{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "744f1882",
   "metadata": {},
   "source": [
    "Convolutional Dictionary Learning\n",
    "=================================\n",
    "\n",
    "This example demonstrates the use of [dictlrn.DictLearn](http://sporco.rtfd.org/en/latest/modules/sporco.dictlrn.dictlrn.html#sporco.dictlrn.dictlrn.DictLearn) to construct a dictionary learning algorithm with the flexibility of choosing the sparse coding and dictionary update classes. In this case they are [cbpdn.ConvBPDNGradReg](http://sporco.rtfd.org/en/latest/modules/sporco.admm.cbpdn.html#sporco.admm.cbpdn.ConvBPDNGradReg) and [admm.ccmod.ConvCnstrMOD](http://sporco.rtfd.org/en/latest/modules/sporco.admm.ccmod.html#sporco.admm.ccmod.ConvCnstrMOD) respectively, so the resulting dictionary learning algorithm is not equivalent to [dictlrn.cbpdndl.ConvBPDNDictLearn](http://sporco.rtfd.org/en/latest/modules/sporco.dictlrn.cbpdndl.html#sporco.dictlrn.cbpdndl.ConvBPDNDictLearn). Sparse coding with a CBPDN variant that includes a gradient regularization term on one of the coefficient maps [[55]](http://sporco.rtfd.org/en/latest/zreferences.html#id55) enables CDL without the need for the usual lowpass/highpass filtering as a pre-processing of the training images."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d77a6c4e",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2025-10-08T19:38:40.382904Z",
     "iopub.status.busy": "2025-10-08T19:38:40.382296Z",
     "iopub.status.idle": "2025-10-08T19:38:41.529040Z",
     "shell.execute_reply": "2025-10-08T19:38:41.528131Z"
    }
   },
   "outputs": [],
   "source": [
    "from __future__ import division\n",
    "from __future__ import print_function\n",
    "from builtins import input\n",
    "\n",
    "import pyfftw   # See https://github.com/pyFFTW/pyFFTW/issues/40\n",
    "import numpy as np\n",
    "\n",
    "from sporco.admm import cbpdn\n",
    "from sporco.admm import ccmod\n",
    "from sporco.dictlrn import dictlrn\n",
    "from sporco import cnvrep\n",
    "from sporco import util\n",
    "from sporco import plot\n",
    "plot.config_notebook_plotting()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30dafdfb",
   "metadata": {},
   "source": [
    "Load training images."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "51947f6a",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2025-10-08T19:38:41.532259Z",
     "iopub.status.busy": "2025-10-08T19:38:41.532021Z",
     "iopub.status.idle": "2025-10-08T19:38:41.972476Z",
     "shell.execute_reply": "2025-10-08T19:38:41.971435Z"
    }
   },
   "outputs": [],
   "source": [
    "exim = util.ExampleImages(scaled=True, zoom=0.5, gray=True)\n",
    "img1 = exim.image('barbara.png', idxexp=np.s_[10:522, 100:612])\n",
    "img2 = exim.image('kodim23.png', idxexp=np.s_[:, 60:572])\n",
    "img3 = exim.image('monarch.png', idxexp=np.s_[:, 160:672])\n",
    "S = np.stack((img1, img2, img3), axis=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ca1e238",
   "metadata": {},
   "source": [
    "Construct initial dictionary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e5348d3e",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2025-10-08T19:38:41.976472Z",
     "iopub.status.busy": "2025-10-08T19:38:41.976280Z",
     "iopub.status.idle": "2025-10-08T19:38:41.979936Z",
     "shell.execute_reply": "2025-10-08T19:38:41.979163Z"
    }
   },
   "outputs": [],
   "source": [
    "np.random.seed(12345)\n",
    "D0 = np.random.randn(8, 8, 64)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "083cf646",
   "metadata": {},
   "source": [
    "Construct object representing problem dimensions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "502ae552",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2025-10-08T19:38:41.983967Z",
     "iopub.status.busy": "2025-10-08T19:38:41.983801Z",
     "iopub.status.idle": "2025-10-08T19:38:41.987238Z",
     "shell.execute_reply": "2025-10-08T19:38:41.986474Z"
    }
   },
   "outputs": [],
   "source": [
    "cri = cnvrep.CDU_ConvRepIndexing(D0.shape, S)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4522e2e",
   "metadata": {},
   "source": [
    "Set up weights for the $\\ell_1$ norm to disable regularization of the coefficient map corresponding to the impulse filter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7cac0bda",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2025-10-08T19:38:41.990689Z",
     "iopub.status.busy": "2025-10-08T19:38:41.990527Z",
     "iopub.status.idle": "2025-10-08T19:38:41.994074Z",
     "shell.execute_reply": "2025-10-08T19:38:41.993301Z"
    }
   },
   "outputs": [],
   "source": [
    "wl1 = np.ones((1,)*4 + (D0.shape[2:]), dtype=np.float32)\n",
    "wl1[..., 0] = 0.0"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "413c0b96",
   "metadata": {},
   "source": [
    "Set of weights for the $\\ell_2$ norm of the gradient to disable regularization of all coefficient maps except for the one corresponding to the impulse filter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "994c210d",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2025-10-08T19:38:41.997413Z",
     "iopub.status.busy": "2025-10-08T19:38:41.997267Z",
     "iopub.status.idle": "2025-10-08T19:38:42.000702Z",
     "shell.execute_reply": "2025-10-08T19:38:41.999908Z"
    }
   },
   "outputs": [],
   "source": [
    "wgr = np.zeros((D0.shape[2]), dtype=np.float32)\n",
    "wgr[0] = 1.0"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "efb72c07",
   "metadata": {},
   "source": [
    "Define X and D update options."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1206e69e",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2025-10-08T19:38:42.004230Z",
     "iopub.status.busy": "2025-10-08T19:38:42.004077Z",
     "iopub.status.idle": "2025-10-08T19:38:42.008620Z",
     "shell.execute_reply": "2025-10-08T19:38:42.007839Z"
    }
   },
   "outputs": [],
   "source": [
    "lmbda = 0.1\n",
    "mu = 0.5\n",
    "optx = cbpdn.ConvBPDNGradReg.Options({'Verbose': False, 'MaxMainIter': 1,\n",
    "            'rho': 20.0*lmbda + 0.5, 'AutoRho': {'Period': 10,\n",
    "            'AutoScaling': False, 'RsdlRatio': 10.0, 'Scaling': 2.0,\n",
    "            'RsdlTarget': 1.0}, 'HighMemSolve': True, 'AuxVarObj': False,\n",
    "            'L1Weight': wl1, 'GradWeight': wgr})\n",
    "optd = ccmod.ConvCnstrMODOptions({'Verbose': False, 'MaxMainIter': 1,\n",
    "            'rho': 5.0*cri.K, 'AutoRho': {'Period': 10, 'AutoScaling': False,\n",
    "            'RsdlRatio': 10.0, 'Scaling': 2.0, 'RsdlTarget': 1.0}},\n",
    "            method='cns')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a61cc6e6",
   "metadata": {},
   "source": [
    "Normalise dictionary according to dictionary Y update options."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "28091e85",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2025-10-08T19:38:42.012083Z",
     "iopub.status.busy": "2025-10-08T19:38:42.011909Z",
     "iopub.status.idle": "2025-10-08T19:38:42.015622Z",
     "shell.execute_reply": "2025-10-08T19:38:42.014846Z"
    }
   },
   "outputs": [],
   "source": [
    "D0n = cnvrep.Pcn(D0, D0.shape, cri.Nv, dimN=2, dimC=0, crp=True,\n",
    "                 zm=optd['ZeroMean'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76e9f6fe",
   "metadata": {},
   "source": [
    "Update D update options to include initial values for Y and U."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "742a24ad",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2025-10-08T19:38:42.019305Z",
     "iopub.status.busy": "2025-10-08T19:38:42.019108Z",
     "iopub.status.idle": "2025-10-08T19:38:42.022825Z",
     "shell.execute_reply": "2025-10-08T19:38:42.022032Z"
    }
   },
   "outputs": [],
   "source": [
    "optd.update({'Y0': cnvrep.zpad(cnvrep.stdformD(D0n, cri.Cd, cri.M), cri.Nv),\n",
    "             'U0': np.zeros(cri.shpD + (cri.K,))})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39cb6466",
   "metadata": {},
   "source": [
    "Create X update object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2cef0cbc",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2025-10-08T19:38:42.026412Z",
     "iopub.status.busy": "2025-10-08T19:38:42.026230Z",
     "iopub.status.idle": "2025-10-08T19:38:46.628902Z",
     "shell.execute_reply": "2025-10-08T19:38:46.627953Z"
    }
   },
   "outputs": [],
   "source": [
    "xstep = cbpdn.ConvBPDNGradReg(D0n, S, lmbda, mu, optx)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "103e3d05",
   "metadata": {},
   "source": [
    "Create D update object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5c6930b2",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2025-10-08T19:38:46.632605Z",
     "iopub.status.busy": "2025-10-08T19:38:46.632433Z",
     "iopub.status.idle": "2025-10-08T19:38:46.662134Z",
     "shell.execute_reply": "2025-10-08T19:38:46.661491Z"
    }
   },
   "outputs": [],
   "source": [
    "dstep = ccmod.ConvCnstrMOD(None, S, D0.shape, optd, method='cns')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "588d2073",
   "metadata": {},
   "source": [
    "Create DictLearn object and solve."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d6391211",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2025-10-08T19:38:46.664515Z",
     "iopub.status.busy": "2025-10-08T19:38:46.664299Z",
     "iopub.status.idle": "2025-10-08T19:43:49.398635Z",
     "shell.execute_reply": "2025-10-08T19:43:49.397950Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Itn   FncX      r_X       s_X       ρ_X       FncD      r_D       s_D       ρ_D     \n",
      "------------------------------------------------------------------------------------\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   0  7.30e+03  9.75e-01  1.02e-01  2.50e+00  1.64e+04  3.27e-01  2.20e-01  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   1  4.09e+03  4.62e-01  1.17e+00  2.50e+00  3.64e+04  3.86e-01  1.20e-01  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   2  5.30e+03  4.28e-01  8.87e-01  2.50e+00  6.12e+03  3.89e-01  1.71e-01  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   3  3.27e+03  3.44e-01  4.56e-01  2.50e+00  3.14e+03  3.67e-01  7.98e-02  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   4  2.01e+03  3.85e-01  5.94e-01  2.50e+00  8.87e+02  3.31e-01  8.04e-02  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   5  3.21e+03  3.98e-01  3.78e-01  2.50e+00  3.12e+03  2.66e-01  6.71e-02  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   6  2.44e+03  2.85e-01  3.80e-01  2.50e+00  5.57e+02  2.22e-01  6.31e-02  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   7  1.58e+03  1.99e-01  4.65e-01  2.50e+00  1.53e+03  1.83e-01  5.45e-02  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   8  2.52e+03  1.90e-01  1.80e-01  2.50e+00  1.22e+03  1.55e-01  5.31e-02  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   9  1.02e+03  1.80e-01  4.36e-01  2.50e+00  3.93e+02  1.30e-01  5.34e-02  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  10  1.91e+03  1.96e-01  1.86e-01  2.50e+00  1.13e+03  1.09e-01  5.38e-02  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  11  1.22e+03  1.39e-01  2.93e-01  2.50e+00  1.35e+02  9.28e-02  5.18e-02  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  12  1.39e+03  1.18e-01  2.24e-01  2.50e+00  7.79e+02  7.88e-02  5.31e-02  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  13  1.23e+03  1.11e-01  1.90e-01  2.50e+00  1.42e+02  6.77e-02  5.23e-02  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  14  9.07e+02  1.06e-01  2.26e-01  2.50e+00  4.18e+02  5.82e-02  4.96e-02  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  15  1.13e+03  9.69e-02  1.14e-01  2.50e+00  1.35e+02  5.19e-02  4.71e-02  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  16  6.80e+02  7.19e-02  1.85e-01  2.50e+00  2.43e+02  4.60e-02  4.40e-02  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  17  9.34e+02  7.11e-02  8.22e-02  2.50e+00  1.12e+02  4.13e-02  4.14e-02  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  18  5.22e+02  6.02e-02  1.48e-01  2.50e+00  1.52e+02  3.76e-02  4.14e-02  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  19  7.62e+02  5.86e-02  6.36e-02  2.50e+00  8.39e+01  3.46e-02  4.13e-02  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  20  4.59e+02  4.46e-02  1.12e-01  2.50e+00  1.06e+02  3.22e-02  4.03e-02  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  21  6.14e+02  4.30e-02  5.30e-02  2.50e+00  6.32e+01  2.99e-02  4.00e-02  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  22  4.27e+02  3.65e-02  8.09e-02  2.50e+00  8.21e+01  2.78e-02  3.96e-02  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  23  4.93e+02  3.39e-02  5.13e-02  2.50e+00  5.07e+01  2.61e-02  3.86e-02  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  24  4.16e+02  2.91e-02  5.76e-02  2.50e+00  6.41e+01  2.44e-02  3.71e-02  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  25  3.95e+02  2.58e-02  4.74e-02  2.50e+00  4.52e+01  2.27e-02  3.55e-02  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  26  3.94e+02  2.44e-02  3.84e-02  2.50e+00  5.30e+01  2.15e-02  3.40e-02  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  27  3.22e+02  2.07e-02  4.35e-02  2.50e+00  4.39e+01  2.03e-02  3.26e-02  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  28  3.57e+02  1.97e-02  2.81e-02  2.50e+00  4.60e+01  1.90e-02  3.15e-02  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  29  2.82e+02  1.67e-02  3.40e-02  2.50e+00  4.52e+01  1.80e-02  3.08e-02  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  30  3.18e+02  1.66e-02  2.49e-02  2.50e+00  4.41e+01  1.72e-02  3.03e-02  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  31  2.76e+02  1.50e-02  2.92e-02  2.50e+00  4.39e+01  1.64e-02  2.96e-02  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      " 190  1.68e+02  2.27e-03  5.43e-03  2.50e+00  3.58e+01  2.98e-03  6.89e-03  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 191  1.68e+02  2.30e-03  5.63e-03  2.50e+00  3.58e+01  2.96e-03  6.88e-03  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 192  1.68e+02  2.32e-03  5.84e-03  2.50e+00  3.58e+01  3.42e-03  6.86e-03  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 193  1.68e+02  2.34e-03  5.98e-03  2.50e+00  3.58e+01  3.29e-03  6.85e-03  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 194  1.68e+02  2.35e-03  6.09e-03  2.50e+00  3.58e+01  3.07e-03  6.84e-03  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 195  1.68e+02  2.32e-03  5.87e-03  2.50e+00  3.58e+01  3.12e-03  6.82e-03  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 196  1.68e+02  2.32e-03  5.90e-03  2.50e+00  3.58e+01  3.14e-03  6.81e-03  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 197  1.68e+02  2.35e-03  6.15e-03  2.50e+00  3.58e+01  3.05e-03  6.80e-03  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 198  1.68e+02  2.32e-03  5.97e-03  2.50e+00  3.58e+01  3.14e-03  6.79e-03  1.50e+01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 199  1.68e+02  2.31e-03  5.90e-03  2.50e+00  3.58e+01  3.43e-03  6.79e-03  1.50e+01\n",
      "------------------------------------------------------------------------------------\n",
      "DictLearn solve time: 302.73s \n",
      "\n"
     ]
    }
   ],
   "source": [
    "opt = dictlrn.DictLearn.Options({'Verbose': True, 'MaxMainIter': 200})\n",
    "d = dictlrn.DictLearn(xstep, dstep, opt)\n",
    "D1 = d.solve()\n",
    "print(\"DictLearn solve time: %.2fs\" % d.timer.elapsed('solve'), \"\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49b9e4f3",
   "metadata": {},
   "source": [
    "Display dictionaries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "94a986d9",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2025-10-08T19:43:49.402316Z",
     "iopub.status.busy": "2025-10-08T19:43:49.402123Z",
     "iopub.status.idle": "2025-10-08T19:43:49.626483Z",
     "shell.execute_reply": "2025-10-08T19:43:49.626023Z"
    }
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1400x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "D1 = D1.squeeze()\n",
    "fig = plot.figure(figsize=(14, 7))\n",
    "plot.subplot(1, 2, 1)\n",
    "plot.imview(util.tiledict(D0), title='D0', fig=fig)\n",
    "plot.subplot(1, 2, 2)\n",
    "plot.imview(util.tiledict(D1), title='D1', fig=fig)\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83954ed1",
   "metadata": {},
   "source": [
    "Plot functional value and residuals."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f65881c5",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2025-10-08T19:43:49.632781Z",
     "iopub.status.busy": "2025-10-08T19:43:49.632607Z",
     "iopub.status.idle": "2025-10-08T19:43:50.382251Z",
     "shell.execute_reply": "2025-10-08T19:43:50.381733Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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kpOx2O+effz5XX32113hqaiqRkZE8+uijJ923b9++mEwmTCYTvr6+NGnShCFDhvDll19WddgiIiIiUuCMSMTMnTuXRo0aMWjQIM9Yt27dsNlsXgvfbtu2jcTERM/Ct71792bTpk2kpKR45ixevJjg4GCio6M9c4oewz3H6MVzrWpNJiIiIgZyVcQ4Cgc8iZjKXxtPREQqxmKxMG/ePBYuXMiHH37oGR83bhyhoaE88cQTp9x/zJgxHDhwgH/++YcvvviC6OhobrjhBu64446qDl1EREREOAMSMQ6Hg7lz5zJixAis1sIla0JCQhg1ahQTJ05k6dKlrF27lttuu43evXtz3nnnAXDppZcSHR3NLbfcwoYNG1i0aBGPPfYYcXFx+Pr6AnDXXXexc+dOHnzwQf766y9ee+01PvvsMyZMmGDI9bpZlIgRERERAwX4WlURIyJyFmnTpg3PPPMM48aN48CBA/z3v//lk08+4b333sPHx+eU+wYEBBAeHk7Tpk0577zzePbZZ3njjTd46623+OGHHwD46aefMJlMXl0p1q9fj8lkYteuXQAcPnyYG2+8kSZNmhAQEEDHjh35+OOPq+qSRURERGoM6+mnVK0ffviBxMREbr/99mLbZs6cidlsZtiwYeTk5BAbG8trr73m2W6xWPjmm28YO3YsvXv3JjAwkBEjRjB16lTPnKioKL799lsmTJjASy+9RNOmTXn77beJjY2tlus7GbMSMSIiImKgIF+Ld0WMX13XuypiRKS2cTohL9OYc9sCoKBtdWmMGzeOr776iltuuYVNmzYxefJkOnfuXK5Tjxgxgvvuu48vv/ySAQMGlGqf7OxsunXrxkMPPURwcDDffvstt9xyC+eccw49e/YsVxwiIiIitYHhiZhLL70U50kWrPfz8yM+Pp74+PiT7t+8eXO+++67U56jb9++/PHHHxWKs7K5W5M5TnLtIiIiIu77ILvdfvrJZRToY8XsTsSYreBXsCaeKmJEpLbJy4TpEcac+5H94BNY6ukmk4nXX3+ddu3a0bFjRx5++OFyn9psNtOmTRtPtUtpNGnShPvvv9/zfdy4cSxatIjPPvtMiRgRERGRUzC8NVltZS546ilfFTEiIiJyEnFxcWzdupXVq1dX+rGDfK1Y3a3JzFbwreP6rESMiMgZbc6cOQQEBJCQkMDevXsrdCyn04mpDBU5drudp556io4dOxIaGkpQUBCLFi0iMTGxQnGIiIiI1HSGV8TUVpaCFJhak4mIiIgRAnytWEwlJGKy1ZpMRGoZW4CrMsWoc5fBsmXLmDlzJt9//z3Tpk1j1KhR/PDDD2VKprjZ7Xb+/vtvevToAbgqZACvjhV5eXle+zz//PO89NJLzJo1i44dOxIYGMj48ePJzc0t8/lFREREahMlYgxiKbjJVSJGREREjOC1RozZAr5qTSYitZTJVKb2YEbJzMxk5MiRjB07ln79+hEVFUXHjh2ZPXs2Y8eOLfPx3n33XY4ePcqwYcMAaNiwIQAHDhygXr16AKxfv95rn99//50rr7yS4cOHA+BwONi+fTvR0dEVuDIRERGRmk+tyQyiihgRERExUqCvFUtBazKnSYkYEZEz3aRJk3A6nTzzzDMAtGjRghdeeIEHH3zwtOu8ZGZmkpSUxN69e1mxYgUPPfQQd911lyepA9CqVSsiIyOZMmUKf//9N99++y0zZszwOk7r1q1ZvHgxy5Yt488//+TOO+8kOTm5Sq5XREREpCZRIsYg1oKKGIdTiRgRERGpfoG+1iIVMUXXiEkDh8O4wEREpJiff/6Z+Ph45s6dS0BAYTuzO++8k/PPP59Ro0Z5tRQ70VtvvUXjxo0555xzuPrqq9m6dSuffvopr732mmeOzWbj448/5q+//qJTp048++yzTJs2zes4jz32GF27diU2Npa+ffsSHh7O0KFDK/16RURERGoatSYziLmgh2++KmJERETEAAE2i1dFjMmdiMEJeRmFiRkRETHcRRddRH5+fonbFi1adMp9f/rpp1Kfp0+fPmzcuNFrrGiCJzQ0lPnz51fa+URERERqC1XEGMTdmsyhRIyIiIgYwGoxE2B13Yc4TBaw+bsqY0DtyUREREREREQqkRIxBrEUtCbTGjEiIiJyMvHx8URHR9OjR48qOX4dH9e7w2RxLVbtroLJTquS84mIiIiIiIjURkrEGMRdEaPWZCIiInIycXFxbN26ldWrV1fJ8QNtrlapDpPFNeBZJ0YVMSIiIiIiIiKVRYkYg1gK1ohxnGJBRREREZGqFGB13Y/YcSdiQlzvOaqIEREREREREaksSsQYxN2aTBUxIiIiYpQgm+s+pDAR466IUSJGREREREREpLIoEWMQd2syhxIxIiIiYpAAm+s9331LqNZkIiIiIiIiIpVOiRiDuCti7ErEiIiIiEECCgph7E4lYkRERERERESqihIxBnFXxCgRIyIiIkYJKGhNlu9uTeYX7HpXIkZERERERESk0igRYxCzqWBxXKcSMSIiImKMAKvrPe/EiphsrREjIiIiIiIiUlmUiDGI1exKxOSrIkZEREQMEmApqIgp1posDXb9DvG9YOfPBkUnIiJG69u3L+PHj68x5xERERExihIxBrEUJGIcSsSIiIjIScTHxxMdHU2PHj2q5Ph+BYmYwoqYENd7znHY9Dkc/Au2zq+Sc4uISNmMHDkSk8mEyWTCZrMRFhbGJZdcwpw5c3A4HKfcd8qUKZ59rVYrLVq0YMKECaSnp59yvy+//JKnnnqqMi9DREREpFZSIsYgFrPrn15rxIiIiMjJxMXFsXXrVlavXl0lx/e3uu5DckuqiDmy0/U541CVnFtERMrusssu48CBA+zatYsFCxbQr18/7r33XgYPHkx+fv4p923fvr1n32effZY333yT++67r8S5ubm5AISGhlKnTp1Kvw4RERGR2kaJGINYCv7llYgRERERo/hZXO+5jhMTMcfhSILrc+bh6g9MRERK5OvrS3h4OE2aNKFr16488sgj/Pe//2XBggXMmzfvlPtarVbCw8Np2rQp119/PTfffDNff/014KqYiYmJ4e233yYqKgo/Pz+geMuwFi1aMG3aNG699VaCgoJo3rw5X3/9NQcPHuTKK68kKCiITp06sWbNGs8+hw8f5sYbb6RJkyYEBATQsWNHPv7440r/txERERE5kykRYxCzydWazO5UIkZERESM4WtxV8SYCgYKEjEZhyBtb+FnEZEazOl0kpmXacjLWQm/D/bv35/OnTvz5Zdflmk/f39/T+ULwI4dO/jiiy/48ssvWb9+/Un3mzlzJn369OGPP/5g0KBB3HLLLdx6660MHz6cdevWcc4553Drrbd6ri07O5tu3brx7bffsnnzZu644w5uueUWVq1aVa7rFRERETkbWY0OoLayWgoSMaqIEREREYP4mQsSMe6KGL9g1/vRXUDBPUqmEjEiUrNl5WfR66Nehpx75U0rCbAFVPg4bdu2ZePGjaWev3btWj766CP69+/vGcvNzeW9996jYcOGp9z38ssv58477wRg8uTJvP766/To0YNrr70WgIceeojevXuTnJzsqd65//77PfuPGzeORYsW8dlnn9GzZ8+yXKaIiIjIWUuJGIN4KmKUiBERERGD+Fpcizvn2N0VMQWJGIrcn2QeAYcdzJbqDU5ERErN6XRiKvgd82Q2bdpEUFAQdrud3NxcBg0axKuvvurZ3rx589MmYQA6derk+RwWFgZAx44di42lpKQQHh6O3W5n+vTpfPbZZ+zbt4/c3FxycnIICKh4AkpERETkbKFEjEEsZiViRERExFi+BRUxOY4TWpN5cULWUQhsUH2BiYhUI3+rPytvWmnYuSvDn3/+SVRU1CnnnHvuuXz99ddYrVYiIiLw8fHx2h4YGFiqc9lsNs9nd/KnpDGHw5Xsf/7553nppZeYNWsWHTt2JDAwkPHjx3u1RRMRERGp6ZSIMYhViRgRERExWGEipqA1maci5gQZh5SIEZEay2QyVUp7MKP8+OOPbNq0iQkTJpxyno+PD61ataqmqAr9/vvvXHnllQwfPhxwJWi2b99OdHR0tcciIiIiYhQlYgziaU1WCYszioiIiJSHj8n1tHKuw0RuvgMfqy+YbeDI856odWJERM4IOTk5JCUlYbfbSU5OZuHChTz99NMMHjyYW2+91ejwStS6dWv+85//sGzZMurVq8eLL75IcnKyEjEiIiJSqygRYxCrxV2urUSMiIiIGMPH7ErE2LGQkZOPT6CPqz1Z1hHXBLMVHPmuihgRETHcwoULady4MVarlXr16tG5c2defvllRowYgdlsNjq8Ej322GPs3LmT2NhYAgICuOOOOxg6dCipqalGhyYiIiJSbZSIMYi7IiZfiRgRERE5ifj4eOLj47Hb7VVyfLPTddx8LKTn5FPvxERMeEfY/wdkHq6S84uISOnNmzePefPmlWvfKVOmMGXKlDJv/+mnn7y+79q1q9gc5wldHlq0aOE1Fhoayvz5808Z34nnEREREalpzsxHZmoBi9aIERERkdOIi4tj69atrF69umpOUJCIsWMmM7cg2eNXsE6MxdeViAElYkREREREREQqQIkYgygRIyIiIoZz5AOFFTEA+BYkYkKjILCh6/OJrcmyjoKjaqp0RERERERERGoaJWIMYiloTWZ3KhEjIiIiBnEUVsRkeBIxdVzvoS0hoIHrc2aRRMyhHfB8K5g/thoDFRERERERETl7KRFjEKvFlYhxqCJGREREjFJQEWPHzC/bD7rGiiZiAgsSMUUrYnYude2XtKkaAxURERERERE5eykRYxBzQUVMvhIxIiIiYhR3IsZp4cOViRzJyIVWA8AvBNrEQkB917yia8Qkb3a952VWc7AiIiIiIiIiZyclYgziXiNGFTEiIiJimIJETIPgALLy7Mz5LQE63wAP7YaoC0uuiElyJ2KyCsf+/AZ2/V5NQYuIiIiIiIicXZSIMYg7EaM1YkRERMQwBWvE9DynEQDvLttFalYeFFTuelXEOJ2u+SlbXWPuREzGIfjsFvh0eHVGLiIiIiIiInLWUCLGIJ5EjCpiRERExCgFFTGtG9eldaMgjufks3DzgcLtAQUVMY48yE6FIwmFLcnc75mHwemArCOexI6IiIiIiIiIFFIixiAWkxIxIiIiYrCCRIzZYqPvuQ0B+PPA8cLtNj/wCXJ9zjwMyZu897Xnea8Vk5tR1RGLiIiIiIiInHWUiDGIuyImX4kYERERMUpBIgazldZhdQD4O+W49xx3e7KMQ4Xrw7jlZUKuEjEiIuJtypQpxMTEGB2GiIiIyBlDiRiDuBMxDiViRERExCjuVmJmC+cWJGK2JaV7TylIxGQcS4LkExMxWd4VMUU/i4hIpRo5ciQmkwmTyYTNZiMsLIxLLrmEOXPm4HA4TrnvlClTPPtarVYaNGjAhRdeyKxZs8jJyammKxARERGpvQxPxOzbt4/hw4dTv359/P396dixI2vWrPFsdzqdTJ48mcaNG+Pv78+AAQP4+++/vY5x5MgRbr75ZoKDg6lbty6jRo0iPd37jwgbN27kggsuwM/Pj8jISJ577rlqub6T8awR41QiRkREREoWHx9PdHQ0PXr0qJoTuBMxJgstGviCJYND6TkcycgF4FB6DusOWQD4YfWWk1TEFKmCUUWMiEiVuuyyyzhw4AC7du1iwYIF9OvXj3vvvZfBgweTn59/yn3bt2/PgQMHSExMZOnSpVx77bU8/fTTnH/++Rw/fvyU+4qIiIhIxRiaiDl69Ch9+vTBZrOxYMECtm7dyowZM6hXr55nznPPPcfLL7/M7NmzWblyJYGBgcTGxpKdne2Zc/PNN7NlyxYWL17MN998wy+//MIdd9zh2Z6Wlsall15K8+bNWbt2Lc8//zxTpkzhzTffrNbrLcqTiFFFjIiIiJxEXFwcW7duZfXq1VVzAkc+v/j7MXznx/T/z7+o03oa1jqb2Z58nD1HMrny1d9JyPQHwHx0J6Ttde1nC3C952W5Xm6qiBERqVK+vr6Eh4fTpEkTunbtyiOPPMJ///tfFixYwLx58065r9VqJTw8nIiICDp27Mi4ceP4+eef2bx5M88++6xnnslkYv78+V771q1b1+v4Dz30EG3atCEgIICWLVvy+OOPk5eXV4lXKiIiIlKzWI08+bPPPktkZCRz5871jEVFRXk+O51OZs2axWOPPcaVV14JwHvvvUdYWBjz58/nhhtu4M8//2ThwoWsXr2a7t27A/DKK69w+eWX88ILLxAREcGHH35Ibm4uc+bMwcfHh/bt27N+/XpefPFFr4RNdbKYlIgRERERgznyeSG0HgmZ+1zfTeAbPp8N+wfyxVon+45lcdy3LgDnZ/zgmhPSDEwmOLbbtT5MnipiROTs5nQ6cWZlnX5iFTD5+2Mq+N2wvPr370/nzp358ssvGT16dJn2bdu2LQMHDuTLL79k2rRppd6vTp06zJs3j4iICDZt2sSYMWOoU6cODz74YFnDFxEREakVDE3EfP3118TGxnLttdfy888/06RJE/7v//6PMWPGAJCQkEBSUhIDBgzw7BMSEkKvXr1Yvnw5N9xwA8uXL6du3bqeJAzAgAEDMJvNrFy5kquuuorly5dz4YUX4uPj45kTGxvLs88+y9GjR70qcABycnK8+uSmpaVV+rV71ohxum78K3rzLSIiIlJWxxw5JPjYAPhk8CfcueABUtnDV7tnc2DHUAA6d2gPW76mvvOoa6fmveHARtfnvExXMsZNiRgROQs5s7LY1rWbIec+d91aTAEBFT5O27Zt2bhxY7n3/f7778u0z2OPPeb53KJFC+6//34++eQTJWJERERETsLQ1mQ7d+7k9ddfp3Xr1ixatIixY8dyzz338O677wKQlJQEQFhYmNd+YWFhnm1JSUk0atTIa7vVaiU0NNRrTknHKHqOop5++mlCQkI8r8jIyEq4Wm/uRAyoKkZERESMsdHhegI8yq8B7eu357qoiTidJvbl/0q6aSv1A32IvuxOXsy7hmfybiD9yrkweBbYXO3KXK3JiiRi1JpMRMQQFXm4rzz7fvrpp/Tp04fw8HCCgoJ47LHHSExMLNf5RURERGoDQytiHA4H3bt3Z/r06QB06dKFzZs3M3v2bEaMGGFYXJMmTWLixIme72lpaZWejPFKxDidxv4gREREpFZaj2vNvZigFgBcHNWD+FW98Aldga3eCi5t0h//OvX40O8GDmfkckXYBUT7BBRZIybTO/miihgROQuZ/P05d91aw85dGf7880+vNt8V2ddkMuF0ej8sWHT9l+XLl3PzzTfz5JNPEhsbS0hICJ988gkzZswoX/AiIiIitYChf/9v3Lgx0dHRXmPt2rXjiy++ACA8PByA5ORkGjdu7JmTnJxMTEyMZ05KSorXMfLz8zly5Ihn//DwcJKTk73muL+75xTl6+uLr69vBa7s9FQRIyIiIkZbb8oFICbY9Qe4cxoGkZ/qSsRYg/7iorZBAISH+HE4I5ektCyiI4K9K2JyVREjImc3k8lUKe3BjPLjjz+yadMmJkyYUOZ9//rrLxYuXMikSZM8Yw0bNuTAgQOe73///TeZmYX//33ZsmU0b96cRx991DO2e/fuckYvIiIiUjsY2pqsT58+bNu2zWts+/btNG/eHICoqCjCw8NZsmSJZ3taWhorV66kd+/eAPTu3Ztjx46xdm3hE0w//vgjDoeDXr16eeb88ssvXk/xLF68mHPPPbfY+jDVxWxSIkZERESMk+fIY7PJdW8UE3wOAH42C82DWmHPDsNktpNmXgdA4xBX4mX/MVcFTWEi5sSKGCViRESqUk5ODklJSezbt49169Yxffp0rrzySgYPHsytt956yn3z8/NJSkpi//79bNq0iVdeeYWLLrqImJgYHnjgAc+8/v378+qrr/LHH3+wZs0a7rrrLmw2m2d769atSUxM5JNPPuGff/7h5Zdf5quvvqqyaxYRERGpCQxNxEyYMIEVK1Ywffp0duzYwUcffcSbb75JXFwc4Hoyafz48UybNo2vv/6aTZs2ceuttxIREcHQoUMBVwXNZZddxpgxY1i1ahW///47d999NzfccAMREREA3HTTTfj4+DBq1Ci2bNnCp59+yksvveTVfqy6WYtUxDgchoUhIiIitdT2o9vJMkGw3U6LwMIK4TZhdchP6wLAwt3fAdA4xA+ApFR3IsbdmuyENWJy06s+cBGRWmzhwoU0btyYFi1acNlll7F06VJefvll/vvf/2KxWE6575YtW2jcuDHNmjWjb9++fPbZZ0yaNIlff/2VoKAgz7wZM2YQGRnJBRdcwE033cT9999PQJGKoSuuuIIJEyZw9913ExMTw7Jly3j88cer7JpFREREagJDW5P16NGDr776ikmTJjF16lSioqKYNWsWN998s2fOgw8+SEZGBnfccQfHjh3jX//6FwsXLsTPz88z58MPP+Tuu+/m4osvxmw2M2zYMF5++WXP9pCQEL7//nvi4uLo1q0bDRo0YPLkydxxxx3Ver1FFW1Nlq9MjIiIiFSz9SnrAeick4vZUvik86gLojjyQz/+ZCFrktaQlJFE47qu+64DqSdWxKg1mYhIdZk3bx7z5s0r175TpkxhypQppZobERHBokWLvMaOHTvm9f25557jueee8xobP358uc4nIiIiUhsYvkb84MGDGTx48Em3m0wmpk6dytSpU086JzQ0lI8++uiU5+nUqRO//vprueOsbCaTCZMJnE6wO9WaTERERKqXOxETk50D5sJbwh4tQvls9OWMWPAp61LWsWjXIhqHXAzAgdQs1yRPRYxak4mIiIiIiIicjqGtyWo7d3syrREjIiIi1W39wfUAxOR4J2Lc+kb2BWDjwY2eNWKSSqyIySjcKa/IZxEREREREREBlIgxlNmkRIyIiIhUP7vDzojoEcTmOOmQkwvm4usKtKrbCoB/jv3jWSNmf2oWTqezSCIm05WMcVNFjIiIiIiIiEgxSsQYyF0RoyViREREpDpZzBaGRw/nheP5BDidJVbEtK7XGoDdabsJDXQlarLzHKRm5RVpTZblXQWTq4oYERERERERkRMpEWMgc0EiJl+ZGBERETGCI9/1XkIiJiwgjCBbEPnOfJKy9lA/0AeAA6nZJ7QmK1IFo9ZkIiIiZ7SrrrqKevXqcc011xgdioiISK2iRIyBLO6KGKdak4mIiIgBTpGIMZlMtKzbEnC1JwsvaE92IDWrSEVMpuvlptZkInIWcer3sLOCfk6V69577+W9994zOgwREZFaR4kYA1k9FTG6sRQREZHi4uPjiY6OpkePHlVzAofd9V5CIgagdV1Xe7Idx3bQOMRVBeNdEXNCIsb9OScdMg5VScgiIhVlsbjaLebm5hociZSG++fk/rlJxfTt25c6deoYHYaIiEitU/Jv3VItzCZXIsauRIyIiIiUIC4ujri4ONLS0ggJCan8E3gqYkr+49Y5dc8BXBUxjUP6AnDgWDaEFlTEZB7x3sG9RsycWDi6GyZsBv+6lRy0iEjFWK1WAgICOHjwIDabDbNZzyeeqRwOBwcPHiQgIACrVX+++OWXX3j++edZu3YtBw4c4KuvvmLo0KFec+Lj43n++edJSkqic+fOvPLKK/Ts2dOYgEVERMRDdzIGclfEaIkYERERMYS7IsZ06kTMjmM7uKyuuzVZkYqYzBOqXnIzwJ4HyZtd31O2QvPzKz1sEZGKMJlMNG7cmISEBHbv3m10OHIaZrOZZs2aYSp4kLE2y8jIoHPnztx+++1cffXVxbZ/+umnTJw4kdmzZ9OrVy9mzZpFbGws27Zto1GjRmU+X05ODjk5OZ7vaWlpFYpfRESkNlMixkBmT2syZWJERETEAKdYIwagVd1WACQeT6RBpOuJ8aS0rMJETNbRE46XB+nJhd+PJCgRIyJnJB8fH1q3bq32ZGcBHx8fVS0VGDhwIAMHDjzp9hdffJExY8Zw2223ATB79my+/fZb5syZw8MPP1zm8z399NM8+eST5Y5XRERECikRYyCLuyJGiw+KiIhIdXM6wXnqNWIa+jck2CeYtNw0sKUAkJSaDba63hN96kDucdfnY3sKx4/srOSgRUQqj9lsxs/Pz+gwRCpFbm4ua9euZdKkSZ4xs9nMgAEDWL58ebmOOWnSJCZOnOj5npaWRmRkZIVjFRERqY2UiDGQOxFjV0GMiIiIVDd3WzI46RoxJpOJVnVbsS5lHRnOfYAfKWk5hRUxbn4hkJfpSuwcSywcVyJGRESkWhw6dAi73U5YWJjXeFhYGH/99Zfn+4ABA9iwYQMZGRk0bdqUzz//nN69e5d4TF9fX3x9fas0bhERkdpCiRgDWUxqTSYiIiIGcbclg5NWxIBrnZh1Kes4nJsItOF4Tj4ZTh8Ci07yCQCfIMhJhWNF1ls4mlDZUYuIiEgF/PDDD0aHICIiUiup0aqBPK3JlIcRERGR6lbKREzreq0BSEj7myBf17zkrBNuIW0BrmQMeCdiVBEjIiJSLRo0aIDFYiE5OdlrPDk5mfDwcIOiEhERETclYgzkTsSoIkZERESqXSkTMe3rtwdgy+EtNAr2ASAp84RJPoGuZAx4tybLToXMI5URrYiIiJyCj48P3bp1Y8mSJZ4xh8PBkiVLTtp6TERERKqPWpMZyFMR43QaHImIiIjUOl5rxJz8lvDc0HOxmqwcyT7CucGZ7DwIyel5YPEFe45rkldFTKL3AY4kQEBoJQcvIiJS+6Snp7Njxw7P94SEBNavX09oaCjNmjVj4sSJjBgxgu7du9OzZ09mzZpFRkYGt912m4FRi4iICCgRYyh3IsaughgRERGpbp6KGBOYT14k7WvxpXW91vx55E98A/cDESSn5YDNv0gixh9sBavGpO71PsDRBGjardLDFxERqW3WrFlDv379PN8nTpwIwIgRI5g3bx7XX389Bw8eZPLkySQlJRETE8PChQsJCwszKmQREREpoESMgSwmdyJGmRgRERGpZs6Cihiz5bRT2zdoz59H/iTfthuIICk121UFk33MNcEn0PWCwgSPTxDkpmudGBERkUrSt29fnKfpqHH33Xdz9913V1NEIiIiUlpaI8ZAZlXEiIiIiFHcCZNTtCVz61C/AwDpzgQAktOyXVUwbkVbk7k16ep6P5JQ4VBFREREREREzmZKxBjI6k7EaI0YERERqW5lScQ0cCViUnJ2AI6CREyRxItPQGFrMremPV3vqogRERERERGRWk6JGAMVrhGjkhgRERGpZo7StyY7p+45+Fp8yXZkYvI5XLhGjFtJFTGRBYmYo6qIERERERERkdpNiRgDWdSaTERERIxShooYq9lK29C2AFj89pJyPBvniYkY24mtybq73tOTISfd9Xnnz/DlHZBxqKLRi4iIiIiIiJw1lIgxkMWkihgRERExRk7CbjIP+uCwnz4RA4XtySz+e8izO8k1+xVu9AkEn6DC7351IbA++Ie6vrvbky39N2z8FLYtqIQrEBERERERETk7KBFjILMqYkRERMQgiQ9MY/eSBuSknr41GUD7+u0B8A08AECW06dwo83fuzVZYAPXe7grecP+dWDPg/3rXd+zUysSuoiIiIiIiMhZRYkYA1ndiRin0+BIREREpLaxBLhai9nzSnc72C60neuDz37AQaZXIuaE1mQBBYmYpgXrxOxZBcmbwZ7j+p5zvAKRi4iIiIiIiJxdSteLQqqEpyJGJTEiIiJSzcxBrkSMI790FTEtQlrga/Elx56Dyecw6XZb4UafQMjPLvzuroiJ7OV637MSIroUblciRkRERERERGoRVcQYyLNGjApiREREpJpZAgsqYnJNpZpvNVtpU6+Na1/f/aQVTcQUq4ip73pv2t31fngH/L24cHtOWrnjFhERERERETnbKBFjIE9rMoeDz1bvYWj87xw8nmNwVCIiIlIbmAtakzlyS3872Da0rWtfv/0cyytSWG0LcFXFuLkrYgJCocG5rs9/LyrcrooYERERERERqUWUiDGQpzWZAz5bs4f1e46xMuGwwVGJiIhIbWAJ9AVKXxEDhYkYi99+DucWaWnmc0Iixr1GDEBkz+IHyk0vU6wiIiIiIiIiZzMlYgzkrohxOJ1k5dkByNN6MSIiIlIgPj6e6OhoevToUenHNgf4AeDIK/0+7ULbufb128+2I0V2tAXgtPkXfg88TSJGFTEiIiIiIiJSiygRYyB3RUy+3Um2OxGTrwVjRERExCUuLo6tW7eyevXqSj+2JcAHAHsZuqK2rtcai8mC2ZrBQVORexafQNYn5Xu+pppDCrdF9ir8HNjQ9a5EjIiIyFmjKh8MERERqS2UiDGQxVTQmszpJDvPVQmT51BFjIiIiFQ9s7+rNZkjt/QPgfhZ/YgKiXLtV8/uGc81+fLRH4c83w866hTuVL81+NV1fW7Z1/WuRIyIiMhZoyofDBEREaktlIgxkMXdmszhJCff9ceMfLsqYkRERKTqlaciBgrXiTnnXJtnbNL//ub7HWme7/tzi6wXYzZDl+HgHwqdbnCN5RTMzc2Ev76F3IyyX4CIiIiIiIjIWUKJGAO5EzH5jiIVMVojRkRERKqBxd+ViHHklO3ew52ISbS7KmAynb588ccBjjsD2O1oxF5nAxJzAr13iv03PJQA4R1c33OOg9MJK2fDJze53kVERERERERqKKvRAdRmVndFjLPIGjGqiBEREZFqYPZ3VbTYy5iIia4fDcDm9D2u/a1+kAMOzNwd8io7DqZze3p+yTv7FrQsczogLxOO7HR9T91X9gsQEREREREROUuoIsZA5oJETE6enXyHKwGTr4oYERERqQYWP9fzOI7sst17tK/fHrPJTHLOUZJ9/PGL6EB042AubtuIy7qeQxZ+HEjNLnlnWwCYCm4/c45D5hHXZ7UmExERERERkRpMFTEGsphciZjM3MLFbtWaTERERKqDOcBVEePIdeDMz8dkLd1tYYAtgDb12vDXkb/YOOxVLmk5mO9sfgB89cdeAJJOlogxmcCnDuSkFiRiDrvGc9MrdjEiIiIiIiIiZzBVxBjIvUZMRm5h+448h1qTiYiISNWz+Fk8nx3p6Rz9+GP+7tuPnL//Pu2+nRp0AmDj8d1QkIQBCA/2B06RiIHC9mQ5xyFLFTEiIiIiIiJS8ykRYyBPIiansCJGrclERESkOpjMYLK47jvsx4+T9t0C8pOSyFi9+rT7dm7UGYANBzd4jTcOcSVlDqRm43Se5OGSookYd0VMXmY5rkBERERERETk7GBoImbKlCmYTCavV9u2bT3bs7OziYuLo379+gQFBTFs2DCSk5O9jpGYmMigQYMICAigUaNGPPDAA+Tney8Q+9NPP9G1a1d8fX1p1aoV8+bNq47LO63CREyRihi7KmJERESkGjjysfi47jscx4+TV3CP5cg4fXWKuyJm6+Gt5NnzPOPhBYmYrDw7aVn5Je7rScRkp0LWUddnVcSIiIiIiIhIDWZ4RUz79u05cOCA5/Xbb795tk2YMIH//e9/fP755/z888/s37+fq6++2rPdbrczaNAgcnNzWbZsGe+++y7z5s1j8uTJnjkJCQkMGjSIfv36sX79esaPH8/o0aNZtGhRtV5nSQpbk2mNGBEREalmjnzMtoKKmLQ08t2JmMzTV6c0D25OiG8IOfYcth3d5hn3s1moW7D2zIG0rJJ3didiUveCs+C+R2vEiIiIiIiISA1meCLGarUSHh7ueTVo0ACA1NRU3nnnHV588UX69+9Pt27dmDt3LsuWLWPFihUAfP/992zdupUPPviAmJgYBg4cyFNPPUV8fDy5ubkAzJ49m6ioKGbMmEG7du24++67ueaaa5g5c6Zh1+xmMbkSMZlF1ojJV0WMiIiIVAdHPhab674jb+9enDk5ruFSVMSYTCZPVcyJ7cnCg11VMSddJ8adiDm6q3AsV63JREREREREpOYyPBHz999/ExERQcuWLbn55ptJTEwEYO3ateTl5TFgwADP3LZt29KsWTOWL18OwPLly+nYsSNhYWGeObGxsaSlpbFlyxbPnKLHcM9xH6MkOTk5pKWleb2qQsmtyVQRIyIiItXAYcfs47rvyPl7R+FwKSpiADo1LDkR414npmyJGLUmExGR6mW32/nll184duyY0aGIiIhILWBoIqZXr17MmzePhQsX8vrrr5OQkMAFF1zA8ePHSUpKwsfHh7p163rtExYWRlJSEgBJSUleSRj3dve2U81JS0sjK6vklhlPP/00ISEhnldkZGRlXG4xhYmYIq3JHKqIERERkWrgyMdS0JosZ0eRREwpKmIAOjfsDMDGgxu9xsND/AE4cNJETLDr/djuwrG8DHDoYRQREak+FouFSy+9lKNHjxodioiIiNQCViNPPnDgQM/nTp060atXL5o3b85nn32Gv7+/YXFNmjSJiRMner6npaVVSTLGXJCIycorkojJ1x8hREREpBo48jH7uB4A8UrElLIipmODjpgwsS99H4eyDtHA39VetvQVMbu9x/MywTcI7PlgMfQWVUREaokOHTqwc+dOoqKijA5FREREajjDW5MVVbduXdq0acOOHTsIDw8nNze3WJlwcnIy4eHhAISHh5NcsLBs0e3ubaeaExwcfNJkj6+vL8HBwV6vqmAtSMQUla+nQUVERKQ6OOyeipj8IvdKpa2ICfIJ4py65wDeVTHhBYmYA2mnScTkn1CZnJcJSZvgmWbwywulikFERKQipk2bxv33388333zDgQMHqqVFuYiIiNROZ1QiJj09nX/++YfGjRvTrVs3bDYbS5Ys8Wzftm0biYmJ9O7dG4DevXuzadMmUlJSPHMWL15McHAw0dHRnjlFj+Ge4z6GkSym4omYPLtak4mIiEg1KFIR4zVcyooYKGxPVnSdmPBgd0VMyS1g8Q0qeTw3HfasdLUpS/il1DGIiIiU1+WXX86GDRu44ooraNq0KfXq1aNevXrUrVuXevXqGR2eiIiI1CCG9n24//77GTJkCM2bN2f//v088cQTWCwWbrzxRkJCQhg1ahQTJ04kNDSU4OBgxo0bR+/evTnvvPMAuPTSS4mOjuaWW27hueeeIykpiccee4y4uDh8fX0BuOuuu3j11Vd58MEHuf322/nxxx/57LPP+Pbbb428dKCwNVlReXZVxIiIiEg1KFIR4zVcyooYcCVivvj7C6+KmFK3JjtRbgZkpxZ+FhERqWJLly41OgQRERGpJQxNxOzdu5cbb7yRw4cP07BhQ/71r3+xYsUKGjZsCMDMmTMxm80MGzaMnJwcYmNjee211zz7WywWvvnmG8aOHUvv3r0JDAxkxIgRTJ061TMnKiqKb7/9lgkTJvDSSy/RtGlT3n77bWJjY6v9ek9UYmsyVcSIiIhIdXDkY/EpIRFThoqYTg07AbDl8BbyHflYzVZPa7K07HwycvIJ9D3hdtP3JC1fczMLEzF5pY9BRESkvC666CKjQxAREZFawtBEzCeffHLK7X5+fsTHxxMfH3/SOc2bN+e777475XH69u3LH3/8Ua4Yq1KJFTFaI0ZERESqgyMfs62E1mQZpU+CRIVEUcdWh+N5x/n76N+0q9+OOn42gv2spGXnk3Aogw5NQrx3OmlFTDpkHSv4rIoYERGpHr/++itvvPEGO3fu5PPPP6dJkya8//77REVF8a9//cvo8ERERKSGOKPWiKltSqqIUWsyERERqRYnqYhxZmbiLOWDIWaTmY4NOwJ4tSeLaebqq78u8WjxnYolYgruh4q2JlNFjIiIVIMvvviC2NhY/P39WbduHTk5OQCkpqYyffp0g6MTERGRmkSJGAOZTWpNJiIiIgZx2L0rYqyFhdKOzKxSH6Zzw84AbDi4wTPWrSARs3Z3KRIxdRq73rVGjIiIVLNp06Yxe/Zs3nrrLWw2m2e8T58+rFu3zsDIREREpKZRIsZAFlXEiIiIiFEc+VhshfcdtiYRUPCQiCOz9IkQ9zoxXomY5q5EzJpdpUjE1I10veedUBGjdq0iIlLFtm3bxoUXXlhsPCQkhGPHjlV/QCIiIlJjKRFjoJJbk6kiRkRERKqBIx9zkdZktrBwzAEBrk0ZpU/EdGzgak2WeDyR1BxXIiWmWV3MJth3LIuk1GzvHXxOSMSENHW9F62IAcgvfVWOiIhIeYSHh7Njx45i47/99hstW7Y0IKIzU3x8PNHR0fTo0cPoUERERM5aSsQYyFxCIiZfFTEiIiJSHRz5mC1gslkAsIaFYQ4MdG3KLP0aLSG+ITQPbg7A5kObAQjytdI2PBgooT2ZxQo2V8IH32Dwd1XPFEvEqD2ZiIhUsTFjxnDvvfeycuVKTCYT+/fv58MPP+T+++9n7NixRod3xoiLi2Pr1q2sXr3a6FBERETOWtbTT5GqUmJFjEMVMSIiIlINnK6HP8wBvthTM7GFh5WrIgagQ4MO7E7bzaZDm+jTpA8A3VvUY+uBNNbuPsqgTo29d/AJcrUf868HPq7kjxIxIiJS3R5++GEcDgcXX3wxmZmZXHjhhfj6+nL//fczbtw4o8MTERGRGkQVMQYym7RGjIiIiBjEkQ+AJcAPAGuj8lXEQGF7MndFDBSuE7M28RTrxATUB1tBIibjEDjyCufklS0GERGRsjKZTDz66KMcOXKEzZs3s2LFCg4ePMhTTz1ldGgiIiJSwygRYyBLia3JVBEjIiIi1cCdiAkJAsDWpElhIqYcFTHgSsQ4na57ma7NXImYLftSycq1e+9QNBHjrohJ2+c9J1eJGBERqVq33347x48fx8fHh+joaHr27ElQUBAZGRncfvvtRocnIiIiNYgSMQYqKRGTq4oYERERqQ4FiZiwW2JpMO5ugi74V2FrsjJWxLQNbYvVZOVw9mGSMpIAaFrPn/BgP/IdTr7ecEKSxZOICT1FIia9bNcjIiJSRu+++y5ZWVnFxrOysnjvvfcMiEhERERqKiViDFRyRYwSMSIiIlINHK4qFf9zm9MwLg6TzVbuihhfiy+t67UGYNOhTYCr3cuof0UB8NzCbaRmFWk75hvseg+o71ovBiDtgPdB1ZpMRESqSFpaGqmpqTidTo4fP05aWprndfToUb777jsaNWpkdJgiIiJSgygRYyBLCWvEOJxgd6g9mYiIiFSxgooYzFbPUHkrYqDkdWJGnN+Clg0DOZyRy0s//F042b+u6z2wAfi4zkn+CU8k52aC0wm/vgg7fihzPCIiIidTt25dQkNDMZlMtGnThnr16nleDRo04PbbbycuLs7oMEVERKQGsZ5+ilSVkipiAPLsDixmSzVHIyIiIrVKSYmYclbEgGudmM+2f+apiAHwsZqZMqQ9t85ZxbvLdzG0SwSdmtaFnmPAngcdr4Mj/5R8wLwMSNoIS56E0JZwzx9ljklERKQkS5cuxel00r9/f7744gtCQ0M923x8fGjevDkREREGRigiIiI1jRIxBjpZIiZfFTEiIiI10lVXXcVPP/3ExRdfzH/+8x9jg3EnYkyFD39UpCKmQ4MOAGw5vAW7w+55qOTCNg0Z2CGcBZuTuPP9tfz37j40iugCw95y7ZieXPIBczMg/aDrc+aRMscjIiJyMhdddBEACQkJNGvWDFMJ3SpEREREKpNakxnopIkYrRMjIiJSI917771nzuK/lVwR0zKkJYG2QLLys9hxbIfXtmev6UTLhoEcSM1m7AfryMm3F270CSz5gLmZkH2s4HO6q02ZiIhIJWrevDm//fYbw4cP5/zzz2ffvn0AvP/++/z2228GRyciIiI1iRIxBjpZIiZXiRgREZEaqW/fvtSpU8foMFwcBfcbRdqhmgPLXxFjMVs868RsOLjBa1uwn423b+1OHT8ra3cf5ZNVewo32gJKPmBeRmEixpEP9twyxyQiInIqX3zxBbGxsfj7+7Nu3TpycnIASE1NZfr06QZHJyIiIjWJEjEGspxQ/uz+mm/XE58iIiLV7ZdffmHIkCFERERgMpmYP39+sTnx8fG0aNECPz8/evXqxapVq6o/0MpSyRUxADGNYgBYn7K+2LaWDYO4/9JzAfhw5W6c7goXnyDviaaC29PcTMg6Vjiek16umERERE5m2rRpzJ49m7feegubzeYZ79OnD+vWrTMwMhEREalplIgxkMVSmIgxm8Df5noiVYkYERGR6peRkUHnzp2Jj48vcfunn37KxIkTeeKJJ1i3bh2dO3cmNjaWlJQUz5yYmBg6dOhQ7LV///7quozSKykRU4E1YgC6NOwCwPqD60vcflXXJvjZzGxPTmft7qOuwRNbkwWFu95zi1TEAOQeL1dMIiIiJ7Nt2zYuvPDCYuMhISEcO3as+gMSERGRGst6+ilSVYpWxPjZLFgLWpWpNZmIiEj1GzhwIAMHDjzp9hdffJExY8Zw2223ATB79my+/fZb5syZw8MPPwzA+vXrKyWWnJwcT3sUgLS0tEo5rpcqqIjp2LAjJkzsOb6HQ1mHaODfwGt7sJ+NIZ0i+HztXj5amUj3FqFg9XVVwTgL7n+CG8Px/QWtyYo8M5RbvphEREROJjw8nB07dtCiRQuv8d9++42WLVsaE5SIiIjUSKqIMVDRNWL8bBZ8rK4fR75DiRgREZEzSW5uLmvXrmXAgAGeMbPZzIABA1i+fHmln+/pp58mJCTE84qMjKz0c3DbdzBuHYR38AxVtCKmjk8dWtVrBcCGlA0lzrmpVzMAvtl0gGOZua7erEXbk9Vp7HpXazIREaliY8aM4d5772XlypWYTCb279/Phx9+yP3338/YsWONDk9ERERqECViDOSViLGasZoLEjFqTSYiInJGOXToEHa7nbCwMK/xsLAwkpKSSn2cAQMGcO211/Ldd9/RtGnTkyZxJk2aRGpqque1Z8+eEudVSEhTqH8O2Pw9Q4UVMeVLxADENIwBTt6eLCayLu0aB5Ob7+DbTQdcg0XbkwVHuN7zMtWaTEREqtTDDz/MTTfdxMUXX0x6ejoXXngho0eP5s4772TcuHFGhyciIiI1iFqTGejE1mR5BZUwak0mIiJSM/3www+lmufr64uvr28VR1NcRStiAGIaxfD59s9Zn7K+xO0mk4kLWzfgzwNp7EgpqHIpKRGTm17YrgzUmkxERCqdyWTi0Ucf5YEHHmDHjh2kp6cTHR1NUFDQ6XcWERERKQMlYgxksRQmYnxtFshzfVZFjIiIyJmlQYMGWCwWkpOTvcaTk5MJDw83KKrK566IIS8PR24uZh+fMh/DXRGz5fAWcu25+FiKH6NpPVcVzt6jWa4BW0DhxjruREwm5GcXjqs1mYiIVBEfHx+io6ONDkNERERqMCViDORdEWPGXlARk6+KGBERkTOKj48P3bp1Y8mSJQwdOhQAh8PBkiVLuPvuu40NrhK5K2IAHBkZ5UrERNaJJNQvlCPZR9h6eCsxjWKKzWlaz3UeTyKm6BoxRVuT5aQVjucqESMiIpUrOzubV155haVLl5KSkoLjhPVa161bZ1BkIiIiUtOUOhGzcePGUh+0U6dO5QqmtjEXWaHHz2ohN1+tyURERIySnp7Ojh07PN8TEhJYv349oaGhNGvWjIkTJzJixAi6d+9Oz549mTVrFhkZGdx2220GRl25TFYrJl9fnDk5rnVi6tUr+zFMJjo37MzSPUvZcHDDSRIx7oqYghZo7tZkZhsENnB9zkmDbCViRESk6owaNYrvv/+ea665hp49e2Iq8rCkiIiISGUqdSImJiYGk8mE01ly2yz3NpPJhN1ur7QAazJrkUyMn81MZp7ru1qTiYiIVL81a9bQr18/z/eJEycCMGLECObNm8f111/PwYMHmTx5MklJScTExLBw4ULCwsKqLKb4+Hji4+Or9d7KHBCAPScHR2b512SJaRTD0j1LWZ+ynhHtRxTb3qQgEXM8O5/UrDxC3IkYv5DCpEx2qvdOak0mIiKV7JtvvuG7776jT58+RociIiIiNVypEzEJCQlVGUetZC7ysI2v1YLNnA9AnipiREREql3fvn1P+sCJ2913312trcji4uKIi4sjLS2NkJCQajmnOTAQ+9GjODIqkIgpWCdm/cH1ngd1igrwsVI/0IfDGbnsO5rlnYixBZZ8UFXEiIhIJWvSpAl16tQxOgwRERGpBUqdiGnevHlVxlErmUwmLGYTdocTP5sZm8VVEZPnUEWMiIiIGMO9TowjM7Pcx4iuH43VbOVQ1iH2pe+jaZ2mxeY0refP4Yxc9h7NJNqrIiag2FwAcsufGBIRESnJjBkzeOihh5g9e7b+5iEiIiJVqtSJmJJs3bqVxMREcnNzvcavuOKKCgVVm1hMJuw48bNZsFpcT4vmqyJGREREDGIOdCVFKlIR42f1Izo0mo2HNrL+4PqTJGIC2LA3lb1HswrbkfmFgNUPMAEnPJiSc7zc8YiIiJSke/fuZGdn07JlSwICArDZbF7bjxw5YlBkIiIiUtOUKxGzc+dOrrrqKjZt2uS1boy77YTWiCk9sxmwg5/NUlgRo0SMiIiIGKQyKmLAtU7MxkMbWZ+ynsEtBxfb3rRgnZi9R7MgpEgixmQCnyDIPSHx4m5Nlpflerf5Vyg+ERGRG2+8kX379jF9+nTCwsKKtdIUERERqSzlSsTce++9REVFsWTJEqKioli1ahWHDx/mvvvu44UXXqjsGGs0q9kMOPC1mbEVVMTk2dWaTERERIzhrog5+v4HpH39Pxrdfx9+0dFlPk5Moxje2/oeGw5uKHF7E08iJhM69natDdOyr2ujT0AJiZgMcNjhtd6u7+PWgtlS5rhERETcli1bxvLly+ncubPRoZzR4uPjiY+P10O3IiIiFVCuRMzy5cv58ccfadCgAWazGbPZzL/+9S+efvpp7rnnHv7444/KjrPGMhc8cONntWAtqIhRazIREZGT+/rrr0s9V+1Sy84SEgxA9pYtABx5twERzz5b5uN0buj6o9b2o9vJyMsg0Bbotd1dEbPvWBa0uAAm7SlMrNiKrBMTFAbpyZCTDhkH4WiCazzjINQJL3NcIiIibm3btiUrK8voMM54cXFxxMXFkZaWRkhIiNHhiIiInJXKlYix2+3UqVMHgAYNGrB//37OPfdcmjdvzrZt2yo1wJrOnXzxs1nw8bQmU0WMiIjIyQwdOrRU80wm01n/5KYRT6DWu+UWnLl5OPPySPvuO7I2bynXcRoFNKJJUBP2pe9jQ8oGzm9yvtf2pvVcyZa9Rwv+AFa0usWnSNImuIkrEZN7HNJTCsczDysRIyIiFfLMM89w33338e9//5uOHTsWWyMmODjYoMhEREROz263k5eXZ3QYNZ6Pjw9ms7nCxylXIqZDhw5s2LCBqKgoevXqxXPPPYePjw9vvvkmLVu2rHBQtYm5oAetn82MtaA8Js+hihgREZGTcdSi/04a8QSqX5s2RDz7DPmHDpH23Xfk7tyJPT0DS1Dg6Xc+QbewbuxL38ea5DXFEjFN6roqYlKz8kjLziPYr8gfv4omYkKawv51rtZkGQcLxzMOlTkeERGRoi677DIALr74Yq9xp9NZIx7oEBGRmsnpdJKUlMSxY8eMDqVWMJvNREVF4ePjU6HjlCsR89hjj5GRkQHA1KlTGTx4MBdccAH169fn008/rVBAtU1BEQx+tqKtyVQRIyIiIsayNmiAtXFj8g8cIHvrFgJ79izzMbqFdePrf75mbfLaYtsCfa2EBvpwJCOXfUezCG5cJBFTtDVZSFPXu7s1mVumEjEiIlIxS5cuNToEERGRMnMnYRo1akRAQACmggf9pfI5HA7279/PgQMHaNasWYX+rcuViImNjfV8btWqFX/99RdHjhyhXr16+sGXkdXsbk1mxsdSUBGjNWJERERKLSMjg59//pnExERyc3O9tt1zzz0GRVUz+HfowPEDB8jetLlciZjuYd0B2HRoE9n52fhZ/by2N63nz5GMXPYezaJd4yLtX05sTQZgz4G0/YXjGYfLHI+IiEhRF110kdEhiIiIlIndbvckYerXr290OLVCw4YN2b9/P/n5+cXamJZFuRIxJQkNDa2sQ9Uq7vZyftbCihitESMiIlI6f/zxB5dffjmZmZlkZGQQGhrKoUOHCAgIoFGjRkrEVJBfhw4cX7yY7C2by7V/ZJ1IGvk3IiUrhY0HN9KzsXcyp2k9fzbuTWXFzsP0aVWfAJ+CW1OvipgmhZ+PJhR+VkWMiIhUkszMzBIf6OjUqZNBEYmIiJTMvSZMQEDAaWZKZXG3JLPb7RVKxJRrlZmMjAwef/xxzj//fFq1akXLli29XlJ6Fs8aMRasBRUx+aqIERERKZUJEyYwZMgQjh49ir+/PytWrGD37t1069aNF154wejwznr+HTsAkLWpfIkYk8lEt/BuAKxJXlNse4v6rsqXd35LoMvUxXy9oaDipWhFTGAjsBT04j1SJBGjNWJERKSCDh48yODBg6lTpw7t27enS5cuXi8REZEzlbpSVZ/K+rcuV0XM6NGj+fnnn7nlllto3LixfvAV4H7yM9jfio+nIkaJGBERkdJYv349b7zxBmazGYvFQk5ODi1btuS5555jxIgRXH311UaHeFbza98egLw9e8g/ehRrvXplPkb3sO4sSFhQYiJm1L+iyMy1s3hrMvuOZfHxykSu6BzhnYjxC3F9z8qFo7sKx1URIyIiFTR+/HiOHTvGypUr6du3L1999RXJyclMmzaNGTNmGB2eiIiI1CDlqohZsGABn3/+Oc8++yzjx4/n3nvv9XqVxzPPPIPJZGL8+PGesezsbOLi4qhfvz5BQUEMGzaM5ORkr/0SExMZNGiQpwXJAw88QH5+vtecn376ia5du+Lr60urVq2YN29euWKsCo8Nbsf4Aa3pElnPs15MnkOtyURERErDZrNhLvjvZ6NGjUhMTAQgJCSEPXv2GBlapYiPjyc6OpoePXoYcn5LSAi25s0AyN6ytVzHcK8Ts/HgRnLt3i1f6gf5MuWK9rwz0jVnw95jrsrgoq3J/OuCTx3X59S9heOZR8oVj4iIiNuPP/7Iiy++SPfu3TGbzTRv3pzhw4fz3HPP8fTTTxsdnoiISK2ya9cuTCYT69evNzqUKlGuREy9evUqdU2Y1atX88YbbxTrvzphwgT+97//8fnnn/Pzzz+zf/9+rydb7XY7gwYNIjc3l2XLlvHuu+8yb948Jk+e7JmTkJDAoEGD6NevH+vXr2f8+PGMHj2aRYsWVVr8FXH+OQ0YP6ANZrPJ05osL18VMSIiIqXRpUsXVq9eDbgW3J08eTIffvgh48ePp0OHDgZHV3FxcXFs3brVc41G8O/QEYDszZvKtX9USBShfqHk2HPYfKjkFmetG9Whjq+VzFw725KPg0+RRIxfXfANKvhS5GEVtSYTEZEKysjIoFGjRoDr7xwHDx4EoGPHjqxbt87I0ERERGqckSNHYjKZMJlM2Gw2oqKiePDBB8nOzjY6tGpRrkTMU089xeTJk8nMzKxwAOnp6dx888289dZb1CvS7iI1NZV33nmHF198kf79+9OtWzfmzp3LsmXLWLFiBQDff/89W7du5YMPPiAmJoaBAwfy1FNPER8f71lkb/bs2URFRTFjxgzatWvH3XffzTXXXMPMmTMrHHtlc7cmy1dFjIiISKlMnz6dxo0bA/Dvf/+bevXqMXbsWA4ePMibb75pcHQ1g39MDADHvvwKR05Omfc3mUx0Czv5OjEAFrOJmGZ1AViXeAx8ChIvJourLVnRVmVuak0mIiIVdO6557Jt2zYAOnfuzBtvvMG+ffuYPXu25/5CREREKs9ll13GgQMH2LlzJzNnzuSNN97giSeeMDqsalGuRMyMGTNYtGgRYWFhdOzYka5du3q9yiIuLo5BgwYxYMAAr/G1a9eSl5fnNd62bVuaNWvG8uXLAVi+fDkdO3YkLCzMMyc2Npa0tDS2bNnimXPisWNjYz3HKElOTg5paWler+rgqYjRGjEiIiKl0r17d/r16we4WpMtXLiQtLQ01q5dS+fOnQ2OrmYIueoqrA0bkpeYyJE5c8p1DHd7sjVJJSdiALo0cz2Qs2730cLWZP51wWQqTMwUlXkEHLpnEhGR8rv33ns5cOAAAE888QQLFiygWbNmvPzyy0yfPt3g6ERERGoeX19fwsPDiYyMZOjQoQwYMIDFixd7zdm5cyf9+vUjICCAzp07F/s7/hdffEH79u3x9fWlRYsWZ826btby7DR06NBKOfknn3zCunXrSmy3kZSUhI+PD3Xr1vUaDwsLIykpyTOnaBLGvd297VRz0tLSyMrKwt/fv9i5n376aZ588slyX1d5WQsqYpSIERERkTOFJSiQRg89xP777+fQG28SPOQKfJo2KdMxuoe7EjHrD64nz5GHzWwrNqerpyLmKHQoSMT4ucYKW5PhStLkZYLTDtnHIKDy2uWKiEjtMnz4cM/nbt26sXv3bv766y+aNWtGgwYNDIxMRESkdJxOJ1l5dkPO7W+zYDKZyr3/5s2bWbZsGc2bN/caf/TRR3nhhRdo3bo1jz76KDfeeCM7duzAarWydu1arrvuOqZMmcL111/PsmXL+L//+z/q16/PyJEjK3hFVatciZjKKBfas2cP9957L4sXL8bPz6/Cx6tMkyZNYuLEiZ7vaWlpREZGVvl5fQoqYvLtak0mIiJSGlFRUae88du5c2c1RlNzBQ+6nGOffkrm6tUceuUVIp59pkz7t6rbihDfEFJzUtl6eCudGxavVuoS6aqI2X04k2M+YdQFqNfCtbFoRUydxpBxEHLSXOvEKBEjIiLlkJeXR9u2bfnmm29o164dAAEBAWXu8iEiImKkrDw70ZONWQt969RYAnzKll745ptvCAoKIj8/n5ycHMxmM6+++qrXnPvvv59BgwYB8OSTT9K+fXt27NhB27ZtefHFF7n44ot5/PHHAWjTpg1bt27l+eefr5mJGLe1a9fy559/AtC+fXu6dOlSpn1TUlK8bnLsdju//PILr776KosWLSI3N5djx455VcUkJycTHh4OQHh4OKtWrfI6bnJysmeb+909VnROcHBwidUw4CqR8vX1LfW1VBaruaAiRmvEiIiIlMr48eO9vufl5fHHH3+wcOFCHnjgAWOCqoFMJhMNxt1N4q0jSP/1V5xOZ5mefDKbzHRt1JWle5ayJmlNiYmYkAAbrRoFsSMlndW5Lbnklq+goesPY16JmKBG4HS4EjGZh4A2Fbw6ERGpjWw2W61ZHFhERORM0a9fP15//XUyMjKYOXMmVquVYcOGec3p1KmT57N7zbaUlBTatm3Ln3/+yZVXXuk1v0+fPsyaNQu73Y7FYqn6iyinciViUlJSuOGGG/jpp588SZJjx47Rr18/PvnkExo2bHjaY1x88cVs2rTJa+y2226jbdu2PPTQQ0RGRmKz2ViyZInnh7Ft2zYSExPp3bs3AL179+bf//43KSkpNGrUCIDFixcTHBxMdHS0Z853333ndZ7Fixd7jnEmsVkLEjH5ak0mIiJSGvfee2+J4/Hx8axZc/L1SKTsAmJiMPn7Yz9yhJy//8avTdkSIN3DurN0z1LWJq9lVMdRJc7p2qwuO1LSWbXrCJcM6l+4oWhrssAG4MiHowmQebg8lyIiIgK41qx99tlnefvtt7FaK/ScqoiIiCH8bRa2To017NxlFRgYSKtWrQCYM2cOnTt35p133mHUqMLfEW22wlbW7gcAHTVgfdBy3WmMGzeO48ePs2XLFk8J79atWxkxYgT33HMPH3/88WmPUadOHTp06OA1FhgYSP369T3jo0aNYuLEiYSGhhIcHMy4cePo3bs35513HgCXXnop0dHR3HLLLTz33HMkJSXx2GOPERcX56loueuuu3j11Vd58MEHuf322/nxxx/57LPP+Pbbb8tz6VXKZi5oTVYD/oclIiJipIEDBzJp0iTmzp1rdCg1hsnHh4AuXchYtozMlavKnogpWCfmj5Q/sDvsWMzFb9p7RdXnszV7efu3BKwWMxMvaYPNYgafwMJJgQ3Bnu/6nHGo3NcjIiKyevVqlixZwvfff0/Hjh0JDAz02v7ll18aFJmIiEjpmEymMrcHO1OYzWYeeeQRJk6cyE033VSqfdq1a8fvv//uNfb777/Tpk2bM7oaBsBcnp0WLlzIa6+95knCAERHRxMfH8+CBQsqLbiZM2cyePBghg0bxoUXXkh4eLjXjZDFYuGbb77BYrHQu3dvhg8fzq233srUqVM9c6Kiovj2229ZvHgxnTt3ZsaMGbz99tvExhqTKTwVq6WgIkZrxIiIiFTIf/7zH0JDz/61Q+Lj44mOjqZHjx5GhwJAQK9eAGSe0Bq2NM6tdy5BtiDS89L56+hfJc65MiaCG3pE4nTC6z/9wxNfb3Ft8KlTOCmwEQTWd33OPAQOBzh17yQiImVXt25dhg0bRmxsLBEREYSEhHi9REREpGpde+21WCwW4uPjSzX/vvvuY8mSJTz11FNs376dd999l1dffZX777+/iiOtuHKlyxwOh1eJkJvNZqtQmdBPP/3k9d3Pz4/4+PhT/iCaN29erPXYifr27csff/xR7riqi83iqojJs6siRkREpDS6dOnitVaJ0+kkKSmJgwcP8tprrxkYWeWIi4sjLi6OtLS0M+IPQgE9XQmhzFWrcDocmMylf6bHYrbQLawbP+/9md/3/U77+u2LzbFazDwzrBOdmtblka828fO2g64NXhUxDSC/oKd/xmH4fATsXgZxK13bRERESkmVsyIiIsayWq3cfffdPPfccwwcOPC087t27cpnn33G5MmTeeqpp2jcuDFTp05l5MiRVR9sBZUrEdO/f3/uvfdePv74YyIiIgDYt28fEyZM4OKLL67UAGsTW0FFTL4qYkREREpl6NChXt/NZjMNGzakb9++tG3b1pigajD/Dh0wBQRgT00lZ/t2/Mr4b9wvsh8/7/2ZH3b/wB2d7jjpvEuiw3jkq03sT80iJ9+Or9caMQ0LEzEHNkDiMtfnXb9C+6vKekkiIiIiIiJSDebNm1fi+MMPP8zDDz8MuB6uLKpu3brFxoYNG+ZZU/5sUq5EzKuvvsoVV1xBixYtiIyMBGDPnj106NCBDz74oFIDrE2sBWvE5GmNGBERkVJ54oknjA6hVjHZbAR060bGr7+SuXJlmRMx/Zv156kVT/HnkT/Zc3wPkXUiS5zXIMiHAB8Lmbl29h7N4pyircmCGkFepuuzOwkDcGCjEjEiIlJm//nPf/jss89ITEwkNzfXa9u6desMikpERERqmnKtERMZGcm6dev49ttvGT9+POPHj+e7775j3bp1NG3atLJjrDVsVvcaMUrEiIiInExaWlqpX1L5Anv1BCD1v1+Tf+iQZ9yenkHWhg3k7t170n3r+dWje1h3AH7Y/cNJ55lMJpqFBgCQeDjzhNZkDSGghBZkSRvLchkiIiK8/PLL3HbbbYSFhfHHH3/Qs2dP6tevz86dO0vVHkVERESktMpVEQOuX5AvueQSLrnkksqMp1azmdWaTERE5HTq1q3rtS7Mqdjt9iqOpvapc8klHHzlVbK3bmXn4CH4d+lCzvbt5O3bB4DJ15fIN2YTeN55pP/6Gzn/7CB44OXYwhoBcEnzS1iZtJIfdv/AbR1uO+l5moUG8FfScRKPZELLE1qT5ZSQZDugRIyIiJTNa6+9xptvvsmNN97IvHnzePDBB2nZsiWTJ0/myJEjRocnIiIiNUipEzEvv/wyd9xxB35+frz88sunnHvPPfdUOLDayGopaE2mihgREZGTWrp0qefzrl27ePjhhxk5ciS9e/cGYPny5bz77rs8/fTTRoVYo/k0b06LTz9h/8OTyPnrL9KL/DzMAQE4MjPZM/b/CDz/fNKXLAEg5fkXCI69lPAnn+Ti5hfz75X/ZuOhjSRlJBEeGF7ieZrXd1XE7D6cCdF1XYNWf/AL8a6IaXAuHP4bMlLgeBLUKfl4IiIiJ0pMTOT8888HwN/fn+PHjwNwyy23cN555/Hqq68aGV6V+uabb7jvvvtwOBw89NBDjB492uiQREREarRSJ2JmzpzJzTffjJ+fHzNnzjzpPJPJpERMOdks7tZkqogRERE5mYsuusjzeerUqbz44ovceOONnrErrriCjh078uabbzJixAgjQqzx/Nq2JeqzT0n93zc4srLwbdMa39atMQcGsjfubjJ+/dWVhDGb8T33XHL+/JO07xaAyUzEC8/TpVEX1qWs44fdPzA8eniJ52hW39WOLPFIBtSNhr6PQEgTMJkgsEgipuO1sOlzOLTNVRWjRIyIiJRSeHg4R44coXnz5jRr1owVK1bQuXNnEhISii0MXJPk5+czceJEli5dSkhICN26deOqq66ifv36RocmIiJSY5U6EZOQkFDiZ6k8toKKmHxVxIiIiJTK8uXLmT17drHx7t2768nOKmby8aHusKuLjTd99RUOPPIoeUlJhD30IP6dOpGxYiWJo0aR9u23BF10IQNaDWBdyjqW7ll60kRM89AiFTEAfR8q3OgT6GpRlnEIoq9wJWEObYOkDdDm0kq/VhERqZn69+/P119/TZcuXbjtttuYMGEC//nPf1izZg1XX138v3E1xapVq2jfvj1NmjQBYODAgXz//fdeD7aIiIhI5TKXZ6epU6eSmZlZbDwrK4upU6dWOKjaShUxIiIiZRMZGclbb71VbPztt98mMjLSgIjE7OtLkxkv0OLDD/Dv1AmAwPN60SDu/wBIenIqF5naArA2eS2pOaklHsfdmizxSGbJTyXf+AkM/w80PBfCXefROjEiIlIWb775Jo8++igAcXFxzJkzh3bt2jF16lRef/31ch1z3759DB8+nPr16+Pv70/Hjh1Zs2ZNpcX8yy+/MGTIECIiIjCZTMyfP7/EefHx8bRo0QI/Pz969erFqlWrPNv279/vScIANGnShH0Fa72JiIhI1ShXIubJJ58kPT292HhmZiZPPvlkhYOqrTxrxDhUESMiIlIaM2fO5JVXXqFjx46MHj2a0aNH06lTJ1555ZVTtlI9W8THxxMdHU2PHj2MDqXCGtx5J/7duuHIyMDx5IucG9wKu9POL3t/KXF+RF1/LGYTOfkOUo7nFJ/QtDu0GuD63LggEZOkRIyIiJTOihUrePzxx5k0aRILFy4E4IYbbuDll19m3Lhx+Pj4lPmYR48epU+fPthsNhYsWMDWrVuZMWMG9erVK3H+77//Tl5eXrHxrVu3kpycXOI+GRkZdO7cmfj4+JPG8emnnzJx4kSeeOIJ1q1bR+fOnYmNjSUlJaXM1yQiIiKVo1yJGKfTiclkKja+YcMGQkNDKxxUbWUzu34cTifYHaqKEREROZ3LL7+c7du3M2TIEI4cOcKRI0cYMmQI27dv5/LLLzc6vAqLi4tj69atrF692uhQKsxksdDkuWcx16lD1vr13LY6CICle5aWON9mMRNR1w8o0p7sZNwVMUd3QXbJFTYiIiJu//nPf+jTpw8vvfQSb7/9NoMGDeKFF16o8HGfffZZIiMjmTt3Lj179iQqKopLL72Uc845p9hch8NBXFwcN910E3a73TO+bds2+vfvz7vvvlviOQYOHMi0adO46qqrThrHiy++yJgxY7jtttuIjo5m9uzZBAQEMGfOHAAiIiK8KmD27dtHREREeS9bRERESqFMiZh69eoRGhqKyWSiTZs2hIaGel4hISFccsklXHfddVUVa41nsxb+OPK0ToyIiEipREZGMn36dL788ku+/PJL/v3vf6st2RnK1qQJ4VOeACBq/jpa73Py277fyLGXUPECNA8NBGD34YxTHzggFIKbuj4nbaq0eEVEpGZ6+umnGTNmDKmpqRw9epRp06Yxffr0Ch/366+/pnv37lx77bU0atSILl26lNhCFcBsNvPdd9/xxx9/cOutt+JwOPjnn3/o378/Q4cO5cEHHyxXDLm5uaxdu5YBAwZ4nWvAgAEsX74cgJ49e7J582b27dtHeno6CxYsIDY29qTHrEkVuiIicuY6VcvNmsBalsmzZs3C6XRy++238+STTxISEuLZ5uPjQ4sWLejdu3elB1lbWM2FVUZ5dgd+NouB0YiIiJyZNm7cSIcOHTCbzWzceOpWVJ0K1iiRM0fIoEGk//wzaV//j1FLbTx8cyYrD6zkwqYXFpvbrH4A7IA9R05TEQMQ3gHS9kLyVmjxryqIXEREaopt27bx6aefYrG4fue+7777mDx5MikpKTRq1Kjcx925cyevv/46EydO5JFHHmH16tXcc889+Pj4MGLEiGLzIyIi+PHHH7ngggu46aabWL58OQMGDCj3+jQAhw4dwm63ExYW5jUeFhbGX3/9BYDVamXGjBn069cPh8PBgw8+SP369U96zLi4OOLi4khLS/P6O5CIiEhZHDx4kMmTJ/Ptt9+SnJxMvXr16Ny5M5MnT6ZPnz4cOHDgpO08a4IyJWLcNw5RUVH06dMHq7VMu8tp2CyFFTH5drUmExERKUlMTAxJSUk0atSImJgYTCZTiYu5m0wmr1YfcuZodP/9HF/8Ay33ZNH9bzNL2iwpORETGgDA7tIkYsLaw/aFkLKlssMVEZEaJjMzk+DgYM93Hx8f/Pz8SE9Pr1AixuFw0L17d091TZcuXdi8eTOzZ88uMRED0KxZM95//30uuugiWrZsyTvvvFNiK/jKdsUVV3DFFVdU+XlERETchg0bRm5uLu+++y4tW7YkOTmZJUuWcPjwYQDCw8MNjrBqlWuNmIyMDJYsWVJsfNGiRSxYsKDCQdVWFrMJd1GMWpOJiIiULCEhgYYNG3o+79y5k4SEhGKvnTt3GhypnIytUSNCb70VgBt/drAkYTF59uKLFTcvSMT8czC9xGSbl0bRrvdkJWJEROT03n77bV5++WXPKz8/n3nz5nmNlVXjxo2Jjo72GmvXrh2JiYkn3Sc5OZk77riDIUOGkJmZyYQJE8p83qIaNGiAxWIhOTm52Hlq+h+4RETkzHXs2DF+/fVXnn32Wfr160fz5s3p2bMnkyZN8jwYcGJrsmXLlhETE4Ofnx/du3dn/vz5mEwm1q9fD8BPP/2EyWRi0aJFdOnSBX9/f/r3709KSgoLFiygXbt2BAcHc9NNN5GZWfhw38KFC/nXv/5F3bp1qV+/PoMHD+aff/6p8n+DcpW0PPzwwzzzzDPFxp1OJw8//DADBw6scGC1ldViJjffQZ5DFTEiIiIlad68eYmf5exSf/Qojn7yCZGHUum2JpXlFy0vVhXTPiIEkwk270vj6QV/MWlg25M/JRzW3vWe8ic4HJCfBQm/QqsBYFEVt4iIFGrWrFmxtVvCw8N5//33Pd9NJhP33HNPmY7bp08ftm3b5jW2ffv2k96vHDp0iIsvvph27drx+eefs337dvr27Yuvry8vvPBCmc7t5uPjQ7du3ViyZAlDhw4FXJU6S5Ys4e677y7XMUVE5AzmdEJeKToIVAVbAJSyijMoKIigoCDmz5/Peeedh6+v7ynnp6WlMWTIEC6//HI++ugjdu/ezfjx40ucO2XKFF599VUCAgK47rrruO666/D19eWjjz4iPT2dq666ildeeYWHHnoIcBWZTJw4kU6dOpGens7kyZO56qqrWL9+PWZzuepWSqVcv5X+/fffxZ7yAGjbti07duyocFC1mc1sIhfIV0WMiIjIab377rs0aNCAQYMGAfDggw/y5ptvEh0dzccff6xEzRnMUqcODcbeRcozz3LTTw4WxH5dLBHTrH4AU69oz+P/3cKbv+zEZjHxQGzbkg9YvxWYbZCbDqmJ8OuLsO5dGPo6xNxUDVckIiJni127dlXJcSdMmMD555/P9OnTue6661i1ahVvvvkmb775ZrG5DoeDgQMH0rx5cz799FOsVivR0dEsXryY/v3706RJkxKrY9LT073+7pKQkMD69esJDQ2lWbNmAEycOJERI0bQvXt3evbsyaxZs8jIyOC2226rkusWERED5WXC9Ahjzv3IfvAJLNVUq9XKvHnzGDNmDLNnz6Zr165cdNFF3HDDDSWu7frRRx9hMpl466238PPzIzo6mn379jFmzJhic6dNm0afPn0AGDVqFJMmTeKff/6hZcuWAFxzzTUsXbrUk4gZNmyY1/5z5syhYcOGbN26lQ4dOpTpn6AsypXiCQkJKbHdx44dOwgMLN0/vpTMZnX9SNSaTERE5PSmT5+Ov78/AMuXL+fVV1/lueeeo0GDBhVu7SFVL/Tmm3G0aEJwFjR8fzE59pxic27p3YJpQ103w2/8vJPM3PySD2axQcNzXZ+TNsGfX7s+H9xW8nwREZFK1qNHD7766is+/vhjOnTowFNPPcWsWbO4+eabi801m81Mnz6dL774Ah8fH894586d+eGHH7j22mtLPMeaNWvo0qULXbp0AVxJly5dujB58mTPnOuvv54XXniByZMnExMTw/r161m4cCFhYWGVfMUiIiKlN2zYMPbv38/XX3/NZZddxk8//UTXrl2ZN29esbnbtm2jU6dO+Pn5ecZ69uxZ4nGLJnLCwsIICAjwJGHcYykpKZ7vf//9NzfeeCMtW7YkODiYFi1aAJyylWhlKFdFzJVXXsn48eP56quvOOeccwBXEua+++7TYm8VZDW7EzFqTSYiInI6e/bsoVWrVgDMnz+fa665hjvuuIM+ffrQt29fY4OT0zLZbDR/8t/sGTGSvmtz2XTfWCJ8G2GuUwdrgwYE9e+HX5s2DD+vOa/+uIOktGw27k3lvJb1Sz5gWHtI3gxr34Wso66xjEPVd0EiIlLrDR48mMGDB5dq7iWXXFLiuDvJUpK+ffueft004O6771YrMhGR2sAW4KpMMercZeTn58cll1zCJZdcwuOPP87o0aN54oknGDlyZPnDsNk8n00mk9d395jDUVj0MGTIEJo3b85bb71FREQEDoeDDh06kJubW+4YSqNcFTHPPfccgYGBtG3blqioKKKiomjXrh3169cvdx9TcfGxuPrq5SsRIyIiclpBQUEcPnwYgO+//97zBw0/Pz+ysrKMDE1KKahXL/b2OQczELhwOan//S9HP/iAg7NmkXDV1aS88AKOjAy6Na8HwNrdR09+sEYFrXN3LC4cyzhYdcGLiIiIiIgYyWRytQcz4lXK9WFOJTo6moyMjGLj5557Lps2bSInp7BrwurVqyt8vsOHD7Nt2zYee+wxzzptR4+e4nfMSlSuipiQkBCWLVvG4sWL2bBhA/7+/nTq1IkLL7zw9DvLKVktrtxYrlqTiYiInNYll1zC6NGj6dKlC9u3b+fyyy8HYMuWLZ7yYjnzRT4xlU+eugWr08St3e/AL9NO9p9/kvHrrxx++x0Oz53HbRHNaW4N55ilF47zmmAuaEnnJax98TElYkRERERERAx1+PBhrr32Wm6//XY6depEnTp1WLNmDc899xxXXnllsfk33XQTjz76KHfccQcPP/wwiYmJngIQUwUSQPXq1aN+/fq8+eabNG7cmMTERB5++OFyH68sypWIAdcFX3rppVx66aWVGU+tZ/VUxCgRIyIicjrx8fE89thj7Nmzhy+++IL69V0tq9auXcuNN95ocHQVFx8fT3x8PHa73ehQqlTbZl35+6oYNh7cSHjXAEZ3HA3A8R9/JPmZZ8lLTCRwz06GsBMSlrFj6bs0nvYUdfr39z6QuyKmqEy1JhMRERERETFSUFAQvXr1YubMmfzzzz/k5eURGRnJmDFjeOSRR4rNDw4O5n//+x9jx44lJiaGjh07MnnyZG666SavdWPKymw288knn3DPPffQoUMHzj33XF5++eVqaW1ucpamuWgJlixZwpIlS0hJSfHqsQYwZ86cSgnuTJGWlkZISAipqakEBwdX6bkum/ULfyUd54NRvfhX6wZVei4REZGKqM7/PtZ2teHfev6O+Tz+++M0CWrCd1d/h9nkqhJ2Op3kJydz/I8NzH3lM7of2EqjrGMA1L3+esIffwyTteDZIqcTnm0B2ccguCmk7XX1LX70gCHXJCIiVaui/3286KKLGDVqFNdeey3+JVVaipfacD8iInKmy87OJiEhgaioqAolJM5GH374IbfddhupqanV+t/tU/2bl+W/jeVaI+bJJ5/k0ksvZcmSJRw6dIijR496vaT83BUxeQ5VxIiIiJTGr7/+yvDhwzn//PPZt28fAO+//z6//fabwZFJWcS2iKWOrQ770vexfP9yz7jJZMIWHk7owFhWXDGK0QMe4uDl14DJxLFPP2X/w5NwuiuGTCYI7+j63OVm13teJuQW7zksIiLSpUsX7r//fsLDwxkzZgwrVqwwOiQREREp8N577/Hbb7+RkJDA/Pnzeeihh7juuuvO2ocnypWImT17NvPmzWPlypXMnz+fr776yusl5Wc1u34k+fZyFSqJiIjUKl988QWxsbH4+/uzbt06z0J+qampTJ8+3eDopCz8rf4MOWcIAJ9u+7TEOV2b1yPPYmPBv66l6Ssvg9VK2jffcGDyZDxF3pdMhfPHQZ/xYC14WilD7clERKS4WbNmsX//fubOnUtKSgoXXngh0dHRvPDCCyQnJxsdnoiISK2WlJTE8OHDadeuHRMmTODaa6/lzTffNDqscitXIiY3N5fzzz+/smMRwMfi+pHkaY0YERGR05o2bRqzZ8/mrbfewmazecb79OnDunXrDIxMyuP6ttcD8NOen0hMSyy2vWuzegCs232MOgMG0OSF58FsJvWLLzk8e7ZrUpOucOk08AmAwIauMSViRETkJKxWK1dffTX//e9/2bt3LzfddBOPP/44kZGRDB06lB9//NHoEEVERGqlBx98kF27dnlag82cOZOAgACjwyq3ciViRo8ezUcffVTZsQhFWpMpESMiInJa27Zt48ILLyw2HhISwrFjx6o/IKmQliEtuaDJBThx8sGfHxTb7k7EbE85zs6D6QRfdhnhU54A4OBLL5O2eLH3DgH1Xe8ZB6s0bhEROfutWrWKJ554ghkzZtCoUSMmTZpEgwYNGDx4MPfff7/R4YmIiMhZzlqenbKzs3nzzTf54Ycf6NSpk9cTqAAvvvhipQRXG1ktak0mIiJSWuHh4ezYsYMWLVp4jf/222+0bNnSmKCkQm5tfyu/7vuV+TvmExcTR4hviGdbwzq+DGjXiB/+TOHlJX8z64Yu1LvuOnK2/83RDz5g/0MP49OkCX7R0a4d3BUxmaqIERGR4lJSUnj//feZO3cuf//9N0OGDOHjjz8mNjYWk8n1kOTIkSO57LLLeOGFFwyOVkREpJCnNbNUucr6ty5XRczGjRuJiYnBbDazefNm/vjjD89r/fr1lRJYbeWjihgREZFSGzNmDPfeey8rV67EZDKxf/9+PvzwQ+677z7Gjh1rdHhSDr3Ce9GmXhuy8rP4fPvnxbaPH9AGgP9u2M+OlOMAhD38EIHn98aZmUninXeSu3efa7KnNZkqYkREpLimTZvy9ttvM2LECPbu3ct//vMfLrvsMk8SBqBTp0706NHDwChFREQKuQsiMjMzDY6k9sjNzQXAYrFU6DjlqohZunRphU4qJ2c1a40YERGR0nr44YdxOBxcfPHFZGZmcuGFF+Lr68sDDzzA6NGjjQ5PysFkMnFr9K089vtjvL/1fW5qexMBtsI+wB2ahBDbPoxFW5KZ+cPfxN/UFZPVSpOXXmL3zcPJ2b6dPXfcQYuPPsQS6G5NdghyM2HT53DuQAhqZNDViYjImWTJkiVccMEFp5wTHBysv4GIiMgZw2KxULduXVJSUgAICAjweoBAKpfD4eDgwYMEBARgtZYrleJRsb2l0vnaXImYnHwlYkRERE7HZDLx6KOP8sADD7Bjxw7S09OJjo7mjTfeICoqiqSkJKNDlHK4vOXlvL7hdfal7+OzbZ8xssNIr+3jB7Rh0ZZkvt14gMcGZdE4xB9LnTpEvvkGu264kdydO9nzf3E0G9PVVf6dcRDWzIHvH4UDo2Cw2uiKiAg88cQTfPnll9StW9drPC0tjaFDh/Ljjz8aE5iIiMgphIeHA3iSMVK1zGYzzZo1q3DCq1yJmH79+p3yxLpZKT8/q6vEyZ2Iyc13sGLnYXq0CMXfp2LlTyIiIjVFTk4OU6ZMYfHixZ4KmKFDhzJ37lyuuuoqLBYLEyZMMDpMKSeb2cadne5k8rLJzNk8h2vPvZZAW6Bne7vGwXRuGsKGvams3HmEoV2auPYLD6fZW2+y66abyVq3jv1zM2nSAkwZh2DvKtfOh/824IpERORM9PPPP3vajRSVnZ3Nr7/+akBEIiIip2cymWjcuDGNGjUiLy/P6HBqPB8fH8zmcq3w4qVciZiYmBiv73l5eaxfv57NmzczYsSICgdVm3kqYvLsAHyyOpHJ/93C+AGtPT3RRUREarvJkyfzxhtvMGDAAJYtW8a1117LbbfdxooVK5gxYwbXXntthfu3irGGnDOEtze9TeLxRD7+62NGd/RuNdejRagrEZNQmIgB8G3dmqbxr7Jn1GiOr/qLAyl1aRyegunIP64Jx/ZU52WIiMgZaOPGjYBr8d2tW7d6VdDa7XYWLlxIkyZNTra7iIjIGcFisej33rNIuRIxM2fOLHF8ypQppKenVyig2s7P5vo/nuyCiph9R7MA2HMky7CYREREzjSff/457733HldccQWbN2+mU6dO5Ofns2HDBvXHrSGsZit3db6LR357hLmb53LDuTcQ5BPk2d4zKpS3f0tgVcLhYvsG9uxJxAsvsG/CBFJ3BeD4bzIRPVMwW4C0feBwQCU80SQiImenmJgYTCYTJpOJ/v37F9vu7+/PK6+8YkBkIiIiUlNV6m+gw4cPZ86cOZV5yFrH1+r6kWQXVMS439NzVGYmIiLitnfvXrp16wZAhw4d8PX1ZcKECUrC1DCXR11OVEgUablpvP/n+17berQIBeCfgxkcSs8ptm9w7KU0fWYyJrOT47utJCxsRPp+X7DnQoZ6KYuI1GYJCQn8888/OJ1OVq1aRUJCgue1b98+0tLSuP32240OU0RERGqQSk3ELF++HD8/v8o8ZK3jrojJyXNVxGQXvKfn5BsWk4iIyJnGbrfj4+Pj+W61WgkKCjrFHnI2spgt/F/n/wPg/S3vk5qT6tlWL9CHc8PqALBm15ES968z8AqaXngEi6+d3ONW9vxSn91L6pPx4wKcTmfVX4CIiJyRmjdvTosWLXA4HHTv3p3mzZt7Xo0bN1abFxEREal05WpNdvXVV3t9dzqdHDhwgDVr1vD4449XSmC1laciJt/u9Z6erUSMiIiIm9PpZOTIkfj6+gKuRXXvuusuAgMDveZ9+eWXRoQnlejSFpfyxsY32HFsB+9vfZ+7u9zt2dYjqh7bko+zMuEIl3VoXHxnmz9BzWycUz+FQ1vqcHR7IJkHfUl86DkCvvqF8Mcfw/ecc6rxakRExGhff/11qedeccUVVRiJiIiI1CZlSsTs3LmTFi1aEBIS4jVuNps599xzmTp1KpdeemmlBljb+BariHElYo6rIkZERMRjxIgRXt+HDx9uUCRS1cwmM3ExcUz4aQIf/PkBw9sNp65fXQB6RtXngxWJrD5JRQwAgQ2w5KYTFpNGaIwvh9flcCwhhMwVK9g59Coa3HknDcbehUlPP4uI1ApDhw4t1TyTyYTdbq/aYERERKTWKFMipnXr1hw4cIC5c+cCcP311/Pyyy8TFhZWJcHVRn4nVsS4W5OpIkZERMTDfS9S08XHxxMfH1/r/xDUv1l/2oa25a8jfzFvyzzGdxsPQM+CdWK27k8jLTuPYD9b8Z0DGsDRXQDYug4knPcIvWYIyb/mkP7rMg69+iqZq1YR8cLz2Bo1qqYrEhERozgcDqNDEBERkVqoTGvEnNhLe8GCBWRkZJT75K+//jqdOnUiODiY4OBgevfuzYIFCzzbs7OziYuLo379+gQFBTFs2DCSk5O9jpGYmMigQYMICAigUaNGPPDAA+TneyctfvrpJ7p27Yqvry+tWrVi3rx55Y65qrkrYtyVMO53rREjIiJS+8TFxbF161ZWr15tdCiGclfFAHz010cczjoMQHiIHy3qB+BwwrIdh0reObCh6z24CUR0AcDHepjI0T2JOO8oZhtkrlrFrmHXkLV5S5Vfi4iIiIiIiNQ+ZUrEnKiii5w2bdqUZ555hrVr17JmzRr69+/PlVdeyZYtrl+CJ0yYwP/+9z8+//xzfv75Z/bv3++1Po3dbmfQoEHk5uaybNky3n33XebNm8fkyZM9cxISEhg0aBD9+vVj/fr1jB8/ntGjR7No0aIKxV5V3BUxOfkFrckK3jNz7dgdWlRWREREaqeLml5Eh/odyMrPYu7mwoqo/m1dldnfb00uecfABq73xp0hJNL1+dgeSFxOSIssWlx6CN9W55B/8CC7b7mF4z/8UJWXISIiZ5iMjAy+++47Zs+ezcsvv+z1EhEREaksZWpNZjKZMJlMxcbKa8iQIV7f//3vf/P666+zYsUKmjZtyjvvvMNHH31E//79AVcbknbt2rFixQrOO+88vv/+e7Zu3coPP/xAWFgYMTExPPXUUzz00ENMmTIFHx8fZs+eTVRUFDNmzACgXbt2/Pbbb8ycOZPY2Nhyx15V/DwVMa4ETE5eYSuS9Jx8QvxLaLkhIiIiUsOZTCbiusQx9oexfLLtE25sdyNNgppwSXQYc35P4Me/Usi3O7BaTnjOqHFn+ON9OKd/YSImdQ9kudaV8a2TS/P46ex76hUyfvuNvePuIfyJJ6h3w/XVfIUiIlLd/vjjDy6//HIyMzPJyMggNDSUQ4cOeTpu3HPPPUaHKCIiIjVEmVuTjRw5kquvvpqrr76a7Oxs7rrrLs9396s87HY7n3zyCRkZGfTu3Zu1a9eSl5fHgAEDPHPatm1Ls2bNWL58OQDLly+nY8eOXmvUxMbGkpaW5qmqWb58udcx3HPcxyhJTk4OaWlpXq/q4uupiPFuTQZqTyYiIiK1W5+IPvQI70GOPYfnVz8PQI8W9agbYONYZh5rdh8tvlOP0XDvBtd7SFPXWE4aHD/gmWLJTSFy9uvUve46cDpJmjKFg6+9VuHqbxERObNNmDCBIUOGcPToUfz9/VmxYgW7d++mW7duvPDCC0aHJyIiIjVImRIxI0aMoFGjRoSEhBASEsLw4cOJiIjwfHe/ymLTpk0EBQXh6+vLXXfdxVdffUV0dDRJSUn4+PhQt25dr/lhYWEkJSUBkJSU5JWEcW93bzvVnLS0NLKyskqM6emnn/a6nsjIyDJdU0W4K2JyCipi3JUxAOnZSsSIiIhI7WUymZjUcxIWk4UliUv4fd/vWC1m+rdtBMDiktqTmUxQr4Xr3TcI/OsVn3NkJyarlfAnp9Dg/8YCcOjlV0h+ahpOLeosIlJjrV+/nvvuuw+z2YzFYiEnJ4fIyEiee+45HnnkEaPDExERkRqkTK3J5s6de/pJZXTuueeyfv16UlNT+X/27js8inJ74Ph3e/qm90IgtNCJNJHeRAELFsCC2BVUQK/KT6/t2r12Y732igVEQSlSld57gJBKSO996++PSTYJCQgSsiGcz/Pss7sz77xzZrjemc2Z874//vgj06dPZ+3atc2+nzMxb9485s6d6/heUlLSYsmYuqHJaipiLFIRI4QQQghRq6NPR6Z2mcpXB7/ihS0vsHDSQsbGBrFgRwYrDmTz+OVdTz10rjECKmsqZ1RqsNug4KjyVaUi4P770fj4kv388xR+8w3WokJCX3wRlV7fAkcnhBCiJel0OtRq5fnUwMBA0tLS6Nq1K0ajkfT0dCdHJ4QQQoi25IwqYs4FvV5PTEwMcXFxvPDCC/Tq1Ys333yT4OBgTCYTRUVFDdpnZ2cTHBwMQHBwMNnZ2Y3W1647VRsvLy9cXV2bjMlgMODl5dXg1VLqhiarrYiRRIwQQgghRH339r4XPxc/UktS+enITwzpGIBeqyatoII9x4pPvbF3ZN3nmDHKe0FSgya+N91I6H9fAZ2Okt9+J/3ue7CWlTfzUQghhHC2Pn36sHXrVgCGDRvGE088wddff83s2bPp3r27k6MTQgghRFvi9ETMiWw2G9XV1cTFxaHT6Vi5cqVj3aFDh0hLS2PQoEEADBo0iL1795KTk+Nos2LFCry8vIiNjXW0qd9HbZvaPlqb+hUxdrtdhiYTQgghhDiBp96Tu3vdDcD7u99HpTZxaTflIZz/W7gXs/UUw4kZ61U5956qvJ+QiAEwXn45Ee+9h8rNjfING0ibMQNLQUGzHYMQQgjne/755wkJCQHgueeew8fHh3vuuYfc3Fw+/PBDJ0cnhBBCiLbEqYmYefPmsW7dOlJSUti7dy/z5s1jzZo13HDDDRiNRm677Tbmzp3L6tWr2b59OzNmzGDQoEEMHDgQgLFjxxIbG8tNN93E7t27WbZsGY8//jgzZ87EYDAAcPfdd5OUlMTDDz9MQkIC7777Lt9//z1z5sxx5qGfVG1FjMVmp7JeNQxAWbXZGSEJIYQQQrQ6kztOJswjjPyqfL4++DWPT+iKt5uO/cdLeHf10ZNvaAxX3v07Q3g/5XNhClgbP/Dicclgoj77FI23N1V795I67QbMGRnNfzBCCCGc4qKLLmLEiBGAMjTZ0qVLKSkpYfv27fTq1cvJ0bUe8fHxxMbG0q9fP2eHIoQQQpy3nJqIycnJ4eabb6Zz586MGjWKrVu3smzZMsaMUYaJeP3115kwYQKTJ09m6NChBAcHs2DBAsf2Go2GxYsXo9FoGDRoEDfeeCM333wzzzzzjKNNdHQ0S5YsYcWKFfTq1YtXX32V//3vf4wbN67Fj/d01FbEABRXNky8lEpFjBBCCCEEADqNjll9ZgHw6b5PMeireXpSNwDeXnWEI9mlTW/Y5TLwjoIBd4JnKGgMYLNAcdNzAbj27EnUN9+gDQ3BlJJCytRpVO7Zc06OSQghhGiNZs6cyYEDBxzDuAkhhBDizGmdufOPP/74lOtdXFyIj48nPj7+pG2ioqL47bffTtnP8OHD2blz5z+KsaXVVsQAFJY3TMTIHDFCCCGEEHUui76MT/d9yuHCw3y872Pm9J3DTzsyWHc4l6X7sugY5Nl4I9/2MLteIsU3GnITlOHJfKOb3I+hfTTtvv2W9Ntvp/pIIqk33EjQ/83De8oUVCrVOTo6IYQQ51p2djYPPfQQK1euJCcnB7vd3mC91Wo9yZZCCCGEEGfGqYkY0ZharUKvUWOy2iiqNDVYJ3PECCGEEELUUavU3N/nfmatmsU3B7/hhi43MKJzAOsO57IjrfD0OvHtUJeIYdRJm+mCgoj69lsy5/0fpStWkPX0M5StWUvwU0+iq5lfQAghxPnllltuIS0tjX//+9+EhIRIcl0IIYQQ54wkYlohg05JxJRUSkWMEEIIIcSpDA0fSp/APuzM2ckHez7gyqj7AdiRVoTNZket/ps/qtVWwRQk1S3b9D5o9XDRrQ2aajw8CHvrTQo+/Yzc11+nbO1akiZMJOCBB/CZNhWVVm6thRDifPLXX3/x559/0rt3b2eHIoQQQog2zqlzxIimGbTKPDFFFSfMESOJGCGEEEKIBlQqFbP7zgZgwZEFGNzycNGpKa40k5RX/vcd+LZX3msTMSnrYekjsHgOlOU0uT+/W2cQvXABrr16YSsvJ/v550mefA0V27Y101EJIYRoCREREY2GIxNCCCGEOBckEdMKueiUf5biEytiZGgyIYQQQohG+gb1ZXj4cKx2K89tfoYe4V4A7Eg9jeHJ/Doo7xnbobIQ1r5Yty5j+0k3M8TEEPXN1wQ//TQao5HqQ4dIvfEmjj/yCJbc3LM5HCGEEC3kjTfe4NFHHyUlJcXZoQghhBCijZNETCvkoqupiJGhyYQQQgghTsu8AfNw07qxM2cnHv5bAU5vnpiIAeDTDspz4YsrIHld3bpjp65wUWk0+Fx/He2X/o73ddeBSkXxol84Ov4yCr74ArtF7t2EEKI1u/7661mzZg0dOnTA09MTX1/fBi8hhBBCiOYiA1m3Qgatkh87cWgyqYgRQgghhGhaqEcos+Nm8/zm59lb8Q0q7f1sT/X4+w11rnDNp/DxWMjcrSxz84eKvFNWxNSn9fEh5Jmn8b72GrKefoaqffvIfv4Fihf9QuhLL2KIiTmLIxNCCHGuvPHGG84OQQghhBAXCEnEtEK1FTElNRUxHgYtZdUWqYgRQgghhDiF6ztfz9LkpezI2YFLyEKOpM+guNKM0VV36g3D+sLY/8DSR0GthYlvwvwbIGMH2GygPr0ictcePWg3/zuKfvyJnFdfpWr/fpKvnkzAA/fje8stqDSaZjhKIYQQzWX69OnODkEIIYQQFwgZmqwVclTEVJoA8PfQA1BaZT7pNkIIIYQQFzq1Ss1TFz+FXq1H63EYrXHH6Q1PBjDgbhj/Clz7OXQaB1pXqC6G/MQzisExXNmvv+I+bCh2k4mcV/5L6o03YUpN/QdHJYQQ4lw6evQojz/+OFOnTiUnJweA33//nf379zs5MiGEEEK0JZKIaYVqK2KKaypi/D0MgDJHjN1ud1pcQgghhBCtXbQxmnt73wuAS9BiXl6xhSqz9e83VKlgwJ3QdQJodBDSS1l+msOTnUgXFEjE++8T8ux/ULu7U7lzJ0lXXkXhd/Plfk4IIVqJtWvX0qNHDzZv3syCBQsoKysDYPfu3Tz55JNOjk4IIYQQbYkkYlohF13DOWJqEzE2O1Sezh8ShBBCCCEuYNO7TSfG2BmVppJk6w/MW7D3zJMf4Rcp7xnb/nEcKpUK72uuof0vi3AbMAB7ZSVZTz1F+t13Y8nN/cf9CiGEaB6PPvoozz77LCtWrECv1zuWjxw5kk2bNjkxMiGEEEK0NZKIaYUM2pqKmJpEjI+7DrVKWVdWJfPECCGEEBeK+Ph4YmNj6devn7NDOa9o1VqeveRpAHTeO1h0cCMLd2acWSdhccr7sX+eiKmlCwsj8tNPCHz0EVR6PeVr15E0cRIlS5dKdYwQQjjR3r17ueqqqxotDwwMJC8vzwkRCSGEEKKtkkRMK1RbEVNaban5rsHDoG2wTAghhBBt38yZMzlw4ABbt251dijnnW7+3bgy5koAXIJ/Zem+42fWQW0iJnsf7PgSLKazikelVuN3yy1E//Qjhq5dsRYVkTF7Dmm33kr1kSNn1bcQQoh/xtvbm8zMzEbLd+7cSVhYmBMiEkIIIURbJYmYVqi2IqaWi06Dp4sOkIoYIYQQQojT9UDfB3DRuKJxTWdr/oozqz7xjoTw/mCzwC+z4K0+sP9nOMsKFkPHjkTP/w7/e+9BpddTsXETSZOuIOPBhyQhI4QQLWzKlCk88sgjZGVloVKpsNlsrF+/noceeoibb77Z2eEJIYQQog2RREwrZNA1/Gdx0dZVxJRJRYwQQgghxGnxd/Xnzh53AWA2/szuzLTT31ilgpsWwJj/gEcwlByDH6bDV1dD+dkNV6PS6wm4/37aL1mM59ixYLdTsmQJSZOu4NgDs6k6dOis+hdCCHF6nn/+ebp06UJERARlZWXExsYydOhQLr74Yh5//HFnhyeEEEKINkQSMa2QS6OKGDXuBmVZqVTECCGEEEKcthk9pmOwRqHSVPGfTc+cWVWMwRMG3w8P7IJhj4DGAEdXwafjofjYWcemj4gg/K03if55IZ7jxoHdTumyZSRfcSXpd99DxY4dZ70PIYQQJ6fX6/noo49ISkpi8eLFfPXVVyQkJPDll1+i0Wj+vgMhhBBCiNMkiZhWqFFFjE6DR+3QZPUqYoorzFhtMsGrEEIIIcTJaNVahvvej92m4XDpFhYcWXDmnehcYcT/wV3rwCsc8g7Dx+Og5AznnTkJly5dCH/zDaJ/WYTXZeNBpaJszRpSp91Ayg03UrpmzZklkIQQQpySzWbjpZdeYvDgwfTr14/4+HhGjBjBddddR8eOHZ0dnhBCCCHaIEnEtEJNVcR41g5NVmUGIKOokn7P/cH93+1s8fiEEEIIIc4nIzv0wJQ3BoBnNz/L5szN/6yjwC5w61Lwi1GGKlv7cjNGCS6dOhH22mt0+P03vK+9FpVOR+X27Ry7+x6Sr7iS4l9/xW6R6mghhDhbzz33HP/3f/+Hh4cHYWFhvPnmm8ycOdPZYQkhhBCiDZNETCvUZEXMCXPEHM4qxWS1cfB4SYvHJ4QQQghxPukb6YMpfyiWkp5YbBZmr57NkcIj/6wz7wiY+JbyedfXzVYVU5++XTtC/vMMHf74A9/bbkXt5kb14cMc/9fDJF0+geJffsFutTb7foUQ4kLxxRdf8O6777Js2TJ+/vlnfv31V77++mtsNpuzQxNCCCFEGyWJmFboxIoYg1aDh4uSiCmtScRUmJQf39UWuVEUQgghhDiVYKMLYd7uVB6/lhivHpSZy3hg9QOUmkr/WYftBkPkILCaYMM7zRtsPbqgQIL+9S9iVq8iYPYDaLy9MaWmcvzhR0iaOIniJUskISOEEP9AWloal112meP76NGjUalUHD/e/Ml1IYQQQgiQREyr5KJrPDSZoyKmSknElJuUd5NVEjFCCCGEEH+nb5QP2HXo82/FRx9Iemk6T2146p/PvTLkIeV92ydQltt8gTZBYzTif/fddPjjDwLmzEFtNGJKSuL4gw+RdMUVlCxdil2e4hZCiNNmsVhwcXFpsEyn02E2m50UkRBCCCHaOq2zAxCNGbSNhybzdGk4NFllTUWMSSpihBBCCCH+1tCO/vy6+zibE6tRu1yDR/QHLE9dzvxD85nSZcqZdxgzCkJ6Q+Yu+OxyuP5LCOjc3GE3oPFwx/+uO/G5YRqFX35J/qefYUo8SsbsOeijovCeOgXvq65CYzSe0ziEEOJ8Z7fbueWWWzAYDI5lVVVV3H333bi7uzuWLViwwBnhCSGEEKINkoqYVqhxRYwG95qKmPLqhhUx1RYZjkIIIYQQ4u9cExfOV7cNYNqASGxVkZhzxwPw+vbXyanIOfMOVSqY9DZ4hkDeIfhgKLzSEV6MhN3fNXP0DWk8PPC/5x5i/liB/6xZqD09MaWmkvPiSxwZNpzjjz1G5Z49/7zaRwgh2rjp06cTGBiI0Wh0vG688UZCQ0MbLBNCCCGEaC5SEdMKGXQN82OuOg1ueiU5IxUxQgghhBBnTqVScUlHfwbH+LEtpYDD2RfTOSKR41UJvL79dV4Y8sKZdxrSE+76E36cASl/gqVKWf7H09DtatDqm/cgTqDx8iJg1kz8ZtxC8eIlFH77LdUJCRT/tIDinxagb98e4xVXYLxiErrg4HMaixBCnE8+/fRTZ4cghBBCiAuMVMS0Qi7ak88RU1GTgCmvVt5tdrDIPDFCCCGEEKdFpVJxTVw4oEZbdBUqVCxOWszOnJ3/rEOPALj5FyUhc9c68AiG0uOw78dmjftU1O7u+Fx/HdELFxD17Td4TZqIysUFU1ISua+/TuKIkaTdeivFv/yCraKixeISQgghhBBCCKGQREwr5KJrPEdM7dBkjooYs8WxvlqqYoQQQgghTtuVvcNQq2B/spFR4RMAeHbTs5ispn/WoVqtVMeE9IKBdyvL1r8FLTw0mEqlwq1PH8JefpmOf/1JyHPP4tavH9jtlG/YyPGHH+HIJUM4Pu//KN+8BbtN7iGFEEIIIYQQoiVIIqYVMpxYEaPV4K6vqYipblgRAzI8mRBCCCHEmQj0cmFYpwAAfE1X4GPw4XDhYd7c8ebZdx43A/SekHsQjiw/+/7+IY2HB96TJxP15Rd0+GMF/vfNQhcZia2iguKFC0mbPp2jY8aS+9ZbmFJTnRanEEIIIYQQQlwIJBHTCp1YEWPQqXE3KMmZ8pqKmApTXUWMSYYmE0IIIYQ4I9fERQCwaHspjw94CoAvDnzBhuMbzq5jV2+Im658/vleSNt0dv01A314OAEzZ9Jh2VKivv4K72uvRe3hgTkjg7x33+PouEtJmTKV/E8+laSMEEIIIYQQQpwDkohphepXxKhUYNDWzRFTbrJgt9sdc8WAVMQIIYQQQpypsd2CCDG6kFdWTWFeDNd3vh6AeX/O41jpsbPrfMiDENwTKvLg84kw/yb46Q44tLQZIv/nVCoVbnFxhPznGTr+9Sehr/4X9yFDQK2mctcucl5+maPjLuXohAnkvPY6lbt3y/BlQgghhBBCCNEMJBHTChnqVcQYtGrlR3NNIsZmhyqzjfJ6iZhqi7VRH0IIIYQQ4uR0GjW3Do4G4KM/k5nTdy6dfTpTUFXAPX/cQ1FV0T/v3M0Xbl0KXSaA1QQHf4G938O3U+DAouY5gLOkdnHBePnlRH70ITFrVhP02GO4DRoIWi2mxKPkf/ghKddPIXHYcI4/9hglS5diLS52dthCCCGEEEIIcV6SREwrZNDW/bO46JTqGDddXZVMWbWFynpDk1VLRYwQQgghxBmb0j8CT4OWxJwyNieVEj8qniC3IFJKUnhg9QNUW6v/eed6d7juS5jyDVz2X4i9ErDDT7dD8p/NdQjNQhcYiO9NNxL16ad0Wv8Xoa+8guf4S1G7u2PJzaX4pwVkzJ7D4UEXkzJ1Grnx8VTu2YPdKg8DCSGEEEIIIcTpkERMK6RSqRzJGJeaYcrUahXu+rp5YsqrZWgyIYQQQoiz4emiY9qASADeWpmIn0sA741+D0+dJztydvDYX49hs5/FfZZaDV0uh/53wDWf1FXIfHMdHPy1mY6ieWmMRowTJxD++ut03LiBiI//h+8tt6CP6QA2G5U7d5L39jukXHc9RwZfQsaDD1G08GcsubnODl0IIYQQQgghWi1JxLRStZUwLvWGKXOrN09MpVkSMUIIIYQQZ2vG4Gjc9Rp2pRfx5sojdPTpyBsj3kCr1rIsZRmvbXuteXak1sDkjyFmNJgrlHljVv4HynKap/9zQK3X4zF4MEGPPkKHxYuJWbWS4GeexnPsWNQeHliLiihZsoTMefM4MmQoSVdcSdazz1GyfDmWwkJnhy+EEEIIIYQQrYYkYlopR0VMvSHJPGoTMdVWyqtlaDIhhBBCiLMVbHThhck9AXhndSLrDufSP6Q//xn8HwA+P/A5f6T+0Tw707nA1PnQ73bADn/+F17tAj/eCpVFzbOPc0gXGorPddcR/tabdNq4gaivv8Lv7rtw6dYNgOpDhyj86isy7n+AI4MuJmniRLKe+Q8lS5diyc93cvRCCCGEEEII4TxaZwcgmlabgDHUS8S4G5TPJZXmBskXqYgRQgghhPjnJvUKZXNSPl9vTmPO/F0smzOUCe0ncKTwCJ/s+4SnNj5Fz4CeBLoFnv3ONFplzpiIgbD5fcjYBvt+gsw9MG0++HU4+320AJVOh1tcHG5xcTB7Npb8fCq2bqViyxYqtm6l+kii41X4zTcA6Nu3x7VPb1x798atd2/0HTqgUstzYUIIIYQQQoi2T375tFK1Q5K5aOsNTaZX8mZ5ZQ0njjVZJREjhBBCCHE2/j0hli7BnuSXm3j0pz3Y7XZm9Z5FV9+uFFcX8+/1/z67+WLqU6mg57Vwx0q4fSV4hUH+EfhoJCSva559tDCtnx9el15K8BNP0P7XX+m4YT1hb76Jz403YujcGQBTUhLFPy0g699PkDRxEocHDCTtttvJfettyv78E2txsZOPQgghLhyLFy+mc+fOdOzYkf/973/ODkcIIYRo86QippUyaGvniGk8NFlu6QmJGKmIEUIIIYQ4Ky46Da9f35sr3lnPHwdz+G5rOlP7R/LikBe5bvF1bDi+gde2vcZD/R5q3h2HXwR3rILvpkHGdvjyKhj/EsTNUOaVOU9pfX3xGjcWr3FjAbAUFlK5cxeVu2pee/diKy2lfP16ytevd2ynb9cOl27d6r1i0Xh4OOswhBCiTbJYLMydO5fVq1djNBqJi4vjqquuws/Pz9mhCSGEEG2WJGJaKUdFjK6uIsa9JhGTc0IiptpibbnAhBBCCCHaqK4hXjw0rhPP/5bAM78eIC7Kh05B7Xn64qd59M9H+fzA5wS7B3Nj7I3Nu2PPYLhlCSyaqQxTtuRBWPsK9LwOLr4PPJphSDQn0/r44DlyBJ4jRwBgt1ioPnyYitrEzK7dmNPSMKWkYEpJoWTJEse2juRM9+64dIvFJVaSM0IIcTa2bNlCt27dCAsLA2D8+PEsX76cqVOnOjkyIYQQou2SoclaqaYqYtz1ymepiBFCCCGEODduv6Q9l8T4U2m2cveX2ymtMnN5+8uZ3Xc2AC9vfZkVqSuaf8c6V5j8MYx5Blx9oSwLNrwFb/WFdf8FS/Xf93EeUWm1uMTG4jttGmEvv0zM8mV03LiBiI8+ImD2A3iOGY02NATAkZjJeekl0m6ezuGL+nH00vFkPPQv8j/9jPItW7CWlTn5iIQQoqEXX3wRlUrF7Nmzm7XfdevWMXHiREJDQ1GpVPz8889NtouPj6ddu3a4uLgwYMAAtmzZ4lh3/PhxRxIGICwsjIyMjGaNUwghhBANOTUR88ILL9CvXz88PT0JDAzkyiuv5NChQw3aVFVVMXPmTPz8/PDw8GDy5MlkZ2c3aJOWlsbll1+Om5sbgYGB/Otf/8JisTRos2bNGvr27YvBYCAmJobPPvvsXB/eWambI6ZeIqZ2aLKyEytiJBEjhBBCCNEc1GoVb07pTYjRhaS8ch6pmS/m1u63cn3n67Fj59F1j7I9e3vz71ylgsEPwIOH4PqvIbQPmEph1X/g47FQkNz8+2xFtD4+eAy5BP+77yb87bfpuGoVHTesJ+KjD5tOzixeXJec6defo+Mvk+SMEKJV2Lp1Kx988AE9e/Y8Zbv169djNpsbLT9w4ECjv3vUKi8vp1evXsTHx5+03/nz5zN37lyefPJJduzYQa9evRg3bhw5OTlndiBCCCGEaDZOTcSsXbuWmTNnsmnTJlasWIHZbGbs2LGUl5c72syZM4dff/2VH374gbVr13L8+HGuvvpqx3qr1crll1+OyWRiw4YNfP7553z22Wc88cQTjjbJyclcfvnljBgxgl27djF79mxuv/12li1b1qLHeyYMutqKmMZDk51YESOJGCGEEEKI5uPnYeDdG/qi06j4bW8WKw/moFKpmNd/HiMiRmCymbh/1f0kFSWdmwC0eug6AW5fBVd/BK4+kLkLPhgKf74GlUXnZr+tkNbXF48hQ06anPEYPQptSAjY7ZiSk0+ZnKnYuhVrWfnf71QIIc5CWVkZN9xwAx999BE+Pj4nbWez2Zg5cybTpk3Daq0bbvzQoUOMHDmSzz//vMntxo8fz7PPPstVV1110r5fe+017rjjDmbMmEFsbCzvv/8+bm5ufPLJJwCEhoY2qIDJyMggNDT0TA9VCCGEEGdAZbfb7c4OolZubi6BgYGsXbuWoUOHUlxcTEBAAN988w3XXHMNAAkJCXTt2pWNGzcycOBAfv/9dyZMmMDx48cJCgoC4P333+eRRx4hNzcXvV7PI488wpIlS9i3b59jX1OmTKGoqIilS5f+bVwlJSUYjUaKi4vx8vI6Nwd/grnf72LBjgzuGtqeeZd1BeCDtUd54fcEXHRqqsx1yZcHRnVkzphOLRKXEEIIUcsZ18cLlZxr53hpaQLvrTlKhwB3ls0eilajptJSye3Lb2dP7h5C3UP56rKvCHALOLeBFB+DH2+F9M3Kd70H9J0OA+8G78hzu+/zhCU/n6oDB6jat4/K/fup2n8AS2Zm44YqVb05Z7rh2q0bhq6xaDzcWz5oIcRZa43Xx+nTp+Pr68vrr7/O8OHD6d27N2+88UaTbY8fP87QoUMZMGAAX375JcnJyQwdOpSJEyfy/vvv/+2+VCoVCxcu5Morr3QsM5lMuLm58eOPPzZYPn36dIqKili0aBEWi4WuXbuyZs0ajEYjcXFxbNiwAT8/vyb3Ex8fT3x8PFarlcOHD7eq8y2EEEI405nci2hbKKbTUlxcDICvry8A27dvx2w2M3r0aEebLl26EBkZ6UjEbNy4kR49ejiSMADjxo3jnnvuYf/+/fTp04eNGzc26KO2zcnGaq2urqa6uq7qpKSkpLkO8bR5uegA8HSp+yeqrYipn4QBMFmlIkYIIYQQorndM7wD87emczS3nPnb0rlhQBSuWlfeGfkON/1+E6klqdy78l4+HfcpHvpzOHm8MRxu+Q32/gAb3oac/bApHja/DwPvgVFPgNZw7vZ/HtD6+eExZAgeQ4Y4llny86nav5+q/fsbJGdMycmO6hlASc5ERyvJmW6xkpwRQvxj3333HTt27GDr1q2n1T40NJRVq1YxZMgQpk2b5vjbxXvvvfePY8jLy8NqtTb4GwlAUFAQCQkJAGi1Wl599VVGjBiBzWbj4YcfPmkSBmDmzJnMnDnT8ccmIYQQQpy5VpOIsdlszJ49m8GDB9O9e3cAsrKy0Ov1eHt7N2gbFBREVlaWo01TNxi1607VpqSkhMrKSlxdXRuse+GFF3j66aeb7dj+iZsGRQEwOS7csczD0PQ/l0mGJhNCCCGEaHZeLjruHxnDU78e4PUVhxkQ7UtMoCc+Lj68N/o9bvztRhIKEpi1ahbvjX4PV63r33f6T2m00Hsq9JoCR1fC+rcgeS1sfEd5v+oDCOp27vZ/HtL6+eExdCgeQ4c6ljVKzuzbjyUrC1NSEqakJEp+/VVpKMkZIcQZSk9P54EHHmDFihW4uLic9naRkZF8+eWXDBs2jPbt2/Pxxx+jUqnOYaSKSZMmMWnSpHO+HyGEEEIonDpHTH0zZ85k3759fPfdd84OhXnz5lFcXOx4paent3gMHQI8eGpSN0KMdT/o3fSaJttWW6xNLhdCCCFE65Gens7w4cOJjY2lZ8+e/PDDD84OSZyGaQOiaB/gTl6Zicvf+osvNqZgt9uJ8Izg3dHv4qHzYHv2duasnoPJajr3AalUEDMapv8CU+eDmx9k7YX3BsPCuyEn4dzHcB6rTc7433MPEe+8Q8c1q+m4/i8iPvyAgAfux2PUKLTBwcqcMzWJmZwXXyL1pps53K+fMufMgw+R/8mnlG/egrW01NmHJIRoJbZv305OTg59+/ZFq9Wi1WpZu3Ytb731FlqttsE8MPVlZ2dz5513MnHiRCoqKpgzZ85ZxeHv749GoyE7O7vRfoKDg8+qbyGEEEL8c62iImbWrFksXryYdevWER5eVwESHByMyWSiqKioQVVM/RuI4OBgtmzZ0qC/2huO+m2augnx8vJqVA0DYDAYMBha3/AOUhEjhBBCnL+0Wi1vvPEGvXv3Jisri7i4OC677DLc3eUJ+9ZMr1Xz7R0DeeiH3fx5JI8nFu2nwmTl7mEd6ObXjfhR8dz9x92sP76eW5fdyn+H/Zdg9xb6Q1fnS+Hu9fD7w3DwF9j9rfIK6AJ9boL+d4JW3zKxnMearJzJy2swpFnVgROGNVuyxNFWFxWJa7duSvVMbCwusbFoZOgeIS44o0aNYu/evQ2WzZgxgy5duvDII4+g0TR+sDIvL49Ro0bRtWtXfvjhBw4fPszw4cMxGAz897///Udx6PV64uLiWLlypWOOGJvNxsqVK5k1a9Y/6lMIIYQQZ8+piRi73c59993HwoULWbNmDdHR0Q3Wx8XFodPpWLlyJZMnTwbg0KFDpKWlMWjQIAAGDRrEc889R05ODoGBgQCsWLECLy8vYmNjHW1+++23Bn2vWLHC0cf5wv2ERIxWrcJiszsSMSaLDY1ahUZ97suYhRBCCHFmQkJCCAkJAZSHRPz9/SkoKJBEzHkgyMuFz2f0J351Iq+uOMxLSxNo5+fOpd2D6RvUl7dHvs2c1XPYnbub6xdfz2vDXyMuKK5lgvMKgeu/hIztsO6/cGQF5CbA8sdg+2cw/iWIGdUysbQhWn9/PIYNw2PYMMcyS34+VQcO1Axtprybjx/HnJqGOTWNkt9+d7TVRUQoiZkunTF07IihUyd0YWGo1K1mQAIhRDPz9PR0DLNey93dHT8/v0bLQUmOjB8/nqioKObPn49WqyU2NpYVK1YwcuRIwsLCmqyOKSsrIzEx0fE9OTmZXbt24evrS2RkJABz585l+vTpXHTRRfTv35833niD8vJyZsyY0cxHLYQQQojT5dREzMyZM/nmm29YtGgRnp6ejjldjEYjrq6uGI1GbrvtNubOnYuvry9eXl7cd999DBo0iIEDBwIwduxYYmNjuemmm3j55ZfJysri8ccfZ+bMmY6qlrvvvpt33nmHhx9+mFtvvZVVq1bx/fffs6Tek2znA3dDwydovN305JVVY7LaMFlsjH5tLT7uehbNHOykCIUQQojz17p163jllVfYvn07mZmZLFy40PEkaa34+HheeeUVsrKy6NWrF2+//Tb9+/c/431t374dq9VKREREM0UvzjW1WsWskTHkllXzxcZUZs/fyUK/wXQN8WJAyADmT5jP3LVzSShI4K4Vd/HqsFcZFjHs7ztuLmFxMPVbqCyC/Qtg9fOQfwS+uhq6TIBxz4FPu5aLpw3S+vnhMWQIHkOGOJZZCgtrkjM1CZoDBzCnpztepUuXOtqq3NwwxMRg6BiDoUMM+uh26Nu1Qx8ejkqnc8YhCSGcSK1W8/zzzzNkyBD0+rrqxV69evHHH38QEBDQ5Hbbtm1jxIgRju9z584FYPr06Xz22WcAXH/99eTm5vLEE0+QlZVF7969Wbp0aaO5c4UQQgjRclR2u93utJ2fZAK6Tz/9lFtuuQWAqqoqHnzwQb799luqq6sZN24c7777boOxTVNTU7nnnntYs2YN7u7uTJ8+nRdffBGtti7PtGbNGubMmcOBAwcIDw/n3//+t2Mff6ekpASj0UhxcTFeXl7/+HjPVmZxJYNeWOX43jHQgyM5ZYzqEsjTV3TjkpdWA5Dwn0tx0TU9n4wQQgjRXFrL9bG5/P7776xfv564uDiuvvrqRomY+fPnc/PNN/P+++8zYMAA3njjDX744QcOHTrkqMrt3bs3FoulUd/Lly8nNDQUgIKCAoYMGcJHH33ExRdffFqxtbVzfT6zWG3c+vk21h3OJTbEi59nDkavVaocqixV/Gvdv1iTvgaNSsPLQ19mbLuxzgm0qhjWvASb3we7FVBB9BDocR10vxr0Uol1rliLi5XkzIEDVB8+TNXhI5iOHsVuOskcQlot+vBw9NHRSmKmJkFjiI5G4+/fIpN2C3G+kutjy5LzLYQQQjR0JtdGpyZizhet5WajpMpMz6eWO773b+fLlpQChnT05/HLYxn3xjoAtjw2ikBPF2eFKYQQ4gLRWq6P54JKpWqUiBkwYAD9+vXjnXfeAZQhRSIiIrjvvvt49NFHT6vf6upqxowZwx133MFNN910ynbV1dWO7yUlJURERLTJc30+yi2tZuzraymsMPPAqI7MGdPJsc5is/Dkhif55egvuGpd+X7C97QztnNesDkHYek8SFpdt8zgBb2mwqB7pUqmhdgtFkxp6VQfPqy8kpMwpaRiSknBXll50u3UHh5KcuaEBI0+Kgq1DGsoRJu+F2mN5HwLIYQQDZ3JtdGpQ5OJM+N2QpWL0U0ZwqDaYqPcVPf0bWmVhUDPFg1NCCGEaNNMJhPbt29n3rx5jmVqtZrRo0ezcePG0+rDbrdzyy23MHLkyFMmYQBeeOEFnn766bOKWZw7AZ4GnrmiO/d9u5P41Yl0C/VibDelWlur1vLMxc+QVZ7FlqwtPLzuYb667Cv0Gv3f9HqOBHaFm3+GwlTY9yPs+BIKk2HLB7D1f9DtSuh5PbQfDlqDc2K8AKi0WgztozG0j4ZLxzmW2202LDk5mJKTMaWkUF3zbkpOwZyRga2sjKp9+6jat69Rn9qgoCaTNLqwMFRa+ZknhBBCCCFEayJ36OcRrUaNi05NldkGgK+b8oPeZLFRUW11tCupNDslPiGEEKKtysvLw2q1NhpbPSgoiISEhNPqY/369cyfP5+ePXvy888/A/Dll1/So0ePRm3nzZvnGPMd6ipiROsxoWcIS/dnsWRPJnd+uZ2r+4bx1KRueLno0Kg1PH/J81zz6zUcLDjIC1te4ImBTzh3iCmfKBjyIAyeo1THbIyHoyth30/Ky2CEHpOh73QI7e28OC8wKrUaXXAwuuBg3AcNarDOZjJhTktrlKAxpaRgLSjAkp2NJTubis2bG3aq06GPiEAfFYUuIhx9eITyHhGBLjwctYtUzgshhBBCCNHSJBFznvEwaKkyK+NLe7vXVcSUVddVxJRUNR6bXgghhBDOdckll2Cz2U6rrcFgwGCQ6oTWTKVS8eq1vQj3duXDP5NYsCODvceK+Xh6PyL93AhyD+LZwc8ya9Usfjz8I65aV/510b+cP9+HWg0xo5RX5m7Y+RUc/BVKM2HbJ8qrw0gY9QSE9nFurBc4tV6PISYGQ0wMJxa7W4uLm0zQmFJSsFdXY0pKwpSU1GS/2oAAdBER6CPC0TVI0kSgDfBHpVaf+4MTQgghhBDiAiOJmPOMm14L1CRiXGsrYqxU1BuaTCpihBBCiObl7++PRqMhOzu7wfLs7GyCg4OdFJVwNhedhnmXdWVMbBAzv9nBkZwyrnx3PR9Pv4g+kT4MixjGE4Oe4JmNz/DlgS8pN5czu+9sfFx8nB26IqSX8rr0JUj5E3Z8AQcWwdFVyiu0D3QaD90ng3+Ms6MV9WiMRlx79cK1V68Gy+02G5asLKqTkzGnp2NKT8ecfgzTsXTMaenYysqw5OZiyc2lcseORv2qDAZ04eE1Q55F1c1J064dGj8/5ycShRBCCCGEOE9JIuY8426o+yfzqZkjxmS1UW6qNzRZlSRihBBCiOak1+uJi4tj5cqVXHnllQDYbDZWrlzJrFmznBuccLqL2vmyaOYl3PHFNvZmFDP9ky38cPfFdA725NpO12Kz2Xh287MsOLKA5SnLuavnXdwYeyNadSu5FVerof0w5VX4b1j9AuyZD8d3Kq81z0PkxTDgTuh6hdJetEoqtRpdaCi60NBG6+x2O7biYkzpxzAfS1fe09OVJE36McyZmUo1zdGjmI4ebbS92tOzbk6aiHC0wcHoQkLQBQejDQlB4+HREocohBBCCCHEeamV/PoTp8vDoHF89q6ZI6babKO8/tBklTI0mRBCCHGmysrKSExMdHxPTk5m165d+Pr6EhkZydy5c5k+fToXXXQR/fv354033qC8vJwZM2Y4MWrRWgQbXZh/10Bu+ngL21MLufmTzfx498VE+LpxfZfriTJG8eq2V0koSODV7a/yW/Jv/Gfwf+js29nZoTfk0w6u/gDGPANHlinDliX+AWkblJd/Z+h5HQR1g7CLwCPA2RGL06RSqdB4e+Pq7Y1rj+6N1tvNZsxZWZhS0zClpmBKScVUM/SZOSMDW2kpVXv3UrV3b5P9qz080IUoSRldcIjyueZdFxyMNjhY5qcRQgghhBAXLEnEnGeUockU9StiKhrMESMVMUIIIcSZ2rZtGyNGjHB8nzt3LgDTp0/ns88+4/rrryc3N5cnnniCrKwsevfuzdKlSwkKCjpnMcXHxxMfH4/Vav37xsLp3PRaPp5+Edd9sJHD2WVc9e4GPripL3FRvgwMGcj8CfNZlLiIV7a9wsGCg9z0+018OOZDegf2dnbojXkGQd+blVfJcdj+GWx+H/IOwar/KG1UaogYCL2uh943gEbn1JDF2VHpdOgjItBHRMAlgxuss1VXY05Pd8xJY87IwJKZhTlLedmKi7GVlVF9JJHqI4kn2QNofHzQhgTXJGpOSNZERKANCJDhz4QQQgghRJukstvtdmcH0dqVlJRgNBopLi7Gy8vLqbHM/HoHS/ZmArB8zlDGvr4ON72Gqf0j+fivZABuGBDJc1f1cGaYQgghLgCt6frY1sm5Pr9kl1Qx/ZMtJGSVotOoeGJCLDcOjHL8gTmvMo9H1z3K5qzNeOm9+OzSz+jo09HJUZ+GqmLY9S1kbIesvZB7sG6dTzvocxPoXMEzWJlbRu/mtFBFy7KVlytJmcwsLFmZmDOzMGdlKsmazEzMWVnYKyv/th+1hwf66GgM7aPRR0ejj26PProd+qgo1AZDCxyJON/I9bFlyfkWQgghGjqTa6NUxJxn3GuGJlOpwNNF+eczWU4YmqxKhiYTQgghhHCWIC8XfrrnYv71425+25vFvxftZ/WhXF6a3JMATwP+rv68NfIt7lxxJ7tzd3Pnijv5aMxHxPjEODv0U3MxwsC7674XpcOBn2H9W1CYUlcpA2Dwgp7Xw4j/Azfflo5UtDC1uzuGDh0wdOjQ5Pra+WmUZE0mlpqkjTkzE0tmppKsyczEVlbW9PBnajW68HAMHTti6BijvMd0xBDdDpVe3wJHKIQQQgghxNmRRMx5pnZoMjedBhetkpSx2OyUVtWfI0aGJhNCCCGEcCZ3g5b4aX35ZH0KLy1NYFVCDle88xdf3zGQaH933HRuxI+KZ8ayGRwpPMIty27h3VHv0jOgp7NDP33eEXDxfXDRrbD1Y8jeBzYrZGxTEjNbP4KExXD5axA9FAwymfuFqnZ+Go23Ny5dujTZxmYyYU5NpTopWZmbJjmJ6uQUTMnJ2EpLMaelYU5Lo2zlyrqNtFr07aJqEjM1CZqOHdFHRqLSaJrcjxBCCCGEEM4giZjzjIehJhFj0KLXqh3LCytMjs8yR4wQQgghhPOpVCpuuySawTF+3PvVDpLyyrn2/Y18ffsAOgd7YjQY+XTcp9y78l725O7htmW3MTtuNlO7TEWtUv/9DloLvTsMvr/uu80GyWvht39B/hH4bqqy3NUH3PzAPRA6jYUe14ExzDkxi1ZHrdc7Ein12e12rHl5VB9NovrIkbpXYiK20lJMiUcxJR6ltN42Kr0efYcOSvVMTEdleLPIKPSREahdXVv2wIQQQgghhEASMecd95pEjLtec0Iipi75IhUxQgghhBCtR5dgL76/exA3/m8zCVmlXPv+Bt69IY5LOvpjNBj5aMxHPLj2Qf7K+IsXt7zI8pTl3NXrLgaFDDo/Jy5Xq6HDCLhrnTJc2e5vobKw7pWfCGkb4I+nIXoI9JwCncfLEGaiSSqVCm1AANqAANwHDnAst9vtWLKzlaTMYSUxU5ugsVdVUX3wINUHDzbqTxsYiD4yEl1UpJKciYpEFx6BLiwUjbf3+fnfnBBCCCGEaPUkEXOeqZ0jxlWvRatWoVaBzQ6F5XUVMaUyR4wQQgghRKvi72Fg/p2DuPXzrWxPLWT6p1t4YkIsNw+KcgxT9sOhH3h1+6vsyNnBXSvuIsY7hheHvEhn387ODv+f0bvBpS8or6piKM5QEjF5h2HvD5C6HpLXKS9UENpHSeB0GAm+7UHrAi7eSmJHiBOoVCp0wcHogoPxGDLEsdxus2E+dkxJzBxWqmdMaWmYUlOxlZRgycnBkpMD27Y17tPVFV1ICLrQUOU9LFT5XPNdGxSESis/oYUQQgghxJmTu8jzjLu+riJGpVKh16qpMtsokKHJhBBCiDYnPj6e+Ph4rFars0MRzcDopuPr2wcwb8FeFu7M4Mlf9rN0XxYvXN2Ddv7uXN/leoaGD+XzA5+z8MhCEosSufn3m3ll2CsMDR/q7PDPjotReQG0GwwXzYDCVNj7PexbADkH4PgO5fXnq3XbaV3Bv2NdkiZyEHgEgVQtiJNQqdXoIyPRR0biOXJkg3XWoqKapEwaprRUzGlpmFJSMR3PwJqbh72yElNSEqakpKY712jQBgXWJGbqJWlC65I3aje3FjhKIYQQQghxvlHZ7Xa7s4No7UpKSjAajRQXF+Pl5eXUWJJyy7gifj23XRLN7NGd6PnUMkqaqIA59OylGLQyQaUQQohzpzVdH9s6Oddti91u59P1Kby8LIEqsw1XnYbnr+7OVX3CHW2Kq4t5cO2DbM7cjFql5u6ed3NHzzvQqtvoc1QlxyFpDRxdrVTIVOSD7SQPF+k9Ibg7dJ2kDGnmHQlque8VZ8dWXY0lKwvz8eM1r8x6n49jzsoC898/8Kbx8WlQTaOtrbAJDZPhz84BuT62LDnfQgghRENncm2URMxpaG03G1abHY1auXm/6Nk/yCurbtRm2+Oj8fcwtHRoQgghLiCt7frYlsm5bpvS8it45Kc9bEzKB2DagEiemBCLi05JKphtZp7b9Bw/HfkJgN4BvXly0JPE+MQ4LeYWZbVAUapSLZO6QUnS5CYAJ/x8UWnAGAb+nSGgMwR0gcCuENwTtHqnhC7aHrvNhiU3D/PxDCyZ9ZI0Gccx13y3lZX9bT8y/Fnzkutjy5LzLYQQQjQkiZhm1ppvNga/uIqMospGy1c9OIz2AR5OiEgIIcSFojVfH9saOddtl9Vm562VR3hr1RHsduge5sV7N8QR4Vs3vNHipMU8t+k5ysxlqFVqJrafyAN9HyDALcCJkTuJpRoKkiF5rTKkWcY2sJ1kfkSdO0RdDO2HQ/RQ8AgEtQ5cvaWCRpwT1pISJSmTUa+SJrPuszU37+87keHPzohcH1uWnG8hhBCiIUnENLPWfLMx8r9rSMord3z3dtNRVGHm55mD6R3h7bzAhBBCtHmt+frY1si5bvvWHs5l9nc7Kaww4+mi5eFxnZk2IMpRBZ1RlsGr215lReoKALz0Xjw24DHGR4+/sIc5slmhLBsKUyD3UM0rAbL2KMObNUWtBa8wMHiBRqfMQdPpUogZDS7y35c4d2wmU8NqmvrDn2VmYs7MPKPhz7TBwWgDAhq/AgPQ+vldEJU1cn1sWXK+hRBCiIYkEdPMWvPNxqVvrCMhq9TxvXOQJ4eyS/ni1v4M7XQBPiUphBCixbTm62NbI+f6wnC8qJJZ3+xgR1oRoFTHPD2pO3FRPo42e3P38uzmZzmQfwCAoeFDeeiih4g2Rjsj5NbLZlOGNEtao7zSN4OpHOzWk2+jdYXYK5TqGbsNtC7gHQE+0eAZ1FKRiwvYSYc/q5ewOZ3hzwBQqdD4+jZO0DRI2gSiDfBHbTh/h7SW62PLkvMthBBCNCSJmGbWmm82Jr3zF3uOFQPgptfQPczIluQC3pnWhwk9Q50cnRBCiLasNV8f2xo51xcOq83ON5tTeWXZIUqqlCG3ro0L5/8u64qPuzLfidlm5uO9H/PBng+w2CxoVVpu6X4L9/a6F51G58zwWz+rBcqyoPiYkpixVEHaJkhYAgVHT76dewD4tIPqUrCawb8TBHeHoO4Q3AN828OFXJkkWkz94c8sOTlYcnOVV/3P+flgPUXS8QRqoxFtgH+T1TW6wEC0AQFovL1Ru7uj0rWu/4+R62PLkvMthBBCNCSJmGbWmm82rn1/A1tTCgEI8DTQK9ybPw5m8/xVPZg2INLJ0QkhhGjLWvP1sa2Ij48nPj4eq9XK4cOH5VxfQPLKqnl5aQLfbzsGQKCngZeu6cmIzoGONinFKby67VXWHFsDQFffrrww5AU6eHdwRsjnN7sdMrbDrm+gMFmZS8ZUBkXpUJwO/M1PJu9IZXizTuOg3RBl+LPyXHDxBp1LSxyBEA52qxVrYWFdYsaRrMltlLixn8ZQaPWpXFxQe3igcXdH7eFR93J3Q+3qhtrNDbWrK2p3N1SurjXf3VC7uoBKDYDW3w+Xrl2b5VjlXqRlyfkWQgghGpJETDNrzTcbN/xvE+sTlfGv2/m50TfKhwU7Mnh0fBfuHiY/woUQQpw7rfn62NbIub5wbU8t5OEfd3M0V5kT8Ireocwb35VgY90f9/9I/YOnNj5FcXUxGpWGK2Ou5J5e9xDkLsNpNQtTBeQchNLjyrwyKpXyPWsvZO+D7ANgra5rr3UFmwVsZiWhExSrJGdirwRXbzi+C9QaaD8c3Hydc0xCAHa7HVtxceOETROJG1tFRbPt1/PSSwl/4/Vm6Uuujy1LzrcQQgjR0JlcG9v+7H1tnF6jdnx202vxclFKxUsqzaw8mM2xwkpuHhR1YU/iKoQQQghxnoqL8mHJ/UN4eekhPt2QzKJdx1lxIJtZI2O47ZJoDFoNo6NG08O/B89tfo7V6av56chPLE5azA1db+DW7rdiNBidfRjnN70bhMcBcXXLoofWfTZVQPJaOPQ7HF6mDH0GgEpJxmTuVl4b32nYr0oN4f2h4xgweMLu76A0E/reDINmgov8u4lzS6VSofH2RuPtjaFjx1O2tZvN2MrLsZaVYysvw1amvKxlZdjKypXvlZXYKiuwVVRgr6jEVlFRs0xZXltYpguTIbSFEEIIceGRipjT0Jqf+rjry20s258NQL92Pgzq4M9bK49w3UXh/Lo7k0qzlQX3XkzfSJ+/6UkIIYQ4M635+tjWyLkWAHuPFfPkL/vYkVYEQLS/O09N6sawTgGONjtzdvLG9jfYkbMDAE+9J7f3uJ1pXabhopUhss45mw3yj4DOFTxDlSqaY1sh4Tc4vBRsVmVOGVMZ5Bw4eT8u3kpCpse1ylBpBUnQYRSE9GyxQxHifCDXx5Yl51sIIYRoSIYma2at+Wbj/m938svu4wAM7xzAJTH+PLvkIMFeLmSVVAEwZ3QnHhh96iechBBCiDPVmq+PbY2ca1HLZrPz864MXvg9gdxSZTisib1C+fflXQn0UhItdrudtcfW8uaON0ksSgQg0C2Qe3vdyxUxV6BVS1G8U9hqJk9Xa5T3onQ4shyOrICqYoidBO4BsPZlyDvUdB+Rg8AzREnkmMqVV3B36Hm9sk7TuiZSF+Jck+tjy5LzLYQQQjQkiZhm1ppvNh76YTc/blcmcb28RwjDOgfw8I97GrS5KMqHH++52BnhCSGEaMNa8/WxrZFzLU5UWmXm9RVH+GxDMjY7uOk13Dm0PXcMaY+7QUm0WG1WFictJn5XPJnlmQC0N7bn/wb8HwNCBjgzfHEqNqsyxNmWDyF1Pfh3Bq8QOLpKmXvmVPSe4B2pVN0YPKA4AzRaiLwYOoyEwC4tcwxCtBC5PrYsOd9CCCFEQzJHzAVEr60/R4zGMUdMfTvTiyipMje5TgghhBBCnH88XXQ8MTGWq/uG8fjP+9iVXsQbfxzh681pzBndiesuCker0XBFzBWMjx7P/EPz+XDPhyQVJ3H78tsZHTmai8MupldALzr5dHL24Yj61Brocpnystuhdq7H4gw4+IvyWe8BendQayFxBexfBNXFYCqFnP3Kq76DvyrvUZdAj8lQXQYVeWA1K3PVhMVBu0uUPlEp8+IIcbos1cr/ptz9Gi7P3A3bPoGLbpNh9YQQQghxwZOKmNPQmp/6ePrX/Xy6PgWAWy5ux9huQUz7aLNjva+7noJyEx/cFMe4bsFOilIIIURb1Jqvj22NnGtxKna7nd/2ZvHS0gTSCioA6BjowaPjuzCySyCqmj/kl5hKeHvH28w/NB87dT8BBgQP4J7e9xAXFNdk/+I8YLMqw5tVFEB+ImTtBUsVGMOU5cl/QtIasFtPrz9jJET0U6poOo0Hqwmy9kB1qZIYqi6Dshxw9Vba+HU4l0cnWitLNWz7FP58FSoL4eoPoPtkZV3OQfjkUqgqAq0rTHobel7b7CHI9bFlyfkWQgghGpKKmAtI/YoYd0PDipgoPzeGdwrg842p/HkkVxIxQgghhBBtkEql4vKeIYyJDeKrTam8teoIR3LKuO3zbQxs78v/XdaVnuHeeOm9eGzgY1zd8WqWpSzjQP4BtmZvZXPWZjYv3cykDpN4tP+jeOo9nX1I4kypNeDmq7z8Y6DzpQ3XXzJHqajZ+j84vlOZi8Y9ALR6JamSuqFhFU1xmvLa99Pp7T+wG1z6vDIE2v6FkL0X/DoqlTbB3ZvvOEXrcXQ1LJ4Dhcl1y366A6pKwCMIljyoJGH0nkql1oLbleH2YidB14ng085ZkQshhBBCOIUkYs5zBk39ocm0GF3rEjEXd/BnSMfaREyeM8ITQgghxFmIj48nPj4eq/U0n2IXFzS9Vs2tl0QzOS6cd9ck8un6FDYlFTDpnfVM6BnCzBExdA3xoqtfV7r6dQUgsyyTj/Z+xE9HfuKXo7+wNWsr07pMY1TkKCK8Ipx8RKJZGcNg9JMnX2+uUipmLNXKkFJpG+HQb0p1jUqtzFXj7q8Ml6Z3B49AKEqF1I1KEueLK8DFW/nje309r4fxL4POFQqS6oZD8++kJILE+cVqVpIsOz5XvnuGwLCHIWM77PwKFs+ua+vfGWb8Bpveg79eg2NblNeBRXD7H04JXwghhBDCWWRostPQmstv31l1hP8uPwzA05O6cWXvMHo9s1xZN60PwzsH0vvp5Vhsdtb9awSRfjLesxBCiObRmq+PbY2ca/FPZBRV8uqyQyzYmeFYNrxzAHcP68CAaF/HkGUAO3N28n9//h/Hyo7VtY0Yzty4uUQbo1s0btHKlOUqc8bo3ZteX1EAa15Qqm3sNvAIhs7jlaRLyp/KMoMRzOVgs9Rt52KEjuOUKh5zBWhdlGWRA6HDqLq5cZzFbofiY2DwUOJXqxuut9kg9yCgAlcf8ApxSpjNriwXlj+mJNXaXQIdxyhJNFCSdD/eCgmLARX0vxNGPg4uXsr5WPFv2PM9eAZBUA8Y9W/wClW2LclUtjv4izLc3aB7myVcuT62LDnfQgghRENncm2URMxpaM03Gx+tS+K53w4C8Mo1Pbm6bzh9nllOlcXGxkdH4udhYOLbf7E3o5gPb4pjrAxPJoQQopm05utjWyPnWpyN/ceLeW/NUX7bm4mt5s6/T6Q39w6PYVSXQNRq5Q/eFeYKFh1dxMq0lWzN2orNbkOj0nBZ9GVM6zqN7v4yxJQ4hewDyjBVMWPqKl3St8CCO+uGrzIYlaSOqQKqi0/eV0gviL0C9B5KQsRcDr7tIfbKhgkacxWUHlfW1VdZCAm/QfvhSiXQmTKVw9fXQepfyne9Jwy4Ey6+H8yVkLQa1r9Vk4ip0edGmPh2XcLGbleO393/n8+hY7Uox2e3g1qrzNFTXQJ+MUoC658qyVT+TcyVSrJFa1CW26zw1dXKfEK1grrDjN9Bo4fvb4Ijy0FjgOu+aDwEnhPI9bFlyfkWQgghGpJETDNrzTcbn29I4clflPGc372hL5f1CGFfRjEWm53eEd4AzPxmB0v2ZPLvCbHcdok80SiEEKJ5tObrY1sj51o0h9T8cj5cl8QP249hstgA6BLsyT3DOzChZygadd0fuJOKknh9++usObbGsSzaGM3QsKFM7DCRzr6dWzp8cb4yVUDmLjBGgDFcSaTYrEqS4ugqZTg0rStYqqA0C/YvUCpkmtJpPEx4HaqK4eCvsOUDKM9V5hwZ94Iy5Nmh32D181BZADo3GDJXSeC4+SkVOdWlSpWGzlVJ2Pz5mrLf0N4Q2gcCu8KPt8HRlYAKqPdzWaVWKnxqaV2VxFJFgdLukjkw4jGlEmjty8rwbmodjHxMSeKoNZCTALu/hdwEpT+DJ4RdBCE962I6vByS10LeYbCaGp8HlQaih0DvG6H71Uq/9ZkrYcUTUJCsDEcX3ENZbrMq87rUDisGENpXSap4Rygxr35OOW89r1eqVyrylQolm0WJSesKU7+FDiPO9H8J54RcH1uWnG8hhBCiIUnENLPWfLPx7ZY05i3YC8BnM/oxvHNgozYv/p7A+2uPcsvF7XhqUreWDlEIIUQb1Zqvj22NnGvRnHJLq/n4r2S+2pRKWbUyVFQ7Pzduq5lfxk1fN43k3ty9fJvwLUtTlmK2mR3Lx7Ubxw1db6Crb1dctC4tfgyiDSvPh+2fKEkEU7mSuFHr4MDPTSclTsZgPHnVjd4TulymTDhfntN0G50b3PyLkiA5shxW/gfyDinJE78Y6DUVLroVXL1h1zfw8z1129UmklQaJdEEyrBrKrWSaDkTGr2ync2iJG10blBSN9wgfjEwaBZ0u1IZTqwgSRk+LHOXsl6thQF3K8mo7Z/Brq8AFXhHQmWRco5cvJU5f/KOAHa48n3oPRWO74RPL6s7Hr0HTPse2g0+s2M4h+T62LLkfAshhBANSSKmmbXmm42fth/jwR92A/Dj3YO4qF3jEvVvNqfxfwv3MrJLIJ/c0g+73U5xpRlvN5kcUwghxD/Xmq+PbY2ca3EuFFeY+XxjCp+sT6aoQkmyGF11TBsQyfRB7Qg21iVYSk2lbDi+gWUpy/gj9Q/sNVUCWpWWPkF9uK7zdVwUdBGZZZkYDUYivSKdckyiDcvcAz/dplSJ6D0hqBv0ux38O8LvD0P6ZiXp4Nse+t0BF82A/T/D+jegKL0uKaN1Uapvavl3gm5XQ/Y+yNihDAWm1sHU76Dj6Lp2NisUpyuT09cO5VXfmpdgzfPKZ1dfpVJlyINK1c/vj4CpTFmn1kLHsRAzWvlcmqVMYJ9bU/2i0UH0UOg0DkJ6K5VEJ85Pk38U9v4Im9+rS+yodUqiprKgLobwfnBkWcNtVWqY/D/oPhmK0mD+TXVJG1DO3eX/rft+8FeljcELbvwJIvr9/b9VC5LrY8uS8y2EEEI0JImYZtaabzYW7znOrG92AvDb/UOIDW0c359Hcrnp4y10DPRgxdxhvLw0gffWHuXr2wdwcQf/lg5ZCCFEG9Gar49tjZxrcS6VV1v4YVs6n25IITVfefJdq1YxoWcIt13Snh7hxgbtDxUc4n97/8eWrC0UVBU02eeNXW/kgb4PSLWMaF52u5J4cPVpOFeM3Q5lOcq8KRpd09taTDVDeKmUIcMO/qrMH9P/zoaJldIspT+vkDOPLeVPpbokqHvD5ElVCRQfU5IgnkFK/M2huhS2fQp75iuJJFD2EXkxXPku+EQpc+Xs+hpS/lIqW658D3pcU9eHpRqS1ymVN96R4NvEUNa5h5SYPRqPvuBscn1sWXK+hRBCiIYkEdPMWvPNxvL9Wdz55XYA1v5rOFF+7o3apOaXM+yVNbjo1Bx85lLGv/knCVmlPHxpZ+4dHtPSIQshhGgjWvP1sa2Rcy1agtVm54+D2Xz8VzJbkusSLH0ivZnaP5IJPUMaDFtmt9s5VnaMRYmL+OHwDxRUFeDv6k9eZR4A7bzacW/vexkbNRbNiXNYCCGaV14imMuVCh+da+P1NpuSiDF4tHxs55BcH1uWnG8hhBCioTO5NmpPuVa0enpt3ZNW7oam/zlDvV1Rq6DKbCOjqJLEHKUsPre0ukViFEIIIYQQrZ9GrWJct2DGdQtm77FiPv4ricV7MtmZVsTOtCL+8+sBrugTypR+kXQPM6JSqYjwjGBWn1nc2/teLDYLeo2edcfW8eSGJ0kpSeHhdQ/ztufbXBVzFWOixuCh90Cv0eOllz/gCdGs/P/mATu1us0lYYQQQgghzifqv29y7qxbt46JEycSGhqKSqXi559/brDebrfzxBNPEBISgqurK6NHj+bIkSMN2hQUFHDDDTfg5eWFt7c3t912G2VlZQ3a7NmzhyFDhuDi4kJERAQvv/zyuT60FmPQ1j1d6K5vOhGj06gJMSpPRa1KyMFiU4qgJBEjhBBCCCGa0iPcyBtT+rBh3kgevrQzUX5ulFZb+GpTGhPe/otJ7/zFN5vTKKu2AKBWqdFrlPkHh4YPZdGVi7i3970YDUbSS9N5a+dbTPx5IiO+H8Hgbwfz0NqHOF523JmHKIQQQgghhBAtxqmJmPLycnr16kV8fHyT619++WXeeust3n//fTZv3oy7uzvjxo2jqqpucsUbbriB/fv3s2LFChYvXsy6deu48847HetLSkoYO3YsUVFRbN++nVdeeYWnnnqKDz/88JwfX0uorYhRq8BFd/J/zkhfNwCW7styLJNEjBBCCNG6xcfHExsbS79+rWtyZHHhCPR04d7hMax+cDhf3z6ACT1D0GlU7DlWzP8t3Ev/5/7g0Z/2sDu9iPojHnvpvbin1z0sn7ycZy5+hn7B/VCr1KhQ5vVYlrKMST9P4uF1D7M0ealjODMhhBBCCCGEaItazRwxKpWKhQsXcuWVVwJKNUxoaCgPPvggDz30EADFxcUEBQXx2WefMWXKFA4ePEhsbCxbt27loosuAmDp0qVcdtllHDt2jNDQUN577z0ee+wxsrKy0OuVp/QeffRRfv75ZxISEk4rttY8Duq+jGImvP0XngYte58ed9J2D/+4m++3HUOjVmGtqYhpH+DOqgeHt1CkQggh2prWfH1sa+Rci9Ykv6yaBTsy+HZLGkl55Y7lnYI8GN01iNGxQfSJ8EZVfzL1ehIKEnhpy0tsy97WYHmQWxA9/HvQzb8b0cZoQtxDCHEPwdtw8r6EEBc2uT62LDnfQgghRENtYo6Y5ORksrKyGD16tGOZ0WhkwIABbNy4kSlTprBx40a8vb0dSRiA0aNHo1ar2bx5M1dddRUbN25k6NChjiQMwLhx43jppZcoLCzEx8en0b6rq6uprq6rFikpKTlHR3n2wn1ccdVp6BLiecp2tRUxtUkYkIoYIYQQQghx5vw8DNwxtD23D4lmS3IB325J47d9WRzOLuNwdhnvrjlKOz83JvcNZ0r/SAI8DQ227+LbhU/GfcLu3N2sTl/NumPrOFp0lOyKbLLTsvkj7Y8G7V00LvQM6Mmt3W/l4tCLJSkjhBBCCCGEOO+02kRMVpYyhFZQUFCD5UFBQY51WVlZBAYGNliv1Wrx9fVt0CY6OrpRH7XrmkrEvPDCCzz99NPNcyDnmLebnvWPjsRNrzllu4iaREx9pVUWqsxWXHSn3lYIIYQQQogTqVQqBrT3Y0B7P56uMLPmcA5/HMxh5cFsUvIreHXFYd5elciEniGM6x7MJTH+uBu0jm17B/amd2Bv5sTNodxczoH8A+zP28/+/P1klGWQWZ5JXmUeVdYqtmRtYUvWFiI8I+ji24WO3h3p4N2Bjj4difKKQq1y6ojLQghx3lm8eDEPPvggNpuNRx55hNtvv93ZIQkhhBBtWqtNxDjTvHnzmDt3ruN7SUkJERERTozo1Hzd9X/bpn4iRq9Vgx1MVhu5pdVNJmmEEEIIIYQ4XUY3HVf0DuOK3mGUV1tYui+LrzansjOtiAU7M1iwMwO9Rk3/aF9GdAnk0u7BhHm7OrZ317nTL7gf/YIbzodkspo4VnaMHw//yA+HfiC9NJ300nRWpK5wtPHQeRDrF0s3/2508emCl8ELV60rblo3PPWehHmESRWNEELUY7FYmDt3LqtXr8ZoNBIXF8dVV12Fn5+fs0MTQggh2qxWm4gJDg4GIDs7m5CQEMfy7Oxsevfu7WiTk5PTYDuLxUJBQYFj++DgYLKzsxu0qf1e2+ZEBoMBg8HQ5LrzVYRPXbKlc5AnBeUmMooqyS2rxs9Dz6qEHIZ1CsDTRefEKIUQQgghxPnO3aBlclw4k+PC2ZlWyKJdx1mVkENaQQV/JebxV2Ie/1l8gLgoHyb0DOHyHiEEerk02Zdeo6e9sT0P93uYu3rexb68fSQWJSqvQuW9zFzmqJhpSlffrszoPoOuvl0xaAzoNXpctC6469zP5WkQQohWa8uWLXTr1o2wsDAAxo8fz/Lly5k6daqTIxNCCCHarlabiImOjiY4OJiVK1c6Ei8lJSVs3ryZe+65B4BBgwZRVFTE9u3biYuLA2DVqlXYbDYGDBjgaPPYY49hNpvR6ZQkw4oVK+jcuXOTw5K1Vf4eelx1GirNVrqFepGQVaokYkqr2Xg0n1eWHWLWiBgeGtfZ2aEKIYQQQog2ok+kD30ifXhyYixJeeWsTshh+YFstqYUsD21kO2phTyz+AB9IrwZ1TWIEZ0D6Rri2WQFi9FgZHDYYAaHDXYss9gsHC06yr68fezL30dSURKVlkoqLBVUmCsorC7kYMFBHl73cKP+4oLi+M/F/yHCq/VWvgshzi/vvfce7733HikpKQB069aNJ554gvHjxzfbPtatW8crr7zC9u3byczMZOHChVx55ZWN2sXHx/PKK6+QlZVFr169ePvtt+nfvz8Ax48fdyRhAMLCwsjIyGi2GIUQQgjRmFMTMWVlZSQmJjq+Jycns2vXLnx9fYmMjGT27Nk8++yzdOzYkejoaP79738TGhrquMno2rUrl156KXfccQfvv/8+ZrOZWbNmMWXKFEJDQwGYNm0aTz/9NLfddhuPPPII+/bt48033+T11193xiE7jUqlItLXjUPZpcSGepFfbgIgt7SaPceKAEjKK3NihEIIIYQQoq1SqVR0CPCgQ4AHtw9pT3ZJFUv2ZLJ4z3F2pBU5Xq8sO0SI0YWB7f3oFupFn0gfeoYb0WmangNGq9bS2bcznX07M5nJjdYXVhXyXcJ3LDq6iBJTCSariWprNQDbs7cz+dfJXBVzFWEeYRgNRvQaPdHGaLr4djmn50MI0TaFh4fz4osv0rFjR+x2O59//jlXXHEFO3fupFu3bo3ar1+/nv79+zseGq114MAB/Pz8Gs2ZC1BeXk6vXr249dZbufrqq5uMY/78+cydO5f333+fAQMG8MYbbzBu3DgOHTrUaJ5dIYQQQrQMld1utztr52vWrGHEiBGNlk+fPp3PPvsMu93Ok08+yYcffkhRURGXXHIJ7777Lp06dXK0LSgoYNasWfz666+o1WomT57MW2+9hYeHh6PNnj17mDlzJlu3bsXf35/77ruPRx555LTjLCkpwWg0UlxcjJeX19kdtBN9uO4o32xO4+s7BhK/OpFvNqfxwKiO/LrnOEm55cRF+fDTPRc7O0whhBDnibZyfTwfyLkWbVlmcSWrE3JZlZDNX4l5VJltDda76TVc1M6Xge19Gdjejx5hJ0/MnA673c6xsmM8ueFJtmZtbbLNgJABjIgYwbHSY1RZq+jh34NeAb2I8opCq657lq3SUkl6aTquWld8XXxluDMhWtj5cH309fXllVde4bbbbmuw3Gaz0bdvXzp27Mh3332HRqMB4NChQwwbNoy5c+fy8MONq/nqU6lUTVbEDBgwgH79+vHOO+849hUREcF9993Ho48+yoYNG3jllVdYuHAhALNnz6Z///5MmzbtlPs7H863EEII0ZLO5Nro1ETM+aIt3my8vuIwb648wjVx4SzcmYHVZifcx5W/Hhnp7NCEEEKcJ9ri9bG1knMtLhRVZiubkwvYk17EnoxitqUUUFhhbtDG3ZGY8WNge196hBnR/oPEjM1u4/fk3zmYf5CsiizKTGVUWirZk7sHi93S5DZ6tZ5Ir0i0ai1VlirSStOw2ZXEkQoVU7tM5eF+D6NRa8784IUQZ6w1Xx+tVis//PAD06dPZ+fOncTGxjZqc/z4cYYOHcqAAQP48ssvSU5OZujQoUycOJH333//b/fRVCLGZDLh5ubGjz/+2GD59OnTKSoqYtGiRVgsFrp27cqaNWswGo3ExcWxYcMG/Pz8mtxPfHw88fHxWK1WDh8+3CrPtxBCCOEMZ3Iv0mrniBHnVoCnAYDNyflYbUouLqekGrvd3uSY3EIIIYQQQpxrLjoNwzoFMKxTAAA2m53DOaVsOprPxqR8NicXUFRhZu3hXNYezgXqEjO9I7zpHelNr3BvfN31f7svtUrN5e0v5/L2lzdYfrzsOF8e+JL00nRHBczu3N0cyD9ApaWSxKLEBu099Z6YrWaqrFV8k/ANhVWFdPPvxl8Zf9HJpxN397obT71nM50hIURrt3fvXgYNGkRVVRUeHh4sXLiwySQMQGhoKKtWrWLIkCFMmzaNjRs3Mnr0aN57771/vP+8vDysVmujYc2CgoJISEgAQKvV8uqrrzJixAhsNhsPP/zwSZMwADNnzmTmzJmOPzYJIYQQ4sxJIuYCVZuISS+odCwzWW0UVphP64erEEIIIYQQ55paraJLsBddgr24ZXA0NpudhKxSNiXls6kmMVNc2TAxAxDl50ZclA/92/nSL9qX9v7up/2wUahHKI/0bzyMsc1uI6M0g/SydOx2O2qVmhjvGALclKTR0pSlzPtzHr+n/M7vKb8DsClzE0uSltDZtzMH8w8S7B7MzN4zGRo+1BFPmamMCksFgW6td96GCnMFc9fOpaN3R+bGzZUHt4Q4hc6dO7Nr1y6Ki4v58ccfmT59OmvXrj1pMiYyMpIvv/ySYcOG0b59ez7++OMW+W9s0qRJTJo06ZzvRwghhBAKScRcoGoTMSfKLqmSRIwQQgjRStQfCkQIoSRmYkO9iA314tZLorHa7CRklbA1uYA9x4rZdayIpNxyUvMrSM2vYMGODAD8PfT0DPeme6gX3cKMdAv1Iszb9Yz+2KlWqYnwiiDCK6LJ9Ze2uxR3rTtPbniScM9whoUP4+fEn0kpSWHD8Q0AFFYXMmvVLLr6dmVgyECKTcX8lvQbVdYq+gf359pO19Leuz1q1Kw/vp6jRUe5vcftRHpFYrfbSS5OJsIrAp1a12QMzeVI4RHmrpnLlC5TuKHrDfyW/BvrM9azPmM9WrWWB/o+cE73L8T5TK/XExMTA0BcXBxbt27lzTff5IMPPmiyfXZ2NnfeeScTJ05k69atzJkzh7fffvsf79/f3x+NRkN2dnaj/QQHB//jfoUQQghxdiQRc4EK8Gg6EZNVUkXXEBnrVQghhGgNZCgQIU5No1bRLdRIt9C6/z6KK83sSi9ia3IBW5IL2HWsiLwyE6sScliVkONo5+2mo1uoF3GRPgyO8adPpA967ZnPNVPfkPAhrLpuleP7TbE3sSRpCSariS5+XViZtpKvD3zNwYKDHCw42GDbLVlb2JK1pVGfGzM38tGYj3hv93v8lvwbsX6xvDb8NcI8ws4q1lN5eevLpJSk8PbOt7miwxX8evRXx7r/7f0fQW5BTOky5ZztX4i2xGazUV1d3eS6vLw8Ro0aRdeuXfnhhx84fPgww4cPx2Aw8N///vcf7U+v1xMXF8fKlSsdc8TYbDZWrlzJrFmz/ulhCCGEEOIsSSLmAnViRYybXkOFyUpOSRVp+RXc8PEmbrk4mtsuiXZShEIIIYQQQpw5o6uuwTwzVWYr+48Xsy+jhH0Zxew/XsLh7FKKKsysT8xnfWI+b61KxE2vYUC0LwPb+9ErwpseYUbcDWf3c0mv0XNVx6sc33sF9OLm2JvZeHwj27O3AzCpwyRC3EP47tB3bDy+kePlx6kwV9AvuB8ZZRmklqRy5aIrsdqVyrgD+Qe4fvH13NLtFvoG9kWtUpNTkYOL1oVwj3D8XP1w1bqiU+tQqVSUmkr59/p/k1iUyKvDXqWzb2cArDYrvxz9hZVpKzEajIR7hnNVzFUkFSexKXMTAOXmct7Z9Q47cnagVqm5vvP1fJvwLf/d9l/GR4/HaJAEsRD1zZs3j/HjxxMZGUlpaSnffPMNa9asYdmyZY3a2mw2xo8fT1RUFPPnz0er1RIbG8uKFSsYOXIkYWFhzJkzp9F2ZWVlJCbWzVWVnJzMrl278PX1JTIyEoC5c+cyffp0LrroIvr3788bb7xBeXk5M2bMOHcHL4QQQohTUtntdruzg2jtap9CLS4uxsur7VSL9HhqGaVVFgBGdglkVUIOc8d0wk2v4dklB+kW6sWS+4c4OUohhBCtVVu9PrZGcq6FaF7VFiuHs8rYm1HMpqR81ifmkV9uatBGrYKOgZ70jvDmonY+xEX5EOnrhlZzdlUzp8Nut6NSqcgqz+KWpbeQUZaBp86TeQPm8fXBr9mfv/9v+wh1D2Vih4msSl/FkcIjABgNRl4f/jpZ5Vl8ceALEgoSGmzjqfPEaDByrOwY4R7hHCs75lh3cejFvD/6fa799VoOFR5iXv95TOs6rXkPXIgz1Nquj7fddhsrV64kMzMTo9FIz549eeSRRxgzZkyT7VesWMGQIUNwcXFpsHznzp0EBAQQHh7eaJs1a9YwYsSIRsunT5/OZ5995vj+zjvv8Morr5CVlUXv3r156623GDBgwFkdX2s730IIIYSzncm1URIxp6Gt3myMfHUNSbnleLlouWVwNG+tPMK0AZGYLDZ+3H4MD4OWvU+Nlck4hRBCNKmtXh9bIznXQpxbNpudhKxS1ifmsT21kN3HisgsrmrUTqtWEennxkVRPgxs78fA9n6Eerue09gyyzJZmLiQy6Ivo52xHdXWan46/BNbs7ayJ3cPWrWWALcAKi2VZJRlUG4ub9SHv6s/gW6BHMg/0GC5h86Dm2NvRqfRsTJ1Jfvy9wHgrnNnwaQFXPvrtZSYSgB4YcgLTGg/gW8OfsMLW16gk08nfpz4o/xWEE4l18eWJedbCCGEaOhMro0yNNkFLMDDQFJuOZ2CPAn2Up7AySmpIrtEGb+2rNpCfrkJ/5PMJyOEEEIIIURboFariA31IjbUiztqluWUVLH7WDE70grZklzAvoxiqi02knLLScot5/ttSrVIpK+bY0izQR2aPzET4hHCvb3vdXw3aAxM6zrtpNUoZpuZCnMF6zPWszBxITa7jWcHP4un3pN7V97LzpyddPTpyMiIkUzrOg1fF18Abul2Cx/v/ZhvEr7hvj73EeoRyrWdruXjfR/jpnVjVOQoAC5vfzmvbnuVw4WHOZB/gG7+3Zr1eEXzyCzLJL8qn+7+3Z0dihBCCCGEQBIxF7TaeWI6BnkS5KV8Pl5UxdHcMkeb1PxyScQIIYQQQogLTqCXC2NiXRgTGwQoVTPZpVUkZJayKTmfTUlKciatoIK0ggp+2K4kZqL83IgN8SLKz50eYUYGtPdt0ftpnVqH0WDksvaXcVn7yxqs+2TcJxRXF+Pn6tdoO61ay1297uKuXnc5lt3c7WYO5B9gROQIXLVKgsloMDKm3RiWJC1h/qH5POn7JBq15twelDgjVpuVGctmkFGWwfWdr+eBvg+wLWsbuZW5TO44Wf69hBBCCCGcQBIxF7BBHfz4bW8mIzoHEFRTEZOQVYKt3mB1KXkVxEX5OilCIYQQQgghWge1WkWI0ZUQoysjugQCSgX5tpQCNiUVsDEpn73HikjNryA1v6LBtp2CPBjU3o8e4d6EebsSE+jheCiqJWnV2iaTMCfj6+LLh2M/bLR8csfJLElawsLEhSxOWkyIewh+rn5EG6O5rfttRHhGsClzE1uztmI0GAlxD2Fo+FBctC5N7KXtMFvNaNQa1KpzP4/QqWzP3k5GWQYA8w/N54fDP2Cz2wClYuqGrjc4JS6b3UZWeRahHqFO2b8QQgghhDNJIuYCdsOAKK7uE46rXkNuqTIcme2EGYNS8xuPMS2EEEIIIYQAD4OW4Z0DGd5ZScyUVpnZnlqoDF+WV8a2lEISsko5nF3G4ewyINWxbUygkpwZ1MGPXhHeeLlocddrUatb/5wrFwVdxNiosaxJX4PJZiKtNI200jR25uzkl8RfiPKK4mjx0QbbBLoFMqXzFI4UHmFv3l4Ghg7ktu63sTlzM/MPzSfWL5aZvWcS4BZwzuO32qz8d9t/CXIL4pbut5zRtna7ncSiRKKN0WjVdT+nDxce5oYlNzA+ejzPDH6mWeJMLk7mf3v/x02xN9HFt8tpb7ckeQkAfQP7klKSQkFVAW5aNyosFXyb8C1Tu0wlsSiRbw5+w9QuU+ns2/mM4vrf3v+RX5nPnLg56DX6095uU+Ym7l5xN5e1v4wXh7x4RvsUQgghhDjfSSLmAueqV8rS/dz1aNQqrDWZGLVKScqkFlRgtdl5eWkCnYM9ubpvuDPDFUIIIYQQotXydNHVJGbqluWXVbMluYBNSfkczS3nWGEFqQUVJOaUkZhTxpeb6pIzrjoNl3T0Z3TXQEZ0CSTQs3VWkKhUKl4d/ipWm5XM8kyyK7LJq8xjUeIi/sz4k6PFR3HRuDAmagwWu4WdOTvJKs/irZ1vOfr48fCP/Hj4R8f3gwUH+S35N67rdB3jo8cT5RVFUXURBo0Bf1d/VKrmS1AtT13OVwe/AiDYPZhLoy9tsL6oqoglyUvIrchlUodJtPdu71j3+o7X+XTfp4S6h3Jzt5u5rtN16DQ6vk34liprFQsTFzKlyxRi/WLPKsb00nRuW3YbuZW5HMg/wI8TfzytIcVMVhMrUlYAMKvPLDr5dCK9NJ12Xu0Y++NYUktSWZayjDd3vElGWQbLU5fz4ZgP0al1rEpfxbh242hvbH/S/nMrcnlzx5uOGF8b/tppJ2O+TfgWO3a8Dd6n1V4IIYQQoi1R2e12+983u7CVlJRgNBopLi7Gy8vL2eGcM4NeWElmcRUAA6J92ZxcQK8Ib+aO6cT0T7bgqtOw96mxaDXOLbUXQgjROlwo18fWQM61EG1LUYWJTUlKcmbD0TyO5pY7Hoiqr3uYF+Hebvh76hkQ7cfQTgEYXXVOiPj0bcncwsGCg0xoP8ExDJrJauKnIz+xOm01XXy70M2/G98c/IYdOTvwc/FjSpcp/HnsT/bk7WmyT1etK5GekUR6RRLpGUmUVxSRXsq7r4svpaZSKi2V+Lv6N6hSSS9J5+2dbzM0YigT2k8AlIqWa3+9lkOFhwDw0Hnww8QfCPcMJ68yj7d3vs2vR3/FbDMDoELFmKgxPDbwMQqrCpn8y2SsdqtjH5M7TubR/o8y4vsRlJmVuTaHhg8lflT8Sc/R9uztFFYVMipyVJMJpuzybKYvne4YXgzgmYuf4aqOVwHKEF8LjiwgxD2EwWGDG2y7MnUls9fMJtAtkBXXrGgwTNorW1/hiwNfoFVpsdgtjuVatRaLTfnu6+LLl+O/JNIrssnYFyctZt6f8xzfh0cM543hb/xtkiijLIPxP43Hjp1frvyFaGP0KdufLrk+tiw530IIIURDZ3JtlIoY4RDk5eJIxIzrFszm5AJS88v583AuAJVmKwlZpXQPMzozTCGEEEIIIc5r3m56Lu0ezKXdgwElOVBtsZGYU8bKgzmsTMhmz7Fi9mWUsC+jBICvNqWhUauI8nOjvb8HHQLc6RDgQa8IbzoFeTRrxcjZ6B/Sn/4h/Rss02v0TO0ylaldpjqWjY0aS3JxMqEeobhoXbir512sTl/NkqQlrDu2jiprFQaNAbPNTKWlkkOFhxzJk/pUqLCjJLG0Ki3hnuFM6zqNS0Iv4dblt5JVnsXvKb+zK2cXj/R7hI2ZGzlUeAg3rRsdvDuwN28vN/9+M518O7EzeycVFmV+n66+XQl0C2TtsbUsT13OkaIj+Bh8sNqtDAsfxqDQQby45UUWHFmA0WCkzFyGr4svxdXFrDu2jj25e+gZ0BO73c7CxIXkVuTS3b87S1OW8nPizwC8MvSVRtU4ZpuZB9c+SEZZBpGekYyKGsWn+z7lnZ3vcGn0pbhqXfnh0A88u/lZAEZGjKSDdwdWpq3Earc65oK5LPqyRnPVTO0yla8OfoXFbkGFivhR8Xyy7xO2ZW9Dq9Li4+JDbmUud624iy8v+xJ/V/9G53tz5mYA+gX3Y0/uHtakr+G35N+Y2GHiKf938f2h77FjZ1DIoGZLwgghhBBCnE+kIuY0XChPfdz15TaW7c8G4K9HRnDJS6sBCDW6cLwmQfP0pG5Mv7ids0IUQgjRilwo18fWQM61EBee7JIqtqUUkl9eTVp+BWsO55KYU9ZkW38PPb0jfOgU5EGnIE86BXnSPsAdF93fD2XVGpmtZix2C65aV8xWM8fKjpFWkkZqSaoyH02JMifN8bLjjiSMRqVpUKlS+93f1Z/8ynzs2Al0C0Sv1nOs7Bi3dLtFSQ4tmUpBVYFju+5+3Xm4/8P0CewDQEJBAvetuo+s8iwAdGodi65YRIRXBP9a+y+Wpix1bHtXz7vIrsjm58Sf6ezTmTdGvMF3Cd/x+YHPmzxOf1d/Fl25CC993f+vv7H9DT7e9zGeOk/mT5xPkFsQk36eREZZBjfH3sydPe9kwsIJFFUXnfIcfj/he7r6dW20/ME1D7I8dTm3dr+VOXFzqLZW8+exP+kZ0BO1Ss1Nv93EsbJjxAXF8cm4Txokc+x2O+N+GkdmeSbvjX6PhIIE3tzxJmEeYfx65a/oNE1Xa1VZqhjz4xiKqot4c8SbjIwcecrYz4RcH1uWnG8hhBCioTO5Nkoi5jRcKDcbTyzaxxcbU/H3MLDt8dEMeP4PskuqG7SZ1CuUt6b2cVKEQgghWpML5frYGsi5FkIAZBVXkZhTxtHcMpJyyziSU8bOtCIqzdZGbdUqaOfnTsea5Ey3UC8GtffH6Na6hzY7EyariaLqIowGIzq1jpyKHFanr+adne9QYioh0jOSTy/9lISCBB7/63EKqwsBJZmydPJSAt0CKTWVsi9vHxllGfi6+DI8YnijSpLMskzuWHEHqSWp3NHjDu7vez8Ax0qPMennSY5hzH676jc0ag3X/HoNpabSBkN+DQ0fytGio/gYfJgdN5tnNz1LSkkK49uNJ9o7mqNFRzFbzaxKXwXAq8NeZWy7sQAsS1nGQ2sfAiDKK4rUklRivGN4/pLn+WjvR9jtdsZEjcFD78GO7B0EuwczpcuUJs9ZmamMPbl7GBg6sNFxAqSWpHLtr9dSaankiUFPcG2nax3r0krSuHzh5WjVWtZPWQ/AZQsuI78qn0f7P4qfix+r01dzqOAQVdYq7u51N1d0uII3d7zJx/s+JsQ9hN+v/v205ro5XXJ9bFlyvoUQQoiGJBHTzC6Um4341Ym8suwQQzr68+VtA7jug41sSVaeDnPRqaky2wjzdmX9o833BJMQQojz14VyfWwN5FwLIU6m2mJld3oxB44XczinjCPZpRzOLqO40tyorVoFXUO8CPZyIcDTQPua4c1iAj0I93FDo24dw5udrcKqQlamrWR4xHDH8FrV1mrWHVvHyrSVDAwZyJUxV55Rn6WmUvbm7m2UwHh126t8tv8z+gb25fPxSuXLsdJjPPrno+zO3Q3QKKEBylw6ty2/rcl9XdfpOv496N8Nln114Cte2vqS4/tHYz9iYMjAMzqG01W7Lw+dB4uuXESgWyCgDC/2n03/IS4ojs8u/QyAbw5+wwtbXjhpXzHeMSQWJQLw+IDHub7L9c0aq1wfW5acbyGEEKIhmSNG/COXdg/mj4PZ3DQwCoB2fm6ORMzU/pF8viGFjKJKsoqr+GpTKiarjXnju7Sa8aiFEEIIIYS40Bi0GvpH+9I/2texzG63k1NazeGapMzhrFK2pxWSmFPG/uMl7D9e0qgfvVZNe393OgR60DPMSO8Ib3qEG3HTn38/GX1cfLim0zUNlhk0BsZEjWFM1Jh/1Ken3pOLwy5utHxWn1kEuwczNHyoY1m4ZzifXfoZPyf+TIh7CIPDBjfarn9If2Z0n8HylOX08O9Bd//uGDQG/F39GRExolH7G2NvxFPvybObnmV89PhzloQBZS6Z35J/Y2/eXu79417u63MfQ8OHOuaHGRAywNH2mk7X8MWBLxwVRZM7TqZ3YG/25e3j/d3vk1iUiFat5bEBjzX6NxFCCCGEuJBIRcxpuFCf+qitkAGYf+dAnll8gP3HSxgTG8SKA8pcMu9M68OEnqHODFMIIYSTXKjXR2eQcy2EaA4ZRZUcOF5CXlk1mcVVHM0t42hOGUl55ZgstkbtNWoVXYI9CfN2xdNFR5i3CzFBnrTzcyPIywV/D0ObqaI5X1Rbq9Gr9ef8YbjDhYe5+febKTeXAzjm67HYLXwx/gvHHDqgDN12pOgIA0MGotfoHcv/PPYnPyf+zI2xNzZo35zk+tiy5HwLIYQQDUlFjGgW7fzcAXDXa+gT6UNclA/7j5c4kjAALy1NYExsEAbt+TkRqBBCCNGaxcfHEx8fj9XaeP4HIYQ4U2HeroR5uzZabrXZySis5GhuGYeyS9mdXsSOtEKyS6pPWkED4OmiZUC0Hxd38OPiGD86BXqilsTMOWXQGFpkP518OvHrlb/y5cEv+f7Q946ETKBrIN39uzdoG+IRQohHSKM+hoQPYUj4kBaJVwghhBCitZOKmNNwoT71UVxpZvonWxgTG8TMETEs2pXBA9/tAiA2xIv88mqyS6p57LKu3DG0vXODFUII0eIu1OujM8i5FkI4Q2ZxJbvTi8krq6akykxafgWHs0s5XlRFblk1VlvDn5I6jQq1SoWrXkOnQE9iQ70YGxvEgPZ+UjlzHqu2VpNdnk1eZR6RXpGOeXdaA7k+tiw530IIIURDUhEjmoXRVcfPM+vGM76onS8qFahVKl6+picHjpfw8E97eOOPwwDcMDDyvBxDWgghhBBCCNFYiNGVEGPjChoAi9XGwcxSNhzNY/3RfLYmF1BptgJ2qi02tqQUsCWlgM82pODlosXfw4C7QUuQlwvhPq50DvYkNsSLUG9XfNx0aDXqlj04cdoMGgORXpFEekU6OxQhhBBCiPOWVMScBnnqo87y/Vm46bVc0tEfq83OlA83sjWlEAC1SpnkM8Toyqe39KOdv7uToxVCCHEuyfWx5ci5FkK0diaLjdyyagCKK8wcyi5h09EClh3IoqjCfMptVSoI9nIhys+NXuHeDOsUQO9Ib3nIS/wtuT62LDnfQgghRENncm2URMxpkJuNkzNbbSzckcE7qxNJK6hwLB/eOYBPb+mHSqWiymzFRSdzyAghRFsj18eWI+daCHG+MlttHM4upazKQmmVhcySKtLyy0nIKuVgZin55dWc7Bepr7ueUG8Xwrxd6RzkybjuwcSGeJ3zierF+UOujy1LzrcQQgjRkAxNJlqMTqPmun4RXBMXTm5ZNccKK5ny4UbWHMpl8Z5Mlu3PYum+LF69rhdX9A5zdrhCCCGEEEKIFqTTqOkWajzpeovVRkGFifSCSpJyy9iUVMC6I7nkllZTUG6ioNzEvowSlu3P5q1Vifh7GAg2GvB21aPTqPBy1TG0YwCjuwZhdNO14JEJIYQQQghx+iQRI5qFWq0iyMuFIC8XbrukPe+vPcp93+50rP/P4gOM7BKIp4uO3NJq/D308iSbEEIIIYQQFzitRk2gpwuBni7ERflw7UURABRXmskorOR4USXHCivYlFTA6kM55JVVk1czBFqtRbuOA+DposXbTeeosOkZbmR4p0B6RXjTzt8Ng1aq9IUQQgghhHNIIkY0u/tGxrBw5zGyS6rxddfjqtOQUVRJ/OqjVJgsfLExldFdA3lnWl8ZskwIIYQQQgjRiNFVh9FVR2yoMsTDLYOjKa+2kJhTRkG5iaJKE2arnfSCCpbvz+ZQdimlNcOf1TpWWMlve7MA0KhVRPm6ERPoQUygBx2DPNCo1eSWVuOiUxMb4kXnYE/HvDQF5SYyiyuJ8HXDy0UqbYQQQgghxNmROWJOg4yDeuZ2pxexcGcGt10SzcHMEu78cnujNkM7BTBjcDvS8ivw89DTPdRIlJ+bVMoIIcR5Qq6PLUfOtRBCnFpxhZncsmqKK82oVVBtsbHxaD5/JeZxOKuU0mrL3/ahUkG0vztatYrD2WWO5YGeBi7u4MfA9n4EeBpw1WkoN1mpMFkI8nIh2t+dIC+Xc3l44iTk+tiy5HwLIYQQDZ3JtVESMadBbjbOjt1uZ8qHm9icXICLTs09w2J4f+1RKs3WRm0jfF25Ni6C9gHupOZX4OWq47Luwfh5GJwQuRBCiFOR62PLkXMthBD/nN1uJ7ukmsScMo7klJKYU0ZiThl2lCRLaZWFA5kl5JY2HPLM201HUYX5tPbRJ9Kb+0bG0D/aD61aRbXZRpnJgp+7XkYBOIfk+tiy5HwLIYQQDUkippnJzcbZO15Uyf/+TObqvmF0DzOyJbmAp37Zj9VmJ8LXjdzSKg5mlWKy2Bptq1WrHEMS6DVqInzdiPB1I9LXjVCjC1qNGp1GRbBRGVtao5aKGiGEaAlyfWw5cq6FEOLcyymt4mBmKdVmK3FRPvh5GCitMrMvo4S/EnPZnV5MaZWZCpMVd4MWF52arOIq0gsrsdqa/lntqtMwsmsgA6N9MbrpMWjVWKx2NGoVAZ4GQr1dCPZykVEB/iG5PrYsOd9CCCFEQ5KIaWZys9EyKk1Wlu7PZMGODMqrLbTzcycxt4w9x4pPuw+1Ctz1Wlz1Gtz0Gtz0WjwMWjxc6t49DcpnV70GF13tS42Ltt7nmnfDCct0GvU5PANCCHF+ketjy5FzLYQQrVdOaRUf/5nM15vTKKs3BJpOo8Js/fuf214uWjoFeRLi7Yqfux6dRoVapUKlUqFRg4dB55gzx9tNR5SfG2HerpK8Qa6PLU3OtxBCCNGQJGKamdxsOFdiTilHc8vRaVRUmKykF1SSVlBBekEF2SVVWG12qi02skuqsJzkSbTmolGrcNGq0WnV6DVqdBo1eq1SkaO81yyrt7zh95qXVoVBo0arUaNRq9CqVXXvGnXD72oVWrUajRo06ibWaVRNLtc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      "text/plain": [
       "<Figure size 2000x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "itsx = xstep.getitstat()\n",
    "itsd = dstep.getitstat()\n",
    "fig = plot.figure(figsize=(20, 5))\n",
    "plot.subplot(1, 3, 1)\n",
    "plot.plot(itsx.ObjFun, xlbl='Iterations', ylbl='Functional', fig=fig)\n",
    "plot.subplot(1, 3, 2)\n",
    "plot.plot(np.vstack((itsx.PrimalRsdl, itsx.DualRsdl, itsd.PrimalRsdl,\n",
    "          itsd.DualRsdl)).T, ptyp='semilogy', xlbl='Iterations',\n",
    "          ylbl='Residual', lgnd=['X Primal', 'X Dual', 'D Primal', 'D Dual'],\n",
    "          fig=fig)\n",
    "plot.subplot(1, 3, 3)\n",
    "plot.plot(np.vstack((itsx.Rho, itsd.Rho)).T,  xlbl='Iterations',\n",
    "          ylbl='Penalty Parameter', ptyp='semilogy', lgnd=['Rho', 'Sigma'],\n",
    "          fig=fig)\n",
    "fig.show()"
   ]
  }
 ],
 "metadata": {
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.13.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}