{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "85e4180c-38f6-4210-b25c-e833649493fe",
   "metadata": {},
   "source": [
    "## Initialize "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6837026f-a1fb-410e-aeae-b89990e70ce3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import necessary modules\n",
    "# uncomment to get plots displayed in notebook\n",
    "%matplotlib inline\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from classy_sz import Class as Class_sz \n",
    "import math\n",
    "\n",
    "font = {'size'   : 16, 'family':'STIXGeneral'}\n",
    "axislabelfontsize='large'\n",
    "matplotlib.rc('font', **font)\n",
    "plt.rcParams[\"figure.figsize\"] = [8.0,8.0]\n",
    "\n",
    "\n",
    "plt.rcParams.update({\n",
    "    \"text.usetex\": True,\n",
    "    \"font.family\": \"sans-serif\",\n",
    "    \"font.sans-serif\": [\"Helvetica\"]})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73b539fc-4bdf-4231-9839-3fc5a501a4dd",
   "metadata": {},
   "source": [
    "## Maniyar et al Model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c4d0248-d0cc-406a-b985-55a4e9d37db2",
   "metadata": {},
   "source": [
    "### Compute"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0fc9626e-c36b-42d9-9919-71096db565ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "# a simple conversion from cl's to dl's\n",
    "def cl_to_dl(lp):\n",
    "    return lp*(lp+1.)/2./np.pi\n",
    "\n",
    "\n",
    "Omegam0 = 0.3075\n",
    "H0 = 67.74\n",
    "Omegab = 0.0486\n",
    "Omegac = 0.2589\n",
    "Omegac + Omegab\n",
    "omega_b = Omegab*(H0/100.)**2\n",
    "omega_c = Omegac*(H0/100.)**2\n",
    "hparam = H0/100.\n",
    "maniyar_cosmo = {\n",
    "'omega_b': omega_b,\n",
    "'omega_cdm':  omega_c,\n",
    "'h': H0/100.,\n",
    "'ln10^{10}A_s': 3.048,\n",
    "'n_s': 0.9665,\n",
    "\n",
    "'m_ncdm': 0.0,\n",
    "'cosmo_model': 1, # set to 1 for mnu-LCDM emulators and set mnu to 0. \n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "502138cf-f074-4984-bb75-232efbfb8abc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 105 µs, sys: 155 µs, total: 260 µs\n",
      "Wall time: 701 µs\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "class_sz = Class_sz()\n",
    "class_sz.set({'output': 'lens_cib_1h,lens_cib_2h'})\n",
    "class_sz.set(maniyar_cosmo)\n",
    "class_sz.set({\n",
    "\n",
    "'mass_function' : 'T08M200c',\n",
    "'use_maniyar_cib_model':1,\n",
    "\n",
    "'maniyar_cib_etamax' : 5.12572945e-01,\n",
    "\n",
    "'maniyar_cib_zc' : 1.5,\n",
    "'maniyar_cib_tau' : 8.25475287e-01,\n",
    "'maniyar_cib_fsub' : 0.134*np.log(10.),\n",
    "'Most_efficient_halo_mass_in_Msun' : 5.34372069e+12,\n",
    "'Size_of_halo_masses_sourcing_CIB_emission' :  1.5583436676980493,\n",
    "#for the Lsat tabulation:\n",
    "'freq_min': 9e1,\n",
    "'freq_max': 8.57e2,\n",
    "'dlogfreq' : 0.1,\n",
    "\n",
    "'concentration_parameter':'fixed', # this sets it to 5\n",
    "\n",
    "'n_z_L_sat' :100,\n",
    "'n_m_L_sat' :100,\n",
    "'n_nu_L_sat':100,\n",
    "\n",
    "'use_nc_1_for_all_halos_cib_HOD': 1,\n",
    "\n",
    "'sub_halo_mass_function' : 'TW10',#'JvdB14',\n",
    "'M_min_subhalo_in_Msun' : 1e5, # 1e5 see https://github.com/abhimaniyar/halomodel_cib_tsz_cibxtsz/blob/master/Cell_cib.py\n",
    "'use_redshift_dependent_M_min': 0,\n",
    "'M_min' : 1e8*hparam,\n",
    "'M_max' : 1e15*hparam,\n",
    "'z_min' : 0.012,\n",
    "'z_max' : 10.,\n",
    "'ell_min': 10.,\n",
    "'ell_max':5e4,\n",
    "'dlogell':0.3,\n",
    "\n",
    "\n",
    "'ndim_redshifts': 210,\n",
    "'ndim_masses':150,\n",
    "\n",
    "'has_cib_flux_cut': 0,\n",
    "'hm_consistency':0,\n",
    "\n",
    "'epsabs_L_sat': 1e-40,\n",
    "'epsrel_L_sat': 1e-9,\n",
    "    \n",
    "'damping_1h_term':0,\n",
    "\n",
    "})\n",
    "\n",
    "class_sz.set({\n",
    "       'cib_frequency_list_num' : 6,\n",
    "       'cib_frequency_list_in_GHz' : '100,143,217,353,545,857',\n",
    "      })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "557eb61b-edb1-4f8b-8b99-0366c5ee0ffb",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/boris/Work/CLASS-SZ/SO-SZ/mcfit/mcfit/mcfit.py:130: UserWarning: use backend='jax' if desired\n",
      "  warnings.warn(\"use backend='jax' if desired\")\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 2min 22s, sys: 4.81 s, total: 2min 27s\n",
      "Wall time: 22.4 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "class_sz.compute_class_szfast()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "20bf4042-fddc-4245-9a89-7b335e3dab80",
   "metadata": {},
   "outputs": [],
   "source": [
    "Dl_lens_cib = class_sz.cl_lens_cib()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a419855-7303-4537-90c3-f3f85f942b16",
   "metadata": {},
   "source": [
    "### Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ef2f13b2-f11c-41f8-9945-92f3d3b4311a",
   "metadata": {},
   "outputs": [],
   "source": [
    "nu_list = class_sz.pars['cib_frequency_list_in_GHz'].split(',')\n",
    "# nu_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "536b5660-d4e0-42a2-92e1-6589c2c9165a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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JGC0QkwW+yT5Cc0KE5oUI14QpqynDYMygnWx2goEg3Z2XSDTsgB9vwKuU1RIwTYzw5I2Phl1ZWamiYfck56mrtYvQ2RCGNgPivMDisWB2mfEd9SEOCwxnDIg2gcFt6DN7Dws4i7oA3oZi+i9OxpmKj5ZIu8RYbST0ryG6Lu8asJ2yfd9LJxq2boadAmvWrAFg8+bNuFwuGhoaqK2tZcWKFXERrl0uF2vXrqWurg6Hw8H27dtZu3btkP2VBzLDzleU3lT0U5nTFFL00MT0xsZGVq5cCUBLS0u/nWUhBNu3b2fRokU4HA6cTicOhwMhBKtXr2bTpk2sWrUqamKfKj/Apz71KX784x/3y5+sjL/85S9ZsWIFAI8//jgPP/xwXP7m5mZWrlyJ0+nka1/7GuvXr4/SSdxZTmU+lbizXEjRQ9NN1xYDLoTooVp63qNhI5RZVSTwiOgSSHfEPM2LMt0KRky5AjL6f7/0UIr0WDOxIIhgjElYYv6gVKu+kUsYIyvH2mp6YnpMGkYQhpj8selGocziEvJjgF5jL5ZxFkSVUIFMRoEYJZCVUk3+McPQcJmNav2r0KL0psvTxSBnuZDJQpvPRmoOBT0aNujRsGMX5DOWs3YBu0DsEcj3JOxG/d8xuPmvNEdMnGcD1wFmEDMFcr6EyajfBuG1kORpOMfyXKTnTc56Zd8u9HmUFcF56D3SS/GpYjij/hcdAumRCF//viKbJNw88DNHQs7SNcO+aJXlkYamLCc2gJSpfT5SIZN70s2bTTmGA0uXLqWpqSkuraWlJenixZo1a9i4cSNbtmzpZzav8RTbvZPl1+pBM9cYSBxcLhcVFRUAbN++PamptZavpqYGl8tFR0dHVGCbmppYunRpWmbYGzdu5Gc/+xkf/ehH894++egLuaKZLZ2cypmPfpE1pVsSaA9g9poRXaJ/5M3Id+mRSLdEdAmEWyQ3r9KhoomOIqpEJ/1MljZES8vhHAd1Ocv8ngt9PhtuFGo96HI2NDp5kTMPsAfYBfI9SXhXGMMeA+JMCvpGkGMk0iwRDQJhESoS9OXAYmAO6v9LALqcDfIsH9BKVKnmPLAMZZ6dLc1clS0BqXS1RFz0ZtgjDW2FT4MQIurvkZieSlHTzDESzTsHWmFJN29svnRpD5auRb7Nhk5DQwMLFiwA1C6u0+kE+iuxQoioUn3LLbdEf9doL1++nMbGxqhSO1D+4uLipPkTn/lv//ZvAKxatYoFCxakbD+73c7jjz/O9u3bcTqdUX6S7YYkqxct3Wzum3kqKiqSHnOm4dChQ9EFhWzqPZM+mW66NlANdXUzWzoD8iRR5kQRnypxXMARKD5eDMdBtsuoPxDBfuQRCIoY/IgDEfmLhUSCiOzCTpfqBeMEfbu5FoFcrJ7PQaI7s8IRST8NOIkeRSHGC+RCCYeA40TPgxTTBPIKCe+iJi3t7MjLBXKGhFdRk1ggstu8SMJ44NcopT8U2Z1ukEoxfZL44CR3SwgDT6m0aECUT0q10vwblUYYRLFAfkDCe8D2SFlQ+QmjgpycjlwZQFpV2aI71KNQPm2jUH5uUyXUoHzf7JFnJumnUXp53qUYqpzlUj6GRc6S5Neepd2TSd6BaGuRoAthV2okd8LyMZbngkYhytlwyMdwy1lsWnFxMXhB7pWI3QK5S/btFB+L2RFFYNQGZSK+wxUCWSOhHrUjeBdQDAahdiwlw9fnR1KehnMsz0X6SMpZNG8R6l1ifAIN2Z/GSMtZKr0rEbqynGck81kOhUJ85jOfSctnORgMYrPZktIuVJw4cYLKysqsfMIefPDBaOc9cOAATqcTKZP79zqdzqgZjOYLqfl7jB+vpPRPf/oT06dPp7y8vF9+k8lEaWkp7e3tcflnz56d1DeisbERgC984Qtx5UnG07333ssDDzyA2WzG7XbHLWBoL3SD+Sx3dHRETUkefvhhzpw5E80rhMBisbB+/XpA+eNoPGXj53b+/HnMZnP0BTUXPster5fq6mqkHJrPciAQYPTo0ZnxJIppf7edotNFGE8aEceF+n7CSPhIGMNxA8KfYK452JESmnmxGZgI/jF+TGdNasW9TGKymwjVh/CN8WE4ZkA6JLJSIsYIDLMM9Fp7kWVSHeFgjvc16sdTCl+j7q4U8uRJ0U5uD/LeFO10Ux9vcf5TdyW0UyDSTh9PaCdfpJ2UJ0Jf39N4+nYfT1arldbWVkxBE8YzRsR5QZGrCIvNQteCLsSvBEW/KkKcFxg7jIgZgt6rezH9yoTxgFHVeSSaaTgQRrQJhBQqWMlJ4GTy9otNCzvChKeGMU03EZ4cxjfBR2hKiJ6xPRTPKMY+Ov8+y2fPno0eHRPXTkPwWe7t7aW6unrIPsvBYJCqqqq88+RyueIi7w52tGFra2s070A+y0IIjEbjJe+znOuxPBc8afOBzWYbFp/lM2fOxO0q5cJn2efzMXbs2CH7LIfDYSorK3PPkx8MhwzYjtmQ70mCO4MY9hgwHjWqRUn6j5Hh6jCGbgPBu4N0XtaJpddCYGkA4xwjZbaE+ckH5rA5usAVe2TQxSpPwzmW54InTc7sdrvyWc7juCeE4MyZM3HRsIfKU7bve8l8lqWUVFRU6D7LhY5c+Sz39PRQVlaW9/LmEp2dnZSWlg55dWjlypU0NjYm9Vl2u91UVFRQW1vLoUOH+tHYuHEjDz74YNSvOFV+UO3xi1/8gs9+9rPR/MnKaDCo0H6xg0kmPGk+yw6HI85vPhHNzc04nc5BfZa1+vn+97/P6tWrMypLIi4on2UJuIBj9B0/cRS1q3pEXeJcGv5VRFZA7ZFd2OslvfRimWRBTBUwBkS1UGcUltG3GyouXF/KVOn5fiZk2cdcwCnUzvg5lP/bPAlPAK+hdqLPgfiEQM6S8BDQDRSDMAvkWAlnQJwfZCHEIBETBbJWqp1obTe6BkSdUJFYE0iMhJylJR9ZlDFbOtnwpPssF6Cc5ZknrX/pPss5kDOEmuua6dst3gXsBxFMPs5JpIozMUrCDJTZ9APAZReGnBXSXKnhkvJZTrMOhkp7JORMPzqqQKC9DGjQVuUS07W8iSgpKaGzs7PfQJYKscIyUN5082UDzQwbkvOUy3QtqFdi3tjnx/6emD+2PZLdq0Ezga6trU1aFs3POBGx/snafS6XK7pLPRBSlQXUWeCNjY2sXr06Gswu8d5UNBORaZ9MN31IfUCidgy3Q3FzMZwlairNUZRCpOVPsSMsrVIpvFKoswSnAnOB9wGTQUwU8T5WEnxudRxEHB/Jdixj6isZz6nyZ1QHw5yeT9pZ97EK1DU3Jh2hdrMfTsiPgBtR5vXngRMgPiDU2ZnzgcMoU/cq4APqf7lbQisqIElk8YU/JilLiYhToKlVl6iJpJfmsQ4GScvVWJsNnWx5Srwnk7zp5E+n7PlOH4ln5msszwWNdNt7qOmZ1kE+5WPIdCRwBOR2ifV1K2K3QDQL5Q+q5Y+dn2yRqxLkpyW9p3spnlKMuEHATBCmC1fOCmmuzNdYnov0kZazkZiHhkIn1e+J0JXlPEMzBxgsTYjUKyYlJSUEAoF+Smiq1Z508kqpzO5i86VTlnTTNUHK1QpWYrq2YuRyueJ+02hoadrRRanya7QT8yeWxW63A0TNwhOxcOHCfrvFmo907KofkHY0bK1ciWVpbGxk7dq11NfX8/3vf3/IK5aJdZAtndj0WHqD0gijfHN3gHhHIHdI9b1V+ftaU0RxkmaJKBfIayRMitCZAywAZoDL5MLusDNglGUZn55YD8l4zSRv2nUwwun5fibkvo8lTR+HuhLz7gcCgBPEAYG8U8JeYDXI7kj0zlkgvy7hdaAZ6EEp0qeF+r47ciXjbYxUCvQ0ENMFcpqE6ajLnrs6SEzLSM7yQCdbnmLvySRvqvyFJmcjJduQPznLxXyQy/eCXNRBvuUjIzphEIcFcptU40/kEh1qHow9pkciVfyJoFBWL7OAO4GvouJPmNRzet29WOwWEBe2nBXSXKkh12N5LtJHWs6GSnsk5rNUvCVCV5bzjGQ+y+FwmE9/+tMZnbMcCASifrmD+RHE5oXU5yxrPtGFes6y5hMjZXL/XofDQVtbW/Q3Ifp8IzT/3rFjx9LZ2Ul5eXm//JrPssFg4OzZs9H8Xq83KU81NTUcPnyYXbt2MWnSpDieVq9ezUc+8pEoT263m6lTpwJkfc5yKBTqd87ykSNHWLlyJXa7nZdeeilnfm5aebQ+kwuf5UAgEOU12vf8YNxnxHbIRnh7GLldYnzXqHxPI4g7p69cEp4SxjjdSODKAP46P+GJYcITwxQ5Uvv3FhcXIzsy5ylWdgbzNYrNG9v3EoPrWa1W/ZzliF/TiJ+zPE5gnxXxw57QjXxGEvAHKO4qpry7HP9MPwF7AIvTguG8AcNZA3wQev+5F/m/qq8iwOA3YOowIY9K9TJ7TihT8Tcjz4n1lR4dVkr0TEHRhCJ6pvUQrgsTrg1TOmboPsta3Q3VZ1lrl0x990KhUFy7ptNOsbIzWN+LzTuQz7J2lvelIE/DOZbngidtPuju7h4Wn+VgMBhXB7nwWdZ8G4fqs2wwGCAMPe/0QDMYdxox7jRietekjhRMDAhpklAMvR/oRY6XmP9iJnhzkKJbijAsNOAOxPDkiT9nWZOddM5ZjpWzVDyZzWasViter/eS91nOZtzLN0+anHm93mHxWU5H18iEp1gld6g+yyaTUm91n+UCRyqf5UtplXuo6QP5LAshqKurw+l0xgm3RkO7d9u2bdHo1pnmTyyjtuP7ta99jXXr1g1Y9tgzlTdv3twvLZ2d5c2bN7N8+fJousvlYtGiRTidTrZt28bChQsLop1SpneBeFcgt0vYgVop361WvhMhDRJGg5glkEulOmbgMtAW0wuGpwIrS67SC6ksuUrPCW0fiE6BrJIquvdvgH3qEl8SyH+Q8AgqqnoZagenVyAPSzgI4uzAJl5yguzbiZ5B3250HSqCuN5OF2R6IZUlV+mFVJZcpQ/LM4NSjRnb6dstfkdAV7/sYELFyAigfn8SWA7iVwJ5jyRmc3lkeboY20nn6ZLkSfdZLhAI0d9nubOzM7oyk5g3FZLdkyx/KvqJeaVUgRqSlWOgshRS+vLly1m/fj2//OUv485ZFkIdE+VwOFi4cOGA+bX6SpY/8Znr169ny5YtPPHEE3zkIx/pd85ybP4HH3ywX3o67R3bX2JXEIUQrFy5EqfTyZYtW6LlzEU9ZtMn+6WfR5lO7xCwA2VK1hLJm+jza0IdKbAE+BCwAMQMER2NYvMPVLZc8wTpy1m6eS8kOcsn7Zz0sSzSB8sbW66k+YvVJRDwYdQFynw/EMlfjzr6ai/qaKzvgbhLwKeBMajjrMwgOySB/QHMh82IgwLaQZwUKpL3HxOeK4ApIKYLpTzHKNJiqoj62mcrH4npIy1nQ5XJQpSzkXhmvuUsWxqDylkOy5hpHeRUPgKocUBTjLdHFGNvkhuKUS5DHtSxfybgahDXCrgWNT/aI8//WOT5xenzlKzMF7KcFdJcmSs5y3UZC0HORmIeGgqdgWQiFrqyPAJIFk05l/ekmzebchQS1qxZw/r161m7dm2csrx+/XpcLhcPP/xwWvm//e1vJ82fCIfDwbp161izZg0LFy5kw4YNcVGoQUWyXrVqVfR86KEgdkVszZo1NDU18fDDD8eVPVdIuy9I1C7aDvp2i99GBVWKQdSUuliqIEjLgSuAhahAW+mNT5mVLQf36XKWPxRqPWRVLgHRY7bvjVwaJH3+8zuB3wN7ACd4/V7M/25WinQNSuk9hvLZ164DqHOuj0SurQnPNqFkaIa6TDUmJVdzAUfmrGjQ5eziQKHWw3CWKx/P6kfTj4pCHVGKaUbJuy/xTsAKTAQ5VdJzdw8lfy5BTBLwb8Ar9AUxNCa5N9XzsylzDvIWav8abhRqPVx0cjbCdDToyrKOCxa1tbWsW7eOtWvXUldXR0NDA06nk6amJurr6/uZSmeaPxk05XjNmjXRq76+nsrKSrZt24bL5aK2tpaWlhZWrFiREz6bmpriIm1rZyvHoqGhod9Od04gUS/wfwBeilztKfLagFuARSBnSjzzPJRPK89IMdah44KHiFxfiUnzoRTjAygl+/9QL9Zm1PFX7wDPoc6yvgI1Mx+K5D+YcHkjvx0C8XtBCSV9zxmPeumeE/M5B/UyrkOHjuzQC8btRiWPWvCt91A7yYmwAdNQbhk9qCj8XcBoYBkEPhJQi2XaSQw35LvwOnToGCp0ZTnPkHLo0bC1/LG/p8qfjH6yvNr/I+0vkE56snJqeb/2ta9RU1PDxo0b2bx5M5WVlVGf4mR1kCz/F7/4Rb797W+nVb9CCFatWsUtt9zCunXreOmll2hubsbhcFBbW8vKlSujO9SPPPIIHR0dKes6FU+x+aWU0WOrILmiDPD444+zYMGCQcueVp85CrwM4g8C+bJEnEpigjNNwnXAWJSCvASEXcSVPewOJ+UzkzIm1kfWPKX53HRkJ5O8F4qcDZcMD+dz08kb2455rYPILnR4bBj5LamUaYkyxUYge6RSmH+MeiG/B8QWgeyVarfpXmA26uzok7JvB3o/BHcGMR00IU4IdSb1KfrtRstxsk9xngPiMoGcLaGSuHrQvqfLa+K96dZN7D25kMlCk7ORnivzUZ6h0Bg2OYsg3TpISqObvlgbmmK8G2whW//nVEhYCGKuQDolrAfagHuAa0BcI9RpDfWAJVIut1S+yXnm6WKSs0KaKzUMVc7yUcaRlrOh0s52HhoKnVS89btHpptTR0bQAnxNmzYt7WjYXV1dSSPUadFDjUYjQvRFw3a73XENrdnnt7e3R/NC6mjYpaWlSCmTRsP2+XxJo+55vd6kEep6enqSRqhLxZPH40kadS8ZT5lGEtSibqbDU2lpabSMWn0VAk9SquibJpMJh8OREU+ZtlNxezE9z/Zg+pMJ0x9NGE/E24LJIkm4NkywPojxfiOmG024ewfmSSt/ZWUlUsqs20kb9O12e8roiEl5Ki6O1rvWroO1k8vlIhgMRmVnoHYKhUK4XK5o3lQ8CaGOfguFQkmjYV9s8pSKJ6vVSmdnJ6FQKNoehcCT1k/NZjN2uz2v415RUREdHR3R/pKSp26wBW0YJhvw/peXop8WYdxjhF5gPYS/EMa72QulELw8SKhcyVmwLUjvjl6M+4wY9hsw7TNhOmCC46REeGyY0MwQoVkh5CxJ8cJivDVe/LYc8xTTTi6Xi1AoFJWdgdrJ7/fj8XiieVP1PaPRSHFxMcFg8JKQp1yOe8PBkyZnFosFm82W13HPZDLR3t6OwWCI1kFKnoSdcHMY/5t+jO8YMb5rxHDAgAj3XyAOV4URiwShmSGChiCiXWDcacT/LT8lN5QQ/HyQrke6IBIjqMjSnycpZbRdU0UkHjJPMdGwNTnTomGnaqfe3l66urqicpaq75nNZoqKiqLRs4fSTheCPOVy3BsOnjQ5s1qtlJaW5nXcS1fXGI73vUSepJTR/OlEwz506NCgAb50ZTlP0KNh6zxlmj5sz2wF/gi8DDSBOJQQxAMJ40G8TyDvk2rn2JqETiHxNIzphVSWXKUXUllylV5IZRlyehg4jIqQPUHC51Bm20dRO85PCuQiqRTjiaC5Pggh1E7WXtQZ0XvUJfYI5SudAnKsTL4TXZV7XguifnOcXkhlyVV6IZUlq3S3iN8tbgZxIIWP0DiQ9VLtCNcDIeAtEH+M0JgK3AjcBNwOorJweC24etd50nkq4PR0o2HrynKeoCnLiQ0gpYomWF5ejhDpOXNmck+6ebMpx8WIQq2HnJbLBbyK8jt+EfXCHPssJHKMRHxcIG4Ryry6v8VZRshV+bOlo8tZYaFQ62E4y5WPZ8lzkp6tPZQsK0EUC6XMjkJF070O+Cx9gcgS0UlUiZa7JcF3g5gOmBBHByjbGGBW5JqtYhN0TujENteGMOpyNtIo1HoYbjnrbOnE1mJTpzNowbdSxdychFKIF0Y+5wMTgKdRC0qfB/l1SeBIAPMyM+Jmoe7Jsmz6fHbho1Dr4YKfz0bgvTGVrpYI3Wd5BJDN+kQm96SbV18nUSjUesi6XN3Aa6id45dRLwuJpOYAU4CPAHeCx6AGjFwG48pVvWZLR5ezwkKh1sNwlivnzxoNgTsC6ogZgVoYexsl/38Gvhj5/g8o5fla4CrUYpgNdTzNEkBCt7tbjQFdqHNhtZ1o7fMIcC5yvaoeLxCUU44sljCTPkVau2ZAbPwxDbqc5Q+FWg95KVcrqn8mXOWtKV56a+jbLV6IOrrJDryJWkxej+r/21ELQxr+BXrcPTmZI/X5bPghpaQ70E27t522njbave3qu7fve2tPK+d7zuMP+QmFQwTCAUwGE8FQEF/Ih0QSCoeQUhKWYYIh5bJVZCzCF/QRkiHMRjO+oA9/yI9E5dPySyRGYcRkMNEb7EUIgVEY6Q32YjFaKCkqocRcQpm5DJvFRrmlHLvFjs1io9RcSmlR6YCfJWZ1fzAQxCZTH5mU63otVJq5LpuuLOvQcaGjF3iDqFk1b6HMNmNRDiwGVqDOio19EZCAGx06dFzosALvi1wa6lBHt72GCiBWhxon/ohSfK9FRdHWYEONFYsTaGtKdMwl90k4CKJXqOjeO5OUaQrxCvRMEOOFGpMKZ1NGRyGjjf5K8R5U/02AQCCFhDoQC0W8YhwJaIcH+A5KOX4dpTDfBHws8glqUUlHQUFKSU+gJ07JTab8tvW0ca7nHK09rXT6Omn3thMIJwtdfnFCICgtKsVqsmIQBqwmKyVFShEvs5QxyjqKcWXjGFM6hlEloxhlHRX3WWmtpMScZJXzEoauLOcZUurRsPORnivaMPxRelOlx6bFtnlSGodAPC2Qz0h4HYQ/we+4VKoX5o+iJv8JCc9MIJfrOoilN5R6yZZOtjylIzuZ5L1Q5Czfz4RLPBp2BEOtg6zko1rCGtQF0BNRKFok/BdKwZ0M5kfNsAoVbbsaMCTQKZVK6VgY/wxXqwt7hx2xX0SVaLFfIPdKRLtQftVHgRcitBDYsSPtEjlLImYJ5EzZp0zXAeYLW85GSrbhAo+G3Sb7rBkiCrHYLeBsUlYV7aky/ri0ueAa58I+LrILHAT8IEoF8m4J/wzUArtALBfI70b6XswUKsjNPDQS81mi3Gi/52Luy6ec+UN+jrqO4nQ5aWlvoaWjhSOuI5zvPk9rTyut3lZcvS6C4WA/WunCbDBTaa0kFA5hs9hwFDuotFYypnQM1aXVTCifwOiS0QghEAjMRnP0u0EY4r73dPdQVlaGQRjUbwiMhkgw35j8sb9r/wtUsEKtn/UEeugOdNMd6KYn0IOn14Pb58YX9NEb6uW457jakQ70csR1hJ5ADz3BHnqDvfiCPnwhH4FQgJAMqfZB0uXvosvflXVdmQwmbEU27MV2RllHqfqxT1AKdUSpLg4XM6lqElUlVYyyjqKypBKRZAU03/IxFDqp5K3fPTLdnDoyQq6jYQeDwWgkxMEi1HV0dMRFTUwVDdtmsxEKhS75aNha2bX6KgSepJSEw2GMRqOKhu0L0PtqL+bfmzE/Y8Z4KD5idbg6THB+EHmlxHK/Be+4zCKoamXX6mCoPGnlr6ioQMqhRcOOjdiZT560KL2a7AwWDdvtdkfzpuJJCEFZWRmBQOCSj4adGHW+EHjS+qnJZBqWaNiJkYqHypP2UlBRUUEwGMyunTxg3GbEMNFAyeIS5BUSjkNoSYjgVUH4FFhrsuOJVjAcMGA8aKT4SDHigCC8J4zhaPKIwwDSJAnXhDHOMRKaHsI7wYucLJGTJUyG8jHJo2GXlJTg9/svCXkazrE8FzxpclZUVKSiYZ/2EtwZxLjPiHGfEdN+E8Z9RjhDSoQnhQnNUpHbjVcYMc834xnvIVySEDnaYMLzqgfzn8yYXzNjetOE/EeJ+JKg52c9BK8MIkfLjHjS+rcWoT3bdpKyL0pvptGwE6MvpxMNOxwOYzAY0oqG3d3dHZ3PBoqGXVxcTG9vb1bRsN0+N4ddhznVe4pjncfYe3YvR1xHOOw+zInOEyq4aBooMhZRZCiiqqSKieUT6fR1UmoqjSq+NaNqGGsdy2jLaEZZR1FRXMHostGMcYwZUpRljad8jOUw9DEiEArQ7e8mZAghzZJWTyser0cp14Ee/JG/Ex0nONN1ht5gLye7TtLa00qQIKc8p3D73Gm3QzKUmEuwmW1Ul1YztnQsJeYSJldM5lPzPsXUkqkD8pTt+16yaNjFxcUUFxfr0bALHQNFw46dyGLTB1v1i71noPyJ9AdarUyGQtrxSpWeS9rZtMdw8CR7JOJloXaQfy0RbX1llEhEnUAul/AJlL+gSE17sPR81IE28A21XrKlky85y1QmU+FiladCkbNM+qk2gRZ6HeRSPlLS8Uu12/waykR1PSra7/slXIOKjn89UDIEOfNKRIvo24XeJ/t2pbsHsc0eC3KKUpyZjDLzjr0c2ddBrtJHUrbzJWcZ0ZAgOgXyuIQTqEBZWiT23UKdBZ4CcpKM2ykWlwnkLNkv6GS/5/4R2ADiJYH0SRWt+kbgJuAKEMah8XShzGexabFzVK7eMVNBCEEwFORk50la2luiO8ROlxNnh5NDbYdw+VwD0jAZTMyumo3NYsNWZGPu6LnUOGqY4pjCBNsEqkqqoibCwzVmD+dYnov0oc5nUko6/Z209bRFzdpbe1pp87bR1tPGMc8x3L1uugPdONudtPe20+Xvwh/y96MZi+c/9jzL6pYNWpbhljM9GvYIY6Bo2G63G7vd3k/QUiGTe9LNm005LkYUXD2cB54F+VsJL4DwxpTJBNwB3AvcTr+XwmyRjzrIFc1s6ehylhpSSkIy1C/4iJQy+pmYpuVN9ftg94TDYXw9PsZXjafcUo7FZBnpagCGt30uaDnrBn6JCuzVBHiB08pku+tQF2XXlyFMOZAzCZykzyd6ryR4IIjpVCRKd3cSgokoJ7kSrV1jiTMxv5gwLP1ZonyITyRcJxP+H8wCdCJRs+noNZvoOcUDogcVOO4vKN/jnwG/B94C2SBxz3Rjr7oA5SyH9+VrPjvdepq2cBvODqUEt3S0RD8Pdxwe1DfYZDBht9gZZxvHtIppzK+ez9K6pdRV1DGmdEzBzJMDoRDndRjZ+cwf8kf9xTXFus3bxrmuc7T3tvO5xZ+jpqJmWMqfCR09GrYOHRcKDgC/BX6Nisopifp9SLtEXCPgQeA2Uh8Do+OCgZTKR0kLStLubafT3xn1PfKH/NGImgP+H04zX5L/h2JmlQuYDebo7oHNYqOsqCz63VZki/+e4rOsqCz63Ww0jyg/Fz1Kgfsjl0QFVjIAb0LZZ8rUm8TNwMeBu4bwHIFSoiYCDcRH6QZoR+1SHo2/5FGJPCIxtBlU8KZdkSsZilBH/yRTpCdHfrtUx9kwqm0TFeHEy5eKQAIqgIkgJ0p8U31YFlgQlwm1a2zPoFxBVJT3F4GtwDaUyXYtKi4HwPsjl0QPWJkD9AZ72X1uN++efZd3z77LzrM72du6lzNdA9jKRzDFPoUZo2bQ2tPKRy77CDWOGqpKqlg8fjFllrJhKL2O4UaRsYjqsmqqy6pHuih5ga4s69Ax3AihVsR/C/wGpSzHYjTIuyVd93VRdl3ZRbsLcqFDSonH56GjtyNO8Y292rxtnPOcozPYGV1tvRgicxqEgbCMD7luNpgxGUz4Qj7CMhxd8Ckxl2A2mnH39vlCBcKBaB3lAhajpZ8y7Sh2MNk+mamOqdFrin3KBbN7UbAQqN1ZgOXgvsmN/ZBd7Ti3RdK/hjrHeSlKia7I0XNHRa4FCb9J8Lg92E12xHGRVKHmKGr30w+0RK5UsKJ2OId6WSBJvJv8IYTadQ2jFFof6rSExO8u+u8Ea7vD6cZPGkPfwsaEmO+xaaWRvBJ63b1Y7JbM6mMj8BzqWCcTygXgk6idZEckz50Z0NPRD1JKjruP88bhN2jpaokqxwfaDkQDRiXCUewgLMOUFZUxqXwSM0bNYH71fK6ddC0Lxy3EZNRVCx0XF/QerUPHcKAH9TKpKcixOoIZddbpjcBq1O6GhJA7NLwvWpc4QuEQxzzHONd2jvM95+MV394EJTii9KZ6mUgHZoM5GinTbFSK5rTKaYwuGc3u87sRCEwGE2ajmSXjl1BuKWfb6W2YDWYsRgtFxiJurrkZIQTvnn0Xi9FCsamYEnMJN069kZ5AD0fdR7GarFjNVhwWB1dUX4E34MXtc2MUxuixEhPtE2nraaOjt0NF7URgL7Yz3jaeM11ncPvcyuJBCEaXjmZM6RgOdxym098XaGpi+URGlYxi7/m99AZ7o4pxXUUd5ZZyXtj7Ar2GXs51n+Nc9zkaahrwBr088foTuHpdeHweOv2drK5fzRHXEX74zg+jCnmxqZj6cfWc6TzDyc6ThGU4akoO4Av58PX4aO1pHbTerSYrUxxTmOqYymT7ZKot1cyqnkVNRQ1THVMZWzpWV6YzgQm4kvijdm4Hfgd8A1iJMpG9FbVIOC+PZSlFmfLOTvF7AKUQHiW5Qn0MpVB6I9cAUZjTgpn0lGpJvDKb+JlmmggKHEP1zxHAOPorv7HXeNRCQC4RRi0MfxOYijrubBeqb/0daoFEXzgeEnoCPew+t5udZ3dGleJ3z75LR29HyntKzCVcM/Ea7rv8PqwmKzNtM5k/Zb4+Ruq4pKD7LOcJA9nBJwZSSAeZ3JNu3mzKcTEib/VwDngGpRy/gNrR0GBCTf4PoMzIkpikDWf75ONZuaKZLZ1k94VlmBOeExxsO8ih9kMcbD/IwfaDHGg9QEtHS1Y7vtqZhlMdUwmEAvhDfmwWGxXFFcyums286nl4A17KLGoVfqpjKhNsEygrKruk5C/TdtTM1V29LroD3cwYNYOTnpO8cvQVXL0uXL0uyorK+Ksr/opf7f0VP3rnR7i8SumePXo2y+cs58l3nmTXuV2YDCb8IT/dgcGdXi1GS1SZnmqf2vc9clWXVWMQ2b21XypyFofT9CmEMwEXyBskokHA5xnU5HlY5zOJWsj0RK7OmO+ZXJ2JhEcAAihGKbXap/a9jNSKcDVKyc8x+rWPDxVAbmvkqgP+D2VqPZr+1gNDeVYOUPBylpD3mPsY75x5h/fOvRc1oz7YdjCpC47JYKKqpIqeQA91FXUsHr+YW6fdSkNNA+XFQ39/vRhRqPWgvzdmRiddn2VdWc4TUkXDBuKOp9EwWNQ97QiAwSIbJqOfKlJcbNS8xGeOVITB4Y4kCJm3x4Dp+4CnI9frxJ05J6ulWi2/FcStAmkaOIpl7LEP+azfnNdBTPm1Y9OyLWM2dKSUnOo8xf7W/RzqOMSh9kNRxfhQ+yF8oYEd7srMZTiKHVw58UpMBhOuXhdV1irGlI1hsn0y86vnU2YuY1TxKEaXjaa0qHTAslwIcpbvZ0Lu+1g6PJ30nOS9s++pBZH2A1w76VoWj1/MPZvvYff53TiKHRQbixlVMop2bzsnPIMfX1JkLIqad0+xT4n7rK2oZbxtfMp7h1oHuZCPXNLJmCcE4V1h5IsSw3YD/BTEGwL5Pal8lJeidi3JbO4rNDkTUiA7ZT8lWnQKpDtJulEgi2Q/xVZYI+kJyq4oTkiP+T1kDmGwGhBmwVBOSMjlGBGdz1oNGH5uQG6VKlicHdXmDSCWCuTY4R1r8i0fwyFnnb5Odp3bpXaJz/XtFnt8nuTPiJz3+4UlX2B82Xi6Al08eu2jmIQJo9FYkHJWSHOlhlyP5blIH+n3xqHSHgk506NhjzAGOmc5GAyyatWqjM5Z9ng8lJWpnajBzlk+efJkNC+kPmcZiJ5bpuFSPGf53LlzFBUVResrU55KTpZQ9KsiQj8OYWxJEM6x4Gvw4fu0j/D8MIj0z1nu6urCZrOpc5bzfDbn6dOnsVqt0TrIxTnLXV1dTJgwASmHds6y1+tl3LhxcefuSSlpD7Rz0nuS3ad3s791P06Xk4MdBzniPkJvqO9M42QoNZdSZa1iSvkUPjDzA1xWfRljTWOpEBXYy1UExcHOWT59+nRUzlLxpA3K2tmUQ2mnC0GeBjpn+ezZs1gslmgfG0meenp72HN6Dy0dLew5t4c7Z97J4prFXPHfV7Dr/C7MRjNV1io+MO0D9IR7ePP4m7h6XbR6WwdVpkvMJdTYa5hqn0qNvYZaRy2zxsxi1phZGLuM2Mps0TrIxTnL3d3dTJgwIftzliN0fD4fY8eOxev1ZnTe6KlTpygpKUmbJ5fLRVdXV1R27OftyB9Jwi+GMe4w4v+Yn97v9mJ32fGX+TnbczaaNxVPRqORUCiExWK5JORpOMfyIfPU6sX6NSvn//E8FZ4KSv9fKf4b/fRe30t4lpoTc9lOJpOJkydPUlpaGq2DofIkpaSnp4fx48cP+Zxlv9/PmDFjMj5n+eTJk/iNfnae28mOczvY3b6bd8+9S0t7S/LdYmGKnkowumQ086rnce3Ea7ly7JVcPvpySs2lcWfanj9/PipnA52zHAgEop9DaacLQZ5yOe4NB0/ae1dlZSWlpaV5HffS1TVy8b6XaTtJKQkGg1RVVennLBc6BtpZdrlc/UKaD7RiEg6H+4VBH2i1J5F+qhVCj8dDeXl5QazEJ0t3uVysXr2adevWUVNTM2De2PR169ZRW1vL8uXL++V3uVysXbuWl156CafTSW1tLZdddhnf+ta3qKurS7uMrl0uHvviY7z0l5dwep24cFFLLfXU88i0R6j/Uj3cBWJSH52mpiaWLVvG8uXL2bJlS8qVxjVr1rBp0yZ+9KMfcf/99w/LCmGmfTKdFU63243D4Uj6vHRpe3o9vO58nTP+M9Gd4f1t+znUfoieQE+/+zUYhZEycxkVJRXUOGqYO3ouN069kfnV85lsn4zJEB+uIRM5yyTvhSBnw/FMyH0fywVPWj+12+1ROfMFfTg7nBxsP8gtNbfgC/lYuWUlB9sPctxzHFuRjV+t/BU7zuzg6QNPqxfpQA+tPa1Rf+pUMBvM1FbUUldRR11lHdMqpzG9cjq1FbVMdUylyNhnk5wOT7mSs2zpwNDns7i8HUArMB3EaoF8UhK8Kojxw0bEvQLGXxhyNpI7XvmSs7TyBlEnOrwA4gWB/FcJDSD/TeL5mIfyKeUFNZ/lWz6GQqfD20Hz6Wa2nd7G9lPbefvk2xz1HE16X5GxiNlVs1lau5RXjr7C31z1N3xo1od4veV1ltQsobSoNCfvmCMlZ4U0V2oYqpzlo4zJ5rNc0k9EOrpGJukjIWfp7izrAb7yDO1lQIOUfSYsyQabgWgk3pMsfyr6g9FOlp4q/3Clb9q0icbGRtatW5d2GdevX88jjzzC6tWrWbFiRdxvbreb2tpaXC5XVJl2Op08/fTTPP3007S0tFBbW5ua/jmgETZ9axNrnGuiybXUUmupxWlw0uhtpPFQI6vfXc2Gz22Io5NOe8e2R+L3ZMhFerZ9crD0TMselmH2t+7njeNv8OaJN3nt2Gvsbd074E5ejaOGcks5ldZK5oyew5IJS1gyYQm1jlp6unoyOq8vEznLJG9s/mTpqfIPd/qF2McGS0+3n8b21WJzMXPGzGHOmDkAlFHGS594CQBvwMtR91FmVc1ikn0SB9oOcLD9IGe7zzLKOopDXzzET3f+lF/v+zWl5lLChHH3ujnqPsph12H8IT/72/azv21/vzIZhIEp9ilKia6YxrTKaVGFurailhJzSVKecjVGZEMnV/NZNG9l5ALYBDwKgV8GML1gUkcOjQceA3GDUIGfjMlpp1P2fKePxDPzLWdJ854A8ZxQcTmaUP7OtwIPgVgkVFCurwPu3PXVgdIzrYN8yke6dFy9LppPN7P91PaoctzSkTxce1lRGWEZpjfYy1THVJZMWMJ1k67j9um3U1vR9/4ipaS+uj6qKA9WxkKfzwpprsyVnOWjjEnH1Tw8NxtdI5304Z7PUv2eCF1ZHgGk2zjZ3pNu3mzKMVxobGxk7dq1Gd3T3Nw84D1r167F5XLx8MMPs27dOkAJ/He+8x2+9KUvsWbNGrZu3Rp/kwt1/vH/AU3QGG5kDUpRXudYx8P/8DDchwpIEinDihUr2LhxIw6HI/qcbDCc7ZOPZw1G09Xr4q2Tb/HG8Td448QbvHH8DTz+/n5WZeYyrpp4FVUlVVRaK1k8fjFXTryS2opaLKbkIVm1gTzXZc4mbyHL2XCiUOshk3JZzVZmVc0CYGbVTDbdtSn6WyAUwGw0c9nYy9h1fhe7zqlrWd0yXvj4C3zv7e/x9vG3GW0bjclgwhv0csR1hEPth2jpaKEn0MNh12EOuw7TRFO/Z0+wTeivSFdMY7xlPPaMDq0dej0M9b6076mBwOoA1q9aQaBOFdiHiphsAO4ANgBFhdu/hhvDUg/7gHdRkc6/iwrUdRvwKCmjVl/s81m66PR3suPIjrhd44PtB5Pmra2opbqsmp5AD/94zT9yPnCe457jXDf5Oq6aeBVlRQOfWazPZ/lDodaDLme5pROlJ1Ptr+sYEtKNsKYjHkuXLqWpKf5FMXHHNxUqKiqAPvPtDRs2xP1eV1dHe3s7HR0dSe91uVzKNKMbFcX656gjTyKuHS5cVEQODN3+7Hbq31+ftBwul4uamhpcLhcdHR1RU5CmpiaWLl0aNcNOhTVr1rBx40a2bNkSZ0p+ISMsw+w5v4c3T7ypdo5Pvsme83v65Ss2FjOveh7VpdXcMeMO7pxxJ+Ns40agxDp05AaaEv3bfb/luUPPRZXof7rpn/jilV/k717+O0wGExNtE7GarXgDXo64j0QD0x1qP6SO7koBgaC2opY5o+fEXbOqZg36Mn3BIgi8AfwZeAR4Fvgv4C7UubsTR65oFx0ksB+1c/w28BNUvb8A/HPk98LUG0Ycnb5OdpzZwbZT29h+ejvbTm3jQNuBpHnHlo6lorgCX8jHyc6TjC0dy5EvHeFs11lOd52mflzy9w0dOnRkj3R1NX1neZghpXI8N5lM6a18SImMBIJI55506WdcjkxQUgJZ0ly6dCn19WpSaGxsxOl0pnXfihUrcLlcbNiwgTVr1iTNU19fT2VlZVyaVg9R3IeKZB17wkylSn/M9xhsgtWrV6dUlIHojvL27dtxOp1RfjJFMBiM7pBqynwqpLugkAz56Aut3a28fux13jr9Fn85+RfeOP5G0mN7bEU2Zo6ayU01N3H3rLtZOH4hZmPfuSVSSgKBQMZly4anTO4pCDm7gFCo9TAc5dL6810z7+L9de/HZFLTbjCsxp1xZeN469RbPL3/afac38NL97/EA/UP8JmnP8PKuSu5bPRlTCyfSE+gh5aOFlo6WqJK9L7WfXT0dkTTf3fgd3HPnmKf0k+Jnl01G3tx/E50tvUwYnJmAq6PXIC8XBK+IYzhKQPi8wIeAr6F8n8exSWjzOWsP3uAl1AK8fPAGeBGlHl1ELguckFadTuc8p+PZ6VDs8vfxTtn3olTjPe37k/qRjTBNoH6cfXUj6vnZ+/9DGeHkxJzCUsmKpPq66dcz8xRMxFCMM42juqy6oznQX0+yx8KtR4uBTkbqbLpyvIIoLu7G7s9TbO5nh6EzZb2sYeC9I5ITDdfVujqgtLSrG59+OGHo9+dTmdayvLGjRtpbGxkw4YNLFq0KGW+fru5QeAPsPHvNuJyuWigAZ6K/DYBeB/wMDBfJTXWNQKkZR6+evXqQfMMhthIgI8++ihtbW398qxfvx6g3yJApsioTyYgGA6y69wutWt84g3+fOzPSf2tDMLAxPKJLBm/hOVzlnNTzU2MKR2Tt7Jlc18m96Sbdyh1ezGhUOthOMulPUsIEVWiP7fkc3yOzwEQCocA8AV9TLFP4a2Tb/HDHT+kpaMF11oXY8vG8qdjf2LJhCV8esGnuWz0ZbS52jjhO8He1r3sOb+HPa172HN+D+e6z3HUfZSj7qM8d+i5uHJMsE3op0BPtExk6tipWfOUr3vSyjsJOtd0Yn/Yrs5JdkXSP4oyGf5A5LoJdczSRYys+/O7QAVq7puOWiS+Dfg+ai4sGaFyFcizYmm6el28c+Ydmk83R6/9bfuTBvebYJvArKpZ2Cw23jf5fXxgyge45+l7+MKSL7C0bikLqhewaPwiJpRPyDlP+nyWPxRqPVxMclYIdDToyrKOCxpOp5M1a9awfPlyVq9eTXNz88A3hIE3oOmJJtb9fh3OgBMnThpoYEvxFlgG/C2whH4r5prinu0OroampqZ+wcdikYyH2EUEDRqNDRs2pIz6lw+c6z7Hmyfe5M0Tb/Ln43/mrZNv0Rvsf1STrcjG1ROv5u7Zd3PVxKuYO2ZuvyjUOnTo6IPRoCJWWQ1WvnHjN6LpvqAPi8mCoceA3WLnuUPP8cTrT1BiLuGNj71Bh+ygyFjEZxd/ltlVszEajLT2tLL3fESBjlGiT3We4mTnSU52nmSrMz5GQ3VZtVKgq+J3o0eXjh7WehgSRkUuUObZr6CshR5EudZcHfm8lWisiUsS54EXgVLgQ8B3UGddfwTYS1+gtUsc57rP0XyqmdePvM7ejr00n2nG2ZF8EX+8bTz14+qZYJtAWIY51XmKbae2RRe4aipqqCqp4q3PvBWNt/HBWR8cTnZ06NCRBfQ310JHSQmys7NfWP9UiA0dP5g5Tbo0synzcGHFihU4HA42bdqUOpME3kEF6fopcEr5HzsjfwDMh/bN7TimO5KS0EygUynKmp9xIpL5J7tcLhobGwfganCsX7+exsZGVq9enZNd7IHgDXh55egrPHfwOZ49+GzSXWOzwcyNU29k7ui5XD3xam6uuRlTwJSf/qVDxyUG7cV6imMK65b2BQ3s9ncT6Alwvvs8T+16ii+/8GWMBiNrr13L317/t1SXVXPZmMuosFZE73H1upIq0cfcxzjTdYYzXWd4+fDLcc+vKqmKKtGzR89mdtVs5oyew7iyAo8nUAQsjVzfiaSdjXz/JHAVSlH8Che/qXYI+AvwHMq0uhllNfVg5Pf/icl7CSrKUkpOdp6M2y1uPt3Myc6TSfNPsU+JmlJPLJ+IL+hjzaI1/Our/8r619dz7aRruX7y9ay9di2LJyym2FQcfe+KPSJOhw4dhQ9dWR4BGAxJQkWmghBQWoohHFamzYMpHlKmlzfdfAWMtWvX0tzczNatW5PvrO5HmVU/BcTG1DDC8iXLWf7l5cgPSn7yfz/hE5/4BAuXLEwa/AuI0k9lFr5w4UIaGhri0hIDlWlIN8BXKiVTixReX1/fL4hZtkjsk4faD/Hcwef4/cHf8/KRl/GH/HG/T6+cTnVZNUtrl/KhWR9i7pi5GEQfDSll3MH0uSxbPu/L5J5082Zb/osNhVoPw1muXD+rxFxCp6FTBcObeSfBcJD3zr4X3aH+xh+/wf/t+j9mVs3k6olX89CVDzGveh5XTbyKqyddHaUjpeRU2ylO9MaYc0euw67DtPa08urRV3n16Ktxz7cV2ZhROYPLxl4WNeeePXo2NY6aaBmGWg85kzNtOK0G3gJOoXae90d+exq1C30Xyh83dfELGnH10I5SfN8E3o/i81bgiygrqrEjVK4RfpaUEmeHM6oQ7zijolOf7znfL69AMH3UdC6vupwlk5awoHoBJoOJ5tPNvH7idR657hHePfsu/9OsVhu+fPWXeeS6R1L2f30+uzhQqPVQSHI2kjRzXTZdWR5mCCEyjo6dyT3p5s2mHIWE5uZm1q9fz8MPPxyvpGonD/0KiN3oLUb5Xa1EBfGyqmSB4P777+fPf/5z1Pc5VQTq2traqB914g5z4g6vy+WKRufOFiUlJf0UZqfTGd1Nf+mll4ZEX4MQArPVzAstL/Dcwed4+sDTHHEdictjNVm5csKV3D/vfj48+8M4ih2D0sxF/8qWji5nhYVCrYfhLFc+npVI02QwsWDcguj/P7/n5/zHbf8RjScgkYRlmJr/qGHGqBlcM/Earp50NTdNvYkJVROYwASunHhl3DO6/d3sb9sfVZ41ZbqlvYVOfyfbz2xn+5ntcfdYjBZmVs1UynNEgZ4zeg7TK6djMVkKQ87GA6ti/q8G3MAK1NFH64BPZEZypCHCgvLd5TADtas+HjgGzEGd7LCYEVkEGEk5C4VDHGg70LdbfKaZHad3JI0wbxRG5oyeE90xrh9XzxVjruBM9xlecr7Ey0deZv2f1+MP+blh6g3cUnMLgVCA+nH1/Pcd/w0QPQ89nbJly1Ou79Hns8xQqPVwsc1nI00nFrqynGdIKUk8ncvn81FUVNTvIO9kp3gJIQiHw/j9/rh7UuVPRj9ZXi3KsNlsTnqYd6qyjES6Vt5YbNu2DVDmyFqQq1hsbN3IRjZSb6ln22Pb2LFwB4999zEagg2sLl5NbIBKn8/HggXqBbOlpSUagTrxmbfccgtOp5Pvf//7/c5PTsz/9ttv9yt7sjZIRkNL9/v9hMNhDAYDUkpcLhdLly4F1K61w+EYUv0eaj/Ec4ee4/lDz/OHI3/o53c8v3o+H73so9xWdxuXjbls0L4Xmy6lxO/3Y7EkPwc53TJmSwfyJ2eZ5L1Q5Gw4ZDjT9hgOnrT+VVRUFJWzfJRFw1DrIBv5qCqp4s4Zd3LnjDsBdYzbb+79Da8ff503T7zJU7ue4i+f/gtH24/y2BuPcfXEq7l64tXMGT0HgzBQYi6hflw9C6oXxNH2BX0caj/Eu6ff5aDrIPta97G3dS/72/bTG+zl3bPv8u7Zd+PuMQojtRW1zKycydwxc6NK9MxRM7FZbP3KPqxytlhd4vsC+YpUO7IStRNbB+JegbxO9lM2R0Ke4tLP0Re1+gWVJDdLxM0CTgOOSP4rI/llCjp55Gm45Ezz0991Zhe723az48wOdp7dSU+gp9/9RcYirhh7BQuqFyjFuLqey8ZchtVsRQjBb/f9ljJzGWVFZdy56U7Gl42noa6Bv7nqb1g0flG/+BuFMp/FvkfEyk4u5r6Rms8Kaa7UkOuxPBfpIz2fDZX2SLw3puItEbqynGcsWrQIo1HNrtqAFQwGWbVqFatWqWVti8WC1WqlO3JElAar1YrFYqGzsxOPx0NZWRlCCEpLSzGbzXg8nriGttlsCCE4f/58NC+A3W4nHA4nNYsVQtDT0zeRGAwGysvL8fv9eL3eaLrJZKKsrIze3t64KM1FRUWUlJTg9Xrx+/tMddPhKRzuixyZjKdAIAD0+VfHoqamhnsa7iF8OIzhuAHhV7y6cNFEEzXlNVxx9RVMmTmFzk934mh10NjYSGtrK/fee2+Up9LSUtxuN2+++SYAM2fOxOv1JuXpH//xH9myZQtPPPEEd9xxB/PmzUvJU+wus8ZTd3d3lJ9kPGntpD3T4/Hg8XhwOBwEg0HuvvtunE4nTz75JNOnTwfIqJ2EWfD8/ud5/tDzPO98nmOdx+KeX2GpYG7VXD4y+yOsvHwlo8tH4/F4CIfDeDyelO0Equ8ZDIYoT1JKurq6mDBhAlLGm2QLIbDb7QSDwWidQPK+J6XE6/Uybty4jPpecXEx7e3tWK3WqByk0/c6OzujspPIU2I7xcpZKp5iX1x6e/sWI0ZCnpK1UyJP2bbTYDxZrVZcLhcWiyXaHoXAk9ZPy8vLsdvteR33ioqKaGtri7MYGSpP2rgyYcKEjNppwbgFzHLM4mMzPqbo+CRdni6qS6v52c6f8eXnv0xdRR2v3vcqzk4n53znmOuYi83Up9BarVbmjplLRbCC26fcHuWp2FrMqZ5TvH3kbfa37+dA+wF1dRzA7XNzsP0gB9sP8syhZ+J4m1A2gZmVM5lROYOZlTNZOHkhdfY6Qp2hqJyl4sloNBIKhQiHw7mRp8WRvucC0+dNWJ+1YlxpRApJ5yudyNGqrWyOkZGnkpIS/D/0Y/xvI8Z3jITmhZC3Sky/M3F60mmsZVaEW4AAq3/kxwhNzhwOBzabbUjyFAqHOBc4x2HPYXac2MGBNtW3DrQfoL23nWQoNZcyv3o+cyvnMm/MPK4YfQUzK2dSVVlFOBzm2PljvHbiNTa+vZEp5VP4f7f8P3af242/189U61Rev+91fL0+xo8fj9/vp7uzj9dMxgjtJX7MmDEZjXsmk4nW1lZKS0ujcjZYO3k8Hrq6uigrK8NgMAzaTrHzWSqezGYzgUCAYDAYfT9L1U6D8VRI81MhjOW54EmTs8rKSkpLS/P6HpGurjEc73uJPEmpjo6K5WnTpk384Ac/iL6vaXyGQiHSgZDpqtU6MoJ20LXL5epnDuByufoFPhps1S8xGNdAqz2J9FOtEHo8HsrLywt2x2vlypU0NjbS0tJCTU2NSmwFngLxvwJiLP9kkYRlsGPlDhbev5BVq1bF+fMKIairq8PpdLJ58+Y4U+tXXnmFm266CYfDgdPpxOFwpCzjpk2bouc4f//7348qxVr+5ubmuKjcy5cvZ/PmzYDaDV62bFnUZznVSuOaNWvYtGkTP/rRj7j//vsxGAysXr2aTZs28bWvfS26q51OPWq7x88dfC6p7/EU+xTumnkX906/l6tqrorz8xjqCqfb7U4ZpTuTFcJs6ED+5CyTvBeCnA3HMyHz9hgOnrT+Zbfbh2Ulfqh1kCv5GIxOMBzkVOcpJtsn89Sup/j7P/w9zg4ns6pm8dCVD7F64WqC4SAmgyltnqSUnOk+w+5zu9lxfAdHuo+wr3Ufe87v4Wz32aTlB3BYHEyrnEZtRS01FTXUVdRR46ihtqKWSfZJmAym4ZGzkEC+LpU/81aUifbdIFb233HOeb/uBGwoU+r/A/ETgfy9VBGtbyXO9zhfcpaL+SATOfMGvBxoOxB1Adjftp+9rXs50HYg6ekLGqY6plJnr2PeuHksHL+Q+up6plVOw2Q0Ren3BHr409E/8fKRl3np8Eu8c+YdZlfN5uaam/nwrA9zU81NwyJn6dKB9Ns1doE2do7Kxdw3UvNZIc2VGnI9lucifaTns6HSHgk50zak3G73gKbb+s5ynqENUhqklNG0ZIPNQDQS70mWPxX9wWgnS0+Vf9jTAyB+J+BHwO9QUT1B9d7rgU+C+IhQPlrNfXQSaW3YsIGlS5eycuVK6uvroz7ImmK7ZcuWOD/jZGXRlOM1a9bw4IMP8uCDD1JfX09lZSXbtm3D5XJRW1tLS0tL9Gin2IFkMF5jy619b2pqikb7FkLwxBNP9LuvoaGB+vp6vAEvfzzyR547pIJzJUauHm8bz7LaZbxvyvu4Z849lFvKowOLwWDIqE8Olp6K70xpZ0Mn33KWSd7Y/OmUfaTS80k72/YYanq6/TRXfXWg9FzVQS7kYzA6ZqOZKY4pANx3+X3cd/l9nOs+x+vHX6eiuAIhBHf8/A7O95znuvHXcdvM27hx6o2UFpUOSH+8bTzjysaxeNTiuJesDm8He1v3svf83qhP9N7WvRxxHcHlc7Ht9Da2nd7WjwejMDLFMYW6ijomlExg1thZ1FXWUVtRS21FbTS2Qk7a1QTifZH0W1DHT20GVoK4ScAvUJG2qwDjEJ8ZAt4GngPxnFBHObUB9USPuxLvH345G+p8kKyvtva0KhP+83ujpvz7WvdxxHUESfKXdIvRwoxRM5g9ejazRs1iVtUsZo+ezYxRM7CarElP+nD3utmwfQNfu+ZrvH78dT77+89yS80tfPXqr3JTzU1Ul1UPyNOFNJ8lvkOk88xCn88Kaa7M11iei/SRns9GQj6GQifV74nQleURgMmUebVnck+6ebMpx7DCFfm8BhXVU8MY4FPAV1EvJmmioaGBlpYW1q5dS1NTE83NzdTW1vKhD32Ib37zm9TV1aVFZ/Xq1TQ0NLBu3booHYfDQW1tLffee2/0TORHH32U9vbkZmHpQDPf146tApL6ZwPc9bm78O3x8Ycjf4jbPRYIJtsnc+eMO1lVv4orxl6RdHDIR1/IFc1s6ehyVlgo1HoYznJdyHI2pnQMH5r1oej/P/nwT3j58Mu8ePBFvvj8F/mfD/wPN069kX/7079x7eRruWriVRSbitN6VoW1gmsmXcM1k66JS+/2d7P71G5O957msOswzg4nLR0tODucHO44jC/kw9nh7Dv3dlf8cyqKK6KKs3bVVdTF7UpnBRNwc+T6T5TFE8BngdeBe1DBJDOJqi2Bn6GOdnoBCKMiVn8OuA21GDwucg1UtAKUs7AMc6L7BK+fe519bfvilOLWntaU91UUV0SPKptVNSv6OdUxNWW0aSklBqOBHad38PKRl3n58Mt8c9k3qa2oZefZnXQHummobeDwQ4cz4kGfz7Ivx8WIQq0HfT7LLR0Nuhl2nqCZYQ+2ta8jAedQK/Y/BN6LSR8DXA38PWp1/RKGlJIdZ3awefdmfrnnlxzqOBT3u9Vk5bZpt/HxKz7OLbW3UG7R+58OHTryCyklXf4uPvvsZ3n58Mt09HZw/eTreeY+5ZtsEIbsldMkCMswpztPR5VlZ4cTp8tJS7tSpgcy7Ya+XenailpqHX3K9FTHVKpKqhhVMgpbkS3tnQcAgqjjp7agTmT4P+AmlAJ9FckV5w3ATOBG4F5UJOvbgSUU/HZGl7+Ls11nOdN1hrPdZ6PndJ/tOsuZ7jPR3850ncEX8qWkM8U+JbpLPHu0UohnVc1idMnotOv/VOcpXmx5kRdaXmBry9a4iNUfveyjjC0bxnOydOjQcUEgXV1NV5bzhFQNIKUK9FNcXJz2JJDJPenmzaYceYMfdd7lkyifLC3ugADmAY+gVurz8OJQUPUQg8RySSnZeXYnm3dv5ufv/Zyj7qPRvCaDicvGXMa1k64dcPc43Wflo/zDTUeXs8JCodbDcJbrUpIzKSX72/az/dR2PnbFx/jFrl+w+pnV3DDlBm6aehM3TLyBBRMX5FXOuv3d0d1o7UrclR4MZoOZSmslo0pGMco6ilElo6gsjv8/mm6tjH4vMhapeUygIlLXoxTlO1HHOR2OfD4O/BSoRVlQDRG56A+9wd44JVdTgjUFOPq96wzdge7BCUagmU5rirC2SzyzauaAxy0NhJ1ndnK2+yzL6pZx3y/vw9nhZFndMm6ceCPX116P2WjOim4iClXOcnWPPp9lhkKtB30+y5xOuspyga9bXpzw+XwUFyc3T8vFPenmzaYcOYMEdqAU5J/QZ3INamV9OfAV1PEdecaI1sMA6O3t5YD7AFv2bOGp957C6XJGfzNg4IrqK/jsos/ykcs+MuTd43zUQa5oZktHl7PCQqHWw3CW61KRMyFEVCkCWDF3BdNHTVdm2y0vsvPUTn408Uf8YtcvcPW6uKX2Fuoq6pK+2GQrZ6VFpVw25jIuG3NZv7ypdqWdHU6Ouo7S5m2jN9hLIBzgbPfZQXepE1FWVKYUZ+soFrcu5sZbbuT6169n3P+MI1gS5MBHDxAaFcK8xUzRrUWYzWbCHWHCsu8KyVDc/4lXKNz/91A4RGd3J1arlTCp7+32d/cpwQk7wsnOHx4IJeYSqsuqqS6rZmzp2KTfx5SOoVyWU1lRmfVLsJSSPef38ELLCxx3H+fbt32blo4WDrUfYlndMn56908xCEM0BkcurRigMOUsl/fo81lmKNR60Oez3NLRoCvLOoYXZ1B+WU8S719WDHwA+AdgzrCXqqCw+9xufrH7F/x8589pcfcF6LIYLdw27TZurbuVj8/7OGVFZSNYSh06dOhIDwZhUGfZjqvnK1d/JXqMijfo5aldT/HF579IdVk1//3+/+aOGXfg6nVFg3PlqzwTyicwoXwC10+5PmmenkAPbT1ttHnbop/t3va+tJj0th71W7u3HUPIwE3v3sSzM55l7O6xfPPH32Rr3Vb+Yd4/8Nw9z3HSfhKAB157gMf/4XGChiCb527me4u+x/7R+/PGcyawGC2MLeuv+MYpw5Hf05mHNAU2U7T1tLHVuZUXW17kxZYX6ejt4MapN3L7tNsBuHv23dG8BmFIRUaHDh06hgRdWdaRf/iBZ4AfAM+jApeAClhyOfAwcDeXdG/ce36vMrHe9XMOtB2IpgsEc8fMZU39Gj4x/xPYLLYBqOjQoUPHhYNPzv8kn5z/SXoCPfz52J+ZPmo6YRmm7jt1jC4Zzc01N3Nd9XXcfcXdFJuHdxenxFxCib2ESfZJA2eUwAHg9xC+I4xrsgvrTVb2fnkvp8tP85vVv6E11Ep1TzV3ee+KKtfN1c0suX4Jc/fMZcXOFcxzzePkhJPcv/1+Xqt7jZOjTmIQhn6X0WBMmm4QBozCiAxLzCbzgPcVm4qT7gJrCrDdYh8x89LXjr3GrKpZFJuKGf/v45ldNZtb627lxx/6MddNvg6LyTIi5dJxkUNKCHZDsAvCAZAB9RkOxnwPgAxCyI+pywVdRSBDMb+lyJ/se2yaoRhMVjAWg1H7jFyG4uTpsb+brOrTUDTStXjR4hJWT0YORUWZd+hM7kk3bzblyAj7UQryj1GBuzSMB74MfAZw5LcI6SDv9ZAC+1v3s3n3Zp7a9RR7W/dG043CyPunv5/ba27nvvn3YS+2570s+aiDXNHMls4lI2cXCAq1HoazXLqcJb+nxFzC0rql0f9bvtjCq0dfpcnZxL+8+S/cMfcO2rxt/Hrfr7lj+h3UVNTkpBxZwwv8ERVj4/fASeAmMFxjoNJWCdugPsNIlJ/gE2ph+R7g26iAYH+FOvkhTddbKSVerxer1VpQvpSQun1a2lt4oeUFZoyaQUNtA99641s8dOVD3Dj1Rk5/5TSV1sx9sXQ5y/yei24+k1Ipvr7z0Hsees+p77H/954B7xn1PdCulNc0IIBCtOsTgF2YwWACsw2ESaUaLPEKdpEDisdC8RgoqkhyVapPczkMYLFxMcpZKugBvvKESzYathdoBDYCr8Wkl6OOwfgHYO7wF6tQcKj9kFKQ33uKXef77NAFgpmjZvKZ+s/w6fpP59UEUYcOHTouNOw8s5OvvPgVXjn6CjNGzeCB+Q/wlWu+MnwF6ARswP8H/C3qvOM7gPejjpDKLkZVcpxHndv8EvBL1DnL+yPPu8A3Vj0+T9R3/YWWFzjhOcF1k6/ji0u+yAdnfXCki6ejUCElBDuVouuLUX5j/+89B71n1eVrUzu8GUOAMCol0WgFGVa7x8IYfxmMYLYrxTLYCcZSpWD6OwCpdnkNZnWJIjAWgaUKTGVqB7uoQu0Gh3og1KsU+1APBL19aeYy9b/3pFJcA53ga03Y9Q6q5+UFQtWBuQwsY8E6VpXZOhYso1Mo2hVpKdqFAj0adg7hcrnYuHEjtbW1OJ1OGhoaqK8feNV4oGjYma78ZnJPunlzvgL9DvA/wP+iXio0XA78C+qFogDtGIZjJd7Z4WTz7s38367/Y+fZnXG/3Vp3K/fOvZcPzfoQFdaKYS1XPp+VK5rZ0rlo5ewCRaHWgy5nQ6MzEnLm7nWz1bkVb8DLx+d9nL//w99zsO0gS6cu5a7Zd1FVWpV2+QeEH2hG7fA+A/w/VFDK/UAImI3ayhkOPAM8BLSjzm9+EFjQP1shyllYhjnYdpBJJZP4bctvuf8391NXUcetdbeqyNVTb6S0qDRnz9PlLPN7CmI+C/mh+wh0Hoxch6CrRe38+s4qpVgGByXTD4ZisIxSSm/FfKWsCsA6AUqnQFkNFI+D4tFKATSVQo7qa9gQDkKoFxny0tvtotgMIuyDkFcp3doV7lXKt9+llO5gJ3Qfg4AHwj713d+hlPVs6joOQi0mGCxQMhEWfhvGJI8PoWEk5EyPhp1DrFixgi1btuBwOABYuHAh27dvz5qe3+/HarXm7Z5082ZTjjh4gKdQ50TuiEkvRU3o/wgM4u5VCBhyPSTBEdcRtuzewlO7nmLHmR1xv02rnMYD8x9gzaI1A5qY5aNcw/msXNHMls5FI2cXCQq1HnQ5Gxqd4ZYze7Gd5XOWR/+/e/bdNO5p5D/e+g9W/341P7/n56ycu5KjrqNMtk/O7KXrNH2m1VtRtpb7gFsA7T1vZvrkcoY7UbvKb6COmnoXpSz/BHUec0yZCkHOznef54WWF7hn9j0cdh2m4X8beO9T79FQ28ChLxxiimNKXp+vy1nm9wzLfBYOQNdhpQhrSnFX5HvXEfoC2gwAgwWs45TyZ58N1kkQcEWU38lQVgulU5WJcXFE+c0DCkHOojCYwFAGplJ8vWaKbfZBFf5BEfIpxTnx8rWDvx3p7yDoOoAp7Eb4XRBwQ6ArRtGWql1ALXakaeI+0nKWCrqyPAhcLhdOpzOqKAPU1tbS2NjI8uXLU994sUKiJuz/QSnKvTG/3YY67ulmoPCtL3KO4+7jbN69mc17NvPWybfifqsfV8+DCx/kw7M/TFVJjnY+dOjQoeMSx/zq+cwbO4+v1n+VLtFFaVEp/pCfBRsWUG4p584Zd3LH9DtYWrc09XFC/4Yyd96JUj7vAP4OmEffXDbS78UCdRazdh6zRAXMXIWy4Po48OmRKVooHOKtk2/x/KHnee7Qc2w/vZ351fO5auJVzK6azdEvHaW7s5vKkuyPjtJxgSAcUIpv16H4XeLOg0pRHkghFmawz1EmzMVjwHEF2GaCbRqUTozs/ObS30FHShgtYK1WVzJISbfbjd2eRDEP9fZXsivm5b/MecRFryw3NzezatWqlDvBLpeLxx57jFGjRgHQ0tLCunXrospxU1NTv3sqKyvZunXrpaUst6JWsTegzNA02IFPoF4sRo9AuUYYPYEefr331/zwnR/y8uGXo+kCwRTHFO6/4n4+v+TzjC69BCtHx/BCSmVKFWd2lfC/ZooV8qnVX80fK+5KTMsyD5G0sPq/JCjBWq5W+o1W9dJjtIKxJP5/U0mKNKvaVdBftnWkwITyCVFl7NRXTvHHI3/kmQPP8LWtX2N7zXaOuo7yUstL3DHzDiZsmKDmrE+iFNEvoxZ8L5S1TIE6hvG/UIr+r4HVYDhogN3Ah8mtH3UCwjKMQRh4tOlRNjZvRErJrdNu5XOLP8et026luqzvJTvXZx7rGGGEg9BzNF4RjppOOxl0h9hYqnZ9y2ph1JUw7lawTVc7xvr4fuHDWKza0jpupEuSM1yUI5jL5WLt2rUAbNu2jebm5pR5b7nlFjZt2hT1QXY6nVEza4fDQXt7e9yuMoDD4cDpdGZdPosl8wgdmdyTbt5B84WBl4FNwK8AzYXBCNyPimZ9NcPnt5UnZNoeUkr+fPzPPPnOk/zfrv+jO9Ad/W2qYyoPX/Mwd8++m7FlY4e1XIX2rFzRzJZOQcpZOKACj8QFJjmvTJhiFdk4xXYA5Vf7PdgL0p8pu8MGgTopLieUYhVqUxJlW0s32aBkkjLNK5msPourVWCWGOhyNjQ6BSlnQLGpmNum3cZtdbcp5fFbMPrXo5njm8OUD0/h450f5/aJt7OSlfBoJqUvMDhQO8qfBiRYzlngX1F+zXcDq4Frh/6YYDjIMfcxaitq+dbr3+JA2wE2fGADN069kbtm3sWSCUswJshWLHQ5GxqdEZGzcAA8+6HjHXDtBNduyj0HlKI8kE+roVjtBIeDMHml8hk22WDsjVA+66JQiIezP2cCXc5yS0fDRaksOxwONmzYAMD69etTKssbN24EiAvWVVtbS319PY899hjr1q1Lep/L5cq6bEKIjO3oM7kn3bwD5jsJPImKaH0sJn00auL9GmpH+SJAJnV71HWUn7z7E36w4wcccR2JppcVlbF89nL+7n1/R11l3bCXqxCflSua2dIZNjkLemOOo2iNP5rCdx7hO481ekzFeQi6M+Zl6BAqKmVZrTpKovuIiuaJUP9XXakCfLj3Rl5ihAqCMvoa8J4Gz16VhlCmcaOvhc4D4DkQyR+hXXUltG9XpnbCoPJXzIfKejjTpIKHaAFHKuvVqvPxX0Wih0Zgm66ih3oOAFLtlsftUshIpNCeLKvCpIKNlE6BksmI0slYY5Xpkskq8mceoMtZ5vdkPZ/1oiJJPxu5zgMNUP7Jcq57/3WcrTrL84eeRxpUfNOvvvhVWntauXPGnSyrW0a55cI8wUIIgWWZRS0O7ED5N29HKcu/QPk2zyPtBe7Tnad5oeUFnjv0HC+2vMhUx1R2rNnBRy//aHS3+NZpt6ZVLn0+K3A5M/rg3Ft9inHHTnDvgnDfImxkFoi50aSOIbJOgPKZULEAxrxP7RYPsHByoWM4+3Mm0OUst3RicVEqy+liy5YtLFq0qF/64sWL2bBhA+vWraOysrKfctze3k5lZeZn/4Halezu7qa0tDSjqIbp3pNu3n75gqigJhsjn1qM9FKUmfVfoXaRLzIMVl/d/m5+tfdXPLnzyTgza4Mw8IEZH+Bvrv4brpt8HYYch8jPpp8U0rNyRTNbOlnLWVcnpaId0Xumb+fX19pvJ1hGjqkQYW92jFmqUCYaIbXibi6D0loon66UzZBXmTIZrCqISemUiHmbUDun5lKwzVCmbN5z6n6NTtFoMBUX1Oq9nPWV5O1x5f/0mY8HOtVxE0YLnH9DReoMdqmgIRPuUEr23m9GAol4VP7ZD6uIqXvXq+M4Qr0q2Mmsr0DbW3DqGWW6LcOqTmVQLRZ0H0ld2KKKqDJNoiKdYnc6rTrQ5SzjezKazzq7Kf1JKeKvBLiBL6B8jzcANwLFfflHMYqPXfGx6P8r5qzgV3t/xTf++A0+9quP8fRHnubWabfS6evEZrGlxVchIK6+6gVxRz3/CbX7PBVlGbaGpIveXf4u/u1P/8Zzh57j3bPvsnj8Ym6bdht/c9XfsGi8el8abxuffbn0+Wxk5UyG1RyjKcQd7yBdOxHdR1MTs02H6mVIXyu9VbdTPPEmRJEdii6SXZMMMZz9ORPocpa/sl3SynJTU1PS3WPtiCiXy0VDQwPt7e398ixdujStZ7jdbhJP53K5XBiNRoqL+2ZvIUS/fLHpgUAg7vdU+YG08kopCQaDyEMSfgg8CeJMX6eSEyV8HnVF/J4EA5cx1+nr16+nqamJbdu2AbBo0SIefvhhGhoaBqTR0dHB6tWrefzxx6mtre1H22AYXLGdcdkMrv7nq9m8ezPeYJ9CNNUxlc8t/hwPLnyQ0qJSXC4Xj6x9hJdeeinaZzTrhEceeYSFCxf2K2dTUxPLli1j+fLlbN68OSn/Uko+//nP8+Mf/5gtW7Zwzz33ZF2Pg6VD/z4zVPpan9W+Z1vGbOkMylOoV/lYefaCZx/Csw9ceyn17ENIX1J6cXRivksEWKsRxlJksFsdl2CphNIpyNHX4e0NYS1zIGx1SiG2VIHBmDu5caRIJ/06y5cMxyJle4BSaDWzKSkRo69OTn/ht5Ok34yYvro/7ZAX6d6nlOmuFmUaOOluxN51yIPfU7vrpjJCRWMwmiyILieEuvsCknS8k5QPGdmdFqVTkCWToXSSUqRLJkNZDaKsBmlIbnQ+VDnLlXwMm5yloBN7T9ZzXwB4HXgW5DJJcHEQ3ga5VMJ04BDKeELLL5PTB1gyYQlLJizh8YbHaeloYXTJaFq7W5n47Ym8b8r7uHv23Xx41ocZUzomrbrJtzxl1R7flfA48Bvg58BnQJwXhF4LYbjDwOoXVrOmfg3zq+dzvvs8X736qyyrWxYNSKk9MxteY9txROsgy3a6kOQstp0CvR6kdze4diJc7yI73gHXu4hgZ/w9WvkAhFmNbxXzkdUNMO52tXgYoevzeLCUlKvF2DTeSXORPlLylM93pnyUcaTlbKi08ylnPp8Pn8/XL7/H40nKWyIuWWV5IFNqzUfZ6XRSX18fVZ41xau5uZlNmzal9ZzJkycnTX/44Yd59FHlKGWxWLBarXR3dxMM9vmBWK1WLBYLnZ2ddHV1AaqRS0tLMZvNeDyeuA5is9kQQsTlBbDb7YTDYTo7IwNkL5ifNVP641IMf+pTHMNVYQIfCGD5sgX/DD9er1e9kLjBZDJRVlZGb29vtMMBFBUVUVJSgtfrxe/vM9dJh6dwuM+8MpGnT37yk/z2t78FoKGhgWAwSFNTE01NTXz729/moYceiucpwq/dbuf73/8+jY2NfP3rX8ftdmMwGCgvL8fvVzzNmzcvmt9gMBAMBhFC4A/5Od12GtdpFwd6DnBg5wEAyovK+cLCL/DxKz7OzOqZdHV1EfQG+Y/v/Qdf/vKXo8+fOnUqU6ZM4ciRIzQ2NtLY2Mjq1atZv359XDtpfAcCAdzuPrPc2HaKFXaAYDBId3efb3QiTxqyaafi4uIoDa3PpNtOGmw2GwaDIcqPlJKuri7sdjtSyqTtlA5PUqrz8hwOR1Y8iYALY/dBjF0HKOp1Yuo+iOx4D+E9hkh4e+57YRBISzXCWg3+dkJGG7JoFGHLGEKV12C1TyQkijjvtVFcUYswlyMMBsVTIBDHkzYoG4uL6e3tVWe3+ruGXZ5StZOGfmNEhu0EA/c9q9WKz+fD7XZH+9iw8OSYR6exFuxqcVOEBPbF/0XwivV4z+9F9BymJ2CheOIN2I9/l/DhnyK6j0DYT2Dc3VA2laLWlwj3tkOwGxHoQMTsTidfsxZI60RCJVMJl9QQLpmKyTETk2MW3q6SaN3mop2kVCvoDodjSO0kpYx+z6TvFRUV0dPTkzFPsXPUYH0vNq+xzYjtNRvh34UxbDUgLZLA0gDBIlWu3u9FeIpUWzbyNK1yGh6PB1PYxFv3v8Uzh57hf3f+L//y6r/w3gPvcbb7LFJKxpWNGzF5GspYHrg9wFvz3+KP2/7I2a1n+c6Pv0O5KOehWx+iyFaEqBZs/MBGxVOAKG9D4UmbD0wmEzabLa/jnslkipZFq4OhtpOUkp6eHhwOx5DaSUoZ/Z5TnqRE+M5i7NxFqf8gsmMnsm0Hju5DiBg3lr45zkCo/HIwlmAyW/DN/Fs6PD1Y7ROQpXWYzGbFk9ereIooFGazGVBjROz7yYU+P2Xa97Id9/LNkyZnRUVFlJaW5vU9Ii1dI0Oesn3fS+RJShn9rvH0+OOPp3StTQdCployuEiwfv161q5d2281wul0UldXx4YNG1i9enXcb01NTSxdupStW7fS0NAQjZi9ePFinE4nDQ0NcX7OyaAddH3s2LF+B127XC7GjBmT9s5yOBzGHQnRrnXKgVZ7XC7XwHmfB/F+9ZtEQh3wNygTraKRX8XbuHEjDz74IPX19Wzbti2a3tzcHDWbb2lpoaamph+NX/7yl6xYsQKAQ4cOJd1ZjkWXv4ufbP8Jv9j/C1499qo6W/PPYHjIwLXzruXr13+dpbVL+9VlY2MjK1euBODxxx/v18eam5tZuXIlTqeTr33ta3FCmu7O8gMPPDBsO8uJfWao9KWUuN3ufsHxMqWdFp1wGHpORHeJ8ewF9z6kaxeGQFvS+wCkMKpjKUYtQRgthB0L6LQuwDZuPsJoTlnOTGRSSonH46G8vDyubjOpg+FIH45V6Fz3sVzwpPUvu92OwWBQ6TIM3jPKHNwyCrHv35Ht29XudGcLLPyu2nl5e7XaQRYmZd4dVkq0GMSfWlqqoKwuctUibNOQZbXq/+JqtWOTJk/DKmc5atdE2RkwbzBM5+5ObJfbEN8X8EUQ9QJ5u1Qm1gsBw/DIWXegm1JzKd/b9j0eev4hloxfwt2z72bl3JVMLJ+Yl2fmSs5ae1r53YHf8dzB53jR+aIKflZ3G7dPv52lU5ZS+Xol/AiYCDwBYodATpfqnOkclDGpnA1zHQxl3CsoOUOqqNPt2xCunWq3uGMnwncu6X1SmIksASNLpsCoJTD+dqj5ePSZhT6fFdJcqWGo81k+yjjScjZU2vmUs4F2lidPnozb7e6nq8Xikt1ZHgiJu84OhyPrFYny8vJ+DWCxWCgqyiw2bCbO6oPmXQryGkl4chjxzwJRVzg+F9AXeC1WkQQViO3xxx/nkUceoampiVWrVkV/W7ZsWdJjvpIhLMO8evRVfrzzx/xi9y/oDUYOiz4F/Bnu/dK9/HD9D7Gaktejy+WKKsrbtm1LunCiKfq1tbU88cQTPProoykHgFQwmYZPPPMRFCJXNKN0Qn6lsHj2Rq794N6tzKhD/X2Ho6vohmLlczX2BkTRKKRjHlRdBcVjo4oJQkA4jMXvVz6vmZQrR/kudhRqPfQrlzBASYxP5uyvxJkboinUV/xT5KiUFuh0wvWNyq996zXKPNtSqfzOiyrBcxC6nQjfeYSvVfnBt/2l75EaaVOpMtW39SnTUcW6dApEFnEGLH+u6iGP9w14TycqONddwF/A9lc2ZVJ9d+Sqpp9JdbblyARlRWVIKfnsos9y96y7+c3+3/Crvb8C4G+u/ht+ve/XXD7mcqZVTstrOQaDVg+tPa387L2f8dCVD/Hq0VfZtH0Tt0+/nbXXrmXBuAUYhKHvpXEZ6tLwdeA14F7gAeAqSDco2GDlGg5cEPNZOpBSxapo34bt/F8Q7negoxkR6DMdjbWIwjYD0XMUOeMhpOMKwsd/i6H6JsSoxeC4XAVPHGK5CnUcH24Uaj3ocpacjsViiUbIHmgBIBUuWWVZC9CVzBxb81HONohXLBYtWoTRqILBxK6iP/DAA1Flb2BzDwtnz3ZFTCOUUqeZRrjdqcw9eqN5IYVpxBZlGhEIBOg+1d80wudLbu7h9SY3jejp6TONKCmB4uLszT1aWloAGDVqFOFwOM6Epbpand146NChOJ6uu+465s6di8ViobGxEafTSWdnZ5wZ9r5z+/jxjh/z090/5WTXyb56K7LxoWkf4g8//AOj5o3ih//6Q0rMJfT09CQ19/inf/onAD7xiU9QV1eH3+9PylNZWRnr1q3jjTfe4N13342af6djhg3E1ZtmwjJ16tR+5jyxePfdd7n88sszNmEJBoPKTDiDdkrH1MhisWRnltPVjtHdjKn9TcyedxDd+5GdTgShfjwL1LuzNNoIl9Ygq5dhttfRU1SLv3S2CnwVy5PHQ9gfBr8njifN/F2rh4HMp7R8Wt7BePL5fHkzCbtQzNzC4XCcf1Ah8eT3+zPnafIKvLFjhBcsprFYb3wO37nt4N6DCHTgnfNdSlufwbzzbwhWXk3YMo6wZRzSMpoi/zGMPUcJew4ivCcQwW5wv6cu4vUTKYzI0ilQWovfMplwSQ2h0lpk2UyKx80jEJJDbichRMpxL1U7+f3+uLFjsHbS+oB2j91uJ3wgjP9XfkwvmjD92UR4ahjjIiOhRSG63+pWCnRxhCdS8+TVzEZj2ykP8lRMMR+Z9hFWLViFwWDA5XLxkx0/4dmWZ5lZOZOVl63krxf9NcZAXyC4XMtTIk9d4S5ePfkqT+99mr+/5u8pMZXw2uHX+MwVn+GeOfewdOLSKE+dns6B2+lZA12vdlH0kyLMd5jp/FMn5bXlhN1hOq3ZjxHBYHBYxj2fzxfXJ3M17hUXF+dkLBdCmbDG8VRcjCV4Bu+JVzG4mjG638HofgdD5ASF2Jd1KUxgtqlI1cEewtZJ9Fz+H4SqbsQePky4bBadPT6oeH8fT8aifm5CWjsFAoG4+WwwnjIdIy6E+SnTvpfpuDecPIXD4WF5j4jtM7nkSXMnGWo7CSHweDyEw2E2bdrED37wg6gepvEZCvV/p0yGS9YMG1Qjrlu3jocffjgufePGjaxZs4aOjo6MdwM1aGbYLper386yx+OJ2vzHliVZGXt6BGX5Ockkb+jslJSWZm+S0dzcjMPhSGpCvXbtWp544gk2bNgQt7McS2PFihU0NjZy6NAhRk8YTePeRn70zo947dhr0Xxmg5lPzv8kn5j3CeaWz2XDhg088sgj0Z3igcpYV1eH0+lMy8w7WXq6Ztif/vSnefLJJ+PMsNevX09bW1u//E888QSgFnoqKioyNqfJpE+maw7U2dmZ0qylHw1fO7T+GXH+z8hzrygTM9l/EJPCqHbqSmtg4l0I2wykfU7fkUND4Ekb5GPvSWU+lW5eKZUPUVlZ2bCarWWaPhxma7nuY7ngSeun2uSftzoIdoPrPbxntmH1O1WQt7l/CzseRhz5KdI+V50/OmqJ+s2zH9FzDKmZfnc7EaHefs/SIA1FKjp6+Wywz4LyOVA+G2GfiTT0P28ymdlaRvIagyHJ2WMC8b8CeUTCDSjT6juAugtTzly9Lp458AxPH3iaH971Q9p62vjBjh+wfM5yLh9zeU77WDgc5p0z7/D7Q7/nuYPP8ZeTf+GKsVdwy+Rb+NI1X2J8+fi06AyaHgDMIJ4XyA9J1T6fAm4DTAUmZxGk2yczMevMRj5S0rHZlOtQ+7bItV3Nef7+wWSlMINtGn7HNRR17VSuRqOvhzHXI6quQVbMV6cIJDwzts5jN2qSlbHQ5ayQ5koNQ53P8lHGkZazodLOuZylQcfj8eBwOHQz7IHQ0NAQ3cWMhRbROFtFORbaIKVBG8QS07W8FwMUb33fU+VJlb5w4cKk6U1NTVGlcOXKlYPW11df/CrPtz/fZ2YN1FXU8YUlX2D1wtVYzVakVL4Njz/+OA0NDXHPTkXf6XQqWnXxZyqny6v2f1NTU9ScOxm0KOCx96xdu7ZfPs1He8OGDVRUVGRUFsiuT6aTrg1G/fJKCd1HEedeg/N/gnOvqBcAjYb2xWBB2mbSO/ZDFI+/FuGYg7BOgMQyJilHtjwluydZ/kzyhsPhpOUYrCzDnZ5P2vnqY4Olp9tPYyf7vJTFXIasugq/eTbWWD+3y/4OJn0Y4d6tXAqMxTDh/bDv38GzF2GfC+OWQe2nwDIK3HvUS3Ykwrf07Af3PnWEmXuXuo7HFsSAKK0B+5yIIj07+inM8S8GKeV1AF4zalcfYAHxPUHRySLEvwrERAHrQDSIOP/YWBoXkpxVWCv4+LyP8/F5yh/0hOcE+9r2cc0Pr2GCbQJ/vfiv+dJVX8r6me5eN609rdRV1nHfr+7j9wd/z9K6pXy6/tNsWbmFcWXjlM9iuT13cqZ5jN0OYq9Qvs2fpe9Yrm4QpQUiZ2Q+1qRLOxv5iKb3nFJKcdvblJx7EzzvJvcxFiYoq1HWUEUVsOAJRHsz0nsK76QvUFQcRBRVxB1dl+ypWlkS6yEXc99IyVkhzZW5ms/yUcaRlrOh0h6SnGVBJ9XvibikleUVK1Yk9UXeunUry5cvz8kztA41WJo2WCWipETg8eQ4wBf5DdRgtSp9aKh0YtPXrVvHI488AsCLL76Iw+Hol7fd285P3/0pzx96HoDf7PsNVMIU+xQeXPQg9112H5Psk+LqAOAb3/gGLpeLxx9/PE7AkpVFM6Wpra2N+13Lv2bNmqSR0mN3kaO7EC4XjY2N/fImw0D10tjYyKpVq1i1alV08Mp0hTCTPplOeiw9GQ6Baxe0vgbn/wznXkV4T/a7X1onIsbfhrTNggl3ga0OCfS63RQ7HH1uiknqPVc8Jd6TLH8meWPrI9UzCyE938+E3PexwdIz6afZyE1O6sBcjhh9DbLq6thMcO0vEJ49SNcupUSHfFA8DvH7K5Rfs30u2OcgF/w7bq8Zh9kNnfuVMh0T6E74O/qOzjr5u/iyWCdEd6KlbRZG4xSwLEZaRkOac0JSnrT8YQk7gGeA3wM+EO8I5A0Sv9uPRVrg/hjaCeQvZDnT0mZVzWLz8s30BHp4/tDz+EIqYu0/v/rPuHvdLJ+znCUTlmA0GFPW757ze9hzfg/L5yznBzt+wN7WvWz6wCa+ufSb/PhDP6bIGB//JF9yJoRA1kj4J+AbgAuQIK4XSJtUu83LgdL+NEZczgbiKQ3acfPZYPm9Z6M7xqKjGdm+DeE9rfIBmv2TFEYonwvWMUrmvWcQnfuQYb9a3Bp7C1TMVxcgXS5kUaWSzQHeUxLlZrB3mgtBzgpprtQwVDnLRxlHWs6GSjsjOcsRnVS8JeKiV5Y1k9VkWL16NevWraOpqYmGhgZA7Ro6nU62bt2ak+cn81kOBoNR5QYG9yMIhzuRsotgUNEYyGdZCEFHR19eSO5HAFBaChZLMBoCHwb2WS4tTc9n2ePJnW/EX/7yF/76r/8ap9OJ3W7nySefZPHixUgpoz6Qb556kyffe5LfHvwtvrBPHc8DLBi7gG/c+Q1unHIjdrs9enRNLE+BQIDvfOc73HDDDdTV1eF2uwf0jYg9VkyjFcvTnDlzuPHGGwEwGtVZuk1NTXH+yRrfH/zgB3nyySej9BOPjvr85z/Pz3/+cyD50VEvvvgijzzyCPPmzWP9+vW43e7CODoq1IvR1Yw4+wr43oPzr/U71xFhIlw6hWDJNIJj7yQw5jaEdYzyYdF8wiLPGMrRUZnyFHsUwmC+RrF5hUjul6M9O9G/6VLzWR6xo6MG4UlKZVZoiBz9VTjHjTgwjL4Od9HloB3t6/Fgv3Mf4Y738J1rxti5D2+nl17PYXjnDmTlIvy2+QQrlxGc+U3VTkVe/K07Cba/h7HrAIau/erTd0YtWHlPwtkmBGCLPEaaKwmVTSdUNotw2QwMFXOxjJ5Pd7iCYCg8ME9+KHu1DNMLJuQzEtEtCNwcIPiJIJYPWTBIA+4JbrrsXYTd4Wh7pHt0VKq+p82xsRFPs22nfPW9hgkN0TghV1RcweZ9m7n9Z7dTZi7jqeVPsaR6CV6vl+5AN386/ideOvYSW49s5Vz3OZZNXUbD+AY+OeuTFFvUKRqV5kq8XV68eKM85eMYwJQ8mUB4BPYX7IT/NwzrwfBFA543PYgJ4uI/OkpKRO8pirp3Y/XuI9z6NqKjGUPvqbhnCNRRTWHbLIK2efhK52Lr3UnPzG8QlGbK3vwAwcqrMUz+GObxN9MZqujjye3OmqfY+Uwb3wYay2PlLNW4px8dpR8dpfEkxIV1dJTus5wCa9asAVREZZfLRUNDA7W1taxYsSKqGIPa3Vu7di11dXU4HA62b9/O2rVro76o2WIgn+VAIIDJZIp2MBh4xSQcDhMMBuPuGWi1J5F+qhXCUCgUVegSn1kIq3ixu7SPP/54nG+5q9fF/+78X7637Xvsb9sfTZ83dh5shp1/2JmWT7F2TNUvfvGLqDnzYGXMxGfZ7XZTUVERt7Ocrs/y6tWr+cEPfpD06Cin08m0adNwOBw4nc44l4FsVggz7ZNx6f4OOP86ovU15NlXof1tdRZtDKQwQMkUGH0dTFqBGHcz0lgyKG1t0NMm6XzylK6cZZL3QpCz4XgmDLGP5YknrX+ZTKZh8fEaah0klQ+/F3P3XhVdu/Ut6D0DNz0Pp7ciDv8IWbkEqq6EigXKzBsQATfSHYkqr+1Gu/dGzo5OsYtgKlV+1eVzYsy55xCwTML0QwviNqEU+2tA3CKQd0i4jj4zXpLLTi5kstDkLN28/pCflw+/zMJxCwmEA8z6r1l4A17G28bzwVkf5I7pd/C+ye/DarYOSCcW+ZKzQfNK4F1gHvAsiEcE8gEJHwc5qjDlLO2dqnCIoGsv5s5d0LEDOt6Bjh0qsn0CJELJRsVChG2qsqzqOQ6tryMv+wbBifdiPvBNdXRTSXpHjmXDU+zYdqHLWSHNlRpyPZbnIn2k57Oh0s72fW8odC55n+UNGzbEfaaCw+EYNM9QoA1SsUh1bFRiPg0GgyHpPanyp5NXCLVKnwqpaA9Xuhakq6GhgS1btkTNrt848QYbtm/gF7t+gS+kVp0MwsA1k67hn2/6Z26YcgMrX1rJTnb2q/tkz9SOqUrmO5yqjA0NDWzcuJGNGzfGmfEny5/M73igMsXm0XZLEvO5XC6WLVPnfLz00ktRP+V0yp5Jn0mZv/s44vyf4PxrcPYV8Ozpy699MVigfKbytRxzPcIxr9+RTKk8RRLrZ7Bj1nLCE5nJWbp5C13OhvOZmbbHcPCU2L8uhDroJx/FpVC8CEYtghmf68tYNgXK5yDOvgR7/g3G3AjXb4GTz4CvHVF1JdR+EkRM/wx6I+bce/sUaM8e6DyoInW3b4dzO2H/dfCOGWp+QdF1v4Y/fhcsTlhshd/NAfscRNk0MPbnN5nsDFUmC1HOBsrrD/np9HUyqmQU/73tv/nU/E9xx/Q7+KvL/4oDbQf407E/cbDtILfedmvG9PMpZwPmFcD8SMLNwMMgfijgURB/Lyj6emHKWb+0kA/cuxEdO6B9h1KKXTspCnb3uxdhjCjGC6ByAVQuQvSeheO/hHMvwfHziMrFMOZ6WPgdxJjrKDIXwWV/m7R8ueQp2dx5IctZIc2VkPuxPBfphTCfDYX2UN73sqWTil4iLlpluVCgrfDFQlvBSGzcgVb9Ev2LU+VPRj9ZXillv+iH6ZRlONLXr18f9cPdsGEDrl4X3/nLd/j+tu+zt3VvNP/lYy7nzul38vC1D2MvtkdpxPKoPSfZM5ubm2lubuaDH/xgNGhFOmV8/PHH2bx5M+vXr2flypUDRs/WLBy08sR+Jn6PpSGljDNBic23YsUKnE4nW7ZsYcGCBUNesYRB+mTIr4JwnfodnHgG0XOk3/2ydCpizI1I23SYdA+ybBqezk7sdntMpuS8DlRGKWXUSiMZsuYpCZ105SyTvIUsZ8P5TMi8PYZrZ1lrx+FYiR9qHWQkH7YZiMv/X2TXT6qI3FJCzwnE4f9FvrUajFZY9J/IKR+l+9gfKRt7GTjmqSv2ueeCyF+fh2fC8EoVWHph4R9h9HOIsA8+uFplfK/vHilMKkp9NLDYHIRjLuGy6Xi6/dF6yIVMFpqcJUs73Xma51ue59kDz7LVuZUHFjzAt2/9No/d/BhTK6ZSZCziv+/4b0DF33jv7HsIIfinV/6J14+/zn2X38eHZn4Im8U2InKWEQ0riPsF8uMSDoHslHhcHuxftiMuE8hPSBiVfVkGSof060AEu5ARhVjbLcazBxEOkAhpsELFFRE/4gXgmK8i19tqESefRh7/Dcz8Ehx5CmwzoW4VouoqpLHPKkBKiScSUyYZcsKT6NtZjpWdC1nOCmmu1JDrsTwX6SM9nw2Vdrbve0Ohk4q3ROjKcp6RC5/lzs7OqB+rEGJQP4LYvJDaZxmUL2wyn+V8n1E3kG/E97//fex2O/d86R4++ZtPsnnP5mhEa4Fgybgl/GvDv3LD5BuUz4QP3D7lC6nxCvQ7ZzmRp9///vcA3HnnnXG+lIPxZDab+cY3vsGXv/xlFi1axH/+53/yuc99Lo6nnTt38uUvfzkaOTuZz/JA5yxLKeN8gjR/jy996Us0NTXx0EMPsXz58pyc+VhcXExPT09cn7EaurC0vkTw8C8xtb6ECPX1EYSRkKWakG0uweo7CYy5lbKquj6/HAky4i9VXl4enWCjt4vMfVjsdvvQeUqj78XKTjq+RlreVDxpg7Lus2ylt7c3rj0KgScplY+Xlp5vPzftmVodDJUnKdXZyum3kxvTuL+ibPqD9Ha7CbbuIFw0irDbTenuRxCvv024pIZg+QJ81n8lNLsa6+/NWO4vJTxvLIGlAQJfCRCaB9bS2ykyf4AzzrcoDR3D1L0fQ+d+zN6DGDz7VJwCT2SH+kQfDwIDpcWTCJbPJlw2E8uYBYRts+gUE8BU1q+dYuVsIJ9lbYGxkORp98nd/HT3T3nxyIvsOr+LxRMWc1vtbTy04CEuH305brebydbJlBWVxY3lRowsGr0IgI/O+igyIFn/2noefOZBNty+gY/Xf5yenp5+fqPZjnt5HSNGe5FVki5PF5YbLFiftMLfQeCDAbzrvEi7zLnPcldXV1wd2Gw2DL7zdJ98DaP7XYye9zB63sXY40xu4VRUQdgxH3/pXELllxO0XU6XYTzjKy0ET75A+PRWzO/9C4R66LnNSVnlInyGUfS63eps44r3K55MJXFnsWtzAZAbntLwWZZSpuWzHCtnA/ksa3Pype6znOuxPBc8afOZ2WweFp/ldHSN4Xjf032WL2AM5LOcGK0aBt/xupCiYQ8l/cjpI9SMr1EJ4/p+KzIWUVFcQXVZNUaDkTVr1qR1zvJAPsXLli2jqamJ5uZm5s2bl/EKoebvrKG+vp7Kykq2bdsWPX5s69atrFixgtra2ox9lh944AF+/OMfR32WtfsAvva1r1FVVdWvjNrxV5muELo6OrBzAnHqGTj5NLT9Je4lQhos6gzYus8gJn0Yaep/zkti+d1ud8rj1zJZIcyGDuRPzjLJW6hyNtwr95B5ewwHT1r/stvtw7ISP9Q6yJV8JKVzxo3jLRPM/gsc2wUrvwgHT8HT8xDFi5HT62DUVTDlo3FH1yTlCZA9J5Q/tHuPMuV270V49qgYBykgSyZHj7kSjrmEbTPxyPGUj556wchZh7eDv335b/nP2/+THWd28O9v/Du3T7+d2+puY0zZmCHR3nVuF5XWSkaVjOKy/76MW2pv4WOXfYxrJ1+LIWJOny85G+qOV5yc7ZbwFPAPwB7gzyD+SiDLciBn4SCe0zuxhQ4jXO/0+RdHIlInQpZMVDvF2o5xxQJE6ZQ+z/1wAHY8TPjUixi696sd5epbYGwDjL4WTCUFNZ9pabF1rikIQ537RkrOCmmu1JDrsTwX6SM9nw2V9ki8N17yPsuFAm2Q0qCtwiSma3kHopF4T7L8qegPRjtZeqr8uU6XUvLmiTfZ2LyRn7/w875MMXObHz9nI3+ghDQV7dhnDFQHTU1NOBwOampqMm4PUCbWS5cujUZUb25uxuFwUFtby7333hsNSPboo4/S3t4eN6CkesZAz4xdmdTOm07EunXrWLhwYXrtEfLBuVeQJ39H+bHfYOg9EZ+5Yr46vmLihxGjrybWtzFVzSfjbah9Jhs6+ZazTPLG5k+n7COVnk/a2bbHUNPTyRtbpguhDnIhH9H048CzwDPgeNkB1SB+1QBLG+AsYJ4AK9+G1r8g2v4Cxxuh5q/g3Ktw5CfIMTdisC5ECEf/cpVOgtJJMP7W2EpAes/SffptSoNHEdEAY3ug9xyi5xj0HIPT6vg/A+AAZFGF8oG21UHZNIRtGtimQdk0KB4DMXUwnH1bSsme83t49sCzPHvwWZ5Y+gT14+oZXTKa3lAviycs5qnlT+XsmZePvRyAsAzz5Iee5Gfv/owPb/4wldZK9vz1HgTJx6Vc8DpUGnFyNlfAv0R+cAH/g/Jx/isBXwDmDEJfSvCeRXTuB88B5Wcf+ZSdLdgTAkxGqCi3gMr6qFJMxXxE8ei+LCE/BLtACMTrH4cpH4EJdyBNVnqnr6Wk5g5EcVXWdRD7fz7HDpEgD+k8s9Dns0KaK/M1lucifaTns5F43xsKnVS/98uv7yznBwPtLIdCIQwGQ78ONtCqXzgcjrsnVf5k9JPl1VYeUwn7cKzWuXpd/PTdn7Jh+wZ2n98d/b3SWsmn5n2Kv73+b6mwViSlk6uyQObtMRyrp1LKaJvndIXQew5O/V6duXr6OUSoz6RHai8Tk1dA7SqEbcqQVjjD4XBckLJsyp4tHcifnGWStxDkLJ30fD8Thl/OMumnOZezPNVBruRDbBLI/5awC7gW5Psl4dvDGC8zkmwlrB8d9x5wPgnn/ggdzVD7aViyQZ3vbK5AlFSnLWdR2r62vujc7j0Iz16ke0/SM9ljIU1lUFanrlgluqwOSiYgUpxhnG279gZ7+cPhP/DsoWf5/cHfc6brDDdPvZn3T38/K+asYHTp6GGVYX/Izztn3mHJhCX851v/ycbmjdx32X189LKPMsUxJWfPHQqNtORsm0B+T8LVwGeAV4H6bkTwENK9DzoPRBRipRSLgKcfDQ3SaFWR22N3iyvmq4jusc9EIjvehTNNcPZlOP8qTFuDqP8W8tyfVR+yjr2g5jMtLbbO4+QsyTMLfT4rpLlSQ67H8lykj/R8NlTaIyFn6e4s68pynqApy9OmTevnsxwOh/n0pz/dz2e5q6srqR+Bx+MhFApFO2TfOcvuuA6i+UYk7rqm8iMoLy9P6UeQyhfW6/WmOGe5J6kfQSJPxcXFvNP6Dv/5xn/SuL8xGtFaILht2m18uf7LLKpeFC1/Nv4egUBgWHmKbadk/h6p2ikdf0TN1yhrnqTE0LWX4ratFJ17Adn6ZtzRMNJgQUz6MN4xH8RXcT1EXiZywZOUEofDMeR2MhqN2Gy2jNups7Mzzh8lHZ5iA72l4xMWO0mk4slms6X0NSrUvpdNOw3GU3d3d1I/t5HmSUqJ0WgcljEisey54AnUqQ4p26nThzfkxfw7M6Y/mQj8e4CyrWX4PX687/MiK9RzNT+3jHlqPYrwuwiXTMG66ysUHXsSYZ+Fz3EtvsmfJGyb048n7YU7nXbqcp3F6D2KsecwRu9higMnCLsPQtchhPdEyqOuIDK+2eoIWmsIWqcSLqkhXFKDqWIWxZUz6OrpTavvdcpOTnWfYpZtFg81PcRLR19i2dRlfHDOB7ml9hYCPfEBoUZqfgoZQzy14yk2793MK8df4f9d8/94+LqHMZgMeLu9IzpGSCkxm839eZIhLMHTWAPH8be+h/Tsx+A5jOmv/xPRNgZu+BHcvAGqWxJaVxAumUyoZBrhsmmESuooqrocU8UcXP4yYi2hEnkyunZgOfxdzO1/glAvwYprCFbdQGDUDcjyudiTyJPG60i9R7hcrjjus4mpMVA79fT0RGVyIJ6sVmtKv9GRHssTebrQxvJc8CSlxGKxDMt7RLq6xnC87yXTNYqLi6M8DeSzfOjQIV1ZHinoPsvx6e5eNz99T+0i7zq3K5rHUezgU/M/xd9d/3dUllQO+6ocDL8vZTo8STkE35OwH3n2j2r3+MRvEd5482ppnaSOspj+WahS5tW5rgOt/LrPcuH4Ug6UPhwr8brP8jD6LB8EngHxrEDukcrc+jhwBrgqz3Lma0Oc/xPy7Msw5T4YdSX8YRnY5yKqbyZcdR1uryE3vpQhH3QfRnoO0tu6B2vwBHS1QOchdWZ0UnNc7QEmZFlNZBe6NrojLWx1BMyVvHluH2+f3s6Xr/oyv9r7K549+Cw/uOsHdPu7KTGXDDpGjNSOl9Ye53vOEwgFGG8bz8z/msmsUbO47/L7uGvmXZSYSzKmP6T5IBzGc95JuTiNoesQ0rMPOg+CZz90tSDC/n73I4G9N8AfvoTcdx38+hH4/9k77/goq+z/v++09AqEEmpCEaSGIDYUpKgUK9gbqyS6a9tVwbK77rruKuhv/a4dsKxrWQV0VYoKAVHsQiii1IReQiCZSZtMZua5vz9uZpJJZpKZyUwyYD6v1/OamTv3Oc85997z3Oc899xzYk6DDhmQ2h+R0FfF0miiDYQQYDsOplRE5V7kN9fDxK+hdDMc+hjRdSIydRToPPOxhko/2mI+c5XVv7e1WM/q8d++Z1mhfc9y+57ldoQQrpuUC/Xf9Hm72TRFo+E53ur7ot8cbW/lvuoHUr712FZe+PEF/rP5P1gd6u2RQHDj0Bu5Pft2zux+pt98hoPHYPsjXOUN26L+TahJGtXFyr364Mdw5FPP6NUISOgPva+FjN+ovYT1EK428Jv3ZsqDoRNuPQukbv36/vDeVuXhpN1WeubvOA3VWG2qPFRt4PVcDcSXApahjr3AOOByEK8INdP3qT2aohMKmaI7Qo/LET0uV781J/TLhaLPYdODiPJd6MbvQtgl4vh3Kg+7McHnNZvUM0N0bVqq06hJOI+Y+g+vmkPtfy7fXXdUFEDFbigvAM2GKN+ljDbghBM+rYTllfBpFegFXJwQjePEv7kyJo0rO6bBhnuJj05T+6SjOrk/RXQaGBMhyLksVOX1+6NzfGd3+Wc3fMY7P73DY18+Rs6yHPbdu4/EqER06NyBwfyh71HmqFSGaHUxwlbs/o7NdRxHuH5XF5Fcz226EWV9tNoClNAfEgfUfU7vD4+nIqxAzCvwD+AFYJY6RHpjvqW9CuPxzxF7vlE5xs1bYPJP6kXI4D+rSh2y1OGNFx/yn0zzWf1r+Ht/i/T5LJLmynDdy0NR3tbzWVvoR0vo+Pq/IdqN5XaEHHannf9t/x/P//A86/avc5cnRSUxc/hM/nTen0iNTW1DDk8RlO+G/Yvh4Edw4nvP/6LSVMTObhdDjyvc7tXtaEc7TiH8D3RpOjgbmA0MA+YCE4DGAevbBjq9ioPQc4b6bS1C2qKUkbrhHqjcA6nZ0ONKGPRACK9rUCvG8RnQdZLnf1JDVh7EUbYDQ+VeJq36O58f38/QmGimxGqsSLYxKhr0ohrKfgLf22PrXc/kYUB7fEZ3Uvfk+t8NcR7GdTiRkZLBH8/7I4+MeYQdJ3aQGpPKu1vf5eHVD3PzsJu5ediN9I5Ndhu5HkZv9XEPA9j9X714F81BUBsTI64nImEAJPZXuYgTa43i2B7gw2gHwJWqeA4wFHgZeAJ4Dbi+9r+C12Dfe1D8JbHGjtBtEgx6EDqPU+0OkD45sIZrRzva0Q7ajeWww+UO01yZEE27FzQ8x1d9b/S91a3vtuHrmoGWHy4/zIINC3h5/cscqzqm/kNw5cAruXXErUzKnOR+CxUpbjkQXH+Eo7x+Wf0+96hbdRj2vwd730aU5nvKYeoIXSch+v8O2WG058NHQzphboP69FrqlhMMnWBl8kd3AqkbDj0LR3m4rwmtr2eBjNNGehYGHutfLyg6EsTPArlUQgpwO8gdEqEJECB/aEBDRqieRXVCVluQqSNh2k5E1UFk0edqpVJK2PoYHFmF6DwW2el8pGkwUiY2Sdtf3q12K8VVxfRM6smNKx8mu1s294y+h3u0rrzWZRjdE7ur+o5qsB1HVh+rNRiPIWzFtb+P1RqNx9zlOCpAqwHrIXX4AamPrjWgOyH00cqYRNTet9V3IYQqr1eGEAgE0lUmRL3/dMTa7WA01ftfV0cHwQAEclsNl1uLiOrg4PX8f/D3L/7CH1Phzx1UF/hrw0udCRHVCRnVqfYlQCeI6uBebZe136WpIxZnCkmpXRC+3EP96D90wBTgYgfigBF57B1YXwy33w1jM+GKy+DCZ7BoXUhKTq4TxId+h1s/2kLPGs5Prv9DMfe11XwWSXOlCy2dz8LBY1vPZy2l3RZ65ku2RudIf2u2IyD8WgJ8GY1G1hev59nvnuXjXR/j0BT/Jp2JywdezqNnPUrXuLqEyadywIewBfiK0XDsWYTc+zaGE+s83Ma0lDOo7nYV9rQLkTHdg5YpmGBYzckkZXuAr/YAX+0BvurLFHBQmCILhq8MGD8zYvzMiDghYAJUTa/CPq2uLZsM8BXAvTyoAF9BBLpxPcx56ydhPYjxxFpiy75HFn1OdY+Z2Preh6E4D5lwGgldBnuVKS4ujurq6kb9VOIo4X9b/8fy3cv58sCXXNL3El6/5HWKa4qJlbEY6q0ZBD32So4iao6jsx1H1BQTp69CVhdRU37IXSZqTmCwn0BWH0No1UQSjjqgUoOM2ATGHbDTPzaRm7r0ITs1E6I6Ik0diE7qgWbqQJUzDmnqgGbqgDAkeA2GFVCAL/zQp3IL8vj3GIvXYDi+Bp3Q0F28nooDXyKtNei+Oouo16LQr9cjbhCYnzJTf6IMxX3PVd4e4Ks9wFd7gK/2AF/tCBFO9dRRFTUVvLnlTV748QV+Kf7FXeeMbmdw1elXcecZdxJliIr4N40Qoamj7JXIgx+j2/9fxJFPQNYZfTImHfrciBhwL9LlXtYCXiD0bSDlyZNqw1XenjoqvG+h21NH+dkGRUAaiI0Ceb6EVGCqOsQ4gYxuuX5Ekp41WdfpRHPY0BmjEesug8OfIBIHILteDAMfUKuZNNazI+VHeP7H51mxawVbj23l7B5nM6XfFKb0m8KgToOavW5Yx56m1e73PVbr0nwModmRUkNFtZJqeVdqCIEql7Lef1rtKmyD+qh6mqZerAvqzhFSetbXGWtXf9Wqr2tFWOpM/HDoB/69+d/8d+t/SU9IZ+3Na1ucDisoPXNUwr53EUc+Qx7NU6vEXSYpl/oukxBx3RvT2Qb8AM4bnOiW6BCVAq4Dolt23zuZ9MxVVr/Nm9WzCJ/PImmudKE9dVR76qh2hAAuY7lhB0jpGaHQHwRyjr91g+EDYPvx7bzwwwv8e9O/qbBXACAQnNPjHJ656Bmyu2X7TSsSEGw7hAWaHY6shL3vIA/+zyMHMtGdYcC90OsaiO8d0suGow1CRTNYOie7np1qiNR2aE2+fF5LAhuBdCAZ6Aj8BHQBdgJDwFcUol+dntWY4egqFcxw5L+gphQ2PoDsejHHY0Zx3/fzePbiZ6moqeDBvAeZ0m8KF/a9kNSYX0eMjFCO5yp7FSsLVnLpgEtZs2cNz3z3DDOHz2TagGmY9Kbw8bXpIeibC1EdYe1k6DIBul6o9rXrvD/8er3WqiTEXwQcA3KBu4DOzZwcCv7DQKd9PossRGo7RMR8FgE0A6Hjy1ZriPY9y+1oFg7NwdIdS3n+h+dZs3eNu7xjbEeuOf0aHhv3GCkxKW3I4UkMqcGxdbDvv7DvXbArVx0BaPo4RPpUxKAHIXV4m7LZjna0I4SoAlYDS4HlQDnwNjANOAy4gkMPbRPuIhemZOg5A9ljOtuPb2f5L++ybOsP3HtgE5fKAoZqp2N32ukek8Rbl/+HJoNGtaNJxBpjuey0ywAY2Gkgo9NHMztvNrnLcnls3GP8dtRvW3YBKcHyCxz5TB29roLMW9WeZ+kEYzxM/DJ4+tNrj9XAv1B61RHYAJzRMtbb0Y52/LrQbiy3wyeKKop4Jf8VXlr/EofK64KWDO88nHkT5zE+Y7zPtBPtaAJSQulG2PuOCtZVVS8Psi4Ket+AzLyVMsNpnkFK2tGOdpzUMKw2qAi+a4BuKOP4DeA8wLVY5z2L0q8eNoeNL/Z9wbKdy1i+azmHyw9zQZ8LuPqMOYw+7TLKqiq4T3cCEdcJ8u+HPf9R2QC6TVauu6b2F7rBoltCN/50/p945LxHWLdvHfraFd67P7mbfqn9uG7IdXSI7dA8oZpSKFpdZyDbTkDa+aqfOo9XdQbeHzrGBSoy/ITa31trvw8E7gZmUKd37WhHO9rhA+3GchsgGPeCQM7xt663elJKvj34LS/8+AKLti7CIdWGeYPOwJR+U3hq4lP069DPb15OBrSaG03ZTrWCXPgmVBbUleuioMMZMPA+6DZFpTyRElHmT76S0CAcbRAqmsHSiWQ9+zUiUtshbHxJ1MP6elSqm4UgpIDzgaeB0/DpXh0ITmU9O1F1gm8PfsvUpKk8+dWTvLrxVab0m8KzFz3LuD7jiDXGAmreKtPKILGvOnH4k9D9Uji8HH7+u9obPOBO2PtfSB4MSYNP2ZeQ4dQzndBxfu/z3b9HdBnBqxtf5f5V93PJgEt4acpLdIzt6HmS9Yjaa97xSuU9tesl5VY9+jVIG6NyLIcYPttgMHAI+DfwF+BDYDGgoSJtB0MzVLyF4bz2+Sx8iNR2aE2+TsXnRp/02vcshwe+Any1RUACf+pW2at456d3eP7H59lStMVdZ1jnYdx31n3MGDSDaGN0xARZiLSADz7LrYeQe9+F/e8iSjbU/wfZ4UyVV7TrxaCPijzef0391C5TxPASqvI24aUcRJ5Q6Z1WovYe1wCfg7j2JJUpzOX1yzSpseHwBj4r+IyHxzzMj4d+5PF1j7P02qXYHDZMelNwgWtcEV6/vkYZ0FEdEN0mI4c+rtx+wyjTqVLeVN0dx3eovM1jHuaXQ1/zzg//j98kCfqf/7ZKvbXlz3DWf1T9MAce8rtcA1EikB0lXAh0Ra02Z0VWuwdaHkm8hKo8kngJVXkk8RKq8kjixd9yfwN8ta8shxnZ2dl+p46qrKz0Gc7dbrej1+sRQrjDuZeVlXkMhISEBIQQlJSUuOuC93DuoMLC7zqxixd+eIG3fn6Lshq1kikQjOwykj+f/WfG9BgDgMPmACNeU3PExsb6TCXgS6by8nKvIeq9yRRo2H1f6bC8pe+Ji4tzh5x3tVdLZRI1JRiPfkz00fcRx79yLx5JdGhxfRB9b0f0y8FSVSt/RTVQ3Sjlg9PpxGAwkJycHJBM8fHxAfVTdHR0o3QuLe0nF/+pqalIKYPuJxdPSUlJYZfJYrHgcDjcutPU2HM6nZjNZnddXzIJIYiNjcXpdFJdXZcuJph+Ohn0yZdM9VN5ufojEmRyjVOj0UhSUlJQ+uTY5cDwmQHjp0YMXxkgE+yT7NgW2HBqToiGmCtiMEkTpaWl7vESCpmkVFE/U1NTW9RPUkqPlB3+jj2TKXCZzGYzTqfTrTu6aB2rClbx4S8fsnLvSqod1UzoPQGLzUJW5yz+Penf7hQlNboarzLp9Xqio6NxOBze+6m6Wsk0ZAEMqiam/EeiTnxOpU2gFX9LzLY/YU+bhL7HNKI6DIp4fWrNe3mzMiUmkOk8wey4asRnZ6Mr2sCe8kSGbatgxJ5x/C7rXqYMeg5nSQlRUVEkJCSE9b5nMBgoKSnxiNLrU6aOSqbKv1UStSAK03kmnEOdGD404Eh1eKSXk1KSkpLSon6SUrrrh02meqkNXXqm0+maHHs2m42Kigq3TvqSyWg0YjKZqKmp8ZoG8GSdnwIde8Hc91pDJtd8FhMTQ1xcXFifI/y1NVrjea+hTFJKd32XTE2ljvIH7SvLYUJTqaPMZnOjKG1NvQHRNK1RZDdf9b3Rb1jXqTlZsWsF//r2X6zZXxewKyMlg+sGX8fdo+9u7ErVDI8n6xssCLw/vJZrNuSBD2Hv28rljPqpnnrAaX+A3te5U500J5OUddH8WiMFQEjawAv/ycnJXq/nL+1g6QQjk796FkhdKSVlZWUkJiZ68BFIG7RGebivCaEfY6GQKWg92wRCCuQIqdyqjcAUYBqIfuFrg1DpR1vr2YnSE3RI6cDMj2by7s/v0ie5D1P6TWFq/6mc0+McjHpj6+lZ9TEVP+LIJ3DsC0S3i5Fj/gf2MjAkQDNzbiStsED49MyjzHZcpXaK64VYMwFZskGldep2EXS9CGK6Ulpdyjs/vUNqTCrXnH4Nz379LKN7j+bM7md65bst2qBRWSnwLojbBXKThM+AWSBTTx49c5XVv7e5DISWzn1tNZ9F0lzpQqjv5aEob+vnxpbSbov5rH1lOULgukm54Hpz0rDcVbcpGg3P8VbfF/363z8t+JRL37vU/TsjJYOHxzzMzOEzmw3Y1RSPoSw3m83MmTOHvLw8CgsLycjIICsri7lz55KRkdGIxsiRI8nPz/dKMyMjg4KCAo/6gdL3xbt57zqeePRe8tZtorBIw1wFGWmQlRnNQ3dcQtbl8xDxvTzOz8vLY+LEiUyfPp3Fixf7pP373/+eN954g8WLFzN9+vRmeQm2PNgx2Vx5/RtoS3gMhk649SyQuvXr+8N7W5WHk3a4xlhz5f6O02bHWLlQbtXFwB3AF4ABRJaAz4EGGW3C2Qah0I9Q0fFXJofm4FDZIXol9+KRNY9QYa3g2anPcuOwG/nT+X+ib2pfn9cMu57FdIaBv1eHvQKqDqjyjferIFQ9roAeV0LHsxE+UhdFim6HTc+kBifWI6oOQY/LYMf/qfJhf4czX0fEdFOxNuohNSaVO8+4083X7tLd/PmrP5ORkkHuyFxuGX4LMcaY0PHoYjXANvAoSwVqg3wLZ63O/w24DvSz9HBGZOuZt2v4dX87CeazSJorw3UvD0W5v/3d0nJ/bI1gaLf2fObr/4ZoN5Z/hbio70UMSRtCWkwaz1z0DEO6DGlrljxgNpvp06cPZrOZjIwMpk+fTmFhIUuWLGHJkiUUFBS4DVoXXIZyVlZWI3qpqZ45NhvSv/TSSzlw4ECT9D1QY4a977Dg+bnkPrffXZyRJsjokUrhUTtLvi1jybeLyMlJZv78+cE3Rjva0Y62QSGwDJXe6QugH3B97X/31qvXfOrXXyVKrCV8uvtTlu1cxie7P2Fo56F8ccsX/G7U79CqlQvchIwJzVBpZRjjIWmg+p79gorcfOADWHcFZD0DfW6A499DahbojG3La7hhOwFHVqqc1kc+A61GeUf1uAyG/g13Wq64nn6Re/y8x3nywid57+f3ePunt7lp2E0crTjKwbKDZHfLDp8cwWIU6kXYZuBZMPxoUCmn1gMjaNf7drTjV4R2Y7kNoNMFnm4pkHOaq2vQGdiUu4mKigoSEiIvT8mcOXMwm83Mnj2buXPnussXLFhAbm4uubm5rFq1yl1uNpsBvK7UNkf/ySefpLy8nISEBBYuXOiVPlCbD/kL2P0KHFjCkm9ryH1O/TV3ZldmP/os9Lgcalce8vPzmTFjBgsWLCA5OdlDDn/h7xuvUCCYMdlaNIOl09Z61hI+TkVEaju4+XIAAuWKeT6wCxiLSu80H2ji/VnA1wohIknPymxlvLz+ZZbtXMY3B75hSOchTO03lU+v/5RR6aMASE9Mp1yU+yIXNF8hb1u9qTb11MUw6iU1Bziq4KsZYC+H7peoVWdXBoMIQdDtIGv3KtqOwxeXQsmPkDxEBaAc8z50PKtOziBSRup0OuJMcdyadSu3Zt0KwJo9a7jug+vo36E/uSNzuXbwtSREtfyZJKRjYRjwCjjKHWBDpZvSAXcCM4Hk1uGtfT4LPxwOqKiAmhqw29Wnt8Nmg7IyE3q993pNnVtTA1YrOJ3qe0UFREVBTIw6YmPV72AOkwkcDgOpqRAd7fmfwQChfqQ8FeczX2jfsxwmuPYsN+cH347GyMzMpKSkhNLS0kb/paSkYDabPfYn5OfnM3LkyEbGdUjoVx2Ewn9DwWtQuQcAcyWk5Ki/N3yTR9ZZ471ep/4KdmlpqXv/RFNu2PWRm5vLggULmnTDbkc72tECWIBqoDMwHHgKlYf1Y2Ac0H7r9gsPrHyA3Oxc0hPSuf6D67mo70VM7jeZ7ond25q10ENqcOIHteJc/BVMWAflO8G8ReV0NkbeC2ifsJ0A6YToNFgxFLKfh07nwp63oMsEiO0WdhbKbeW889M7zN8wn4v7Xszfx/+doooiOsd3Dvu1g4Id+B/wL1QaqsLa8lPXhjxpISVYLHDsmPejqAiOHFHH8ePQitk62wQGAxiNyiBPSYGuXZWBnpICaWmQnKyOlJS67/WPxETQn2IeFf7aapHzOvRXAiklNTU1mEwm/1YOpUQ6Kv0+x1/6AfMRCPSxLXqFlZWV1ch1uikUFqrZatSoUQHT99kO+5fA7lfh6GeohKmAMELauTzxYRfgv+Tk5Pg0lAH3ivKGDRsoLCz06iLuC1JKr1H6XMa8LzTrQu7jWqEeC6GiGSydYM4L5JyI0LOTCBHVDrtRrtXLQH4pcf7Zif6PesT/BPRErS5f2jSJYHCq6NmR8iOs2LWCZbuWcVHmRdwy5BZ6JPVAIIgxxvDB1R+E7FoRqWdCBx3PVIcLVQdg69/g25uhy0To/1u1It3KaLYdpAalG+HQCuVeXfIjjHgaTrsXzl0C8X2UfBk3tRpfCVEJ5GbnkjMyB7tmR5MaoxaOomtCV3JH5nL16VcTZ4oLybVCwr9RwFWo4yjKSH4I2ATcD1wAvvKnt89nLUd1NRQX+zaAjx5Vxm9REZw4oVZwg4FerwxJTVNGt16vjE29HoxGiV4vSU0VdOggKCtThmRqKpSWqsdf14qua4U3JgY6dIC4OLWCnZioynU6tdJcVVX36foeFweVlUqWxMQ6w99m81zFttsVn0ajpLJS/e90ikayOxzqsFpV2+zeHXi7xMQoOTt2lAih2qBjR0GnTtCxo2+Du0sXJW9TaOvnxqbQbiyHGa6ohPVRVVWF0ei530kIH1HenFWIxQk0M8bq6oNfdf2tFwzkjHIwxPmWqZnyRYsWKTq1dVzlCxYswGw2M2HCBI//CwoK3J8TJ04kLy+P5ORksrOzefLJJ8nKyvK4ZkP6rv5Y8K+/KPpDDMrVziVP4iAYNAd6XIkwxrEkJxOA2bNnN+KxIXJycjwiFdb/bPi9Pg0ppUdof1f5gw8+yIkTJxrVf+qppwBlTDek21y7128Df+r7Uy6lpKqqCpPJ1OLoiMHQCVamhud4qx9IXSklVqu1ER/+8NKa5eG+JoR+jDVX7lG2E1gILAOxRyDHSrgU5AJJeWo5STIJ0ae2vmyedjDl0PI2CJV+BEJHSsnGoxtZunMpy3ctZ+ORjZyRfgZT+k1hbO+xVFVVceeoOz1oBqJnodDJNtezLhNh8k9QtgMOfICoKVV1t/4NojpC98shpkvb6VnpRuSO5+DIp+CsUgZ95m2IMe8jY7oqayChn6pcG7gnlPeI+v3YVH1j7T7wLbdv4e2f3uZf3/+LOXlz2HfPPqIN0Y0CCbVUz1qsH50BCeIegXxBwtWoF26vACPaZj5zlZ2MemazQWGhYOdOya5dUFCgDMWiIkFRkaSoCCorAzd+4uIkRiMMGiSIi5NoGqSnQ48ekJkJvXsL0tIkaWnKKDUalcHbVH+Yza7o4cHJGo57gZT1o2ELHA6JzYbHUVMjKCuTmM1gNivjvrQUysoE+/ZJiovVb4tFrbRXVAisVonDoQS1WuHQITh0SODzrZAXfPSRZNq0pmVqi+dGX/3bEO3GcpjhLc+yw+Fg1qxZ/uVZPgldHiwWC1Fx+pDkc/v+++95+umn2b17N3v37mXs2LG88sorSCnd+dy+/fZbQBmSGRkZXHHFFRQUFJCXl0d2djZvvPEGN910k9ccdd+uW80//voA+/fto/BoDRMGw+K7HKCLwtFlMlV9H0SL7w9AlENHjLFuJbtDhw5YLJaAZXLVWbVqFZdffrm73Gg0uo1kKSWbNm1y/+fKUZebmwt45ke8+uqrAXjmmWfck1iguTld7eJ6GAlFnuWKigqSkpKQsmV5lq1WK8nJya0iU0VFhfuc5vIj1q/rSybXtWtqan71eZZtNhsWi8XdJmGXyZqE/Iek7LEy9Lv1mI6YcDzsIO7yOBzRdXmWKyoq3HlIw52bs6qqyt22oegnKSWVlZUtzsUupXR/t1qtlFWVkV+Uz9npZ7N412LuX30/43uN59bBtzJhygQ6xnZ0y3T48OGAZaqvO82Nvfp1fcnkmmNtNlsb61MX6P5bt0w2LR5TwVvoN9yNM3kUhnPfwhHdM6x5lq21Y0xftZfo3XORZ75BlL2cGpGAfdhCHCmjQWesk6k2L7NvmRRaco9w6ZnBYPA7z/L1/a9n5uCZHLIeQqvR+H/f/j8W71jMzMEzuXbotSTHJ/vMSezixTUmWyqT6+E7OTnZez91iaf64Wpst9swvWPCHmvHWGwk9t1YrNdYqTHV5Vl2yRdonuVAZao/nzWXZ7mmpsZDz5rKswzqHhFsnmW7HYqL49i718jmzdUUFOgoLNSxe7eOgwd1qKHozQirK1Mru5CZKejSRaOkRCMtTaNrV0mvXpLBg6Pp1q2GxEQrHTtKoqPr9Mlq9T72qqqstX2rDMLWvpe7aLU0z3JFRQUmk4m4uDhqaqzY7TXodGpFODlZyVRR4b2fysq83/csljJsNklZmcBiEdjtcZjNgsLCEmy2GEpKdBQXCyyWKE6ckBQXa+j1YDYLiooEDoeOhAQnFktFkzIF+7znLc+y63t7nuUIR8jyLAOavSKkeZYhzPnyat2wQ/HmbMmSJTz44INuA3XChAm8/PLLZGZmuutOmjSJvLw8Xn75ZXJzc93leXl5TJo0CYDS0lKSkpJcwqu9ZoWvsWTROzz4jp3CY+qvCSM78vK/niDzrFuQovGbCovFQkpKChkZGeyu58Pi4j03N5eFCxc2Om/69OnuFe36fPmDxYsXc+WVV3ptr7lz5/Lggw8ya9Ysd9TtYFYj2vMst+dZDveKV9jzLO+Uyr16OfACaqX4bxL+hIcbje838ZGfzzxU+uGNzs8Hf2Zd0TquG3wdZbYyLnz7QjblbkKTGnqd3r3q11KZGupOKHQy0vSsUZn1KBz8EJFxM7LqMHxzjVpt7nEFJJ7W8ms6qqDocziyAu3gcsT41YjozrD3P5CZi9Ab2vQeEQo9K7WW8tZPbzF/w3wOlx/mxckvcs3gaxrVhxbkWfZRHpSe7QRxo0DuknA7cBfILr+OPMtOJ+zfD7t2we7daqV4927YuRP27FEuwr6QkADx8ZKUFOjbFzIyoE8fQd++kowM6NwZkpKaXvk9GeazcPDY1vNZ02NM0mDIhHU+a8+zfJLBdZNyQUrpdkXydrPxSsMYjzFGIIxxnorppa6UEmMMzdeVEkN0Y5pN0Q6q3JdMfpbPmDGDGTOUS/SSJUuYMWMG2dnZlJaWuus2jFztKp84cSI5OTksWLCARYsWkXPjNCh8AwoWQoUyvmeMgulnJUKfG3h/5whm3DCL7KkPUFp6m1eZXMpXWFjotf+ys7PZs2ePR3leXp4HX67PpgJ8SSm59dZbef311322l+tFQlZWFgsWLPDaBg3hrTyoMelHeX33r2BpBEsnWJm8neNVPwKoazAYvPIRqEy+yqUEh0Ngtar9XNXV1Pvuu9zhUA81dYdo8Fvtg/JW7qu+73KJEAnExwuio4U78md0NMTEiHrfAyg3QMxG0K8AloLYI2AccAWQBkSD+Lt/47Shboann0KjZ6HQDwBNavxw6AeW7VzGsl3L+KX4F87tcS4TMibQv0N/tv1um9fzQiFTw3NCoZPh1rNAyz3KYrtC/ztUeUxn6H83HHhfuWl3GQ9jlyOkw2s6Kp/XrNwHh5fDoeVw7HOI7oLsNpmaof8kOjYdYYiB/r8Ln0wBlrdUz1JjU7l79N3cdcZdfHPgGzrGdkQiue7965jafyrTB00n2hAd8JgM2zw0APgexDqhAgf+EXgVjLbwz2eusnDqmZSCw4eVQbxzp/rctUu43afrLf7h7QkxLk4FlcrKgsmToVs3GDpUBZ3yzmfr67Cv8nDdy0NR3tbzmS8aOl34nvdaQsfX/43qt68shwft0bDDg0AjRC9Z9C4zrr6W2ddkMnfaHqDWxURngj43Qp9boNM5UKsw/tDPzMyksLDQr2BaZrOZlJQUD8M4FNGwCwsLyczMJDk5mT179vh8g9aOyILDoaJuuoKRuAKVmM34MGrrvvsqs1qVwfxrQBLQFdgO/Bu4GPhEwMoo+DYetLg64zo+Xu1L69lT7U2rf3Tq5Fb5XxWkVPtR//7l3/nX9//CoTmY3G8yU/tP5cLMC0mJSWlrFn9dsFeoF7cpQ+GHO1TArV7XQa+rITa9cf3SzVBTCp3HwrorocasInB3mwyJp/3qBrXNYeOl9S/x8vqXKa4q5pZhtzBv4jz0ugjcv+ZEBQMbAvQGHgDOw/dKQ4TAboft22HzZvj55zrjePduNff4gsGgAjqNH68CPAkBo0bB+efDoEEqsFU72tHWaI+GHaGQUlJdXU10dLTfbzQCOcffusHw0RrIz8/niSeecK8KN8TIkSOBun3DPlFeALsXkPyzck2mQgUByz/ckSdWdmbi5TnkXHN3o3bwh/6ECRNYsGAB8+fPbzZV1fr165vm0wek9AzwVR9ms5mJEycCsHr16hYbyuEYC6GiGSyd1tIzkykai0V4ROP0jNQpOXpUo7hY7ekpKfFbhKARFaUClFRWqgcSnU7lXxw2TD3c7NunHmR0OhXM5Iwz4PBh9fAjhCrv0gWys+vc5lx0MjPVSsCGDSrIh6t8+HAYPBjWrFFRNvV6Reu886BfP/jf/yQnTmhYrTqsVsHEicqYffnlusieDgdMnKheAqxZo2TR6aAvMCkF3pFweSmcL+Em4PfAbwBNotI/VQPH/W+j7t2hRw9Jt25OevXS06uX8DCoXa5+oUJb6dnxquN0jO3Ivzf9mzV71vCfy//Dmd3P5Lxe53FWj7Mw6AxuOi5jOpwy/Zrms2ZhjFeGMsDwJ1Q6qr3vwKY5cO4iFXH7wEdgK4Ihj4J5K1QfUcbyuUsaDdBIbYdw8RVliOLeM+/lntH38OW+L/li3xfohI7PdnyG2W7mioFXYNR73z4QCELCv76WzsfVRC+MRlwmoD+wBmgm2Hdr6Vl1dTQ//STYtAk2bVIG8tatDVeJPeGaR7p2hf79YcQIuPFG6NXr1Esz5MKvTc9a61pt/dzYFNqN5TaAzWYjOjo6bOf4WzcYPsKN5ORklixZgtls9mosb9iwAcCdhsm1wpqVlcWGH79Xbmk7X4SjKwFYla/OG3XuZLj4CZJL41nyQCZmuZScO+4GPNuhIX1vmDt3LosWLWLevHlcffXVTdZ1BeQKBr4CD8yYMYPCwkIWL14cUDqqphCOsRAqmsHSCVbPNC2aoqLGKSnqG8FFRXD0aBQlJc2lphCA5xODEGplU9PqHjQSE9Xb9p49YccOVS8mRuVDHDZM7dPatUv9jo9Xx4AB6rOqShl2SUnqd1s+oNx9t/fy668Hi6W8UfTQv/61cV2tBso+BbEcoleB8SDoRsOjr8Hqz6GiEp4pV6vpN94I330Hb7wBFRXq6NIFbr0V3nsP3nxT9Y8rl6SmqdQiNptyFSwoEPiaBuPjG69I9+jhuVIdGxtY+7SGnjk0B98c+IalO5aybNcyzNVmDv3hEBdmXsh5vc4DYHxG45R3ra1nv4b5LCCYkqHPzZA0CPa9Bz/9Fcw/qf8SB0DCAOhxGRhqLSsfD4GR2g7h5EsIwfm9z+f83ucjpeRo2VH++s1f+cPKP3D7yNvJzc4lLS6tRdcI2XyWaiP6H9HwMPApylBeBewAZuLTcA6lnkmpXprWN4rz803s3+99TEVHq5enx45JLr20mjFj1EvisWOV904E2Yuthl+jnrXGtdr6udEX2o3ldkQUMjIyyMjIIC8vjyVLlni4H+fn57NgwQJ3WihX/azhQ9R/d6aRc25pXf2i7sxbdpCMjAymz1mu6qfgQb9+4Cxv9L3BlT85NzeXkSNHMn/+/EaGfX5+PrNmzWp+BTxA5ObmkpeXx+zZs/1yQ2+Hb5SWwi+/wLZtdZ9btiRy+LA/M7+gvv9ccrIyyOx2ZbSmpqq37KNHS6qrraSlxdCvnyAzU+UiPFXfuLcIGjATdEsh2QBMAZ4GJgIJ0BG42ks8n6lT1dEQF10Er76qcm4WFqo9cllZ8M9/wjvvwN69ahV8/Hg7/fsbyMsTlJbW5bmsqFBjYlsTW3dTUz0N6T591Oq764iPD0G7+AGn5mTxL4tZtmsZn+z6BIPOwJT+U3h83ONMzJyITujomtC1dZhpR2CwlYA+WuVA/jgDpAO6XgQD71efdgvs+y9sfUytOF+6BxwVoIsGvamtuY9IXNrvUq7Pup5PCz7luR+eI6trFlP6T2Hrsa0MThvc1uwpJACuDJUCeA14FPgdcCcq7kIIUF2t7mEuo9j16Rmc2fOlrsGgPG9GjIAJE2D6dLW/WEqwWGwkJUX/Kg3kdvx60W4styPiMH/+fCZOnMiMGTPIysoiIyODwsJC8vPVMvHixYtJTkqCoi9g5wssvvlnJh6G3JdKmf+Znoxe3SksiSN/8y8kJyc32hfckH6PHj04cOCAJ/1mXJtdxnFubq77yMrKIjU1lfXr12M2m8nIyKCgoMAdoKylyMvL8wjkNW/evEZ1JkyYELLV5lMBUqqV4PpG8c8/q6O4uGHtOgM4OloZQjabWvVNTVVuZuPGKaPLaJR06VLBkCHxpKUJonwkLVcPFzUkJcW0P1w0RDUQDdwNjAJuBM5ERY49g4YL8kFBp1MrH+n1tn/+4Q/qAKislJSUVNK9exJvvgkbNyrDuqAAHnhAnXfTTXX776RULzoOH1bGdEmJOjZv9n79zp09jeeuXY0MGaKivHbs2PIVmVc3vkpmXCbnJ57Pf7b8h5FdR7Li+hWckX4GOtG+KTAiISVYfgahU6vIn18Ig/8E3S+BcZ9C8lDQ1Xs0i+4Ig/8Ipz8C1iPqvJ0vwLanoecM6H0ddDpXlbfDDb1Oz9T+U5naX71JO1R2iLNePYtBnQZx1xl3MWPQDKIMPm7crY0JwAbgc9QLwh9RLwv3o/I2+4niYk+DeMOGBHbu9O79ZDSqe1BMDNx1l6SkpJpu3aIZN07Qr1/7nuJ2tKM+2o3lNoDJFPjb4EDO8bduMHy0BiZMmEBBQQFz5swhLy+P/Px8tTo8fTpz//ZHMsSXsOIesPwCQEYabHi6M3OW9mZ9QQ1LPt9IRkYGOTk5zJ07t5Hh2yT9uXObDdrlQk5ODhMmTGDu3LluOsnJyWRkZHD11Vcze/ZsAB566CFKgtisqm+w/Gg2m93fvRnKoFzEgzGWwzEWQkXTHzpSwsGDyhh2HVu3JrBtW8M36J7o3FlF4JQSRoyQnHWWjTFjoujYsbk9XmC16omJad7giVQ9a22YdCZYByxDpXgaBryL2oDsWvS8o3V5io0FIVT/3HSTOupDSvjgA2VAu46nnlIrz716KSM6LU25eF9yiQqE8/PP6gVNaaly1y8qgm++AfUips7HMiFBGc31jenMTFWWnt7Y+8DutLNu/zqW7VxGekI69519H1a7FadwIoTgk+s/aVFbBDtO2+czP+CwqtROh5ero/oYDH1cGcsTvgBDrT9/ahP3biEgtpv6PmgOdBqjVpzXXQkDZ8OgB6DyACZjh/DLEwRas3+8XSs9MZ1DfzjEG5ve4G9f/o2F+Qv54pYv/N6nH/b5TAAX1B4Ae4HTUFEM7wfTiLrzNE290HO5UbuO2pTn9Qiqm0htYGCcTuUBM348vPSS8oSKilL3GqtVts9nASBS26Gt9SxSaIaat/Zo2GFCezTsEKNkI+x6Cfa8CVq1KtPHQIczYPiTKhBKO055OJ3KfdZlEG/bpgKQbNumDBhf6NJFReKMjlaBqMaOVUGp2lUzzKhA5T1eBqxARYOdAkwDJqFcEU9S1NSoXKKFheoh9ZZb4Kuv1D7qvXuVAX3WWXDzzWpf9fbtKur5gQPqxU5TMJmUS3ePAScQ/VZwPHUZO7RPiTPGMW3AVG4Ydi3j+oxrBSnb0SKUbIAtj0LRGojuDN2mQPoUSBsLhpjQXEOzg9Om5sOPeoIxAXpdq6JqJ/YLzTVOMWhS40j5EdIT07npfzdhc9q464y7OKfHOREVsImDIP8F2stwYAS8eCasX68CLJaVeT8lLk65TvfpA0uWwOjRKtDiGWeo+BcxIRp27WjHqQB/bbV2YzlMcHWA2Wxu1AFVVVXExMQ0yk3WVMJ4q9XqcY6v+t7oe6vbVLS4pnhp1XJnNexfjNj1Epz4ro53YxJi0EPIvrlgSmrRNSHw/ghXef0yKaW7z1sjuXyo28DFf6yPCEj+0Cgrg2++kXzzjYOCAiM//aSCX9ntvh5mJF26wDnnKEOld+8aJk40MniwcD8ghELPAql7UuhZqGnXSHgRyAUKgWuAqVA9sZqoMVEIQ+voWSDjNNR6Vl4u2bZNuWqPGwf//rcKZrZvn6BjR8lvfwtXXw1Ll8LBgwKrVaqcpbsle8t34Dh6GnT/Dn4zBo4Og53TYOdUOJKFQNC9e92qdN++NQwZYmLgQBV8rL77pL+8B6uv0PL5LBQ6GRF6pjmUK3R1EeKr6cjxa1UWhkNLlZGcOBAR7nu5owrbnveJOvKByr186QGQDoTTiozp1iL6oZgPIm0+E0Kw+8RuXlz/Iq9vep3eyb1598p3GdBxQEj0I1A9A8HevZL16+uM4g0bQDMLegNbgCdQi85v6yVRyQK7XVJWJvh//09y223wxhuCG26QJCXJU0bPImmudKGlz0zh4LGt9ayltFtLzzyfM8tITk5uN5bbCi5juW/fvm53WtcNy+FwMGvWLGbNmgVAVFQUMTExVFRUeKQLiomJISoqCovFQllZGfHx8QghiIuLw2g0YrFYPAZIQkICQggOHTrkrguQlJSEpmmUl5c34jM2NpaqektyOp2OxMREbDYb1npJ9AwGA/Hx8VitVmw2m7vcZDK5adTUyy/QnExlZWVomuYury+TqCjEtP81TAfeROdQPrQSHc6E07H2fwhn2kUkJSc3kkkIQVJSEna7ncrKSr9kiouL49ixY5hMJnd7hUOmhv2k0+mwNPAPrt9PUkoqKipISEggOTk5IJkC7afo6GiOHDnicdNrqUwu/tPT05FS+tVPJ07o2bQpgbVrHXzxBWzdqkfKxoaxEJKOHSVjx2r07m2gWzcbo0fb6NtXw2gMXiaz2Ux5eblbd5rqJ6fTyZEjR9x1fcnkuilHR0dTXV3don5qrbHXXD81GnsO0P+gJ2pVFKarTFiHWdHfosf6Jyuyh8RkMhETE0NRURFRUVHu/ogEmVzjNDExkaSkpLDe90wmEzt3HmH//niio2HIEI25cxN48x0Hhwxf0kGext8fieHh4wO4L34DRlsXiisPUl3cnb17dezZo2fvXh0VFb5XvmJjJX37OunXT+O00zSGD48mI6OG9PQq9756bzJJKbHZbHTu3Bmr1RqQTIcPHyY2Ntbdr831k9lspqKiwq07TfVTTU0NRUVF7rq+7nt6vR6n00lUVFTr6pPJjq5oJfZ9H2IoXk3lyDdxppxFUtlqtK5TKa+q4yWY+alF93KtGvQxxJ1YjvGHW3CknkVN1yuxd7kEaUpp1XuES8+Sk5NJSEgI633PYDBw6NAh4uLi3GPSH5kq7ZUs2bGEq067CrvRzrPfPctNA2+iZ2JPpJRUVVXRrVs3ampqgu4nKSU1NTWkpaVRUVHJ3r1ONm82sHGjni1bTOTn67ymFzQaJaefbsfp1JG1Xc/9DuhugPwxYL6lkqFnOenYUXrIVFZW5tYznU7XZD9VV1dTXFzs1jNfMhmNRux2u/uzJf3UavNTEP3UnEzB3PdaQyaXnqWmphIXFxfW5wh/bY1AZHIZuV27dqW6ujrofpJSpV7t2LEj5eXlaJrGwoULefXVV93Pay45nU4nu3fvbjeW2wpNrSybzebaFCr+v4m3WCwe5zT1tqchfV9vCMvKykhMTIyMFS/NgTiyArnjRUTRyjo+DQmI0x9E9vkNxHQOOS8QeH+0xttTKaW7z1vjDWGo28DFv+9AaYI9eyTr1sG6dbB2rSuNjyfi4iQXX6xWznr3Vm6tffrU7ecMpUz+6lkgdSNOz5qQKSAaZQL5qaxzrxbAFBD3CuTwyNCzQMZpa+vZ8arjrNi9gmU7l/FZwWckmpKYPeA1Lh86kS7d7Nx0g5F16wSHD0PXrpLly5UL5auvCjp3lphMal/0zp2wZYudggIju3b59rrQ6yUZGTBwoEo5NmiQ4LTTJKedpqK3N6ev4dSzUOhkq+qZZRsc/BAOr4AT3yGShyC7TVarx6lngE7fpiteXvvDegS57z3Y/y50GA0j/wWWXxAJ/ZC6xnmIQ70SFqnzmS8au0t282DegyzbtYzJfSfz+zN/z5CkIQHrh6tcSrVV48cfJV9/beOXX6JZvx6Kixvrq8GgdLVHD+V9sn692vLxz3+a+emnJOLiBEOHgGENiJcF8i2pgiVqQKf615Qnt561Eu1gyqHl81k4eGxrPWsp7WDnoZbQ8XdluT3AV5jhukm5IKV0l3m72TRFo+E53ur7ot8cbW/lvuqHtNx6FApeUfuRrYfrkvHEZ8KghxEZN6uHjzDxEmx/hKu8YZ/VvwmF65rhaoP6vGua2mO8bh18+aX6PHSoMY3Bg9X+4ksugcsvV4GOLBYrSUmmVpHJX90JpG79+v7w3lblzdY1A8koF+t7QfQTau/xx6go1q4XGF60ta30zN9x2lp6trxgOS9ufpEfDv3AiK4jmNZ/Go+MeYThXYbXO8fEu++qbyq9mYpOW1Ghckf//LPg6FGlGzt3So4etbJtm5GsLEF5udq/v317XdqrbdugvFywa5fK1V2PS0BFeR84EDIyYhk6VBnSAweq8vpihFPPQqGT9et7K/dV36/y0i1QvgPRcwbseR3KdkLGTXDuuxDbPWzzU6DlPvsjthti4O9h4O9r3cUF/JAD5TsRva+HjFsgZXiz9Fs6H0TifOatrF+Hfrx/9fvst+zn5fUv8/2h7xmaPJRvDnzDsC7DiDc1zglXn86RI8p9WrlTC9avV4H+lM7VbRo2GJTudetWd962bYKKClU+axbccAOYTJLycsGYMfVkukgdAgFvAfehtr7cD6JbXTufVHrWRHkkzZWhms/CwWNb61lLaYeKd3/p+Pq/IdqN5TZAlK88MyE6x9+6wfAREkgJx75U6S8OfADU5jUwJkFmDvT9DSSe1mrstFk7NIPW5CvU17LbYfPmaL7/HvfqccOo1EKoaNSjR8OVV8KUKSpFU31IGTxvv3o9aymcwHpUGqd1wExgNzAV9aDmX9B4NyK1HcLFl0NzYNAZ+ObANzz7/bP898r/khqfyszhM3n/qvf9ynuckqL24LuQl6c+S0rU/v2YGDh4MIr77lO/+/RRL5r+7/9UnagoFfFbPYQ3NqSPHHEdgjVrPNshMRFOO009tA8cWPc9I0M95LtwSupZ5T44tBxKN8HoBVBzAsp3q/9GPBW664QBzbaDKy3VxHVqHi78N6w6D6ZuB1OyyuEcHaIkv4HwFYHX6pnUk3+M/wdSKvfQP3/+ZzYc2cDM4TP53Rm/IzOlL4cPq5Rz+fl1BrJnVGoFvR5OP10ybJiT6Gg9N9wg6NRJvSCuqlJBuK65Rn326VP3oiomxo958HYgG/g7kAn8F7j0JNCzkxiR2g4no56Fg2aoefPbDfvpp5/mxIkTIb14QzzxxBNhpd+aaI+G7QU1FtjzH9j9sjvtE6CihA64B077Peij246/dgSNykoV8XfdOvjiC/j2W5WjuD50OhWA6MILlXvZ6NHqQb4dEYRy4DNUaqcVKNe+LUAaUAUktR1rJwOOVhxlxa4VLN25lM/3fE7hPYU4NSf5R/K5sO+FYb222awe1MvK4Ior4PHH4S9/gdNPV5Fwr7oKJk5sfM6OHY0N6YIClZ7GG0wmlyu359G3r/rvpITmgOPfqbROh5ZB2XaVuzh9Cgz4PehCkPQ7kuGsVnPvoeWw7nLoNhn63KI+9Sdrp4YWTqfy5Fj07be8u+c5drGcpNf2U3I0DqQeZF1kPZ1OvVjKzlZB+DRNRcC/+Wb18mvCBHj5ZaUzhw975oFvMbYCXQATMBv4PTAghPTb0Y5TCCGPht2vXz/mzp3r03e9pcjJyQm7Md6a8NUBUkoqKys9gk80h0DO8bduMHwEDfPPsPM5KHyjLu2TIU7t8RpwN3Q6p+nzw4hWbYcA0Jp8BXOtkhKVJsdlHG/cCPXiRQDKdeyiiyA7WzBunDKOjY23x4Wct2DPO+n1rCV4HbUasRboi1o9ngacRUj8jyK1HULB15aiLXy842OW7lzKhsMbyO6WzbT+05jafypDOw910w1HGzRFU0o4dAh++EEdw4erlavp05VL6BlnqGP8eOjQwZOOzaZctuuvQm/fro568WU8YDBAv36Njej+/VXKNn94DkS+YOp5wFailu+MSfBxBjgqlXHYbQp0naRWWU8yhGSMlReol9p73oAuE2D0K1BTCqaUtuWrFa9ltcJPP6ncxRs3wqZNki1boKqqHr2oMrAlIs54EeOY/2No9Z1M7zuTzO4JFBfj9qrauxdGjFArxjNnSvr0aaX5rEjieMCBYZEBcYmAh4HhLaMfqffx1kaktsPJpmfhohkIHX+NZb8fg6SUXHHFFf5zGyDmzJkTNtqRBkdDqyLE5/hbNxg+/IbmhMPLYMdzULS6rtyYCBm3wpBHG6V9aiuEtR1agNbkq7lr2WzKKF66FD7/HH7+uXGdrl3V3quLLoKrrpJ0724hJSWJlt5Hg22HX4WetQTvovIg3wYcASaj9iP3Dc/lIrUdguHr2wPfIoTgzO5nct/K+0gwJZA7Mpcp10yhc3xnn+eFow180RQCundXR/2p++9/V54fP/wATz8NHTrABRfAzTebGDwYzjxT5SQfPFgd9aFpsG9fXZ7zX36R/PSTkx079FRUCLdh/f77defodGp1zWU8qxRXkuxslRM2WPkCrielWjF2VECHUfDdTEifCn1nwbiVKk7GKbCC3OIxlpAJQ/+q5mh7bTLfNReCtKvV5t7XQXSn1ucrTNcqKXEZxHWf27erleQ6qEksNlYybJhgxAgYPjyRESOg32m38a9Vyby27f94ovrPjM1fyPEvZ3DeefDCC3D22WpLA6ghaLG00nyWBpXPVpL0tyR4GliAur+XAKmNq5/081krI1LbIVL1rLVphpo3v43lCRMmhPTCrU2/Ha2EmlIoeA12PAtV+2sLBSSdDkP+Cj0up8XWUztaBcePw4oV8PHH6rPhilJSklqtmjZNPYz37l3XteqhoLU5bodPVAArUdGrS4APgXTA5Sr/cNuwdbKgqKKI5buWY64284ez/sCmo5sw6U2c2f1MVt6wMqJWF5rDgAHquOWWujK7Hc44w8HWrUbefVetKh87pvT4zTfVCnRWlto20aePOqZMcel5BYmJSRw6VN+IrjvMZtzBxT76CJThkYAQkt69G69EDxwICQkhEtZpg2NfKNfqQ8ug+gic9gdlLJ+7CPS1+9oS+4fogqcQhK5udX38ati/BPb8GzY9AFN3QmwPQAMv0bQjEVLC/v2NDeP9+73X79SJWqMYhg+X9O1bzogRCZjNsGaNih/w5pvwxRcmLkq/jg6Hr2XYtK/pHNeZjMed3LfyPjoPu5nExBGtJ6Q39ASeq/0ugbGobTV/BM4Hr9Hp2tGOdnjAb2P55ZdfbrbO3r176d27d1CM+EO/HREMb67WQq/eQg/9G8T1alv+2tEspFT7F5cuVQbyN9947luMjoaLL1bROc89F9JCHwOmHaHEftTe46XA50AflGv1zNr/x7QRXycBpJRsKdrC0p1L3e7Vo9JHcc3p1wBwx6g73HVPJkPZFwwGyM2tISkpBiGUgZucrFJTffEFzJunXp5NmACffgrFxSpgUUqtZ64QKtVNjx4qJoELUsLRow0NaMnWrZKSEh179sCePbB8uSc/PXrUGc69epkYMkS5c6enq5XqZlG2CzbNhqOrwJSqXKuzn4PO48BQGyhBH9oAMKc0jAmQOVMdlfshricc/gy+vRF631AbTXtoW3PphsMBW7fC5s3KKHYZxqWl3utnZNQZxiNGqMMVDd7phFWr4D//MZGTA1u2wJAhavvCQw+pMa62NAjgXADKbGU4NAfnvn4uZ6SfwX1n3cfU/lNbS3zfEKitNs8ClwOno16ixjRxTjva0Y7Q5VmeNGkSq1evprS0tD2gFU3vWa6pqcFk8p0KpyECOcffusHw0QguV+vtz8KxNXXlxmTo91sY/Ejdg0mEIiTtEAa0Fl8OB3z1leTDD518/LGePXs8r9Wnj3Ij++1vlXumXw+qhI7/YOmcUnoWCH5ERak2Ap1RKZ2m1R79wn95XzgZ9MzmtGHQGThRdYJRC0dRYi1hUuYkpvWfxuR+k5t0rw70WqHc49UaeialClB08KDKdf7MM3D//TB0qGTMGCdXX63nnHMC0zOLxcS2baLRSvTRo77PjYpSLt19+6r90X37Qt9Mjb59naR1tBLz9QTE2OWAVCkJu02F5CG/Gm+mVtUzqalV+8J/q1XnjJth1IuqXHhOFOHgS9NUcKydO+uOXbtUEK7CQnA4Gl/HYFAB7+obxsOGKS+phnj2WRg3Tr20yc6WjBihMXGijvHjhd8vikusJby8/mV+Kf6Ft654i8KSQlJNqSTFJbX9fFaGCuJ4DcjVEkexA8MMA0IfQfNZhCJS26E1+TqZ57P6CHmAr+bw1FNPkZWVxfjx40NB7qTHKR0N26urNdD9MhhwL6Sd96t5ODkZYbGo1aGPP4Zly1T0XBeEUA+jV10FOTnQq90hILJRCawCOqIWNaYB9wLjUa7XjdOBtqMejlYcxak5SU9MZ9ALg3ju4ue4oM8FrNmzhnN6nkO0oT06vy8UFcHaterIzoZbb4UHHlCG9dixMGaMdyOkOZSUqH3PLuN5507YvZtaA0jViY8uZ8LgPKaOWMaU4cv5w9v/5P311/GbC5dyXD+OHn3ilSFde/Ts6Znuqh0hhr0cqo+p/c5rJoEhXq02d7u4RW7aUiqPBmUE1zeI1aevYHMA8fF1BrHrc9Ag9cKl4TW2bYPVq5Vr9XnnwX33qRdC55+vth+ECjM/msmyncv4bfZv+d0ZvyMtLkLcs5agomYnoLbkXEN7Ytl2/CrQJsbyAw880Gy9hx566JRKEeULTa0sl5eXk5CQENAbQn/P8bduMHzUuVr/B7TaWUrooevFkPX/Tsp9X0G1Qysg1Hzt2aPcqz/6SD3c1nevTkmRTJhgZ8IEI9dcIwjFu51Q8R8snZNaz5rDQercq9cAPVD7z24ODflwIFL0zJt79WNjHuOhsQ9RVFlE57jOYeMvHG0QaXr24YcqvsHatSr91Jo1yuBYvVq5qiYkBK9nzrL9mLd+BIeXkVKzluPVmXy9dyof/jCV99edRZXVd4ojo1F5yXisSNcevXufOoZ0ROhZ+W4VTdu1JWvi18j4zCb5Ki/3bhDv3Km2BPiCwaBcqPv3V/2qPiVdu1YwYEA8eh+rpMeOwSef1BnIpaXKSB4/XsXgGDjQs36o2lXTND7++WPmb57P2n1r+eG2HxjSeUiz57XKfHainIT/JSDmCngJmAjYUV5KLeDjVESktkNr8nWqzGchj4bdHHJycnj66aeZMGECw4cP91mvsLAwVJdsNRQWFjJnzhweeughskLwmlHzlcAyROf4W9ever5crZOHQq/roP+dYPQjpGmAGDlyJPn5+V7/y8jIoKCgoFka8+bNIyMjg+nTp4eFPoDZbOaJJ54gLy+PwsJCzGYzGRkZZGVl+RwveXl5TJw4kenTp7N48WKftO+++27eeOMNFi9e7FWGpqBpKtrtxx/Dhx+qN+f1kZysIuDecYdaBaqoqCIpqeWRqz15CHych5LOSaVnTUGi9pp9A9yJynt8Diq90z9ROTQjZ772iVCNh2Dw87GfeeHHF1i2cxml1aVMypzEHdl3cHHfi4lyqKWmLvFdws5HONogkvTsssvUAcplu0MHZezk5Kgo2tnZ6n5z//1+XqtiD/r9n8Hpt2M4vISOtk9hyFRIf5HOCX25ArgCeM0h+eWXMoqKEigoEOzerQyu3buV0W6z1RlfDWEwKIPZZUhnZECXLiomQ+fO6rNDB/+3oLQ12lLPAEjoC0MfgyF/gWNfQlwfOPEDMd/9jsOJt5N/4hq27Yr3MIibcr0H5RlQ3yB2Hb16NU5HqILOORv115EjsHAh/PnP8PXX8NJLag/+22+rrQXRzTiRhKJdhRCM7TGWSwdfyvbj2xnQcQA/HPqBv6z9C/eddR8X9LnA58N92Oczg6YyI8wE9KgtPZcDDwCzgNjg+TgVEant0Jp8nYrzmS+EzFh+8MEHKSkpYf78+ZSUlJCRkUFqqmd8+pKSEp8GSqQiLy8PgPz8fEpKStqYm1aEy9V65/NQubeuPHEADHkcel4ZVldr1zjxZmw2HFfeMG/ePObMmUNOTo5XQ7M+fafTiV5flzLEH/oACxYsIDc31/07IyODjIwMCgsLWbJkCUuWLCEnJ4f58+f7Ra+lqKxUb8ld7tXHjnn+P3gwzJyp3pz3q7d/NUyp09vRElSh3uiXAmejDOTuwH3AxXhN/dEOTxwuP8yfP/8zC6ctxFxtRid0LJy2kLG9xxJlUAaylBJLe9j2sKB7d/UZE6MM1v371Yrz5s3KQF2xAv7xD2U8jxunYiPE6k/A4U9UYK7Rr4CjEkPZZkVo4H3q8AK9Hnr21BgyBCZO9PxP05Thvnt33eEypHfvhurquu+ffupdFp1ORUd2Gc+uz/rf65c1Z3idCrDZ4MQJ5Sbt+qz//cQJHcePj+X4caiynMb5vX5DzgXPc37aH1i37o+8smy2B720NO8GcWamGkOB8vb553VRq5csUWnKiouVK//ll6ujLTGwk1q+7pPch+xu2Vy95Gq6J3bnb+P+xrQB09qOMZdVMBIVCOzvtceT1AWHbEc7fmUImbH83nvvkZ2dzZVXXumzTmlp6Um3suxKaZWcnNy2jLQWvEW1NiZBp3Mg65/KWA43C7V+V82tvPpCfn5+k3m769NftGgRFouldlXVf+N/yZIlbkN57ty5zJ7tOfHn5+czY8YMFixYQHJyMnPnzg1YDn9w6JAyjD/+WEXstNvr/ouJUe5kN90EN94Ifr4DaEdb4TAqtdNSIK/2czzwGmBCpQC5vs24i2g0dK9+/dLXyUjJICMlA7tm55ye53BOz3Pams1fNXr2VPciV1q5kSPhttsku378hR/fWEa3X5YxqNO3lBuGcTxqKl0qqolOHox10JP4drBuHjqdunbPnsqbpj5cQaLqG9J79qj92MeOqc+SElWvqEgd/iAxsWmDuv5/ycltH+KjuloZud6MXl/fKyoCuUISv+z6LW//cDuXjVlP/4wKHn0Upvf/I/FpvUgZcQ1JHVqWL2zXLuVa/dlnsHZtEklJauX4jjtUfyQmwnPPNU+ntdEprhOPjXuMB899kP9s/g82p8rn99H2jziv13mkxKS0DWM6lNvG5UC9l0iGFQbl1XSKheJpRzuaQsiM5YyMDFauXNlsvVJfsfv9RH5+PrNmzWLDhg1e/3e5xXbo0AGAgoIC5s6dG1HGblxc4G7LgZzjb113Pc0Jh5ergF1Fq+sqGJPg9D9B/ztaNaq164VKRkZGUOePHz+e5ORkt1HcHP1A+8NsNjNjxgwANmzY4HX1Oysriw0bNtCnTx/mzZvHQw89FPAYNDb0L6vF8eOweDG89ZZK71Qf8fFqpebee1WAHZOfT5nBjMnWohksnYjTM2/4GRVcZSmwibro1XOBgSj36vP8ZimiEeoxVu2o5vM9n7N051KW7Vzmjl59+8jb6ZbQjWhDNA+PaT6BdDjGfmte66TTM2c1cYZqkjonMbPneIj9DtllIjUdb4I+7/Lic+k8+6wyys48U/Lww/GNVotDxb9Op1bAu3dX901vsNvViqTLeG7q89gxVb+sTB27djXPg9GoXmzqdOrQ65v/3vR/ic3SqKnxNIArKwNuOkDRS02Fjh3V0aGDr++Snj0ddO1qQKc7o47A7t6w6wVY/QfofT0MfRyiO/p9/W++UYb+BReo4HKVlSp12eOPOxg2zBBS1/lw61msMZbbs28HwKk5ee6H57j+g+uZOXwm94y+h16JgUfbDMl8JlDeTAA1EPtiLNwDzAZ+h9s9+9eG1pw3AkH7fBZaOi6EzFj2dwUwmBU2s9nsXilcv359k67c48ePZ+HChW4DprCwkJEjR7Jhw4aIMJiFED6NoFCc429dIQRGWQHbX4Mdz0HVvro/E/qrSavn9DZ55e0yZkeNGhXwuTNmzMBsNjN//nwPF2lf9IPpD1eAupycnCb3sLtWlDds2EBhYWFA+92FEOjqzfQVFSo41y23pOBwmH2et2lTAZmZgb1kCKYNWotmsHQiSs8a1tsKbEatEr9f+/suYDLQKSCWTxqEajwUVxZzuPwww7oM49aPb2XdvnVM6z+NBdMWMLb32ICjV4dj7LfmtU4aPbMegcp90PFMxKbZGA3xMPwJyH4B4nojDDG4ghTPmQOzZ6tV3jVrBN26GZBSRSUeMQImT1au1vUjbYezH41G6NZNHc1BSrVH2x/DuqhIBbay2z09gloGQbABDPT6OgPXt9Hr+ZmU5O9eboFHpCgX+t4GmbdCyQbY8wYY46FiLxxdCb2uVfmda6FpkJ+vVo4tFpX7u7AQqqqUsfy//7keV3xcqwVobT3T6/Tk3ZTH+sPr+ee3/2TsG2PZc88e7Jodo87olxdcWOYzk0CsE/AZ8GfgTdRcdhLEzAglWnPeCATt81lo6dRHyIzlPn36+Pxv7969APTu3bvJer6QnJzs3vc5b948n8byggULAM99rq5gS0888UTYXGEDgZSSsrIyEhMT/brhSSmprKmkrLyMxITmz5FSNl+3ch9y2/+DvW8jNOXyg86ognVlzoK4nqrMXhWIaG7EGmNbFMnOZcwWFhYyceJE8vLySE5OJjs7m7lz5/o0OhcsWMCSJUuYP38+2dnZIafvwpIlSwCadPV2IScnp9k63iClxGqtAVQKixtvVG/Q4SHgBLGxysXaFWF23rx5AHToELivdaBjsjVpBksnmPMCOcffulJKyneVk/BlAuIbAa8C5cDu2gp/9k+ekx0t6cefjv1E/pF8bhl+Cyt2reCr/V+x8JKFvDTlJRJMLY+aGeqx35rXilg9kxqUboRDy9RRuhF6zoCOZyKHPUlZpZ1EKRFJA73SFcIVtVrRlzKRZ54RrFgBf/kLXH+9MjaFgAMHYNAgSXl56/RjUxACUlLUMcCPHUtWq1q1ttnA6VQGoaZ5fm/4u+n/JOXlVURHxyKl8FnXYGhsCCcmhi+IWZPjSwjokK0OUCmods2H/PuoSruO1ft/y7ufDWPlSrUiPmECXHKJqnrDDZ5kmr1WOPgPI53sbtm8c+U7VNZUUlVRxT9++Adf7PuC+866j8sHXo5B5/sRPlzzWVlZGYkXJiIuFLAPZSj/A+WWPQvcb71OYbTmvBEI2uez8PEW0gBfe/bsITU1lRkzZnDBBRewceNGJkyYQGpqKiNGjEAIwXvvvReqSzbC4sWLvRpJo0aNYv78+W5jecGCBT7duIGw7jEF1ZH+ospeRcKTLdvL4x/ssOsZ4JkWU6p4qII4U/AuED/++COgjFFXNOvCwkLy8vIYOXKk1+jQhYWF5ObmMn36dHJycpr0PmhI/9JLL+XAgQNN0m94LQjeTdyFvLw8tzu3C668kvv3w969SgaXq3XfvjB16mxmzVL5Il1w0Zg/f37Q3hMhyiAXFprB0gnmvEDO8VlXogJy1aZ3StiQAKNQ7tU1wFm1x68M/ratzWFj7d617v3Hx6uOc3Hfi7lx6I3cPPxmbh6ucmQlRoVm01w4xn5rXiui9Mx2AjY9qLb12Cug20XQ/y6Vbze61m3CEIvE/6BqUkp0OuUmPXasWlE8ckTt9V2zBqZOVcbe+PHRXHstTJoUlBhtgpgYtZc6VFD7we0kJbX9PuiG8Hd8ff7TGYw8ZwM1RzfwwbwF2IyfkZExjDXvrmPg2cMwxDSv96eansUaY7FUWXh4zMN0ie/CA6seYHbebN6+4m3O7nF2SK7lb113PQH0ri0cDDyC2j70R1QgsJYEGjgJ0JrzRiBon89CS8eFkBnLo0aNIjMzk1mzZrnLZsyYwfjx41m0aBEAFouFp59+mvvvvz9Ul/VAXl6eVyPXFaHYbDaTnJwc9GpfMLBYLI06zWKxEBUVRXS9kJlCiIhVvkAhpURK6VOm5spde41ffvllcnJy3OV5eXlMmjSJGTNmUFpaSlI9P7wZM2aQnJzMwoUL3df3xUtD+mazmaSkJFavXu2TvguuyLkZGRkeMrjo5+bmsnDhwkbnuYKJuXhy8eFapW4KWVnw7LOSs86q//ZcXXPu3LksWbKEWbNmMWvWrKDavX47+VPfn/KGfRAMjZbQCVamhud4q++1rg2ETSATJGQDO4FJIHMk5WPKSeibUOeqJn3TbqvycF8Tmu8PTWpc9/51rNi9gpToFKb0n8JLk19iXJ9xHu7VoZSpfj9GQhsEKlOb65nUVPlXVyEH/wkSBoAxCXHWW8iO54C+3hNzvTauf71gdLJLF4mUKor2iROwdq3ko49UBOSJEyVvvKFy506eDP37C9xKF0TbBFPeVroNob+Xh4JGU3rmikKelSU4+2zJ3/8OTzwB2dlZTJ/3MqmpAqlp8PlfYel3yF7XQGYOosMoL70aWBuEWz9CPZ8lmhK598x7ufOMO3l/2/tkpmRSVl3Gi+tfJHdkLsnRyY30piV61rC+T96ngZgqkIsk/D/gMtRKswGE8eTVp9a8l4eivK3ns5bSDqee2Ww2bDZbo/plZWVeZWuIkBnLe/bs8TCC33//ffbs2eOxwpeUlOTVAAkFfAVzgrpI1oHuG/WXflPo6eO18ezZs3nooYcAiIqKIiYmhsrKShwOh7tOTEwMsaZYDv3uEOVl5cTFxyGEIC42DqPRiKXM0xBPiFcuiYcPH3bXBUhKTELTNMoryhvxERsTS5W1zt1ap9ORmJCIzWbDWm11lxsMBuLj4rFardhqbO5yk9FEbGwsVVVV1NiV27C9yk61Vu1TpqioKMrLyz3yoMXFKZnKyso89r9rmoZOp8NisTBq1Chuvvlm3njjDd577z1uu+02ysvLefTRR8nPz+fDDz8kOTkZu91ORW2ozpqaGsrLy0lMTKSmpgar1eqmbzAY3HUa0l+0aBE33HADNTU1bl6ioqI8xpLLcK4v06BBgxhbGylGr9cjhCAvLw+73e6u75J7woRLOeOM//LeeyYKCupSV8XHS6ZMqeHIkVl8+eWbPPQQnHGGg7KyuigsOp2OlStX8uCDDzJs2DDmzZuHxWJR/RQfT3V1tfvGAGAyqX6yWq2NZIqOjsZqVX3tGjP+9JPH2EtIcPcTqBtURUUFSUlJSKkSxLsghCApKQmHw0FlpadM9fvJRcdqtZKcnNwqMrnGjRCikUwuJCUpfaraV4W+XI82RCM2NxbTCBOO+xxYn7Wi9dUguu6mXFNTQ3V1tZtGMP0UrD411U8NZQq2n5qTKSYmBpvNhsVicbeJQ+egU1InbvvwNrLSsrj+9Os5r9t53Df6Pkb1GOWWyVZpw4YtLDK5xqlOpyMpKSkgmQLtJ5PJRFVVlZuPUPSTlJLKykqSk5Nb1E9SSvf3JmWy29Cb12Ms+hTT8VWIPjdQlXwjxk6X4qyJRlZYiRvyhJLJYkHKuuvWl6m+njXXT/Xr+pLp3HP1nHWWk6goDYvFhqYZWb7cxEMPGejeHZ55xsaYMdU4nWpv7smuT615Lw+FTC49U3NuAp98UsNnn8GaNQYOH9Zx9tka/fvrsVqtLFmiZLJYICYmCoihsqoKx8gl6C2bMR14A9PaKXDJTsorrGhOuwpGWiuTwWBw8+Jqg5bKJKWkqqqK5OTkFvWTay4AAhp73mSaMXAGOp2OTfs2sXT7Uv6x7h/MHDKTB89/kLTYNI/5zHV/a6qf6uuZL5lc+0CtViv2ehvt3fo0tRLHRUom07MmohdGIx4VlE8tRxMnnz615r08FDK59MxkMhEXFxfW5wghhMeYCYVMwT7vNZRJSun+7pLpySefbJnHsAwRFixY4PE7NzdX9u3bt9l6gWLu3LnSG9sFBQUSkPPnz2/036pVqyQgV61aFfD1Vq1a5b7m9OnTvdL3BovFIgG5f/9+aTabPY4TJ07IqqoqqWma+5BSevyuX+50OqXdbpdOp7PZ+pqm+VXX6XRKh8PhUc8fXtq6fNGiRRKQs2fPlpqmyfXr10tAPvDAAx51XeWzZs0KqL0a0vfGS0ZGhgTk7t27m+W9tLTUPXY0TZOHD2vyjjtWSkDCdKnWiKUUQpPDh0v56quarKpS/XPbbbdJQC5evLgR3d27d0tAJicny5KSkha1r7cx09J+co1ZX9fzl3awdIKRqVk9c2pS26JJ+Q8ptbM0qek1qd1S+59Zk1I7efUs3NfUNE1WWCvkp7s+lXeuuFP2/r/e8urFV0sppdx4eKM8Wn60TWSq3+et0QYt1bNQ6UdAdGxmKfctktrXN0ptcQepLU6V2tc3SG3Pf6VWXdJiPWuRTtbj35uelZdrculSKffs0WR1tSY7d9bk5MmafO45TRYWnrz6FM4xFmqZHA5NFhaqflywwCkNBin799fknXdqctky1UcB03fYVHnBG1J7N1Zq386U2rFvpVYrt79tEBL9aAU6/sj01b6v5LR3pslbP7pVapomK22VraZnXuvXaFJ7VZOyt5TaaZrUvj759CmcehYOHtt6Pmsp7XDqmdVqddtgFovF/X3//v0SkBaLRTaFkK0su1I1uZCXl8dEL/keUtsg2Wuwq8Kg8ixPmDChUR5df5GYmEhiYt0eGyEEmqa538T4A9cbdX/O8beuECIgHiIFDffkrl+/HoCnnnqKp556qlH9hQsXuqOju+rWR8P28mfP7/jx4yksLPTYB+8Lrmvu3asiuK5ZA1LWtfukSZJLL1UBa5KSlOlcy5nP/jGbzUyq3ZjnCk7WEgQyvoKhKX248ISTTjAy+TynCBXAZBlwFJgIzAS5WCK61dZNRLlZN2DxZNWzUKHMVsaH2z9k2a5lfLb7MxKjEpnafyrPX/w843qPA2BYl2Ftxl84xn5rXitselZTCodXqHQ+Bz+GbfOg2xQ473/Q4SzQ6evOi+D5LC5O7WmWta8l8/JULt4PPoAHH1R7nysr4eef4dxz/U+1F6lozfHcFEpLYfVquPxy+PFHuO46we7dOqZMEezcCb17t/ACutpIt31uhKTBULAQPp8EA+9DDHkUHfLk0LMgzvOFs3uczUfXfIRWuzVi5MKRjOgyggfPeZAhnYe0mL6rnt/takDtXb4B+DeQBtiBT1B5mvU+z4x4RIqeNUT7fOabTlRUFFFRUe7/A6UfMmO5oKDA/X3jxo0UFhY2Cl60adOmsHWiywj3ZhiXlJR41GlNZGdno9fXPVgIIXA4HO49ptC8a0RZWRllZWXEx8crN+xmXCMOHTrkrgveXSNccLlQu9AaLixNuXts2bKF4cOHM2zYMNauXdvIhWXZsmXudtU0jc6dO3PppZe66RiNRjRNo6SkhLVr19K7d2+GDRvGgAEDqKmpYdu2bYwYMYJhw4bx1VdfERcXR3FxMSaTCSGEm/6oUaN8yvTnP/+ZRYsW8dRTTzFlyhTOPPPMRjJVV8OXX8bzm9+o9FXKZlb9kZoqKSmBCy+08+67LrdlkLKun2Ste6UL9V1YrrjiCgoLC1m8eDGDBw/2cO8J1nXv6NGjxMTEhNwNOz09PSRu2F27dg27TBaLhfLycqU7UpD4u0SYCxZpIaY8BvsTdhznOUjqkoTT6eTIkSPEm+Pdeu1NJtdNOTo6+lfjhl1WWcYz3z1D7vBcDpQd4Nnvn+WSAZeQMzCHkd1GulOiCafql7aUyTVOExMTW8UN+8iRI8TG1mULCJUbdnp6esvcsJ12tGPrSLV+jbXP3dhraojd/W8qky4kqsuVxGTcSGVFhZKpvKJFMpnNZioqKtxzVFP9ZLfbKSoqctf1JZNer8fpdBIVFdVkP3XvDrNmwZ13Kvdep7OSH3+EnJxYKioE48dr3HCDnkmTIkef2vpe7o9MNTUOvvuumlWrDOTlGVm/Xs+gQZCVVcOAAVa+/15y+HAFycnJdOuWgNUawvuePgP6PwEZfyQuWmBwOnAuPQ2tw1nU9MrBmTQ0ZG7Y3bp1C4kbdlpaWsBu2IcPHyYurm57XXMyvTPlHf75/T8Z/cpoxvYay4fXfohO6ryOPZvNRnFxsVvPmnLDttvt7k+/++kaJZMuX0f87fGIzgL+BmVjypBErj615r08FDK55rPU1NRWccP2x9Zojec9X27YHTt2dMu0cOFCXn31VffzmktOp9OJX2hy3TkAFBYWyhkzZsjbb79dpqSkyBkzZrj/W716tczNzZWpqaly48aNLbqOLzdsKaUE5Ny5cxuVz58/XwKytLS0RdcOBC43bLPZ3MhloKSkJGC3tYbn+Krvjb63uk6nU5aWlkake2hWVpYE5Msvv+xR7nKtzsjIaJZGU27YDem72isQ+i+//HKtK7WiI6WUdrsmV67U5C23aDI6er2ELHedhITp8tFHNblzpyZXrlzp4ZrtrV2cTqe8+eabG7lhz5o1y+12Hqp2D2ZMNlfuGrO+rucv7WDpBCSTU5PyFymdTzplzZk10rmw9pxXNCmPeHef8lcnI1nPQkWj2l4tV+5eKe9acZdct2+ddDqdcuaHM+V+8/6wjrFQyFS/H8Pd7qFog1Dph5RSatUnpFb4ltS+ulZqi5Klc3EnqX1zi9TK94ZVpoa6E0hdX/VbqmdOpybz8zX5+OOa/Ne/VPlzz2nymWc0uXdv6+pTW48x/8ee+pwzR8ouXTSZkKDJyy/X5IIFmty/v231zLL3S6l9l6NctD87R2r2qhb108k0n7nKXNc6aD4o569X2wfX7lkrV+xc0UifWnU+q9Sk9k9NyjQptXsjW5/87g+nU2rV1VKWl0vtxAmpHTkitf37pbZ7t9S2bZPyp5+ktn691L79VmpffCG1vDwpP/1Uah99JLUPPpDasmVSW7VKauvWSfnjj1LbvFlq27dLbe9eRaukRMqqKqk5HE3y2NbzWUtpt4Wemc3m1nXD7tOnDwsXLiQvL4+cnBxGjBgBqMBfBQUFjBw5kpEjR1JYWMjw4cNDdVkPTJgwwWOF2wWz2UxGRkaL3VXb0TpYtGgRkyZN4vbbb2fBggXuaOb5+fkkJye7o0qHin737t05ePBgQPRdEdVvv/1292EwZOFwpALrATNCZDBpUgH79s3g9NPh0UfVubVpxwNGXl6eR6TtefPmNXIlmTBhAiNHjgzuAr8m2IF1qPROy4BDIC4Q1FxVQ8zkGFXnN3h1rW4HnKg6wfJdy1m2cxmfFXxGvCmeKf2mkBSVhBCCVy95ta1ZbEdDSAnOKjDEwYZ7YefzkDQE0qcgx36CRd+P5JTW976KBAgBw4erQwjVVCkp8Prr8MADqvyll+DXfGuVUuW27txZRau2WODJJyE7Gy68EM4+O3Lc2J2Jg5E9XkIMfxKKVoM+Gg5+BCfWQ99ciE1vaxZbDV0TujIrS3kx7rPs476V99EjsQdzzpnDlQOvxKAPmRngH2KAe4EcoAQ1v84CrgHGgztrRDjgcKjcnMXFcOxY489jx6CiQrkGujzBqqpUQnS7ve5wOEi221XScocDUW910hf73soDqesqlzodGI3qMJnqPqOjER06EC8lwmBQSdT1enXExEBsbN1nfLzKvec6XMnhk5LUZ6QocgQhpFqSlJTElVde6VHWp08fj3RS4cSMGTO87iFdtWpVk3lzw4lQuGGXl5d7RJ0LRYQ6FxwOR0S5YZeVldGhQwfWrFnDo48+yk8//cSSJUvo3bs3N998M3/961/dEZYbyuQtgqq3aNgu+n/961/ZvHkzH3/8sQf9Tp1UPtCmZCovdyDEDYwcOZYNG/4J5OFw5APJdOjQh6lTL2f+/AeJijLyn//cQ0lJSaNo2PUjZDfsJymlh5uTw+Hg8OHD7t/e9mcDPPbYY4wcObLNI6jKSIyG7YghaksU5dnlGF42EP3PaOwX2tHP02O4yIClptYNOyYeYWk+GnZ9PWvKDRs46aNhV1RUcMJ6go6xHVmweQEPf/EwQ9OGMqn3JD66/COGpQ3DaDQSH18bMb+ZaNiRINOvIhp2XDT2Ez9RZcrEtHcBhhNfUTP6HeIzZmLr/Tuq9Z3cdPyKhh0imUIdDds1x9ZPDxJsP7nG3pQpGlOmQEmJ4PPP4+jWzcDBg2VMnRrHpEkOpk2zc/bZsej1keM2Gup7ucUiWLvWwKpVBtasMdG9O6xcaWHyZB1Go6SsDKZPT8JuVzK52G8qGnZCQkJY73uNIkcnTiBB09BFdcJR/COGbfOwp02muu99JPQ456SNht3cPcJbNOxrB13LhG4TeOvnt7h/5f1EySguG3IZldbK0ETDDuReHmekzFGGPC6J6hZF1FVRiCEC+ZjEMtxPfUpIwFFUhHXfPsSJE+iKi9GdOEFMeTna0aM4jx5FV1yMKC5GV1QE5eUIH3tVAzVcm4MUQhmqOp3baNUSEiAuDlFRgejQAWJj0Y4fB7sdUWuI6xwOpMOhDHtA1NMHAKFpYLOpoyGPu3ZhbIFM7uyWRiMyNhaRkADJyTgSE5GdOqkjKQlTWhqkpODUNCo7d4bkZGRyMgk9eqAlJFBez6aIlGjYLXXDFrLh0pQPrF69mgULFnD77bczbtw4v4iHA3PmzPG6ouZCZmYm8+fPZ8KECYBK8TNx4kSvK87hRFlZGUlJSZjNZr8CfPnacO4ql9IzYEVT9RvSDyaYRFO8REJ5KGkH0h+bNgleeUXy1ltQVua+tdCvn+C++yS33OL5Uq4lMrn6vDXaN5gx2Vy5lBKdTtdiHoOlo2kaYpdQK8e3AwdA3CKQX0u1smwEdJ50/NGzQHXSFyJdn+xOO98f+p5zepzDl/u+ZNayWey8cycHyw4ihCA9ofHqTGuPsUBl8lZ+MumZX/pRXayCcx1ejjjyGTKmG0z5BexlKjCSITb0etaC+SxUc58vhLL/rFbJxx/D//4Hy5dDRoaaE6xWSVSUei4O9TVbe4xZLPDSS4JPPpF8+y0MGAAXXwyTJ6s8yA0XnU46PasohF0vQfpURNr5yH3vQrepYIxvknZEzGd+9Gv9sqb0zO60Y9AZ2HBkA5e9dxl/OPMP5IzMId4U3yQvvtDi/rMA/wfiG4H8oBJ+KYRDu6GoCFFcjCwqUqvCrpXgI0cQJSX1o6L6BQkQFaWi/yUlQYcOiM6dkV27qshzPXuCwQAmEyI6Gln7vf5KrjCZ0AwGhMnk8Z8wmVR9gwH8fI5otl2kVMZ0TQ3Saq0zlKurwWZrVC6rqxGu8qoqFcGwqkodlZWI6mpkaakq1zQ4dAjKyhDV1Ur5QwAZHa1WtlNTQadD9OyJ/OMfYdSoZtugtfWsrKyM5ORkLBaLh63W6Bx/jWVQuZPnz5/Phg0byMnJ4eqrrw6bS3VD5OaqQEmLFi3CbDYzYcIEMjIymDFjhtswBuVyPWfOHDIzM0lOTmbDhg3MmTOHjIyMVuHTBZex3LADpJTu3MENb3y+EMg5/tYNho9TEf60g9kM77wD8+fDli115T17wo03wm23hSC6ZxB8RfK1QkUzYDoO4BuQH0vlYr0XxAUCXgZ6he5ap6qelVpL+WT3JyzduZRPdn1CjDGG/Jx8OsV1wuawEWeKC4pupLbDKaNnaIjtT8GhZXDiB0gZAelTIX2a+h6i8RyK804VPauuVlG0R46EuXPh//5PRX++4go4/3z1/NzaaEk73Hkn3HMPpKfDTTfBpElw0UVqnmtLvsJ6LdsJWDMBKvZAxi3Q77eQ2L9lNEPFWwvP8/ccTWp8tP0j/vHVPygsLeQfF/yD3OzckPLvFVYrFBTArl2we7f63LULdu6Cw4eADwET8AiwsWlaBoNyL05IUG7EnTpB167QowcMGaK+p6WpIzVV1W8BTtn5zOmEsjL10Gs2q7D2Db8fOABWK7KyUvVfWRmiokIZ4/W8Chph1SqoZ6uFhf8g6Piy1RoiIGPZBYvFwqJFi5g/fz4Wi4Xc3FxycnKavNCvDb5WlkEZ9ElJSX69LXe9UbRYLB7nNPXWryF9X29dysrKSExMbNXVnlCVh4o2eO8PEKxdK3nlFXj/fbDZXG0JI0ZIHnpIPRiFciWh4RthV5+H4i1bMG3QEvou/n3FCQhkNaJZOpoEAfwReAm1YjwZKi+oJPbSWERCaPUskLoni54tzF/IOz+9w9cHvmZw2mCm9pvKtP7TyE7PRnhx2oqEMdZceSDj9GTRs0b6sfXvVMYMIz5jCqy/A1KzVYqnmK4B0Q5WX4ORqaHuhEIn21rPamokX36pUlJ9+CHMmiX4y18kmzapldmYmNbhBZrvDynhp59gxQr49FPBVVdJfvtbWLBA7T3u1Sv0c26k6pmaPzQ4/i3segEqCmDSd4iaEqQhyZ0irVXns1DIJKVHm/ujZ2azmfzSfByagwv7XsibW95kXO9xdE/s7sF/QHpWXY3Yswe5c2edMVxQADt3Ig4e9Cqn+1q6NDD9CWy3IlK/RI56AzJSlPGbmQkdOyK6dEF26NDozVQk6FlzdMLBY1vrmbDb1ap1AwNbWCzIadPUS4smaLeFnvm7shzU65WkpCT3nts9e/Ywf/58srKyyMzMZMaMGdx2223BkD0l0b5nObJT3cTFxVFTU+PeS3nkiGDRohj+/W8Te/fW3QQHDXIybZqT++83ER2tZHJdOhwySan2eAkhfO9HjNB9bhD+PcvCLDCmGYl9IxbHBgeVT1diyDIg35MYzjYQHRtN2ZEy7E47wuK/TPV155TasyyVK1eC0YiupoayY8dYtG8pFTUV3NrjciwHdzEj+RxeTL+THlFp4HQidpSjO/A9DpsNa2Wl2ivldCKkJC46Gkd1NTar1V2uF4JooxF7dTV2m00FPdE09EJg0unQVVVhTUiAmBhkTAzGxESikpOxSonDaFSuW7GxRKekEJWU1L5nuYFMwlZEYvlXcGgptqjeVJ/2Fwz6dCqd0cQLgWPE82rs1QA1lsBSR8n2PcsumYK571VVWcjOVsGuHn8coqKUTLNmSbZv1zNxop1p0xxce20sBkPr38vt9iiWLrXy2Wd6Vq0yYjYLLrhAcu21gvPPL8di0bj6akVH00I/57r0rE32LNPMPUJKyk2nw+kvguZAlJWRtO0BKFpLdY+Z1HS/Ac2YfErsWW6qnyorKxmZOhIhBGXlZSzbuYycpTlcddpV3D3ybvqm9PW+Z9lmI/rwYaIPHKDm559h1y50hYXoCwrcBrGvtT0pBDItDce55yITEjD264cYN46yzp2RtalexcEaEt+chPbYBCoKKsAEsqOsG3t2O5X12qC1UkeFOv6Ei1YoUkeZTKZWSR3l1dbo2JHyqCgVCbC+TH70k6/nvUD7Sco23LPsDzZu3Mj8+fNZtGgREydOJDc3lwsuuCBU5E8qtK8st/2qkT/lAMXFZr76KonXXxesWAGaptpDr5ecey489hicey4I0XoytfUbwpbSD/kbwoRk+Ja66NVVQCGIQwLpkNC75TK1+sqypiFde7AsFncETmGzqb1Groic9cutVvXbtUepulrtQWpQF6vVo9wsrXzWF5b2h9IYWPE2fNMDavQwdq/XLmpzyOhotSRXL5KnqDW0PcqjoxGxsY3qi4QEZHq6csXr0UPtUeMk0jNAlm6CQ0vh8HIo2YBIzUamT4Xul0PSoJNqxctVfiquLPte3VNbdz74AD7+GPLyBJomWboULrkEOnYMLS9Q1x8lJYK8PLj6anjlFcFTT0kmT4bJk+G889S2zdaacyNVz3zSlg7kgf/Bzheg5EfkuFVYjINOCj2rG3uBrn0vBQABAABJREFUrSx707Nfjv3CU988xeo9q9k9bRW6n7dh//kXog8erHOd3rfPZ9AsABIS1H7gmhq1X23wYDjzTMT55yO7dVMPVs20gbvsaeBvwP3A70EktM0zKbSvLIN/tkYg5W0xn4Vlz3IgeP/993nvvfdYvXo1V111Fbm5ua22vzkS0NSeZV+Tui8Eco6/dYPh41TDjh3w6quS116TnDihc5ePHAnTp8Ndd7mfr1sdrdk/4bhWSGjaQX4osX9gx5hnREgBU4BpwCSgiV0fbaJnTiecOOGRjkIWFWE7eJCosjJEcbH6z/X/iRN+8RUsdqcq43jpAFjXEwYeh2k74JIdMPpQvYoJCSpap9nsSaBnTyXToUN1DzQGAwwapNJr7NmjyoRQijJiBBw9qtzsXPXT0iA7u84Nr3Zlmk6dlKVw4IDa6yRlwIFaAkZKijKae/Z0G9Cye3cqU1OJGzgQ0b17WFNm+DW+NAcUrYEuE6F4HaydDF0vVAGIuk2GmM6B0wwVbyE679c+n23ZAjk5sGEDjBkDN9+sjpbCZoMvvpD87381PPGEifJywe23KyNdiLotQ22Bk3o+K92CTOhHxbHtxG+9C9H/d9DjStAHfq84KfSsuhq2boXNm9WxaRPWXzYTU1LO/ZNgVyo8+gVkHWlAxGRS+4F79oTf/Ab691f3+dNPb2QQtwjrgDlAIbACyAodaX8Rqfebk1rPQkgzEDph3bMcCLztb54+fTq9Qx0RKcLgbwe0o3VRWQlLlsArr8BXX9WVm0wwZYpyoRs0qO34awfwOjAKGAiMA85GGchnAvpW5MOL8ev1u+t3sMavXq+M0IQE6NNHBdgoLa3LpZierh449u9XK9DR0WpZaMAAdezerd7ax8bydexxBvQ/G1P3XnT7fCrnxA1kWtq5TO0xnt6nnalyKtbUKBo6nedTdGtM+lKq62uaWgHev18Z2BUVSjkzM5Uh+9ZbyrguL1ftMWmSegh7/HGV7NVqVccNNyiZXEnMXXJMmaLO/fHHutyYzUEI5TrmWomuZ1S7jy5dVH+FEtYjcGg5dB4LsT3hs2wY+ylEdwbpAH1UaK/XjojAwYNqf7PdDr//vcrp7HTClVeq9zr+0lixQh15eeq8yZOVOnTrFlb2f32wl0HBa2q12VEOpz8MA+5ua66Ch5Tq3lvPKGbzZrWKUM8V1w2jkaPJBuZd0oGXux9lYkUa/0q6ht7nXQKjR6s5pdV4Bz4GJgJlwJfADHz7e7ejHT4QMcZyfezZs4clS5Ywf/58UlJSyM3N5aqrrjoljcmm3LDtdjsGgyEgtzWHw+FxTlOuEQ3p+3Jbczqd6PX6iHBbC7Q8kLpSwvr1KuXT229DVZWSV6eTDBkCd9zh4JZb9JhMrRN4yB+ZpJTuPm8Nd5pAx6Q/7kAOh8O9x8lnXQ34AeVe3RPE7QL5hIQxwDkB0AlWJqsVduxAbN+O/OUX5C+/IHbuhCNHoKSkafcyL5CgjKnoaLXaeu65aDEx6EpLlaHVvbvKN5OVpdJVpKQ0iswZSLtbqi2sLFxJWmwa5/c+nyveu4K7zriLcX3GYXPaiPJiaLWWS1hDhHqMeZRLWZcmo6ICevZEWK3I99+HwkLYvl2tkD/yCOLTT5ELFqj+0evVg2FlJaLe/ihfkAaDennRoweidmXaw6ju0weRktJ0G+j1CPNmOLwUDi1HlOYjO5wBw+dBp3OalNWbu1kw+hEqOm6ZWjCfhWLui7T5LBgab7wheeEFZa9ceCH8/e8wZIhnfYdDOYB06iS48UbJf/8LZ52F2716yBBwOMKjZ6GYDyJtPgtKP6QGR1aCowx6XgUFryES+yE7ntusS3Fr6JmrrH6bC4cDsXMnctMmZRRv2QKbNyOOHfN6LQkIvV65Tw8eDLfcApdfjjQYcDqdFFuL+ed3/2T22bOJNkSz88ROstOz20af1km4BkgHngTGnRzzWTh4bGs9aynttpjP2twNuzmc6vubXcZy3759/Q7wVVFR4XXTvcVioaysjPj4eISoC/BlsVg8Bohr0/2hQ4fcdaHpAF+xsbFeA3zZbDavgQSsVqvXTfdVVVVeN937kqmsrMxrIAFvMgUaHMFut1NZWUlJiWDRIiNvvRXFzz/r611L45prapg920m/frEcO3YMk8nkbq9IkElKFaghISGB5ORkt0zh6Kfo6GiOHDlCTEyMuw1aKpOL//T0dKRsEOCrUpD0QxLaR5pyo7KDY6ID+3V24i6L85BJShXwoWvXrj4DPvgjkygtJWbvXkwFBdg2b0a3fbs6Dh5s8mW0VI0KUVHIuDgcQ4ZgzMxEHj1KZUIC+t69kb16oQ0cSEL//tgTEqisx6PrphwdHe01wFcw+rT7xG4+LfyUT/d8yjeHvqFfh37cO/JeZgyYEXA/ueCPPrkQzNiLiYmhqKiIqKgo9xhrs3tEVBT2bduwbduGbu9ebE4nurvuImnhQpUL0m5HJiai9e+P7NkT47ZtaEeOIMxm/16epKbizMjA2asXWp8+aH36YBgwAOPAgZgP/5ekff8P4SjH3ukCdD0uwdBjGhZbVFAySSmprKwkPT3dZ1AYf/pJShXgq3Pnzj4DqHjrJ5PJxOHDh4mNjXX3a3P9ZDabqaiocM9RTY29mpoaioqK3HV9yaTX63E6nURFRZ0085Ovfios1PHBB0auuUYjMzOOG290MHhwDb/9bQ3/+lcUBw8aeeMNA+vXW+nQoYaUFOmWKRz38lDI5JoPkpOTSUhICOtzhMFg4NChQ8TFxbnboKUySSmpqqqiW7dujYIsxRQ8RdSel9Ci06nulUtNtxmgj/Iqk5QqwFdaWprPIEstlklKtI0bsX3/PdqmTUTv2IF+2zZEvXZ1QQIiIQHnhRdiq6zEICWO889HTphA3IgRWGtqPPrJaDRit9vdnwBf7P+C65Zdx/g+43ngjAcY0mGIXzKFdOwVVxI1P4ro/4um+rFqYu6JCeszbDD3vdaYc116lpqaSlxcXFjve/7aGoHIFOzzXkOZXMZyx44d/QrwtXv37sg1lutj9erV5OXl8cQTT7Q1KyFDe4CvtnlzD4Jvv5W8+CK89x44HC4DGIYOlfzpT8pDs/7+rdZOaeOPTFJGZkAUf8td/LsDLBwBugJfARNB9BLIqVK5V5+DOy6/tzeEfgd8kFL5JW7bBtu3U7N5M6aCAvj5Z0QTLtKyQwe1GtixI7YBAzCddx5i+HDo1g3RsSOygdttIDoZCj2TUkX+/PrA1+Quy2XniZ2c3+t8pvZX6Z0yUzPbdNXIn3I4SVJHOZ3KNbGwULmJjxyJeP555HvvQWEh4sgRZG4ujB2LePVVZHGxWpm2WqG01HOcxQMVwCDgSuBvIAdGQd+uYBwCvftBZiaiXz9kRoZaoa7nZeCPTAHpRxjoQHuAr1Drk6apxb/ly2HpUsGGDZKoKDUcr74annsODIbW17NQzAeRNp+FTD+cVuSet6BgAYz9BHQmcFYjYjqHT880DbF3L3LjRuWOsGULbNqE2L/f6/no9Ugh1ADLzIQ331Tpl6qr0QYObJGeFVUU8fS3T/PS+pd4fNzj3Hvmvc3KFJaxdwLQg9AL5AMSHsQj+GckzWfhuP+0tZ61lHZbzGdhTR0VaowfP57x48e3NRthgethwAXXg2/Dclfdpmg0PMdbfV/0m6PtrdxX/Ugpr19WWQnvvEOtG1tdeceO8Kc/wU03QXKy/+0Vbt59lTfss/o3oXBdMyxtoIEh3wBDQcQI6IsKyDES2AL0x2v+Xm+0G7WBw6GCSG3bBtu2IbZtg59/Vq629bwkGjkg9+yp9qQmJcHw4XD++TB6NKJTJ1dDUG2xENVwAvTBYzj1rKKmgihDFKXWUkYtHMW2322jV1IvHj3/US7MvJCk6CS/6AQ79kJd3lZ65k9dDz3T65WbdXp6XeW77kLcdZf6XlWlUmIlJEBNDSI/XxnWhYXqBnRaFMy9BvqVQ4cqePc8sDvh0wKkrgixzQbb9gJ7PfkBZSj37q0eZGsP0bev+p6R4U7W26x+BNlewdAJ1XwWirmvfn1/eA93eaA0KisFd98Nn3yi5rNJk+C3v4WLLhJ06QLffgu//KKGyeuvw08/Ca69VqWrcpEMt561hEakzmch0Q9DLKJfDvTLUb/3/he+/w30vh4x4PeQfLp/dHzJ5HSiLyxE7Nih7jnr10N+PpSXe59Fu3dHDhoEK1eqLSOjRsG4cepz2LBG+4tFC58buyR04elJT/PA2Q/glE7smp07lt3B7dm3Myp9VOuNvdro8hSDqBbqReXtwCN1/0XSfBYOPWhrPWuLeagldHz93xARYSz/2qALIixlIOf4WzcYPiIN27bBSy/Ba6+pBwxQq8ajR8MTTyh7qDlEaju0Jl8huVYlkIfaf7wc4q3x8B5wIVAMuCKL9/eTnpSwaxemdeuUMbJjh4rSuXu3ioTjC6edhuzZE4fJhOGssxBjx8LQoRAf3+wl21LP9pn3sXTnUpbuXMravWtZcd0KLuhzAe9Nf49oQzQ9knrQI6mH3/xFGk4JPYuNrft+003qOPwJHPwQDs2E78ph7DAoSocvJBQVqOhLNhtcfDH29HQMnTsjoqIUrcJC9eKnsFDV2b1bHd6Qnu5hSFNrSOvS09ULoBYi2P5pn88Ch5QqsJfJBNdfD7NmqbmqZ0/1zuWccxoHZj/7bHUADBwIX34JEyaogPP/+pfaswyR2w4n3XzWEpq9r4XkIbDj/1TAvuFz3cHAmqUjpbonbNigjOING2DDBhLLyhrX1evVizuHQ6XwuOUWePddePBB6NGD8qNHSejate5tSgjka6pe53gVrb+ipoLUmFTGvjGWcb3H8ej5jzIqfZRf9EOCTsAbwH3AQ8AfgZep9TsPzSXa9SwC9KwV6LgQMjfshx566JRyo24pfLlhC3FyuYRFYrndDh9/LHj+eckXX9Td+Tp2lDz0kODmmyW1+ewjjvemyiOJl4DKPwFeBLFaINMlTEW5V48BYQqAtsOB2LwZ+eWXKlT5l1/6dJ+WOh0MGYIYOBBpMqkoN+eeq9JVGI0nTT9tPLKRxb8sZtnOZWw7vo0xPccwrf80pvafSt/UvmG77ikz9pooD+s1Dy1D6IzIrhfCt7eAKQnSp0Gn8xCGqMb1KyoQn3+O3LpVeUEcPw6ffgorViAeeUStAnXvXuf5sHs3orAQWVCgvnt7UK6PHj2QAwfCaacpS6r2U6Sl4W2C/9X0UxuVeyuz2eDLLwXLlklWrIDLLoOnnoI1a+D00wWdOwd+zepq9S5m0CDo1w8mTxZccIHkmmugV6/wy3Syl7fKNa1FIJ0Q1RG+mAo9ZyD63IjURSnDuLDQbRiL/Hxkfj6iYRo/avPOd+2qtnwUF0NqKuLcc5GjRin3gjPOgFr36Ehp32OVx3j6m6fZZ9nHe9Pfo7KmkjhT43z3zdFpMS81gAQxTiBvlnArHkuFp+zYa5ep2fJWD/DVoUMH8vPz6VX/Dv0rRlMBvjRN49Zbbw0owJfNZsNYawQ0t+n++PHj7rrgO8CXKwjIyRLg6/BhwX/+E8Xrr5s4dsz11kjSv7/GnDnVXH65g5SUwIIjxMXFuXlxtVckBIWRUmK32zGZTK0S4Ku0tNQ9Pv2RqWJ1BcZFRqxzrRiXG4k5EIO4RGDpagGBm/+OHTsipfQe8KG0FNuXX6L/7jsM33yDYf16RL2xCCCFwDlwIPpzzsERFYUtKwtndjYyPR1TdHRIZTKbzdTU1Lh1p6l+cjqdlJSUuOu6ZWrQT0IIoqOjax9sVYCvrcVb+bnkZ3JH5/KXNX9ha9FWLsq4iAm9JpCWmNbmY69RP4UowJeLF1d/RIJMrnEaFRVFUlKSd5ni4qg++i3y4FKEs4rq0/5KzLGPiIpJoKrDRQEFhTlx4oRHxOa4qiqM331H9YYN6LZtQ3bujPWJJ0h84QXEBx9g79cP58CBOIYPh7g44ouKkLt24dixA92ePegKCtAdP45PpKai9e+PvV8/tP79cfbvjxg4kLhBgzyC97j6JSkpKeAAX41k8iPAlytIkOuaTQX4MpvN7rpNBfgymUxomhZRAb727q1g+XIjK1caWLvWSHKy4OKLnVxwgZXzznMQFxe6e7lOF8WHH0bz5pt21q41kp3t5KOPKkhKavt7hEvPYmJiWiXA1/Hjxz2i9IYiwJfT6aRDhw6NAnz53U9OG6YtLxJ9+FWEoxRtSxritVJ0Zk+eAKTJhLNPH5znnIPBbkd39CjH//1vYtauRWc24zj7bOIGD0an1/uUqayszK1nOp2uyX6qrq6mrKzMrWe+ZDIajRgMBhwOhzvAlz/95BpjkxdPJt4Uz1/G/oWze53duvOT3kD8yni0hzQ1H/+5Gvs0e9ABvgK977VWgC+73U5cXFyrBPjyx9YIRCYpVRT3lJSUFgf4crVNxAX40ul0TJw4kYkTJ/4q8ig3h/YAX6EplxI+/1zwwguSjz4CTVO8duoEt94qyclR2/yCvSa0B/iCZtrACqxBuVevBrFJIEslrAOubZp/jwALRUXw9dfw1VeIr79GbtiAaJjPMSUFmZkJI0bA1Vcjzz4bS3V1mwQe8lU/kLpSSrYd2sbaI2v57uB3vHHZG/xw6AdW71nNI+c9EjF6Fu5rwkkS4MtV32GFotVwaCni8HKkvRy6XgQ9r1TpYsLdBvv2IX78Ua1E//ILXHAB3H474tprkXv2qKXE009HXnIJZXo9SUeOuPfws2OH+ty713f07uho5IABagX6tNOQp51GeY8eJGZlec2XGk49C4VORtJ8VloKr74quO8+yapV8NhjykV6yhQYNkyAl7X+UOtZTU0SX30luOIK5db9n/8IrrlGcvnlnh77rXWPiKj5LAjaXuezpuhIqXQ4Px9Zz5ValJSo/wejtiN9AHKgAbr0AbojHA7kgQOwbx+MGQNr16pVZIcDc0xMQDLVb/NI0bPiymKe/vZpXvzxRc7rdR7vXPGO1/gbYZ2faiS8DqwHFgBlIJJafz4Lx/2nrfWspbQD1rMQ0Gn1AF9z587lgQceAFR068WLF5OZmckVV1wRqkuclHDdpFyQsj3Alz/lZjPcdNM8Vq58D5stH8gAsujSZS4PPpjBHXfgkRd55MiR5Ofne6WZkZFBQUGBh0I3h6ysLDZs2NAs72azmSeeeIK8vDwKCwsxm81kZGSQlZXFQw89RFZWVqPr5eXluV8qLV682Cft3//+97zxxhssXryY6dOnN8tLsOVex8xRYBmIpULtQ05DuVa/BESB6CHguiboS4l+zx4VnfPrr+GLL9Q+rPp1Qe3bHDRIRbO56irlWl1/r4mU7vy3LZapifqucn91p6m6mtTIP5LPxzs+ZunOpfxU9BPn9DiHaQOm4ZAOzuxxJmf2ODNgmcJdHk7awfZHS8v9qevmqeowIioVaizwcR+I6apcq896A9HpPNCbmqTTXHlAbdC7N/TujZgxw7P8r39FbNqk9u5/841yvxw2DGbORPTqpVwxr7lGvWyy22HnThX4zmVIb9+uyqqrEZs3qyi6KF1MQm1vEH36eLhyuw7h5cEjVHoWCp2sX99bua/6LS23WGDlSsHy5fDII2pr+aZNYLUKLrxQ5UxuQCVsvLj6Iy1NcOWV6v+xY+HwYXjuOcEdd8DcuXDPPcHRbwmP/vZ3S8sDHZP+0vbJu5TqxVR+vtsoZv16qN1CVL+2NBpxnn46+n79EOedB3eejnjtchi/C44eA/sExPD74dwxkJKiTkpLUzJZLAHLFGl6lhafxryJ83jg7Ad4+6e3SYpOYuuxrVTZqxjdfbRfMrW43CQgF3WA2jaWCOIJAUP9oxOq+SwcetDWetZS2qHi3V86vv5viJAZyy5DGeqiW1ssFp566imEEO2rze3wC/n58OKL8NprI5FSGckGw3QSEgopLV3C0aNLGDNmAyZTVoPzlKGclZXViGZqgw3MDes4nU63q7zZbKawsLDROd6wYMECcnNz3b8zMjLIyMigsLCQJUuWsGTJEnJycpg/f75fsrc5ttV+DkJNICaUgfx34HR8PeMpOBzqwbt2rzFffEGit/3GqanqAf+WW1QUm549QypCW6HKXoXNYSM5OpnhLw9nv2U/F/W9iPvOuo9z0s6hd+feft+U29FKkBJKNqCzSkjKhjXjYdTL0Pl8uHgTJPSDSOuzAQPUcfXV6reUailz9mz48Ue1gfUvf1HGtF4Pr7yiDOhrr1X7+XU6FRxvzx4PI1pu34785Rd0Fot6qVVQAMuWeV67c2dlOA8aVHecdprXlehTGVKqxfvly9Wxbp3qkilTwBW37e2325rLOqSnwwMPqGP7dlUmJYwbB4MHq1vxyJGRN9QjEna76vyNG9UbEdenlz3GGI0wZIh6cdW5s9prfNllVAwdStKYMXDffUo3xYcwtDccehN2vQCj74XkZHBWg/7U1K1OcZ3c6aXyj+TzuxW/47xe5/Ho+Y96GM2tgv+hnnFGA1ehFgRimzyjHb9GyFbCU089JbOzs+XChQtb65JtCovFIgFpsVg8yjVNk+Xl5VLTNL9pBXKOv3WD4SNcsFqlfOMNKUePllJN4/MlII3G6fLBB6UsK1P1Vq1aJQGZlZXlcX5paakE5PTp0wO+dsN2mD17tgRkQUFBk+ctXrxYovzp5Ny5cxv9v2HDBpmRkSEBOXv2bI//XHI0xa+maXLmzJkSkIsXLw5YLr9QLaX8VErtzdo2+Ksm5b9q/7M1c25FhZSrV0v5179KOX68lHFxrs5zH5oQUhs9WsoHHpDynXekLCkJmMVgx2lr6NnOIzvl+kPrpZRS3vDBDXLuV2oc/HzsZ1njqGkR/6caIqYd7JVSHvhYyu9zpPygm9TeS5DWrc8qvhzVYb10ONrAK03X94MHpbznHinPOkvKqCgpBwxQ5du3S/nhh1IePuxJp6xMaocPS/n551K++KKUd90l5YQJUnbv3ki3PfQ8JUVq55wj5axZUj7zjJSffSbl/v11fLSgHSJlPquulnLvXvX99ttVc158sZTPPy/lnj1huWRQ8L+9pPzySyl/8xsp4+OlPP10KU+caHu+Iupa5eVSfvWVlM8/L7Xf/EY6RoyQWlSUdz0wGqUcPlzK226T8tlnpVywQM2NF14oZUKClElJUk6eLLW8PMWb0+n9mo5q1TnF30q5pKOUm/8kZdWRU/658VjFMTln1RwZ9/c4+cmuT0JK228USin/KqXUpJR7pJTlvqtGzHzWACelnoWBZiB0fNlqDRGyPcu+8MorrzB//nw2bNhAVlYWubm5mM3mU361uT0advPlBQUwf75a/DCb1Wttg0ESF3cVFssSfvxxPSNHZnnQ6du3L4WFhe49OaBWlbOzs3nggQeYN29e0Dy66Dz55JPMmTPHZ/3S0lL3yvP69evdrtYN67tcss1mMyUlJe79E3l5eUyaNInp06ezaNEinzzm5uaycOFCFi9ezJVXXhmUTI3Kj0lYDiwDVgIpIG4RyL82Q8fhUG6fy5YhvvxS7TdumL4pORnZvbuKTH3VVXDOOYiYmIgce8GUSynZeHQjy3YuY+nOpWw6uomZI2ayYOoCjyifkch7c+WRxEuoyj3Kjn8PP/8dilYjorsg06e6o1e73KsjiXe/ZAqkvKYGDhxQKac++khF4N62TS073n47PPwwHDiASElBekmxJioqkNu3q/3TLpfuX35BFBZCw7gDtZAJCXUr0bWfYtAgZK9eaoW7FdqrJeWHDytv2ksuUXPUihWCDz6QHDigHGTi4nzTaWveAymvqFB7qy+9VKXlfe45uOUWwdSp0sNpIBJ5D5k+FRXVrRJv2oTYuBG5e7fXPf8yMRGGD1fbH0aMgOHDEVFRyJ9+UvPeI4/Aq6/CeechxoxBjhmjVphrvdf84lFKFTNhx//B0dWIwX9Env5IYDKFob3CXV5cWUxqbCq7TuzivpX38ccxf+SsHme1Pi/3AItB/EMgb5Kga6Z+EOWR1O6hKo8kXvwtb/U9yx988IF7f/KmTZuYP38+CxYsQEpJTk4OCxYsYMSIER7nrF69mry8PG677bZQsRFxyM7ObhQN2+l0cttttzWKhl1ZWdk4Qp0pirIjZVRXV2MymRCiLhp2maXMYyC4ItQVFxe764LvaNhRUVHo9Xqv0bBrbN4jPlZbvUeos1bVi6AaC1HR3mUymWJYvTqKZ591kpenx+XbGxMj+e1vBbm55Tz/fDeefRYOHjzAiBHDPSIJnqh17ZVSumXaunUrAIMHDwbA4XB4jbrnLYplXFwc5loXqunTpzNs2DDuuusuAJ9RYR977DEAbr75ZjIzM6mpqSEqKsoddc+F+Ph45s6dy7fffsuWLVsYNmwYgLuO3W73iJDYMBq2K3pyfZl69+7dKKpifWzZsoUhQ4bURRKsHR4mi4nY62LhB3AOd2K/yI79XjvGkUaiY6IbRXaMiYkhqqqK/8/eeYdHVXR//HM32fRKDz2F0IsJSEdKAKUoKtiw0ywvtlfB3l4LxZ+9AYqKKAqoIJ0EUJAOofcktFDSe9+95/fHZZf07Ca7Kcj3ee6TZDL3zJlyZu6ZOXNOzh9/4LhqFY7r16PLzDSXowDi6oqxbVsKBg/G6b770HXrRtqVMSYi5Ken09BZC6NTmndES/pJ5KoX0rK8I5bWTy4uZdSplH4q7PExLy/PLDsmL5YnLp5g1s5ZrDu9jvS8dIYHDeeJ7k/Qs15Pmno3Jc10l8xZKdUbtole4f40y5MVdSpzjiinTunpJecIaz1zWiNPZdXJ1dW1hHdyu9Xpyv+89emwazJpNyzCIacAR89QDEGv4Nmsp7lOkpFNfn4qLi4uZu/LltbJ2n5ycnIq4aW3qv0kIuTn59OoUaOK+6lBA0hLwzEsDI8xY8iNi8O4axfi6IghNRW3l17CadEipF07CkJCyH/4YYzdu+Ps5ISrpydZ7dtjaNNG06pMdTIaSdm5E6foaBxPnkR34gT6kydRoqJQMjJg1y7tKQxXVwxBQajt2qG2bYtLSAhq27ZkNGoEjtoniWnsFRQUkJKSYpahssaeg4MDjlferWw/qSocPerG+vVOLF+ucuSIjp49jfTrl8n997szcaL+inMjwWDQ7irXlDzZct4zGtMZNEhIT4dWrRR69fLk5Zdh8mTh5ZdzmTQpv8p1Mo1TNzc3PD097TrvOTo6Eh8fX+QbyNxPKSnozpzB4eBBHA4exPn4cdi3D+XyZYpDAVQ/P4ydO2Po3Jn8Dh3wuukm8hs3Jn/3bhz//huHZcvIv/NOPI4exbBtG1nDh8NTT8ELL+Dk7Hzl2yib/Ctrp0nGfXx8Kq6Taw/o9hPuxnM4qpnEx8fjdfYjjD43YGg4FE8v73LHXnp6Ovn5+Tg5OZm9YZfVT3l5eaSlpZnbrKyxZ/Ksrapqqd6wqzqX1/esj07R4Wxwpq1PW4YtHEbfZn2ZNXwWnRp2qj55egtce7ri/Koz6icqmYsyET8x18kec7kt6mSSMw8PD9zd3e36HWGprlEd33vF6yQiZvqWeMO2CGIj1KtXz2xqrdPpJDQ0VObOnWvRu7Nnz7YVG7UGpqP91NRUUVW1yJOcnCxGo7FImoiUyKeqqkimiHaGWnceNaNknS5fVuXdd1Vp3lyVwtZLAQGqzJ2rSkHB1fxRUVECSEBAgKxfv15ERKKiomTs2LECyAsvvFCE9owZMwSQGTNmSFhYmADi4+MjYWFhsmfPnnLb19QfJhoV5RcRs3l1VFRUxf1XSvr69euFK2bYZeU1Go3y0EMPCVfMsAvX9YUXXpAXXnhBpk2bZv6dKybhycnJInkiasoVujeqom5WRTWoIt+JqBfKbgOjwSDq0aOizpolav/+Ig4OUrizVL1e1JtuElm4UNQzZ8qtq9FolOTk5DLb3NL2qiwdq+VMxFyWwWAQg9EgY38dK2dSzkhqTqo8uepJWXtqreTk5xTJW5h+abSNRqOkpKSU4MPaMWPvdHuXWZn+sCq9IFvU83+KumOSqL83FTVxr4gxX9ToH0Q15FU4To1XzCJrexvYSj7KpBMbK+rvv4v64ouibtmi/b9dO5G+fUV9+mlRf/5Z1LS0iuuUlyfq4cOiLl4s6ptvinrPPaJ26SLi5FRkTikyvzg5idqpk6h33SXqG2+ILF4sxgMHJPnSJbvKmcGgyiOPqNKokSo+Pqrcc4/IggWqxMfXXnmyt5ypqsiWLaps26b9PWGCKjNmiMTGVo7HGpGz7GxR9+wR9ZtvRH3ySZF+/UT19Cx97CmKqG3bauN0xgyRdetEvXxZo2c0ivHYMcmaPVvUO+7Qrh14e4t6222ifvqpqLm51stZGXyXW6ekJDEeelfU35qI+mdbUU98KWLILZNG8TWqPB5r63p2OeOyPLPmGflkxyeiqqqkZKfYvcwi6Tki6teqqAWqqNGqqKdsJ2f24L2m17Oq0q6J78bU1FShOs2wdTodiqIwadIkpkyZUuIUuTzMmzfPfMp6rcBkhl38aF9ESrjoLxdZQEmruNqNTOCKiVpkJHz6qebwxLSR5eSkbb5OmQJBQaWTiIyMJDQ0tET6tGnTmDlzZpG0cePGsXTpUgCzJ+qYmBiz06/yvEmb+sPf35/u3bsTHh5eYfVM/VZZ0TF5w/bx8SEsLKzMfHv27OHMmTPl8g9X6z/nkTlMzpwM64AXgFeBA2ghKlzLeDk/H9myhbzffsN5zRqUM2eK/r9NG83c7PHH4aabipiSlQerx7mN6VjznohwKP4Qy48vZ/mx5UzpMYVJoZNYcGABNwfdTCP3RpWmb6t2qOuwSzvkXIILK+HCCrgcAS5NNNPq5kXNq6udr2osq1rk7NgxzXnYrl2wYwf88Qd4eMBLLyEDB5IeEoJXmzaWlW8wIDExZO3Zg/uZMyhXzLk5fhyKxVg386Yo0LIlSlCQtmAUfgICwM3NqnbIyQFXV20N6tkTxo/XTI+7doU+fcyH23US9hrPixbBd99pkYyGDIEffoBGJafFaucLES200smT5kdOnkQ9dgxdVBRKodMzM5ydoUsXzZT6ihk1Xbpctas3YdkyzSle+/ZI69YYWrfGcdgwlKFDISSkUgPFJuuZmg9nf4WzP8OA5VCQBoojONcr+x0L1sDavp5l5GXQ6uNW3NXxLl6/6XWaejat1vL5Cvgv8CTIK0IatW9dv76eWU+nLF2tNKI2QWBgoKSmplr1ztKlS69Zp1/lOfhKSUkx725UCFU7qU2JTRE148pJczmPpXmtoWntk58nsnixSN++RTdwGzTQfF5kZ5df5ZSUFAkJCRGuOPOaPHmy+VTZx8dHwsPDi+Q3nSbPmTOnSLrJkRYgKSkppTevqspTTz0lgOzdu7d8xuSqM7GAgIBS/z958mRzmYWfws68CvNlyVOmg69zIjNHzBRAJiuTRUJE5A0R2SOak4qykJAgsmCByNixJR1zKYpIUJDI9OkiFTg5Kw9Wj3Mb06novdyCXFl7aq08uepJaflRS3F/111u/+V2+fyfzyUuI85mfNmqHeo6bNYO6VEilzdqv29/WGRdH5HD74ukHNYc49QUXzVUVo3J2aVLIs8+K2q3bqLqdJoliohIfLw2v1hbltGoecpatUpk9myRRx4R6dmzzJPAIk+zZqIOGCC5998v6rvvaotPZKTIlbU3L0/zRfjccyLBwSL33qsVuXOnyPnzVjZULYe9x/O5cyIffihiMIjs2SMyZYrI9u0Vi16V+UpL0wr8+WeRN98Uue8+kR49NMdZ5Y0NX1+RwYO1zv/xR5FDh0QKCkrST03VnN5Nnao56BLRPlbWrdP4LyiovevZsQ9FfnUX2fucSNZ5y96pJF81vZ4dSzgm4xaPE7d33eSNTW9UPwMHRGSIiNpAlfS/02vdun59PbOejqUOvmymLE+fPt3qdyIjI2XKlClyuja5k7QRylOWs7KyrBoM1rxjad7K8FEREhJE3ntPpGnTq2uVTqd9nEREWE6nLOU3OjpafHx8ylV+i8OkvBanZUJycrIAEhYWZjF/JiW2NMyZM0fCwsKKPGUpyxV5w3700UdLKsvxIvKO9uuSlzWP3CEtQ0Riy2FYVbWPhPff13YwFKWoCZq3txQ88ICoP/xw1fV4FWGr8VVZOqW9l5abJmtPrRURkU93fCotPmwhT6x8QtacWiM5BTl1Rs7qIirdDoYckdhVIoff1f4+97vIvitrjVqGR9nq4KuWlFUr5Oz8eVG3b9cSvvhCm186dxZ56imRLVuqVJZqNEpWTIyoW7aIfP+9yKuvitxzj0j37uUqSql4yQ88IOOcl4mXQ4Y0cU2RCSGR8sezf0vGX3vs6/q5BlGd4/nkSZHJk7VuaNtWi2hRJb5yc0WOHBH54w+RWbM0z9IDBog0aVK+QqwoIq1biwwbJvKf/4j68ceS88cfop4+XbYWn5srsnGjyCuvaGE4HBy0SjzxhOYNvjL8WwBbrmdFkLBTZPMdIoucRc4vt7qsurae7bmwR77b952IiGw7t00y8spxXW1rqCLqSlWykrNEPaeKrK2+oivC9fXMejrVrixfR1FY2gHXAvbv10JQFI6qoNeL3HmnyNmz1tGq6OR2zhwtrFRp4ZpKgynEU/HwTcXpWROeyXRnuaLwUiKlh7WyRFkWuaroL3lqicg9ooUzSBeRiSLRR6PNJ+2lbhzk5oqsXSvyn/+UHvqlfn1tF2PzZu1U5xqEqqpyKO6QvL/lfcnKz5KTiSel3/x+YlSNkm/Ir/EF/zrKQPYlkahvRP6+TeQXN5FlrUR2TxUxGmqas+uwBPHxIkuWiDz5pMjXX2tpL74o8swzIn/+qZ3i2QKqKpKYKLJjh8jChZL72jvybrfFkttzgBzy7S/d2SVv8rrsIUSMKCXnQF9f7XTy3ntFXntN0/a2btVOzQ3Xx5qlyM4WWbRIO5gVEXn3Xe0AuFQLMoNBJCZGO7H97DPtJHf4cBF/f21nvTyluHFjkf79tY+NGTNEfv9d5PBhLfakJUhJ0TZzRLRx6Ocn8sAD2ibMtWJikHZcJC9FJDdJZMvdIgk7apoju2PkTyOl0exG8smOTyS3wL7h/0pgrYj4iMgIETlavUVfh21gqa5m9aWLjRs3MnjwYGtfu44rEBFycnJwdXW12CbfmncszVsZPgrDYIDly+GTT2DLlqvp7drBvffC9Ona1SBrERMTA0BISEip/w8ICAAgOjraInqmcE1lYc6cOQClhmYqC2FhYcydO5c5c+aUuD9dHHv27LGYrhknQf4U5M8rd6KXAQ8AToAnpM5OZWjoUEDzKG+uY1wcrF4Nf/4J4eFQyPMgigKdOml3j0eNghYtzP8SEXKysys9FkpDVcdXZenkG/PZfHYzy08sZ+WJlcRlxREWEMb4zuNpU78NWx7RBqvOQVfi3dooZ9cKym0HERADKA4QcRMkbof6N2r3j7u8A94dtfFb3XzVgbJqSs7Kfa9hQxg7VntM6NMHVq2CF15ATp0ib/lynEeORNm0CXr0AE9Pq/nKzlHYuL0+K1fW54EHbqTbmByiYl1JemccnZrC7rQ0iL4NojpCVJT2REdrPy9ehJQU7S727t0lC1YUzXt4o0ZXn4YNi/5d+PHystsYtQY1Md+4ugj3jMiAxETYlYjraQ/e/rwpjz3qyt3tDvBOp19pmB6FnDqFEh2NUsiTbQl4emr3hIs/bdqAt7dF/JjXs9hYlI0bISICZs/W+ik8HB55BEaMgAsXLO6zWilnpcGrrfYzLxlxaw4bw6Bed5ROr0CTsv2j1OX1bMW9K1h5ciWvbHyFT3Z+wuHHD+OqL8tJi21gbodhrihRCrwJhAC/ArfatWjL+Lq+ntmcN6uV5enTp7O7tMXlOkqFyNV4wCbk5eXhUjiAIRXHAyv+Tln5S6NfWl4RzcV8cT4q4iUpSfjmG/jiCzh//uog7NRJ+Pxz6N+/8Ppjfewzk2O4yMhIc57C+ffu3QtAaGgoIkJMTAxBQUGEhISwZ8+eErTXr18PQI8ePUqUGRkZSWRkJLfeemuJ/5XH44wZM1i8eDGzZs3irrvuKjPOMmixkk0w/b9wPhEBA5AIip+CTBb4ARgIxlZGuAz8H8idV94RzaFXTEwMSxYv5gZFQf73Py126pW2MdNu1EiLbzphAtx+O4qHx9WybTAmy0s3jVnXKsZZtoSOqqooisKLES/y9d6v8XDyYGSbkbw/4H1GdxyNm5ObmZYt5MyavJWVs+pOt3eZUGyMGXMhLwnFvTmy9R5o2B+Cn4QOL0O9EBTXxkXplDIX2KJOhfux2tugEnQqIx+2krMq1WnUKBg1SkuPjSVHUXDKyIDJk1HOnEG6d9ccCL71lnmXtTQ5O3dOi3e8cqWwcSM0bQojR4KPj1BQkM8337igKFeGi5cX3HADSkhISd6zslBOn0ZOnSqhSCvnz2sEEhK058iRUutdGOLkVER5Vho1QkpRrpXGjZEGDcCGc21xVHkuz85GSUpCEhI0BfjKY05LSiqanpgIhcIIPQs8A+ziRn7aPx7P/V9ylsYs5i4e5nsaOaUhQUFXleArCrHStq3WZsU+bs08ViTfqamwYQOsW4fz2rUQH4/07at5JHN11da/338vSgPsKh/VLmemNCdfpNss0ps/iVfczxD3F0qTMCTtOHgEgs6xBI3avJ6VlxdgVPAoRrQZwa4Lu3BxdGHZ8WUYVAN3tLujiLJkFzmrB3wKPA5KcwVJElgIPIZ2wFGJOlX1u6um1rOq0q4JOSurbsVhtbK8d+9e6tevb+1r3H333cyYMaN8b2PXIEqLs2wwGJg0aZJlcZavxD7LvBKnT1GUCmOfFc4LZcdZBi1+b6lxlovFcztxwolvv3VjwQLIy9PoOjkJw4YZ+eQTR5o00WKfpadjcZ3UMuK5DRw4kL/++ou3336bV1991Ryj7syZM7z//vsADBkyBFVVqV+/Pl27diUyMpJPP/2Up59+2hzP7cCBA8yePZvWrVszduxY8vLyitRp9erVAIwePdocK1erV/nx3PR6PW+88QbPPvss3bt35/PPP+fJJ58sUqcDBw7w7LPPmk/KC8dUNuUxXDJosQ3nO6HfoMdxpSPqdJWM1zIQDyH3P7mw82o/ZWVl8cwzzxAREcF/b7yRsdOmQTHv1WrDhuhuuYXcRx8lt3Nn0GknqE46HW6UHjvaxcXF3C6mNqhqrFsRITMzE29vb0SqFmc5JycHHx+fIrEEk3KSaOrTlO8Of8fu2N18PPhjejfuzS2338KNLW7E1dWVS5cukZ+dT0FOgcV1Kiw7FcVHLJy3rDqZ2jM/P/9fH2e5ICOWnPML0SesQ5/4F8Zmt+PY70eyg9+kwKGeFrjWvTeuOlecoVrqZBqnpjik9o6zbJprTePCFnGWs7Ky8PHxqVI/mT4uoOz48jark7u7JjtGI8qePXinpyObNlGwYwc5OTk4rl2Ly/vv4xgWRn7vfhx3b0e7bvDRRy68954L/frBzTcbeeONbNq0UVEULc6y0ah9vFncT507k+Xvj6GQpZyrqyvODg5knDmDxMWhS0xESUjAJT0dh6Qk8mNjURISUK6k6xITUTIztdPS2FjtuYKyzjMUQDw9URs0QBo0QBo1Qu/piaqqGEW0OVunQ9Hp0Ds7YzSlKwrodOj0evROThiMxqv5FQUHJyccHR1xzM4mz9kZ5Uq6o5MTjk5O5OXnI9rAgIICnNLTcUhOxnD5MkpSErqkJJTkZBTTWlAG72VB3NyQevVQ69eH+vXp2aQJob4nKfB+jsu5bVmxdhivnZrBraMVpjyWT/fuV8ekuZ+ys62a97L27EGSkjD26oX7/ffjeOYMDBtG8vvv4zhoEMoVD9eenp7oRCo974kI2dnZ+Pj4VGmOMCmbgNWxo028mOSsojkiPT2dzDxHpPkT2vwGyI4JSPYF8gKmkt/8PnR6d/McUXg9Ky/OMmhzhD3iLFd1fWrv2Z6MjAxyCnL47/r/8t7f7/Fan9cY2HIger2+REzizMxM8/d1bGws8fHxZGZmmmPvKopCVlYW+fn5GAyGQjHZVfM3o9FoJD8/31yPepfqMT1yOg4vOfBpy0/Z4r0Fg9FgXv91Oh3Z2dnmzfusrCwcHR3N66S7uzsuLi44OTnh4eGBh4cHzs7OeHt7a/Lt6Gj+bnNzc8PHxwedTodOp8PFxQVVVWnUqBG+vr6ICA4ODri4uJhpuLu72yzOsiW6RlW/90ywNs6y6XdbxVm2OnSUTqcjJCTEbA5rCVJTU82nfqdPn/5XKMwmd+Spqakl6puamlrCpXl5OyYmwSz8Tnm7PcXpl7VDmJ6ejpeXVxE+Cuc3GmHFCi30019/Xc3TrJnw9ttwzz1aCA5b72ClpqYSEBBg/hkSEkJqaioREREAfP3110yZMsVMIyYmhmHDhhETE2Mem6bQUT4+PkRERJhPogtj2LBhREREEBkZSdeuXa3egZw7dy6PPfaY+X8hISHUq1ePPXv2mHkPDw9n3LhxBAQEsHjGYlgBEQsiGLZvGGN9xrI4YbF2suwIimPR3a5HH32UH374gSVLlnBnaCgR77zDsPnzAZgG1AdEp4O2bcHfH7p1I+yOO0qta3l1AuvHpCW7dWlpaWWawVuzQ5iWloa7pzv/nPuHFSdXsPLUSoyqkaipUVzKvIQqKs29mle5TpbKmTV5LZGzyraNLdPtRjsnDmK+1UI8Je+Get2h2ShoOhp8OqPodDVaJ9P48vb2RmdHXkyoqpyVJR+2kjNr6VSmTsVlp3je1FMJ/PXpQcak/8i6NUZeynuTvakBxG06hnM9d+rd0Lp2yVlODhIfD4UeJSEBiYvTTqYLp8fHX42hWJuh12unvA0amB+lQQOkfv0iaTRogNKwIVKvHri5FSFRmpwlJHgzf76ORo2EZ5+Fdeu0qE1+fha2e2ys9lJsLMqbbyI//KCZG7z6KuTlma0SLB2T9paPmpAzU1rhuc0sZ8YCOLcEjs2E3HgYdRzFybvWr2eW5M3LyyMpKYmEhASSkpKIjYvljwt/sD5rPbcm3wrxkJCQQEJCApcuXSI1NbWIwmhLOODAJCbxNm/zPu/zER/ZpZzKQKfT4erqiqenJ82bN8fLyws3NzeaNWuGj48Pvr6+RZ569eqZ0728vNDprl5hs0TXsCa9JuQsPT0dHx+fCkNHWa0sDxs2zGzaai3mzp3Lvn37+Oqrryr1fl1CeXGWc3NzzWYSlsCadyzNW16+lBSYP1+LPXn27NX09u3h3XdhzBhtk9vemD59ulmZNSnNL730Uqn3mVNTU5k+fTp79uwx5w8LC2PmzJnlCoyPjw8XL160qj8KIyYmhpkzZxIREUFMTAw+Pj4EBARw9913M23aNFBh6Z1LSd6ZzOTEyXATRARHMPTLoYwdO5YlS5aUSldEmDh+PPMXLWJJUBBjo6JYCoyrgJ+ZM2dq5VqByozJ6qBZYCxgydElLD+2nPWn1+Ps4Myo4FHc2vZWhvgPwd3Jvcx364KcXbMw5sO+/0LX9yAvCSKfQZqOJrf+YFx8WteqdqjO/qmtclYVOraSs5MnYeVK7dmyRfN98ddf4O2lkpuQgLtfI5QXX4QPP9RMd2+5BSZN0jJWgf9qh4hmLlxMuSY/H1RV+7+qFn2Kp5XztxiNGA0GHBRFc2lWVn5HxxJKb5HH09Omi3xZ/XPXXVo441GjYPJkGDoUrhjiacjJgc2bNQV53To4cUILjj16NLz4olVl2YP/6qJj8/VMBFIioV4onFuKJG4nt/XjuPgG1pr1LC8vj7NnzxIfH09iYiKJiYkkJCSYf09MTCQ+Pp6kpCTi4uKKWEgWgR4oANoCNwAbgfir/3ZwcDArj97e3gQHB6PX60lISCAgIACj0UhKSgp6vR4nJyf0ej16vR6dToe7uztNmjRBVVVEBO8r9+odHBxwdnbGvcAdJ0cnFFeFTks7cebOM6gNVfMJsV6vR1EU8+lqbm6u+cnOziY7O5uCggISExOvHGAZiY2NJSsri9zcXHJycsjLyzOfwrq4uJCfn09iYiJ6vZ6cnBxycnLK3ICxFoqi4OHhgaenJ02aNDGfVjds2JDGjRtTv379Esq26fH29jZb2ZaFmpAzS+MsW60s33XXXSxevNiaV4pg9uzZvPDCC5V+v67A4kDXtQhHj2oK8vffg8li1NtbW8QmTtSuFl1HBRA0W7W30CbmW4FXga7AcKCioZCSAr//Dj/9pH0tFhbPgQPh7ru1L4x69ezAfO3AvL3z6Nm8Jx0bdmTYwmH0ad6H0W1H071pd3RKScdc11HDyE2Ai6vgwgoInARNb4aDr0ObJ8C1SU1zdx21GG+8AYsWaZuygwdrStPIkdC6dRkvpKdrDpvWrIGHHoK+fTWluVcvTYFu1qw62b8OGyAmBr79Fn7+WfO15hRznAyvZjTzU7Uj54YNYfhw7RkyBCpw2nkdViJ5Hxx+Gy6ugdbjodMr4GG55WhVkJmZSXR0NFFRUUV+njx5kthCVxqsgbOzM25ubnh6euLr60v79u0JCgoiQzI47HuYLVlbCGsaxku9XyLEPwS3YhYRdkE82h3mCOAV4Gmg5LVvu8FgMJiV8JycHNLT00lJSTE/ycnJpKamcunSJWJjY0lOTiYlJYX09HSysrLIzs6u8in88uXLufXWGvR+VgYs1dWsvrNckfffiuDr61ul9+s6RLQ7Zu7u7lbtEFr6jqV5TflcXNxZs0bh00+1bxATvL3hscfg9ddLWFddU6hMf5TAWWDFlacVMBfoBZgcTr9TwftZWZq9+88/a96sC92hED8/lHvvheees9uHoE3aoJI0DaqB7ee38+eJP/H39eeJHk+QnpdOVn4WDjoHIh6IqBRvtU3ObNm2NQ4RSDuqKccXVkDSTvC9QfNe7XVlR63L28VeqZ3tUJ181aSc2YuONe8VFIBeD//3f4LBkM+0aU74+SnMnq3pQB4eFtD38oI77tAe0E5kAwKQ77/XFqxOnVC2bdMKUhTtBPVfhLooZwHeSbzbJYJ3RrZEadCb3/sv4J6od7hlhI7Jn8Vw80ONcXC079xfXTSrQ86sfqfeDUj/38m+vBe3M1+gZMeCuz+kHgLfLlXiQ0RITk4uoRCbnvj4+HLfN5kMu7trd6vr169PQEAA3bp1Q6/X06RJE1q3bk2DBg1o0KCB+TpNeTiTeoY3/3qTDKcM3NzcSMlJwdfVOr3E6v5oBPwO/AU8B5wDvrCqyCrxZbqD7lHaJGsh8vLySijYly9fJisri4SEBC5fvmw2gU9NTTWflsfHx5Ofn2+R7lfTclYerF5J/P39rS5k//79+Pj40Lp1a1JSUqx+/1pDZXZorHnHkrypqfDVVw7Mm3fVR5ROp0X4+M9/tMPLWrTW2hVW94cK7OaqgnwU6A+M5mrYgOEV0MjL08zKFi3S7NAKOX/C3x+ZMIGMESPw7NatWjrCHnd3yqKZlpvGuuh1rDi5gtWnVuOgODAyeCS3tLkFgP/2+a9NeKsNclZZPmodjPmQGQXeHeDoTDj8NtIkDGn9IGrvRajOTRARzRwtO9tslmb6aTQayc7OxsXFBcdapsBUZ/9Up5xVF52y3hOBw4e1fcCVKzX99e+/YdAgrjhucaKQy4fK8eXkBC+/DC+9RPrp03gdPqzt7v72m3biPGyYFibo5ps1j9T/AtTW+cbMl8EAO3deNa3es0fb5Hj5ZaA3dxx9l1PnFObPhylvNOG+EzBrlmaR7WphRKB/k5zZ6p0C1yDoNV/73siIhvA+Wgi/9tPBb5j5O6Q4TRHh0qVLJU6HTQpxcWddxeHg4EBoaCh+fn7o9XpuvfVWWrduTdu2bWnYsKHNN31a+7Tm+zHfA3A04Sg95vXgqRufYlrfaVYpzZXqx4Fo346ZaKbhE4GpQHfrSdmULwvg7OxMkyZNaNJEsxYrfie+POTn51e4iWFCTctZWaiWr5aQkBB8fX2ZOXMmYWFlx3q7jurBhg1w662Qna2tPDqd9i3xxRflmL/922EAVgEjgMPAzVd+f+nK7z4W0DAaYdMm+OUXWLwYCnsn9/DQ7mFNmwbduoEIagWLTF3CubRztPRuyZazWxiyYAhtG7RldPBoVt67khub3YiDrvy7LNcqTAtOWlqa2ZNv4TtIZT3l/d+S/+Xn56OqagmltnCaj6uRwR0MLN6p0DtQ5cPxBrq/5oCXi5GcfMgtMO0WWQeTmZy7u3uRn6WlVeZ/zs7OtepU7d+E/HxtfTHdP05M1PTVCRM0nRXghhsgLU0tn1AlIPXqaXMoaI41mjfXLHU++0z7+csvmrbu5gahoeZIAddRDUhK0k75f/tNu9Ol12sXlJ94Qhsgfn5X8yoKrVppUcReew0yM7WNl86dtSvqkyZppvq1bM/t2oJnINx2Dk59CdvvR/xu5kLL9zl69CiHDh3i4sWLREdHm5/C3sFLg5+fH7m5ufj5+REYGEjXrl3p1asXvXv3pl4NXinr0LADmx7axMsbXsb/E39mD53NpNBJ9i3UAfAGctCsD28C7gXeQzuBvgbh5ORU0yxUGdUy3cyYMYOYmBjCwsJofV0bq3GEhmqLj4eHMHEi/O9/SqkmcP96xAIrgb5AR2A20BnohnYHRW8BDRHYsUM7Qf7lF807qgnu7trKf++90KPHNXWUb1SN7Ly4k26O3XDTu9Hhiw5EPRVFaNNQTvznBP6+1luo1HaIaCGyEhMTSUpKMjskiY2NJTMz05xmekx/Wxq6oDrQrimMDoHRN0DvNnDgHKzbD38dhRteBjCSklUBkQpgUtztZWWk0+nMd9aaN29Oy5YtzU+LFi3Mv9vj1OLfim+/1TZgHRxg6lTt+vDcuVro5FJCstofDg6aI6iePTWty3TKsGGDFt7B2Vlj8vnnoVOnGmDwGkdenuap7aab4Phxzd/GyZPa3fKICAgJsWizwtHx6hXlv/7SHI9OnQpPPqn5WKkj7mDqFHJycjhy5AgHDx7kwIEEThxpT3LsCnafWMgnD8KRs/DjP2Aotmw5ODjg5eVFkyZNGDlyJP369cPJyYm+ffvWar89Nza7kYgHI9gQs4ECVQuHteXsFvq06GPfTXxXtCt6E9FMsycDy+xX3HVUDVY7+LoOy1CeN+z8/HycnJysunti6TuW5j1xQmjVKh9nZ8v5uBZRpL1EgUiumlcfRFOU30bb/bOcKBw8qCnHCxcWib2JXg/9+mkfacOHF3P9WQZf1XCX0hZlZeRlsD56PStOrmDVqVWoqsqiOxcxLGgYOQU5uOottKGzAW9VlTOA7OzsIspt8SchIYHk5OQiXjqrYvqjKAouLi40aNCA7OxssrKycHBwwMHBgcaNGxMQEEBsbCzp6elmj5xt27YlKCiIw4cPk5OTg7OzM87OzoSGhtKqVStzyD4XFxdcXV258cYbady4Mfv27TN7ptTpdIxoGUm9tBU45F3mknQgVm4gVrrSY8AYMjIyOHjwIDqdDkVRcHZ2pm/fvsTHx3P8+HFzWBIfHx969OjB+fPnOXXqlDl/48aN6datG9u3b+fw4cNm5yEBAQF06dKFn376iQMHDpCVlUVWVhZ9+/YlODiYuXPncuHCBXM8xBtvvBFvb2+2bdtm3mAQEZycnMjNzS0S99MSODk50aJFC5o3b07r1q2LKNImxboqd7yKwx4ybSua1tBRVdi7Vzs57tFDGDo0n6efduKJJxS6dCn3VavLsjSvVe1gMMD27dpp8733QocOmqY/YIB2/N25c53duKzOdaMEzpzRHK+tWQMbN2pa7qZNEBiIpKeTfyUud1X5Mhph1y7o3VtbYr//XnNCOnq0trxeK3JW1fcqekdEOH/+/BWl+AD79+/n8OHDnDx5skisXRMcHByYFObC9JEFeHq4sDKmEycK+tCzdz/69+9foyfEtkR2QTZdv+6Kq6MrHwz7gGGBw0rksYucZaM5/ZoAPIx135z25Ksay6oJObO5N+zhw4ezbt066zi2AvamX92oi96w/5VQAR3wE/ACkIVmVj0auAUtmLGliI7WnHQtWgTHjl1N1+m0lXzSJM307BowSSmMVSdX8dmuz9h0ZhMBvgGMDh7N6ODR9G7RG0dd7bWVU1WVc+fOceLECU6cOMHJkyfNv8fFxRUJfG8NFEVBr9fj7OyMl5cXAwYMQK/Xk5SURKNGjWjatCkBAQGEhoZSr149GjZsiEt1Hr+pBtA5wskvoSAVOr4MMT+Aky80GQKOZYfkqm6YQkBkZGTg6emJq6srW7duJTU1lYyMDDIyMrjjjjtwd3fn5ZdfJjk5mbS0NFJTU3n77bfZt28fTz/9NE5OTri5ueHo6IizszMXLlywqHxfX98SJ9KF/27atGmtu4NtD2RmQni4piCvWqW5WLj5Zs2KdsCAmuauisjLgx9+0JTniAjN+/Lx49r/HByu2/tWhFmz4LvvICpK805+yy3aUw2bDpcuaafN8+ZpY/LDD+G+++xaZJ1EVlZWodPiAxw8eJCDBw+Smppa5juOjo74+fnRuXNnRo8ezcMPP6ytU6oRzi2GmO/gphVgyAJHN3CoCRMS+yDPkMeXu7/kf5v/x4g2I1h4x8LqKdgAfIR2QDMazZrxuoN/u8LmynKbNm04deqUzRisbvrVjfJOlk0fftbsEFr6jqV5K8PHNYM4oDGwA2SKkLE5A89znijxiuaoyxpdNitLu4f1zTea2ZkJDg5aYOr//AceeMBql+LV2T/Wji9FUdh+fjs/HvyRL0d+yYoTK4hKjmJ029EE1QuyKf+VpVP4vdTU1CKK8IkTJzh+/DinTp2qUCE27Uw6OzubPXL26tWL5s2bExsbS6tWrWjRogWtW7emTZs2NGzYsEgoilojZ+knr3qvNmTCzXs0b6ciJTye2gM12Q4ZGRnExMQQHR1NSkoKEyZMYNWqVTzxxBNmpbl9+/b069ePffv2cfbsWdLT08uO21kIOp2OZs2amZVnf39/goKCCAwMJCgoCD8/P7NjE3u0gT3lLDFRC7m7ZAncf7/mz2LUKG3fr2/fq6d419R6lpcH+/dr5tvz58P06XDbbZr37SFDNPPtWgy7y1lamhYq46uv4Nw5eP99bYPYxQXCwsq0i7Y3X6qq7XPUrw833CBMmlTAzTfrGTNGQW/JdakKUJvWs4rk4OzZsxw8eJD9+/cTGRnJsWPHOHXqVKmxdh0cHFAUhbvuugsvLy+SkpIICwtjyJAhBAQElCirVD4Ovg7R30KH6VroQEfrLchqK5JzkjkSf4T+rfqz5MgS+rbsS1PPpvaXs4vANLSrgH9Z/lpt/W6sbprW0LF56CgR4YMPPrCcWytgci9/LUJESkxSRqOxRJqiKKVOZqb04u+Ulb80+qXlLezUp6wya3O6VTQElAMK8qdod5D3AWeALsDPV9qro0CnQvnLo6+qmjfP+fNh0SKUrCzTa9CxI/z3v3D77XDFS6CIFI2XbEGdCve5vdsXyh+TWflZhMeEs+LkCjac3sDhxw/TwqsFg1oPQkQYFTyqTP5Nv1eWR0vp5OfnExMTU+SU+MiRI5w6darCuaVp06bceOONODk50bRpUwYNGkSnTp1o0KABnp6epZapqmoJT5AmXorzXyNyphpRErciF/6ECysh6yw0Hgwt70JpNlrL793JxGSNjzF7lGtK8/DwoEuXLnQpZCM8YsQIzpw5Q0FBAUeOHMHBwYEuXbqwbNkyfvzxR7PjmpEjR/LQQw8xZ84cYmJiUBSF/Px8MjMziYuLo6CggPPnz3P+/Hm2bdtWgi8XFxcCAwPNT9OmTenUqRNBQUG0atUKR0fHKs17tpSz/HwjW7dC9+7C2bOavhgfr50cHzqk0KZN8TVE+1nV9cwWa5/N5MzJCaVnTy39wQchKEiLe//44ygjRyJffAFHjmg7B+7uZdKpqbUSbCxnubmweTOsWYOydi3i4KC5OB8yRLOHFkG5776rNMroJ3uvZ4qiGW2BZmXfvLmBadP0PPOMMGUKvPgiODlVvp+qez0rDcX7NTMzkyNHjnDgwAEOHDjAoUOHOHjwIOnp6aW+7+joiNFoxMnJicDAQD755BN69uxJfHw8rVu3NisNxdezwihVzjq9AT5dUQ7/DznyPvT/HRr0srptykuvKXmq51qPfi37oaoqf574k0eWP8JzvZ/jhT4vVFnOys3rJ/Ajmmm2CjwByq0Kckv5tGv6u7GqtGtCzsqqW4l3xMKcv/32m0UEq4I777zT7mVUF0wTT1BQkPluoKJod/sMBgOTJk1i0iTN656zszOurq5kZmYWufPo6uqKs7MzaWlppKen4+HhgaIouLu7o9frSUtLK9LRpl2UCxcumPMCeHt7o6oqGYW9L1+Bm5tbkRMUnU6Hl5cXeXl5RTwcmuK05eTkXAn7ocFk3pidnV3klK6iOqWnpxe5F1NenXQ6XYnwA6XVSVEUvL29KSgoICsrC8e/HdH/qUe/To8uTYdxqJHcobkYhhqQBoKjoyPu7u7Ex8cXudtQZp1SUnBduhR1zhx00dHmdPHwQLn7bjKefRZjoVjIVamTiJCZmYmnpyc+Pj7mOtmjn1xcXLh06RKurq7mNkgqSGL9mfX8cfQPNp/fTDOPZtwScAtjOoxhYMBAsjKyyq2Tif9mzZohIuX2U3l1EhFycnLw8/MjJyeHc+fOER0dzalTp4iJiSEmJoajR49y9uzZUu9ZFa5/y5YtGTJkCJ6enrRq1YqePXvi7++Pt7c3er3ebNprkp3y+sloNHLp0iVz3rLqZJqUXVxcyC0UHsxe8qS/9CcG3+64efuhX9eN/HoDKGh0MwUNbgJHjyrJU3n9VFGdXF1diYuLK+KpujbMEaZx6uXlhbe3d4mxp6oq9evXZ+XKlWzbto3Tp09z+vRpHn/8cR544AF69OhBamoqvr6+uLi40KJFC5KTkzl58iTnz58v13mbo6MjrVq1IigoiBYtWuDv70/r1q0JCAigU6dOuLu7V1gnES2eZLNmzTAYDFb3U3x8Hps26Vm71pH16/XodApr1uQSFJRHerp2iFhePzk5OXHx4kXc3NzM/VpRP6WmppKZmWmWnfL6KT8/n7i4OHPesurk4OCA0WjE2dnZPuuTmxv6/HzSjEbcxo3DccsWCoYMwXHcOJTbbyet2Nxjb3myZi63Wp4cHNAtWIBhxQoct2xBfHwoGDoUp1tvRR00iIxCJzWW1skkZz4+Pnh6etr1O8LR0ZELFy7g4uJOeLieiAg9c+Y4cuSIjkuXMunZ04ipCpb2k4iQnZ1N06ZNzfFjK9NPpo3dRo0akZWVZXGdcnJyWL9+PSdOnDArx2WdFut0Ovz9/enevTt///03DRs2pFevXoSFhdGlSxcaN25sHhumfsrNzSUhIcEsZ2XVSa/XU1BQYP5ZpJ9cXMiJXky+dy8QI06xP6Fr+wTO7vWrZS4vXH97fcPuurSLN7a+QWJOIhFjIvDy8LJ43qt0nVRw/s4Z13ddMfYxkvW/LFR/tdQ6meSsXr16uLu72/W73FJdo7Lfe6bIHZXpJxHBYDDQoEEDMjIyUFWVefPm8e2335q/10z1NBqN5hBnNjHDvg7rYFKWU1NTS3RAampqidhk5e2YlHeKVRqK0y9rhzA9PR0vL68ifFTES21JLzPvBgXJFLgNeALtPvKtoAxUECfL2qsI/YIC7S7bd9/BypUoVwRMdDrNNO/JJ6F/fxQHB5vWSeRqDDudTmf3HcLU1FTO551HURQ6NerEjd/ciKujK6OCRzE6eDRt67etcOyVxr+PyZWplTzm5eWxb98+tm3bxs6dOzl9+jQnTpwodcPHBJ1OZ47nPmrUKBo0aEC3bt3o2rWr+YTYFnJmTV67y1lGFMrFlYh7ADS/FXY/Aa3vh4Z9ULhi7WABnerYhbZ23quO04iqytmRI0c4efIkMTExREVF8eyzz9KyZUs8PT1p1qwZAQEBNGzYkJ49e3Lu3DmOHTvGhQsXiI6OLrJ5UhqaN29e5FQ6KCiIoKAgAgIC8Pb2LsK/tXL2888K330nbN4MwcGaefVNN2UwdKhnqT4HbdmvxWXHFjJZreuZiOaT4vff4Y8/UBYtQlq0gAULNJPtJk1qbK2EKsjZq6/C+PEQGIhy++3IwIHa3eOOHaGCfrK3nNmiDRYsUHj6acHfX7trf9994O5u+UlVVdYza+ikpaURGRnJ3r172bdvH3v37uXkyZOl0jf5YcjKyiIsLIxZs2Zx5MgROnToQOfOnWtOztKOwc5HtfWp/X+RNk+A3qvs/BWk16ZvTxHhRNIJmjg24Y8zf9DEvQk3B91cYRtXuU5JCvKywC40R7RKyfw1LWdVpV2dcmbKn56ejo+Pz3VluaZQ3p1lg8FgNr+zBNa8Y2neyvBR6yDAITTP1XHAp8BytIDv4y0kUVY7HD2qKcjffafFiDShaVN47DF4+mm7xq2ojv7JKchhw+kNJGYnMr7jeGZtn4W3izf/ufE/FBgL0DtU/rKXtfxfunSJ7du3s23bNrZv387u3bvL9G7s7u5OkyZNCAgI4NZbb6V9+/a0a9eOpk2bmsu6ZuVMNUDi9qv3jzOjodFACJ4KzUdXnb6dUFvnG3vwJSLEx8dz5MgRDh8+zJEjR/jiiy/Yu3cvffv2JSgoiI4dO9K2bVuGDx/O8ePHzSfWUVFRREVFlWlKaUKDBg3MynNwcDAdOnSgQ4cOBAUFoS92SVMEFAWWL4dDhzSd6JtvICdHU5L9/SvfDtesnFmLmBjNbHvHDujTR/Oy/fjj1c6Gxe0QE3PVc/Xw4Vo8pi++0O4dt21bc3zZuaysLPjpJ62qL7yg3cW/fBmaNKk8zarwVlgxNj1l+e5p3rw5TZo04dixY+Tn5xMaGkr//v0JDQ0lNDSUgIAAs38Ea3m2i5yJwOVwOPSWZpYd8n8gKih1P765qR3mH5jPyxtfJsQvhNlDZ9OtSTf7F54NuAHPA72AOzErzrVFzmqapjV0bO7g6zqsw3Vv2HZCHprDgxVo948TgKHA7cBDVaSdlga//qp9Se7efTXd3V3bin7kEc1pVx3GpYxLrDy5khUnVxARE4Gfpx8PdX2I1296vdp4MBgMHDx40KwYb9myhfPnz5fIp9frCQgIoFevXgwbNoyuXbsSFBSEcy13smNzGHI0pymXN8DWuwEFmo6EZqPBb2iJHfvrqJ0QEWJjYzl8+DCHDx8mMzOTt956i6+++opnnnmGdu3a0alTJ26++WZuueUWDh06RGxsrFmJNt2jjouLK7MMR0fHKyfR3XF0HE18/I0kJzdi926Vs2c9uXhR042uw064dEnblbh8Gd58U4ttFBOjWSG1a1dzfJniHq9erT0xMdC/v3ZyPGaMdjf7XwSTgUBSErRsCYMGaUZit9xiUfjnSiElJaWEYhxd6DpXYTRq1Ij69etz77334uHhwWeffUZMTAxnzpzh4sWLhIaG1p11UATUK2aza7pBy7ug7dPgfG2EmkrJSeG9Le8xN3IuUVOjaOjesHoK/gHNCVhntIOiDtVT7LWG68pyDaO8k+WyzFjKgjXvWJq3MnzUGBLRArirQHPAE82t/mhg0JX/VRJiNJK1Zg3uv/6KsmSJ9lFhQseO2vbzffdhE5ea1vBlo/4REQQhMTuRkT+PZN+lffRs3pPRwaO5te2ttG+gKf+2HguF+U9KSmLHjh1s27aNrVu3smvXrlJNUH19fbnpppu4/fbb6d27N0FXPuAqw1udl7PM0+DSGAoyYEUg3H4RjHmQcQrq9wRd6fG5aytq63xTnXyVVZbRaOT06dNmJbpJkyZMnDiRyZMn89NPP9GxY0c6derEAw88wKBBg4iNjSUxMZHo6GhOnDjBwYMHiYqK5tgxlezsQWgTY1/gMFd3FXfRvHlz2rVrR/v27c3WGO3bt6dx48bAv1TO7I116+DLL7WfAQHwwQdaLGc7oUg7ZGRo8b7uvBN+/BFeekkre8QIzUFXKc4Lq4WvGpKzsnDpEsydC3PmaHsGmzdXnWZycrJZITYpyDExMaXmbdGiBc2aNcPBwYGEhASio6Np2rQp/fv357PPPsPHxweRq74V6rScxf0Nh/8HSbug7VPQ5X+a2UsdQ2ntkJabhreLN9/t+46YlBim9Z2Gp7OdZSwNeAv4EzgO4lB75aw6aVpDx+besK/DdqjM/oQ171iat9bukwhgClPcARgBvIb2DbgTaIvZ7KTSOHdOi6357bd4nD17NT0oCKZM0eyzKrLNsjMq2z+5hlxyDbn4uPjQY14PPhz+If1a9uOZns8wLHBYiZ1PkZIe2ysLo9HI0aNH2bZtG3///Te7d+8mKiqqRD4nJycGDx5M+/bt6d+/P0OGDCl1oqoKb3VKzlQjJO28al6dfgKGbIBGA2DkUe30WA+4VNOutR1QW+eb6uSrtLIcHBzMJtVjxowxp3/++ec8/fTTZiXa5LxkzJgxREdHX/HYfjuPPPIyt9zSmVatoFevXDp1OkP9+j9y+fJujh07xrFjZ4mLg9jYWGJjY4mIiChSvo+PD+3atSMwMJAuXbqYlWl/f3+zc0pr62TLd+r0ejZ8uPZkZmomz61ba3GO+vWDXr20E+c+fWxznGkwwPbtOP/xh6YYqyp8+inceqtmFn7//TWqmNS0nJUFPz944w14+WUtVDTA889DSop22hwSUj7NuLg49u3bZ75fvHfvXs6cOVNqXn9/f7p06YKXlxd9+vRh3LhxdO/enYyMDPr160f//v3p168frVq1KlGfa0LOGt+kPQlbIX6LNh6T94Jrc3BtXHm6NYDi7eDtovmR6NSoE/P3z2du5FzeGvgWE0Mm4qizk6rlDXwIvIOmzb0Kjs0cYTJQDfvp9pBpW9G0NW/XleXrqB0oADajHYSsAC5C5GORTNo8ib1b94Ip3n07iIyMZPr06ezZs4fJkyczc+ZMy8rIzdVM5L79VttxR9O5xcEB+vVDefNNuOmmOrnTGZcZx6pTq1hxcgXh0eG81O8lXhnwCt+P+Z429dqgU3SM72LhRW4rkJaWxs6dO80m1du3by/VCZe/vz9+fn4MHTqUO++8k44dOxa5X/WvxeWNcHoBXFyl3edqOgI6vQ5+w8FJW3xxb1mzPF5HjcDJyYmOHTvSsWNH7r77bkAzG33ssa2kpsbSqNFWPvywNadOuTJ0aC4GQyAODp3w8upJSEhPhg37GCcnLWh8SkoKx48f59ixY+afx44d4/Tp06SmprJjxw527NjBTz/9VKT84ODgEifRbdu2xdX12omlWi3w8IBx47TfjUZ45RXNQdiYMeDkBCdOgKsrODhYt/4kJGhK+OrV2um1oyO6IUM05dzfH/7+2y7VuRah11+9ZfXww/D555qlepcuMG8eNG2qcurUKfbv38/+/fvZt28f+/fv5/Lly6XSCwwM5IYbbjB7qr9w4QI33HADTz31FF999RUtW7akfv36HD169N8nTw37ag9AzA9anOagKdDhBXD1q1neqogezXqw+eHNLD+xnBcjXqR38950aayFLbTbaa/blZ/dwPUZV1gAfA6E2qe4fyOuK8vXUbM4hLYrthZwh9ShqUxvMx0Gwp7Ne4iMjLyqKF9BSEgI4eHhBAYGWlbGvn1aTOQFC6Cw45xBg5B77yVt5Ei8/fzqnJJ8NvUsPx36iT9P/Mmei3vo3rQ7o4NH89bAt+jcqDOg7XLaEtHR0WzZsoVt27bxzz//cOzYsRJ5dDodLVq0ICQkhHHjxjF8+HDq1bs27ifZBIk7IOEfaP88ZJzQTov7/wYN+oC9dqCvo05CrjheXrECVq6E7duhWzdnnnwykAcfDOSBBzSvn3q9njVrVrBz50527tzJ8uXLGTZsGEeOHOGdd96hZ8+e9OzZk/vuuw8Xl6sTam5uLqdOneLo0aPs37+fM2fOcOzYMU6cOEFubq75VLswFEWhdevWdOjQgXbt2tG6dWtCQ0Pp0KGD2Uv3dZQDBwcYOVJ75szR1idPT1i4UPO+du+92tO5c8k1SVU13xq+vpoDsblztaPPESPg2WchNJTszMzr/VBFtGmTx6RJh+nY8SiLF7syYcJ8jhw5QlbWRGAuEGvOqygKwcHBdOvWjdDQUPz8/Dhx4gR79uxh/fr1qKpKnz596N+/PzfffDMAL774ollx+tcpysXR/VMIeEgzz/4zEEYeAQ//muaqSlAUhTHtxnBr21vRKTqWHl3KV3u+4oOhH3CD3w32K/hOSO+djveX3lpEmJNcVaSvo0q4fmfZTijvzrKqquh0Oqvunlj6jqV5K8OHTSBAEvAgsAyIAX4GbgVC0EI9XcGsWbOYPn16meYUoaGhhIWFlX6ynJKifXx88w0cPHg13dVV281/6y1o06bm2qEClMVXfFY8X+3+ijcGvsG6qHV8tecrRgePZmTwSJp4VM5svLw2yMnJ4a+//mL16tWsWLGCs4VN1q/Aw8OD9u3bM3bsWIYNG0anTp1wcHCwSbtWtn9qjZypRtTEnegurUJJ3A6DwiH9qKYwB02yuD51HXVNzmqyrIIC7ZRr3z7tuunly5qT4tGjNf2qaVPLaZ4/f54ffvjBrET7+/uzc+dOtm/fzqlTp+jZsydt2rQxh48x0TEajZw9e7bIKfTx48c5evQoKSkpZfLu5+dH+/bt6dChg/lEukOHDjRq1KiIp/prZj2zJfLytNPhRYvgzz81ZfiDD+DCBTh7VjPX/uAD2LtXy3PoEDRsWOS6UG1th9ooZyakpKRw4MAB80nxvn37OHbsWJE4tBr80ek+QWQ4rVod5vbbz3Hbbb6EhoaSlJTE1KlTWb58Of/88w+ffvqp2aS6S5cuODo6Voq3ytbJ2ndqjZylnwSvYLgcAed/hw4v1krLKmvbITU3lRn/zODTnZ8yruM43h38Ls29mtuXr1xF8+czC/AApmBT02x7jAVb0bSGznUHXzWM8jpARKweCNa8Y2neyvBhNQzAVq6aV88FBgDzgfso1zmX1cqyCOzaBV9/DT//DKbA5Xq9dm/rrru0r89i9/CqpR0qARHNMdfqU6tZcXIFs4fOpqF7Q6aFT+Oj4R/h7Gg7b5iF2+DUqVOsWbOGNWvW8NdffxVxxqUoCu3atWP06NF069aNwYMHmx0ElUfTVrzZ+z2byJkhS1vsL6yACysRYx5K01s079Utx4Kuep3F1RbUZjmrLr7KKishAVxctKmpeXNND/LwgH/+gcGDtT0+a2mWli81NRVfX1+WLFnCRx99RGRkJK6urrz++us888wzHD16lEaNGtGwYel340W0sFgmBfrYsWMcPXqUY8eOcfHixTLL9vX1LaI8t2vXjg4dOtCyZcsKr2PUqvWsOiCiRWNYvlwbAJs3a4Ph7be1HZNWrcp1OFlb26Gm5czkjb6wUmyypigN9erV44YbbqBbt27ccMMN5mgMa9Yc4+OPczh37jSXL0/k++8PM3hwM1as+JkHH3zQrBhbw1tl62TLd2qVnKWfgINvQOwyCJwAHV8Bt6YVvladqEw7nE09yysbX+HRGx5lsP9gsguycdPb9ui3BF9rgaloCvNnQD87llWLaFpK57qyXMMo72S5ePD3imDNO5bmrQwfFiMVTUBXAGvQHBONQnPQNRRwt4yMxcrya69pyvHXX2vHMSbUqwcTJsD06VC/fqk07NoOlYCIcCzxGH+e+JNlR5ex5/IeujXpxujg0UzpPqXSp8flISsri1WrVrF582ZWrlxZ4vS4YcOGdOrUiQcffJA777wTTwu8qNqqXStLp9rlLDsWUMC1KawIAsUBmo1Gmo4izakT3r4NasX4qinUNjkzoTr5KlwWKBw+rJlWr1ih7fEtXqz5ezpxAoKDLbsVUlX+8/PzOXjwIC4uLjRv3pzJkyezZMkS/P396dmzJy+++CJdu3aloKCgRPzm4uWnp6ebT58LK9KnT58ucw53c3Mr4qHbdCIdGBiIXq+vHetZdSE3VzOlXr0aEhM1j9UjRkDv3toAWbRIC/907JjmKCwjQzPHLoTa2g7VLWdJSUlcvny5yInx/v37SUpKKvWd1q1bF1GMu3XrRvPmzc28zp8/nyVLlrBt2zYcHBwYNGgQQ4YMYfDgwbz4Yls2blR48EF46ilNdsvjrU6sZzbmv9JIOwoHX4dW90LLO6EgE/Qe9i+3AtiiHXbG7mT0Iu3a3KTQSTZxAlYmX3lojsA+RnOea4NbcfYYCzXx3XjdG7aNEBMTw9KlSwEIDw9n5syZhJjcI1Yj8vLySE9PL7Xj9Xo9rq6u5OTkmP+flZWFqqql0nJxcUGv15OZmYm7uzt6vZ70wnd5i8Hd3R2dTkdGRgaenp6oqkpWVlbRTJeAKHAOc8b5NWf4G820eg3QgyLm1TZDTg6sX0/kp5+yJzeXVCBcUZjZpw8hb76pfWzUog+GslBgLCAuK47mXs2ZvGIyCw8tJMw/jHva38Nv9/xGM69mNi1PRMynx6tWreLvv/8m33QKj3Z63KpVK0aPHs2UKVPo0KFDrfrwqhUQFYfUfZBj0GIdH3oLfLpC2//AsB1XvVaLaHcMr+M6gMhIB377DVatgvh4GDoUHn0UfvtN88oL0LZt9fHj5ORE9+7dzR8Xv/76K3PmzGH37t3s3LkTFxcXCgoKaNCgAcHBwfTq1YsBAwYwevToInefAby9vc13owsjJyeHkydPFjmFPnz4MNHR0WRnZxMZGan5pigEvV5PUFCQ+fS5Q4cOBAUFERgYSLNmza4N54Ame/uJEzWrp6FDoVEj7R7yTTdppgYmdO0KkyZpcY6aNIGtW7X17eabtfvNo0eDu4W70NcITFYOJ06c4OTJk5w8edL8e3R0NAUFBSXecXR0pEOHDiVOjH18fMw0TR/Pn3/+Ofn5+Tz33HPEx8fTp08f3nnnHW644YYi42/ZMm0v47PPoFs3iI6Gxo21T4/ry2YV4d0B+mvf36SfgnXdoe0z0O65qw4w6yhubHYj39z6DS+Ev8CXe77kq5Ff0a+lDY99C8MZeAl4Bs2a8ys0p7pPcF0LtBRyHeVi2rRp5t/37t0rgERHR1f4XlpamgCSlpZWJF1VVUlJSRFVVS3mQVVVmT59uqDd+C3xTJgwQUREJkyYIK+//rqkpKTIsGHDysw/b948UVVV2rVrJ2vWrBEREU9PzzLzHz58uEh9Dh8+XGbeN954Q6TA4qqVi5kzZ0qJIZqdLfLDDyK9e0sISBhIOIh4eoo89ZQsmT9ffHx8rGpba/vDFkjKTpL1UetFROSr3V/J7b/cLiIi51LPSVZ+ls35ysrKkpUrV8qTTz4p/v7+JfqtSZMmMnHiRPnhhx8kPT29yuXZiv/K0qmsnJX7TkGWyPk/RXZMEvV3P1F/9RQ1cprpZZvyf62htrZDdfB18KDIu+9qZX3xRZZMmaLKqlXaVFZVVIecqaoqMTExsmjRInnqqackNDRUsrOz5dSpUzJ16lRZuHChJCUlWV1WXl6eHD9+XP744w9577335P7775fQ0FBxd3cvc30BxNnZWdq1aycjRoyQqVOnyscffyx//vmn7NixQ7Jt0aj2Ql6eyIYNIs89J9K2rcjUqVr6xo0isbHW0zt5UuTtt0XatRMZNUpERNRz5yQlLu6akrPMzEyJjIyUX375Rd566y0ZP3689OjRQ7y8vModJx4eHtKvXz/5z3/+I99++63s3btXcnNzS9C/cOGC/Pjjj/Lwww9Ly5YtpW/fviIiEhkZKfv377eK/+Rk7eePP4p07Sry/fcihYusVetZJfLW+Dwe/49I+E0iS3xFTs2pGR7Etu2QZ8iTD7Z+IMuPLxcRkcSsRPvzFSEibUTkBhHZaeeyaoCmNXTK0tWK47oZdjmIjIwkNDSUlJQU886jr68vM2fOZPLkyeW+a2sz7Pj4eFxcXCw6Wc7NzcXR0bHCk+ULFy7QuHFjy0+WL2Tguc0T9U+VrNVZWtylYWimHVfu1Dk7O+PsbJu7tEXMsE+c0DyHfvut2aN1KICrK3t/+klz2qUoxMTEEBgYyN69ey2yAKhMf1QWJxJPsOLkClacXMHWc1sJbRrKtke3YRQjjjpHdMrV3eqq8iXFTo//+uuvIjvtJo+2o0aNYuLEibRo0QIfH586bU5T1ffKfCfrPOx+AuI2gEsTzby62WjSnLvg7duwdpmt1VLU1nawNV8i2n3jFSu06HRr1sDFi/DHH/Df/14bZmsmREVF8eGHH7JhwwaioqIYN24cv/zyC5cvX8bd3b3M6xoVlaWqKrGxsRw9epSjR49y5MgRYmNjiYmJ4cyZM6U4XiqKZs2aERgYWOIJCAigXr161Tv+kpO1u8erVsH69drd4xEjNE9tYWGaF+yqQkQzyfbyQu6+GwkPRxk7FmX8eC32US04ha+ozw0GA2fOnClyOmz6/cKFC2XS1el0tG7dmuDgYNq2bUtwcDBt2rQxO5orK074pUuXeOedd9i4cSMnT54kNDTUbFbdt29f3NyK3iO1Vj6ys+HHH+GjjyA1VTt1HjeulqxnVchbK+ZxEW0tLsiEFmO08IsN+4KD7Xy4VMyCfdrhbOpZOn7Zkad7Ps1L/V/Cw8k6k3Or+MoFZgP/BxwHrLzhZ482qIn17Jq9sxwZGcmkSZPYu3dvqf9PTU3l/fffp/6VO6rR0dHMnDnTrOxai1mzZjFt2jTz34qiMGfOnEory1DHHDWoaCbUd6LdQW6Ddvd4NNALuwY+n/Xee0x/5RVk8GDYuPHqP9zc4IknCF23ju69ezNnzhzzv0wObCxVlsG+DitUUZkWPo0/T/zJ2bSzDPEfwujg0YwKHkUL7xY25Ss7O5tNmzaZFeTijkvc3NwYOnQojz76KIMHD8bD4+pEbI82sBXNytKplJypKooYtBBOETdBz2/BrTmc+FRz0OXdwWxbZ1M5+xegtrZDVfnKzYW//roa3ikpCYYNg1GjNAvZws65rlU5u3z5MhcvXiQ0NJSXX36ZWbNm0b17dwYNGsQ999xD165dK11W4bwGg4Hz588THR1NdHQ0MTEx5t+jo6NLjfFeGN7e3qUq0YGBgTRv3rxM5coqpKfDV1/BtGlanOPp0zXleNQozU7XnsqrqiJbt6IsWgRLlsCvv8KgQXDggGbKXYPyp6oqCQkJRRTiisymTWjQoIFZGS78MzAwsNTN+cJjJjMzE9BCNPXp04fFixfj4+PDa6+9xpAhQ7jpppss+j6sjHyoKqxdq1nO33CDFoRj7FihU6frDr5sArUA1vWCvATo9LoWgqqaHGjaqx32XtzL02ufJiYlhs9u+Yw7O9xpX77SAG/gV7S7zQ+gHYTZo6xqpGkpnWtKWU5NTWX69OkA7Nmjxd4ti+3Q0FDmzZtnVpRiYmIYOnQoe/furbTCbEJERATjxo3j9OnTFdKqs6GjVCARaITmQa8p2l2HlUB7wMLQxlXCmTMwbx6zPv6Y6dnZmHu6bVt4+WUYPx4cHAgNDaV79+5VUpYr0x8V0VMUhdc3vU6IXwhj2o3hrb/eokvjLgwNHGrxTqGlfJ08eZLVq1eb7x4X/uBwcnKiW7du9OrVi4kTJ9KpU6dSadm6DWxJs7J0rHrPmAtxm5DYP+HiKmj/PErbp+DSeqjfs9S7UVWWs38Zams7VJavuDhNSW7VCnr00P4ePVp7Bg4set20qmXZg39b0SnrvfPnz7Np0yY2btzIiBEjuOuuu3jyySdp0KABAwcO5MYbb8TNzc2m65nRaCQ5OZmYmJgSSnR0dDSXLl0qtywnJydat26Nv78/jRo1okGDBjRs2LDUn76+vlfvraana2YEq1bB889DYKDmWPLLL6Gcjy97oEh7GQyaYh4bC506aZfi77tP28Gx4cX47Oxs4uPjSUhIMD/F/758+TJRUVGkleO/wcXFheDg4BIKcZs2bahXz3KPRPn5+Wzbto2NGzeyceNGdu7cyXfffcf999/PunXr6NOnj0VOKgvDFnKWnQ3/+Y+waJF2Jf3ZZxWGDbPckV+t/m6sSYgKZxfDoTdA7wXDd9l9U8je7SAi/HrkV3SKjrs63sWF9AsW+a6pEl8rgceBAOBLoKMdy7IzTWvoXFPKcmGU5yF57ty5zJkzp8Sp87hx4wgICCg9Hq8VCA0NZebMmYSFhVWYt055w85SIBzt5HgV0P3Kzxg0r3k+FrFZNRiNmhfQr77SbBfRwsNNB+TVVzXnJi2LxtuzlbJcVbOPqOQoVpzQzKtb+7Rm/m3ziYiJoLlXc9o1aFcpmmXxJSIcOXKEJUuWsGjRIk6dOlXkPXd3d3r27MkjjzzCmDFjipweW1tWVVDrzbBzE66EdloBl8PBuQHSdBRZPoNx9x+B4liKtlMJvmqF2VotQG1tB8v7UQvXfuGCZkX7/POaI+JXXtEcdTVsWPH32b9Szgrhhx9+YP369WzatImUlBT27NlDu3bt2LFjBz169MDJyanS9C3Jl52dzenTp4so0Cal+vTp0+WebBZHW0XhLjc3RogQmpPDZQ8Pjvn7c6h/fxzbtCmiWJt+dy0v/peNUGY75ORoyvyiRZqTsG3bICZGU+YbNChCIysrq0Llt/Df2dnZFvNnuv5TXCEODg6mefPmVXLctmrVKubOncvGjRtxdXVl6NChDBkyhEGDBuHv719pumBbOYuKSuenn7zYuVNh9Wrtmka9ehWHh6t13421aB4HQDVA2hHw7QqnvgaXxtB8jF0U5+psh9TcVFp/3Jox7cbw/pD38fP0sx9fGcBbaA7ATgDlhIK+Vtazf6U37CVLltC9e/cS6T169GDOnDlmZXnu3LllmnED+Pj4lFCsp0+fbrGiXCeQBk7fOMEGYBPQCs20ejHQ90qegGrg49Il7R7yV19pq4YJrVppdkvLlsH//lcNjFgOo2pke+x2s4IclRzFIP9B3NH+DkYHjwYgLMB240REOHz4MEuWLGHJkiUcP37c/D9FUejbty9jxoxh+PDhdOzYsfYtYrUFufFwcS0EPAixyyH6G820usvb4N0JAENaWrXefbqO2ovC5tUrVmjXTx98UFOWZ826ak3bqFGNslln8NBDD/HQQw+hqip79+4lODiYs2fPMmbMGHJycujXrx/Dhg3jueees0v5bm5udOzYkY4dSx6ZGI1GYmNjiY6O5uzZsyQmJpKQkEBiYiKJiYmkxsXhEBfHvtRU3kxL4zERNmVlsRC4FziTkaHtphw8WG75xU+oTb+X5eejrLm8tHRFURARcnNzi/g3KZJ3wABQVXJnziT0jz+4afdudjVsyG8eHizPz+dSYiI5OTllN2IZcHZ2pmHDhjRs2NAcp7v406hRI7p162aTTYPs7Gzc3NzYuHEjCxcuZP78+RQUFNCvXz/eeustWrZsia+vb61cCxs2FN5446oON2MG/PILPP44PPGEZrJ9HZWAzlFTlEEzxd79BBx5D7q8A34WHuHXQvi4+HDgsQNMi5hG28/b8u7gd5nac6p9CvMEPgCeQ7MqXQfkALdhsWn2tYprSlmOiIgo9fQ4ICCAmJgYUlNT8fHxqfC+cXHMnTuXoUOHmhXlyMjIGgkfZVPkg365XhOCT4ByYgPaHKqq3UH+6itNGTY5IvP0hClTtFgq7duTdMX03hokJyfbltcrMKgGlh9fzq1tb+Vw/GHG/DKGkcEjeXvQ2wwLHIaXs23N7UwK8tq1a/nll1+Ijo42/0+n09GuXTsmTpzIo48+eiVu63WUgDFPM6W+uAq6/E/7+8Kf4H8/BE6AoIlF89ctI5vrsBMSE2HyZM0fU716mmn13LlFzatrgc+kOgtFUWjTpg2Ojo4EBAQQFxfHkSNH2LRpE7GxsQAsWrSIn3/+mYEDB9KrVy969+5tV8XHwcGBVq1a0apVq6uJFy/Czp1w++2wYIEWCHvlSvLPnCHZaKR5djZ3JCQw4IpCbVKuS/tZUFBAdnY2Z8+eLRHHviYRBDwYF8czcXHEAkvQblpdcHEpU/kt7W8PDw+LTiSLhxuzFAUFBezatYv169ezfv16Dh06RGJiIu3ateO+++4DYMyYMUXKqiv49FO47TbNGVjr1tqnUZ8+Nc1VHUfgBC0288kvYOejcPNe7aS5jirMrXxa8evYX9l8djPn0s4BmkVjoG+gfebFpld+pqOZZn8LfAa0tn1RdQXXjLKcmppa5v9M94tjYmKsVnIjIiKoV68e3bt3JzU11XwfylI6aWlpJUzG09PTcXZ2LrJwmHaFi6NweuH/l5XfhIrySgMhe2W2dndHAQr92xJeKpWemAjff48yZ44WjNDEi58fTJ2K8uyziLMzU6ZMATRLAYChQ4fi7+/PuHHjCAsLIzIykrlz5xIZGUlMTAwiwsyZM1m8eLE5JvakSZO46667zM7ZrGkvU/7TKadZcXIFN7W6iU6NOvH5rs8J8Quha+OuXP7vZRx0DuXSqEybHThwgCVLlrB06VJOnjxZJH/37t156qmnGD16NF5eXmYapfFuTZnltUFl61T4/1VpF6vp5CbAxdVwcSXeF9eBkw/SbCQYssCtOUr/pVfzlyEjFcmONXlNd9jtIk82TLd3mSZUZ7mW5FVVIS9P42vECIU33xRCQ+HGG+GNN6BzZ9DpCvd35Xk0oSptUGX5sDGdst6xZo7Q6XR06tSJTp06mdO7d+/OhQsXiIiI4PXXX2fy5Ml8+OGHHDt2jFatWpXqrbjKY8ZohN27UVavRlat0k6Je/fWnHPdey/KAw8gIuhbtaIx0Lgc+sXrmZGRYT6lNpkvF/67NA/fZbV3aemF0/Lz89Hr9eX2mV6vp0GDBjRu3JgGDRpwoH59nqtfn1kuLrQaOBCCg+Ghh7Q7zlfMtCtqR0vmSWv6SUSYM2cO69evZ+PGjej1eoYOHcrkyZMZOnQoLi4u+Pn54efnV2GblMW/veXDUjpDhmjPiRMK/v7CiRPaSfMzz2jDz7Q5Z02dire9Lb4xa2o9qxQNB1do/zy0fVo7aT7xCcqltUjn/0H97pbTqaa53JL0/i37A5BvyCdsQRjB9YP5aPhHdGjYoQg/NuunsaAMVpDpot1hPkwJhbmiMWNNuj3lLC8vj7y8vBL5y4sEVKQMKW8k1EKUdWfZFDKoNE/VERERDB06lPDwcKvMqE33X4sjOjqagIDybZRNdvBlYfr06bz44ouAZsLk6upKZmZmkUXT1dUVZ2dn0tPTi4SBcnd3R6/Xl1DEPT090el0JXZVvb29UVW1iKdQRVHw9vamoKCArKwsc7pOp8PLy4u8vLwi5liOjo54eHiQk5NjHnCgOUVxc3MjOzub/Px8c3rhOsnOnTjPm4f+t980pyOAeHiQP2oUeRMmoHbvXivqZFSN7I3bS/jZcNaeXsvxxOP0adqH6b2m06dZH7v0k4hw6NAhli1bxrJlyzh9+rQ5n16vJygoiMcee4zx48cX8dRqj36qk2PP1ZXsjETyVT2uh57BKXYh4t0VXYvbyK43hHy3q96r60ydrsV+qkV1cnb2JiLCwLJlRtat0/PAA3m8+GIBe/d60a5dHq6uda9O12I/meqUlJRERkYGDRs25O677+bvv/9mwIABhIWFceedd1K/fv2q1amgANenn0a/Zg2K0Yhy881kDx5MweDByBXHUv+qfhJB9/vvGOfPR4mLI2P3bpTUVLyaNUN1cLBbnUzhL2fNmkWnTp0ICwvj6aefpl27dvTr14+OHTua7zRf6/KUnCzMn+/EN9844+6u8OabCqNG1e061Xg/qWl4nP0SOfk5BQ0Gk9vuLVT3gDpbp7S8NGbvms23B7/lsRse440+b9i1n5QTCmqwisMuB9wUN3RhdWvszZgxo1y/Vf8aB1/lKctLly5l3LhxVivLVYFJWT537lyJDigoKMDd3d3ik2VVVTEYDDg6Oha5h1RW1xUUFFSYV0TzHurg4FDCjMMmO1K5uShLliCff46ye/fVcuvXR3nsMWT6dC3mZCXp22oX06gaUY0qi44u4oXwFzCoBka0GcHo4NEMDxyOt4u3RXSsSRcR9u/fz+LFi/ntt9+Iiooqkr9Tp048//zzjBw50ux11Z47uVByzFSVvohgMBjQ60sP42DNDmEJOsZ8yIwC7w4oxz5AUvZDn4WQHKnFQXZrWqk6WSpn1uS1u5zZKL06TpZtPcasqVNiIvz5pxbaKTxcoV49YdQoGDlS6N/fgIeHY52QM4vkoxI8VpZOZepUXHasySsiREVFsXLlSlavXs0nn3xCQEAAr732GkOHDuWmm24qcf+3CH0RzS2xuzvKc88hnTpp136+/ho6doTevVH0+lovT9UmZxkZ2vWoadNQfvgBue8+7cS5WzeraJvGl6PjVTkzGAzs2bOH9evXEx4eTseOHfn6669Zvnw5rVq1otuVMuzZBvaWj6rSyc/XIoCJwN13F7BihSM33qjQvHnFdSrc5tbKWVm0a2o9synt7IvaXeY2j4FnO8g+i+IZWONzeWXTjyceZ9eFXTzY9UEOxR0iyCcIFycX+61nP6GZZt8GBe8V4Ni8Yr2kNshZeSfLLVu2rFBZvmZuXplCC5Rmjm26x2pN+AFbwcvLq8Tj4OBQpkOPslB4R8ZWea2haTHOn9dcxLZoAQ89hLJ7N6LXI717I+vXQ0ICvPNOCUW5OnExQ3Mktv38drrN6UZmZiahfqEsGbeEuOfj+PH2H7m7090lFOWqQETYu3cvL774Iv7+/mbP6lFRUbi6uhIaGsoXX3xBWloaBw4c4P7776+SZ1BrYY+xYCuaWVlZkJcMpxfC1nvgj0baTxHt3vGNc7WM9ULMinJly68zclYHUd3tkJys6UAAmzdr945DQmDrVi063RdfwC23gNFYfXzVejmrpvcqK2eKohAcHMyzzz5LeHg4HTp0ICMjg9TUVB555BEaNmzI+PHjzcoCAHl5WminZ57RzIsfflhLf+ABLSA2wGOPQf/+4Fj3b6bZdIyZQizNmKHFbE5JgX794McftfQrMYwt5SsxMZG5c+cybtw4GjVqxMiRIzl69CgPP/wwr776KgC33XabWVGuLK4VOXNy0iJl3n8/ZGZm8fvvCkFBWlqhMwiblPWvWc9c/aD7Z+DdEZJ3waqOsHMSZJ23ikxtaYf2DdvzYNcHEREmr5xMj296sOnMJvsVOB44CuSDY2dHiKroBetgLzlzdnYuVR8rT0EujLq/MlyBJTGUKzKdtge6d+9uNp817e4ZDAYmTZrEpEmTgKtmBFlZWaWaEWRkZJB5ZVFSFMVsGpGenl7C3ENRlCJ5oXTTCBMMBkOR0A8m04j8/PxSTSNyc3NLNYXNW7sW3VdfoV+5EsXEk7Mz+fffT8706UjDhlqd8vPNdSrN3KO0OllrwmIwGIoKigKnMk/xx9E/WHFyBceTj7P/4f208WzDb3f9RkFBAc2cmtHcuTlZGVlmc4+cnJxSzT3K66fCdXJzc+PgwYMsXLiQZcuWFXHsotPp6NChAxMmTGDs2LF4eHiY62Qy48nMzERRFHx8fErUqVL9VEadXFxczDRMY6asOlnaTyb+vb29ERGL+qlEndR8nE5/jdvlNZC+G/HqRH7D4RT0WIbRuytOubm4ufmSk51NftbV8VGVOhWWnYrGXuG8ZdXJVHZ+fj65ublV6idrxp495akyY8/V1ZW8vDzNnOtKm9i6TvHxafzzjyNr1+p59tlcmjTxIjwc7rgjzXwn0FSnggKtTqZxqtPp8Pb2tok8lVUnJycn81xraoOq9pOIkJWVVeU5QkTMv1dHnQrLTkVjr3De0urk5OTERx99xOzZszlx4gSHDh0i/fx5tv33vziHh9MvNxfx8oIRIzD8738YBgzAOTcX15AQsjIzNQ/4hepUF+SpOudyc50GDkQdMICMd97RLtGeO4d3x44QFobx/vvJHDBA0+6K1SkuLo4ZM2bwwgsvcO7cORYuXMigQYN4/PHH6datGw4ODhWaWFozRzg6Oprb19QGVe0nESE7OxsfH58q9ZOImH+3tk7Z2Vl8/jk8/7wDc+Y4c+utThw/LuTnp6HXg+mA01SnwuuZaX4rb+wVlrOy6mQ6qcvJySkSaq3Ork/6DjgP3onrqZmwsi057d8hv9WECutkj7ncFnVaPmY5n+36jNt/vZ3hQcOZf8t8jAaj7fupqSfKLwoJfybg1MAJh+0OKHkKHgM9qlQnESEnJwcfH58qrbmmk2XAXKd58+bx7bffmr/XTPU0Gq+2T7mQOoaZM2dKWWyHhYXJ5MmTS30nICDA3qwVQVpamgCSmpoqqqoWeZKTk8VoNBZJE5ES+UzpRqOxxDtl5S+Nfml5jUajpKSklOCjIl6KpKWni/rllyLt24to53wiIGrz5qJ++qlIQYFldCqRbkneNSfXyITlE6TJB03E+31vuWfJPbLwwEJJzEqsUn+Ul240GmXHjh3y3//+V5o3by5ortMEEDc3Nxk+fLh89dVXkpmZWS7twn1ui/aydsxUlb6J/7LKK5fGoXdFjd8mqtEo6j/jJXPfB6JmnrN7nSyVM2vy2kTOqiG9Lo4xVVXl8mVVvv1WlTvuEPHwUKVFC1Uef1yVM2esG6d1Rc6Kp1VJzmxApzJ1Ki47tpBJo9EoKfHxYnzjDVEzMkQ9cULyu3eXfePGyUu33CL1fH1l2rRpoqqq7Ny5UxITE+u0PFW3nJWZNypK5I03RG3dWtTgYDEaDHJ43z75YPZsGTp0qGzfvl0KCgrkmWeekTNnztQ6ObO3fNhTzvLytPwzZ6rSqZMqP/xwNa1wWTaXsxpYz6pFnpIPiJq0X/vuODVP1Lx0u8qZPepk6seLaRdlwf4FoqqqRF6MlPTcdJuXW6QN5quiuqoiT4ioydUvH1Whk5qaKoCkpaVJebhmTpYBxo0bV+oF7vDwcMaOHVsDHJWOypjXWvOOpXkrbeZ78iR8+SXMn49yZTdSFAUmTICnn9bufoG21VnGfQ97YV3UOjLzM7mj/R2sjlqNp5MnP93+E/1b9cdRV/pwt4W58+HDh1mwYAE///wzFy5cKEK7a9euTJkyhfvvv9/sxbW8uzC25MtS2KMsi2jmp8LFNXBxJXi1h86vgWtTcHADRUF6L8CQkQFu1ofmqvNydo3BFu0gAmlp4O2tWdV+8QWEhmrhnV57Dbp0uXrCUp181WRZtqJZWTo1JmcZGZp59apVMH48uu7dURISNFPhNm1w3LWLrkA3ReHtQqca//vf/1izZg09e/ZkxIgRTJw4kUbXULDsap1vAgJIf+45wjt2ZO+SJSz09+exixe5x82NHkOG4O/ujoODA//3f/9XqmWbvfBvkDPTdcypU8HLC95+W5sDv/9eC3NnbVn/+vXMp7P2Mz8VYr6Dg69C5zch4FHNm3Yx1NZ20Ol0NPZozANdNe/9M/6ZwT/n/2HGkBmM7zy+xF3zqpYFwMNAX2Aq0A74G2hbRZpVhK37p845+Jo+fTqzZs0qU9EwOfkyOfKKiYlh6NChReLUVgdMDr6CgoJKmGGLCI8++mgJM+za6nUPwFFR8NiyBePHH+OwYYM5XdzdUR56iOyXXybf3d2cXh11MhqNbI3ZypqYNSRkJ/DhkA/ZfHkzaTlpjGo9quI6VdHrXlxcHL/99huLFi3i8OHDRep48803M2zYMB588EEKCgqqr5/qgBdLh+zTeKb/hXr+T5TEf1A92lLQ6GYMzW7Ho3nfOlmna7Gfakud8vLg+HEH+vRxYf58PcuWFbBoURbHj+vw9RWCgjzqXJ2uxX6qjjo5nD6N5+bNqH/+ibJlC6q/PwXDhmF8+GHcQ0MtrlNiYiJ//fUXy5cv5/3338fT05M33niDkSNHcsstt6Cq6vV+KqdOZ8+e5cyZMwwcOJBp06axefNmBg8ezJAhQ+jTpQveERG4/vorsnUr2V98QcG4cSCCo15fa+tU1/vJaIQVK/QMGOBGkyYqM2fm8+CD+dSvL3W2TjXWTyK4Jq/D+egbGNyDyQz5sc7WKS8vj99O/sYb/7xBC68WbHp4E8Y8o336ycsbdZVKRp8MdLE6yAVpJ7Vi7JVnhh0VFXXteMM2xd5dvHgxqamphIWFERAQYI69a0JqairTp08nMDAQHx8f9u7dy/Tp06v9vrJJWU5NTS3RAXl5eTg5OVnlPTQ/P7/IO+WdTBanX5ZXw4KCAnM8xuJlmvMnJ8N338EXX6CcOaO9CzBqlHaSPHo0ioNDtXn+zCnIYePpjaw8tZKVJ1eSnpfO8MDhjGk3hvs631cpz5nW9EdOTg7Lli1jwYIFhIeHF5lYOnfuzFNPPcV9992Hq6ur1XUt7j3U1OfV4aXX2jFZUbqJf2dnZ1CNkLwb6veEuE3w9wiUxoOQpqOg2Shwb1Um7SJ0SoEt62SpnFmT12I5q+F0e5cJ1vVHYqLCqlXCypWwbh34+cHRo5Cbq+DgIBQKJFClOtU1ObOVfNQmOSs3b14eBSdOoO/UCWXmTHjzTZSbbkJGjtQC0AYGmvmvipzFxcUxc+ZMVq9ezZkzZxg3bhw//vij1XQqk9eW6WD7uRw0J6kRERHs27eP999/n1WrVhEREcFHH31kbvdSacTEgLs74uYGoaEot9+OMmkScqXfaroN7C0fNSFniqIQGys88ghs26Y5e//vf6F166qvfTW1ntXYWikGJCsWPFrDoTegyXBo2Aew/Vxui/Ty1rOs/CxWnlrJPZ3uYf+l/TT1bEpD94aVLhcq0DW+AZ4CngbldQVxrZ1ylp6ejo+PT4XKMmID7Nu3zxZkrimY7iwXt4NXVVVSUlLMdvOWwJp3LM1bYb79+0UmThRxdRUx3Ud2cBAZPlzkyBGLebcFLmVckoy8DEnPTReP9zyk9cetZerqqbI+ar3kGfKqRNuS9jIajbJp0yZ55JFHxM3NTSh0D7l3797yf//3f+Y7cLZCZcZJbSpLzUuVzKPfiZp5TqQgS+SPFiLZl0QMeSL56Xbnrc7I2b8ElsmZyPvvi/Tpo001PXqIvPWWSGSkiL2ar87LmY1o1io5S0gQWbRIRFVF3bBBCrp00fImJIiklz532LJtjx8/LsuWLRMRkffee0/CwsLkyy+/lEuXLlWZtr1hq3YwGAyyc+dOeeutt6RXr16i0+mkS5cuMm3aNCkoKLCeL4NBMn7/XdSxY0WcnERGjNAE3g64LmdX39m0KV3uukuVO+/U0rKyqkb/X7ueqUaRA2+I/OousvkOUVOP18p2sLR/nlnzjPjO8JUvd30pBqPBfmXtE5FeIrLehjRtTKcsXa04rL6zfPfddzNz5kxat25tTvP29mbevHkEBgYyePBga0le0xCREjshpaVVtGNS/J3ydnssySuFdqLMKCiAP/5A+fxz+Oefq3k7doRRo1Bee03bIdZetJh3a9NFhINxB3HQOdCpUSfG/DKGV/q/wqjgUUROjiTQN7DIrpkl7WJNe5nyHzt2jB9//JEFCxYUuYfs4eHBbbfdxiuvvEK7du1s1gbFdwhNT3WcRlRmTJZIzzqDxP4JF1ZCwt+4uAVAvQDErTncegZMO7A6PQpYRLswX9VRJ0tkx5q8leH9Wj1ZLqs/Zs0SunbVovjExmphXJcuVWjSpHhb2r5OdU3ObCUftUnOFEAOHtSCYK9aBbt2QbduKEOGIAMGkLFxI94iUL/+1fx2lLO2bdsSHByMiDB+/HhcXV1ZtGgRU6dO5fPPP2fKlCmkpKTg6+tbJp2aPFmu7BiLi4tDr9fj7e1NYGAg6enpDBs2jEmTJrF06VKaNWtW6TVXFIWCgQOR225DSUhAtm/X1oP58+HYMZg0CeVKm1dnG9hbPmpCzgrLWNeuBhYtEjQDOIUhQwRfX5g2DQYM0LqgLqxnNb9WKtD5DQiaAoffgs23IX3/selcbot0S9ezD4d/SFhAGE+vfZpv9n1D+P3h1HOrV+UxWSJvV2ArKLraK2dl1a04rFaWU1JSSqT5+/szadIkfv/9d2vJXfOoC6Gjck6fxumHH3D+7jt0ly8D2rGp2q4d2W+9hXHoUO2ukbs7uWXcI6hqqBtHZ0f+ufAPvx36jTUxa0jNS+XlXi/ToUEH/n74b3Iyc0hLS6OhQ0OziXtZ9z2scbvv7u5Ofn6+OaRNYmIiy5cv5+effyYyMtKc18PDgzvuuIPx48fTu3dvs7v5tLQ0u4RHEKk7oaP0F5bgduZTJOMEBt8+FDS6mYJ2s0hXG9CsYTOkBkIJXA8dVfNhLArXyRQ66vTpdMLD9axbp6dFCx2ffupIs2a5uLoaSEsz8s47he8a2b9OJjm7HjqqZkJHub7zDs5LlkByMgUDB2K4914K5s8HPz+tn/LzyczOBp0ORSk9dBRgXmPz8vJsKk9eXl48/PDDPPnkkyQnJ5ORkUFKSgrt27fHz8+PsWPHcuedd+Ln51duP5napzaFjnJ2dmbv3r1kZWXRs2dPHnroIW6//XYmTZrE6tWrady4MY6F4k6LSKXrZJIzR0dHPBs3Jnf4cPLS0nBo0QLnlSvRd+4MffuS9+ab5HbpYnU/mXAth46ytk6lhY5assTARx8ZGTPGiTZtVJYuzaZVq39x6Kgy+qnsOrni2vUTnHRGsuNTcD3+JKL3Jc//Cdy9G9d4nUxy5uTkhLu7e7l16tuoL//c9w/LTi3DTefGicQT6A166rnUs6ifLNU1quN7rzpCR1l9Z3nDhg306NEDLy8v0tLSCA0N5fTp0wQEBDB27Fjef/99a8hdsyjvznJmZibu7u7mAQbl75ioqkpWVlaRd8rb7SlOv9QdQlUld9MmXL/9FpYuRTENMk9PlKFDkQ8+gELWAxXxWNn0A5cP8Pbmt1kfvR5fV19GBY9idJvRDPIfhIujS5l0bLkTn5iYyMaNG1m4cCFr1qwpIjzt27dn6tSpPPzww7i4uFTb7qnpI9jd3b1a7lJaPCaNuSiOrsjlTXDyM+j/G8T/DblxKH43I3qvIvx7eHiUWp41O4SVoWNVnbBOzqzJa/rIcnNzK8KHNW1QHen2oi2iHRytXAnLlxvZtcuBbt00lwe33w5du9ZsnWqtnFlYpzolZzk5KG5uqLNnU5CVhf7111F+/BGlSRNkwACKX0SvzXKWnJzMihUr+P3339m0aRPnzp1DVVXOnTtH165d7T6WKtsfFy9eZM2aNaxdu5aIiAicnJx4/vnneeGFFzAYDGbl2NZzRIVylpCAsmABMnSoFk3j7bfh3nuhXTu7ypm95aMm5MyUVrjNTQqCKW9GBvz+Ozz4IOzapXDokMrtt2dRr17tkrPqoF2ZdLjSH9n7UQ5Mh8zT0PlNlMAJiOJQY3Wqynr2ysZX+HrP17wz+B0mh0zGQedQbn5zG1igl9RmObP0znKlHHx98803TJw4kXnz5hEQEED37t0BbVfhOjSYlOUKL43XBPbsgcmTYd++q2nNmsHs2XDnneDkZJdiTUMtKSeJe3+7l1X3reJM6hl+Pfwro9uOpmvjriUmYHtBRNi6dSsLFixg0aJF5h0y0KwBbr/99msupEilkXUOLqzQnoR/YEwsqAZIPw6N+tU0d9dRy2A0wt9/w003QWSkZu4XFqaFdxo5UptqruNfAFWF3bu1nZKVK8HZGXbsgCNHtDWmTZua5tAmyM3NxcXFhfDwcO644w4aNWrEHXfcwd13323+NqopqKqKTqdjz549TJw4kcOHD9OzZ09uueUWbrnlFm644YbaFwInOVmLh/Tbb9CzJ0yZAvfdV9NcXbMID9cifqalaeH4pkzRQlFdh4UQgdg/4NgHMHC1FvZSp4dq+pa1JVafWs1Ta57C28Wb1fetprFH45pmye6wVFertDfs3377DUVRuOOOOyrN5LWM8k6Wc3JyzKeUJlR04mVakC05WS5Ov0Tes2fB5B08JARmzoRCd81tueOVZ8hj89nNrDi5gpUnV/LN6G8Y5D+IHw78wF0d7sJV71op+pXlMSoqynwP+ezZs+b/u7q6MmrUKF555RW6dOlS46d7ImLu8+o48SoxJpN2o1z4E7mwAtKOQMP+mufqZqNRvCq+W2bi3+QVvLI8VpZOqXWqoFxL5cyavCKaeauzs/M1fbKclARr1sCNNyoEBAihobBsGbRqBbm54OZmfX9UR51qXM6qWKdaJ2e5ubByJcrq1cjq1ZofjFtu0XZJhg9HqV+/hOzYQiZri5zl5uayfv16/vjjDxo3bsyMGTNYtmwZ3t7e9O/fH71eb/cxdvr0aRo1aoTBYKBNmzYcP34co9HIhg0bGDZsGPXq1auQjj1Olq2Ws8RE+PFHlOPHkTlztE1+V1ft5LmCNrBUzuwtHzWxnhU+WbZUzgwGld9/L+Djj5148kltbyI9XcHLq3bIWW1aK00osz8OvAbxf0G3WdCgV7XWyRbrWa4hlx8P/siEGyZwLu0cHk4eNHBrYFEbVLVONSFndjtZfuyxxwgKCiIgIICYmBief/55a17/16C8OMtl3VkuK05YWloa6enpeHh4oChX7yyXFvtMURQuXLhgzgul3yPQr1iBoXdvXFu2NN85A9vEPkvOSWbThU2sO72OdVHrcNe7M9x/ODcH3MzwNsPx8fCp1hh1qamprFmzhu+//55du3YVKXPMmDEMGzaMkSNHmvupNsQSFNHunnh6euLj42PXGHUuLi5cunQJz5x9oHPE6NsTr713o3OpR3a9MAoaDEH0PlbVycS/ySlMZWMJimh3WPz8/Mq8w1JenVxdXc1yUFE/paamkpGRYZadimJ8X7p0yZy3rDqZJmUXF5dS7yzX5TiWhw7ls2yZkTVr9Oza5UDHjioffuhAnz4l6+Tq6kpcXFyRj6zaUCfTOPXy8sLb29uuMR+dnJy4ePFiERPGqtZJRDM3a9asWZl3wiypk+kjuHHjxmXeCSuvTr4//UTBqFGIhwfeo0ah3HILmQMHYujRA66Y9prqlJqaSmZmpll2yuun/Px84uLizHnLqpODgwNGoxFnZ+daJ0+zZs3im2++QVVVxowZw/PPP0+TJk0q1U+l1cloNLJnzx42bdpEeHg4+/fv59dffyUsLIzDhw8TGhqKq6trjcaFNcmZj48Pnp6elZInh//7P1xmzMDYtSsyaRL6e+8l3WAo9X7vhQsXipiHVrVOIpr5cdOmTcu8N2pJnUS0O8uNGjUq835vaf1UmTqZ7ix7eHiY7yyX1U+5ubkkJCTg7q7JWVycntBQd+6918ATT2QTEKDxo9frzaGjSruzXJvWpxqby5NicY7+DOfTX1DQcAgOPT5B59GqWupkkrN69erh7u5e5Xnvo8iP+HT3p7zW+zUe6vSQ2TTbGl2jOr73itdJRLuz3KBBA4vuLNslzvKGDRtYsmQJERERxMTEMHToUIYOHUpISMh1T9iFUN7JcmpqKt7e3hafLqiqSlpaWpF3ytvxKk6/rJ349PR0vLy8bLJDeD7tPCcSTzAkYAhPrX2Kbee3MTp4NKPajCLEL8Tmp0kV5c3Pz2f16tUsWLCAlStXFpkY2rRpwxNPPMGkSZNwc3Ozuj+qY/dURMx9brcTr+wLcHElFKST6jcJ74RFKI7u4P9glemb+Pfx8SmRzxralaUD9pMza/LaWs5qw8794sXw2msK584Jgwdr949HjYIWLWzbH9VRp2qRs0KoahvYSj6qRKegQIuWsHIl9OlD6pAh+Lz7rnYBsnPnCsstLju2kMnaJmfF04xGI9u2beP3339n6tSpNG7cmCeffJJbb72Vm2++GXd3d6vLXLt2LQsWLGDdunUoisLw4cO55ZZb6NmzJ4GBgdW+5paXbjM5S06GhQth3jyUDz5Ahg2DEyfgSlQKEyyVM3uvQzWxnpnSCrd5ZeTs8GGF2bOFX3/Vbu19+mnNyVltWitNqLA/ci7BkXegwysozvWQ/DRwbVx6XhvxaI/1bE3UGrNp9h93/UEL7xZltkFV61QTcmbpybLV3rCHDBnCkCFDAM0TcEREBOHh4Xz99dcoisKpU6esJXlNwzRJmSAi5rTSJpvyaBR/p7T8ZdGviHZp6WXlBygwFvDPuX/459w/vDrgVY4nHmfJ0SWEBYbx8fCPzTtQZaE8+jExMcyZM4fU1FRiYmLw8fFh5syZBFwxHY+MjGT69Ons2bOHyZMnM3PmTPP7x48f59tvv2X+/PkkJyeb07t168aoUaN4/PHHadq0qTm9sv1hr/TifVZ4EqpymSIoKZEQe+X+ceoBaNAHaXWPVlabx23aBrbivTJ07C1n1uQtnN8S3msqvbS09HTw8lLYuROefx62bIGgIJg1C4YOVXB3t4x2TcmZpePUpnJWRrqt2sAW8mE1ncRElDVrNAV57VrNrn7kSKRpUy3/7NlVkjNbyGTh/BXVtTrSC6c5OjoyYMAABgwYAGgOwpo2bcpLL73Egw8+yLhx4/juu+/KpGMaO3///TcbNmzg7bff5tKlSwQFBfHUU0/Ro0cPHBwczB+J9pKzqq4HVR6r9etrF2yfekpLP3sWQkOha1dNo7vrLuTKaZ+lbWDPdchWdKydOwqXUVk569wZFixQ+N//IDoaFAW+/BJat3ZgxIjql7PatFZa1B9uTaHHl9rv55ag7HgU2k+D9s+Bo7vdeLT1ejaizQgG+w9m3t55NPFsQlxWHA6KAw3cGlg9N1uSXt1yVtb/S0Cuwy4oK9C1qqqSlZVldXB5S9+xNG9l+EjOTpafD/4s9yy9R3xm+Ejj2Y1lwvIJklOQYzGNihAdHS2TJ08ukjZt2jQBJDo6ukh6QECATJs2TTIyMmT+/PnSu3dvQYt6JYA4OzvLbbfdJnv37i2zvMq0Q3XAZnwVZIsYC0SyL4n83lTkV0+RLeNEYhaI5CbatqxCsBXNytKpy3JW0zh5UuT//k9k4EARR0eRCxdEMjJEduyoPM3a2g7VyVedkjNVFTlzRvv9119FHBxEQkNFXn9dZPduEaOx0uVfl7OrUFVVDh48KD/99JOIiHz99dcyYcIE2bBhg8TFxclPP/0k999/vwQHB0tBQYEcP35cfv3113Lp1cZ2sCtfyckin30m0rmzSN++WllJSXVDzuz4nj3k7J13VPH2VmXAAFXCw7Vp4t+ISvVj3BaRtb1EfmsicnF97eHLSszeOlt8Z/jKF7u+kPSM9DovZ2XpasVRaQdf11E+arU3bCtgUA046hy57ZfbWH1qNR0bdmR08GhGtx1N96bd0Sm29aQ5ZcoUZs6cWcJ8wtfXl4CAAPbu3QtoO3vt27fH2dmZU6dOme886HQ6hg4dyj333MP48ePNcQH/Vci5DGIEt2awoi30+AoaD9KcTjToCw728XZ+HXUXW7dqDrlWrIDTp2HQIM179ahRmpOu6/gXIC8P/voL+veHS5egRw+4fBmysiAnBwpZ5FyH7SEi/PLLL3zyySdERkZSUFCAv78/9957LyNGjKB37961z3N1bYKI5mmwQQMYPx6iouA//4G77tI8sV+HTZCaCp99Bt9+C/v3a0Ym+rrp/Ln6IQLnfwefLuDhD5c3gN+wOtd4hb1mLxm3hADfgJpmqdKwmzfs7t27ExgYyMsvv0zXrl2rzOi1ivLuLGdnZxdxPARXTa2KQ1G0+yQ5OTlF3ikrf2n0S8srIiW8jIKmHCdkJ+Dn4ceTq5+kqWdTXun/Cmuj1tKxUUdaeLWgOMrj3dp0X19fwsLCWLx4cZH0cePGsXTpUuLj4/nxxx+ZP38+R44cMefx9PTk7rvv5q233sLPz8/iMsH6/rBXeuE0ETH3eYV3T0Q0k+oLK+DCSpSUSKTbLGj3LOTEgUsjUMq+s2SPNjDx7+bmVmp5ltKuLJ3K1MlSObMmb1lyZk0b2Cv9778158XDh8PDDyvodMKoUTB0KHh62rZMqH45s2acWiRnVUyHqreBreTDnH75MqxerZlXh4dDvXqwahV06qQNDpeyY91Xtk7FZccWMlnb5MyavKtXr6Zly5Z07NiRbt260bZtW4YNG4anpyehoaH4+/szaNAgBg4cyL333kv79u1rRM5ssR5Um5xduoTr0qUoX36p3XNeuxYlJKTSdapL65kprXCb20POVFXBwQHefx/++ANefVVh9GgpoffV9NizRzrYYC5PP4ZEDASvdhDyIdQLrTKP1SlnuYZcPtr6EVN7TSVfzceoGmnk0ahKtGtCzux2Z3nDhg289957DBo0iKFDh/Lrr79aS+Jfhe7du1vsDbss74gZGRmlesNOT08vMkBMHuqSk5Mr9FBngqOjI5dSLrHx7EbWnl5L+Jlw+rTow293/sZjnR/D18WXtLQ0+vv1L9eTYFkeVMurU2neEbt160ZBQYHZc6CnpyciQmxsLAB+fn4YjUZzezZv3pzZs2czbNiwCj0+lubF0t3dnczMTPLz883tZes6ldZP1nrDLtXTrXoe9fhnKBdXohSkUtBgMGrLR3EZuJJc8SQvLQ1wgfz0cuvk4uJCWlpakTaoap1M/Lu6uiJSujfssrz3Fu6nwpNeWd4RbVknS71hq6paRM7KqpNpUtbpdKV6w7amTlUde6dP69i0yYOEBJg+PY3oaD25uQppafl8//3VsaeqWsxNa/qpojq5urqSkZFRpD+qU54K90fhOpnGqckbtjV1srafnJycSE1NLdIGVa2TiOYN283Nzbp++uUX5OuvUfbvxxgSQsHw4WRNnUr9gQPJyc0l31TulTAxtq6Tpd6wDQZDETkrq04mb9iKolSbPFnTT6Y6eXl5sW/fPv7880+8vLx49NFH2bFjByJCcHAwf//9t7kdTWMvIyODyZMns3TpUmbPnk379u3ZsWMHRqOxWubyiupkyRxhkjOTN2x7znuOjo6kqCr5DzyA8sADOP79N65t26KLiaHgqafInzQJw4ABYMHYK+4N283NrUpzhIjmfNTNzc36OqWkFOnXynjDLq+fCstZWXUyecEWEbM37IceAgcHd6ZM0fPqqyqffJJNSIixwjpV19irTD9Bdczl7VFHnqDgwDs4hw8gN/gl8gOfqlKdTHJm8oZtz3nP2cGZBwIfwJBj4LuD3/Hu9nd5d8i7TOw2keysqxF2quN7r3idRDRv2G5ubhZ5w7YEVTLDnjt3LvPmzWPp0qW0um6rVwS29oYdHx9fZNfcyckJFxcXcnJySrjwz8nJwdHR0TwYFEXB2dkZJycnMjMzUVWVlJwUvt/zPZsTNvNP7D8EugVyc9DNjAgaQfdm3fH00AS8+OTk4eGBqqolFAJPT08MBoM5fEdFdbIk/cyZM3z33Xd8//33nD9/3pzesGFDpkyZwooVKxg2bBh33303e/bsITU1lYiICGbMmEFISIhFZVamP6rjNEKkDK+G2Rfh8JvQYw5K0nbkzE/QdDQ0HggO5Z8CVWcbmPivC95DTenXmjdsVVXYtk1YsUI7OIyKgoED4c47FSZPrt6de7juDRtqyBu2wQAODvDbb7B7N8yYgbJoEZKfr8VAbtSoRuXMFjJZk3JWUT9lZWWxYcMGVq9ezZo1a0hKSmLIkCE88MADjB071iraaWlpbN26lZEjR/LDDz8wf/587r33XsaOHUv9+vUB+8mZLdaDGpWz+Hjk44/hm280U+2XXkJ58EG7rkM1sZ6Z0gq3eXXIWW6uwrx5mnVS06awfDnceSc4Ol6bJ8s2ncszz4CaB57BKKe/Q1reC46uFdIonl6Tcrb61GqeXvs03i7e/HzHzwTXD7aadk3ImaUny0gVER0dLXfddVeFl6P/bSjPwVdKSorVjhqmT58uFHJeNWHCBBERmTBhQpH0119/XVJSUmTYsGFF0ufNmyciIh06dCiSPuWjKRKTHCOenp5F0g8fPmyuQ+EnLS1NDh8+XCTN09NTRETWrl0rb7zxRpXaLTc3V3755RcJCwsrUXZoaKjsKORlKCQkRMLCwiQ8PNyctmTJEvHx8bGqba3tj+qAqqqSkpwsavJBkcPviazrLZJ6VMSQI3LofyKGPNuWZeM2sBXNytKprJxZ+o6leat7fKWlieTkaD8bNBCpX1/kwQdFlizR0moKtVrOqomvapWzc+dEvvpKZMQIkYAAzSHXiRMimzfblLd/q5xVhOjoaFFVVXbv3i1OTk4SGBgoTz31lKxbt05ycmzjEDMuLk4+++wz6d27t+j1evnggw9EVVVJsrFzK1ugVslZTo7IggUi8+drfy9eLHLkSNVo2oo3G75Xk3J25IhIq1YiwcEiP/wgUlBgMdt1AnYbzzkJImu6i/zRUuTML1Z7UKtpOcspyJH3t7wvSdlJkpGXIfGZ8VWmaSveykK1OvhKTU3lpZde4quvvirxv99//x0RITk5mcDAwH9NLOayLo2LlH23qiyIlAwHodfrcXV1LXGy7OTkhIhgNBqLmFO4uLgUOVk28VGvXj2zmUVhuLu7lzhZBs38ovjJMoCXl1eJk2VrcPDgQb799lt+/PFHUlJSzOmtW7cmKyuLBx54gP/7v/8r8k5oqHbHw+T0CyAmJobAwED27t1b4nS5NFSmP+wKYz7E/41c+BOJXYGSF4/SZCg0Gw0tx4KTj82LtEcb2IpmZelUVs4sfcfSvNUxvs6cAYNBC+kUEgJvvaU559q7F7p10w4Vaxq1Ts6uoDr5squcOTmh7N6tmRCsXAmHD0PfvpqHttGjoW1bsMM4/TfJWXnIy8tj8+bNdOjQgfr16+Pn58eBAwdo3LgxZ8+eJTg4uGIiVcDp06cxGAwEBgbSo0cPgoODue+++xg+fDhOTjXv0LFWy9nUqTBvHvTpozkEu/VWcCx6Q/H6emY9zYIC+PFHePddzQ/G119bxHadgF3Hs6hweiEceBGajoSe82oHX1aW9f3+73lu3XO8O/hdJodOrjCcrCU0bcVbYVjsjNkCJb0Ihg0bJi+++KJs2LChSHpkZGSJNBGRWbNmmX9PTU2VuXPnyunTp0vkW7p0aanpdRWW7lb8m5GWliZff/21hIaGFjlB9vLykrvvvltOnjwpY8eOLRFKyoSQkJAS/0tJSRGg3HBRtQ65SSIph7Tft47XQjztnCISu1IL/XQd11EIBoPItm0iL70k0qmTFt5pxgztf9enm38ZVq0SOX5cOzlu107kvvtEfv5ZJCmppjm75nH+/HmZM2eO3HbbbeLu7i7NmzeX9eu1kDB5ebaz/LEWBw8elBdffFFatWolvr6+cunSJTEYDGK8Eu7rOkpBQoI2ibZtKxIfL5KeLnL5ck1zdU2goEBrXqNRZORIkS+/FMnNrWmu6gDyM0TSTmq/H/tQJCu2ZvmpBFaeWCkBnwTIihMrapqVMmGprmZ1HIKkpCRSUlKYPHkyDg4O9OjRg8cff5zTp08TGRlZIn9ISAh33303Bw4cwNvbm0mTJpGSksLGjRuL5LvzzjuJiIiwlp1aD7lyd6TwY7pwXjitrLxAIcc7aoX5S6NfWl5VVYucMlvKS1XTVVVly5YtPPzwwzRq1IjHHnvMfDJ84403smrVKpKSkli0aBG///47/v7+zJkzp1TapdEvL93S9rJ3G4iqQtoxJGaB9nfsMuSYdmouoZ8jt51H7f4lGV4DUHXO9uXFTm1Q2FFZVXisLJ3K1MlSObMmry3l7OhReOQRwc9PGDlSOHdOeOUVITERpk3T8nh6Vo5+XRxjtqhT4X6sC23A6dPIp58iC67MHVu3kn3kCKIoyJEjyMKFyD33IL6+VtGuSTmzhUxW53r2888/07VrV1q3bs3ChQvp1asXW7du5ezZs4SFhSEiZgsve/NS2tO6dWveffddoqOjWbduHY0bN2bt2rW0bNmS5557jj179ljU9rbksbbKmTmtfn1k2jTk6FGkQQNYuxZp1Qq5/35k+3ZUo7HOrGemtNoiZ46OUL++9vv99wtffCEEBgqffgoGg/3HXk2PsUrXydEd8QxCDLlI0m5Y2RY59DZSkFUmnZqWs+J5R7QZwZHHjzCyzUiLaNfUd6MlsNob9ksvvcSdd95p/nvDhg2Eh4fz3nvvcfr0aZ5//vki+YcMGUL37t2ZNGkS+/btY+zYsdx9990EBATwzTffMHHiRHPeevXqWctOrYctvWEbDAYUpWJv2Kmpqea8UL43bIPBQHb2Vc919vIkGB8fz6JFi1iwYAExMTHmfF5eXowbN44333yT5s2bmz2lLl++nNzcXN5//31ENDP0AwcOmMOVeXt7A5Cfn2/2MljY3CIzM9OcXpE37Ozs7CLtZRcPqsZ8HFN24Bi3BufE9UjOBQz1B5HlOxLq3Q7178BbBNXRk4wr/ZqZmYnRaCzbG7aN+snFxYWMjIwibWArb9ju7u7mSdUERbHeO2J11anwOxV55iwsZ2XVyeRIIj8/v1LesBMTFf77X1cWLjRiMLji5VXAd9/l0bOnEUfHq3VKT6/d3kYLy42pP2qTN2xVVavFG3bhedyiOiUn47BrF/p169CvW4cuOhr69yd7/HgK0tKQ558nKyuLZoDBaKx0P4kIeXl55ogHdq3TlTne9E5F/VRYziryhp2Xl2dTL8smZzm7d+/mk08+4aeffsLb25unn36awYMH06JFC3OdCl9lqinvvcXnvTZt2pCfn8/w4cP59NNPWbJkCYMGDeKOO+7gm2++AbQQOPaeI0xyZqJvb2/YaWlpRcak1XUaNw5D+/aon3+O0/Dh5E2dSvoTT+Dh7k5+QUGVvWF7eHjYvU4mb9gGg8Eib9iF5aw8b9gGg6FUx7KW9NPNN8OwYRAe7kFEhCOZmemcO6dQv76Ku3vd8oZdmXmv0nXq/DVewf9B9jyN8UI4Wb1WlFonk5zpdDq7e8O2VNdQFAUXbxeLotRU9nuvLG/YpmgCak17w7YWMTExzJo1i8WLF5OWlkZAQAAhISEEBASQkpJCUFBQCWW7rsLW3rAt8VRYFn1rvRraylueiLBhwwbmzJnDsmXLzINSp9Nxzz338OSTT9KrV68SfEZGRhIREcG0adOKpM+aNatIWmhoKKGhocyZM8dcZlpaGr6+vuzZs6fInWV7ejUsNx1Btj0IF1eCgys0G8n/t3fe4VFU6x//TCohbakioEjAgoooRUVsSOgJFkDs5Spgv6hIROXaUIr1en8qodjvVQG9SkIzsSs2CHrtAlFQpIdNSC97fn8Ms25CNtkyszsb3s/z+AiHM++87znzndkzc8576JyJdmg6Krq1VxvGC4JIydLrzf9IyB5qlPuqM3/q+qMzlwvWroXcXI2cHMUzz8DJJ8PTT8O110JiYngzRwdTDpING3xsg337IDkZbd061IgRetmoUZCRgTZ8OKpBDoxI15kZmjTreeZyufj6669Zvnw5K1asYP369ezcuZOamhoKCwsZMGCAT3as1lMw/VFRUcGuXbs4/PDDGTJkCHFxcVx55ZWcd955tGpiX20znge20pmvtktLUWVlFEdH4xg6FNLT4frrIS3Nbx9DoTOjzLPN7aYzz/Kbb4bXX4dbb4Ubb4TUVPvoycrnWUC+uOqgfCskHgY/zIaOg9E6nGobnQVrOxzPM8v2WX7kkUfo378/gwcP9vdQ0tLSmDdvHvPmzXNP2y4qKiIvL4+FCxc2vbg6QjFuUgZKKXdZYzebpmw0PKax+t7sN2e7sXJv9Zsr37lzJ88//zzz5s3jt99+c5cfcsghXHfdddx+++0kJyc3aufXX39l0qRJTJgwgUceecRdvmfPHgoKCsjKymrS/6KiIq9x+dNevsZ6QHlthZ7yf+3N0HYApF2BdsiZcPQt0K4/aH+tfGjMSsM+87wJ+e2Lj+Wmt0GDPwfrYyB2rNaZP3U96zekvFwjPx9ycmD5cqis1HfzycrSOP54Pc/M3//um+/BlkfiNdZcua/Xadh1tmkTtG0LCQn6vis//gi9eqG9/Tacemq9LG0NLbcEnZmhSc/6vvjuWf7222+zbNkyVqxYQXl5OcOGDWPy5MmMHDnSPYupffv2jdrwZt/Ka8lbuS/90bp1a/dWny+99BKvvPIK9913HzfccANffPEFRx55pNuOmT7aQmeBxJScDElJaE4nPPkk2jPPwLHHwvDh+nZssbF++RgKnXmew046a6z8X//S8xA++CA8+ih8/DEcd5w99OSt3Kznmd++REVD0uGgFLhq4L0hcNj5aH1m6QNowq+zcDyHgrHj7d8PQPlJQUGBatOmjYqKilLDhw9XjzzyiFq/fr2/ZupRWFjYopJ7KdX01lGVlZV+bwHg6zG+1g3Ej6aoq6tT7777rho/fryKjo5WoCfriouLUzfeeKP6+uuvfbKTlpbmPrbhf+PGjVNKKbVu3To1Sd8oVjkcDjVp0iS1d+9elZ2d7d5yqm/fvmrOnDnNni/odqirUWr7B0qtu12pZUcp9fGFevmedUqVbQ3Mphl+hflcZtkM1I6ddbZ9u56gSymlJk9WqmdPpW69Van331equtpndyOKUF7P/hA2ndXUKPXhh0pNnaonFYqLU+qtt/SKfiQWEp0F7sdrr72mVq1apZRS6vLLL1dTp05V7733nqqOYBEG04+ff/65qqurU2vWrFHHHHOMmj17ttq6NfBnmBl+2eVcB9j84w+lnn9e/3NurlKPP67U3r2W+RbJOvMVl0u/JdbWKvXdd0rdfbdSu3ebfhpTsM3zrHSLnhT27TSl6mpbns5CYMfSraMKCwvp2bMnDoeD/v3743A4GDZsWL31xwc7Pqcjj3B27drFCy+8wLx58+qtRe7YsSPXX389d9xxB4mJiWH00AJqSuHP5bA1B/5cAVqMPr26SyZ0GgqxjX81Fw5OXC4oKIA1a+CWW+Dtt/UPEi+9pH9Jjo8HX19uChHOhx/C/PmwcqU+bWD0aH1rp6FD9S9YgiVUVVXx4Ycfsnz5cmJjY3n00Ud57bXXcDgcjDCmuguAPk37rbfe4oUXXuD999/npptu4vHHHw+3W/YlPx/uuw/Wr4erroKpU6F793B7FdF89x3cdht89pk+s+r226FNm3B7ZWOq90JcG/j+YWjdDY64uN4sRsE7vo7VAhosP/roo+7M1t749ddfmT9/PpMnT+aII47w9xQRT1P7LO/bt8+9QN4X/DnG17qB+OF57Icffkh2djZLly51L6pPTEzk0ksv5corr+S0007zy2a48LkdXDWw4VnoOQlKf4VPLtQHx10yod3J4MMecpb4ZdNzmWUzUDvh1ll5Obz7LixbpsjNVZSVaYwYofHyy/rg+GAjlNezP4TEr/Xr4T//Qc2dS8XLL5Pw889omZn6gvSo4H7QHOw6a6re1q1bWbFiBcuXLyc/P582bdowevRoLrjgAoYNG+ZzjJGE2dfz77//zo4dO+jfvz/XXHMN8fHxXHXVVQwYMMDy6yRQwvY8KyiAJ56Aq6+GwYPhyy91jXvUb4k6s5LPPoMZM/T3iX//O9TWHrAFdliw7fPst1dRBXegJXZF6/cktD/VunO1kN+Nvg6W/b7sFi5cSPfu3etlxG6M7t27M2nSJGbPnk3//v3lq7MHntnmrDjG17r++rF7925efPFF5s+fzy+//OIub9++Pddddx1ZWVkkJSX5ZdMONNoOrjrY87n+9bjLufpNZ8+X0PU8SO0Fo78Nj18RdC6zbAZqJ9Q6M147XnwxLFsGnTrpa7GefbackSMTD8pBsiehvJ79wVS/amrgk08gNxfeeUf/tde+PRx1FADVY8aQkJpq6lSCg01n3qirq2PNmjX069ePP/74g2OOOYZTTz2V0aNH88ADD9C7d29b/bC1CjOv58MOO4zDDtPXQl577bU8//zzDB06lK5du/Lmm29y9NFHh8WvcJyrWZt9+8LLL+t//uMPPSnf4YfDlClw0UXut6SRrrNQ9uPAgfqHe5cLtm+HAQP0QfMNN0Dr1s0fbyW2fJ51u4iS5LNI/XMBfDwWMn6ydKZjS/zd6A2/B8vZ2dl89dVXPtXt3r078+bN49133+XRRx9tMZmu/UGpA/fxaqxM05rO8tbwGG/1G7PfWF3j702d0/j3jz/+mPnz57N48WL3V+SkpCSGDRvGLbfcwllnndWoveZiCqbcLNuGz0opqNkH21bD1ly0bSv0ss6jICpOrzjw5ZDF5NnnVp7zgDYwyb6nvWB8DNROoDH5oh2jzOVSFBUp2rbVfw8dcYTGlCmK88+He+7Rc8CAoqSklthYRcPTWt2v4dBTKK+x5sr9uU6D0llRkT6tOjcXVq1CS0hAjR6tZ6yJiYGuXfV05ia0gVn6sJPOgtFkUVERq1ev5thjj6Vbt27cfPPNvPDCCxx//PHs2LGDtm3bHtDf/rZNIOXh0jZYp7OBAwdy6qmn8sQTT/DWW29x+OGH88svv3Drrbdy5ZVXMmbMGOI93ghGwvPMMn106QJbtqC9/DJq1ix45RV45x1Ugz2PrYzJTJ15+uCv72aUR0VpdOyoyM7WvzQ/9hg88ABMnBi5OrPCR6UUKioBddwMtGPvREXFwfdzoLYUjs2CmERL2yBY2+F4nnmLrSF+D5bbBLBwYMiQITidTt577z3OOeccv4+PZMzaZ9nYo1DTmt9n2bMuBLbP8vbt23n++ed58cUX631Fbtu2LVdeeSW33norycnJxMXpA0kr93Ozct+9xLg6vfzbJ2n9w524WnfH1Xk0sWf8l4rEPlTX7N+Drbg4pDEppe+Xp2laSPZZNmwY14xZ+yynpqaiVPD7LDscjpDE5KmdxvqpshIKClJZtgxycpI5/vhaXn+9nMsvj6Zbt2Rqa2sZPlyPqaTkr3MHus+yZ0yRoKem9lmuqqqiuLjY3SZ2iMm4To19SH2NKf7330mIiaGiWzfi0tPB5aJ2+HDIyaHVoEGU7d+7ncpKqKx0781p3GuNNgg2JqUUZWVlQd8jlFLuP/u732ggMXnqrLl+8qyraRpbtmzh7bffZvny5Xz11Vccc8wx3HvvvRxxxBF88sknVFdXU1JSQnR0NBUVFS1ST6G8lzeMqaamhtGjR1NVVUV0dDSnnXYad999N5MnT+bGG29k6tSpXvdZjomJCck+y0b7Gm0QbD8ppSgvL8fhcPjXT9ddR8Vll1Hz+++o4mISJk3SXyjceSdl3bpZGpPn88yXfZY9ddbUPstAwPsse8bk77WnlIvTTttHfj4sXx7Lzp1RQALr1tXSrVuZkZQ8ZPssm30vN2wFc48wdBYXF6fvs1xeTl1ifxJ+mE7UxkXUHv8gcUddTVlZuSn7LPsy1gjF7z1v+ywD4dtnecCAAT5/WW7InXfeyezZswM6NtJoap/lmpoaYmJi3BcYNP3GxOVyUVtbW++Ypt72NLTv7Q1hXV0d0dHR7npKKT799FPmz5/P66+/7r4Ztm7dmhNPPJGHHnqIs846q1EfQ/12L2AbygVFBdC2H+z+FD67kpoRPxJT/SeaqoXknraIyRB7TExMSPbL8/ea9OVtXW1trfvhGqiPgdoJJCZvOispUSxdqm/vlJcH7dppZGQoRo2q5ZxzomnVyj+d+dsGoSgPxZt4s68xM2LyWWfV1fqeJjt36lMq778fLTkZddtt+gJ1jzmBVraBWfqwk86aq1tVVUV8fDxvvPEGU6dOZffu3aSnpzNy5EhGjRrFYYcdZjudhUvbYJ3OmuunNWvWsGfPHsaMGcNDDz1Eq1atuPTSS+nUqZNtn2dW6+MAO998g3rySaJeew3OPhsWLtS/QlsQk2eb+6IzX35jhktn3ttF49RTFUVFcO+9+q05OjoydGaFj151phRseR2+vRftnHxU68OCPi/4NtbwpzwczzNf91n2e7Dcv39/1q5d688hbtavX8+77757UEzHjqRs2Hv37uWll15i/vz5/PDDD+7yNm3aMHXqVG666Sbbx9AktWWwLW9/9url4KqGUd9Cq476v8U5wu2hYDNcLpg9G66/Hioq4IIL9CQjmZnQuzc0+J0gtET27Kk3vZqEBLjySv3CECxly5YtHHbYYRQUFHDJJZfw008/8eOPP7JlyxbOPvtsWrVqFW4XhSZYunQp8+bN46OPPmLYsGE8+eST9OzZM9xu2YcdO2DRIj3N859/6tkgL71Uv8cIflFbqy8Vv/9+cDjgq6/cW18LDVEu0KLgl6dhz1dw4mxI6BRur8KKr2M1v1Nxpqens3DhwoCcOumkk8jLywvo2JaCUori4mKvb2qCPcbXui6Xi3feeYcrr7ySTp06MWXKFH744QdiY2M55ZRTeOedd9izZw933XVX5A6Uf30Z3h8FS9vB19P0QfGg1+GCndC6C0TFomJT/e6PUBDIdWKnc5llM1A7/h5XVQWrVysmTapixQpFVJT+EbGoCDp3hs8/h7vvhhNO0AfKvtoPZT/aGbu2Qz2/jP927YIzz4RDDoHHH4ejj9azzGzdGtRAWXTm/Zja2lo+/vhjsrKyOP744+nRowebN2/muOOO45VXXkEpxbHHHsuIESMaHSjb9foKNXZph3HjxpGfn8+GDRs45ZRTaNOmDZs2bWLatGls3LjR0nNHhM46doS77tKTfm3fDk8+qScD+8c/9L+bcH4rfjfa5fryJCZGT0D+yy96M8bGwn//C8uX/5V002zs2A7gg1/GdlJdMvQPRblHw09P6Lu9mH2uAAj386wp/B4sT5gwgTlz5pjmwMFIIB3o702yKb788kt69+7N8OHDeemll6iurqZ9+/Y8/fTT7Nq1i88//5yhQ4ceMM0mItiyBDYt0v9cthk6nQOjvoHMX6DvY3DIWRBVf6m+3W54BqH0y4pzmWUzUDvNHbd7t77X8bhxesLia67R31J32v+i9cknoUeP4P2y6/UVamzZDjU1KGMt2aBBeibrdu30L8ibN+vbwdx/P/TvH/Q2T3Bw6qypY958800uvvhiOnbsyNixY9mxYwf33nsvu3fv5ogjjiA+Pp4jjzzSp2eRLa+vMGCndujWrRszZsygbdu2lJaWul+ApKens2zZMsvOG1E6GzgQvv0W/v1v/bOo8TFqx46gz2/m78Zg/AgFcXH6zHYAp1MfQJ92mv7R3grs2g4++ZXYDc5YAme8CYXPQ8nP1p0rTDbN9s3vp/9JJ51E9+7dueuuuwI6YVFRUUDHCebRuXNnfvzxRzRNo3///qxcuZKdO3dyww03kJqaGm73fKe2HP7IgS8mwUcX6GWtDoGE/WuAjr8Hek2FFN+3txBaLkrBtm36nxct0gfFTz2lfy3+6CPYsgUef7yCk04Kr5+CxezZo2ennTABOnQgft48vfzpp6FfP31QfM019dYSCubx1ltv8dprrwGwbt06evbsycqVK9m+fTsvvPAC48ePj6znkOAT3bp147XXXuP3339n+PDh7iVfq1evZsOGDWH2LsxoGgwbpi/7uPtuqK7WH0zp6fonUjtuU2Rjrr4aCgv1ZVPjx8NPP4XbI5vSaQiM/AYcx8Ov/4aPx0PZlnB7ZUsC2t573rx59OzZkx49enDNNdf4daxd38YcTHTt2pW33nqLE044gW7dukXWF+TyP+HPXH2QvONdSDgUumRCt5v0f+94Znj9E2yFse3tmWfqL+9HjdJn02ZmwsiR+hRrA7k1tWB+/x1efVVff7xmDfTpo18EU6dS1aMHrQB5S2I+5eXlvP/++yxfvpzdu3ezePFiUlJS3NlPH3rooTB7KISajh07cscdd7j/vnz5crKzsxk0aBCTJ0/mvPPOq7cF1UGHpumfSb//HrKz9W3nOnXSl4IIPpOUpM90nzJFz7/4xhv6S/IHH9TfiQr7MX7/dxoC2/MgtxccN13/0BQtuSEM/E7wZTB//nyuu+46LrzwQubPn+/TutZHH32UjRs3Ms94k9+C8bZoXCmFy+UiKirK50GqP8f4WjcQP8LG3q+h9eEQFQtvdIB2/fUBcpdMSOkVVLYlu7ZDKP2y4lxm2QzETlERLF+uyMlRrF6tkZio8dFHkJam7+Tjkbg44HO1SJ1ZSFjaYdcumDdP35hz5Ur9y3FmJmRkuL8ai86Cs9PYcVu2bGH58uXk5uby3nvvceihh5KRkUFmZiZDhw4VnVmIXduhOb927tzJiy++yKJFi1i1ahUdO3bk999/5+ij/Z8V1uJ0VlWFWrcO1ymnEDV/Ptrvv8ONNzY780V0Vp/du2HuXP0xMHw4PPwwHHNMYLbs2g6m+LXrM1h3M/T7J3QYZO25LLLpjx1fE3wFPFgGmDt3LnfeeSdt27Zl+vTpTJw40evJ3njjDSZNmsS6des44ogjAj1lxNBUByil/L4Q/DnG17qB+BES6iphx/sQmwodToP3R8Ixt8Khw6CmBGLNTThm13YIpV9WnMssm77aee45ePFF+PRTfQZbRoZizBiNvn19X256UOksxFjeDkb26pwcPRFXhw5w223wr3/piXTC5ZfF5wq1zho77rPPPuPoo48mJiaGQw45hFNPPZXRo0eTkZHBMcccc4Bd0Zl12LUdfPHLqPPhhx8ybNgwTjvtNCZNmsQFF1zg19fmlqoz7bPP9Hvb6tX6/OL7728ysYbo7EC2bYNZs2DMGH2W+++/w2EH7qTULHZtB1P8Ukr/CLV1BWx4Gvo+CSlHWnMui2z6aseybNieTJs2jXnz5lFUVMS0adNo06YNw4cPZ/r06bz55pu8+eabPProowwfPpwLL7yQ6dOnHxQD5aaQrIZeqNgBm56Dj86HN9rDV9dDyY/6v529Qh8ogyUDZVu1w35C6VckZzVculR/Wwz6V+OLLoJff4V16xS33VZMv37Kr4Fyi9dZmLCkHZTSF6M98shf2aufeEL/XNCqlT4Pb/78ZgfKojP/7RQVFfGf//yHDz74gOLiYubMmcO3335Lamoqe/bs4YMPPuCOO+6gV69ejQ6URWfWYNd28NUv41o566yz+OOPPxg9ejT33nsv119/PQBlZWWmncsfwqWzA44bOBCWLYPvvoPUVH2dUU0NfPjhAeuIRGeNc+iheq6S9HTYsAGOPBL+9jf47Tffbdi1HUzzy7hntz8VEo+AlX3g67ugptT8c3kQbp01RUBrlj2ZNGkS6enpTJ48mXfffZe8vDzyPdZWGM5mZ2czceLEYE8ntDR2fwHr/g5Fa6Ht/unVJzwAqcf/JVgbvr0TQo/TqW93m5OjD4jXrNFfqjsc+r/fcMNfdW32DBPMoqZG39OrSxeYPFnfYHPIEH2P0ldflaRcFqGU4vvvvyc3N5fc3Fw+//xzevfuzbRp0zjxxBN566233AOdxMTEMHsrtAQ6dOjA1KlTuf322ykpKQH0QXRSUpL7a/NBu9/2kUfq84kB/vc/OP986NkTsrLgvPMgOjqs7kUKRx6pv3e4/379Hes//qGvcxb2E98WBjwNPSfC2pth54fQZfRB+QMr6MEyQFpaGnl5eRQXF5Odnc3atWtxOp04HA4GDBjAuHHj6N69uxmnijiUUge83WisTNO0Rt+CGOUNj/FWvzH7jdU1/t7UOU0vr6sCLRqtxonKPwuGfwWtOkGPiXDGW2itO9Wvv//Pjdk3y0f9NP73hxXlnmWefW61L57nM8u+p71gfNy4UbFkSRzvvgsffaTo1UtfcnrzzXqC0JNOMvRhTky+aMefumHRWQDltr7Gior0LZzS09EWLkTl5enZWu6+W98HOTHxr/o+3CMjWWcNy8zSWWN26urqiI6O5t133+Xaa69l586dpKenc/nll/Pqq6/StWtXAJxOZ1A6M0OTdtNZuLQN1j3PzHgeBKqzlJQUlFKsXLmSF198kfvvv5+bb76Z//3vf3T2zNK4H1/bIBh9hNJOkzH17q1vc7dgAUyZgrZqFWr+/BalMytt9+gBL7+sceedij179EfI66/DiBHgcITuXm5GuWXPM0cfGPKhXrDzE/jfDOj7JEo7vNlrxp/ycOjMW/8ecIzytabgF8Y8+J49exK9/y2fpmlomkZtbS0TJ050f2mPj48nISGB0tJSamtr3TYSEhKIj4+nuLiYkpISkpKS0DSNxMREYmNjD5hmkJycjKZpbN261V0XIDU1FZfLxb59+w7ws3Xr1pSXl7v/HhUVRUpKClVVVe6MpQAxMTEkJSVRUVFBVVWVuzwuLs5to7q62l3ujmnPr2jbVhG7cxWxu96jZuCrxB02mrLCZdS0OU1P2gX1YjrxxBPZu3cvJ510Eu3ataNt27bk5eW5y7p160ZZWRmFhYVs2rSJK6+8kvvvvx9N00hNTaWmpqbedK2mYkpMTGTnzp3ExcW526u5mD755BNmzJjB+vXrufLKK5k7dy7x8fGUlJTg8tjioal+ioqKotjY33U/nv2klKK0tJTk5GQcDodfMfnbT61atWLbtm0kJCS428C49gKNyfC/S5cuKKXqXXvN9dNHH1XTvn0F7dopevVK4YQTqhk7Np6hQ6vo1KnSspicTif79u1za6epfqqrq2Pbtm3uut5iMm7KrVq1orLyL98D1pOXe4SZ156v/eTPtZeQkMCOHTuIj49394fXmFq1Iv6336hcupSYlSuJ/uIL6k48EfXJJ8RGRVFcVobS/pptEkxMxnWakpJCamqqefe9RvopLi6OP//8k9atW7vbINh+UkpRVlZGly5dqK2tDbiflFJUVVVRV1dHYmIiW7du5ayzzuLbb7+ltLSUjRs30r9/f2JjY4OOyel0Ulpa6tZOU/1UXV3Njh073HW9xRQdHU1dXR3x8fEHhZ5CeS83IyZDZw6Hg+Tk5KD11KpVK7755ht69erF6tWrefLJJ7nyyiu5+OKLSUpKYuvWrSTuf4lmRkxKKcrLy+ncuTPV1dUB95NSiurqajp27EhZWZnP115MTIxvMVVXk1pbi6tjR+rGjKH8xBNRkycT5XA02U+VlZXs2rXLrTNvMcXGxlJTU+P+f3P9ZIdrz99+qq6G8eMT+f77GO65R+Pyy0uJjrb+Xm5GTIbO2rZtS2JiojX3vdp9pGz5J9qGpyk95CLqjv0HxLUxJSalFBUVFRx66KFUVlYGfI9QSlFbW0v79u3Zt28fLpeLBQsWsGjRIvfvNSPOuro6Nm7caH6CrzfeeIOvvvrK3QgTJkzgxBNP9MfEQYExWHY6nfU6wOgkzePHnlHe3Fs/z2Oaqt/QflNvKxsj6DdSJb/AH/9F+zMXtftzaHMSdMmAzpnQ5kS0qKgm7bRt25a1a9eSlpbmLp82bRqPPPIIGzduJC0tzV137ty5rF27lsWLF/vl49y5c5k2bVrA/QHQs2dPxo4dy5w5cyx5e2r0eSi+UgTaBs35H9VMXwOUlOj5SpKTYeRIjauvVlxwgf4FuboaYmN9s2NGTL7ozF9NeiMU/WqHN/dGebP94XLBHXdATg7a77+jzjlHvwgyMtwZWKyIKZJ01liZrzprrNzlcrFu3Tpyc3NZvnw533zzDR999BGnnnoqP/74o3vNsZU6M+vZ542WqqdQ3svNsGGVzvbs2cNLL73EggUL2LlzJw899BCTJk3yqQ38OWcwOjPDjq/9qmkayuXSt8mbO1ffM/G669Duvx/VSP6GSNBZqPWklJ4r8s47NZKSFJ9+Cp5Nb9W93BzfQ/Q8K/kZtfZm6H65/p9Szf6296U81DorKSnB4XA0O1j2exr22rVrmT17tvvvCxYs4PXXX2fy5MlNJu969913adeu3UE3sDYuWgPPm1JjQvNGY8c0Vt+b/YZ1jR9K3lKre/PFa3nJz+D8BrpNgM3/gaIC6H4l2qDF0PrAaVLe7BQXFzNp0iR6NMjw6PnDyvPPWVlZTJ48udl28SwvLCysdzPxtz+McmPQ7ulPU/V9KTfKGvtBGaxtb+XBtEFz/jd8ABv8+ivk5Gjk5Oi5SY48Uk9cDPD883/Vj4tTuFze7ZgZk3GcLzrzta7pOrOwPOTX2P6HlTZjBvTvr6+1S03VE3YNHYrmZe2rmTFFos4ae574q4/8/Hz+85//sGLFCqqqqhgxYgRTpkxh5MiRtG/fHoDjjjvOkpiM43y5dzZWt7H6dtRZOM5pxb3cDBtW6qx9+/bcdttt3HrrrXzyySfuH8h33HEHp556Kuedd557RkSgMQWqM7Ps+NuvWlQUKjMT1+jRRH32GdqSJRAfj/bLL/rWEEd6z2xsV52FUk+aBqNH61OxN23SiIqCJ5+E446DoUOtuZebUR7S51nK0bjOWqlfC8Xfw5eToe8TaO1PDth2OHTW1LPHE7+zYe/du5fNmze7/z5x4kRmzZpFXl4ejz76qNfjhgwZwrx589yJGg5mGpsO7Q2l9Gl227dvp6yszKf/fK3rj80D/ivaQtn3Cyn78BrKSksp27uVsm1r9amNve+Ds3P0pACNDJSboqioiAEDBvh1jMPI8OQjS5curfd3f/ojlITSLyvO5WnTeLm3bh307g1HH60n9czI0BMaf/89XHONub4Fcpw/x/ha167XV6jZt28f/PILPPYYnH22noIU9D/36qX/+d579UFzCJNEtSSdNcVHH33EU089BcC3335Lu3bteP3119m5cyf/+c9/yMzMpF27dpadP9BjRGf+Ydd2sNovTdM444wzOO200ygpKaFTp05MmzaNI444ggceeKDe9NVAMMv/kD/PBg2Cf/5THwGuXq0/gMePh7VrA7Jv1+vLCqKj4aij/vr7hRfCsGGwfr192yGkz7PSUv26SuoBnYbCu4Phi2uhcmfgNsOsM2/4/WV5zpw5DBkyhGHDhnHnnXe6P1tPnDiR4uJirrvuOubOndvo5+x58+bx6KOPMnXq1OA9P0goLy8nOTk53G40w3Mef36U0tLSoDKi9u3b16/6/gyu8/PzycrKYs6cOf66JfhJaSnExUFdnT44/uYb6NYNZsyA4cP1j4hCC6e2Vt/4etkykpct0ze1POccmDBBf1MC+h4egqnU1NSwZs0acnNzWb9+PXl5eSQmJpK6X3S33nprvfr+TLcUBLujaRpTp07ltttuY+XKlSxevJi4uDg2bNjArl27GDhwoM9flFoUt9yiv4x84gk46yw9k5VxHxaaZMoUuPJKfY/mUaP0dw3yG2Y/MQlwwn2QdhUU3AabX4Ojb9G/krQQnfn9ZTk1NZW1a9eyYcMG2rRpw/XXX8/777/v/rd58+bx8MMP8/XXX5vtq3AQkJaW5l6r7Cvjxo0D9KysWVlZzJ8/n/nz55OVlUVhYaG73tKlS1myZAmAe+nA5MmT+eabb+rZW7p0aT0bc42NfIVm2bJF39Fi5Ejo0SOVVav0tcjLlkGbNtC+vf52Vh4yLZjaWnjtNX2bp2+/1QfGe/dSed99sGsXrFgB11/vXocsmMOePXt45ZVXuOiii+jQoQMXXnghe/bs4YYbbsDlctGvXz+uvPLKcLspCCEjOjqajIwMXnrpJaKjo1m3bh0jR46kf//+PPfcc0F/bY5IDj9cHyxv2aJ/Jt24EU4+mdg339Tv3YJX2rTRl4EXFkJSkj5wvvVW2L073J7ZhKQj4Mw34aibofQ3WH0K7PggzE6ZQ8BbRy1ZsoSCggLuvPNOhgwZQps2bRg6dCgTJkzguuuuIzs7G03T6NOnT73jNm7cGLTTkY4/bzRbt27Nvn37KCkpISUlpdljlVI+1fVar9oJFdsgtZe+BiExDY7LgqL1kHwkxCb55HOoKSgoYPz48axbt67etOx+/foxZ84c0tPTGTduHOPGjSM/P58JEyYwbdo0dzsY5Ofnk52dTV5enrts6NChfPXVV+6BdqgI5ZvvYM713Xf6C+qcHP3Pp5+uv6x+6KFS+vbVr5f+/UPvWyDH+XOMr3UPii8YGzfqF8A55+jT/BYuhFNPhRNPhD//BE2jtqRE/4VhMyJFZ43xww8/oGl65vZevXpx2GGHkZGRwW233Ub//v2JivL9fbjorGVg13awg84uuugiMjIy+Pe//80TTzxBeXk5N910E0VFRbRt2zYgm2b5ZsVxTR5jLLno3BmuuIKE++6Dhx6CBx7Q96030Y+WRkIC1NRoZGTA9On69lN33gl//zuE4edvPeygMzQNEjrD4ePhw0wY9Dp0GRWcTbN8CxRlAuvWrVOTJk1Sbdq0UZqmqaioKNW2bVsVFRWlrrvuOnXdddepCy+8UPXs2VO98cYbZpzS9hQXFytAFRcXh9uV5inZoNSPjyuVP1ip/8Qo9cklenlVkVKuuvD6tp9p06YpQG3atKnRf09LS1Nz5sw5oHzJkiXK4XD4VNez/t69e91l2dnZB9hQSqn09HQ1bdo0P6JoOfz4o1LPPqv/+amnlLroIqX+/W+l9uwJr19CiKipUeqjj5S64w6ljjlGqbg4pYYNU+qTT8LtWYumoqJCrVy5Uq1cuVIppdTVV1+tXnnlFaWUioxnjSDYBJfLpWpqalRJSYlKSkpSmZmZavXq1aquzh6/eUJOdbVSr7yi1PPP639/5x2liorC6lKk8P77Sp1+ulKFhfqjsaYm3B7ZiPI/laqzb4P4Olbzexp2Y/Tt25fs7GyKiorYtGkTzz77LOPHj2fs2LF89dVXrF27lv79+7N27VouuOACM04ZsSilqKmp8WuNmD/H+FpXVeykbt3tqNxesPxY+HMVdL0AxmyEQf/WK8W1Ac2US8RSCgsLKSwsJL2RtY/p6ek4nU7y8/MbPbZhe40bN469e/ficDhwOp0UFBSwadMmnE6nlSE061e4z7V1K2Rn63lBamr02Vp//qn/2803w6uvwiWXgPFy3iz/A7VjG52FsB8tx5ii98ILcMghcMEFsHMnzJypz0NbvVpPJtMIdm0Hu+msMbZt28aiRYs4//zzad++PZMmTeL7778HYNGiRVx44YUopZrc9sIq30Rn9sKu7WBHnWmavp9wcnIyP/74I3369OHyyy9n4MCBjW5D0+KfZ4C65BK46ip9rek//6lP2b79dvjjj6D8b2k0bIezz4aPP4bu3eHFF+GEE/SlZ6FuJjvqjIRDIcq3Sczh1llTmD4S6t69O5MmTWLevHksXryYtWvXMmTIEMaPH+9OLnKw47kxtxXH+FQ3Ko660m1wwoMwdg+csxqOvgkSu/ntW7gpKCgIqk7D9srPz6dfv36MHz+etWvXHrCNVagI5Dox61xK6Zmr77sP+vXTE3O98goMGKDvfXz88fpsLX9smuWblceZrrMA/bANW7bo///0UzCW1Jxyir6H5/bt+sB57Fh9YXoz2LUdwqmzxjAe8D/88AMnn3wyhx12GIsWLeLkk0/ms88+Y/Pmzdx+++1+2TTLN7OOE51Zh13bwW4686Rr1648+OCDbNmyhWeffRZN03j44Ye5/vrr+e677wKyaZZvwRwXsM40Tb/H5+fr+zyecAKUl8P+3VkE7217+eVw441w7bVw5pnwv//Zw69IOVe4deaNkHw2nD17NuvWrQvFqQRfiUulvM8zcNhYiLV7tu2mMRKCFRUVHfBvRllTScNeeOEF95/nz5/P+PHjmTNnDnl5eUyaNKnZtUwthYqKvxJVnHEGDB4MP/ygZ4Hcvl1/czptWkh39RFCTV2dPjC+8059U8mePfUpBH376j+eQN/maeBAfV8NwRTKy8spLy+nqqqK7t2788cff9ClSxduuukmtm3bxpo1a5g+fTq9e/eWNYOCYBHx8fHu3ThGjRpFVVUVAwYM4JxzznHP5jioOOUUePNNfcu/1q3hscdo/be/6YkbhUaJi9MHy5s2wZAhepnLpaf1ECKXkM2xHTt2bKhOZSoFBQXk5+ezdOlSd6ZlwV707duXtLS0Rr8eFxQU4HA4Gp2i3RhZWVlMnz69Xn3PQXjDPZojne3b4Ysv9D/fcQc8+aT+5+ee0wfOixfrb0rbtw+bi0IoyMnR98Xo1AnOPVcfIN93n34RdO6sZzTp3j3cXrYojK9Yo0ePpl27dvz3v/8lPj6eJUuWcMghh5CamsoVV1xBhw4dwu2qIBx0nHjiiTz33HP8/vvvjBgxgpSUFMrLy5k1axY7duwIt3uhxfgBcOGFqLZt9UH02LEyAmyC5GT9EXrCCfosvd694brrYNu2cHsmBIL9F6SGmSH7Xw2NGzfOvdWQ53ZEgeBPhtJAjvG1biB+hBtva4eXLFlCdnZ2vb5xOp3MmjWLBQsW1MuQnZ6ezldffeX+e8Mvx3v27Gn0nE6ns97AuaioyNK1zFb0j1L6fsczZ+rPu65d4amn9HM98gg8+KBe76ij9DekwWCW/4HaEZ01w/LlYLz8ee89faD83//Cjh3w0kv6AvUg1sE2xK7tEEq/fvrpJ+655x5OPPFE0tLSeP311xk8eDDr16/nkksuAfR942NjY322KToL3I+WiF3bIZR+mXmu9u3bM23aNLp168auXbt4//33Ofzww7nsssv4wnjTHALfbKGzww+n6vHHYcMGfes/l0tPYrJ2rd++RTr+tO2AAfrvrt274cgj9Y8RdvDLjucK9/PMG5o62FfqN8PSpUtJT0/H4XBQUFBAv3793AmgmqKkpITU1FSKi4uDSrxyMFNYWMicOXMoKioiPz8fp9NJ37596d+/Pw6Hgzlz5jRa31hjvGnTJiZPnuyeVmXgdDoZP348aWlp9OjRg0mTJrn7s6CggFmzZpGWlsaAAQMA/UXJ3Llz2bRpE0OHDiUtLY3s7Gzmz5+Pw+HgwgsvZM6cOc1eE+GkqAhmzNBn0u7ZA8OHQ2YmjBoFHTuG2zvBcurq4Msv9S/Iu3fD/Pn6YLmiAvbvUy5Ywy+//EJubi633XYbixYt4r333iMjI4Phw4cfNEs8BKEl8fPPP/PMM89QWVlJdnY2P/30E926dSMhISHcroWetWv1DFennw7/+Aecdlq4PbI1n38OsbH6yqY33oAxY4L/MCEEjq9jtYgbLBcUFDBx4kSva6CNr4nt9u8ht2nTJtMGMnPnzvV5v11vHaCUorq6mri4OJ/XnvlzjK91A/GjJWLXdgjWL6X0HB1XXKGvMz7qKH2a9ciR+nOtVSvzzmWF/8HaEZ0BpaXwzjv6AHn5cj2b9ahRcN55IR8gt1SdNUZhYSG5ubmsXr2apUuXsnXrVl566SXuv/9+0Zmfx0SEzmyEXdshlH6F43l2/vnn8/HHH3P99ddz44030qlTJ1N9s73Odu2CJ56A//s/fXuMhx7yycdIxYxrrKhITwBWWQlz5uibSwR7ubZ0nVlhx9fBsm/5vMOM0+kkKysLgLVr1zaZ2XjIkCEsWLDA/TWxsLCQfv36sW7duoAHzIWFhSxdutTngXJzVFRUEOfnqyR/jvG1biB+tETs2g7++KUUfP+9vl1BTo5+473jDhg9Wt/OKS5O3wnCjHP5ilk2A7VzUOpsyxZo00a/IA45RE9jnpmpT7c+7TSICd8tvyXorDFqa2v5/PPPycnJITc3lw0bNnDWWWeRkZFBXV0dPXv25IEHHkApJToL4Bhb6szG2LUdQulXqHX25ptv8t577/HEE09wxBFH8PPPP9OtW+M7i7RInXXoAA8/DFOnQnGx/vy54QZ9XfOQIcGPAm1IsNdY27b61Oznn9cTgi1fbs707Jass3DYMbDn4pYGOBwOsrOzyc7OZsKECV7rGcm3PKfdpqWl0bdvX2bNmhXw+dPS0pg2bRppaWlMnjw5YDuCYCbV1ZCXB7fcAmlpcPLJerKua67Rk3IBTJig52cSWigul97p33yj/33CBPjkE32t8S+/wE8/wSOP6K+wwzhQbmk4nU62bt0KwNlnn83555/P9u3buf/++9m9ezd5eXn8/e9/JykpKcyeCoJgNZqmMWTIEHJzc91TsnNzc91lLpcr3C6GhrZt9USQtbXQpYv+PDrtNFi5MvSbDkcA0dH6FlMbNujbWQM8+6yeX1OwFy3q19OSJUvo37//AeUDBgwgOzvbvcZ1/vz5TW5l1dh6WIDp06fTpk0b+vXrx6RJk8xzXBB8pKwMPvhA/2K8eDFkZekfDv/v/+Ccc/SkxUILp6xMf0tiTK+urNQztvXpo+/vZQyKDzssvH62MDZs2MDmzZtJT09n5syZtGrVipkzZ/Lqq6/SuXNnomUrLUE46DniiCMAGDhwIP/73/+YOHEiKSkpPPLII2RmZobXuVARGwv33AN//zs884yeRXTECNi7FxyOFvmlORiSk/WdGmtr4aOP9FmB06bpH+pbtw63dwK0sMFyfn5+o4PctLQ0CgsLcTqdOBwOnwe6BQUFDBkyhF9//RWHw+H+b9OmTUH5GRPAFx5/jvG1biB+tETs2g4xMTEopX8czMmBv/1Nz9N0//2Qng4XXQSXXmrOc8eKNjDLZqB2WpzOCgr0LG3vvqsPhDMz4dVX9cQqRvZkm17LYG+dNUZtbS2ffvopOTk55OTk8Ntvv3HxxReTnp7O7Nmz3ccd5sdLCdGZ/8fI88w/7NoOofTLDjpr164dd911F7fffjuvv/46ycnJALz88stccMEFdOnSxdLz+3uMJTpLTtbf6O9fRslNN+nrxWbMgPPPB5tmbvcFa64x/ZG+Zg3ceiusWAGffebfb7yDTWdW23HbM9VaGGlqCx9jrXJhYeEBmZGbw8i8bJzD6XQydOhQn48vLi7GM4eapmkopaiqqiI+Pv6A8oYYi9MTExMB3HWaqu9r3aSkJJRSB/xbU7btUm6m7YbtFe6Yamr0D4Q5OYnk5sIff8CQIYpzz9UTdRm7VRj1G5q3SxskJiaa0i6B2vE3JuNcnsd46ydf60KAOqur0/+ye7eejCs/Hy05GXXWWfDoo3D00e4nqN30ZBed+XOdGiilKCoq4uabb2blypXExcUxevRoHn74YYYOHUpycjJKKfdX5FC3gZn6sIvOzHr22el5Fs5npVU6M+N5YGCXNgiFPuLi4rh8/7qoyspK3nnnHe644w4mTJjAlClTOPHEEy2LyXY6W7AAFi6Ev/8d7d57UcuW6dO2m7Fjp9+eRrkV93KjfOBAxZo1egoSgMceg1NP1TjtNHvrLJzPoebsVFVVUVVVdUD9kpKSA+w1RosZLBv73zaVxMtzj1xf6Nu3L5MnT3avhc7Ly2PJkiWkp6f7bOPwww9vtHzatGlMnz4dgPj4eBISEigrK6O2ttZdJyEhgfj4eEpKSqisrHRndktMTCQ2NpaSkpJ6F0hycjKaprFr1656WeBSU1NxuVzs27evng/x8fFER0dTXl7uLouKiiIlJYXq6moqKirc5TExMSQlJVFZWem+4ADi4uJo3bo1FRUVVFdX17PdVEz79u2rt46nqZiioqIoLi6u53tjMWmaRmpqKrW1tZSVlfkUU2JiovtFi9Fe4YjJ5YI2bVJ54AHF449rtGqlSE+v4oEH6jj33CTi4vSYjEPM7KdWrVqxZ88eoqOj3W0QbExK6dkIO3TogFIq4H5SSlFXV0e7du0sj6m4uJiqqiq3dpq69urq6tizZ4+7rreYjB9LmqZRWVnZdD+Vl5OwZg3x77yDysmhfP58ak8/ndirriKqooKEI4+k7IYb9Gtv/w3ebnrydu0lJCSwd+9ed1sZ/RTue4RSirKyMpKTk7n66qu57bbb6NWrF926dWPx4sWccsoppKSkUFFRQVVVlfscgdwj4uLi2L17NzExMfV+oAYTk6Gzjh07BtVPxo/fNm3aWB6T0+msl6m0qX6qqalh79697rreYoqOjnZ/SWhJzyc73MvNiMm4Tlu3bk1ycrKlvyNiYmLYuXNnvd9AwcaklKK2tpb27dsH1U9KKV566SV27NjBo48+ymWXXcbHH3/Mvn376NixIwkJCabFVFJS4tZZVFRUk/1k3NsM+95iio2NJSoqCpfLRU1NTWD9dMUVJP7tb8QuWUJJSgra+vVEf/klNePGkdymja30FMp7eWMxtWmj50vbt68VI0a0YvhwFzNmlNGtm6vRmAydJSUlkZiYaOl9z9exRih+7zWMSSnltm/ENHv27EZnHvtKxG0dNXfuXLKysg54i1BYWEiPHj3Izs4+YJr10qVLGT9+PHl5eX4NdIPBSEe+ZcuWA9KRO51OOnbsSCuP/XuaemPicrkoLi4mNTXVfVF6q2/Yb66uUoqSkhJSUlLc9XzxxS7lZtmGA9srVDFt36445BB4+WV9mvWSJRoffaSIj4d+/RT79ul9HhUVZakvYH4bKKUoLi72+vLKV9uB2gkkJl915k/dZnW2dau++XVuLuTnQ+fOaJmZqIwMOOOMehswRoKe7KQzb3Vra2v5+OOPyc3NJTc3l/PPP59Zs2bx7rvv0qdPHzp06GC6LwbBtoFZ+rCTzszQpN2eZ+F6VoJ1OjPjeWC355nV+mjOTl1dHdHR0dx0003k5+czZcoULr/88npfBwONybPNba2zjz7S15hpGkyfjnbFFagGU2jt9NvTwOx7eXPl27Zp3H23Ii8PNm3666eBnXQWrG0rn2dNfVk+/PDDW8bWUb7Qtm1boPHp2MYXZaNOKElJSTmgA1wuV70p2MLBQW2tvhYlJ0cfG+3ZA9u2wfDhcNZZep0zztD/H1mvsAS/+PVX+PlnfdPrOXNg3TrIyIDZs6FXL30dl1wAprJ3715WrFhBbm4uq1atIiEhgVGjRjFnzhwGDBgAwNChQ73+KBAEQTAbYznH448/zuLFi3niiSe4++67yc3NZeDAgWH2LkSceaaemOXVV/X9mUtL9S0+lNIH0AKg72ry3HN6jrS4OH01VkqKnk07gpd+h4z4+Hj3uKupFwDeaDGDZV/2UE5LS7PekQb079/ffUM03u7V1tYyceJEJk6cCPg2NaK0tNRto7mpEZ51wfs0bNC/shzs07Crq6spLi52t5eZMUVHx/LiixWsXBlDfn4M0dF6JuuHH4aTTy6mtBTi4/X/lPorJqUUpaWlaJqGw+GwfOqeYcNoAzOmYZeWlpKamopSwU3DrqiowOFwhCQmT+00d+151vUWk6ZpUFlJ7Ycf4vroIyrvuYeY//2PuHfeIW7kSCpnz6bKw/e4ysqI1lNT07CNqX5Gf1gdk1KKJUuWcOGFF5Kbm8tjjz3GiBEjyMvL44QTTnBPDy0tLWXfvn2kpqZaPnXPuNcabWDGNOyysrKg7xFKKfefQxGTp3aau/Y86zY1DRvqfz0ItJ8iQU+hvJebEZOhs5iYmJBMwzZ8MdrAjGnY5eXlOByOoKdhG39uGNOFF17IZZddxooVKzj88MP56aefeOCBB7j11lsZMGCA3zF5Ps+am4ZdXV1dT2dNTcMG/R4R8DRsvFx7Y8aQfNFFRLlclGzcSNLo0VRdey3VV1xB6iGH2G4attn3cl9jioqqoLgYunSJYcaM1jz9tMasWdUMGvSXzuLi4kIyDduXsUYofu81Ng3b+LMR04IFC1i0aJH795oRZ52RH6YZWsw0bNC/DKSlpZGdnX3AMdnZ2UFnsfYHYxq20+k84MtyeXk5CQkJfk1bq6ioqHdMU29GGtr3Np2msrKSVq1a2WLamr/lZk4l8bc/fCl/5RU48kg45RSNzExF79568uKTT9b31msuJuOmkZCQEJLpNGa3geF/ay/7HvgznSYQO4HE5KvOfKq7Y4e+rVNOjj69un17/evxI4/A/uUXLVFPodaZZ3lVVRUff/wxOTk53HHHHSQlJXHFFVfw+uuvEx8f32g/RZrOzNKHnXRmhibt9jwLl7bBOp2Z8Tywm86s1kcwdrZt28bMmTN58cUXGTBgAFOmTGHMmDE+x+TZ5hGns7o6ePNNePBB2LsX7cEHUVdeaY5tm+jMDF+qquDppzUee0zx9dfQvn14dRas7XDorKSkBIfD0ew07Bb18X78+PHk5+cfUJ6Xl8e4cePC4NGBaJpG69atD7jRmHWMr3UD8aMlYkY71NXBp5/CnXfC3Ll62b59UF4OmqaPlx5+GAYO1AfKofLLV6w4l1k2A7UTcp0pBdXV+v+HD4euXfWsnwMGoH32GRQWwr/+5R4oH2xYdT3v2bOHl19+mQkTJtCxY0cuv/xyysrKqK6uxuFw8Pbbbzf6w85qv0J1roNOZyb60RKxazuIzvy3c+ihh/L000+zZcsWRowYwS+//IKmaXzxxRf1vtCZcS7b6SwqSt8FYv16fb7x3r16+Y8/6j+4woxddBYfD7ffrq/s6tABnnpK4847W1NUJDoz3Tfl7ZWBTcnKymLu3Lle33QYSb6MRF6FhYUMHTo0pF+V4a8vyz179jxgGnZdXR3XXnvtAdOwS0tLG50aUVxcTEVFhfvriGf2Xs92MKZG7Ny5s96XFG/TsOPi4oiJiWl0GnZVVVWjU1iMrLCeNlq3bk15eXmjUyO8xVRSUuI1I3HDmPydwlJTU+M142Nj07AbZun1Jaa9e2t5//1YVq6MJT8/FqU0hg6tZuzYatLTa4OOSSl9WmSrVq1wOBx+xeRvP7Vq1eqAzI7B9pPhf8eOHVGq8WnYvsSk1F9ZSL1NyzErJqfTSWVlpVs7zWXD3r11KwnFxajDD6f1LbcQ16sXNVOnUrViBXW9eqE6dkTTNHcG0cayYbc0PXmLKSEhgaKiIqKiotz9EUhMMTExbN26laSkJGbMmMGzzz7LCSecQEZGBoMHD6ZPnz5E7V/A5UtMxnWakJBAamqqpfe9uLg4du3aRWxsrLsNgu0npZrPhu1LTEopXC4Xbdu29TrNzayYnE6ne+tEoz+89VN1dTVFRUXuut5iio6OdmeAPhj0FMp7uRkxGTpLTEwkOTnZ0vueWdmwPWNSSlFTU0OHDh28Tu/1JSbji2+bNm2a3P2kqZjGjh3L+vXrmThxIn//+98PSAbmOQ3b0JkxDdtbP1VWVuJ0Ot068xaToXOjPYLpJ7+vvbo61CmnoJWVUXnnndSeey6pbdpYqqdQ3ssh+HvEjz9q3HNPK9avj+PeezWuuqocpay57/k61gjF772GMSml3Of1ZRr2xo0bm/2yHDGD5cmTJwOwePFinE4n6enppKWlMX78+HoZrp1OJ1lZWfTo0QOHw8G6devIysoK+XrlpqZhW5Wl15v9xuoqZa/sof6Wm2UbfO+PzZuhWzeNr79WnHoq9Oihz6zNzITTTtOIijIvJqXsmT3U13LD/0jO0tto/V270Fas0PeHzMuDoUPhjTdg61a09u1RDRL3RYrOrD4nBH6N1dTU8MMPP9CnTx8WLlzIW2+9RW5uLt999x0Oh4OuXbsGHFOk6cwsfdhJZ2Y8++yms3BpGyQbNkRONuxAYgL46KOPeOKJJ3jvvff4/fff3eWeNjzbPNJ15i6rrtazXM2cCYceirZmDWr/OupQ+WIQ6mzY/ujs889TmT49ildfVRx9tFHP3PNCZGXD9lbf12nYEZPgy1iH3HA9ckMcDkezdUKJcZMyUEq5yxq72TRlo+ExjdX3Zr85242Ve6tvl3IzbDTVH0ppfPWVPqO2Y0fo1w8+/xyOP17j+++hZ88DzhC0Pw37zPMmFKxtb+WBXpPNlZvleyB2TNVZUREsWADLlsGXX0LfvpCRQemtt5I0aJBet2tXvW6jliNDZ3a6xvbs2cPKlSvJyclh1apVtG/fnl9++YWLLrqISy65BE3T6N27tykxRZrOzNCHWXbM0pkZzz7P+r74bnV5OM5p1b3cDBt21ZmV+jDLTsOYzj77bM4++2y2b9+Ow+Fg/vz5vPPOO2RlZTFgwIB652gpOtM0TZ93fP31cNVV8OGHEBeHtnIlxMbCkCF/jQgt9sWqe7kZ5ZqmMWKExogR+p9feEHPofP443DCCeadN5Cxhi/lodaZt39vSMQMliMV4w1fc2Wa1vQbk4bHeKvfmP3G6nq+ifJ2TjuXm2Ub6rdXWZn+wTA3V2P5ckVVlf4i87zz4I8/ICFBt9OjR/3dfcyOybPPrW7fhm1ghn1Pe8G+aQzETqAxuY9xueD229GmT0dpGnz2GVx9NSxdita5M8rlora4GKWfyKvtSNGZHa4xl8vFo48+yvLly1mzZg19+vRh9OjRTJ06lb59+xIVFUVycrLP/erPdRopOjNLH7bRmZ91vdW3m87CpW0w/15uhg276sxqfVits06dOqGUYsSIEfz4448MHjyYU045hUceeYQTTzyxxejsgLJWrWD4cDRA/fEHTJsGffroX5z3v8C2yzXmc0wmlHv2Y1SUXn7uufDdd3DKKXDppfDQQxodO1rTBsHGFA6deYutITJYtpjGto7ytma5qXTuntsbNZfOveFWSN7WLMfHx8vWUfvX+7z9dhnPPNOKjz+O4bDDFGPGaLz4YhX9+lUSEwPFxezfoy00MSmlr0U0pohYvd1IXV1dvWvGjK2jqqur3TetYLYSMFL7WxpTSQkVS5eSsHw55RdcQO1555HSvj1UVlKcmgovvvhXPyl9faenzrzFpGka8fHxVFdXN7pmuaXpqamto5RSjW4dNWvWLI455hjOOeccNm7cyIUXXsirr75KcnKyO6Z9+/ZZEpNxnYZq66ja2tp6bWDG1lHG+YPdOso4fyhi8tROc/3kWbepraPi4+Nl6ygL7uVmxGRcp2VlZSHZOqrhbyAzto4yzh/s1lEGZsaUkpLCvffeyy233MLLL7+My+Vi7969LF++nFGjRhEbG9tsP3nab2rrqPj4eGu2jgq0n666ivJhw4h/5hniR42iLC+PpFNOibh7eb2YArxHGDqrqKhwbx2ladXccw9cemkU99+fyK+/RtO6dRkVFbXExQXeT76ONULxe6+xraOM3CUH7dZRkYK3NcsH01tuu8akFGgafPMNPP64xosvKt59V0+8mJkJRx8deTHZtdxOvtQrVwp+/llPVZ6bq6996tNHX4R+6aX6vl+RFlMQ5aE6pzG9Ojc3l86dO/PEE0/wxhtv0K1bN/r16xeRMYWy3E6+mFVuJ1/MKreTL2aV28kXs8rt5ItZ5UbZb7/9xuDBg4mJiWHq1KlcddVV+1/4h9/HQGNqtm5JCaSk6HlFFi6E++8Hj+U6ERmTBeUrVihuvRWeeAJGjmwZMQVa7uuaZRksW4QxWG7YAUopysrKSExMRNN8myvvzzG+1g3Ej0imvBzefVcfG61cCevW6ds4rVypOPfcMpKS7NUOoewfK85lls1A7TR6XE2NPkA+/nh45hl9z4UhQ/Q3JBkZqM6dRWcmo5Ti559/ZtmyZbz99tt88cUXnHDCCWRmZnL++edz4oknht0/0ZnJOjPxGNGZf9i1HURnwdkJVGdOp5Pc3Fzmzp1Ljx49eOutt1BKHWCjxels2zZ9v86FC+H88+GBBxpLMhMwdm0HX/2qrYXsbJgxAwYNgief1BPWWnGucNj0x463sVpDZBq2xSh14LqGmpoav9/INDzGW/3G7DdWVyl9WlG43+oEWu5L3W3bIDdX/y8/X+PQQxUZGfD88+Bw6DkhLr0UnE7/+8PqmDz73GpfILBrsqlyw3/jz4H6GKgdd0xFRbB6NYwdCz/9hDZ2LOrnn+GSS+CKK2D/NHxN01Aul0/a8UeTkaIzM23X1tby8ccfc8YZZ/D1119zxhlnMGTIEMaOHcurr77KYYcdVu+YcMYUaTozSx+m6yyI55kZzz676Sxc2gbz7+Vm2LCrzqzWRzh0ZpQZ5ZdeeimXXnopRUVFKKU4//zzOeqoo5gyZQqHHnpoROjMbxuHHop66in9hfjMmfr+zD16oO3aherQIWgfwfx7uRnlvuosOlpxww1w4YX6gHnDBujRQ6OyUtFw8kGwYw1/ysOhM2+xNUQGyxbT2Jrl2tpaJk6c6Nea5dLSUreN5tYReNYF72uWgRa3ZnnbtlSKi2s5+ugyLrkkkYoKjVGjapk9uxVpadVUVuoxlZf/tWa54boLO8SklKK0tBRN00KyZtmwYbSBGWuWS0tLSU1NRang1ixXVFTgcDh8jqnV778T/847tH7jDbSvvsJ19NGUHXcc8UcfTfwvv7CvtBQX6K9Xi4vrxeSpneb6ybOut5iM9mzpa5b37t3L+++/z6BBg+jSpQs33XQTr7zyCmlpaWzZsoX27duzY8cO977xdonJuE6NfUitXudm3GuN68KMNctlZWVB3yOUUu4/hyImT+0010+edZtaswzImmUL7uVmxGToLCYmJiRrlg1fjDYwY81yeXk5Docj6DXLxp+tjsnzeRYVFUX79u2pqanh+uuv55///Cc9evRgwoQJPPXUU8TFxdXTWVNrlgF7rVlu6tpzOODRR3Xfv/uOxJNPpu6KKyibMgV16KFe+6m5mKy4l/scUzNrlktLS4mLi3OvWW4qpthYmD1bj+nLL+M5/3zFffdVMG5cDZrW/JplX8YaVv/e87Zm2fizrFm2ObLPcmjeNFZWwnvvwZYtcN118PTTGqWliqwsfSwUE9O0bbBuX8pAYwK9f+y4L6Wv5Yb/Id2X8rHH9GlXv/4KgwdTMWQIrcaNQzviCJ/O66vO/KkbKToLxMZPP/1ETk4Oubm5fPrpp/Tu3ZtHH32Uc84554D6EHqd+XOdRorOgtKHBXYCiUn2Wbb3NWZFTHbVmdX6CIfOjDLPNm9MZ99//z0vvPACc+bMYePGjWzdupWzzz7bljozzfaPP6Luu0+fbnjffXDHHQHZAXvvsxyIzkDjP/9RTJsGaWnwr3/pCcaDHWv4Ux4Oncma5TDT1Jrl6upq4uLiDrjZeMOfY3ytG4gfdmHHDv1el5Ojb/PUvj1cdBHMmeO/Lbu2Qyj9suJcZtn0asd4E/LSS/DTT/r6pFdf1fdhHDoUlZQkOrOIxYsXc88997BlyxbOOeccMjMzycjIOGB6tSd2bQfRWXB25HlmL+zaDqKz4OxYrbOXX36Z6667jtNOO42srCyGDBnS6DF2vb78Zv162LMH0tPhjTfgnHOgTRufD7drO5jhV2mp/nPqpJNg/HioqICEBGvOZZVNf+z4umZZBssW4WsHCM2jlP5fWRkMHQpr10L//npepsxMPdmhje5XglUUFupvSHJy9E2vf/wRfvlFv7t7ZFIWzMP4ivDll19y22238cknn1BQUMCWLVtIT08nKSkp3C4KgiAIQbJnzx7+7//+j3/96188+eSTXHbZZSh1YDKwFkVdHYweDV98AVOnwi23QHJyuL2yDRs2wGmn6UnFJ0/Wk+K2NGSwHGaamoZdUlLinvNv0NxUtH379tU7pqmpEQ3te5tOU1paSlJSki2mrTUsr6rSx0Dt2kF6usbf/67IzITXX4ezz4ZOncybsuVvf4RiqpFSyt3noZi2ZnYbGP57u/n4ZNvlQn32GdVvvkn8O+/oA+OzznJnr6Z7d1Nj8lVn/tS1u84aK9uwYYN7evUnn3zCb7/9RkpKCt9//z0nn3xyo1P6mjsnhF5n/lynkaKzxqabBa2zIOwEElND7ZihSbvpLFzaBut0ZsbzwG46s1of4dCZUebZ5v7orLy8nOjoaIqKihg+fDi33HILV1xxBfHx8WHTmeV6+uAD1D33QFERfPcd7N+jN5T3cjPKrdDZihUat96qaN1an5p9+ul//ZsvYw1/ysOhM1+nYUuCL4sxK8FXSUkJLpcLTWs+wZdnXYicBF9OZyxLl1awalUM770Xy9/+VsXjj8fyf/8XRZs2xRQXw4gRen2lzEmOkJiYSGVlZb32skNSGOOhpJQKSYKvsrKyem1gVoKv5ORk943L136qfestKo44AlfXriRfcQWuvn3hH/+g8qyzqPJI1RhXUWF6TMa/aVrzCb48deYtJuOmbPcEX99//z1vvvkmOTk5bN68mTPOOIPRo0fzwgsv0LFjR8rKyjjmmGMoKSkJ6NpLSEigoqKiXn/YISmMcZ0a9a1O8FVaWlqvDcxK8JWSkmJKgq+UlJSQxOR5THP95KmzphJ8uVwuSfBlwb3cjJgMnUVFRYUkwZfnfdyMmJTSE3wF20/GsyAlJcXymIwEXy6Xy53AsKl+avi7sWPHjtx66608+uij/OMf/2Dq1Klcf/31uFyuyEnw5Ws/DR5MxTvvULNpE2rfPuL/7/+I7tyZuKuuoqy8PCT3cjNiMnRm/LY1Q0+jR8cyYEAJzz4bxzffKHr3riYqKpmkJN/GGv7EpJSe4CslJUUSfB0sSIIv38q3boWXX9bXIH/xhcZJJ+nbO40ZYyQXsPatHEiCLwhzgq/Nm/Wp1R07ok2YgLrtNhg5EtLTUS4Xxfvf/DVrJ8iYDrYEXx988AGVlZWMGDGCq6++Gk3TyMzMZOjQoe7p1Xa+xpor9+c6jRSdNfYGPVISDxnlkuDL3teYFTHZVWdW6yMcOjPKPNs8GJ25XC6WL1/Ozp07+dvf/sby5cs57bTTaNu2bUBtEEh5yPX06qswfTpa27ao2bNh2LADjmlpCb58Ld+xA447Dm67Df72NyeHHHJwJPiSL8sWY9ykDJRS7rLGHupN2Wh4TGP1vdlvznZj5d7qB1vudMIjj2g8+KCexXrNGrjySliyBDp39t2OGb4E2h9WlTfsM8+bkFXntKoNvPrucsGXX6IZ649/+AHOOENPZw5ojz/+V92oqIDawGqd+VPXs74vvltVvnHjRnJycti5cyezZs1i165dlJWVoWkaL7zwQqPHm+VLuHTm63UaSTrzdmywPoZTZ2Zo0rO+L75bXR6Oc1qts2CfB3bUmZX6MMtOoDGZobPo6GjGjBnj9mPBggVceuml3HLLLdx66620a9euSV/MKg+pni65BC64AJ5+Gu3aa/WEYO3awf46Vt3LzSi3WmedOsGyZXDzzYqFC1P41780MjJ8uzf7Uh5qnXn79wPqK2+vsYSgaCobdm1tLTExMT53kj/H+Fo3ED8CpagIVq7Ux0V33QW9esGtt8KsWeHPpRDKdvCHUPplxbkOsFlZCa1aweefw3nn6YvSR43S1x+PGAFe3gAG6tvBqLOG1NbW8tlnn5GTk0NOTg6FhYWcffbZjB07lkmTJoXUF9FZiHQWYjuiM3th13YQnQVnx246+/zzz5k5cyZr165l8+bNLTvRo7Hzxty58PXX8NBDqCOOOOh1VlurWLiwjurqaG65RWPfvuB/z4dDZ5LgK8wc7Nmwf/75r8TFn34KJ5ygj4uuvRaa2GFGaEn88QekpOjJMjp1go0b9T0I1q+HQYMgNjbcHrY4iouLiYuLo6amhrS0NDRNY/To0WRmZjJs2DCSw/12ShAEQWgR/PbbbxxxxBEsWrSIH3/8kalTp9KpU6dwu2UNv/8O//gHvPaanhp6xgz9a7NAaam+N/OVV+rNEklDHl/HalEh9Engr7n0/ryj8OcYX+sG4kdT1NbqU6oBbr9dX2/83ntw8cXw229QUKCnn7fbQNnsdjCLUPpl2rlcLn1fr3/8A3XSSagjjkB9+CEkJelTrTt1gtRUPZ25jwPlQH1rqTprjMLCQjZs2ADAOeecQ35+PikpKeTl5bF9+3ZeeOEFxo4dG9aBsujMmnOZZVN01jKwazuIzoKzY1edHXHEEQD07duXDRs2kJaWxpQpU9i+fbvPfkYMhx0Gzz8PX36JKixk32+/oWpqwCNBbrgJl86SkiA/H776Co46Cl55JXibZvlmFrJm2WKUUgd0mMvlOqBM05peuN7wGG/1G7PfWF3Dr6bO2Vy50wmffQajRmksXqxYtEgXzG236QPjpCTPxAP+22+q3AwbBoH0hxXlnmWefW61LxBkG6xZAy+8gLZ8OaqsDEaMQN16KyWDBpGalqbXPeyweheBr7aNdjD+bHVMvmjHn7pm6KxheV1dHV988QXLli0jNzeXX375hZkzZzJt2jTee+89UlJSUEpx0kknNXpuK/VkJ535UjeidNZIWaD6sJPOzNCkFToLpjwc5zSwSmfB2LCrzoUId8UAAEgVSURBVKzWRzh0ZpSFUmcnnngib7/9NgUFBcycOZNt27bRoUMHtm3bRteuXSNWT42WH388vP02tU4n6s039R+8990HV12FFhsb1pjCqbPeveH99/WxwK+/6j/3iov17yN21pm32Boig2WLMWvrKGN7E01rfusoz7pg7tZR339fxbJlilWrYvjssxiOOUYxeLDG6NEVjBhRTXExJCZCdHQ8YP+tBBITE6murqa4uNjdXnbYbkQpfQsATdNCsnWUYcNog+ZiKv3kE2JWr6Zq6lRi//c/EhIS4KWXKD7hBIiLc/ufov7a7zGQflJK30rA4XBYHpOx1YZxTHP95FnXW0zGuc3YOqqwsJCHH36Y1atX43K5GDp0KHfddRcZGRlomub2s7i42BbbWHjGlJCQQFVVVT2d2WG7EeM6NbZWsXrrKONea7SBWVtHBXuPUEq5/xyKmDy101w/edZtausoQLaOCvC+Z3VMhs5iYmJCsnWU4YvRBmZtHeVwOEzZOgqwPCbP55kvW0d56sxbTLH7Z4R52zrqqKOO4rnnngPg3Xff5dxzz+XKK6/kxhtv5DCPaYWRoqdm73tDhxI3cyatHnyQqMcfhwULKOndO2wxGTqLi4szbesof8ca48bpMe3eDf36pTB4cC2PPBJHmzbW/d6TraMimJa2ddSMGfDmmxqbNinOPhsyMvQ1yN26Rfabe5Cto6CZNlBKT2yRkwPvvov2zjuo33+H5cvh739v0v9I2GrDKLfj1lG7d+9m8uTJvP766/z000+89NJLjBkzhoEDB9Z7CWcXPdlJZ76+ibeNzgKIqSXozAxNBqszs8vDcU4D2TpKto5SyrytozzxV2c//PADDz/8MG+88QYzZ87k9ttvbzZWO+nJ5/6oroYFC9AGDkSddBJ88w2ceGLIYwq3zhrWLSyEqVNh2jSNU0+1p8583TpKBssW0VQ2bGOT+IY3G2/4c4yvdZuqpxRoGsyZA4ceCldcoScC7NFD326uJeUICqQ/QkEo/Wr0XJWV+qLznBx9E2ynE4YP19+QTJigZ7YOgf+B2okEnTXE5XLx5ZdfurNXP/vsswwcOJBnnnmGa665Rv96H6GIzqw5l+gsOP9bGnZtB9FZcHYiXWc///wzFRUVnHjiicyaNYuxY8dy1FFH+Xy83WiyHX76Cfr3138vPfwwHH20PfyKgHOFQ2eSDTvMNNUBSim/LwR/jvG1rme93377K3t1SgosXQoffQRt2uhrEVoygfRHKAilX0optJ079S2dDj8c+vWDPXv0wXFmJpx1FsTH+2/TBP8DtWNHnTWktLSUvLw8cnJyWL58OTU1NYwcOZIxY8YwcuTIFpVJX3RmzblEZ4H70RKxazuIzoKz0xJ0VlVVxQ033MC///1vxo0bx913302vXr0CshVummyH7dv1xD0vvAAPPAB33GEPvyLgXKHWmWTDtinG9AB/3lH4c4yvdWtrFXl5pUyfrujdG448Et56S9/6ds4cvc6ZZx4cA+WDNnuoUvDtt6iVK/VzzZ79VxrDVavg11/hX//SpxMEMFCW7KGN19uxYwdr1qwB4I477uDOO++kbdu2LF68mJ07d/Lvf/+bCRMmtLiB8kGrMwvPJToL3I+WiF3bQXQWnJ2WorP4+Hj3NlOJiYlccsklKKXqrZ+NBJpth06d4Nln9eVr55yjl730EjSSNyikftn8XOHWWVNIgq+DlM2bYcKEREaPhnvugREj9Kx1Qgunqgo+/PCvaQS7dunz7AcOhEcfhf3rYOnQIbx+tiBcLhdfffUVa9asYcqUKXz11VcsXbqU0047jSeffJJ4P19ECIIgCEKk0r17d7Kzs6mpqUHTNK688kqqqqqYMWMGffr0Cbd75mFMwS4rgwUL9AW8//gHTJoEcXHh9U3wC/myfJCSlgYbN5bw8sv6ElQZKLdwdu+GceOgfXu45hp9Y+xnn9WnWj/zjF4nSm4HZlFeXs6yZcuYOHEixx57LMOGDeOLL76gsrKSjIwMXnjhBQAZKAuCIAgHJUZ27blz59KlSxcGDhzIeeedx7Zt28LsmckkJurrGhct0n93ZWaG2yPBT+TLssUodeCedI2VNZflreExTWWo86WuUoroaJo8p53LzczoF0h/WFHeMKuh8Z/ftgFVXa2/uczI0PcAPPFEtL59UXfdpWdpPCCpm7lt4GkvmHYJ1E6gMfmiHW91jXNefPHFLFu2jC5dupCZmcmCBQsYNmwYcfvfJBvHtEQ92Uln/lynAeksDG1glj7spDMzNGlGG5hZHi5tg3U6M+N5YDedWa2PcOisoW4iQWedO3fmySefJCsri2eeeYbU1FS2bt3K77//zimnnGKrZ6WB3zoD/ffYiBGwZYv+O23mTBg0CM4+2xQfw62zYG2HQ2feYmuIDJYtprF9ll0uF9dcc43P+ywbe5mVlJQAze9R51kX7LdHXbj3fGwYU6tWreq1l51i2rdvn28xVVcT89lnxL3zDnErV1J72WWU3XYbMZMmUde+PbE1NbS+6y4qysup9ojViCkmJqZeG5gVE2BKP2maRkVFhV/Xnr8xGT4axzTVT8bN+LfffqNt27ZkZWVx1FFHccsttzB69Ghuu+02jjrqKKKjo0lOTnbv5e0ZU0vVk7eY4uLi6vWHnWIqLS0NyX0vKiqqXhuYFZOmadTU1ATdT5qmUV5ebmlMRl3j/031k7EHplG3qZhSU1MPKj2Zdd8LZUzl5eUhue9pmlavDcyKSdM0qqqqguqnVq1aoWn6HrWhiKmkpKTZfjL2TDbsNxVTamqqZXo65JBDePDBBykuLubDDz9k0qRJDBo0iNmzZ3PCCSfY6jdsUPfydu1IdblQ0dFo555L7RlnUPHAA6iePU2JqbKyMiT3PbBmrBHI773GYjI0I/ss25im9lmuq6s7IKV5U29MXC7XAWnQm3rb09C+tzeExtsnTz+a88Uu5Wa+HfO3P0IRk1J/pb73ul+e04lavlxfe7xqlT7VZ/RotDFjUOecA61bh60NDP+NF0WB2AjGTiAx+aKziooK3nvvPZYtW8by5cvp06cPy5cv55dffqF9+/a0bdu2nt1I0Vko3kKHWmf+XKdN6sxGbWCWPuykMzOefXbTWbi0DdbpzIzngd10ZrU+wqEzo8yzzSNVZ7t27WL27NnMmzePBQsWcMkll1h+Tl/KwcR7+c6d+jrm1avhl1/Q4uIiVmfB2g6HzmSf5TDT1D7LDTd/bw5/jvG1biB+tETs2g5e/XI64b//hauvhsWLYfbsv7Z36ts3oHXHVrSBWTYDtWOmzkpKSli8eDE5OTnk5eVxyCGHkJGRwTnnnMOoUaOaXHds1+sr1Ni1HULpl+jM/2PkeeYfdm0H0Vlwdg52nW3dupXExEQAsrKyyMrKIi0tLWTnb4gl7VBaCklJ+rK59u1h8mTYv6Y7rH6F8Fzh0JlsHSUILYHaWvjgA7j9dti2DVwueOMNPav1+PFQUKDv59e/vyToMhGXy8XMmTPZs2cP5eXlPPfcc5x66ql8+eWXFBYW8tRTTzF48GD3OmRBEARBEMynS5cuOBwOqqqqqKqq4thjj+W6665j69at4XbNPJKS9P+fcgr83//BCSfAihX6Fp9C2JFf14JgN5xOePVVWl97LXTsqKcr37sXKiuhbVvIzdX3PbbRl4NIp7KyklWrVjF16lSWL19OVFQUe/fupbi4mE6dOrFmzRqmT5/O8ccfb6svNoIgCIJwMHDIIYfwwgsvsH79evbu3cvMmTMB39edRgQjR8K338INN8CNN+q/B4WwIwm+wkAgP7b9OcbXuvKjX8cW7VBerq8xnjYNnngCevXCNWyY/mbxlFMs/2psRRuYZTNQO80dt3PnTpYvX05OTg7vvPMObdu2ZcSIEXTp0gWAxx57zBS/bHF92QC7tkMo/ToYdRbsMaIz/7BrO4jOgrMjOvuLXr168frrr+NyuaipqaFPnz6cf/75TJ06lTZt2oTEB0vbITYWbr4Zrr8eYmLg4Yfhjz/0WYQdOoTPrxCcK9w682pP1ixbg6/z4IWDlNpa+OUXOPZYWLgQ3npL/2L8zTf6ptdHHBFuD1scSim2bt1K165dWbhwIddddx39+vUjMzOTzMxMTjjhBFv8EBAEQRAEwTc+++wzZsyYwbp167jjjjuYPn16y3qW//IL3HEHfPgh3HOPPpBuIleK4Du+jtVksGwRTSX4qq2tdadH9wV/jvG1biB+tERC2g4lJXrGw5wc/YtxYiIUFuqJHaKiIDk5LH5ZcS6zbAZqxziurq6Ojz/+mMGDB/Ptt98yevRo/vjjD/bs2UNdXR2dOnUK6FyiM/+wazuIzoKzI88ze2HXdhCdBWdHdOYb77//Pu+//z4PPPAAGzZsoGvXriQkJJh+nrC1w7vvwl13wWuvQbdu+lI8j/OLzvy3Iwm+bIznXmNWHONr3UD8aIlY2g4uFzz1FAwdqmc4vO8+6NwZli3TB8rR0fqXZI+Bckj8CsG5zLLpr51du3bx4osvMm7cODp27MjVV1/Nb7/9Rp8+fSgsLCQqKooOHTrUGygHci7RmX/YtR1EZ8HZkeeZvbBrO4jOgrMjOmuewYMH88ADDwDwj3/8gyOPPJJ58+bV25fXLMLSDkOGwOefQ/fuMH8+DB6sJ3kNk18tUWfekMGyIFjBv/4F772nfzH+5hvIyICffoLvv9e3ezrtNH2gLJjGwoULOf300zn00EN5+umnOe644/jggw/4/fff6dmzJ1FRUbRq1SrcbgqCIAiCYCH//ve/eeKJJ/jnP//JMcccw44dO8LtkjkYX0ovvVT/HXn66fpWotu2hdevFo4k+LIYYxP35sqa22y74TFNbRTuS13PTcy9ndPO5WZurB5If9SjtBQtLw+VkwOHHAKzZkG7dtC6NRqgFi70PJlPMXn2udXta0obePHf+HOgPnqzY7TLkiVL2LRpE3feeSd1dXVcfvnlvP7663Tu3Bmn00lqamq9Y83QmT91I0VnkXiNNVfuz3UaKTrzVR/+2g7UTqAxeR5jhibtprNwaRus05kZzwO76cxqfYRDZw110xJ0FogNTdMYN24c559/PitXrqRDhw588cUXbN68mbFjxxLlkTTVDvdyv8uTkuChh9AmTULdeSfs3Inq0AFVXo5KSQmLzoK1HQ6deYutITJYtpj+/fsTvf8LoiHguro6rr32WiZOnAhAfHw8CQkJlJWVUVtb6z42ISGB+Ph49u3bR3l5udtGYmIisbGxlJSU1Ovo5ORkNE2rVxcgNTUVl8vFvn376vkWFRVFbW2tu75RlpKSQnV1NRUVFe7ymJgYkpKSqKyspKqqyl0eFxdH69atqaioqDfVxZeYXC6Xu7ypmKKioiguLq7ne2MxaZpGamoqtbW19aZgNBVTYmIitbW1FBcXu9vLl5jqfv2V2NWriV21ipiPP4bu3akeNozq9HTqioth1Cg9JggoJqUU5eXlaJqGw+HwKyZ/+6lVq1ZUVVXVa4Ng+8nwPzU1FaVUwP2klHLH8eeff7J8+XJWrVrFli1b+OijjzjqqKNISkqiuLiYCRMmuGMCAorJUzvN9ZNnXW8xaZpGVFQU1dXVVFZWBtVPkaAnbzElJCRQU1NTrz/sEJNxnUZFRZGammrpfS8uLs59DRhtEGxMhv/B3iOUUtTU1ACEJCZP7TTXT551vcUUHR1NVFSUex/WYPopEvQUynu5GTEZ12lMTAzJycmW3vdiYmIO+A0UbExKKfd1Hkw/KaXccVgdk+fzzLi/NdVPnva9xRQbG0tUVBQVFRXu+0Wg/RRKPZ1xxhmUl5ezc+dOpkyZwsyZM7n77rsZNmwY8fHxtriX+xsT7L/2jjiCyuefp6qqipglS0i+/XaqZ86k1VVXUVFZadl9z9exRqC/94K5Ryil3NuJGTEtWLCARYsWuX+vGXH6uu2YJPiyCGPRuNPprLdo/GB6y92iYnK5YN06tP79UevXw6mn6tNfMjIgMxPtqKMiL6YQlAdr4+effyYnJ4fc3Fw+/fRTevfuTUZGBpmZmfTv35+oqKiIi8mO5XbyxaxyO/liVrmdfDGr3E6+mFVuJ1/MKreTL2aV28kXs8rt5ItZ5WbaLi8vZ968ecyePZtFixaRkZER8TG5y10u+M9/YPp0tC5dUM88A337RnZMFpeXlJTgcDgkG3a4aCobdnV1NXFxcWia71kNfT3G17qB+NESabIdysshPx+OO07PPHj00Xo26+7d9czWFu7nF8r+seJcwdj89NNP6datGx06dKBr167069ePMWPGkJmZyWGHHWbZ+UVn1mHXdjiYdWaGHdGZvbBrO4jOgrMjOjOf0tJSEhIS+PHHH5kyZQoPPvggAwcO9OlYu7aD26/aWrTHH4fBg2HQINixAxpJaGrKuSJcZ5IN28Z4TqOw4hhf6wbiR0ukXjts3QrZ2foX43bt4LbbYMMGfWP4jRuhZ089MVcINr4PZf9YcS5fbTqdTl577TVWrFgBwHPPPcf//vc/4uPj2bp1K6+99hrXX3+9zwNlf88f6DGiM/+wazscLDqzyo7ozF7YtR1EZ8HZEZ2ZS1JSEtHR0XTt2pVTTz2VoUOHkpGRwTfffOPT8XZth4qKCmjdGmbM0Gc/fvstpKXpW041WIppyrlMJtw684YMlgUB4O23oV8//QvyK6/AmWfqKfk3bIARI/Q6NnqDGOls3LiRJ554gnPOOYcOHTrw8MMPs337dgAWLVrEqFGjAH2NlCAIgiAIgtk4HA5mzpzJr7/+ytFHH81nn30GcECOn4jlhBPg44/hk0/gyCPhjTfC7VFEIgm+hIOPqiqIj4d16+Duu/UN3g85BKZMgZEj9f2QBVNxuVxERUWxbt06LrvsMjZt2sTgwYO54IILeO655zjiiCPC7aIgCIIgCAchHTp04LHHHgOgqKiInj17cs0113DXXXfRJgQzCS2lXz/48EN4800wphpv2KAPngWfkC/LYSAmxv93FP4c42vdQPyIWLZvh4UL4dxzoW1b+OMP/UZx++3EREfrCbsuv9xWA+VQ9o8V5yovL6eiooLS0lI6d+7Mrl27OOKII3jwwQfZvXs3q1ev5qabbmp2oByob6Ize2HXdoh0nZllU3TWMrBrO4jOgrMjOgsdbdu25d133+Wbb76hR48ePP300wfUsWs7ePVL02DsWBg6VN+TuW9f/Tfvn3+af64gCLfOvCEJvizC10XjgoV8+60+vTonR/+KfPLJkJmp/3fccTKt2gJ+++03cnJyyMnJ4YMPPmDx4sWcd955rF+/nhNOOMG9jZogCIIgCIKdycvLY/PmzVx77bV8//339OrVq94ezRHL77/DHXfA8uXw2GMwaVK4PQoLvo7VZLBsEU1lw66srKRVq1Z+ZTX09Rhf6wbiR0Tw5Zd6Fuuzz4Zx4/RU+mPGwKhR0LHjAdXt2g6h9CvYc/3vf//jtddeIycnh59//pkzzzyTjIwMhg0bRq9evYLOahiIb6Ize2HXdogknVlpU3TWMrBrO4jOgrMjOgs/LpeLk046CU3TmDNnDmeeeabt2iGg/vnwQz3xV0YG/Pgj9Opl3blCZNMfO5IN28Z4brRtxTG+1g3ED9uxcyc8/zw89JD+959+gh9+0P+8dKm+RuOqqxodKBvYtR1C6Ze/5/rxxx/dU5M+/vhjtmzZwowZM9i1axf5+fn8/e9/p3PnzmHxLZjjRGfWYdd2sLPOQmlTdNYysGs7iM6CsyM6Cy9RUVF88cUXXH755Vx88cWMGTMGl8sVbrcOwO/+OessfaC8b5/+58xMfecXK84VQptm+yaDZSGyUAq++w5mzYKBA6FzZ3j2WYiL0//9iivghhvC62MLZMuWLTzzzDNccMEFVFdX43K52LVrFwA33ngjr7zyChdeeCGpqalh9lQQBEEQBMFcWrVqxe23387GjRu56qqriIqK4r333mPz5s3hdi14kpP1D01dukDv3vDww+H2yFbIYFmwP9XV+n/79kGPHvra488/h2uu0dddfPmlvvZCMA2Xy8WXX37JjBkzOPHEE+nRowdLlizh9NNPp6amhuOOO4777rsv3G4KgiAIgiCEjDZt2jB69GgAVq5cSa9evbj99tspKioKs2dB0r49zJsHa9bA0UfrZd99p3+kOsiRwbIfLF26lKVLlwZtJ874CmrRMb7WDcSPkLFnjz4QBn3P4+XL9Tdfixbp//b223DttXDooUGfyq7tEEq/4uLiKC8vd38tPuOMMxg2bBgbN24kKyuLnTt38v7773PbbbeRmJjos02zfAvVcQedzkKIXdsh1Dqzq03RWcvAru0gOgvOjujMXhjt8Mgjj/DZZ5/x/fff07NnT/bs2WMLv4LipJP0zNk1NXDeefpv8K+/tuZcFtk02zdJ8OUjTqeTfv36MWfOHMaNG9dsfcmG7SdK6euNc3L0/z77DO65B+67T9/26ZBDJHu1BWzdupXNmzdz2mmncdNNN9GmTRsefPBBNm3axOGHH05sbGy4XRQEQRAEQbA13333HccffzwrVqxg165dXHbZZZG/A0hpqZ4T6J//hOuv1zNntyAkwZfJLF68mL59+wZtRylFeXk5/ryj8OcYX+sG4oclbNwIt96q73ncty989BFcdhls3qwPlAE6dbJsoGybdmiAVX4ppVi3bh333Xcf/fr1o1u3bjzzzDOUl5fzyCOP8MADDwDQo0ePoAbKZvkfqB3Rmb2wazuE0i8rziU6C9yPlohd20F0Fpwd0Zm98NYOxx9/PKAvY7v33ns56aSTWLlyZcjay5L+SUrScwR98w3076+Xffstqra2xemsKSJusFxQUEC/fv28/rvT6SQrK4u5c+cyd+5cJk+ejNPpDOqc+fn5pKenB2XDk+rqakuP8bVuIH6YQlGRPoW6rk5fh7xvHzz+OOzeDbm5MHmynmQgRIStHZrBTL/27NnD5MmT6dq1K4MHD+aHH35gypQpbN++nZdffpnq6mrTt0Ewy/9A7Rz0OrMZdm2HUPplxblEZ4H70RKxazuIzoKzIzqzF021Q0ZGBj///DNXXXUVl112GWvXrrWFX0Fx5JFw8cX6LNC//Q3696fugw9MP024deaNGFOtWYQxAAZYu3YtBQUFXusOGTKEBQsWuL8CFxYW0q9fP9atW4fD4Qjo3E6nk7S0tIB8F4ANG/6aXj1nDvTrB926QUWFvjZi4cJwe9jiUEqhaRqXXXYZWVlZHH300SQmJvLCCy9w1lln1VvPcbC/JRYEQRAEQTCL+Ph4brvtNiZNmkRSUhLvvfceCxcu5KGHHqJ79+7hdi9wNA0++QSeeIKkCRPg/PPhxRdb/DLJiPiy7HA4yM7OJjs7mwkTJnitN3/+fIB606XT0tLo27cvs2bNCujcixcv9mmNsuBBba0+nfqOO+CYY+D44yEvD8aPh+7dIToaZszQp3cIpqCU4ptvvuHBBx/k5JNP5pFHHgHg/PPPp3379sTFxfH4448zdOhQSdAhCIIgCIJgMUn7f+ceddRRxMfHc+yxx3LrrbeGPRFYUMTHQ1YWJV98Aenp+kD5f/+DFrwHd0R8WfaVJUuW0N+YU+/BgAEDyM7OZs6cOYA+qF63bp1XOw6Hgzlz5lBQUGDq9GuD+Ph4S4/xtW4gfnilpAR++w1OOAGmTIHFi2H0aH2vtqFD9UzWNsXUdjCR5vyqqqri/fffJycnh5ycHIqKihg+fDg33HCDe1uDsWPHmnKuQDDLZqB2WqTOIhi7tkMo/RKd+X+M6Mw/7NoOorPg7IjO7IW/7dC1a1eef/55br31Vu68807ee+89xo8fT21tLTEx5g3FQtk/cd27Q69e+l+ysvRZpE88ARkZAX9pDrfOvNGiBsv5+fnuAbEnaWlpFBYW4nQ6cTgcTJo0ySd7RUVF5Ofnu/9uTP8uKiry2UZxcXG9aa6aprmnyHp2plHeEGMNaatWrYC/psw2Vd/XugkJCSilDvi3pmzXKy8shI8/RrvqKtQ778C//w1vvgkzZ8KTT6LFxPxVv5E2aNZ+E+Vm2DDKG7aX2fYDjcnwy9O30tJS3n33Xc4991yWLFnCXXfdRUZGBvPnz+fss88mPj7ebcefNrOiDYw10MG2S6B2/I3JOJfnMd76yde6YILOQlAeinOGWmf+XKcGdm8DM/VhF52Z9eyzk87CqW2rdGbG88DALm0QCn2EWmeeZS1FZ3Z6Vhrlgeqsd+/eLF++HIBvv/2W0aNH89BDD3HJJZcQFRUVuTrLyUFbuBB11VVw6qn6WGD/LEY76Kyqqoqq/V++PctLSkoOsNcYLWaw3FQSL2OtcmFhoV8ZrdPT0+t9Wc7Ly2Po0KE+D5QBDj/88EbLp02bxvTp0wH9DUhCQgJlZWXU1ta66yQkJBAfH09JSQllZWUkJCSgaRqJiYnExsZSUlJS7wJJTk5G0zS2b9/urguQmpqKy+Vi37599XyIiYlx769rEBUVRUpKCtXV1VRUVNSrm5SQQPVHH6HefpvY1auJ2rQJ1+mnEz1hAhUjR1Kdng7FxXpMNTUkxMR4jWnfvn24XC53eVMxRUVFUbzfrkFjMWmaRmpqKrW1tZSVlfkUU2JiIkVFRe6bFOj7s7Vu3ZqKiop6SQKa6yczY1JKUVFRQUJCAlu3buWtt97ioosuQinFww8/zKBBg7j44osZN24clZWVAFRWVlJbW0tSUhKVlZXuG0NzMbVq1Ypdu3YRGxvrboNgYzL879SpE0qpgPtJKUVNTQ0dOnSwPKbi4mLKy8vd2mmqn+rq6ti5c6e7rreYNE0jOjqamJgYdz/Bfj352U+huvYC6afmYkpISGD37t3ExMS4+8MOMRnXaWJiIqmpqX7F5G8/xcXFsWPHDvcLLTNiUkpRWVlJp06dguonpRS1tbW0b9/e8picTqf73mb0h7d+qqmpYffu3e663mKKjo52a+1g0FMo7+VmxGToLDk5meTkZEvvezExMWzfvr1ewspgY1JKUVVVxSGHHBJUPymlcLlctG3b1vKYSkpK3DqLiopqsp+qqqooKipy68xbTLGxse6Bd01NTVD9FAl6svpefswxx3DPPfdw55138s9//pPZs2eTnp4ecEyGzlJTU0lMTLT0vtfoWGPyZFxjx1K5dCk1FRVErVuHOvxwUrt2tfT3XsOYjJc5bdq0ccc0e/bsRj+m+krE7bM8d+5csrKyDniLUFhYSI8ePcjOzj5gMJufn8/QoUPJy8sLaFq10+lk/vz5zJo1i/79+zN58uRm1zEbe3dt2bLlgL27nE4nHTt2rPcGqKk3Ji6Xi+LiYlJTU90Xpbf6hv3m6iqlKCkpISUlxV2vSV+uvx7tjTf08lGj9GkWw4fD/uMj9c09HNheVvveXEzV1dV88MEHvPnmm7zzzjvs2LGDoUOH8sgjj3DkkUea7guY3wZKKYqLi70m1fPVdqB2AonJV535U9dvnYWp3OpzQuh15s91mpqaSlRUlO3bwCx92ElnZmjSbjoLl7bBOp2Z8Tywm86s1kc4dGaUebZ5pOvMTs9KAzPv5eXl5Tz22GMcdthhXH311ZSUlJDcYOliJOjsgLpXXw3vvIO2dClq4MBmbVups6a+LB9++OHN7rPcYr4sN0WwW0c5HA6mTZvGtGnT/D42JSXlgA5wuVz2XvdRWakv4L/nHjj0ULjxRhgwQN//+NRT9QRdgqnU1dURExPD/fffz+OPP05KSgpDhw7lqaeeIj09vd6LFUEQBEEQBCHyad26NTNmzABg586dHHXUUdx4443ceeed7gRhEclzz+n5izw+8oSL+Ph497irqRcA3mgxg+W2bdsCjQ+Mi4qK6tUJJf379yd6/+DSeLtXW1vLxIkTmThxIuDb1IjS0lK3jeamRnjWBe/TsAFqa2spLy0luqCA2FWriF29muguXah++22qR4xApabiKi4mZsIEfbpHRQVV++1D5E9zS0xMpLq6muLiYnd7hSImTdP4+eefOfTQQ3n11VdZsWIFy5YtY8iQIQwePJjevXtTVlZGcnIyCQkJ1NTUWDp1z7BhtIEZ07BLS0tJTU1FqeCmYVdUVOBwOEISk6d2mrv2POt6i8k4d3V19UE/DbuqqqqezuwQk3GdGtMUrZ66Zyx5MdrAjGnYZWVlOByOoKdhG38ORUye2mmunzzrNjUNG+p/PQi0nyJBT6G8l5sRk6GzmJiYkEzDNnwx2sCMadjl5eU4HI6gp2Ebf7Y6Js/nWXPTsKurq+vprKlp2KDfIw72adhm38sNWx07dmTVqlVMmTKFRYsW8eCDDzJhwgSfp2GXlpYSFxcXkmnYvow1tJEj9X7y4TdsoL/3GpuGbfzZiGnBggUsWrTI/XvNiLOurg5faDHTsEHvsDlz5hzwBXj+/PlMnjyZvXv3BrTXciAY07CdTucBX5arqqqIi4vza9padXV1vWOaejPS0H6j02nKy6lbsYKYlSthxQo95fvIkZCZiTZyJCo1tVFf7DIVxsypJP72R6DlNTU1fPzxx+Tk5JCbm0tJSQl//vknRUVFVFdX07Vr13rTaYw+D8V0GrPbwPDf2wwKf6bTBGInkJh81Zk/dZXS1+B4riH0tw1CUR6KaWuh0pk/dSNNZ2bpw046M0OTdtNZuLQN1unMjOeB3XRmtT7CoTOjzLPNI11ndnpWGph9L29YrpRiyZIlFBcXM3HiRP788086d+7cpO1w6yxY2+HQWUlJCQ6H4+Cahp2ens6mTZsOKHc6naSlpYVsoOyJcZPyxNuU2ob1DKKioho9xlt9X+pqe/cSdc89+trj116DQYNg/1tDAG9J372dMxzlZtn2tz/8KXe5XLz22mvk5OSwcuVKEhISyMjI4PHHH2fIkCHExMTQsWPHA2xomnbAmnarfATz26Ch/4HaDsaOlTrzta6maV5v2E35Esl6CofOvJX7e51GQhuYpQ876SxYTdpRZ+HStpU6M+t5YKc2sFof4dCZ8buzJenMTs9KMP9e3rBc0zQmTJgA6IPS/v37M2jQIObOnUtaWlqjduygs2Bsh0Nn3uw1pEUNlsePH99otrO8vLxmE3JZhfGGyBNj8X7Dzm3qrd++ffvqHeOtfmP2G6urOnem9KuvSEpK+suP/XWa8sUu5WbZBv/7w5fyF198kaOOOoqBAweyePFijj/+eFavXk3//v3rbQ/geYynDaWUu89D8YbQ7DYw/Pf2ps5X24HaCSQmX3XmT12l9GlR9XTmZxuEotzqc4I1Ogs2pkjTmVn6sJPOzNCk3XQWLm2DdToz43lgN51ZrY9w6Mzzt0VL0ZmdnpUGZt/LmyqPi4tj/fr13H333Rx33HHcfvvtPPjggwfUD7fOgrUdDp15i60hETdY3rNnj9d/mzRpEnPmzCE/P9+d9bqwsJDCwkLy8vJC5WI9zFqzXFJSgsvlQtOaX7PsWRd8WLPs69ZRLXCNZWJiIpWVlfXay9+YYmNjWbt2LUuXLqVNmzbcdttt7u1RNE3jxRdfdMfkeSNrbuuo0tJSlFJBr0dsLqZWrVpRVlZWrw3MWrOcnJzsvnEF0k9K6WtYUlJSQhKT8W+a1vyaZU+deYvJuCnLmuUEKioq6vWHHWIyrlOjvtXr3EpLS+u1gVlrllNSUkxZs5ySkhKSmDyPaa6fPHXW1Jpll8sla5YtuJebEZOhs6ioqJCsWfa8j5sRk1L6muVg+8l4FqSkpFgek7Fm2eVy+bRm2VNnTa1ZdrlcsmbZgnt5czF16tSJZ555hquuuor169dTXFzMxo0bOemkk6iurq63Ztn4bWv1mmVfxhqh+L0na5Y9mDx5MgCLFy/G6XSSnp5OWloa48ePr7cdlNPpJCsrix49euBwOFi3bh1ZWVkHTFuwmqbWLFu1pY03+97eENppqw1/y818OxbIFgDFxcWsWrWK3NxcVqxYQUxMDKNHj+aiiy5i2LBhQcdknMNuW234Wm74HwlbbRjlsnVUZF1jZsQUaTozSx920pkZmrSbzsKlbZCto0C2jlJKto6yyzVmVUxKKQYNGkR5eTmPP/4455xzTth1ZkZModZZi1uznJ2dXe//3nA4HM3WEYRA+PXXX+nevTtff/01J598MscccwwZGRnk5uZyyimnEBUVFW4XBUEQBEEQhBaMpmm8//77PP3001xwwQUMGTKE119/PdxutVgiZrAcqTQ2DdvlcnHNNdf4NQ27rq7Ove1Kc1MjPOuC92nYiYmJMg17/xSnxraOKi0t5dNPP6Vr164ccsghnHLKKaxZs4bu3buzdu1aunXr5o4pOjraPSXFjJiUUtTV1bnfelk9dU/TtHptYMY07Lq6Ovcb7mCm5Rg+hSImT+0010+edb3FZOhVpmHrGrHj1lF1dXXs27cvJNOwjTfeRhuYMQ3baKNgp2Ebz6lQxOSpneb6ybNuU9OwExMTZRq2BfdyM2IydGZsh2j1NGzjS6nRBmZMwzbaKNhp2DEx+s9uq2PyfJ75Mg3bU2dNTcM2pvce7NOwzb6XBxrT7bffzvjx41m1ahWlpaUUFBRw9NFH07VrV8unYfsy1gjF773GpmHHxcUBB+E07EjD2zTsg2lKWCTGtG/fPlavXu2eXq2UYuHChZx77rnuVPSRFlM4y+3ki1nldvLFrHI7+WJWuZ18MavcTr6YVW4nX8wqt5MvZpXbyRezyu3ki1nldvLFrHI7+WJWuVW2//a3v5GTk8P999/PpEmT3C9nIjkmK8t9nYYtg2WLMAbLDTtAKe9rPrzhzzG+1g3Ej5aI0Q6ff/45jz32GB988AE9e/YkMzOTzMxMBg4c6P7iEg6/QtE/VpzLLJuB2hGd2Qu7toPoLDg7ojN7Ydd2EJ0FZ0d0Zi/s2g5K6V+7P/nkE6ZOnUp0dDSfffZZk4PAYM7VEnTmbazWEFlkGQYCeT/hzzG+1j1Y35MYcX/zzTdcdtllKKWIjY1l5MiR/PDDD/zwww/MmTOH008/PSwD5YZ+Ruq5zLIZqB3Rmb2wazuIzoKzIzqzF3ZtB9FZcHZEZ/bCzu0wevRovv32W2bPnk1KSgpffvklP/74o+nnaYk684asWbYYpQ7cZ7mxsuamCzQ8xlv9xuw3Vtf4e7inQARa7q+N8vJy3nnnHZYtW8aqVasoKCiga9euZGZm4nK5OPvssxk8eDDw17qJcMbk2edW++J5PrPse9oLxsdA7QQaky/a8adupOgsEq+x5sr9uU4jRWdm6cNOOjNDk3bTWbi0DdbpzIzngd10ZrU+wqGzhrppCTqz07PSwOx7uRnlnv0YGxtLRkYGSik++ugjZsyYwTXXXMP9999P27Ztgz6v5/nMiikcOvMWW0NksGwxZu2zbOwFqmnNJ/jyrAsH7z7LW7duZdWqVaxevZqPPvqIww47jBEjRrBw4UJiY2OJj49nwoQJ7Ny5s9EEX+GMSSl9vzxN00KS4MuwYbSBWfssp6amolTw+yw7HI6QxOSpneb6ybOut5iMc0uCrwSqqqpsmeDL2P81FAm+jHut0QZm7bMc7D1CKeX+cyhi8tROc/3kWbepBF+AJPiy4F5uRkyGzmJiYkKS4MvwxWgDs/ZZdjgcpuyzDP4n+PI3Js/nmS8Jvjx11lSCL0ASfFlwLzcjJkNncXFx9fZZvvbaaznzzDO599576dmzJx9//DFdu3YNqp98HWuE4vee7LMcwTS1z3JdXR1RUVHuCwyafmPicrncG8sbxzT1tqehfW9vCI23vJ5+NOeLXcq91f3xxx8pLS1lwIABDB8+nKqqKjIyMhgzZgxHHXXUAfXB//4IRUxKKXefh2K/PLPbwPDf2zR2f94QBmInkJh81Zk/dSNFZ6F4Ex9qnflznUaKzszSh510ZoYm7aazcGkbrNOZGc8Du+nMan2EQ2dGmWebR7rO7PSsNDD7Xm5GuS86++KLLzj55JP5+uuvcTqd7hmVZrRBsDGFQ2eS4CvMNLVo3LjZ+IM/x/haNxA/7EZ5eTnvvvsuv//+OzfccAPPPvss+/btY9q0adTV1fm05tiu7RBKv6w4l1k2A7UjOrMXdm0H0VlwdkRn9sKu7SA6C86O6Mxe2LUdfPXrtddeY9KkSYwcOZJHH32Uww47zLJzhcOmr3YkwZdNUUrPVufPOwp/jvG1biB+2IU///yT+fPnk5mZSfv27ZkyZQpbt24F4Prrr2fatGkAPg+U7dgOofTLinOZZTNQO6Ize2HXdhCdBWdHdGYv7NoOorPg7IjO7IVd28Efvy666CJ+/vln4uPj6dWrF19//bVl5wq1TSt8kzXLFqOUJPgKthz06R7l5eUMGTKE9evXc+qpp5KRkcHs2bPp1auX+9hAbAfSH1aUN5xOY/wXivY1uw087QXjY6B2Ao3JF+34UzdSdBaJ11hz5f5cp5GiM7P0YSedmaFJu+ksnM9Kq3RmxvPAbjqzWh/h0FlD3bQEndnpWWlg9r3cjHJ/ddapUydefPFFbrnlFnr37s2PP/7IL7/8QmZmpvurbLBjDX/Kw6Ezb7E1RAbLFiMJvgJL+FBZWUlpaSndu3dnyJAhTJo0iVGjRnHzzTczaNAg0tLS3DGVlJS44/U3OUJiYiLV1dWS4EsSfEmCL0nwFXFJYZSSBF8gCb6MmCTBlyT4kgRfkuCrYYKv5mLq3bs30dHRfP/990yePJmTTjqJ2bNn06dPH0nwZcShfB1WC37RVIIvp9NJamqq+wKDpt+YuFwuiouL6x3T1Nuehva9vSH0tml3uN7W7dy5k5ycHHJzc8nLy+PGG29kzpw5bNiwgcMPP5z4+HjTzwn+90co3p4qpdx9HoqEKGa3geG/w+Fo9Hz+vCEMxE4gMfmqM3/q2lFn4XhzD6HXmT/XaaTozCx92ElnZmjSbjoLl7bBOp2Z8Tywm86s1kc4dGaUebZ5pOvMTs9KA7Pv5WaUm6GzoqIi/vGPf/Dcc8+xcuVKzj777KDGGv6Uh0NnkuArzEiCL9/4/fffeemll8jJyWHt2rX079+fzMxMxowZw/HHHx8S/+zQDo0RSr+sOJdZNgO1IzqzF3ZtB9FZcHZEZ/bCru0gOgvOjujMXti1Hczy69tvv+Xoo49mx44dfPjhh1x66aUH2G0JOpMEXzZFKT2luT/vKPw5xte6gfhhFk6nk6ysLFwuF3/88Qdr165l0qRJbN26lc8//5y7776b3r17h2ygHK52aIpQ+mXFucyyGagd0Zm9sGs7iM6CsyM6sxd2bQfRWXB2RGf2wq7tYKZfvXv3Ji4ujs2bN3PnnXdyxhlnsH79ekvOZbZNK3yTwXIYaGztsJnH+Fo3ED8CYdeuXbz44ouMGzeOb775hsTERCorKykrK2PgwIH897//5W9/+xuHHHJISPxpSKjawV9C6ZcV5zLLZqB2Djad2R27toPoLDg7ojN7Ydd2EJ0FZ0d0Zi/s2g5m+3X66afz008/ccYZZ3Daaafx1ltvWXYuM22a7Zsk+LIYpQ6+bNhKKX744Qdyc3PJycnhiy++4KSTTiIjI4O2bdsSGxvLk08+ecD5w7Hew/DBKvuBxuTZ56FYf2N2G3jaC/ZaCsROoDH5oh1/6lqpMzPLI/Eaa67cn+s0UnRmlj7spDMzNGk3nYVL22Cdzsx4HthNZ1brIxw6a6iblqAzOz0rDcy+l5tRbpXOEhMTefjhh7n66qs59NBD2b17N2+88QZjx45t9prxpzwcOvPWvw2RwbLFHCzZsEtKSti8eTOHH344d911F88//zzp6elcdNFFLFy4kEMPPdQdE/yVoc4gXJkEJRu2ZMOWbNiSDVuyYUs2bCOmSNCTZMOWbNiSDVuyYZuVDdvXfurYsSN1dXVs3ryZRx55hH/961888sgjDBw40JSYAv29J9mwI5imsmEbC8kNkUHTb0yMG5/nMU298Wpo39sbwn379rkH2A3P6cvbmz179vDZZ5+RmZnJq6++ynPPPcc777zDtm3bSE1NJTEx0fZvGsH//ghFTEr9lXUyFNlDzW4Dw//U1NRGz+fPG8JA7AQSk68686euGToLRXko3sSHWmf+XKeRojOz9GEnnZmhSbvpLFzaBut0ZsbzwG46s1of4dCZUebZ5pGuMzs9Kw3MvpebUR5KnVVVVfHwww/z+OOP89RTT3H11VcHbTscOpNs2GHG1wxrkcbPP//MsmXLyMnJYc2aNfTp04dPPvmE2NhYoqKiiIqSZfCCIAiCIAiC0JL5448/3F+mFy1axPXXX09cXFy43fIZyYZtU5RS1NTUeH1bFewxvtb114+77rqLo446ij59+vDBBx9wySWX8Ntvv7Fu3ToSEhKIiYmJyIFyIP0RCkLplxXnMstmoHYiVWctFbu2g+gsODuiM3th13YQnQVnR3RmL+zaDuHQWZcuXWjXrh3btm3jueeeo3fv3qxatSoom+HSWVNE3uimBeA5b9+KY3yt662eMZd/1qxZvPDCCwC0bduWuXPnsmfPHpYvX851111H165dffbJzgTSH6EglH5ZcS6zbAZqx+46O9iwazuIzoKzIzqzF3ZtB9FZcHZEZ/bCru0QLp0deeSRrFu3jltuuYWLL76YRx99NGibZvlmBpLgSwBg48aN5OTkkJOTQ2pqKv/9738588wzcTgcAEydOjW8DgqCIAiCIAiCYDtiYmK48cYbmTBhArW1tVRXV/PUU09x/fXXk5iYGG73gkIGywcptbW1rFmzhvfff5/c3FwKCws5++yzueCCC8jMzARg0KBBYfZSEARBEARBEIRIoH379gBs3bqVN998k5NOOokhQ4aE2avgkMFyGAhkba8/x/hS948//uCKK64gIyODmTNnMmzYMJKTk/32qyVg17XWofTLinOZZTNQO3bQWaB+tETs2g6is+DsiM7shV3bQXQWnB3Rmb2wazvYSWddunTh008/PSBzejA2Q23HQLJhW4S3raPstI2FUqrRi9huafojYSsBiUliiuRyO/liVrmdfDGr3E6+mFVuJ1/MKreTL2aV28kXs8rt5ItZ5XbyxaxyO/liVrmdfDGr3E6++Fru69ZR8mXZYvr37090dDSgd5Cm6XvaXXPNNUycOBFofqPwkpISqqqqiI2NRdO0Jjc/1zSN3bt3u+tC4xuFG/Y1TXNvrg6h2dDdTpvUJyYmUlJSgsvlcreXHWJSSs/mFxcXh8PhCHiTel/6qVWrVuzdu9d9fZoRk+F/+/btUUoFtUm9Uoo2bdpYHlNxcTHV1dVu7TTVT3V1dRQVFbnreotJ0zRatWqFUorKysqg+ikS9OQtpoSEBIqLi1Hqrxd0dojJuE7j4+NJTU219L4XFxfHnj17iI6OdrdBsDEppaitraV9+/ZB9ZPRL6mpqZbH5HQ6qampcWunqX6qqanB6XS663qLKTo6mri4OFwu10Ghp1Dey82IydBZQkICycnJlt73YmJi2L17NzExMe42CDYmpRR1dXW0a9cuqH5SSrnbxuqYSkpK3DqLiopqsp+qqqooKSlx68xbTLGxscTExFBbW0tNTU1Q/RQJegrlvdyMmAydJSYmkpiYaOl9z9exRih+7zWMSSnlbhsjpgULFrBo0aJ64zCAuro6fEG+LFuEty/LAE6nk9TUVPcFBk2/AXG5XBQXF9c7xlv9xuw3VlcpdcAm9L74Ypdys2yD//0RipiUUu4+t3pzeTC/DQz/jQRxgfoYqJ1AYvJVZ/7UjRSdWX1OCL3O/LlOI0VnZunDTjozQ5N201m4tA3W6cyM54HddGa1PsKhM6PMs80jXWd2elYamH0vN6M83DoL1nY4dCZflm2CcZMyUEq5yxq72TRlo+ExjdX3Zr85242Ve6tvl3IzbATaH1aVN+wzz5uQVee0qg3M8j0QO1brzJ+6nvV98T1c5ZF4jTVX7ut1Gkk6M0MfZtkxS2dmaNKzvi++W10ejnNarbNgnwd21JmV+jDLTqAxtSSd2elZadW93IzycOssHPoIxo63f2+IPVeoC4IgCIIgCIIgCEIYkcFyGIiJ8f+Dvj/H+Fo3ED9aInZth1D6ZcW5zLIZqB3Rmb2wazuIzoKzIzqzF3ZtB9FZcHZEZ/bCru0gOjPXjoGsWbYIY81yc/PgBUEQBEEQBEEQhNDh61hNviyHGKWUOzukFcf4WjcQP1oidm2HUPplxbnMshmoHdGZvbBrO4jOgrMjOrMXdm0H0VlwdkRn9sKu7SA6M9eOJzJYDgOe6dCtOMbXuoH40RKxazuE0i8rzmWWzUDtiM7shV3bQXQWnB3Rmb2wazuIzoKzIzqzF3ZtB9GZuXYMZLAsCIIgCIIgCIIgCA2QwXIYWLBggaXH+FK3qqqK2bNn2/btWCgJpD9CQSj9suJcZtkM1I7ozF6IzkRngRwjOvMP0ZnoLJBjRGf+ITprmTrzhiT4sghj0bjT6Txg0fhxxx3Hd999d8DeZE1tGH/88cfXO8Zb/cbsN1a3uLiYNm3asHfvXlJTUw84p502gG+s3CzbAMceeyzff/+9z/0RipiUUu4+D8Xm8v5ek82VG/5///33jZ7PV9uB2gkkJl915k/dSNGZ1ecE868xM2KKNJ2ZpQ876cwMTdpNZ+HSNlinMzOeB3bTmdX6aMzOcccdxw8//OCXnUBi8mzzSNeZnZ6VBmbfy80oD7fOgrUdDp2VlJTgcDiaTfBlz9znLYj+/fsTHR0N4L5h/fbbbzz22GNMnDgRgPj4eBISEigrK6O2ttZ9bEJCAvHx8ZSWltK5c2dKSkoASExMJDY2lpKSknoXSHJyMlFRUfXqAqSmpuJyudi3b5+7rKysDACXy0VxcbG7PCoqipSUFKqrq6moqHCXx8TEkJSURGVlZb23inFxcbRu3ZqKigqqq6vd5c3FtG/fPlwul7u8uZg8ffQWk6ZppKamUltb647Pl5hSUlLqtZddYurcuTP79u0LKCZ/+6ldu3b12sCMmDp37gwQdD+1a9cOTdOoqKiwNKZ9+/bV005T/aSUqlfXW0xGHLW1tfXstGQ9eYvJ4XDU6w+7xNS5c2dKS0tDct/r2LFjvTYwI6ZDDz0UTdOoqakJqp8cDgeaplFeXm5pTCUlJfW001Q/1dXV1avrLabKykoAampq6rVZS9ZTKO/lZsTUuXNnysvLQ3LfO/TQQ+u1gRkxdezYEU3TqKqqCqqfUlJS0DSN0tJSy2MytNNcP9XU1NTTmbeYjDiqq6vrnbcl6ymU93IzYurcuTOVlZUhue/5MtYI1e+9hjElJiaiaRolJSW4XC4WLFjAokWL3OMwI866ujp8Qb4sW4R8WY6MN40gX5ZBviz7o7NIeBPvb3kkvIm3IqZI05mVb+Lly7J55ZH8xcuKmOyqMzt/8Qo2Js82j3Sd2elZaSBfluXLsmAixk3KQCmFy+U6oNyo681GY8c0Vt+bfW/nasyP5nyxS7kZNoyHir/9YVW5Z5nRj1afM9Brsrly481dsD4GYsdqnflaN5J0FonXWHPlvl6nkaQzM/Rhlh2zdBasJu2os3Cc02qdBfs8sKPOrNRHY+XGj/RQxNSSdGanZ6VV93IzysOts3A8hxor91Vn3v69IZLgSxAEQRAEQRAEQRAaIF+WLcLzE3/D8rq6Ovc6El9t+XqMr3UNv/zxoyUSSH+EglD6ZcW5zLIZqB3Rmb0QnYnOAjlGdOYfojPRWSDHiM78Q3TWcnRmXNPeppobyJpli/jjjz847LDDwu2GIAiCIAiCIAiC0Ai///47Xbt29frvMli2CJfLxZ9//klycrKt3jwJgiAIgiAIgiAczCil3Jnjo6K8r0yWwbIgCIIgCIIgCIIgNEASfAmCIAiCIAiCIAhCA2SwLAiCIAiCIAiCIAgNkMGyIAiCIAiCIAiCIDRABsuCIAiCIAiCIAiC0AAZLAuCIAiCIAiCIAhCA2SwLAiCIAiCIAiCIAgNkMGyIAiCIAiCIAiCIDRABsuCIAiCIAiCIAiC0AAZLAuCIAiCIAiCIAhCA2SwLAiCIAiCIAiCIAgNiAm3A4I9KSwsJCsri+nTp9O3b99wuyMILY7CwkKWLl0KQF5eHnPmzBGtCYLJFBQUUFRUhNPp5KuvvqJHjx5MmjQp3G4JQovFeK6NGzcuzJ4IgjnIYFk4gPz8fOCvHxmCIJhPdnY2c+bMASA9PZ1+/fqxadMm0tLSwuyZILQchgwZwpIlSxg3bhx9+/alR48epKeni84EwQKcTidZWVnuZ5sgtARkGrZwAOnp6aSnp+NwOMLtiiC0SAoKCpg7dy5OpxOAvn374nA43C+qBEEwhwULFtC/f38At97atm0bRo8EoeWyePFimSEltDjky3ILo6CggIkTJ7Ju3bpG/93pdDJr1izatWsHwKZNm5gzZ44MjAXBD4LVWd++fQ/QnfFDXhAEHTOeZ55TQfPz8xk3bpw87wTBA7N+N+bn55Oenk5eXp7VLgtCSJHBcgvAmPYCsHbtWgoKCrzWHTJkCAsWLHC/+SssLKRfv36sW7dOfkAIQhOYrbNp06a56+fn5+NwOLjwwgutC0AQIgArnmdGfoCvvvqKJUuWWOq/IEQCZuvM6XTidDpleYPQIpFp2C0Ah8NBdnY22dnZTJgwwWu9+fPnA9SbIpOWlkbfvn2ZNWuW5X4KQiRjpc6ysrJYsmSJvLASDnqs0FlaWhrTpk0jLS2NyZMnW+O4IEQQZuts8eLFktBLaLHIYPkgYsmSJe61W54MGDDAnb1QEITg8FdnRjKU9PT0ULgnCC2CQJ5n06dPZ/78+e4BgCAITeOLzgoKCuT5JbRoZLB8EJGfn0+PHj0OKE9LS6OwsFDWTAqCCfijs/nz5zN06FD3D42mpsIJgvAXvuisoKCANm3auDXncDhwOBxs2rQpxN4KQmTii86KiopYunQpc+fOZe7cuRQUFPD666/LSymhxSBrlg8SmhoIG1M/CwsL6021kcGzIPiHPzrLz8+nbdu29O/fH6fTSWFh4QEaFAThQHzVGUD//v0PWFc5dOhQiz0UhMjHV50ZO6gY5OXlMXToUNnPXGgxyGD5IMHYL7mpNZFGnfz8fAoKCigsLCQ7O5vCwkK56QmCD/iqM28/2OWLlyA0j686S09PZ/Lkye4vXHl5eSxZskSmjAqCD/jzuxH0wfX8+fNZu3YtoG/RJuuYhZaADJaFA94eGm8JPbP1CoIQHJ46czgcKKXC54wgtFAaPs88f6zLS19BMIfGvjo7HA6mTZsmvx2FFoesWT5IaNu2LdD4Dc54M2jUEQQhMERngmA9ojNBsB7RmSDoyGD5IMGXLWlkfzxBCA7RmSBYj+hMEKxHdCYIOjJYPohIT09vdE2ksZG87PEqCMEjOhME6xGdCYL1iM4EQQbLBxXjx48nPz//gPK8vDxJwiAIJiE6EwTrEZ0JgvWIzgRBBsstjj179nj9NyO5ieeNz9iuZs6cOZb7JggtBdGZIFiP6EwQrEd0JghNI9mwWwiTJ08GYPHixQAMHTqUtLQ0xo8fX2+bjHXr1pGVlUVBQQEOh4N169aRl5cXFp8FIdIQnQmC9YjOBMF6RGeC4Buakv1LBEEQBEEQBEEQBKEeMg1bEARBEARBEARBEBogg2VBEARBEARBEARBaIAMlgVBEARBEARBEAShATJYFgRBEARBEARBEIQGyGBZEARBEARBEARBEBogg2VBEARBEARBEARBaIDssywIgiAIgk84nU6ysrJwOBwMGDCAcePGhdslQRAEQbAM+bIsCIIgCIJPDBkyhKysLObMmcNXX30VbncEQRAEwVJksCwIgiAIQrOMHz+e9PR00tLSwu2KIAiCIIQEmYYtCIIgCEKTFBQUsHTpUvbu3RtuVwRBEAQhZGhKKRVuJwRBEARBsC9Dhw4lLS2N7OxsAObPn0///v3p27dvmD0TBEEQBOuQadiCIAiCIHiloKCA/Px8xo8f7y7btGmTDJQFQRCEFo8MlgVBEARB8Ep2djYOh4P09HRA/6o8efLkMHslCIIgCNYjg2VBEARBELyyePFi90DZ6XTidDolyZcgCIJwUCAJvgRBEARBaJTCwkKcTicTJkwAICsry71uWRAEQRBaOvJlWRAEQRCERsnPzwegb9++7v2VBUEQBOFgQQbLgiAIgiA0yrp163A4HGRnZzNhwgQcDke4XRIEQRCEkCGDZUEQBEEQGqWoqMg9DVuyXwuCIAgHGzJYFgRBEAShUQoLC5k0aZIMlAVBEISDEhksC4IgCIJwAFlZWeF2QRAEQRDCigyWBUEQBEGox9KlS5k8eTITJkygqKjogH83En8JgiAIQktGU0qpcDshCIIgCIL9cDqd9OvXj02bNrnLJk+ezOTJk2VqtiAIgtDikcGyIAiCIAheWbp0KXl5efTo0QOASZMmSVZsQRAE4aBABsuCIAiCIAiCIAiC0ABZsywIgiAIgiAIgiAIDZDBsiAIgiAIgiAIgiA0QAbLgiAIgiAIgiAIgtAAGSwLgiAIgiAIgiAIQgNksCwIgiAIgiAIgiAIDZDBsiAIgiAIgiAIgiA0QAbLgiAIgiAIgiAIgtAAGSwLgiAIgiAIgiAIQgNksCwIgiAIgiAIgiAIDZDBsiAIgiAIgiAIgiA0QAbLgiAIgiAIgiAIgtAAGSwLgiAIgiAIgiAIQgNksCwIgiAIgiAIgiAIDfh/F+r4cfKmm2EAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.lines import Line2D\n",
    "Planck = {'name': 'Planck',\n",
    "          'do_cib': 1, 'do_tsz': 1, 'do_cibxtsz': 1,\n",
    "          'freq_cib': [100., 143., 217., 353., 545., 857.],\n",
    "          'cc': np.array([1.076, 1.017, 1.119, 1.097, 1.068, 0.995, 0.960]),\n",
    "          'cc_cibmean': np.array([1.076, 1.017, 1.119, 1.097, 1.068, 0.995, 0.960]),\n",
    "          'freq_cibmean': np.array([100., 143., 217., 353., 545., 857.]),\n",
    "          'fc': np.ones(7),\n",
    "          }\n",
    "#Get Data\n",
    "ells = np.asarray(Dl_lens_cib['545']['ell'])\n",
    "factor = cl_to_dl(ells)\n",
    "\n",
    "#Font Settings\n",
    "label_size = 15\n",
    "title_size = 18\n",
    "legend_size = 13\n",
    "\n",
    "\n",
    "fig, (ax1) = plt.subplots(1,1,figsize=(10,5))\n",
    "ax = ax1\n",
    "ax.tick_params(axis = 'x',which='both',length=5,direction='in', pad=10)\n",
    "ax.tick_params(axis = 'y',which='both',length=5,direction='in', pad=5)\n",
    "ax.xaxis.set_ticks_position('both')\n",
    "ax.yaxis.set_ticks_position('both')\n",
    "plt.setp(ax.get_yticklabels(), rotation='horizontal', fontsize=label_size)\n",
    "plt.setp(ax.get_xticklabels(), fontsize=label_size)\n",
    "\n",
    "#Colors\n",
    "colors = ['k','r','b','orange','green','magenta']\n",
    "alphas = [1,1,1,1,1,1]\n",
    "\n",
    "for i, nu in enumerate(nu_list):\n",
    "    #Extract Data\n",
    "    nu_name = str(nu)\n",
    "    cl_1h = np.asarray(Dl_lens_cib[str(nu)]['1h']) / factor\n",
    "    cl_2h = np.asarray(Dl_lens_cib[str(nu)]['2h']) / factor\n",
    "    cl_tot = cl_1h + cl_2h\n",
    "\n",
    "    #Plot\n",
    "    plt.plot(ells, ells * cl_tot, label= nu_name + ' GHz', color=colors[i],alpha= alphas[i])\n",
    "    plt.plot(ells, ells * cl_1h, '-.', alpha= alphas[i], color=colors[i],lw=0.8)\n",
    "    plt.plot(ells, ells * cl_2h, '--', alpha= alphas[i], color=colors[i],lw=0.8)\n",
    "\n",
    "    #Figure Size\n",
    "    # ax = plt.gca()\n",
    "    # fig = plt.gcf()\n",
    "    # fig.set_figheight(6)\n",
    "    # fig.set_figwidth(11)\n",
    "    \n",
    "\n",
    "    #Legend\n",
    "    line_1h = Line2D([0], [0], label=r'$\\mathrm{1h}$', color='black', linestyle='-.', lw=0.8)\n",
    "    line_2h = Line2D([0], [0], label=r'$\\mathrm{2h}$', color='black', linestyle='--',lw=0.8)\n",
    "    line_1p2h = Line2D([0], [0], label=r'$\\mathrm{Total}$', color='black', linestyle='-')\n",
    "    h, l = ax.get_legend_handles_labels()\n",
    "    h.extend([line_1h, line_2h,line_1p2h])\n",
    "    plt.legend(handles= h,frameon=False,fontsize=15,loc=2)\n",
    "\n",
    "    #Labels\n",
    "    plt.xlabel(r'$\\ell$', fontsize= title_size,labelpad=1)\n",
    "    plt.ylabel(r'$\\ell C^{\\nu\\kappa}_{\\ell}$\\quad$\\mathrm{[Jy/sr]}$', size= title_size)\n",
    "    plt.xscale('log')\n",
    "    plt.yscale('log')\n",
    "    \n",
    "    #Ticks\n",
    "    # ax.yaxis.set_minor_locator(matpotlib.ticker.MultipleLocator(0.5))\n",
    "    ax.tick_params(axis = 'x',which='both',length=5,direction='in', top=True, right=True, labelsize=label_size, pad=10)\n",
    "    ax.tick_params(axis = 'y',which='both',length=5,direction='in', top=True, right=True, labelsize=label_size, pad=5)\n",
    "    ax.grid( visible=True, which=\"both\", alpha=0.2, linestyle='--')\n",
    "    \n",
    "plt.tight_layout()\n",
    "# plt.savefig('figures/cls_CIB_CMB_lensing.pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "feb5e904-6a01-40fc-97bd-985bff2aefb8",
   "metadata": {},
   "source": [
    "## McCarthy & Madavacheril Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "bda37918-5055-471a-9f89-9e9d055de1d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib as mpl\n",
    "import matplotlib\n",
    "from matplotlib import pyplot as plt\n",
    "from matplotlib_inline.backend_inline import set_matplotlib_formats\n",
    "set_matplotlib_formats('svg')\n",
    "from classy_sz import Class as Class_sz \n",
    "import os\n",
    "import time\n",
    "\n",
    "font = {'family':'STIXGeneral'}\n",
    "axislabelfontsize='large'\n",
    "matplotlib.rc('font', **font)\n",
    "\n",
    "plt.rcParams.update({\n",
    "    \"text.usetex\": True,\n",
    "    \"font.family\": \"sans-serif\",\n",
    "    \"font.sans-serif\": [\"Helvetica\"]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d0fcc9e2-e45c-4bd1-be36-405c70726961",
   "metadata": {},
   "outputs": [],
   "source": [
    "# a simple conversion from cl's to dl's\n",
    "def cl_to_dl(lp):\n",
    "    return lp*(lp+1.)/2./np.pi"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57abc8e1-9b7a-4d94-b12e-080fb22c3eba",
   "metadata": {},
   "source": [
    "You can set `class_sz` parameters individually, in a single dictionary, or multiple dictionaries."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1477594e-18e9-4573-a987-a00afcb2401c",
   "metadata": {},
   "source": [
    "First, let's establish the cosmology. McCarthy et al. use Planck 2014 cosmology (https://arxiv.org/pdf/1303.5076.pdf, best-fit)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6683d84d-735f-490a-b45c-db20e9230109",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Parameters for Cosmology Planck 14, https://arxiv.org/pdf/1303.5076.pdf, best-fit\n",
    "p14_dict={}\n",
    "p14_dict['h'] = 0.6711      \n",
    "p14_dict['omega_b'] = 0.022068\n",
    "p14_dict['Omega_cdm'] = 0.3175-0.022068/0.6711/0.6711\n",
    "p14_dict['A_s'] = 2.2e-9\n",
    "p14_dict['n_s'] = .9624\n",
    "# p14_dict['k_pivot'] = 0.05\n",
    "p14_dict['tau_reio'] = 0.0925\n",
    "# p14_dict['N_ncdm'] = 1\n",
    "# p14_dict['N_ur'] = 0.00641\n",
    "# p14_dict['deg_ncdm'] = 3\n",
    "# p14_dict['m_ncdm'] = 0.02\n",
    "# p14_dict['T_ncdm'] = 0.71611\n",
    "p14_dict['cosmo_model'] = 1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7b15265-7107-40e0-a644-8b89d1feb88f",
   "metadata": {},
   "source": [
    "Halo Model and grid parameters:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2e563761-66ed-4d1a-8f8e-60771714b5c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "p_hm_dict = {}\n",
    "\n",
    "p_hm_dict['mass_function'] = 'T10M200m'\n",
    "p_hm_dict['concentration_parameter'] = 'D08'\n",
    "p_hm_dict['delta_for_cib'] = '200m'\n",
    "p_hm_dict['hm_consistency'] = 1\n",
    "p_hm_dict['damping_1h_term'] = 0\n",
    "\n",
    "\n",
    "# HOD parameters for CIB\n",
    "p_hm_dict['M_min_HOD'] = pow(10.,10)  # was M_min_HOD_cib\n",
    "\n",
    "#Grid Parameters\n",
    "# Mass bounds\n",
    "p_hm_dict['M_min'] = 1e8 * p14_dict['h']    # was M_min_cib\n",
    "p_hm_dict['M_max'] = 1e16 * p14_dict['h']   # was M_max_cib\n",
    "# Redshift bounds\n",
    "p_hm_dict['z_min'] = 0.07\n",
    "p_hm_dict['z_max'] = 6. # fiducial for MM20 : 6\n",
    "p_hm_dict['freq_min'] = 10.\n",
    "p_hm_dict['freq_max'] = 5e4 \n",
    "p_hm_dict['z_max_pk'] = p_hm_dict['z_max']\n",
    "\n",
    "#Precision Parameters\n",
    "# Precision for redshift integal\n",
    "p_hm_dict['redshift_epsabs'] = 1e-40#1.e-40\n",
    "p_hm_dict['redshift_epsrel'] = 1e-4#1.e-10 # fiducial value 1e-8\n",
    "# Precision for mass integal\n",
    "p_hm_dict['mass_epsabs'] = 1e-40 #1.e-40\n",
    "p_hm_dict['mass_epsrel'] = 1e-4#1e-10\n",
    "# Precision for Luminosity integral (sub-halo mass function)\n",
    "p_hm_dict['L_sat_epsabs'] = 1e-40 #1.e-40\n",
    "p_hm_dict['L_sat_epsrel'] = 1e-3#1e-10\n",
    "# Multipole array\n",
    "p_hm_dict['dlogell'] = 0.1\n",
    "p_hm_dict['ell_max'] = 8000.\n",
    "p_hm_dict['ell_min'] = 10"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a551a5ab-e3c5-4b74-9b01-8f59f034657b",
   "metadata": {},
   "source": [
    "CIB Model parameters (see McCarthy et al. at https://journals.aps.org/prd/abstract/10.1103/PhysRevD.103.103515):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8cce4aa1-fea9-410c-9234-dd94d7de5233",
   "metadata": {},
   "outputs": [],
   "source": [
    "#CIB Parameters\n",
    "p_CIB_dict = {}\n",
    "p_CIB_dict['Redshift_evolution_of_L_-_M_normalisation'] =  0.36\n",
    "p_CIB_dict['Dust_temperature_today_in_Kelvins'] = 24.4\n",
    "p_CIB_dict['Emissivity_index_of_sed'] = 1.75\n",
    "p_CIB_dict['Power_law_index_of_SED_at_high_frequency'] = 1.7\n",
    "p_CIB_dict['Redshift_evolution_of_L_-_M_normalisation'] = 3.6\n",
    "p_CIB_dict['Most_efficient_halo_mass_in_Msun'] = 10**12.6\n",
    "p_CIB_dict['Normalisation_of_L_-_M_relation_in_[JyMPc2/Msun]'] = 6.4e-8\n",
    "p_CIB_dict['Size_of_halo_masses_sourcing_CIB_emission'] = 0.5"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61a63031-5532-4fed-8cdb-d24b14f02b59",
   "metadata": {},
   "source": [
    "Select your frequencies. Here, we choose some Planck frequencies. Note that the frequency lists (both for the observed CIB frequencies and the flux cuts) must be a single string, not a list of strings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "21b243dd-5820-40fc-909a-6af1cfc1a5f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "nu_list = [353,545,857]\n",
    "nu_list_str = str(nu_list)[1:-1]  # Note: this must be a single string, not a list of strings!\n",
    "\n",
    "#Frequency Parameters\n",
    "p_freq_dict = {}\n",
    "p_freq_dict['cib_frequency_list_num'] = len(nu_list)\n",
    "p_freq_dict['cib_frequency_list_in_GHz'] = nu_list_str"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1d14285-d1e4-4b71-a465-8c1257921fda",
   "metadata": {},
   "source": [
    "We choose flux cuts from Planck 2013 (see https://journals.aps.org/prd/abstract/10.1103/PhysRevD.103.103515)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "45c4daa4-2d50-47e8-879a-5d596423045f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Flux Cuts\n",
    "cib_fcut_dict = {}\n",
    "\n",
    "#Planck flux cut, Table 1 in https://arxiv.org/pdf/1309.0382.pdf\n",
    "cib_fcut_dict['100'] = 400\n",
    "cib_fcut_dict['143'] = 350\n",
    "cib_fcut_dict['217'] = 225\n",
    "cib_fcut_dict['353'] = 315\n",
    "cib_fcut_dict['545'] = 350\n",
    "cib_fcut_dict['857'] = 710\n",
    "cib_fcut_dict['3000'] = 1000\n",
    "\n",
    "def make_flux_cut_list(cib_flux, nu_list):\n",
    "    \"\"\"\n",
    "    Make a string of flux cut values for given frequency list to pass into class_sz\n",
    "    Beware: if frequency not in the flux_cut dictionary, it assigns 0\n",
    "    \"\"\"\n",
    "    cib_flux_list = []\n",
    "    keys = list(cib_flux.keys())\n",
    "    for i,nu in enumerate(nu_list):\n",
    "        if str(nu) in keys:\n",
    "            cib_flux_list.append(cib_flux[str(nu)])\n",
    "        else:\n",
    "            cib_flux_list.append(0)\n",
    "    return cib_flux_list\n",
    "\n",
    "cib_flux_list = make_flux_cut_list(cib_fcut_dict, nu_list)\n",
    "\n",
    "#Add Flux Cuts\n",
    "p_freq_dict['cib_Snu_cutoff_list_in_mJy'] = str(list(cib_flux_list))[1:-1]\n",
    "p_freq_dict['has_cib_flux_cut'] = 1\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "710fc63b-4c23-4bf0-9b9a-c94018954598",
   "metadata": {},
   "source": [
    "### Compute "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "eb6208ff-cadc-4e03-be4a-67ef0da42ac8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class_sz = Class_sz()\n",
    "\n",
    "#Set Parameters\n",
    "class_sz.set(p14_dict)\n",
    "class_sz.set(p_hm_dict)\n",
    "class_sz.set(p_CIB_dict)\n",
    "class_sz.set(p_freq_dict)\n",
    "\n",
    "#Tell It What You Want\n",
    "class_sz.set({\n",
    "    'output': 'lens_cib_1h,lens_cib_2h'\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5e795df3-1a9b-45cf-b995-2af9f6bec7f2",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/boris/Work/CLASS-SZ/SO-SZ/mcfit/mcfit/mcfit.py:130: UserWarning: use backend='jax' if desired\n",
      "  warnings.warn(\"use backend='jax' if desired\")\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 36.8 s, sys: 4.53 s, total: 41.3 s\n",
      "Wall time: 7.75 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "#Calculate Spectra\n",
    "class_sz.set({\n",
    "# 'ndim_redshifts':150,\n",
    "})\n",
    "class_sz.compute_class_szfast()\n",
    "Dl_lens_cib = class_sz.cl_lens_cib()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9091f954-4fa4-43a5-acf5-c28b3d339820",
   "metadata": {},
   "source": [
    "### Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "de064b0d-b643-411b-9bca-ae0f2fe0f57d",
   "metadata": {},
   "outputs": [
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   "source": [
    "#Get Data\n",
    "ells = np.asarray(Dl_lens_cib['545']['ell'])\n",
    "factor = cl_to_dl(ells)\n",
    "\n",
    "#Font Settings\n",
    "label_size = 15\n",
    "title_size = 18\n",
    "legend_size = 13\n",
    "\n",
    "\n",
    "fig, (ax1) = plt.subplots(1,1,figsize=(10,5))\n",
    "ax = ax1\n",
    "ax.tick_params(axis = 'x',which='both',length=5,direction='in', pad=10)\n",
    "ax.tick_params(axis = 'y',which='both',length=5,direction='in', pad=5)\n",
    "ax.xaxis.set_ticks_position('both')\n",
    "ax.yaxis.set_ticks_position('both')\n",
    "plt.setp(ax.get_yticklabels(), rotation='horizontal', fontsize=label_size)\n",
    "plt.setp(ax.get_xticklabels(), fontsize=label_size)\n",
    "\n",
    "#Colors\n",
    "colors = ['k','r','b']\n",
    "alphas = [1,0.6,0.4]\n",
    "\n",
    "for i, nu in enumerate(nu_list):\n",
    "    #Extract Data\n",
    "    nu_name = str(nu)\n",
    "    cl_1h = np.asarray(Dl_lens_cib[str(nu)]['1h']) / factor\n",
    "    cl_2h = np.asarray(Dl_lens_cib[str(nu)]['2h']) / factor\n",
    "    cl_tot = cl_1h + cl_2h\n",
    "\n",
    "    #Plot\n",
    "    plt.plot(ells, ells * cl_tot, label= nu_name + ' GHz', color=colors[i],alpha= alphas[i])\n",
    "    plt.plot(ells, ells * cl_1h, '-.', alpha= alphas[i], color=colors[i],lw=0.8)\n",
    "    plt.plot(ells, ells * cl_2h, '--', alpha= alphas[i], color=colors[i],lw=0.8)\n",
    "\n",
    "    #Figure Size\n",
    "    # ax = plt.gca()\n",
    "    # fig = plt.gcf()\n",
    "    # fig.set_figheight(6)\n",
    "    # fig.set_figwidth(11)\n",
    "    \n",
    "\n",
    "    #Legend\n",
    "    line_1h = mpl.lines.Line2D([0], [0], label=r'$\\mathrm{1h}$', color='black', linestyle='-.', lw=0.8)\n",
    "    line_2h = mpl.lines.Line2D([0], [0], label=r'$\\mathrm{2h}$', color='black', linestyle='--',lw=0.8)\n",
    "    line_1p2h = mpl.lines.Line2D([0], [0], label=r'$\\mathrm{Total}$', color='black', linestyle='-')\n",
    "    h, l = ax.get_legend_handles_labels()\n",
    "    h.extend([line_1h, line_2h,line_1p2h])\n",
    "    plt.legend(handles= h,frameon=False,fontsize=15,loc=2)\n",
    "\n",
    "    #Labels\n",
    "    plt.xlabel(r'$\\ell$', fontsize= title_size,labelpad=1)\n",
    "    plt.ylabel(r'$\\ell C^{\\nu\\kappa}_{\\ell}$\\quad$\\mathrm{[Jy/sr]}$', size= title_size)\n",
    "    plt.xscale('log')\n",
    "    # plt.yscale('log')\n",
    "    \n",
    "    #Ticks\n",
    "    ax.yaxis.set_minor_locator(mpl.ticker.MultipleLocator(0.5))\n",
    "    ax.tick_params(axis = 'x',which='both',length=5,direction='in', top=True, right=True, labelsize=label_size, pad=10)\n",
    "    ax.tick_params(axis = 'y',which='both',length=5,direction='in', top=True, right=True, labelsize=label_size, pad=5)\n",
    "    ax.grid( visible=True, which=\"both\", alpha=0.2, linestyle='--')\n",
    "    \n",
    "plt.tight_layout()\n",
    "# plt.savefig('figures/cls_CIB_CMB_lensing.pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37d770a2-de95-4c54-ab6d-668a7f2c9142",
   "metadata": {},
   "source": [
    "## Primordial non-Gaussianity"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6e7dafb-fa41-4e08-a091-bcffbb655ca3",
   "metadata": {},
   "source": [
    "Initialize cosmology part"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "bb3c055c-647f-4ac2-af4f-fad47767d76e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1min 2s, sys: 600 ms, total: 1min 3s\n",
      "Wall time: 10.5 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "class_sz = Class_sz()\n",
    "#Set Parameters\n",
    "class_sz.set(p14_dict)\n",
    "class_sz.set(p_hm_dict)\n",
    "class_sz.set(p_CIB_dict)\n",
    "class_sz.set(p_freq_dict)\n",
    "#Calculate Spectra\n",
    "class_sz.set({\n",
    "# 'ndim_redshifts':150,\n",
    "'use_png_scale_dependent_bias' : 1,\n",
    "'fNL' : 100.,\n",
    "'hm_consistency': 1,\n",
    "'ndim_redshifts': 100,\n",
    "'ndim_masses': 100,\n",
    "})\n",
    "#Tell It What You Want\n",
    "class_sz.set({\n",
    "    'output': 'lens_cib_1h,lens_cib_2h'\n",
    "})\n",
    "class_sz.compute()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62bb12b8-a011-4da1-8967-f3d86c33aae4",
   "metadata": {},
   "source": [
    "Compute (without redoing cosmo) but only changing fNL:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "057d3e9a-bce6-4c0d-8dcb-dbed6c528dbd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 4min 35s, sys: 1.17 s, total: 4min 36s\n",
      "Wall time: 40 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "## define array of fNL\n",
    "fnl_ar = np.linspace(-100,100,10)\n",
    "Dl_lens_cib_all = []\n",
    "for fnl in fnl_ar:\n",
    "    class_sz.compute_class_sz({'fNL':fnl})\n",
    "    Dl_lens_cib_all.append(class_sz.cl_lens_cib())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "14de9146-738c-40eb-bd55-f86d476858b4",
   "metadata": {},
   "outputs": [
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   ],
   "source": [
    "#Get Data\n",
    "ells = np.asarray(Dl_lens_cib['545']['ell'])\n",
    "factor = cl_to_dl(ells)\n",
    "\n",
    "#Font Settings\n",
    "label_size = 15\n",
    "title_size = 18\n",
    "legend_size = 13\n",
    "\n",
    "\n",
    "fig, (ax1) = plt.subplots(1,1,figsize=(10,5))\n",
    "ax = ax1\n",
    "ax.tick_params(axis = 'x',which='both',length=5,direction='in', pad=10)\n",
    "ax.tick_params(axis = 'y',which='both',length=5,direction='in', pad=5)\n",
    "ax.xaxis.set_ticks_position('both')\n",
    "ax.yaxis.set_ticks_position('both')\n",
    "plt.setp(ax.get_yticklabels(), rotation='horizontal', fontsize=label_size)\n",
    "plt.setp(ax.get_xticklabels(), fontsize=label_size)\n",
    "\n",
    "#Colors\n",
    "colors = ['k','r','b']\n",
    "alphas = [1,0.6,0.4]\n",
    "for ifnl,fnl in enumerate(fnl_ar):\n",
    "    Dl_lens_cib = Dl_lens_cib_all[ifnl]\n",
    "    for i, nu in enumerate(nu_list):\n",
    "        #Extract Data\n",
    "        nu_name = str(nu)\n",
    "        cl_1h = np.asarray(Dl_lens_cib[str(nu)]['1h']) / factor\n",
    "        cl_2h = np.asarray(Dl_lens_cib[str(nu)]['2h']) / factor\n",
    "        cl_tot = cl_1h + cl_2h\n",
    "\n",
    "        #Plot\n",
    "        plt.plot(ells, ells * cl_tot, label= nu_name + ' GHz', color=colors[i],alpha= alphas[i])\n",
    "        plt.plot(ells, ells * cl_1h, '-.', alpha= alphas[i], color=colors[i],lw=0.8)\n",
    "        plt.plot(ells, ells * cl_2h, '--', alpha= alphas[i], color=colors[i],lw=0.8)\n",
    "\n",
    "        #Figure Size\n",
    "        # ax = plt.gca()\n",
    "        # fig = plt.gcf()\n",
    "        # fig.set_figheight(6)\n",
    "        # fig.set_figwidth(11)\n",
    "\n",
    "\n",
    "        #Legend\n",
    "        line_1h = mpl.lines.Line2D([0], [0], #label=r'$\\mathrm{1h}$', \n",
    "                                   color='black', linestyle='-.', lw=0.8)\n",
    "        line_2h = mpl.lines.Line2D([0], [0], #label=r'$\\mathrm{2h}$', \n",
    "                                   color='black', linestyle='--',lw=0.8)\n",
    "        line_1p2h = mpl.lines.Line2D([0], [0], #label=r'$\\mathrm{Total}$', \n",
    "                                     color='black', linestyle='-')\n",
    "        # h, l = ax.get_legend_handles_labels()\n",
    "        h.extend([line_1h, line_2h,line_1p2h])\n",
    "        # plt.legend(handles= h,frameon=False,fontsize=15,loc=2)\n",
    "\n",
    "        #Labels\n",
    "        plt.xlabel(r'$\\ell$', fontsize= title_size,labelpad=1)\n",
    "        plt.ylabel(r'$\\ell C^{\\nu\\kappa}_{\\ell}$\\quad$\\mathrm{[Jy/sr]}$', size= title_size)\n",
    "        plt.xscale('log')\n",
    "        # plt.yscale('log')\n",
    "\n",
    "        #Ticks\n",
    "        ax.yaxis.set_minor_locator(mpl.ticker.MultipleLocator(0.5))\n",
    "        ax.tick_params(axis = 'x',which='both',length=5,direction='in', top=True, right=True, labelsize=label_size, pad=10)\n",
    "        ax.tick_params(axis = 'y',which='both',length=5,direction='in', top=True, right=True, labelsize=label_size, pad=5)\n",
    "        ax.grid( visible=True, which=\"both\", alpha=0.2, linestyle='--')\n",
    "\n",
    "plt.tight_layout()\n",
    "# plt.savefig('figures/cls_CIB_CMB_lensing.pdf')"
   ]
  }
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