0ezghaTL/HldiTZqyyl6d28j5X4HwFvI5CKJQoabMuNIP3cXJQ6/v4CZaGRbUTON5gVN2jj+7aXn7Ep/XQlCbWuWTc06TqnCJncfGL+ZWWYtgy+nEmlaRradNy+p5femFoGayOLY0D5jW2p6HazNp0w2allpllO1BTXshTr4c3XS1YsSrNqHycWdIijnwkzBm/LkJ7kqL2/fYUZyVBdPHvtywcKHIFWIpShlbmiQNWqJAAxmKpVSy9cTkNJMs+5qiKjFGaXNuH35HqDGt+KEdtUU2edJKCulESM1ZdUOtMFYhGt3snpjTWHY/001+C3uFCHpWyhVg8CHM1GrpaTMbYbXHLwKeqnXFOZSC5eGULOZU/GnHiiDf/R34SraZrGUx8S8u4k9jjprn8vKYK+sbEJu3gffXPy+vPdj+bsL5+g1EXj3p/f8VMN2TkVFOg5tMY2FgFtw14hoqjNTtmwIB24K0L/3bF463DoRefejS+HDr9MuHJ9J0ZORY+nb4bgJ2QXHhvUdj/8FCx4CtpddDkLsMwIC2kUT7qXh/4ifxgfxX5W9+bI6OXj0t/w7QlxbE+46+OxnP4uf//mfx0c/+lEAwMc+9jH86q/+ahXmK1/5Cp4+fQoA+NrXvobf/u3fBgD84A/+YBXu85//PH7sx37smiXIQtYWWzSIuMApWmxxih4tepwOalXNoCnH66fFHKUafDvFOU7TxqaCgEwibWGS2uewXd4KtrO9aIHY1yYlaTrS++YiAfMCNWHBewGWTotM0lGbiUSJN9n4jnwPkie19iDhKUHxJvw0veYESBuM1g1qitCbcmRd/DrD/2b9PUhskuA6mQgDTCvcTNmTpUwlIROH7ENqGipYbv8uIOGl8l/tM8pgaUJyAzOFqaQQ86TmG/kBTj5MX80fKoHEsIxntgVz2v4oj68f21UPfxtTXY8d1m/KwpkPx7GmGoMMN2XOk3GB3K6nrp4e3kSkhlPznczTE2hT6Wq5fN20b6aeDy/7FjJemzT5OpU8h8cpJSCMPbEZb2sZ5S5LhS4bN8bUkLBzz/kumyUOYUmOtSWEbzRfgamGVNqIescGkl1puO8153zYKa57nO/UaTUjdyw8z8b7cFNCOmufelBkxcy6LSyNcdnMvNk0cptff7GZx820jsYwZSOhnzGnMeaaLyP6dodJQ3mI3WHzXyNtv+uhn8L12qymg+8Gxr17Nee7S99P9/a8X/YsHj78awkYT/8aZgqXtQzH4nIe+TrgEYU8o9p7irYnQlkuHIO2XNYdOxaNqjTozeY3z/gtfQy47rvkcHAxmFu5LfLhq+NtqQXHiC2O1EbVgkcHEnFX2V8teDnwBx71uPKCu8W4rRv8X/F/w/IcHBGmFHyuE/cIca8k3Hvf+1785m/+5s4wKdmj98EPfrD6fRtwK3mGi8EoE814xeLvwTiGHg26cpY3FH9wKHcC1kW8cIot2tQDMYt+Tl/0WCUg0E/ZBTLJYoXIGWwAvOhrjS5m4Qck75OgU7KFkjdPfNFcZShl8GYRvfYTNaSmtOj4kJAQ6qGSeavXi820pJtpeikZUOdHckbNV7YwMkqlwn6dzXTOYaSRklgkq6gZpvUfSzHHxAyZjhP5raYdmbbWUUnLKakg21avA0bqeEJriqSZ4j70/pTfs4h6/FBCxu+qPddLWPYJx3aQsKwP256HETnWPJmmhNtcHVSaqm27Lel7Cawn9KbIQu/fjfc7KfOcfSntc95TAlZV0fxY95LGBKTgqLESltebCMQANMHc8CUAMYRsilKqZgjVt6YUIje3ntiv6D8XOw7kBwWmYYg/wRKOvkslB2HsFJFTk1T6SfGfpsEaazMbiWb5ZGNMl4t2PSlWk5C+PN633G6h/jTpNy7TVbTmuPicur5Ly23XPb7zdgkj+UhPD2Arg4WehxGel0PHQKpa8vI8DNcX7U/1/02g02sul/bD7TcPmnY9nTbuyuFgVw/H4dlLCDIq6jGcZ6z6bpJnRFPnmLITPI2bDTj3pSF9m3noP5Nh9WzNXB0eL3iMbfooA81W7pqp9h/61qxrebh1IjIhF6/0Rj0k2FygBOpCONU4rj5fsODq+GH87/BF/GukA1tLLThG+NPQCx4C4z330hf3hVpEvaxEjhbeqtx14x4h7pWEe0hENDjBKzjBfxpeZStsERBwgg3Wg5gw4TW8M2zMMgeViTcgoUebNeCwQRcbvNoVP3Ap4eRCTo02MOJMzf8lZC00HUAb2Iyj0hySOTQ9SVkGNa14Xwk5tSHjNc1IOCiJdYExscb01HSiaiipnSHvp20DI3O81p5qYulvTRvu02tEaXok7rR+UeIyH5KcAeYbTdvIz/FTskwl+bS+7KezFRC7miglseMtkCX3XYlJrrfYR62k4aH+yC4jtdhXSr7pui6gbguWUYm3hLFWnirR9JIOoeVgXIZR7Tm/19E+UH9unuDUMTjl826a96nbSyXn+kxNzYZTEnDI96l1WTP+PlQ5WNGjEpYlPZJyIWVSDu2c+ataVO0VVbOYsC1FMM8vuRzWoFk7ygTeU5XLzWr+1xqoiUHynQk0WxmHGlpKY5GlCdDntr1xKJP6+rJrfuhpqZPrnCnCbV4UN74WZsIbeWm/Y8Vue8wvMHeRdJeLrqcngtwnzHM+7zTcn87jMhJQy3kZ4adlY8lj9SK8HEaKXN9Dk5Eut9tU5f7K+d+XWFd71V5hvs77tzn05IS9qu2FWs8i4/s2P+iLj0cNuH7LdFw/eskqAvS0ixnJ1XA+/SB3+iEGy5dJmLHpIK+Zx3hzr+njhL0x7DeQ3zx5EdKOZuRDAslGI3Tpi9FCHBLqxQ3p7eMcrzYuj7N+N0V6hPPUggUZ/yP+LRYCbsE+wLx8LrPwQ0KlDUtf3A/S8LlLmLlgwfHiaEm4Dits8Axv43W8B9/EKzjHqrzaTnFRiZA3OMMaW5zhBV7DC0ScIGKDFToEBJxigxN0aJKc7w8BMdjUUdksJanSAPg2ar9hXqWS9+gHjWQYJI6acqQ/uABg9V7grW/W6fl1JK2VqIabklckUpjuVPmUMKKpyyCfDOPDJeQRxvqwbBeYlgfzPgk0JZJIDPXApBn4ORKqRzZpqFpRXlurVmWo05s6+r4CcNGN3xee2GE8JXgS6ng+fy9vDO6aknXa7hFm8nFKcqt5ey2+ub7w2ma+nqoNp3VRglnjeTOd7NupNiSYvr+ueetY9H059z5XE5+adztxTdP1ZkanxsAERuYnGbYQb0DWhusbEiclQjFLOSCQ5MqFNAG3/c53SYrVyM1u203Pn1q3U8ytWmHjjjLuVgdV40KNyxGLQFxF23YGP5Ry1OShXxZb/WrC0Jt8TO63oSYz9JHUadCGFssMqbfFtKE/TcDNEXnMY84PWt2G4wGd71M3o75+Fd9t9jqYX/xq3S9PZ3daVrZMjFibXF7Ouantsnj++5TPwJvhbskwey5zDamQbGP/OjV/OdAzJTYfkfjq3V1ro4QV0mgGmvrunwvOPXPmTnf1Q3Cf/oU/TiMOtprD6Dr9Z3LeSVghH+OqS0vrC49rezlX21CZBj5cM5VK6Bq9M6dcv/+wuaVHTSoeD8JAmF7tqMhjQTZAHq60Gliw4LjwHM8uD7RgwUvF8nZ6KFjLL31wnxjLRJa2PmosmnAjHC0JF4aNfcIWJ1ijxwmeIyBhgxOcDXYj84nmhKaQbtyENlgjYYUuX0/ZHGXfNGhixGoTESiX2SL7ZeP8QbKLpNoUSUFNsHPkHbs3DcnwHVCZ/U8wP27pmzWRwTSVcGIck07XaQG1pp3eo2acmtsk+ECoppRq0qmUmu2ipjqBWh6mbYJa5JVKOsP0zDqqxEMJJpJgvPaihKcsjWlw9E/JzD0JpW0z5wt6QrNpRMaVulRlXbk4WymvXmc/ruUaULf/qbvmZY4sp44Zff95oknJLbafl31qW3lNOT/DaH2VyOM9puGJPi8D9eWc60Mva9VnYYrz2LUGmLLIqHkqT+HKH0IJKmVqAPTld0DWfEsAYhOAEJBUCy4lpEaJt5oE8+DQy1WlaCU3IIWFRtIo0WXxWXg11UZxo5E+oRBwWm1qsai2ml/M2gCdEovrs2/Tid3RMF4LUGmKOe5zl5jJkzZm/K7GuNWIscDeiKM4E2d+4OU61hNSLleQesyl6VVdDVam3QTbVck8y/OqmJugdqds09blou5xX15OJl4FYx2o20Fbwp6ngH5SpP9wG5Wp+SZVHrL0haefwHQdbrLxusvN2lXSIaE4jpeG4w76bLaIUMOa7WROU/PVcW9Bff9zRmwqrcLDE3vkevQyptWk4+HVhf2SZo6FHDrM4DTfJYdJAt8lbD15mATyggULFhwHArqjffseAupd6XF6yt0HHOMxrwU7sJBwIxwtCXeGCwSc4l14jhUitjhBjwus0OMcr+B1vIWAhDW25cRnRCsqWKe4wAm2aNAhokHLLXZoENuANkbENbA6RyY/VsjkmGqKJYy1y0gIvSPXvC+5DYyoeQYzwUiTl2rHK5RrhGq8qc8z9b8GmGRb86L/LZIHcSKMQvM4n7hPhBboe6SSXopATEBYAaEBQkJW+ElA32fCIiDfY/GpSZQAhB5ISsqxDCTnVDtLw7ANlTxlIjRb6evnySzA+tu/l6dsUWkc1bzy5hG93zO2t/YX3Pc5+b+Xuyu5RagmmDI2bDtqqgG1hGLK1OZU+oT66GNZVEVLSdw5eELOt7EnfKc02Yg5Uk4JyNVEeI3D63PSQiXoJD8l4lKouzEhj3EScCOEgJCSFDcO5JgRVCoIbkooLQiJNC4tp0xdqkZVmCXq6qFVl/cyg09JQurvhLqJE4xstNxTFY9lJcESYNpzGkNhWjpTZTKyMlV3g9xX84keOpim7iZXZuY9HlA6Hfj0Irwm0BhGtY7LQtOhftyM0/AE6DQsvatvVq5iGGxqqvNjchfytMbxcrON1NRjfxeadNo7ZtqSuTDMw2zEfbsboZ9fEEYw+X6YK+/chHnom9tpgq4mJjP0eeYYpm9i3uEcduitcjnqRYS+T7jsue82CDID3UVq9duRBjfjxEg4BITZJ/YYMLe0f7ywlcbc+bQFCxYsWHB/0B1Zv8zEDwrKNg5x9XYIsH2QF9otOFqQn7hp3CPE0ZJwWcTRox3YjKyxsUKPtpgLyv6QskmhTLopu2XiwTNcoAkJ29BilTqcXnSZKIrI5NNzmCyG2m8kznTgkKDzGmd+UG5Rm6RU05FeYy65T34nqUYShPmoNlvvwqZyX3enJK90PUDiRckar1EldUmvniB1L5C6TL4xXCp1bFbA9gJoSLqFTEiskK+lUje1zDe3cU6l3UODkSU/KGmoBBdg/aVabjocNA2mQ8JSZS/9RJwpIo3pkxTcQN3W2H3/fiJpx/tq5pJka+vCM05C/cQz/ymzk55LmGrwKSmjI56GT08Aaxk4RjXenAIFx+EueS7ja/t5DbWpY+qemIP77aXxQa7r/Zn1RJIvHJcxALHUPQWgjdn3WmxCZqRbtwgvg9pEtkatUWuG5hiNhJlDbiBqVAWpdL0wGpNDRLaKWovsVZRjU8RUo6IcbahJP5ZFiQ41c2jdZPFo3nJqSOp0awTcmN4xQrGp+PSs62Ls9hwBkx+z+cVkGOIyP7VpOo98d6wFN1WHmtjzufs0dy9+mQ/DNmgRJ1ZCu0nJcZr2fdfkYne0Prs1/6byanEb70a1xl0sY+F2ugJs17ouDycOrqdjLhb4jNjEbaYjlfi/rMxh5vvLRVvsHTyk35fsR0zbLEw+A2mYAU2bDqNQx4Xeqf3z6Zgi4e8K9zcW6vccTW82OCxzpCSJgd3nyw4PXC/kVUZzqzfEMYEt0EL1OhcsWLDgEPA63oO38O2HLsatYfuzBS8bCzl0f9D1vLngWGjOBY8XR0vCtdiiRYNznOEM52iKeliPgNfxrGi35a1XnmZX6BHQIuIU52Vz1uFdeGc4yRoaIG57NF1CeAE0L4DBlHhEJrI2MK0wSoAp11IC6Bw2E6lmEIkzxt+gNr/oNdKoQRfK9xdSHt7r5U+hpINaSYvyeyv3VZq9sTCp3A/AoLEWGpjm27MX6LrMKQCZgGjbHDYloCuafMo5hAB0PdCUME0oxS0EBgm7rvhFWxWTfyHkayTumjb/hSmC0IMmN6nlpqSKJ6poQpTyWK8h5tvZk0za1p6cmtLG8iRrkLCeWPLacJ6o9eVjGZSY8mX2fIwn25ScJM/hScypuMZ01PWjpp4P7+ulZJg33zlFkGm7w4VnneckMj4fn+YccdgDybcvswzjKAHlUeWAlqz7lBCDaUHVxbSwcWBo7VqDvpAq1JAjwRWG62Y80pjOTDzkDjEtOs3dk3Q1cajachaP4uU6LEs71vLyRJPvoDGRYQrJjYtnD5OROzWxMl56B/lGYf6Y2MqaLXWdzKfXroE1TXQaCWSIM3HsnuqR3HyBq2ZI/2v8N/gT/JG7rxpwV0nPPi/bZPrpzsbL1TdFSf6/OfSlEaqxfBPkqW0/DdXFylEqP/W5mDbJeAh4N17DT+C/xW/inz9gKXYtQMYvuYSmzNnaN3wKoszWh06QjOfyvKzh+IvFKPKhQReQqew57tqQ7X3CNOmPTzvTDtT0jgJecFw9vWDBgseBYyDgbIe6zMMvG1d1c7DgevC78DnLLwuOHIs5yhGOloQDuHVMaNGhKaIvE8f2aLHBChENeqywRYcV1niOE2yKOcuEDu1ALCEBZ13E6hnQdBgL3UnCqXA+yTUl0ZSUY5Gew8gHkmiqPadkHZAH5Qa5F3tkAol+2QAj3gLqvBgWGJNrgJlppCYftfIo6REtvQSgLwRc25AoyOVqiyw49kCUPGLMBFvb5LittCO111ICtuWhW6/E71MC+omHMcYcLqX8nZpGqbfvI9JG4du1DYW5w/gYsKah5hyZxukJkDZjObgSYj7vaT7D+oFt1AE4cemQtJsjATERFqjHr2qqAWMtPJaLWo8o5dByzuXrrSrw9y555FS8KVJQy7aLZJyS3Xutuqn4U+Y9FcqF8LuazfR5QcZ4U5KOQF9+02RlbJqxFlyVqZFaXDgquTalrUXqazsij2zwqaE5uw9oR0T3e4WEFf4WzvEfJjWiPIlUkyo24Osc6+v1wlgHppJf9bVYdVxTYqm5SiUfx6gJMAvzHvzP8U18dVSn/Kh6shCoWVutqw6oMaFnpNMU4TgHH34cNu24p3lr//xP+PJMGD8xjtMyglMdmO4m0/go3VTr7N14Hd8eTshcH370zb84bgtN+37BuYJm+GzKCjDSfapsh49v4+0HJuDmsLt946B16Ml2fRGGQvEclqbVLvjxGKp7hzIqA1AR2/upcbXbLGco/1vZ97EOt4PNhwvyOMhGQaY13xcsWLBgwf0gr9MP+1jVoeIu3AwsqKF6KPy9mFl9pNji5l2/vTzIIeJoSbjsk6PBu/CsbByziOI1vIOINTqkIo7uscYWK1wgIJTvPRqkok0X0YcGbeoRUsLZW0CjssQ1MnmmWjQETRwSW7nmw21nvnvtOSUFVPuO6ZPko5k/yO9GwkHSgIS7kDwbCStlTuW/BNNuiwnotmWDXki0PmaNtRiNqAshh40pk3NMO5X00AOrxsg9lpd8hJJ5RAj5+oX41WuasSlLmsIcNOlUa8trgXXJ7vn3ci2lNiKHY6Df1KSMynET8pjxsv2adzAyln1IWfQUWaRE3dQawpd9DqY6VJN+eq1x4ad8unlJzdSaJmGaDPRhfHtMSYB8flp+/p5LXzVAr4IpmfwU5+GvOcI+IV8bulL43tEQL6xdBIDGzEzmU9yWeBy+U9/LGkUJpYDguqvW3mKMWIg9S01JPgsbAZzjz+AHoJE0nmqw9KaIwgYmZK7JNAw119/W5Fbmfkg7DwZLx1hSLd/0IBiXLwL4Fr5a1ZHXp+oCaD8Ed286/Rxnmnyqybua8Ks3bml0P39SnDq/ErLHefrBtDr7SXMuLNv9assNS/9mG6KETLpcR2tOtQvHvvJutinTKZ+pxNGd+9nw+d6wcqyGMZSqcOMxtWxGHxq79I/8/JBnTXvOmUL9eVjQmT0Ns8iUle5DQZT5eV/OW88RcCc4wQYboLz38w4qoMfU0ZRDhl8RPXZkz5Sd6GwvOHyc4BSbyh/FggUL9gm2U3xIw+mPD0tb3w/qvTx27GcWPApMWeS7TtwjxNGScF74EAC8C8/QY40eEa+UU/Ir9DhBh4QV6FWJxtxeKQxUjxXaENH0ESE50ZkSbc/leyy/G/n+AmZqMkrcjXzXNbKSaErocCBTTshwUeL5Aav+5HifxBrJJh8vGuHGv67cX7mD8yS5EoBUSLc2GBG27cyEpEd0b8DzjRFlNEu56TIR1wTTnKM23cj3GzIpl1KOwzInJ6FqKNVRosuTXaqZqCSQ9rMSOQzjyRol7KKEZRrMo5V7KqdVkm2LsRZa49KZ03LTsmi5VXaPietTYPuwXnOSsam0fPl822seTJ/l1bprOG9W06cR3pNVWNM7lkc+8ltzEloeX27e95pu2l8+f2q4lX4JAGIYi+L7gGyesmnQpIQYI1IIiDRL2VArwpNuHqE0YxyIMy/yy7oT/Gb6wXUa5jvF7gf5X8mfZhjWkHuME9ydNDRwAkk9H6aON00OTNE/NYFESnFsam+cSi2K89ugfkIoblPBtGpnTUFa+aYW/fn6PPkWS72MPvEkm9cWrMth6V+OXZsSa/PdRFVdz92Lb7U+C9TE61XKODUer4r8GhxrK94GNZnelP+TG5v3hygTGk0aptEzNEW8LdhvzPVVcs+MHTggfdWMZq9DQSh6fvpszs2i+wxdDO5P2ec04TbYVKHiYEzTvEMelnnNOWR/4fQP97hx3ZXCgkNBQsS78QRv4+lDF+Ua8HP+Yb7BFiy4CuojOseP/y1+DP8a//KhiyFY5pe7gMkrVKC3tO2CBR5HS8Jxa3iBFifo0VSn+hqsiorPC7yKNTq0iGWbSXl/2Y6lLLg4eXGOk01CXJsJObxAbe5RCTQ1Dbktv5V0UxOHXjNNCYIkadBMZCdpME0SbEDNNlNKfCGNwhlSTVwyvq5zy72UgFAIuK4QVrEQdE0wkq0vph+bEn/bA6E3jTbVTAshh08wUo0adKXZh/RIsjHvVWtVTSXtVTMm4rxpSk2fJF3j47ENKpKxATZFlW/t2ojtS5mnakl6TTGfj5qGVNOIiinugf2nJF4p5nDPv/t27V/8HkcJSJJanjD0aWm+muZU2NPXgfTWfFku22tN1Q+YVu5huVfvBfBtU6UMGBOTPs+rHlVXPgmonyOXRgpALNdGVjrLw5P4Xcg3DlLVHlORqhJkmT4KAx+L6t6YGGoQy+xoPmA8IWV5UCPOCCmasFRtOiXgxmloGZphGjIaJFRhxyWeInxyefqBcJyqwxRJFkHfQ8ad1wOLQ7tue5/iFIVYP8719/HgmmovgNP0PME2ldd14MmsqxBFlz0W+fUzN/nVeZPEjNIDV3nw8hSb+5108XUJOEtJJ7/bg/qoupm+S2OBvs/9q0Wvxtll3rIpOh5MLRRoZpS/vUPhQ4IuTPi+MS+mXLK9/FLd5Mx6nqOi0yV+KNzszH3e9ByPWLweU48b9cpxwXFgiy22e0nAXWXTe9V0lqd3weFiyqLKMeP38a/RokX/QGouef/ZyK/H0/b3icWc6oJJLD7hRjhaEo6G17Y4Q4NzJERs0WKNHic4F70NEyHns7YmCj3HKd7dvYWzuEGbgFUEAvcn78B8tqkkjDJEmnVMyK28let6ELaHmfx7LnGoqXYu3wEj9JKEUw03yHWCxN0UEaTlJopUnP7Xum0mvnpJM6WsnUZ08n3QYOuylltA/lTC6/zcstwiE3erlZFkxKbkrdpu1GzT9LoIrFYBKVr/KYFH0g3I2nGp1CH2RbvOK7IoiRmitZfuTb2fM8rJPRHHdOYkpkHSYLqe7HLmDEfabl4bj2l72b2XcU+VRa95pojhdvl0Y7p+dumRSczurdoXGyS9y9gETxSyLdYub0UDIH6zbi8l4KbWC7vWZXO/WaYJviSV77HUM6Vaua8PMPZakkok4qCm7HKhzEgXyaYs+ueMRuLIk1I6BFRgr/7k8j8TFRq5YbOllQMSV6mq2nQl65Cg/tliOfrQDHrIJMRy+Hqg0cxkrl8Y3asNZaWqXkTNlVoa1DTzHRxcOL2O4V6dtqVlITkzzdmdT8P/dq+r0hmHv+oy16Yui6VjYFymufJlqBnUKcwRwPNh/YN9OSh8BwJanKC7ZCOnBKuOyjQ8tDfHFJF5X+Jcjn3TieSzmmRTuWyAHh+m+tyuqcnVw/XIYC/WfnjS8ptUDcm+rNF/c6NRfLvy7XIV3d99gS001Yvs4YO9EA/4+bgLHI9vyQX7BFutqIfJ/IsSm1T2HBkNbDuth8OCrGY5l07P+otgfcH9I5Td7G1gFjkez3h9KPKNqGUIj6fd7xP1YeL7bdP34r34Jr55r3ksuEOQ77hp3CPE0ZJwFGgmNDgtZlU2WOMUz3CKC8Ry/uI1PBs2kSe4wAk6tOjQoMdZOsdp3AApIa4Cuj5hTcLsDMAzGLnFzw1qrTMt0AY1KbNBJvN4nxJ5knVq6nLqcKKSaxFG1vlwJO2AWlooZGDqgHiR0wylDJsLDNpkCbWm23biQSLhtdmMswHMZ1vT1EUkSec130ia9bGYv3T5RGnL9f/p/4zu9/4F0p//eVYgAgb/c0CtKefNV8aUebYwoU03tO0UKTO1vlfTj8n9kXhSE44+L6bBOFNmGlV6rsTbLrKI+xTWp0U95jwZp/Gn0tFxqZpyUySZT5d1ZDqqXeil2VP5X/Zdy0loPbW8l8mrfftOkXZT/VHiVM+B8VbDOGsA9C2yGUodmIV8S+VBSMHILiPYmL2d3zaNrSwgM4G8UTxmdCkMpFhNwkyZh8TEFSWXrCFkWhnSYT4k7erUVKhnv1QUZCSSa2C5X5vwMIIwb+pZViXc7AHcpUE1dToxDG3HttZyqEprqO6NB0/G6/gufAvfmMh3XlciIJNhuXenNetqkxC+DtpuV18sX0bA3Z6AmiYA/SOnY/YyAg6guIdENUT4c/Wy1uPTREo6NXutybuCPUt5AotyJ79WdPJdsMCD810cZsSp19jhQGtghzjCMCvvO/R9W2vG7T+4nmiQyhvhOJBHTo+I78Z34Rv4+i2I1kOFNx++YMHtwBmO3zOSfIvVeU/dGq+GMIAniG1X06M+mHfYb7YFh4XbEnCAUdILXg4e21v9ZeGqh2/vAgsBd2DocHOTJYsm3GGBE+y78PawJFujQ8AKwAYRK6yxRXY43uPdeKcsEhNW6HCKiyykCw146qqh7zY196gSVh5iUXkgSZe3UQ8i1WBjOK4VNxhbDqL5yyS/SdQxvpJ2mn/t2sFIQhKKJVyUcvedEVgxZW24gZzrMmFF85IxZqIswAg7XwTFhuYpYTwTAGyl7BWnkYyf8H7gtttcjv5/+L+jbY28SyXeQLxJVZXoG+pbzISuWiD4w0hKDnkprJoh5JE91fJSRiI0whzCmBQlevQeSaopuaonq/S+kmwMN3aJZWGBWpOM9xvUhKKSayuXFsuuFug8yMboJKwycK/9t8KYfJ46KHaC+fr7chFT+zLlq7Qc6u+ulvtZnbRe0i4hlKYg6VY0MmmSUrsoq4sCKdg2tYkRsWmkWFlzTDXgjFAiJcDTojWx1kuhrWkCMMQxUq0mtEj0REx5FkpDujXhptx1PtFqJB3D+U6h+McTdEHysDytE2vSQ69jdK0mJmtyzse1jVFwKSXoqUWb9nVwzw2wmqhq0eBtvAUPI+305eBRa9bp66F+MHdherHsufic7nya9atsmtxiTZrq9+7FenLfr1c3y9PGTI6fZtt0Po0p0yVZs7OHEWR3s/lgfqHoCc4Tsn5SX7BgF/IcnsdUP1JKJ/ZfdDk1v2bRrlJE+w+W1owj73e7A/peMsH3IfsdVOR3y9fx5hHU5SaYO4S1YMF1QC03Hlnj1Yx6d3L5am569Wtpmh2PmjYPALoht2VUL9hnzAl8Ftw1vKThGHEzc+k3g+WyjN8FC66KoyXhgIBVIdJMpp8Zqw1O8ARPcYYLBDQ4E/OUXMRxRtmsT3H24gVOn0e0mzK1dMimIzcB6EtA+nXzvte2yCYlVXWF4Uhm0OSk+nkDaoKNpiop7T15DXjnmd1Xk5UEzVmqNLyDkX9KVjCrlAmpbW+/6Vdt8KeWgFg08EIwn29AvcDWa6qo57kN8o1z03aAmaBsS73awmdFIQ8TspZeUwhCRR+BFPL9vgfCyQpN340Iua4H1ipHZduyIKLNNAltV43fAviODwDf/Kt6x6HydW08vyPRhvNyef/O87JaXwcvdfP5JdR11bQiagIOUj9flh6ZINP0PCnWnABhU+fBe2xDxlmhrpuXd/v20bBT/eX7cW6fxucU7p5vw107ylCaksQ161fixCYAhXxL9APH20UTjvBapLb9NOHjLqKMxe5KCkpoGVkRRtUD0uA7TvPw5hiDXItlGaip2Exbe+r0RFf2E1Y/GpDUFFZyy8MTiRhEAVOkhT1MRriMw9Ukl10zcmqqrmNSSsmnDgm1Q1GGmc/f+mlsL9bGxG4YoVU/CKwPtcborW2OYLI24QM+P0Ga777cGyZynguvpJ5evVr9LI2psl/tSFbm2H3f6uSaW+mmWx0fj+PdTLyuULfpIRALNbxQ7Krhp6ZinaPmjbuOydtla6iwl20shytIJOvygmZPD6vd9B1wCKY3de6lAbZDGKt+wWXmqA8f4UDH/t0gItzijbZgAZFXRrrFvY/nya+U/UqpLbuj/fDCuWBBDbOeASxj835Ri9se9rhJgwZ/C38L/x7//l7Sv28CTvdp3Nsv43fBLLa4uYmS7eVBDhGHY/3kmlhhg4CAt/EubBGwxgXWRfSXNd3Oh636ZlClCYMGXIcVIhqsN1u8+jyi3aJe4UUA25Ql2dQ0o5YcYBLOC5h/uAtkbTb1/baVMNRk0xWkZ7AY78WzWlrSu3ib8sdZktp7F5ZmApCKZs7FOQa/ad7UJIk4/g2/kxFw0ZIdPqNkq1Yya3G5xYfcx8zvVMqz7Wvyr2lKcyQrI/+GPJRsFId23gRl8oTpEND9nlvPm3S7xjf+yswwMp6vHGXqnj9gA07JYafynwPl1xpnimCa65y59PnunWo71keJRw4OVdP0svU5SSzTnOoPz6vwc6q9ptKYynPXumJV/rwCVPkbCIoApAbZ7GSL7Fsy1FlFALFtgbZFSmkothY9a8bo75r9o1cci5Pkf/p7y8i0Asm23HE5vbHYMgHopIGzn7gW2XtKHT4WcW4+F98gohHxrv2fhl9h4jFoBqIqf2+HstYEWyqPhj4YJJDyX6ryHpOTU8vwKPGNgtQ5rh3KPTYbqeFDJQau85smtLLZbH1gDGFoGw46Y8R1uM9tLrQOcVB19QSctm8o8/c4nIUP0l7jeiX3xzDJta9HDyUDdZzvNoVpOfDazbTT8hSl3pqm0rn5hoNt7aesvjxbdX5zk/N+wc9XCiV4GDbKn39tjtvF5gJ7tptqbaHhY5l7YnkGYyH1e9Qmc219EkrYw8MpTm4Y0+anVNa8uR3aoZ3jwYrjQzXe9h+5tHenS/uyYKsLPapwuLBF4D4YCF2PzFS8DLQjYywLFlyOegXAueyhqC9dPc0fH1uwYB9weG/+Q8PUPvQhERDw4/g/PGgZbgM9rLcQcAsuRX/LvyPE0WrCUeSSNTECTrDBFi1W6PEKng+i4QRgXVTQznCOdbHRmEJAh4RXz7cIXdked8gk1ha1dhnK9bdgREKHrIUGUII9ZqI2yIQcMJYrUluNUlkvZWK8F3LNk24kDuk3rpQjifA/RWCzzZ99NMU+gkSWElXJfaoQTZ+TFuNmUqhbMJ+mVjnABqo2oYpGkTKxMfiRk4KEALRt1oKbqhfjNCVcIJk0tYJXKfoOM4SjSqtMfCqOfp9iKKdl8mN4KSfLMifDpdTEk3PJhdHvUwSVJ9CIXsKouckE4HSiLFo+b54ySdwpssy3m/8+1Se+rOsd9fCaeVNKP20AmjQ8Y1M70CHbEndYGBY/cAAQQqHFmgYhhCIQscYIhTDTc9rWbDQ3lv3CzQkiUyH0xgSKVYon9JJ0tPnWqhdcLM/0ib7ab5rRYWn4rvMJy9aAxCDv1fZR+52Lv10sri7I68GRf6mHHpJtY9bWyLQx6eXNbXoyVVNg2Cntu3GN6rhaSztROV2mAFTajD5t7WUL047C5b/G5TVdXpagH5VpTNbZGGa6Pu3pdtF3SDNc292Wu8pLEsfi363AZtxnDUyX9eEFvx7aNwnAGc5wPhxkyjBfgTWd5q0zz2mjcqaKZXTp2mHs66WeYJPEtLQgYUhNc+yrD5kGqeqRvupxK7tp9e7bVvOisjl+E7BG3oDjNJl/aDikWhxSWTPsjaGa64dVh3k8dF22D3YEmJYI+mrmXbBgHnz6+71bxdTr+oWAW7A/uPsdxoIp0HXHvrR1jx7/HL/z0MW4EWzPvZDHC66IfML85nGPEEdLwq2H888J78bboA+fpggrWvGQlLXesn+4Di1O8CIv2FKPiIRQpI7hGWpJ8RqZaNsi+3zTQbIpib8FG3SeyKFWGu9RIkefcChhSK7xTU2zlZi4pkxYK2ltLetQ8owk4AohRQKu6zC4LqOvt7a1+DHaZrvD/DPFk5QqVvRclDYZXdRNnWpXLkfjjwLBTFEO/uBSqU9TE3HU7GOcFBzh6Ake/nlFIVZQ5Y+7jv6x8VTBQ99jEXVFp7gEvU82s5m45sknb9nMk0R63RNzej1g7NttKrwvN+H9uCkvMLV780ohPUwDbSr9Fvn5TABe/TBw8cV6trO9orWB10Ak1hLHayiyXANhmJBohlPKlUo4HUYI5dlpAkKoCRUgk3IxZOF8P2gmZN2wvmiGqQCZ4uHcFakMIyPwdHglKGEzNvVn5WyHqhphVDdSLfj2YurabKVprtDPXYKRgSZQn35sVJiuxJ0K8pWaMoFyTU6F0j5+RqkF7qyLzUlj0gjD9XGJzVgee21qFvSEz3Q+rGsaatO4MiihaOXxRIIvoc9jnPt0nVM1aUynntxnxvyCvZ5udfLbvcCvffFx3PtJ9HJoee/TIJwnl9LQlw8ntvKvGI41Pgvq1yWhwXNsq9FcP4c6MabSlqmIciFhplAfLIijkk3HyWE93efj2UiMo7FRzy2kQ1P1nHE+6KsYL3P7+TJ9PDBHwJOoh2OqLhZyO8/hlxm/3Qfk90Qjb7v9Lati6q17qKjfhNofjwvsU/OLvmDBPGy22td5Nq9K1Lz6ggUPCxuL+/a0HBdsj7Jfq6r/gD+/JITuZx4OWgJvgWnBggU3w9GScPWUZRuoZkKt7ARbnGCLbDStwXOc4TsuvoVXLzq0AYgt0CjTpHPP8/Kn0lqaoXwmhSEpQtaJvuJU6vVCvpNY03z7Eobug5gfNfPqw9w1QSANs+3lVRQw+Hyj9tjIHCWyrzQl1bRac9puU+F0umZTKX8196phFaYGLNOlucnYAytHlLGOHUk3GLEIwDToipnOdtf6nASQFkBNOnoCzmuxadw5GaNqy01ZQ9N34JwlOM+v+DS8lTfFrh2UHiT0a0cvzb/K+5lhPKmn9xv50abxuqRxn+rr8MX/a5qsC8gEm5bTD+Yp4k6f2VY+XfratSlg0I5LKZNycWinsnFNyT0zNAeWKgF9VznHs0bIj/yYNMlCFNscq/aYCpm5GajJKqbdlObRxkqFXMrX2HReIK9NG2F+2XJ+NOsYqphGAjZSjpqh9aJwTyrWc0m+TvJxyuSmnuI305f6kFjnWx1qwsuTGP771ENhBF9dYiU3Wf/kBprGmtvI0VzmnHcbT5Z5gsLPydZLfOnMP+TjMTEen74eOcV6zM+VOWNMYF7HqJvWYP7M//U3GmrWkz5nSTT3Vc1f/iaGY7KBmhLhHe17htL+MOH09Ftbf992o3bdeLvy02v+4EF9byxytnpSw7h+Dknd5ZF8X736cgk4zbVBL6u1XtpjvwkK6xl+p4HS/YUe2Dgk8RzXAf2tn/r9wuP2D8eDXvv9zCy4De72cMd+m9StV7b7WsoFjwVB/l9wP7B9/+Fpwa6xekBt+Bq2ll4OMCy4ATrcfOjcVINuz3G0JFxftkwRafAFB0S8hnfwKi7Qo0WDiFO8wGt4XoTTK5ziLaxSj1U8w6p/G6kF4imALdBQMkPLP6pSQjVLMkvUmtvCNNnsiH3tO05/M80XsEGXynf6joOE3cLYseCut3bfm17cdPl7TFk7rCu/1YyjZsOi28vM7k2d0wioq6vXyVtMabzx8yZCrKoJYk2y+WsRQJMw8gc3hFPNLhZc398d5okd/9uTUmy0ldzz+wFqefH6FNmVALz2OnDx1nzeuyY8LynxBJrmQ8LKpzeXp5KIcOWfItumNAG1/WnbdLXG8AAGl0bND8n9raVHbTem7ddkvvyeq5kblEKyDcQe8rUIIFLTrtQvBSA1JLYAhIBUHs7YNOiDZ1bVDKR1AsWfpvdrdBWQ5L5Vi2mav6SmSkMbL4D+ybxo2W8dQplD6wbVxW/nHhgjn8JQVhsC9eANJQ2rW32mwXJsh9/KpyrJ6Ak1C8+yKNHWSBj6kqrDafpzi9SpOdKGeXAh7Vtf9Xd9jEHT2y2qvZzMqokY3y7UhoygRnlG7evO5852mtIiZBxtKRvfu4UjJv4f96PhegSc9f/dbS5sfKpQfTx2Xhbq8ZLLkpDc+PPtPz02+fsJ3oXX8Ar+Ev95R7h9x1UoAx1r4+fbnvsMrw20yyz3/oPjVed2nbfbwbLE/oL9loZZtB7ZL1vD8DLYYsnrkO47uA5pisbJIZV9Gvl5pwHwxwjdBiw4PtzV3BfK0cFDQIuEiIiHtECwYMHjfKO8HOiB2t3mqebx0GvDhybgTEYAPNTedcGRgC6ybhr3CHG0JFw+jdXhDJsybVAs3SIiYFUEzqvSs8YzdFljat0jvAM0F0CKQHgbJoN4gSxdo6aaknAKSry93CLCfLoRtOt44eIw3sZdU/IvumskCbc5zYSs4YUkE2rM2m0AsE1Ze4ykHNuCfGJyyWvx1LKiXtem0M1bwnwzsXlVnMO0mT5NVl42cGMCQsRgmvIyJGnboIVVuRv9gqk/NxZaSafk7qsMS+9XBZawPg9gXoL3/C1gFYCQ6jBeOWSKyPMaeo3707hTphr9+7iFmbukspaXObNuns+Z2uVrWYZBuLHywKWh5VKmZq7svo76mySowhOLIpdNM/kkbffybKWmCPxTyn7gAKS2RSy/68fcWNgEI8lIQtAvF4X8FBOpry7TYstx+qEB1Vyi5Wg5hyEXM/94lUVYrV03Z4JwitTTevCqdmd+DKeZ4PE5dTPqVRM3GsfTXtoeNcnkSbm6Llof07ibe2ynSZ85MkmZ+Hp66WfaFlBNrPo+27D2QTadhrYpNYCyZ9VertexjZCcrn1tSbmZEN/vJuDm/cVdHbkNxqL428C/suuSvZwTmDqNetpINSrhnrPL23LcNmus8BTP8HRQ+T927CLrpkk3u1pv5LVvDgtjItaLcPe3TtZ/yc06noreJyT3ua/lrBFg5mHjrQXzpzjFRXUC8WVCNwRXwxon2N7aT+O+II4OFSxYMIXkjvrtO5rRm+Dl46GF/AseBranXIiN+0J9sPNmK+7H/myqaLKWoSxYcE30uLlvt4d+Ud8TjpaEW6PDCQCgxTlOcYaLYamzwRprPMcpLgC0eIZTvIILvIrnwzz97rdeIBTVr+YcpolDzbPz8kfoPN2h3jHzSHwHI/ConAdJc+viqcM1td1IAhDym+8YVQ0pxFpKmUikucauz77ghuJ2Y6LNb/y9/zYWL0kcveZ5HK+Voulo9TxMeF5fIx9G+N9TqIi2mfshoPIbN/neVmJqlwxbr3t59BQhpmsxzVfJvuD+GNhL9VR5hhp3ug6Z0vLSvDRvJRc96YaJcGqaUz/9dY+IrEWmijZqVlLb3afv7zUS10ujgfGgmutzn7amJdcGLVOScijEHGRclzRjkyMMZHdTfLA1TcUac8FT+y6wxlAtOLtr5iETEvrChhoxAhgBx/Pd2gBpuIchTpDPIOnktDRt0nX2u5U2IAWgg9SuqR8283dnMVVrrybLSN41Uu6a4LPut/pRv4DvBR1mfrFphFktCk1D3joorPxx0B6rH/i67dOQx5haqvtDyze1NfCki8/T6qICk/EDqf3tiarerYZ8ecZ+Buuy6ZxfpzBHBJJw9A8lZuNMpaHfdxGqV4WW2DTLmom71yvrVfL14DOftU/q8WLGxKbaT3G9cm5fkn2I7JFo3zfC9TOSfU7W812aeDIOi1iZQ0AcTt/URyT2tV76Tm2w796BeFBh38vpYTP+bUnOqxBw+XDIfcxJeUfE98ZV+uB4CLgMv4xesGCMeuW676Bc4qHxHnwnvlVZElhw7Kj3Pvv7Vl9hje4oVFCWt9Z1YXLgxo3XBQsW3BWOloR7htdwhi3aYpgyATjDc5ziHC0STnExXF+jR0DEu/AMCUDbd9n0ZAME+luj3OpbJYM1MqF2DlOTDADegRFnZKLacv283KP2mvqH8ytCva87oFjS4XtbbbIRZb8aY9aAi+UvpSIm2bHynFtG7zJBmeR+cuFVQKtkHcNMWXScKpPHVBkvS6dpjGhrG0yaogxw130YT9bop8o2PWHj01hL3CmThwn2dCrv4LXXqH2l+SpxpSYvfRwvAfQEk97zZTzFNHxeHP9TbaAEpmrQKfGlaZ7OpMN22ZXPXB01vrZHOxHGE64k5ltJujxjsUExOWlk3ADRgMsRy/dCwJEsMi0nM0WZi27kUeec1mlRjchSim1aM8qE9I171mlgxiqap6qcji7UxukaURWGvJluA6O/mE5TYoVCUNm9ICTClDgvQU1Z1mVh2jpnqBYU684pONe/hRJ29YxHw4z1tbo8nPGUGLTPWpzK+q9KvH5oCQ6+XDaWk9TdmNirCdXx5k7beXcYncDmURNa0xOeJzi13uMJZyqPIHlcX8jjY8yRhJelwcdfS1yTzbteHHcPMzFr7VebvUxDuLsmAl8m9p+Am4KSnkSeU4x6tzmklToerqCbz1Umgm+rPZONXN7X8Ud9P4TJ3tof2IIjvwvnPVfuM+57XN8PAQeooJTP76G1/e1Ab3iP1xzngquAx/AOY27qrrkGvC8sBNzjAvcRwJRVl/3CoRJwttfmDu0QZqT9QW61+T39ggU3wuITboSjJeFQhNYBPdbY4t14Gy16nJQN7Auc4b/AX6NFhzU2eA+eIqV8jjyiQQyFyyhSt7BFNiGpms00RUk8hw2UVu5vkIkxf68p9wJMO46qZNz3UWNufOze4gH21umNfJtEyppvfUk3xTFhxuRImFFBz2tfeMGq3575Ioz9N5kG29wU7zkn5nfdV8NKRvqIJ2qMmKv8yKmkQwvibXAqOebl31Py3rV8V3JHC7d2v6cqyvKRVFOtN0zE0/ro2oR5kcjyeWi5tdHV8kitrz7dboB1tjdLGSQNP6DmreWNTUqO5Z7jsmg+SmZrmrsGltPESzXLhT4AqeSbXJn6ENCvCpmVEmIISCGgCw1SahACvQup6UHThjMCrhau+2evk0roEFLBL5DJqCwCDkPaasLBCCiawRxryfkB3g9xLGw2f8h5pX54UmnUTPNZmY2O0gFBsiG3YYO+HKFoQKoOEpr1sRZg+Vg/TxRa547nNQtba+QpWQdp3zEZNU2+ed2RVTV3WpuxTRuJr+LjeULLiFY/0dQxrfzTE8/4cIVPb6rO7O+EftAbnCfCdGqyMaa4+Yag35HvLngNCHsf8vTA/WxStL11euX4j67nLPTU54KHR0BtnC/3X4+aGmeP9bj8dbRfaIb3Ui9PMQ0DXwdTBNz9aEXmMrK0Uz7j9gdpNA/tZzkBnbHus5z3b85tf1v4ZeIuiPUFxwyuj/2KeV+x36XbB2QbUueXB1xwLdTvw2Uc3iVUdnm5fawFl2MZnwvuCFNuu64T9whxtCTcGhdYIXucOcV5EXC02OAEp9ggYoU1tghIOMOF0/+ASdl6IDxHLRkMAJ7BiDRG9YdGegDfRtZc84wU46lpSV6neUmqmdASiw5g3mNcSg8603oDMGh+8fu23I+eUStJb2FCH1rfnIIKiyaS2o3Bdp9VZe5VOTX9T4map8SqtOynSkdtqCz+DQRcCDMEnCdrIL97oNg8HfMEXurgTSgyrBJfU2yj5ucbWQmkIGXRPNVkJsN5gpB11DL6PH35k8TRcmkd1jN1Un9xTMsTcJoWyUXfpqzTGtMDZZf0UhV9pngJzUvbw8m1E/NphfRZ5T8GMME50K9tyk2NmIlsViU4PXXRB9z0Yj1PTWpv01D7zKqJdUuXJF9NhtUp1SYvzXwhT0bXxIDlbQRiALBFg1CFV32iugxG4MVCwNkgNFOY9qD2hbyzPGuBnJGIrAdQE5wM1yJOGKex+J4MswddtfBYBiXlanKrTi9NTgxzsPiemBuHGddjlxjc0zhTUB9zVxGtjkUxeWzsKocRlXNn7qcJRn7qdxs5YTQOrgpLk+X2tNh0mW4DnS84wSr1kGb7+zg2S4dhfvKuwD4z8irPKTbO6NeywT5ra2X8r/Ff49/ifyq/7PmICGjvYBd1H+OiQTMcDogyV+yrmUq/NNl/cG1xP1oyL89ny2Vv0WPH4Wg5XYYVVuiO9Wj1gyLITArQOP1Di8J1bRigps0X7MJCwN0fHssK92XCnnE9cLrgKtB3e209ZcGCO8JtFGsPUyn3UhztLHWKDQI6vAtvYS26XDTf1qATkW5mvULIv0+254gNEM6B8K2SoJIcCUasbZDZqw1qzZ4tsrlKXfmZDnom1mjOkuYpKdu9KH9Jwp+Xa7tYsf/L/4B0cgaEog1XtOIuNsC2BzZd1pDzWnJeAEvybVdWalHz2qKVVC8/dpF4cce9qXBtk/9WsupPKbdDAEYmKBsJXxWL/UUtM0LNJU7Jz6e0ybwvNDacElLMZ42xqUclwpoSh8TTWu77hlpJPDVb2cg1JbCSC8cyT6kqtu77yUQefnbxaSuU5FItOaAmyfQ+28K3P9uH4Vblj3F8/bwGI/M+celI3ARkbTfWW4o3PMfJxnaC+YLjICTP3jXG2OWweSMb0RRyKFcyTzctNlhjOzCc6u/MTBZiSJGUVoMea6hvKCMolGxTn2Fw4XPjk3SaJuDqzt2WvPl82gm1mkhSwsbIqVrUaLp61tpp6HB7CLyoOiIU95qmMafpaf0STNvPkzumvaUEZ247auMpMVY/7sG1FbWZrkJ/sb5GfXmTm1OE2DDuMO4bGys677N92iocy3tVk4vzNZkX4en4uM7in22bydj8vKRSh/r31fWJ/HRa97tX4b0d+qH8fhmgC4asPUUi/H+D/9Wt891nPB4CTlGTuvYs2nwT4X177h+MgBvD5iJ7H+wDYln4RDdX7Ev5agREKducL+X9gY7p8eGXQ4LqsB5qHW6O/O6LleHcw8VCwN0n8tollU0TzYm/7HGj698eTVlDNQdqzHfBMSFi5fZ/C26DfV4THxKsHZdxuWDBy8DRasLl004nWKMrp3B5PYu6TnGOHsATPAPQoEkRp9giJWC1SVi/yKRMNRVRQ+1tmClJIAvqN8it2SP7f3sHdhxfCYt3YPYdpw4ZUeaSkMm9C5h0jhUDbHVpq0yk3/p/oEsrpAlNuJQy4bSZYNZM+Jmh7uimwk6ZlbwNdi1F5tx8TVkPXLfwSnYDeE3db4WQP5upzKdIpKlCeJn01DVPEs3dJwnm00qozVPqeIoujseUvJjElLIotcx3GkpUKT/iNfDmwNnGqz5O+XrTMk3J0FkvbzuKcn5PPPr7Ph9IPE/QJYlXCDicjNPJRJs8ewC6BojrdR5sfURKJwhhMwzEvlnZoIT6JfNkD69NnynNVmzb4fxpL+KiBmkww1cTQSYUy9ONNXKeA1qkCbETy8jyUXutNmmJQkytSr4kt3JDqu+q+nFVgjB/MwJM82bXeOKJ5UhDHdMQejzIjFxSMg6otfV8vDSUO1baiFmDUcuYv4eZdIL7HA9PJdyMUNU4+gBbu+exFGFE5a40eyQXTt8HNf1Xl9fDl78m/+ZJuwAd87uhpOi0Vtju/Haly+kkVpOqTgA+r+tD/aL65zn7guTCwSOP5X+zg+hYcCyYUiG30a5ntuZGyz7C3icrhL0WhOe5vMc+aiBmj6ksY36fRuyvqUB7g0QcrknDbFQ1ViuDx4cpiwkLFnjwmecBkvzZ3JMXLNUi121t7a+YYZbRu+Dloj4oe5mgZcF1oWthyiIOBe/Gu/E23n7oYgyyirTMkQvuC/SvcNO4R4ijJeEIm1AiTnCBd+EZ3oXnWGE7iKyREk7jBuuuMFQBCHQCo7LOHtNzU0Am1p7JNUoxGU/JNIb38XVvSgKO91Vyt5XfDJOA9K/+JUB/bzAyIKb6t7VNhmq16fUpsDVvu42+ynOofAg5I++bZ4p/8tpuvJZgpGRDsqSsjkZxpvrZM39K1M1VSBsszITXe5SoqYLPtETc4rQuPsmkKXhSip9zyh28To0wlndXPlPprN1v3cnzc46gVCmjfyZ9XiTe5vgC/5tackrmMQ01E0rTs6p0JUXoAXRFq7BvkNUrU0IU26epbRCxQaaqvhd98x+B4E0LJnTSGbXAQwcSSa2ArZvGk+vkHkmIJhTNrVa2rOPB3mMl96wURpiQYEujNPKU1wxkWH2yKgymYNREZV1+a48samSjp0IM5g6O1flaK19uExusyQ0snUq1XSy+tZ+VJclfKOnUA7jWMASMDBy3j30L7tu4Dbxm49RDV8/HzWS9tf7aH2MNN20DkkJXX5j3VbuM47CPbiqMtd64+Uah3gyrH8ZaK7P+vH4e+mljjkJ03p2a9A4Hc0LZqTF9Wc10RBxWK7wMhCLC5FyYx0rYc12oDHtfEPp+2L++5jvWjrDsE1I52MJfteh5H+FngkNEPqjSi7jvccFWcvv5zC7YL/j1U0Isc369k6lDKVIVu/7O94cdpNN16jJCF+wHdCRe9aDhguvDzPcfFh6agKv3wsAyPhfcGzrcfHjt85nNW+BoSbgWWzzB+eDvLaFBhxO8C/+pCJFX2OIUHc6xQo82Gs2avJbO8/J9i6y9RlKArFAs15UkU1BjTm036h4uyfUXyMQa2SYlB8iW1ba7MgEXLbgmy3xiBLquvsdkVNHOcz5VWrib6XlKOYngdU+2eQ5Lww6n3qLz64ai6VYinYnGlpJxQCYqmwDgdA30W0t86glRE4W67p86vK5aXlNkEStwiroynqzTxtJ81BwjG23KRKOm60kqk0XXZVDfbVqOhDFRFeSP1157AmyeThNhNINJM5HKA3CcKvGnbaD+3LRsrfvOwewHjdZJiTZPrApSKUsATNsylCxaoG+B2AakJt9MKAHlM5TvsWkQ8VfA4M+MpsZse0qTiEDebHbIftXszHsoTdUMYQDVzMoFzE2wkrR4FnV8WoyLsbGZDCOjbMqzhmNX5DMKoYQL0KFU50GTwHEoO//i8Jt37SGriZdMHqlHNm7Ga5OcYwJM/e0xRKzCWjlqZtuTYfOzIevTY0yiedLL89G9lOMEZ3iBCwChvMcsBn20GcXqT1nWrW9zu5/UaiIwje5N1zO/spifegKZP2LBPqQwkz1/nc0p62HE2U0Fola3uxKp6oaGdTNyT/Uj9/s07FR78DlJrs+9eD3K/SnzbXNpW1zOIqkKr1P5frbaywBrbuOeo+wygeZ+wHo6a0fna2MjwvuCWmC8X6hF0vtZxnkcWnkzuJbTN/HjQSNeexcsuB641kpOzsDDJGk4mNUPa6h6R2OpcK3gVxNTm+0FCx4O1P5+nG+M+0W9d97fI137hjT8TQktFyy4Jywk3AhHS8K9gnOcocMa2yLwC1hhU4k+Ixq8wCs4C8/RocGJ6B6kAIQtbNC8BVMXo7+3ANOAewGTEG2GLMzkpEmw7De/k9yjjzklTNT2mmriURtO2LQEoHNacF2JH4VZ0xP6c6TbFMgD3qXAkunyc0qpC6ir7t20DYvyBDQJlVYbv69ajLTdWpEgBn7vtrXvPwULSHJKM1dCDjANqynWkMRTlO8+jPEo9VO6lnueMDqVNFh2NctY8zPW0Ky7L6/XXmPnMM0o8Tzn0QLontbk3EnJ25OE/vCS5uPDedIPE59ATZQzLe0fulTTtP1sWOqXQgkfLekUys9VaWqGA4aBlviZEkLReEsJiCkMYahkV5sD1K1nvpengqaYeFTvJDl8AE0vMp1YxNi1plfdcFMLVtHcAw0aUuSUt8h91amaNrXfMJBPJNyordHDHjoK6xnWzhw0Q0lq/ze+g9Wf3a43uxKSY5KIdZtvi/r6FLHIEhTvotD2qYmxMfk2TwYCG5wP5ajHhNYlzYjG6t9xNGFYOfi4XGVut7zrmXvXe6Q2wagk8eW+l5S6CpJ3TbReXmYNdddbjTp9ey7SqJ32R0hkG7FcrkZ60Gt52sjUcjcIqD241c9oi77MFtR85JLF9LnsW52fldHSVZ0vtvhu83AP38r3Ae0bI+Qz2Lr56jwd/pDQudEWPM3eaZzVM/L+Qt8++w6+r9No2XdISMNq5XDRoEW81pgJwzy+YMHN4PcuRCpr+fo4Rj6AWK+T1Y4A01ywYN9Q7+t2+QxZcF3oYT89zLu07+XQA9mGpd0WLHgIHC0Jl8VFuqkPeA++PfxKAE5wjgY9ztIGoQFWFwnNNqHtkcmw5xK4K38vkH3CkZW9KGHXME23gEyqkbHiXl6PdHfySS26OedoWxjpxvhCwA2TakLl+yyV8DRP2U8QcR5z1+tF8PW3nxqHJ9q9gMi745pLx99rm1xfT7415dog5ikEHQm5BCDMmVP0hWCBPUE3JdmdI/GYBok0r9Hl02FaQcKr1po2GAkz34i+fkxPCbSpupy6a359w/y93zXyBCQXucc/kTAkH32ekGtXcQao8TW8trEnCKfqYnJA+15Iy6ThmhykL/cTtShLVO3HACAOWm9C0LTGbDLLmkywTWgartUC81gNHCuD+ZLLDeL9yjHcHBHUoYVplVHobX7etMFItqn/Ok2rr1RF84JPhe66YKZm2rZca4dFdf1oUJif45vo1tdHBdF2jXVSs5xEI23OT587JJ6F16Fj9WMbemKmTkt/eWKJaY7JED8paei6vsH9HreTPpJmOPSyzcx8LcbgmCXZlrX5Ll/w27iyPuAI0+fisryD+279fjOn6Al+XNoo5P16IlHc/0bHv3LmctRNrM47NSXajL4nl4Nt6Ma+BzPaIjJTii85sm5cyiQ9Z/OYnw1y+nEYF0onR8zRu/XTMv0KPhzMr5ju26eZb8fr4xCEAA16RLSlDfmevdnscb84nDHMNUA8kPJ65JHfy3x5mPW4/tOrWrcLFtwduO72pyV1L5QRR2/vqe/7iwYrxGM91r9ghDm3AAtuDt2jTpP6C+bg5RQLFrw03Oa1d6SvzKMl4ZrCgOUtdIMW3WBKo0WH1/EULSJewzt4V3gBNA1SClgNjtRga8G3YD7bAjKhwAFB6Tq/05ebXgvIxB1lRC/kO2CSQUpteZ2knua9Q51zW8rEKvQlzkDOtQGxT0P1rK1E4e611xDe+150X/3qKH0VbSu/wul87kwlOY050aSG88tv5VWCfIZQNNkYNmAg4ZoGFSG3XuX6r1oLy/SHik3Juk1mPy6or1yAmVgcMsZY2vdffAB4+vWxdBgwTTQSuScYl4tQza2V5KXlVkn7Gka4JYk7V3dfX5JZDK8saoSRgJqeat/xk4NNnfypnTG29ylqpmRq4PhBAQBnco3lYbrMl2XX9vHlLtdCId5iI8m1RsbFFohN1t1qUspEUhmMKRSiql1LJlbt7UBemTlKIBTzFQAJBwrMsybcmKo2gXpTXfOkSy7BeOD1km4a8iRppuJHCurN1KQ3SNcN2nC1eMpMJwYxY0jhP5s9leFAIVdblZZlMy9I02J2f22KIAhShppA412j0GpyEaXNwhA7odYONJN98+Wz9rM+16nf4u3SZZl6eO2XEkYqNtZ+C8OLzoxd+rT4GCoV4icwH9PMG+vsP2+uUl+HYSgfqrC7RH8sY1v9bqry2vvpZpuPeryrr0NqIl72hrs7zAnc+cyGYuyTswrNqzZVPwA6+VHA6q9jclxcZwM8d3JiHv4I1Xy8+RVDbgvTlfVjzKfGOeaw3LrvQq7lbX2ahWpcGG5HwI1R+/To3ZvlIZF3ETrOeqiR5/2AN0u6H203h1C9Uw4P9v47zPJnWwnXx/hdv2DB3eKykXXYI+/+CDhdq90MJzgr1jcW3Ba3Oez3WPD9+F/iT/D/vnL4el+54Kow+cmckG/BgpcANWl3k7hHiKMl4U5xjlewxVocsZ1iixZbvI63BmHUC7yCd+FFnpyaBugiwlMgPEMeLNSKU2G+ttoLuUfNNg40Xt/CpD4bCQsYKaHrsk7uqwadxnPrrT7WxFNvbyv0kcScN+NgfMVw/dkz4PXXMQVWwS/zmK1aKPSCAPIwHmpd0YvTxp6pgHWb60MCbqT91hQNOAAn4gMOwOD/bcjHv4+Ux9BPoD7+rNdJbE1JE7SxTkvYp1+3e/yj1hjjr2AklE8PJeyp+633NXxA7VtN66Y4k7r4RlfCjeVVkoudpeGUgPME2xo1Occ6e1mqObqq25x1WsGeDzXrqYOP8bxpTdZD66v9m+xWr77xCnoA/ToMAzAha76ltkUQH3B9sIYa+wbLheDRALveCNceUPtgqzsuW8htYSdII0z3wQZgnlJyRZoyQfVQP3T2DI59qgEUalO/pEGq4gYAHVZSjrqeenItVHmOB20m6tgZ9B1kA4CEAsOogZvehdGBYPOMiZWNfNOHVQetXcmkjtU3FuKKenlmWnHsyNBP4WriU9OzEON27KUNa4KEpjaZnn8A6ldUfV3THI8tu0IdNhXHy9hHQFvu1G1ah51agVnZPDVytdVazj+PhVhGNePfZrOmJKmZXZyqx8vd2Hiilr1dP8t8DnRcm+GWeTH9ZfW6WR3PcILzYfFzOa4uSrq8PFPat7qwGub5QduvKTpkNoccLllgddAxchUjvhb/ZZFN+kJuEeVN9PBtr++DMV19G8yRnNcHD1KM9af3E7m87UTd34134228fe0UX8WreD6YMLl/+H3O8SOU+fGwTXEuMLyCV/HiJT4zC+4Lt3+HLATczaH7hXrt/LjeENfBdQg421cuuAnqlfQyJhc8ABZNuBGOloTzduuboqPxKs6R0CCgR0oBLXo0MZ8aX2/LiWGSZjliJjw4ANQs5DvIpJoQXsM7l4f/I8zcZJDriqn9DMP08lu178pnaoBUSLZBiS/VWaQdazMvyAOA+Fd/NRlWp2yvoGRivvo681BOhuKvKfdehHImTPukaLitJtg81X5brTIRB2CkMZcABC3kkACMCOJT4QlXyoimTDwCtdSaUjuSU2p+cS3h9R4kjIY1KSvwCuoG82uSKcJs1zqQ49tzPCx768ICdfvoeJ8j8bRcU/c828rBor/X8l0JQCUE5w6d+Twb1OmUOqVSPpoqHfy+ufGTAHTrUJFtOXy+NliKDS1SaEqTZE0cEtVdYQ2NOBkXmnlt5WlQo3F5SlB1Sd0IUKAc0CEgykDq0QppYQRSQBqIJhNAW7r0O2babDTNSFpOB54nZBS1++ScJ0m9ZiAO+FuHb06vrTY7NLumpEsmtdpBHKlzEYV/XouPbW7hSNjlzzjRLrW5Tr8B05RRka06f45JT9MbTlXcqbS9rzttLe2/OXOBdR/r99qQXQRcGjWJaTTHVcXmLGVd/6th/N4y8ikWavRuQALHnzp4OZg61KIUsWmO+jcxMTch3p5Yuw6uQ8DdLTgutI7JjZnxetEI9dz/PNpQp3ooUC1QG8/UJ96v7Xi9AFE9431CmnzWro/vxgfwdXzjDucrgO2mngL3GVPj7yYEHICXSsAR+zY27x9hWFdx3C4GwQ4XCwG3YMHtwEOac3v5BbeDHaoFlva8Huq2I5Y2XLBgH3C0JBwAbLDGu/AMQMJreAcXOEXAU7RFZIsU8d70LYSUTKPqHJloA+oj58+hUsO86/CmIZUs4e8t6r06iQMeOLpALVFTZDWXWr5GMk6Itthnv2+DPziXDEmolIA21cp1FPBqEVUBaQom4seVXovkc5RzGet61BYTNe4gfJyRU6xXmYSLRRuQvuDo+w2SZqCcmn80X9i7zEmeTZFKU4SRspB6zRNZ+sR5kk/DMB+Nq/7htCxabtXg07pCwioDoFppSph5zbHTdwPd20YK+kEwdSycrJNvA0h+Wka2t5ZH84HcJ/h9SlWBBJ2avqwtQ1pRygCtxnMA4gqDX7i+yUQbUgKa2tRgbFukENCFFqlpMTggLNigQUJbhKERfdXxSuqYRlQm0MYV67FCQi/EGq9bYyrBEcX2qJFIZtqRedGMJIYw+Vsc7tPUZP07jmYD+07RtTZ5Ln8DUlsWrk6L5AJPXWcTnisheuDCssSAaf00cs3ue395w1xaDcQw5Dunk0ETf73bgNVknT2cpB+tnqRUkoSsTVOyjnPEyXjz58uoQuP5mVrzHOv+jDUD6zhap93gaywTTGyTcbl350fNTr7NrG7JTTYrtOiuYMvAzIjGyTF2l5h6x8WSd/00sV3zE5fJoLYYm/TaXVrOl0uyHRZs/iOiG2FTfR+HZ5WaZIdCyl3+XO0XEacI1UjfD+SS9ABu62/vr/D1OymRQd8L4dblu1/Yc+WXeYeE/X127gtheMeGssJcsGDBgscIE/k9rrfAywIP3i64OupDy8C09seCBS8ZiybcCIe677kUHRqc4RxN0YfY4gSv4MUgBgeA03CBhswOVcnWQKBJSZRPr6HfAXiGsROaDmZKMiETcM9gLBfvqZYc5Tp0snYBO/6uEhLuc7aSXgK6TSaflKDqpUo00RgC0LbGaSjHRJ7CcxueC/JG1sYi6TEaGM+l8Ui4sRzkh6bSHkTZ7h2yXgFnp/lzvQJOT4x4a1cwjTctJ005nrqCeTOKapqQ8aYqR7KKlTpFJsuYxhmM+NHKakdoejRFeYppE4r6qQQT01q7PNjI/+WP5nJp/U5QNzrjM1/6mmsBfPd/YyY1mabKLU8lX6azkraYgnIHrIuSZFNtxPJp2fzg1Tby+XleQQc6bNEy8N1tNkfZrYB+FdC3+TMqwbZaYdu22DaZgEsopilLGhuskaSjItYD4WQaF9SQa4rR3NWkwbA8FSREpwFHP2tMjz7nTFOOwkzTZItldhybycQg9swilhY9VqXcK2TfdPnPRNFKWPH8gGlp9UVU0w8ac7kteqzKn9dF0TrntLvyAKVqgBuBQZ95CatCXvKutt2YgEMV0oTwDF+XuW4ngL6ratosDm3dlL92aE8jTny8On1tv+lxAImXJu6H0ke7F+B+zI81BMfh6pL72X2qXesSxDJRmPh1N1EQh+eF8ZWq0vjjdF7B6WzaHKfWx4CNsSly+XbI/doM9YnDd3spkFD2pz9TmbBi9XvZ+N8NplYcvm1tsWfzQz2H7y+m6jdNbu8X8go1Thot3B/sZ9kOw7shZ9r9bMNdsHdSD0/LPwbY6mTBggULHhvqQ4u6Q1pwt1ja9TqoZR13v49dsOBG6JA5jJv83SMJ961vfQtvvPEGnjx5gidPnuCNN97At7/97dnw2+0Wv/ALv4Af+IEfwGuvvYbv+Z7vwc/8zM/gL//yL6+d99FqwmVhsTEM+ex4qnizdeiwvugQQsBqG9FS8ywnYKTYFlng/zwAXcrXaZaSxNpzVOTY8JvzHiXTJOeAek5kWjrQNC5/p/p2uwK6rV2b0xYjZ0CNMcZnkeaEMN5KoeoT6OZZ+cQTGI9IrmYlVVNeha+Hszab0WTZ2sb82jWFQGybXP4E4OykrhexaoFmXa6zgiSjEsx05JSPtIBpModknfdNptbfTiUNlWwpiacaayEAJ+XHGWq1Q//OVH9rXn1QcVbCcNyRJGwAfP3fWjgl+LRDSTzysWE51gD++l/VWnZKlLUSlvG8SiPTZ3mSXIP7zTIoW8z2WaNOV+XQnilWX3j9RLhGtGoaIAVkk5QJCCkTb6zXMMxogjIlbE9OkEJASgEp2CLc/JlR06s+62A+3jLVleelXJE4xDUBb/4eSjWsUByONG+pPrkygVMP5lClqVpbYfikachM4Kj+qg3s5NIIQzpjAifBNiim6cZ0akIqlVCe1OmHPMeqqToUqHVXk1ZGqkVXZ5IZjSsPfUNlw4ZsG+oAWgl6KXtEkHbQ/H3eCdZLCvYvSg/YGPB1TUPfZn1Ia2fz1+d9v+1agNd+z+YJOKZi5kLZt7sRwFdjkJi7NwRjcoBmPKfi7U7rbTwXLTfTkQTYVzoh3S3hpm1U+5XT8eG1GBfdgoeHHwfT86ARHeYd8jC2vDYnNTJbXP5kvmxYqcwQ8z4glJmjPuSxX213GGKry99Q+wwz0cx5YH/G6P0jHy7aP3OxCxYsWHBf4Lu+3u8us+BdIc18XzAPlePWfu6XcblgT3C5QaL7iXsJPvGJT+BrX/saPve5zwEAPvWpT+GNN97A7/zO70yGf/78Ob785S/jl37pl/B3/s7fwbe+9S38w3/4D/Gxj30MX/rSl66V99GScEDWnIgIOMMFznCBHg1OsEWDiFfxHK/iAm1KWL2IaPQQC4X+EcDTklgDYJuMfFsh+4RjeJIfQBb+X4ASVUOAsbqAzY/qV05JHCUFWQYlWZAJqlWxfqfmGGPK36OT5qX3vAf45reH7IF6CaF8x9TUTW6FPAubyXMwKphnlQb3aA3QljJuI7AugdsGtS+3QrytCkGnvt1oFdCjWRtRlxOFEW8qb/UmD32F+Z2kEdMK7g+AU0oyqCabl/WuyhfVTNM8CNXyUv9nTEeJMi2n1hkA+gvrNKbDjmnkt5bT11MP1TDtXX7Y9LqSiFP1JEGndVOuiGX3eXipp35XIpCEXClzSnX2qZSXRFxqbToYqpJg5k1DAEIhXoL5GKrfEwEdVgPpYAska5xMGuWKqVZZPQWoTmpNQJmmE4mgiG0ZNEb2mEbV1KYhnwtoBzFnJ/nEoQUiqLmUJK7lwNPgzeDJh+UHjCBKaId2iFU6FOUYgWnloAFbI8QoQA6gppqZyRybTpwzlZjDxqE0poWUy14TUury0fpJ/aNMCeq9NoK1uZJ6OZ1UlaEmcZhGPduyPTX/gHVJrRYQT9VeSaEpAm5qCV/XaRxe62TkHkXWftKt4+nrL9d2uv3mf0/TmzY2zEzbGLffrPhUax96fnK9mzwXvGyMnwjvc+4wejU/FTZ/Axyf+2XGMAz/75NIQdcHugy5b5zgBJtLfSxyxrvKkYeHhRn+mJuX9x3+rfVYYOsU+6X3HmObLFiw4NDwOr4Db+Fbk/c+iP8Kf44/ra7ZzHa/6/gVVuiO1Q7bDlznEOmCWmSclnZbsODK+JM/+RN87nOfwxe+8AX88A//MADgN37jN/CRj3wEX/nKV/ChD31oFOfJkyf43d/93erar/zKr+DDH/4w/uIv/gJ/82/+zSvnf7Qk3CleYI0er+EZTrFBg76c2mvRI+AUW6QEnJ+tcRo3OM2u4xB6AK8CeAvAt1FLFc/l+1zLBQw+2wasALxALT9NyOTbFjWRQpUy729O028kDDLpdLIGzi/K75BJK8CIORJy7dtvAS3QFbbAk26qeETRu1aFYVSTLbr7vrj8PCnkGwm0VSHd2gaVRttafLmx7A1q0m3VZk25hhpNIacVkivHlGRE/ZrNQbXf1EmeJ3o8mRRQk2aEEngklIxvqNP1DvQaucY06cMuzoTTMrMstMqmfI6YYhzKxbKo1hqv0WwqQbVHHTRnEo7lUfdlU32i17QunhxknyghOZWuW4Po2KmJFGS/b6WeKQDdKiA1QN/kBFd9jxQC+rYdBm8X1IRcFPOHqLSiVJssa5etKhpC/b8BNfGT0Ay+xowmsiboHMubw7RIaCR9mkRMiCVvpSTyNNIgDWShtRDjZoLM6pFE6ByQzV5S6y7fU60vEmRJwhDN0Iapqre1g5XSSDdqgqUhLYbVyVrTGj/sQf6vHxjLpSYamSfpslzmWLH5czmwTXyaGX318HPOndKnGZdzagrrHRWs7TkWHV9twc4xPd58zsM0IOdPi5ppRU9j8LNum8vzHIv/kvsNhIkxcbXNin8Xjkm36ZFWf182RscFP18ckvDZH1AI5Vq/Z144UpmD9A320E+SaU0DuYQvo80uJ+CIfdMenIK9y4E4LCEPc4ac1oJr0JSaHSPS0H+1weh9HnMZDVrE+zxevWDBgoPAHAEHYJKAy3trbyfq7vGYCLj64O717Um8ilfxHM/vtlAHggCK5XaZylqw4IHhuZHroCzV3nrrrery6ekpTk/nXY5chj/4gz/AkydPBgIOAH7kR34ET548we///u9PknBTePr0KUIIeM973nOt/I+WhDvDBd6Lt7FGh4gWZzjHGtuBU4ldwmm/QWwatM8D8CJl4qeHkW+vAvhG+X4BIxYCMiGXJdCmDkZ/bVvU8s4eqhKSYbNm/Z5pYJpykDwphecOVY/eFp9wNDUZQia4Uspm9ppGSIiiGrcqRFywJAYot3SC+rnx1v0SxgpMvkqsVgNUZjFTyr7cqL3XNlYPIH8X3mPQ6lsXAma1KvVqLE7VTvT/5tuOhaO2GAnPU7nHT08e8fvK/Vbi7HSioZhXKvdekXyUEON4YYPR/5mSUFoW/qnpRa0Dyb45n2laT2+ick5m7MvutdtoMpKzywnqdvdYSRhfxyn5u2rN6fNA0o7kX0ECqj7pExDKWEvSNikAsc2EXAoBTUqIbYsuBKQQEIqft217AkVfKmraUS1iIb0ADKIJI6hohjITMEaskXQaq2nmb6GYmfQkgmqeqVYYtcRMJdBshOdU6BfOGtMTGGoyknmshjr0o04KskkheUgikZNWLl0mm6yjOPTHnV5r8KXqd6yus5RmVdg0BanzZmLcmhDrK6JK62smp3JZqM1XD2ivzZVAIsqM/vpT492obdWM6FjMbOlaCepvPpyv627SLU5cJUm4i3DSDVTWfSN2EXvs/1wD/ptm068GrVk9jsdprtCiu6YQTutr41zzIlGrZkcPU6S84DqwkeEtS/uxuH+jYVwivbIfIzjPW6m84MPePF065zbljbpPCEXzd/99dzXytnoInOAUG1zcIgWzRqBj4HgJOEBnN7719n2cEQsBt2DBgqvitiTRgt2IgwDoZqu6x0jA2YFdbbNlTC7YU9wBCfe93/u91eV//I//MT796U/fuEhvvvkm3v/+94+uv//978ebb755pTTOz8/xi7/4i/jEJz6B119//Vr5Hy0Jd4oLrIdTJNl8WQDwCs7xanqO1/qs1tbEHgiycdA9GKVrFPC3yBptWxgJdwEj6XpAsqzjA7VzQR2IJPKA2mQh5ZE9jIhr5HvZ/6RUtMIKGdWXwRoCspnN8l21zUIA2mRkWN9bcRvUingcJKplQKW9BsAqlHhBBNGFWOu68l203dYrMzHZRyHgYKQbkMulGnGrdfaBF0SqFfx7JyFrYlFDi4WmGUbVZuM6ipzKKUyji+SZvs/4m5pe7CumRat5JM1IRCkZqKwlpAzmvtC01pRk0w7Q+q7kT6Gknvej5gm5ExdfSb8kYU4wIriGOniLiePD9Za39oszEzm0I+NpuRv5VK1SJUrZ5qUMKU3Ur8nkNAD0LdDEIkwPwLYNaPyDkhJiGcAJmaBj8fKnaX/1RZQUBg03CudSIcMC6DC3KSKLbWkYFV+kKvWMPCzb6hrFVupLLiFrp5nQMlVhmW5XtObm3olTxEUtTK61ozwJRUKJBrHSEEPrlSRsrQ1HIm/KvCSGuOMymJZavTjlIn+KLOtd2SD3aErTCJem9GIuuWoFGiFjbU9tQKNm9MSAmVvMeXmNPMOYoGyGMlrbkIwkTedPxlntlaQLUgsz9UkfZr79SR6bnqKdwhuvsea2VPXj7Sfb3ZjKo85vTB57zBFwuwiT2tyHjqT6RXEIokhPCPt7U+SRj7Nru/zwBMlDgC+yjCj+kjgmzAfldUf9y4L1upkK3Jdymi4ycLmOLHWw7x9GRNx8l3kf4Lsvv7P2c7ztB25HwCn2bQzcN2xExaJftmDBggUA8BrejWd4+6GLcWtwfzd/OnjBTeFlEwt2I0sealnAggV7jw648fKwxPvqV79aEV1zWnCf/vSn8cu//Ms7k/w3/+bfAABCGM85KaXJ6x7b7RYf//jHEWPEr/3ar10a3uNoSbhXcIGunHVfY4tTnOMUFzjD+SBUBYCQEnAKpP8MhA0wWHrpkDXiUK6RwW1gRF0A8BrMN5xJI3PLklzryp9qxDE+3DVVMeM9ynU07U7uuXGi/tI0KdWI8z7VQgBCyuYiEQoHFIzg0+Ly83RVp0eQcCP5FmPtz21dRt26BZo+E2+81vUYtJSC28cqQZczKpVTM4tK5JAp7FGTWsB4j6zE0aC2h5qIOoURWtRSmyLqWhjh17jyqQIP0/WklpqIVHkvv7/yOrB9y+qpZimZnmr1kZ+hht0KZmqS9xg/wkhGtpPWQdefVCv1JJ+2iV8bqE3FExgh6a36KU/BNLWdlFz182QAEstLUlSi9as8tiIy8datchLdKg+wQWm1kG6xsNt+4dOXzDY4BRvXTC7G4b42Sibg2lI0+kFj5ShgrIkqLvzVh1juskxyGQESh/u1hlFNXBkBY55/jDjir6aUJROLqqBYC2S9/7cWtYadzUBKVlmZQmWO0cqRqrIpYab10eFBgqz2AackpJYaCDhFj65Kv556bdDV04Zpq9GcovreqY0yheGalTQM9+o0JxYCw5/Gnxbwkcyk1qGNZD9Bado2Xn36afTwKkLp411UzrwoMlXfrifmV8KVvevNd15XBMq2Y+yp6ce+7f8pWI4b/87W1u6lljRU20s4tWhMYlMNxPKV4q0qo4oXhhGpZWOZ9rcF7wrqmzND53fvRXQ/kA8Q2FjIvbsf2lR8QvMJI75B52aPl0PAsVx5Z+F9JO8HajHXPiIfTIp72n6XQVt2//3w3T12rwMWLFjwOHEMBBzg99ULbgN9Pz6m4yq3BWUfdZst4/EyNAe4olwwjddff/1K2mY/93M/h49//OM7w3zwgx/EH//xH+PrX//66N43vvENfOADH9gZf7vd4qd/+qfxZ3/2Z/i93/u9a2vBAUdMwmUfSFn77XW8hXfjHTQAtljhJHR4vjrDK905mm3C6j8XwkddLTwrn1QL43c1KwhkCdQW9R4kuT+al6RW1AWMwNA4gBElgGnZMZ8AU1Fbw8i9xhFvhfxCyuRW12etMxJjAfab2mY0CxmoIdQX8qzNf5tSh6bM99S6o6+5lPK9pkWlwdY0wMkqm470ZUwpX6cpyRCA1YndG4i4FghKqAHZJKJZ2bN2VK0suN/6rlIzlWahz8LRJxy5jQjre16fevcpWaREHe9pucjdQPIhsTSVHkp6eGakGCXRHENr+U3wNwlKXmtdGDUrqflPae4xHNsXqAk2D/Vpp0Sm5ueZECVTg/zWfvJjoCkEHMO1NuZig8H/WwcgiYPCGAJS21ZJA6V7mqbw5zQ3FIZQmzIoUtVI2YcazVGSUkrluhU3oC/xGmSPMmbeMRaturbMZbkBQ6HejETThVk7CAExEApGuOVhvAIJLtPUCojVQ5OvMP9MuJixIdU+q8k2Xhvbym/xKiJeVGJcagvqlIkhfgTJJP+I1yY0Y6nzLs83lif9CuU0zN4+ydCmtB7zMJ86JAUbaW8bxEZfKrE4RX3UG5A5vwJKNI39w9XkZC6/r6vSJbZw18e31tqotQanyjK/aRqXXUuqdbE6AfUkOr+R0LGRfxt5VB+quvpmpO4Dpmm92A+TWT3uHwJ+/NtSIbdD68gRI9jMMKh/OqJrdyXfWO/6EAHbwAyWMk2G82bY1AdkX0IyJvVjH4/o1vdQ/p3XqA85uubgn/axKd2Hw3iOi2VMPSyokV4fpdkPNMBeEr4Ktl8/8ybaf/g3/+PDvo+xBQsWLJiHHqCsD6/OCTcWXAfcP9N0M48vLdgNylvqXeDSbpche4hatPP3ArTidxNcswvf97734X3ve9+l4T7ykY/g6dOn+OIXv4gPf/jDAIA//MM/xNOnT/GjP/qjs/FIwP3pn/4pPv/5z+M7v/M7r1fAgqMl4U6wxQkSTvEcr+DFMFVtcIamf473dOdYP4s4fQabx06RzUwCRpw1Q8SaWCOUNCNhsQXwHDX5tpEwSjKQjOPgVI23Fka6Bfeb92FcRCxppJI+iay2zURZ35v4pwUQVhamaTJZx6qtivlN/n6lLeYlSx4xGXnWNECKwMm6kHKpaLQJyadmJYfXh1zz7xKShYNWF7+v5DtcPBIyK9R7QU8cUfYbStonqCXE3iyimpcEarOVVaEx1mKbO4ChxBjVCbwGnyf7SEal3sqjZVSTmyybEm+aNwnFyya2k4n4nsxs3B+lrmxjkpHaf/4olBJy7O8OtWlM1l9Nt2obC8GZkMm2kPL3TjUDgWxSUgZg1zRI6oww5TP2KQR04QRdNQi8kE0FqhHd4ODOyLJcnQZxIOvoPSYTTdtKxZAEG4XYNmCj2wiEUmEl1NRcXgLQFTF9brqEMJBYarhQiZ1sqrLedOQa0xtPlM8xCUOyKpWYAee4GEhJozzyoQiWiG2Yy92IqJ+C1nZofV2Eqo1+owgiTHCsBGEzWsJqm5qWYk1S+jQgeQJGiIQqT08w1VTI3FqkzmOa+hoTWlMbRGpvWjpmPJMI1Xc+lqqNEICKkEmTdRvXge089pJ0+ak0fc2mqm7ac7uI1/ky+TzGZis58Wr+9wdPsun1euPFJwDV7EMCwsLXxFn+ZurLbdWG+XO8CdanRH9buOTGQBqe+TTR50DWCuNMNO45LRXrcrwCbWtLbbdw76PtuqjHEufq/egTmvrN5Mf+IL8n24mn6uGwT2W5DDT3fIiYnttefilelhlWy7He1C5YsGDB4cDvzWpT/se7En2Z8Hsuc1cBLO07j/E+ej/WGfuMelTt167q0aIoDd0I97Ss/P7v/378+I//OD75yU/i13/91wEAn/rUp/ATP/ET+NCHPjSE+77v+z585jOfwU/+5E+i6zr81E/9FL785S/jn//zf46+7wf/ce9973txcnIymdcUjpaEA/IU3+MUlaGz1OOsP0e7jVhT220NI8laZDOUXsPNy6NImnlmlxJMfZ+o9o7GEdJgUMogiZJgWk0k8zQNX6YV0KyA1JXlQgC24uJAteCATMyllMk5chFtA3RloNN0pZqspEnJk3WOt+ks7fU6x2G6bQOEQoKENpN0Wg6kfL9psvYcpcWVuUmSWnDX/Ce1q3ybA7UJyQ5GuJGoWsHk1z2m1wEk9iBxlDgKMJLJ+4pjfPIyqnmH8vkKbI3HfNT0KO9z3NBnnMITg450qiSa6n+ObeNJO5aTdWLbNKjrq2NY28eTh95spBJ02o4nck/JUaYpPv2SHE4juZvK736Vtd5YtxQy8RZSQh+AftWiiXHoktia2cEYAvqmHTShauIrlGvt4P/NxPUBfakoF032yUGRSnc0g7bKWNPJQo2F3+q9KxOFeVik4V7WuDOGMmvSsQNiIdJ0cCbUAnvrUCVltjKIs9ko80+nhAu7LA71o1BeSSvT7sthSbTRRKaJ4jMhpoNWSwt39pxai7aBoo8Spm2+4phe3e5KnF62McjTeeOujMMYUcj0xuYfrwMj1sZk2Jgc9fWbsWNchbFHzTZNu1dPgxnXkd3Y3fH8a82us4x+4rg6fJqq9YZL2uEm+V23TOxHJdHGbaFjm6d+tE8TutFErDlpHZpr0BVTxNzu8JfTM015ZnSusaeaT60+U/TuSBz+lt2X3F7OqcyWU6EeHvsmiBo/KZ5ofziEcrZqX8ivuhT7005TCHtevnlkUjgOT/NN6/AaXsOzwRzLTcrxEAI607xesGDBgkPB+CBcfYBzwX1jaWcPO5Y3h6u12XfiO/HX+Os7KtVhoCbUl7G1N9hDEg4APvvZz+Lnf/7n8dGPfhQA8LGPfQy/+qu/WoX5yle+gqdPnwIAvva1r+G3f/u3AQA/+IM/WIX7/Oc/jx/7sR+7ct5HTcJl4dYWWzR4F86BBLzaPcNp2iCuAgLVZAJMy+wFjKjokDXavAToFQBvld/UpDuBmY9UNYKETKKREDiHER00J+nRojaBybKwnByMnjSKmdhi3s0KiCWdpi0kGbWECuG17TKhBuTfZydZY45FT6WJSMCtV8DJSRGtboHNJmvIKXkWAtCugNW6FpOkVkg4+thjPGqkxdJe1A7zpBY1xdQMocrUIfc8iUcJHzXBVhKG16lx1QBIpQHZ7oCRViqPejdUWm3hSV6t5DfJQPUn17h0mQ/7XskzlpF5eI5mrKQzNp2pJL1qoNEE51byJgGoEnmWg22uZjWZJv9Uy09lx13JDzDiTvsNri1IyJEgLHVKJ0Bk+/RAbHO3aRqpBbarFWKbmbsYApqUENsWXWgQQ1sI44QUgC1apKIvki2grgZqiARHj3UhggI60bpKEqYmEUwzjFWuCThU97ZYDaKoDtkfHPPPpFsD6k2YqUlrYC487BqvkxCsCTcu+LIY2HzFMUzniMgtViNlVHNzODaFOa9OmWuVKqN6JABrA3e1YG7KLroSepYHyVIM9TOSLw1llXmqKl99PbhvY01A9Q6npfLESSM52/WIgP8OP47fxf+zql1EGE1z9UvGvqXq3hRqgtXHJWHoNQ53Icf1E9I8Aaf0l06bpid1O4G/pjd+Qeg4ux9h5ZQQWU0+5pxto99XMXUMsozanj7lub7e382Hn/fYC93EeItl0mdL6HkRjb+/td2FcampUT1996Fgs+TUk/RwsNmCv/YDfPdg8Lf40MjvkEMQLR66TzW2cn/jOtyGgHtI+IMkh9uHCxYsOHZwDc6VON1N2Cp8mcHuEvUBV2JpYw8j4G6vwfXYCDhieu+/YMEY733ve/Gbv/mbO8Mk0Uj64Ac/WP2+DY6WhAvo8S48wwk2WJPRCsA6bocwfQO0F0A4R94tb1Cbf1RTeBcw4qyW1tbysRcwKZEqb1xIuoynUmz6eEuAHAe3+wyr/U7Tlywj5HcA2jOg2cDecUWTKgCD77aTdfb3FmMm6UIDrJps2nLVFlIvZbIuNNmHG5pS5DWwFp9bQDZFSV9vQ1+U+gQtJ9tiDdOqAoz8oWYiyblQrlHjK0j7nMDMhVIrTtsO8l1JL7Yry/+KixO6Wr55IuXk2PBujbhue1V+e+nrGczkJAklNZPJT62D5nEqafs6UrNO/RsyTf8+V5m5Wh+klp3Kexn3BEaIbSUd1Z5T7on+2ZgXw7ANINfg4q4wLnch1dKq/DF/AClkQi4VM6ptIZC3TUBsG6Ti9w0AYoxoUwLVMSOyBlyHNZpKeJIJpDg0thr0C6UZ1iDNlv3GNdV9K3pAhzBo2GWTVVXVCgG3BgU52YSlETAkpoxI83kaaoJItxpKwOk1M1/IzUn+vpbY9EpndFYPFA1AtpidBScRwrCe3KO4zYgqrybMMmr5Wb9mEFX72nvCrm4Tu66tY9N6nZpqsXklZ3/ivCawVB+vJpU88Wd9FSoCzsoUdoj06nSpVTh1X8tJoay+Hmq17TquJyN5p9Yuw2RcnwbvKmlYa2JeDdp+jD3dSpe33W3gp+KpOiR5iftDXfWY86ntH8Vwf5iqY5Bn3dAhjTSNeADCZrPDRD1b7BMoGLBZZh/KuZ+C/zBoYe8HqL++/8/F/vThTTBeqzwecA136H24YMGCYwb3ubq/fLzz9v3B9ofE8ma4ChYi+PoYP7tL++0VqOBxE+zTVuoOcbQk3LvwHK+JI7aIBiFFdGhxgg6rZwkraq6puT3FFpkY65HJhOflN4kuO9KeSY+I2q+c+vsi1siEHCS+khdK0FG1ROeR1n33A1PJrASEU0nHhQ1Fs+z0FOgvkE1aFm21flvMRBbtuvUJEGLJupQxBABnpdgbIKTyfDFPtge1vKgVpmQc6+7ljq/A2p2jVLXfGJZEzZmLfybp9gBeQ+0vLcA07xhvaq4+LWlQS4wmLX1YHQ9qGlM/1zBSSgk6NS+p6dKHmvdjZ4yMxaF22xp13RJyW/K+lp/tqn2h9fBSLW1TwDTitH78zjhaL2oy9q6NWE81ESoagMmVI66AVLQxY5PJNiCPx9gAscmEW58SQkroTk5yVYqTwgQghKx90gfzEdaVBkmiiUDzijwdF4vGFpuo1prrS/xcYZJmfXko6RNOtXz6QmdF0OdOJv0SVIuNXngierRFp06JwDFh5c0/slzmQ8dOCqVCmlE7Zzt0Tg/TUDPKhJpyPfj4mFjPT1c97HShbnpyG9qDYI8172vYXG4K1a3capYxSQnLfD/kxDZpEaStGJ99y42CfbcJvibLxoRm3f4kslr08pCe4BQbXFSh1Z+dYrx0nF+5eHKM+XlQNKblNoOsng4e52FjilvWHmamcjqOvtLqUdQMsa8KrV8Cqj7jNasfqZi7W4BPnXnQMtWv8VCVrn71XlamZdMwxnSb9GVmsVe9adEGefpuf570ZaGu59SYe3iYL4/9IJlCmU9iNYs9PPZRnJeqeXm/xhURYFr582VcY42IWEz07hO0dfdxDNwnwuhwz4IFCxY8NPxMXFvuOJwV4iFA3363tWry2GCivf1doe07annSgr2Bmcu6Po50KX20JNwJzsEHcIMTvJou0KQOp7FDkyJarylExDbbY9zCzEuqv7bnMImaknGAkSoeeq1FJkE6GOHAspCQY1pqwo+DVwkXkhmaL8kXMdk3EInqb47kTCl/63yRNSXdUEigBjBTkUrCFKlHeA3ASQNcRCPLvEIFy+O1y7xJTtabBJxqWlFRpilxpw7YMN5UOZQI4u8TmCYiiSgSiNQ6Y7m9dpiSeGcwkovptMhaceoTTsv7CjArOWJZvdU5LQ/7n+0VXPgValOXat4SEq6VNBJqk5TaB1oHTzTShCavqQlOJfWUvGM9tW3XQEryaJVy9JAwJOeK1mYA0Lcha8GFgNTkDHoE9KEFdbeAAITcTZuQmVWSCdQmyxprymbndCLCoKUGALEy8NQUbQ2ah1QSjLRA7RsulVJ1wurWi69QyhNL2qtSFi5tcwuR0CJxyDLSQ1OeHrLOCEvbDTW1/2uNtiT1MMJGF9QkGkz4TY23WNoDSMNDGEsZjVTTAcQa92iHIaY+43Ir1G/h/KstbWBt/f9v78+jdMvq+n78tc85z1BV997q4fYok2O3IPITiHRjFAdsMSImcYEEcoP+DGrWIoTEDGh+/mzW+uYnZnBI0DgslCyH6DKCWQaD0RVAErobhMYZBG0mpZue7lhVz/Occ/bvj70/5/PZ53mqbtXtureeqrvfd9Wtes6zz57Hz3t/Ph9L0gnpaJUtoX8LUswBWrVYujf62nuLJx3bdvpciCEHbDFFKD89nFhCLx1STRd6XjDf3563XXqWJVfUXb4XiV8XT0B90isd3B7V7JrPX0oL6xKl5ZG63x3mCbjtPO7Is/3R99ADkZLiRUfDp2UjEgG237SdODlj/yF9vuzmRIvGLIaiAXRYxC26Fs17sFwmLE++ZP25dBOAlwvLJUop4py0H4aOLif04st29TdjtuDpMkEtAFwtWGONr+Mufou30Xa7zIyMjIyDhezf1fh7X4CR8USgFyP1GuJRq9uKinqhD6EnDq27TFzuBva0J+fw1ARlrr+lg1gavBQcUSHKkSXhKmYIWzVkykq7wersPEW0nVhYhYSaoL32OOAbiSAltmqUjOiTEK15Vpp3hcwQQsyaJbREjmgLyffW/5sQJ30zmELg2Q4taUne5JQtpg/HJh8T0jxYsmQWv+pLUocOnJ9/3sXRBlJJiMv+RVBrJhLzniWSGkIcok23iMTrE0sNsFZBU2v9WNKrb0POoX7cZih5JL7Nxmgdp1JorVchQkUOIHW4hvaRqF2YtFNfolb0nkuexexjS9rnbPgVVK6+SBlFSDRLiNm+asNBSrRFMiwhARf5oBv3njmUiNzOVKdDTVr29sG+IpgvbcFL/ReBlEPMT7pIojhwLrzTOGiKQHY00exkaMIyEHYUnVZWIJy0YMFnWCAUwjuhMaxXLE8RiTkR6gbhrxBvtgHn72+rVpx8liaoE+Yy3USIloGYm5RNhu9UDgPl1MbPLVWnmSYkmhB4QkY1Ji6lqoQwSLWKrO80JRBTKIElW+7gLy/t0CFuS+H0p1BpB22DviPD7bf1Oi0KKy4kkdWFEp98Qog6lJIUcij8ZcX5kucm+eRiffUHg/6t054l6VLiSYjAPqGj9bOYSJL+KKFSQjMlDXUKTknPReiXpH9fIKUy5+NosEq6qfc9S4xeDCnpRVLTdHFfvo32os29TlxKeJZ4+r4Y5+snE3BXBvbQ2qepw99C2x6O45kexvVeVn+kHzysvz446Ly5uG4si8lFMTm9DHkRSL8qlpKwPDrQfVPRrRhHHxe4wG/xm1FMGfahajQ8IyNj2aHXBI8G9Cwj5y+5PGg9Uy/GUauLy4n+WekoXj+5XAQcLM8uetlhxYipnMIKVHMdZhwOHGESrmGV81GQ7phSBAuHhaOYeQoXBPuuJmi3WRII0ivwNaoVJ9pkjQlrZZtCmlh5sCW4JuaZEBFtTGNYQV1rPq69GR55MIQpe+nZ/EkrlrEswglYKeoaKiEV84cT5iWtlpCTuIcx7cKrFpPMdVMTzhI2Re+5QMoqJGKBmjmU+usTUxaitWbrvAJ8rYSXpCNxCLlm20ZIuhXS+doqdyyy0CYEkvQDiafovVMAx+LfVuo3QH3YSTh5jnkmZKaF+M8TbUYrkVt0xV+It6F5ZssqSkqyolUmrLTbIrnmoivUzvyMmV8D7V7X1pVdSYtIrAGuAF8E85LOxeRNnPUwMHCubWmLgjb6eqNtaaoK0bhquhlANc+0QPNVrN8GLRaPY8ogvq8kkZ9jGPV3X/MlcNKiyRaEEn3tNx3SrotfNmVt1OMIw6NAaJaQx1GvHFZ7y8fyF0la1jykapfJxrlPxIFsqH33vgqWWvNOugnX30IaiSeshgLxliYt0lJEU5sXF1i13e/SaNvJswof82fLISJsaVObhiVIw90JPaT1zU8q4WI7s21BS9QoJaX1qzUjphyFFkzrzpJOOuRUG1Hz2e/D9j2HaBAuph4W8ODMk6AXv8/fxPIJLUgyVnZHeyj55nrP++TdzmTibrCYaAtoUHNaljBNJ0sljVPkzf/BY74NXK8FDw9s3+9T4wcNnQNFu+rg6zZdUw8eBW2nJ74sUIKXpWizefT3acuYx90hXCFZLiL28qIvqDzquuBrHOcC5w46GxkZ+4KjRDrpbmn78/pOOEp1cSXQP0s9UVzLdTzOY/sY48HDnsa3c0eRsT1k1DbdWfzw7g6vOmRNuDkcWRKOqLkyYBoEMA6qNoo9oxlJ1wKbMbgQMmcJjb2JkmFTE0bIkj6BJUSVkEsNocPJ2i/fFeazxCkEm6+D6cI2pnn+QSU9Fpm5FELIEhmi4TVFzVWCykW9pIVqUknYfhlL1MRhg2p9WYJOfvoXREQZRspniSPrU038ubkFcWDCyO++DLyfZn8XICSf5F1IIvEBaDUeJbz9W9KWPPbNNUq++qYx5X3RJpPyS3wD8xNYmvny2r2ihJV4rQ81CSPlFqLP5h/znbQppuzS1tCXNWv8VuFL8mH79KJ3QAk9b377mIwhAD3QjsG34Jqg3dZGX38+EnGuhbZw0b8bgYCrqi6pWRU7l/NRy0wFTqliY4F4CxOhv63CQL6po0Ehr0SfKhVkqeBPtIHqWGENJSUNbTfVFtSJBpbmaZbY7yRq2Tk8VUy/ipRXG7tMv3FtXiQkvWe2YURAYukwIWCUcLPhhTwTIs6bsquoexG5GEgLJRK1XutuIGq8GifJ30GTz7L9lmwSYkrMU9r/7ffyjmqSabv3VVVdr4yLxKm2L/TrLO0fqt1la4ckvNSXkqeepqMD0wNkf1+iGnshdEolSRkl9f5zSw4uGsjzsERtGn4RvTf/7qJDW9G1X/+9JyZalzEshGxIK71HJ38FwWlr3ulP7nnjf3igJmfDEqQXMC6fPuXlwTzdvwywc9EynJTKuC1p5ua4K49gtrcxhp4vF3YvANPZf3nFP9pyh5fAsmv6cpKdlxcyLxRxxVXNy4oB9dKbE704HO4JEXBlnBsyMjKeGPpXDcPneYsqGZcT+3vd6HEe4xqu4TSn9y3Og4S6xLJX1ubry+FYY43znL9ymTtEOOxXs65aiAuvS8FhPQZcBEeYhAtoKRlQs1JPu7ZvB6QaXMLOipRGnluSTiPUMMfj90LoCJkmZMUM1TYqoHNTV7KYtLLaWwVK4hHzO41xVeY7Sy6V5l3xNWdM+uHMZ0lTiCLBzOStb2VLSChLuo1iuoNeXUm4PjljCUlL7DiCtp4nEJxillK+F1JzYD6XvXdKE2+fPKt66dk+YLUGxYeb1S60effmfUsqLmJVrLlIIfusLzpriVCkgSWqnSf9ymqyYcJLmYTEE7LL+oaTeKQstk0WkZZShqr3XMxsOtTsqRB6to3kue3LJv9e4hhEYsakVcRx2JQwGzsoHK714YegxUppZnAH08EwNLePxJQrKN1iAqeminfS++SV3FVXk48iMrPNKWYcJU4R54tuVdC8G1KjWnfB55uQhOH/4J/NMrOu818km4tQlaGy1CCGkFZlR7KBaqOJByolFR07mQFUM5yiq6TvpiRQWo/6vmrr6abSx+8AE7dqVqUmQFKCI7xvtb1C9xXzj+AT266Sa/mk6Yf6EWGsmg+17/m5fgCpKq7mSt+39aFadUpGkYTBPO8TlJKWahkWvTdSIm93Xnz6k20/TSExhapUXci+SdJFkHLKdDVvg31neqD/bb+kO++zdr97WxSvtpH2a9XItDqm4c1mYf+48jjBCmfnNiIZe4POgU13/1R723KRWjtDboAun5m7ZSEblGQ6+LxIDkout/nH3Z9R+xvaZUS6zh58Oz4RLNs4vZKwRKR+OgoEHMju9tKRCbiMDMGlr0d6wbGIJ8g2E3CXAeLPvX9S9KTn2/3EUSHglDjqn/MXhfWZgOshnN/7F8nz+D5UEJn2pWBZjypPEEeWhBMR7oApAz/BF2oUq7T7/wGBHOufCaz/MkvSOFICbYyaqpx3oqN/i+RSCBYhrVR1Jn2/f6FENJuEfBrFPAsBY31+CaxZSSFgtgiEivUxJ+lZ041CtEilOYJ5RZG3CvkGStism7rZTn4pJCKxDiQfQgg51Cec1NvUlM9q11mNsQGq4daa70fx/TVUmi5yfNFqs/LjkXnX1r9wN57U35mVSFsSamDCqxxG45HvaxNGzFxabTnLT9Sk5JvNY98sJua90oSRftT2wtr+azUVW0J7SBtLW4nWnKQhZFppPvfHhIQFfBH/LPVzW8GsdHjnoYjEQRG10JwLWm9FgfeetijxRUnjQgSFrylcICo8nhklTTQj6RDNGtuBxACgw1EarbmUoPGIYUohqISgC09aHDUlBZZIE9JEqJaq+1s9zYHc1W9MRxGipVnQOBqHhFPyJpB9g947abWLPzg9piiJpuY6ZZM9bzFfninhVnQ1KFCyTAlAfVvv3ttNu51ag7ZhSgZqevK3/GXDSArzJGLTlUTJTEsXqXlLza+dEhZpEKZadRJL+q7WiYTVyUbIIIetXcE8GWc1N22+Ur9kNsXFO53Uu5qjTdj2tD6lbJ4g8LeEW5NMnCz4vRh9wjKdJmyr7H2n1s9RvyyLtetCukFbZTmRCbj9hlxeUGpWRu9ya8bpvCsUekl/gT1YLI/pPZ2194oiznb7nZ9l7Fdy/Fi2fCnStfpwIb1ac/VC9q0VDe3SzVkZGRnLgO3X6+18s82fcsNvP3eLOOOJQs6csg9tkvpd7l3EssCb/3N9XRxWDtN2coFcbxlHB0eWhINg+G3FbzJwNZQF00HF2tma0l4wsKSRsLQim2kJ5M15lGQRsqRFiarV+N2ElKCTcEKe2PitBpjIcEWDSOQsdYxbMCMlC4W0kTRtPELwCJko5MvAhBGNM3lXiLrKxGVlx5iwQmJJ3TmUIHTMk5oCyYP4gZN6trZihcSSvB2P30ucsr+yRJ/VeAPVQvOEOhRiqTZhxFyn5QmEgLNtJcRcZcLR+1vqo0+EKd+iebbac6IlJu0s2piOVANO8i1EI6SkqxCD0k/FzKd9t09iWgjJCqGebb1IP7aEX79f2LLJI9tOlhuqTBXGseCA6QB85WK8HlwQo81GQ1oXfZs5B95H85Ra0Y0bMI1mG2fmFtxO2/BpV8g2IUPEj1z4FOKYUOGpTKgQTv2btYYYU9JG6b6yE8dYI4G1MVOpYuF0oxE2vIOuPC1Fl0LdETn9zYkSKqIpJ9posnm2pISQZmqWMiWtUk0yi5S40fpIv5e4pGs3XV3ZfLQmf2WkHcsuZ1B0ZcG8GaaOYaSJlKhUKDkGqa6bbu7SMFajUNPdniCyfuZkKizMd/26CsSWPm27RUFC9LUBtb60BC21GXA2vN28kvy9KP/pZ2/ekLpOSb7dbYRlapfWVbJNn/TJzb3AllFikL5UmCOiange7c37xQTV+ejXhx3j0HYzztWBipJ636hnu14th+bSpfo8uxwE3MV1jA8CYU48eJOdO2E5ycvdQewjHFYScb+Q7sU8V7NmYEZGxl6hF1B1j6/ODxadT+zvjP2AM/8vsyHrZUD/3L3z+TvDon+1N3WxkXHosczHjQPAkSbhPAW1C8LrIVN8UeBF1plKBZXAES0huXwu5glPkxJxIqlRRyOB+LGmJoXEEnOPQ+jMxwvpIWEr1BSkzEIrJn8QCMEpQZtNTFEK+TQ1YYQgkbAqY05nt7UY/5Ypq2BMqhE2Q0mfCalZw8K8L9LnISmxJvV+HK1ngZBENbAyhCIWRsIIMSik1IqJU8pSmjroE2tCRkGq5WUh5RBiUcxNjs13fdJJyi+aYsQ8jGNZRL41Rs2bShrCOcxItcqk/ykPonFYX24STsJaTcmeCciEWLV5F4xMvQh5JyRxv6x9DUTLHRRmw2HDDzSaRWiApqLTenPO4b2nif7ePC5wb5Kgk9vRSutYf2YtQ0Pc6BASckkW9qBBViE6YD4K79VkZRCebXWDIRWmNRRR+0zIolT82FIw6zq70mt6m0zzFfJUIt4ztL5cp+mkMYvZzECMyfHEiqt8F6+n7QjA7Q8nfTJKwtnnaelVu0xIN/U05kwMasKi7z9uXkRaxO60Rt0N5v67dltbUKPaj/20U8GwkpEh9x4RF9t86RCxpJd83n73EDT4NK1A3Mmn9MAi5W6SAZ6G0Py6JBfphDsfb4slnLQHeVOmi2F7sfXO79pcan1J6n267NLEktLv7ZTc4IxoT+YB/Xs5KIG9l7hPFktrikCir2gN1lk1JrTtg2GOW44aWTaEXqQGAJZRi8WuJXo3/FLac/8IOIEKypahf4W10S9Bf3dPcNa7HEhnk+XI02Isc94uBk+697t60b8WlHF5kOs442jBzhzWGkf/nJxxeaAXZyHX88WhRLGeflOh89WJAQNmFzFDbS9K6+er5VpkxtWII0zCeYYy4D0431JOCsqWQDIMgY34e4KuLeIrzMo6vQlnURCnEThcAACDXElEQVQIGEvOCXkjcdk5RwgbG88Y1TySPM1QUuSCSUvSG8c0JN8iYxOzjZLWiNS8pJBdx7HScfWrtolKOIV3kNlQiCxv6ghSSaBouYnE0JrDHJt8W2ltgWqBlUDpQ9yiBdiiZNYAJZOEaJP8gJpbdPH9iak3S7batpbvhSyypKeQXtacoyUaRePMEn5CaA1NOEtGWY0yqxVo/dnZOmxjXFYzEFJiTfLhzXNLMAvJZ/cAkrbUXWmeW8me632P+U6ex7L5mAdv8tkMgplJH+vBEwm6FtrS0RQuGWoOmFUVvohabygZ0eJpGOApovE4zzQWTgRtYm7PkiINZTTFWEVtKdVKk84rJiYbSqxO01anVho+B42yYJ5yZmyOSv6ELlJNKn237oidIEBtYuWLuUtwzJAuE3IxoyIl7dSHV9vF5Ux60glkWiq67zRvAhGa9skriT8IMiX/bZePYAgRREOLmOPGbECVGpNaCV1VxX6W19ZDVkHDNBFbWQEmyROrtZeiL/Kx047UnaW35HCnwzMl8Kz4tE8lpaRb/z3XK+OiAZaSt5JvCds3d9mHmP9cbIvW4y+yzEve1N1pP53tD179em6TSV/K2r8BsjfY2lKTr2IeU9K19Sq5urIHxkWEiG177eP6eVEO++H1eWo6NEyz6tfPd/ONHeFWBX9+9MtPv4WeCLlzuKHmccXT5/LVgc7D6TWC5cDB5yXNwcGTTH1d6GWArGWF6fHLie1XvnkU8V8953T7oKBXk/YTJeUh8yemthbCp2UaC0cJV6+QN+PooN+LG/o3f/PscTmhJymVp2Ttt+0x3yNl1e+f9K7efrsTAdfvb0Eekcm3jKOPI0vCDdhiGA9iLS1lUzOoxzi3pTNmiWqBgRIq4kcsSMHD82Px+WOkUksIxJiYuFQZeHjnDCmxI8ScmIS0BEvfRKHEIXmx87gQeqJBJ37PtlCCpTbxrpISO3bVEC2qEYGIG5h0pEygPtRmBCIP1GymQzXchAy0UsdxLy6Jf0RqltPNNKzUi5jzHKGknN2TST6tRplozknehGhbjXUk5J0lvERbrU+uSTqSZupaTAk3+c733mlJTXeK9mSB+tmz8YvMVLTwpN4s4SptBGldrJpwUsZ+HEJK9k13imbdIlgZbtS086J9WH0BDErgIyGZMhBtdSTgnI+vmv5bDwp8WWp3bFsK72mKkrYsw/DyULtqzl8bQE3ZLdKW+FEizdNQMOtMMEoFVz3BNp2Yta8RNX8Lx8e0B/FQYMkg0UIquqqqKZJhplVpNcrC3+LnLQwvoQEX6WIUzKIGnwhi6crvkr+EhJP3mm5L2FJTJuVt40AoYm2EYVB1ZRlzgvOc72JYZKG/7epZ0ps/Mlmb8uqWUkJV3SclhdS8p2zQUqXU7Te1TVd/SjZKeYPWY3ii4VwSm5IUfZVQS1gt0kdKIf7gUhLRof1M89V/U+Mtkyf2t+9uEvRJKOjnq0+uyPjTiWFnSMx6K3KR+T5L9uzt0LGoJtMbmNq/5nH5DzqL8yd1J/2zmWsXlwhMS8RTpfjH0Xawd3+KuT4yn/o8+Tu/cKdasG2ntSvf+W7UF70FwPaoq+MIni6WDc7MlMsGXbcuxcTbgIrZvpMVy0LozF90yFiM5fHjtwguzqi7E8m08d9yQfZ2+0foHy4CDsLFmZDng7kik5FxuLCdH7Sjiv6pRS/2Lb5cmGeQ/YG9jKf1XpDO1Lmud8L8KD189bYMF3vkUtgyXn3MyLgcuKxylccff5xTp06xvr7O+vo6p06d4vTp07t+/7u/+7txzvFjP/Zje0677LwTtYzrKVXbUA/OURcEk5RChPXJeVmFhuZHUBFIjhFKXMg7Qoj19worBMJKfkbANcD1qKaVaC8NgPX4IyfOCjiBmrq0EM2sMapNJnkT/2F9K3SWsAElt0YEEu+a+P6xmO5a/E7ysBp/rMnHKoYZxDDr8b31GM91vXSL+M4xUn9hNl9ifrGK8ZyI8Uv9L4IlpqTcoml3XSxbGfN2PD6TdhGlJinLNTF/Qub1/fyKn7gi5lU0FCvzTOqqX87VWJ7j5pn9XtrEkn3KIfWVHfS5JdykX9ELNzJ5k3LZfmzbp0dC+jH4E+BXwUv7r0A9/hiz4UeoB1CXMFsJP/UA2hind0a86VwwMxk13XCOZjhkOhwxHQyYUbLFgC23woyh8b1W9IiM8NvhqBkwZcyMITMGTBlEU5BCGtl3hKByzKhoqDpSpSForNWGwBM0FEwZdu80SX4CHdEyoKGipqBhEDW7JP9KttRURkOvjO8WtFRRYGN9yGneAn9bdWYH246MtORXyLuSl6LhJRRc2cUf0hR/c46aMv4MET9yLXCWjfiekoUBVuAlpENaz4I65jnUdxmJROlollAlaefADZddvYQ6KzrizKZ1jOti3ad9penqqoq0Uai3KVVsp6rjoCUP4d62iODFGTUx7b5d1ovBbsittk00x9rVtfSV7eNtk59FG/3F7wlZrXcxhAzu3ziYf09JT0s6hXdVU3XRRL4z+nFD2v7qQ26+bx0EpN6a2IYpAQc6viS/8kz7oMZUcAPXd3ObbARaShoTx8XNiNpFwmI74s7N5U/isVpgesmhP+bT+ji6YiIh4padfrw0PZv9J+CAbfrJ1Y7lHifLm7OALJA5GtA9n4/7hoyMjMW4mgg4weJ1ctE8cTjmjtt4xkFnYVv0z7l051q9uHvYiKQrDTm/6/Xl7c7ky4+DJODsRfpldAKQkXG5cFk14V7xilfw6U9/mne84x0AfNd3fRenTp3iN3/zNy/67m/8xm9w3333ceutt15S2lavYVYOWWm2wDmmY1i54FVDScxByqWPVYJJSJlDLbEmdlGEmBPzgn0/XFa7rk/ejM339rqPJfsKAkmzSWoSU+R0U9QcomCVYH7RXqmHQADN4o/IC8X8ZP8ykUMJLonDmni0JKD1d9f3fWdltEJMrRJMa47NOzVa96IVKCYkJS0hKiVOCWv9701REs2Z/FhyUCXIARJe2vQEqgkn9eDQ/gHaBqso+enNcxuvcAuiiSj5wqQjfgStyU3X+5G4bZ32w223V+qvZdWCcLbfSblj3XvL7Ug5ilhkD6415iaBpnCUXvWTHA5fOFrvg483F0xMtmVF7Qoq3+Cdw7ugPVa7klmXoTaSHmUyTMAbEiYISAtU7V+IKXAEj2HylmoetcA0mrVUXalQ6EAUDRBPSpbxlI2WN40ghMGMAal2VdWFASEWKhpS0w4eSxF6Q5ZpmWeGlEs1Y0QDT2Y7KaVo4aQElZZf8l0m39ORTvJMylJiNcocqbNc18Uf2sN2XTGDKeEXEQoSq25etSZsHab1qf7SVImz4DSnu9S1Nvq30qR++5NaiY/9zptwLQ6t7fm8929w6vQ7LxyXuNvkjUXkVdo2dgyEv63uQr9c6Xs2bebe3R7atyw9M5/nvW6aFy09HiipcFRMuhsy82ldbmjbuI6uVDOvKWk1X//bfQ6lW3R790HO8LncwgM8tMP7VxJV0tKt2cBYbWMosPq84XuZU+yR9LDCrhzLplkzD50FDxI67x48sSMrnc7mB5kXO3ssG5Z/nB50++0P7PqdISNzWbWMMzKWGwUl7aHTiA2wM7od/3qhdLsQhwsf4U8OOgvbIuzk+7f07e+MRZg/j1v5U8ZukMoy5uVVuQ8eVQgZcanvHj1cNhLuz/7sz3jHO97Bvffey/Oe9zwAfvZnf5Y777yTj3zkI9x2223bvvtXf/VXvOY1r+G3f/u3+aZv+qZLSr/pGBZP6xytCz6IhpMWSjofVa4lkEP2+r2QQSI7c6hPNZFDCRkj5iDleQucRkkWB5wzcVsCSgieVHKrxJ1VtFhDTViuxectgTAUImk9hhWfalYzTMwyislLycOGSQNTDguRVcvzcUzjmHkupi8FomWFKcu6SV/SsqYh5b3C1IsQVmKCUXzc2boSTT7J+wXU1GNf4isk09jEYfd9YlrS1sdqfCZpWPmwtKe8K2YepV4k7iHpHmdGaqLSQgg8KbdyNlYBZV6qo/yVtpkl6VhQH1IGT+ebzkcNUV+Ci+3SOjqzko7wXVtBG/PeFg5cIOc0W+GIPSsH1NUgNLtzXbKBgx10WWmiJpBWUWAN1USiY4vVWKTQiWpc5zVNiBglhCqKmAfxx1bgo5819ZDWxjREG0mJICUrLKmkn12n5SalbuOWrKVI+POa0lBlkquU2rG3AEM8q8w6Vlr9lvkkJFhCz2pYpcRQ0X2v5JOQTCJkT80xWjMgopFURHosEJWWjhCCUkkm8WHVxA7ZF0L57qffSVNixxrYVBJRffEFI1R9mkxMWOrg9kmtKCmVwpkYMGGFSkxJIclVGKq+qwMbn/RLKc28nVubjvaFtC1sejtrRqn2WJ8+mSeBtks79TSlPgXnTcPsHhJ/E8lvj9LALS56ApSN1uUTWS+qBZ/8tuMNFgsFdq7LxQeKxeFTAm4ZsTj/4tlS5l5v5s6W3VK9y45lFxLb+eygj65ypeTg290ZXeiDz41cF1m2vmSvWSwvDj+hH+Ap6Jv7vbpx8CMzI+Ow4jATcO2CudBefDzoncxRg4iEZI+op+lcz3uByFDkFKzylIzdQuQLIsk6rJqDGZeCOv5c6rtHD5eNhLvnnntYX1/vCDiAO+64g/X1dd773vduS8K1bcupU6f4F//iX/CMZ1xclXsymTCZTLrPZ8+ejX+J8TaHc55pMeTYZEsPnN24d7DiAxkkvrocQTNqI36+lkCspTNvwICgsSYr3DS+C+pPrjLxitaWlUSLNpdo4AmptBq/k1baIiXrxFdd32fcEPXZNo35ENOVAiG+jsU0hFg8EeM5H98TM4dTE0a0xKxigBBUmzEdIYmsQoslsaROxI+dN+/14y5JTTdWBJJUvu+bThT/d/15vULNiAoR1petuhhm1ntmzUPKKuxR/4HelElISps3y8hIXVjZ7jHSviXvSB8akRJ5Epctp5TLavTJ96KRKGlI/qRM1rpeAX4UiWqTVuug9HEj4qKpyUIDeByT4QCcwzctbVlR0VIXQUvMOVH6CwRKG1nDIh4mrPaYpVPaKLSfxs6hZFMgBSaUkSIJmlYq7lIfZUJcKCGiz4MJQGWMG1PJ1oSkmEOUt4LZykFH94CQUCVCPgVaYYCLHUfE1pZ8aLq8uS4Oh6emxnfstMQfasd+9t0brvNVJ6SQkotat7bLBC5ebIuKfmFK5Mh7og9lfa25jmDTvId3y7n3tO58V58gfg9SAhCsqT9Mm4iGkuarjmm57j1NV9tZW2kRwWPJO9Bh6bGdXPuQ5Ftjsqb8UqRiTiUU+2n3pyxLtfqu789PbPNP+pvakt2K/vRQoUc1yfcTER966PxrYdKoLyN1sKhe+6lJX2+Txcr13ob5POZDQxgLZfLZ1lcwEJCKVg4LtCRCFy9zGfQqw8HCzrUHn59lEvEHoWPq3fJgodqqy923Zf+3/GThztBdVYZgOQj7jIyMy490R63nXD1zHO4ZfhmhZ1K9dOy3PdNk7A79s3Cux53QF1O2C2Q6GRlXKy4bCffggw9y4403zj2/8cYbefDBB7d974d/+IepqorXvva1u0rnh37oh3jDG96w4JuSljqIH72nrUrOuzHr0y2i0k4wtzf0uIpARIn2kieQSSp/DD7cJub7c6QmBCWcEDag2lbBkZASX1arUkgTqzUGSpiI6UnRApN0bd5WY/wSr5WLVTG81SgboWTZqoOpD2Gsu5rjqOYfKHFoiT5ritP6Q2tR322ibSfEIrFMmPhWCWSc1J+0hZCAjlT7TUglkZkKTyEmLaUMQh5KPGvmO8z3Nt4V1PeckFWiALOKkm/W2qDkRQhTW1eiRVeb8PIOaNuIBp4QYkLoSVxD876saJYUFpKwbzIUtP9I2UvwazFsQ9AGdWahjG1cj6CagfPQxPw0BRQteAdbKxW+KHBtS9G2NFX4DNCWwe/YDILpSidZKhJximisyTPlqktUV8xqPylqKuqo1WbpKWsOUNIUoktIN6FSRAzlu3fVx5VuDSRvSizNKKgjk+oRb2FCRkUNQKNuGUKEI0fNIJIPIa5gctPHbwNNoURhSiJJ87ddeRw1ztSMUklNV5Zi7jvffW/rtKCmQXQP5+kiJSMlvlCXQsdZ8kbJrFCTpXlHv5FnliQJ9xIKU9+2w4u2Gcn7IvhdlD6oFqGm4ehTWm1XNqE2i+T7tD9KXrRmfFLmPuZJHMmHLUtJuyDPfdooRZ86uhg0fo1PXcDvj1mIxXVQ0CykwfYP/ePlonzYcbTKmNt4Mr/PA9u8lQ8IO2MROanjsCU153hYalPHUbhU0cRZdy9eIK8slkWY7bv/D7KOwp5gWTQF7E5CZ9qDxmGa4ZbD3OqlQufDBt/bxV7NCHtiWTEKDp64z8jIuHT0L77pGUdO5rqDCrYSDnqncHRhL7oqHLm+d4aVD2yPXIc7wdactVI0f07J9Xh1IZuj7GPPJNzdd9+9DemleP/73w+Ac/MDzHu/8DnABz7wAX78x3+cD37wg9uG6eP7vu/7+Gf/7J91n8+ePcuTn/xkGko2WcNRc5zzeOdoq4rT16xy3emNmD9oxlBugTuOEm+SdI1qkJ2Pz+S7Maq5JuYHxzGOTRNWyLNNAgkkxM9pUwjR3BKiTjSfhGwbmucnY7znUJOTWyaO86S7oDGBUGtifkXjaiU+K70SPXWMS2S/townYv5E6++a+HkrlqskJdeEcBsA15GSaNbXmJCCQuAJkSW+3KQON1AiS7TETiCnt5C2NTEpPXtkyiA/jYlH0l6NzyQ+2Sv2/f9J+exvMb0p7SfEmlWgEV9xfV90tpvL8wFKnno6M5FCmnXkqDVLKrDxrdCZXZU66kicAbjoy83NgmnJLtgA6kHQOKqVk6D10AwKnPeBtCuK7mfmKopWVUmFHHLAzFUMohhTNM5SbbAC8VNGJMnq6AuuoI3fqfp6QxU1vug0vGSTX3bph8po4rvi18x1z8uYj0BEhbdFt0kGm+9EyJK2iIKaRJQiRJpW/ix+b8m8kI+hyYMSQ2rmUsmZVB9QbrPRlW+G+FiTO+11FGSkvvEkPhk2HiE105uHIdwANdNpyRnry0xz5XrlwHxryR45bNlDmTUXJtp1SmTZw4IzcUu+pZTe1EvRCy2EWZo/eSaCPSUV9WZDWu+LNpX9lGRqcSYG1di0pGBaxiL5Xvj+RaZCvHk/NXrZ93y0u7VTdB8tgXipkLxJaSD0xnDM9tvW2X7Am9+esusX9jv93k66nnPMIgF3efJ2dcKOHB0Jh/ueszP/LxvmtYgPBql21cHCIf4MiwMn41QMuSwEHBw8Ubo3LE+97R39HclhLst+Iux/7H6zPPCxmpGRsVfYs0fbjWhZ+eR2+yJx4+FZgZYVcvZaTK9tdwUxow89R4osgu4sKa46sgnP3UHdV/TlOLnurm5kc5R97JmEe81rXsPLX/7yHcM87WlP4w//8A956KF5XycPP/wwN91008L33vOe9/DZz36WpzzlKd2zpmn43u/9Xn7sx36Mj3/843PvjEYjRqPR3PNNVjjBFlDSuBLxTzQoZmyOKlYmoUF9EbR8Sg9ughIaFWrSEQLhMUPVDiqCjzMhX6wfMgikkJBNKyhRN0QJqoZUA+26+FsIX/E3Z89sQhwNY5qul89rUUKsITX1uE5K2PQtSY1Rk4dCFop5yhmqqVWgGmrWtKZoWwkZJc/FBOQFwjgSrbgVUw+Sl9KEEXIN1DQn6H5OfiQf1kyoxTiWoyGQiIV5R9pLfNAtcnkkRKykMSFdV6TclflbTIdKPVRAUUDV6nuL5NJDtL6E0LOkZYlqDQrnJf1K4pwStTx76axCHS+j+SIQbxDJuErTq0eGGGpanIPJsGJWhQirZobzLb6MRJYPPs8oXEeRNJRRDzU4nJuiPtIaHDWDrov6pBMpPDDB0UaNMxeJvL5dVSEQHAWTLp4imqe0ZJTqazXd91VnXMAjBhyFJBJfdGVHJFniQ32o+ehzrjQpCK2nuZ0xoDCaTkIGWZJO86vUV5888F1Yq44JUNHGCarpwledhlcbc62+ywwp29Vmv1OKCU9lsSW8tGebaC2mdI6ko2JI+Uk3aKkvtXlBld0gq0ZbkWyOLfGi5VpsepKkFZj7u58TSdcSPo2JW/Q7haptOpGrltEDbTKYF+VB0+hz/mkbtDGti9+rT0kqfWNeI1FRmtG2m3hVk1XGTOh/zYK4nyhs+9p2ak2/SDXuGjN+Frd3xuWAzsVEMtb2/MNX+3qsXB7otYSDr9P51eNgIdd3DhrL6B/RLZxHlxVHw2xhNr84j7TnNRQcFR+AGRlHGYtmstRved8nRsZ+wl7KlDq3l6AWG5vO7bAIUl9qSL1/ibPIK/cuIZIrlb0cXM1VlNT5Ys8SoebSNdoyCQfAyZMnOXny5EXD3XnnnZw5c4b3ve99fPmXfzkA9913H2fOnOH5z3/+wndOnTrFC1/4wuTZN3zDN3Dq1Cm+4zu+Y0/51OUomLuTKbSh4PzaGPwFXAG+dDD0jC54ikjUOMB54DHSK/6inTYgJY5mqKlKIcW2SP2WiQaUvCO+3ITccqgmlF0nV004R2qW8Nr4+wIqXRVtMPGbJu6exPykkEMu5uFCDD+OaayiskrhAsYE4icSPAgpJyYpRfNvQNCQkzFmzUk64AZU203mRZFJt+az1BWomUZPql0nGmweNV9ZmHckfWsGU+KWeUC0AuU7q20m67DkT0aKEJBCBFrpl5Wvr6CmRCXdqlWCVAi7iUlngPY1a45SyDgpu5Qz5tVX4CKxJnlt4/femFj1UfPNe2jL2CwO2iqSIoXDF+C9LKNQF47ZcBgIxJjwpBpT+xLnW0pX07gKH21ohhswQb+s7URPYQz6SA4FwkUqK9USsk+CCE0d4YmpSNVhCto2TSTxyjhJCyEkxwDRepIbTY3pLGImMtAHZUIh9QmcmiFVTGPGgODnRUwSKkkWSMhQxkAdyt3AIpJXbRe/kDlKaEi9CPlVxjiVzgjl7hNw0sxqmlNrVkWjnoIZVkPFde/p9qlPZCkBpTRGoJ3UhKMQbVKbRRd3XxNNN7jiN6qfsoRSPUJtCxtPqgEizr6VpHRJGJvXRehvFVMiT8Vn0jf1k/RV+dw3l2nrsX8IWkTEpaSkpZnT3O1cFpsfi5SgWnwoW0TA9UlbyYGtC6vNuB+wy29Kq4WJzRIQkgf9LePgslndztglfLew9kfGYYLDGlRaFqTGhp84Sgqa5LbWbpGKMA6+jZdBQzC9GLI8WKZ2ujisYOzwotlhxb7aob1R9nGHoV9mZFwN6K8TQRxhHRZYo/jhZ5VjbLBxZTN6FcC2Rd9iTHouzTPo3tC/QgyZfNsd5utoOYjfTMBlLDsum3Tqi7/4i3nRi17Eq1/9an76p38agO/6ru/ixS9+MbfddlsX7vbbb+eHfuiH+Dt/5+9w/fXXc/311yfxDAYDbr755uSd3aFliyEjaiYMGUeVqpqSAQ1bq2PGs8iSFY5m4GlHUG3SyYr8NcDZSMh51GQh6KwzIZA8q8AZlEAZo5JDIbpkPyJk2jS+61Azg5aMK02abYz3DKqGItp1dcyHbU2RkBYxrVVUk+s8So6toPunKammnPhR65NcmLIVqP80a7pRnjcxXktCig++vp80Yr015nOBaszZfAlJJfFtETT9JF9CwAlhautFNOBEU83HcooPuym60xECzq4ja+bdddQfnGingZqolDikbmx5nEnXytRb8660ddQI9EUgzjwx/WhysgXK+Nw7aKMJ07py4DyFkfXXAIWjbCMxXThmg0Hw5+YcxWwWknaOpqwiz1l29FBNsF3pqZgwmNu61FRMIhmk2m+qKWZDW3pBTCQWUXOloUrEaEro6U/dmXeEmkHkxgPpVfccGQY/bSHflpRXtrlPaAlJpCYQa4aGmFFNL8zblhAQgkvMcIp+XVCCtR0kCDAlXiEMJUYh46w5RgtL8gh9h/FvJwjdVgazrQNjd7S7yThf90IJ6Tv9WwNKwvTTVhLHmToU8yWevsnK+bTniVoNE/KtxG8Kdf69eENo21Rqsq/9J9qF1seVLbfmVSFDWMnV/reL85KOKOtTYTF55hf+nRKWF0t3u7z061vbUFrNsV2+LgXz40lIAbvogPTToBO4+/rNOBj4uEAeXkF0ekt2ObD/XlUujYADISmXp32XgTSV9aPpPi0LlqutdoKsfYcbh6OuDxKyswg4LARxRsZRwvzZSs8A4afvGdeSb4pMwO0f5ORn12xn/g+wZ6OM7dA/K6sVnVx/e4X19p264MjIWITsE66Py3pF/Jd+6Zd47Wtfy1133QXAS17yEt70pjclYT7ykY9w5syZfU/7AsdYYStqz6ywxRYjZjj556ZsFjBuJ+A9k9UKioKV6Yyqicr0Y/AnoKjBbRCIrnPgrKm/VVQTbJ3UlKSYkxQu4ER4vyOlVkgl9+fj70H8+wRBgnAGbakbUM27E6hvsAsokScYkppXFOLu2pgPTL5E40rIpXPx/SGBsNpi3q+bhBftOav5ZwksId+s/MqagpT6GKNaakIQii87UCLRxfwI2Se/rYbYKqp1t2XyZQlSmB8BA9SkpBCg4gdP4jiOaszJrmiMtrfkQcg3b35Dqglo+wDmXZE3l4FUs37hfNwvtA78OJJuDnzkXppBII7rytEW4W+7RfaFA+eoC/CuwJfBr1v40tOWJa0rmLoh4lfFUaNs5azbmAdtroIqFjwQNFVHGqlGjettftSsIZTMEid62rWqhBAp4vMyapqJhpuYmAx+YEQraZEyfBvTCTSfkhpCbsld/kA8Bu09q9uziOyQb8XJfBPnGEHQAlRzfUGDrDIhhLRKCSwxXRh06cruhvAi0UQb6z2kV3TtIrpJmteCEatM2OrKaWkW8ZAW3lKzmJYEC5CJpb9p1XCWcNX07XMta+p1L8Qj+esf7xrTsqpnUCTTS2tKoRpwab01wIgxU7bwSd8ru3S1n6dk7jz5qOWzNTGvoTUvWvKk9ZMes2yM2x8QJGSbPNk53e1gyy59Wsyi9su0X1pvQiJLWhKrEs724G/TXD5DbxnbQYlbfXKYION/P3U9nxiW7Z7u/lHx+4Gd9J4z4DCYR7T7j2XP6/7C9ebKow/dvcn/87u2jIyM7SC2QMLcsXtxeP+8tkgTVc77i0dkHqWXE3KZN/zd97u7XLuuZYaV2VjpwvLs6A8HdL7QHXaT7NWuLIYMmB5RkuZoIfuE6+OyknDXXXcdv/iLv7hjGDF9tx0W+YHbDTwFE1YYRt2XmiHj2IhTBszcEAaw1W6xPjsbze3B+fUhxRYM6ylF5aEIJEd5DNwx8NeDexidzQuUfDu/ICOiLCJkixBLZ5jX0BJSzRG4DtFcEw04YrrXxTDi3wwCObdBSjiNUV91x2N6E1LtNTEzmVZeyIsQgqAknfV/1pdwrA7BTdWsZYFq7knZpqjWnpiDtONSiCyprwGqbSaEW0nQHpOyQmqGc4RKoyUdK8O1hCPmuTfvDM13YqqzQv21AQlHYclAqw03IJUjOFTjrref9dBpHromkm8F+CpothXRdl/SXPKhhKYMWm11VUWmzuELh289bd1Q+ZamKJgNKqq6hqKgLkstelSxm7ohjUvVKn3UL5tRRzIomIRTwmocDRG6RCweSCzldkFNNYr5R9VAEjKLqMVWUBvvbeJxrokaXjUVJZ4mEjB1R1rJBsFHUqyI5us0DdVsCk9mXeOJZo2qP4qBw0Awlt3mTc1bViZepQ9CPquOCAtPS/N2/x6bbAh1k6N1X8W8lagPO6XYVFNOTFhac5ttrI2CghUuxMsI3vyoPzXxfxfqwuZ1MRU5Xw7RGFMSRzSV5jdperiwZJ1LnosHOz0EShirFRg++ST+ohviqgln6eCCCTP6A1HCpv1Y/tcB11+59Jao5Kcxz7S+0jjnN7MpFm9oF91WlTItfnf7jfGiFTjw+arddyXE2aHWgmq3EpNZjH60UHbXEA7rsdfOl8uC3VPslw/WG+rBQlaBy+GR8lKQXi85eOj655emji6G5Rpve0dKp+2mzoNNiKvJnJLsAUvkKtyyzbMZGcuMtjsb7m7c2H12ejGS5JwpxN4yrO5XA7Rd7Ayo8+NBEh6HBYtqyMoLVFaQ63AvkAuz213APghkAu6wIPuE6+PIOksJujuuM2hXU3ZOn2cMGIhvJzfk/OAYYx+0ITaqVdwxwHuOTS8waiZ4p5P2rITmZhg9CsUU3AD8ANwI9QM2ib9rlDQS8kpMTa7GvzeBRwjzlxBGntRXmAOeHD+LD7mWQE5JC4rJSCGnhBe4FjXdKD7cJF/jmI8NmDvrCSkoPuSOx98NOoZEOQqJa2obIOB6UgmRpGst4IkWmY95uQbrPiaUqYzpiixfnm2hxJY14ygYIyxLwJD5tULSEY09ISilOI5Q19J2dnVfi3/L2i6mNgeo9pupJ+8C18UYNb0p5i/HQYuNEbhZSMvHfuPL0Peq2mi9pLsLvIOmjAJ/FxJqXEk7KJlVHu8crY832kYhXO0jEebKIGh3PmqEaRGFsGqjHzTRixNtr9BUSgKFrhi27ZOuUgMVFwwzlcnyLZsh1eYpO6G/iwfyKUEbpujChA4kNMeMEk8ZR3nocE2MtTGm6tSsg2pTNZ16pZRYN2a6USu6eISQUVGykhXqP6zsxMxSX23ULvNR38zGmmpCWTKtv2Ekag3SUTdW9Kn/205axClDB0Lqx63Amha0sUpelISRuFMtNb3DnOZFiRVLgtkyKmEn8YvfP0umeVML7VxZpTbSttC8g7LwVo+l/9uWUEyRLtqsLxYNpUSVi8Sp5sOZ32ke++21PXzv79CXJU+XfqCQelIK+fJsqG2bhxqz/UwPmQGOIRXTJd183cgJHubskRUTVhTUe7hNvVuIOddlII72hnmxyEEjrNXLQGg2T2D22X8sR53oVZHlgqw3+2/KdP8h68RhR/+y08Xr/eoi4CxkZ9cu3DdlZGTsHYvGUEu5TajCnJH1m4zLi/lz9HazX54Nd4JcpJ3fq1upRa7DnbD4cu7h34llZCwTjiwJd45j1Iw4Q8s1PE5DwWmupcWxzmmu5zSOli03ZuBqHvXXRrNxJRUzxm7C+eEa9cyxOV5jPJtyYus8G6sD2rJkstYyOjdh5QLdbNWW4FbCD5tABV60qC5EYkWIGyF41lDSZ0vjYhDDOFSTa4oSOzH+bh2xRN8NBFm0mMkUIk3iPU4g+a6JzxxKBHnUt9oF1AyikFeifSek3gpKRE1MOt48b1C/eJIHK9O3Skrj+O5qrDdvvl+Pz6TurJabhBFSrox5E4ZDNORmKKEu/uLEdKgl6MTP38TkS/ZDWybMojVpROIPzpcEzTbJYwFtBYWQdhVMhzG4i1zrigv9qQHvHL4MibdFy6CJZFfpaMXfWwFbwwHOOUMvhXc7rTgc3hU03lNTBVG489SR0FJzFB7xKlbEbcyEIRVK0KXES5lsZ4K/NuuPTX04yeaoRTSb9P58S8GsY2eFhGtoGUT/cKA0mm5OSz6HCY9EEqmi6sQX4hOt6MiUYCqz6A4fLhqmtIKpxpRRyiVaf7aMPpqZFCLBEmWYv0WDTjW3lMwLZibrWC/B/17REUX6vhAXLpZfTVfqt7ppSmOx7RQ0E/vEh5qlVI21wpQnJYhUrFgintyUjPRdzvo+4WxsGmsx9xxE7OYRUUzaRkkPN2/ND0afhE033lov+rYStNInNcyibXvbse7FXIi0nJbMbM3fuxcx9Q9oQpsuMrW5HWxZbZzhb9Hu2z9vQf2SpfRs0HLUeSfViAWWloAD+CxnDzoLlxWXg4CD0O+Xj5jYDRzLQu4oUuH+QWG5zMa5blY8WIjJ62WqG4GudMuNsFIuX/1dCmS/lgXaF4f4h26T/dPR6AcZGZcH9kyzaG5PT0zWTE/GQaB/yUDOzf1Lnxm7g64VerlXJBTLtktdZiy6/LKsO9mMw4RsjrKPI0vCqU+qioe4iTETBlE0/zjXMzBskQNwOr3UDGj8lBkj6kEwJ7cxWmFaFQyKGgc0VclsrcFVNdUWNIMgCBmeCxpK7RpUIm8fA8fBnwc3YR4r4Gtwge0IEB5iSCCfQM1NFqj/NJVVh3DWROUKSj5NYlyjGGYdJYpWCaY0jxMIrgrVettknpw6Hj/XMQ1i2BWUNLPjReqgjmmNSUnH7lTqoPIhzDTGV8ewo/je0OTPrquOsKcUM5pC1LXmu0F8V74brcLJl8GFtyixKbJgGRmisWjLIit9YT57gvlIwFXh747WiX7x/BBcC20BzQgaHzTbPIGgqwsY1OHdtoqERwVNUUR2zuMGMC09rYPZeIz34NuWthzQ4qjaWXgfmPiVqBnWggvaoJ6C1lktspY6mkwU7axQNHkefKOBpzbUkGzmg1nIgiqOpxlVJOA8VfeGbC6J2qgOy9XWkWSjC6l3tGeMEL9s4bk2xrTTFHusM4GoW65BbH7RqfKIvzoLIfdEkDmLg0e9w0k+HbUxoSlUkIiyxGiilkC1iUTUpXFpeg4xhSlMcRhKTUdmWbGv+quzmmmByFQvaLL5bCL5KG0o74hxz5KCJtEJ1P9tW+hm1hJnoX6slqEQZI1p9ZToUm3H8F1KwGmdaR/QstqJTtpZN4TSn+wW0fpHs4SkPdrU5sDTbEMMiNnSUDoNo9SnrQNtX6VC5blqSaaY39imhGRaL1qm3W+ItX5DHtqONvXmueCJiWYtZTu/gU9pP390tyAZ2yL4uywOnabHsgmsdD48aBIOlktIviz5uBiGDJkyvXjAfcdhIbaWo2/vF0RzdfnJz2WAeEAWv9Cq9Xs4+m5GxpVDenJzvZNdSugoMZFH0pWC7/3tkzNtetE1t8vFsejMH+p10fm6/yxjJ7RY20Eqz8jIeGKYcenmKI+mydEjKwETofkmq4BnhYmZRArOs8IqW5zjGBuscoyzDKIXp5qCB91NFMBJ/0jQWPKwUaxxnPOURnjUDgumQ52ozq2PwRXgW9Yf2aCy66s1QSlyd9HiGhMIpA2CHzBJIJpldOJH7VpUS01IIGJ8jkCQnTYVIdpx4jPNoeYeZ+bZMRPOnnLEhKVojIkWnUdJN8EI1S7bRE1c+hj/ECXnxvH9TVSLr/Qp6SZmHR1K9lmupvp82PpECCjEZGneb+KPfNfGPIgZyMEGbL1F8yWabRLHAnhiXkYxG7GevGgSukCczaJGn5if9CVUU2g9TMYFbUnQUiuK8LAIETUDT1G34D24uPA5ve3ugbZwTKohtR9QuoamUJXIaREyVtDSuqIj0IRCEGJESJIpAxoGqFHCcMjdoqJlkJBQEHR4gqXOkoYRQkMFiq7oSCqHi8SP696fMMBTUEXqI+SqMjSFQv2QFTFdR0kbrXeGTlnEdAJ5oh2jxprTDP7IlDACu20LxFyB+FzzMfezWF+VmK3tCDhr2tJ3YoKme1aYfKuGmU1V/bcJNVlGbS8tvaeIVkzLGJvqKAm1o21SAlVHqob2FbJF5yaB+Iyr4wQk2khaJ7LpkolADmuLbIC73mfJv6Lt0nTJsSPV6HJIa3mErtteQ6qfhta37espgSV12CyIi1ivtg5SyijkvF3Yh7YnzNqF6cyjH0d4V12K7pUY8+a3ainaQ7dDvdbtnLfdQK30hslTSXN7MFpEg2ZcnQgrjVwSWH7I9YNlO4oug7lDWTUP2rxhurIcLOxqvj2uPAGX7jKWG3q16mhALg/pqp5xMYiFDgg9dhnG9lFH/7JUxrJg0fpq9/pyvkpN0y8aMXkUXQmkp9GAeU03eKIXH68G9M/I6Y5c7QJl7A39a9ey1gZpgTeWgTIyniiyJlwfR5aE+xS3cj01K5ExmlFRRL2Y84z5LCdZ5zRFXPwe5SQjtjjOOWYM8DiGzHjEneQY55m4IcfY5BzHGDFhqx1CeYy1dpNz1QkArq8fCQQcgCvYOFHRtgPWN4NdSNfCdBUGDpyHpoCtqqKoPIO6pao9rEFTRXOFns6UoTsXl+kh+IJApEWtOSeEXksg1kbAGZToExJqgJqFlDACh5rJlFn5GCpdtfOw+HWboONiJf4I6SXEXR3jVb4mEI1iVvM4SrSB+khbi2Gn5jkEGe96/O7ES+Ha/zf82ReFeKxNvhlKqNl8g2rReVKffQUdudaVq0S1E2XfJBp1jdkAx3hqaR+HWIHs8t4MYFoFH20JSiXGvHM0w4K6jfd5HDgXiKJpMcQNoPEV3pV475kxoHS12XwTtcfoTLk0Xd+PeTW/a4bxbzV1OKMM8dJEbbgWMW4Y4i/xiS1UF025Cqki+RhQxTgkHfDUDHHMOqKtjWYj7ZEhPE9Jkc2OGAwhxHm0+JhTYk9vTysdoAf50CTBF1z/sJK6gw/mMQPBtugmlYvaeEq8eTzpHSJJM+iczZMyQjamJEWblEjErEpoNh2xkpIaQdNPiLumC58eqTUeEG1Aoe7UlKTGr/7+bAwyhCxN17+VFr6ruu/EHGla/nnn020SRtECxzjOzdzAA3yKhlkvRKohJuZIwdPEPj5vvtGj7aB9R8m6xXlRgknNJm0X93YQksxqzGk9aQteLB4bn0W6gV500Htim2slK622YiB22yTfy0G8XS4/Zxm7hfTQ+lAd7WTmPvj89knsg9KqsdcTrM7xwcHRoqv8QUKuIi2rKHtZ+vLOOEzzQ8blRP/yldjtWM7RdfiR6/ViqKio91ko2L8Ot+hy33Z9Xvb5ej08z5wHhfkdGoif8/RSKeR22hm+9xusBZr0TJkJuN1DL/putxNU60cZGRmXD0eWhDvNdQyYMOBRSloe41rKSMaJ2b1HGLLCJsciMzVlwJRxJ/b2ODZZoabiAqvUnOEY5znNNdRFYJFm5eluY/RQdRN4x/XuMVoP54bX4Sk4PV7HUdNSMWDGsdVzjKc159dWKIqwpGx5z+jCjPH5hnoFyhnBjCHht1sFPwPvoF4BXAhTXIjLUeQJfBP/vhkYRCJoFghA74F1cIE3CCTbGQIhNkVJKPl7FSXytkj92InW20ZItzNR2RBIK/E9J5ptVg58IuSp24+Ilptoo62QOi2qUbOPJ+LzEpi+ER55I1xvGr5BNd4GzGuwlrEezDruZKWJxKAHnHAZDto21FkTtQSLJhbHKHZM1hyu9R0Hi3wPTFYC+1fNatpS2LlYPCHYXEVFy9AHEfGsHFK7gsLXzNyQ1gWNsZYC56KoP2rICWksy2YbK6uJC2xLRUvb6UrVHXHkzXuB2Jmx0pEiQYtOPZ4NIrPpE2Yz3FcWk4oFSse0FGxGhtXesAlxH0vSLwyFFUxcBs008VI17cpoCbNACDaxZLrhLWNTq4lMezewr/0mFIKSYYDJHaR3/FPhWonv0uyL3XSD46kMcdZ2caoHtbRc/U5kzXo2yWclcFVjSmYwMcnbN+cod8ek5JaQszGmG1v1cEMX1jp09NRd+/jYb/p0lNaJTSWtOW/C9OGAc1zgHBvxSYnVIVx8Ww60PPNkqjd1pW3dsJh47aehpOFizYKdDwZC9opntFSEvDviSsaM79ViOon249ifA8u8oDk88V36y3UwygTcQcJ3YqLDQAIIlsd8nMxoMrMug9jhoDVTRBPQL02fWp4rBxa6+1qOvnz14uA1Vw8zim7P1ETPzH2R7FHDMdY5n9yYzTho7CScvoGbeJiH9hxHn3xbRNyEi6cNcmGQ5CfjICEnxkWn1nxlYG+wVzf1HG8v2Pb7fO7/F4OV/mTttoyDQc2lm5XMmnCHDIFA+yw3ADBiwowRK2x0vuFaSs6xjvi/2mKFKY+xygUGzDjPMbYYUeAZUFMz5DGu5zjnOhH4FmNW2QJgy43ZYpULrDFyU9Y41x14t1iloWLTw9ZwxLXlY1Ck5MTZtWM8vlqwvnWaybCiKSpK37Iym3BhraRoHKNpHcg5H7Suhlt0mlY+RIOPml3NIBJGjkCiyV5OWn1MINSC3Tv8mRCXi6SYF2UnB+76+G5D8F8n5JnwGDKfl3TmLr0HNyaMubMxbw7cCDX5ODSvCzlodysj8NEfnCuAiQkffa/R9pYTqYMyPq8J2mkF+AG0q3QrexH90vkC6mimsvBQtMFCZFPA1vUFg6nHRbaujZVdFDAdBM1FCocvHWVjNM6co65KcI7aFUzHIcMDr5PJ1FU0RSDpplTMGkdbVDQuOOJzrkm2b6G46lWtQUxOuu7bEjXvV1NQM0C8hrWU1FQdUefiE3BMWEXIOasxJGEnFB1ZJv1tRhV1GsJPG98tIPpQCyOgMvltI3HVGOLK01Ljo7+yKsbfMjPkiOE8zUGl6jqNbtTCOzWlIYpEJ0q0nKTOAinWdISbxCR/y+eiE39qXTd42t6GRnuiHKLsGA8QDSd9b/6ml8L6XpPLAeEigZCWafpaR329gDSM76Uv31qxqvzVoFqCfS9ytkX6cfaNIIrB0+CTTkudeuhMN9iiXDufK/0sG3UR0CgJtBiWqvKmj+nvxccp+VY17NI87BbpDT+pu71virXc2rfSFPYP861t+01fhJBt8GfMwx6sDweWyxtCeoXhoJAanz04yCp20GYwLdpuXV6O/Aj0us/yo+3miJ1X8cOJbI7yicCuHmJm/miLFc9zhpKS5tD5UD26aHYQCF6MgNOza5jZnJnv5FnRnR77K6xcsj2KPX05EPQNF8/P6TVWCW8vU6aXktLzd8Z2WERI69nWWgWyITJ2A0toNmx3Xs/IuBLI5ij7OLIk3CZjHuNGVpjg8AyZ4oCKGU/iU3GZDaKMz3ALxzkPOB7nes5ygjFbbLEKQMWUEs86j1Ph2WLEmAkNJZ/lVo6xwZgtakaAp46qXZuc5HoeN0L0QMxsssb56jgnOMM6ZzvaY8oAnOOh8Q1QlB1ZuDHbZDoY4SkYNptU3jMrKtamm8zWG9ysZTjzbB0b0FYw2JjCIJBCeI+rHStF2/l18zNwUSOsrhxF62lH4I5F4mwD2rVA4JVxJfSy/49roShz+bVAhBVb4CKZ5aNpRxfXznYI3AIuatN5B25IR8SJJpqHziSmE7K8AB+4oRAm7FSDmc4qvh81AH30peeKGH/cR/kS6ipmfRBJSQeNg2Y9vlsGX2vSRrQtReuYDUvasmQ6dgwmM4bThsm4ZDoK/tIGk2kgKYG6KNkaRgazCSzewAfPW60r4kpYsOVHtB5qBlRFerBqymGyyW4pKCJJJoJ6j2ODcaLxpctowQQocNFsZBGbrKKOZimbLnQgoWYMIzFXdIYhZwyjGcHaiPULpp1WWR37u0uWcyHtasQ0ItS4WIJA7KkIQvNRdyYuLTEz7KixoDMmBu7EpGRhqDP7pvpLk/JL3ambQkvpFN1bWgYNI4Rd28XXxvRD/ook5ZTESkWEdrMzLxITstNu3FPiLxCXcjBLiRz7t5BS4n9ERbbCcdu6s/XXd8g7i/FJHc3rhGgbCrlaJHUXjpLht5p8BL3jGdokCFTaSC5KKqLdJ17r+pv1tmuN+fxsh7S+thMjp+9rXVvS1C8M24d1IJ0e4vq1v/vNsNSj9Q2YHlT2d2Md+r4S97Y/7dbvXcbVivmZ6nD0EjUrePCwhpUPDmIIeVmI1GUiSAOWUdMsvTi1HP15Z1jzzEcLyzJyDjP6+w0rYjyayATc8qKI5Auk+/vWnGLCneCqI+/a7uJq+OS6/1XjrX/Nje5ZxuWEJeAWzdRN1y59SyOBND1cK+3BYP7yKARJTJt8L8/T3xk7YbsZQ2VDHttnMzKuHGZcuibcpb633DiyJNzDnGQtCpqtJ6aaAX/EMznBGW7gERoqTrPOkCkjZnjgLGtY30A+qoBtssIjXMNxNjqB+RobnGaEp+Ukp3HAJiuc5wQQzF8GUmLAcc6zxgaiyfIo1/NZbmSd06y5TYKIo+CCC7YdC2qO+/NsVsJCebaKFUoXCImzo+N4V4D3jN0Wg7jBO7e6wrFWHMY5pqOKYT2jaD2+hY31MoiXRkGby7ee1c2ZBKc5FpJrKnDToBXWGq2ydgxlcHOHXw0E1vQ4NKXDeahaj/PgJvHdlUDoMYwEWAOti2lDIOoG4EeBkKtHgRBztQ9hBoYgMM6nqs1QK7ORoxn4QMy1UM0AH9KsB7A5dvhBicMxmtSUbViG6iqQjE3laIqCZlDFDAaUTYN3jrYIjOV0NGBaVczK6EvMwWxURA9IRdfHGgpmVWABW+8onBBqsRUdtG6Ap6TGd1piLcG0pOjECNFUM6SI4q8mUrNCPYmfsUHcGgaXeuOk38tmRjThrL+xoAVadAeGJpIgs+jHK5QuUGi1IYDazjzkvDmq4MtKSaYgqC9xXR7UoJY9sOgNHR/JJilj0L7SZ0GoFEzLipadkD5FN76sZ5b+Nk4OQUJiCJE5oYplVOpEttphFggbbSmD3H5Lj1NC6kVytotThdFWC0zyY0Ws4T1LbKUl0duUrvue7vvFmysf6dxQT6qFWGIPHWVn7ET6CwgNlhI8fR0RET+2yQFFSZp0U60addrOVReu6Y46RRe+TzaKVt3FNpJCZEodpcbcLr4JtQSc9qRFXvDm37M3+XzXRoJL3wA3c7cD9/+gYjfz3owCn7RtRsZOkJU73YctJ1KKfDk0h5Ruv5x52R01sCx0u85M1ojvwSK9lrA8EHOdh4n+OXiy+fJAfCtnXA4ssjmxjMhabUcDYRUqqPHYC2rhXGatawSP4KHF+yI3H8/L/R67fFc5rgYsuuSpV1ftiWi7y6jbfc6A/lm67yLDded0e27O2Bm2z6o8JK3h1IJTRkbGsuDIknB/xVNYxfEk/opVNmjifaUpA1pKHuMkE0asMKXF8XGeGk1OBp9X1/A4N/Fwt6naZMwjXEsgI2YEbaQVNlnhWh6nYchD3EBJQ82AMRMqGrYYI7olZzhByQxHMOM3ZYin5BFuouURStrov0qF0rUr2WLIasznljsGOIZsMXLTqJHmmPmKgaupKdkYrFHOzjJuJ7TO0ZYFZ0+M2CjWqJoJlKr9MWwmlEVLXRUM6iA+b8oosPaBMJoMCjaPDQEoZw2rmzNmA5iMK+phRTVtKKKJRQ80jaNqPFsnStrCMd6qg/ZbGcg3XwRyrPBQlyWzYRFMOdYtw62aehyIRT8sqBojZncOX4R8OWA2grZUk484R9E0eBqqBjZWK5rS0ZQlrYehb5mMK8rG07Y1rii4UAyhchS+TQg4gNY56iIygK7A+YZpGbWzXPQ/5kpmcbNQRrueU0bxuO2oXcsgaknWUTNNF8lwi66JvrQayiiqbDttOCE6WlqmjLs6Dk+CGcvQr4tIsqjPtrYTfjZRy80zw6Ge1YK2XE3VkUItRA03TLorMZ0+DaJO0kHuoRbdc0tKSn00Mc8SR4GYvdS421gXXX/qpinV+qnxkQgMxFjRORkMtVobsiBoYgXiUvIrlIyk03Y006CLQ8vYGMLLdX9LjfhIw9LlRbc6NdakiBIlTcyJxCnv1CjBqtsoq3ukfVR0EoLGWhHbOj1GyPZWCKd5McW8lyYZcfYg2SZUnRCA222SU01CjXsxiafaVPZ7JSX7t+akBfv9xqL/NGg+Sz9q2E4DLt3Qas5VjCn53Ishtv0jyNYYcy6aP9Y4+wfEJwYpq5+L+/Bu4CtK6iz4OgDorLvcEC+WAQdJWAwomUXPn2AvYGje9hsXL6sVkhwkySQrvppkPthZKV2dlnGGXA7idPdQkdzRwjJosh5diBZKoDTAXu7bzVWtK4dMwB0OLJqD1BCznAvEdoicxcRjG723tyPVlqlnXl2wrZS2qXy//fl2588ZffQ12+bt2kjNL48H5mXFotnFXqwPCFSmrjS5j2YsA7I5yj6OLAkHMGGFv+DzOcZ5ADZY4Wl8gse4npaSimv5XB7oBOozht27Z1nnHCdwOG7kMQZcQI5RMwbMGFDH3x4XDQ0MGDIFHBuscT2P8BA3ciOPRgF7xV/weZzgLEOmUY8HwPEwN+CA45ztdJ3A8zA3Eg74ZymNRd8pw+CNK9pynDKkZtBNxhcGx6h9RRW3hJt+jMMxK4dUXo3yta7Aec/meMjUB1Kg9o612RZtUbAxHoJTQUdTlTyyNmK1ntCMornBUcVgEkX5PugWnV8ZM60q8J7J6pDRrKZoG1wbaIeN1TGDtgnaZ4BrW9qqoB5EAb0LC/W0qhjVwaSjdzApKwZNQ1sUTIYDKl9DoVpobVEwGxQUZY0rHdNiQONCPgZMcM5Rl4CrmJVDpsUA70oKakbMcI5gktPBlht15JQDJk4IGqk9MfUYSKMpVdRQKYwWWskmUbXQQDYhDSUlqk9WUzBlTElLFZfQGQMaik6XKvSlgZaZIpIMfbInHEeDr7ewyfHAIFJUwQRqFcjemKOgJWRvJglxSKTL2pinIpbeRW29IhJsPt4NDJQQhvwQo0hKzzlmpg5l49Ci2mcN1sOKEkOqHxDSqBl2odREn/iKc4hJWGlNK3BVk5f2QKRiYyXqhDRSMk7qrKWMuS+6jU+fpJKNaBvzpTe+lBy8jifxKH9t+ohAySiSd6wfNqk7JQSF4PO9962nNh+1FBtTI/T6UnozTYyKamze/NYRs/i2oIotxfymHG3TA6mtY03fml9MRcc2D/0tZ5rT+U2+3BZL/S9qHS8qx6I45FufhLVe8Xa/Ge7H54AzTJgnAPd7g22J46MhDs0E3JVGWBX3j36+PHBGO6VPwB1UnmfxsLGdoV39fKW0wOxt5YOCrg8OvUJz8Ah5KlAzncsBWYWXXfu0j2Un6y8dYgnjcLXHYYOcEaS2ZX+7DHPF0YI1r3iYsGhf3acD+mH1s/Vj3t8pbHdZLY/4ZcNOq4y9QKxYrtX9sEBGRJBIiARru9GWybfdQGRGIplI5UyH7cpVxtWFmks3K3n49hq7wZEm4WYMqKh5kJuiCUnHg9zcmf+bMeQP+VKOcZab+CxjJgsEqPAxnsKILW7lr6loeJTrqJiyyTGmDFlhk2s4HYXfImgueITr8Tj+ilsZMaWloKJlk1U+y01cy2mOc55NRmyyAhQ8xnWM2GCFKTWOE2wSKLjjVNSMo2+7raALx5gtPHRGL2cMO0rgrDtBHTWPSie6UlOucRdweDb9KhPXMHITKtfQes+WGwbRTllSREJKfKMATN2Qpiw5Vw1Z8ZvULvgSq0vPuJnSukDc+TKYvMR5Wgd1NaBz3OY8uAJfzCh9an97MqyCmMV76nKALwo8WxRtQ1tUTAcjpkNtnVnrOlLRAY1zbFYrtEVJ2UzwxSAIbArHVjsED5NiBFURtRIDOdO6IpJZQQPOAa5omeGigb6CTcYUtAyoCaYjh9GwSNFppQUipIhtXUcxWbq5sORPS8GkI39VuC7aY02n0SWabX7uXqcHzrNKRQ3xvZCvkrqjyujyNYuabw4f+0f/OKLGIetovrKh6Ajd8KykoY3Ehfh/CykJIVbEmEqjxyUEm6YUyBqpIbnhXiNewqzpO91sWApNNaZCih5rb19JUvk7vC8EkJCNYp5S26hPBoq7bLldKzOF9X8mt7jEt5lqtVkCDoQgVfOaIdWHeAioFm5HvQmp9WFD2rIpRG/AmspcjJSETNNON3d625Okba0G4SL051YtRz/N7Q6yF99YSl9P7csvSkPSV4LR0/d5tLuNrB09e6mP7dB0vUf9+TQXbb+9Y5FoPbUZf/WhiHNafaAaP4cNqWirYNnvYKZaZjsJ4q4s2nihJvU4sghXzrCd+IE8SM0z20LLQNT0e0v/5HDQZjL1es7hgeyIjyoOmsi+WiD7pJ0va8Gyrk2HA4eBgNO9uJ5HrWfs/n6675PKmwu16bl70XqUe9Myo39GUxlFWHlk3yVhM/YGleekVpPUQhJkMnN3OMlJHuaRpKb0SqO9XA65PjMOD7ImXB9HloQ7xzGm9Trn/TH8oGDsP85n3OcALbfz55S0TBhwlhM8yvV8ks9ljXNcz8Ncw+MMaRkypaVkyogpIx5gyCpbVJEI81SAZ8aQFTYYMWGLEUMmBIHugAkVFbDBMQoaruU0j3M9HvgsN/Egt7DKeVYIPtzCMXTARtR02qBlSM1ZrsFTUkUzlyJod7SssoFsKeyxe4WNbnqeMCZM1qv4SB7hApVwhhOc4CxTN+60AUs3oaKlcSUVNVWkgOT7FbY45453+SirAbMq6FU1FBTedzse2fxM3Zg2ap0NqaldyYQReGhcQelaKlfjPUzdCHCMmDAdHo8UQhArBG3DGG9RUlNSt2Gr3LgS7wBKmnIVBwyY4nFMi9WOoHKROArkjNRZ2RFNwRNa8InlaGkooyZVuAk3ZWjaoDECs+DXLZBRJWMmCZkzM36v6MihsMUvEXV8uelSMGWAeOYSf2pKDEHQ4CxoqZhSdBpKTaRlRdPI4ag7okyohhK9Te7isSQQilb7LGyuqqjppr7S2ujrLNUqU7KvjQK7BjHhGExltnGzq6Savt9Ewk/SVVGpbjX6JBMmrja2ht5XTO/ApjcfxXxIaTbnZRdju1CUrCS79ZEm5WkT0Zd+X0QiUk0qSjkklwMm5ghgqWmtoTFtN/fYPPVT65N+Wpf97Zq8I6TPojjTG5++i88eQ1OCb3G+9IqCFeWmYTu/ib13diKGfPK3bkz10Ly9WE8JOHlfNrh94nFn2NLMk2V72ySLHozkx3fGS/dfPNmv51RIcXUScWF+ysfw3WF7QdRy9hxLk+sFieUQDYjR5pS6P5h8hdRFU1DydzDQNlsekiZd/fqrxcEQcJqfvRhLXh4cNq29vSKTcFcO/X2lGj0XLMc8kvFE0R9RcknStq/4JVfI+bffT4JkQK9GLjJSvfsLgRlXDulZVPdR4Zk8SdvQzgj5yt3esPh0aOs1lRlk7B6P8Gj3t1737l8CyMjIOOw4siTcn525He/XKcuWa8tH+VOeQRHnrT/iS7iVv8bRssEqBS01FTUlFzjOp/DRXGTDST7LOueYUnGONR6NIolb+WvGkQwCzzmO8xjXM6DmGOfxeB7jOlyMSwxjnGeF6zibCOu3WGHIjNJsH0TEf4E1ziPmBgsq1CRfIMVGtDiOcyHZdgqhUkVxYiCTgjBjixFrbNICFwg+5h5mzDiShw1VJIvC1nXMJiWz6NMsVOIkEnmyxNYMKJhxhhNAQeHqoFnoAhUjZGYgJBxDaqaMggaVkxwHcqh1VVc3QfusiSRiwYBZNKYYNNEKBlScpypaaspOE7Ay9MmMIVNSU5JVbHP7bMaALcZRfN92vtFcNAPqCaYcZ6z0BP9KG0ypEAOLQXsuhA0mGsVfm/g9i6Y4Cb4A1dSFNQgZPFhFg5y0sQcICsRcZdWjvgRhK2ppQikLNOaGIJ3W5OIDrKYo/UncUEu/Up93Wi/Bv536gWuo4iFHNxRBq07qUI1uWhog1EMwDSlW+Iv4TESXcmjyuFhfugUM9/iLpK2aTrxYxrR8V0PSFiquFTLSdd8qcakb/HkiK9yIF1HmPEEUUp0Yikrekb+brmxyc1O0/ebJMMm/+JprUdMafTG55KeJ7Sae7ZTklbqyebdkjXrYu9im0PrQ6yPkV8eR9R+lB+f5jXy/zKkNfzlkz6e1GLbud7/BXVT/lwqph5TM9L1n+w8pQxsvlWjaGRm7h3jVPGgvXTtBPQfB3mj2y4F01uiv2lceNj9y9eOgSQPZyS7TjBSuEsmKvRzCfPVpeNAttjek6/3ytPHlwEHPN1crXPejp4IWe9UtJdAXX1bLbXflcbHdqJ6RRZNJz4CtOfWqT2ffe7MP2SHYVA9+Zc7YGR57BgR1bSHykD6BkUf0biF127+aGWZR+/c8ybks19uWGf2eaM8m6fWuXI8Zhx0zLt0c5aW+t9w4siTc5JMrlNeNKW65wCMP38TaDeeYnB7hW8fWYMBfT27l+PgcNx97kBkVE1YAzzpnGDFlyoCShse4jhlDzrEGOIZMOc45/ppbOMZ5RkzZZMT1PB7Jq5JP8DkcYwPRlFvnMTxVFE+1nAGmkTjxOI5xjtOss8bZaDZzTIlnkxUcnmOcY8SU8xxnRsVxzgGwwSo+klsNJSMmDI3K5lnWWIlaYAU+mit0bDHmLCcomeGpqKJ3uxnBFOQsPgvbmYKaimmy/IqpPiFKHJuscJ5VhtTRUGHFJlWnQq1m1cJG+RxrBF96YXsccuBpGeAoGTLrCIIpI8RgYYuL7TOMmkotDcNoIJKokRR89ZWicRdJHiUXhPQpYr5aGgKhGUojm3AV7AdzfiUNA1yn86QEo++GUtj4hft0SljUjHA0DGm7PEhdqladbHVCCDUCKlpekp+GAdPYVgPjs0y8Bvp4KJEcuahLI1RwSHlGGX3LpdtXuvf06Op77wocwTxlCKOEiR6OdIppu14QtCFlu2a1fDSMiyUPxJOUvY70m5jmlLpRo5SuqzO9QRQ0GBuEnpLwBWKQUu/NCfGhZdZtpe/yKqJm2SRZf2lWi1B7vxUlKsmaCgZsvatoat5XnfrFa5MwSnOJnzopo76r/VbLEr4TSl2Nuy4iFG188u7iA01KKi3+vknSCW0ih+ftPCL1fauoVufFIeV28fiwGwLxYtD6ujRxrD3U2H5EF+/l0WtYLKzNG/2M7aBXCDzqlaDAXgqxdP0ywI6j1IDLweZSrkjYSy4HJaS310P6q9FBYv5qxcHlQyH9fdm0zQ6rqEZ9mxxltN2u87C1z9GBrfmUsrafFhFwYlHiuXw5f84fcS76mc8i5kvHYuHzzt+nnrzlOmQI2Z8/5MynrdwnB7ZDbtFlh21JPWfO+8re3g1BbuPtYC+W6s5QrAKJJMSOqj5Vfjh3IkUnV7pykMvSIo2RZymZebjqMSNje2QSro8jS8K1f7xCe2yV2XgMxxz1yRE1K2E+OzalWJ/x+IXrGI23GJZTiGKlKUMe5Xpm7YDrisfAQUXNCpuUtGywxgar3MBneYQbY2qOx7iOYadP51lhhhjRu8Bx6mjGsabkNNdS0DBkGomFqptmy6i1JF65SmY8yg2scZ4SzxarbLJCi+MYG136m6yxyVqiIzNiixljVtnA46OpP9hiFQ8Mohm+lirSNxVbrACOCS3HuNCJ7U9zDTUlxzgXy7SOGmvwkdbyeGqaSG61cWM8ZQWAVTYo8NGn2CASGMFX3YxBt7wraVFGE4gq/Au+egYd3RTqdMAkvl9EQ4we2GSVTQId5ikZMCPVOiOaDi2ill646yNClib6PrMCM2J91R1hEOK3hgVrhrE2hDgrEeKnJvX0pgc8unJLGmFxtsSOiKMqpl2+0kW6jkO6jRuKAqIeptaPi6VRMqfA2tsVUipQtlVX+3LMsffjhcRQI5VC+hVYskrLF3LcdJqZLho7FdpFjI7S9VfJtVJi4Vmou9KkrAY7PUWXl3QjLlpz8p2tV0s0q+jPip6VVLR3wHTbakWYYkTUkivWVAZdvFb7KiVDrR8Cuz3UsqUb3qYrhTW1ouUI74iWa5p3qVeZC7cTStg4+39r/5W4FpNvoQ/0D0iSzzIxIWrfsTfyrAjlYuj313mya+9QocAT04Wwh8krSQ3Y9lVPGHnDnwHzfaHtfHva0b7cem86lgpD9wuufL515Ql5SsWHB6NRpSYw+6KUK4v0kpeubgffv8I+XPaErnu2HNBrOIdr9tadgu7ojjLmLyxkHDTsxS4hc1K/YP095u9zX/xbL8IVNIdo3F06+sTHojLrhcbwaZFfU7vW9E8fag5dznHBO7gaZVOixV4GnIeeKZbpSlDGpcH2E+uyQE/T260eV8PI3H+ku+Wdds1HhyS6EgSclZOBzpBhp2tlSjZURsZRQfYJ18eRJeHABYn0pIAa6tMrcK2H64FZgffgnGcyGzOdjVgZb9LWJQ9t3kRdDfAUTIoxx0bn8N7hfMOTir+mcLDFmNNcwwpbyER5geM0lNzAI5TM2GQlkl8umnujCxcO9XVHMA2YMWbCBY5znuORQPAc5wxEAmuTMSd5tKNWPAVbDBlFHbXzrEUtspYRk47YGlCzyThq3ZUMIy0FjhktK2xQ4tlgyCjSLgEFZzgeicLVTmvvcQbRJOQQId8qZpFI9NQ4Y6bTscUA0Qg8xzEcBSucZxZNR24xZsQkmsIL2jhDWmoGPYIlCOdlmaoR3UKYUFF2pAx4Wqbx/WAiq4x1vxIpiKCpB0HbEGqKSNqEeimpKcx2P9SX6P3UFMwYUFFHU4GDeDwQekroLx/bWGmY1hB7ZdeWUucNcshoYn1aHa+UunFMqaiM0UAx8qhhy0gFqy+4QHO1tJGAVfKoZNKlqFp5IiCcRaK4iC3QdJsH69tMqKqU4PDmxr8QF8H8Ydu1mYi6fAwfTIv42E6uCycl1MOe3agEo58u2ZD75K/FGmbOfLbiP60fLYXENO/zK5Cu8yYRU90QIQJBjpE+6QNEjb1+3qwPPXuExdRP/zCSHpXbmJ8UizfafXoqPVynpJtLwtij9/YE3F7Qz0foS0LX7v4QsNib0MXf7QsQiJ4v90OLzpKWuyUU9xPauw9e3yTjoGGJkHmCfpEwa7l7jK5JByvgl5WnXTg7HyTpVSyY1Q+GzJHdXXpt5eBgRXzqITJdqQ8a2n6HS9Cc7maOPvp70IzlwbyAuUVF/CoY7YfXUddGHYp+bDvhUtbSvfadnYiynZ6LAL6/XrkkrJZ/MYkGciXQJ8/DSVVoNplh52d7+Vya3/1ayyPpKMHuNu1vkH5pd3Rqz+agdwqHGfbiVXp+383YyuOvj/5FNk+/jsPcqqaQ7be5H18urLJ60FnIyFiII0zCRdTo3Lblgkbjww7/iTH+QsGj16/CF83gXAG+YnjiAsPrtgBo2orWOzbqYIryz+tjfN7gLyldzVm3zmmuYZ0zBHODwe/VQ9zE5/BXPMSNiJGVkzzCGluJqFwnZ73vO4lmJQsaRkw4wzqekooZq2xwjjVKGobMcMBZTrDBKsc5Z26cFcyoGFIzYUzDLPpHC8tCE4kbCTtlSCCejnOeGatsUdFEam1g/ISFvLpoJlGpumA8rmbYlWHCFgUNnkEU+zexJgIB1XAc1ZQrI9XiY14KWra6d/qbshkDClpmjKOxibCxL2liPkZMGaBeu8RoZdkRK9IlRJwy5RgDZp1+Wx1r2UedN2LYJr4/i1qNM+NnLpRlEMNaPSLJf4E3uj8zinh4Ew3FAgxZZvvILPavoqNC1YDkrIvZ0TKMTxsqGmYMYhoq9gjxVsltzxqrHSX9SEpRdrluEQ027Wt9wkXs6WsphKQrOg1JNT25yKCTj/3RxX5mtQP75E56ez58Eo0/K9BLxcpyDBSy21KgGHJHyt2PS3pV/+Dd12dYfMvTwsVxoZ+JcViabX5jrASh/t5546zH5d1BZiohvNL8BYgfhvS9+XwsOlzZXO02XxJazK/uRhw6T7juLU01AdsnP/dnwyzt7xcKGfaGMUO2ugsQu4OS4NuX5/AJeTP2BplhZJ6TbaEYVV7m1rd+KQWy21iGfKcz0Dyhf6XzYlMN+uZ9XLl8pWI2e6Xk4MU7QgRuZxT5oLHdtZjlx8GT4lcSKZl7mNrp6obuxRc/t3/7Xo+2NF66W7QXL2RvKevXIp+Ou9mxbkdNbbdv6xMcVv/DmvULZ6C+N3G673wSk/WQqVcWFq8uYquGXhyLsJyz796xf7voKnqmP+xY1OrpZUTby0LfSi9kHub+cGXRr+tFc1N43j+/52sju4El2+x8aS9Op/316rmCdNA4wXH+Pi/l/8f/96CzkkHNpZuVzJpwhwt/AlwH3IxeotoAHgAeLeGGGO5hYGsI1zk47pmeXaVam1KMWmhamjYQI23rmGytcf/p/xe3nHyQqgzbywusssomq2x1SZ/mmNGjKjjPCWaMKWhY4zwDGjZZiZp0ng1WqNkwN4FLZlTdNF0z5AKOC6wwYoZ4Gws+1RrOsc4KGwyZMWHMFqsM2IqkywonONMZFZwwpGSroy3ETKaDSNqNGDCNWl0i8FZXxdbURPjfM6OipYz+umASybchs0imiaAs3NudMSCYiBTTiSu0zDpyZsaIOhqjDNp1RedRLxBPYtqvoohhpnhqRjFPA0MyOaaMKBNziy56JHOdhteMYfQj10RCK9CHk46ICcts09WAEj/ht25lXFdHYnCRWE7RjiPmVYWF6W3ANpa7jPSkxFVEPbM2kn5FLHsRCauQQh19EzrE9GLbHRKlJWYd4SdlSQ8I1n+ZbCDU6XXIY/q+CvSFbLN1QNQ8VCOcmm6LElQF2leC38H04NevX0FKlqh/PLk1l4qrbLmkvOJDzULj0luiNg0xkOlN2bQ2W5yprYL5/qA3+/rCSNVv1DLaWtOxuXgjp3S/tTe+eEPdJ8f07/5Be1E6qqu5SDOs/6aUuekO7NtDj/ehHlPfeztDyaV+H98bEdknP/frIG1FF/Pk8qXhUgi4gJ29Gy0DlZGxH5gf4WJKK50D0jvq8sZyQEel3IYu45qq60OqX37Q6BM5V16soat3ev/2IKmQ/qzS31leaehVGRkHamI8YDn60/y1kOXI18UgO4X5HeAy4Rqu5QZu4KP8+T7G2lxkhc3YDwRP5gchqJnX4uqTTE0yq8lYCDOMaNvJeS7dR8v6ay/vygUBK+iVFbE1qSh5obNF0+VX4+zPdAJLmHn0LLfoEtp2OnPbYbnJtRVW2ezcfewH9m9VOwwEnPSYovdZeq3tSfrb9d5YPg/Dy4z+ZVd51g+TGvn2xpjuonG4fGNzGWD7d7ojUxo5YDeXCzIuJ85yjp/mLQedjQwgm6Ocx9El4e4llO4GAhl3GvgK4DiwhRqWxgVi9nFg6GDm2HjwWlzV4kvP5pOPs3bjGS48doK2DuYjHzt9LTdc9wjT6ZCZO85kuMLDDDjBGUZsASdYiRplLUQvcSNaHOcYs84FBjRciJpTQcjcMI7+0SaMqChZjdpznuD3bErJgHO0VNHMIXi2GDJjk1UaJh1BMWPUHQXOcqLzx+YpmHAzmzzGiE1W4oYumIIcBQueDBky7dJeYSuKucJiI0L9GY4NjkeTmCqsFu0l1VobMGHIKpvUFEwZ42gZM+uWq2AWMmzABsyiFfiQVtDGC4YVawr6ftB8zLsVu4kYMRAoRdRaaztSS+IQTxpFjGXKsDt6iPnDcLTxnQZc8OLnO9JRSCP1JadW7BuKjv4KOnlVVzJibHJEUzGQEppyiLK3/WtTx5aYCCVQtLGTq78Dl8SZwmqcaTvLd+pAVii5vlixoAaEWNVtiJJdljwL5IPkQ+moFqshV8QDqqSY2ve3tFLwBmeJUBU0ht9W0yglVdI0LezBs18m/d1itYnszT09EgsppPSftl3/jlTqK22ePtN87ESOSX60nvptbsvV4jqivU9oLiLW1KzC7ggxsKLgnZHqwArhvneo0HmeHNwN5umH/RV56hg4uA159lBz1LGdCCNouS2il/uk0TJBPY1iLq/oSiFhrnz+7SzdnzNSHETdFr3fAQc9+mWlTH0qHUzf071rX4NyOcZCqkm1k9hsGbFo5V/GvJ/mcU7z+D7FpjOqX+I59ajgYAi47dCf0fp7aL3oYr9fbFpPznZyshRiT057Orurl9btqG7rU63sTmbz4fplOfgZ+kpiJwJODSFmCPqXToVwa1TQZlBEeVLfwsjiPnZUNP/2E7bG5IwrNW8lGXpR2Ho8s+P56I/lJ4r+qd8Syos0fUVFIp3Lcz1fadh229jXCxUZl44Zl047Hc014OiScDJTPgbd+PtD4CaCX7jPEEr/IPCMGPaPgZPAUx1+VkIDzUbF2Y8Pgms2B+DYPHuch9uCSTvCuZbrb3gMgIfdTRz3j/EIJ3HOcYwzXMtpNhmzRTBpOWTCeaaUzLiZh6JfMMfD3IjDUTFllQ3KaDayjAYl5Ri3xYiWkkE8cIQ7ljPAscFKJO5IaJKaivOsUcU4zrMFrDJhhbPAmAsMkht0JZusdZ+2WGGFDcBRR79zLlKBDQUzhozZ6rb0qu3mo1ZUwBmOG30s8cTWRp95auYw+HMbMuh0mWDSkVKildcyY4hjwDDqxrWR4Apk1zDST8EUZdsRZnZy1g2iaEL5qFEWcqd3tiZUiGHHmmGcSlIhltx3nVIAZdTFEs0mJSwCwSV+0ojGI4sYn4/xlIYMwYTXhV1NIqq+gt3SinnMEddyLbfxKB+iYMgFziV3H+V3bbZvzvzvYx6VFEmPek1H9PUPmpbESxXwQz8pFzxXUjGULCXHVKdLb8ot8g3WYgm7vslMS4QJeZd+p+lpXtOSpSQw5meetEsJwSZ5T7Xrym6j7JL3RMtRYm67OkqR3nLrU3epmcy0HmQMCKxQIBUTtF2+L06m2XqWfO8Orveze8zTZJdOcln6WJ/sH5bhGK+EMSw+DGccbqhmcUDb9et0LVymVu/PHKA9NR01LdZrzJUcUfO3XUUEavWUDs60YjoT7u/1gUvNS5oLtUdwkLBCQk/fvPJyINXstBe2lmfM7oxFo/noY36+ysgIWGT9YNGosKtz1Xu+SHxT9sLISVRdUiyOP2O3uJoJuP7OzF6wtFIK7YfbWfnY/pJnH5mAC1i0K9brS7qH0XO8yKdcT36R0Yftx9b6h+3n9vLx9jvqVAaUcXCwrXTQ1w0zMrbDESfhPJxx8EeENerp0PlnXAHOx78/QCDnIJinXI3fP0Lgt06W0HhYbaF2cG7AVrmKr1rcZIQ/6boR/8jsRpyDqqyZuBEb7jgnONP5cZsypKaiYsJf8Pk4WlY4z3pkChtWCQL5JlI5Qb9oQM0kvjuMHsUANjjGJkOGzGgYsMVq3HJ7ruUsAFuMgWBzfoXNSF+o2vQma9RMGTMl3NGzek6BnLrAsShQaqOhQ/Wh1VJE84YtnqC5pn6/HENmTBniKaiYIpuClopR3GDZ5ayN991n0BFZosc0o2KG0i/gmMaFc0ZBQdXlvoGoY5Z6PXJdOmVvuRSNsrIz8SghdYOp4o8pBWI+Ue51hW8rE38gWWvjh00ISBCSJZgTmXbDUe7NWiGa0G2i7abkoX5D98ySD1uc5pN8ILbJVuxdDjFgojpaQoqJWVRLmdr8FF1NzBJCpq8jp6Rg8NdXUNKaFiljWQvE12DaQnrjsIk+/exdUSuUanBJykKwWTGI5MUaW+uTNFZbTTX1XPedFWZrqZ0Jk5JgQmb279br5kDfVQ04he/Cl/FWoZQkJcm0fPOk26LNoqSbiuLnw4kYOT147v7gLm2/23d0+7u3dPRda3Lt4EW828F1/x9c/nT8NL1eu1hvJuOgME9GzR8EFxMdIORQSrEvH3QllBWpSL7VqxagZah6M+uVgZiEDjlRr77yrc7ql7+u+32gf/nCmf8PCunujrjeH+zsZ30gioB6mUQnurL3NTuXc/zuBG8ujB3G/F8abP++WsqcsTcUZiW5XGiY19fIyNgO9gws6F/6UBmKPf8L+mfPRetqnhH3gsUXe1PZy3zrZOJtJ1jizcod5Hp3+r3Y1dkf1xEZ+4tFl+fld1ASyO21HMjmKPs4uiTcR7fg/CYUAxhfC40LZNtHgCHwuYQ2XQWeDIyBdcKo/Xj8vQmcBSoHf8PBVhHm4UfAU8G4wJeOxz99I+P1C7jhDJynmQ2YtGNGKxOa0jFwWwyKQKY1lLQMIy0W9Lq2WOMEmzhz8PYUnOd4J6pY5ULUhgsaU+c5ho8kxoQxU2assImY5WuBx1mPxJoQBCUrfA4bfAaryxNMUVacY4THs8JGotdkCYvgT62MRh6lPOKnLdwCX2HSvVMziOYk5fOQEWK2ccBWImDQqbSMcQefdY4i0j2SBx9pGdHCajvtMkt+hPCzSAqWNAyoIyFUxpqy9sktGWPNdhTI7XZJw5oblFSbuFCL0KSmoIl+6oIPvLJ7t6CJdVdiBXliaMtuCKSNRDyp2nEuli+EFU3HkFrRaeopgSV5drEkQiyVRghfxPC2PawhShWSithUcmyJQOnJQbxfIjexmgWbmBDXsEtLzKiKAabUNGLQnZQtUWvIUnGPnhJwaggpGBWVek1JLEs8iD5D/yiSisLU62Dag235+t+ldbPIeKZtdx138mk77Tchj7e/H68zi91wXlxEpJv+3YlM+6TgXkSt0hcvdYOrtbhYU/BSEEQk+3+Y8eb/AMeAitkV3mg4Ug0iWSmUKO+Lfw+bDsbB4Vk8jT/g43t8y84Sduyo3qzMC97Mdjq36cy4vELgRX0oHfmpAeX5awxXHkpuyZFOdjz9Ge7yiz7C6NQdAV0OdOW2M/GVRP+6Sds9US2zgzgSW5I6iBll9PQNaB8s7LhXg3GHC6ptq3vC9HrN1YSrscwZu8PlJuAyMnYL2Un6eMXanuX1ZJ16hV9s9rQ/4+UZcDss2gn33VbY/aXd6y2+mpPrerdICeT0EmNqutN+n3Glsd317z7tLOEaczrbSXaWcRCouXSzkkeThDu6PfPsY9DOoN6A2QZcIPiCe9TDhg+fpwRfcX8BfBj4KPAx4M8J31uZ1v3Ap+P3DwOPVUErDqg3R5x/fJ2tc6tsPn6cejakaUo2N8ZMmxEPzW7h0/UtnOU4NRUThh1xAzBhzIPcxCOc5BxrnOYaNhlHE4lKnAR/bSs8yvVciCp9IgKasMIkEj4h7IAJq0wYdiYhPfA5fAmbHOcsx9lkxCTmp6akZkDDgPOc4HzUsJtSMWHAFmOmDLqj9IRx9EY3oqGiZkQwf1ixxQp11LYSEkTvuJdGUwVmDKMO3pgZoyiKK6kR7y8iehwwo6KmNO9U1AxpGSCadU0koJq4VZxFksZTUDNkixFTRrTxXY9jwogJYyYM8VxD3XHTSjz6SGS1HaHVR6COmpiHUJ+VIXkC4dYwMGkHrT0RqgZCK42/IZjnDGRm0aUvdOCU4GfOd7qTUv6KKYP4boW926P+5ITAKWL+VAQv5FYb89UQyL5AYEp/LE3dyg9MKZkyYMYwltdhF0TfpWM3/bqcNrHuxEypbkNl81kyo+RzeT5K3knfKrs61BswIe15PQrph4WJwy7aKVGY5sPRX9wXESv0PjWmnXV861bbbsrlyLPd5m+eTJtPMw0vdb+7aT8lYne3ARWhcOjr5a7TknefKJS43J+4Ai6PGDTUjoyLltkSmV0JvURGftApTiFHdhn1h09UfPkQ6uYveTD5PF9HwRhxeqWh6WYGqXtZQa1fpSIJH56XcVaRqx271z+9nOiXXWYtKZ94Tk1H7cEdeXU8zhsxmTcv2a/jy5tn2QXqLB5WPclLeg0jXTWvBHxcvZuuH8uOJr0GdKWR9r8S1aA8+PGxOxye2dV3F/RkZ6lX2K4GCEGuhrszMjIyDgqLdp6+91ufF91vvTJjLWNYeY61XHN0Z7oBA4p9EpXq5cYAKxkBudBdxt1x+o5cSF60olZHWJ9iv2BJmnnrPH3ZUIHusA9u53q1Q06HIs/tz1t2PIVL/1bmlMm35UP9BH8uDx5//HFOnTrF+vo66+vrnDp1itOnT+/4zt13383tt9/O2toa1157LS984Qu577779pz2EZ65zVLXTsBHH2feweYmsJLK22sCuTaM78wIGnISZkYg7GQe3gIuOGg9OA8TmJ0/hlupKUZb4MF7FaxfmB6nKhtwYYNznmOssYGYK2yjCcQpQ0ZsMWHMgCkOT0XNOVZY50wUHgRRx4SKAQ1iPnKLEeFmbzCv4/CRWKuoafAUvI/3x0Yv2GSVEVuRpFOr0cGnWyBxBky6ThJ8uDVxogsbgkE0+CiETriJXTBlREndUW72ZsmUQRQYuahpEibKEqJpSbr8V3HgKRGmU3Ew9yh5VrOOVjSW2ni299Z9bOYK1Zoq2GKCDAuJQXWetH4aCuPDS7QPRcdt2JEB1nyhelCT2zYudj+lO7QtpKRWPyDoQsrC0zDoytJGTTuL+Tv69k7yvKaLGIpK9Q9cJ1ZINyq69Ml95xBvObdBVKOMmr4ll+meOhPz9ounlOuj/H7vub0xtmjDlJY/bR/93sIu8PPknT4XH3WL7rZJvlIiD/plVAOx0qqLRVc63lJyc7tNYnozSLDzhnI7v3MXw8Xabr/TE6R1svyb5XSrL2PdXxatu0uFHe1hzHrzXTDAq+UQc7KWVLb9rX9vbdkxT4crdpf/81zoZkUdq9ZfmMxSHtGD7s8MizTAJMyV9YG2F4i5xn57a7/QvYbvVsaD6SGtaaOQsl2PrVEa0TcWXP48psfOfp+wo+xgfc+lxo3hoEe4jl258gLQkPpWWx70qVPNszxdfsjqu8iGwNWFoptJMjIyMq4ctru8JJaC5Hxkr39axwoKe+ULDtfefX+xl8uR/fq3ZIE9mzZGGiCmDtMLpOF6VZvsDbZHfUS1RC4FIh+zNSl/W1cu829dvX38IJGeY/SZla9BkPeWxsGQtR+WzU1mPBG84hWv4NOf/jTveMc7APiu7/ouTp06xW/+5m9u+84XfdEX8aY3vYnP+7zPY3Nzkx/90R/lrrvu4mMf+xg33HDDrtN23vtlleRcEs6ePcv6+jrwI8Dzgc8CjwF/m2BvcgpswpPX4NqK6MosmJ30wJMI/uEuEFih1fhzHPhiYA1hS+AawvuDGPZGD62nWKtxo4bBtecZjht86/C+4PjoDKNyiyFtN8Gc4Bw1BatsAWG6GbFJiadiag7jngETruFcvF086/SUqng7WokMz7FovjKEE9Iq+FgbdsSW6meVUWdMhMCWuBqzhVJjNapXR7yjoJs78Aw7P29tnDSdES43DGkMMaB3qywtooIm0dFS7S0VqjgGcVmdRW0vocjEFKNsJIVIaxAjjr67DRPuHFnhtw4JEdE5SMopk76YtNRbSurDY97HlxVnl92TOtkmaN06WppINKb0kRBjlkNvTRlFN8J1oUXEK6Skmhe1ubJkmpQzNc1pYYkPazYpDT8vRta8+dgPrPGxdNOpcUq+t7fJbTdefVJnjWs4z+mYt+03XGl97Gz/e97U4nbhVLS2XZiURN4elvDe7WZxcT/cv/jTtKyIfXfQO+NPzN66HGv2k4hTP4BXZpNXs+jguxywPrBAKSUdo9vXUxHXgcUeHe33Jf05YG/ov2tHtHy3SOggJUif6SWOvtaqXaMsyR1qxZn52OZs0QEdlq2l94JF87bqVc+vK/M+rq5UPvtkm679/d5o1xFM6MufR6VrdWSJKGZ+dTxIpPs12F4AuBPWWOECm/ubNVNLpTGr1fczuBxIW9SZ/njwbbwX+GS+W8a8r7LGBhcuU+zajoXpcxkZGRmXG5bsWXT1KT3LqmzHJzv4PGs9EWwnSN39eTq3w8UgO+S+N3v7fbtg95RecWTu+4wri/55Zv7c5RbMTXrJa6875MnZLd60/m84c+YMJ06ceIK5z9grlJf5/xB8f10KtoD/Z9/b8M/+7M94+tOfzr333svznvc8AO69917uvPNOPvzhD3PbbbftKh4p4+/+7u/ydV/3dbtO/8hpwimn+CngPagY5b8DLyAcwyfwqYfhr0u44VpYKTTYZ4ARQTPuOKHdJ8AGgb97MoEzOA6d7OAm9IL7yNOeD4/bjYZmdUZRtDR1STMqWRnD6mCGwzOjpGKKA87TMGJCSc2MmpoWxyQxAjaLBh5HTAEYRpKuiGSXS8I6qqjnVUbyKBhsrNmK5J0YraqoGXeklYvGIMWYpGcWDQO2wMCI9K04sohUEsAk0kAhjAhp5KZOy5QadXQaQtmDqxp/HNAyixRZG4kMxzFuZYNPISKVQII1vfjUuKKdrFOhui4BJbMunF3YlWgJPy5qFEpObVptrGdnFot5TSsfc6RLSUrCKXTz7LtPYMW9GpMVM3vElFYqWlSNNPvUiqfnyZMmlmwR1PNHhSybvguflkfIz/4d4SZqB24Hu7HdiRRrEYJR2iLd9I7wbCRm9bYnwy6WluZ9MZG3KNxutLxEw/Ri2CtJJnWz27jDO5cmrgyjfO/aXKF3X7o/uH5c+0GcPVFC8lLSmydMuSJpXyrsFlk0OXV90LnJUvHp2FLtlPQAtV2ZL0ZH9Kkt1XTSbbwzIfRwJkLkRTHaZw2qLWVnO4+mGGi57Y7laU6Xt3V3BzHuIrRM8BW7KNylETX7A7tOu4Vt7jn4PGpv0gOoW5DHg0U6Uz2R/Ezi5bP9hYxy7Ye7qbdwz1Z77jjapLj8WGb/jTshnf2XVzM3YHLZ2jLdgR+cVmrGfmDIkGk8Y2dkHCbImdyeIuYtvoSnh2mlWWbovj9dBeQMMG/68Gjh+dzBe7n3sqfT12KzeoI3cROf4SEO4/WlqxEih5XrrXJx+/JcygPORnn00dI5OoS4wKWblQz797NnzyZPR6MRo9HoknN0zz33sL6+3hFwAHfccQfr6+u8973v3RUJN51O+Zmf+RnW19d51rOetaf0jxwJ9+ijj8a/fvTigRvo3LZYfGx/8uJJXRDWBN7uMfNsUfIZGRkZGRkZGRkZGRkZGRkZGRkZGcuC/3DQGcjI2CUeffTRqJGVcSUxHA65+eabefDBXfAyO+DYsWM8+clPTp794A/+IHffffclx/nggw9y4403zj2/8cYbefDBnRma//E//gcvf/nL2djY4JZbbuF3fud3OHny5J7SP3Ik3HXXXQfAJz/5yTzYMq5KnD17lic/+cl86lOfyqrXGVcl8hjIuJqR+3/G1Y48BjKuduQxkHE1I/f/jKsdeQxkXO04c+YMT3nKUzp+IOPKYjwe88ADDzCdPjELA957nEs1XbfTgrv77rt5wxvesGN873//+wHm4twurT6+5mu+hg996EM88sgj/OzP/iwve9nLuO+++xaSetvhyJFwRRHUldfX1/OCk3FV48SJE3kMZFzVyGMg42pG7v8ZVzvyGMi42pHHQMbVjNz/M6525DGQcbVD+IGMK4/xeMx4fKn+4PaO17zmNbz85S/fMczTnvY0/vAP/5CHHnpo7ruHH36Ym266acf319bW+IIv+AK+4Au+gDvuuIMv/MIv5M1vfjPf933ft+t8HjkSLiMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjI+Po4uTJk7syDXnnnXdy5swZ3ve+9/HlX/7lANx3332cOXOG5z//+XtK03vPZLI339OZFs7IyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMg4cvjiL/5iXvSiF/HqV7+ae++9l3vvvZdXv/rVvPjFL+a2227rwt1+++287W1vA+DChQt8//d/P/feey+f+MQn+OAHP8g//If/kE9/+tO89KUv3VP6R46EG41G/OAP/uC2dkIzMo468hjIuNqRx0DG1Yzc/zOuduQxkHG1I4+BjKsZuf9nXO3IYyDjakceAxk74Zd+6Zd45jOfyV133cVdd93Fl37pl/ILv/ALSZiPfOQjnDlzBoCyLPnwhz/Mt37rt/JFX/RFvPjFL+bhhx/mPe95D894xjP2lLbz3vt9K0lGRkZGRkZGRkZGRkZGRkZGRkZGRkZGRkZGRsbR04TLyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyDhoZBIuIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjI2OfkUm4jIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIx9RibhMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjL2GYeShPs3/+bf8PznP5/V1VWuueaaXb3jvefuu+/m1ltvZWVlha/+6q/mT/7kT5Iwk8mEf/yP/zEnT55kbW2Nl7zkJXz605++DCXIyLh0PP7445w6dYr19XXW19c5deoUp0+f3vEd59zCn3/37/5dF+arv/qr575/+ctffplLk5Gxd1zKGPj2b//2uf59xx13JGHyGpBxWLDXMTCbzfhX/+pf8cxnPpO1tTVuvfVW/sE/+Af89V//dRIurwMZy4if/Mmf5HM/93MZj8c85znP4T3vec+O4d/97nfznOc8h/F4zOd93ufxUz/1U3Nhfv3Xf52nP/3pjEYjnv70p/O2t73tcmU/I+MJYy9j4K1vfStf//Vfzw033MCJEye48847+e3f/u0kzFve8paF54Ktra3LXZSMjEvCXsbAu971roX9+8Mf/nASLq8DGYcFe+n/i868zjme8YxndGHyGpBxmPB7v/d7fPM3fzO33norzjl+4zd+46Lv5LNAxrLiUJJw0+mUl770pfyjf/SPdv3Ov/23/5Yf+ZEf4U1vehPvf//7ufnmm/n6r/96zp0714V53etex9ve9jZ+5Vd+hf/zf/4P58+f58UvfjFN01yOYmRkXBJe8YpX8KEPfYh3vOMdvOMd7+BDH/oQp06d2vGdz3zmM8nPz/3cz+Gc41u/9VuTcK9+9auTcD/90z99OYuSkXFJuJQxAPCiF70o6d+/9Vu/lXyf14CMw4K9joGNjQ0++MEP8gM/8AN88IMf5K1vfSt//ud/zkte8pK5sHkdyFgm/Oqv/iqve93r+Nf/+l9z//3385Vf+ZV84zd+I5/85CcXhn/ggQf4W3/rb/GVX/mV3H///Xz/938/r33ta/n1X//1Lsw999zDt33bt3Hq1Cn+4A/+gFOnTvGyl72M++6770oVKyNj19jrGPi93/s9vv7rv57f+q3f4gMf+ABf8zVfwzd/8zdz//33J+FOnDgxdz4Yj8dXokgZGXvCXseA4CMf+UjSv7/wC7+w+y6vAxmHBXvt/z/+4z+e9PtPfepTXHfddbz0pS9NwuU1IOOw4MKFCzzrWc/iTW96067C57NAxlLDH2L8/M//vF9fX79ouLZt/c033+zf+MY3ds+2trb8+vq6/6mf+invvfenT5/2g8HA/8qv/EoX5q/+6q98URT+He94x77nPSPjUvCnf/qnHvD33ntv9+yee+7xgP/whz+863i+5Vu+xX/t135t8uwFL3iB/yf/5J/sV1YzMi4LLnUMvOpVr/Lf8i3fsu33eQ3IOCzYr3Xgfe97nwf8Jz7xie5ZXgcylg1f/uVf7r/ne74neXb77bf717/+9QvD/8t/+S/97bffnjz77u/+bn/HHXd0n1/2spf5F73oRUmYb/iGb/Avf/nL9ynXGRn7h72OgUV4+tOf7t/whjd0n3d7hs7IWAbsdQy8853v9IB//PHHt40zrwMZhwVPdA1429ve5p1z/uMf/3j3LK8BGYcVgH/b2962Y5h8FshYZhxKTbi94oEHHuDBBx/krrvu6p6NRiNe8IIX8N73vheAD3zgA8xmsyTMrbfeypd8yZd0YTIyDhr33HMP6+vrPO95z+ue3XHHHayvr++6nz700EO8/e1v5zu/8zvnvvulX/olTp48yTOe8Qz++T//54mmaEbGMuCJjIF3vetd3HjjjXzRF30Rr371q/nsZz/bfZfXgIzDgv1YBwDOnDmDc27OrHdeBzKWBdPplA984APJvAxw1113bdvX77nnnrnw3/AN38Dv//7vM5vNdgyT5/qMZcOljIE+2rbl3LlzXHfddcnz8+fP89SnPpUnPelJvPjFL57TlMvIWAY8kTHwZV/2Zdxyyy183dd9He985zuT7/I6kHEYsB9rwJvf/GZe+MIX8tSnPjV5nteAjKOKfBbIWGZUB52BK4EHH3wQgJtuuil5ftNNN/GJT3yiCzMcDrn22mvnwsj7GRkHjQcffJAbb7xx7vmNN9646376X/7Lf+H48eP83b/7d5Pnr3zlK/ncz/1cbr75Zv74j/+Y7/u+7+MP/uAP+J3f+Z19yXtGxn7gUsfAN37jN/LSl76Upz71qTzwwAP8wA/8AF/7tV/LBz7wAUajUV4DMg4N9mMd2Nra4vWvfz2veMUrOHHiRPc8rwMZy4RHHnmEpmkW7t+36+sPPvjgwvB1XfPII49wyy23bBsmz/UZy4ZLGQN9/If/8B+4cOECL3vZy7pnt99+O295y1t45jOfydmzZ/nxH/9xvuIrvoI/+IM/SEz2ZWQcNC5lDNxyyy38zM/8DM95znOYTCb8wi/8Al/3dV/Hu971Lr7qq74K2H6tyOtAxjLhia4Bn/nMZ/if//N/8su//MvJ87wGZBxl5LNAxjJjaUi4u+++mze84Q07hnn/+9/Pc5/73EtOwzmXfPbezz3rYzdhMjKeKHbb/2G+H8Pe+unP/dzP8cpXvnLO5verX/3q7u8v+ZIv4Qu/8At57nOfywc/+EGe/exn7yrujIxLxeUeA9/2bd/W/f0lX/IlPPe5z+WpT30qb3/72+cI6b3Em5GxX7hS68BsNuPlL385bdvykz/5k8l3eR3IWEbsdf++KHz/+aWcCTIyDgqX2l//63/9r9x999389//+35PLG3fccQd33HFH9/krvuIrePazn81/+k//if/4H//j/mU8I2OfsJcxcNttt3Hbbbd1n++8804+9alP8e///b/vSLi9xpmRcZC41L76lre8hWuuuYa//bf/dvI8rwEZRx35LJCxrFgaEu41r3kNL3/5y3cM87SnPe2S4r755puBwIjfcsst3fPPfvazHft98803M51OefzxxxNNiM9+9rM8//nPv6R0MzJ2i932/z/8wz/koYcemvvu4YcfnrvJsQjvec97+MhHPsKv/uqvXjTss5/9bAaDAR/96Eez8DXjsuNKjQHBLbfcwlOf+lQ++tGPAnkNyDh4XIkxMJvNeNnLXsYDDzzA//7f/zvRgluEvA5kHCROnjxJWZZzt1Lt/r2Pm2++eWH4qqq4/vrrdwyzlzUkI+NK4FLGgOBXf/VX+c7v/E5+7dd+jRe+8IU7hi2Kgr/xN/5GtyfKyFgWPJExYHHHHXfwi7/4i93nvA5kHAY8kf7vvefnfu7nOHXqFMPhcMeweQ3IOErIZ4GMZcbS+IQ7efIkt99++44/fc2d3UJMK1lzStPplHe/+92dcPU5z3kOg8EgCfOZz3yGP/7jP84C2IzLjt32/zvvvJMzZ87wvve9r3v3vvvu48yZM7vqp29+85t5znOew7Oe9ayLhv2TP/kTZrNZQlxnZFwuXKkxIHj00Uf51Kc+1fXvvAZkHDQu9xgQAu6jH/0ov/u7v9sdQnZCXgcyDhLD4ZDnPOc5c+ZQf+d3fmfbvn7nnXfOhf9f/+t/8dznPpfBYLBjmDzXZywbLmUMQNCA+/Zv/3Z++Zd/mW/6pm+6aDreez70oQ/luT5j6XCpY6CP+++/P+nfeR3IOAx4Iv3/3e9+Nx/72Mf4zu/8zoumk9eAjKOEfBbIWGr4Q4hPfOIT/v777/dveMMb/LFjx/z999/v77//fn/u3LkuzG233ebf+ta3dp/f+MY3+vX1df/Wt77V/9Ef/ZH/e3/v7/lbbrnFnz17tgvzPd/zPf5JT3qS/93f/V3/wQ9+0H/t136tf9aznuXrur6i5cvI2AkvetGL/Jd+6Zf6e+65x99zzz3+mc98pn/xi1+chOn3f++9P3PmjF9dXfX/+T//57k4P/axj/k3vOEN/v3vf79/4IEH/Nvf/nZ/++23+y/7si/L/T9j6bDXMXDu3Dn/vd/7vf69732vf+CBB/w73/lOf+edd/rP+ZzPyWtAxqHEXsfAbDbzL3nJS/yTnvQk/6EPfch/5jOf6X4mk4n3Pq8DGcuJX/mVX/GDwcC/+c1v9n/6p3/qX/e61/m1tTX/8Y9/3Hvv/etf/3p/6tSpLvxf/uVf+tXVVf9P/+k/9X/6p3/q3/zmN/vBYOD/23/7b12Y//t//68vy9K/8Y1v9H/2Z3/m3/jGN/qqqvy99957xcuXkXEx7HUM/PIv/7Kvqsr/xE/8RDLXnz59ugtz9913+3e84x3+L/7iL/z999/vv+M7vsNXVeXvu+++K16+jIyLYa9j4Ed/9Ef92972Nv/nf/7n/o//+I/961//eg/4X//1X+/C5HUg47Bgr/1f8Pf//t/3z3ve8xbGmdeAjMOEc+fOdTJ/wP/Ij/yIv//++/0nPvEJ730+C2QcLhxKEu5Vr3qVB+Z+3vnOd3ZhAP/zP//z3ee2bf0P/uAP+ptvvtmPRiP/VV/1Vf6P/uiPkng3Nzf9a17zGn/dddf5lZUV/+IXv9h/8pOfvEKlysjYHR599FH/yle+0h8/ftwfP37cv/KVr/SPP/54Eqbf/733/qd/+qf9yspKcggXfPKTn/Rf9VVf5a+77jo/HA7953/+5/vXvva1/tFHH72MJcnIuDTsdQxsbGz4u+66y99www1+MBj4pzzlKf5Vr3rV3Pye14CMw4K9joEHHnhg4b7J7p3yOpCxrPiJn/gJ/9SnPtUPh0P/7Gc/27/73e/uvnvVq17lX/CCFyTh3/Wud/kv+7Iv88Ph0D/taU9bePno137t1/xtt93mB4OBv/322xPhbEbGsmEvY+AFL3jBwrn+Va96VRfmda97nX/KU57ih8Ohv+GGG/xdd93l3/ve917BEmVk7A17GQM//MM/7D//8z/fj8djf+211/q/+Tf/pn/7298+F2deBzIOC/a6Dzp9+rRfWVnxP/MzP7MwvrwGZBwmvPOd79xxX5PPAhmHCc776KEwIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyNjX7A0PuEyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMo4KMgmXkZGRkZGRkZGRkZGRkZGRkZGRkZGRkZGRkbHPyCRcRkZGRkZGRkZGRkZGRkZGRkZGRkZGRkZGRsY+I5NwGRkZGRkZGRkZGRkZGRkZGRkZGRkZGRkZGRn7jEzCZWRkZGRkZGRkZGRkZGRkZGRkZGRkZGRkZGTsMzIJl5GRkZGRkZGRkZGRkZGRkZGRkZGRkZGRkZGxz8gkXEZGRkZGRkZGRkZGRkZGRkZGRkZGRkZGRkbGPiOTcBkZGRkZGRkZGRkZGRkZGRkZGRkZGRkZGRkZ+4xMwmVkZGRkZGRkZGRkZGRkZGRkZGRkZGRkZGRk7DMyCZeRkZGRkZGRkZGRkZGRkZGRkZGRkZGRkZGRsc/IJFxGRkZGRkZGRkZGRkZGRkZGRkZGRkZGRkZGxj4jk3AZGRkZGRkZGRkZGRkZGRkZGRkZGRkZGRkZGfuM/z++Lxco6rL20gAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 2000x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 2000x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "color_legend = [[0,1.5],[-0.3,0.3],[0,35]]\n",
    "\n",
    "for idx in range(3):\n",
    "    plt.figure(figsize=(20,4))\n",
    "    plt.scatter(samples[:,0],\n",
    "               samples[:,1],\n",
    "               c = result[:,idx],\n",
    "               cmap= 'jet',\n",
    "               s=2)\n",
    "    plt.colorbar()\n",
    "    plt.clim(color_legend[idx])\n",
    "    plt.xlim((0-L/2, L-L/2))\n",
    "    plt.ylim((0-D/2, D-D/2))\n",
    "    plt.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "26d53bb1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cc48fb25",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
