Learning_Curves.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "407eb044",
   "metadata": {},
   "source": [
    "# Learning Curves and Step Timings\n",
    "In this notebook, we: \n",
    "1. Create learning curves using varying number of structures and sparse points per species\n",
    "2. Determine how step timings increase using an increasing number of sparse points per species\n",
    "   - Also see how step timings compare when computing representations __with__ and __without__ gradients "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b68f9441",
   "metadata": {},
   "source": [
    "## Importing modules and defining functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1a9b1ef1",
   "metadata": {
    "code_folding": [
     0
    ],
    "hide_input": false
   },
   "outputs": [],
   "source": [
    "#Import modules\n",
    "from matplotlib import pylab as plt\n",
    "from tqdm import tqdm\n",
    "import itertools\n",
    "from joblib import Parallel, delayed, parallel_backend\n",
    "\n",
    "import pickle\n",
    "import numpy as np\n",
    "from numpy.random import RandomState\n",
    "random_state = RandomState(0)\n",
    "from sklearn import linear_model\n",
    "import skcosmo.feature_selection\n",
    "#from skcosmo.feature_selection import FPS\n",
    "from skcosmo.sample_selection import PCovCUR, FPS\n",
    "from skcosmo.preprocessing import StandardFlexibleScaler\n",
    "from sklearn.linear_model import LinearRegression, Ridge\n",
    "from sklearn.model_selection import GroupKFold, cross_val_score\n",
    "from sklearn.kernel_ridge import KernelRidge\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.pipeline import Pipeline\n",
    "from skopt.space import Real, Integer\n",
    "from skopt.utils import use_named_args\n",
    "from skopt import gp_minimize, callbacks, dump, load\n",
    "from skopt.callbacks import CheckpointSaver\n",
    "from skopt.plots import plot_convergence, plot_objective\n",
    "\n",
    "import ase\n",
    "from ase.io import read, write\n",
    "from ase.build import make_supercell\n",
    "from ase.visualize import view\n",
    "import numpy as np\n",
    "# If installed -- not essential, though\n",
    "try:\n",
    "    from tqdm.notebook import tqdm\n",
    "except ImportError:\n",
    "    tqdm = (lambda i, **kwargs: i)\n",
    "\n",
    "from time import time, sleep\n",
    "\n",
    "from rascal.models import KRR\n",
    "from rascal.utils import dump_obj, load_obj\n",
    "\n",
    "from rascal.models import Kernel, train_gap_model, compute_KNM\n",
    "from rascal.representations import SphericalInvariants\n",
    "from rascal.neighbourlist.structure_manager import mask_center_atoms_by_species\n",
    "from rascal.representations.spherical_invariants import get_power_spectrum_index_mapping\n",
    "from rascal.utils import from_dict, to_dict, CURFilter, FPSFilter, dump_obj, load_obj\n",
    "from rascal.utils import get_score, get_optimal_radial_basis_hypers\n",
    "from rascal.utils.io import load_json, dump_json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d3e3877a",
   "metadata": {
    "code_folding": [
     0,
     1
    ]
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   "outputs": [],
   "source": [
    "#Define functions\n",
    "def do_fps(x, d=0):\n",
    "    \"\"\"\n",
    "    Function for performing farthest-point-sampling for a given feature matrix.\n",
    "    d gives the number of farthest-point-sampled feature vectors that will be outputted. \n",
    "    If d==0, the entire set will be FPS-sorted.\n",
    "    Returns the FPS-sorted IDs, as well as the FPS distances.\n",
    "    \"\"\"\n",
    "    if d == 0 : d = len(x)\n",
    "    n = len(x)\n",
    "    iy = np.zeros(d, int)\n",
    "    # faster evaluation of Euclidean distance\n",
    "    n2 = np.sum(x**2,axis=1)\n",
    "    iy[0] = 0\n",
    "    dl = n2 + n2[iy[0]] - 2* np.dot(x, x[iy[0]])\n",
    "    dss = []\n",
    "    for i in range(1,d):\n",
    "        iy[i] = np.argmax(dl)\n",
    "        nd = n2 + n2[iy[i]] - 2*np.dot(x,x[iy[i]])\n",
    "        dl = np.minimum(dl, nd)\n",
    "        dss.append(max(dl))\n",
    "    return iy,dss\n",
    "\n",
    "def get_forces(frames, force_array='PBE-D2_forces'):\n",
    "    frc = np.zeros((np.sum([len(frm) for frm in frames]),3))\n",
    "    iat = 0\n",
    "    for ifrm,frm in enumerate(frames):\n",
    "        frc[iat:iat+len(frm)] = frm.get_array(force_array) \n",
    "        iat += len(frm)\n",
    "    return frc\n",
    "\n",
    "def get_features(atoms_objects, batch_size=1000, progress_bar=True):\n",
    "    '''Get average feature vectors for each structure in a list of atoms objects. \n",
    "        This requires soap to be set.\n",
    "    '''\n",
    "    struct_feat=[] #create empty feature vector of n_structures x n_features\n",
    "    got_frame = False\n",
    "    for frm in atoms_objects:\n",
    "        if got_frame == False: \n",
    "            if all(sp in frm.get_atomic_numbers() for sp in [1,6,7,8]) == True:\n",
    "                all_species = frm\n",
    "                got_frame = True\n",
    "    for i in tqdm(range(int(len(atoms_objects)/batch_size+0.9999)), disable = not progress_bar): #Get feature vectors in batches of 1000\n",
    "        for frm in atoms_objects[i*batch_size:(i+1)*batch_size]:\n",
    "            frm.wrap(eps=1e-13) #wrap atoms in unit cell\n",
    "        frames=[all_species.copy()] #add initial frame with all species\n",
    "        frames.extend(atoms_objects[i*batch_size:(i+1)*batch_size]) #extend initial frame with frames of batch\n",
    "        manager = soap.transform(frames) #calculate soap features for all structures in batch\n",
    "        env_feat_batch = manager.get_features(soap)[len(all_species):] #get feature vectors for all structures in batch\n",
    "        # (having removed the initial frame)\n",
    "        atom_counter=0 #count atoms\n",
    "        for ifrm,frm in enumerate(atoms_objects[i*batch_size:(i+1)*batch_size]): #iterate over frames in batch\n",
    "            nat=len(frm) #count atoms in frame\n",
    "            struct_feat.append(np.mean(env_feat_batch[atom_counter:atom_counter+nat],axis=0)) #average vectors\n",
    "            # for atoms in the same frame. Done by averaging vectors between atom_counter and atom_counter + nat\n",
    "            atom_counter+=nat #add atoms to counter\n",
    "    struct_feat_array=np.asarray(struct_feat)\n",
    "    return struct_feat_array\n",
    "\n",
    "def get_features_parallel(atoms_objects, bsize=1000, n_cores = 4):\n",
    "    '''Get average feature vectors for each structure in a list of atoms objects. \n",
    "        Do this in parallel\n",
    "    '''\n",
    "    blocks = []\n",
    "    for iblk, blk in enumerate(range(0, len(atoms_objects), bsize)):\n",
    "        blocks.append(atoms_objects[iblk*bsize:(iblk+1)*bsize])\n",
    "    with parallel_backend(backend=\"loky\"):\n",
    "        results = Parallel(n_jobs=n_cores)(delayed(get_features)(frames, batch_size=10000, \n",
    "                                                                 progress_bar=False) for frames in blocks)\n",
    "\n",
    "    return np.concatenate(results)\n",
    "\n",
    "def predict_batched(frames, ml_model):\n",
    "    man_pred = soap.transform(frames)\n",
    "    return ml_model.predict(man_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a282ca9",
   "metadata": {},
   "source": [
    "# Create Learning Curves\n",
    "Here we create learning curves using various numbers of sparse points __per species__"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ecce45b",
   "metadata": {},
   "source": [
    "## Reload database with DFTB+ results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1ee6f3eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "#(Re-)Open dataset\n",
    "with open('delta_data/CSD-10k_combined_w_kpts_11_pc_w_DFTB.pickle', 'rb') as f:\n",
    "    db = pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2ebdf87b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Add energy and force deltas to frame\n",
    "for name in db.keys():\n",
    "    for frm in db[name]:\n",
    "        frm.info['Delta_energy'] = frm.info['PBE-D2_energy'] - frm.info['DFTB_energy']\n",
    "        frm.arrays['Delta_forces'] = frm.arrays['PBE-D2_forces'] - frm.arrays['DFTB_forces']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "334fbe69",
   "metadata": {},
   "source": [
    "## Get PCovFPS Sample and Feature IDs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "04b7b7c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Reload PCovFPS-sorted sample IDs\n",
    "pcf_ids = np.load('delta_data/pcovfps_selected_idx.npy')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8f70609",
   "metadata": {},
   "source": [
    "## Create training and validation sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0c22cec1",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create training and val sets\n",
    "all_frames = []\n",
    "for name in db.keys():\n",
    "    for frm in db[name]:\n",
    "        all_frames.append(frm)\n",
    "\n",
    "ids = np.arange(len(all_frames))\n",
    "np.random.seed(10)\n",
    "np.random.shuffle(ids)\n",
    "\n",
    "train_frames_unsorted = [all_frames[i] for i in ids[1000:]]\n",
    "val_frames = [all_frames[i] for i in ids[:1000]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "964572f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Sort PCov-FPS sort training IDs\n",
    "train_frames = [train_frames_unsorted[i] for i in pcf_ids]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "cc4b25c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create composition (X), energy deltas (y) and force deltas (f) arrays\n",
    "X_all = np.zeros((len(all_frames), 4), dtype=int) ; y_all = np.zeros(len(all_frames), dtype=float)\n",
    "f_all = np.zeros((np.sum([len(frm) for frm in all_frames]),3), dtype=float)\n",
    "\n",
    "counter = 0\n",
    "for ifrm, frm in enumerate(all_frames):\n",
    "    symbols = frm.get_atomic_numbers()\n",
    "    X_all[ifrm] = [len(np.where(symbols == sp)[0]) for sp in [1,6,7,8]]\n",
    "    y_all[ifrm] = frm.info['PBE-D2_energy']\n",
    "    f_all[counter:counter+len(frm)] = frm.arrays['PBE-D2_forces']\n",
    "    counter += len(frm)\n",
    "    \n",
    "X_train = np.zeros((len(train_frames), 4), dtype=int) ; y_train = np.zeros(len(train_frames), dtype=float)\n",
    "f_train = np.zeros((np.sum([len(frm) for frm in train_frames]),3), dtype=float)\n",
    "\n",
    "counter = 0\n",
    "for ifrm, frm in enumerate(train_frames):\n",
    "    symbols = frm.get_atomic_numbers()\n",
    "    X_train[ifrm] = [len(np.where(symbols == sp)[0]) for sp in [1,6,7,8]]\n",
    "    y_train[ifrm] = frm.info['PBE-D2_energy']\n",
    "    f_train[counter:counter+len(frm)] = frm.arrays['PBE-D2_forces']\n",
    "    counter += len(frm)\n",
    "\n",
    "X_val = np.zeros((len(val_frames), 4), dtype=int) ; y_val = np.zeros(len(val_frames), dtype=float)\n",
    "f_val = np.zeros((np.sum([len(frm) for frm in val_frames]),3), dtype=float)\n",
    "\n",
    "counter = 0\n",
    "for ifrm, frm in enumerate(val_frames):\n",
    "    symbols = frm.get_atomic_numbers()\n",
    "    X_val[ifrm] = [len(np.where(symbols == sp)[0]) for sp in [1,6,7,8]]\n",
    "    y_val[ifrm] = frm.info['PBE-D2_energy']\n",
    "    f_val[counter:counter+len(frm)] = frm.arrays['PBE-D2_forces']\n",
    "    counter += len(frm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d23599ab",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Per-atom energy for species H : -16.468153332304215 eV\n",
      "Per-atom energy for species C : -154.72001091746486 eV\n",
      "Per-atom energy for species N : -383.34420755813 eV\n",
      "Per-atom energy for species O : -566.227034198853 eV\n"
     ]
    }
   ],
   "source": [
    "#Create baselined per-atom energy model\n",
    "mlr = Ridge(fit_intercept=False, alpha=1e-4)\n",
    "mlr.fit(X_all, y_all)\n",
    "for spec, coef in zip(['H', 'C', 'N', 'O'], mlr.coef_):\n",
    "    print(f\"Per-atom energy for species {spec} : {coef} eV\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "687fa29b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#get dressed atom energies for reduced training set\n",
    "y_dressed = {}\n",
    "for i,j in enumerate([1,6,7,8]):\n",
    "    y_dressed[j] = mlr.coef_[i]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a34590f",
   "metadata": {},
   "source": [
    "## Create test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e6c046ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import test set\n",
    "with open('raw_data/CSD-1k_hcno_w_DFTB.pickle', 'rb') as f:\n",
    "    csd_2k = pickle.load(f)\n",
    "#csd_2k = read('raw_data/CSD-1k_hcno_w_DFTB.pickle', ':')\n",
    "#Only get CHNO\n",
    "csd_1k = []\n",
    "for frm in csd_2k:\n",
    "    frm.info['Delta_energy'] = frm.info['PBE-D2_energy'] - frm.info['DFTB_energy']\n",
    "    frm.arrays['Delta_forces'] = frm.arrays['PBE-D2_forces'] - frm.arrays['DFTB_forces']\n",
    "    good = True\n",
    "    for elem in np.unique(frm.get_chemical_symbols()):\n",
    "        if elem not in ['H', 'C', 'N', 'O']:\n",
    "            good = False\n",
    "    if good == True:\n",
    "        csd_1k.append(frm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "6bcca38d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create composition (X), energy deltas (y) and force deltas (f) arrays\n",
    "X_test = np.zeros((len(csd_1k), 4), dtype=int) ; y_test = np.zeros(len(csd_1k), dtype=float)\n",
    "f_test = np.zeros((np.sum([len(frm) for frm in csd_1k]),3), dtype=float)\n",
    "\n",
    "counter = 0\n",
    "for ifrm, frm in enumerate(csd_1k):\n",
    "    symbols = frm.get_atomic_numbers()\n",
    "    X_test[ifrm] = [len(np.where(symbols == sp)[0]) for sp in [1,6,7,8]]\n",
    "    y_test[ifrm] = frm.info['PBE-D2_energy']\n",
    "    f_test[counter:counter+len(frm)] = frm.arrays['PBE-D2_forces']\n",
    "    counter += len(frm)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65633e0a",
   "metadata": {},
   "source": [
    "## Create Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "6d627986",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Define soap hyperparameters\n",
    "zeta=3\n",
    "hypers = dict(soap_type=\"PowerSpectrum\",\n",
    "              interaction_cutoff=6, #cutoff distance in angstroms\n",
    "              max_radial=9, #no. of radial basis functions\n",
    "              max_angular=6, #no. of angular basis functions\n",
    "              gaussian_sigma_constant=0.4, #sigma width (i.e. amount of 'smearing')\n",
    "              gaussian_sigma_type=\"Constant\",\n",
    "              cutoff_function_type=\"RadialScaling\",\n",
    "              cutoff_smooth_width=0.5,\n",
    "              cutoff_function_parameters=\n",
    "                    dict(\n",
    "                            rate=1,\n",
    "                            scale=2,\n",
    "                            exponent=4\n",
    "                        ),\n",
    "              radial_basis=\"GTO\",\n",
    "              normalize=True,\n",
    "              optimization=\n",
    "                    dict(\n",
    "                            Spline=dict(\n",
    "                               accuracy=1.0e-05\n",
    "                            )\n",
    "                        ),\n",
    "              compute_gradients=False #don't care about forces right now\n",
    "              )\n",
    "soap = SphericalInvariants(**hypers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "110d0cd1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computing feature matrices...\n",
      "Time taken:  491.1472556591034 s\n"
     ]
    }
   ],
   "source": [
    "# Compute feature matrices\n",
    "print(\"Computing feature matrices...\")\n",
    "start=time()\n",
    "manager_train = soap.transform(train_frames) #calculating features\n",
    "print(\"Time taken: \", time()-start, \"s\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "76f36656",
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_sparse_points(ns):\n",
    "    nsparse    = {1: ns, 6: ns, 7: ns, 8: ns}\n",
    "    compressor = FPSFilter(soap,nsparse,act_on='sample per species')\n",
    "    X_sparse = compressor.select_and_filter(manager_train)\n",
    "    dump_obj(f'learning_curve_data/X_sparse_PCovFPS_{ns}_696.json', X_sparse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "b17c4d9b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done for 500\n",
      "Calculated sparse points for n_sparse = 500\n",
      "Done for 1000\n",
      "Calculated sparse points for n_sparse = 1000\n",
      "Done for 1500\n",
      "Calculated sparse points for n_sparse = 1500\n",
      "Done for 2000\n",
      "Calculated sparse points for n_sparse = 2000\n",
      "Done for 2500\n",
      "Calculated sparse points for n_sparse = 2500\n",
      "Done for 3000\n",
      "Calculated sparse points for n_sparse = 3000\n",
      "Done for 3500\n",
      "Calculated sparse points for n_sparse = 3500\n",
      "Done for 4000\n",
      "Calculated sparse points for n_sparse = 4000\n"
     ]
    }
   ],
   "source": [
    "for i in range(500, 4001, 500):\n",
    "    calculate_sparse_points(i)\n",
    "    print(f'Calculated sparse points for n_sparse = {i}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "e3888dc0",
   "metadata": {},
   "outputs": [],
   "source": [
    "kernel = Kernel(soap, name='GAP', zeta=zeta, target_type='Structure', kernel_type='Sparse') #Define Kernel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4f6d6be2",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Compute kernel for all n_sparse\n",
    "for i in range(500, 4001, 500):\n",
    "    X_sparse = load_obj(f'learning_curve_data/X_sparse_PCovFPS_{i}_696.json')\n",
    "    Knm = np.zeros((len(train_frames), X_sparse.get_features().shape[0]), dtype=float)\n",
    "    start=time()\n",
    "    for ifrm, frm in enumerate(tqdm(train_frames, desc=f\"Computing KNM for n_sparse = {i}\", leave=False)):\n",
    "        feat = soap.transform(frm)\n",
    "        Knm[ifrm] = kernel(feat, X_sparse)\n",
    "    np.save(f'learning_curve_data/Knm_learning_curve_{i}', Knm)\n",
    "    print(f\"Knm saved using N sparse = {i}. Time taken :\", time()-start, \"s\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "5ccfdeca",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training GAP model using N_sparse=500 and N_train=2000...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Making predictions for test set...:   0%|          | 0/250 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE for N_sparse=500 and N_train=2000 : 0.04474911686521567\n",
      "Time taken: 57.273845 s\n",
      "\n",
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     "text": [
      "RMSE for N_sparse=500 and N_train=4000 : 0.017392658552732324\n",
      "Time taken: 50.98416 s\n",
      "\n",
      "Training GAP model using N_sparse=500 and N_train=6000...\n"
     ]
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      "RMSE for N_sparse=500 and N_train=6000 : 0.016759043469544214\n",
      "Time taken: 50.953467 s\n",
      "\n",
      "Training GAP model using N_sparse=500 and N_train=8000...\n"
     ]
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     "text": [
      "RMSE for N_sparse=500 and N_train=8000 : 0.015998318090743258\n",
      "Time taken: 46.57688 s\n",
      "\n",
      "Training GAP model using N_sparse=500 and N_train=10000...\n"
     ]
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     "text": [
      "RMSE for N_sparse=500 and N_train=10000 : 0.015805926824126463\n",
      "Time taken: 49.834427 s\n",
      "\n",
      "Training GAP model using N_sparse=500 and N_train=12000...\n"
     ]
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     "text": [
      "RMSE for N_sparse=500 and N_train=12000 : 0.015698847826189487\n",
      "Time taken: 65.710396 s\n",
      "\n",
      "Training GAP model using N_sparse=500 and N_train=14000...\n"
     ]
    },
    {
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     "text": [
      "RMSE for N_sparse=500 and N_train=14000 : 0.015904624983398084\n",
      "Time taken: 52.587997 s\n",
      "\n",
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     ]
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    {
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     "text": [
      "RMSE for N_sparse=500 and N_train=16000 : 0.01600553059250229\n",
      "Time taken: 47.743929 s\n",
      "\n",
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     ]
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     "text": [
      "RMSE for N_sparse=500 and N_train=18000 : 0.016077700495233112\n",
      "Time taken: 52.090206 s\n",
      "\n",
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     ]
    },
    {
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     "text": [
      "RMSE for N_sparse=500 and N_train=20000 : 0.016052636780244055\n",
      "Time taken: 51.902212 s\n",
      "\n",
      "Training GAP model using N_sparse=1000 and N_train=2000...\n"
     ]
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    {
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     "text": [
      "RMSE for N_sparse=1000 and N_train=2000 : 0.01884427716415382\n",
      "Time taken: 93.4545 s\n",
      "\n",
      "Training GAP model using N_sparse=1000 and N_train=4000...\n"
     ]
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    {
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     "text": [
      "RMSE for N_sparse=1000 and N_train=4000 : 0.027200693411847728\n",
      "Time taken: 93.928015 s\n",
      "\n",
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     ]
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     "text": [
      "RMSE for N_sparse=1000 and N_train=6000 : 0.021386500267007946\n",
      "Time taken: 95.226659 s\n",
      "\n",
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     ]
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     "text": [
      "RMSE for N_sparse=1000 and N_train=8000 : 0.018118673414529832\n",
      "Time taken: 97.708845 s\n",
      "\n",
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     ]
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     "text": [
      "RMSE for N_sparse=1000 and N_train=10000 : 0.01694793670002991\n",
      "Time taken: 94.909727 s\n",
      "\n",
      "Training GAP model using N_sparse=1000 and N_train=12000...\n"
     ]
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     "text": [
      "RMSE for N_sparse=1000 and N_train=12000 : 0.016372641145092796\n",
      "Time taken: 95.841299 s\n",
      "\n",
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     ]
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    {
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     "text": [
      "RMSE for N_sparse=1000 and N_train=14000 : 0.01615962254754772\n",
      "Time taken: 97.045477 s\n",
      "\n",
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     ]
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      "RMSE for N_sparse=1000 and N_train=16000 : 0.016271225837308414\n",
      "Time taken: 95.982339 s\n",
      "\n",
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     ]
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      "RMSE for N_sparse=1000 and N_train=18000 : 0.01623965280282031\n",
      "Time taken: 97.196959 s\n",
      "\n",
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     ]
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      "RMSE for N_sparse=1000 and N_train=20000 : 0.016307590136595197\n",
      "Time taken: 96.231286 s\n",
      "\n",
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     ]
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      "RMSE for N_sparse=1500 and N_train=2000 : 0.017051274100795683\n",
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      "\n",
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     ]
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      "RMSE for N_sparse=1500 and N_train=4000 : 0.01929763750302782\n",
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      "\n",
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     ]
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      "RMSE for N_sparse=1500 and N_train=6000 : 0.023520980384212138\n",
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     ]
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      "RMSE for N_sparse=1500 and N_train=8000 : 0.017065281734567294\n",
      "Time taken: 167.08891 s\n",
      "\n",
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     ]
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      "RMSE for N_sparse=1500 and N_train=10000 : 0.015763403565158873\n",
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      "\n",
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     ]
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      "RMSE for N_sparse=1500 and N_train=12000 : 0.015233064089835163\n",
      "Time taken: 157.318789 s\n",
      "\n",
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     ]
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      "RMSE for N_sparse=1500 and N_train=14000 : 0.013942538675610005\n",
      "Time taken: 163.95337 s\n",
      "\n",
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     ]
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      "RMSE for N_sparse=1500 and N_train=16000 : 0.01374016855207032\n",
      "Time taken: 158.680254 s\n",
      "\n",
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     ]
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      "RMSE for N_sparse=1500 and N_train=18000 : 0.013907177533089007\n",
      "Time taken: 154.935242 s\n",
      "\n",
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     ]
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      "RMSE for N_sparse=1500 and N_train=20000 : 0.0139857495890493\n",
      "Time taken: 169.643443 s\n",
      "\n",
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     ]
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     "text": [
      "RMSE for N_sparse=2000 and N_train=2000 : 0.016160347058798524\n",
      "Time taken: 211.254875 s\n",
      "\n",
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     ]
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      "RMSE for N_sparse=2000 and N_train=4000 : 0.01822060639846712\n",
      "Time taken: 206.241188 s\n",
      "\n",
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     ]
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      "RMSE for N_sparse=2000 and N_train=6000 : 0.019006295414358408\n",
      "Time taken: 211.608876 s\n",
      "\n",
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     "text": [
      "RMSE for N_sparse=2000 and N_train=8000 : 0.017872542005953077\n",
      "Time taken: 205.145809 s\n",
      "\n",
      "Training GAP model using N_sparse=2000 and N_train=10000...\n"
     ]
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     "text": [
      "RMSE for N_sparse=2000 and N_train=10000 : 0.01698105009890131\n",
      "Time taken: 204.195159 s\n",
      "\n",
      "Training GAP model using N_sparse=2000 and N_train=12000...\n"
     ]
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     "text": [
      "RMSE for N_sparse=2000 and N_train=12000 : 0.015963058219578285\n",
      "Time taken: 208.70991 s\n",
      "\n",
      "Training GAP model using N_sparse=2000 and N_train=14000...\n"
     ]
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     "text": [
      "RMSE for N_sparse=2000 and N_train=14000 : 0.015052024208715264\n",
      "Time taken: 210.307071 s\n",
      "\n",
      "Training GAP model using N_sparse=2000 and N_train=16000...\n"
     ]
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     "text": [
      "RMSE for N_sparse=2000 and N_train=16000 : 0.014785024245317205\n",
      "Time taken: 247.943376 s\n",
      "\n",
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     ]
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     "text": [
      "RMSE for N_sparse=2000 and N_train=18000 : 0.01446898817593982\n",
      "Time taken: 223.44241 s\n",
      "\n",
      "Training GAP model using N_sparse=2000 and N_train=20000...\n"
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     "text": [
      "RMSE for N_sparse=2000 and N_train=20000 : 0.013754029235336323\n",
      "Time taken: 217.380264 s\n",
      "\n",
      "Training GAP model using N_sparse=2500 and N_train=2000...\n"
     ]
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     "text": [
      "RMSE for N_sparse=2500 and N_train=2000 : 0.015596735516599984\n",
      "Time taken: 266.31238 s\n",
      "\n",
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     ]
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     "text": [
      "RMSE for N_sparse=2500 and N_train=4000 : 0.01710455040073881\n",
      "Time taken: 325.540094 s\n",
      "\n",
      "Training GAP model using N_sparse=2500 and N_train=6000...\n"
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     "text": [
      "RMSE for N_sparse=2500 and N_train=6000 : 0.017827937983194535\n",
      "Time taken: 277.015832 s\n",
      "\n",
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     "text": [
      "RMSE for N_sparse=2500 and N_train=8000 : 0.018188645432757906\n",
      "Time taken: 275.24958 s\n",
      "\n",
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     "text": [
      "RMSE for N_sparse=2500 and N_train=10000 : 0.01745624018583442\n",
      "Time taken: 284.535 s\n",
      "\n",
      "Training GAP model using N_sparse=2500 and N_train=12000...\n"
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     "text": [
      "RMSE for N_sparse=2500 and N_train=12000 : 0.01631023126386724\n",
      "Time taken: 288.623395 s\n",
      "\n",
      "Training GAP model using N_sparse=2500 and N_train=14000...\n"
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     "text": [
      "RMSE for N_sparse=2500 and N_train=14000 : 0.016444405205842852\n",
      "Time taken: 281.210669 s\n",
      "\n",
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     "text": [
      "RMSE for N_sparse=2500 and N_train=16000 : 0.015981427619125004\n",
      "Time taken: 282.961491 s\n",
      "\n",
      "Training GAP model using N_sparse=2500 and N_train=18000...\n"
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     "text": [
      "RMSE for N_sparse=2500 and N_train=18000 : 0.01543334038136208\n",
      "Time taken: 281.749068 s\n",
      "\n",
      "Training GAP model using N_sparse=2500 and N_train=20000...\n"
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     "text": [
      "RMSE for N_sparse=2500 and N_train=20000 : 0.014749884532900317\n",
      "Time taken: 292.611565 s\n",
      "\n",
      "Training GAP model using N_sparse=3000 and N_train=2000...\n"
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     "text": [
      "RMSE for N_sparse=3000 and N_train=2000 : 0.015596157663599675\n",
      "Time taken: 392.964633 s\n",
      "\n",
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     "text": [
      "RMSE for N_sparse=3000 and N_train=4000 : 0.016742557048281415\n",
      "Time taken: 344.190443 s\n",
      "\n",
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      "RMSE for N_sparse=3000 and N_train=6000 : 0.017392994428025946\n",
      "Time taken: 378.122519 s\n",
      "\n",
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      "RMSE for N_sparse=3000 and N_train=8000 : 0.017289780419828482\n",
      "Time taken: 359.551313 s\n",
      "\n",
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     "text": [
      "RMSE for N_sparse=3000 and N_train=10000 : 0.016097370291550447\n",
      "Time taken: 384.235544 s\n",
      "\n",
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     "text": [
      "RMSE for N_sparse=3000 and N_train=12000 : 0.016908451962791116\n",
      "Time taken: 350.372857 s\n",
      "\n",
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     "text": [
      "RMSE for N_sparse=3000 and N_train=14000 : 0.016944146379946706\n",
      "Time taken: 353.948761 s\n",
      "\n",
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     "text": [
      "RMSE for N_sparse=3000 and N_train=16000 : 0.016572547913100956\n",
      "Time taken: 342.282206 s\n",
      "\n",
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     "text": [
      "RMSE for N_sparse=3000 and N_train=18000 : 0.01589348424686457\n",
      "Time taken: 342.842676 s\n",
      "\n",
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     "text": [
      "RMSE for N_sparse=3000 and N_train=20000 : 0.014940915215178555\n",
      "Time taken: 347.16153 s\n",
      "\n",
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     "text": [
      "RMSE for N_sparse=3500 and N_train=2000 : 0.015433690141798226\n",
      "Time taken: 496.331522 s\n",
      "\n",
      "Training GAP model using N_sparse=3500 and N_train=4000...\n"
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     "text": [
      "RMSE for N_sparse=3500 and N_train=4000 : 0.016377917139289537\n",
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      "\n",
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     "text": [
      "RMSE for N_sparse=3500 and N_train=6000 : 0.016961417471298073\n",
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      "\n",
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      "RMSE for N_sparse=3500 and N_train=8000 : 0.016000404888217728\n",
      "Time taken: 417.268245 s\n",
      "\n",
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      "RMSE for N_sparse=3500 and N_train=10000 : 0.015664873713241968\n",
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      "\n",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE for N_sparse=3500 and N_train=12000 : 0.0160718371427035\n",
      "Time taken: 468.032864 s\n",
      "\n",
      "Training GAP model using N_sparse=3500 and N_train=14000...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE for N_sparse=3500 and N_train=14000 : 0.017028342349708847\n",
      "Time taken: 465.219663 s\n",
      "\n",
      "Training GAP model using N_sparse=3500 and N_train=16000...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE for N_sparse=3500 and N_train=16000 : 0.016093239441495478\n",
      "Time taken: 481.906879 s\n",
      "\n",
      "Training GAP model using N_sparse=3500 and N_train=18000...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE for N_sparse=3500 and N_train=18000 : 0.016004132036110792\n",
      "Time taken: 458.808002 s\n",
      "\n",
      "Training GAP model using N_sparse=3500 and N_train=20000...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE for N_sparse=3500 and N_train=20000 : 0.014884261645590199\n",
      "Time taken: 451.757903 s\n",
      "\n",
      "Training GAP model using N_sparse=4000 and N_train=2000...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE for N_sparse=4000 and N_train=2000 : 0.015313045113365151\n",
      "Time taken: 489.798233 s\n",
      "\n",
      "Training GAP model using N_sparse=4000 and N_train=4000...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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      ]
     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE for N_sparse=4000 and N_train=4000 : 0.016130453125940157\n",
      "Time taken: 487.275652 s\n",
      "\n",
      "Training GAP model using N_sparse=4000 and N_train=6000...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE for N_sparse=4000 and N_train=6000 : 0.016371860029461633\n",
      "Time taken: 514.037921 s\n",
      "\n",
      "Training GAP model using N_sparse=4000 and N_train=8000...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE for N_sparse=4000 and N_train=8000 : 0.015373172291759218\n",
      "Time taken: 537.268956 s\n",
      "\n",
      "Training GAP model using N_sparse=4000 and N_train=10000...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE for N_sparse=4000 and N_train=10000 : 0.014799969701013617\n",
      "Time taken: 519.781794 s\n",
      "\n",
      "Training GAP model using N_sparse=4000 and N_train=12000...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE for N_sparse=4000 and N_train=12000 : 0.014366234636147194\n",
      "Time taken: 595.684417 s\n",
      "\n",
      "Training GAP model using N_sparse=4000 and N_train=14000...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE for N_sparse=4000 and N_train=14000 : 0.014612305914612045\n",
      "Time taken: 657.62026 s\n",
      "\n",
      "Training GAP model using N_sparse=4000 and N_train=16000...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
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       "version_minor": 0
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE for N_sparse=4000 and N_train=16000 : 0.014761256685343135\n",
      "Time taken: 545.860454 s\n",
      "\n",
      "Training GAP model using N_sparse=4000 and N_train=18000...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
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       "version_minor": 0
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE for N_sparse=4000 and N_train=18000 : 0.01509733157175387\n",
      "Time taken: 518.810282 s\n",
      "\n",
      "Training GAP model using N_sparse=4000 and N_train=20000...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE for N_sparse=4000 and N_train=20000 : 0.014483022416708236\n",
      "Time taken: 518.91681 s\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#Train and test GAP model using various slices of training frames and testing on the first 100 CSD-1k structures\n",
    "spar_low=500; spar_high=4001; spar_gap=500\n",
    "ntrain_low=2000; ntrain_high=20001; ntrain_gap=2000\n",
    "\n",
    "for ni, i in enumerate(range(spar_low, spar_high, spar_gap)):\n",
    "    K_NM_total = np.load(f'learning_curve_data/Knm_learning_curve_{i}.npy') #import full KNM for this number of sparse points\n",
    "    X_sparse = load_obj(f'learning_curve_data/X_sparse_PCovFPS_{i}_696.json') #Import X_sparse for this number of sparse points\n",
    "    results=np.zeros((len(range(ntrain_low, ntrain_high, ntrain_gap)), 2), dtype=float)\n",
    "    \n",
    "    for nj, j in enumerate(range(ntrain_low, ntrain_high, ntrain_gap)):\n",
    "        start = time()\n",
    "        K_NM = K_NM_total[:j] #get correct slice of KNM\n",
    "        print(f\"Training GAP model using N_sparse={i} and N_train={j}...\")\n",
    "        start = time()\n",
    "    \n",
    "        model = train_gap_model(kernel, train_frames[:j], K_NM, X_sparse, y_train[:j], y_dressed, \n",
    "                                grad_train=None, lambdas=[0.01], jitter=1e-9, solver = 'RKHS') #train model\n",
    "        \n",
    "        #test model on full CSD-1k set (only on first 250 structures)\n",
    "        npred=250\n",
    "        ytp = []\n",
    "        for frm in tqdm(csd_1k[:npred], desc = \"Making predictions for test set...\", leave=False):\n",
    "            manager_test = soap.transform(frm)\n",
    "            ytp.append(model.predict(manager_test))\n",
    "\n",
    "        y_test_pred = np.array(ytp).flatten()\n",
    "        rmse = np.sqrt(np.mean(((y_test[:npred] - mlr.predict(X_test[:npred]))/np.sum(X_test[:npred],axis=1) \n",
    "                               - (y_test_pred[:npred] - mlr.predict(X_test[:npred]))/np.sum(X_test[:npred],axis=1))**2))\n",
    "        \n",
    "        print(f\"RMSE for N_sparse={i} and N_train={j} : {rmse}\")\n",
    "        \n",
    "        results[nj] = [float(j), rmse]\n",
    "        np.save(f'learning_curve_data/predictions/0.01_reg/y_test_pred_S{i}_N{j}', y_test_pred)\n",
    "        \n",
    "        print(\"Time taken:\", np.round(time()-start, 6), \"s\")\n",
    "        print(\"\")\n",
    "    \n",
    "    np.save(f'learning_curve_data/rmses/0.01_reg/RMSEs_{i}_sparse_points', results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "d00fe559",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training GAP model using N_sparse=500 and N_train=2000...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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       "version_minor": 0
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE : 0.034443884051348767\n",
      "Time taken: 45.790192 s\n",
      "\n",
      "Training GAP model using N_sparse=500 and N_train=4000...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
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       "version_minor": 0
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE : 0.01733368486430909\n",
      "Time taken: 47.085859 s\n",
      "\n",
      "Training GAP model using N_sparse=500 and N_train=6000...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
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       "version_minor": 0
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE : 0.01673256659832386\n",
      "Time taken: 48.927427 s\n",
      "\n",
      "Training GAP model using N_sparse=500 and N_train=8000...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
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       "version_minor": 0
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE : 0.0159828177591577\n",
      "Time taken: 44.44847 s\n",
      "\n",
      "Training GAP model using N_sparse=500 and N_train=10000...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
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       "version_minor": 0
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE : 0.0157931998975896\n",
      "Time taken: 50.473994 s\n",
      "\n",
      "Training GAP model using N_sparse=500 and N_train=12000...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
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       "version_minor": 0
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE : 0.015687645950870393\n",
      "Time taken: 46.075531 s\n",
      "\n",
      "Training GAP model using N_sparse=500 and N_train=14000...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
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       "version_minor": 0
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE : 0.015896568492178225\n",
      "Time taken: 46.07317 s\n",
      "\n",
      "Training GAP model using N_sparse=500 and N_train=16000...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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       "version_minor": 0
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE : 0.015997963104292218\n",
      "Time taken: 49.005035 s\n",
      "\n",
      "Training GAP model using N_sparse=500 and N_train=18000...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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       "version_minor": 0
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE : 0.01607141848495364\n",
      "Time taken: 59.367811 s\n",
      "\n",
      "Training GAP model using N_sparse=500 and N_train=20000...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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       "version_minor": 0
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE : 0.01604702880859275\n",
      "Time taken: 43.02018 s\n",
      "\n",
      "Saved RMSEs for 500 sparse points\n",
      "\n",
      "\n",
      "Training GAP model using N_sparse=1000 and N_train=2000...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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       "version_minor": 0
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE : 0.01879428434564959\n",
      "Time taken: 89.486737 s\n",
      "\n",
      "Training GAP model using N_sparse=1000 and N_train=4000...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE : 0.0220976362339825\n",
      "Time taken: 93.141049 s\n",
      "\n",
      "Training GAP model using N_sparse=1000 and N_train=6000...\n"
     ]
    },
    {
     "data": {
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE : 0.02061320745128857\n",
      "Time taken: 95.43188 s\n",
      "\n",
      "Training GAP model using N_sparse=1000 and N_train=8000...\n"
     ]
    },
    {
     "data": {
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     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE : 0.01790255210062641\n",
      "Time taken: 96.115762 s\n",
      "\n",
      "Training GAP model using N_sparse=1000 and N_train=10000...\n"
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      "RMSE : 0.01684872036467483\n",
      "Time taken: 95.071982 s\n",
      "\n",
      "Training GAP model using N_sparse=1000 and N_train=12000...\n"
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     "text": [
      "RMSE : 0.01631370316539334\n",
      "Time taken: 99.307029 s\n",
      "\n",
      "Training GAP model using N_sparse=1000 and N_train=14000...\n"
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     "text": [
      "RMSE : 0.016107124511940508\n",
      "Time taken: 98.362662 s\n",
      "\n",
      "Training GAP model using N_sparse=1000 and N_train=16000...\n"
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     "text": [
      "RMSE : 0.016226078583119207\n",
      "Time taken: 96.589701 s\n",
      "\n",
      "Training GAP model using N_sparse=1000 and N_train=18000...\n"
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     "text": [
      "RMSE : 0.016203715493016118\n",
      "Time taken: 100.622207 s\n",
      "\n",
      "Training GAP model using N_sparse=1000 and N_train=20000...\n"
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      "RMSE : 0.016280221824684363\n",
      "Time taken: 101.915618 s\n",
      "\n",
      "Saved RMSEs for 1000 sparse points\n",
      "\n",
      "\n",
      "Training GAP model using N_sparse=1500 and N_train=2000...\n"
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      "Time taken: 190.85778 s\n",
      "\n",
      "Training GAP model using N_sparse=1500 and N_train=4000...\n"
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     "text": [
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      "Time taken: 184.790398 s\n",
      "\n",
      "Training GAP model using N_sparse=1500 and N_train=6000...\n"
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     "text": [
      "RMSE : 0.019379461086662917\n",
      "Time taken: 157.015654 s\n",
      "\n",
      "Training GAP model using N_sparse=1500 and N_train=8000...\n"
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      "RMSE : 0.01596380833099353\n",
      "Time taken: 146.337155 s\n",
      "\n",
      "Training GAP model using N_sparse=1500 and N_train=10000...\n"
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      "Time taken: 154.594914 s\n",
      "\n",
      "Training GAP model using N_sparse=1500 and N_train=12000...\n"
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      "RMSE : 0.015004968089367682\n",
      "Time taken: 156.465153 s\n",
      "\n",
      "Training GAP model using N_sparse=1500 and N_train=14000...\n"
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      "\n",
      "Training GAP model using N_sparse=1500 and N_train=16000...\n"
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      "\n",
      "Training GAP model using N_sparse=1500 and N_train=18000...\n"
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      "RMSE : 0.013847684858336382\n",
      "Time taken: 155.715054 s\n",
      "\n",
      "Training GAP model using N_sparse=1500 and N_train=20000...\n"
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      "\n",
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      "\n",
      "Training GAP model using N_sparse=2000 and N_train=4000...\n"
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      "Time taken: 199.844655 s\n",
      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "RMSE : 0.014808913742414487\n",
      "Time taken: 266.06336 s\n",
      "\n",
      "Training GAP model using N_sparse=2500 and N_train=18000...\n"
     ]
    },
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE : 0.014591566099594848\n",
      "Time taken: 274.373828 s\n",
      "\n",
      "Training GAP model using N_sparse=2500 and N_train=20000...\n"
     ]
    },
    {
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE : 0.014194525165510364\n",
      "Time taken: 272.803511 s\n",
      "\n",
      "Saved RMSEs for 2500 sparse points\n",
      "\n",
      "\n",
      "Training GAP model using N_sparse=3000 and N_train=2000...\n"
     ]
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     "text": [
      "RMSE : 0.01559010091740165\n",
      "Time taken: 334.519853 s\n",
      "\n",
      "Training GAP model using N_sparse=3000 and N_train=4000...\n"
     ]
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE : 0.016634402584972948\n",
      "Time taken: 335.498544 s\n",
      "\n",
      "Training GAP model using N_sparse=3000 and N_train=6000...\n"
     ]
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    {
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE : 0.0168708838727514\n",
      "Time taken: 331.818626 s\n",
      "\n",
      "Training GAP model using N_sparse=3000 and N_train=8000...\n"
     ]
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    {
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     "output_type": "stream",
     "text": [
      "RMSE : 0.01585722829436709\n",
      "Time taken: 334.684443 s\n",
      "\n",
      "Training GAP model using N_sparse=3000 and N_train=10000...\n"
     ]
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    {
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     "output_type": "stream",
     "text": [
      "RMSE : 0.014750276546187055\n",
      "Time taken: 351.479439 s\n",
      "\n",
      "Training GAP model using N_sparse=3000 and N_train=12000...\n"
     ]
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     "output_type": "stream",
     "text": [
      "RMSE : 0.01450255415573452\n",
      "Time taken: 339.838127 s\n",
      "\n",
      "Training GAP model using N_sparse=3000 and N_train=14000...\n"
     ]
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE : 0.014925280441433611\n",
      "Time taken: 341.199785 s\n",
      "\n",
      "Training GAP model using N_sparse=3000 and N_train=16000...\n"
     ]
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     "output_type": "stream",
     "text": [
      "RMSE : 0.014538904291051194\n",
      "Time taken: 337.248355 s\n",
      "\n",
      "Training GAP model using N_sparse=3000 and N_train=18000...\n"
     ]
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE : 0.014427865391382234\n",
      "Time taken: 351.349971 s\n",
      "\n",
      "Training GAP model using N_sparse=3000 and N_train=20000...\n"
     ]
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     "text": [
      "RMSE : 0.013951304236404404\n",
      "Time taken: 351.442954 s\n",
      "\n",
      "Saved RMSEs for 3000 sparse points\n",
      "\n",
      "\n",
      "Training GAP model using N_sparse=3500 and N_train=2000...\n"
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     "output_type": "stream",
     "text": [
      "RMSE : 0.015428734806620367\n",
      "Time taken: 416.673888 s\n",
      "\n",
      "Training GAP model using N_sparse=3500 and N_train=4000...\n"
     ]
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE : 0.016290834556936147\n",
      "Time taken: 404.838768 s\n",
      "\n",
      "Training GAP model using N_sparse=3500 and N_train=6000...\n"
     ]
    },
    {
     "data": {
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE : 0.016533178410558978\n",
      "Time taken: 433.142729 s\n",
      "\n",
      "Training GAP model using N_sparse=3500 and N_train=8000...\n"
     ]
    },
    {
     "data": {
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE : 0.01511590464104271\n",
      "Time taken: 434.934945 s\n",
      "\n",
      "Training GAP model using N_sparse=3500 and N_train=10000...\n"
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE : 0.014537417536224891\n",
      "Time taken: 483.489578 s\n",
      "\n",
      "Training GAP model using N_sparse=3500 and N_train=12000...\n"
     ]
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    {
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     "output_type": "stream",
     "text": [
      "RMSE : 0.014269885746930805\n",
      "Time taken: 419.559547 s\n",
      "\n",
      "Training GAP model using N_sparse=3500 and N_train=14000...\n"
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    {
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     "output_type": "stream",
     "text": [
      "RMSE : 0.014885465496334316\n",
      "Time taken: 417.699314 s\n",
      "\n",
      "Training GAP model using N_sparse=3500 and N_train=16000...\n"
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    {
     "data": {
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     "text": [
      "RMSE : 0.01409804006535623\n",
      "Time taken: 419.918037 s\n",
      "\n",
      "Training GAP model using N_sparse=3500 and N_train=18000...\n"
     ]
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    {
     "data": {
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE : 0.014214155002420382\n",
      "Time taken: 422.034024 s\n",
      "\n",
      "Training GAP model using N_sparse=3500 and N_train=20000...\n"
     ]
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    {
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     "text": [
      "RMSE : 0.013715389136209703\n",
      "Time taken: 428.785269 s\n",
      "\n",
      "Saved RMSEs for 3500 sparse points\n",
      "\n",
      "\n",
      "Training GAP model using N_sparse=4000 and N_train=2000...\n"
     ]
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    {
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     "output_type": "stream",
     "text": [
      "RMSE : 0.015308738733107835\n",
      "Time taken: 488.560271 s\n",
      "\n",
      "Training GAP model using N_sparse=4000 and N_train=4000...\n"
     ]
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    {
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE : 0.016056430511438662\n",
      "Time taken: 483.396353 s\n",
      "\n",
      "Training GAP model using N_sparse=4000 and N_train=6000...\n"
     ]
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    {
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE : 0.016037661561061487\n",
      "Time taken: 493.062907 s\n",
      "\n",
      "Training GAP model using N_sparse=4000 and N_train=8000...\n"
     ]
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    {
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     "text": [
      "RMSE : 0.01478121805147613\n",
      "Time taken: 513.403887 s\n",
      "\n",
      "Training GAP model using N_sparse=4000 and N_train=10000...\n"
     ]
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    {
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     "text": [
      "RMSE : 0.01404491066087965\n",
      "Time taken: 492.009734 s\n",
      "\n",
      "Training GAP model using N_sparse=4000 and N_train=12000...\n"
     ]
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     "output_type": "stream",
     "text": [
      "RMSE : 0.013366080550634218\n",
      "Time taken: 513.922554 s\n",
      "\n",
      "Training GAP model using N_sparse=4000 and N_train=14000...\n"
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    {
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE : 0.013810577440636924\n",
      "Time taken: 531.983113 s\n",
      "\n",
      "Training GAP model using N_sparse=4000 and N_train=16000...\n"
     ]
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    {
     "data": {
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     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE : 0.013165897614464116\n",
      "Time taken: 502.789961 s\n",
      "\n",
      "Training GAP model using N_sparse=4000 and N_train=18000...\n"
     ]
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    {
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     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE : 0.013442707111295875\n",
      "Time taken: 507.728656 s\n",
      "\n",
      "Training GAP model using N_sparse=4000 and N_train=20000...\n"
     ]
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    {
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     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE : 0.013219707090291506\n",
      "Time taken: 499.639566 s\n",
      "\n",
      "Saved RMSEs for 4000 sparse points\n",
      "\n",
      "\n"
     ]
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   ],
   "source": [
    "#Train and test GAP model using various slices of training frames and testing on the first 100 CSD-1k structures\n",
    "spar_low=500; spar_high=4001; spar_gap=500\n",
    "ntrain_low=2000; ntrain_high=20001; ntrain_gap=2000\n",
    "\n",
    "for ni, i in enumerate(range(spar_low, spar_high, spar_gap)):\n",
    "    K_NM_total = np.load(f'learning_curve_data/Knm_learning_curve_{i}.npy') #import full KNM for this number of sparse points\n",
    "    X_sparse = load_obj(f'learning_curve_data/X_sparse_PCovFPS_{i}_696.json') #Import X_sparse for this number of sparse points\n",
    "    results=np.zeros((len(range(ntrain_low, ntrain_high, ntrain_gap)), 2), dtype=float)\n",
    "    \n",
    "    for nj, j in enumerate(range(ntrain_low, ntrain_high, ntrain_gap)):\n",
    "        start = time()\n",
    "        K_NM = K_NM_total[:j] #get correct slice of KNM\n",
    "        print(f\"Training GAP model using N_sparse={i} and N_train={j}...\")\n",
    "        start = time()\n",
    "    \n",
    "        model = train_gap_model(kernel, train_frames[:j], K_NM, X_sparse, y_train[:j], y_dressed, \n",
    "                                grad_train=None, lambdas=[0.02], jitter=1e-9, solver = 'RKHS') #train model\n",
    "        \n",
    "        #test model on full CSD-1k set (only on first 250 structures)\n",
    "        npred=250\n",
    "        ytp = []\n",
    "        for frm in tqdm(csd_1k[:npred], desc = \"Making predictions for test set...\", leave=False):\n",
    "            manager_test = soap.transform(frm)\n",
    "            ytp.append(model.predict(manager_test))\n",
    "\n",
    "        y_test_pred = np.array(ytp).flatten()\n",
    "        rmse = np.sqrt(np.mean(((y_test[:npred] - mlr.predict(X_test[:npred]))/np.sum(X_test[:npred],axis=1) \n",
    "                               - (y_test_pred[:npred] - mlr.predict(X_test[:npred]))/np.sum(X_test[:npred],axis=1))**2))\n",
    "        \n",
    "        print(f\"RMSE : {rmse}\")\n",
    "        \n",
    "        results[nj] = [float(j), rmse]\n",
    "        np.save(f'learning_curve_data/predictions/0.02_reg/y_test_pred_S{i}_N{j}', y_test_pred)\n",
    "        \n",
    "        print(\"Time taken:\", np.round(time()-start, 6), \"s\")\n",
    "        print(\"\")\n",
    "    \n",
    "    np.save(f'learning_curve_data/rmses/0.02_reg/RMSEs_{i}_sparse_points', results)\n",
    "    print(f\"Saved RMSEs for {i} sparse points\")\n",
    "    print(\"\")\n",
    "    print(\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68dda017",
   "metadata": {},
   "source": [
    "## Plot Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a9274736",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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MPaoR8dTV3hJg75Rt/DJH/b+mrNuog/IKiPvqHHWfop4/UjPqVpH8gZMr/tR/lDTivW5P7nkj9ixGfyljGlOI/QWFTdI16vtEkpTIbifvuf6omWBYvX9Yu4E6Zdkfktw9lbn94zRiEvTq70TeCc08+xiV4/0ZWaTPcb3f41K+briPLdk+NkccXUiupM7sezmwQR5tBGBajueQ6iRbdRvdGhl/o49X6vn8NOpYpYmf5Vyfw0+pJ9mbVf93dXwPNmmgXvZ+YUwjn/fIHP33bqBOs9k3V/fz3RzPcUSO7W7N2ubFFG3fkKPtHYr9nFxa3lLPfi0CF6Vs46Dq34Hs+l9poF7Z/u4txv68ie9DKY+hWut7vn+OeFfRiAs7SS6w2KiBbYbl6G9YirZL/hqV8vPn0rIX56STJLUkvyCZsHq1j4EDY4zz0zYQY3wJ+F5W8f4hhO1T1E3dT1a9VSRj2L+fUdwJOLYx7WW4NMZ4RxPbyPSTGONjKbf9A8mQY5n2K2As+Sh63CGEtYFvZRXPB46OMS5qqH5Mhpb8NsnwIKW0CbnnjZhR4jiag8Z8n24iufoyU655pOpUx7B2/4oNDy9X8v1hCKEryd1Omf4aY/KXZz5ijCtiMrdfi9cCXjf3sSnFGBcCt2cVV1H7udVnL5K7AzO9FGNMPVxfhR2vFPpYpV7Vw3WdnuOhkTHGl9O0EWP8FfBwVvFa5D8fUUk0031M9vCqkNydni17iNftQghb5Nn2MmBy2sCkFMbEGH/R8GYQY3wAuCDHQ9lDpmcr69+9dSjp/hwqav/W0t/z/8lRdlGM8Y9p+86IYUmMsVh/a5b0Naqgz59aAJN0kqQWIYTQm9pzNp0TY5zbiOZuJpmPJNPwRrSTWoxxCZD9R83uTWhyAcmVfoUyDfhT2o1jjMtJJk/OtGkIYYMCxpRGqeI+HuicVfa/+fwBUn2i+Zdpty+Q7nWUzytlEM1Ao75PMcY51J5rav8QwkZ5NDOC2sfsN9RXoYz7w1yTwH/aiD5bm+b8urmPzd/1OcrySd5/J2WbRVHg45VCH6ukcQTJXDCZHowx3p9nO6eT3DWS6ZTqJGClaY77mB2y1pcAueaae7D6sUxH1NVoHXPdTfbEqAqsoWRLtotJhrrLdGgIIXtfBVTs373l2J9D5ezfWux7HkLYAxiSVTyFZOjtilGm16hSPn9qAUzSSZJaiuEkV6OvtohkCJy8VV/59FBW8Z6NCysv2QeCuzWhrduqr9gvlL9VX0Gfj//mKPtyIYLJQ6nizr7TaSVwY579QjIc27xG1GusjnWUN+ouixasKd+n7IRaFfDNPOqPyFp/h2TItPoMpzz7wzkkw7xkasrFBq1Fc37d3MfmKcY4Fng9q7h/CGGXhupW31F4eFbxUpK7dkupUMcrhT5WSeNrOcr+nG8jMca3gUeyiteldnKpEjSrfUwIoT3JkGWZXqq+I7aG6s9P9vtwZD3Nb0fN30eA1HehSik8G2N8JZ8K1Rc//COruArYp44qw6m8v3vLsT+Hyti/tfT3/JAcZZfk2ieX2XBK/xpVwudPLYRJOklSS5F90PRijHFxE9p7J2t9UL4NhBDWDSEMDyH8Twjh5hDCQyGEZ0IIk0IIL2Yv1L7C/0uNDR54ogl1c3myEXXezlHWramB5KlUcWefoJwUY/w4346r/2BLO2xcIeQa6hJq/7HR2jXl+/QotYf3S3XXTAjhK0D2sF2jUgyhUpb9YfXnN3u4uB+HEOq8q0HN/nVzH9s4f8tRlma/cCy1L664p/qu3UYr4/FKoY9V0hiatb6I2kNXppU9dGmu9suuGe5jBlJzuDLIPdTlav/KWt+5eqjoXNIOoyk11j2NrJc9dCvUfQFExf3dS3n255Wyf7unkfWay3s+LGt9OY1MfhVZyV+jCvn8qYXIvoJIkqTmKvukSP/qE0mN1SNrvVsIoV31UF31CiHsDZxFMs9OU4Y9qgohdGnkVYmFPuGQfdV8Grnuxip1kq7ocYcQ1gE2zSpuyus/iXqGaiqwz+soXweYWaIYmoNGv58xxpUhhL+TzJGw2jYhhJ1jjM83UH1kdnOku3uonPvDG4DLMtY7AHeGEJ4lif2BGGP28D5qvq+b+9jG+TtwITX/Hj82hPDDGGNd+2XInchr9FCXFXC8UtLkSAihM7BVVvGkGGP2sJVp5dqHV+KddNC89jG5XsP6Piv3k5w0zvwMH07N51tf295Jp0Jq7OfpFWp/jnMllaGC/u7NUM5kd7n3by32PQ8hrEXt5FRTk1/FUq7vRbk/f2ohTNJJkpq96mFx1s8qXqd6KaQe1JO4qB6G6jpqj4XeFN2AxiTpZhUwBkiGcshXrj/sSj1XSyniXjdH2buN6He17Kv2iqmu12cdTNJlaur36QZqJukgOdleZ5Ku+mTyUVnFj8cYp9fXUQXsD68BjgOyh+7brXq5OoTwFvAMMKH635cbM8F6C9NcXzf3sY0QY5wZQniAmvObdAMOI5kHpZYQwjbArlnF02nEnYEVdLxS6GOVhqxL7TvIc81zltbrwCpqjlC0XhPaK6bmtI/J6263GOO8EMLjJMnm1Y4kd5Iuu+0VJCfKpUJ5ozGVYoxLQwjvAn0zimvN11oBx3l1KfX+PFO5928t+T1fD2ibVVZx+8wyv0bl/vyphXC4S0lSS5DrBF4x1DV/1+oTXo9Q2BNe0Pik1meFDCLPKykrRoni7p6jrClzupVyPrgPyT205YYljKE5aNL3KcY4FRibVfyN6qtT63Ik0CWrLNcQednKuj+MMS4Fvg6MrqfuliR3CV4FvAh8EkL4RwjhwBBCq/z7pLm+bu5jmyTXHXDfqWf7XI/dkO+cgBV2vFLQY5UUcn2W5jW2serXPvs5ZF95XxGa2T4m+263ZcDkBupkD3k5JITQM7Og+je3f9Z2U6qHK5MKpZC/T+vk2Kbsf/fWodT78zUqYP/Wkt/zXL9pc4sdSCOU7TWqgM+fWgg/CJKkliDXSZdSu4TcY8hPBS4lORm2C7AxydXma8UYQ+ZCynmq0qjAiZxbslyJlmVNaG9pE+rmJca4jNx3pOxUqhiagwJ9n7ITbN2peRdNtuz9wXzg7hT9lH1/GGP8BNgXOIZ0QwD1AL4J/Bt4tYTzOOR6XzsUqO1cJzrq/Rw1o9et1JrtPrYBDwEfZZV9NYTQK3vDEEIVyXudKQKjGtFvxRyvlOFYpWuOskVNbDO7fq4+KkJz2MeEENoB22YVT64+XqnPPUDmsKWB5M7UTNuS31x3UmM0ZZ+SZn9S9uO8XMr9t2eZ928t+T1fO0dZY0b5Kbayfi+aw++rKp9JOklSS5Br/pbLsk8qFWB5N1fnIYRtqX3CaiHJgdeXY4w/jDHeEWN8Psb4cYzxszpONuR7xaIqQ66rJ5tyki7XH0PFlOsPiezhOtR0t1P7D/GcJ7pDCJsDX8kqvrWBuapWK+v+cLWYuD3GuBOwDfBjknmDPm0g/i+TzONwdQghe1i6QpuXoyz77sXGytVOg1ceN5PXrdSa+z42p+p50LLnmAzUnosSkiu0s+9wHt3Q8LfZPF5hQY6yzk1sM7t+rj4qRjPYx/SndmK+wURajHE28HRW8ZFZ67mG0XQ+OhVaU/YpafYnFXGcV4nKuH9rye95rjskC3WsXEhl/140g99XVTiTdJKkluCTHGWbl7D/Y6j9mzoixnhTjHmNNV6RQySpQblOvDdlyI1SDdex2pgcZV8JIWTPP9DclHr+w3rFGBcCd2YVfy2EsEmOzUdSe96kNENdQvn3h7XEGF+LMf4xxnhIjHE9YCvgROAf5I4X4DTgl0UOLdd3d50CtZ2rnbyGB6rg163Umvs+tj5/o/aQwyNznKTJNdRlruEyG9Laj1cK+p2vHqIqO+nbmHka81WQ37cK3cfkNR9dluwhL78SQsico6gpbQPJvEchhGNCCP8MIbwaQpgbQlgRQlgYQng3hPCfEMKFIYS96jqOCiF0DyHEjGXdEEKbEMJBIYRrQwiTQwifhhCWhRA+DCHcFkIYkkeMXUMIR4QQLg8hPFndxuLq9maHEJ4OIZwbQtg4RVu9M+JcEULoUF3eJ4RwfghhQnWbq7fZqBivWVZ7PUMIZ4cQHq6u/3kI4bMQwpQQwhUhhL4NtVFk3QpYd16ObSruOK8SlXj/1pLf81wJpkq8m7OivhcV+vuqCmeSTpLU7MUYF1D76qmBJQzha1nrU2KMdzWinT6FCEYlN4vaw6dlD9WUj1J+diG5wi/75GxP4JASx7Fa9hxXjT0ZWUkn4lfLTrS1Ab6dWVB9cn5E1navxhj/m6aDCtgfNijGODXG+LcY47eBjYCDgedzbPrLEMJ6RQwl1x/JWze10eo5v7JPfkaaePK+gl63Umvu+9g6xRjfAp7KKu4N7LV6JYSwAXBg1jZzSIb3y1drP175hNq/d/2a0N6XqX1Opa6Tb1Dhv28Vso/Jno8O0ifS7qbm+9sWODRjPTtJtxJ4KW1gIYQDgbeAW4HjST4761T30xnoBewD/AJ4nLqHhd0+4//vk9wZ/CzJ8dgpJHcT9iD5fGxMMgTt2BBCgydvQwg/Idln3gmcQXJX/sYkd7+2A9YDdgfOA14PITQ0N2VmrFOBlSGEXwOvAb8meU1XfxZmxRhnZMVTqNeMEEKnEMJlwDSSYXv3q67fgeQ13Ab4AfBaCOHHDTyvYtqqMZVCCO1J9v+ZZmVv1xyO8ypRkfdvLfk9/4Taw7VX3Oet0r8XFfL7qgpnkk6S1FJkn8DePITw5RL1/aWs9ezhdtIa3NRAVHoxxuUkE0Bn2iU0fhLoXHMFFU2M8T3gsRwPfa+UcWTIHlalsUPTbdnUQAotxvgU8HZW8cis9b1ITjpluiHPrsq5P8xLjHFljPEBYAjJHF2ZOgH7F7H7XCd+C/EH/YAcZVNijEsK0DZQ9tetpJr7PjaFXHfEZZ6o/jZQlfX4P2OMjZlbr1Ufr8QYFwNvZBVv34Q7x3fOUVbf8InN5vetjPuY7ETaCuDlNBVjjB8Cz2UVHwlrToZn75tfq/5MNCiE8B3gAWp+hz4EngFGk/zuZt9x8mIdzQ3K+P/s6jZWf5YWk/w2PZWjvd+FELLn2cs2nC/mVo3AO8B44AngVWoOu90VuCmEsGs97W2f8f8pwC3A+UD76raer471Q7ISnoV8zUIyV+ezwJkZz28JMIEkufcaXyRo2wJ/CCGcUc/zKqZcd2ymMZDaifu69ifN5jivEhVh/9Zi3/OYDHmdfby8fQihqUNFF0Oz+F60pmN45ccknSSppfhPjrJvlqjv7Kud8r5bIiTzxDTlam6V1/is9Y3IuBMirRDCVsBOBYkoP5flKNsnlGcS63lZ671zDP2Wxp4FiKUYRmWtb5U1jFX2VeQrSIZGyUc594eNEmNcQXIlfbZiXgU7ieSEaKa+BRgq66AcZWOb2GZOZXrdyqG572Prcye15907IoSwegisXHeWpB3+NpvHKzAua70LjT8hdlSK9jPNy1qv+N+3Uu5jqpOl2e2+FtPNx7pa9pCXXw0hrEOSoGuf9ViqO/SqT/JezRfDUN9IMofjpjHGPWKMX4sx7poxpNkvgenAK3U0mZmk2wHYhCTh9FOgZ4xxxxjjniTzUJ5BcsffahfX9ZmpvnChF/AXkrs0usUY+8QYh8QYvxpj7A9sQDLk2uo7PquAc+t5+ttn/P8A4AiS/cZ3gfVjjLvEGPeMMW4KfCsjloK9ZiEZsnQ0X9xBPQs4CegeY9w5xrh3jHGb6nYez6j6h5B7SPFiG97IeofnKHu2jm2b3XFeJSrg/m14I0NoLu/5mKz1KuAbJeo7H83qe9GKjuGVkkk6SVJLcV+OsjNCCKWYN2VR1npjhij4YSECUdnclqPsJ41opzF1mizG+G9y31FxbQihZyH7CiEc28A8KNl3OXQhGcIonz72ogLvpKs2CliVVTYSknlkqP0H+4Mxxpl59lHO/WFTvJ6jrCnzfNSr+o/jXJ/7b+UoS6X6JPOxOR56PEdZoZT0dSuTZr2PrU91AuKWrOKOwDeq73DJ3v+9EGNMPURfFo9X4JEcZafl20gIYXNqJ/c+of7ET3P9fSvVPmYbks9+przmjKN2kq4dyZCXue50qe+ux0w/5IsE3x9jjCNjjG/m2rB6SLOLgC3qudt1+6z1+cDQGOMfYoxr7rasvtviSpKhKVfrC+xSR7sB6BtjPDXG+ED18G/Z8S2OMf6NZAjN1eqbCy4z1k7Ae8BuMcZrspOnWccqBXnNqhOS/+SLz/yLwLYxxuuz706vHj74QL64o689cGodz6uYBocQ+udTIYSwFrWPPVaQJCdzaa7HeZWoEPu3lv6e352j7OwQQvZd/uXWHL8XreEYXimZpJMktQgxxleofWC2NvCPJgyJldbHWev75NNnCGEfas9BpWYkxvgsyV05mfYLIaS+ei+E8FWSq5vL5VSSK7kzrQs8FkLIHiItbyGE9iGE/wNupvbV7JlynTQ7Lo9+2gH/m2d4JRNj/IDaf4AfE0LoCBxDchIsU75DXZZ7f9gUuRLCs4vc51U5ys4OIWzWyPbOovb8Ih+Q+wRHoZTjdSupFrKPrU9dQ15+J+W2aXm8knwXP8oqO6h67qx8XEntYUivrR6etS7N9fetVPuYXIm0vJJ0McZ3qL2vOIKmzXWXOZfj1SnjWJmrPITQgZpzn0bgqBhjfbFcQc3js5xJuuqkXqq7DmOMT/BF0r4DORL21XfzZg6/vQQ4JMY4NUUXhXrNvgPsW/3/j4H9Y4y15uzKaGMpNb8jX03TdxFcnuf2PwU2zSq7N8aYvc8GmvVxXiUq1P6txb7n1cdg2fPn9icZ+rZiNNPvRYs/hld6lfohlSSpMX5NzSFhILmi8obqP4rzFkLoH0L4ewihez2bZd+J0YeUV2WHEHYguYK+McMdqbL8JkfZ9SGEXEOZ1BBC+ApwD2X8HMQYXyMZVilbP2B8CKHRw2uFEPYmORn2oxSbj6P20G9nVM9H0lA/bUiSLnVdZV4pshNva5OcRMwe1m4W8O9G9lHy/WEIYUgI4czqOwIb48wcZY29Yyitf5PMZZOpC3BnCGHdfBoKIewP/DbHQ5fXd+K+mb5u5dCs97H1iTFOoPa8W7tS+yr7XHfd5aPVH69UfxdzJedvDCGkuqsthPAb4OtZxUuAPzdQtSy/b81oH9OURFqmu7LW96X2EKGrqHvOuGyZd/9nn1TP17bUTO7eFGPMNUTbGtV312W+3k0ewrE64Zs5F1auBNn2Wev/l8ddvE1+zarvNMr8TT075cgCmfNSNfjdKpK9QwgXpNkwhHAA8D85Hrqigarl+ru3olTQ/q2lv+e5jsF+EULI++76EMJaIYSN8q2XUklfowr6/KkFMEknSWoxqv9w/GmOh74NPBtCODjN3B8hhO4hhO+EEB4lmRvhWySTkNfl9hxll4cQvlfPvBFtQwjfJ5nIffXVq5/l2lbNQ4zxHmp/FtoD/woh3BZC2CP7Kr4Qwk4hhGtJPgerD+6z514qmRjjdcDvcjy0CTAmhPDv6j9GGjyGDCF0DCEcHUJ4muTOsVTDwFRfBX5zVnEXYHR9J1BDCFsC9wOnVBdl3xVYSe4G5maV/ZJkAvFM/2zgrow6lWl/uAHJ/IYfhBCuCSHsW30isKE+uoQQLia5Cy3TfOCBhuo3RYwxAieTDDGUaWfguRDCEQ29TtWv0QUkV+9mD9U2geSOm/o0u9etHFrCPrYBue6Qy/483RVjnNeEPjxeSfyR2gma9Uh+5+qcZyeEsE4I4a/kPrn6sxhj9h16NZTx96257GOy76TLJ5GWKXvIy7WoPY/imzHGhSnby/y83xBC2LkRMa02KGs914UduWTeXdOlvg2rv7N7hBDODiFcW71/fDCE8PDqheS4bPWoBsvIPT/l9hn/XwJckjJWKMxrdgxf3GXyRowx17DHuWR+Pzo3ot+mWt3/r0II14Uv5hetIYTQJoRwFklSOfv7OCrGmH3nUg1l/Lu30lTC/q3Fv+cxxseAS7ObAv4YQrgzJPNQNtTv5iGEXwHv0vi5YOtVhteoEj5/aiEqbfxYSVLrsFMI4cUCtHNTjPEPmQUxxkuqT3RkD2m1HckJ1PdCCE+Q/NH/KclBdTegO8nwMzuSJBNS/0bGGB8LITwFfCWjuAr4E3BmCOFu4FWSK+DXJ5m8/lBqXmU6k+Sk0e/T9quK9F2SE0HbZpUfXb0sDiHMILnCrye1T7RMJLlS8aGs8pzDJhVDjPGcEMIikmRd9h8xB1Yvc0IIo0k+159ULwDrkNyZsSMwmNpDN6Z1EXA8yR1mq20JvBRCuJ9kAvMZJEM0bQLsVb2s/t5+QjLsTNqTXyUVY1waQrgF+F5GcfYJRGjEUJdZ/ZR8f1htbZLhU08FFoQQJpIMP/YOMI9kiK0OJFfX7wDsR+75F36cduiupogxjg0h/IxkH5xpC+BO4P0QwuMkV7Z+CiwmeY02BIYCe5D7ROAckmHM6pqXKFuzet3KpNnvY+vxT5JjgLXq2aYpQ116vFItxrgshHAcyR03mZ+R9YFbQgjnktx5+RawgOS7vitwCF8kezP9m4aT8auV8/etYvcx1Qn27bKK80mkrRFjfC2E8Bq5f1dXSzsfHcAdfPF7vTnJBRyPkyS9H2goOZtl+4z/j6trnrYcMn+Hc772IRk2+2ckydx85hN+K8aYPVcu1Iz1gRhj9sVF9SnEa5Y5FGyn6uRiGpnf6XJcUPBrvtg/ngQcHUK4B3ieZISEdUiOsY4Acg2tPR04O01HZTzOq0Tl3L+1lvf8pyT7hb2yyo8ADgshTAAeI3k+c0he7x4kxxI7U3sfXxRleo0q9vdVzUiM0cXFxcXFpWgLydw8sUjLZXX0GYBfkZx0K1Rf6zXwPDcjOanSmLbnkxwMjszxWO8Ur/F52fWa+J4VpL063vuRKeoNy1FvWKXHnVF/PZKD8nw/B6+QnFTZJ8dj25Xhu7sf8GEBv0ORJLlxEbBWiv5HNLKPBSQnUyvi+1RPPzs18Dz+W6B+SrY/BIYXsI9LyvCZ/z7JHQWFiP91oF/Kfkv6uuE+tiL2sXU8t1vrif9tIBSgj7IcrxTq81OIz3JG/Z1Iko5N+c79C+iQZ9wjGtlXo37faAb7ZmCbHH3d1IT2ftvA8/hhHm11BZ6so51VwLMkJ9jr/Vuhuq3xGXXPySOG5zLqnZ3j8f7AtEa+p7fW0eekjG1Oy/P1b9JrRnKX0eICfF7HFuPzmhHneTn6DMBtjYz3faBPnjGU4+/eWs+7mK9zA7EML+DzTnMM1Srf84y+OwA3FaC/kQ30MyxHnWGV9hqV+vPn0rIXh7uUJLU4MfE7khNxE5rY3GfAX4F6r+SNMb5HMjn5G3m2/wYwJMaYzxW9qmAxxk+A3Ugm005zB81K4C/A0JhMFp5rToDsOWyKLsb4CPBlkjvq5jWxuYUkz3GrGOMvYoo7i2KMNwLfofYwhPWZBuweY3yucWGWTkzmoHqlnk1uKFA/pdwfzqfpw4x+ApwYY8x7joumijH+ieSkwPNNaGYZcB2wS0zmeUyjWb9updZS9rF1qO9Oub/FGGNTO/B45QvV++HBJEP/5WsRcC7J3bJ5fX/L8PvWHPYxhZqPbrXsIS+zpf4cxxgXkNw5ckKOmAJJ4vQS4N0Qwi/qGTq2DTAwo+jZNP2HEKpI7kRZ7fWsx/uQJMQ2ry5aTpIwGEHyuq4HdIwxhtULkHlXWq3XubrPzCFYU8W6WgFes37UHu63MdLeqVgw1fvp44Fr8qw6Ftgzxjgt3/5K/XdvhSn7/q01vecxxiUxxuOBH5DcJdgYK8k9xG7BlPA1KvvnTy2HSTpJUosVY3wixrgzyZjndwCzU1Z9h+RA7ChgoxjjyWlOwMQYXyW5KvtXJFep1+c14AxgYIxxSsq41EzEGJfGGM8juWPkdOBRkmGzFpGcxJ9BMqTVuUDfGOOpMcbVQ/Lkmki7qH/I1CXGuDDGeA7J0BwnkMyJk3booFkkQ4p8C9iw+jl+kGf/N5BcHX4H9Z+M/4hkjqBtYzIXQXNRVyJuCXBLITsqxf4wxvgEsC7J8Hh/JhlGJu0wgi8APwa2jDH+LWWdgosxjosx7kKSxLiNmvMA1WUV8DLwv8DmMcZTMr7Pafps9q9bqbWUfWwOo0nmasm2CrixUJ14vPKFGOO0GOPXgH1JfuMWNVBlGsm8PFvGGH8Tcw8TmKbfkv2+NZN9TPZ8dNCEJF2M8UWS9yrnwyR3ieXT3qoY46gY444k+52TSY5xMt+7zsCFJK9XLl+m5jDg76TsfueMeiupPa/mtSTvLyQJvAExxm/EGP8eY5wUY/w083c7hNCWJDm9Wq7XYhtqzln3aspY12jia7Z5xv+nZSYY81xOyDfuQogxrogxfpckQfA4yT68LpNIXps98k3WZPVZ0r97K0Wl7N9a23seY7yKZIqDn5IMHd3Qb+EqkjuC/wfoFWO8L5/+GqvYr1GlfP7UMoTY9IvxJElqFqqv0twG2IrkYGpdkgtWFpDcKfQ28HrMb86F+vralmSC+PVIrgZdQDJG+4sxxneb2odaphDCTdSch+P9GGOu+QvKovpK8K2AvsCXSOb+WIvk8z2XZGz/V2KM0wvcbyeS+b96kXynIskwZS+RfKc8qM1DqfaHIYTOJJ+VPiTJka4kw1gtJLn69C3g5RhjpdzJVEsIYUuS16pH9dKRJPY5JJ/BCYWOvyW8bpWq0vexpebxSk0hhPYkd/l8iWSOus4kv2uzgckx/Rxi+fRZ8t839zGFEULoTnKS+qd8cRH8ezHGXjm2PRa4OaOoR5rf2BDClSQXIwA8FmPcJ+OxL/PFnXUR6B8buIs7hDAMeCKjaP2Y3KGcuc23+eLCgEkxxlx3OjZKmtcsq/9nY4yDqUAhhPNILgZZIyZ3KmZvtx7JHeBbkBw3f0ZyEdCkGOPbRYqtZH/3Vppi7t98z+vsuzvJBQUbkvx2diS56GUOyR2tr+ZzAVuxlOI18vdVjWWSTpIkqUKEENYC3gM2yCj+V4zxyDKFJEkthvtYScUQQvgVcEH16qoYY9sc2/we+ElG0UYxxpkNtLs2yd21q4fpPSLGeFfG498Hrqpe/W+McdcUsd5OcncIwAcxxi/l2OYSkjnjIBlq98SG2s1Xfa9ZCOE4knmvAKbEGAdk168EaRM2ajl8zyUVi8NdSpIkVY4R1Dx5DPBUOQKRpBbIfaykYngm4/+f1LHNoKz1rVK0+yu+SNBNBu7OenyTFP2uEUIYAmRelFDXsJ/bp9imqep7zTKHpNsqhNC1SDFIklQRTNJJkiRVgBDCFsDvs4o/B/5ZhnAkqUVxHyspH9XDoqV1QMb/s+eMW237rPUjGuh/L764mw3glzmGPs2c++jLDbTXE7gVyHxedc37t13G/1Mn6Qr4mj3PF8+tHfD9PNqVJKnZMUknSZJUQCGEC0IIec1vFEIYDDwJdMt66KYY45yCBSdJzZz7WEnFFkLYEHglhDCies7A+rb9FvCjjKJrcmyzKcl8g5m+W31nW642DwLuIklQAVwZY7w/x6ZTMv6/RXUsudobBIwlmWsxU60EXAjhSyTzr0Iyz91LudrMUa9gr1mMcR7wYEbRb0IIx6SIYdMQwm9DCD9OE7MkSZXCOekkSZIKKISwBKgCHgfuBZ4GXosxLs/argcwFPgOcAi1L576GBgYY2xw+CJJai3cx0oqthDC/sBD1auLgEeAccAbwHxgfWAL4NtA5nxpOedvCyEcDNxXvTqTZOjKvYElJAmqR6v/vzlwNLAvX9zxNg7YK8a4LEe72XPWrQKur25vNrAZcDDJXXttSOZ5+wawev63XjHG9+qJ9c0YY7136GXUK/RrNgD4L9Axo/gp4F/Aa8BioCuwITCQZH+/C8nrVmPuvmJxfrLWx/dcUrFUlTsASZKkFqgt8LXqBWB5CGEWyUmKKpKTKetRc8ihTMuAb3nyWJJych8rqZi2z/h/Z+Dw6qU+11L3sIyZ89FNBH5Kcmfb2sBZ1Usuj5IknGol6ABijJ+FEH7AF8P2tgFOrl6yXQPcCBxfvT4nO0FXbfuM/+czH11mvSa/ZjHGydV3z91S3R7AV6qXhhRrHj1JkorC4S4lSZKKrx2wCbANsBXJ1cR1nTyeRXLF9GMlik2Smjv3sZIK6THgauCtBraL1dt+LcZ4WoxxZR3b1UjSxRgnA3sCr9Sx/XskibYDYowL6w0gxpuAY0nunMvleWC/GON3STfXXOY2L9bXd5ZCv2ZUD/E5iGQuveV1bVdtNnAzcFCM8Z3UUUuSVAEc7lKSJKmAQgi3kAwt1LmhbbMsBP4C/D7GOLPggUlSC+A+VlIphRDWI0lc9SGZq62K5K7dd4HnYox1Jccy23gH6F29eniM8e6Mx3YlGf5xA+AT4GXgvzHPk3XV88DtA2wNrAQ+BJ6PMb6dTzuFUIjXLEebawO7An35Yn7Rz4D3gddjjG82PfK8YzoPhz5sVXzPJRWLSTpJkqQCCyF0BPYAhpCcpNgc2JjkpHIHkpPFc0ju6JhAMsfGozHGuWUJWJKaEfexkpqLEMI6QOa+p3eMcXqZwlEBmbBpfXzPJRWLSTpJkiRJkiSpwEIIewGPV6/OiTGuW854JElS5XFOOkmSJEmSJKnwasxHV7YoJElSxTJJJ0mSJEmSJBXe9hn/N0knSZJqMUknSZIkSZIkFZ530kmSpHqZpJMkSZIkSZIKKITQAdg6o8gknSRJqiXEGMsdgyRJkiRJkiRJktSqeCedJEmSJEmSJEmSVGIm6SRJkiRJkiRJkqQSM0knSZIkSZIkSZIklZhJOkmSJEmSJEmSJKnETNJJkiRJkiRJkiRJJWaSTpIkSZIkSZIkSSoxk3SSJEmSJEmSJElSiZmkkyRJkiRJkiRJkkqsqtwBSFK5hBC6AXtmFL0PLCtTOJIkSZIkSZKkpmsPfClj/ckY4/xyBVMfk3SSWrM9gXvLHYQkSZIkSZIkqWgOBe4rdxC5ONylJEmSJEmSJEmSVGIm6SRJkiRJkiRJkqQSc7hLSa3Z+5kr99xzD1tuuWW5YpEkSZIkSZIkNdFbb73F8OHDM4ver2PTsjNJJ6k1W5a5suWWW9K/f/9yxSJJkiRJkiRJKrxlDW9SHg53KUmSJEmSJEmSJJWYSTpJkiRJkiRJkiSpxEzSSZIkSZIkSZIkSSVmkk6SJEmSJEmSJEkqMZN0kiRJkiRJkiRJUomZpJMkSZIkSZIkSZJKzCSdJEmSJEmSJEmSVGIm6SRJkiRJkiRJkqQSM0knSZIkSZIkSZIklZhJOkmSJEmSJEmSJKnETNJJkiRJkiRJkiRJJWaSTpIkSZIkSZIkSSoxk3SSJEmSJEmSJElSiZmkkyRJkiRJkiRJkkqsqtwBSJIkSZIkSZJUnxgjq1atIsZY7lAkNUIIgTZt2hBCKHcoFcUknSRJkiRJkiSposQYWbJkCQsWLGDBggUsW7as3CFJKoC2bdvSuXNnunbtSufOnWnbtm25Qyork3SSJEmSJEmSpIqxePFiPvroI5YvX17uUCQV2MqVK/nss8/47LPPAOjatSs9e/Zstck656STJEmSJEmSJFWExYsX895775mgk1qJBQsWMH369Fb7nTdJJ0mSJEmSJEkqu9UJOuedk1qXpUuX8u6777J06dJyh1JyDncpSZIkSZIkSSqrGCMfffRRrQRdu3btWHvttenSpQvt2rUjhFCmCCU1RYyRlStX8vnnn7NgwQIWL15c4/u+YsUKZs6cyWabbVbGKEvPJJ0kSZIkSZIkqayWLFlSa7i7rl27sskmm5iYk1qIdu3a0aFDB7p3786yZct4//33WbZs2ZrHFy1axPLly2nXrl0Zoywth7uUJEmSJEmSJJXVggULaqy3a9fOBJ3UgrVv355evXrRpk3NNNX8+fPLFFF5mKSTJEmSJEmSJJVVdpJu7bXXNkEntXBVVVWsvfbaNcpM0kmSJEmSJEmSVCIxxhpD3gF06dKlTNFIKqXsJN3y5ctrzU3ZkpmkkyRJkiRJkiSVzapVq2qVtaY5qaTWLPu7HmM0SSdJkiRJkiRJUinkOiHvUJdS65A9Jx3kTty3VCbpJEmSJEmSJEmSpBIzSSdJkiRJkiRJkiSVmEk6SZIkSZIkSZIkqcRM0kmSJEmSJEmSJEklZpJOkiRJkiRJkiRJKjGTdJIkSZIkSZIkSVKJmaSTJEmSJEmSJEmSSqyq3AFIkiRJkiRJkiRVuuXLlzN27Fjee+89Pv74Y7p06cLGG2/MoEGD6N27d0H7euedd3jxxRf56KOPWLhwIT179qRXr14MGTKEdu3aFbQvlY9JOkmSJEmSJEmS1Cycd955nH/++Y2uP2LECEaNGpVXndmzZ3Puuedy2223MWfOnJzbDBkyhB/+8IccccQRjY4N4M477+SSSy5h/PjxOR/v0aMHxxxzDL/5zW9Yb731mtSXys/hLiVJkiRJkiRJknJ46KGHGDBgAFdffXWdCTqAcePGceSRR/LNb36TRYsW5d3PwoULOfbYYznqqKPqTNABzJkzh6uvvpoBAwbwyCOP5N2PKot30kmSJEmSJEmSJGUZM2YMw4cPZ9myZWvKQgjssMMO9OnTh3nz5jFp0iQ++eSTNY/fdNNNfPbZZ9xzzz20aZPuPqmVK1dyzDHH8OCDD9YoX3/99Rk0aBDdunXj7bffZtKkScQYAZg5cyaHHnooo0ePZvfddy/As1U5mKSTJEmSJEmSJEnN0i233MJuu+2WevsuXbqk2u6DDz7g8MMPr5GgGzp0KNdddx39+vVbU7Z06VKuvfZafvzjH7N8+XIA7r//fs455xwuvPDCVH39/Oc/r5Gga9euHZdccgmnnHIK7du3X1P+6quvctJJJ625027p0qUMHz6cV155hZ49e6bqS5XFJJ0kSZIkSZIkSWqWNtpoI3r37l3wds8991zmzp27Zn3IkCGMHj2aDh061NhurbXW4owzzmCzzTbjsMMOW1N+ySWXcOqpp9KrV696+5k2bRqXX355jbI77riDQw89tNa222yzDY899hh77733mkTdp59+yvnnn88111yT93NU+TknnaSSCyFsEUL4fgjhthDCayGEBSGEZSGEWSGEMSGEX4YQNix3nJIkSZIkSZJan6lTp3LjjTeuWW/fvj2jRo2qlaDLNHz4cEaMGLFmfenSpZx//vkN9nX++eevuQMPYOTIkTkTdKt17NiRUaNG1bjD7vrrr2fatGkN9qXKY5JOUkmFEEYBbwFXAUcDWwNdgHbA+sCewO+AN0MI3yxTmJIkSZIkSZJaqZtvvpmVK1euWT/88MPp27dvg/V+9rOf1Vi//fbbWbJkSZ3bf/7559x55531tpHLVlttxfDhw9esr1ixgptvvrnBeqo8Jukkldqm1f8uBm4FTiFJzO0AHAr8E4jA2sDfQwjHlCNISZIkSZIkSa3T3XffXWP9hBNOSFWvX79+7LrrrmvWFy1axKOPPlrn9o888giLFy9esz548GC23nrrVH1lx3TXXXelqqfKYpJOUql9BJwFbBhjPDbGeF2M8akY46QY430xxm8Bx1VvG4CrQgidyhWsJEmSJEmSpNZjxowZvPTSS2vWq6qqGDp0aOr6w4YNq7H+0EMP1bntww8/XG/d+uyxxx5UVVWtWZ80aRIzZ85MXV+VwSSdpJKKMX47xnh5jHFhPdvcCtxTvboesE8pYpMkSZIkSZLUuk2ePLnG+sCBA+ncuXPq+kOGDKmxPmXKlNR9DR48OHU/nTt3Ztttt03dlyqTSTpJleqxjP9vVbYoJEmSJEmSJFWsa6+9ln322YdNNtmEDh060LVrV3r37s2ee+7Jr371K55++um82nv11VdrrG+55ZZ51d9iiy3qbS/Ta6+9VrK+VJmqGt5ExRZCaAtsCWwDbAx0A5YCc4G3gQkxxkWtPSblFkLYHNie5H3qAnwMTAfGxRiXlzG0pmqf8f+VdW4lSZIkSZIkqdW69dZba6wvXbqUhQsXMn36dJ566ikuvPBCdtppJy666CL22afhAbveeuutGuubbbZZXvH06tWrxvqnn37K3Llz6d69e43yOXPmMGfOnCb1lb391KlT86qv8jNJVyYhhM2Aw0mG8dsDWLuezVeGEP4DXBVj/Hdriqm5CSH0AXYGdqr+dwega8Ym02OMvQvU15HAD4G67oGeE0K4Dfh1jPGTQvRZYl/N+L/3aUuSJEmSJKmWFStX8fH8JeUOo1Xo2a0DVW2b5+B8EyZMYN999+UXv/gFF1xwASGEOredN29ejfUNNtggr766dOlChw4dWLLki8/l/PnzayXpsvvp1KlTXsNq5opt/vz5edVX+ZmkK4MQws3AsXlUaQvsD+wfQngAOCnGWNAZICsxpuYihDAM+AVJYq5HCfrrAlwHfKOBTXsA3wUODyGMiDE+UuzYCiWEsAtwQPXqR8CY8kXTgj31B9h0Z9h8T6jnwESSJEmSJKlSfTx/CXv8/olyh9EqPP3TvfhSj07lDmONTTbZhAMPPJBddtmFfv360aNHD9q0acOnn37KxIkTeeCBB3jkkS9OicYYufDCC1m1ahUXXXRRne0uXLiwxnrHjh3zjq1jx441knQLFiwoWj+ZcvWjymaSrjzqml/rQ2AqMJPkvekDbEfNuQMPAp4KIewZY5zRwmNqLrYH9i1FR9XDkN4GHJj10GxgEjAf2AIYBKzOumwI3BtC2CfG+Ewp4myKEMLawCi++Iz9Msa4rHwRtVCfvAWP/w6I0HM7GHom9DsU2vqzIEmSJEmSpMq1yy678Mgjj/C1r32tzjvihgwZwumnn86ECRM47rjjagwD+b//+7/stttuHHrooTnrZifPOnTokHeMHTt2ZO7cuXW2Wch+6mtTlc+zseU3Cfgb8FCM8e3sB0MImwC/Bk7JKN4KuCOE8JUYY2wlMTVHS4EPSJJmhfK/1EzQLScZ8vIvmYmsEMI2wF/5YijMtYB7Qgjbxhg/rq+DEMJwkrntmmpcjHFaPhWqk5C3AP2qi26PMd5YgFiUbfyVQPVX9eOX4M7vQPfeMPh02P54aF85V0VJkiRJkiRJqx14YPb9C3XbaaedePbZZxk8eDBvvvnmmvKf//znHHTQQbRt27bBNuobGrM51lFlMUlXHhH4N3BejHFCvRvG+CFwagjhJeBPGQ/tDhwD3JqzYsuIqTlZTjJv2gTg+ep/XwGGAgW53756vrszs4qPijHem71tjPHVEMLewGN8kahbFzgXOK2Bri4DejWwTRonAKmTdCH5RbmeL5KQE4ATCxCHsi2YCS/eUrt87rvw4I9hzEWwy6mwy8nQqegjuEqSJEmSJElF06NHD2655RZ22mknVt9f8vrrr/PEE0+wzz771Nq+S5ea9y98/vnnefeZXSe7zVL2o8rWPGd5bP6OijEe1FAyLFOM8c/Av7KKv9XCYwIghLBOCKF/gdraIYSQ/+C+9bsRWDvGOCjGeHKM8S8xxokxxuUF7udcoF3G+qhcCbrVYoyfAyOBzKEiT6xO9lWU6gTd1cCI6qKXgP1ijN6fXQz/vRZWLq378cWfwpgL4dL+8NDPYO700sUmSZIkSZIkFdgOO+zAvvvWnLHo4YcfzrmtSTqVkkm6MogxvtvIqn/KWt+riaGsUYkxAYQQugGPkMx5N6iJbe0JPAXcX8hEXYxxboxxScNbNl51vEdmFV/cUL0Y45vAPRlFVcBxDVQbCHQvwHJzQ/FluBI4tfr/k4F9Yoxz8qivtJYtguf/mm7b5YvhuWvgikHwr5NgxivFjU2SJEmSJEkqkv3337/G+ssvv5xzu27dutVYnz17dl79LFy4sFbybJ111mmwn8WLF7No0aK8+po1a1aD/aiyOdxl8zIpa71jCGGdGOO8cgRTrWgxhRC6Ag8Du1QXPRZC+FqM8YVGtPVV4H6gE7A3cF8I4eBiJ9cKaD+S2FcbH2N8PWXdG4CjM9YPBy6oa+MY42f5h9d4IYQrge9Xr04Bvhpj/KSUMbQq7TrBsbfC2MvhzdxXC9USV8IrdyTLFnvD0DNh86+AY15LkiRJkqQy69mtA0//tKD3DagOPbt1KHcITdK7d+8a63Ul3/r27Vtjffr0/EaZyt6+R48edO/evdZ26667Lt27d2fu3Llryt577z369evX6L6yY1flM0nXvKzIUda+5FHUVMyYlgGfZqx3B0aHEPaLMf43bSMhhK8B9wKZd8/NJplHrrnYP2t9TB51nyZ5n1Z/3weFEDaMMc4sRGBNEUK4Aji9enV1gi6/S1OUnxCg15BkmfUajL0CXrkdVuX6Kufw9mPJsvGgJFnX7xBo0/AEu5IkSZIkScVQ1bYNX+rRqeEN1ep17FhzcLW6hpfMTpK99dZbefUzbdq0GuvbbLNNndv269ePcePG1egrnyRddl/51FVlcLjL5mXLrPUVQLnvOCpaTDHGpSR3fd2fUbwO8J8Qwm5p2ggh7AfcR80E3U3At2KMKwsRZ4kMyFofn7ZijHERkD1OYUHm+GuKEMLlwA+qV1cn6GbVU0WFtkE/OOxqOPNlGHw6tM9jzOqPJsEdI+HKHZLhM5fnP2a2JEmSJEmSVCqffFLztPV6662Xc7sBA2qein355ZdZvHhx6n7Gjh1bb3v1PTZ+fOrTvixatKjWkJ319aXKZJKuecmek2xCjHFVWSL5QlFjijEuq+7jvozitYFHQwhD6qsbQvg6yR10mfdh/wP4djNL0AFkXwKR3+Ub8HbWet2Xb5RACOFS4Izq1VcxQVde3TaB/X4HZ0+Bvc+Fzhukrzv3Xfj3j+DSAfDkH2CxUwlKkiRJkiSp8jz33HM11jfeeOOc2/Xs2ZOBAweuWV+xYgXPPPNM6n7GjBlTY/2AAw6oc9vsefKy69bn6aefZsWKL0bHGjRoEBtuuGHq+qoMJumaiRBCF+DErOK7yxHLaqWKKSNRd09GcVfgkRDCHnXEdghwF7BWRvGNwMgKSGzmJYTQA+iRVfxens1kb1+2wYlDCBcDZ1WvfkJyN90GIYQB9SyblCveVqXjOrDHD+GsV+Dgy6HHFunrLv4EnrggSdY99HOYl+9HVJIkSZIkSSqOJUuWcNddd9UoGzZsWJ3bH3bYYTXWb7jhhlT9vP766zWSgZ07d2bfffetc/v99tuvxjCc48eP5/XXX0/V16hRo2qsZ8es5sEkXfNxEbBRxvo84K/lCWWNksUUY1wOHE2SeFutC/BQCGHPzG1DCIcBd1JzbrwbgO80twRdtXWy1hdXD2GZj+y71Lo1PpwmOybj/+sBj5EMx1nf8rsSx9i6tesAO46E05+HY/4Jm+yUvu7yRfDc1XD59nDXKTBjcrGilCRJkiRJklK5+OKL+fDDD9est23blq9//et1bn/88cfTtm3bNet33XUXU6dOTdVPpqOPPpoOHTrUsTV06tSJI4+sOVhddhu5vPnmm9x99xf3y1RVVXHcccc1WE+VxyRdM1CddDo9q/hXMcayjStXjpiqE3XHkCTgVusMPBhC+Gp1XEcCtwPtMrb5K3BiM03QQZKMzNSYyb+y63RtZCxqTdq0hX4Hw0mjYeSD0He/9HXjSnj5NrhmKPzzCHjnKYixeLFKkiRJkiSpxfvHP/7BzJkz86pz3XXXcf7559coGzlyJL169aqzTt++fRkxYsSa9WXLljFy5EiWLFlSZ5177723xt1t7du359xzz20wvvPOO4927b44nT1q1Cjuu+++OrdfsmQJJ5xwAsuWLVtTduKJJ7LFFnmMiqWKYZKuwoUQtgP+nlX8KHB1GcIByhtTjHEFcCxJIm61TsADIYSLgFuAqozH/gKcEmOzzg5kJ+nq/iWoW3aSLrvNkokx9o4xhjyXkU3pM4QwMoQwJnshucNSDQkBeg+F42+H746D7Y6FNlUN11vtrdFw48Fw3Vdhyj2wqrlNCSlJkiRJkqRKcP3117P55pszYsQI/v3vf7NoUd0Djk2YMIHDDz+cU045hczTw5tssgkXXHBBg32df/75dO/efc36uHHj2GeffWoNR7l06VKuvPJKjjrqqBrlP/rRj+pNBK7Wp08fzjzzzBplRx55JFdddVWNRBzAa6+9xt577824cePWlK277rqpkoGqTKF55y5athDCZsA4IHM+runAzjHG2a05phBCW+Amag6dmO0a4HvlTNCFEIYBT2QUTY8x9s6zjSHA2IyiD2KMX8qzjZOA6zKKHo0x5nFbVPMWQjgPaPCXavLkyfTv37/4AbUE8z+AZ6+GF0bBsoX51e2+OQz5AWx/HLTr2PD2kiRJkiSpRVuxYkWtoQT79u1LVVUeFwmrVRg2bBhPPvnkmvU2bdrQt29fevfuTbdu3Wjbti2ffvopL730Us477nr06MGTTz7JgAEDUvU3ZswY9ttvvxrJshACO+64I3369GH+/PlMnDiR2bNrnho/6KCDuOeee2oMmVmflStXcvDBB/PQQw/VKN9ggw3YYYcd6Nq1K9OmTWPixIk1Eo7t27dn9OjR7LHHHqn6qUTF+P5PmTIl+z0eEGOc0ugGi8i9XIUKIWwA/IeaybAZwNfKmKCrmJhijCtDCMcDq0jurMv2J+AHzfwOutWyMyCNyWpk18kzq9LsvQs8maO8E7BzaUNpIbptCvv9Dr7yY3j+enjuGliUcjcw9x349w9hzEWw66mw04nQqUdx45UkSZIkSVKLs2rVKt544w3eeOONBrfde++9GTVqFJtuumnq9ocNG8bdd9/NyJEj1yTiYoxMmDCBCRMm5Kxz7LHHct1116VO0EEyR97tt9/OSSedxG233bamfNasWTz88MM562ywwQbceOONzTpBJ4e7rEghhB7AaGCrjOJPgH1ijA3PTtlKYooxrgQeyfUQ8EgLSdCBSbomizGOijEOy16AE8odW7PXsXuSqDtrMhx0GfTok77uotnw+AVw6QB4+Bcw7/2ihSlJkiRJkqTm78wzz+S4445LNYwkQOfOnTnssMMYPXo0o0ePzitBt9qBBx7I5MmTOe2002oMf5ltt91248477+Tmm2+mc+fOeffTpUsXbr31Vu644w522223Orfr0aMH3/3ud5k8eTL7779/3v2osjjcZYUJIXQDHgN2zCieC3w1xviiMX0hhDAC+Bu5k83LgKNjjPeWNqqaCjTc5bokCdFMXWKMdQ+4XLuN/wN+lFF0RYzxzLq2by1CCP2ByavXHe6yAFathNcfgGcug48m5le3TRUMOBKGngEb+j5IkiRJktRaONylGmPevHlMmTKF999/n5kzZ7J48WJWrVrFOuusQ/fu3enXrx8DBw7M6462hixbtoyxY8cyffp0ZsyYQefOndlkk00YNGgQm2++ecH6AXjnnXeYOHEiH330EYsWLWKjjTaiV69eDB06lPbt2xe0r3JyuEtVjBBCV+BhaibDPgP2L2OCruJiAgghnAD8lZoJumeB1ZcYtAfuCCF8I8Z4V6njK6QY46chhLlA5mUamwGv5dFM9qUlZbn7Ua1Am7awzaHQ7xCYPjZJ1r31n3R1V62Al29Nli2/BrufBb2GQgjFjFiSJEmSJEnN0DrrrMPQoUNL2mf79u3Za6+9StLX5ptvXvDEnyqPw11WiBBCZ+BBvkgyQTIk4QExxv8a0xdCCCcD11Pz8/vLGONg4LKMsnbAbSGEo0oYXrFkJ+S2zLN+9hiE+ST4pPyFAL13h2/eCaeNhYHfSO6US+ut/8Cor8Nf94ZX703u0JMkSZIkSZKkFsQkXQUIIXQEHgB2zyheDHw9xjjOmL4QQjgNuBbIvLXmpzHGiwBijGcDf8x4rAq4JYTwjdJFWRSTs9YHp61YnWwd2EB7UvFsNAAOvxbOeBF2+z60y2NM7g9fgNu/DVftDBNugOVLihamJEmSJEmSJJWSSboyCyF0AO4DhmUULwEOiTE+ZUxfCCF8H/gzNRN0P4ox/iFzuxjjj4GLM4raAv8MIRxf/CiL5uGs9WF51N2DmkPbTooxzmxyRFK+1vkS7H8hnD0ZvnoOdFovfd05b8MDZ8Fl28JT/wefzy1amJIkSZIkSZJUCibpyiiE0B64C9gno3gpMDzG+JgxfSGEcAZwFTUTdGfGGC/JtX2M8efA7zKK2gJ/DyF8u3hRFtUjwOcZ64NDCFunrDsya/3ugkQkNVanHvCVnyTJuq9fAt3zGFt70Sx4/Ldw6QB45Fcw/4PixSlJkiRJkiRJRWSSrkxCCFXA7cABGcXLgSNjjI8Y0xdCCD8ELs8oisDpMcYr6qsXYzwH+E1GURvghhDCdwofZXHFGBcDd2YV/6yheiGErYDDMopWADcXMDSp8dp1hJ1PhB+8AEfdCBsPSl932UIYfxVcvh3cfRrMfLV4cUqSJEmSJElSEZikK4MQQlvgJuDQjOIVwDExxgcK3Nd5IYSYsYwpd0z5CCH8hJpzzEXgezHGP6WpH2M8F/h1RlEb4K8hhJMLF2XJnEeSNF1tZAjhkLo2rh629AagfUbx9THGt4sTntRIbdpC/+Fw8hMw4gHYcp8Gq6yxagW8dAtcPRhuOhreHQsxFi1USZIkSZIkSSqUqoY3URH8DTg6q+yXwKQQQu8825oRY1zSEmMKIawH/DSjKAKnxhivy6edGONvQwgrgAtXNw38TwjhlhjjwqbGWR3rpuT+Pm2UtV5Vz+u5MMb4SV19xBinhRAuB36cUXxn9Z2Gf4kxLsuIpx/wV2BIxrafAufX/SykMgsBNt8jWWZMhnFXwCt3QlyZrv7UR5Jlk51g6Jmw9deTBKAkSZIkSZIkVaAQveOg5EIIhXzR94oxjqmnr/OAczOKnowxDitnTPkIIQwEHge6AyfHGP/WhLZ+Avwe+KA6xrcKEWN12+8CvZrYzI0xxpEN9NMWuJ+aQ5ICzAImAguAPsAO1Jy/bxmwT4zx6SbG2KKEEPoDk1evT548mf79+5cxItUy7z0Y/2eYeCMsX5xf3XW3hCE/gIHfgHYdihOfJEmSJElqshUrVjB16tQaZX379qWqyntMpJauGN//KVOmMGDAgMyiATHGKY1usIgc7lIVLcb4MrA38O2mJOiq2/oDcBoFTtCVUoxxJckdj7dlPbQBsD9wFLAjNRN0s4BDTdCpWVpnMzjgf+HsKbDXOdBpvfR1P30L7j8TLtsWnv4jfD6vaGFKkiRJkiRJUr5M0qnixRhfijHeVKC2rm2uCbrVYowLY4zfIEnIPVvPpnOAq0muEni4JMFJxdKpB+z5Ezh7Mnz9j9C9d/q6i2bBY7+BS/vDI7+C+R8WLUxJkiRJkiRJSsv7hcsgxhga3qpgfZ0HnJdiu5LF1BLFGHuXoc87Seak25xkeMuNgc7ADGA6MDZznjqpRWjXEXY+CXY8AV67D565DD5+MV3dZQth/FXw3DWw7dEw9AzYoF8xo5UkSZIkSZKkOpmkk5q5GOM7wDvljkMqqTZtof9hsM1weOcpGHs5vP1YurqrVsBLNyfLVvvD0DNhs8EQvFZBkiRJkiRJUuk43KUkqfkKAfrsCd+6C057BrY9CkLb9PXffBhuOACu3xdeewBWrSperJIkSZIkSZKUwSSdJKll2GhbOOKvcMYk2PU0aNcpfd0P/gu3HQ9/2hleuBFWLC1enJIkSZIkSZKESTpJUkvTvRcccDGcPQX2+hV0Wjd93U/fgvvPgMu2hWcuhc/nFS1MSZIkSZIkSa2bSTpJUsvUqQfs+VM4azIc+H+wTq/0dRfOhNHnwaUD4NFz4LOPihamJEmSJEmSpNbJJJ0kqWVr3wl2ORl+MBGO/Bv03C593WULYNyVcNlAuOf7MOv14sUpSZIkSZIkqVUxSSdJah3aVsGAI+CUJ+Hb98IWX01fd9VyePGf8Odd4eZvwPTxxYtTkiRJkiRJUqtgkk6S1LqEAH2GwbfuhlOfggFHQmibvv6bD8EN+8P1+8Lr/4ZVq4oWqiRJkiRJkqSWyySdJKn16rkdHHk9nDERdjkVqjqmr/v+c3DrcfCnXWDi32HF0uLFKUmSJEmSJKnFMUknSVL33nDg7+HsKTDsF9CxR/q6n06F+36QzFv3zGWwZH6xopQkSZIkSZLUgpikkyRptc7rwrCfJ8m6A/8P1tksfd2FM2D0uXDpAPjPr+Gzj4sXpyRJkiRJkqRmzySdJEnZ2neCXU6GH0yCI66HjQamr7v0Mxh7OVy2Ldz7fZj9RvHilCRJkiRJktRsmaSTJKkubatg2yPh1KfgW/dAn73S1121HCb9M5mz7pZj4b3nihamJEmSJEmSpObHJJ0kSQ0JAbbYC759D5zyJAw4AkIeP6FvPAh/2xeu3w9efxBWrSpaqJIkSZIkSZKaB5N0kiTlY+Pt4ci/wRmTYOeToapj+rrvPwu3Hgt/3i25y27F0qKFKUmSJEmSJKmymaSTJKkxuveGr/8fnD0Z9vw5dOyevu4nbyTz1V2+XTJ/3ZLPihamJEmSJEmSpMpkkk6SpKbovB7s9Qs4ewoc8AdYZ7P0dRd8DP/5NVzaH/5zLiyYUbw4JUmSJEmSJFWUqnIHIElSi9C+M+x6Cuz0HXj1Hhh7Gcx4JV3dpZ8l2z/7Zxh4DAw5A9bfqojBSpIkSZIkqTGWL1/O2LFjee+99/j444/p0qULG2+8MYMGDaJ3794F7eudd97hxRdf5KOPPmLhwoX07NmTXr16MWTIENq1a1ewfkr5nFSTSTpJkgqpbRVseyQMOAKmPQHPXAbvPJmu7splMOkfyXx1W38dhp4JX9qlqOFKkiRJkiQ1Z9OmTeP5559nwoQJPP/880ycOJEFCxasebxXr168++67Te5n9uzZnHvuudx2223MmTMn5zZDhgzhhz/8IUcccUST+rrzzju55JJLGD9+fM7He/TowTHHHMNvfvMb1ltvvUb3U8rnpNxCjLHcMUhSWYQQ+gOTV69PnjyZ/v37lzEitVgfTYKxVyR32MVV+dXdbDAMPQv67gttHKVakiRJktTyrFixgqlTp9Yo69u3L1VV3mOi3MaMGcNFF13EhAkT6kwurVaIJN1DDz3EyJEjmTVrVqrtjz/+eK699lo6d+6cVz8LFy7k5JNP5tZbb021/YYbbsiNN97Ifvvtl1c/ULrn1JBifP+nTJnCgAEDMosGxBinNLrBInIvJ0lSsW08CI66Aeb8GsZfldwpt2JJurrvjU+W9bdOhsHc9iioal/ceCVJkiRJkirYiy++yKOPPlqSvsaMGcPw4cNZtmzZmrIQAjvssAN9+vRh3rx5TJo0iU8++WTN4zfddBOfffYZ99xzD21SXnS9cuVKjjnmGB588MEa5euvvz6DBg2iW7duvP3220yaNInVN1/NnDmTQw89lNGjR7P77rtX3HNSw3wlJUkqlR6bw9f/CGdPgT1/Bh27p687+3W493tw+XYw7kpY8lnx4pQkSZIkSWqG1lprLbbYYouCtffBBx9w+OGH10hmDR06lClTpjBhwgRuv/12Hn30UT744AMuv/zyGvPE3X///Zxzzjmp+/r5z39eI0HXrl07rrzySj744AMeeeQRbr/9dl544QUmT57M4MGD12y3dOlShg8fzscff1xxz0kNM0knSVKpdV4P9vplkqzb/2Lo9qX0dRd8BI+eA5cOgNHnwYIZRQtTkiRJkiSpUrVr147tt9+ek046iWuvvZYXXniBBQsW8Ne//rVgfZx77rnMnTt3zfqQIUMYPXo0/fr1q7HdWmutxRlnnMHtt99eo/ySSy5h+vTpDfYzbdo0Lr/88hpld9xxB6effjrt29ccUWmbbbbhscceq5Go+/TTTzn//PMr6jkpHZN0kiSVS/vOsNtpcMYkOPyvsOG26esunQ/PXAqXbQv3/QA+mdpwHUmSJEmSpBZgxIgRfPbZZ0yaNInrrruOU045hR122KHGXV9NNXXqVG688cY16+3bt2fUqFF06NChzjrDhw9nxIgRa9aXLl2aKnl2/vnns3z58jXrI0eO5NBDD61z+44dOzJq1KgaCbzrr7+eadOm1dtPKZ+T0jFJJ0lSubVtBwOPgtOehm/+Czb/Svq6K5fBxL/DVTvDrcfD+88XL05JkiRJkqQK0L1793oTS4Vw8803s3LlyjXrhx9+OH379m2w3s9+9rMa67fffjtLliypc/vPP/+cO++8s942ctlqq60YPnz4mvUVK1Zw880311unVM9J6ZmkkySpUoQAW+4DI+6Hk5+A/odBSPtTHeH1B+D6feCGA+HNR2DVqqKGK0mSJEmS1FLdfffdNdZPOOGEVPX69evHrrvuumZ90aJFPProo3Vu/8gjj7B48eI164MHD2brrbdO1Vd2THfddVe925fqOSk9k3SSJFWiTXaAo0bB6RNgpxOhKo+rw6aPhZuPhquHwIs3w4plDdeRJEmSJEkSADNmzOCll15as15VVcXQoUNT1x82bFiN9YceeqjObR9++OF669Znjz32oKqqas36pEmTmDlzZs5tS/mclJ5JOkmSKtm6W8BBl8BZk+ErP4UO66SvO/s1uOe7cMX2MO4qWLqgWFFKkiRJkiS1GJMnT66xPnDgQDp37py6/pAhQ2qsT5kyJXVfgwcPTt1P586d2XbbbVP1VcrnpPRM0kmS1Bx0WR+++is4ewrs/7/Q7Uvp6372ITz6K7ikP4w+HxbkvqJKkiRJkiRJ8Oqrr9ZY33LLLfOqv8UWW9TbXqbXXnutJH2V8jkpPZN0kiQ1J2t1gd2+C2dMgsP+Ahv0T1936Xx45hK4bFu4/0z45K3ixSlJkiRJktRMvfVWzXMmm222WV71e/XqVWP9008/Ze7cubW2mzNnDnPmzGlSX9nbT506Ned2pXpOyk9Vw5tIkqSK07YdbHcMDDwa3noMxl4G7z6dru7KpfDCKHjhRuh3EAw9CzbdqYjBSpIkSZJUJCtXJCPIqPjW3gTato6Uwrx582qsb7DBBnnV79KlCx06dGDJkiVryubPn0/37t3r7adTp055DUGZK7b58+fn3K5Uz0n5aR3fKEmSWqoQoO8+yfLhCzD2cnj1PiCmqBzhtfuTpdfuMPRM6Pu1pE1JkiRJkpqDzz6EyweWO4rW4cyXoXuvhrdrARYuXFhjvWPHjnm30bFjxxoJrQULFhStn0y5+ilkXw09J+XH4S4lSWopNtkRjv47/OAF2Ok70Hat9HWnPwM3HwVXD4EXb4GVy4sXpyRJkiRJUgXLTmh16NAh7zayk2DZbZayn1L3pfRM0kmS1NKsuwUcdCmcPRn2+DF06Ja+7qxX4Z7T4PLtYfyfYKlXREmSJEmSpNYtNGLUoUquU+q+VDeTdJIktVRdNoC9/wfOngL7XQRrb5q+7mcfwCO/hEv7w2O/hYWzihenJEmSJElSBenSpUuN9c8//zzvNrLrZLdZyn5K3ZfSM0knSVJLt1ZXGPw9OPNFOOxa2GCb9HWXzIen/w8uHQD3nwWfvl2sKCVJkiRJkiqCSbqm9aX0TNJJktRatG0H230DvjsOjr8Teu+Rvu7KpfDCDXDljnDbt+DDF4oXpyRJkiRJUhl161Zz6pDZs2fnVX/hwoW1ElrrrLNOg/0sXryYRYsW5dXXrFk1Rz/K1U+uvor1nJSfqnIHIEmSSiwE6Pu1ZPlgAoy9HF67H4gpKkd47b5k6b0HDD0TttwnaVOSJEmSpFJbexM48+VyR9E6rL1JuSMomb59+9ZYnz59el71s7fv0aMH3bt3r7XduuuuS/fu3Zk7d+6asvfee49+/fo1uq/s2OsqL9ZzUn5M0kmS1JptuhMc849kGMtxV8CLtyR3zaXx7tPJskH/JFk34PDkbj1JkiRJkkqlbRV071XuKNTCZCfJ3nrrrbzqT5s2rcb6NtvUPfVIv379GDduXI2+8knSZfdVV91SPiel53CXkiQJ1t0CDr4cznoF9vgRdOjWcJ3VZk2Bu0+By7eH8X+GpQuLFqYkSZIkSVKxDRgwoMb6yy+/zOLFi1PXHzt2bL3t1ffY+PHjU/ezaNEiXn655p2kdfVVyuek9EzSSZKkL3TdEPb+NZw9Bfa7ML+hLD77AB75BVzaHx6/ABbmN7a5JEmSJElSJejZsycDBw5cs75ixQqeeeaZ1PXHjBlTY/2AAw6oc9v999+/3rr1efrpp1mxYsWa9UGDBrHhhhvm3LaUz0npmaSTJEm1rdUVBn8fzngRhl8D66cfZoEl8+CpP8BlA+CBs5OhNCVJkiRJkpqRww47rMb6DTfckKre66+/znPPPbdmvXPnzuy77751br/ffvvRsWPHNevjx4/n9ddfT9XXqFGjaqxnx5ytVM9J6ZmkkyRJdatqD9sfC98bD8fdDr2Gpq+7YglM+BtctRPcPgI+nFi8OCVJkiRJkgro+OOPp23btmvW77rrLqZOndpgvYsvvrjG+tFHH02HDh3q3L5Tp04ceeSR9baRy5tvvsndd9+9Zr2qqorjjjuu3jqlek5KzySdJElqWAiw1X5wwoNw4mjodzAQ0tWNq+DVe+C6vWDUQfDWaIixmNFKkiRJkiQ1Sd++fRkxYsSa9WXLljFy5EiWLFlSZ5177723xt1t7du359xzz22wr/POO4927dqtWR81ahT33XdfndsvWbKEE044gWXLlq0pO/HEE9liiy3q7aeUz0npmKSTJEn5+dLOcMw/4fQJsMMIaNs+fd13n4Z/HgHX7A4v3w4rlxcvTkmSJEmS1GJ98MEHvPvuu7WWGTNm1NhuxYoVObd79913+eSTT+rt4/zzz6d79+5r1seNG8c+++xTazjKpUuXcuWVV3LUUUfVKP/Rj35Er169Gnwuffr04cwzz6xRduSRR3LVVVfVSMQBvPbaa+y9996MGzduTdm6666bOnFWquekdEL0SnZJrVQIoT8wefX65MmT6d+/fxkjkpqpBTPhuWvg+eth6fz86nbbLJn7bodvQfvOxYlPkiRJklTRVqxYUWvIvb59+1JVVVWmiNQc9O7dm+nTpzepjREjRtSa1y3bmDFj2G+//Woky0II7LjjjvTp04f58+czceJEZs+eXaPeQQcdxD333FNjeMn6rFy5koMPPpiHHnqoRvkGG2zADjvsQNeuXZk2bRoTJ04kM6/Tvn17Ro8ezR577JGqn1I+pzSK8f2fMmUKAwYMyCwaEGOc0ugGi8gknaRWyySdVGBLF8ALo2D8n2HBR/nV7dgddj4ZdjkFuqxflPAkSZIkSZXJJJ0ao1RJOoAHH3yQkSNH1kpa1eXYY4/luuuuo3Pn/C5IXrhwISeddBK33XZbqu032GADbrzxRvbff/+8+oHSPaeGtPYkncNdSpKkwlirKwz5AZz5Egy/GtbfOn3dz+fCU7+HywbAv38Ec6YVL05JkiRJkqQ8HHjggUyePJnTTjutxlCR2XbbbTfuvPNObr755kYls7p06cKtt97KHXfcwW677Vbndj169OC73/0ukydPblSCDkr3nFQ/76ST1Gp5J51UZKtWwdRHYezl8N64hrfPFNrANofC0DNh40HFiU+SJEmSVBG8k07NybJlyxg7dizTp09nxowZdO7cmU022YRBgwax+eabF7Svd955h4kTJ/LRRx+xaNEiNtpoI3r16sXQoUNp3759wfop5XPK1trvpHMvJ0mSiqNNG/jy/sny/n+TZN3r/wZSXCAUV8GUu5Nl8z2TZN0WX4UQih62JEmSJElSXdq3b89ee+1Vkr4233zzoifJoLTPSTU53KUkSSq+L+0C37gJTn8edvg2tM3jaq93noR/Hg7X7AEv3wErVxQvTkmSJEmSJKlETNJJkqTSWa8vHHIlnPUKDD0L1lo7fd2Zr8BdJ8EVg+C5a2HZoqKFKUmSJEmSJBWbSTpJklR6XTeCr50PZ0+Br/0WuvZMX3f+e/DQT+HS/vDEhbDok+LFKUmSJEmSJBWJSTpJklQ+HdaGoWfAmS/DoX+G9b6cvu7nc+HJi+HSAfDvH8Ocd4oXpyRJkiRJklRgJukkSVL5VbWHQcfD956FY2+FzQanr7vic3j+OrhyB7jzO/DRi0ULU5IkSZIkSSoUk3SSJKlytGkDXz4AvvMwfOdR+PLX09eNq2Dyv+Ave8LfD4W3H4cYixerJEmSJEmS1ARV5Q5AkiQpp812hc1uhtlvwrgr4KVbYdXydHWnjUmWjQbC0DNhm+HQ1sMeSZIkSZIkVQ7vpJMkSZVt/a3g0KvgrFeShNtaa6evO+Nl+NeJcOUgeO4vsGxx8eKUJEmSJEmS8mCSTpIkNQ9r94Sv/QbOnpz822Wj9HXnvQcP/QQu7Q9j/hcWfVq8OCVJkiRJkqQUTNJJkqTmpUO35I66s16GQ66C9bZKX/fzOTDmoiRZ9+BPYO704sUpSZIkSZIk1cMknSRJap6q1oIdvgXfew6+cQt8adf0dVd8Dv/9C1wxCO48ET5+qXhxSpIkSZIkSTmYpJMkSc1bmzaw9YFw4qPwnUfgywemrxtXwuQ74dqvwN+Hw9tPQIxFC1WSJEmSJElararcAUiSJBXMZrsly+w3YNwV8NJtsGp5urrTnkiWntslw2n2OxTaeqgkSZIkSZKk4vBOOkmS1PKs/2U49E/JvHVDzoD2XdPX/fgluPM7cNWO8N/rYNni4sUpSZIkSZKkVssknSRJarnW3hj2/S38cArscz502Sh93bnvwoM/hssGwJiLYfGcooUpSZIkSZKk1scknSRJavk6dIPdz0rurDvkSli3b/q6iz+FMRfCpf3hwZ/C3OlFC1OSJEmSJEmth0k6SZLUelStBTt8G77/X/jGzbDpLunrLl8M/70WrhgE/zoJPn65eHFKkiRJkiSpxTNJJ0mSWp82bWDrr8NJ/4ETHoatDkhfN66EV+6Aa/eAfxwG056EGIsXqyRJkiRJklqkqnIHIEmSVFa9BifLrNdh3BXw8u2wanm6um8/niw9t4ehZ8I2h0KbtkUNV5IkSZIkSS2Dd9JJkiQBbLA1DP8znPkSDPkBtO+avu7HL8KdJ8CVO8Dzf4XlnxctTEmSJEmSJLUMJukkSZIyddsE9r0Azp4Me58LnTdIX3fuu/DvH8GlA+DJ38PiOUULU5IkSZIkSc2bSTpJkqRcOq4De/wQznoFDr4C1t0yfd3Fn8ATv4NL+8NDP4d57xUtTEmSJEmSJDVPJukkSZLq064D7DgCvv88HPNP2GSn9HWXL4bnrobLt4d/nQwzJhctTEmSJEmSJDUvJukkSZLSaNMG+h0MJ42GEx6CrfZPXzeuhFduh2uGwj+PgHeeghiLF6skSZIkSZIqXlW5A5AkSWpWQoBeQ5Jl5qsw7sokAbdqRbr6b41Olo13gKFnJom/Nm2LG7MkSZIkSZIqjnfSSZIkNdaG28BhV8OZL8Hg06F9l/R1P5oId4yAK3eE56+H5Z8XL05JkiRJkiRVHJN0kiRJTdVtU9jvd3D2FNj719B5g/R1574D//4hXDoAnvwDLJ5TvDglSZIkSZJUMUzSSZIkFUrHdWCPH8FZr8DBl0OPLdLXXfwJPHFBkqx7+Bcw7/2ihSlJkiRJkqTyc046SZKkQmvXAXYcCYO+Ba//G8ZeBh++kK7u8kXw7J/hv3+BAUfC0DNgw/7FjFaSJEmSJFWgd955hxdffJGPPvqIhQsX0rNnT3r16sWQIUNo165dwfpZvnw5Y8eO5b333uPjjz+mS5cubLzxxgwaNIjevXsXrB/VZpJOkiSpWNq0hW0OgX4Hw/RxSbJu6qPp6q5aAS/fmixbfg2Gngm9d4cQihqyJEmSJEnNybRp03j++eeZMGECzz//PBMnTmTBggVrHu/Vqxfvvvtuo9oOTfwb/J133mlUkuvOO+/kkksuYfz48Tkf79GjB8cccwy/+c1vWG+99Rod3+zZszn33HO57bbbmDMn9/QbQ4YM4Yc//CFHHHFEo/tR3UzSSZIkFVsI0HtossycAuOuhFfuSBJxabz1n2TZeAfY/SzY+qAkAShJkiRJUis0ZswYLrroIiZMmFBncqk5WrhwISeffDK33nprvdvNmTOHq6++mrvuuosbb7yR/fbbL+++HnroIUaOHMmsWbPq3W7cuHGMGzeO448/nmuvvZbOnTvn3ZfqZpJOkiSplDbsD4ddA189B8b/GV4YlQxxmcZHE+H2b0OPPjDkB7DdccnQmpIkSZIktSIvvvgijz6acqSaZmLlypUcc8wxPPjggzXK119/fQYNGkS3bt14++23mTRpEjFGAGbOnMmhhx7K6NGj2X333VP3NWbMGIYPH86yZcvWlIUQ2GGHHejTpw/z5s1j0qRJfPLJJ2sev+mmm/jss8+45557aNOmTROfrVYzSSdJklQO3TaF/S+EPX8Cz18Pz10Di2anqztnGjxwNjxxIex6Gux8InTsXtx4JUmSJEmqcGuttRabbropb7/9dsHb3nXXXRu8wy3bpptumnrbn//85zUSdO3ateOSSy7hlFNOoX379mvKX331VU466aQ1Q2EuXbqU4cOH88orr9CzZ88G+/nggw84/PDDayTohg4dynXXXUe/fv3WlC1dupRrr72WH//4xyxfvhyA+++/n3POOYcLL7ww9fNS/UzSSZIklVPH7vCVH8Pg0+Glm5OhMOdMS1d30Wx4/Lfw9CWw40gY/L0k+SdJkiRJUgvXrl07+vfvz0477cTOO+/MTjvtxLbbbsvYsWPZa6+9Ct5fhw4dGjW/XBrTpk3j8ssvr1F2xx13cOihh9badptttuGxxx5j7733XpOo+/TTTzn//PO55pprGuzr3HPPZe7cuWvWhwwZwujRo+nQoeZIPWuttRZnnHEGm222GYcddtia8ksuuYRTTz2VXr165fUclZv3JEqSJFWCdh1gp+/A6RPg6L8n88+ltXwRPPsnuHw7uPs0mPlq8eKUJEmSJKnMRowYwWeffcakSZO47rrrOOWUU9hhhx1o165duUNrlPPPP3/N3WoAI0eOzJmgW61jx46MGjWqxh12119/PdOm1X/R79SpU7nxxhvXrLdv355Ro0bVStBlGj58OCNGjFizvnTpUs4///x6+1F6JukkSZIqSZu2sM2hcPLjMPLfsOXX0tddtQJeugWuHgw3HQXvPgPV49RLkiRJktRSdO/evd7EUnPy+eefc+edd9Yo+9nPftZgva222orhw4evWV+xYgU333xzvXVuvvlmVq5cuWb98MMPp2/fvg32lR3P7bffzpIlSxqsp4aZpJMkSapEIUDv3eGbd8JpY2HgN6BNHiOVT30URn0d/roPvHofrFrZcB1JkiRJklRSjzzyCIsXL16zPnjwYLbeeutUdU844YQa63fddVe9299999311q9Lv3792HXXXdesL1q0iEcffTRVXdXPJJ0kSVKl22gAHH4tnPEi7PY9aNc5fd0PJ8Dt34I/7QIvjILlXukmSZIkSVKlePjhh2usDxs2LHXdPfbYg6qqLy7onTRpEjNnzsy57YwZM3jppZfWrFdVVTF06NDUfWXH9dBDD6Wuq7qZpJMkSWou1vkS7H8RnD0ZvnoOdFovfd1P34L7z4TLtoWn/wifzytamJIkSZIkKZ3JkyfXWB88eHDqup07d2bbbbetUTZlypRU/QwcOJDOndNfBDxkyJBU/Sg/JukkSZKam0494Cs/SZJ1X78Eum+evu6iWfDYb+DS/vDIr2D+h8WLU5IkSZKkFuK9997jhBNOoH///nTv3p327duz4YYb0r9/f775zW/yl7/8hTlz5uTd7muvvVZjfcstt8yr/hZbbFFj/dVXX825XXZ5sfpRfkzSSZIkNVftOsLOJ8IPXoCjboSNB6Wvu2whjL8KLh8Id38XZr3WcB1JkiRJklqpd955h1GjRvHqq68yb948li9fzqxZs3j11Ve56aabOPXUU9lss804++yzWbhwYao258yZUyuxt9lmm+UVV/b2U6dOzbndW2+91aR+evXqVWP9008/Ze7cuXm1odqqGt5EkiRJFa1NW+g/HLY5FN59GsZeDm+NTld31Qp46eZk2Wp/GHombDYYQihqyJIkSZJUCCtWrWDm4txzcKmwNuy0IVVtTCnUZ9GiRVx22WU8+OCD3HXXXfTv37/e7efNm1djvVOnTnkNQQmwwQYb1FifP39+qr6y6zWkS5cudOjQgSVLvpjrfv78+XTv3j2vdlST3yhJkqSWIgTY/CvJMuMVGHsFTP4XxJXp6r/5cLJsunOSrPvy16GNAy9IkiRJqlwzF89k/3/tX+4wWoWHj3iYTbpsUu4wSq6qqordd9+dffbZh4EDB7LpppvStWtXFi5cyHvvvcfTTz/N3//+d2bNmrWmzptvvsk+++zDs88+W+sOtEzZd9x17Ngx7/iy6yxYsKCofWUm6erqS+mZpJMkSWqJNtoWjrgO9v4fGP9nmHgjLF+cru4Hz8Nt34R1t4QhZ8B234CqtYobryRJkiRJFeaCCy7g5JNPrvOus+23355DDjmE3/72t5x//vlcfPHFxBgBmDFjBocffjgTJkwg1DFaTXbirEOHDnnHmJ1sq2uozUL1lTnEZdphPVU3L42WJElqydbZDA74Xzh7Cux1DnRaL33dT9+C+8+Ay7aFpy+Bz+cVLUxJkiRJkirNr371q1TDQnbo0IGLLrqIK6+8skb5xIkTueWWW1L3V1cyr9B1St2X6maSTpIkqTXo1AP2/AmcPRm+/kfo3jt93YUz4bHz4dIB8Og58NlHRQtTkiRJkqTm6vvf/z6HHHJIjbI///nPdW7fpUuXGuuff/553n1m18lusxx9KT2TdJIkSa1Ju46w80nwg4lw5A3Qc/v0dZctgHFXwmUD4Z7vwazXixamJEmSJEnN0S9+8Ysa688++yzz5s3Lua1JOpmkkyRJao3atIUBh8MpY+Db98EWe6evu2o5vHgT/HlXuPkbMH180cKUJEmSJKk52WWXXejevfua9ZUrV/Lqq6/m3LZbt2411hcvXsyiRYvy6m/WrFk11tdZZ51Ufc2ePTuvfhYuXFgrSVdXX0qvqtwBSJIkqYxCgD57JsvHL8O4K2DyXRBXpqv/5kPJ8qVdYeiZsNUB0MbrwCRJkiSVxoadNuThIx4udxitwoadNix3CM1CmzZt2GyzzZg7d+6asroSYuuuuy7du3evse17771Hv379Uvc3ffr0Gut9+/bNuV12eXa9fPvp0aNHjWSkGscknSRJkhI9B8IRf4Wv/g88+2eY+HdYvjhd3fefg1uPg3X7wtAzYOAxULVWceOVJEmS1OpVtaliky6blDsMqYaOHTvWWK9vaMl+/foxbty4NetvvfVWXkm6adOm1Wqvrn4yvfXWW6n7yNXPNttsk1d95eZlzpIkSaqpey844GI4ewoM+yV0Wjd93U+nwn0/SOate+YyWDK/aGFKkiRJklSJPvnkkxrr6623Xp3bDhgwoMb6+PHpp5RYtGgRL7/8cr3t1VX+8ssvs3hxygtzgbFjx6bqR/kxSSdJkqTcOvWAYT+DsybDgf8H6/RKX3fhDBh9LlzSHx79H/jso+LFKUmSJElShfjkk09q3XW28cYb17n9/vvvX2N9zJgxqft6+umnWbFixZr1QYMGseGGuYcl7dmzJwMHDlyzvmLFCp555pnUfWXHdcABB6Suq7qZpJMkSVL92neCXU6GH0yEI/8GGw1suM5qyxYk89xdNhDu/T7MfqN4cUqSJEmSVGa33norq1atWrO+4YYb1jt85X777VdjeMzx48fz+uuvp+pr1KhRNdYPO+ywerfPfvyGG25I1c/rr7/Oc889t2a9c+fO7Lvvvqnqqn4m6SRJkpRO2yoYcASc+hR8+17Y4qvp665aDpP+CX/aBW45Ft57tnhxSpIkSZJUBjNnzuSCCy6oUXbwwQcTQqizTqdOnTjyyCNrlF188cUN9vXmm29y9913r1mvqqriuOOOq7fO8ccfT9u2bdes33XXXUydOrXBvrLjOfroo+nQoUOD9dQwk3SSJEnKTwjQZxh86+4kYTfgSAh5HFa+8SD8bT+4fl94/UHIuMJQkiRJkqRye+ONN7j//vvzqjNjxgwOOuggZs6cuaasffv2/OIXv2iw7nnnnUe7du3WrI8aNYr77ruvzu2XLFnCCSecwLJly9aUnXjiiWyxxRb19tO3b19GjBixZn3ZsmWMHDmSJUuW1Fnn3nvvrXHHXvv27Tn33HPr7UfpmaSTJElS4/XcDo68Hs6YBLucAlUdG66z2vvPwa3Hwp93hYn/gBVLixenJEmSJKlF+eCDD3j33XdrLTNmzKix3YoVK3Ju9+677/LJJ5/kbPvjjz/mkEMOYeDAgfz+97+v926zBQsWcNVVV7H99tszYcKEGo+dc8459OnTp8Hn0qdPH84888waZUceeSRXXXVVjUQcwGuvvcbee+/NuHHj1pStu+66qRNn559/Pt27d1+zPm7cOPbZZ59aQ2wuXbqUK6+8kqOOOqpG+Y9+9CN69cpjznrVK8QYyx2DJJVFCKE/MHn1+uTJk+nfv38ZI5KkFmDRp/D8dfDctfD5nPzqdu0Ju30XdhwJHboVJTxJkiRJlWfFihW1kiB9+/alqqqqTBGpOejduzfTp09vUhsjRoyoNa8bwJgxY9hrr71qlHXr1o0BAwaw3nrr0bVrVxYuXMj777/PSy+9xIoVK2q1ccopp3DttdemjmXlypUcfPDBPPTQQzXKN9hgA3bYYQe6du3KtGnTmDhxIpl5nfbt2zN69Gj22GOP1H2NGTOG/fbbr0YCMITAjjvuSJ8+fZg/fz4TJ05k9uzZNeoddNBB3HPPPTWGzGyqYnz/p0yZwoABAzKLBsQYpzS6wSJyLydJkqTC6bwuDPs5DDkDXrwJxl0B895LV3fBx/CfX8NT/wc7nQC7fQ+6blTceCVJkiRJSmH+/PmMHTu2we06d+7MpZdeysknn5xX+23btuX222/npJNO4rbbbltTPmvWLB5++OGcdTbYYANuvPHGvBJ0AMOGDePuu+9m5MiRaxJxMUYmTJhQ627A1Y499liuu+66gibo5HCXkiRJKob2nWCXk+EHk+CI62GjgenrLv0Mxl4Ol20L954Os98sXpySJEmSJGXp168fv/zlLxk6dCgdO6ab1mGrrbbiwgsv5N133807Qbdaly5duPXWW7njjjvYbbfd6tyuR48efPe732Xy5Mnsv//+jerrwAMPZPLkyZx22mk1hr/Mtttuu3HnnXdy880307lz50b1pbo53KWkVsvhLiWphGKEaU8kybdpY/Kv/+Wvw+5nwZd2KXRkkiRJksrM4S5VyVatWsXUqVN5++23+fDDD5k3bx5LliyhY8eOdO/enZ49e7Lzzjuz/vrrF7zvd955h4kTJ/LRRx+xaNEiNtpoI3r16sXQoUNp3759wfpZtmwZY8eOZfr06cyYMYPOnTuzySabMGjQIDbffPOC9ZOLw11KkiRJxRYCbPHVZPnoxWQYzCl3Q1yVrv4b/06WzQbD0DOh737QxkEhJEmSJEnF1aZNG7785S/z5S9/ueR9b7755kVPkkEyr132HHwqDc9sSJIkqbQ23h6O/Bv8YCLsfDJUpRs6BID3xsMt34CrB8Okm2DFsobrSJIkSZIkVSCTdJIkSSqPHpvD1/8Pzp4Me/4cOtY9Bn4ts1+He78Hl28HY6+AJZ8VL05JkiRJkqQiMEknSZKk8uq8Huz1Czh7Chzwe+i2Wfq6Cz6C//wPXDoARp8HC2YULUxJkiRJkqRCMkknSZKkytC+M+x6KpwxCY64HjbcNn3dpfPhmUvhsm3hvh/AJ1MbriNJkiRJklRGJukkSZJUWdpWwbZHwmlPwzfvgs33TF935TKY+He4ame49Xh4//nixSlJkiRJktQEJukkSZJUmUKALfeGEffBKWOg/+EQ0h6+Rnj9Abh+H/jbAfDGw7BqVTGjlSRJkiRJyotJOkmSJFW+jQfBUTfAD16AnU+Cqg7p6743Dm45Bq4eAi/eDCuWFS9OSZIkSZKklEzSSZIkqfno0Qe+/kc4ewrs+TPo2D193dmvwT3fhSu2h3FXwdIFRQtTkiRJkiSpISbpJEmS1Px0Xg/2+iWcNRn2vxi6fSl93c8+hEd/BZf0h9Hnw4KZxYtTkiRJkiSpDibpJEmS1Hyt1QV2Ow3OmASHXwcbDkhfd+l8eOYSuGwA/OfXsHJF8eKUJEmSJEnKYpJOkiRJzV/bdjDwaDjtGfjmv2Dzr6Svu3IZjL0cbv8WrFpVvBglSZIkSZIymKSTJElSyxECbLkPjLgfTn4CthkOIeUh7xsPwthLixqeJEmSJEnSaibpJEmS1DJtsgMcfSOcPgF2OhGqOjRc5/EL4N1nih+bJEmSJElq9UzSSZIkqWVbdws46BI4azJ85SfQYZ26t42r4M7vwMJZJQtPkiRJkiS1TibpJEmS1Dp0WR++eg6cPQX2/19o3yX3dgtnwr9OhFUrSxufJEmSJElqVUzSSZIkqXVZqwvs9l045Iq6t3nnKXjy4tLFJEmSJEmSWh2TdJIkSWqdBhwBO59c9+NP/h7eeqx08UiSJEmSpFbFJJ0kSZJar/1+Bz23r+PBCHedDJ99VMqIJEmSJElSK2GSTpIkSa1X1Vpw9I2wVrfcjy/+FO78DqxcUdq4JEmSJElSi2eSTpIkSa1b995w2NV1P/7eeHj8tyULR5IkSZIktQ4m6SRJkqStvw6DT6/78bGXwRsPlywcSZIkSZLU8pmkkyRJkgD2OQ823aXux+8+Fea9V7JwJEmSJElSy2aSTpIkSQJo2w6OugE69sj9+JJ5cMdIWLGslFFJkiRJkqQWyiSdJEmStFq3TeHw6+p+/MMX4D+/Ll08kiRJkiSpxTJJJ0mSJGXquw/s8eO6H3/uanj13tLFI0mSJEmSWiSTdJIkSVK2Yb+A3nvU/fi9p8Onb5cuHkmSJEmS1OKYpJMkSZKyta2CI/4KnTfI/fjSz+COEbB8SWnjkiRJkiRJLUZVuQOQJEmSKlLXjeDI6+Hvh0JcVfvxGa/Awz+Hgy8reWiSJEmSJFi5ciVvvfUWr776Kh999NH/s3fncVWW+f/HXzebILiAyKKOuJYLmbiAYpqV41Jmapptk5qmNdXUWM1MTTO2z6+msSlryqzUFnPh65KWLVqWuYCKS7jvpoKoiAKCbPfvD/TEgXPgAIdzQN7Px4OHXtfnuu/P526yET7nvi7OnTtHvXr1CAwMpG3btvTo0QN/f3+n5szLy2Pt2rUcPXqU5ORkAgICaNasGVFRUbRq1cqpuQ4dOsTWrVs5ceIEmZmZhIeHExERQWxsLN7e3k7L48pnEmtq0omIiIiI2NO6H/R/Bn54yXZ88yyIiIUud7i2LhERERERkTrq6NGjLFq0iJUrV7JmzRrOnz9vd62npye///3veeSRR7jllluqlPfUqVNMnTqV+fPnk5aWZnNNbGwsU6ZM4fbbb69Srri4OKZNm8b69ettxoOCghgzZgwvvPACwcHBlc7jymcS2wzTNN1dg4iIWxiG0RlIujxOSkqic+fObqxIRERqpMJC+Ox2OPC97bi3P0z6AZpe7dq6RERERESuEPn5+ezbt89qrn379nh56R0TsXb33Xfz+eefV+raoUOH8sEHHxAaGlrha1esWMG4ceNITU11aP0999zDjBkzKvwWX2ZmJg888ADz5s1zaH1oaChz5sxh0KBBFcoDrnum8lTHn/8dO3YQGRlZfCrSNM0dlb5hNdJ/5UREREREyuLhASNnwnt9IeNE6XheFiwYCw+sAh/nfrMiIiIiIiIiv9m7d6/N+ebNm9O+fXtCQ0PJz8/n4MGDbNu2jcLC344uWL58Of369ePHH38kLCzM4ZyrV69m+PDh5ObmWuYMw6Bbt260adOG9PR0tmzZwunTpy3xzz77jPPnz7NkyRI8PDwcylNQUMCYMWP46quvrOabNm1KVFQUjRo14sCBA2zZsoXLL1+dPHmS2267jZUrV3LdddfVuGeS8umfpIiIiIhIefyDYdRHYHjajp/aBV8+AdqlQkRERERExCWioqKYPn06+/fv59ixY/zwww/MmzePuLg4EhMTOXr0KJMmTbK6Zu/evYwePRpHdxg8duwYI0eOtGpm9enThx07drBp0yYWLFjAt99+y7Fjx3jzzTetzolbtmwZzz77rMPP87e//c2qQeft7c306dM5duwY33zzDQsWLGDz5s0kJSXRu3dvy7qLFy8yfPhwkpOTa9wzSfnUpBMRERERcUREbxgw1X582+ew5VPX1SMiIiIiIlLHGIbBLbfcwsaNG0lMTOSRRx6hbdu2Ntc2b96cGTNm8M4771jN//zzz8yfP9+hfFOnTuXs2bOWcWxsLCtXrqRjx45W6+rVq8ef/vQnFixYYDU/bdo0jhw5Um6egwcP8uabb1rNLVy4kEceeQQfHx+r+U6dOrFq1SqrRt2ZM2d4/vnna9QziWPUpBMRERERcVTvR+GqIfbjXz0JKUn24yIiIiIiIlJpCxcuZPny5fTo0cPha/74xz9y++23W8198skn5V63b98+5syZYxn7+Pgwe/ZsfH197V4zfPhwxo4daxlfvHjRoebZ888/T15enmU8btw4brvtNrvr/fz8mD17tlUD78MPP+TgwYNl5nHlM4lj1KQTEREREXGUhwcM/x80amk7np8DC+6DnPOurUtERERERKQOaNWqVaWue/jhh63GP/zwQ7nXzJ07l4KCAst45MiRtG/fvtzr/vrXv1qNFyxYQE5Ojt312dnZxMXFlXkPW6666iqGDx9uGefn5zN37twyr3HVM4nj1KQTEREREamI+kEwejZ4eNuOpx2AZY/pfDoREREREZEaIioqymqcnZ1Nenp6mdcsXrzYajx+/HiHcnXs2JGYmBjLOCsri2+//dbu+m+++YYLFy5Yxr1796ZDhw4O5SpZ06JFi8pc76pnEsepSSciIiIiUlEtusOgl+3HdyyCjR+4rh4RERERERGxy8vLq9Rcbm6u3fUpKSls27bN6vo+ffo4nK9///5W4xUrVthd+/XXX5d5bVn69u1r9Wxbtmzh5MmTNte68pnEcWrSiYiIiIhURvQk6GT/jAC+eQZObHFdPSIiIiIiImLT/v37rcZeXl4EBwfbXZ+UZH3WeJcuXfD393c4X2xsrNV4x44dDufq3bu3w3n8/f255pprHMrlymcSx6lJJyIiIiJSGYYBw6ZDUBvb8YJcWDAWstNdWpaIiIiIiIhYK3nmW48ePfDwsN8e2blzp9W4Xbt2FcrXtm3bMu9X3K5du1ySy5XPJI5Tk05EREREpLJ8G8HoOeBZz3Y8/QgsfVjn04mIiIiIiLhJZmYmH374odXciBEjyrym5Jt3LVu2rFDOiIgIq/GZM2c4e/ZsqXVpaWmkpaVVKVfJ9fv27bO5zlXPJBVTeiNWERERERFxXHgXuPk1WPaY7fju5bDhf9D7YdfWJSIiIiJSB5j5+eSl2D6DS5zLOywUw8bZbjXd008/TUpKimXcuHFjJk6cWOY16enpVuOQkJAK5QwICMDX15ecnBzL3Llz5wgMDCwzT/369Su0BaWt2s6dO2dznaueSSqm9v2JEhERERGpabqNhSPrYPt82/Hv/gktesLvol1bl4iIiIjIFS4v5SQHBgxwdxl1QtuVK/Fp0dzdZVTI4sWLefvtt63mXn75ZYKCgsq8LjMz02rs5+dX4dx+fn5WDa2MjIxqy1OcrTzOzFXeM0nFaLtLEREREZGqMgy4ZRoEX207XpgPC8dB1hmXliUiIiIiIlJXbdu2jfvuu89qbuDAgTz00EPlXluyoeXr61vh/CWbYCXv6co8rs4ljlOTTkRERETEGeoFwB0fg3d92/Hzx2HxZCgsdG1dIiIiIiIidczRo0e55ZZbrJpIERERfPrppxiGUeH7XWnXuDqX2KcmnYiIiIiIs4R0gKFv2I/v/w7WlhEXERERERGRKklNTeX3v/89x48ft8yFhYXx3Xff0bRpU4fuERAQYDXOzs6ucB0lryl5T1fmcXUucZyadCIiIiIiznTtndDtPvvx71+Cwz+7rh4REREREZE6Ii0tjQEDBrB3717LXHBwMCtXrqR9+/YO30dNuqrlEsepSSciIiIi4mxDXoPQSNsxsxDi7ofMVNfWJCIiIiIicgU7d+4cAwcO5JdffrHMBQYG8t1339G5c+cK3atRo0ZW41OnTlXo+szMzFINrcaNG5eb58KFC2RlZVUoV2qq9feWtvLYylVdzyQV4+XuAkRERERErjjefjB6Drx/PeTaOEg78yT83wT4wxLw8HR5eSIiIiIiVwrvsFDarlzp7jLqBO+wUHeXYFdGRgaDBw9m8+bNlrmGDRvy9ddf07Vr1wrfr+Rbd0eOHKnQ9SXXBwUFERgYWGpdkyZNCAwM5OzZs5a5o0eP0rFjx0rnsvfGoKueSSpGTToRERERkeoQ3A6GvVX01pwth36CH1+FG55xbV0iIiIiIlcQw8sLnxbN3V2GuFFWVhY333wzGzZssMwFBASwYsUKoqOjK3XPkk2y/fv3V+j6gwcPWo07depUZq5169ZZ5apIk65kLnvXuvKZxHHa7lJEREREpLpE3g49H7Af//E12L/KdfWIiIiIiIhcQbKzsxk6dCg///zbud/169fnyy+/JDY2ttL3jYy0Pr5g+/btXLhwweHr165dW+b9yoqtX7/e4TxZWVls377doVyufCZxnJp0IiIiIiLVadDLEN7VTtCERQ/A+ROurEhERERERKTWy8nJYdiwYaxevdoy5+vryxdffEG/fv2qdO/w8HC6dOliGefn51s1AstTvCaAIUOG2F07ePDgMq8ty5o1a8jPz7eMo6KiCA21vS2pK59JHKcmnYiIiIhIdfKqB3fMgXqNbMcvnCnaErMgz7V1iYiIiIiI1FK5ubmMHDmSlcXOI6xXrx5LlizhpptuckqOESNGWI1nzZrl0HW7d+8mPj7eMvb392fgwIF21w8aNAg/Pz/LeP369ezevduhXLNnz7Yal6y5JFc9kzhOTToRERERkeoW2ApGvGs/fnQ9fP+iy8oRERERERGprfLz87njjjtYsWKFZc7b25u4uDgGDRrktDz33HMPnp6elvGiRYvYt29fude9+uqrVuM77rgDX19fu+vr16/PqFGjyryHLXv37mXx4sWWsZeXF3fffXeZ17jqmcRxatKJiIiIiLhCh1ug9yP242vfhD0r7MdFRERERETquIKCAu655x6WLl1qmfPy8mL+/PkMHTrUqbnat2/P2LFjLePc3FzGjRtHTk6O3WuWLl1q9Xabj48PU6dOLTfXc889h7e3t2U8e/ZsvvjiC7vrc3JyGD9+PLm5uZa5CRMm0LZt2zLzuPKZxDFq0omIiIiIuMqA56BFtP344gfh7BGXlSMiIiIiIlKb3H///SxYsMBq7pVXXiEqKorDhw9X6KusxtRlzz//PIGBgZbxunXrGDBgQKntKC9evMj06dMZPXq01fwTTzxBREREuXnatGnDY489ZjU3atQo3n77batGHMCuXbu46aabWLdunWWuSZMmDjfOXPVM4hjDNE131yAidYhhGG2BwUA/oAvQAqgHpAM7gW+BD03TPOmCWjoDSZfHSUlJdO7cubrTiohIXXfuGLzXF7LTbMebdYP7vwEvH9fWJSIiIiLiJvn5+aW23Gvfvj1eXl5uqkhqKsMwnHavH374gf79+5e7bvXq1QwaNMiqWWYYBt27d6dNmzacO3eOxMRETp06ZXXd0KFDWbJkidX2kmUpKCjg1ltvtdrGEyAkJIRu3brRoEEDDh48SGJiIsX7Oj4+PqxcuZK+ffs6lMeVz+SI6vjzv2PHDiIjI4tPRZqmuaPSN6xGatKJiMsYhjEbGFveOuA88LBpmp9Wcz1q0omIiHvsWwmf3W4/HvMgDCn/DAIRERERkSuBmnTiKHc06QC++uorxo0bV6ppZc9dd93FzJkz8ff3r1BNmZmZTJw4kfnz5zu0PiQkhDlz5jB48OAK5QHXPVN56nqTTttdiogrtbj06wVgHjAJuB7oBtwGfAqYQEPgY8MwxrijSBERkWrXfgD0fdJ+PP492LnUflxERERERERc5uabbyYpKYkHH3zQaqvIknr16kVcXBxz586tVDMrICCAefPmsXDhQnr16mV3XVBQEA899BBJSUmVatCB655JyqY36UTEZQzD+BjYTNF2lpl21twJfH5peBqIME3zQjXVozfpRETEfQry4ZPhcHiN7Xi9hjBpNTQp++BvEREREZHaTm/SSW2Sm5vL2rVrOXLkCCkpKfj7+9O8eXOioqJo3bq1U3MdOnSIxMRETpw4QVZWFmFhYURERNCnTx98fJx3RIIrn6mkuv4mnf4rJyIuY5rmfQ6smXfpDbrhQDAwAPiimksTERFxPU8vuP2DovPpslJLxy+eh4VjYcJK8PZ1fX0iIiIiIiJSio+PDzfccINLcrVu3bram2Tg2mcSa9ruUkRqolXFfn+V26oQERGpbg3CYNSHYNj5a3nKL/D131xbk4iIiIiIiIi4hN6kE5sMw/AE2gGdgGZAI+AicBY4AGwyTTPLfRXKZYZhtAa6UvS/UwCQDBwB1pmmmefG0qqi+LvaBW6rQkRExBVa94P+z8APL9mOb54FEbHQ5Q7X1iUiIiIiIiIi1UpNOrEwDKMlMJKi7QX7Ag3LWF5gGMZ3wNumaX7pivpqOsMw2gA9gR6Xfu0GNCi25Ihpmq2cmG8UMAXobWdJmmEY84F/mqZ52ll5XeTGYr+vkXsFi4iIOFXfJ+DoejiwynZ82eMQfi00vdqlZYmIiIiIiIhI9dF2lwKAYRhzKXr76g3gFspu0AF4AoOB5YZhLDMMI7SaS6yRDMPobxjGN4ZhnKHoDcN5wJPA9Vg36JyZM8AwjM+Bhdhv0AEEAQ8BSYZhDKqOWqqDYRjRwJBLwxPAavdVIyIi4iIeHjDyfWjQzHY8LwsWjIVcbWQgIiIiIiIicqVQk04us3fu13GKmiTzgf8DtgCFJdYMBX4yDCOs2qqruboCAylqiFW7S9uQzgfuLBE6BXxLUeMuETCLxUKBpYZhXOeKGqvCMIyGwGx++2/TM6Zp5rqvIhERERfyD4ZRH4HhaTt+ahd8+QSYpu24iIiIiIiIiNQq2u5SbNkCfASsME3zQMmgYRjNgX8Ck4pNXwUsNAyjn2nqJ0cUnd93DGjr5Pv+P+DmYuM8ira8fL94M8swjE7AB/z2pl09YIlhGNeYpplcVgLDMIZTdLZdVa0zTfOgo4svNSA/BzpemlpgmuYcJ9QhIiJSe0T0hgFT4bt/2o5v+xwi+kC3P7i2LhERERERERFxOjXp5DIT+BJ4zjTNTWUuNM3jwGTDMLYB7xQLXQeMoWjLx7okj6Jz0zYBGy/9+gvQB/jBWUkunXn3WInp0aZpLi251jTNnYZh3ASs4rdGXRNgKvBgOan+C0RUrVoAxgMONekMwzCAD/mtAbkJmOCEGkRERGqf3o/CkfWwd4Xt+FdPQrMoCIt0bV0iIiIiIiIi4lTa7lIuG22a5tDyGnTFmab5P4q2wCzOqR/rNgyjsWEYnZ10r26GYfg5417FzAEamqYZZZrmA6Zpvm+aZqJpmnlOzgNFDTbvYuPZthp0l5mmmQ2MA4pvFznhUrOvxrjUoHsXGHtpahswyDTNTPdVJSIi4kYeHjD8f9Cope14fg4suA9yzru2LhERERERERFxKjXpBADTNA9X8tJ3SoxvqGIpFoZhNAK+oei8u6gq3ut64CdgmTMbdaZpnjVNM8dZ97PnUs2jSky/Wt51pmnuBZYUm/IC7i7nsi5AoBO+5pZX3yXTgcmXfp8EDDBNM83Ba0VERK5M9YNg9Gzw8LYdTzsAyx7T+XQiIiIiIiIitZiadFJVW0qM/QzDaFzVmxqG0QD4GogGgoBVhmF0r+S9bgS+AvyBm4AvDMPwrWqNLjYIqF9svN40zd0OXjurxHhkWYtN0zxvmma6E75yy8oDYBjGdODhS8MdwI2maZ528LlERESubC26w6CX7cd3LIKNH7iuHhERERERERFxKjXppKrybcz5OOG+ucCZYuNAYKVhGNEVuYlhGL8HlmPd4DpF0TlytcngEuPVFbh2Ddb/O0UZhhFa5YqqyDCMt4BHLg0vN+hOubEkERGRmid6EnS6zX78m2fgeKLr6hERERERERERp1GTTqqqXYlxPlDlN6FM07xI0Rtfy4pNNwa+MwyjlyP3MAxjEPAFUHx7y8+AP5imWVDVGl0sssR4vaMXmqaZBfxSYtop5/xVlmEYbwKPXhpebtClurEkERGRmskwYNh0CLJzpGxBLiwcC9lnXVuXiIiIiIiIiFSZmnRSVSXPSdtkmmahM258abvEURQ12i5rCHxrGEZsWdcahnELsBQovq3lJ8B9tbBBB9CxxHh/Ba8/UGLcqQq1VIlhGG8Af7o03IkadCIiImXzbQSj54BnPdvx9KOw5GGdTyciIiIiIiJSy6hJJ5VmGEYAMKHE9GJn5ijWqFtSbLoB8I1hGH3t1DUMWAQU/0nWHGCcsxqIrmQYRhBF5/IVd7SCtym5vn3lK6o8wzBeBR6/NDxN0dt0IYZhRJbx1dwdtYqIiNQo4V3g5tfsx/d8CevfcV09IiIiIiIiIlJlatJJVfwLCCs2Tgc+cHYS0zTzgDsoarxdFgCsMAzj+uJrDcMYAcRhfS7eLOD+2tigu6RxifGFS1tYVkTJN9UaVb6cKhlT7PfBwCqKtuIs6+tlF9coIiJSM3UbC13G2I+vnApH411Xj4iIiIiIiIhUiZp0UimXmmGPlJj+u2maadWR71KjbgxFDbjL/IGvDMO48VJNo4AFgHexNR8AE2pxgw6KGpLFZVfiHiWvaVDJWkRERMRdDANumQbBV9uOF+ZD3HjIOuPaukRERERERESkUrzcXYDUPoZhXAt8XGL6W+Dd6sxrmma+YRh3AYUUvVkHUB9YbhjGm8CTWP87/T7woGnW+gNaSjbpcipxj5JNupL3dAnTNFu5I69hGOOAcTZC9V1biYiISBXVC4A7PoaZN0DehdLx88dh8WS4ewF46PN4IiIiIiIiIjWZvnOXCjEMoyXwJdZNniPAva5ohpmmmQ/cDcwvNu0H/A3rBt17XBkNOlsq80xX4j+HimgFXG/jq6cbaxIREamckA4w9A378f3fwdoy4iIiIiIiIiJSI+hNOnGYYRghwHdA82LTKcDvTdM85ao6TNMsMAzjHoreqLvLxpJ3gEevoAZdZomxXyXuUfKakve80h0GfrQxXx816kREpDa69k44shYSS25ucMn3L8HvYqDVda6tS0REREREREQcpjfpxCGGYQQBK4Grik2fBgaYprnP1fWYplkAfGMrBHxzBTXoQE26KjNNc7Zpmv1LfgHj3V2biIhIpQ15DUIjbcfMQoi7HzJTXVuTiIiIiIiIiDhMTTopl2EYjSg6c+6aYtNnKXqDboebahoLfGQrBMQZhnGbi0uqTudKjOsbhuFfwXuElBinV74cERERqRG8/WD0HPCxc9Rs5kn4vwlQWODaukRERERERETEIWrSSZkMw2gAfA10LzZ9HhhsmuZWN9U0nqIGXfF/fzcU+70PsNAwjJEuLayamKZ5hqKmaHEtK3ibiBJjl7/9KCIiItUguB0Me8t+/NBP8OOrrqtHRERERERERBymJp3Ydeltra+AXsWmM4EhpmkmuKmmB4APsf539xnTNHsD/y025w3MNwxjtAvLq067SozbVfD6NuXcT0RERGqryNuh5wP24z++BvtXua4eEREREREREXGImnRik2EYfsBy4Lpi0xeAW0zTXOemmh4EZlC0peVlfzFN818Apmn+GfhPsZgX8LlhGHe6rspqk1Ri3NvRCy81W7uUcz8RERGpzQa9DOFd7QRNWPQAnD/hyopEREREREREpBxq0kkphmH4Al8A/YtN5wDDTNP8yU01PQz8D+sG3ROmaf67+DrTNJ8Eiu/p5Al8ahjGPdVfZbX6usS4fwWu7UtRw/KyLaZpnqxyRSIiIlJzeNWDO+ZAvUa24xfOQNz9UJDn2rpERERERERExC416cSKYRg+wCJgQLHpi8Bw0zTdsk+SYRh/At7GukH3mGma02ytN03zb8DLxaY8gY8Nw7iv+qqsdt8A2cXGvQ3D6ODgteNKjBc7pSIRERGpWQJbwYh37ceProfvX3RZOSIiIiIiIiJSNjXpxMIwDC9gATCk2HQeMMo0zW/cVNMU4M1iUybwiGmab5V1nWmazwIvFJvyAGYZhnG/86usfqZpXgDiSkz/tbzrDMO4ChhRbCofmOvE0kRERKQm6XAL9H7Efnztm7BnhevqERERERERERG71KQTAAzD8AQ+A24rNp0PjDFNc7mbanoK6zPmTOCPpmm+48j1pmlOBf5ZbMoD+MAwjAecV6VLPUdR0/SycYZhDLO3+NK2pbMAn2LTH5qmeaB6yhMREZEaYcBz0CLafnzxg3D2iMvKERERERERERHbvMpfInXER8AdJeaeAbYYhtGqgvdKMU0zpyrFGIYRDPyl2JQJTDZNc2ZF7mOa5ouGYeQDr1y+NfAPwzA+N00zsyo1Fqu1Bbb/LIWVGHuV8c8y0zTN02XlMU3zoGEYbwJPFpuOu/S24fumaeYWq6kj8AEQW2ztGeD5snKIiIjIFcDTG0bPgvf6QnZa6XhOOiwcB/d/A14+peMiIiIiIiK1REFBAfv372fnzp2cOHGCc+fOUa9ePQIDA2nbti09evTA39/f3WVW2qFDh9i6dSsnTpwgMzOT8PBwIiIiiI2Nxdvb22l58vLyWLt2LUePHiU5OZmAgACaNWtGVFQUrVq1cloeKU1NOrnM1nltr136qqgbgNVVKcY0zdOGYdwEfA8EAg+YpvlRJe/1r0uNuteAY8CNzmrQXfIzEOHAuubAITuxOZQ+O86WvwGd+W1LUm9gOkWNx0QgA2gDdMP6DL9cYIRpmskO5BAREZHarlELGDkTPrvddvxEInz3DxjyqmvrEhERERERqaKjR4+yaNEiVq5cyZo1azh//rzdtZ6envz+97/nkUce4ZZbbqlwLsMwyl9UhkOHDlWqyRUXF8e0adNYv369zXhQUBBjxozhhRdeIDg4uNL1nTp1iqlTpzJ//nzS0mx8yBOIjY1lypQp3H67ne8vpUq03aXUWKZpbgduAu6rbIOu2L3+DTwI3GCa5n5n1OcOpmkWUPTG4/wSoRBgMDAa6I51gy4VuM00zTUuKVJERERqhvYDoO+T9uPx78GOJS4rR0REREREpKruvvtuIiIi+POf/8yXX35ZZoMOit60+/rrrxk6dCi33norJ0+edFGllZOZmcldd93F6NGj7TboANLS0nj33XeJjIzkm2++qVSuFStWEBkZybvvvmu3QQewbt06Ro0axb333ktWVlalcol9epNOajTTNLcB25x0rxnOuI+7XXoL8E7DMOKAJ4BedpamUdTMm2qa5ilX1SciIiI1SP+n4dd4OGznszpLH4Gwa6BJW9fWJSIiIiIiUgl79+61Od+8eXPat29PaGgo+fn5HDx4kG3btlFYWGhZs3z5cvr168ePP/5IWFjJk4rcr6CggDFjxvDVV19ZzTdt2pSoqCgaNWrEgQMH2LJlC6ZpAnDy5Eluu+02Vq5cyXXXXedwrtWrVzN8+HBycy0nKGEYBt26daNNmzakp6ezZcsWTp/+7YSmzz77jPPnz7NkyRI8PPT+l7OoSScAmKZZtfd26zDTNFu5KW8cRWfStaZoe8tmgD+QAhwB1hY/p05ERETqIE8vuP2DovPpslJLx3MzYOFYmPAdePu5vj4REREREZFKioqK4v7772fIkCG0bVv6g4fHjx/nhRde4P3337fM7d27l9GjR/PTTz9VeCvLmJgY5s2bV6FrWrRo4fDav/3tb1YNOm9vb6ZNm8akSZPw8fntPPGdO3cyceJEy5t2Fy9eZPjw4fzyyy+Eh4eXm+fYsWOMHDnSqkHXp08fZs6cSceOHS1zFy9eZMaMGTz55JPk5eUBsGzZMp599lleeeUVh59LymZc7riKiNQ1hmF0BpIuj5OSkujcubMbKxIREakmh36Cj28Ds9B2vPs4uPVNl5YkIiIiInJZfn4++/bts5pr3749Xl56x0Ss9ezZk9DQUJ577jl69Ojh0DX/+9//ePjhh63mPv/8c+68885yry3eyLv++utZvXp1hep11MGDB+nQoYOlGQawZMkSbrvtNpvrs7Ozuemmm6y2xJw8eTLvvfdeubkmTJjARx/9drpUbGwsq1atwtfX1+b6JUuWMGLECMu4Xr167Nmzh4iIiHJzOaI6/vzv2LGDyMjI4lORpmnuqPQNq5HeSRQRERERudK17gf9n7Ef3zwbti9wWTkiIiIiIiKVsXDhQpYvX+5wgw7gj3/8I7fffrvV3CeffOLs0qrk+eeft2rQjRs3zm6DDsDPz4/Zs2dbvWH34YcfcvDgwTLz7Nu3jzlz5ljGPj4+zJ49226DDmD48OGMHTvWMr548SLPP/98mXnEcWrSiYiIiIjUBX2fgLY32Y8vexxO7XFZOSIiIiIiIhXVqlWrSl1X8k26H374wQnVOEd2djZxcXFWc3/961/Lve6qq65i+PDhlnF+fj5z584t85q5c+dSUFBgGY8cOZL27duXm6tkPQsWLCAnJ6fc66R8atKJiIiIiNQFHh4w8n1o0Mx2PC8LFoyF3CzX1iUiIiIiIlLNoqKirMbZ2dmkp6e7p5gSvvnmGy5cuGAZ9+7dmw4dOjh07fjx463GixYtKnP94sWLy7zeno4dOxITE2MZZ2Vl8e233zp0rZRNTToRERERkbrCPxhGzwLD03b81C748gnQudUiIiIiInIFsXW+WW5urhsqKe3rr7+2Gvfv39/ha/v27Wv1bFu2bOHkyZM216akpLBt2zbL2MvLiz59+jicq2RdK1ascPhasU9NOhERERGRuqRlLxgw1X582+ew5VPX1SMiIiIiIlLN9u/fbzX28vIiODjYTdVYS0pKshr37t3b4Wv9/f255pprrOZ27NjhUJ4uXbrg7+/vcK7Y2FiH8kjFqEknIiIiIlLX9H4UrhpiP/7Vk5CSZD8uIiIiIiJSi5Q8861Hjx54eFSsPXL06FHGjx9P586dCQwMxMfHh9DQUDp37sy9997L+++/T1paWoVr27Vrl9W4Xbt2Fbq+bdu2VuOdO3faXFdyvrrySMWoSSciIiIiUtd4eMDw/0Gjlrbj+Tmw4D7IOe/aukRERERERJwsMzOTDz/80GpuxIgRFb7PoUOHmD17Njt37iQ9PZ28vDxSU1PZuXMnn332GZMnT6Zly5b8+c9/JjMz06F7pqWllWrstWxp5/s0O0qu37dvn811Jd8mrGieiIgIq/GZM2c4e/Zshe4hpZXeiFVERERERK589YNg9Gz4aBAU5pWOpx2AZY/BqI/AMFxenoiIiIiIIwoLCsk8e9HdZdQJAYH18PCsfe/9PP3006SkpFjGjRs3ZuLEidWSKysri//+97989dVXLFq0iM6dO5e5Pj093Wpcv379Cm1BCRASEmI1PnfunEO5Sl5XnoCAAHx9fcnJybHKFRgYWKH7iDU16URERERE6qoW3WHQy7DiL7bjOxZBRCxEP+DaukREREREHJR59iKfPLve3WXUCX94qTcNg/3cXUaFLF68mLfffttq7uWXXyYoKMjhe3h5eXHdddcxYMAAunTpQosWLWjQoAGZmZkcPXqUNWvW8PHHH5Oammq5Zu/evQwYMIANGzaUegOtuJJv3Pn5Vfyfb8lrMjIyqjVX8SadvVziODXpRERERETqsuhJcGQt7FxqO/7NM9C8OzTv5tq6REREREREqmDbtm3cd999VnMDBw7koYcecvgeL730Eg888IDdt866du3KsGHDePHFF3n++ed59dVXMU0TgJSUFEaOHMmmTZsw7OxOUrJx5uvr63Btl5VsttnbatNZuYpvcenotp5iX+17N1VERERERJzHMGDYdAhqYztekAsLx0K2zhoQEREREZHa4ejRo9xyyy1WTaSIiAg+/fRTuw0zW/7+9787tC2kr68v//rXv5g+fbrVfGJiIp9//rnD+SpSW1WucXUusU9NOhERERGRus63EYyeA571bMfTj8KSh+HSJ0JFRERERERqqtTUVH7/+99z/Phxy1xYWBjfffcdTZs2rdbcDz/8MMOGDbOa+9///md3fUBAgNU4Ozu7wjlLXlPynu7IJY5Tk05ERERERCC8C9z8mv34ni9h/Tuuq0dERERERKSC0tLSGDBgAHv37rXMBQcHs3LlStq3b++SGp5++mmr8YYNG0hPT7e5Vk06UZNORERERESKdBsLXcbYj6+cCkfjXVePiIiIiIiIg86dO8fAgQP55ZdfLHOBgYF89913dO7c2WV1REdHExgYaBkXFBSwc+dOm2sbNWpkNb5w4QJZWVkVypeammo1bty4sUO5Tp06VaE8mZmZpZp09nKJ47zcXYCIiIiIiNQQhgG3TIMTW+H0ntLxwnyIGw+T14B/E5eXJyIiIiJSUkBgPf7wUm93l1EnBATa2R6/BsjIyGDw4MFs3rzZMtewYUO+/vprunbt6tJaPDw8aNmyJWfP/naut72GWJMmTQgMDLRae/ToUTp27OhwviNHjliN7b0xWHK+5HUVzRMUFGTVjJTKUZNORERERER+Uy8A7vgYZt4AeRdKx88fh8WT4O6F4KGNOURERETEvTw8PWgY7OfuMsSNsrKyuPnmm9mwYYNlLiAggBUrVhAdHe2Wmvz8rP+dLGtryY4dO7Ju3TrLeP/+/RVq0h08eLDU/ezlKW7//v0O57CVp1OnThW6XmzTd9UiIiIiImItpAMMfcN+fP9K+Hma6+oRERERERGxITs7m6FDh/Lzzz9b5urXr8+XX35JbGys2+o6ffq01Tg4ONju2sjISKvx+vXrHc6TlZXF9u3by7yfvfnt27dz4YKND2basXbtWofySMWoSSciIiIiIqVdeyd0u89+/IeX4dAa19UjIiIiIiJSTE5ODsOGDWP16tWWOV9fX7744gv69evntrpOnz5d6q2zZs2a2V0/ePBgq3Hx5ynPmjVryM/Pt4yjoqIIDQ21uTY8PJwuXbpYxvn5+VbNzfKUrGvIkCEOXyv2qUknIiIiIiK2DXkNQu18OtIshP+bABknXVuTiIiIiIjUebm5uYwcOZKVK1da5urVq8eSJUu46aab3FgZzJs3j8LCQss4NDS0zO0rBw0aZLU95vr169m9e7dDuWbPnm01HjFiRJnrS8ZnzZrlUJ7du3cTHx9vGfv7+zNw4ECHrpWyqUknIiIiIiK2efvB6Dng08B2PPNkUaOusMC1dYmIiIiISJ2Vn5/PHXfcwYoVKyxz3t7exMXFMWjQIDdWBidPnuSll16ymrv11lsxDMPuNfXr12fUqFFWc6+++mq5ufbu3cvixYstYy8vL+6+++4yr7nnnnvw9PS0jBctWsS+ffvKzVWynjvuuANfX99yr5PyqUknIiIiIiL2BbeDYW/Zjx9eAz+W/w2kiIiIiIhIVRUUFHDPPfewdOlSy5yXlxfz589n6NChTsuzZ88eli1bVqFrUlJSGDp0KCdP/rbbiI+PD08//XS51z733HN4e3tbxrNnz+aLL76wuz4nJ4fx48eTm5trmZswYQJt27YtM0/79u0ZO3asZZybm8u4cePIycmxe83SpUut3tjz8fFh6tSpZeYRx6lJJyIiIiIiZYscCT0fsB//8TXYv8p19YiIiIiISJ10//33s2DBAqu5V155haioKA4fPlyhr7IaU8nJyQwbNowuXbrw2muvlfm2WUZGBm+//TZdu3Zl06ZNVrFnn32WNm3alPtcbdq04bHHHrOaGzVqFG+//bZVIw5g165d3HTTTaxbt84y16RJE4cbZ88//zyBgYGW8bp16xgwYECpLTYvXrzI9OnTGT16tNX8E088QUREhEO5pHyGaZrurkFExC0Mw+gMJF0eJyUl0blzZzdWJCIiUoPlX4QPB0LyVtvx+k3gwZ+hof0D0UVEREREbMnPzy/VBGnfvj1eXl5uqkhqqrK2jayoH374gf79+9uMrV69mhtuuMFqrlGjRkRGRhIcHEyDBg3IzMzk119/Zdu2beTn55e6x6RJk5gxY4bD9RQUFHDrrbdabeMJEBISQrdu3WjQoAEHDx4kMTGR4n0dHx8fVq5cSd++fR3OtXr1agYNGmTVADQMg+7du9OmTRvOnTtHYmIip06dsrpu6NChLFmyxGrLzKqqjj//O3bsIDLS6nz1SNM0d1T6htVI/5UTEREREZHyedWDO+bAe/3g4rnS8QtnIO5+GLsMPL1Lx0VERERERGqxc+fOsXbt2nLX+fv788Ybb/DAA2XsRmKDp6cnCxYsYOLEicyfP98yn5qaytdff23zmpCQEObMmVOhBh1A//79Wbx4MePGjbM04kzTZNOmTaXeBrzsrrvuYubMmU5t0Im2uxQREREREUcFtoIR79qPH10P37/osnJERERERESqQ8eOHXnmmWfo06cPfn5+Dl1z1VVX8corr3D48OEKN+guCwgIYN68eSxcuJBevXrZXRcUFMRDDz1EUlISgwcPrlSum2++maSkJB588EGr7S9L6tWrF3FxccydOxd/f/9K5RL7tN2liNRZ2u5SRESkkr75O6x/2378rnlw9RDX1SMiIiIitZq2u5SarLCwkH379nHgwAGOHz9Oeno6OTk5+Pn5ERgYSHh4OD179qRp06ZOz33o0CESExM5ceIEWVlZhIWFERERQZ8+ffDx8XFantzcXNauXcuRI0dISUnB39+f5s2bExUVRevWrZ2WxxZtdykiIiIiIlIRA56DXxPgWILt+OIHYfJPEKjDxEVEREREpHbz8PDg6quv5uqrr3Z57tatW1d7kwyKzrUreQafuIa2uxQRERERkYrx9IbRs8AvyHY8Jx0WjoP8XNtxEREREREREVGTTkREREREKqFRCxg50378RCJ89w/X1SMiIiIiIiJSy6hJJyIiIiIildN+APR90n48/j3YscRl5YiIiIiIiIjUJmrSiYiIiIhI5fV/Glr1tR9f+gicOeC6ekRERERERERqCTXpRERERESk8jy94PYPwD/Edjw3AxaOhbxs19YlIiIiIiIiUsOpSSciIiIiIlXTIAxGfQiGnW8vUn6Br//m2ppEREREREREajg16UREREREpOpa94P+z9iPb54N2+a7rBwRERERERGRmk5NOhERERERcY6+T0Dbm+zHlz8OqbtdVo6IiIiIiIhITaYmnYiIiIiIOIeHB4x8Hxo0sx3Pu1B0Pl1ulmvrEhEREREREamB1KQTERERERHn8Q+G0bPA8LQdP7Ublk8B03RtXSIiIiIiIiI1jJp0IiIiIiLiXC17wYDn7Me3z4Mtn7isHBEREREREZGaSE06ERERERFxvthH4aoh9uNfPQUpSa6rR0RERERERKSGUZNORERERESczzBg+P+gUUvb8fwcWHAf5Jx3bV0iIiIiIiIiNYSadCIiIiIiUj3qB8Ho2eDhbTuedgCWPabz6URERERERKROUpNORERERESqT4vuMOhl+/Edi2DjB66rR0RERERERKSGUJNORERERESqV/Qk6HSb/fg3z8DxRNfVIyIiIiIiIlIDqEknIiIiIiLVyzBg2HQIamM7XpALC8dC9lnX1iUiIiIiIiLiRmrSiYiIiIhI9fNtBKPngGc92/H0o7DkYZ1PJyIiIiIiInWGmnQiIiIiIuIa4V3g5tfsx/d8CevfcV09IiIiIiIiIm6kJp2IiIiIiLhOt7HQZYz9+MqpcDTedfWIiIiIiIiIuImXuwsQEREREXGlszln2ZiykYSUBHac3kGBWcDAVgMZ23ks3h7e7i7vymcYcMs0OLEVTu8pHS/Mh7jxMHkN+DdxeXkiIiIiIiIirqImnYiIiIhc0TJzM0lMTWRD8gYSkhPYc7Z0Y2hX2i6OZx5nau+pbqiwDqoXAHd8DDNvgLwLpePnj8PiSXD3QvDQ5h8iIiIiIiJyZVKTTkRERESuKDn5OWw9tZWE5ATiU+Itb8uVJ25vHEPbDKV7aHcXVCmEdIChb8Diybbj+1fCz9Og35OurUtERERERETERdSkExEREZFaLa8wjx2ndxCfHE9CSgJbU7eSW5hbqXtN2zSNT2/+FMMwnFyl2HTtnXBkLSR+bDv+w8vwuxho3de1dYmIiIiIiIi4gJp0IiIiIlKrFJqF7EnbQ0JKAvHJ8Ww+uZkL+Ta2TKyE7ae3882RbxjcarBT7icOGPIaHE+Ek0mlY2Yh/N+EovPpGoS6vjYRERERERGRaqQmnYiIiIjUaKZpcuj8oaLtK5Pj2XhyI+cunqu2fG9ufpMbf3cjPp4+1ZZDivH2g9Fz4P3+kJtROp55sqhRd99S8PB0eXkiIiIiIiIi1UVNOhERERGpcU5kniA+OZ74lHgSkhM4lX3K6Tl8PX3JKcgpNX8s8xjz98znD53+4PScYkdwOxj2FsSNtx0/vAZW/z+48e+urUtERERERESkGqlJJyIiIiJudzr7NAnJCZYtLI9lHnN6jmC/YKLDoukV3oueYT25WHCRkV+MpNAsLLV2xvYZ3NbuNhr6NHR6HWJH5Eg4uh4S3rcd/+nf0DIG2g1wbV0iIiIiIiIi1URNOhERERFxuXMXz7Hp5CZLY25/+n6n52jo05CeYT2JCY8hJiyG1o1aYxiG1ZqR7UcStzfOZn0fbP+AKT2mOL0uKcPAl+DYRjixxUbQhEWTis6na9Tc5aWJiIiIiEjNk52dze7duzly5AgnTpwgIyODvLw8GjZsSJMmTYiMjKRz5854eTmnFZKXl8fatWs5evQoycnJBAQE0KxZM6KiomjVqpVTclx26NAhtm7dyokTJ8jMzCQ8PJyIiAhiY2Px9vZ2Wh5XPpOUpiadiIiIiFS7C3kX2JK6xbJ95a60XTbfYKsKPy8/uod2JyYshujwaK4OvBrPcs4we7jrw3x58Euy87NLxT7b9Rl3driTZgHNnFqnlMGrHoyeDe/1A1vnDl44A3H3w7jl4Om8b0pFRERERKT2mDVrFt9//z3x8fEcOHCAwsKyv7cMCAjgjjvu4NFHH6Vr166Vynnq1CmmTp3K/PnzSUtLs7kmNjaWKVOmcPvtt1cqx2VxcXFMmzaN9evX24wHBQUxZswYXnjhBYKDgyudx5XPJPYZpmm6uwYREbcwDKMzkHR5nJSUROfOnd1YkYjIlSO3IJftp7Zbtq/cfno7+YX5Ts3h7eFN15CuRIdFExMeQ2STSLwr0bh5d+u7/G/b/2zGbmlzC/+v7/+raqlSUbu/hHl324/H/gkGvui6ekRERESkWuXn57Nv3z6rufbt2zvt7Se5srRo0YLjx49X+DpPT08effRR/v3vf1fo360VK1Ywbtw4UlNTHVp/zz33MGPGDPz9/StUX2ZmJg888ADz5s1zaH1oaChz5sxh0KBBFcoDrnsmR1THn/8dO3YQGRlZfCrSNM0dlb5hNdJ/5URERESkygoKC9iVtov45HgSUhJIPJlITkGOU3N4GB5ENokkOjya6LBookKi8PXyrfJ9x3Yey4K9CzidfbpU7MuDX/KHTn+gcxN9iMOlOtwCvR+B9W/bjq97CyJi4eohrq1LRERERERqnPr169O2bVtatmxJw4YNKSwsJC0tjV9++YWUlBTLuoKCAv773/9y+PBh4uLi8PQse+cVgNWrVzN8+HByc3Mtc4Zh0K1bN9q0aUN6ejpbtmzh9Onfvp/87LPPOH/+PEuWLMHDw8OhZygoKGDMmDF89dVXVvNNmzYlKiqKRo0aceDAAbZs2cLlF69OnjzJbbfdxsqVK7nuuuscyuPKZxLH6E06Eamz9CadiEjlmabJ/vT9JKQksCF5A5tTNpORl+H0PFcFXmV5U657aHca+DRweg6AuL1xPL/+eZux6LBoPhj4Qanz7KSaFeTBrJvhWILtuG9jmPwTBEa4tCwRERERcT69SScVcfXVV9OhQweGDBlCbGwskZGRdhtHGzZs4Nlnn2XVqlVW86+99hpPPfVUmXmOHTtGly5dOHv2rGWuT58+zJw5k44dO1rmLl68yIwZM3jyySfJy8uzzD/99NO88sorDj3TU089xeuvv24Ze3t7M23aNCZNmoSPj49lfufOnUycONFqK8wmTZrwyy+/EB4eXm4eVz6To+r6m3Rq0olInaUmnYiI40zT5FjGMTakbCAhOYGElATScmzvWV8VEQ0jLE25nmE9CfINcnoOW/IL8xn1xSgOnDtgM/7OTe/Qr0U/l9QixZw7Bu/1hWw7/6416wb3fwNePrbjIiIiIlIrqEknFZGXl4e3t+NHHRQWFjJ27Fg+/fRTy1yjRo04efIk9erVs3vdhAkT+Oijjyzj2NhYVq1aha+v7R1dlixZwogRIyzjevXqsWfPHiIiyv5g4cGDB+nQoYNVM2zJkiXcdtttNtdnZ2dz0003WTXqJk+ezHvvvVdmHlc+U0WoSacmnYjUUWrSiYiU7WTWScuZcgkpCSRnJTs9R2j9UGLCY4gJjyE6LJow/zCn53DUT8d+4uFVD9uMtW3UlrhhcXh56IcELrdvJXxWxiHlMQ/CkFddV4+IiIiIOJ2adFLdzp8/T7NmzcjKyrLMrVixgsGDB9tcv2/fPjp27EhBQQEAPj4+JCUl0b59+zLzjBs3jjlz5ljG48ePt2qK2TJ27Fg+/vhjq3vMmjWrzGv27t3LNddcY9my0svLiz179tCmTRu717jymSqirjfptHmoiIiIiABwNucs3x7+lpc2vMSti29lQNwAnvn5GZYeWOq0Bl1gvUAGtRrEP3r9g+UjlvPdqO94+bqXGdZ2mFsbdAB9m/clOizaZuzAuQMs2b/EtQVJkfYDoO+T9uPx78GOJS4rR0REREREap+GDRuWOrdt//79dtfPnTvX0swCGDlyZLnNLIC//vWvVuMFCxaQk2P/vPbs7Gzi4uLKvIctV111FcOHD7eM8/PzmTt3bpnXuOqZpGLUpBMRERGpozJzM/np2E/8e+O/Gb1sNP3m9+OJH59g/p75HD5/2Ck5ArwD6N+iP3/p+Rfibo1j9ZjVvH7969xx9R1ENIyoUee8GYbBlB5T7Mbf2foOF/IuuLAisej/NLTqaz++9BE4Y3urUhEREREREYCgIOvjFDIy7J+rvnjxYqvx+PHjHcrRsWNHYmJiLOOsrCy+/fZbu+u/+eYbLlz47fvM3r1706FDB4dylaxp0aJFZa531TNJxeh9YREREZE6Iic/h22nthGfHE98Sjw7Tu+gwCwo/8IKqOdZj6iQKMv2lZ2adKpVW0R2btKZoW2Gsvzg8lKx09mnmb1jNn/s+kc3VFbHeXrB7R8UnU+XlVo6npsBC8fChO/A28/19YmIiIiISI135MgRq3GzZs1srktJSWHbtm2WsZeXF3369HE4T//+/YmPj7eMV6xYwbBhw2yu/frrr0td66i+ffvi5eVFfn4+AFu2bOHkyZOEhoaWWuvKZ5KKqT0/MRERERGRCskrzGPH6R2Wc+W2pm4ltzDXqTm8DC+uaXoN0WHRxITHcG3Ta/Hx9HFqDld7NOpRvj38rc1/VrN3zGb0VaNpWr+pGyqr4xqEwagP4ePbwCwsHU/5Bb7+G9z6putrExERERGRGm3v3r1WTSbDMLj++uttrk1KSrIad+nSBX9/f4dzxcbGWo137LB/FFrJXL1793Y4j7+/P9dccw1btmyxymWrSefKZ5KKUZNORERE5ApRaBay9+zeojflkuPZfHIzF/Kduz2jgUGHoA70Cu9FdHg03UK6Ud+7vlNzuFuzgGbc0+keZiWVPqg7Oz+bd7a+w3Oxz7m+MIHW/aD/M/DDS7bjm2dDy1i4doxLyxIRERERkZorOTmZ0aNHW53HNmrUKFq1amVz/c6dO63G7dq1q1C+tm3blnm/4nbt2lXlXMWbdDt37uTGG28stc6VzyQVoyadiIiISC1lmiaHzh8iITmBhJSir3MXzzk9T5tGbYgJjyEmLIYeYT1oVK+R03PUNBOvmcjifYtJv5heKrZ4/2Lu7Xgv7QIr9k2NOEnfJ+DoejiwynZ8+eMQfi2EOHaOg4iIiIiIXFny8/M5e/Ysu3btYvny5cyYMYPz589b4m3atOHtt9+2e/3+/futxi1btqxQ/oiICKvxmTNnOHv2LIGBgVbzaWlppKWlVSlXyfX79u2zuc5VzyQVpyadiIiISC1yIvME8cnxRU255ARSs22cz1VFzQOaW86Uiw6LrpNbOzb0acjkLpN5deOrpWKFZiFvJL7BOze944bKBA8PGPl+0fl0GSdKx/MuFJ1P98D34OP49i0iIiIiUjsVFhSQcea0u8uoExo0CcbD09PdZZTy+OOP8+abjm17f8MNN/DJJ58QEhJid016errVuKy1tgQEBODr60tOTo5l7ty5c6UaWiXz1K9fv0JbUNqq7dw52x/cddUzScWpSSciIiJSg53OPs3GlI2WLSyPZR5zeo5gv2DLmXLRYdG0aNDC6TlqozFXj2Hu7rn8mvFrqdhPx34iITmB6PBoN1Qm+AfD6Fkw62YwC0rHT+2G5VNgxHtgGK6vT0RERERcJuPMaT54dIK7y6gTJk7/kEYhpc87qw2GDRvGww8/zMCBA8tdm5mZaTX28/OrcD4/Pz+rhlZGRka15SnOVh5n5irvmaTi1KQTERERqUHO555nU8omElISiE+OZ3/6/vIvqqAGPg0sb8n1Cu9F60atMdTIKMXb05vHuj3Gkz8+aTP++qbXmTd0Hh6Gh4srEwBa9oIBz8F3/7Ad3z4PWvWBbve5tCwREREREal5VqxYQUFBAb6+vvTr16/MtSUbWr6+vhXO5+fnx9mzZ+3e05l5yrqns3OV90xScWrSiYiIiLjRhbwLbE3dyoaUDSQkJ7ArbReFZqFTc/h5+dEttBsxYTHEhMdwdeDVeHrUvC1KaqKBEQPp0rQL209tLxXblbaLrw59xdA2Q91QmQAQ+ygcWQd7V9iOf/UUNIuCsGtcW5eIiIiIiLjMP//5Tx5//HHLODs7mzNnzrB161YWL17M999/T15eHl9++SVffvklDz/8MG+++SaeDm7dWZkPtdbka1ydS8qmJp2IiIiIC+UV5LHt1DbLm3LbT28nvzDfqTm8Pby5tum1RIcXvSkX2SQSb09vp+aoKwzD4MkeT3LfCttvY72V+Ba/j/g99TzrubgyAYq2shzxLrzXD84dLR3Pz4EFY2HSavBt6PLyRERERESk+gUFBREUFFRq/rrrruORRx7h559/5t577+XIkSMAvPPOO2RnZ/Phhx/avF9AQIDVODs7u8I1lbym5D1dmcfVuaRi1KQTERERqUYFhQXsSttFfHI8CSkJJJ5MJKcgp/wLK8DD8KBzk86WM+W6hnTFz6vi+8uLbVEhUdzU8iZWHV1VKpaclczcXXMZHzneDZUJAH6BMHo2fDQICvNKx9MOwBePFq3RJz9FREREROqc6667jh9++IGePXty5swZAD766COGDRvGbbfdVmq9mnRVyyUVoyadiIiIiBOZpsn+9P2WN+U2pWwiI8/5hylfFXgV0WHRxITH0D20Ow18Gjg9h/zm8W6P8+OvP5Jvln7rceb2mYxoN4LGvo1dX5gUadEdBr0MK/5iO75zCWz8AKIfcGlZIiIiIiJSM7Ru3Zp//vOfPPbYY5a51157zWaTrlGjRlbjU6dOVShXZmZmqYZW48aNy81z4cIFsrKy8Pf3dzhXampquXls5aquZ5KKU5NOREREpApM0+RYxjHiU+JJSE4gPiWetJw0p+eJaBhBdFg00eHR9AztSRO/Jk7PIfa1atSKUVeNYt6eeaViGXkZzNg+g79G/9UNlYlF9CQ4shZ2LrUd//ppaN4Nmnd3bV0iIiIiUq0aNAlm4nTb2xaKczVoEuzuEqrkzjvvtGrSbdiwgfT09FLNpvbt21uNL2+T6aiS64OCgggMDCy1rkmTJgQGBnL27FnL3NGjR+nYsWOlc5Ws3d58dT2TVJyadCIiIiIVdDLrpOVNuYSUBJKzkp2eI6R+CL3CexU15sKiCQ8Id3oOqZiHuj7EsoPLyMrLKhWbt2ced3e4m981/J0bKhOgaCvLYdMh5RdIO1g6XpgHC8fB5J+KtsgUERERkSuCh6cnjUJC3V2G1AIhISFWTbHCwkIOHTpEVFSU1bqSTbL9+/dXKM/Bg9bfj3Tq1Mnu2o4dO7Ju3TqrXBVp0pXMZe9aVz6TVIyadCIiIiLlSM9JZ+PJjcQnxxOfHM/h84edniOwXiA9w3oSEx5DTHgMLRu0xND5WTVKkG8QEyIn8NaWt0rF8gvzeXPLm7x+/etuqEwsfBvB6DnwwQAouFg6nn4UljwMd36m8+lEREREROogb29vq/HFi6W/b4iMjLQab9++nQsXLlC/fn2Hcqxdu7bM+5WMFW/SrV+/nltvvdWhPFlZWWzfvt2hXK58JqkYNelERERESsjKy2Lzyc2WN+X2pO3BxHRqDn9vf3qE9iAmPIbosGjaB7bHw/Bwag5xvns73cu8PfNIvZBaKvbN4W+4r9N9dGnaxQ2ViUV4F7j5NVj2mO34ni9h/TsQ+4hr6xIREREREbfKycnh9OnTVnOhoaXfwgwPD6dLly6WBlh+fj4///wzAwcOdCjP6tWrrcZDhgyxu3bw4MG8//77dq8ty5o1a8jP/+3c9KioKJvPA659JqkYNelERESkzrtYcJGtqVstTbmk00kUmAVOzVHPsx5dQ7patrDs1KQTXh76q1ht4+flx6NRj/KPtf+wGf/Ppv8we/BsvQXpbt3GwpF1sH2+7fjKqdCiJ7SMcW1dIiIiIiLiNqtWraKwsNAyrl+/Ps2bN7e5dsSIEVZvqc2aNcuhhtbu3buJj4+3jP39/cu8btCgQfj5+ZGdnQ0UvUm3e/duOnToUG6u2bNnl6q5LK56JqmYWvtxbcMw/AzDaGgYhqe7axEREZHaJa8wj62pW3l/+/tM/GYisXNjmfjtRGb+MpNtp7Y5pUHnZXgRFRLF5C6T+WjQR6y9ay0fDPyAiddMpEvTLmrQ1WK3trmVqwKvshlLTE3k+1+/d3FFUophwC3TIPhq2/HCfIgbD1lnXFuXiIiIiIi4RWFhIS+++KLV3ODBg/Hx8bG5/p577sHT87fWw6JFi9i3b1+5eV599VWr8R133IGvr6/d9fXr12fUqFFl3sOWvXv3snjxYsvYy8uLu+++u8xrXPVMUjGGaTp36yZnMwyjO9AbiAauAloBwUDxjyfnAL8Ch4CtQALwk2ma+q5bROwyDKMzkHR5nJSUROfOnd1YkYhUl0KzkL1n91rOlNt8cjMX8i84NYeBQYegDpbtK7uFdsPf29+pOaTmWHd8HZNXTrYZa9WwFYtuW4S3h7fNuLhQ6m6YeQPk2fnz3m4A3L0QPGrtZxdFRERErgj5+fmlmgXt27fHy0sfbhRr06dPZ9SoUYSHhzt8TV5eHpMmTSr15tmqVau48cYb7V43YcIEPvroI8s4NjaWVatW2W1QLV26lOHDh1vGPj4+7N27l4iIiDLrO3jwIB06dCAvL8/qXsOGDbO5Picnh5tuusnqLLvJkyfz3nvvlZnHlc9UEdXx53/Hjh0lz82LNE1zR6VvWI1q5HejhmH83jCMWYZhnKSo4fYmcA9FjboQiuo2in35UdTAGwj8BYgDUgzDWGcYxl8Mw2jmhscQERERNzFNk0PnDjF/93ymrJ7C9fOvZ/Sy0by+6XXWHF/jtAZdm0ZtuPPqO3mj/xusuXMNC25dwBM9nqBvi75q0F3hYpvHEtss1mbs8PnD/N/e/3NxRWJTSAcY+ob9+P6V8PM019UjIiIiIiJV8uGHH9K2bVvuvfdeli1bRkZGht212dnZfP7550RFRZVq0P3hD38os0EH8PzzzxMYGGgZr1u3jgEDBrB7926rdRcvXmT69OmMHj3aav6JJ55wqJnVpk0bHnvM+kztUaNG8fbbb5Obm2s1v2vXrlINuiZNmjB16tRy87jymcRxNeZNOsMwgoA/ApOAyxvBFn9briKFXr7u8jWFwDfAG6ZprqpKnSJy5dCbdCJXluTMZDYkbyAhJYGE5ARSs1OdnqOZfzNiwmMsb8s1rd/U6Tmk9tiTtofRy0Zj2vhrapBvEF+O+JIAnwA3VCalfPEoJH5sO2Z4wH1fQOu+rq1JRERERCz0Jp04qmvXrmzbts0yNgyDdu3a0apVKxo3boyPjw8ZGRkcOXKEnTt3Wr2ddtnQoUOJi4ujXr165eZbvXo1gwYNsmqWGYZB9+7dadOmDefOnSMxMZFTp06VyrFkyRKr7SXLUlBQwK233sqKFSus5kNCQujWrRsNGjTg4MGDJCYmUryn4+Pjw8qVK+nb1/HvZ1z1TI6q62/Sub1JZxhGMPAsMJGiN+JKNtiMEpcUApnABSD/0jX+gK33MYvf4/LvtwPPmaa51Bn1i0jtpSadSO12Ovs0G1M2Ep8cT0JKAr9m/Or0HE18mxAdHk2v8F5Eh0XTokELp+eQ2u3Zn59l6QHbf6184JoH+FO3P7m4IrEpLxs+GAAnk2zHA0Jh8hpoEOraukREREQEUJNOHFeySVcRfn5+PPvsszz11FN4ezt+PMFXX33FuHHjSjWt7LnrrruYOXMm/v4V22EnMzOTiRMnMn/+fIfWh4SEMGfOHAYPHlyhPOC6Z3KEmnRuatIZhuEL/A2YQlGT7XIjrXhTbi/wE0XnzG0HjgAnTNMssHG/xhS9gdcR6ELR1ph9Lt2bEvc2gQ3Ak6ZprnfiY4lILaImnUjtcj73PJtSNpGQkkB8cjz70/c7PUcDnwZEh0UTHRZNTHgMbRq1wTBKfl5I5DcpWSncuvhWcgpySsV8PX1ZPmI5of5q/NQIp/fD+/0h1852OK36wn1LwcO5nwoVERERkfKpSSeO2rhxI1988QWrVq0iMTGRixcvlntNhw4duOeeexg3bhwtWlTuw7epqalMnTqV+fPnc/bsWZtrevXqxZNPPsntt99eqRyXxcXF8Z///IcNGzbYjAcFBTFmzBief/55mjat/A4/rnymsqhJ54YmnWEYw4D/AhFYN+UKKGrKLQC+ME0zuYp5vIDewCjgduDy2XRmsV9nA381TfNMVXKJSO2jJp1IzXYh7wJbU7cSnxJPfHI8u9J2UWgWOjWHn5cf3UK7ERMWQ3R4NB0CO+CpH9BLBb2V+BYzf5lpMza83XBe7POiiysSu5IWQdx4+/F+f4Eb/+66ekREREQEUJNOKicvL49du3Zx8OBBjh8/TmZmJnl5eQQEBNCwYUNatWpFVFSU1RlsVZWbm8vatWs5cuQIKSkp+Pv707x5c6KiomjdurXT8gAcOnSIxMRETpw4QVZWFmFhYURERNCnTx98fHyclseVz2SLmnTuadIVYv1m2wngA2CmaZrHqymnAdwCTAaGAB6XQibwvGmaL1RHXhGpudSkE6lZ8gry2H56OwnJCWxI3sD209vJL8x3ag5vD2+ubXot0eHRxITFcE3wNXh7Or7FhYgtmbmZ3LL4FtJy0krFDAwW3rqQq4OudkNlYtNXT0HC+3aCBtwbB+0GuLQkERERkbpOTTqRuquuN+nc+V85AzgIvArMNk2z9AmOTmQWdSOXA8sNw7ga+DtwJ6CPy4uIiLhBQWEBu9N2syF5AwkpCWxJ3UJ2frZTc3gYHnRu0rloC8vwaKJCovDz8nNqDpEAnwAevPZBXol/pVTMxOSNzW/w3u/fc0NlYtPAl+DYRjixxUbQhEWTis6na9Tc5aWJiIiIiIhI3eKuJt0p4Hlghq3z5aqbaZp7gPsMw3iZoiahew7mExERqUNM02R/+n7LmXKbUjaRkWfnbKgqaB/YnpiwGGLCY+ge2p0GPg2cnkOkpFFXjWLurrkcPn+4VGztibWsO7GO2Gaxri9MSvOqB6Nnw4x+kHOudPzCGYi7H8YtB71pKyIiIiIiItXIXU26dqZpOv+nchV0qVk33DAM/fRORETEyUzT5FjGMeJT4klITiA+Jd7mdoBV1bJBy6LtK8Nj6BnakyZ+TZyeQ6Q83h7ePN7tcR5f/bjN+LRN04gZGqMzD2uKwFYw/F2Yd7ft+K8bYNULMFDnCYqIiIiIiEj1cUuTriY06IqrafWIiIjUVqkXUolPjichJYGE5AROZJ1weo6Q+iH0Cu9VtIVlWDThAeFOzyFSGTe2vJGokCi2pJbeRnHP2T0sP7ic29rd5obKxKYOt0DvR2D927bj696Clr2hw82urUtERERERETqDJ28KSIiIpWWnpPOxpMbLY25Q+cOOT1H43qNiQ4relMuOiyaiIYRGIbh9DwiVWUYBk/0eIJ7v7rXZnz6lukMajUIXy9fF1cmdg14Dn5NgGMJtuNLHiw6ny4wwqVliYiIiIiISN2gJp2IiIg4LCsvi80nN1u2r9yTtgfTyUe7+nv70yO0h6Ux1z6wPR6Gh1NziFSXa5tey8CIgXx75NtSsZMXTvLprk+ZeM1EN1QmNnl6w+hZ8F5fyLaxHW/OOVg4Du7/uugsOxEREREREREnUpNORERE7LpYcJFtqdvYkLyBhJQEkk4nUWAWODVHPc96dA3pSkxYDNHh0XRu0hkvD/0VRWqvx7s9zve/fk9+YX6p2Ae/fMDI9iMJ8g1yQ2ViU6MWMHImfHa77fiJRPj2H3Dza66tS0RERERERK54+gmYiIiIWOQX5rPjzI6i7SuTE9iSuoXcwlyn5vAyvIgMjiQ6PJqYsBiuDbmWep56Q0WuHL9r+DvuvPpOPt31aalYVl4W7217j2dinnFDZWJX+wHQ90lY87rteMIMiOgNnUe4ti4RERERERG5oqlJJyIiUocVmoXsPbvXcqbc5pObycrLcmoOA4MOQR0sZ8p1C+2Gv7e/U3OI1DSTu0xm6f6lZORllIot3LOQuzvcTatGrVxfmNjX/2n4NR4Or7EdX/oohHWBJm1dW5eIiIiIiIhcsWpNk84wjBuAm4CuQCjQEPCu4G1M0zT1XbWIiNRZpmly+Pxhy5lyG1M2kn4x3el52jRqYzlTrkdoDxr7NnZ6DpGarLFvYyZ2mcgbm98oFcs383kz8U3euKF0TNzI0wtu/6DofLqs1NLx3AxYOBYmfAfefq6vT0RERERERK44Nb5JZxjGMGAa0Lr4dCVvZ1a9IhERkdolOTOZ+JR4S2Mu9YKNHz5XUTP/ZkVvyoVHEx0WTUj9EKfnEKlt7ul4D/N2zyM5K7lUbOXRlWxJ3UJUSJQbKhO7GoTBqA/h49vALCwdT/kFvv4b3Pqm62sTERERERGRK06NbtIZhvEi8Ay/NeXMEr9W6HZOKUpERKSGO5N9ho0pG4lPiSc+OZ5fM351eo4mvk0sZ8pFh0fTIqAFhqH/qxUprp5nPR6NepRnfrZ9/tzrm17n0yGf6s9OTdO6H/R/Bn54yXZ882xoGQvXjnFpWSIiIiIiInLlqbFNOsMw7gH+fml4uSl3+ScYmcA5IN/VdYmIiNQ053PPszlls6Uptz99v9NzNPBpQM/QnpbGXNvGbdVYEHHALW1u4ZOdn7ArbVep2PZT2/n2yLcMajXIDZVJmfo+AUfXw4FVtuPLH4fwayGkg0vLEhERERERkStLjWzSGUU/9Xv10tCkqDm3HfgP8K1pmifdVZuIiIi7Zedns+XkFssWljvTdlJoa1u2KvDz8qNbSLeiplx4DB0CO+Dp4enUHCJ1gYfhwRM9nmDitxNtxt9MfJMbf3cj3p4VPWpZqpWHB4x8v+h8uowTpeN5F4rOp3vge/Dxd319IiIiIiIickWokU06oDfQjN/eoFsEjDFNs8B9JYmIiLhHXkEe209vt5wpt+3UNvILnfsyuZeHF9c2vZaY8BhiwmK4JvgaNQ1EnCQmPIZ+Lfrx07GfSsV+zfiV+Xvmc2+ne91QmZTJPxhGz4JZN4Otb0NO7YblU2DEe6A3i0VERERERKQSamqTLvLSrwaQDTygBp2IiNQVBYUF7E7bbXlTLjE1kez8bKfm8DA86BTUiZjwojPlokKi8PPyc2oOEfnNlO5T+Pn4zzbfen1v+3sMazeMhj4N3VCZlKllLxjwHHz3D9vx7fOgVR/odp9LyxIREREREZErQ01t0jW59KsJrDNN86w7ixEREalOpmlyIP2ApSm38eRGMnIznJ6nfWB7YsJiiA6LpntYdzUERFyobeO2jGg3gv/b93+lYucunuODXz5gSvcpbqhMyhX7KBxZB3tX2I5/9RQ0i4Kwa1xbl4iIiIiIiNR6NbVJV/x1gRS3VSEiIlINTNPkWOaxou0rk+NJSEngTM4Zp+dp2aBl0ZlyYTH0COtBsF+w03OIiOMe7vowXx36yuabsZ/t/Iw7r76TZgHN3FCZlMkwYMS7MKMfpB8tHc/PgQVjYdJq8NWHH0RERERERMRxNbVJt6/Y7xu5rQoREREnSb2QamnIJSQncCLrhNNzhPiFWLavjAmLITwg3Ok5RKTymtZvyrjO43h327ulYrmFuUzfMp1/9f2XGyqTcvkFwujZ8OEgKMwrHU87AF88WrRG59OJiIiIiIiIg2pqk+5nII+i+qLcXIuIiEiFpeeks/HkRktj7tC5Q07P0bheY3qG9SQmLIaY8BgiGkZg6IfDIjXauM7jWLh3IaezT5eKLT+4nD90+gOdmnRyQ2VSrubdYdDLsOIvtuM7l8DGDyD6AZeWJSIiIiIiIrVXjWzSmaZ5zjCMhcDdQDPDMPqbprnazWWJiIjYlZWXxeaTm0lITiAhJYHdabsxMZ2ao75XfXqE9bA05doHtsfD8HBqDhGpXvW96/PHrn/khfUv2IxP2zSNmQNnquFeU0VPgiNrYedS2/Gvn4bm3YoaeiIiIiIiIiLlqJFNukueAW4BGgJvGYYRa5pmpptrEhERAeBiwUW2pW4jPiWehOQEkk4nkW/mOzVHPc96dA3pSkxY0RaWnZp0wtvD26k5RMT1RrQbwac7P+XguYOlYvEp8aw5voZ+Lfq5oTIpl2HAsOmQ8guklf7fj8I8WDgOJv9UtEWmiIiIiIiISBlqbJPONM2jhmGMAZYCnYHvDMO40zTNI24uTURE6qD8wnx2nNlBQnIC8cnxbEndQm5hrlNzeBqeXBN8jeVMuWtDrqWeZz2n5hAR9/Py8GJK9yk88v0jNuNvbH6D2GaxeHnU2L+q122+jWD0HPhgABRcLB1PPwpL/gh3ztX5dCIiIiIiIlKmGv2dv2ma3xqGMQBYAMQAuw3DWAB8DewC0oHCCt7zqLPrFBGRK0+hWci+s/uIT44nPiWezSc3k5WX5dQcBgYdgjoQHRZNdHg03UO74+/t79QcIlIz9WvRj55hPdmYsrFUbH/6fpbuX8rtV93uhsrEIeFd4ObXYNljtuN7voL1b0Pso66tS0RERERERGqVGt2kAzBNc61hGEOBlUAgcO+lr0rdjlrwzCIi4nqmaXLk/BFLU25jykbSL6Y7PU/rRq2JDosmJjyGnqE9aezb2Ok5RKTmMwyDJ3o8wZ3L77QZf2frOwxpPYT63vVdXJk4rNtYOLIOts+3Hf9uKrToCS17ubYuERERERERqTVqdMPKMAwv4FXgUcCToiab9owRERGnSM5MtpwpF58ST+qFVKfnCPcPJyY8hpjwGKLDogmpH+L0HCJSO3Vu0pmbW9/MV4e+KhU7lX2KOTvn8NC1D7mhMnGIYcAt0+DEVji9p3TcLICF4+HBNeAf7PLyREREREREpOarsU06wzA8gWXAQIoac+alkBp1IiJSKWeyz7AxZaOlMXc0w/k7IAf5BhETdqkpFx5Ni4AWGDqTSETs+FO3P/Hdke/IK8wrFZuVNIvRV40m2E8NnhqrXgDc8THMvAHyLpSOZ5yARZPgnjjw8HB9fSIiIiIiIlKj1dgmHfAsMIiiptzlxpwJ7AX2AeeAfLdVJyIiNd753PNsTtlMQkoCG5I3sD99v9NzNPBpQM/QnkSHRxMTFkPbxm3VlBMRhzUPaM49He9h9o7ZpWLZ+dn8b+v/+Gfvf7q+MHFcSAcY+gYsnmw7fmAV/DwN+j3p2rpERERERESkxquRTTrDMOoDU7B+a+5d4P+Zpvmr2woTEZEaLTs/my2pW4q2r0yOZ2faTgrNQqfm8PPyo1tIN0tTrkNQBzw9PJ2aQ0TqlonXTGTRvkWczz1fKrZo3yLu7XgvbRq3cUNl4rBr74QjayHxY9vxH16G38VA676urUtERERE5AqXnZ3N7t27OXLkCCdOnCAjI4O8vDwaNmxIkyZNiIyMpHPnznh51chWSJkOHTrE1q1bOXHiBJmZmYSHhxMREUFsbCze3t5Oy5OXl8fatWs5evQoycnJBAQE0KxZM6KiomjVqpXT8ohtNfXfzOuBBvz2Ft1zpmm+6N6SRESkpskryOOX078QnxxPfEo8205tI7/QuS9Ze3l4cW3Ta4kJK9q+sktwF7w9nfcXIRGRRvUaMbnLZP696d+lYgVmAW9sfoPpN013Q2VSIUNeg+OJcDKpdMwshP+bAJPXQINQ19cmIiIiInIFmTVrFt9//z3x8fEcOHCAwsKyP6AdEBDAHXfcwaOPPkrXrl0dzlPVnZIOHTpUqSZXXFwc06ZNY/369TbjQUFBjBkzhhdeeIHg4Mofj3Dq1CmmTp3K/PnzSUtLs7kmNjaWKVOmcPvtt1c6j5TNME2z/FUuZhjGn4H/XBqeAcJM0yxwY0kicgUyDKMzYPlJWlJSEp07d3ZjRVKegsICdqfttpwpl5iaSHZ+tlNzeBgedArqZHlTLio0Cj8vP6fmEBEpKbcgl2FLhnE887jN+EeDPqJnWE8XVyUVdno/vN8fcjNsx1v1hfuWgt7AFhEREbGSn5/Pvn37rObat29fK99+kurXokULjh+3/b1TWTw9PXn00Uf597//7dC/W65u0mVmZvLAAw8wb948h9aHhoYyZ84cBg0aVOHaVqxYwbhx40hNTXVo/T333MOMGTPw9/evcK7yVMef/x07dhAZGVl8KtI0zR2VvmE1qqn/lfO59KsJbFCDTkSkbjJNkwPpByxNuY0nN5Jh7wefVdCucTtiwmOICYuhe1h3Gvo0dHoOEZGy+Hj68Hi3x3nqp6dsxv+z6T/MvWUuHoaHiyuTCgluB8PegrjxtuOH18Dq/wc3/t21dYmIiIiIXMHq169P27ZtadmyJQ0bNqSwsJC0tDR++eUXUlJSLOsKCgr473//y+HDh4mLi8PTs+Z8eK6goIAxY8bw1VdfWc03bdqUqKgoGjVqxIEDB9iyZQuXX7w6efIkt912GytXruS6665zONfq1asZPnw4ubm5ljnDMOjWrRtt2rQhPT2dLVu2cPr0aUv8s88+4/z58yxZsgQPD31f6kw1tUmXXOz359xWhYiIuNyvGb8WnSl3qTF3JueM03P8rsHvLE25HmE9CPar/NYAIiLOMqjVID7e+TG/nP6lVGzHmR18fehrbm5zsxsqkwqJHAlH10PC+7bjP/0bWsZAuwGurUtERERE5Arh7+/PsGHDGDJkCLGxsURGRtptHG3YsIFnn32WVatWWeaWLFnCtGnTeOop2x+StCUmJsbhN9wua9GihcNr//a3v1k16Ly9vZk2bRqTJk3Cx8fHMr9z504mTpxo2Qrz4sWLDB8+nF9++YXw8PBy8xw7doyRI0daNej69OnDzJkz6dixo2Xu4sWLzJgxgyeffJK8vDwAli1bxrPPPssrr7zi8HNJ+Wrqdpd9gR8pepPuO9M0B7u5JBG5Amm7y5oh9UIqCSkJJCQnkJCSYHert6oI8QshJrzoTLnosGiaBTRzeg4REWfYfHIz474eZzPWPKA5Xwz/Ah9PH5txqUHyL8JHg+DEFtvx+k2Kzqdr1Ny1dYmIiIjUUNruUioiLy8Pb29vh9cXFhYyduxYPv30U8tco0aNOHnyJPXq1bN7XfHtLq+//npWr15dqXrLc/DgQTp06GBphkFRI/G2226zuT47O5ubbrrJ6sy6yZMn895775Wba8KECXz00UeWcWxsLKtWrcLX19fm+iVLljBixAjLuF69euzZs4eIiIhyczmqrm93WVObdJ7AMSCUojfpgrXlpYg4m5p07nHu4jk2pmxkQ/IGElISOHTukNNzNK7XmJ5hPYkJK2rMtWrYqsr7iIuIuMqfvv8TP/z6g83Ykz2eZGznsS6uSCrl7GGY0Q9y7GwM8rteMG45eDr+wwURERGRK5WadFLdzp8/T7NmzcjKyrLMrVixgsGD7b8f5Kom3dixY/n4448t43HjxjFr1qwyr9m7dy/XXHON5Y04Ly8v9uzZQ5s2bexes2/fPjp27EhBQVGrxcfHh6SkJNq3b19mrnHjxjFnzhzLePz48VaNvqqq6026Grl56KWG3OxLw4aAfhIhIlJLZeVl8dOxn3h94+vcsewO+s7ry59X/5n5e+Y7rUFX36s+/Vr048keT7Lw1oX8OOZHpvWfxpgOY2jdqLUadCJSq/y5+5/xNGyfjTBj+wzOXdRu8LVCYCsY/q79+K8bYNULLitHRERERKQua9iwYalz2/bv3++man6TnZ1NXFyc1dxf//rXcq+76qqrGD58uGWcn5/P3Llzy7xm7ty5lgYdwMiRI8tt0NmqZ8GCBeTk5JR7nTimJn8U4WVgJNAe+LdhGOtN09zl5ppERKQcFwsusi11m+VMuaTTSeSb+U7N4ePhQ1RIFNHh0cSEx9CpSSe8PfQmgohcGVo3as2oq0Yxf8/8UrGM3Aze3/4+T/V0/OwEcaMOt0DvR2D927bj696Clr2hg84aFBERERGpbkFBQVbjjIwMN1Xym2+++YYLFy5Yxr1796ZDhw4OXTt+/HgWLFhgGS9atIhnn33W7vrFixeXut4RHTt2JCYmhvj4eACysrL49ttvGTZsmEPXS9lqbJPONM1MwzAGA98BbYC1hmH8CfjMrIl7dIqI1FH5hfnsOLODhOQE4lPi2Zq6lYsFF52aw9PwJDI4kuiwaHqF9+LakGup52l/z3ARkdruwWsfZNmBZVzIv1Aq9vnuz7mrw120aOD4IeTiRgOeg18T4FiC7fiSB4vOpwt03pkOIiIiIiJS2pEjR6zGzZo1c1Mlv/n666+txv3793f42r59++Ll5UV+ftGH47ds2cLJkycJDQ0ttTYlJYVt27ZZxl5eXvTp08fhXP3797c06aBoq1A16ZyjxjbpDMO479Jv3wb+AQQCc4CXDMP4FtgFnAUKK3Jf0zQ/Ln+ViIjYU2gWsu/sPuKT40lISWDTyU1k5WWVf2EFdQjqQHRY0Zty3UO74+/t7/QcIiI1VbBfMPdH3s/bW0u/gZVXmMdbiW/x2vWvuaEyqTBPbxg9C97rC9lppeM552DhOLj/a/DSB1BERERERKrD3r17rZpMhmFw/fXXu7GiIklJSVbj3r17O3ytv78/11xzDVu2bLHM7dixw2aTrmSeLl264O/v+M/aYmNjrcY7dtTI491qpRrbpKPoTLrib8yZgAG0BCZU4b5q0omIVIBpmhw5f4SElATik+PZmLKRsxfPOj1Pq4atiAmPISY8hh6hPQj0DXR6DhGR2uS+zvexYM8CUrNTS8VWHF7BfZ3vIzI40saVUuM0agEjZ8Jnt9uOn0iEb/8BN6vxKiIiIiLibMnJyYwePdrqPLZRo0bRqlUrh+9x9OhRxo8fT0JCAidOnCArK4vAwECCg4OJioqiX79+jBo1qtSWmuXZtcv6hK927dpV6Pq2bdtaNel27tzJjTfeWGrdzp07q5ynrPtJ5dXkJt1lBr8168wS84663ODTNpkiIg5IyUohPjm+6CslntQLpX9AXFXh/uHEhMcQHRZNdFg0of6lP+UjIlKX+Xn58UjUI/xz3T9txl/f9DqzBs3CMCry12Jxm/YDoO+TsOZ12/GEGRDRGzqPcG1dIiIiIiJXmPz8fM6ePcuuXbtYvnw5M2bM4Pz585Z4mzZtePttO+dG23Ho0CEOHTpkNZeamkpqaio7d+7ks88+Y8qUKTzwwAO8+OKLBAQElHvPtLQ00tKsd9to2bJlheoquX7fvn021+3fv79KeSIirLfnP3PmDGfPniUwUB+yr6qa3qQzSvxa1fuIiIgNZ7LPsPHkxqItLJMTOJpx1Ok5gnyDiAmLITo8mpiwGFo0aKEfLIuIlGNY22F8susT9p0t/Y3W5pObWf3ram5oeYPrC5PK6f80/BoPh9fYji99FMK6QJO2tuMiIiIiUopZYFJw7qK7y6gTPBvVw/CseT/Lefzxx3nzzTcdWnvDDTfwySefEBIS4vQ6srKy+O9//8tXX33FokWL6Ny5c5nr09PTrcb169ev0BaUQKnnOHfunEO5Kvr8AQEB+Pr6kpOTY5VLTbqqq8lNuvHuLkBE5EqVkZvBppRNRVtYpsTb/OFvVTXwbkCPsB6Wt+XaNW6nppyISAV5engypfsUHlr5kM34G4lv0LdFX7w8avJf68XC0wtu/6DofLosG2+p52bAgrEw8Tvw9nN9fSIiIiK1UMG5i6S8ttHdZdQJYX/piVeQr7vLqJRhw4bx8MMPM3DgwApd5+XlxXXXXceAAQPo0qULLVq0oEGDBmRmZnL06FHWrFnDxx9/TGrqb3+/37t3LwMGDGDDhg2l3kArLjMz02rs51fx7wFKXpORkVGtuYo36ezlkoqpsd/Nm6Y5x901iIhcif6x9h98ceALCs1Cp97X19OXbqHdiA6Lpld4LzoEdcDTw9OpOURE6qI+zfrQK7wXG5I3lIodOneIRfsWccfVd7ihMqmUBmEw6kP4+Daw9f/FJ3+BFX+FYW+5vjYRERERkSvUihUrKCgowNfXl379+jl0zUsvvcQDDzxg962zrl27MmzYMF588UWef/55Xn31VUyz6MStlJQURo4cyaZNm+x+aL1k48zXt+IN0JLNtpL3dHaus2fPlptLKsbD3QWIiIhrBfkGOaVB5+XhRbeQbvzx2j8ye/Bs1t61lhm/n8GEaybQObizGnQiIk5iGAZP9HgCw84O7u9sfYesvCwXVyVV0rof9H/GfjxxDmyb57p6RERERERqsX/+85+WM+MOHTrEzp07WbNmDdOnT+fGG28EIC8vjy+//JLrr7+eRx55hIKCgnLv+/e//92hbSF9fX3517/+xfTp063mExMT+fzzzx1+jsrsQFXZXatcmUvKpiadiEgdExMWU6nrPAwPOjfpzP2R9zNjwAzW3rmWOUPm8FDXh+ge2h0fTx8nVyoiIpd1COrArW1vtRlLy0njo6SPXFyRVFnfJ6DtTfbjy/8MqbtdV4+IiIiISC0VFBREq1atLF8dO3bkuuuu45FHHmHVqlWsWbPGatvJd955h0mTJjm9jocffphhw4ZZzf3vf/+zuz4gIMBqnJ2dXeGcJa8peU935JKKUZNORKSO6RrS1eGzi9o1bsc9He/hzRve5KcxPzFv6Dz+3P3PxDaPpb53/WquVEREins06lHqedazGft4x8eczDrp4oqkSjw8YOT70KCZ7XjeBVg4FnL1lqSIiIiISFVcd911/PDDDzRp0sQy99FHH7F06VKn53r66aetxhs2bCA9Pd3mWjXpBNSkExGpc+p716dLcBebsd81+B23t7+d1/q9xg93/MDi2xbzt+i/cWPLG2lUr5GLKxURkeLC/MO4t+O9NmM5BTm8s/UdF1ckVeYfDKNngWFni+hTu2H5FLh0roWIiIiIiFRO69at+ec//2k199prrzk9T3R0NIGBgZZxQUEBO3futLm2USPrn7VduHCBrKyKfUgvNTXVaty4cWOHcp06dapCeTIzM0s16ezlkopx7FWKGsQwDG+gO9ATCAGCABM4C6QCG4HNpmnmua1IEZEarld4LxJTEwnxCyE6PJrosGhiwmNoFmDn0/wiIlIjTLhmAov2LeLsxbOlYkv2L+HeTvdyVeBVbqhMKq1lLxjwHHz3D9vx7fOgVR/odp9LyxIRERGpLTwb1SPsLz3dXUad4NnI9s4etcWdd97JY489ZhlffsvNmc0mDw8PWrZsydmzv33PZq8h1qRJEwIDA63WHj16lI4dOzqc78iRI1bj9u3b21xXcr7kdRXNExQUZNWMlMqrNU06wzA6A38G7gJ8y1meYxjG58B/TdNMqvbiRERqmZHtRzK49WBaNWylQ19FRGqRBj4NmHztZP5fwv8rFTMxmbZ5Gu8NeM8NlUmVxD4KR9bB3hW24189Bc2iIOwa19YlIiIiUgsYngZeQeX9uFgEQkJCrJpihYWFHDp0iKioKKfm8fPzsxqXtbVkx44dWbdunWW8f//+CjXpDh48WOp+9vIUt3//fodz2MrTqVOnCl0v9tX47S4Nw/AwDOMlYCswHvADjEtfpZZf+vK7tHarYRgvG4a9/WNEROqmUP9QWjdqrQadiEgtdMdVdxDRMMJmbO3xtaw/sd7FFUmVGQaMeBcat7Qdz8+BBWMh57xr6xIRERERucJ4e3tbjS9evOj0HKdPn7YaBwcH210bGRlpNV6/3vHv57Kysti+fXuZ97M3v337di5cuOBwrrVr1zqURyquRjfpLjXXlgFPA54UNeDMS1/wW1OueNOueNwD+BuwXI06EREREbkSeHt683i3x+3Gp22eRqFZ6LqCxDn8AmH0bPDwth1POwBfPKrz6UREREREKiknJ6dUAy00NNSpOU6fPl3qrbNmzewfLzN48GCr8erVqx3OtWbNGvLz8y3jqKgou88THh5Oly5dLOP8/Hx+/vlnh3OVrGvIkCEOXytlq9FNOuAdYAi/Nee49PstwNvAI8AfLn09AkwHEvmtaWde+nUg8K4rCxcRERERqS43tbyJqBDbW7LsTtvN8oPLXVyROEXz7jDoFfvxnUtg4wcuK0dERERE5EqyatUqCgt/+0Bj/fr1ad68uVNzzJs3zypHaGhomdtXDho0yGp7zPXr17N7926Hcs2ePdtqPGLEiDLXl4zPmjXLoTy7d+8mPj7eMvb392fgwIEOXSvlq7FNOsMwooFJ/PZmnAEsByJN0+xhmuafTNP8n2man136+p9pmo+ZptkT6Ax8gXWjboJhGDHueRoREREREecxDIMp3afYjU/fMp2c/BwXViROE/0AdBpuP/7103B8s8vKERERERG5EhQWFvLiiy9azQ0ePBgfHx+n5Th58iQvvfSS1dytt95a5nEz9evXZ9SoUVZzr776arm59u7dy+LFiy1jLy8v7r777jKvueeee/D0/G3DwUWLFrFv375yc5Ws54477sDXV+dAOkuNbdIBz1369fK/wU+ZpjnMNM2d5V1omuYu0zSHA09g/RbeVGcXKSIiIiLiDl1DuvL7iN/bjKVkpfDprk9dXJE4hWHAsLcgqI3teGEeLBwH2WddWpaIiIiISE0wffp0kpOTK3RNXl4eEyZMsHobDODhhx+2uX7Pnj0sW7asQjlSUlIYOnQoJ0+etMz5+Pjw9NNPl3vtc889Z3VW3uzZs/niiy/srs/JyWH8+PHk5uZa5iZMmEDbtm3LzNO+fXvGjh1rGefm5jJu3Dhycux/wHPp0qVWb+z5+PgwdaraLM5UI5t0hmH4Azfy21t0M0zT/E9F72Oa5hsUbXN5efvLGy/dW0RERESk1nu82+N4eXjZjH34y4ek5aS5uCJxCt9GMHoOeNazHU8/Ckv+qPPpRERERKTO+fDDD2nbti333nsvy5YtIyMjw+7a7OxsPv/8c6KiokptDfmHP/yBG2+80eZ1ycnJDBs2jC5duvDaa6+V+bZZRkYGb7/9Nl27dmXTpk1WsWeffZY2bex8+K6YNm3a8Nhjj1nNjRo1irffftuqEQewa9cubrrpJtatW2eZa9KkicONs+eff57AwEDLeN26dQwYMKDUFpsXL15k+vTpjB492mr+iSeeICIiwqFc4hjDrIHf2BmGMQhYcWmYD4SbpnmmkvdqAiQDXhQ1/G42TfMbpxQqIrWaYRidgaTL46SkJDp37uzGikRERCru/yX8Pz7b9ZnN2N0d7ubpmPI/uSk11ObZsOwx+/GBL0Hsoy4rR0RERKS65Ofnl2qEtG/fHi8v2x9Ik7qra9eubNu2zTI2DIN27drRqlUrGjdujI+PDxkZGRw5coSdO3eSl5dX6h5Dhw4lLi6OevVsfyhu9erV3HDDDVZzjRo1IjIykuDgYBo0aEBmZia//vor27ZtIz8/v9Q9Jk2axIwZMxx+roKCAm699VZWrFhhNR8SEkK3bt1o0KABBw8eJDExkeI9HR8fH1auXEnfvn0dzrV69WoGDRpk1QA0DIPu3bvTpk0bzp07R2JiIqdOnbK6bujQoSxZssRqy0xnqI4//zt27CAyMrL4VKRpmjsqfcNqVFP/K3f5tEYTSKhsgw7ANM0zhmEkALGXplpUtTgRERERkZpicpfJLN2/lMy8zFKxBXsWcHfHu4loqE861krdxsKRdbB9vu34d1OhRU9o2cu1dYmIiIiI1BCmabJv3z6Hzlbz8/Pj2Wef5amnnrLaXtIR586dY+3ateWu8/f354033uCBBx6o0P09PT1ZsGABEydOZP783/7+n5qaytdff23zmpCQEObMmVOhBh1A//79Wbx4MePGjbM04kzTZNOmTaXeBrzsrrvuYubMmU5v0EkN3e4SaFrs90edcL9fi/0+2An3ExERERGpEQJ9A5l4zUSbsXwznzcT33RxReI0hgG3TIPgq23HzQJYOB6yTru2LhERERERN5k5cybPPvssvXv3tvsmXEkdOnTgxRdfZO/evTzzzDPlNug6duzIM888Q58+ffDz83Mox1VXXcUrr7zC4cOHK9yguywgIIB58+axcOFCevWy/0G8oKAgHnroIZKSkhg8eHClct18880kJSXx4IMPWm1/WVKvXr2Ii4tj7ty5+PvrJLHqUFO3u3wcmEbRm3TLTNMcXsX7LQZuu3S/J0zT/G8VSxSRK4C2uxQRkStFTn4Oty65lZSsFJvxT4Z8QteQrq4tSpwndTfMvAHyLtiOt70J7okDj5r6GUwRERGRsmm7S6mMvLw8du3axcGDBzl+/DiZmZnk5eUREBBAw4YNadWqFVFRUWU2ocpTWFjIvn37OHDgAMePHyc9PZ2cnBz8/PwIDAwkPDycnj170rRp0/JvVkGHDh0iMTGREydOkJWVRVhYGBEREfTp0wcfHx+n5cnNzWXt2rUcOXKElJQU/P39ad68OVFRUbRu3dppeezRdpc1U2qx33dywv2K/9T9lN1VIiIiIiK1kK+XL3+K+hPP/PyMzfjrm17nkyGfYBiGiysTpwjpAEPfgMWTbccPrIKf/wP9nnJtXSIiIiIibuTt7U2XLl3o0qVLteXw8PDg6quv5uqr7exuUY1at27tkiaZj49PqTP4xHVq6kctd1/61QDaGoYRU9kbGYYRDbSzcW8RERERkSvGLW1uoWNQR5uxbae2sfLoShdXJE517Z3Q7T778R9egUM/ua4eERERERERqbIa2aQzTTORojfeTIoade8YhuFb0ftcuuadYlOnTdPc7JwqRURERERqDg/Dgyk9ptiN/3fzf8kryHNhReJ0Q16D0EjbMbMQ4iZAxknX1iQiIiIiIiKVViObdJd8RlGDzgSigK8Nwwh19GLDMEKAL4Hul6bMS/cUEREREbki9QrvxXXNr7MZO5pxlAV7F7i4InEqbz8YPQd8GtiOZ6XC/02AwgLX1iUiIiIiIiKVUpObdC8DGZd+bwB9gd2GYbxoGEYHexcZhnG1YRgvULStZX+KmnMAmcAr1VeuiIiIiIj7Tek+BQ/D9l/z39v2Hhm5GTZjUksEt4Nhb9mPH14Dq//lunpERERERESk0mpsk840zTPAWIqabJcbbY2AZ4AdhmGcMQxjg2EY3xiG8bVhGOsNwzgD7AT+DjS+dI0BFADjTdM87dKHEBERERFxsfaB7RnebrjNWPrFdD785UPXFiTOFzkSoifZj//0OuzXGYQiIiIiIiI1XY1t0gGYprkEmARcPjzj8hl1BhAIRAMDgN9f+n1gsfjltbnAQ6ZpLnJl7SIiIiIi7vJw14fx8/KzGft016ekZKW4uCJxuoEvQbMoO0ETFk2Cc8ddWpKIiIiIiIhUTI1u0gGYpvkR0AvYSlHTDX57u84subzYnHHpmt6maX5Q7YWKiIiIiNQQIfVDuK/TfTZjFwsuMn3LdBdXJE7nVQ9GzwbfRrbjF85A3P1QkGc7LiIiIiIiIm5X45t0AKZpbjVNsztwIzAHOMhvb8yV/Dp4ac1Npml2M01zi3uqFhERERFxn/GR4wnyDbIZW3ZgGbvTdru4InG6wFYw/D378V83wKoXXFaOiIiIiIiIVEytaNJdZprmatM0x5um2R4IAq6m6C273pd+H2SaZvtLa35wZ60iIiIiIu7k7+3Pw10fthkzMfnPpv9gmiU3ppBap8PNEPuo/fi6t2D3V66rR0RERERERBxWq5p0xZmmmW6a5j7TNBNM04y/9Pt0d9clIiIiIlJTjGw/ktaNWtuMbUjewNoTa11ckVSLm6bC72Lsx5c8CGePuK4eERERERERcUitbdKJiIiIiEjZvDy8+HO3P9uN/2fTfygoLHBhRVItPL1h1EfgZ3t7U3LOwcJxkH/RpWWJiIiIiIhI2Wpsk84wjH8W+2pYhfs0Kn4vZ9YoIiIiIlLT9f9df7qHdrcZ25++ny8OfOHiiqRaNGoBI2faj59IhG//4bp6REREREREpFw1tkkHPAdMvfTVuAr3aVziXiIiIiIidYZhGDzZ40m78be3vM2FvAsurEiqTfsB0Nf+/9YkzIAdi11Xj4iIiIiIiJSpJjfpAIwaei8RERERkVojMjiSIa2G2IylZqfyyc5PXFyRVJv+T0OrvvbjSx+FMwdcV4+IiIiIiIjYVdObdCIiIiIi4gR/6vYnvD28bcY+SvqI09mnXVyRVAtPL7j9A/APsR3PzYAFYyEv27V1iYiIiIiISCl1oUlX/A26QrdVISIiIiLiRi0atOCuDnfZjF3Iv8B7295zcUVSbRqEwagPwbDz7d7JX2DFX11bk4iIiIiIiJRSF5p0jYr9XodtiIiIiEidNanLJBr4NLAZi9sbx8FzB11ckVSb1v2g/zP244lzYNs819UjIiIiIiIipdSFJl3XS7+agPbwEREREZE6q1G9RkzuMtlmrMAs4L+b/+vagqR69X0C2t5kP778z5C623X1iIiIiIiIiJUruklnGEZ74G/Fpna6qxYRERERkZrgrg530Tyguc3YD7/+wKaUTS6uSKqNhweMfB8aNLMdz7sAC+6D3CzX1iUiIiIiIiIAeLkzuWEY3zu4dJ5hGDkVuHU9IByIKDG/qgL3EBERERG54vh4+vCnqD/x1zW2zyT7z6b/8Nktn+Fh7zwzqV38g2H0LJh1M5gFpeOn98DyKTDiPTCM0nERERERERGpNm5t0gH9KdqGsiwGEFOJe1/+DvPy/dOBTytxHxERERGRK8rg1oP5eOfH7Dizo1Qs6UwS3xz+hiGth7ihMqkWLXvBgOfgu3/Yjm+fBxGx0H2sS8sSERERERGp667kj8debs4ZQAZwt2maOpNOREREROo8D8ODJ3o8YTf+ZuKb5BbkurAiqXaxj8JVZTRev3oKkre7rh4RERERERGpEU06w86XI2vsfeUCqcBqYCrQwTTNb6r5OUREREREao2eYT3p36K/zdjxzON8vvtz1xYk1cswYMS70Lil7XjBRVg4FnLOu7YuERERERGROsytTTrTND3sfV1ecumrVVlrbXz5maYZbprmjaZpvmiaZrIbH1NEREREpEb6c/c/42l42oy9v/19zl085+KKpFr5BcLo2eDhbTuedhC+eBTM8k4kEBEREREREWeoCW/SlUUnl4uIiIiIVJM2jdswsv1Im7HzueeZuX2miyuSate8Owx6xX585xLY+IHLyhEREREREanLanKT7ifgx0u/5ri5FhERERGRK9Ifu/4RPy8/m7G5u+dyLOOYiyuSahf9AHQabj/+9dNwfLPLyhEREREREamramyTzjTN/qZp3nDpK9Xd9YiIiIiIXImC/YK5P/J+m7G8wjze2vKWiyuSamcYMGw6BLWxHS/Mg4XjIPusS8sSERERERGpa2psk05ERERERFzjvk730dSvqc3YikMr2HF6h4srkmrn2xBGzwHPerbj6UdhyR91Pp2IiIiIiEg18nJ3ASIiIiIi4l71vevzSNQjTF031Wb89U2v89GgjzAMHRl9RQnvAje/Bssesx3f8xWsfxtiH3VtXSIiIiIiNVReXh5r167l6NGjJCcnExAQQLNmzYiKiqJVq1ZOzXXo0CG2bt3KiRMnyMzMJDw8nIiICGJjY/H29nZaHlc+k5SmJp2IiIiIiHBb29v4ZOcn7E/fXyq26eQmfjz2I/1/19/1hUn16jYWjqyD7fNtx7+bCi16Qsterq1LRERERKSK7rzzTubPt/57bkREBIcPH67wvU6dOsXUqVOZP38+aWlpNtfExsYyZcoUbr/99sqUaxEXF8e0adNYT8ZILQABAABJREFUv369zXhQUBBjxozhhRdeIDg4uNJ5XPlMYp9h1rLtS4z/z959h0dV5X8cf5+ZSU8ICZ1AEgRFBAUE7FhxbaiIfdVVsK669l1d11111/2t7q5tddeG2DuCYu8FOx0BK5iE3tJIz8yc3x8z6ZMGmTuZ5PN6njzJvefcud+hJvOZ8z3G9AJGAGlAKu1s2WmtfTIcdYlI9DHGjASW1xwvX76ckSNHRrAiERGRyJq3dh6XfnBpyLEhqUOYfcJsPC69z6/LqSyBRw6HrT+EHk8ZCJfMg6QdfwFAREREpCVer5effvqpwbldd90Vj0ffe8qOmTt3LieeeGKT8zsS0r311lucd955bN68uU3zzzrrLB566CGSkpLadZ+SkhIuvPBCnn/++TbN79evH0888QRHHXVUu+4Dzj2ntgjH3/8VK1YwatSo+qdGWWs75T4OUfGvnDGmN3ApcBYwbCcfTiGdiIiIiEgIB2UcxL4D9uXrDV83Gful6Bdm/zSb04afFoHKJKzikuG0J+GRw6C6rOn49vUw+yI4axa4tK25iIiIiHRuhYWF/Pa3v+2Qx/r444+ZMmUKVVVVteeMMey9997ssssuFBYWsnjxYrZu3Vo7/swzz1BcXMwrr7yCq43fP/t8Pk4//XTefPPNBuf79OnD2LFjSU1NZdWqVSxevJiahVebNm3ixBNP5P333+eggw7qdM9J2qbT/2oaY6YCPwM3A7sCZgc+qPdZRERERERCMMZw7bhrMc186/y/Jf+jtLrU4arEEX13h8l3Nz++6gP47E7n6hERERER2UHXXnst69evByAlJWWHH2ft2rVMnTq1QZh14IEHsmLFChYsWMCLL77Iu+++y9q1a7n33nsb7BP32muvcdNNN7X5XjfccEODgC4mJob77ruPtWvX8s477/Diiy+ycOFCli9fzv777187r7KykilTprBhw4ZO95ykbTp1SGeMOQt4CehB05DN1vtorPGYAjoRERERkTYY0WsEk3eZHHJsW8U2Hl/xuLMFiXNGnwF7/6b58Y/+D3751Ll6RERERETa6f3332fmzJkAeDwe/vrXv+7wY918880UFBTUHh9wwAG8//77jBgxosG8uLg4rrjiCl588cUG5++66y5yc3Nbvc/q1au59957G5x76aWXuPzyy4mNjW1wfo899uCDDz5oENRt27aNW2+9tVM9J2m7ThvSGWOGAA8TCNhqwrZlwK3ADTRcJTcNuBK4A5gXnF8zthm4IjhnuhO1i4iIiIhEs9+N/R2xrtiQY0+seILNZW3bt0Ci0DH/hH6jQo9ZP8w6H7ZvcrYmEREREZE2KC0t5cILL6w9vuaaaxgzZswOPdZPP/3EE088UXscGxvL448/Tnx8fLPXTJkyhXPPPbf2uLKysk3h2a233kp1dXXt8XnnnRdyP70aCQkJPP744w0CvEcffZTVq1e3eB8nn5O0XacN6YDrgATqArpbgbHW2luBF+pPtNY+Ya29z1r7R2vtIcDuwCsEgro+wMXAu9baJxARERERkRYNSB7A2XucHXKs3FvO/5b8z+GKxDExCXDqExDbTFug0s3w8vng9zlbl4iIiIhIK/74xz+Sk5MDwC677MItt9yyw4/17LPP4vPVfc87depUdt1111avu/766xscv/jii1RUVDQ7v7y8nFmzZrX4GKHstttuTJkypfbY6/Xy7LPPtniNU89J2qdThnTGGBdwNnUB3UvW2lttzY6IrbDW/mytnQr8jUBQtwfwmjEmpuUrRUREREQE4II9L6BnXM+QY3N+nsPPBT87W5A4p/cwOOE/zY/nzIOP/+FcPSIiIiIirfjiiy/473//W3v80EMPkZCQsMOPN2fOnAbH06ZNa9N1I0aMYN999609Li0t5d133212/jvvvENZWVnt8f7778/uu+/epns1rmn27NktznfqOUn7dMqQDtgLSKGuZeUONY611t5M3Yq6sQTaXoqIiIiISCtSYlO4ZPQlIcf81s9dC+9yuCJx1KipsM9FzY9/+i/46X3n6hERERERaUZlZSXTp0/H7/cDcO655zJp0qQdfryNGzeydOnS2mOPx8OBBx7Y5usPPfTQBsdvvfVWs3PffvvtFq9tycSJE/F4PLXHixcvZtOm0K3pnXxO0j6dNaSr2QTBAnnW2pUtTTbGmBaG/1jv6/N3tjAR2XnGmKHGmMuMMS8YY74zxmw3xlQZYzYbYz42xtxojOkX6TpFRES6u9N2O43MlMyQY/PWzePrDV87XJE46le3wcCxzY/PvhCK1jpXj4iIiIhICLfccgs//PADAH369OHOO+/cqcdbvnx5g+O99tqLpKSkNl9/wAEHNDhesWJFm++1//77t/k+SUlJ7Lnnnm26l5PPSdqns4Z06fW+DvW73bjtZbM7G1prfwC+I7CabrgxZuTOlyciO8oY8zjwM3A/cBqBPSSTgRgCe0geAvwd+NEYE3ozHBEREXFEjDuGK/e+stnxOxfcid/6HaxIHOWJg1Mfh/jU0OPl+TBrOviqQ4+LiIiIiITZokWL+Pe//117fM8999CrV6+desyVKxuuGRo2bFi7rh86dGiLj1ffd99958i9nHxO0j6dNaSrv0t5QYjx0hbmh/Jjva9H7FBFItJRBgU/lwHPAxcRCOb2Bk4EniYQxPcAnjTGnB6JIkVERCTgyKwjGd1ndMix7/K/443VbzhckTgqLRumPNj8+Jqv4YNbHStHRERERKSG1+tl+vTpeL1eAI4++mh+/etf7/Tj/vxzw/23MzNDdxdpTlZWVoPjbdu2UVDQNObIz88nPz9/p+7VeP5PP/0Ucp5Tz0naz9P6lIioH8LFhBjf3ug4A9jcwuOV1Pu6/44WJSIdYj1wFfCotbak0dhiYK4x5g3gOQIrYO83xrxmrS1DREREHGeM4brx13HOW+eEHL9v8X38KvtXxLnjHK5MHLP7sXDA7+CL+0KPf3EfZB4QmCciIiLiMJ/PR3FxcaTL6BZ69OiB2+2OdBm1br/99tp91pKSknjggQc65HELCwsbHPft27dd1ycnJxMfH09FRUXtuaKiItLS0lq8T2JiYrtaUIaqraioKOQ8p56TtF9nDem21vu6R+NBa22VMWYr0Dt4ahSBF/ebM6De18k7X56I7Chr7W/aMOf54Aq6KQT+nk8C5oa5NBEREWnGmL5jmJQ5iffz3m8ytqF0A8989wzTR02PQGXimCNuhjXfBFbOhfLKJXDxPEjLCj0uIiIiEibFxcXce++9kS6jW7jyyis7TSizcuVKbrvtttrjv/3tb2RnZ3fIY5eUNFxXkJCQ0O7HSEhIaBBobd/eeN1Rx92nvlD36ch7tfacpP06a7vL+u0pd21mTv296o5o7oGMMUnAPvVOaQ2mSHT4oN7Xu0WsChEREQHgqnFX4TGh3+M3Y9kMCisKnS1InOWOgVMeg4T00OMVRfDSeeCtdLQsEREREel+/H4/559/PpWVge89x40bxxVXXNFhj9840IqPj2/3YzQOwRo/ppP3cfpe0j6dNaRbCfgItLobYoxJDDFnXvCzAU41xjT3ls0baLh6bkUz8yQEY0yCMWZ/Y8x0Y8y1xpg/GWN+Z4w53RizqzHGRLrG7s4YM8QYc5Ix5jJjzPXGmN8YYw4xxoRqFRtNYut97YtYFSIiIgJAVo8sTh1+asix7dXbeWjZQw5XJI5LzYCpjzQ/vn4RvPtn5+oRERERkW7p3nvv5auvvgLA4/EwY8aMsLbh3JGXwDvzNU7fS1rWKUO64D5Vi4KHhtAr5V6omQ4kAO8aYw6uGTTGpBpjbgP+FJwDkA80058lMowxuwQDr38ZYz42xhQbY2y9j5wI1bW/MeYFoBD4AngU+DdwG/Af4HkCKx7XGGNuNcY085ba7sXJ309jzCnGmC+A1cBs4H7gduAJ4GNgozHmf8aY3s0/Sqd2eL2vFa6LiIh0ApeMvoTkmNDd45//4XnWFK9xuCJx3K6TYOJ1zY9/8xCsmONcPSIiIiLSraxevZqbbrqp9viaa65hzJgxHXqP5OSGP/OUl5e3+zEaX9P4MZ28j9P3kvbplCFd0Dv1vj6h8aC1dgXwKoEQzxJoi/lRMBRZC2wB/hicXjPnfmttdVirbgNjzKHGmHeMMduAVQQCr+uAQ4CUCNfmMcbcD3wOnEbD1UyhZAB/AVYaY44Od32dkdO/n8aYZGPMc8BLwP4tTE0HfgssN8Yc1dF1hJMxZh/gmODhegKho4iIiERYenw65+95fsgxr9/LPYvucbYgiYxD/wjZE5sff/V3sG2Vc/WIiIiISLdgreXCCy+krKwMgF122YVbbrmlw++jkG7n7iXt05lDupqVcgY40xiTGmLOlcDG4Nc2ODcZGAh4qAvnABYA/xe2attnDPArAiFKpxFsXfkccBmBX7v6vgfmAM8C79F0b79+wKvdNKgbg0O/n8YYN4G/G2c0GtoCvEsguFtE3Z97qPu9OSjc9XUEY0wP4HHq/n260VpbFbmKREREpL6zR5xNv8R+IcfezX2XpVuWOlyROM7tgZNnQFLf0ONV2+HFc6G6/T/4i4iIiIg055FHHuHDDz+sPX7ooYea7JPWEVJTG0YRW7Zsadf1JSUlTQKtnj17tnqfsrIySktL23WvzZs3t3qfUPcK13OS9gu983snYK1dYYwZR90L9U32pLLW5hljjiCwcmnP+kPBzyb48Rbw686wiq4VlcBaYGiE7n8BcEqjc58Cl1lrl9c/aYzxAOcAdwM1f8NjgSeMMbtZa4vCXWwUCMfv5+3AsfWOq4FrgIfrB1nGmD2AGdSttIsDXjHG7Gmt3dDSDYwxU2i4j+OO+sJau7o9FwRDyOeAEcFTL1prn+iAWkRERKSDxHvi+d3Y33HT5zeFHL9zwZ08cfQT2q+gq0vpD6c8Ck+eCNbfdHzTt/DW9XDCf5yvTURERLqVHj16cOWVV0a6jG6hR48eEb3/zTffXPv1sccey7Bhw8jJyWnxmo0bNzY49nq9Ta4ZOHAgsbF1DeV23XXXBuO5ubntqrPx/PT0dNLS0prM69WrF2lpaRQU1K2HycvLY8SIEU3mtvVejWtv7ny4npO0X6cN6QCstYvbMOc7Y8zewFTgRAJtL3sSWOm1FHjBWvth848QMdUE9tlaAMwPfv4WOBD4KEI13djo+FNgUqhw01rrBR4zxnwLfEYgBALoC1wC3BHOQjuhsP9+GmN2IbB6tL5TrbWvNp5rrV0ZDLA/oC6o6wXcTOD3pyX3AFk7Vy0A0wjsl9cmwZWcj1IXQi4AQvfTEhERkYiavMtknlr5FD8U/NBkbPHmxXyY9yFHZIXaVlq6lCEHw6E3wke3hR5f9ARkHQCjGzeBEBEREek4brdbYUE3UX8l15tvvsmQIUPa/Rjr1q1rct3ixYsb7GvXOCT7+eef23WP1asbviS6xx57NDt3xIgRfPHFFw3u1Z6QrvG9mrvWyeck7dOZ2122mbXWZ619yVp7trV2X2vtcGvtftbaiztpQPcE0MNaO9Zae6G19mFr7aJIrvQzxuwJZDc6fUVrNVlrFwCPNDp9fAeWhjGmpzFmZAc91t7GmI5eA+3U7+fNQEy948dDBXQ1rLXlwHlA/VaR5wfDvk4lGNA9AJwbPLUUOMpaWxK5qkRERKQ5bpeba8Zf0+z43Yvuptrf2ZtYSIeYeC0MbSGQff1q2Py9c/WIiIiIiOykUaNGNThetmxZ7T54bfH555+3+HgtjX355Zdtvk9paSnLli1r072cfE7SPl0ipIs21toCa21FpOtopHFws8Za29YNRRoHRaHX1O6A4F6E7wCfGmPG7uRjHUJgdeBrHRnUOfH7Gay3cSvSVlcrWmt/BF6pd8oD/LqVy/YC0jrg49nW6qvnPuDi4NfLCazgzG/H9SIiIuKwAwYewIEDDww5llucy6wfZzlckUSEywVTH4aUgaHHq8vgxd9AVfv21hARERERiZQBAwaw11571R57vV4+++yzNl//8ccfNzg+5phjmp179NFHt3htS+bNm4fX6609Hjt2LP36hd4/3MnnJO0TsZDOGPNnY4zi1s4jqdHx2nZcu6bRcYesLzfGpABvA/sA6cAHwX0Kd+SxDgfeJPA8jwDmGmPiO6JOhxwFJNY7/tJa29a3JD/W6HhqS5OttcXW2sIO+Khq6T41jDH3AZcFD1cAh1trt7bxuYmIiEgEXT3uagyh9557YMkDbK/a7nBFEhFJveHUx8C4Q49v/QFevwasDT0uIiIiItIGhYWFWGvb9fHRRw13IsrKymoyp36ryxonnXRSg+PHHmv8Emto33//PV9//XXtcVJSEr/61a+anX/UUUeRkFC3nuTLL7/k++/b9rLv448/3uC4cc2NOfWcpH0iuZLuVmCpMeYnY8w/jTH7t3qFhNPGRsftCbAaz+2oFVBVwLZ6x2nA+8aYfdrzIMaYI4HXaRhybSGwj1y0OLrR8cftuHYe4K13PNYYE/otFQ4zxvwHuDx4WBPQbYlgSSIiItIOw9OHc+KwE0OOFVQWMHP5TIcrkojJ3A8m3dL8+LLnYdGTjpUjIiIiIrIzzjrrLNzuujehzZ49m59++qnV6+64o2Hzs9NOO434+OZfak9MTOSUUxo2UGv8GKH8+OOPzJkzp/bY4/Hw61+33EDNqeck7dMZ2l0OBa4FPjPGrDfGPGiMOcoY44l0Yd3MfKCy3vGIdrSEbLy6bX5HFGStrSSw6uu1eqd7Au8ZY/Zry2MYY44C5gL1n8szwDnWWl9H1OmQxqtO29yc2FpbCnzb6HSH7PG3M4wx9wK/Cx7WBHSbI1iSiIiI7IDLx1xOvDv0D2hPrXyKjaWN3wsmXdYBv4PdWmh78+bvYcOy5sdFRERERDqJXXfdlXPPPbf2uKqqivPOO4+KiuZ3PXr11VcbrG6LjY3l5ptvbvVet9xyCzExMbXHjz/+OHPnzm12fkVFBdOmTaOqqq6R2fnnn8/QoUNbvI+Tz0naLpIhXRHU9sYxwY/+wIUE2hJuMcY8Y4w5xRjTuBWjdDBr7Xag/ltb44HzW7vOGOOmbiVUjSc6sK4qAnux1f9XqQfwrjHmgFZqO47Afnn1XzV6CvhNlAV0ACMaHf/czutXNTreYydq2WnGmLuBK4KHK1FAJyIiErX6JfXjnD3OCTlW6avkvsX3OVyRRIwxcNID0DMz9LivEl46FyqKna1LRERERGQH3HrrraSl1e3s9MUXXzBp0qQm7SgrKyu57777OPXUUxucv/baa8nKymr1PrvssgtXXnllg3OnnHIK999/f4MgDuC7777jiCOO4Isvvqg916tXrzYHZ049J2k7YyO0L0BwpdyhwEnAiUConcZriqsEPgDmAHO78n5VxphDgfqNcnOttdkO3TsdWAjU3K8cOMFa+34z82OAB4Hp9U5/CEyyHfwHK3ivF4Ep9U6XAMdaa+eFmH8C8BIQW+/0E8B0a62/I2trSUf8fgZ/X7Y1Op0cXCHX1se4E7im3qn/WGuvbG5+OBlj7gD+EDzcCpwOtBbQFVhr14WhlpHA8prj5cuXM3JkxBcZioiIRJ2SqhKOm3Mc+RVNu54bDC8d/xLD04dHoDKJiHUL4dGjwN9Md/k9psCpjwdCPRERERHA6/U2abu366674vGo2ZrsnI8//pjDDjus9jgrK4ucnJx2XX/UUUc1CMuMMYwbN45ddtmFoqIiFi1axJYtDXfwmTx5Mq+88kqD9pIt8fl8HH/88bz11lsNzvft25e9996blJQUVq9ezaJFi6j/0ntsbCzvv/8+EydO7HTPqa3C8fd/xYoVjBrVoDndKGvtih1+wDCK2Eo6a63XWvu+tfYya+0gYH/gn8CP9abV/NQWDxwLPAJsMMZ8bIy50hijyLYDWWvzgcOAxcFTCcA7xpgXjDGnGmP2NMYMM8bsZ4y5mkALxfoB3TfAKR0d0AVrqwZOA2bXO50MvGWMOaT+XGPMScAsGgZ0j+FwQNeBejY6LmtPQBfUOARL3fFydtrp9b7uTSCA/7aVj787XKOIiIi0Q3JsMr8d/duQYxbLnQvudLgiiaiMcXDU/zU/vvIV+OYRx8oREREREdlRhx56KHPmzKFPnz6156y1LFiwgBdffJF33nmnSZh15pln8vzzz7crzHK73bz44oucfvrpDc5v3ryZt99+m5deeomFCxc2COj69u3Lq6++2q6AzsnnJG3TGfakA8Ba+7W19gZr7e4E9su6icCqrvotMQHcwETgLmC1MWaRMebPxpjGe3bJDrDW5gD7AhcBCwj8up9GYBXbMuAnAvuh3QXUvB16G/An4CBrbUEYa6smEPDMqnc6CXjTGHM4gDHmlGCtMfXmzADOj9KADgJhZH3lO/AYja9J2cFaopIx5rxguN/gg0B4KyIiIh3g5N1OJrtHdsixLzd8yefrPne2IImsfS4MrJhrzjs3BlbciYiIiIh0csceeyzLly/nkksuadAqsrH99tuPWbNm8eyzz5KU1P4dvJKTk3n++ed56aWX2G+//Zqdl56ezm9/+1uWL1/O0Ucf3e77gHPPSVoXsXaXbWWMGUSgJeZJBMK5mqjWEgiQ6j+B1QRaYs6x1n7pZJ0dJZLtLuvVEA+cB5xNYIVjS2HuGuAvwPPW2uZ3mOxAwVapzxAID2uUA/cC1wH118E+DFwSjtV9bdFB7S4PAOq/qrUuuPq0PY9xIYFfixrvWmuPas9jRDNjzC1Aq42Z1e5SRERk53yQ9wFXfXRVyLFd03blpckv4XbpnZfdRkUxPHwI5K8OPZ6aCZd8CgnNvyggIiIi3YPaXUq0qKqq4vPPPyc3N5eNGzeSlJRERkYGY8eOZciQIR16r19++YVFixaxfv16SktL6d+/P1lZWRx44IHExsa2/gBt5ORzCqW7t7vs9P/KWWvXAvcB9wX35jqeQGB3JIF2jDVBnQGGAtcC1xpjNgOvEgjtPrDWeiNQftQxxhwIPE3dvnStGUxgNdI/jTE3WmtnhKu2GtZarzHm1wR+32vW/yYANzSa+iBwaaQCujDakefT1X4N2isH+CTE+URggrOliIiIdF2HDz6cvfvuzaLNi5qM/VTwE3NXzeWkXU+KQGUSEfE94NQnYMYk8FU2HS/Kg1cuhTOe1f50IiIiIhIVYmNjG+xxF05DhgxxJCRz8jlJU52m3WVbWGvzrbVPWGunAH2AUwisqCqqN80EP/oBFwJvAluMMc8YY04xxmhNZjOMMUcA79MwoFtHIPwaS2BvtFigP3A08ARQE372AR4xxjxsTPh/wrbW+oCzgOeamfJfuk5AV9LoOGEHHqPxNY0fs0uz1j5urT208QcwLdK1iYiIdCXGGK4df22z4/cvvp9y74507paoNWAvOPafzY//8CZ8eb9z9YiIiIiIiHQiURXS1WetLbPWzrbWngP0BX4FPACsrzetJrBLBc4AXiAQ2L1mjJkeXJkngDGmD4HAK77e6deAPay1d1hrl1hri6y11dbaTdbad6y15xFoQbqt3jUXAn9wouZgUPdOqCHgnS4S0IFCOgmDqrXr8G3fHukyRESkC9qrz14clR26q/bm8s08tfIphyuSiNv7XNjr9ObH37sZ8r5yrh4REREREZFOImpDuvqstV5r7fvW2suCe3XtD/wT+LHetJrVXfHAscAjwOXOVtqpXUNgNVyN74HTrLXFLV1krf2KupaTNW42xvTt4PqaMMacC8wMNQTMMsacGO4aHFLU6DhxB1aENv79KNzxcqQrWH/ttfw4YR9+PPAgcs46m/U3/omtDz9C8bvvUvHDj/grHNliUkREuqgr974Sjyt0Z/2Zy2eyrXxbyDHpooyB4+6C3sNDj1sfvDQNSrc6W5eIiIiIiEiEdfo96XaEtfZr4GvgBmPMCAJ72J0EjAtOqdnHTuqc2uj4Dmttm16lt9Z+YIyZR2BVHQRWbZ0B/KcD62vAGDMNmEHDoPkrYL/g17HAS8aYM6y1s8NVhxOstduMMQVAWr3TmcB37XiYrEbHP4WcJd1GVU4OAL5t2yjfto3yhQubzPEMHEBsVhax2dnEZWcTk5UV+JyRgYmJcbhiERGJJoNTBnPm7meGXDVXWl3KA0sf4Kb9bopAZRIxcclw2pPwyGFQXdZ0fPt6mH0RnDULXF3ivaQiIiIiIiKt6pIhXX3W2u8IhBn/Z4wZRCCsmwIcHMm6OpPgqqyhjU5/0M6HeZ+6kA5g350qqgXGmAuBh6hbHQlwo7X2H8aYu4GrgudigBeMMb+21r4Urnoc8h1wQL3jYbQvpNslxONJN+UtKMBX1HiBZoh56zfgXb+Bsi8btZ/yeIjNyCA2Ozv4EQjyYrOy8PTvj9ELayIiAly818W88vMrbK9q2l551o+zOGvEWQxJDf8m6NKJ9N0dJt8Ncy4OPb7qA/jsTjj4987WJSIiIiIiEiFdPqSrz1q7FrgPuC+4H12vCJfUWfQMcW5jOx+j8fzeO1ZKy4wxlwD/o2FA9wdr7b8ArLVXG2N8wLXBMQ/wnDHGba19Phw1OWQ5DUO6/QnsGdiqYAi7V4jHk26qOjd35x7A66UqN5eq3Fz45JMGQyYuLrD6LrgCr36I505PxxjTzIOKiEhXkxqXykV7XsSdC+9sMuazPu5ZeA/3Hn5vBCqTiBp9BuR+DoueDD3+0f/B4H1hiN5TKSIiIiIiXV+nDemMMfsG21aGhbU2H8gP1+NHmcIQ55KaOd+c5EbHJTtaTHOMMZcRCFnrv8p/rbX2rvrzrLXXGWO8wPXBU27g6WBQ90xH1+WQt4GL6h0f2o5rJ9Lw7/pia+2mjihKolNlsNVlONjKSip//JHKH39sMuZKSWkY3tV+nYU7JSVsNYmISOScOeJMnvv+OdaXrm8y9uGaD1m4aSHj+o0LcaV0acf8E9Ytgk0h3jdm/TDrfLjkM0jp53xtIiIiIiIiDuq0IR3wpTFmBfAo8JS1VrvLh4m1ttQYUwz0qHd6LPBROx6m8asr7V2J1yJjzBVA47daX2mtDbnvnbX2hmBQ96fgKTfwZDCoa+Ztu53aO0A5gf3+APY3xuxurf2+Ddee1+h4TkcWJtGnOi8vIvf1b99OxfLlVCxv+oKcu1evulV3WfVaaGZm4oqPj0C1IiLSEeLccVyx9xXcMO+GkON3LbiLp499Wiutu5uYBDj1CXj4UAjRDpXSzTBrGpz5PMT3aDouIiIiIiLSRXTmkA5gD+BO4HZjzKvAo9badyNcU1f1MXBCveOLaGNIZ4zp3+hagHkdUxYYY64h8OeghgV+Z639b0vXWWtvCra+/EvwlAt4zBjjsdbO7Kj6nGCtLTPGzALOqXf6emBaS9cZY3YjsA9jDS/wbMdXKNGk9+WX0/PUU6nKyQl+5NZ9vW4deL2O1+Tbto3ybdsoX7iw4YAxeAb0r111F5edTUxWVuBzRgYmJsbxWkVEpH2OGXIMT658kpXbVjYZW7Z1Ge/kvsPR2UdHoDKJqN7D4IT/BMK4UHI/h4cmwikzIUOrLUVEREREpGvq7CEdBFobxgKnAKcYY9YAM4HHrbWRWQ4SJYwxtwA31zv1ibX20Gamv0DDoO10Y8wb1tqnW7lHHPAUDdtdlhBY+bXTjDG/B/5Z75QFLrXWPtiW6621NwdX1P01eMoFzAiuqHukI2p00C3AGUBNKnGeMWaOtXZuqMnGmHjgMQJ/f2o8aq1dFdYqpdMzLhcxAwYQM2AASfvv32DMVldTvW4dlTk5VOfmUlkT3uXm4l2/wflircW7fgPe9Rso+/KrhmMeD7EZGU32vovNzsbTrx/G5XK+XhERacJlXFw3/jqmvzM95Pi9C+/l8MGHE+uODTkuXdioqZD3JXzzcOjxghx49FdwxM2w/+Wg/9tFRERERKSL6cwh3cfAIcGvbfCzATIJBE9/Mca8D8wAXrXWVjte4U4wxgwi9K9//0bHHmNMdjMPU2Kt3dpBJT0P/AEYHTw2BNpDTgBut9Y2eXXeGHMYcBcwptHQHdbagp0tyBjTO1hTDQtc3N5wzVr7t2BQ9381Dw382RjznLW2Q/bOc+L301q72hhzL3BdvdOzgisNH7bWVtWrZwSBvxsH1Ju7Dbi1+WchAiYmpjboasxfUUFVbh5VucHVd7l1q/B82yLQkdjrpSo3l6rcXPjkkwZDJi6ubs+7ms9DAl+709PVVk1ExGET+k/gkEGH8MnaT5qMrS1Zyws/vMA5e5wT4krp8n51G6ydD+sXhx73e+G9P8Mvn8CUByG5j7P1iYiIiIiIhJGx1rY+K0KCYcb5wLnAoODp+oFdzdfbCKzmmmmtXeFkjTvKGJMDZO3kwzxhrT2vhXvcQttX0mGMGQZ8DvRtNOQHlgGrCeyLlk5gz7rGARTAm8CUjgpNjTF7AR8CacCFO9Omst6qvLXAYdbanzuixuBj5xDm38/gfdzAa8AxjYY2A4uA7cAuwN4E/o7UqAImWWs7rA1pV2CMGQnUbpC2fPlyRo4cGcGKopdv+/a6tpm59dpn5uTgL+mQLLzDuFJSGoZ3tV9n4U5JiXR5IiJd1qrCVUydOxW/9TcZS41L5c2pb9IjVvuPdUsFOYH96cpbeZ9fcj846SEYepgTVYmIiIiDvF4vP/30U4Nzu+66Kx5PZ15jIiIdIRx//1esWMGoUaPqnxrVWbOjTv2vnLU2h8CKp78ARxEI7I4n0L6vfljXG7gKuMoY8w2BFUTPW2tLna45mllrfzbGHEIg8Bxfb8hFYLXcmJYuBx4BrurIVY3W2mXGmCMI/CV6Zicf61/GmGLgg44M6JxkrfUZY04j8Gf89HpDfYHmNnPZDJyrgE7CyZ2SQsKeo0jYs8F/flhr8eXnN937LrgCzlZWOl6rf/t2Kr79lopvv20y5u7duzawaxDkZWbiio93vFYRka5kaM+hTN11KrN+nNVkrKiyiBnLZnDN+GsiUJlEXFo2nPcGvHA25K9ufl7JJnjqJDjoajjsRnBrb1oREREREYlunTqkq2EDy/3eBt42xvQCfgNMA2peDa4f2O0T/LjbGPMigT24vnS45Khlrf3eGLM/8GvgEmA/Gq7IaqwcmA3cb639qoV5O1PTUmBpBz3WQx3xOJEUbNF5hjFmFnAtgd+jUPIJ7DV4s7V2i1P1idRnjMHTqxeeXr1IHDeuwZj1+/Fu3Nho5V3w67VrwedzvF7f1q2Ub91K+cKFDQeMwTOgP3HZ2cRkZRFXbwVeTEYGJkYvEoqItMVlYy7jjdVvUO4tbzL2zHfPcMbuZzAweWAEKpOI6zcSLv4U3rgWlr3QwkQLn90FOZ/ByTMgbWebWYiIiIiIiEROp2532ZrgfmkXEFhRVNMbx1IXKtU8ue8JrDx6qgP3cOsWjDGpBFbVDQF6AnEEWioWEGgT+K211huxAgVjzBAC7S0HAknARiAX+Lz+PnXSlNpddl62uprqdeuozMmhOjeXynor8Lzrm2yRGVkeD7GDBjXZ+y42OxtPv34YlyvSFYqIdCoPLHmA/y39X8ix43Y5jtsn3u5wRdLpLHkuENZVt9IYJS4VTvgPjJziSFkiIiISPmp3KdJ9dfd2l1Ed0tUwxiQApxFYXXdw8HT91XU1x1XW2gSHyxORTkohXXTyl5dTlbeGqtycJvvg+bZti3R5DZj4eGIzMxvufTck8LU7LQ1jWlqoLCLSNZVVl3HcnOPYWh76vXPPT36ekb30/3G3t/UnmDUNNjZtT93EuGlw9D8gRj/qiYiIRCuFdCLdl0K6LhDS1WeMGUZg77rfAAOCp2tW11lrrTtStYlI56KQruvxbd/edO+74Nf+kpJIl9eAKyWlYXhX+zkLd0pKpMsTEQmrWT/O4tYvbw05tk//fZjxqxl6I4OAtxLe+wt8/WDrc/uMgFMfg74jwl+XiIiIdDifz8ePP/7Y4NywYcOI0fYSIl1eVVUVq1atanBu+PDhuHaiO5VCuk7AGOMCpgAPAL1RSCcijSik6z6stfjy85vufZeTQ1VeHrayMtIlNuDu3bs2sGsQ4mVm4oqPj3R5IiI7zev3csrcU1hVtCrk+H+P+C8HDzo45Jh0Q9+/Ca9eCuUFLc/zJARW1I07DxTyioiIRBVrLd9//32Dc1lZWSQmJkaoIhFxSmlpKXl5ebXHxhiGDx++U2/cjKaQrkuuFzbGjCKwmu4soFeEyxERkQgzxuDp1QtPr14kjhvXYMz6/Xg3bmyw6q42xFu7Fnw+x+v1bd1K+datlC9c2HDAGDwD+hPXeAVedjYxGRkYtQERkSjhcXm4Zvw1XPbBZSHH71pwFwcMPACPS/+uCbD7sTDgc5h9IeR+3vw8bzm8fhWs/hiOvxcSejpUoIiIiOwsYwyxsbFUVVXVnispKVFIJ9INFBcXNziOiYnpVp1VusxPvcaYFOBMAuHc+JrT1O1NJyIi0oRxuYgZOJCYgQNJ2n//BmO2uprqdeuobNI+Mxfvhg3OF2st3vUb8K7fQOkXXzYc83iIHTSoyd53sVlZePr1w+xEiwARkXCYmDGRffrvwzcbv2kytqpoFa/8/Aqn7HZKBCqTTik1A859DT79F3xyB1h/83NXvgLrF8HJM2HwBMdKFBERkZ2TkpLCtnp7zRcXF9OnT59u9WK9SHfj9XqbhHSpqakRqiYyor7dpTHmYALB3MlAAoFgDurCuZrjRcCj1toHnK1QRDortbuUneEvL6cqb02Tve+qcnPx1fuhojMw8fGB4K7+3nfBEM+dlqYfeEQkYlZsW8EZr58Rcqx3Qm/eOOkNEmP07mlpJOdzePkC2L6+5XnGDYffBAdeBXqzioiISKdXXl5OTk5Og3MpKSlkZGTo51aRLqiqqoo1a9Y0WEELHbMfZTS1u4zKkM4YMwA4D5gGDK05Hfxs631dCDxDIJxb4lyFIhINFNJJuPiKi4PBXcPwrionB39JSaTLa8CVklLbMrN++8zY7CzcycmRLk9EuoEb5t3AG6vfCDl26ehL+e2Y3zpckUSFsnx49TL44c3W5+5yKJz0MKT0C3tZIiIisuOstaxatYrq6uoG52NiYujRowfJycl4PB5cevONSFSy1uLz+SgrK6OkpISysjIa51NJSUlkZmbu9L0U0oWBMcYNHE9g1dxRgJvQq+Ys8AkwA3jZWlvpcKkiEiUU0onTrLX48vOb7n2Xk0NVXh62snP9l+Xu3ZvY7KyG4V1WFrGZmbji4yNdnoh0EetL1nP8nOOp8lc1GUvwJPDm1DfpndA7ApVJp2ctfPMIvPsn8DX989NAUh846UEYNsmZ2kRERGSHlJWVkZeX1+SFexHp+mJiYhg8eDBxcXE7/VgK6TqQMWZ3AsHcOUCfmtPBz/VXza0HniCwam61o0WKSFRSSCedifX78W7c2GDVXWVODtU5uVStXQs+X6RLrGMMMQMGBAK8RqvwYjIyMJ4us+WtiDjkrgV38diKx0KOnbLbKdy8/80OVyRRZcMymDUdtv3U+twDroDD/wye2PDXJSIiIjtEQZ1I9xMXF8fgwYN3us1lDYV0HcAYM51AOLdfzang5/qr5rzAG8CjwJvWtrR7uIhIQwrpJFrY6mqq1q5tsvddVU4u3g0bIl1eQx4PsYMGNdn7LjYrC0+/fhi1JRGREIqrijl29rEUVRY1GXMZF7NPmM3QnkNDXCkSVFkCb/0BljzT+tyMcXDyo5A+JPx1iYiIyA4pKytj/fr1TVpfikjXk5KSwoABA3C73R32mArpOoAxxk/dSrn6wRzAj8BM4HFr7eYIlCciXYBCOukK/OXlVOWtabL3XVVuLr5t2yJdXgMmPj4Q3DXa+y42Oxt3Wpo2Ahfp5p5a+RT/nP/PkGOHDDqE+4+43+GKJCotexFevxqqWtkDNq4HTL4b9jzFmbpERESk3ay1VFZWUlxczPbt26mqaqW9tYhEBbfbTXJyMsnJySQlJXVoOFdDIV0HqBfSQSCcKwNmEWhnOS9ihYlIl6GQTro6X3Fx7Yq7xiGev6SVFy8d5urRo154l0VsVl2I505OjnR5IuKAal81J7xyAmtL1oYcf/RXj7LPgH0crkqi0rZVgfaXG5a0PnfsOXDMHRCbFPayREREZOdYa/H7/WqDKRKljDG4XC5H3qStkK4DBEM6gIXADOA5a21xBEsSkS5GIZ10V9ZafNu2NWyfmVO3As92sncnunv3rtv/LhjkxWVnE5OZiasDNhMWkc7j7Zy3+f0nvw85NiJ9BM9Pfh6XUdtcaQNvFXxwK3zZhhWYvXeDUx6D/qNanysiIiIiIp2eQroOYIz5DzDDWrss0rWISNekkE6kKev34924kaqcHCpzcqjOzaUyGORVr10HPl+kS6xjDDEDBjRom1kT5MVkZGA8nkhXKCLtZK3l7DfPZtnW0D8C/GPiP5i8y2SHq5Ko9uO78MolUNZKC2h3HBz1d5hwAaj9soiIiIhIVFNIJyISBRTSibSPra6mau3aJnvfVeXk4t2wIdLlNeTxEDtoUJO972Kzs/H07YtxaSWOSGe1aNMizn373JBjA5IG8NpJrxHn1ipaaYfiDTDnIvjl09bn7j4ZTrgPEtPDX5eIiIiIiIRFNIV0eou5iIiItImJiSFuyBDihgxpMuYvL6cqb02Tve+qcnLw5ec7X6zXW3v/xkx8fN3+d1lZDYI8d1qaI73RRaR5e/fbm8MHH86Haz5sMrahdAPPfvcs00ZNi0BlErV6DIBzXoHP7oKP/gG2hVXh378O65fAyTMga3+nKhQRERERkW5KK+lEpNvSSjoRZ/iKi+uFdrkNgjx/SUmky2vA1aNHvfCuJsgLBnjJyZEuT6Tb+KXoF0569SR8IcKUlJgU3pz6Jj3jezpfmES/vK/h5fOhaE3L84wLDr0RJl4DLrcztYmIiIiISIeIppV0URXSGWNigP2BicBQIB1IAbDWHhHB0kQkCimkE4ksay2+bduarLyrysmlKjcXW1UV6RIbcPfuXS+4C3yOy84mJjMTV5xa74l0tNu+uo0Xfngh5NjZI87m+n2ud7gi6TLKC2Du7+C711qfmz0Rpj4SWI0nIiIiIiJRQSFdBzPGJAFXA5cDfRoPA9Za2+TtjcaYM4G/Bw/zgQk2Gp6wiDhCIZ1I52X9frwbNlCVm0tlTg7Vwc9VOTlUr10HvhZalTnNGGIGDGiy911sVhYxGRkYj7qLi+yIbeXbOG7OcZRWlzYZ87g8zD1xLoN7DI5AZdIlWAsLZsLbfwRfZctzE3vBlAdgt6OcqU1ERERERHaKQroOZIzZC3gR2JVAIAdQv+iWQrpkYB2B1XYWOMZa+254KxaRaKGQTiQ62epqqtaubbQCL7D6zrthQ6TLa8jjIXbw4CZ738VmZ+Pp2xfjckW6QpFO7eFlD3Pf4vtCjh2VfRT/PuTfDlckXc6mFfDSNNj6Q+tz97sMJt0MHq2eFhERERHpzBTSdRBjzB7A50APgmEcdUEd9Y5DhnTBx5gBTA/OnWGtvTisRYtI1FBIJ9L1+MvLqcrLa7L3XVVODr78/EiX14BJSCA2M7PByruaEM+dloYxpvUHEeniyr3lTJ4zmc1lm0OOP3PsM+zVZy+Hq5Iup6oU3r4BFj3Z+twBo+GUx6DX0PDXJSIiIiIiO0QhXQcwxsQDK4Fs6sK4ZcC9wEdAHPBdcHpLId3xwKvBx/jFWjssvJWLSLRQSCfSvfiKi+tW3v3ScB88f2nTdnqR5OrRo15wV7MPXjDAS06OdHkijprz0xz+8sVfQo7t3XdvHj/6cYXa0jGWvwyvXQWVxS3Pi02G4+6C0ac7UpaIiIiIiLSPQroOYIz5A3A7da0t7wOusdb6g+NZwC/BsZZCumSgiLqVeAOstaHfiisi3YpCOhEBsNbi27atUfvMuhaatqoq0iU24O7Tu3bVXVx2NjFZWYHPmZm44tSCTboen9/Hqa+fyk8FP4Ucv+ewezgi8wiHq5IuqyAHZk2HdQtbnzv613DsvyBOb54QEREREelMFNJ1AGPMGmBg8PAVa+3JjcbbFNIF564ChhAI6Q631n4ShpJFJMoopBOR1li/H++GDVTl5lLZqH1m9dp14PNFusQ6xhAzYECTve9is7OJGTgQ4/FEukKRHfb5us+55P1LQo5l98hm9omziXHFOFyVdFm+avjwb/D5va3P7TUMTpkZaIMpIiIiIiKdQjSFdJ3y1ZrgXnQZwUML/H4nH7ImpAPYBVBIJyIiIq0yLhcxGRnEZGSQdMABDcZsVRVV69bVW3WXU7sXnnfjRueLtZbq9eupXr+e0i++aDgWE0PsoEFN9r6Lzc7G07cvxuVyvl6Rdjgw40AOGHgAX6z/oslYTnEOL//4MmfsfkYEKpMuyR0DR/4VhhwCcy6G0i3Nz932M8yYBEf+Dfa9GNR6VURERERE2qFThnTAmOBnCyy31q7eyccrrPd16k4+loiIiAgmNpa4IUOIGzKkyZi/vJyqvLwme99V5ebiy893vtjqaqp++YWqX35pMmQSEojNzKxbgVcT4g3Jxt2zp/b6kk7jmnHX8OX6L7E07QTywNIHmLzLZJJj1XZQOtCwI+CSzwNB3eqPmp/nq4K3r4fVH8OJ/4WkXo6VKCIiIiIi0a2zhnR96n0devOJ9qms93ViBzyeiIiISLNcCQnEDx9O/PDhTcZ8xcV1wV2jEM9fWup4rba8nMoffqDyhx+ajLl69KhbdZeVVS/Iy8adnOR4rdK9DU8fzglDT+DVVa82GcuvyGfm8plcsfcVEahMurSUfnD2bPjiP4EWmH5v83N/fAsePAhOfgSyD3KuRhERERERiVqdNaSLr/d1ZbOz2q7+6rntHfB4IiIiIjvE3aMHCXvuScKeezY4b63Ft21bg1V3dV/nYauqHK/VX1xMxbJlVCxb1mTM3ac3cVnZxGRnEVdvFV5MZiauuDjHa5Xu4fKxl/NOzjtU+CqajD218ilOH346/ZL6RaAy6dJcLjjoqkDwNmsaFOY1P3f7enjieDj4D3Dw78HdWX/kFhERERGRzqCz/sSwtd7XvTvg8Xap9/W2Dng8ERERkQ5ljMHTuzee3r1JHD++wZj1+/Fu2EBlk/Aul+q168Dnc7xe35atlG3ZCgsWNBwwhpiBAxuuvAvufxczcCDG01m//ZRo0D+pP+fscQ6PfPtIk7EKXwX3L7mfvx34twhUJt3CoPFw8Tx4/SpYMaf5edYPn9wOOfNg6iOQmtH8XBERERER6daMtU33dIg0Y8xxwGvBw63W2r4h5mQBNRurWGutu5nH6gVsBgyBPe72t9Z+0/FVi0i0McaMBJbXHC9fvpyRI0dGsCIRkfazVVVUrV1HVW4OVTm5DVbheTdujHR5DcXEEDtoUMO974IhnqdfP+1/J21SUlXCcXOOI7+i6f6OBsNLx7/E8PSmrWZFOoy1sOhJeOt68Ja3PDchDU78H+x+rDO1iYiIiIgIK1asYNSoUfVPjbLWrohUPS3prCFdGrAFcBEI1o601n7YaE5bQ7rfA3cED0uAntZaf1gKF5GoopBORLo6f3k5VXl5Tfa+q8rNxZffNOCIJJOQEAjuGux9l0XskGzcPXsqwJMGnvv+Of7v6/8LOXbgwAN58MgHHa5IuqXN3wfaX25e2frcfS6GI/8KMfGtzxURERERkZ2ikK4DGGO+BPYlENJ9BRxk6xXblpDOGJMBLAN6Bk+9Yq09OZx1i0j0UEgnIt2Zr6goENzl5gZCvHor8PylpZEurwFXamowvMtquAovKxt3clKky5MIqPZXM/XVqeQU54Qcf+jIhzhg4AHOFiXdU3U5vHMjLJjZ+tx+e8Kpj0HvXcNfl4iIiIhIN6aQrgMYY84AniUQ0gHMsNZeXG+8xZDOGNMPeBMYWzMHONha+3lYCxeRqKGQTkSkKWstvq1bm6y8C3zOw1ZVRbrEBtx9ehOXlU3skIYtNGMGD8YVFxfp8iSMPsj9gKs+virk2PC04bww+QXcrpDNNkQ63spXYe7voKKo5XkxSXDsv2DMr0ErhEVEREREwkIhXQcxxiwCRlO3n9wXwI3W2nnNhXTGmCTgHOBmoP5edu9aa49xrHgR6fQU0omItI/1+fBu3Ehl4/AuJ5fqdevA54t0iXWMIWbgwCZ738VmZxMzcCDG44l0hbKTrLWc+/a5LN68OOT4bQfexonDTnS4KunWCvPg5Qtgzdetz93zNJh8F8SlhL8uEREREZFuRiFdBzHG7EKg1WWvmlMEwrqNwM/AxOB5CzwM7AbsD8TVm2uAdcBYa+1Wx4oXkU5PIZ2ISMexVVVUrV1HVW4gtKsf4nk3box0eQ3FxBA7aFBteBc3bCjJhxyCp3fvSFcm7bR0y1LOfvPskGP9Evvx+kmvE+/RHmDiIJ8XPv4HzLuTuqYwzUgbAqfMhIy9HSlNRERERKS7UEjXgYwx+wBzgAHUhW40+rrmmBDja4HJ1tpl4a9WRKKJQjoREWf4y8qoWrOmyd53VTk5+AoKIl0eAK7ERAbccTs9jjwy0qVIO1378bW8m/tuyLEr976SC/a8wOGKRIDVH8Psi6BkU8vzXDEw6RbY71JwuZyoTERERESky1NI18GC+8vNBGraVbZWdE1Q9x5wrrW2k719W0Q6A4V0IiKR5ysqatA2s/4+eP7SUmeLcbkY+I//I/VEtUiMJmuK13DCqyfg9XubjCXFJPHm1DdJj0+PQGXS7ZVsgVd+Cz+/1/rcYUfCSQ9Cklb0ioiIiIjsLIV0YWKMGQdcCRxBYGVdKEXAB8B91tpPnKpNRKKPQjoRkc7LWotv69YGK+8qc3Kozs2lKjcPW1UVtnv3v+Vm0s44I2yPLx3vjm/u4Onvng45dubuZ3Ljvjc6XJFIkN8PX/0P3r8F/NUtz03uD1Mfhl0OcaQ0EREREZGuSiGdA4L71Q0msF9dLLAV2ASssNb6I1mbiEQHhXQiItHJ+nx4N26kMqdx+8xcqtetA59vp+/R9/e/p9f50zugWnFCYUUhx84+lu3V25uMeYyHOSfOITs12/nCRGqsWwSzpkPBL61MNDDxWjj0j+D2OFKaiIiIiEhXo5BORCQKKKQTEel6bFUVVWvXNdn7rio3F+/G9nVA733ZZfS+/DKMMa1PloibuXwmdy+8O+TYpMxJ3H1Y6DERx1QUwxvXwLcvtT538L5w8gzomRn+ukREREREuhiFdCIiUUAhnYhI9+IvK6MqL69277uKFcvZ/t77LV6Tft559L3+DwrqokClr5Lj5xzPhtINIcefPOZJxvYd63BVIo1YC0uehTevg+qylufGp8IJ98MeJzhTm4iIiIhIFxFNIZ0r0gWIiIiIiDjBlZhI/O670+Poo+h9ycUMuu8++lxzTYvX5D/+OBtvuRXrVzf1zi7OHcfvxv6u2fE7F9yJ3qAoEWcMjD0LLvoE+u3Z8tyKInjxHHj9Gqgud6Y+ERERERFxlEI6EREREem2el90If1uuqnFOYUvvMD6G27Aer0OVSU76rhdjmNE+oiQY0u3LOW93PccrkikGX12gwveh30ubn3ugkfhkSNg8/fhr0tERERERBylkE5EREREurX0s89iwN//Dq7mvzUunvsa666+Bn9VlYOVSXu5jItrx1/b7Pg9i+6h2lftYEUiLYiJh2P/CWc8CwlpLc/dvAIePhQWPhFomSkiIiIiIl1CREI6Y8znxph9I3HvRnUkGGNuNsY0/5O8iIiIiHR5PU+eSsad/waPp9k52997j7WXXY6/XG3nOrN9B+zLxIyJIcfWbF/Diz++6HBFIq3Y/Ti45DPIPKDled5yeO0KmDU90ApTRERERESiXqRW0u0PfGGMedkYs7fTNw+Gc1cBPwN/AZKcrkFEREREOpcexxzDoPv+g4mNbXZO6bx5rLnoYnwlpQ5WJu11zbhrcJnQP+o8sPQBiquKHa5IpBWpg+Dc1+CQ66GZP7u1VsyGByfC2gXO1CYiIiIiImET6XaXU4D5xpi3jTHHG2NMOG9mjMk0xtwG5AJ3AgPCeT8RERERiS4phx3G4IcexCQmNjunbP588qZPx1dY6Fxh0i7D0oZx0rCTQo4VVRYx49sZDlck0gZuDxx2YyCsSxnY8tzCXJh5FHx2D/j9jpQnIiIiIiIdL1Ih3a1AJWCCH0cCrwC5xph/GmMmdNSNjDHpxpjzjTFvA6uAPwK9g/cFWAC81FH3ExEREZHolrT//mTOmIErJaXZORXLlpF77nl4t251sDJpj8vGXEaCJyHk2DMrn2F9yXqHKxJpo+yDAu0vdzu65Xl+L7x/MzxzMpRsdqY2ERERERHpUBEJ6ay1twKjgNeDp2rCukHAtcBXxpgNxpjnjDGXGmMOMMY0/ypJzYME7GqMOSUY9s0HNgEPEwgC3fWmbwUuBfaz1n7Xcc9ORERERKJd4t5jyXz8Mdw9ezY7p/KHH8g9+xyqN250rjBpsz6JfTh35Lkhx6r8Vdy3+D6HKxJph6RecObzcPQd4G6+BS8Aqz6EBw4MfBYRERERkahirLWRLcCYg4B/AAcGT1nqVrk1Lq4IWAsUAOWAF0ggsKfcgOBH/SCufvvMmsfdDtwD/MtaW9JRz0NEoo8xZiSwvOZ4+fLljBw5MoIViYhIZ1P588/kTZuOd8uWZufEZGSQ+dhMYjMzHaxM2qKsuoxjZx/LtoptIcdfmPwCe/Taw+GqRNppw1J4aRrkr2p97oFXweE3gTsm7GWJiIiIiHRWK1asYNSoUfVPjbLWrohUPS2J9J50WGs/s9ZOBA4B5lIXzNV8NvU+ehJYgXcQgZVxxwCHAvsAgwFPvbn1HwNgA3ADMNhae7MCOhERERFpTdywYWQ98zQxA5vfH6p63Tpyzz6HylVteAFdHJUYk8ilYy5tdvyuBXcR6TctirRqwGi4+BMYfWbrcz+/B2YeDQU54a5KREREREQ6QMRDuhrW2nnW2ilAJvAnAqtbGq+Eq/locnmIMQOUAC8AxwGZ1tp/WmuLO756EREREemqYjMzyXrmaWKzs5ud4928mdyzz6Fi5UrnCpM2mbrrVHZJ3SXk2Ncbv2beunkOVySyA+JS4KQH4aSHITa55bnrFsCDE2H5bGdqExERERGRHdZpQroa1tr11tp/WGtHEwjsLgJmAt8CFTRcWVf/A2Aj8DbwN2AS0Ntae6a19i1rrd/ZZyIiIiIiXUXMgAFkPf0Ucbvu2uwcX0EBueeeR9nixQ5WJq3xuDxcPe7qZsfvXng3Xr/XwYpEdsLo0+HiTwOr61pSWQyzpsHcK6CqzJnaRERERESk3TpdSFeftXattXaGtfYCa+0Ya23N3nOjgf2BicB4YDcg0VqbYa09NtjO8kNrbXUEyxcRERGRLsTTuzeZTz5BfMO+9g34t28n7/wLKP3qKwcrk9YcMugQxvcbH3Ls58KfefXnVx2uSGQn9BoK578H+zXfyrXWoifgkcNgU6fcfkNEREREpNvr1CFdKNbaTdbab621X1trP7fWLrLW/mytrYx0bSIiIiLStXnS0sh8/DESxo9rdo4tK2PNRRez/eOPnStMWmSM4brx1zU7/t8l/6WsWquNJIp44uDof8CvX4SE9JbnbvkeHjkc5j8K2oNRRERERKRTibqQTkREREQkktzJyWQ+8ghJBx7Y7BxbVcXay39H8dtvO1iZtGRk75EcM+SYkGNbyrfwxMonHK5IpAPsdhT89nPIntjyPG8FvHENvPgbKC9wpjYREREREWmVQjoRERERkXZyJSQw6IH/kTzpiOYneb2su+ZaCmfPca4wadGVe19JjCsm5Nhjyx9ja/lWhysS6QA9BsJvXoXDbgLTyo/4382FBydC3tfO1CYiIiIiIi1SSCciIiIisgNcsbEMuvtuekye3Pwkv58NN95I/jPPOFeYNCsjOYNf7/7rkGPl3nL+t+R/Dlck0kFcbjjk93Dem9BjUMtzi9bAY8fAp/8Gv8+Z+kREREREJCSFdCIiIiIiO8jExDDwjtvpeeqpLc7b9Lfb2PrIIw5VJS25cK8L6RHbI+TY7J9ms7pwtcMViXSgrP3hknmwewtvHgCwPvjwb/DUFNi+0ZHSRERERESkKYV0IiIiIiI7wbjd9P/rraSfe26L87bceReb770Xa61DlUkoqXGpXLTXRSHHfNbH3QvvdrgikQ6WmA6nPw3H/hvccS3P/eVTeOBA+Ok9Z2oTEREREZEGFNKJiHRDsxet5doXl/L8N3n8vLlELxiLiOwkYwx9b7ie3pde2uK8bQ88yObbb9e/uxF25u5nkpGcEXLs47UfM3/jfIcrEulgxsA+F8KFH0Dv3VqeW7YVnjkF3vkTeKucqU9ERERERACFdCIi3dJ7Kzfx8qK13DD7Wybd9QnjbnufC59cwMOfrmJRXgFVXn+kSxQRiTrGGPpc8Tv6/v66FuflP/EkG//yF6xPe0FFSqw7liv3vrLZ8TsX3Inf6v9C6QL67wkXfQxjz2l97pf3w8xfwbZVYS9LREREREQCjN7FKyLdlTFmJLC85nj58uWMHDkyghU5w1rLhL9/wNaSymbnxHlcjBnckwnZ6YzPTmPvrDR6xMc4WKWISHTLf/ZZNv31by3O6TF5MgP/8X+YGP37GgnWWs568yy+3fptyPE7Jt7Bsbsc63BVImH07Sx47Sqo2t7yvNgUmHw37NXyXpsiIiIiIp3VihUrGDVqVP1To6y1KyJVT0sU0olIt9VdQ7qcraUc+u+P23WNMbB7/x7sk53G+GBwNyA1ITwFioh0EYVzXmHDn/4E/uZXZCUfcQQZd9+FKzbWwcqkxsJNCznv7fNCjmUkZzB3ylxi3fq9kS4kfzXMOh/WL2p97piz4dh/QmxS+OsSEREREelA0RTSqd2liEg3Mz8nv93XWAvfbSjmiS9z+d1zi9n/Hx9y0B0fcvULS3jm61x+3LQdv19v+hARqa/nSVPIuOsuaGGlXMkHH7D2t5fiLy93sDKpMa7fOA4bfFjIsXUl63ju++ccrkgkzNJ3genvwAFXtD53ydPw0CGwYVn46xIRERER6aa0kk5Euq3uupLu+lnLeGHBmg5/3NSEGMZnBVbaTchOY89BqcR53B1+HxGRaFPyySesveJKbGXzbYYTxo9j8IMP4k5OdrAyAfil6BdOevUkfLbpHoEpsSm8NfUtUuNSI1CZSJj99D7MuRjKtrY8zx0Hv7oN9rkw0F5BRERERKSTi6aVdArpRKTb6q4h3ac/buGjHzazIKeAFeuLCNcCuFiPi9GDUmtDu3GZ6aQmat8lEemeSr/6mjWXXootK2t2TvyoUQx+5GE8aWkOViYAt311Gy/88ELIsd/s8Rt+P+H3Dlck4pDtGwNB3eqPW587/Dg48X5ITA97WSIiIiIiO0MhnYhIFOiuIV19JZVeluQVMj8nnwW5+SzKLaS8uulKgo4yvF8K47PTmBDc1y6jZwJG78gWkW6ifMkS8i66GH9xcbNz4nbdlcyZj+Lp08fBymRr+VaOm30cZd6mIWqMK4a5U+YyKGVQBCoTcYDfD5/fAx/eBiFWlDbQIwNOngFZBzhSmoiIiIjIjlBIJyISBRTSNVXt8/PdhmLm5xSwICef+TkFbC1pvj3bzhqQGl+70m58VjrD+6fgdim0E5Guq+K778g7/wJ8+c3vDxqblUXmYzOJGTjQwcrkoaUPcf+S+0OOHZN9DP885J8OVyTisDXfwKzzoSiv5XnGBYfcAAdfBy61NhcRERGRzkchnYhIFFBI1zprLbnbygIr7XIKmJ+bz+otpWG7X0qch72z0gKhXXY6Ywb3JD5GL/6ISNdSuXo1eedNw7t5c7NzPAMHkPXYY8RmZTlYWfdW7i1n8uzJbC4P/fvy3HHPMar3qJBjIl1GeSG8dgWsfLX1uVkHwdSHITUj7GWJiIiIiLSHQjoRkSigkG7HbC2pZGFu3Uq75euK8IZpY7sYt2FURmqgPWZWILhLT4oNy71ERJxUtWYNedOmU712bbNz3H16kzVzJnG77upgZd3bnJ/m8Jcv/hJybFy/cTx21GNq0yxdn7Ww8HF4+wbwVrQ8NyEdpjwAw492pDQRERERkbZQSCciEgUU0nWM8iofS9YUBkK73AIW5RZQUukN2/2G9kkK7mkXaJOZmZ6oF0xFJCpVb9xI3rTpVP3yS7Nz3D17MnjGDBJG6f8nJ/j8Pk59/VR+Kvgp5Ph/DvsPh2Ue5nBVIhGyaSXMmg5bvmt97r6/hSNvBU9c+OsSEREREWmFQrpOxBgTA4yoObbWLotgOSLSiSikCw+f3/L9xuJAe8ycfObn5LOpOHz72vVJiavd025CdjojBqTgcbvCdj8RkY7k3baNvOnnU/nDD83OcSUnM/jhh0jce28HK+u+Plv3Gb99/7chx4akDmH2CbPxuDwOVyUSIVVl8M6NsPCx1uf23wtOeQx6Dwt/XSIiIiIiLVBI15YbG7Mo+KUFjrPWbmxl/g6FbcaYLGB13WVWP1GLCKCQzinWWtYWlLMgN9Aec0FOPj9uKgnb/RJj3eydmcb47DQmBPe1S4rTP/0i0nn5CgvJu+hiKpY1/+2tSUhg8H/vJ+mAAxysrHuy1nLRexfx1YavQo7/eb8/c9rw0xyuSiTCVsyBuVdCZVHL82KS4Lg7YcyZztQlIiIiIhKCQrq23NgYf/BLCwyx1ua1Mn+HwrbgdTU9hKy11r0j9YpI16OQLnIKy6pYmFtQG9otW1tElc/f+oU7wO0yjBzYI7jSLrCvXZ8UtWISkc7FV1LK2t/+lrL585udY2JiyLj3XlIOV7vFcPs+/3tOe+00LE1/VkqPT+fNqW+SFJMUgcpEIqggF14+H9Y2/+9Urb1OD4R1cSnhr0tEREREpBGFdG258Y6FdO0O2xTSiUhzFNJ1HhXVPr5dV8T8nHwWBIO74orw7WuX3SuR8dnp7JOdzvjsNIb0TtK+diIScf7yctZecSWl8+Y1P8njIeOfd9Dj2GOdK6yb+tNnf2Luqrkhxy7e62IuH3u5wxWJdAK+avjo/+CzuyFEiN1A+i6B9pcDxzhRmYiIiIhILYV0bbmxQjoRiTCFdJ2X32/5cfP22pV2C3IKWFdYHrb79UqKrW2POT47nZEDexCjfe1EJAL8VVWsv/Y6tr/3XvOTjGHAbX+j58knO1dYN7SxdCOT50ym0td0X9UETwKvn/Q6fRP7RqAykU5g1Ucw52Io2dTyPFcMHPlX2O+3oDdEiYiIiIhDFNK15cYK6UQkwhTSRZd1heW1gd38nHx+2LSdcP0XFh/jYuzgtNr2mGMze5ISHxOem4mINGK9Xjb86U8UvRp6FVeNfjfeSPpvznGoqu7pnoX38OjyR0OOTd11KrcecKvDFYl0IiVbAkHdqg9an7vrUTDlf5DUO/x1iYiIiEi3p5CuLTdWSCciEaaQLroVlVezKC+w0m5+TgFL1xRS6Q3PvnYuAyMG9AiutEtjfFY6/VPjw3IvEREA6/ez8a9/pfD5F1qc1+eqq+h9ycUOVdX9bK/aznGzj6OgsqDJmMu4eOn4l9gtbbcIVCbSSfj98OX98MGt4G+lVXnKAJj6CAyZ6ExtIiIiItJtKaRry40V0olIhCmk61oqvT6WryuuDe0W5OZTWFYdtvsNTk9gQlagPeaE7DSG9knG5VIbJxHpONZaNv/r3+TPnNnivF4XXUSfq6/S3pph8sx3z3D7N7eHHDsw40AenPSgwxWJdEJrF8LL06Egp5WJBg7+PRxyPbg9TlQmIiIiIt2QQrq23FghnYhEmEK6rs3vt6zeWsL8YHvMBTkF5OWXhe1+PRNjGJ+VVhvajcpIJc6j/3JEZOdYa9n6v/+x9b77W5yXdvbZ9LvxjxiX9tPsaNW+aqa8OoW87aF/XHn4yIfZf+D+Dlcl0glVFMHrV8Pyl1ufm7l/YFVdz8Hhr0tEREREuh2FdG25sUI6EYkwhXTdz6biito97Rbk5rNyfTH+MP03GOtxMWZQT8ZnpzEhO529s9JITdC+diKyY7bNfIzN//xni3NSp05lwN/+inHr292O9l7ue1zz8TUhx3ZP350XJr+AyyggFcFaWPw0vPUHqG7lzVHxPeHE/8KIyY6UJiIiIiLdh0K6ttxYIZ2IRJhCOimp9LI4ryDQHjMnn8V5hZRX+8JyL2NgeL+U2tBufHY6GT0TwnIvEemaCp5/gY233hp4EbwZPY49hoF33IGJ0ZsCOpK1lt+89RuWbFkScvzvB/2dE4ae4GxRIp3Zlh9g1nTYtLz1uRMugF/9HWK036+IiIiIdAyFdG25sUI6EYkwhXTSWLXPz8r1xbXtMRfk5rO1pCps9xuYGl/bHnN8djq79UvBrX3tRKQFRXPnsv6PN4Kv+TcUJB92GBn33I0rLs7Byrq+JZuXcM5b54Qc65/Un9emvEa8RyGDSK3qCnj3Jpj/SOtz+42CU2ZCn+Hhr0tEREREujyFdG25sUI6EYkwhXTSGmstOdvKgqFdILhbvbU0bPdLifcwLiu40i4rjdGDexIfo/+2RKSh4vfeY90110J1dbNzEvffj8H//S+uxEQHK+v6rvn4Gt7LfS/k2JV7X8kFe17gcEUiUeC71+DVy6GisOV5MYlwzD9h7NmBFgQiIiIiIjtIIV1bbqyQTkQiTCGd7Igt2ytZmBtojzk/t4AV64rwhmljuxi3Yc+M1Nr2mOOz0khLig3LvUQkupTMm8fay3+Hraxsdk7C3nsz+KEHcaekOFhZ15ZbnMuUV6bgtd4mY8kxybwx9Q3S49MjUJlIJ1e4Bl6+ANZ81frcUafA5Lshvkf46xIRERGRLkkhXVturJBORCJMIZ10hLIqL0vWFLIgp4D5Ofksyi2gtCo8+9oBDOubHGiPmZXOhOx0BqcnYPRuc5FuqfSbb1h7yW/xl5U1Oyd+jz0Y/OgMPGlpDlbWtf3j63/w7PfPhhz79e6/5o/7/tHhikSihM8Ln9wBn/6LwMsALUjLDrS/zBjnRGUiIiIi0sUopGvLjRXSiUiEKaSTcPD6/Hy/cXvtSrv5v+SzeXvzK112Vt+UuOBKu0CbzN37p+Bxu8J2PxHpXMqXLSPvwovwFxU1Oyd22FAyZ84kpm9fByvrugoqCjh29rGUVJc0GfMYD69MeYWsHlkRqEwkSvzyKcy+CLZvaHmeywNH3Az7Xw4ufW8jIiIiIm2nkK4tNw6EdDU3/xqoaOWSeGC/4NcW+KSNt2pwnUI6EamhkE6cYK1lTX55YF+73Hzm5xTw8+amL+x2lOQ4D2Mze9YGd2MG9yQx1hO2+4lI5FX88AN508/Ht21bs3NiMjPJemwmMRkZDlbWdc34dgb3Lro35NiRWUdy16F3OVyRSJQp3QqvXAo/vdP63KFHwEkPQXKf8NclIiIiIl2CQrq23LgupDO02uui7rJ6X7e3cINCOhGpRyGdREp+aVXdvnY5+Xy7rohqX3j+P/a4DCMzUpmQlRbY1y47jd7JcWG5l4hETuXqX8ibPh3vxo3NzvEMGEDmzEeJGzLEwcq6pgpvBce/cjwbS0P/ej91zFOM6TvG2aJEoo218NUD8N5fwF/d8tzkfoGgbuhhztQmIiIiIlFNIV1bbtxwJZ0jt0QhnYjUo5BOOouKah9L1xSyIDewr93C3AK2V3jDdr9deicxPjsQ2k3ITie7V6L2tRPpAqrWriNv2jSq16xpdo67d28yH32U+OG7OVhZ1zR31Vz+9NmfQo6N7jOap455Sv+2irTF+sUwazrkr25looGDrobDbgR3jCOliYiIiEh0UkjXlhvX7UnnJIV0IlJLIZ10Vj6/5cdNwX3tcgLB3Yai1rpC77jeybGMz6rb126PgT2I0b52IlGpetNm8qZPp2rVqmbnuFNTGTxjBgl7jmp2jrTOb/2c/vrpfJ//fcjxuw69iyOzjnS4KpEoVbkd3rgOlj3f+txBE+DkRyFNez+KiIiISGgK6dpyY2POjcR9rbVPROK+ItL5KKSTaLKusLy2PeaCnAJ+2LSdcP0XnhDjZmxmz+BKuzTGZqaRHKd97USihTc/n7zzL6Dyu++aneNKSmLwQw+SOH68g5V1PV+u/5KL3rso5FhmSiavnPgKMVrxI9J2S56DN66F6tKW58Wlwgn3wsiTnKlLRERERKKKQjoRkSigkE6iWVFZNYvyCmpDuyVrC6nyhmeRusvAHgN7MD4r0B5zfHYa/XrEh+VeItIxfMXFrLnwIsqXLm12jomPZ9D995N80IEOVtb1XPL+JXy+7vOQYzfscwNnjTjL4YpEotzWn2HWNNi4rPW546bB0f+AmITw1yUiIiIiUUMhnYhIFFBIJ11JpdfH8nVFzM8pqG2TWVReHbb7ZaYn1rbHnJCdxtA+ydp7SaST8ZeWsubSyyj7+utm55iYGDLuvouUSZMcrKxr+bHgR0597VT8tukbJXrG9eTNqW+SEpsSgcpEopi3Et67Gb5+oPW5fUbAqY9B3xHhr0tEREREooJCOhGRKKCQTroyv9+yaktJXWiXm8+a/PKw3S8tMYZxWYHAbnx2OntmpBLr0b52IpHmr6hg3ZVXUfLJJ81PcrsZePvtpB4/2bnCupi/fP4X5vw8J+TY+aPO56pxVzlbkEhX8cNb8MqlUJ7f8jxPPBx9O4w7D/SmIREREZFuTyGdiEgUUEgn3c3GogoW5AbaY87Pyee7DcX4w/RtQJzHxejBPWtDu70z00hN0L5MIpFgq6pY94fr2f72281PMob+t95C2mmnOVdYF7KpdBOT50ymwlfRZCzOHcfrJ71O/6T+EahMpAsoXg8vXwi5n7U+d48pcPy9kNAz3FWJiIiISCemkE5EJAoopJPubntFNYvzCmvbYy5eU0BFdXj2tTMGhvdLqd3TbkJ2OgN7av8YEadYr5cNN/2ZoldeaXFevz/eQPq55zpTVBdz3+L7eHjZwyHHThh6An8/6O8OVyTShfh98Om/4ZPbIURr2QZSM+GUR2HwPs7UJiIiIiKdjkK6CDDGxAFpQJG1Nnz9vESky1BIJ9JQtc/PivXFwdAusOJuW2lV2O6X0TOB8cGVdhOy09itbwoul1pUiYSL9fvZdNvfKXj22Rbn9bnyCnpdcon2mWyn0upSjp19LPkVTdvyGQwvHv8iu6fvHoHKRLqQnM9h9oVQvK7lecYNh98EB14FLrXfFhEREeluFNI5xBizO3A18Csgs95QEfAh8Iy1NvTmECLS7SmkE2mZtZZftpbWtsdckFvAL1tLw3a/HvEexmXVhHbp7DUolfgYd9juJ9IdWWvZctddbHtkRovzel1wPn2uvVZBXTu9+MOL/O2rv4Uc22/Afjx85MP6NRXZWWX58Orl8MMbrc/d5VA46WFI6Rf2skRERESk81BI15YbGxMLHF3v1FZr7RftuP6vwB8BFxDqJ92aJ/YxcKa1dvMOlioiXZRCOpH227K9koW5+XzzSwELcvNZsb4YX5g2tot1u9hrUGrtSrtxWWn0TIwNy71EuhNrLdseeogt99zb4ry0X59Jv5tuwmgVSpt5/V6mzp3KL0W/hBx/YNIDHJRxkMNViXRB1sL8GfDOjeBrZdV/Uh846UEYNsmZ2kREREQk4hTSteXGxkwC3qUuTLvJWvuPNl57L3A5deFcc0/CBMdWAIdaa5v2nhGRbkshncjOK630smRNYW17zEV5BZRV+cJ2v936JdeGduOz0hmUlqBVKd2E9Vt8Xj/eaj8+rx9fzeeac/XOe6v9+BvMtfi8vuCxDcyt9uHzWrzVfhJTYsgYnsaQ0b1xubtPIJX/xBNs+sftLc5JnTKFAbf9DePxOFRV9Pso7yOu+OiKkGPDeg5j1vGzcLu0SlikQ2xYBrOmw7afWp97wBVw+J/Bozf8iIiIiHR1CunacmNj/gVcGzysAAZba7e14bopwGwaBnPNvTpnqQvqnrbWnrvDBYtIl6OQTqTjeX1+vtuwPdgeM5/5OQVs2V4Ztvv17xHP+Ow0JmSnMz47jd3798Ctfe06nN9XE3zZYNDlCwZfjUKzan/IIK3FOfXOtxTA+X3h/541JT2e0UcMZsSBA4iN7x6hVMFLL7HxLzcHVqU0I+Xoo8n45x2YWL2w3RbWWqa9M42FmxaGHP/rAX/lpF1PcrgqkS6sqhTe/AMsebr1uQP3hlNmQvqQ8NclIiIiIhGjkK4tNzbma2ACgQDtFWvtyW24xgOsBIZRF8ABLATuBZYAPmAP4GJgEg2Dun2staF/WhaRbkchnUj4WWvJyy9jfk4BC3LymZ+Tz6ot4dvXLjnOw95ZaUwI7m03ZnBPEmKjd8WKtTYQjHkbrhRrbvVYg9Ar1ByvH3/wc8hrG831BVee2TC1NO2sYhM8jDxoIHsdPojktPhIlxN2Ra+/wfrrrwdf86tgkw85hIx778EV3/V/PTrC8q3LOfONM0OO9U3oy2snvUZiTKLDVYl0cctegtevhqrtLc+LTYHj74E9T3GkLBERERFxnkK61m4aCNu2AzVvxz3XWtvq296MMScAr9AweHsRONta2+RVBWPMLcBfqFt194C19vKdrV9EugaFdCKRsa2kkoW5BSzILWB+Tj7fri3CG6YQyOMyjMpIDbTHzE5nfFYavZLj2nStP9hesSPCsdrzoVaT1bRg9PrxBlsw1p8jkeNyGYZN6MuYSZn0GZwS6XLCavsHH7Duqqux1dXNzkncd18G/++/uJKSHKwsev3hkz/wVs5bIccuH3M5F4++2OGKRLqB/NWB9pfrF7c+d+zZcMw/IVb/pomIiIh0NQrpWrupMXtQ98K4BQZYaze34boXgVOoC+k2AsOstWUtXPMBcFjwcIu1tt/O1C4iXYdCOpHOobzKx9K1hcGVdgUsyimgtNKLG/BYgp9N7bEHg7v2PLgxwfMEz5u66+p97Q5emxLjJi0uhpQYN4keN25LYIVZo3DNifaKEj0yhqcx9shMMkemd9l9EEs+/5y1l12Orahodk7CmDEMfvgh3D16OFhZdFq7fS0nvHIC1f6mwWeiJ5E3pr5B74TeEahMpIvzVsEHt8KX97c+t/ducMpj0H9U63NFREREJGoopGvtpsYcC7wePFxnrR3chmsMsBXoSd0qupustf9o5bojgPeChxbIttau2cHSRaQLUUgn0pS1wdVjwdVc3mpf3T5kIVshttxasfF+Y7VBWL2xUPuVRagbt0ir0gYkMWbSYHbbpx+emOhtpdqcsgULWHPxJfhLm29LGzdiBJmPzsCTnu5gZdHpX/P/xZMrnww5dvrw07lpv5scrkikG/npPZhzCZRtbXmeOw6O+jtMuAC66JswRERERLobhXSt3dSYi4AHCYRmH1prj2zDNXsCS2nY6nJXa+3qVq5zAflAj+A1J1hr39i5ZyAiXYFCOuls/H7bjn3C2j6nQYjWOFRrMMeqvaJENZfL4I5x4fa4Ap+DX3tqzxncHnfwODC3utLHL0u3tnvlZEKPWPY6NINRBw8iPjkmTM8oMsq/Xc6aCy7AV1TU7JzYoUPJnPkoMf3UpKIlRZVFHDP7GLaH2CPLbdzMPnE2u6TuEoHKRLqJ7Rth9oXwy6etz919MpxwHyTqDQgiIiIi0S6aQjpPhO5bf1OP/DZes1+j47WtBXQA1lq/MeZb4MDgqf5tvJ+IiHQT1lr8PtvucMzfXPDVJESz+Ly+2iAsMKdu7zGv14+/2o8/TPuyiTjB7akLvpqGY/WO653zeFy4gp8bnG90nbu1OcGvXa4dWwFRWlTJtx+tZfmn66gs87bpmvLiKr6e+wsL38pl9/0HMPqIwfTsl7hD9+9sEvYcReZTT5I3/Xx8W0OvQKlatYrcs88h87HHiB2U4XCF0SM1LpWL97qYfy/4d5Mxn/Vxz8J7+M/h/4lAZSLdREp/OOcV+Oxu+Oj/oOlW9nW+fx3WL4GTZ0DW/k5VKCIiIiLdXKRCuvh6X1e18ZoJ9b62wNftuN/Gel9rAw0RkU7E+m1t68MGAZe3URBWE3ZV++qCr2p/IPwK1Y6xUUvFUAFa/QAO5WMSrQwNAqsmAVeIcKw2NNvBcMzlMXhi3MHzBrfbhdnBgKwzSEqNY78pQxl3TDbffbGBpR/kUby1+X3Z6vNW+1n+6TqWz1vHkL16M/bITPoPTY36fevid9uN7KefInfadLwbNoScU71mDblnnUXmY48Rt8sQhyuMHmfufibPff8c60rWNRn7aM1HLNy0kHH9xkWgMpFuwuWGg6+D7Inw8vlQ1MLuF8Vr4fFj4dAbYeI1gWtFRERERMIoUiFd/U0u2hqa7UPDVpeL23G/8npfd423OIuIdAC/r/7eY43Cq2ZXhTUzp3ELxVCtFhtf5/Xj9yodk+jlw+IDfK7ASrLYWDcJ8R6SEjy1IVaoFV91wZcJznE3WIXm8biDQZgLd72xUCGay2WiPhDqLGLi3Ox12CBGHZLBL0u3sOS9NWxc3XzLxwYs/LJ0K78s3Uq/IT0YMymTXcb0xuV2hbfoMIrNzg4EddOnU52bF3KOd9Mmcs85h8yZjxI/fLjDFUaHWHcsV4y9guvnXR9y/M4Fd/LMsc/o77FIuGXuC5fMg7lXwHdzm59n/fDRbfDLJzD1EegxwLkaRURERKTbidSedOcBMwmEbT9Ya/doZX4SUAi4qAvpjrHWvtvG+80FJgevu85ae/cOFy8iXUZ33ZNu4+oiXr13Cb5qP1btFSWK1Q++Gq/2cocIuKr8lq3lVWwurWRdcSWbSivxAl4TDNoMeAGfscHzdSFc4OvAXG9wrg+wzbymHudxMWZwTyZkpzM+O429s9LoEd+19i3rLjauLmLJe3msXrKF9n7bnNIrntFHDGbEAQOIjY/Ue+N2XvXmzeRNn07Vz6uaneNKTSXzkYdJ2GsvByuLHn7r59dv/JoV20JvgfCvg//F0UOOdrgqkW7KWlgwE965EbytrJpO7AVTHoDdjnKmNhERERHpENG0J12kQrqDgJqdm31AhrV2cwvzjwNeo24lnQ9It9Y23YE99PVfENjTzgIXWWsf3YnyRaSL6K4h3aacYmbdviDSZUgUM4YWwrGd2Hcs1LXNtF90eXZ+9VhxRTWL8wpZkJPPN7/ks2RNIZVefwf9KjVkDOzevwf7ZKcxPhjcDUhNCMu9JDyKtpSx9IO1fPfFerxV7ftzEpfoYeTEgex56GCS0+LCVGF4eQsKWHP+BVSsXNnsHFdiIoMefICkffZxsLLoMX/jfKa/Mz3kWEZyBnOnzCXWHetwVSLd2KYV8NI02PpD63P3uxQm3QKe6Pw3XERERKS7UUjX2k2NSQSKCQRuADdaa+9oYf5TwFnUhXQLrbUTmpsf4voCIDV4/dHW2vd2tHYR6Tq6a0i3bV0Jz//tm0iXITvI5W7U9rCFcKylPcUazjF4PO6G7Rjrjbk97nr7kLmiun1fS6q8fpavL2JBTj7zcwpYkJNPQVl12O43KC2hdqXdhOx0hvVJxhXF+7p1FxWl1ayYt45lH66lrLitWysHuNyGXSf0Y8ykTHoPSg5TheHj276dNRddTPni5rvOm7g4Bt1/H8kTJzpYWfT43Qe/4+O1H4cc+/343/Obkb9xtiCR7q6qDN6+ARY90frcAaPhlMeg19Dw1yUiIiIiO0UhXVtubMwHwGHBwyJgX2vtjyHm7Q4sAWKoa3XZYqjX6Po9qHsR3gJDrbU5O1W8iHQJ3TWkK9xUxjM3fxXpMqJSTcDlqr+nWKiArDbkMoF2i/XbL3rcweCr3mM1t3qscQDncWEU4jjGWsuqLaV1oV1uPrnbysJ2v9SEGMZnBVbaTchOY89BqcR53GG7n+wcX7WfH+dvYsn7eeSvL239gkYGj0hjzKRMBu+RHlV7kfnLylhz2WWUfdnC/yMxMWTc+W96/OpXzhUWJVYXrmbq3Kn4rK/JWI/YHrw59U1S41IjUJlIN7d8Nrx2JVQWtzwvNhmOuwtGn+5MXSIiIiKyQxTSteXGxpwJPEPd6rhNwNXAy9baahN4tWIS8BCQXW9eNYGgbW0b7/M74N7gYZG1Nq0jn4eIRK/uGtJtz6/gyRu/iHQZ7WJcpm5FWHB/sZq9xkKHYw0DtLauPGtpjsu98+0VJfptLq5gQW4B83PyWZBTwIr1RYRra8dYj4vRg1JrQ7txmemkJmpfu87GWsualfkseT+PNd8VtPv69IFJjJk0mN0m9McdEx2rVP2Vlay76mpKPvqo+UluNwP/8X+knnCCc4VFib9++Vde+vGlkGPn7nEu1024zuGKRASAghyYdT6sa0Nb+NFnwrH/hrjoWxUtIiIi0h0opGvLjQOvdH4FjK85RSCIqwa2EmhPmVjvfM3nGdbai9txn2+C97DAe9Za7cguIkD3DenKiqt47A+ftXm+y2Pavu9YG4KvUCFai3M8psu2V5ToV1LpZUleYSC0y81nUW4h5dVNV8h0lOH9UmrbY47PTiOjZ4LC405k69oSlr6fx4/zN+H3te977MQesex52CBGHZxBfFLnD2NtdTXrr7+e4jffan6SMfS/+WbSztCKk/q2lm/l2NnHUu4tbzIW44ph7pS5DEoZFIHKRARfNXx4G3x+T+tzew2DU2YG2mCKiIiISKeikK6tNzdmBDAPqFndFupVJltvbC0w1lq7rY2PX9PqsuYx/mKt/fuOVywiXUl3Del8Xj95K/NDtl9sEsC51V5RpD2qfX6+21Bcu6fd/JwCtpZUhu1+A1Lja1fajc9KZ3j/FNz6OxtxpYWVLPtoLSvmraOyzNuuaz2xLkbsP4DRkwaT2icxTBV2DOvzseEvf6Ho5dktzuv7hz/Qa/o0h6qKDg8sfYD/LflfyLFjhhzDPw/+p8MViUgDP38Acy6G0i0tz3PHwpF/g30vBr1pRkRERKTTUEjXngKMGQO8BmRQF6Y1mQasA46z1i5rx2M/AZxT73H3ttYu3fFqRaQr6a4hnYg4x1pL7ray2vaY83PzWb2l/fuXtVVKnIe9s9ICoV12OmMG9yQ+RvvaNWatpbK0lNLCAsqKCnB5Yug/dFfcHk+H3qeqwst3X2xg2YdrKN5a0b6LDewypg9jJmUyYGjn3aPM+v1s+sftFDz1VIvzel9+Ob0vu1QrP4PKqsuYPGcyW8pDBwDPH/c8I3vrexKRiCrZHAjqVn3Y+tzdjoET/wtJvcJfl4iIiIi0SiFde4swJhG4HjgTGNZoeCPwNHBHW1fQBR9zKPA9UPPKVK61dkgHlCsiXYRCOhGJhK0llSzMrVtpt3xdEd4wbWwX4zaMykgNtMfMCgR36UmxYblXZ1BdVUlZYQGltR+FgSCusIDSorrzZYUF+LwNV7glp6Vz0JnnssfEwzCujm2x6/dbVi/ewpL389j0S3G7r++/Sw/GTMpkyJg+uDrhSklrLVvuuZdtDz3U4rz0adPo+4ffK6gLevnHl7nly1tCjo3vN56ZR83Ur5VIpPn98MV/4MO/gb+VldEpA+HkRyD7IGdqExEREZFmKaTbCcaY/sBAwANsttbm7ODjJAP138ZWZq1tpVeFiHQnCulEpDMor/KxZE1hILTLLWBRbgElle1rkdgeQ/skBfe0C7TJzExP7NRBgN/no6y4qC5sq/koCoRw9c9VlZft9P36D9uNw8+7mAG7Du+A6huy1rJxVRFL3l/D6qVbmu8h0YweveMZfUQmIw4YQExc51shufWhh9ly990tzul5xun0/8tfOjwIjUY+v49TXjuFnwt/Djl+3+H3cejgQ50tSkRCW7sAZk2DwryW5xkXHPwHOPj34O7Y1dkiIiIi0nYK6UREooBCOhHpjHx+y/cbiwPtMXPymZ+Tz6bi8O1r1yclrnZPuwnZ6YwYkILHHd4ApX67yZrAreEKuJrVb4WUFRdBBL5f3ePgw5l45rkkp4endVnh5jKWfrCG77/YgLfa365r4xI9jDw4g70OG0RSalxY6ttR+U89zaa/t7wFdOqJJzDg73/HdHB70Wj06dpPueyDy0KODUkdwuwTZuNx6ddJpFOoKILXroIVLe/DCUDmAYFVdamDwl6WiIiIiDSlkE5EJAoopBORaGCtZW1BOQtyA+0xF+Tk8+OmkrDdLzHWzd6ZaYzPTmNCcF+7pLi2hQQ7026yM4qJi2ffqacz7tgT8cSGp01oRUk1yz9dx7KP11JeXNWua11uw2779GPMpEx6ZSSHpb4dUfjyy2z4818CbeKakXLkkWTc+W9MmH5do4W1lgvfvZCvN34dcvzP+/2Z04af5nBVItIsa2HxU/DmH8Bb3vJcTwKMOw8OuFxhnYiIiIjDFNKJiEQBhXQiEq0Ky6pYmFtQG9otW1tEla99q7HaymMsY/p4GNPLzW49LIPjfbgqtoet3WRnlNqvP4eecwFDx+8bttag3mofP36ziSXvr6FgQ2m7rx+8RzpjJ2UyaERap2hfWvzmm6z7w/XQQhibdPBEBv3nP7ji4x2srPNZuW0lp79+esixXvG9eGPqGyTFJDlclYi0aPP3gfaXm1e2PtcVA6PPgIOuhl5Dw1+biIiIiCikExGJBgrpRKSrqKj28e26Iubn5LMgGNwVV7SwUs1aYv1VJPnKSKz3keQrb3Cc6CsjwVeBq72bp3VRWXuN5dDfXEDvwVlhu4e1lryV+Sx5L4+13xe0+/peGcmMmTSYXSf0w+2J7L5v2z/8iHVXXYWtan6FYOKECQx64AHcyd07hPrjvD/y+urXQ45dMvoSLhsTuiWmiERQdTm88ydY8Gjb5hsX7DEFJl4D/fcMa2kiIiIi3Z1COhGRKKCQTkS6mkC7yUJKCvL5KXc9P/6ygbXrN7FtyzYoLw4Gb4EgzmN9kS43rDwxsSSlpZHYM42k1DSSetZ9JPZMI6lnT5J6plFdUcm85x5n1YLQ7QYbMy4XY351HAecehbxyeFtMbllzXaWvr+Gn+Zvwu9v3/fsSamx7HnYIEZOzCA+KSZMFbau9MsvWXPpZdjy5tvCxY/ei8yHH8admupgZZ3L+pL1HD/neKr8TQPNBE8Cr5/0On0T+0agMhFp1cq5MPfywJ51bbXrUTDxWsjcN3x1iYiIiHRjCulERKKAQjoRiQZ+v4/y4uJ6+7zV7etWWrvXW6DtZGVZ+9skRhNjXCT27BkM3XoGw7Z6H6lptediExLa1fYxZ8lCPnpyBvnr1rRpfnxKDw487Wz2mnQULpd7R59Sm5QUVLDso7WsmLeeqvL27eXniXOzxwEDGH3EYHr0TghThS0rW7SINRddjL+k+b0U43bfncxHZ+Dp1cvByjqXuxbexWPLHws5dvKuJ3PLAbc4W5CItF1hHrx8Aaxp2xs+amUdFFhZN/Rw6AStikVERES6CoV0bbmxMasjcFtrrVUTeBEBFNKJSORYa6ksK20YthUWUlpU0GCPt9LCAsqLi7E2PPvNdRZxSUm1q92aBG/1PuJTUsIaiPm8Xpa++wZfvPRsmwPPPpnZHHbeRQweuVfY6qpRVeHlu883sPSDNWzPr2jXtcbALmP7MGZSJv13cX7FWvmKFaw5/wJ8hYXNzokdMoTMx2YS07+/c4V1IsVVxRw3+zgKKwubjLmMi5ePf5lhacOcL0xE2sbnhW8ehs/vgZJN7bt2wJjAyrrdJ4Mrsq2KRURERLoChXRtubExfsACTr5dzFprw/tWZxGJGgrpRKSj1bSbrFnh1jBwC6x2C6x8K8BXXR3pcsPKa9yUuhMpq/1IoMydWHsuJT2N4dkDGbNbJvsM68vQPsm4XJ1jFUFZcRGfv/AUyz54B9r4vfJu+x7IwWdPJ7VvvzBXB36fn1WLt7DkvTw2525v9/UDhqYyZlIm2aN7O/prXvnTT+RNPx/vli3NzonJyCDz8ceIHTzYsbo6k6dXPs0d8+8IOTYxYyL/m/Q/hysSkXarroAlzwTCusK89l3bezgcdDXseQq4I9eqWERERCTaKaRry43rQjrHbolCOhGpRyGdiLSF2k3W8WMoD4ZtZe6ERiFcTQCXQJknkSoT267WXT0TYxiflcb47HQmZKcxKiOVOE9kv23bnLOajx5/mLXfLW99MoF98MafMJV9TjiFmPj4MFcXWJG54ecilryfxy/Ltrb7O+vUPgmMPmIwux8wgJhYZ36tq3JzyZ02De/6Dc3O8fTtS+ZjM4kb2v0aYFT7qjnx1RNZsz1029UZv5rBvgO0h5VIVPB5YfnL8NldsOX79l2bmgkHXgFjz4aYyLQqFhEREYlmCunacuOGIZ1Tb+FVSCcitRTSiXRfajfZUIvtJlPr9n4rJpZFecXMz8lnQW4+K9cX4w/Tt5KxHhdjBvVkfHYaE7LT2TsrjdQE51cVWGv58avP+OSpmWzf1vwKsPqSe/Xm4LOmsfsBB7drX7ydUbipjCUfrOH7Lzfgq27fn9e4JA+jDs5gz0MHkZQaF6YK61Rv2EDeedOoys1tdo47LY3MmY8SP2JE2OvpbN7JeYfrPrku5NiI9BE8P/l5XEbt8ESiht8PP7wJ8/4N6xe379qkvrD/ZTB+OsT3CE99IiIiIl2QQrq23Lhhu8sq4DVgJvBNOO9rrd0WzscXkeihkE6k61G7yTqemNhguNazNnBLTG26z1tiak88sbE7dI+SSi+L8wqYn1PAgpx8FucVUl7t6+BnEmAMDO+XUhvajc9OJ6Onc6sLqisrmD93NvPnvoy3qrJN1wwcvgeHn3cR/XZxbh+x8pIqln+yjm8/Xkv59vb9GXd5DMP36c/oSYPpNTA5TBUGeLdsIW/6+VT+9FPz9fToQebDD5EwZkxYa+lsrLWc/dbZLNuyLOT4/x30fxw/9HiHqxKRnWYtrP4Y5t0JOfPad218KuxzMex7CST1Ckt5IiIiIl2JQrq23Lj5dpcrCYR1T1tr2/Z2ZRGRHaCQTiQ6qN1kHWNcJKam1q12S60L4RqvgItNSHRsFVeNap+fleuDK+1yCliQm8/Wkqqw3W9ganxte8zx2ens1i8Fd5j3WCveuplPn36MH75s4wusxjDq0COZeOZvSEztGdba6vNW+/jx600seT+Pgo1l7b4+c2Q6Y47MZNDwtLD9OfIWFLDmwouoWN58O1GTmMjg//2PpP26V4vHxZsX85u3fhNybEDSAF476TXi3OFf9SgiYbLmG5h3F/z4Vvuui0mEcdPggMuhx8Dw1CYiIiLSBSika8uNjZkOTAMODDFsAS/wJoHA7k1rbXjeli0i3ZZCOpHIUbvJhtrabjKhRw9crujp3G2tJWdbWTC0CwR3q7eGL0hNifcwLiu40i4rjdGDexIfE55fr7XfLefDxx9mS87qNs2PTUhk/5PPYOwxx+P2ONe20/otuSu2seT9PNb9UNju63sNSmbspMEMG98Pt6fjWyz6SkpYc/EllC9c2OwcExfHoP/cS/Ihh3T4/Tuzqz66ig/yPgg5dvW4q5k+arrDFYlIh9u4HD67G1bMhvZ8r+OOhdFnwoFXQq/ut3+niIiISGsU0rWnAGN2Bc4HzgEGBE/XtMGsKW4z8CTwuLX2O8eLFJEuSSGdSMdTu8k6TrSbjEZbtleyMDfQHnN+bgEr1hXhDdPGdjFuw54ZqbXtMcdnpZGW1HG/1n6/j+Ufvcdnzz1J+fbiNl2TNnAQh/3mAoaMHd9hdbTVlrztLHk/j58WbMa289c8qWccex02iJETBxKX2LEho7+sjLWX/47SL75oflJMDBn/+hc9jj6qQ+/dmeUU5XDSqyfhtd4mYykxKbw59U16xvd0vjAR6XjbVsHn98KSZ8Hfju+PjAtGnQwHXQ399HOMiIiISA2FdDvAGOMCjgGmA5OBxj/91xT6DYHVdc9ba7c7V6GIdDUK6UTaRu0m63T2dpPRqKzKy5I1hSzIKWB+Tj6LcgsorQpfA4VhfZMD7TGz0pmQnc7g9ISd/n2qKC3hy1nPseSd1/H72lb7kLHjOfQ3F5I+MGOn7r0jtudXsOyjtayct46qivb9WsfEuRlx4ABGHz6YHr07bk9Af1UV666+hpIPQq8cA8DlYsDf/07Pk6Z02H07u79/9Xee/+H5kGNnjzib6/e53uGKRCSsitbBl/fDwsehup2tiocfCwddA4MnhKU0ERERkWiikG4nGWN6A+cC5wE1r5g3Xl1XDrxMYHXdR07XKCLRTyGddGdqN9lQV203GY28Pj/fb9xeu9Ju/i/5bN5eGbb79U2JY8KQdI4Z1Z+jRvYnxr3jLR23rV3Dx08+Qs7SRW2a73K7GXvMCex/8hnEJSbt8H13VFW5l5Wfr2fph2soyW/fr7ExMHTvvoyZlEm/IT06pB5bXc36P95I8euvtziv31/+TPqvf90h9+zs8ivyOXb2sZRWN30DhMflYe6JcxncY3AEKhORsCrdCl8/CF8/DJVF7bt2yMEw8VoYckjgH2sRERGRbkghXQcyxuxDoB3maUBq8HTjwC4XeAx4wlqb53iRIhKVFNJJV+StqqoL2Lp5u0l3TAxJPdPVbjLKWWtZk18e2NcuN5/5OQX8vLkkLPfq1yOOc/bL4sx9MumVHLdDj2GtZfWi+Xz85CMUbtzQpmsSU3ty0Bm/YdShkzCujt/3rTV+n59Vi7aw5P08Nue2v1HFgGGpjJmUyZC9emNcO/eCsPX52HjLLRS+NKvFeX2vu5ZeF1ywU/eKFjO+ncG9i+4NOfarrF9x56F3OlyRiDimohgWPApf/hdKt7Tv2oxxgbBut2MgAv+3iIiIiESSQrowMMbEA6cC04BDCIR09dngx0fAo8DL1tqu/eqjiOwUhXQSLdRuso7aTQpAfmlV3b52Ofl8u66Ial/HfU8b63FxwuiBnHdANqMyUlu/IARvdTWL35rLV7Ofp6q8vE3X9NtlGIedexEZu++xQ/fcWdZaNvxcyOL31pCzbGu7r0/tm8CYSZkM368/MbE7vuLUWsvm228n/4knW5zX+9Lf0vt3v+vyf88rvBVMnjOZTWWbQo4/fezTjO4z2uGqRMRR1eWw+OnAvnVFa9p3bZ8RMPEaGDkV3J7w1CciIiLSySikCzNjzBACq+vOAUL1d7HARGttC7vPi0h3p5BOIkntJhtSu0nZGRXVPpauKWRBbmBfu4U5BWyv9HbIY4/PSuO8A7N3uBVmaWEB8557ghUfv9/ma3Y/8BAOPmsaKb16t/t+HaVgYylLPljDD19txFfdvn9/4pNiGHVIBnseOojEHju2StVay9b77mPr/x5ocV76uefS94bru3xQ9+rPr3LT5zeFHBvbdyxPHP1El/81EBHAVw3fvgTz7oJtP7Xv2p5ZcNBVMPrXEBMflvJEREREOguFdA4xgZ9EfwVMB04AYqlrg6mQTkRapJBOwsFbVUVZUWHTVW9FTdtOequrIl1uWKndpESKz2/5cVNwX7ucQHC3oahipx6zf494ztk/izMmDN6hVpgbf/6RD594mA0/ft+m+Z64OPY98VTGHX8SMbE71nqzI5Rvr+LbT9ax/JO1lG9vX5MKt8fF8H37MXpSJukDdmzPvW0zZrD53y23c+x56qn0v+VmjLvrBvg+v4/TXz+dHwp+CDl+z6H3cETWEQ5XJSIR4/fB96/DvDthw9L2XZvcHw64HMZNg7jk8NQnIiIiEmEK6RxkjDmYQEh3CpCAQjoRaSOFdNJW9dtNBvZ0K2y27WRlqdpNqt2kdEbrCstr22MuyCngh03b2ZFvg2M9Lk4cPZBzd6AVprWW7z77mHnPPEZJQX6brunRpx+HnDOdXfc5IKJ/n7xVPn74eiNL3l9D4aaydl+fNaoXY47MJGO3nu1+HvnPPsumv/6txTk9Jk9m4O3/wHi6biu3L9Z/wcXvXRxyLKtHFnNOnEOMK8bhqkQkoqyFVR8EVtblft6+axPSYN9LYJ+LIDE9PPWJiIiIRIhCujAzxgwCzgXOA3ZpPIxCOhFpA4V03Zu1lqrystBhW7DtZM25sqIitZtUu0npYorKqlmUV8A7KzYyZ/E6Kr3t/zs+ITuN8w4YwlEj++FpRyvMqopyvnnlJRa8PgdfddtWpw0euReHnXshfbKGtLvOjmT9ltzl21j8Xh7rfyps9/W9Bycz9shMho7ri7sdv2aFc15hw5/+BP7mf5+SJx1Bxl134erCK3Mvee8SPl8f+oX4G/e9kTN3P9PhikSk08j9Ej67C356t33XxSbD+Gmw/+WQ0j88tYmIiIg4TCFdGBhjYoGTCKyaOxxwEQjk6vMDHwAzgZettR2zGYmIdEkK6bomtZuso3aTIm1TUFrFCwvW8NSXuawrLG/39QNS4zl7vyzO3CeT9KS2/10q3LSRT5+eyU/ftO19Zca42GvS0Rx4+tkkpPRod50dbXNuMUveX8PPCzdj/e37mSI5LY69DhvMHhMHEpfQttVvxW+/zbrrfg/e5r/FTzrwQAbdfx+uhIR21RMtfsj/gVNfOxVL01/v9Ph03jjpDZJj1b5OpFvbsBQ+uxtWvAIh/q1oljsOxp4FB14JadlhKk5ERETEGQrpOpAxZhyBYO4MoGfNaQLfbdaEdL8AjwOPW2vXOFyiiEQphXTRQ+0m66jdpEj4eH1+3v9uE499nsPXv7StHWV9sR4XU8YEWmGOHNj2Vpi53y7h4yceYeua3DbNj09KZv9Tz2LMr47F1Qn2YdueX8HSD9ew8rP1VFf42nVtTLybPQ4ayOjDB5OSHt/6vT7+mHVXXImtav5NFgnjxzH4wQdxJ3fNsOrPn/+ZV35+JeTYhXteyBV7X+FsQSLSOW39CT6/B5Y+D/52vH/ZuGHPU+Cgq6HviLCVJyIiIhJOCul2kjGmN3AOMA2oecW8cTBXDrwMzLTWfux0jSIS/RTSRZbaTTakdpMinct3G4p54oucHW6Fuc+QdKYdkM2Re7StFabf52Pp+2/xxQtPU1Fa0qZ79BqUyWHnXkTWXmPaXV84VJZ7WTlvPcs+WkNJQWW7rjUuw7C9+zDmyEz6ZrW8SrD0q69Zc+ml2LLm98aL33NPMh95GHfPnu2qIxpsKt3E5DmTqfBVNBmLc8fx+kmv0z9JLetEJKhwDXx5Pyx8ArztXC2++2SYeA1kjAtPbSIiIiJhopBuBxhjXMBxBIK544CavjeNw7mvCbSzfN5au93pOkWk61BIFx5qN1kn0G4ysNotELLVtJ1MV7tJkShRUFrF8/PX8NSXOawvahqKtGZgajxn75/FGRPa1gqzfHsxX7z0DEvffavNb04YOn4/Dj3nfHr2H9Du+sLB5/OzauFmlry/hi157f92feCuPRlzZCbZo3phXKFXA5ctXsyaiy7Gv735x4/bbTcyZz6Kp3fvdtfQ2f1n0X945NtHQo6dOPREbjvoNocrEpFOr2QLfP0AfPMIVBa379pdDoOJ10L2QaAuDSIiIhIFFNK1pwBjRhAI5s4G+tWcpmEwtwl4msCque8cL1JEuiSFdG2ndpP1GENij9SGIVszbSfjEpPUblKki/D6/Ly3chOPfZHDNzvQCjPO42LKmIz/Z+++49uszv6Pf24ty3vHGV7ZCzLIIIMAIWGUEQh7hYSwWroobel4nqfl1/E8baG0hdJBGQl7jwBhhYSQQfYkkwxvx3vbstb5/SE5lh1ZlhJJlu3r/XrlZevWOfd9EjuJpe99XYfFs3IZN7j7/eQqCvL4YvlTFHy9x6/z6w0Gply5kHMX3ojJHBn7sSmlKD5cy65VBeTvrQp4flJGDJPmZzH63IEYTKdWEFv276fgrrtx1NR0eQ5TTg7Zy57DOCgyAsxgabQ2csU7V1BtOfV7UUPjjaveYHTK6B5YmRAi4rXUwtanYdM/oDnAf5szp7vCulGXSlgnhBBCiIgmIZ0/F9a0e3GFc9PbDnUaYgdW4qqa+1ApFdgGF0II0Y3+HtJJu8mOomJi26vdumo7mZRMdHxCROwBJYToOftK6li+MY93d5VgPY1WmOcOTWGJH60wlVIc2fIVX7zwDPUVZX6dOzY5hfNvXcLY8y5E03XfZjNcqkub2P15IYc2ncAR4J9ZdLyRs84fwlkXZBKT0LEasfXoUQruXIq9vLzL+cbBg8l+7llMOTmntfZI9erBV/n95t97fW7moJk8dclTYV6REKJXsTbDjudh4+NQXxzY3IyzXHvWjV8I0oZdCCGEEBFIQjp/LqxpTjpWy7U5ADwHPK+U6vrVthBCnKH+GtId3ryBtS8828/bTXpWvyUTk5SE0RTV08sVQvQy1U1WXtlSwIub8ik9zVaYi2bmcvO0LJJ9tMK0W61s++AdNr/7OvZW//Z6GzRiNHPvvJdBIyKrmqq53sretUV8/UUxliZbQHP1Bh2jZw5k0rwskgfGnjxuLSykYMmd2Iq7fpPZkJ5O9nPPEjVixGmvPdLYnDaufe9a8urzvD7/r/n/YvaQ2eFdlBCi97FbYc9rsP4vUH00sLkpw2D2AzDxZjDIz9JCCCGEiBwS0vlz4Y4hnRX4AFfV3KZQXlcpFXh/IiFEn9RfQ7pvtmxkxZ//t6eXcfqk3aQQIsLYHU4+3V/Gsg15bMk7vVaYCye7WmGOHdR1K8yG6krWvbSMA+u/8Pvc4y+Yx3m3LCYuOSXgdYWSzerg0KYT7FpVQF15S8Dzc89OZdLF2QwemYSmadhOnKDgzqVYjx/vco4+KYmsZ54mug/9X/95wec8sOYBr8+NTB7JG1e+gV6qXIQQ/nA6YP97sO4xKNsb2Nz4wTDr+zBlMZhiux8vhBBCCBFiEtL5c+GOIV24FqGUUoYwXUsIEeH6a0hXcvgAr/zPT3t6GaeQdpNCiL7g62JXK8z3dp9eK8wZw1JYMmso88cO6LIVZvGhA6xZ9hRlx77x65xGczQzrr2Jcy6/GoPRGPCaQkk5FXl7K9n5WQGlR+oCnp+eHc+ki7MYfs4AVE01BXfdTeuhQ12O18XFkfXUU8ScM/lMlh0xlFIs+XgJO8p3eH3+N7N+w8KRC8O8KiFEr6YUfPMZrHsUCjcHNjc6BWbcD9Pvhujk0KxPCCGEEMIPEtL5c+H2kO7koTBcViml5J1dIQTQf0O6uvITPP39u8NyLWk3KYTor6oaW3l1ayEvfJXPifrAW2EOSYpm0cwcbp6WRVLMqa0wldPJvrWfs+6V5TTX1fp1zqSMQVxwx90MnzI9IquMy/Lq2bWqgKM7KlDOwF6jxKVEMfGiLEafFcuJH9yPZc+eLsdq0dFk/eNJYmfOPNMlR4Q9FXu4beVtXp8bED2AD679gGhDdJhXJYTo9ZSC/I2w7s9w9PPA5priYdpdMPO7EDcgNOsTQgghhPBBQjp/LnxqSBfySyIhnRDCQ38N6WytFh6/4/rTP4G0mxRCCL/ZHE4+3VfGso3H2ZpXE/B8s7G9FeaYgae2wmxtbmbT26+yY+UKnA67X+fMmTCZuYvvITUzO+D1hEN9ZQt7Vhexf0MJtlZHQHNNZj1jzx1A2sq/obau7XKcZjIx5K9/Jf6iuWe63Ijwk7U/4ZO8T7w+9/3J3+feCfeGeUVCiD6lZKerDeaB9wnobRyDGSYvgtk/gKTI/D9HCCGEEH2ThHT+XFjT8ghvSAeAUmpouK8phIhM/TWkA3hiyY1YW5o7HJN2k0IIEVpfF9exbGMeK3aVYHUE3gpz5rBUlszOZf7YDPS6jjdAVJcUs/aFpzm2Y6tf59J0OiZfeiUzr78Vc1xcwGsJh9ZmG/vWl7BndRFNta0BzdXpNAbZjzN4x6vENxZ6H2QwMORPfyTh8suDsNqeVdhQyIJ3F2B3nhrUxhpj+XDhh6RGp/bAyoQQfUrFIVj/V9jzGqgAbqLQGeDsG+G8ByB9dKhWJ4QQQghxkoR0QgjRC/TnkO7A+i8wREVJu0khhOgBlY2tvLqlgBc3FZx2K8zFs3K4aWo2iTEd95g7vms7a5b/h5qSIr/OFR2fwOybFnH2vEvQ6SLzBgyH3cmR7eXsWlVAZWFjwPOTag6RXfg5qdX70TrfI6jTMei3vyXpumuDtNqe88ctf+TFAy96fe6m0Tfx3zP+O8wrEkL0WTX5sPEJ2PE8OAK5iUKDsVfBnAdhcN/YG1QIIYQQkUlCOiGE6AX6c0gnhBCi59kcTj7Zd4JlG/LYln+6rTAzWTIrl9ED408ed9jt7PrkAza+8fIpVdNdSc8Zytwl95I17uyA1xEuSimKDtWw67NCCvZVBTw/pukE2UWfk1G2BX2nirOM//ovUhbdHqyl9ohaSy2Xv305DbaGU57Ta3reufodhiZKUxEhRBA1lMGmf8DWZ8B66r89Pg2fB3N+DDmzQNrjCyGEECLIJKQTQoheQEI6IYQQkWJvkasV5vu7T68V5qzhqSyZlcs8j1aYzXW1rH/tBfau/hT8/Jl/1IzzuOD2pSSkDwh4DeFUVdLI7s8LObT5BE57YK9njNZ6Mou/ZEjJl5hsTSePp//oR6Td17v3bnvu6+d4bPtjXp+7KOsi/nbR38K8IiFEv9BSA1uedgV2LdWBzc2a4QrrRl4sYZ0QQgghgkZCOiGE6AUkpBNCCBFpKhtbeWVzAS9syqe8IbB92AAyk6O5Y2bHVphlx4+yZtm/KT64369zGIwmpi64julXX4cxyhzwGsKpqa6Vr9cWs3dtEa1Np+7H5ovOYWXQic1kFa0mpqUcgNT77iP9gR+i9dI3ilsdrSx4ZwElTSVen1922TKmZEwJ86qEEP2GtQm2L3e1wmzw/u9QlzLOdrXBHHc1RGj7ZSGEEEL0HhLSCSFELyAhnRBCiEhlczj56OsTLN+Yx/bTaIUZbdSz8JwhLJmVy6iMeJRSHPpqHV+++BwNVRV+nSM+NZ3zb1vC6FnnR3xoZWt1cPCrUnZ/XkhdRUtgk5WTtKqvyS5cRWLdUVIWLSLjFz9H0+lCs9gQ++DYB/xi3S+8PjchbQIvXv5ixH89hRC9nL0Vdr8K6/8CNccDm5syHM77EUy4CQym0KxPCCGEEH2ehHQRSNO0FOAhpdTPe3otQojIICGdEEKI3mBPUS3LNubxwe7S02qFOXtEKktmDeWiMQNw2lrZuuIttr73Fnab1a/5Q8aMY+6S+8gYOjzga4eb06nI21PJrs8KKD1aF/D8+Po8sgs/Z8ScoQz57f9D0/e+ag6ncnLzBzdzoPqA1+cfueARLsu9LMyrEkL0Sw477H8X1j0G5QG+J5YwBGb9AM65A0wxIVmeEEIIIfouCekiiKZpicBPgB8AcUqp3vdKWwgREhLSCSGEUEr1mqqiioZWXtlSwIun2QozKyWaxTNzuWFqFlpjDWtfeo7DX63zb7KmcfZFl3DezXcQk5AY8LV7wonjdez6rJBjO8v93ZLvJLOlihHxJ5jxv3cTFR8dmgWG0JbSLdz16V1en8uMy+S9a97DpJcKFSFEmCgFhz+BdY9C0dbA5sakwYzvwLS7ITopJMsTQgghRN8jIV0E0DQtHviR+1cCoAFKQjohRBsJ6YQQou9SDoWz0YqjwYqjvv2js8H9eYMVZ70VR6MNzaQjenwaid/KRR8X+cGF1e7k430nWLbhODsKagOeH23Uc627Faa5Ko81y56iIt+/dmRRMbHMvP4WJl16BXqDMeBr94T6yhZ2f17I/o2l2FsdAc01YOWseUOZOD+HuOTI3p+vs+9+/l2+LPrS63MPTXuIReMWhXlFQoh+TynIWw/r/gzH1gQ2NyrBFdTNuB/i0kOzPiGEEEL0GRLS9SBN02JxVc39GEjGFc61kZBOCHGShHRCCNH7KJuzPWRr8Ajh6jseczbZIMAfc/UpZtIWj8OYERuaxYfA7sJalm/M4/09Jdgcgf9cf96INBbPyCatZCcbX38RS0O9X/OSB2cyd/E9DJ00JeBr9hRLk43960vYs7qQpjr/Wn220ek0RkwbwKT52aRnxYdohcF1tPYo1664Fqc6tUVqYlQiK69dSYIpoQdWJoQQQPF2VxvMgx8ENs8QDVMWw6zvQ2JmaNYmhBBCiF5PQrrToGmaEbjI/SsLSAEswDHgC2ClUsruY34U8D3gZ0Aq7eFc229QA75RSo0OxfqFEL2PhHRCCBE5nK2O9uq2ztVuHtVwqqXLHweDQovSk3rrGMyjU0J6nWArb7DwyuZCXtycT8VptMLMTolh0eR0svLXs//zlSinf3vfDTtnGhfecTfJg4YEfM2e4rA7+WZbGbs+K6SquDHg+Zljkpk0P5vs8SkR3yr14Y0P89Y3b3l97s7xd/Lg1AfDvCIhhOik/ACs/wvsfRNUANXOOgNMvBlm/wjSRoRufUIIIYTolSSkC3QRmnYT8Edc4VxX8oHvKKU+8TJ/IfAX93yv4Rzwe+BFpbzcSiqE6JckpBNCiNBSSqFa7B6hm61jCFffirPBhqPeirIG1oYwpDRIvHIYcbMGR3wI05nV7uSjr0t5bkMeuwprA54fY9Jzw3Ajw499TsXBPX7N0ekNnHP5AmZcezNRMTEBX7OnKKUoOlDDrlUFFOyvDnh+8qBYJs3PYtT0DAzGyGzWUdFcwRXvXEGLveWU50w6E+8vfJ/BcYN7YGVCCNFJ9XHY+DjsfBEcgVQ7azD+GjjvQRg0IVSrE0IIIUQvIyFdIAvQtF8Bv6ZjW0roGLK1cQC3K6Vec8+NAv4F3IH3cO4w8DvgZQnnhBCdSUgnhBCnRzkVzmbbKXu8nXxc3x7KYe+9P4LFnjuQpAXD0fS6nl7KadnlboX5wem0wlSKK5KqGZP/Bdaacr+mxCQmMeeWxYy/YB6arnf9mVUVN7L9jd0c2d+E0hkCmhudYGLChUM46/xMzHGRt0/fP3b9g3/u/qfX564YdgV/mPOHMK9ICCF8aDgBX/0dtj4LtqbA5o68BOb8GLJnhGZtQgghhOg1JKTz9+Ka9i3gQ/dDz4V02Eeu0/FmYCxQDnwCzHEf9wznDuEK516RcE4I0RUJ6YQQoiPlUDgbO7aX7LDvW1v7yUYbOHu+G0M4RI1IIvXWMehiIi988Vd5g4WXNxfw4qYCKhsDa4WpUw7m2g4wrnwL2PybmzFsBHOX3MeQ0WNPZ7k9qnr7Pjb97nUKU6ZiNwa2N6HBqGPMrEFMnJdF0oDIqShstjVzxTtXUNlS6fX5V698lfGp8vOPECLCNFfDlqdg0z/BUhvY3JzZMOdBGD4PellFvBBCCCGCQ0I6fy+uad8Aw+kYsFUCW4EaIAE4BxjsHtMWxv0TcOLag85z7hFcVXmvqp4uERRCRDwJ6YQQ/YWyO7sO3Tw/b7J1vD2qr9Br6ONM6BJM6ONN6Ns+xptoOVCF5YDvVoeGtGhSl4zHmBYdpgWHhtXuZOXeUp7bmMfuAFthxtibmFO3hVH1B/2eM/a8C5lz2xLiU9ICXGnPaj12nGNL76VIP5yCzLlYotMDO4EGQyekMfnibAYOT4yIlqlvHH6D33z1G6/PTR84nacveToi1imEEKdobYDty2Dj36HxRGBzB010VdaNuQp6WYW3EEIIIc6MhHT+XFjTLgRW0x6+NeIK3V7oHLBpmrYA+DcwwH2oDogBTO7HTcCvgCeUUvZQr10I0TdISCeE6O2cVoerss1zn7e2areG9hDO2dw3fzzSjDp08R2DN51HAKdPMKGLN6GLNqDpvAcQyqmo+ziPxi+LfF8r2kDqbWMxj0gKwe8k/HYW1LB8Yx4f7i0NqBVmRmsZ51etZ2Crfy0wDVFRnHvNjUy9ciEGk6n7CRHCWlREwZ1LsRYWUZE2gYKs+dQnDgv4PBlDE5g0P5thk9LQ9WDbVLvTznUrruNY3TGvzz8570nOzzw/zKsSQogA2Cyw+2VY/1eozQ9sbtooOO9HcPYNoO+9lfFCCCGE8J+EdP5cWNMeAX7sfugA5iul1voYPxZXhV3bLcxtVXV5wJVKqQOhW60Qoi+SkE4IEYmUUiiL49QWk533fWuwolodPb3ckNCi9O2hm2cIl9DxsRalD1r1T9O2E9S8cwR8BVY6jaSrhxN37qCgXDMSlNdbeGlzAS9tDqAVplKMbjzMrJpNxDma/ZqSkJ7BhYvuYsT0mb2mYstWVuYK6o65gq26hKEUZM2jIm0iaIEFbglpZiZclMXYWYMwmQPb8y5Y1hau5Xurv+f1ueGJw3lzwZsYAtyPTwghws5hh31vw7rHoCLAt4ESs2D2D2Hy7WDs3dXxQgghhPBNQjp/LqxpnwHzcAVt7yqlrvNjzmPAA7RX3zUDE5VSR0O4VCFEHyUhnRAinJRT4Wy24WiwebSYbPUI3WwnQzjsfXNLXV2MwRWyeVS76TwCuLbHOpO+R9bXeqyOqhf3d1t5GDd7MIlXDOuyOq83arU7XK0wN+Sxp6jOrzlGp5WptTuYXLcbPf59z2afNYELF99LenbuGaw2fOxVVRTcfQ+tB9rfCG42p1GUOZeSQTNx6qMCOl9UjIHxcwZz9oVZxCUHNvdMKaW469O72Hpiq9fnfz3z11w/6vqwrkkIIU6b0wmHP4IvH4WSHYHNjU2Hmd+FqXeBOSE06xNCCCFEj5KQzp8La1oekI0rcLtDKfWSH3NmA+to3y3lH0qp74dskUKIPk1COiFEMCiHwtnUseXkycq3DvvA2cDZBzd800AXZzw1dIv3aD+ZYEIfZ0IzRP5+MPaqFiqX78Ne3uJznHl0Mim3jEHXQ1VRoaKUYmdhLcs25LFybyl2P75nE2x1nFf9FcObj/t1DU3TMfGSbzHrhtuIjo/8N0cddXUU3nsfLbt3dzhuM8RQPPg8ioZciDUqMaBz6vQaI6dlMGl+NmmZccFcrk/7qvZx8wc3e30uLTqNDxd+SIwxJmzrEUKIM6YUHF8L6/4Mx78MbK45EabfC+d+B2JTQ7M+IYQQQvQICen8ubCm1QCJuAK3KUqpXX7MSQaqaA/pFiilPgzZIoUQfZqEdEIIX5Td6XWPt5OVb20BXJOt/SeTvkSnoY83okuIcgduRu97vsWa0PR9p6IMwGmxU/XyQVoP1/gcZ8iIIW3xeAwp5jCtLLzK3K0wX96cT2WjtdvxWS1FzKlaT6rN959bG3NcPLNuvI2J87+FTt8z1ZP+cjQ2UXT//TRv2XLKc07NQFnGVAqy5tEUOzjgc2eNTWbS/GyyxqWEpRXoz9f9nA+PeX8Jdf/E+/nOpO+EfA1CCBEShVth/WNwaGVg84wxMGUJzPweJA4JydKEEEIIEV4S0vlzYU2zAzpcb2uNUEp1e+ut5nrV2rb5isL1Byt70QkhTouEdEL0T06ro8s93jwr37predhrGXTtlW5e9n3TuQM5XYyxT7VzDJRyKOo+PEbjxhKf43SxRlIXjSUqN7BKqt6k1e7gwz2uVph7i323wtSUk7Pr93Fu7RbMzu6DPYC0rBzmLrmX7LMmBmO5IeO0WCj64Q9pWuu9UkMB1anjKZ97H6WVgYeOKYNjmTQ/m1HTMtAbQ1d1WtJYwlXvXIXVy9cn2hDNymtXkhadFrLrCyFEyJXtg/V/ga/fAhVAC3GdESbdArMfgNThIVueEEIIIUJPQjp/LqxpbT8pKWCoUqoglPOEEKIzCemE6DuUUiiLw2fo1va5anV0f8JeSIvSn9puMuHUFpSaWR+Wap2+onFTCbUrjuJzyzW9RvJ1I4k9JyNs6+oJSil2FNSybGMeH3XTCtPsaOHcmq2c1bAfnZ+lpiOmzeTCO+4iccDAYC056JTVSvFPH6Lhk0+6HqRpRP30dxzTjeGbrWU4HYG93opJMHH23EzOOn8I5ljjGa7Yu8e2PcZz+57z+tz1o67n1zN/HZLrCiFEWFUdhQ1/g10vg9Pm/zxNB+OvhfN+BAPP6n68EEIIISKOhHT+XFhCOiFED5OQTojIp5wKZ7MNR4OtY4tJLyGcsgVwp3QvoosxtFe6ebab7LTvm84U2e0CezPLNzVUvXQAZfEd8MZfmEXCJTn9ogKxrN7CS5vyeWlzAVVNXVfMpVqrOL9qPZkW3xWJbfRGI1OvXMj0a27AZI4O1nKDStntlP73/1D37rs+x2X84udEXX0ze9YUsW9dMa0BVucaTDrGzhrMxHmZJKYHd5+4ems9l799OXWtp1ZG6jQdby94m+FJUkUihOgj6ktg499h+3Ngaw5s7qhvwZwfQ9a00KxNCCGEECEhIZ0/F5aQTgjRwySkE6LnKKfC2WjDUd/qZd83W/vjRisEWIXSK2iuNokdW0x6VL55BHCaIXRt74T/bOXNVC7fh6PK4nOceXwqKTeN7jehqcXmboW58ThfF9d7H6QUw5uPcV71VyTYG/w6b1xyCnNuu5Ox510YkZWfyumk7He/o+blV3yOS3/gh6Tedx+2VgcHNpayZ3Uh9ZW+v4dOocGwSelMvjibgcOC11b1hf0v8Ketf/L63AWZF/D3eX8P2rWEECIiNFXB5n/Bln+DxXf75lPkznGFdcMuhAj8f0kIIYQQHUlI58+FJaQTQvQwCemECD5ld+JodLec7GrftwYrzkYbfnbA6110mms/t86hm+fjBBO6WBOaXt7g6W0cTTaqXzpA6zHfb+wZB8eSung8hsSoMK2s57laYdbw3IY8Pv76hNdWmHqnncl1u5latwOj8q+qbNCoMVy0+F4GjhgV7CWfMaUUFX/+M1VPP+NzXOo9d5P+4INomobTqTi2s4JdqwooO95FqOnDwGEJTJqfzdBJ6ejOsGLT5rCx4N0FFDUWeX3+2UufZdpAqRwRQvRBlnrY9ix89SQ0lQc2d/A5rrBu9OWgkxuphBBCiEglIZ0/F3aFbW0XvwU44efUL9wfVYDzXJOU8r7TuxCi35GQTgj/Oa2Oji0mO4VubY+dAbZz6zUMOo/2ksaO+7557PmmizH2i1aH/ZmyO6l97yhNW33/CKqLN5F2xzhMWfFhWlnkOFFn4aXN+bzcRSvMWHsjs6s3MbrpG7/POf7C+cy5ZTGxScnBXOoZU0pR9a9/UfG3x32OS771VjL++7/Q3G/oKqU4cbSOXasKOba7IuCbFhLSzEycl83YWYMwRp1+1ebHeR/z07U/9frcuNRxvHLFK+g0eRNaCNFH2Vpg54uw4XGoC/D+7/QxcN6DcNZ1oDeEZn1CCCGEOG0S0vlz4faQTiOwl6We73wFunillJKfnoQQgIR0QiilUK2Ojvu81Xvf9021+t6Lq7fSTPr2dpMJnfd9M7a3nIw2RGTLPdEzlFI0ri+mbuVx3z+NGnSk3DiKmAnpYVtbJLHYHHywp5TnNhxnX8mpVWODLKWcX7WeAdZKv85nio5mxrU3M/lbCzAYjcFe7hmpWraM8j/80eeYxIULGfTb36AZOr4cqS1rZvfqQg5uLMUe4N6aUTEGxp8/hAlzM4k9jcpNpRS3r7ydPZV7vD7/hzl/4IphVwR8XiGE6FUcNtj7Jqx/DCoPBzY3KQdm/xAm3QZGc2jWJ4QQQoiASUjnz4U7VtIF8q6X54IDfbdMKaX6xwYhQohuSUgn+iqlFM5me3vo1nnPN48QTgX4hnBvoUUbutzjzfOx7gwqUIRoOVBF9SuHUFbfIXbCxTnEX5TVb4NepRTb82t4bqOrFabDoxWmppyMbTzEzOrNxDhb/Dpf0sBBXHjHPQw7Z1pE/ZnWvP46J379MPh4fRV/2WUM+dMf0UymU56zNNr4+sti9nxRREv9qRWIvuj0GqOmZzBpfjapQ+ICmrujbAeLP17s9bnBsYNZsXAFUfr+07pVCNGPOZ1w8ANY92co3RXY3LgMmPk9mHonRPW/KnohhBAi0khI58+FO4Z0YbkkEtIJITxISCd6G+VUOBttp4ZunsFbvRVHoxUcfXDDNw10scYO7SW97vsWb0IzSns2ER62E01ULtuHo7bV57joSemkXDeq339vlta18OImVyvMmmbbyeMmZyvTarYzsX4vevy7eSB34jlcuPgeUodkhWq5Aat7/wNKfv5zcHQd3MZdeCFD/vZXdFHegy+7zcHhLWXsWlVITWlTwGvIHpfCpPnZZI5N9jvE/OHqH7K6cLXX5x6c8iB3nnVnwOsQQoheSyk4uhrWPQb56wOba06Cc78N594HMSkhWZ4QQgghuichnT8X1rQ8whvSAaCUGhruawohIpOEdCJSKLsTR2Onfd7qrTgb3IFcfasrgGu09cD/nGGgA31cp2q3BI/QrS2EizOi6ft3wCEik6PRStXz+7EWNPgcZ8qKJ/WOcejjT62i6m8sNgcrdpewbEMe+0vbW2EmWWuYU72R3Bb/9gbSdHomX3YlM6+/BXNsYBVkodKwahXFP3oQZbN1OSZmxgyynvw7utjYLscopSjYX82uzwooOlgT8DpSh8Qx6eIsRk7NQG/w/W/n8brjLHxvIQ51argYb4xn5bUrSTInBbwGIYTo9Qo2ucK6bz4JbJ4x1lVVN/N7kDAoNGsTQgghRJckpBNCiF5AQjoRak6ro0Po1mHfN4/Pnc32nl5qaBg0ry0mO4RwCSZ0MUY0XeS0rBPidCibk5q3DtO8q8LnOH1iFKlLxmMa1HU4058opdiWX8OyDXl8vK+9FWZOcz5zqjeQbKvz6zyGmDguvHUxZ8+7BJ2u5xtnNK7fQNH3voeyWLocEz1pEllP/Rt9QkK356sobGDXqgKObC3H6Qzs9Vtsoomz52Yyfs4QzLFd7+X3u02/47VDr3l97vaxt/Oz6T8L6LpCCNGnlO6B9X+Bfe8Q0F1zepNrv7rZP4QUuWdcCCGECBcJ6YQQoheQkE6cDqUUqtXhNXTr3IJSWXzvU9VbaSa9O2Qz+qx806INEbVflBChppSi4YtC6j/J9zlOM+lIuXkM0eNSw7Sy3qGk1tUK85UtrlaYOuVgQv3XTK/ZRpTyb482Q3omV9z7HUZMmBji1Xaveds2Cu/7Ns6mrltWRo0bS/bTT2NI8a8lWmONhT1riti3rgRrS2A3eBii9IybNYiJ87JISIs+5fmqliqueOcKmmynrtegM7Di6hVkJUROa1EhhOgRlUdgw19h96vg7Lpi+hSaHs66Ds77EWSMC9nyhBBCCOEiIZ0QQvQCEtIJT0opnM12n6Fb22Nl82+/pN5GizZ43+MtoeM+cLooQ08vVYiI1ry3kprXD/n+t0KDxG8NJW7OEAmzO2lrhfnchjwOlNYT7WhmZvUWxjUewN8/KXvORBbccy9jR+aEdK3dadm7l8K778FR13VFoGn4cLKffRZjxgC/z2u12DmwoZTdnxfSUN11tZ43mgbDJqcz6eJsBg5N7PDcU3ue4omdT3idd2nupTx6waMBXUsIIfqsuiLY+HfYvgzsLYHNHXMlnPcgZE4JydKEEEIIISGdEEL0ChLS9Q/KqXA22dor3zxDN8894Bqs4Oib/yfqYo3e93jz+Fwfb0Qz9nyLOCH6CmtRA5XP78dZ77sCLGZKBskLR6B1s2dYf6SUYmteDcs2HueTfWWktJRzftV6Bree8Gu+TTNQO2I237rlVs4fN7jHwlDLocMU3HUXjsrKLscYs7LIfu45TJlDAjq30+Hk6M4Kdn1WQHm+7z0RvRk0PJFJ87PJnZiGTqfRYm/hyneupLy53Ov4ly5/iQnpEwK+jhBC9FlNlbDpH7DlP9Ba3/14T8MuhDk/htw5rjsohBBCCBE0EtIJIUQvICFd76YcThwNNo/Kt1Z36GbrGMI1WgPaNqLX0IEuzlfo5t4DLs6Ippc3/4XoCY66Viqf34+tuNHnONPQBFJvH4fex35h/V1xWyvMzfmkVxxgdvVXxDu6biPpqV4fx7Ghc7nkyku5bkomMabwVwO3Hj9OwdK7sJeWdjnGMHAg2c8+S9SwwPcsUkpReqSOXasKOL6nMuD/9xLTo5k4L4sxswbxQf4KfrXxV17HnTPgHJZdtkyqP4UQojNLHWx9Gr76BzR3fVOGV5nTXGHdyEtBJz+3CyGEEMEgIZ0QQvQCEtJFJmVzeG836bkHXIMVZ1Nge/H0Gnqt4x5vnUO3ts9jjWg6eZNUiEjntDqoef0QLV9X+RynTzGTtmQ8xgExYVpZ72SxOXhvVzHLvzxM/OF1nFO3C4Pyb//PYvMgtg+6gIvPO4c7ZuaSnRreP2tbcTH5dy7FVlDQ5Rh9airZzz6DefTo075ObVkzuz4v5OBXpTgCbM8cFWtg/JzB/M32MPuad3sd89e5f2Ve9rzTXp8QQvRp1mbY+QJseBzqiwKbO2C8a8+68QtBL+3lhRBCiDMhIZ0QQvQCEtKFj1IK1erw2mLSUd+xBaWy+Pdma2+jmXSnVrt5aUGpRRukQkGIPkY5FfWf5dOwptDnOM2sJ/XWsZhHJYdpZb2XUootx6t5cdVO7JtWMKLpmF/znGjsix/L5pTpzB6fy52zc5k1PDVs/+7aysspWLoU65GjXY7RJSaS/Z+niJ5wZm0lWxqtfL22mL1fFNHSYAtorqaH/Smb2DN4DTUxHduL5ibk8vbVb2PUSeWnEEJ0yW6FvW/A+seg6khgc5NzYdYPYNSlkJgZkuUJIYQQfZ2EdEII0QtISHfmlFI4m+3toVunajfPfeBUgHfz9xaa2YA+wXhqtVunFpS6KLkbVoj+rnlnOdVvHQa7j5+/dZB05XDiZg0O38J6uaKaZl56ZzUNX7xBcqvvisU2rToTm5OmsjfhLIZnJLJkdi4LJw8JSytMe00NBXfdRev+A12O0cXEkPXvfxEzbdqZX8/m4PDmMnatKqDmRHPA8wuS9rN78BqKEw6DO8v8r3P/i5vH3HzGaxNCiD7P6YAD78O6P8OJPYHPjx8MmVMhazpkTodBE8FoDv46hRBCiD5GQjohhOgFJKTrmnIqnE22jqGbZwtKj8c4+ub/I7pY4yktJvXxRo8QLgp9vBHNqO/ppQohepHW/HqqXtiPs9F3ZVPsjEEkXTUcTS+Vtf5qarHy6ouvU/7Fe5jsLX7NqTYm82XKbApjskgwG7hpWhZ3zMwlKyW0rTAd9fUU3vdtWnbu7HKMZjaT+cQTxM05LyjXVE5F/r4qdq0qoPhQbcDzK2OK2D14DUdTd5AUk8SHCz8kzhQXlLUJIUSfpxQc+RzWPQoFX53+efQmV1CXOR2yprk+Jg4J3jqFEEKIPkJCOiGE6AX6a0jnbLFjr2zpMnRzNFhxNlqhLxa+6UAX5xG6ebabbHucYEIfZ0TTy6btQojQsNdYqFq+D1s3VU1RI5NIvXUsumipxA1Ec0M97zzzHKWbPkdT/v1ndiwmh/Ups6kzJqJpMH9sBnfOymVmCFthOpuaKPzu92jetKnrQUYjQx77MwkXXxzUa1cUNLBrVQHfbCtHOQN7PdhoquXrgWuZNnckP5j5vaCuSwgh+oX8ja7KuiOrgnO+hCGQOc2j2m4CGKKCc24hhBCil5KQTggheoH+GtI1bTlBzdvf9PQygkuvdR26eX4ea0TTSVWKEKLnOVvtVL9yCMvBap/jDOnRpC0ejyEtOkwr6zsqC/P5+Ol/UXZwr1/jHejYlTiBrUlTsOlMAIzKiGPJrKFcM3lwSFphOltbKf7hAzR+8UXXg/R6Bv/f/5K4YEHQr99QbWHPmiL2ryvGGuCesDZ9K2NnD2LmJaNJkO9PIYQIXMku1551+1cAQXxvTm+CQZPcoZ07vEuQNtpCCNEvKQWWOqgrhNpCGDwZEgb19KrCQkI6IYToBfprSNdysJqqZRH5f9IpNKPO+x5vnR7rYgwhq3QQQohQUU5F3UfHaVxX7HOcLsZA6u1jiRqWFJ6F9SFKKY5u28ya55+mvvyEX3Oa9DFsTD6Xg3Gjwf1/S2K0kZunZXH7jJygt8JUNhvFDz1Ew0cfdz1I0xj48MMk33RjUK/dxtpiZ/+GEnavLqSxujWguZoGw88ZwKT52WQMTQjJ+oQQok+rOAwb/gp7XgOnPTTXSMjstLedVNsJIUSf4HRCY5krhGsL4jp/tDa0j7/2aZhwQ8+tN4wkpBNCiF6gv4Z01uJGyp/oeg+ccNDM+pPVbrqE9sDtZAjnfqxF6SV8E0L0eU1bT1DzzhHw1XZQr5F8zQhipw0M38L6ELvNxvYP32Xz269ha7X4NafMNIC1qbMpM7f/mevcrTCXzM5l5rDgtcJUDgel//Mr6t5+2+e4AT/7Gal3LgnKNb1xOpwc3VHBzs8KqCho6H5CJ4NGJDJpfjZDJ6RJ5boQQgSqtgA2/xsOfwxVR0J7LX2Ua287qbYTQojIZrdCfVGn8K0I6gpcn9cXg8Pq//nm/RrmPBi69UYQCemEEKIX6K8hnaPBSunvN4fk3LpYg/dqt04hnM6kD8n1hRCit7IcraX6pQM4m33fQR83ZwiJ3xoqAchpaqyuYt0ry9n/5Wq/5xyMG8XG5Bk0GWI7HB+dEc+S2blcM2kI0UH4f005nZT97/9R8+KLPselff97pN1/f0hvYlFKUfJNLbtWFZK3pzLg+UkZMUycl8XoGQMxyv/5QggRuOZqKNoKhVugaAsU7wBrY2ivmZAJWdMg61xXtd3As8FgCu01hRCiv7PUu6vgilw3a3hWwdUVQcMJgtoSeepSuPIvwTtfBJOQTggheoH+GtIpp6L4v9b7/3+8Brq4ztVuRvfjqPZ93+KMaAZdSNcuhBB9mb2yhcrl+7BXtPgcZx6TQsoto9FFBX+PtP6i5PBB1ix/ihNHDvs13qoZ2J50DjsTJuLQdfxzT4w2cvP0LBbNyCEz+cxaYSqlqPjLX6l66imf41KWLmXAT38SlmrzmhNN7FiVz76NReidgX3PmWONnHXBEM6+MJOYBHmjVwghTpvTAeX73aGdO7yrPhraaxrM7r3tprlCu6zpEC8V/UII4TeloKnCHboVuIM4z4q4Atd+ceE04mK4/c3wXrOHSEgnhBC9QH8N6QBKfr8ZZ7PNa4vJDpVvCSZ0sUap2BBCiDBxNtuoevkgrUdqfY4zDowhdfF4DMnm8CysD1JOJ/vXrWHdy8toqq3xa06dIYH1KbM4FpN7cr+6NjoNLh6XwZJZQ5kxLOWMArTKf/2bir/+1eeYpJtvYuCvfoWmC88NMh8fWMWrb33CWSfOI9oeH9BcvUHH6HMzmDg/m5RBsd1PEEII0b2mKldgV7TFFdoV7wBbU2ivmZjdcW87qbYTQvRnDhvUl3TaA84dxrVVwtn9a7UfNulj4Luh6a4VaSSkE0KIXqA/h3ROi132exNCiAilHE5q3z9G06ZSn+N0cUZSF40jKichTCvrm6wtzWx653V2fPguDrvvdqNtCs1D+DL1PKpNKV6fHzMwniWzcrn6DFphVj//AmX/+78+xyRevYBBv/89miH0VZVKKRZ/vJg9pV8zqnIqE0rmkmzJCPg8OWenMml+NkNGJcnPIUIIEUwOu6varmgLFLrDu+pjob2mVNsJIfoya9Ope8B5BnINpaCcPb1K/2l6SBsF393U0ysJCwnphBCiF+jPIZ0QQojI1/hVCbXvHwVfr/sMGinXjSJm8oCwrauvqjlRwtoXnuHoNv/uLHWisTdhPJuTptGq917RmBRj5OZp2SyamcOQpOiA11T71luU/vf/uFrldCH+kksY8ugjaKbQVzLsKt/Foo8WuR4ojezacUwqmcvg+pEBnystK47JF2czfMoA9Hpply2EECHRVOmxt91WKN4OtubQXjMx22Nvu2muaju9MbTXFEKIQCnl2v/TM3zrvC9cS3VPrzIwhmhIyoLELI+P2a6PiZkQPwj0/WfLBAnpgkDTtG8ppT7q6XUIIfouCemEEEJEOsvhGqpeOoBqdfgcF39RFgnzc6Q9cRDk7d7BmuX/obq40K/xLbooNidP5+v4cSjNe9ik0+CScQNZMjuXc4cG1gqz7sMPKfnZz8FHlV/s+XPIfPxxdObQtz998IsH+Sz/sw7H0huzmFAyl5HV54AK7HswLjmKCXOzGDdnMFHR/edNAyGE6BEOO5Tv67i3Xc3x0F7TEA2DJ3estouTm4tEhFEKGsuh6ojHr6OuvR9bakAfBYYoV/Wor4/GaP/GGcydfnV6Tm88pbW6CJDD7qp0a2s96Rm+tQVyob5pIdiiU9rDtw5BXJbrBomYFPm+8SAhXRBomuYEjgNPAc8qpSp6eElCiD5GQjohhBC9ga28mcpl+3BU+97PIPrsNJJvGIXuNNsrinYOu53dn61k4xsv0drk3/4+lcYUvkw9j+LoIT7HjRkYz52zXa0wzUb/vlYNq1dT/MMHUDZbl2Nipk8n8x//QB8X2j3fCuoLuPq9q7E7Tw0N0x2D+WXMoxzbVI3N4jtY7sxo1jPuvMFMvCiL+BTZa1EIIcKmscJjb7utULIj9G9cJ+W4quyypku1nQgvS317ANc5kLM29PTqPGinGQT6CgP9CQ3bQsJecOOUraVj5VtdUcd2lPXFoAL7ebRHaTpXpdsp4VtWeyVcVFxPr7JXkZAuCNwhXdvibMDbwL+UUl/23KqEEH2JhHRCCCF6C0eTjaoX92M9Xu9znDEzjrQ7xqFPiArTyvq25vo6Nr7+IntWfYLyc7+JIzHD2JAyk3qj770Ck2KM3DI9m0UzchjsRyvMpo0bKfzu91AtLV2OMU+cQPZTT6FPTPRrrafrD1v+wEsHXvL63C1jbuHHEx5i/7oS9qwppLGmNaBzazqNEeekM/2qYSRlxARjuUIIIQLhsEPZ1x5tMrdATV5orynVdiKY7K1QffzUEK7qCDSV9/Tqegedwc9gz/NjgOGhsasqwihXNZil9tQ94GoL2ivjmnpZPY/B7AraEjM7tqFsC+ISBsvNCkEmIV0QeIR0bTWabQs9CPwTeF4p5ftdCiGE8EFCOiGEEL2JsjupeecIzdvLfI7TJZhIWzwe0xC50zJYyvOOsWbZUxQd+Lr7wYBd07MjcRLbEydj1/l+sa3XaVw6PoPFM3OZ3k0rzOYdOyi89z6cjY1djokaM4bsZ57GkJrq11pPR42lhsvfvpxG26nrMGgG3rn6HXITc3E4nBzdXs6uVYVUFAR2d7rBqGPWdSM464IhAbUHFUIIEQKNFe5Ku7a97XaAveubRoIiKcddaTfdFd5lnCVvYIt2TocrrPFWFVdXCH7eXCUilM4AXro2RDRzoqvlZJJH5VtbG8qkLIhNl1aUYSYhXRBomvYmsAAw0B7QeQZ2LcArwL+VUtvCv0IhRG8nIZ0QQojeRilF45fF1H18vP0nZC80o47kG0cTc3Za+BbXxymlOLxpA2tffIaGSv/u3G3Ux7IhZQaHY0f69aJ87KAE7pyVy4JJg7tshdny9T4K774bR21tl+cxDRtG9nPPYszI8Gudp+OZvc/w1x1/9frc/Oz5/GXuX04+VkpRfLiWXasKyN9bFdB1cs5K5aI7xhKTYDqT5QohhAgmh81VbVfobpNZtDX01XbGGFe13ck2mdMhLj201xQ9SyloquxUEde2V9wxcARWrS/E6dMgfqBHFVxWx2q4xEww++6iIcJPQrog0TQtA7gHuBvIdh/2Vl23A1d13StKqRDfyiOE6CskpBNCCNFbteyvovrVgyir77uEEy7JIX5ullQiBZHN2sq2FW+z5b03sVv9e3OoJGogX6aeR0WUf28mJrtbYd7eRSvM1m++IX/pUhwVlV2ew5iZSfay5zBlZvp1zUBZ7BauevcqTjSd8Pr88996nskDJp9yvLq0id2rCji0uQyH3b+73KPjjcxdNJahEyR0FkKIiNVY3t4es3ArlOwMfbVd8tD2fe0y26rtesFeWqKj1gaParijHcO41rqeXp3oD3TGjpVvJz93B3IJQ1ztOEWvIiFdkGmudxWuAL4NXAbo8F5dVw88j6u6bn+41ymE6F0kpBNCCNGbWUsaqVq+H0ed76AoZvIAkq8diWbUhWll/UN9ZQVfvvQchzb6t2W2AvbHjeGrlHNp0fu315pep3HZ+IEsmZ3L1JzkDmGrNT+f/DvvxF5S2uV8Q0YG2c89S9SwYX5dL1DvH32fX67/pdfnJqZP5IVvvdBlQNxcb2Xv2iK+/qIYS5PNr+uNnzOY2dePxBjlvcpQCCFEBHHY4MTejnvb1RaE9prGGBh8Tse97WLlBo+IYLe6qi297RPX6P2Gnx5jToTUkZA6wvUrMROUA+wW1353bR9tLR0f+/2xpfe1cuztTPEdQzfPj4lZEJcBOnmt1NdISBdCmqZlAfcBdwKD3Ie9VdetB/4BvK2U8u9VnxCiX5GQTgghRG/naLBS9fx+rIW+9/sy5SSQumgs+jhpGRhsRQe+Zs2y/1Ced9Sv8VadkS1JU9mdcDZOzf+wafzgBBbPymXBxPZWmLaSEgruXIo1P7/LefqUFLKfeRrz2LF+X8tfTuXk5g9u5kD1Aa/P//mCP3NJ7iU+z2GzOji06QS7VhVQV959xUXigGguXjqejFxpKSSEEL1OQ1nHve1KdrqCi1DyrLbLmg4Dxku1Xag4nVBffGoIV3UEavMja584gxlShkPq8PYwru1XTEro9w5z2F3tOk+Gd34EfLbO4wIIBTuHi7765vdGsQM67QWX3TGIi07q6RWKHiAhXRhomqYHFuIK7C7CFdJ5Vte1fV4JPAM8pZTKC/MyhRARTEI6IYQQfYGyOah+8xtadvveJ02fFEXakvEYB8aGaWX9h9Pp4Os1q1j/6vO01PvXlqkxKpk1STPJi8kJ6FopsSZumZ7F7TNyGJQYjb2igoKld9H6zTddztElJJD91L+JnjQpoGv5Y3PpZu7+9G6vz2XFZ/He1e9h1Bu7PY9yKvauLWbj20dw2Hy/iafTaUy7MpdzLs1Bp5e7noUQoteyW6Fsb8e97UJebRcLQ87puLddbGpor9mXKAXN1V3sE3c09KFrIDQdJOV4BHAegVzCkP5bOaWUq5LPa6jnKxTs7mPnz32MDYTOAAmDXW0oO1TBZba3pjSaQ/NnJXo1CenCTNO04cB3gMVA2/+snavrFPAJ8C/gA9UXfuNCiDMiIZ0QQoi+QilFw+pC6j/ruqIKQDPpSbl1DNFjUsK0sv7F0tTIprdeZefH7+N0OPya05A+gvdMU6kxJQd0Lb1O47KzBnLnrFwmJWgU3nMPln1dv+bUYmLI+uc/iT13ekDX8cf9q+5nXfE6r8/9fPrPuW3sbX6fq7qkic+e20dlYWO3YwcOS2T+neNITD913z4hhBC9VMOJU/e2c/i3B+xpSxnmbo/pbpM5YJxU21mbutgn7ghYant6dR3FDTw1hEsdAcm5YJAuEhFHKXBYu28fGhXnCuTiB4JOWp2LwElI10M0TTMBN+Lau26W+7C3VphFwFO4qut833IshOizJKQTQgjR1zTvqaD69cNg91GJpEHi5cOIO29wl/uFiTNTVVzIF88/Td6u7X6N13R6WkfP4g37WKrtgb8JcdaQBJZOGsCkf/6G1h07ur5OVBSZj/+NuAsuCPgavhypOcJ171+H00sbq6SoJD689kMSTP63p3TYnWx5/xg7Pi3othuT0azn/JtGMXrGQPl+FkKIvshude9t59Ems64wtNdsq7Zrq7TLnNY3q+0cNqjJ914V11DS06vrKCqxUwg3vP1jVHxPr04IEYEkpIsAmqZNA94Asjo/5f6oACvwEvBbpZTv246FEH2OhHRCCCH6ImthA5XP78fZYPU5Lnb6QJIWDEcz9NNWPyGmlOL4zm188fx/qCn1740uc3wihnOv4LXagRyr6n5/ts4GRSn+uOtF0g/t7nqQ0ciQRx8l4VLfe8UF6uGND/PWN295fW7pWUv50ZQfBXzO4sM1rHpuP4013VdQDD8nnQtvHYM5rvvWmkIIIXq5+tJOe9vtCkO13fBOe9uN6x3VPU4nNJR63yeuJg+Uf5X/YaGPclU1etsnLjYt9PvECSH6FAnpepCmaWNxVdItAhI7P+3+2Lm6zgr8P6XUH8KySCH6OXeL2suA84EJQCYQBdQC+4FPgWeUUmUhXoeEdEIIIfoke10rVcv3YStp8jkualgiqbePRRcjwUaoOOw2dnz0PpveegVri3/B24Chw0medyNvFRlZcyiwxh9Gh41fbnuRGaU+Xn/qdAz639+TdM01AZ3bl4rmCq545wpa7Kf+Hk06Ex8s/IBBcYMCPm9rs421rxzmm63d/1gYm2hi3pJxZI2Vdq5CCNGv2K1wYk97m8yibaGvtjPFufe2m94e3sX04P8/zdWntqVs2yfO1txz6zqFBknZnUI4dyiXmNk7gk8hRK8gIV2YaZpmBG7AFc7NbjtMe3MUDXACHwF1wHW4AgHVaewLSqkl4Vm1EP2TpmnLcO0f2Z164LtKqRdDuBYJ6YQQQvRZTquD6tcOYdlX5XOcIdVM6pLxGNNjwrSy/qmptob1rz7P11+scu3F4Ycxsy8g57IbePNgE29uL6Kx1e7XPL3TwY93vMrcop0+xw389a9IvuUWv87pjyd3Pcm/dv/L63NXDbuK/53zv6d97sNbTrD2lcNYW7r/M5g4L4sZ1wzDYJQ3+oQQot+qL2mvtCvcAqW7XPtghVLqiE57240NbuhkbYLqY16q4o5CS3XwrhMMsQO87xOXMhQMUT29OiFEPyAhXZhomjYCuBdYArQ1h24L3Noq5aqAZ4F/KqXy3POSgaXAD3C1w1Qe865TSr0blt+AEP2QpmmrgHlAM7ACWA0cAhpw/X28AbiN9r+TtyilXgvRWiSkE0II0acpp6L+03wavvB9N7lm1pN621jMI5PDtLL+68TRb1iz7ClKDh/wa7zBFMX0q69nzCVX8d7eCpZ/lc/xSt8VkgA65eR7u97iW/mbfY4b8NOfkHrXXX6tpTvNtmYuf/tyqizeg+HXrnyNcanjTvv89VUtfL7sACXf1HY7NnVILBcvHU/qkLjTvp4QQog+xN4KpXs6tsmsLw7tNU3xnfa2m9p9tZ3DBrUF3veJC/V6A2WK99Kacrjrl7lzczMhhAgvCelCSNM0PXANrqq5ubjeyPfWxnIr8CTwmlLKa2NqdwXeL4FfeRz+SCl1ZfBXLoQA0DTteWA7rnaWjV2MuRl4xf2wEshRSgW9P4OEdEIIIfqLpu1l1Lz9DTh8/Oyvg6QFw4mbMTh8C+unlFIc3LCWL196jsZq35WObRLSB3DB7UsZPm0WXx6pZPnGPL7orhWmUty3dwXXHFvnc1ja/feT9v3voQVhr5fXD73Obzf91utz5w48l/9c8p8zuo7Tqdj1WQGbVxzD6ev7GdAZNGZeM5yJF2Wh6WQfGyGEEJ3UFbtDu62uj6W7w1BtN7K9PWZyjmtfuM77xDn9q5wPC70Jkod6r4qLGyD7xAkhIpaEdCGgaVo2rqq5pUBG22E6tqy0AK8BTyqltgVw7n+5zw1QrpQaGJRFCyFOm6Zp7+AK5AGuVkqtCME1JKQTQgjRb7Tm1VH1wn6cTb7f+ImbNZjEK4ah6eVNl1CzWlrY8u6bbPvgbRw2m19zssadzYWL72FA7jCOVTTy/Ff5vLGtkCarw/sEpVh04BNuPbzK53lTFi9mwM9/dsZBnd1p57oV13Gs7pjX55+c9yTnZ55/RtcAqCho4LNn91Fzovv7uDLHJDNv8TjikqW9lhBCCB/sra6grm1vu8Kt0FDS06sKAw2SsrrYJy5L9okTQvRKEtIFieZ6hXglcB9wKaDDe9XcceCfwLNKqYCbMGuaNgdY637oUEoZz2TdQogzp2na94An3A9/qpR6NATXkJBOCCFEv2KvtlC5fB/2Mt/BRtSoZFJvHYPObAjTyvq3uvITrH3hWb7ZstGv8ZqmY8L8S5l14+3EJCTSYLHx5vYilm/MI6/K+9f2hsOrWbp/pc/zRi28jqG/+39o+jN7M+6Lwi/4/urve31uRNII3rjqDQy6M//eslsdbHz7KHu/KOp2bFSMgQtvG8OIKQPO+LpCCCH6kbqiTnvb7QanfzfWRJzYdO8VcclDwWju6dUJIURQSUgXBJqm/Q9wN5DZdoiOVXNO4BNcLS0/UmfwG9E0bShw1P1QKaXkFhEvNE0bA0zE9TWJxlW5WA4cAXYrpbrfHEMEnfv7dxIwGIgDSoF8YKNSqpf+5Aiapj0I/Nn98EGl1F9CcA0J6YQQQvQ7Toud6lcOYjlU43OcYUA0aYvHY0iNDtPKRMHXu1mz7CkqC/P9Gh8VG8usG25j4sWXozcYcDoVa7+pYNmGPNYePrUV5pXHNvDdPe/4POfhs2eT9af/Y3Ju2mlX1SmlWPrJUraVeW9u8vDMh7lu1HWndW5v8r+u4vPnD9BS332LsjEzBjLnplGYoiWAFkIIcRpsFldQ57m3XUNpT6+qnSnO+z5xKcMhOqmnVyeEEGEjIV0QaJrmpL1azjOcqwaeBf6plDoepGvl4KrGgzCGdJqmDQOmAVPdH88B4j2G5CulcsOxlq5ompYE/BBXm9FsH0MdwC7gTaXUH0K/ssgUzq+ppmnXAw8CM7sYUo2r/euvlFKVwbhmOGma9gFwhfvhpUqpT0NwDQnphBBC9EvKqaj78BiNG3y3cNLFGEhdNI6ooYlhWplwOhzsWfUxG15/EUtjg19zUjOzuXDxPeROmHzy2NGKRp7fmMeb24s6tMKcn7+VB3a+jp6uXwduGHQWK6+5n0Xnj+TyswcRZQj85dG+yn3c/OHNXp9Lj07ng4UfEGOMCfi8XWlpsLLmxYMc3939j70JaWbmLxnHoBFJQbu+EEKIfkopV7Vdh73t9oS22k5nhJSu9onLkH3ihBACCemColNIB7AdV9XcK0qp1iBfKxU42UpPKXVnMM/f6VoXAr/AFeKkdDO8R0M6TdNuwNVGNDWAaWX9bU+/cH9NNU2LA/4DeH/X41RlwGKl1Cdnct1w0jRtOvAVrha3JcBQpVTQd2+WkE4IIUR/17i5lNr3joLTx2sCvUbywpHETs3oeowIupbGBja+/hK7P1uJcjr9mjN86gwuXHQXSQMHnTxWb7Hx5rYiln+VR767FeZ5xbv52baXMKiuz7ttwGh+N30x8Unx3HpuNrefm82AhMBaYT305UN8dPwjr8/dP+l+vjPxOwGdrztKKQ5sKGXd64exW33/mWkaTPlWLlOvyEWv1wV1HUIIIfo5mwVKd3Xc267xRODnSczyXhWXmA16qQgXfYfTqahsaqWsrpWyegsVja0oBdEmHdFGPWajnmijnmiTvv2xx+d6nQTT4lQS0gWBO6RrxVUJ9KRSamsPLykoNE17APC3bV+PhXSapv0aeNjLUwXAYaACMAODgLOBWPfz/TGke4AwfU01TdMDK4DLOz1VAewE6oDhwGTaA25w/V2ar5Raf7rXDhdN0xKATcBY96ElSqnlIbqWhHRCCCH6PcuRWqpeOoBqsfscF3dBJomX5qLJi+CwqizIY83ypyj4eo9f4/UGA1OuuIZzF96IKbq9Us3pVHxxuJxlG/P58nAF007s57+3PI/J2fXXfW/qMB6esZRmoxmjXuPyswexZFYuk7OT/VpLcWMxV71zFTYv1QTRhmhWXruStOg0v84ViNqyZj57bj/lefXdjh2QE8/FS8eTlBG8qj4hhBCiA1/VdjGpHQM4z33iTPJ/k+j9mlrtlNVbOFFvcX10B3Enj9VZKG9oxe7rpsFumPQ6zEad1xAv2qjH7PF5tMkj9HPPMXcTBEYb9UQZdOjkdVCvIiFdEGia9hDwjFKqqqfXEkw+Ap1WoAhXwNKmR0I6TdN+jEdlodsrwP8ppfZ6Ga/D1XLxOlxtCftVyhHOr6mmaY8AP/E4ZMPV8vIpz0ozTdPGAU/TsRVmFXC2Uspns3RN067BtbfdmdqolDoWyAQvIeTrSqmbgrCWrq7XL0M6i8XCzp070TTtjH8BQTlPKH8JIYTonq2imarl+7FXtvgcZx6XSspNo9FFyRbO4aSU4si2Tax9/mnqysv8mhObnMKcWxYzbs5cNF3HSrEj5Y08/1UeB1eu4efrnyba0XXDgkNJWfz3rHto9HijcGJWEnfOyuXyswdhMviuQnt066Ms3+/9fqsbRt3Ar2b+yq/fT6AcDifbVuaxfWUe3b3kNZh0nHfDSMadN1h+dhBCCBEeTgc4rGCUvX9F7+RwKiobWzlR5xnAWSirb+0QwDW0+r4RsDcxG3VdBn9RhrZQT9dNMNjpsWcQaNQRZdDJz6NBIiGd6JI70PkTsA/YBmx1f9wLzAbWeAwPe0inadpE93ra6uZtwK1KqTf9nG9QSvWdf339EK6vqXu/u4OA0ePwNUqp97oYHw18Tseg7t9KqW93c508IOd01tjJnUqpZf4O1lz/Az0HLHYf2gbMVUo1BmEtXV2zX4Z0VVVVPPHEEz29jLDp6ZBQfvkfpoby3EKI7jmbbVS9dIDWo3U+xxkHxZK6eByGpMBaH4ozZ7da2f7hu2x+53VsrRa/5gwaMZq5S+5l0MjRpzxXb7Hx0WufMeLP/02MteuA9njCIH45615qzfEdjqfHR3Hbudncem42A+K9fz/UtdZx+duXU289tapNr+l5e8HbDEsa5tfv5XScOFbHZ8/uo76y+z+v3AlpzL19DDEJppCtRwghhBAi0jVYbCer3toCuPYQzhXAVTS0+uyYL06PptEeBHqtCNR1GfxFdXis8xoExkQZiIvqH+1yJaQTXdI0LRloUUqd8ipRc+1t1mMhnaZpBmAzcI7H4aVKqefCtQZvNE1LAoYE4y+RpmnnAAeUUr5vEw/snGH5mmqathy4w+PQsu72T9Q0bRSusLDtnQY7MNpXhZvWAyGd5noX/Z/Afe5Du4GLlFLVQViHr+v2y5CusrKSv//97z29DCHCpqcDSPkVvl9dfb2F/5TDSe2KozRt9r1vii7OSOod44jKTgjTyoSnhupK1r28nAPr1nQ/2G3c+Rcx59YlxCWfuoVy09f7OL70LvT1XQe0RXHp/GLWfVTGJJ3ynFGvccXZg1gyeyiTsk59fvm+5Ty6rXOjDpcLMy/kiXmhvXnIarGz/vVvOLDRZ0MJAKLjjVx0x1hyzw5+G04hhBBCiJ5kczipaHAFb+Xu0O1EW+VbnYWyBlf1W5PV0dNLFSEye0QqL909o6eXERYS0onTovV8SHcL8LLHoc+VUvPDdX1vNE1LBD4FRuDaU23nGZzrAuBDXPudXRXMoM7HNS8kCF9TzVUVVwl4NiQfq5Q66Mfc14AbPQ79j1Lqdz7GJwC++xb5p9mzBacvmqb9Hfiu++HXwFylVGUQ1tDddftlSFdRUcGTTz7Z08sQQoiw6S7ci4mJYdy4cZx//vmYTFJBo5SicUMJdR8eA18vFQwaKTeMImbigLCtTXRUcvgAa5Y9xYmj3/g13miO5tyFNzLlimswGI0dnms9coSCO5dir6jocn5ZdDK/OO8+SmO7DrAmZSVx5+xcvnVWeytMq8PKgncXUNxY7HXOs5c+y7SB0/z6PZyJozvLWfPiQVqbum/8cdYFQ5h13QiMJmntKoQQQojIppSivsVOWYOlvf2kRxvKsnpXMFfZ2NptG3DRt80fO4CnF4f+5+5IICGdOC0RENJ9AVzgcWieUmp1uK7fmaZp8bgCurZ4vwa4WCm1/TTOdRHwPu0h1ypcQZ1/fYJOUxBDumuAdzwOfaWUmuXn3MuAjzwO7VRKndPV+HDTNO0J4Hvuh/twBXRdvzsU3Gv3y5CuvLycf/zjHz29DCGEiDjp6enceOONpKen9/RSIkLLoWqqXz6IavV9J238vGwS5mdL1WIPUU4n+75czbqXl9FcV+vXnKSMQVyw6C6GTz23w9fNWlBAwZI7sZWUdDm3ypzAL2fdS0HCQJ/XSI+P4vZzc7j13GzS46P46PhHPPTlQ17Hjk8dz8tXvIxOC8Z9Yr411bby+fL9FB6o6XZs8sAYLl46nvTs+G7HCiGEEEKEgtXupLzB0m37SYvN2dNLDQqDTmNAfBQmg44Wm4MWqwOLzYnV0Td+fz3tygmD+PutEfO2cEhJSBcEmqbd0f0ovymgAagDTgAHVQT+xnsypNM0bQTgeQtuHjCsJ/+cNE2LAt4CrvA4XAtcqpTaEsB5LgbeAzx3430FWKSUCmn9dhBDun/R3goS4P+UUr/0c24srj83z4bDA5VSZYGuI9g0TXsc+L774T5cLS7Lw3j9fhnSlZWV8c9//rOnlyGEEBHJaDSyYMECzj777J5eSkSwlTVRuXw/jmrf9zVFT0gj5YZRaEapOuoprc3NbH7nNbZ/+B5Oh39bROdMmMzcxfeQmpl98pittJSCO5dizcvrcl6dKZb/mnUPR5Myu72GUa9x1YTB3DEzhz/uuZ+vq772Ou6Pc/7I5cMu92vdZ0o5FXu+KOKrt4/isPt+w0en05i+YCiTL8lBp5MgWgghhBDBoZSittnGifr2yre2ijfPAK6qya8mVb1CUoyRjHgzGYlmBiZEMTCh7XMzGe5fqbEmrz9z2R1OLHanO7RznAzwWtyfWzw/tzldY7p4vsM5bA5arK7xzVZ7n99n74YpmTxyw8SeXkZYSEgXBJqmOfHdYOdMNAFbgeXAa0qp1hBdJyA9HNLdAzzlcegZpdTd4bi2L5qmmYA3gas8DtfjCuo2+TH/UuBdwHMn+5eAxaEO6NzXv5DghHTrgdkehxYopd4PYP4OYLLHoR6tkgTQNO1vwA/cD8Me0LnX0C9DuqqqKt544w2UUiH9JYQQvdm0adO49NJLMRj6x6bavjgarVS9eABrXr3PccbMONLuGI8+QVqG9qSa0mK+eOEZjm337542Tadj0qVXMOv62zDHxQFgr6ykYOldtB4+3OW8RoOZX828mwOpuX6vbUxuBcXRf/b63JC4Iay4ZgUmffi+f6qKG/ns2f1UFTd2O3bQiETmLxlHQlp0t2OFEEII0b9ZbA7K60+tejsZwLlbUFq7uVmotzDpdQzoFLoNTDCfPDYw0RXAmSP8hj6lFDaHcgd9p4aBFneg123w5zHeYnN6DQZ76m2zO2bm8Jurz+p+YB8gIV0QeIR0obpdse03fgK4Uyn1aYiu47ceDun+A3iGcncrpZ7RXP1v5gG3AecCQ3BVZFXiqrxbBbyqlMoL4dpMwBvAAo/DDcBlSqmNPuZdgasSL8rj8AvAEqVUWP4XDGJIVwWkeBwap5Q6EMD8N4DrPQ59Xyn190DXESyapv0FeMD9cD+uFpdhDejc6+iXIV24hDoE7Iu/+tqfmxC93eDBg7nhhhtITk7u6aX0OGV3UvP2NzTv8P3ftT7RROod4zENiQvTykRX8nZtZ83y/1BdUuTXeHN8AufddDtnz7sUnU6Po7aWgnvuxbJ3b5dzLAYTD597J7vTR/q9LnPmcozx3n+M/cnUn7B4/GK/zxUMDpuTTSuOseuzgm7Hmsx6zr9lNKOmZ0h7VyGEEKIfcjoV1c1Wj+Ct1cv+bxZqmm09vdSgSYk1kZHgrnxLNDMg3hW6tVW/DUw0kxxjlJ+NAqCUotXu9DsE9Az32oPBU6sKO1YHOmj1EgLfd/4wfnH52B74XYefhHRB4A7pPHku1Nff+s6/IW9jVafnFPADpdST/q8w+Ho4pNsJTPI4NBsoAZ4BLupmug34D/BTpVRziNZnBF4HrvE43AhcrpRa52X8AlzBnuetuMuBpSpMAZ17HRdyhl9TTdNSgKpOh+OUUk0BnOPPwIMehx5XSv0wkHUEi6ZpfwTaNiSpBG4CugvoapRSxSFYi4R0QoRQT4eE/f1XJHwN+gKz2czChQsZPXp0Ty+lxymlaFhbRP0neT77XWhGHSk3jyZ6fFrY1ia8c9jt7PrkQ75682Vam/370TE9O5e5S+4la/wEHI2NFH37OzRv29bleGU08tqV97NcZfl1fp2pnJhhf+XUl3sQb4rno2s/IjEq0a9zBVPRwWpWLTtAU233TVZGTh3A+beMxhxrDMPKhBBCCBEuzVY7RTUtFFY3n/xY6g7gTtRZKG+wYHP0jdc5UQadq8LNo/1kW+jWFsANSIgiyhDZ1W+ia06nwmL3DAGdJJgNDEgwdz+5D5CQLgg0TWu7hTIe+BWQiitUcwKbcbWrLMDV+tCEq8robGAO0LaLucIV7HyMaz+yJGCce0wOHcM6O67KrB5rAdjDId0JIMPj0MW49m0L5N2VncAVSqnSYK6tjTuoexW41uNwk/uaaz3GLQReAzxfNT+HqzowrHXkQQrphgFHPQ41K6ViAzzHz4A/eBxarpRaEsg5gkXTtDxcf/8CEZL1SkgnhBCh1dMhoT+/du7cyWEfLf3anHfeecydOxe9Xl6ktuyrpPrVQyhfm9NrkHBpLvEXZMpdtRGgub6ODa++wJ7Vn4Cfr/9GnTubCxbdRVxcPEXf/wFN69d3PdhgQPuv/8fz5pG8vaMIi6/vDSBq4DuYkjd7fe72sYv42fSHvD4XapYmG2tfPsSR7d03eIhLjmLeknFkjpZKWyGEEKK3sNqdlNS2UFjTTGG162N7KNdMZWPf2P8tLa6t+s1zz7eOIVxitFS/ib5NQrog0TRtJLACGOU+9DTwO6VUoY85OuBq4FFgKK5Q7zdKqd90Gnc58DdgOO1tNXcrpSbTQ3o4pLPQsS1kEdC2E3wT8C/gI/fxWGAisBQ4r9OpvgIuUEqFpK5b0zQDrvDQs3VjM3CVUmq1pmnXu5/33EDmaeBe1QPf7EEK6SYAuz0OVSmlAro1XdO0H+D6fm/ztlLqukDOESw9EdJpmrYE8DY/BpjW9kBCOiGE6H+cTicbN27k888/77b6Lycnh+uvv574+PgwrS5yWYsbqXp+H446329kxJwzgORrR6IZdGFamfCl7PhR1ix7iuKD/r02NRhNTF1wLVMvW0DFf/0XDZ+t6nqwTseg3/4W7VtX8fq2QpZ/lUdRTYvXoZq+gdjhj6DpvXz/KD3XpP+NKYNHkJsWy9DUWBJjwlexppTi8JYyvnzlEFZLN1tYazB5fjbnLhiG3ijf40IIIURPczgVJ+otFFU3U+gO3wprmilyB3In6i3+3q8UkaKNevf+blEnq90yPPZ8G5hoJj0uCpP87C2EhHTBoGlaDK5qubG42ikuUkq9HsD8WFyh0nm4Qri7lFLLOo2JB77A1eZRc4+7TCn12Zn/DgLXUyGdpmlRgKWLp/fj+jPxGoxqmvZjXIGop/9RSv0uiEvsfE0D8BJwo8fhFlwh1E/oGNA9BXy7JwI6CFpINwvY4HGoWCmV2dX4Ls5xD64/izafKqUuDeQcvZmmaQ8Dv+5unIR0QgjRf+Xl5fHmm2/S2Njoc1xsbCzXX389Q4cODdPKIpej3krl8/uwFfn+MzPlJpB6+1j0cSaf40R4KKU4vGk9a194loaqCr/mxKWmcf4ti4l/byUNH3zgc2zGf/83KbffhsOp+PxAGcs25rHxaOfO7WBK+5yodO8vu2x1E7GU3HLycXKM8WRgl5vm/pUaQ25aLAnm0AR49ZUtrFq2n9Ijdd2OTc2M4+Kl40gdLHsxCiGEEKGklKKy0equhHNVwRV5VMWV1Lb0ynaUmgbpcVEeoVtUhz3f2o4nmA1S/SaEnySkCwJN0x7FtYeWAn6vlPrVaZwjDTiIqxVmMzBUKVXRacxQ95i2YOcxpdRPz2Ttp6sHQ7oYXNVyndUBZ/uqXHTPfwz4kcehaiBHKeX7HZszoGmaHldQd5OPYf8C7u+pgA5CFtIVKeXnph/t57gb176BbfpbSLcEqaQTQgjRjYaGBt566y3y8vJ8jtM0jYsuuojZs2ej0/Xvu1SVzUH1G4dp2VPpc5w+OYq0JeMxZgTUsVuEkK3VwtYVb7H1vbew2/xr7TR49FgmWEC3wndQl/7gg6Tde8/Jx4dONLBsYx7v7PRohalZiR3+CDpjg9dzNB3/Lk5L9z/ypsaa3KFdLEPTYk5+npsWS1yUodv5vjidip2f5rNlxXGcTt8vKfQGHTOvHc6ECzPRdPLmmRBCCHG66lpsJ9tPntqSsoUWWzeV7hEm1qQ/2XKyq/aT6XFRGPT9+3WFEMEmId0ZcldKleDaD80CpCul/Nvp/NRz/R74Ba6w70Gl1N+8jHkRuNU9ZrNSatbprv1M9HC7SwfQ+X+DX3duE9rF3FigGPDc4f1GpdQbQVyit+vqgReAW7w8/STw/Z4M6CCi2l1+H3jc41CPtbuMJLInnRBCiM4cDgdffPEF69at63bsyJEjWbhwITExMWFYWeRSSlG/qoCGzwt8jtOi9KTcOobo0SlhWpnwR31lOV+++ByHvur+ex4ATWNESgY5X24iyt71m2Sp376P9B/+sMPd3rXNVlcrzI35FNe2YEzcinnwW17n25tzacm/m45NMgKTFhflCu7cod3QkwFeDDEm/89bnl/PZ8/up7asudux2eNSuGjxWGITo7odK4QQQvRHLVaHK4BrC+Gq2z8vqmmm3mLv6SX6Ra/TXNVviWYGJnhUwXVqP3mmNw0JIU6PhHRnyB1srMYVmq1TSl14Bue6AFdIooAvlVJzvYxZBCx3PyxSSmWf7vXORA+HdPVA5w1Whiql8vyc/yxwp8ehfyilvhuk5fm67mJgWafDCrhaKfV+qK/fnSCFdMOAox6HmpVSAd2GrmnaQ8AfPQ49r5RaHMg5+iIJ6YQQQnTl8OHDvP3221gsXXUEd0lMTOTGG29kyJAhYVpZ5GreXU71G4fB7uP1hQaJVwwjbvZgadUTYYr2f83q5U9RkXfMr/FGvYHhBSfIrapD18WXPPmORWT84henfK0dTsWqA2U8t+EYe9Sv0JvLvM53tqbRWv4t7I3jcO1OEDwZCVHkpLa30GyrwstJiSXapD9lvM3qYOObR/j6y+Juz22ONTL39jEMm5we1DULIYQQvYHV7qSktmMFXOHJSrhmKhv9q+DvSZoGgxLMZKbEkJkczaCT1W/t4VtaXBR6qZ4XImJJSHeGNE27A1fwooDXlFK3nsG5RuFqZ6lwBSTDvIyZCmxxP2xSSnUOq8Kih0O6AsCzn0yZUmpgAPPvBf7tcWiNUuqiYK2vi2suBp7l1ApAACuuar73QrmG7gQppEsFOveQigukutTdPvbHHoceV0r9MJB19EUS0gkhhPCltraW119/nZKSEp/j9Ho9l156KdOmTev3wVNrQT1Vz+/H2WjzOS723IEkLRiOJm19IorT6eDr1Z+x/tXnaWmo92tOrMXK2JIqBjR4rzJLvP46Bv2//4emPzX4Anhl7yf8746f+LyGvWkYrWVX4mwd7NeaztSgRPPJijvPKrzslBhOHKxh9fMHaGnw/T0OMHbWIM67cSQms9xBL4QQou9wOBVl9ZZO4Zs7lKtu5kS9hW66REeEtDgTmckxZKXEkJUcTZY7kMtKjmFwUjQmg/ycKkRv1ptCukh9tTDI4/Mz3biirf+QBnQVOtV4fN5f+5IcpmNIVxrg/M7vXqWe2XJ80zTtTuBpOgZ0m4AZ7s9NwBuapt2slHo7lGsJNaVUlaZpNUCyx+Fs4EAAp8np9PibM16YEEII0cclJSWxdOlSPv30U7Zs2dLlOIfDwcqVKykoKOCqq64iKqq//jgJUdkJDPjeZKqW78NW2vX9RE2bT2CvspB66xh0McYwrlD4otPpmTD/MkbNOI+v3nqFXZ98gNPhe9+XJrOJbcMGkV7fxNjiKuKsHcOrujffQjW3MPiPf0Aznvq1vvmsS/i89Fw2l27u8hqG2GPohz6BrW4K1vJLUY7Q3lNZWmehtM7CV8eqOhzXNBicGM3IYdGcXaJhrvBdCXBgYynF39Ry8Z3jGDgs0edYIYQQIlIopahqsp5SAdfWjrK4tgWbI/JTuASzwR3CuYK3rJT2z4ckRwfU+loIIUIpUv818rxtc9wZnsuzLKaxizFmj89bzvB6vdU+YJ7H49YA53ceb/Y6Kgg0TbsHV9We563qv1RK/Z+maX8BHnAfMwKvaZp2a6j3xwuDA4DnXokjCCyk61xBGshcIYQQot8yGAxcfvnlZGdns2LFCqzWrt+U//rrryktLeWmm25iwIABYVxlZDEkRZH+7YlUv3YIy/6qLse1Hqml/B+7SV0yHmNadBhXKLpjjotj7uJ7mDDvMtYsf4r8PTu7nVOREEtlXAy5lXWMKKvB6HSefK5+5UqcFgtD/vIYuk4htqZp/HTqT7lt5W20Orp+CaJpClPSNqIS92KrmktL5WxQ4Q14lYLi2haKa1v4QsHEaD1zW4wYfbTirK9o4e1HtzP1W7lMvTwXnVSPCiGEiAB1LbYOwVvnqrgWm++bdCKB2ahrD9+So08Gcm3VcYnRciOYEKJ3iNR2l1cBbW0KFTBLKdX1rZW+z/UxcIn7PLuVUud4GTMP+Mw95rhSasRpLfwM9XC7y7twVaa1OaSUGhPA/BuA1z0OfaWUmtXV+NOladq3gX/QMaB7SCn1iMeYzq0dHcDtSqlXg72e7gTra6pp2r+Bez0O/Z9S6pd+zo0FaukYyg9USnnf/KMfkXaXQgghAlFZWclrr71GRUWFz3FGo5Err7ySiRMnhmllkUk5FXWf5NG4tsjnOC3aQOptYzGPSArPwkRAlFIc27GVL57/D7Un/Gu2YbLZGX2imszqhg4/tMfOmkXm359AFxNzypyvSr7igTUP0Gz33jazswHRg7gq625SmU5+VTN5VU3kVTVTUNWM1eHs/gRBkuzQuKLZxCBH9+FbY6yO1ilJZOUkulpopsaSmRyNQYI7IYQQQdZidbjCN8994apdLSkLq5upt9h7eondMuo1Bie1VcG1h29tLSnT4kz9vtW8EKJrvandZaSGdAlABa5QQQN2AnOUUv69Yms/z03AK7jCN4BHlVI/8zLup8Af3ePWKKXmn8HyT1sPh3SDgSLawy8bkKCUsvg5//8Bv/I49IZS6sYgr/G7wBN0DOh+rJR6zMvYPwCeX2sHsFgp9VIw19SdIIZ0CwHPtp1+h6Capl0GfORxaKe3sLo/kpBOCCFEoKxWKx988AF79uzpduyUKVO47LLLMHpp8defNG0ro+adb8BXWySdRtLVw4k7d1DXY0SPstts7Fj5Hpvefg2bxb/mIwnNFsYXV5Lc3F4hFz1lCln/+if6+FNbVlY0V/DEzid498i7KPx7nToxfSIPTXuICekTANc+OSW1La7QrrKJ45XNJz8vqG7GHoJNcnQKZlkMnNtqQOejqg7AimJ1tI29JgdoYNBpZCZHk5sWS26qa++7tgBvcJJZAjwhhBBe2RxOSmpbOgRvbfvCFVa3UNkYaIOs8NM0GJRgJjM5hkzPlpTu/eEyEszodRLCCSFOj4R0QaBp2lvAQtoDto3ArUqpQj/n3w38HVfLQw1wApOVUnu9jF0FXOS+1l+VUj/uPCYcejKkc19/Ax1bKl6plPrQz7mbgekeh36glHoiiGv7AfC3Tod/qJR63Mec3wH/5XHICdyplHo+WOvqThBDuhigEvDsBTVWKXXQj7mvAjd5HPqVUuq3ga6hL5KQTgghxOlQSrFjxw5WrlyJo5v9ugYOHMiNN95ISkpKmFYXmVqP11H1wn6czb7v2o6bPZjEK4ahyRsyEauptoZ1ryxn3xer/J4zuKaB0aVVRLtbZ5nHjyfr6f9gSE72Ov5A1QEe2fYIW09s9fsalw+9nB9N+REDY7vahhzsDicltRaOnwzwmk4GeIU1LTjOMMAbYtdxebORJGf3wdpho4NPo620+Bhq1GtkpcSQm9oW4MWcDPMGJ0XLG5dCCNGHOZyK8gaLK4Srbj4ZvhXWNFNc00JpXQshuO8k6NLiTKdUwLXtCzc4KRqTQW5GEUKEhoR0QaBp2lBc+6S1bVqgAU3Ai8AbwDalVH2nOaOAucDdwDm0V1wp4Gml1H1erpMFHPcYe51S6t2g/mb8FAEh3d3AfzwOrVZKzetqvMe8OcCXHoecwCil1NEgretB4M8ehxTwfaXUk37M7Vzh5wTuUUo9G4y1+XH9CwnS11TTtOeBRR6Hliml7uxmzihgL2ByH7IDY4L1tentJKQTQghxJkpLS3n99depqanxOS4qKoprrrmGsWPHhmllkcle1ULl8n3Yy31XYZlHJ5Nyyxh05kjdPlsAnDhymNXL/k3pN4f8Gq93OBleXsPQijr0ShE1ciTZzz6DIT3d63ilFKsLV/PYtscoaCjw6xpR+iiWjF/C0rOWEmM8taWmLzaHk6Ka9gq8vMomjlc1k1fZRFFNs99vhJoUzGs2cpat++/fRk3xUYyVPGPg7TlNeh3ZqW0Bniu8a6vCG5RgRicBnhBCRDSlFFVN1lMq4Nr2hyuubcHmqwtBhIg3GzoEb1kp7a0pM5OjiTHJz3NCiJ4hIV2QaJp2M/AC0HZbhQYd+p7UAw24AohE2oMIz3BOAzYBFyulmrxc40ngO+6HFiDd27hwCHKg8zDwa49Da5VSF3YzR48r0PF8B8lrO0mPOQNwVTkO9zj8qlLqlkDX3MX5fwr8yeOQAu5XSv0rgHP8D/CbTue4Tyn1ny6mBE2Qv6bDgIO4qkPbXK2UWtHFeDPwOR2rI/+tlPr26Vy/L5KQTgghxJlqaWnhvffe4+DBbovbmTVrFvPmzUOv14dhZZHJabFT9fJBWg/7DjYNGTGkLR6PIcUcppWJ06GcTg5sWMu6l56jsabarznRrTbGlFYxsK4JU042Oc89h3Hw4C7H2xw2Xj74Mv/e/W8abA1+XSM9Op0fnPMDFgxfgE478zv0rXYnhTXN5Fe522e6K/COVzZRXNuCt5fUo616Lm4xEq26D8u2m+x8GW3DHqRcLcqgI8cd4A1NiyUnNZbctBiGpsWSES8BnhBC9IQWq4NdhbVsy6tmW34NOwtqesW+cGajjix34OZqRdlxf7jE6P7d1l0IEbkkpAsiTdNuBJ4CEmgP6Lp7VeE57hPgZqVUXRfnn0F7tV6jUmr7ma24e5qmZeLab6+zGbj20GtTDJzXxWkalVKVPq7xMAGGdO55FwMf0x6MAjwOPKyUquk0dj7wT2CEx+EaYIpS6nh31/JjLWnAASDNfei0wzVN034B/K/HoUJgnFKq8UzX6T5/yL+m7us8AvzE45ANeBB4Sill9Rg3FniajgFdFXC2UqrU1zX6EwnphBBCBINSiq+++orPPvuM7n62zs7O5vrrrychISFMq4s8yqGo+/AYjRtLfI7TxRpIXTSOqNzEMK1MnC6rpYUt777BtvffxmH37w3H1IYWxpZUkpKSSs6zz2LKzfU5vsZSwz92/YM3Dr+BQ/luM9tmbMpYHpr2EFMHTvVr/OlotTsorG7meGVbiNfWQrOZ+poWvtVkIsfefTBfqXPyYYyVckNoX5+bjbqT7TNd1Xcx5LjDvAHxUWiaBHhCCBEM5fUWtuXXsC2vhu351ewrqQ/JvqhnyqDTGOLRhjKzUyCXFmeS/xuEEL2ShHRB5g5AHgWupT0I8bZwz/81jgO/D1dbw0BompYH5JzhaZYrpZb4uMbDnEZI5577PaDzfnI2XBWJxbj2RZvEqb8HK7BAKfWJP9fxcy0TgNVAMmfYptKjKq8ImKuUOhKcVYbna+q+jh54H/hWp6fKgR24KkuH0bHdK7i+NvOVUuvOcI19ioR0Qgghgik/P58333yThgbf1T4xMTFcf/31DBs2LEwri0yNm0qpXXHE1Yy8K3qN5OtGEntORtjWJU5fbdkJ1r7wDEe2fuXfBKXIrqpnrE1jxNNPYx41qtspR2uP8ui2R1lfvN7vdc3Pns+DUx4kKyHL7znBYLE5yK9sYtfnhVRuLPP9vQ44UKwz29kaZe/+ttQQiDHp3YFdjEeIF0tOagzpcRLgCSFEV5xOxTfljWzLr2Z7Xg1b86sprPbd3jtcNA0GJphd1XAeLSnbgriBCWbZ41QI0SdJSBcimqYNAq7HVR00EVeFVRLQiquCKx9XkLQK+FRF6G8u0kM69/zv4ApG/d3MoQy4Vim10d9rBLCWibj+Er0UhHPdB3wezIDOfd48whDSua8Vh6tK7iY/z1sOLFZKfXwGa+uTJKQTQggRbI2Njbz99tscO3as27Fz585lzpw56HRn3o6vt7J8U0PVSwdR3bR7ir8wi4RLctDkTaReIX/vLtYse4qqIv/2kjPaHYyua+G8xx4ndsIEv+ZsKN7AI1sf4Widf1stG3VGbht7G/dOuJd4U7xfc4KpsqiRz57dR3VJ9zsrFBgcrIyx0aCLnJezcVEGVwvNtFiGelTh5abGkhIrVRZCiP6lxepgd1Et2/Nr2JpXzY78nm1dmRZnOqUCrm2PuMFJ0ZgM/fdnTSFE/yUhnfCpN4R07nMMBx4Grga6eiV7AvgX8NeuWor2B+EM6TyueT3wY1wtNb2pBl4Dfq2UqjjDtfVJEtIJIYQIBafTydq1a1m7dm23Y0eMGMHChQuJjY0Nw8oik62imapl+7BXWXyOM49PJeWm0ehM/XdPv97E6XCw+7OVbHz9JSxN/nWYj7fauXDRXYy69ga/xtuddt46/BZP7nqSmlbf+xy2SY5K5ruTvst1o67DoPPWrT507DYHm949xu7PC7sda4jSk3xBBhXJhpN74OVVNVPR0BqGlQYm3mxor7xzB3ltYV5yrKn7EwghRIQrb7CwPa/G1b4yv4Z9xXVhbV0ZbzZ0CN5OhnHuirgYU3j/PxNCiN5AQrozpGnaSDq283tdKXWip9bT32maFg3MBjKBgbhaJ1YAu5VSe3pybQI0TRuKq73lYCAWV3CaD2zw3KdOnEpCOiGEEKF05MgR3nrrLVpafLc7SkhI4IYbbiArK7yt+CKJo8lG9UsHaD3m+54v4+BYUhePx5AY5XOciBwtDfVsfOMldn/6EUp10+/RLXf4KC7+0c9JSB/g1/h6az3/2fMfXjzwInanf5UMI5JG8JOpP2H2kNl+jQ+mwv3VfL58P0113f+oPmp6BuffPIqoGCMAja329tCusonjlc3kVTWRX9VEZWPk/eifGG10hXaprqq7oR4BXqL79ySEEJHE6VQcqWhkW16Nq31lfg35Vc0hvabZqCMzOYasTpVwrmMx8u+lEEKcBgnpzpCmad8H/up+WAMMUkrZem5FQoi+SEI6IYQQoVZXV8cbb7xBUVGRz3E6nY5LLrmEc889t9+2jVN2J7XvHaVpq+9783TxJtLuGIcpK/wtC8XpqyjIY82ypyjc5989fiajiasf+h+yJ0z2+xoF9QU8tv0xPi/43O855w05j59O/SnDksK7R6Sl0cYXLx3k6M7uG17EpURx8Z3jGDwy2ee4eouN/MpmjrsDvLzKppOf1zRH3svp5BjjycAuJzWW3LSYkyFeglnekBZChIfF5mB3YS3b8mvY7v5V1xK6fzMTzAam5qYwJSeZKTnJDE+PIy1O2gYLIUSwSUh3hjRN+wXwe0ABq5VSF/fwkoQQfZCEdEIIIcLBbrezatUqNm3a1O3YcePGsWDBAsxmcxhWFnmUUjSuL6Fu5THXK4GuGHSk3DiKmAnpYVubOHNKKY5s+Yovnn+a+srybsfrlGL2mEmc8/0HMKT7/7XeemIrj2x9hAPVB/war9f03Dj6Ru6feD9J5iS/r3OmlFIc2nSCL189jK3V4XuwBudcksP0q4aiP429heqabe6WmU0cPxngNZNX2RTSN6NPV2qsyV2B59r7LsejCi8uStq6CSFOX2VjK9vyatieX83WvBr2ldRhc4TuvdGc1Bim5CQzNSeFqbnJjEiPQyd77AohRMhJSHeGNE27B/g3rpfmryilbu/hJQkh+iAJ6YQQQoTT/v37effdd7FafbekS0lJ4cYbb2TgwIFhWlnkaTlYTfXLB1FW38FFwsU5xF+UJXef9zI2ayvb33uLTW++gsNnGusyuqyGydNnk7LoDqLPPqvb8QAOp4MVR1fw+M7HqWyp9GtOvCmeb0/4NreMuQWjPnyVXHUVLax6bj8numn3CpCeHc/FS8eRPDB4+1jWNFk57m6Zebyy+WQ7zeOVTTRY/GsfGk5pcVEMTYtp3wfPHeblpMYQKwGeEMKD06k4VtnI1ryak8FcXghbVxp0GuOHJDI1J5lpucmck5PMgPj+eeOVEEL0NAnpzpCmaVcCK3CFdB8qpRb08JKEEH2QhHRCCCHCraqqitdff52ysjKf4wwGA1dccQWTJ/vf6q+vsZ1oonLZPhy1rT7HRU9MJ+X6kWhGfZhWJoKlvuwEn/7ix+Q3dR9OZVXWMb64kthJk0i5YxHxF1+MZuw+SGu2NfPM18+wfN9yWh2+v5fa5CTk8OCUB5mbNTdsAbDT4WTHJ/ls+SAP5fT9Gt1g1DHruhGcdcGQkK5PKUV1k9W9/11zexWe+3Fja+QFeAPio0620HQFeDHkpsWSkxJLtEn+jRCir7PYHOwpqnPtJZdXw/aCGmpD2O43wWzgnJxkprnbV07MTJJ/a4QQIkJISHeGNE1LB0oAHZCvlArvBgFCiH5BQjohhBA9wWazsXLlSnbu3Nnt2MmTJ3P55Zdj9COM6IscjVaqXjiANb/e5zhTVjypd4xDH28K08pEsCink72/eIjN+3dRH+O72iC9vonJ+WUYnApDRgbJt9xC0o03YEhJ6fY6pY2l/HXHX1l5fKXfa5s+cDo/nfZTxqSM8XvOmSo7Xs9nz+2jrryl27E5Z6Vy0R1jiUkI//e9UorKRuvJ4C7fHdy1hXjN3VTB9oSBCeb2fe88qvCyU2IwS8gvRK9U1dh6ci+5bXnV7C0ObevK7JQYpuYkMyXX1b5y5ABpXSmEEJFKQrog0DTtM2Aermq6c5VS23p4SUKIPkZCOiGEED1p586dfPjhh9jtvqtRMjIyuPHGG0lNTQ3TyiKLsjmpefsbmnf63sNMnxhF6uJxmAbHhWllIliUUpT//e+sXfEmhSnxPscmNLcy9XgpZrsrBNJMJhKuupKURYswj+k+TNtdsZs/bf0Teyr2+LU2DY2FIxfy/cnfJy06za85Z8pqsbPhrSPsX1fS7djoeCNzbx/D0ImRsz+jUoqKhtaTgZ1nC838qmZabJEV4GkaDE6MJic15pQqvKyUGKIMEuAJEQmUUhytaGJ7fjXb8mrYll/D8cqmkF3PoNMYPziBKe695KbmJDMgQVpXCiFEbyEhXRBomjYHWANowEZgrlIq8vppCCF6LQnphBBC9LQTJ07w+uuvU11d7XOcyWTimmuuYdy4cWFaWWRRStHwRSH1n+T7HKeZdKTcPIbocf0z0OztLEePsuGvj7CrvMjnOLPVxrRjpcS3dmxhFjNtGsmLbif+oovQDF3vTaaU4uO8j/nL9r9Q2lTq19piDDHcffbdLBq3CLMhPG/SHt9dweoXDmJp7L5V27g5gznv+pEYoyI7UFJKUVbf6tE2s60Sz9VOs9Xu7OkldqDTYHBSdId974amuUK8rOQYTAZdTy9RiD7LYnPwdXEdW917yW3Pr6EmhK0r480Gzsl2hXFTc1OYmJVIjEn2uRRCiN5KQrog0TTtN8B/46qmex9YopSq7dFFCSH6DAnphBBCRAKLxcKKFSvYv39/t2NnzJjB/PnzMfgIIPqy5r2V1Lx+CGXz8Ua+BomXDSXu/NDu1yVCZ99nH/PpM//Aqbr+OhscDqYcP0Fqk+XU5wYPIuW220i67jr0SUldnsNit/DC/hd4eu/TNNub/Vrb4NjBPDDlAS7LvSws319Nda2sfv4gBfuquh2bOCCai5eOJyM3IeTrCgWnU3Gi3uIK7k4GeK7wrqCqGasjsgI8vU5jSFK0u/rOVYXX1kYzMzkao14CPCECUd1kZVueK4zbll/D3qK6kP69z0yOPhnITc1NZtSAeGldKYQQfYiEdEGkadoPgUcAPVAB/Bt4D9gjlXVCiDMhIZ0QQohIoZRi8+bNfPrppzidvt+QyszM5IYbbiAxMTFMq4ss1uJGKpfvw1lv9TkuZkoGyQtHoEmlS69UtP9r3n3kt7Q2d93KTHMqJhSWM6S20fvzZjOJCxaQsuh2okaO7PI8Fc0VPLHzCd498i4K/14fT0yfyEPTHmJC+gS/xp8JpRRfry1mw1tHcPgKqAFNpzH9ylzOuTQHXR8KiRxORWldi2vfO3eA1xbmFVY3h3QPqtOhaZAaayI93kxGQhQD4qMY4P48Pd7MgIQoMhLMpMdFSTWe6JeUUhyrbGJ7Xg3b8qvZll/DsYrQta7Un2xd6dpLbmpuMhnSulIIIfo0CemCQNO0Yx4PM4Bo9+dtC3YAdUBDAKdVSqnhQVieEKIPkJBOCCFEpCksLOSNN96gvr7e57jo6Giuu+46RowYEaaVRRZHfSuVy/djK/YezrQxDU0g9fZx6GONYVqZCKaqokLe/sPD1FeU+Rw3qrSK4eW1+Kp/iJk5g5RFdxB3wfloeu8tIQ9UHeCRbY+w9cRWv9d4+dDL+dGUHzEwdqDfc05XdWkTq57bT0VB9y+BBw5LZP6d40hMj+52bG9ndzgprbN47IHnDvGqmimsbsbujMz3PNokxxhdgZ1HkDcg3hXiDUhwHUuPj8JsjOxWpkL40mp3ta5s20tue34N1U2+b7Y5E/FRBibnuFtX5iQzMSuJ2Kj+2YVACCH6KwnpgkDTNCd4vY3xTGrPlVJKfrIVQgAS0gkhhIhMTU1NvP322xw9erTbsRdccAEXXHABOl3/q8RwWh3UvHGYlr2VPsfpU8ykLRmPcUBMmFYmgqmptoZ3/vj/KDt2xOe4rKp6xhdV0N3fBGNWFsm33epqhRkff8rzSilWF67msW2PUdBQ4Ncao/RRLBm/hKVnLSXGGNrvM4fdyZb3j7Pj03zvr5Y9GM16zr9pFKNnDOy3rV/tDidFNS0n97/Lq2o+GeYV1bTgiPAAz1NitLE9vIuPIj0hioz49iAvw/0x2iRveYieV9NkZXt+DVvzq9meV8Oe4jqsIdxzckhSNFNz2/eTG5URj15aVwohRL8mIV0Q+AjpTvuUSEgnhPAgIZ0QQohI5XQ6WbduHWvWrOl27LBhw7j22muJi4sLw8oii3Iq6lfl07C60Oc4zawn9daxmEclh2llIphsFgsfPP4njm3f4nNcen0zk/NPYPAjeNFiYki65hqSb7+dqGFDT72mw8bLB1/m37v/TYPNv+Yt6dHp/OCcH7Bg+AJ0WmiD85Jvavjsuf00Vrd2O3b4OelceOsYzHFSUerJ5nBSWN3sDvCa26vwqpoormmhF+V3HcSbDR3aaw5wh3onP7qDPqkqEsGilCKvqpmtedUn21ceDXHryrGD4k+2rZyak8LARGldKYQQoiMJ6YJA07Q8ghvSAaCUOvUVmBCiX5KQTgghRKQ7evQob731Fs3NzT7HxcfHc8MNN5CdnR2mlUWW5p3lVL91GOw+Xj7oIOnK4cTNGhy+hYmgcTodrH7uKXZ/+qHPcQktrUw9VorZ7vD73LFz5pByxyJiZ89G61SVWmOp4R+7/sEbh9/Aofw759iUsTw07SGmDpzq9xpOR2uLnS9fPcThzb7bgQLEJpqYt3gcWeNSQrqmvqLV7qCwusVdfdd0Msg7XtlESV0LEfo2SkBiTfr2NpsJZjLio05W5Xl+jI8y9NtKTOGdq3VlPdvzq9mW52pdWRXC1pVxUQYmZyedDOUmSetKIYQQfpCQTgghegEJ6YQQQvQG9fX1vPnmmxQU+G69p2kaF198MTNnzuyXb6i25tdT9cJ+nI02n+NiZwwi6aphaPr+1yK0t1NKse2Dd/jyxWd9jou2O5l6tJh4S2BvGptyc0m+/XYSr7kGfVxsh+eO1h7l0W2Psr54vd/nm589nwenPEhWQlZA6wjUN1vL+OLlQ1hb7N2OnXhRFjMWDsMg+5udNovNQY/nNyMAAQAASURBVGF1s8ceeM3ku9tpltRZenp5QRdt1LtDu/ZqvIyTVXntbTYToiXM66tqm12tK7fl17A9r4ZdRbUhb105JSeZqbnJTMlJZszABGldKYQQImAS0gkhRC8gIZ0QQojewuFw8Pnnn7Nx48Zux44ZM4arr76a6OjoMKwssthrLFQt34fthO/Kw6gRSaTeNhZdtNyJ3xsd+modHz35GA5b14Gs0Whihk1H/O7AX4fr4uJIuu5akm+7DVOn6tQNxRt4ZOsjHK3rfs9IAKPOyG1jb+PeCfcSbzp1D7xgaai28Pmy/RQfru12bMrgWC5eOp60zP7XIjfULDYHBdXNlNVbKKtvpbzBQnmHj62U1VtoDWHA0VOiDLr2Cjx3kJfu0V5zgHsPvaQYo4R5EUwpRX5VM9vya9iWV822/BqOlDeG7Ho6DcYOSji5l9zU3GQGJfa/n1+EEEIEn4R0QgjRC0hIJ4QQorc5cOAA7777Lq2tvvehSk5O5sYbb2TQoEFhWlnkcLbaqX7lEJaD1T7HGdKjSVs8HkOavBnYGxUd3Md7j/wOS2PX+8Xp9HouvHwhA3bvp/7jj8FHqOeVphF34YWk3LGImBkzTgYLdqedtw6/xZO7nqSmtcavUyVHJfPdSd/lulHXYdCFJhxWTsXOVQVsfu8YTofv1/k6g8bMa4Yz8aIsNKlQCSulFPUWO+X1FsobXAFeWX1rpzDPdazF5n/b1t7CpNe5W2xGnVKNl+4O8gYkRJESY0In35shZ7U72VdSxzb3XnLb82uobAxd68pYk57J2ckn95KblJ1EnLSuFEIIEQIS0gkhRC8gIZ0QQojeqLq6mtdff50TJ074HKfX67n88ss555xz+l3VgnIq6j4+TuOXxT7H6WIMpNw2FvPwpPAsTARVdUkRb//fr6kr970n26wbbmPKnIuoe+11al57DUdVVcDXMo0YTsrti0hccBW6mBgA6q31/GfPf3jxwIvYnd23mgQYkTSCn0z9CbOHzA54Df6qKGzgs2f3U1Pa1O3YzDHJzFs8lrhkc8jWI06PUorGVrsryPMI8Mo8wr226rzGVv++/3oTg047WYk3oHN7TY8981Jjo6QVYgDqmm1sL6h2h3I17C6sDWll5+BEM1NyU5ia09a6Mh6DtJsWQggRBhLSCSFELyAhnRBCiN7KZrPx8ccfs3379m7HTpw4kSuuuAKTyRSGlUWWpq0nqHnnCDh9vObRaSQvHEHstIHhW5gImua6Wt750284ceSwz3HjL5zPxfd8D83ppH7lSmqefwHL/v0BX0+XmEjS9deRcuutGIcMAaCgvoDHtj/G5wWf+32e84acx0+n/pRhScMCXoM/7FYHG985yt41Rd2OjYoxcOFtYxgxZUBI1iJCr+lkmGc52VKzoqG9vWbbxwZL3wvz9DqN1FhT+z55HgFeW1XegHgzaXGmfhcOKaUoqG4+Gchtz6/mcFloW1eOGZhwci+5qbkpDEmSanUhhBA9Q0K6MNE0LQmIx/X7KOjh5QghehkJ6YQQQvR2u3fv5oMPPsDWTRu/9PR0brrpJtLS0sK0ssjReqyWqhcP4Gz2/eZ03JwhJH5rqLT+64VsrRY+fPxRjm7b5HNczoTJXPWjXxAVE4NSipadO6l+4QUaPv0MHAG2FdTpiJ83j+RFtxMzbRqaprH1xFYe2foIB6oP+HUKvabnxtE3cv/E+0kyJwV2fT/l76ti9fIDNNd3375uzIyBzLlpFCbZq7HParE6qGhopaxTW83yBleo1xbo1TYH2Bq2F9A0SI1t2yOvPchrr9Jr30fP2EvDPJvDyb6Setdecu5grrLRd3vsMxFj0jM5O4kpOSlMy01mUlYS8WZjyK4nhBBCBEJCuhDRNO0aYAEwB8gF2n5yUkqpU15JaJqWC7Tt9N2klOr+VmMhRL8hIZ0QQoi+oLy8nNdee42qblr4mUwmFixY0PmFSr9gr2yhcvk+7BUtPseZx6SQcstodLI/Tq/jdDr4YvnT7Pz4fZ/j0rNzWfjzh4lPbQ+sbaWl1LzyKrWvv46jtjbga0eNGUPKottJuPJKlNHAiqMreHzn41S2VPo1P94Uz7cnfJtbxtyCUR/8N7hbGq2seeEgx3d3v574VDMX3zmOQSOSgr4O0XtYbI6TlXin7J3ncay6KXR7l/Wk1FiTe988Mxke1XkZCVGkx7dX7EUZ9D26zroWGzvyXXvJbcurYXdRLRZb6FpXDkwwu/eSc1XJSetKIYQQkUxCuiDTNO1S4HFgRNuhTkOUUuqUn440TZsHfAYowAoMVkr5t7O3EKLPk5BOCCFEX9Ha2sr777/P119/3e3Y6dOnc8kll2Aw9K8gytlip+rlA7R+U+tznHFgDKmLx2OQPbp6HaUUO1a+xxcvPAM+XufGpaRy7c8fJj1naIfjTouF+g8+oPqFF2k9dCjg6+uTk0m68UaSb7kZW2oCz3z9DMv3LafV4V8lS05CDg9OeZC5WXODvo+kUooDG0tZ9/o32Ft9Vw1qGkz5Vi5Tr8hFL2/ACx+sdicVjR5B3smPntV6rVQ1tfr6K9lrJcUYT7bUTHdX4nXYO8/9nNl45mGeUorC6hZXIJdfw/a8Gg6XN4Tsz1Vra12Zk3yyfeWQpOh+t8etEEKI3ktCuiDSNO1XwK9wBXMarsCt7aeCts+9hnTu+fuAse6x31VK/SvkixZC9AoS0gkhhOhLlFJs3bqVjz/+GKfT9530Q4YM4YYbbiApKSk8i4sQyqGoff8oTZtKfY7TxRlJXTSOqJyEMK1MBNPhzRv46Ik/Y7d1XeVjio7mqgd/Se6Eyac8p5SieetWal54gYbPV0M3f59OodeTcOklJN++iNqRGfxt599YeXyl39OnD5zOT6f9lDEpYwK7rh9qy5tZ9dx+yo7Xdzt2QE48Fy8dT1JGTNDXIfoXm8NJVaP1ZDvNtqq8CneQ1xboVTa2+txCtLdKMBtOttVs3zuvY5vNAQlRxJjab56xOZzsL6k/uZfc1rwaKhpC27pyUlYSU3OSmZKbwuTsJBKkdaUQQoheTEK6INE07QfAX90P2wI5C7AFqAeubHvOR0j3K+Bh9/yVSqmrQrhkIUQvIiGdEEKIvqioqIg33niDuro6n+PMZjPXXnsto0aNCtPKIkfjVyXUvn8UfGUvBo2U60YRM3lA2NYlgqfk8AHe+dNvsTR0HUbp9Houvud7nDX34i7HWIuKqXn5ZWrffBNnfffBVmfms84iZdHt5E0dwp92/4U9FXv8mqehsXDkQr4/+fukRQd3L0mnw8m2j/LZtjIP1U0iYjDpOO+GkYw7b7BU0IiQczgVVY2u6rsyL1V5FW3hXmMrjj6Y5sVFGRiQEEW82cjhEw202ALcKzMAGQlRTM1JcbevTGHsIGldKYQQom+RkC4INE0bCewD2sK3VlwVdU8qpVo0TcsBjruf8xXSTQJ2uB/WA8kqUn/TQoiwkpBOCCFEX9Xc3Mw777zDN9980+3YOXPmcOGFF6LX9+zeOuFmOVxD1csHUBbfb4LGz80i4eIcNJ0EFL1NTWkxb//hYWpP+K6cnHHdLcy64VafIZSzuZm6FSuofuFFrEePBrwWfVoaSTfdyK5ZA3j0+DOUNvleU5sYQwx3n303i8YtwmwIbgvWE8fq+Oy5/dR3s1cjQO6ENObePoaYBFNQ1yDE6XA6FdXN7ZV5FfXtod7JY+6KPZtD3v7RNBidEX8ykJuSk0xmsrSuFEII0bdJSBcEmqa9AtzkftgCzFdKfeXxvL8hnQ5oBMy4qunGKqUOh2zhQoheQ0I6IYQQfZnT6WTDhg2sXr2a7n7mz83N5brrriM+Pj5Mq4sMtvJmKpfvw1Fl8Tku+uw0km8Yhc7Uv4LMvqC5vo53//QbSr/xvcfcuPMv4pL7vo/e4Lu9m1KK5q++ovr5F2hcu9bn3ndeGY3EXnoJG2Yl8teWD2i2N/s1bXDsYB6Y8gCX5V4W1DfWrRY769/4hgMbug8No+ONXHTHWHLPDm5lnxCh4nQqaltsJ9trdt47r739ZitWe4BtbSNYtNHdutK9l9zk7GQSo6V1pRBCiP5FQrozpGlaFFCNK1gD+IlS6i+dxvgV0rnHbgcm4wrpFiqlVgR/1UKI3kZCOiGEEP3B8ePHefPNN2lqavI5Li4ujuuvv57c3NzwLCxCOJpsVL24H2s3e3QZh8SRdsc49IlRYVqZCBabtZWPnvgz32zZ6HNc9lkTWPDj/yIqJtav81rz86l+6SXq3nobZzd/v7wxTBjP2hlxPJm0Hbuf+e/E9Ik8NO0hJqRPCPh6vhzbWcGaFw9iabJ1O/as84cw6/oRGCW0Fn2EUoq6Flt7e02PvfPK690f3aGexRZ5Yd6A+Ch3IJfC1Jxkxg1OwCitK4UQQvRzEtKdIU3TLgJWuR82AWlKqdZOYwIJ6d4HrsAV0n1XKfWv4K9aCNHbSEgnhBCiv2hoaODNN98kPz/f5zhN05g3bx6zZs1Cp+s/b/Apu5Oad4/QvK3M5zhdgom0O8ZhyuxfFYd9gdPp4MsXn2X7h+/5HJeWlcPCnz9MQlq63+d2NDZR98471Lz4ItZu/o55v2gK686NZdmIEhpi/KuSu2LYFTxwzgMMjB0Y+PW60FTbyufPH6Bwf3W3Y5MyYrh46TgG5CQE7fpCRDqlFA2tdlclXn3rKdV4ZfUWKtwfm62h2U9O02DUgHim5CYzzd2+UlpXCiGEEKeSkO4MaZq2FHgaV6j2hVJqnpcxgYR0LwG3uM/3c6XUI8FftRCit5GQTgghRH/icDhYs2YN69ev73bsqFGjWLhwIdHR0WFYWWRQStG4rpi6j467XjV0QTPqSL5xNDHS8q9X2vHRCtYs/4/PNpVxySks/PnDDMgdFtC5ldNJ0/r1VD//Ak1+/D07Zb7RwNYJZt6Y2EJ+RvdvuJv1ZhaPX8zSs5YSY4wJ+Hpe1+BU7F1bxMa3juLopv2fTqcxfcFQJl+Sg072bBSig0Z3mFfmrsSr8Az06lspa7BQUd9KQ6vd53nMRp2rdWVOClNykzlHWlcKIYQQfpGQ7gxpmvYz4P9wvTx+VSl1m5cxgYR0bfvbKeCXSqk/Bn/VQojeRkI6IYQQ/dGhQ4d45513sFh878OWlJTEDTfcwJAhQ8K0ssjQsr+K6lcPoqy+A4qES3KIn5sl1Qu90DdbNrLy8Uex26xdjjGao7nqRz9n6KQpp3WN1mPH+P/s3XV4lGe+N/DvM56RuDtJkAQnWHAoFC3F67Jy1q1dP+c9u9097znr7cq7elbqghYp3mKFUpwAwQLEXWcmyehzv38EaGmTkcgkk3w/19Xr2jK/+3l+BDLdPN+5f3fjq6+h6e23IVp9O3fu466lqrA9V8bJYRJkLwFYTEgMvjHhG1ieuRwKqWd2wNZXWLHvnwWoL7N6rU3ICsP8p3MQGj14Qn2intLqcN3dlXfn7LzmVgciDBqMT43ASI6uJCIi6hKGdN0kSdK3ADyP9lBtixBiTQc1/oR0ewHMv329Lwsh/tbjTRNR0GFIR0REg1VjYyPWr1+PyspKj3VKpRKLFi3CxIkTB1UY5aiwov6lArib7R7r9ONjEbFqKCQ1H6AGm4prV/D2L3+KNkvnZxFKCgXmf/6rGHPfwi7fx202o2nzZjS++hqcZWV+r68NBfbkKvDuWAktIZ6/B7Mjs/G9Sd/DxPiJXW33Hm6njA+33cTZ/SUed5cCgEanxKxHhmPY5LhB9V5BRERERP0TQ7pukiTpMQCvoP1HgZNCiKkd1PgT0lUDiLl9vTVCiC093zURBRuGdERENJi5XC7s2bMHJ0+e9Fo7evRoLFu2DFqtNgCd9Q9uiwP1rxTAUWLxWKdJC0XUE9lQGjUB6ox6SlNVJTb//MdorKzwWDdl5UOY/tDj3QqfhNsN66FDaHjlFbR+cNzv9XYVcGSUhF0TFSiN8dzH/NT5eDb3WaSEpnS13XuUXW3Euy8WwNroObQGgKETYzHrkeHQGTiOj4iIiIj6DkO6bpIkKQ/A0dv/6gAQJYRo+USNTyGdJEkTAJy6UwcgQwjRhdO8iWigYUhHREQEXLhwAdu2bYPT6fRYFx0djXXr1iE2NjZAnfU94ZTRsPEa2s7XeqxThmsR/fRIqOMNAeqMekqruRlbf/3fqLha4LEue+ZcLPzSN6BUdT98sl27hsZXX0Pztm0QXsbOduRCmoSdkyScyZQgOhmFqVao8Vj2Y/jCmC/ApDF1t2XYWpw4/MZVXD9V47XWGKHFfU/nIHl4RLfvS0RERETUFQzpukmSJCWAegB3fpr4lhDiD5+o8TWkexXAo7f/tUgI4d/p30Q0YDGkIyIialdbW4v169ejttZzGKVWq/HAAw9gzJgxAeqs7wkhYHmvFOZ9nj/nJ2mUiHxkOEKyowLUGfUUl8OBXX98HteOv++xLmXkGCz/9r9DZzD2zH0bG9G8aRMaXnsdLi+jZztSFd4+CvPAGAmtuo7DughtBL467qtYPWw1VApVt/oVQuDaiWocfuMqHDa352IJGDc/FVOXZ0DJcbBEREREFGAM6XqAJEmvAXjk9r82ARgrhCj92OteQzpJklYC2ISPJuj/XAjxH73WNBEFFYZ0REREH3E4HNi+fTsuXLjgtXbixIlYuHAh1OrBM9KuNb8WjRuuQTjlzoskIGzJEBhnJPFcriAjZBmHX38Rp7Zv9lgXlZyKVT94DqExPbejVLhcsLz7HhpeeRltp077vd6mBg6OlrB7ogIVUR3/vcsKz8J3Jn4H05Omd7ddmOvbsP9fBagsbPZaG5VsxILP5iAqsWeCTSIiIiIiXzCk6wGSJI1A+8Nz6fY/twAsv/OF9BbSSZL0NIA/A9DcXt8GYIgQwvt8DiIaFBjSERER3UsIgdOnT2PXrl1wuz3vlElISMC6desQETF4Rto5yiyoe6kAssXhsc4wKR7hD2ZCUnEHUbA5u2cHDvzrbxCi8zDWEB6Bld//MeIysnr8/raCAjS8+hrMO3ZAODz/PevIuSHtozDPZ0gQHQTFM5Jm4LsTv4uM8O4NmJFlgbN7i3Fi2y3IsudnCkqVAnmrMjFmTjKkTsZzEhERERH1JIZ0PUSSpBcAfBPtO+EkAE4ArwJYD6ABwIe3S4UQQilJUgqA+wF8HsDk22twe/33hRC/DmD7RNTPMaQjIiLqWEVFBdavX4+mpiaPdTqdDitWrMCIESMC01g/4Gq2o/6lS3BWtHis02aEIfKxbCgNg2e34UBReOpDvPO7X8LlsHdao9bqsOyZ7yNj/KRe6cHV0ICm9evR+PobcNX4/znTighg90QFDo6WYNPeG4wpJSXWDV+Hr4z9CsJ14d3qs7bEgr3/uISm6lavtak5kZj3ZDYM4dpu3ZOIiIiIyBuGdD1EkiQFgF0AFuCjoO6TDd/5NRsAXQe/LgHYLIRY0+sNE1FQYUhHRETUuba2Nrz99tu4evWq19rp06dj3rx5UCo7PCZ6wJEdbjS+dRVtl+o91qmidIh6aiTUsfoAdUY9parwGrb88qdobW7qtEaSFLjvc1/G2AWLe60P4XTCvHcvGl95FW3nzvm9vlUDHBgjYXeuAtWR94Z1Jo0JXxrzJTwy4hGolV0Pk50ON45tKsTFQ+Vea3UGNeY+PgIZ42O6fD8iIiIiIm8Y0vUgSZJ0AP4E4Gl8FNB9fIccPvFrd379zr//E8CXhBCuXmyTiIIQQzoiIiLPhBA4duwY9u/fD28/N6SlpWHNmjUwmUwB6q5vCVnAvK8YlgOlHusknRJRj2VDN3TwjAUdKJprqrDpZ8+hsaLMY93kB9dgxsNPQlL07njTtgsX0PDKKzDv2g04nX6tlQGczZKwc6KEC+kS8LFRmGmhaXg291nMTZnbrbMUiy7U4b2XL6PN4r237GkJmLFuKDQ6VZfvR0RERETUGYZ0vUCSpLUA/hPAx7+yHTV/56eKGwB+JIR4o7d7I6LgxJCOiIjIN8XFxdiwYQOsVqvHOoPBgNWrVyMjo3vnXQWTljPVaNx0HXB7+LlKAYQvz4RxamLgGqMe0Wa1YOuv/i/Kr3j+eX74tFlY9JVnoFL3/nhTV20tGt98C41vvgl3vefdnB0pjQZ25ypweJQEu+ajUG5y/GR8d9J3MSKy6+NrW80OHHj1Cory67zWhsaEYMFnchCfEdbl+xERERERdYQhXS+SJGku2sdfzgCQAiAKgAZAHYBqAMcA7AGwSwjh+bR7IhrUGNIRERH5zmq1YtOmTbh165bHOkmSMHfuXMyYMQOKXt5Z1F/Yi5pR/8plyC2edxAZpyUibGkGJGXXdytR4LkcDuz+829x9dhhj3XJ2aOw/Dv/gRBjYHaTyg4HLLt2oeHlV2C75P/zBqsOeG+shD0TFKgNb/87KUHCyqEr8fXxX0d0SHSX+hJCoOD9Cry/4TpcDtljraSQMHFxGiYuSYdCOTjeL4iIiIio9zGkIyIKAgzpiIiI/CPLMg4ePIjDhz2HFQCQlZWFVatWQa8fHOexuRpsqHvpElzVrR7rtMMiEPXoCCg45i+oCFnGkTdfxsmtGz3WRSYmY9UPn0NYbHyAOmsPxdrOnkPjq6/AvGcv4Pbvs6qyBJwcKmHXRAkFqe2jMPUqPT4/+vN4IucJ6FQ67xfpQFN1K/b98xJqii1ea+OGhGL+Z3IQzvMbiYiIiKgHMKQjIgoCDOmIiIi65vr169i8eTPa2to81oWFhWHt2rVITk4OUGd9S7a50PDGFdiuNnqsU8WGIPqpkVBFhQSoM+op5/ftxLv/+AuE6HyHmD4sHCu//2PEZw4NYGftnFVVaHzjTTS99RbcTU1+ry+KBXZNVOD9HAlOtYREQyK+lfstLEpf1KXz6txuGafeKcLpXUXw9uhBpVVi5rqhyJ6W0K2z8YiIiIiIGNIREQUBhnRERERd19TUhA0bNqC8vNxjnUKhwMKFCzF58uRB8eBdyALNO2/B+r6Xr4tehajHc6DleVxB5+aZk9jx21/Aabd1WqPSarHsm99DZu6UAHb2Edlmg/mdd9Dw8iuwX73q93pzCPDuuPZRmA2hEsbGjMX3Jn0PY2LGdKmfysIm7H+xAOa6zr9mdwwZG425T4xAiFHTpXsRERERETGkIyIKAgzpiIiIusflcmHfvn348MMPvdaOHDkSy5cvh1arDUBnfc96ohJNb98AZA8/byklRKzMgmFi4EYjUs+ovlmILb/4CVqaOt81KUkKzPvMFzFu4dIAdnYvIQRaT55E4yuvwvLuu4Ds+Yy4T3JLwIcjJOyaqMDVJGBp5jJ8a8K3EG/w/++so82FI+uv4coHVV5r9aEa3PdUNlJHRvl9HyIiIiIihnREREGAIR0REVHPuHTpErZu3QqHw+GxLioqCuvWrUNcXFyAOutbthtNqH/1MkSby2OdcVYywhalQ1IM/J2GA4m5tgabfvZjNJSXeqyb+MAqzHr0aUgKRYA665izvBwNr7+Opg0bIZvNfq+/Ed8+CvPMqBA8NvZpfHbUZ6FX+3+GXOHpGhx87QrsrZ6/LwBg9NxkTFuZCZVG6fd9iIiIiGjwYkjXiyRJigYQCyAMgNrf9UII76fcE9GgwJCOiIio59TV1WH9+vWoqanxWKdSqbBs2TKMGzcuMI31MWddG+pfvARXnefz+3TZkYh8eAQUWoYRwcRmtWLbb/4bpQUXPNYNmzoDi7/6LFSavh/hKLe2onnbdjS8+gochTf8Xt+kB/aNl3BmWgyenv0Mlmcuh0LyL4C0Ntrx7ksFKLvi+fxGAIhIMGDBZ3MQk2Lyu1ciIiIiGpwY0vUwSZKmA/gCgHkAErtxKSGEUPVMV0QU7BjSERER9SyHw4F33nkH58+f91o7YcIELF68GGq135+7CzpyqxP1r1+BvbDJY506wYCop3KgCtcFpjHqES6nE3v+/FtcOXrIY13i8Bys+O7/QYgpNECdeSaEQOvx42h4+RVYDx4E/Hw24FIAH2RLuDwvA4+tfg4T4yf6d39ZIP9AGT7YcgNul+cxnAqlhCkPZmD8/FTuOCUiIiIirxjS9RBJkkIB/BXAuju/1M1LCiEEP5pKRAAY0hEREfUGIQTOnj2Ld955B26322NtfHw81q1bh8jIyAB113eEW0bTthto+dDzeVwKoxpRT+ZAm9o/ghzyjRACR996FR9uectjXURCElb94DmExycEqDPfOEpK0Pjaa2jatBmy1er3+muJQOmiMVj+uZ8hNSrDr7X15Vbs++cl1Je3eK1NGhaO+57OgSmSQTYRERERdY4hXQ+QJEkHYB+AaWgP5+402p2gjiEdEd3FkI6IiKj3VFZWYv369Whs9DzOTqvVYsWKFcjOzg5QZ31HCAHrsQo077j50U83HVFJiFw7DPqxsQHrjXpG/ru7sf/vf4KQO98ZFhIahpXf+xEShg4PYGe+cVtb0Lz1bTS+8iocRUV+r280AjWLcjHnq/+N8IQ0n9e5nG4c33oT5/d7Pt8PADQhKsx5dDiGThocZ1sSERERkf8Y0vUASZL+E8BPcG845wBwDMBlAI0AnP5eVwjxk57qkYiCG0M6IiKi3mWz2bB161ZcvnzZa21eXh7mz58PpXLgf6au7WoDGl6/AmH3vNPQdF8qQuenQpI43i+Y3Dp7Ctt/+ws4bZ2fQ6jSaLH0G99F1qSpAezMd0KW0XL0KBpefgUtR474vd6pBCyzx2HsV/4dxlGjfV5XeqUB7754GS1Ndq+1wybHYdbDw6DVD/yRuURERETkH4Z03SRJkgpAHQATPto59zsAPxVCeD9ZmojIBwzpiIiIep8QAsePH8e+ffsge9hdBAApKSlYs2YNwsLCAtRd33FWt6DupQK4G2we60LGRCNy7TBI6oEfXg4k1bduYMsvfoKWxobOiyQJc5/6AiYsfiBwjXWB/eat9lGYmzdDtHUePHbGOWoo0j//VZjm3wdJ5f2IeFuLEwdfu4IbZ2q91hojtZj/dA6ShkX43RcRERERDVwM6bpJkqSZAA7ho110PxdC/EcftkREAxBDOiIiosApKSnBhg0bYLFYPNbp9XqsXr0amZmZAeqs77itDtS/ehmOIrPHOnWyEdFPjoQyVBOgzqgnmOtqsPlnz6G+rMRjXe7SFZj9+GchKRQB6qxr3BYLmjdvRs3LL0GUV/p/gbgYxDz+BMLXrIEqwnOoJoTA1Q+rcPjNa3DaPO84hQRMuD8Nkx8YAqWqf38NiYiIiCgwGNJ1kyRJ/wbgr7f/1QwgTgjhfd4FEZEfGNIREREFVktLCzZt2oSbN296rZ0zZw5mzZoFRT8PLrpLuGQ0br6O1jM1HuuUoRpEPTUSmiRjgDqjnmBrsWL78/+Dkov5HuuGTpmGxV/7NtQabYA66zrhdsN66DCK/v7/oDpT4P8FtBqEL1+OiMefgG74MI+l5ro27P9XASpvNHu9bHSKEQs+OxKRCQb/eyIiIiKiAYUhXTdJkvQDAP+D9p10+4QQi/q4JSIagBjSERERBZ4syzh8+DAOHjzotTYzMxOrVq2CwTCwH7oLIWA9XIbm3UUfzRLpgKRWIPKh4QgZFR2w3qj73C4n9v71Dyg4/J7HuoRhI7Diu/8JfWjwjHttvXoV5/78PzC8ewIav0+MB/RTpiDyySdgnDMHUifnUcpuGWf2lODkjluQZc/PL5RqBaavzsKo2Uk8y5GIiIhoEGNI102SJH0ZwB/R/iPq60KIJ/q4JSIagBjSERER9Z3CwkJs3rwZra2tHutMJhPWrl2L1NTUAHXWd9ou1aHhzasQTs9n94UuSodpdjJDiCAihMCxDa/j+KY3PNaFxydg1Q9/goj4xAB11jMsdZU48Kf/QMSO44g2+/+MQZ2cjIjHHkP46lVQhoZ2WFNdZMb+fxWgqdrzewYApI6MwrwnR8AQ1v93JhIRERFRz2NI102SJD0AYCvaQ7p3hBDL+7glIhqAGNIRERH1rebmZmzcuBGlpaUe6xQKBRYsWICpU6cO+GDKUWFF/UuX4G52eKzTT4hFxKqhkHgGV1C5cGAv9v/vHyG7Oz9nTWcKxcrv/ScSh2UHsLOeUdFUhi0v/hBxO09hpOej+DokhYQgbMWDiHziCWgzMj71utPuxtFNhbh0uNzrtXRGNeY9MQJDxsb43wgRERERBTWGdN0kSVIUgAoAKgDFQohP/79zIqJuYkhHRETU99xuN/bv348PPvjAa212djYefPBB6HS6AHTWd9xmB+peKYCz1OKxTpMWiqgnsqE0agLUGfWEovNnsP2Fn8HR1tZpjUqtweKvfxvDpkwPYGc9J782Hy++/WNk7ruKGZcENJ1nkp0yTJ+OyCefgGHmTEifOJvyVn4dDrxyGW0W7zM2c2YmYsaaoVBrOx6nSUREREQDD0O6HiBJ0hYAD6J9N90kIcSZPm6JiAYYhnRERET9R0FBAbZu3Qq73e6xLjIyEmvXrkVCQkKAOusbwulGw4ZraMuv81injNAi+umRUMcN7HP7BpqaopvY8oufwNpQ33mRJGHOE59H7tIHA9dYDxJCYHfRbvzt8K8x+lgV7j8jI8pz7twhdVoqIh97HGGrVkJpNN799VazA++9chnFFzx8DW8Liw3Bgs+MRNyQjkdpEhEREdHAwpCuB0iSNALAKQAhAA4BmC+E8Hw4AxGRHxjSERER9S/19fVYv349qqurPdapVCosWbIEEyZMCFBnfUMIAfP+Elje9Tw3UNIqEfnoCIQMjwxQZ9QTLPV12Pzz51BXUuSxbvziBzDnyc9DoQjOnWA2lw2vFLyCf537X4y61ILFp2SM8D6t8lMUBgPCVq1C5OOPQZOWBqD9e+TS4XIc3VgIl5ezHCWFhMnL0jFhYRoUSo6JJSIiIhrIGNL1EEmSPgPg77f/9TUA/yaE8PzRWiIiHzGkIyIi6n+cTid27tyJs2fPeq0dN24clixZAo1mYI97bD1fg4YN1wGXhxBCAsKWZsA4PXHAn9s3kNhbW7Dt+Z+h5MI5j3VZk6Ziyde/A7U2eEe91rXV4Q9n/4At17dgSKWMxadkTC8QUPn7UVxJgnHWLEQ88QQM06dBkiQ0VrVg3z8LUFvifatefEYY5n8mB2ExIV37jRARERFRv8eQrgdJkrQGwEsAdAAKAfwawE4hRBc+e0dE9BGGdERERP3XuXPnsGPHDrhcLo91sbGxWLduHaKjowPUWd+wl5hR/3IBZKvnM7gMU+IRvjwTEncKBQ23y4V9f/t/uHRov8e6hKzhWPG9/4Q+LDwwjfWSKw1X8MuTv8TJqpMIswosOCtw/1kZ4S3+X0uTmYnIxx9D2IMPQmh0OLHjFs7sKW4/NMMDtU6JWQ8Nw/Cp8Qy1iYiIiAYghnQ9QJKkmx/71xgAdw5ZuNOwFUAjAH8+dyeEEJk90B4RDQAM6YiIiPq36upqrF+/HvX1ns+c0mg0ePDBBwf8f8ddTXbUv3QJzkrPaYY2MwxRj2VDoVcHqDPqLiEEjm9+E8fWv+axLiwuHqt+8BNEJiYFqLPeIYTAe6Xv4flTz6PEUgKlW2DaZYHFJ2VkVfl/PUVoKMJXr0bEY4+irs2I/f8qgKXB5nVd5oQYzHl0BHRGfq8QERERDSQM6XqAJEky2gM5Cfd+Dq47H3MTQojgHORPRD2OIR0REVH/Z7PZsH37dly65P3nqSlTpmDBggVQqVQB6KxvyHY3Gt66CluB5+BSFR2CqKdyoI7RB6gz6gmXDr2LvX/9PWS3u9MandGEFd/9TySNyAlgZ73D6Xbi9Suv46/n/wqL0wIIgaHlwJJTMqZc7cIoTIUCxnlzYVz3OE4VmnDthOfzLQHAEKbBfU/lICWHZzoSERERDRQM6XrAx0K6HrskGNIR0ccwpCMiIgoOQgicOHECe/bsgSx7fmqflJSEtWvXIjw8PDDN9QEhC5j3FMFyqMxjnRSiQtRj2dBlhQemMeoRxRfOYdtv/geOttZOa5RqNRZ/9dsYnjcjgJ31nkZbI/507k/YcG0D3KI9oIywCNx/RsaCswKhbf5fUztsGJoXfBanbobD0dZ56HnH2HkpmLoyAyo1HxkQERERBTuGdD1AkqQi9GxIBwAQQgzp6WsSUXBiSEdERBRcysrKsH79epjNZo91ISEhWLVqFYYOHRqgzvpGy6lqNG65Drg9/NikkBD+YCaMUxIC1xh1W21JETb//DlY6+s81s1+/LPIXbZywJyrdqPpBn596td4v/z9u7+mdglMLxBYclJGeo3/13RGp+LKpK+gts3ktTYy0YAFnx2J6GSj/zciIiIion6DIR0RURBgSEdERBR8WltbsXnzZhQWFnqtnTVrFubMmQOFQhGAzvqG/VYz6l8pgNzq8lhnnJ6IsKUZkBQDI8wZDCwNddjy85+gtviWx7pxC5di7tNfgEIxcHaAHS0/il+d/BVuNN/46BeFwIgyYMlJGZOvCSj8eJQhIKE09T7czFgOGZ6/TgqVhLwVmRg7L4XfL0RERERBiiEdEVEQYEhHREQUnGRZxvvvv48DBw7A288zQ4YMwerVq2E0DtydMa76NtS9VABXTefjEQFANzwCkY+MgEI3cM/sG2gcba3Y/sLPUXT+jMe6jNzJWPaN70Gt0wWos97nkl3YdG0T/njuj2i0N97zWlSzwKIzMu47J2C0+X5NiyEJBTlPo8WQ6LU2eUQE7nsqG8aIgfM1JSIiIhosGNIREQUBhnRERETB7ebNm9i0aRNaWlo81hmNRqxduxZpaWkB6izwZJsL9a9fgf1ao8c6VZwe0U+NhCqSwUOwcLtcePcff8KF9/Z6rIvLGIqV3/8RDOERAeosMMwOM/43/3/x6uVX4ZLv3TGqcQrMvCSw+KSMVM+TQe9yK9S4kbEcZcnzvNZq9SrMeWwEsnJju9I6EREREfURhnREREGAIR0REVHwM5vN2LhxI0pKSjzWSZKE+fPnY9q0aQPm/K5PEm6B5nduwnqswmOdwqBC1BM50KaHBagz6i4hBD7csh5H33rFY11oTBxW/fA5RCWlBKizwCk1l+L5089jf8n+T78oBEYWCyw5JZB7XcCXAbf1ESNwecQTcGjDvdYOnxqPWQ8NgyaEu1CJiIiIggFDOiKiIMCQjoiIaGBwu9147733cPToUa+1w4cPx4oVKxASEhKAzvqG9XglmrYVArKHIqWEiNVDYZgQF7C+qPsKjhzAnj//DrK78zMIdQYjHvzO/0FyzqhOa4LZyaqT+NXJX+Fyw+UOX49tFFh4Rsa88wIGu+drOdQGXB32KGpjxnm9rylKh/mfyUFiVrj/TRMRERFRQDGkIyIKAgzpiIiIBpYrV65gy5YtsNs9P5kPDw/HunXrkJjo/VyqYGW73oj6165A2DoPcwDANCcFofenQVIMzN2FA1HJxXxs+81/w97a+ZhXpUqFRV95BiOmzw5gZ4Hjlt3YdmMbfn/296hr63jOpdYhMPuCwOJTMpIaOr+WAFAZPxXXs9bCrfI8BlaSgAmL0jBp2RAolb7s1yMiIiKivsCQzpcbS9Lyj/3rXiGEH8c9+3WfGAB/vf2vQgixujfuQ0TBhyEdERHRwNPQ0IANGzagsrLSY51SqcTixYuRm5s7YMdfOmtbUf9SAVx1bR7rdCOjEPnQcCg0ygB1Rt1VV1qMzT9/Dpa6Wo91Mx99GpOWrx6wf8dbna34x8V/4KVLL8Hu7jicl4TAmFsCi08KTLjZ+fOPVl00CrKfgjksw+t9Y9NMWPDZkQiP03e5dyIiIiLqPQzpfLmxJMlo/9AaAAwRQng8RKKrYZskSWkAbt25lxCCP3kSEQCGdERERAOV0+nE7t27cfr0aa+1Y8aMwbJly6DRaALQWeDJrU7Uv3oZ9pvNHuvUCQZEPT0SqjBtgDqj7rI2NmDLL36Cmls3PNaNXbAY8z7zJSiUA/dH4UprJX575rfYeWunx7qEeoFFp2XMuSAQ4vj067KkQHHqQhSlL4aQPH+9VBoFpq8ZipEzEwdsCEpEREQUrBjS+XLj9pAOaA/PfAnpuhS2fWzd7WUM6YioHUM6IiKigS0/Px/bt2+H0+n0WBcTE4N169YhJiYmQJ0FlnDLaNp6Ay0nqjzWKUxqRD85EpoUU4A6o+5y2Nqw47e/wK2zpzzWZUyYhKXf/B40uoF7FiMA5Nfm4xcnf4H82nyPdSE2gTm3R2HGN3369WZTOgqyn0KbPtbrPdPHRGPu4yOgDx2YQT8RERFRMGJI58uN/d9J16WwjSEdEXWGIR0REdHAV1NTg/Xr16OuruNzq+5Qq9VYvnw5Ro8eHaDOAksIAevRCjS/c/Ojn8I6olIgct0w6McMzMByIJLdbrz7zz8jf/9uj3WxQzKx6gfPwRAeEaDO+oYQAruLduOF0y+gssXz2FtJFhh/Q2DZGQmjbrrvec2l1OJ65mpUJk73ek+dXoH7PjMK6aOju9U7EREREfUMhnS+3LjrO+kAhnRE1AMY0hEREQ0Odrsd27dvx8WLF73WTpo0CQsXLoRKpQpAZ4HXdqUBDW9cgbC7PdaFzk+F6b5UjvELEkIInNy2CUdef9FjXWhMLFb94DlEJacGprE+ZHPZ8ErBK/j7hb+j1dXqtT6pTmDN+RDknXdAYf9oFmZt9BhcGf4YnGqj12sMyxCY/fWZ0IRwVx0RERFRX2JI58uNGdIRUR9jSEdERDR4CCFw6tQp7N69G26354AqMTERa9euRUTEwNxx5KxqQd2Ll+BusnusCxkbg8g1QyGp+SNUsLh89BD2/OkFuF2uTmu0egMe/M5/IGXkmAB21nfq2urwh7N/wJbrWyA8biNtZ2gTePxGAuadtEOqqgUA2DWhuDz8cTREef9ZweCox/RxdqSvngt1UlK3+yciIiIi/zGk8+XGDOmIqI8xpCMiIhp8ysvLsX79ejQ3N3us0+l0WLlyJYYPHx6gzgLLbXWg/pXLcBSbPdZpUkyIejIHShN3BgWLsoKLePvX/wV7S0unNQqlCou+/E1kz5wbwM761pWGK/jVyV/hRNUJn+olWeBL5glYcMIJ1+lzEADKk2ajMGMFZKXn7wdJdiOtZA8ycBXhU3JhyMuDfspkqAZo8E9ERETU3zCk8+XGDOmIqI8xpCMiIhqcWltb8fbbb+PatWtea2fMmIG5c+dCqRx4P0YIp4zGzdfRerbGY50yTIuop3KgSfQ+7o/6h/qyUmz++XMw11Z7rJvx8JOYvGLtoBlrKoTAgdID+M2p36DE4vERxF06pQ5fNS7FfSdsaHlnN6zKCFzKfhpWU4rXtSpXK5LLDiG5/AA0rlbosrNhmJYH/dQ86HMnQBES0t3fEhERERF1gCGdLzdmSEdEfYwhHRER0eAlyzKOHTuGd999F95+JkpLS8OaNWtgMpkC1F3gCCFgOVgG854ij3WSRoHIh0cgJCcqMI1Rt7U0NWLLL36C6puFHutG37cQ8z/3FSgGYBDdGafbidevvI6/nv8rLE6LT2tiQmLwTObnMPWUFQ1vrMc1/SSUpMwHJIXXtUq3HUnlR5BS9i60jvbdq5JajZAJE2DImwpDXh50I0dCGqBnYRIREREFGkM6X27MkI6I+hhDOiIiIioqKsLGjRthtVo91hkMBqxZswZDhgwJUGeB1XaxDg1vXYVwyp0XSUDYoiEwzkoaNDuvgp3TZsOO3/8SN097HvGYPi4XD3zr+9CE6APUWf/QaGvEn879CRuubYBbeD6r8o7syGx8b/yzGHahEddf34+zyjzYdZE+rVXITiRUHkNayT7o7I33vmY0Qj9lCgx5eTDkTYUmI4PfZ0RERERdxJDOlxszpCOiPsaQjoiIiADAYrFg06ZNKCoq8lgnSRLmzZuH6dOnQ6Hwvnsm2DjKrah/6RLcZofHOn1uHCJWZkFSDbyvwUAky26896+/4fzedzzWxaRnYNX3fwxj5ODbLXmj6QZ+ferXeL/8fZ/XzE+dj2dzn4XpehMOvZiPMmeCz2sl2Y346uNIK9kHfVtthzWq2FgY8qZCn5cHQ14e1HFxPl+fiIiIaLBjSOfLjRnSEVEfY0hHREREd7jdbhw8eBBHjhzxWjt06FCsXLkSev3A23XkNttR93IBnGWedxZq0kMR9UQOlAZ1gDqj7hBC4NSOLTj86j891pmiYrDqBz9GdGp6YBrrZ46WH8WvTv4KN5pv+FSvVqjxWPZj+MKYL6DsSBXe31wEh9uPRw5CRlzNKaQX74GhtcpjqSYjo32X3bQ86CdPhnIAjt8lIiIi6ikM6Xy5MUM6IupjDOmIiIjok65du4bNmzfDZrN5rAsLC8O6deuQlJQUoM4CR3a40bjhGtou1HmsU0bqEP1UDtRxhgB1Rt119YMj2PXH5+F2Ojut0YTo8eB3/gOpo8YGsLP+wyW7sOnaJvzx3B/R+ImRlJ2J0Ebgq+O+imXJy1FwqArn95fA3ubb+Mw7YmrPIr14N0zWMu/FCgV0o0fBMLV9l13I+HFQaLV+3Y+IiIhoIGNI58uNGdIRUR9jSEdEREQdaWxsxIYNG1BRUeGxTqFQYNGiRZg0adKAOztKyALm/cWwvFfqsU7SKhH1WDZ0wyIC1Bl1V9mVS9j6q/8Lm9XSaY1CqcLCL30DObPmBbCz/sXsMON/8/8Xr15+FS7Z5dOarPAsfGfidzApagouHirHuf0laLN0Hoh2JKr+ItKLdyPMfMt78W2SVgt9bm77LrupedBlj4Ck5KMPIiIiGrwY0vlyY4Z0RNTHGNIRERFRZ1wuF/bu3YsTJ054rR01ahQeeOABaAfgTpbWczVo2HgNcHn4uVECwh/IhHFaYuAao25pqCjD5p/9GM011R7rpq17DFNXPTzgQmh/lJpL8fzp57G/ZL/Pa2YkzcCaYWswMmw0as7YcWZPCVqa7H7dN6LxKtKLdyG86Tr8/eorw8KgnzoVhryp7efZpaYO6j9DIiIiGnwY0vly4/aQ7s7NHwHgeQA7EA/gzdv/WwCYA/j0/1XvWceQjojuYEhHRERE3ly8eBHbtm2Dw+HwWBcVFYWHHnoIsbGxAeoscOzFZtS/UgDZ6nlHkGFqAsIfyICkVASoM+qO1uYmbPnlT1FVeM1j3ai5CzD/81+FUqUKUGf908mqk/jVyV/hcsNlv9YlGZMwJnIsRtRNgTgTBVujf2Mww5pvIL14NyIbCvwO6+5QJSa0n2eXNw2GqVOgio7u4pWIiIiIggNDOl9u/FFIJ+GjsM7rso/9b38bl8CQjog+hiEdERER+aK2thbr169HbW2txzq1Wo1ly5Zh7NiBd5aXq9GG+pcuwVnV6rFOmxWOqMeyoQgZ3IFOsHDabXjn97/GjVPHPdaljRmPB575IbR6fYA665/cshvbbmzD78/+HnVtns9s7IgkFMhumIJJFYsQYg33a63JUoL04t2IrsuH5PfjkHtphw2DIW8q9Hl50E+cBKWR50oSERHRwMKQzpcb37uTztcPhH28WX8+RHY3DGRIR0R3MKQjIiIiXzkcDuzYsQP5+flea3Nzc7Fo0SKo1eoAdBY4st2FhjeuwnalwWOdKiYE0U+NhCo6JECdUXfIshsHX/o7zu7e7rEuJjUdK3/wHExR3IXV6mzFPy7+Ay9degl2t39jLAFAEhKGNIzFhLL7Ed2a5NdaQ0sF0op3I67mTLfDOgCASoWQMWNu77SbipAxYyBpNN2/LhEREVEfYkjny43vDekCckswpCOij2FIR0RERP4QQuDMmTPYuXMn3G7PI+vi4+Oxbt06REZGBqi7wBCyQPPuIlgPl3msU+hViHwsG7rM8MA0Rt0ihMCZnVtx8JV/AB6eERgjo7DqB88hJm1IALvrvyqtlfjtmd9i562dXbuAANIaR2JC+f2Is6b7tdQAK9KKdiG2+DAUQu7a/Tsg6fXQT5oIw9Q8GKblQTt0KCQFR9gSERFRcGFI58uNJakIgQ3pAABCCP40QUQAGNIRERFR11RUVGDDhg1obGz0WKfVarFixQpkZ2cHqLPAaTlZhca3CwG3hx/pFBIiVmbBMCk+cI1Rt1z78Ch2/eE3cDk7P4NRExKCB579d6SPGR/Azvq3/Np8/PLkL3G+9nzXLiCApOZhyC2/H4nmoX4tNRgVGB5ehbjCfXCcOQXh5fxMfymjomCYMgWGaXnQT82DJtm/nX9EREREfYEhHRFREGBIR0RERF3V1taGrVu34sqVK15rp02bhvvuuw9K5cAa6mG/2YT6Vy9DbnV5rDPOSELYkiGQFP6cWEB9peLaZWz55X/BZjF3WqNQKrHg376GUXMXBLCz/k0IgYOlB7GraBfya/NRbi3v0nXizRmYUHY/Upv9C/d1oSpMmJuMIfpKOE59iJYPPoDt0iWPOyO7Qp2aenc0pn7KFKgiInr0+kREREQ9gSEdEVEQYEhHRERE3SGEwAcffIB9+/bB289VqampWLNmDUJDQwPUXWC46ttQ9+IluGrbPNbpRkQi8pHhUGhVAeqMuqOxshybf/4cmqoqPdblrXkEeWsehSQxgP2kurY6nK893/5PzXkU1BfA5rb5vD7GmooJZQswpHGMX/d1a+0IGd+G8fPSkWNIhuv0ObR88AFaj30AR3Gxv78NzyQJ2uwRt0O7adDnToAihGdREhERUd9jSEdEFAQY0hEREVFPKC4uxsaNG2GxWDzW6fV6rFmzBhkZGQHqLDDkNhfqX78M+/Umj3XqeD2inhoJVYQuMI1Rt7Sam/H2L3+KyutXPdaNnH0fFnzha1Cq1AHqLDg5ZSeuNV7D+ZrzyK/Lx/ma8yizej7bEQAiWxIwvnwBsurHQ4LvZ8PZla0oSDgK58hq5CQNx9jYsRjtiof+/A20fnAcLcePw11X153f0qdIajVCxo+HIW8qDHl50I0aBUnFYJ6IiIgCjyEdEVEQYEhHREREPcVqtWLTpk24deuW19q5c+di5syZUCh8f+De3wm3QNOOG2j5wPPOK4VRjagncqBNG1g7Cgcqp8OOXX/4Da6fOOaxLnXUWCz/9r9DqzcEqLOBoa6tDhdqL9zdcXep/hLaXB3vSg1ri8X48vkYVjsRCvg+OtepsONS/Ps4n3AAbRoLYkNiMTZ2LMZEjca4ligkXqmD/fhJtJ48Cbmlpad+awAAhdEI/eTJd8djajIzueuSiIiIAoIhHRFREGBIR0RERD1JlmUcPHgQhw8f9lqbmZmJVatWwWAYWKGG9YMKNG2/AcgeipQSItcMg358bMD6oq6TZTcOv/pPnH5nq8e66JQ0rPzBcwiNjglQZwOPS3bheuP1u6Fdfm0+Siwl99SYbJEYV3EfRtRMhVL4vkvNJTlwOe44ziW+ixZt091fVylUyI7MxtiIUZjUGI70a2YoTl9C2/nzgNPZU7+19nvFxMAwLQ/6qe2hnTo+vkevT0RERHQHQzoioiDAkI6IiIh6w/Xr17F582a0tXk+py00NBRr165FSkpKgDoLDNu1RtS/fhnC5vZYZ5qbgtAFaZAU3FkTDM7s2oYDL/0v4OEZgjEiEit/8Bxi0wfWSNe+1GBrQH5t/t3Q7kLdBbS52mCwh2Fs5TxkV0+DWtb4fD235MK1mBM4m7QfZl19hzXRIdHINY3E9NoIZN5oheFcIRxXr/XUb+kuTUYGDFOntgd3kydDOcDO7CQiIqK+w5COiCgIMKQjIiKi3tLc3IwNGzagrMzzmVMKhQL3338/pkyZMqDGwDlrWlH30iW4620e60JGRyNi7TAoNL6P76O+c/3EMez8/a/hcjo6rVHrQrD8mR8gfVxuADsbPFyyC4VNhXeDu8tl1xBxLQujqmZCI/t+3qMMGYXRp3E2aR8a9dUea1WSCuPVGZhTG4XhNxwIu1AMUVHV3d/KvRQK6EaNujsaM2T8eCi02p69BxEREQ0aDOmIiIIAQzoiIiLqTS6XC/v378fx48e91ubk5GD58uXQ6Xx/yN7fuVucqH/1Mhy3mj3WqZOMiH4yB8owPpAPBhXXruDtX/4UbRZzpzWSQoH5n/8qxty3MICdDV6NtkacLc3HpYMVkPPDoXL6/r0kIONm5HmcSd6HekO5z+uGt4bhvtpYjLolI+pSOSSztSutd0rSaqHPnQB9Xh4MedOgyx4BSckwn4iIiHzDkI6IKAgwpCMiIqJAuHTpErZu3QqHo/PdRwAQGRmJdevWIX4AndMkXDIa3y5E6ynPO3UUoRpEP5kDTbIpQJ1RdzRVVWLzz3+MxsoKj3VTVj6E6Q89PqB2ifZ3jjYX8g+V4uy+YjhaPB0O+WnF4ZdwOnkPakzFfq2ThMCQWgXmVkVhXLGEmOt1UNh79jw7RVgYDFOmwJA3FYa8PKjT0vj3ioiIiDrFkI6IKAgwpCMiIqJAqaurw4YNG1Bd7WWsnEqFpUuXYvz48QHqrPcJIWA9Uo7mXbcADz9+SmoFItYNg350TOCaoy5rNTdj66//GxVXCzzWZc+ci4Vf+gaUKnWAOiMAcDrcKHi/Amf3lqClye7X2rKwqziTtBcVoYVAF3IwlUtgaAUwuUyL3BI14orNkOSeffakSkyAYWpe+3jMqVOgiuH7BhEREX2EIR0RURBgSEdERESB5HA4sHPnTpw7d85r7fjx47FkyRKo1QMn2GgrqEfDm1chHG6PdaH3p8E0N4W7ZIKAy+HArj8+j2vH3/dYlzJyDJZ/+9+hMxgD1Bnd4XbKuHK8Eqd3F8Pi5YzIT6o03cSZpD0oDb/SpbDujhC7QE6JwJgiCRNKVYir9i809IV26FAYpuVBP3Uq9JMmQ2k09Pg9iIiIKHgwpCMiCgIM6YiIiKgvnDlzBjt37oTL5fJYFxcXh3Xr1iEqKipAnfU+R2UL6l+6BLeXnT3aoeEInZsKzZBQhnX9nJBlHH79RZzavtljXVRyKlb94DmExsQGqDP6OLdbxvWT1Ti9qxhN1a1+ra0xlOBM8l4URVwEpO4/Qwq3CowuEhhVJDC2WEKk2b+xnF6pVAgZPbp9l920PISMGQNJo+nZexAREVG/xpCOiCgIMKQjIiKivlJVVYX169ejoaHBY51Go8GKFSuQk5MToM56n9viQP0rBXCUWLzWatJCYZqdDN2ISEgKhnX92dk9O3DgX3+DEJ0HLobwCKz8/o8Rl5EVwM7o42RZ4MaZGpzeVYz6cqtfa63GehxP2IEbUWcheiCsAwAIgYQGYHRRe3A3skTA6N+GP68kvR76ibnt4zGn5UE7bBgkhaJnb0JERET9CkM6IqIgwJCOiIiI+pLNZsPWrVtx+fJlr7VTp07F/PnzoVKpAtBZ7xNOGQ2brqHtXK1P9ao4PUxzUqAfEwNJybCuvyo89SHe+d0v4XJ0vlNSrdVh2TPfR8b4SQHsjD5JCIGiC/U49c4t1BR7D8w/ThspwTGmAvkR7yO/4TwsDv/WeyLJAhlV7aHdqGKBEaUCGs8Tcv2mjIyEYeoU6PPyYMibBk1yUs/egIiIiPocQzoioiDAkI6IiIj6mhACH374Ifbu3QtZ9jzyLTk5GWvXrkVYWFiAuutdQghY3iuFeV+xz2uUEVqYZiXDMDEOklrZi91RV1UVXsOWX/4Urc1NndZICgXmf+4rGDN/UeAaow4JIVB2uRGndhWh4nqTX2tNkTqMuz8FISPtuNh0Aedrz+N87XncaLoBgZ551qR2Cowoaw/sRt9qD/B6eg+cOiWlfTRm3lTop06FKiKih+9AREREgcaQjogoCDCkIyIiov6itLQUGzZsgNls9lgXEhKC1atXIytr4IwLbM2vReOGaxBO38+lUhjVME5PgnFqAhQhA2N34UDSXFOFTT97Do0VZR7rJq9YixkPPcHRg/1ExfVGnNpVjNICz2N4P0kfpsH4BakYOTMJaq0SFocFF+su3g3t8mvzYXZ4fm/zlaGtfSTmnfGYif616hNtTnb7aMy8POhzJ0Ch1/f8TYiIiKhXMaQjIgoCDOmIiIioP2lpacHmzZtx48YNr7WzZ8/G7NmzoRgg4YajwoqmLYVwlPo3Nk/SKmGcmgDjjCQoTZpe6o66os1qwdZf/V+UX/H8LGTE9NlY+OVvQaVWB6gz8qb6lhmndhWhKL/Or3U6oxpj70vB6DnJ0H4sPJeFjGJz8T2hXWFTIWQP5xf6KsosMKpIYMzt0C68pduXvJdaDf24cTBMy4N+6lSEjB4NaYCMHSYiIhrIGNIREQUBhnRERETU38iyjMOHD+PgwYNeazMyMrBq1SoYjcbebywAhBCw32yG5WAp7H6O3YNKgiE3DqZZyVBFhfRKf+Q/l8OB3X/+La4eO+yxLjl7FJZ/5z8QYjQFqDPyRV2ZFad3F6HwdA38mV6pCVFhzNxkjJ2XAp2x4/C1xdmCC3UXcL7mPPLr8nG+9jya7c3da1gIJNfh7i67nBIBvaN7l/wkhdEI/aRJ7eMxp+VBk5kJSeI5mURERP0NQzoioiDAkI6IiIj6qxs3bmDTpk1obW31WGcymbB27VqkpqYGqLPAcJRZYDlUhraLdX6FA5CAkDExMM1JgSbB0Gv9ke+ELOPImy/j5NaNHusiE5Ox6ofPISw2PkCdka8aq1pwZncxrp6ohpB9/4ZUaZUYPSsJY+enwBCm9VgrhECxubg9sKtp33F3vel6t3bbKd0CmZV3QjsZw8oBVfc3791DFRMDfd7U9vGY0/KgjuffXyIiov6AIR0RURBgSEdERET9mdlsxoYNG1BaWuqxTpIkLFiwAHl5eQNuR4ezthWWQ2VoPVsDuP372VU3IhKmOcnQpof1Unfkj/P7duLdf/wFwkPoog8Lx8rv/xjxmUMD2Bn5ylzXhjN7inH5g0rILt+/H5VqBXJmJGL8glSYInU+r2t1tn7qbLtGe2NXWgcAaB0CI0rbR2OOKhYYUt3lS3VKM2QIDHlToc/Lg2HyZCjD+P5DRETUFxjSEREFAYZ0RERE1N+53W7s378fH3zwgdfaESNG4MEHH0RIyMAb9+hqtsN6pBwtJyohHP5thdGkhcI0NwW64REDLsQMNjfPnMSO3/4CTrut0xqVVotl3/weMnOnBLAz8oe10Yaz+0pQcKQCLqfv348KpYQReQmYsDAVYTF6v+8rhECppfSe0O5a4zW4hdvvawGAqbX9PLvRxe3jMeOaunSZzikU0I0c2T4aM28qQiZMgELreUchERER9QyGdEREQYAhHREREQWLy5cv4+2334bdbvdYFxERgXXr1iEhISFAnQWWu8WJlg8qYD1WAbnV5ddadbwepjkpCBkdA0nJsK6vVN8sxJZf/AQtTZ3viJIkBeZ95osYt3BpADsjf7WaHTj/bgkuHCyH0+57UCZJwNDJcchdlI7Ibo6lbXW24lL9pXuCuwZbQ5euFdv4UWA3qkggtK1brX2KpNVCnzsB+ql5MOTlQZeTDUmp7NmbEBEREQCGdEREQYEhHREREQWThoYGrF+/HlVVVR7rlEollixZggkTJgzYnWOyw42WE1WwHimDu9nh11plpA6mWUkw5MZDUit6qUPyxFxbg00/+zEayj2Pcp34wCrMevRpSAr+OfVnthYn8g+UIf+9Utj9Cc8lIHN8DHIXpyMmxdQjvQghUGYtaw/tas4jvy4fVxuu+r3bThICqTV3zrMTyC4V0Dl7pMW7FGFhMEyeDMO0POinToUmPX3AvmcTEREFGkM6IqIgwJCOiIiIgo3T6cSuXbtw5swZr7Vjx47F0qVLodFoAtBZ3xAuGa3namA5VAZXrX/bXhRGNYwzkmCcmgCFTtVLHVJnbFYrtv3mv1FacMFj3bC8mVj8lWegGsB/jwcKR5sLFw+X49z+ErRZ/Eu00kdHIXdJOuKH9PwZbm2uNlyqu3R3p9352vOot9X7dQ2lW2BYOTC6SMboIoGsCkDZw4/TVAkJMEydCsO09p12qujonr0BERHRIMKQjogoCDCkIyIiomB17tw57NixAy6X510rMTExeOihhxA9wB/2ClnAVlAP88FSOMusfq2VtEoY8xJhnJ4IpYlBUCC5nE7s+fNvceXoIY91SSNy8OB3/g9CTKEB6oy6w+lwo+BIBc7uLUaLnztdk0dEYOKSdCQODe+1XWVCCJRby+8Gdudrz+Nqw1W4hO+7AEPsAjklt8+0KxJIrevhJhUKhC5ahJhvfgOatLQevjgREdHAx5COiCgIMKQjIiKiYFZdXY3169ejvt7zjhCNRoPly5d/8ofUAUkIAfuNJlgOlsFe2OTfYpUCholxMM1KhipS1yv90acJIXD0rVfx4Za3PNZFJCRh1Q9/gvC4+AB1Rt3ldsq4/EElzuwphqXe5tfahKwwTFycjpScyICMgLS5bCioL7gb2p2vPY+6Nt+Tt3DrR4Hd6CKBaEsPNaZSIXztGsR85StQxcT00EWJiIgGPoZ0RERBgCEdERERBTu73Y5t27bh0iXvP29OnjwZ999/P1SqwTHa0VFqgeVgKdoK6gF/fuxVAPoxMTDNSYE63tBr/dG98t/djf1//xOELHdaExIahpXf/xESsoYHsDPqLrdbxvUT1Ti9uxhN1a1+rY1NMyF3cTqGjImGpAjceW1CCFS2VN4zIvNyw2W4ZB922wmBhIbb59kVC4wsFjD6l1F+ihQSgsinnkTU5z4Hpalnzu8jIiIayBjSEREFAYZ0RERENBAIIXDy5Ens3r0bsoeAAwASExOxbt06hIeHB6a5fsBZ0wrLoTK0nq0BZP9+/tWNiIRpbgq0aRyzGAi3zp7C9t/+Ak5b5+cLqjRaLP3Gd5E1aWoAO6OeIMsCN87U4PSuItSXt/i1NjLRgImL05GZGwtFAMO6j7O5bLjccPmjMZk151HTVuN1nSQLZFQBo4rbd9mNKBXQuLvWgzIsDFFf/CIiHnsUCq22axchIiIaBBjSEREFAYZ0RERENJCUlZVhw4YNaG5u9lin0+mwatUqDBs2LECd9Q+uJjusR8rQcqIKwuk5zPwkTXooTHNToBsWEZDRe4NZ9a0b2PKLn6ClsaHzIknC3Ke+gAmLHwhcY9RjhCxQdKEOp3YWoabYv7mQ4XF6TFiYhmFT4qBUKnqpQ98IIVDdWo1ztedwvuY88uvycbn+Mpyy0+M6tVNgePlHozEzKgF/fyeq+HjEfP1rCHvwQUiDZHc0ERGRPxjSEREFAYZ0RERENNC0trZiy5YtuH79utfamTNnYs6cOVAqlQHorP9wtzhhPVYB67EKiDYfRtd9jDrBANOcZISMioGkZFjXW8x1Ndj8s+dQX1bisS536QrMfvyzkBR9G9ZQ1wghUHq5Aad2FqGy0POHCz7JFKnDhEVpGJEXD5W6/7yH2d12XK6/fM+YzOrWao9rDG0COSUCY4oExt4UiG/y/X6azEzEPvMtGO+7jx8gCBKyW0Z9eQvqyiyQ3QLhsXrEDQmFStN//h4TEQ0EDOmIiIIAQzoiIiIaiGRZxtGjR/Hee+/B28976enpWL16NUyD8Iwj2e5Gy4lKWI6UQzY7/FqrjNLBNCsZhglxkNQMiHqDrcWK7c//D0ou5nusGzplGhZ/7dtQazj6L5hVXG/EqZ1FKL3c6Nc6Q5gG4+9PQ86MRKi1/TPkqGqp+mhEZu15FNQXdLrbTpIFZlwSeOiIjFg/csuQsWMR8+1nYZg8uYe6pp7S0mRH1a1mVN8yo/qWGTXFZrgc9+7mViglxKSakJgVjoSh4UjIDIPOoO6jjomIBgaGdEREQYAhHREREQ1kt27dwsaNG9HS4vnsJ6PRiDVr1iA9PT0wjfUzwiWj9WwNLIfK4Krr/Cy0jihMaphmJMEwJQEKHUfO9TS3y4m9f/0DCg6/57EuYdgIrPjuf0IfGhagzqi3VN1qxuldxSjKr/NrXYhJjbH3pWD07GRoQvr396LD7cCVhit3Q7v82nxUtlTeU6NyCSw4K7D6qIxQP96WDLNmIvaZZ6DLzu7hrskXLocbtSUWVN0yo/p2MGdttHfpWpGJBiRkhiEhKxwJWWEIjQrp4W6JiAY2hnREREGAIR0RERENdBaLBRs3bkRxcbHHOkmScN9992HatGlQDNLRgUIWaLtUB8vBMjjLrX6tlXQqGPMSYJyeCKVR00sdDk5CCBzb8DqOb3rDY114fAJW/fAniIhPDFBn1Jvqyiw4vasYhWdqAD8eW2n1Koyem4yx81KCaidSTWsNztacxU+O/QQW50fn9OnsAg+ckLHshECIHxt+Q5ctQ8w3vwFNSkovdEtA+3tTc00bqovMqL7ZjKpbZtSXWSHLvfOc1RihRUJWOBKz2oO7yAQDJAVHnBIRdYYhHRFREGBIR0RERIOB2+3Ge++9h6NHj3qtHTZsGFauXImQkMH7iX0hBOyFTbAcLIX9hn/nZEGlgGFSHEwzk6GK1PVOg4PUhQN7sf9//wjZ7e60JsQUihXf+08kDuMuooGisaoFp3cX49qJagg/wg+1VolRs5Mwbn4q9KHBE5zvvrUb3z383U/9emiLwKpjMhafU0Bydf49cA+1GhHr1iH6y1+CKjq6hzsdfOytzvZA7tZH/9haOh5bGghavepjO+3CEZtmglI1OD9kQ0TUEYZ0RERBgCEdERERDSZXr17Fli1bYLPZPNaFh4dj7dq1SEpKClBn/Ze9xAzLwTLYCur9W6gA9GNjYZqTDHWcoXeaG4SKzp/B9hd+Bkdb5/P/VGoNlnz9Oxg6ZVoAO6Pe1lzbhjN7i3HlWCVkt+/PsZRqBUbOSMT4+1NhjAiO4PxHR3+ELYVbOnwtxaLFC4WTIe85CPj4PE/S6xH19FOI/OxnoTQae7DTgUt2y2iobEHVTfPdnXKNVa193ZZHSrUCcemhSLi90y4hI6zfj34lIupNDOmIiIIAQzoiIiIabBobG7F+/XpUVlZ6rFMqlVi0aBEmTpwISeI4LWdNKywHS9F6rhbwc5SZLjsSprkp0KaG9lJ3g0tN0U1s+cVPYG3wEJxKEuY88XnkLn0wcI1RQFgbbTi7twSX3q+A2yn7vE6hlDBiWgIm3J+GsJj+vVO41dmKh3Y8hCJzUYevZ0dm4x+ZP0Lj7/6AlkOHfb6uMjwcUV/6IiIefRQKTfDsLgyElmb77d1xzai6aUZNiQUuu487FrtIqVJAqVbA0ebqletLEhCVbLw9IrP9XDtDmLZX7kVE1B8xpCMiCgIM6YiIiGgwcrlc2LNnD06ePOm1dvTo0Vi2bBm0Wj7YAwBXkw3Ww+VoOVkF4UdAAACaIWEInZsC7dBwBp/dZKmvw+afP4e6kiKPdRMWL8fsJz8HhUIZmMYoYFrNDpzbX4KLh8rh9CNMkRQShk2Kw4RFaYhM6L+7XAvqC/DYzsfgkjsOcJ7MeRLfnfRdtJ48iZrfPI+2c+d8vrYqMQExX/8GwpY/AEk5+L43XE436kqtqLrZjOpbZlTdaoa1wd7r9w2NCUH8kFDEDQlF3JAwRCcboVBIaKhsQWVhEyoKm1FZ2ARrY+/1EhoTgsTMMCQMbQ/uwmJD+N8jIhqwGNIREQUBhnREREQ0mOXn52P79u1wOj2fqRMdHY1169YhNjY2QJ31f26rA9ZjFbAeq4Sw+bcLQp1ogGlOCkJGRUNS8OFoV9lbW7Dt+Z+h5MI5j3VZk/Kw5OvfhlobHKMOyT+2Fify3ytF/oEy2Fv9+F6UgMzxsZi4JA3Ryabea7AbXrr0En596tedvv7n+X/GjKQZEELAeuAAap5/Ho7CGz5fXzs0CzHPPAPj3LkDNqgRQsBc13bP2Mq6MqtfI1O7Qq1TIi49FPEZYYhLbw/mQkz37l6UZTdcDgc0unt3dprr21B5O7CrvNGMhoqWXuszxKRuH42ZGYbEoeHtwaGS59oR0cDAkI6IKAgwpCMiIqLBrra2Fm+99Rbq6uo81qnVajzwwAMYM2ZMgDoLDrLdhZYPq2A5Ug7Z4vBrrSpKB+PsZBgmxEFS8aFoV7hdLuz72//DpUP7PdYlZA3Hiu/9J/Rh4YFpjALO0ebChUNlOLe/FDar5w8efFL6mGjkLk5D/JCwXuqua2Qh4yvvfgVHy492+HqkLhKblm9CdEg0AEC43Wjeug21f/gDXF5GGn9cyIQJiP32s9Dn5vZI333J3uZCTVH72Mr2XXJmv/8++EuSgMhEA+KGhN3eJReKiHgDFJ18CKO+rATHN7+Fm2dOwGmzI37oMExZsRYZEyZ3GJbarE5U3mhCZWEzKgqbUFtsgezn2GVfqbRKxA8JReLQ9uAubkgY1NrBt9uSiAYGhnREREGAIR0RERERYLfbsWPHDly4cMFr7cSJE7Fw4UKo1eoAdBY8hEtG65kaWA6VwlVv82utwqSBaWYSDFPiodCqeqnDgUsIgeOb38Sx9a95rAuLi8eqH/wEkYlJAeqM+oLT7kbB+xU4u7cYLc3+Becp2RGYuCQdiUMjeqk7/9W11WHNtjWot3V8BuP0xOn40/w/QSF9FPTLdjsa33gD9X/5K9xNTT7fyzhnDmKeeQa64cO623ZAyLJAY2XLx8ZWmtFY1QL08mPOEJMacUPCEJ/RPrYyNs0Ejc77e3d9WSmOb34TV44dBjp4Fps4LBszH3kKyTmjOlj9EafDjZpbZlTc3mlXdaPZr5Gv/lAoJESnmpCYFda+4y4rDCFGnmdIRMGBIR0RURBgSEdERETUTgiBU6dOYffu3XC7PT/sS0hIwLp16xAR0X8eZPcXQhZou1gHy8FSOP0cUSbpVDBOS4BxWiKUfAjqt0uH3sXev/4esoe/vzqjCSu++59IGpETwM6oL7icblz5oApn9hTD4mdwnpAVhomL05GSE9kvxkAeLT+KL+3/Uqevf2fid/DUyKc+9etuiwX1//wnGl58CaKtzbebSRLClj+A6K9/A5rk/hVot5od7YHc7Z1yNUWWXgun7lCoJMSkmBD/sV1ypiidX38v6stLcXxT5+HcJ6WPy8WMh59E3JBMn64vu2XUl7eg4npT+9l2N5rRZvYvoPZHRLweCVnhd4M7f78eRESBwpCOiCgIMKQjIiIiuldFRQXWr1+PJi+7L3Q6HVasWIERI0YEprEgI4SA/XoTLAdLYb/Z7NdaSa2AYVI8jLOSoArnOWr+KL5wDtt+8z9wtLV2WqNUq7H4q9/G8LwZAeyM+orbLeP6iWqc3l2MpurO/150JDbNhNzF6Rgypu/Pj/zVyV/h5YKXO3xNpVDhtSWvISeq4/DZVVuLuj//BY3r1wMuH8/tU6sR8fDDiP7yl6CKjOxq213mdsqoLbWg+lZ7IFd1y+x32NoVodG6u2Mr44eEITrZCKW6a+OIGyrK2sO5o4chhOz3+uF5MzH9occRkeBfWCqEQHNNGypvNKHi9tl2zTU+hrRdYAjXfmynXTiiEg19/v1CRAQwpCMiCgoM6YiIiIg+ra2tDW+//TauXr3qtXb69OmYN28elEqeWdMZe4kZlgOlsF1u8G+hQoJ+XAxMs5OhjjP0TnMDUG1JETb//DlY6z2fszj78c8id9lK7gAZJGRZ4MaZGpzaWYQGP3e5RiUZkLs4HZkTYjs9Z6y3OdwOPL7zcVxuuNzh6+mh6Xhr2VvQq/WdX6OkBLW/+z3M77zj830Vej0iP/tZRD79NJTG3nkfEkLAUm9D1a1mVN80o7rIjNpSC2RX7z6vVOuUiE0LRfyQUMRlhCEuPRT60O7vYm6oKG8fa/n+oS6Fcx8nKRQYPfd+TF3zMEyR0V2+TkuzHZWFzXfPtqsrtfiyqa9LNCEqJGSGIeF2cBeXFtrloJOIqDsY0hERBQGGdEREREQdE0Lg2LFj2L9/P7z9zJiamoo1a9YgNDQ0QN0FJ2d1CywHy9B6vgbw87mtLicKpjnJ0Kbya+wLS0Mdtvz8J6gtvuWxbtzCZZj79L9BoWDIPFgIWeBWfh1O7ypCTbHFr7XhcXrkLkrD0MlxUCoDHzrcar6Fh3Y8hDZXx7uiVmStwH9N/y+v17Fdvoya519Ay5EjPt9bGRmJ6C99CeEPPwSFpntBlsPmQk1R+xlyd3bKtVmc3bqmVxIQmWC4u0MubkgoIhIMPRq6NlSU48PNb+Kyj+FcnC4NCkmFBnsl7LLnXZ4qtQbjFi3D5AfXIMTU/f8OOGwuVN1sbg/uCptQdcsMt7N7gWJnlCoFYtNN7TvtMsOQkBkGrZ7n2hJR72NIR0QUBBjSEREREXlWVFSEjRs3wmq1eqwzGAxYvXo1MjIyAtRZ8HI12mA5XIaWk9WAy7+HotqMMJjmpEA7NJw7wLxwtLVi+ws/R9H5Mx7rMidOwdKvfxdqHUeLDiZCCJQWNODUziJU3vBvJK0pSocJC9OQnZcQ8B1CW65vwY+O/ajT138161dYNGSRT9dq+fAEap7/DWzn832+vzopCTHf/AZCly6F5MMOalkWaKxsaT9H7mb72MqGyhaglx9FhpjUd8dWxg0JRVxaKDQhql65V2NlOY5vfguXjxz0KZyL1CZgSvRShGqiAAAOtw0n63ajrNX77nVNiB6Tlq/GhCXLodGFdLf1u9wuGbUlFlQUNt3dcWdv8XE0qr8kICrR2D4ic2g4EjLDYYzQ9s69iGhQY0hHRBQEGNIREREReWe1WrFx40YUFRV5rJMkCXPnzsWMGTOgUHC0lTduqwPWoxWwflABYXP7tVadZIRpTjJCRvb9WVn9mdvlwrv/+BMuvLfXY1185lCs+N6PYAiPCFBn1F8IIVBxvQmndhah7EqjX2sNYRqMvz8NOTMTodYEZjemEALfO/w97C7a3eHrRrURG5dvRJLRt3PMhBCw7N+P2hd+C8fNmz73oR02DDHPPgPj7Nn3fGCg1ey4G8hVF7WPrnT6+f7mL4VSQnSK6fbYylDEpYchNFrX6x9kaKyqwIeb30LBkQMQsm8fuBgeNhljImZDId3730gBGe9XbUZF2w2frqMPC8fUVQ9hzPxFUKp6fleakAUaqlru7rSrKGyCtcHe4/e5IzRah4TM8LsjMiPi9fwgChF1G0M6IqIgwJCOiIiIyDeyLOPAgQM44sN4tKysLKxatQp6fednI9FHZJsLLR9WwfJ+GWQ/R76pokNgmp0M/fhYSCoGox0RQuDDLetx9K1XPNaFxcZh1Q9/gsjE5AB1Rv1N1c1mnN5VhKIL9X6tCzGpMfa+FIyendxru7U+zuwwY+22tahoqejw9bExY/HiohehUvjei3C50Lx1K2r/8P/gqqryaY0sKeGcNB+u+Q+hwRmK6lvNMNfZfL5nV5midPeMrYxOMUKlDtzI2qaqShzf/BYKjrznczinVYRgRsoaRCsSO60RCuC0Yz9ulJ72uZfQmDhMX/cYRsyY3etjey0NNlTe3mlXUdjk99mO/tAZ1bfPtQtHYlY4olONfTJiloiCG0M6IqIgwJCOiIiIyD/Xrl3Dli1b0NbW8ZlId4SFhWHt2rVITmbg4SvhlNFyphqWw2Vw1/v3oFsZqoFxZhIMkxOg0PJ8tY4UHDmAPX/+HWR35yPcdAYjHvzu/0Fy9qhOa2jgqy214PSuYtw4W+PXWEatXoUxc5MxZl4KdIbePXPrXM05PL37abhFx7vUvjjmi/ja+K/5fV3ZZkPja6+j7m9/g9z80RhQAcCmi4TZlA5z6BA0h6bDakqBrOjd36dKq0Rcuglx6R+NrjSE9c1oxKaqShzf8hYKDvsezgFARlIuck33QWH3vjNM0ilhmWjDkd2vwVxb7fM9opJTMePhJ5E5cUrAdqDZWpyoutF8d0RmTbEZsrt3njGrNArEDQlrH5GZFY64IaHQ6Ho/ECei4MaQjogoCDCkIyIiIvJfU1MTNmzYgPLyco91CoUCCxcuxOTJkzm2yg/CLdB2sRaWg2VwVvq3U0EKUcE4LRHGaYlQ9nJIEIxKLuZj22/+G/bWzr+uSpUKi776LEZMmxXAzqg/aqhswZndxbh2shpC9v3ZmVqrxOg5SRh7Xyr0oZpe6+8v5/+CP577Y4evSZDwj4X/wKT4SV26dltNI278bQPKjl1Fsz4Z5tAhcGhCu9OuTyISDO1jK4eEIm5IGCITDVD08UjfpuoqfLjlLVw69K5f4VxkQjJmjX4E2kKFX2GvwqRB1BdyUHD6II5vfgutzU0+r00YOhwzH3kKKSPH+H7DHuJyuFFTbEbF9fYRmZU3m3tt1KmkkBCTYry70y4+M6xXv9eIKDgxpCMiCgIM6YiIiIi6xuVyYe/evThx4oTX2pycHCxfvhw6nS4AnQ0cQgjYrjXCcrAUjltmv9ZKagUMk+NhnJkMVXjf7Drpr+pKi7H558/BUlfrsW7mo09j0vLVDJgJzbWtOLOnBFc+qPRrp5BKrUDOzESMX5AKY0TPv/+5ZTc+t/dzOF3d8XjEOH0cNi3fhDBtmMfrCFmgsaoVVbeaUX3LjOpbZjRUWNHbjwt1BjXiMkJvh3JhiE0PhTYA40J91VxTheOb16Pg8LuQ3b6HTREJichb8giibkbCUeTfe/cdqugQxHxpDNwqN87s3IaT2zbB0dbq8/r0sRMw4+EnEZeR1aX79wRZFqgvs6LyRtPd4K7V7Oi1+4XH6ZGQFYbErPaz7UKjQ/j+TTTIMaQjIgoCDOmIiIiIuufixYvYtm0bHA7PD96ioqKwbt06xMXFBaizgcVebIblQClsVxr8W6iQoB8fC9PsZKhjeUbgHdbGBmz5xU9Qc+uGx7qxCxZj3me+BIWSI0Sp/Uyus/tKUPB+BdxO33dUKVQSRuQlYML9aQiLCenRnqpaqrB622qYHR2HQfel3ocX5rxwT1jRZnXcDeOqbjajpsgMRy/teLpDoZAQnWJE3O1z5OIzQvttiNJcU31355y/4dzUVQ8jPWo0mjYVQm7tfLQuAECCxx126iQjYr4wGgqtCm1WC05u3Yizu7bD5fQ96Bo2dQamP/R4vzhrUwgBc11be2B3o31EZlO178Gjv/RhmruBXUJWOKKSjH2+K5OIAoshHRFREGBIR0RERNR9dXV1WL9+PWpqajzWqVQqLFu2DOPGjQtMYwOQs6oFloOlaM2vBXzPCAAJCMmJgmlOCjQppl7rL5g4bG3Y8dtf4NbZUx7rMiZMwtJvfg8aXc+GKxS8Ws0OnNtfgguHyuGy+x7iSAoJwybHIXdRGiLiDT3Wz7vF7+JbB7/V4WsKWYnvZfwIo+XJqL7VjKpbZphrPZ8p2hO0tgaEmW8h1FyECKkR6U8sRczDayFp+u9IQnNtDY5veQuXDu73K5wLj0/A1FUPY8TUWbDsK4X1fc+joAFAnWBA5MPD0by7CLbLnX/4QpsVjuinR0JSKQAAloY6HN/0Ji68t9fn0ZuSQoFRc+Yjb82jMEVF+/abCpBWs6M9sLsd3NWWWv0aLesPjU6J+MwwJGSGI3Fo+85NlZofwCAayBjSEREFAYZ0RERERD3D4XDgnXfewfnz573WTpgwAYsXL4ZazTPTusrVYIPlcBlaTlUDLn/SuvaHvqbZydBmhffLXSyBJLvdePeff0b+/t0e62KHZGLVD56DITwiQJ1RMLBZnTh/oBT575XB0eZl19THSUDWhFjkLk5HdLKxR3r56Qc/xYarG2B0RCDOkoZYazrirGmItqZAJXr3vVbhtiPUUoxQc9HdYE7bwc4+dUoKYr7xDYQuXQJJoejVnvxhrq3Bh1vW4+LB/ZDdvv85hsclYOrqh5E9Yw7kJgfq37gCZ5nV6zpDXgLCl2RAUisgnG7U/uOix7GYIaOjEfnICEgf2wXWWFmOo+tfw9Vjh33uV6lWY9zCZZiyYi1CTL1/vmBXOGwuVN8yo6KwCZWFTai+aYbLj12r/lCoJMSmhiJxaHtwF58ZBh3PciUaUBjSEREFAYZ0RERERD1HCIEzZ85g586dcHvZhRAfH49169YhMjIyQN0NTG6LA9ajFbB+UAHhx44eAFAnG2GanYKQkVH3PPwdbIQQOLltE468/qLHutCYWKz6wXOISk4NTGMUNOxtLlw8VIZz+0thszr9Wps+JhoTF6cjboj/oYnT7kZNcfvYyvIbDbh+tRRae8+Efp5ExOsRNyQUcUNCYay8BOe/fgdX0S2f12tHjEDss8/AMHNmn35QwFx3O5w74F84FxYXj6mrHkbOzLlQKJVoPV+Lxs3Xvb4HSzoVItcMRcioe3ezyW0u1P41H86qlk7XGqYmIPzBzE99vapv3cDRN1/GrXMdn0vYEU2IHhMfWIncpSv6/Q5ht1tGbYkFlYXtZ9pVFjbD1uLf95g/IhMN7SMybwd3pkiepUsUzBjSEREFAYZ0RERERD2vsrIS69evR2Njo8c6rVaLBx98EDk5OQHqbOCSbS5Yj1fC+n45ZD9DAlVMCEyzk6EfF3t3pNpgdPnoIez50wtwuzp/WK81GPDgd/4PUnJGB7AzChZOuxuXjpTj7L4StDb7fm4YAKTkRGLi4jQkDu14t6aQBZpqWlF103x3bGVDuRW9/UhPa1Ah/vY5cnFDQhGXHgqt/t7dRsLlQtPmzaj7f3+Ey8vY44/TT5qE2G8/i5AAj0A219XixNvrceG9ff6HcysfQvbMuVCqVJAdbjTvuImWE1Ve12pSTYh8ZARUER2HPm6zAzV/Pgd3o73Ta4TOT0Xo/LQOXysruIgjb7yEimuXffvNANCHhWPKynUYM38xVEGys13IAo3VrXcDu4rCJljqbb12P1Ok7u6Zdqk5kQiN7t+hJhHdiyEdEVEQYEhHRERE1DtsNhvefvttXLlyxWttXl4e5s+fD6WSZ8N0l3DKaDldDcvhMrgb/HtwqQzTwDgzGYZJ8VBoB+efRVnBRbz96/+CvaXzHS0KpQqLvvItZM+YE7jGKKi4nG5cOVaJM3tKYPHz+zAhKwwTl6QjNjUUVbeaUV3UvlOu+pbZv5GaXaBQSIhKNiL+TiA3JAxhsSE+73aT29rQ8OqrqP/fv0M2dz6+8ZOM8+9D7DPPQJuZ2dXWfdIezm3Ahff2+hfOxcZhyqqHkDNzHpQqFQDAWd2C+tevwFXd6nW9aXYyQu9Pg6T0/CEIZ10bav98HrKHnWLhKzJhnJrY4WtCCNw8cxLvv/ky6kqKvPZ1R2hMLKatfQzZM+dAoQi+935ro+1uYFdZ2Iz6CivQS4+6Y9NDkZUbi6zcWO6yIwoCDOmIiIIAQzoiIiKi3iOEwAcffID9+/dDlj2fKZOSkoI1a9YgLCwsQN0NbMIt0HahFpaDZR5HqHVEoVfBOC0RhrxEKAfh+Tz1ZaXY/PPnYK6t9lg34+EnMXnF2kF/rh91zu2Wce3DapzeXYTmmra+budTjBHau2Fc3JBQxKSaoNZ0P6RxNzej/u9/R8PLr0DYO98Zdg+FAmErVyDma1+DOiGh2z18nKW+Dh++vQEX39vjcafsJ4XGxGHqqoeQM+ujcE4IgdaT1WjafgPCy1lpCoMakQ8Nh26Y72dZOsqtqP1bfuejMyUg8pER0I+J6fQasuzG1aOHcXT9q2iu8fw+9nFRyamY/vATyJo4Najf12wtTlTdbL47IrO62AzZ1fPPvuMzQpGVG4fMCbEwRmh7/PpE1H0M6YiIggBDOiIiIqLeV1JSgg0bNsBisXis0+v1WL16NTJ7eTfFYCKEgO1qIywHS+Eo8n1nCwBIGgUMkxNgnJkEVdjgegDZ0tSILb/4CapvFnqsG33fQsz/3Feg4C5Q8kCWBW6crsGpXUVoqPAvNO8pToUDtYYS1JiKEZqixo9XfB+hEb07us9ZXY26P/4JTZs2AV7OKb1D0mgQ8fjjiPq3z0MV4Xu41RFLQ137zrl3/Q3nYjFl5UMYOfu+u+Ec0D5WuHFLIdrO13q9hjYrHJHrhkMZqvG7b1thE+r+dRFwd/K8Vikh+jMjocvy/PVxu5y48O5efLDpDbQ2N/l8/4Ss4ZjxyFNIHTXGj677L5fTjZoiy92ddlU3muCw+XeGq0cSkJAZdjuwi4FhkP33kqg/Y0hHRBQEGNIRERERBUZLSws2bdqEmzdveq2dM2cOZs2aBYVi8J6P1hvsRc2wHCyD7UqDfwuVEvTjY2GanQx1jL53muuHnDYbdvz+l7h5+oTHuiHjcrHsW9+HJmTwfG2oa4QscCu/Dqd2FqG2xPOHFrpLGyUhX/Ehqo1FqDYVoTGkErLio51f35rwLXxu9Od6tYc77DdvofZ3v4Nlzx6f1yiMRkR9/vOIfPIJKPT+fW+1h3MbceHd3V0I59bdDufu3UXsKLOg/o0rcHs7/0wCQhekwTQnBZKi67vRWi/UouH1K52ObZQ0SsR8YTQ0ySav13LabDizaxtObtsEe6vvIXHamPGY8fCTiM8c6vOaYCDLAg0VVlRcb0bljSZUXm9Ci59nSHZKApKGhiNrYhwyx8cgxOR/SEtEPYchHRFREGBIR0RERBQ4sizj0KFDOHTokNfatLQ0LFu2DDExnY/0oq5xVLbAcqi0fTeIP48DJCBkZBRMc1J8ejA8EMiyG+/96284v/cdj3Wx6ZlY+f0fwRgZFaDOKJgJIVBS0IDTO4tQeaO529fT6lX3jK2MSw+FzqDGC6dfwD8v/rPDNSpJhZcXv4zRMaO7fX9ftV24gJrfPI/W48d9XqOMiUbMV76C8DVrIKk9j9+1NtTjxNaNyH93N9zOzs91+yRTdAymrnwII+d8OpwTQsB6tALNu251vrPtTq9hGkQ+MgLa9J4Z22w9XommtzvfzaswqBHz5bFQR/u2I7LNasHJbZtwdtd2uBw+jiEFMGzKdEx76HFEJaX4vCaYCCFgrrPdDewqbzSjscr7WYPeSAoJScPCMXRiHDLGxUBnHHzjo4n6GkM6IqIgwJCOiIiIKPAKCwuxefNmtLZ6fgimUCgwffp0zJo1C2ovD2fJf676NliOlKPlVBXg53k92qxwmOakQJsZFtRnF/lCCIFTO7bg8Ksdhx13mKJisOoHP0Z0anpgGqOgJ4RAxfUmnNpZhLIrjT6tkRQSopIMiB8ShriM9kAuPFbf4a4tp+zEkzufxMX6ix1cCUg2JmPDAxtg1Bi79fvwl/XoUdT+5nnYCgp8XqNOS0XsN78J06JFkD6xy9ra2IATWzcgf7+f4VxUDKasXIdRc+d/KpwDAHeLE40br8F22fvuY112JCLWDOvxczzN+4th3l/S6evKCC1ivzwWylDfRyxaG+pxfPObuPDeXsi+jiGVFBg5Zz7y1jyC0OiB/+GZNosDlTeaUVHYhPKrjagrtXbregqFhOTsCGTlxmHI2GjoBuF5r0R9gSEdEVEQYEhHRERE1Deam5uxYcMGlJWVea2NiIjAkiVLMHTowBq51V+4LQ5Y3y+H9XglhN2/c3rUKSaEzk6GLieqW6PdgsHVD45g1x+f9xgCaEL0ePA7/4HUUWMD2BkNBFU3m3F6VxGKLtTf8+uGMA3iMtp3yMUPCUNMmglqje9nIJaaS7F2x1q0ODsec7gsYxl+NvNn3eq9K4Qsw7JnD2p++1s4izsPoT5Jl5ODmGefhWH6NLQ0NeLk1o3I378bLqfv4wrbw7m1GDlnAVSdfADEfqsZDW9egdvbGESlhLDFQ2CcntgrH1gQQqBp2w20fFDZaY06Xo+YL46FIkTVaU1HGqsqcGz9a7hy1Pvu9juUajXG3b8Uk1eshT60Z3YMBoOmmlYUnq5B4eka1Jd1M7BTSkjNiURWbiyGjI2Bxs8/NyLyHUM6IqIgwJCOiIiIqO+43W7s27cPx30cfZaTk4NFixYhNDS0lzsbnOQ2F6zHK2E9Wg7Z6vtuFABQxYTANDsF+nExkFQD9yzBsiuXsPVX/xc2a+fniSmUKiz80jeQM2teADujgaKxqgVVN5uhDVEjNt0EY4Su29fcfmM7/v39f+/09f+Z8T94IPOBbt+nK4TTiaZNm1D7xz/CXVvn0xq7SoniMdm4JTn9OnPOGBWNKSvWYdTczsM5IQtYDpTCvL/Y6zhgVZQOkY+M6PXxv0IWaHjzCtryO//6aNJDEfO5UZDUvge4d9QU3cTRt17BzTMnfV6jCQnBxGWrkLv0wUF3HmdjVcvdwK6hwvcz/jqiVCmQOjISWRNjkT46GhodAzuinsSQjogoCDCkIyIiIup7BQUF2Lp1K+x272fkaDQazJs3D5MmTYJS6f/DSPJOON1oOVUNy+EyuBt9P7cIAJRhWhhnJsEwOR4KP3b7BJOGijJs/tmP0VxT7bFu1NwFGDJ+IpKzRw2qHSfUP/3wyA+x4+aODl/Tq/TY+MBGpIT23ZljcmsrGl55FfV//ztkS8chuF2lxI3YcJREhUJW+P5hAGNkVHs4N+/+TsM5AHCbHWh46wrsPpwTGDIuBhErsqAIUKgiXDLqXrwEe2FTpzW67EhEPZ4DSdm1HX1lVy7h/TdeQvkV38eQhoSGYerKdRizYInHr+1AVV9hbQ/sTtWgqbp759gp1Qqkj4pC1sQ4pI2Kglo7MP8bShRIDOmIiIIAQzoiIiKi/qG+vh7bt29HUVGRT/UJCQlYtmwZkpKSerexQUy4Bdrya2E+WAqXnw8fFXoVjNOTYMxLgEI/8B7ctjY3Ycsvf4qqwms+1UenpiMlZzRSckYjKXskQzsKOKvDirXb16LM2vGI4VFRo/Dy4pehVvbt96ursRH1f/87Gl95FcLRPmrSrlLiZkw4iqP9D+cmr1iL0fMWeg2QbNca0fDWVcgtnncRS2oFwpdnQj8xLuDnccp2F2r/dgHO8s7HLeonxiFi9dAu9yaEwK1zp/D+6y+htqTI53Wm6BhMW/MocmbNg2IQfoBGCIH68hYUnqrG9dM1MNe2det6Ko0C6WOikZUbi7SRUVAN0A+9EPU2hnREREGAIR0RERFR/yGEQH5+Pvbs2YPWVt9CoUmTJmHevHkICQnp5e4GLyEL2K42wHKwDI5is19rJY0ShinxMM1IgjJM20sd9g2n3YZ3fv9r3Djl27jWj2NoR33hQu0FPLnrSbhExyMiPzvqs3gm95kAd9UxZ1UVSn/7As6dOIbiKJNf4ZwhPAJTVq5rD+c0Go+1wi2jeW8xrIe8n4+qitMj6tERUMcZfO6lp7mtDtT+JR+uus5DINOcZIQtGtKt+whZxpUPjuDYW6+iqbrz8/A+KTIpBTMeegJZk/MCHmL2F0II1JVaUXi6GtdP1cBSb+vW9dRaJdLHRGPoxFik5kRBqR64I6WJehpDOiKiIMCQjoiIiKj/aW1txbvvvovTp0/7VG8wGLBo0SKMGjVq0D4UDAQhBBxFZlgOlsJ2tdG/xUoJhglxMM5Kgjpm4JxfJMtuHHzp7zi7e3u3rsPQjgLl7xf+jt+d+V2Hr0mQ8Lf7/4apCVMD3NW9WpubcHL7Zpzb+w5cPoxBvkPrdCGzphFpTiD2c59H5BOPQ+HhAxyuBhsa3rwCR0nnZ0zeYZgcj/AHMrp05ltPczXYUPPn85Atjk5rwpYOgWlmcrfv5Xa5cPHAXnyw6U20NDb4vC4+axhmPPwk0kaP63YPwUwIgZoiCwpPV6PwdA2sfo6Q/iSNTokh42KQlRuLlOxIKAfwGbBEPYEhHRFREGBIR0RERNR/lZaWYseOHaiu9nz21x0ZGRlYunQpoqKierkzclRYYTlUhrb8WsCfRwoSEDIqGqY5KdAkGXutv0ASQuDMzq04+Mo/gB56vsLQjnqLLGR8Yd8X8GHlhx2+HhMSg43LNyJSFxngzoBWczNObtvUxXCuCSn1Zig/9j2oio1F9Fe/ivBVKyF9Ytxl28U6NGy8DmHreFfhHZJWiYjVQ6EfE+Pfb6aXOataUPOXfI/9R6wbBsOEuJ65n92Gs7t34MTWDbC3tPi8LnXUWMx85CnEZw3rkT6CmZAFqovMuH6qGjdO16ClufOQ1RdavQoZtwO7pBERUCoZ2BF9EkM6IqIgwJCOiIiIqH9zu9348MMPceDAATidns8KAgClUokZM2ZgxowZUHs5g4i6z1XXBsuRMrScqgbc/j1b0A4Nh2lOCrQZYQNiB+T1D49h/z/+hNbmph6/NkM76kk1rTVYvW01muxNHb4+J3kOfj/v9wH7vmw1N+PU9s04u2eH3+FcRk0TUj8Rzn2SJj0dMd/6JkwLFwIugaZ3bqLluPcRjupkI6IeGQFVVP8cp2wvakbt3y8CLrnjAoWEqCdzEDKi5wJXm9WKk9s34czObXA5fP+zGjplGqY/9ASiklJ6rJdgJmSByhvNKDxdg8IzNWgzdy+w0xnUyBgfg6yJsUgaGg4FAzsiAAzpiIiCAkM6IiIiouDQ3NyMXbt24cqVKz7VR0ZGYunSpcjMzOzlzggA3GYHLO+Xo+V4JYTD7ddaTYoJpjkp0GVHQlIEd1jntNlw7cOjKC24gLKCC2iu8W0XqL9iUtORPLI9tEvOHoUQU2iv3IcGroOlB/H1977e6es/nPxDPJr9aK/20GpuxqkdW3Bu9w447b6f26UPC8eYoTmI3nsQcmmpz+t042dAN/pJuH04WtM4MwlhC9Mh9fNxgm0F9ah/tQDoJKeT1ApEf340tGk9+x5hbWzA8c1v4cK7uyG7fXvPlyQFRs65D3lrHkFodGyP9hPMZFmg8noTrp+uwc2zNWizeP9AkichJjUyx8cia2IsErLCoQjy/64SdQdDOiKiIMCQjoiIiCi4XL16FTt37kRzc7NP9aNGjcLChQthMpl6uTMCALnVCevxSliPVkBu8e9BoypWD9PsZOjHxUAaILsAzLU1KC24wNCO+qX/+fB/8MaVNzp8TaPQ4PWlr2N45PAev2+ruRmnd2zB2S6Ec5MfXIMx8xdBrdVBOBxo3LgRdX/6M9x1dR7XqlKmQjf2UUgqncc6hV6FiHXDe3T3WW9rOVWNxo3XOn1dClEh9ktjoI4z9Pi9m6oqcWzDa7h89JDP436VKhXG3r8UU1au467gT5DdMsqvNaHwVDVunKuFvcXzOFZv9GEaZE6IRVZuLBIywoL+gzBE/mJIR0QUBBjSEREREQUfh8OBw4cP49ixY5DlTrYPfIxWq8V9992HiRMnQqEYGOFPfyc73Gg9VQ3L4TK4m3wfiQYAynAtTDOToJ8UD4VG2Usd9o3mmmqUXb6I0kvtwZ25lqEd9R27245H3nkE1xuvd/h6RlgG3lz2JkJUPTPusc1ixqk74Zytzed1+rBwTFq+GmMXLIZa++mQTW5pQcPLL6P+7/+A/Mnz0pRa6MY+AnXqNK/30QwJQ9TDw6EM0/rcW39hOVSG5l23On1dGapBzFfGQhXuOaTsqtriW3j/zZdx88xJn9eodSGYuGwFcpeuhFav75W+gpnbLaPsSiMKT9fg1rla2Fu7F9gZI7R3A7u4IaEDYsw0kTcM6YiIggBDOiIiIqLgVVNTgx07dqCkpMSn+sTERCxbtgyJiYm93BndIdwyWs/XwnKwDK6aVr/WKgwqGKclwZiXAIV+YJ4vyNCO+tqNpht4aMdDsLs7DtPXDluLH+X9qFv3aLNacHrHFpzZtd3/cO6BVRh7/5IOw7lPcjU2ov6vf0Pja69BOJ1QhCZDN+kLUJrivawUMEyNRvjy7KDeadT0zk1Yj5R3+roqJgQxXxoLpaH33k/LrxTgyBsvofyK78/AQ0yhmLJyHcYuWAKVRtNrvQUzt0tG6eUGFJ6qwc3ztXDa/Bsr/UnGSC2ycuMwdGIsYlJNDOxowGJIR0QUBBjSEREREQU3WZZx/vx57N27F21t3h/+SpKEyZMnY+7cudDpemdHAX2akAVsVxpgOVgKR4nFr7WSRgnD1HiYZiRDGTqwH+AytKO+sP7qevzX8f/q9PUX5ryA+Wnz/b5uezj3Ns7u3gaHD+/Pd4SEhmHS8tUYt2AJ1F14n3aUl6P2D9shu4dBUnoOpOS2JthO/x2ytQSRTzyBqH/7PJShwfn9IGSBxo3X0HqmptMadYoJMZ8fDYW293YpCyFQdO40jrz5MmqLbvq8zhQVg7y1j2DkrPugUA6sXdQ9yeV0o+RSAwpP16Aovw5Oe/cCu9BoHbJy45CVG4voFCMDOxpQGNIREQUBhnREREREA0NLSwv279+Ps2fP+lRvMpmwaNEi5OTk8IFUAAkh4LjVDPPBMtivNfq3WCnBkBsH06xkqKJ7ZvxefxeQ0E6SEJOa3h7Yjbwd2hl5huNgIoTAswefxf6S/R2+HqoJxablmxBv8LYjrZ3NasXpd7bgzC4/wzlTaHs4d//SLoVzQPu5mI2brqPtUr3XWlfVBdjO/AvCYb37a4qwMER/4d8Q8dhjUAThBzmEW0b9K5dhu9LQaY12WASin8yBpOrd8c9ClnH1gyM4uv5VNFVV+rwuMjEZ0x9+AkMnT+N/n71wOdwovljfHthdqIPL4X0EuCdhsSHIyo3F0IlxiEw08OtPQY8hHRFREGBIR0RERDSwFBcXY8eOHaitrfWpPisrC0uWLEFkZGQvd0af5Ci3wnKoFG0X6gB/HktIQMjoaJjmpECTaOy1/vqje0O7fJhrO98x02UM7QalZnszVm9bjerWjoPgiXET8ff7/w6lovMdTjarFad3vo0zO7fB0eb7eNsQUygmPrAK4xYuhUbX9QDeXmxGwxtXvJ6DKWQ37AWb4Szcj87efFTx8Yj52lcRtmIFJJWqyz31BdnhRt0/LsJRbO60JmRsDCIfGh6Q8Z5ulwuXDu7HBxtfh7Wx8/Dwk+IyhmLmI08hbcy43mtuAHHa3Si6UIfC0zUovlgPt7N7gV1EvB5ZubHImhiHyARDD3VJFFgM6YiIggBDOiIiIqKBx+Vy4fjx4zh48CBcLpfXepVKhVmzZmHatGlQBdnD2IHAWdcG6+EytJyuBtz+PZ/QDotA6JwUaIaEDspP/DfXVKO04ALKCi6g5FI+LHW+hdN+YWg3aJysOonP7fkcRCfB1dfGfQ1fHPvFT/26rcWK0+9sxZmdW/0K53SmUEzqgXBOyAKWw2Uw7y0CvOQSwtGM1g/+BLnxlk/X1mRkIOZb34RpwYKgeo+RW52o+Ws+XNWd/3kYpyUi7IGMgP2+nA47zu3egRNvb4Ctxep9wW2po8ZgxiNPISFreC92N7A4bC4U5dfh+qkalBTUQ3Z179l/ZKIBQyfGIis3DuFx+h7qkqj3MaQjIgoCDOmIiIiIBq7Gxkbs2rUL165d86k+OjoaS5cuxZAhQ3q5M+qI22yH5f1ytByvgnD4d8aOJtUE05wU6EZEBmRnSH/F0I666w9n/4C/5f+tw9eUkhIvLnoR42LHAWgP587s3IozO7fB3tri8z10plBMXLYS4xct61Y4BwBuqwMN66/5ND43ZHQ0wldkwvrubtT+7vdwlpf7fB/dmDGIffZZGKZO6U67AeVqtqP2z+c97iwMXZiG0LmpAeyq/e/Nqe1bcHrn23DZPe96/LisSXmY8fATiEoObL/Bzt7mwq3ztSg8XYPSggbIfn4Y5pOiU4ztO+xy4xAWMzhGT1PwYkhHRBQEGNIRERERDWxCCFy5cgW7du2C2dz56K+PGzt2LBYsWACjcXCNUuwv5FYnrB9UwnqsHHKL952QH6eK08M0JwX6MdGQlL173lIwYGhH/nLJLjy9+2mcrz3f4euJhkS8Nv8lXNv3Hs7s3OpfOGc0fRTOhXR/N46tsBENb12FbHF6LlQpEP5ABgyT4+/uGhMOBxrfWo+6P/8Z7gbfRzAaZsxA7LPPQJeT053WA8ZZ04rav5yH3Nr5e2nEqqEwTPbtvMGe1NLUiOOb30L+/t2Q3b6910uSAjmz5mHa2kcRGhPbyx0OPLYWZ3tgd6oGpVcaIeTuZQKxaSZk5sYiKzcWoVEM7Kj/YUhHRBQEGNIRERERDQ52ux0HDx7E8ePH4cvPwDqdDvPnz8eECROgUDDs6Quyw43Wk1WwHCn3esbUJykjtDDNSoZhYhwkdednaA02AQvt0oYgJWc0UnJGIyl7JEO7IFNuLcfabWthcVru+XW1U0JOUSjGlkRCYff9vKueDueEW8C8vxiWg6Vez7NUxYQg6rFsqOM7PlPLbW1Bw4svouGf/4Tc6vuoztAlSxDzrW9Ck9r/d3U5Si2o/d98CEcnf2YSEPVYNkJGRQe2sduaa6pwbP1rKHj/IODjM2qlSoWxC5Zgysp10IeF92p/A1Wb1YGbZ9t32JVfbfT1S9+puCGht3fYxcIYoeuZJom6iSEdEVEQYEhHRERENLhUVVVhx44dKCsr86k+OTkZy5YtQ3x84HcZUDvhltF6rhaWQ6Vw1bT5tVZhUMM4IxHGqYlQhPC8wU9iaEed2X1rN757+LsA2sO57OJQjLxlgtbpe+itMxiRu2wlxi96AFp9z5xj5Wqyo+HNK3AUed8Zrc+NQ/iDmVBovPfsamhA3V/+gqY33oRwetmZd4dKhYh1axH95S9DFRPj25o+YrvWiLqXLnV+7qdKQsxnR0GbER7Qvj6urqQI77/1Km6cOu7zGrUuBLlLV2DispU99ndsMGo1O3DzbE17YHe9yWv47U1CZhiyJsYic3wsDOHaHumRqCsY0hERBQGGdERERESDjyzLOHPmDPbv3w+bzea1XpIkTJ06FXPmzIFWy4dNfUXIArbL9TAfLIOz1OJ9wcdIWiUMUxNgmpEEpUnTSx0GNyEEzLXVKL10AaUFF1B66QIs9QztBrP/fO/fcfPAIYy8FepXOKc1GDBx6UqMX7y8R4OTtoJ6NG685nF0IwBIGiUiVmZBP97/cYiOsnLU/eEPaN62zeddXVJICCKfehJRn/sclKb++3e59VwNGt662mkAI2mViPniGGgS+3bUc8W1yzjyxksoK7jovfg2nSkUUx5cg3ELl0Gl4Xt8d7Q023HjTC0KT1ejsrC5exeTgMSscGTlxiJzQiz0ofyzocBiSEdEFAQY0hERERENXlarFfv27cP58x2fvfRJoaGhWLx4MUaMGHH3XCMKPCEE7DebYTlYCvv1Jv8WqyQYcuNgmpUMFc/P8aivQrvk7FHQ8TzIPuVoa8XZ3Ttwcsdm2K1Wn9dpDQbkLl2BCYuXQ6vveLxkVwiXjOZdt2A9WuG1Vp1oQOSj2VBHd+/723b1Gmp/+1tYDxzweY0yPBxRX/wiIh59BIp++oEO69FyNG2/2enrCqMasV8e2+fvj0IIFJ8/gyNvvIyaohs+rzNGRSNv9SMYNWc+FEqOOu4ua6MdN87UoPB0Napu+naub2ckCUgcFoGhE2ORMT4GIUYGdtT7GNIREQUBhnREREREdOvWLezYsQP19fU+1Q8bNgxLlixBeHh47zZGXjnKLLAcKkPbxTr/xnNJQMiYGJhmJ/f5rpFgwdBu4LsTzp3asQU2q++7VbX62+Hckp4N5wDAVdeG+jeuwFnuPSw0TktE2JIhkFQ9d45o6+nTqPnN82g7c8bnNaqEBMR87WsIW/EgpH4YFDXvLYLlvdJOX1dG6RD7pbH9YtexkGVc+/Aojr71ChorvYe0d0QkJmPGQ49j6JTp/FBNDzHXt7XvsDtVjZpi/3azf5KkkJA8IgJZubHIGBcDnUHdQ10S3YshHRFREGBIR0REREQA4HK5cOzYMRw+fBgul+dRagCgVqsxe/Zs5OXlQdkPH8IONs7aVlgOlaH1bE3nZy51Qjc8AqY5KdAOCeul7gamQIZ2sWkZSBk5Csk5Y5A8YiRDux7maGvF2T3vtIdzFt93yzhUMi4NMePzTz+HOVnze7yv1nM1aNxSCGF3e6yTQlSIXDMMISOjerwHoP3vuvXAQdS+8Dzs1wt9XqfJykTsM8/AOG9evwqKhBBo2lKIlhNVndaoEwyI+eIYKHT94yxPt8uFS4f244ONb8Da4NsHagAgLiMLMx5+EmljxverP4Ng11zbhhtnanD9VDXqSn3fbdsRhUJCSk4ksnJjMWRcDLQ8P5Z6EEM6IqIgwJCOiIiIiD6uoaEBO3fuRGGhbw9iY2JisGzZMqSlpfVyZ+QLd7MdliPlaDlRCeGQ/VqrSQuFaU4ydCMi+TC3C4QQaK6pRmlBPsouXUBpwUWGdv2cw9aGc3vewcntm/0O5wrSzSgYYoZDLRCpi8Sm5ZsQHRLdI33JDjeatt1A66lqr7WatFBEPjIcqnBdj9zbE+F2o3n7dtT9/g9wVvi+qytk3DjEfvtZ6CdN6sXu/CNkgYbXLqPtUueBlzYjDNGfGQVJ3XM7E7vL6bDj3J53cOLtDX7t9kwZOQYzH3kKCUOH92J3g1NTdSsKT9eg8HQN6n3Y8eqJQiUhNSeqPbAbEw0NAzvqJoZ0RERBgCEdEREREX2SEAIFBQXYtWsXrD6exzR+/HjMnz8fBkPPjnqjrnG3ONHyQQWsxyogt3rfGflx6ng9THNSEDI6BpKSYV1XfTK0Kym4AGt9Xc/fiKGd3+6Ec6e2b0abH+GcUyVwKb0ZBUMscKjvDcGnJ03Hn+77ExRS9wIdZ1UL6l+/AldNq+dCCTDNSUHo/LSAf5/KDgea3ngDdX/5K9yNjT6vM8yaidhnn4VuxIhe7M53wimj7l8XYb/Z3GlNyMgoRD6WDUnRv94L7a0tOLVjC07veBtOu83ndVmTpmL6Q08gOoUfrOkNDZUtt3fY1aCxsqVb11KqFEgbHYXxC1IRn8Gd7tQ1DOmIiIIAQzoiIiIi6ozNZsOBAwdw4sQJ+PJzc0hICO6//36MGzeOO7H6CdnhRsuJKliPlMHd7PBrrTJSB9OsJBhy4yCpOdK0uxja9T2nzYZze9/ByW2b/ArnNCEhmLDkQTjHxeHrHzzbad13J34XT458sku9CSHQcrIKTdtuAi7Pu2AVRjUiHxoO3dCILt2rp7itVjT881+of/FFiFYvoeIdkoTQZcsQ842vQ5OS0rsN+kC2uVD713w4PQQqhsnxCF+Z1S//u9bS1IgP316P83t3QXb7+IEMSULOzLmYtvYxhMXG9W6Dg1h9hRWFp9p32DVV+/j90REJGDc/FVOXZ0DZj3Z1UnBgSEdEFAQY0hERERGRNxUVFdixYwcqfBxvlpqaimXLliE2NraXOyNfCZeM1nO1sBwqhau2za+1CqMaxhlJME5N6DfnMw0EDO0Cx2mz4dy+ne3hnLnzXVOfpAkJwYTFyzFh6QqEGE0AgF+e/CVeKXilw3qVQoXXlryGnKgcv/qTbS40br6Otnzvf/7aoeGIXDccSpPGr3v0JlddHer+8lc0vvUW4HT6tkitRsS6dYj+8pegiu6ZMaFd5bY4UPOX83DXd74jzTQvBWH3pweuKT8111Tjg42vo+DwAQjh26hjhVKFsQsWY8rKdTCE923gO5AJIVBfbsX1UzUoPFUNc53vOx8/LirZiAWfzUFU4uB6/6buYUhHRBQEGNIRERERkS9kWcapU6fw7rvvwm63e61XKBSYNm0aZs2aBY2m/zxMHuyELGArqIf5YCmcZf6dnSNplTDmJcA4PalfBQQDRUBDu/QMpOSMRsrI0UgaMRI6w8B86Ou023B+706c3L4Zrc1NPq9T69rDudylDyLEFHrPaw63A4/vfByXGy53uDY9NB1vLXsLerXep3s5Si2of+MK3A1eHtwrgND702GaldzvRi/e4SgtRe3v/wDzjh2Aj89aJb0eYUuXQDdyFHQjc6AdNgwKrbaXO/00V30bav58HrK185Ax/IEMGKcnBbAr/9WVFuPoW6+g8ORxn9eotTrkLn0QEx9YBa2eI6t7kxACtSWW9jPsTtXA4u37/hOUagWmrcrE6DnJ/XJnJ/U/DOmIiIIAQzoiIiIi8ofFYsGePXtw8eJF78UAwsPDsWTJEgwbNqyXOyN/CCFgv9EEy8Ey2Aub/FuskmCYGA/TrGSoInW90h/dDu2qq1BacOHuPwztfOO023B+3y6c3LapC+HcA8hduuJT4dzH3Wq+hYd2PIQ2V8e7UldmrcRPp//U472EELC+X47m3UWA2/NzSWW4FpGPjIA2rfOe+hPblSuoeeEFtBw67P9ipRLazEzocnJu/5MN7YhsKI29Hx45Kqyo/Ws+hN3dcYEERD48HPqx/X+XeMW1K3j/zZdReinf5zU6owmTV6zFuIVLodYEPigdbIQQqC4yo/B0DW6croG10fsHoO5IzYnEvKeyYQjjnxN5xpCOiCgIMKQjIiIioq64ceMG3nnnHTQ0NPhUn52djUWLFiEsLKyXOyN/OUotsBwqRdulesCfxyMKQD8mBqY5KVDHc/dFb/tUaHcpH9aG+p6/URCHdk67Dfn7d+PE1o3+hXNaHcbfDuf0ob69R225vgU/OvajTl//1exfYVH6og5fc7c40bjhGmxXvL9/6nKiELlmKBR6tU999SetJ0+i5te/Qdv58927kCRBk5Z2N7TT5eRAm50NVUTPj2i032xC7T8vAq5O3gyVEqKfGgndsP4/HlIIgeIL5/D+Gy+h+mahz+uMkVHIW/MIRs1ZAIWS55EGgpAFqm6ZUXiqGoVnatDqwxmyOoMac58YgYxxMQHokIIVQzoioiDAkI6IiIiIusrpdOL999/H+++/D7e7k50HH6PRaDBnzhxMmTIFSj7463ecNa2wHCpD69kaQPbvOYluRCRMc5KhTWcIGygM7T7idNiRv283Tm7biJamRp/XqbU6jF+0DLnLVvoczt0hhMB3D38Xe4r2dPi6SW3ChuUbkGS8dzyi/WYzGt68ArfZy0N4pYTwpRkw5CUE9Vg7IQSs772Hmhf+P3v3HRbVmfYP/Hum0zvSURTsPSLWGGMX1FjS1ZTNJtny22zN9k22vZtt7+67LW2TGNMtMYq9d7FXLKBSlN7LMP35/QESNMwMZebAwPdzXV6ROfc55zYi6PnOcz//C1P2dZdeWxUV2WLFXeMPtQv2Qm24WIbyDy/bfdOCpFEg7LkR0MT6dfpechBCICvjMA59+gEqC261+bygyGhMeuRJJI2fBEmhcGOH1JKwCdw8X4Z9H11Fg7OvEwCGTIrEpGWJ0HDPWGoFQzoiIg/AkI6IiIiIOqusrAxbtmzBjRs32lTfp08fpKamIjY21s2dUUdYqoyoO3gL9ceLIMy2dp2r6esPv2mx0A0M8uhgwRP1xtDObDLiQtPKufaGc6PmpOK+DoRzLdWYarBs4zIU1Be0enxU2Ci8O+ddqBQqCJtA7Z481OzOc7piVRXqheDHBkET3b3C0M4QViuqN3yB0n/+E5bCQrfdRxka2rzarjm4i45u99ej+uNFqFyfZfe4wluFsBdGQh3etr0HuwOb1YpL+3fjyNqP2jU6N7xvf0x5bAXiR47h13UZ6WtM2PvBFeScd/57FRDmhRnPDEFEP75Rhu7GkI6IyAMwpCMiIiIiVxBC4OLFi9i2bRvq6+vbdM7YsWMxY8YMeHl5ubk76ghrvRl1RwpQd6QAosHSrnPVkT7wHh0OXVIQVH28+WC3C/Tk0M5sMuLC7u2N4Vxl20buAoBKq8Xo2am4L21xp8K5ls6UnMFT256CTbQeaD8/4nm82P85VHxyFcYb1U6v5z06HIGL+kOh7ZmrYmxGIyo/+hjlr78Oa7Xz/x+uoPD3bwzsBjeFd0OHQBMfD8nJiu6avXmo2Z5r97gyUIuwF0dC5WH7gllMJpzdsRkZG9bAUFvT5vOComIQHt8PoXF9ERrXF2Fx8fAPDecqOzcSQiDzUAEOrcmCxeT4TTOSQsK4+X0xdk48FEr+nlAjhnRERB6AIR0RERERuVJDQwP27NmDEydOtKne29sbs2fPxogRIxjkdFM2oxX1x4tQd/CW8xF9rVD4qaFLDIIuMQjaxEAofTVu6JKcEUKgqrgQ+Zcu4FZTcOeO0E6SFAjvl4CYIcMRO2Q4ogcNcVloZzGZcH73dhz/Yk27w7lRs+ZjXNpieAcEuqSXll4/9zr+dfZfrR4bVz8Ur5R+E4oGx9eQ1AoELhwA77HhveJrobWuHnV796Dh7DkYLl+G4coVCL1etvtL3t7QDRz45Yq7oUOg7d8fkvrLvf+EEKjedAN1R1pfKQkAqj7eCH9+hEfuGWjU63Fq8+c4mb4BZoOTT1A71DovhMbGITQ2vjG8i+2L0Lh4l4Xg1KiqWI+d71xCSW6t09qIBH/MeHooAsL4BihiSEdE5BEY0hERERGRO9y6dQvp6ekoKipqU33fvn0xf/58hIWFubkz6ihhsUF/pgS1+2/BUtaxB7oAoI7yaQrsgqDt6w9JxXf8d4WuCu1iBg+F1tunXdewmEy4sGc7jm9Yg7r2hHMaLUbNdl84d4fVZsWzO57FqeJTza8phQIrSxZiWcVMp+erI7wR/Phgjxqd6GrCaoUpNxeGS5kwZGY2BneZmbDVtH2lV2dJajW0SUnNoZ1u8GBoEpNQ9UUuGs6V2j1PE++P0GeHQaHxzL1W9TXVyPj8M5zbsRlWS/tWTdvjExjUFNrFN/83JCYWaq3OJdfvjaxWG06k38TpbblwFmWotUpMeSQJgyZE9IrQn+xjSEdE5AEY0hERERGRu1itVpw4cQJ79uyByeR8BZZCocDkyZMxZcoUqNWetyqhtxA2gYZL5ajdlw/z7bpOXUtSK6BNCIA2MahxNGaYFx8odpGvhHaXzrcrEGur9oR2FpMJF/buaAzn2hEgqjRajJw1D+PSFsMnMMhVrTtUVF+EJRuXoMZUgz6mEPz49jMYZOjn9DyflEgEzu8HSe2ZAY87CSFgvn27Mbi73BTeZV6Gtazt+6l1mlIJbf8B0AxeDkjhdst0g4IRsnwwJA8eM1hTWoIjaz9C5v49EHbGt3aKJCEoIrJ5td2dlXeBERFQKPj531YF2VXY9W4massNTmv7jwnDtMcHQefLv1P1VgzpiIg8AEM6IiIiInK3mpoabNu2DZmZmW2qDwoKwvz58zFgwAA3d0adIYSAMbsKtfvyYbzumr2llAFaaBMDoUsKgrZ/IJQ+fLDYVboytFOqNbi4ZwcyNnzWvnBOrWkM5xYskS2ca2lX7i6s2/g+vlP4BHxtjlfFSTolgpYkwns4Vw+3l7mkpCmw+/KHpaDQvTdVauE96XtQBtsPXr1HhyNoWRIkhWe/0aD8Vh4Of/oBso4fkeV+KrUGwTGxCGu58i6uL3wCg/imDTuMDRYc/PQarh5zPq3AJ0CDB58agtjBwTJ0Rt0NQzoiIg/AkI6IiIiI5JKVlYXNmzejqqqqTfVDhw7F7Nmz4e/v797GqNNM+bWo2ZcPwyUXjkuUAHWMH3SJgdAlBkET5+fRq1Q8nZyhndbbG4b6tq/SbAzn5mLcgqVdEs4BgDBbUZV+A/UZzh+aa2L9EPzYIKiCOfrPVSyVlTA2jci8s+LOlJPj0ntIGl94TfkhlH6RdmuE6Qp0/U3Ne92pwj13j8HC7Ks49Mlq5F042yX31/n5I6zFuMzQuHiExsZD49V7x8LeK+tkMfZ/dBVGvfMxpSMfjEXKogSouGq3V2FIR0TkARjSEREREZGcTCYTDh48iMOHD8Nmcz5OS6PR4MEHH8S4ceOgUDCg6e7MpXo0XCiDIasSptxawOa65y2SVgltQgB0SUHQJQZBGaLz2IffPYEQAlVFBcjPvNAc3LkjtHNEpdZgxMy5GLdgCXyDum6ViLlEj4qPrsBcVO+0dlfUSTz6wgvQabxk6Kx3s9bVwXjlSnNoZ8jMhPH6dcBq7fA1Ja9geE/5ERTe9j/fDBfXwpy9AwCgDAlpDux0gwdDN3QI1DExHvW1q7qkCIVZV1GWn4ey/ByU5eWguqS4y/rxD+uD0Lj4L1fexcYjKCoGSpWqy3rqSnWVBux67zJuX610WhsS7YOZzwxFSLSvDJ1Rd8CQjojIAzCkIyIiIqKuUFpaivT0dOTm5rapPjIyEqmpqYiOjnZzZ+QqNqMFxuvVMGRVwphVBUtZg0uvrwzWNa+y0w4IhELXOx/Qdhf3hnb5mRdQ76bQTqlWY+SMuRi3cGmXhnNCCOhPlaDqi2wIs+M3HVQra/HnqFU46ZuJxwc9jp+M/4lMXVJLNoMBxqysxn3umlbdGa9ehTCb23wNhV8kvKf8CJKm9b0UAaDh9Luw5B1t/Xx//8bArim00w0ZAk3fvpCUnrPCydSgbxHa5aIsLwel+bkw1NZ0ST8KpQrB0THN4zLD4hr3vfMLCfOoQLSjhE3g7O58HPviOmwWxzmHUqXAhIf6Y8QDMR4/mpWcY0hHROQBGNIRERERUVcRQuDcuXPYsWMH9Hp9m85JTk7G9OnTodNxTJynsVQYmgK7ShiyqyAMHV/N8hUKQBPrD11iILRJQdBE+0FS8uFjV3JHaKdUqzFixhwkL1gK3+AQF3XaMTajBVUbrkN/psRp7Vnvq/hT1HuoUH+5d+M/p/8T98fe784WqY2E2QzjjRt3BXeGK1cgHHxfUgQlwHvS9yCpNK1f02ZFw/H/wFp0vk09SF5e0A0c2Ljirim40/bvD0nT+vW7IyEE9NVVKM1rXG1XlpeLsvwclOfnwWI2dUlPWm8fhMTGIzQ2rjG8i23c707n2zNXkpXdqsWO/2aistD5qt6YQUF4cOUQ+AZpZeiMugpDOiIiD8CQjoiIiIi6ml6vx65du3D69Ok21fv6+mL27NkYNmxYr3iHfE8krAKmW7WNgV1WFUx5NYALH81IOhV0AwKgTQyCLikIqiCGul2tM6GdUq3GiAfnYNzCJfALDnVzp86Zbteh4uMrTleH2mDDB6Gb8WnoNtikuz/Bg7RBWLdgHcK8w9zZKnWQsFphys37MrS73Dgy01b9ZdCqDB8Gr5RvQlK0vgJOWE1oOPw3WCuyO9SDpFZDm5gI3dAh0A4eDK8hQ6AdOBAKL88alWqzWVFVVPTlqrum/1YVFUII52Ov3cE3OKR5r7uwuL4IiY1HSHQsVB4UitpjMVlx9PPrOL/3ltNarY8KDzwxCP3HhMvQGXUFhnRERB6AIR0RERERdRd5eXlIT09HSYnzlSkAkJCQgPnz5yMkpGtX1FDn2RosMF6vgiGrEoZrlbBWGl16fVWoF7R3RmP2D4RC6zlj5XqqtoR2SpUKwx+cg+RFS7tFOCeEQP2RAlRtuQlYnYyU89dAtTgKj5xfidKG0lZrxkeOx5sz34RC4n6bnkAIAfPtAhgyL8FwuXGPO3OpBtqkh+2fY6qH/tCfYau57ZomFApo+ydAO3jwXXvdKf38XHN9GZlNRlTcyv9y5V1+Lsryc902JtcZSaFAUERUY3gXF9+88i4gvA8kD9wTN+9SOXavugx9jfNVjIMmRGDKI0nQcGx0j8OQjojIAzCkIyIiIqLuxGq1IiMjA3v37oW5DXsEKZVKTJkyBZMnT4ZKxYdLPYEQApZyQ+Mqu2uVMF6vhjC5cDSmUoImzh+6pMbQTh3ly315ugEhBCoLC3Ar8wIqCm/DJzAIgyZOhV9I14dzAGDTm1GxNguGzHKntbpBwQhalgSljxpHC47i+Z3PQ9hZKvrSmJfw7PBnXd0uyahq6xXU7W89iAUAW0MV9Adfg9A7/9zpKHV8HHSDh3wZ3A0ZDFVw1+3X2Bn6mmqU5+eitHnVXQ7K8vNgNrh2X9O2Umm1CI2Ja1p51xjghcX1hXdAYJf00x4NdSbsXX0FN8+VOa31D9VhxtNDEdk/QIbOSC4M6YiIPABDOiIiIiLqjqqqqrBt2zZcuXKlTfUhISGYP38+EhIS3NwZyU1YbTDl1jaussuqhPl2nUtHYyq8VY1jMRMDoU0MgiqA+/PQ3Yy5Naj4+AqsVU5WeColBMzpB9/JUXeN4v3rqb/i3YvvtnqKSlLh/bnvY3jYcFe2TDKr3noTtfvtjxe0GSug3/N7CGONbD2pIiLuCu10Q4ZA1aePR46JFjYbaspKGlfb5eU2r76rLLwNm9WFb+JoB++AQITGxjePzQyNi0doTDzU3WzPXCEELh8pxMHPsmAxOv5/JUnA2Hl9cd+8vlAqPW/1IH0VQzoiIg/AkI6IiIiIurMrV65g69atqG6xD5Ajw4cPx6xZs+DngaO/qG2s9WYYsxtHYxqzKmGtdj7Kqz1UfbyhawrtNP0CoNBwNGZvJWwCtftvoWZnDuBk6yxlsA4hjw+CJuarX3vMVjNWbF2Bi+UXWzkTiPWLxZq0NfBR+7iga+oKQghUrsuC/mSx3Rp1pDd8xwsYr11uHpdpzM4GZAyZlMHBzSMydUMbAzx1bKxHBncAYLWYUXH71l3jMkvzclBbZn9lo1tJEgLDI5rHZYbGNgZ4QZFRUCi79ntJVbEeO9/NREmO86C4Tz9/zHh6CALDvWXojNyJIR0RkQdgSEdERERE3Z3JZML+/ftx9OhR2GxOnpQD0Gq1mDFjBsaOHQuFB+4jQ20nhICltKFxLGZWJYw3qiHMzj9H2kwlQds3oHEvu8RAqCN9PPZhNrWPtdaEis+uwphV5bTWa0QoghYnQuFgP6f8mnws3bQUeou+1eNpCWn4/ZTfd7Rd6gaEVaD8g0wYLtvfU007IBChTw2FpGr83mQzGmG8lgVDZmbzD+PVqxAm1775wBGFnx90gwY1hndNwZ2mXz9IXRwqdYZRX4+ypnGZpXm5TSFeDoz19V3Sj1KtRnB0LMLurLxr2vfONyhE1u8pVqsNJ7fk4NSWHDiLQ1RaJaYsS8TgSZH8vufBGNIREXkAhnRERERE5CmKi4uRnp6O/Pz8NtVHR0cjNTUVkZGRbu6MugthscGYU9O4yu5aJcyFrn0gq/BVNwZ2SUHQDQiE0k/j0utT92DIqkTFp1dhq3O8L6akViAwrT+8x7VthOCm65vw00M/tXv8f6b8D1ITUtvdL3UfwmxF6X8vwuRgtZLXiFAEPzrI7l6YwmyG8cbNu4O7y5dh07ce8LqDpNNBN3Bgc2inGzIE2qQkSB6896sQAnWV5Y3hXdO4zNL8XFTczoe1DXvguoPOxxehcX0REhuPsLj45j3vtN7uXVVbdKMaO9+5hJoyg9PafiND8cDyQfDy5fc7T8SQjojIAzCkIyIiIiJPYrPZcPbsWezcuRMNDQ1O6yVJwvjx4/HAAw9Aq+VeY72NtdbUOBrzWuN+ds5Cl/ZSR/o07meXFAhtfAAkNVduejJhFajZlYvafflO9z1UhXsj5IlBUPdp38P0Hx/8MTbf2NzqMR+1D9akrkGsf2y7rkndi63BgtI3zsFcZD9U85kQicAF/du8QknYbDDl5t4V2hkuZcLaxlHQrqAMDob/3LkISEuFbuTIHrO6yma1orKwAGX5TSMz83JQlpeLqpIiOF1u5iZ+oWEIi+uL6EFDMeLBOdD5+rr8HiaDBQc/y8KVI4VOa739NXhw5WDEDQ1xeR/kXgzpiIg8AEM6IiIiIvJE9fX12LlzJ86ePdumej8/P8ydOxeDBw/uMQ8WqX2EEDAX6WHMagzsjDerAYvrngdJagU0/RpHY+qSAqEK9+bnmgexVBlQ8fFVmHKd79fkMy4CAWkJHdqvsM5Uh2WbluFW3a1Wjw8PHY5Vc1dBrVC3+9rUfVhrjCj5zzlYK412a/xnxMF/RnyH7yGEgKWgAA0tQjtDZiYspe7fj00dF4eA1FT4p6VC26+f2+/XFcwGA8pu5X658q5pzzt9dZWsfWi8vDDv2z9A/7Hj3XL97FMl2PfhFRj1Fqe1Ix6IwYSH+kPFvVo9BkM6IiIPwJCOiIiIiDxZTk4ONm/ejNI2PpRMTEzEvHnzEBQU5ObOqLuzmaww5dQ0r7KzFLt2lJzSX/PlKrsBQVD6MHTprhoulaNi7TWIBscPqSWtEkGLB8B7ZHin7ne+9DxWbl0Ji2j9fs8OexYvjX2pU/egrmcua0Dpf87BVm9/BW/gogHwTXHtSGZLaSkMly83rrprCu7Mt2+79B4t6YYNQ0BaKvznzYMqLMxt9+ku9NVVKG1abVeW3zg2s+xWHixG+4FsZ0mSAtNWfg1j5i5wy/XrKo3YvSoTt65UOq0NjvLBzGeGIDTGzy29kGsxpCMi8gAM6YiIiIjI01ksFhw9ehT79++HxeL8neAqlQr3338/JkyYAJUH769DrmWtNsKQVdW4yi67ErZ6559LbSYB6mjfxlV2iYHQxPlDUnE0ZlcTFhuqt9xE3ZECp7XqaF+EPDYIqlAvl9z77Qtv4++n/97qMQkS3pr1FsZHumflDMnHdKsWpW9egDBZWy+QgODHB8F7uHvDLWtVFQxXrjSHdobLl2G6edO14xwVCvhMmAD/tFT4zZgJpa9791XrToTNhqqSorvGZZbl5aCysABC2Fx2n9Fz0jBt5degULh+JZuwCZzbk4+jG67D5mSVuUIlIWVBf4yaEWt3b0XqHhjSERF5AIZ0RERERNRTVFZWYsuWLcjKympTfWhoKFJTU9G3b1/3NkYeR9gEzAV1MGRVwZhVCWNuDWB14WhMjRLa/o2jMbWJgVCFenE0pszMZQ2o+OgyzAX1Tmt9J0UhYG4/lwarNmHD13d8HRlFGa0eD/MKw7oF6xCk46pfT2fIrkTZu5fsfw1RSgh9ehh0AwJl7ctWXw/D1auNwV3TyjtjdjbQhje7OCPpdPCbPh3+aanwnTwZkrp3riS2mEwov51/17jMsrwc1FWUd/iaCWPGYf53fgSNzjVvGLhX+e067PjvJVS04Wtj9MBAPLhyCPyCdW7phTqPIR0RkQdgSEdEREREPYkQApcvX8bWrVtRW1vbpnNGjhyJWbNmwcen97zrn9rHZrTCeKMKxqaVdpbSBpdeXxmkbQrsgqDrHwCFd+98oC0X/ZkSVH6ebX91UxOFtwpBS5PgNSTELX2U6EuwZOMSVBmrWj0+LWYa/m/6/zHA7QH050tR8fEVwM4jaEmjRNjXh0PTxSMEbUYjjNeyYLjctOIu8zKMV65AmEwdvqYyMBB+c+cgIC0NXqNH8/MZQENdLcrzclF6Z1xmXmOAZ2po29jlsL4JeOjlX8IvONQt/VnMVhzbcAPnduc7rdV6q3D/4wOReF8ft/RCncOQjojIAzCkIyIiIqKeyGg0Yt++fTh27Bja8m9+nU6HmTNnYvTo0VAoOIaQHLNUGhrHYmZVwZBVBWFw7WhMTaxf0352QdDE+EFS8qG2K9hMVlR9cR36U8VOazV9/RH86CCoArVu7Wlf/j58e8+37R7/SfJP8Pjgx93aA8mj7lgBqjZct3tc4aNG2IsjoXbRSFVXsen1qN2zF9WbNqL+0GHA6jjcdkQdEwP/1PkISEuDtn9/F3bp+YQQqC0vRVleLm6cOYlzOzY7rPcNDsFDL/8K4X0T3NZTfmYFdq/KRH2185B24PgITH00CRovjhHvThjSERF5AIZ0RERERNSTFRYWIj09Hbdv325TfWxsLFJTU9GnD98RTm0jbAKmW7UwXquEIasKpvwawHVbEEHSKaHrHwhtUhB0iUFQcaxYh5iL6lH+0WVYSpysgpQAvwdi4f9gvGzh6O+O/Q6fXP2k1WMahQYfzf8IA4MHytILuVfNrlzU7Mqze1wZpEX4iyOh9HdvONxRlooK1GzdippN6Wg4e7ZT19IOGYyAtAXwnzcP6j7hrmmwB7m4dyd2vvVP2ByEomqtDqkvvYyEMePc1oehzox9H17B9TOlTmv9QnSY8fQQRMk8upXsY0hHROQBGNIRERERUU9ns9lw+vRp7Nq1CwaDwWm9JEmYMGECpk2bBo1GI0OH1JPYDBYYr1fB0BTaWSucf861hypE17zKTpsQAIWOqxYcEUKg/ngRqjbdACyO01OFnxrBjwySfW8wg8WAx7c8jqzK1vfT7B/QHx+nfgwvVfdaYUXtJ4RA1RfXUX+s0G6NOsIbYc+PhKKbr0gy5eWhOj0dNZvSYbp5s+MXkiR4p4xHQGoa/GbNhNKva0d+did5F89h419+D6Pe/v5wkqTAA09/HaNnp7qtDyEErhwtxMFPs2A2Ol5JKUnAmDnxGJfaD0olJxN0NYZ0REQegCEdEREREfUWdXV12LFjB86fP9+m+oCAAMydOxeDBg1yc2fUk1nKG2DIqoThWhWM16sgnDzgbBeFBE2cH3RNoZ062heSgqMx77AZLKhcl4WGC2VOa7VJQQh+OAlK364J5q9XXccj6Y/AaDW2evzhpIfxiwm/kLkrcgdhE6j4+IrDz0tNX3+EPTsMklopY2cdI4SA4VImajZtQvWWzbCWOv/zZo+k1cL3gQcQkJYK3ylTIPGNMii/lY/1f3gFNaWOx/SOmbcQ9y9/BgqF+z5nqkv12PlOJopv1jitDY/3w8xnhiKwj7fb+iHnGNIREXkAhnRERERE1NvcuHEDmzdvRnl5eZvqBw4ciLlz5yIwMNC9jVGPJ6w2mPJrYbjWuJ+d6VYt4MJHUgpvFbQDAqFLDII2Mcjt+6l1Z6b8WpR/fMX5SkaFhIDZ8fCdEtPlAednVz/Db479xu7xv037Gx6Mf1DGjshdhMWGsvcuwZhdZbdGNzgYIU8O8ag9KYXVivpjx1CzKR21O3bAptd3+FqKgAD4z56NgAVp8BozBlIv3i9WX12FDX/6DQqzrjqs63/feMz/9g+h1rlvLLLNasPJrbk4uSUHwub4G5hKo8DkZYkYMjkKkuQ5n8c9CUM6IiIPwJCOiIiIiHoji8WCw4cP48CBA7A62O/lDrVajWnTpiElJQVKZfdf2UCewaY3w5BdBWNW43hMa3Xrq6g6ShXu1RzYaRMCoND0/M9dYROoO3Qb1dtyACcPkJWBWgQ/PgjaOH95mnNCCIGX9r6EPfl7Wj3ur/HHugXrEOETIXNn5A42owWlb16A+Xad3Rrv+/ogaEmiRwYctoYG1O3di+pN6ag7eBCwWDp8LVVUJALmp8I/LRW6pCQXduk5zCYjtv3rf3Ht2CGHdeH9+uOhH/0SvsEhbu2n6EY1dr6biZpSJ/t8Aug7IhTTlw+Clx9XRsqNIR0RkQdgSEdEREREvVl5eTm2bNmC69evt6k+PDwcqampiIuLc3Nn1NsIIWApbRyNacxqGo1pdryHWrsoJWj7+jfuZ5cYBHWkT5evHHM1a50JlWuuwXC10mmt19AQBC1JhMJbLUNnbVdlqMKSTUtQoi9p9fh9fe7D27PehtKNI+1IPtY6E0pfPw9Lmf2gw29aDALm9JOxK9ezVFaidvt2VG/chIbTpzt1Le2gQQhIS4X//PlQR/SuwFrYbDj0yfs4/sVah3W+IaFY/PKvEBbv3s8bk8GCQ2uycPmw/T0W7/Dy12D68kHoOzzUrT3R3RjSERF5AIZ0RERERNTbCSFw6dIlbNu2DXV19lc0tDRmzBjMmDED3t7ca4XcQ1hsMObWwJhVCUNWlcPVNh2h8FVDNyCwObRT+nv2CgfD9SpUfHoVthqT40KVhMDUBPiMj+y2q5NOFJ3As9ufhbAzC/Vbo76F50c+L3NX5C6WCgNK/nMOtlr7n7sB8xPgNyVaxq7cx3TrNmrS01G9aRNMbXyDTKskCd7jxsE/LRX+s2dD6d89VsTK4fzu7dj19r8gbPbfyKHWeSHtuz9Gv1Fj3d7P9TMl2PvBFRjrna+WHH5/NCYsGQB1L1jZ3R0wpCMi8gAM6YiIiIiIGhkMBuzZswcnTpxAW54TeHt7Y+bMmRg1alS3fdhPPYe1zgRjduNYTENWlcMH+h2hjvCBNqlpP7u+/pDUnvEAVdgEanbnoXZPntP9/VRhXgh+bBA0Ub7yNNcJ/zjzD7x5/s1WjyklJd6b8x5GhY+StylyG3NRPUpePw9hsB9yBD0yED6jw2Xsyr2EEDBevozqTemo2bwZlpLWV4+2haRWw3faNPinpcJ32jQoNJ79poO2yD1/Fhv/+nuYGuzv+ycpFJj+9AsYNWue2/uprzJi9/uXkZ9Z4bQ2KMIbM58ZirA4P7f31dsxpCMi8gAM6YiIiIiI7nb79m2kp6ejsND5+CYAiI+Px/z58xEe3nMenlL3JoSApVjfFNhVwnizBrC4cDSmSgFtQgB0iY2hnaqPd7cMoq3VRpR/chWmm9VOa73HhCNw4QAotJ4RPlpsFjy17SmcKz3X6vFo32isSVsDPw0fcvcUxpxqlL590f6fZYWEkJVD4DUwWN7GZCCsVuhPnED1pk2o3b4Dtjauam+Nws8P/nNmwz81Dd7j7oOkULiw0+6lLD8Xn7/2KmpKHQecY+cvwtQnn4bCzWNyhU3g/L5bOLr+OqxOvicplBLGL0jAqJlxUPSw0cvdCUM6IiIPwJCOiIiIiOirbDYbTp48id27d8NoNDqtVygUmDhxIqZOnQpNL3gHP3UvwmyFMacGhmuVMGZVwlxkf2VFRyj8NdAlBkGXGAjtgEAofbv+c7zhSgUq11yFzcl4NUmjQOCiAfAZ00emzlzndt1tLN24FHXm1gOLuX3n4rWpr3XLAJU6piGzHOUfZAJ28g1JrUDoc8Ohjeu5ox1tBgPq9u1Hdfom1O0/AJjNHb6WKiIC/vPnIWDBAugGDnRhl91HfVUlNvzpNyjKvuawbsC4FMz71g+g1unc3lP57TrsfCcT5W0Y0xyVGIgZTw+BX7D7++qNGNIREXkAhnRERERERPbV1tZi+/btuHjxovNiAIGBgZg3bx6SkpLc3BmRfdYaIwxZVY2r7LKqYKvv+EPu1qijfRsDu8QgaOP9IankW6kiLDZUb89B3cHbTmvVkT4IfnwQ1GGeu3fk1ptb8aMDP7J7/DeTfoNFAxbJ1xC5Xf3JIlSuzbJ7XOGtQtjzI6Du4yNjV13DWlWFmu07ULNpE/QnT3bqWtrERPinpSEgdT7UUVEu6rB7MBsN2PrPvyLr+BGHdX0SEvHQy7+ET2CQ23uymm049sV1nN2V77RW46XC/Y8nIWlchNv76m0Y0hEReQCGdEREREREzmVnZ2Pz5s2orKxsU/3gwYMxZ84cBAQEuLkzIseETcBcWN8U2FXCmFMDWF33HEzSKKBNCIQ2MRC6pCCoQr3ctrLLUt6A8o+vwHzL+eoMnwmRCJyXAEnt+aPufn7o5/ji+hetHvNSeeGz1M/QN6CvvE2RW9Xuz0f11hy7x5UBGoS9OAqqQK18TXUx8+3bqN68BTWbNsGYZT/EbAvv++6Df1oa/GfPgjIw0DUNdjFhs+HAR+/h5Kb1Duv8QsOw+OVfITSuryx95V+pwO73LqO+yvlUgqTkPpj6aBK03moZOusdGNIREXkAhnRERERERG1jNptx6NAhHDp0CFar1Wm9RqPBAw88gOTkZCiVnrEPFvV8NpMVxhvVMGY17mdnKWlw6fWVgVroEoMaQ7sBgVC46GGr/nwpKtdlQRgd/9mTdCoEL02E17BQl9y3O9Cb9Xg4/WHk1uS2enxw8GB8MO8DaJRdP4aUXEMIgerNN1F3yP6KUVWYF8JeGAmlT+8LNAxXr6Jm0yZUp2+Gpaio4xdSq+E7dSoC0lLhO20aFDKMgnS3czu3Yvc7/4Gw2d8TTuPlhbSXfoy+o8bK0pOh3ox9H17F9dOO984DAN9gLWY+PQRRie5f7dcbMKQjIvIADOmIiIiIiNqnrKwMmzdvxs2bN9tUHxERgdTUVMTExLi5M6L2s1QZYGwajWnIqoJocLzHW7tIgCbGr3mVnSbWD5KyfSvbhNmKqk03UH/c+YN4TZwfgh8bBFWQ5z9ov9el8kt4csuTsNha//1ZOWQlfjDuBzJ3Re4kbAKVa65Bf8Z+sKGJ9UPoc8Oh0PTON4IImw36EydRk74JNdu2w1Zb2+FrKXx94TdrFgLSUuGdnAzJg99ck3P2FDb97Q8wNdh/E4akUGDGs9/AiBlzZOlJCIGrGUU48Mk1mA1O3ugkAWNmxSM5rR+UMo5T7okY0hEReQCGdERERERE7SeEwIULF7B9+3bU19e36Zz77rsPDz74ILy8vNzcHVHHCJuA+XYdDNcaV9mZ8moBmwtHY2qV0PYPhC4pELrEIKhCHP9ZMBfXo/yjK7AU651e229aDPxnxrc7BPQkqy6twp9P/tnu8ddnvI5J0ZNk7IjcTVhtKH8/E4ar9kcta5OCELpiiKx7Q3ZHNpMJdfv3o2bjJtTt2wdh7vhenKrwcPjPn4+AtFRoBw922whfdyrLy8H6115FbVmpw7r70hZj6uNPQVLI8/lTU9aAne9kouhGtdPasDg/zHxmCIIiev7+i+7CkI6IyAMwpCMiIiIi6riGhgbs3r0bJ0+ebFO9j48PZs+ejeHDh3vkQz/qXWwGC4zXq5v3s7OUG1x6fWWIDrrEIOgSA6HtHwiFTgWgMQTXnyxG1cbrEGb7I9sAQOGrRvDDA6FL6vmj0WzChhd3vYgjBUdaPR6iC8HaBWsR6tVzRn1S44jasrcvNIbmdniNCkPwwwMhKfh9BQCsNTWo3bED1ZvSoT9+HOjEs39N//4ISEuFf2oqNB62Ir6usgIb/vgbFN9wvIdf4viJmPvN70GtlWcVss1qw+ntuTiengPh5I0gKrUCk5YOwNCp0fx7UwcwpCMi8gAM6YiIiIiIOu/WrVtIT09HURv3xunXrx/mz5+P0FA+TCfPYSlvgKFpNKYxu8rp/nDtogA0cf7QJQbBXKpHw1nHqz8AQDsgEMEPD4TSv/fsxVbWUIYlG5egwlDR6vFJ0ZPw7wf/DYXUu1dV9TQ2vRklr5+HpcT+qlLfSVEISE1gkHEPc1ERajZvRvWmdBivXOnUtbzGjEFAWir85syBKsgz3hhgNhiw5Z9/RvaJYw7rIgYkYdEPfwGfQPl+XcU3a7Dz3UuobsPeqPHDQzB9+WB496Kv967AkI6IyAMwpCMiIiIicg2r1Yrjx49j7969MJlMTuuVSiUmT56MyZMnQ61Wy9AhkesIq4ApvwaGrCoYsyphyq8F5Hq8JgH+M+PhNy22V64cOnjrIL6x+xt2j//wvh9ixdAVMnZEcrBUG1H673OwVhvt1vjP7gv/B2Jl7MqzGK5dQ82mdFRvToeloLDjF1Kp4Dt5MgIWpMH3gQeg6OZjrG02Kw58+B5OpX/usM4/LBwPvfwrhMbGy9QZYDJYcHhtNjIPFTit9fJTY/ryweg7gm9waiuGdEREHoAhHRERERGRa1VXV2Pbtm24fPlym+qDgoIwf/58DBgwwM2dEbmPTW+G4XoVjFlVMFyrhLXKfpDQGcoADYIfGwRt3wC3XN9T/PHEH7E6c3Wrx1QKFT6a9xEGhwyWuStyN3OJHqWvn4NNb7FbE7Q4ET7JETJ25XmEzYaG06dRvSkdNdu2wVbtfH80exTe3vCbORP+aWnwSRkPSaVyYaeudXbHFux553UIYX+MsMbLGwu+91PEjxglX2MAbpwtxd4PrsBQ53wvwaFTozFpyQCotUoZOvNsDOmIiDwAQzoiIiIiIve4du0atmzZgqqqqjbVDx06FHPmzIGfn597GyNyMyEELGUNMF6rbFxpd6MKwuR4b7m20A0ORtDSJCh9uPLUZDXhyS1P4nJF628G6OvfF5+mfgpvtbfMnZG7mfJrUfrWeft/piQg5MnB8BrK1UZtIUwm1B08iOpN6ajbuxfC2PE3GCjDQhEwbx78U9OgGza0W44evXnmJDb97TWYDfZHTCqUSjz47Dcw4sHZMnYG1Fcbsef9y8i71Po435YC+3hj5jNDEB7vL0NnnoshHRGRB2BIR0RERETkPiaTCQcPHsThw4dhszkPKbRaLaZPn45x48ZBoeCeUtQzCIsNprwaGK417mdnLqhr32hMpYSAuf3gOymqWz707io3q2/ikfRH0GBp/WH74sTFeHXiqzJ3RXIwXKtE2apLgNXOHySVhLBnhkGbEChrX57OWleH2h07Ub1pI/THMoBOZAaafv3gnzofAWlp0MTFubDLzivNvYn1r72KuvIyh3XJC5di8qMrIMn49xEhBC7su40j67NhNTv+e5NCIWFcWj+MmR0PRS8cfdwWDOmIiDwAQzoiIiIiIvcrKSnB5s2bkZub26b6yMhIpKWlISoqys2dEcnPWmeC8XpVc2hnq7G/h6MqRIfgxwZBE8MVpq1Zn7UevzryK7vH/3T/nzCn7xwZOyK56M+WoOKTq3aPS1olwp4fAU2Ur4xd9Rzm4hLUbNmCmk2bYMjM7NS1vEaOhH9aGvznzYUqONhFHXZOXUU5Pv/jr1Fy87rDuqSUyZjzze9CrdHK1FmjioJ67Hz3Esry65zWRg4IwIynhsA/tHvvDdgVGNIREXkAhnRERERERPIQQuDcuXPYsWMH9Hq903pJkjBu3DhMnz4dOp1Ohg6J5CeEgKVE3xzYmW5WQ5htgFKCz9g+CJjXDwpd993jqasJIfCD/T/AjtwdrR73U/thzYI1iPaNlrkzkkPt4duo3nTD7nGFrxrhL46EKoThRWcYr19H9aZNqNmUDvPt2x2/kFIJn8mTEJCaBr8Hp0Ph3bXjaM0GAzb/40+4fjLDYV1k4kAs+uEv4B0QKE9jTaxmGzI23cCZnXlOV19rdEpMfWwgkpL7cMV1CwzpiIg8AEM6IiIiIiJ56fV67Nq1C6dPn25Tva+vL+bMmYOhQ7vn/jZEriTMNljrTFBolVB4c++5tqgx1WDpxqUorC9s9fiosFF4d867UCkYdvZE1dtzULs33+5xZYgO4S+MhNJPI2NXPZMQAg1nzqJ600bUbt0Gaxv3nG2N5O0NvwcfRMCCNPhMmABJ1TV/Pm02K/avfgent3zhsC4gvA8eevkVhMTEytTZl25drcTu9zJRV+l8v8AB94Xj/scGQse9SwEwpCMi8ggM6YiIiIiIukZeXh7S09NRUlLSpvr+/ftj/vz5CO4mo7KIqPs4U3IGT217CjbR+h5OL4x8Ad8c9U2ZuyI5CCFQ9Xk26o8X2a1RR/kg7OsjuCrVhYTJhLrDh1GzKR21e/ZAGAwdvpYyJAT+c+ciIC0VuhEjuuQNOWe2bcLe996CsPM1BAC03j5Y8P2fIm7YSBk7a2SoN+PAx1eRddL535l8g7R48KkhiBkYJENn3RtDOiIiD8CQjoiIiIio61itVhw7dgz79u2D2Wx2Wq9UKjF16lRMmjQJqi561z0RdU//Ofcf/Pvsv1s9ppAUeGf2OxjbZ6zMXZEchE2g/MPLMFwqt1ujTQhA6NPDIKkVMnbWO1jr6lG7aydqNqWj/uhRwGY/6HJE0gVAkzgGXqOnQR09AOrwIPikREIVJM/I6xunTyD9b6/BbLQfOCqUSsx47psY/sAsWXpqSQiBa8eLceDjqzAZrI6LJWD0jDiMX5AAZS/+nGdIR0TkARjSERERERF1vaqqKmzduhVXr15tU31ISAjmz5+PhIQEN3dGRJ7CarPime3P4HRJ66N0I3wisDZtLQK0ATJ3RnIQZhtK37kI081quzVeQ0MQ/MRgSAqOTnYXS2kparZuRfXGTTBcvNh6kaSAwrcPFAGxUAbEQhEQC0VADBRa/6/WKoDgRwfBe0SYextvUpJzA5+/9irqKuwHvgAw/qGHMenhJyEp5A/AasoasOu9TBRm2/9cvyM01hcznx6K4CgfGTrrfhjSERF5AIZ0RERERETdx5UrV7BlyxbU1NS0qX7EiBGYNWsWfH193dwZEXmCwrpCLNm0BLWm2laPz4yfib/c/xfub9lD2QwWlL5xHubCers1PuMjELhoAD8HZGC8cRPVG7egdv9pCIP6y1DOPxqSsj17BAoELhwA3wlRbuu1pdqKMnz+2q9RmnPDYd3ACVMw5xvfhUoj/36HNpvAmR25OL7xJmw2x9mOUq3AxMX9MXxaTK/7vGdIR0TkARjSERERERF1L0ajEfv378fRo0fRlucVOp0OM2bMwJgxY6Dogne0E1H3sjN3J76373t2j/9ywi+xLGmZjB2RnKy1JpT85xysFfZHFvpNj0XArL7yNdULCCFgqzXBVFAPc2EdzAX1MBfWw1LeALgoevB7MA7+M+JkCZpMhgZs/vsfceP0CYd1UUmDsfCHP4e3f9es0C3JrcHOdzJRVax3Whs3NBjTVwyGT4BWhs66B4Z0REQegCEdEREREVH3VFxcjPT0dOTn57epPjo6GqmpqYiMjHRzZ0TU3b1y5BWsy1rX6jGdUodPUj9B/8D+MndFcrGUN6DkP+dgq7O/12nggv7wnSjPyqyeRlgFLGV6mAvr7wrlbPXO95btLJ+USAQu6C/LyFKbzYp9q97GmW2bHNYF9InA4h+/guCoGLf31Bqz0YrD67Jx6cBtp7U6XzUeeHIQEkbJMz60qzGkIyLyAAzpiIiIiIi6L5vNhrNnz2Lnzp1oaGhwWi9JEsaPH48HHngAWm3veac4Ed2twdKAR9MfxY3q1sfVJQUl4aP5H0Gr5NeJnsp0uw6lb56HMFpbL5Ca9job2TvCio6yGa0wF9XDXFDXFMrVwVykByy2LuvJa3gogh8ZCEklz+r501s3Yt+qtyGE/V+zzscXC77/U8QOHSFLT63JOV+GPasvo6HWeVg6ZnY8JjzU89+owJCOiMgDMKQjIiIiIur+6uvrsXPnTpw9e7ZN9X5+fpg7dy4GDx7c6/ZfIaJGVyuu4rHNj8Fsa/2B9RODn8CPk38sc1ckJ8P1KpS9exGw2Hn2rZQQ+tRQ6BKD5G2sG5JjXGVb2RoqofBy/HuiHRCIkOWDodCqZOnp+qkMpP/9j7AYjXZrFEoVZj3/bQy9/0FZemqNvsaEPasvI/dCucO61G+PRPzQEJm66joM6YiIPABDOiIiIiIiz5GTk4P09HSUlZW1qT4xMRHz5s1DUBAfwBL1Rh9e/hB/OP4Hu8f/Of2fuD/2fhk7Irk1XCxD+YeX7QZNkkaBsOdGQBPrJ29jXagrx1XeRQGow72hjvSFOsqn8b+RPqhe/ykqPjwI7cjHIUn2V8upo30R+vRQKH01srRbfCMbG/74a9RVVjisS1nyKCYue6LL3iQkhMClA7dxeG02LOavrv4b/kAMpj6S1AWdyY8hHRGRB2BIR0RERETkWSwWC44ePYr9+/fDYrE4rVepVLj//vsxYcIEqFTyvOOeiLoHIQS+tedbOHDrQKvHg7RBWLdgHcK8OfKwJ6s7Xoiq9dl2jyu8VQh7cSTUYd4ydiWP7jKuUtIqoY70gSaqMYhTR/lCHe4NSd16CFf+33dQ8eFu6MY+C0mptntdVagXQp8ZBlWwzl2t36W2vAyfv/YqSnNvOqwbNOl+zH7hO1Bp5AkQW1NZVI+d72SiNK+2+bWgSB88/JP7oNIou6wvOTGkIyLyAAzpiIiIiIg8U0VFBbZu3YqsrKw21YeFhWH+/Pno27evexsjom6lwlCBJRuXoKyh9RW4KZEpeGPmG1A4WLFDnq9mTx5qduTaPa4M1CL8xZFQBnjmPoXdaVylMkDbtDLuy1BOGaSDpGjfyrKy//wHFR9uh9f4b0JS2w/hFH4ahD07DOoIn8623iamBj3S//5H3Dxz0mFd9KAhWPD9n8HbP0CWvlpjtdhwPP0mTm/PhUIhYemP70NYL1o1ypCOiMgDMKQjIiIiIvJcQghcvnwZW7duRW1trfMTAIwaNQozZ86Ej488D/OIqOsdLTiK53c+D2Enrfju2O/imWHPyNwVyUkIgepNN1B3pMBujaqPN8KfHwGFt/2VW91Bdx9XqfRx3f+/kr//HZUfbYHXxP8Hhdbfbp2kUyH0qSHQ9pUnELNZrdjz3ps4t2Ozw7rAiEg89PIrCI6KlqUvewqyKlFV3IAhk6O6tA+5MaQjIvIADOmIiIiIiDyf0WjE3r17kZGRgbY84/Dy8sLMmTMxatQoKBRcPUPUG/z11F/x7sV3Wz2mklRYPW81hoUOa/U49QzCJlDx6VU0nCu1W6OJ90fos8Og6CbjAD11XKWrCCFQ8qc/o/LTdHhPfAkKn1D7xSoFQp4YBK/BIW7tqWVvp7dsxL7VbwMO/u6h8/XDwu//DDFD+PVFbgzpiIg8AEM6IiIiIqKeo7CwEOnp6bh9+3ab6mNjY5Gamoo+ffq4uTMi6mpmqxnLty7HpfLWn8/G+sViTdoa+Ki5yrYnExYbylZdgjGrym6NblAwQpYPhqSU700cjeMqzTA1j6ps/K8nj6t0FSEEin//P6hasxFeE74DZUCM/WIFELQkCT5j5fu+nn3iGDb/40+wGI3221KqMPvF72DIlAdk64sY0hEReQSGdEREREREPYvNZsOpU6ewa9cuGB08MLtDoVBgwoQJuP/++6HRaGTokIi6Sl5NHpZtWga9Rd/q8QX9F+B3k38nc1ckN5vRitK3L8Ccb39MsveYcAQtTXJLMCVsApayBpgL6mAq/HKVnK2u542rdBUhBIpeeRVV6zfCa/y3oApNdFgfMLcf/O53EOa5WPGNbHz+2quor6p0WDdh6WOYsPRxSFLXBJ69DUM6IiIHJEnqD2AOgKkARgCIAaAFUAUgE8AOAP8VQhS7uQ+GdEREREREPVBdXR22b9+OCxcutKk+ICAA8+bNw8CBA93cGRF1pY3XN+Jnh35m9/j/TPkfpCakytgRdQVrvRmlr5+DpbTBbo3v1GgEzkvo1H2ax1U2rYwzFdbDUlQPYe4d4ypdSdhsKPzZz1H9RTq8xn0dqsiRDut9p0YjYG4/2QKxmrISfP7ar1GWl+OwbvCUBzDr+f8Hlbr7haE9DUM6IiI7JEl6D8DKNpTWAPimEOIDN/bCkI6IiIiIqAe7ceMGNm/ejPLy8jbVDxo0CHPmzEFgYKB7GyOiLiGEwI8P/hhbbm5p9biP2gdr0tYg1i9W5s5IbpYqA0r/fQ7WGpPdmrauyOK4SnkIqxUFP3oZNVu2QjdqOdTxkxzWe48JR9CSJEhKeX7tRr0e6X/7A3LOnXZYFz1oKBb+4Gfw8vOXpa/eiiEdEZEdkiTtAvAgAD2AjQD2ALgKoBZALIBlAJ4AIKHxrzKPCSE+dVMvDOmIiIiIiHo4i8WCw4cP48CBA7BarU7r1Wo1pk2bhpSUFCiVShk6JCI51ZnqsHTTUtyua33/yuGhw7Fq7iqoFVzp0tOZi+tR8vp5iAaL3ZqgZXfvccZxlV1LWCy4/b3vo3bHDmiGLIY2aY7Det2gYAQ/PggKjTzfz21WK3a/8x+c37XNYV1QZBQe+vErCIqIkqWv3oghHRGRHZIkvQ/gFBrHWdbZqXkUwMdNH5YBiBdCtD40vnO9MKQjIiIiIuolysvLsXnzZty4caNN9eHh4UhNTUVcXJybOyMiuZ0vPY+VW1fCIloPZ742/Gv4zpjvyNwVdQVjbg3K3r5gfwSlAvB7IA62WlOXj6u8a2RlHx+PGlfpSsJkwq3vvIS6vXuhHjATumHLHNZr4v0RunIIFN7yBJhCCJxK/xz7P3wXcJC96Pz8sfAHP0PMID6LdAeGdEREnSRJ0ucAFjV9uFAIsdEN92BIR0RERETUiwghcOnSJWzbtg11da2+Z/ArxowZgxkzZsDb29vN3RGRnN6+8Db+fvrvrR6TIOGtWW9hfOR4mbuirtBwpQLl718C5M3eWqUM0Ny1Ok4T1XPGVbqSzWTCrW98E/WHDkEVmwLd6JWQFPZXy6n6eCPs2WFQ+mtl6zHr+BFs+cdfYDEZ7dYoVSrM/sZ3MXjS/bL11VswpCMi6iRJkr4F4B9NH/5QCPFnN9yDIR0RERERUS9kMBiwZ88eHD9+vE313t7emDVrFkaOHAlJ4oNSop7AarPi6zu/juNFrX8dCPcKx9oFaxGkC5K5M+oK9aeLUfnZNfluqABUYd5froxrCuV6+rhKV7IZDMh//gXoMzKg7DMcXuOeh6TS2K1XBmoR+uwwqMPke9NNUfY1fP7HX0NfXeWwbuLDTyBl8aP8O4YLMaQjItlIktQPwCgAUQB8ARQCyAVwRAgh80Bs15Ek6XsA/tL04feEEP/rhnswpCMiIiIi6sVu376N9PR0FBYWtqk+Pj4eqampCAsLc3NnRCSH4vpiLN20FFXGqlaPT4uZhv+b/n98cN5L1B68herNN11+XY6rdB+bXo+8576OhlOnoAhOgHfKtyFpfOzWK3xUCH16GDQxfrL1WFNagvV/eAXlt/Ic1g2ZOh2znv82lCoGta7AkI7aRJKkBADjANzX9N8xAFp+hcgVQvTtgtYckiTpEwCP3PNyt+xVbnL+nkqStBTA9wBMsFNSAeBTAL8UQpS54p5ykiQpHcD8pg9nCyF2uOEeDOmIiIiIiHo5m82GEydOYPfu3TCZTE7rFQoFJk2ahClTpkCjsf+OfSLyDHvy9uA7e+3vP/fT8T/FY4Mek7Ej6krVW2+idv+tDp/PcZXys9bVIe/ZZ2E4dx4Kvyh4TfwOFF72V8BKGiVCVgyGboB8q2SN+nps+t8/IPf8GYd1sUOGI+37P4WXr3whYk/FkI7skiRpGoCfoDHECXZS3u2CL0mSFgD4opVD3a5Xucj9eypJki+AtwA82sZTigGsFEJs78x95SRJUjKAowAUAAoA9BNCOP/Xcvvvw5COiIiIiIgAADU1Ndi+fTsuXWrb85vAwEDMnz8fiYmJbu6MiNztd8d+h0+uftLqMY1Cg49TP0ZSUJLMXVFXEEKgcm0W9KeKHRdyXGW3Yq2pQd5TT8OQmQnJKxjeE1+Cwi/C/glKCcGPDIT3CPlWxlstFux+5z+4sNvxI9qgqBgsfvlXCIyIlKmznokhHdklSdJLANo6tq9bBV+SJAUCuITGsYr36la9yknO31NJkpQANgKYd8+hUgBnAFQD6A9gNICWb9ExApghhDjU0XvLRZIkfwDHAAxueukpIcQqN92LIR0REREREd0lOzsbmzdvRmVlZZvqhwwZgjlz5sDf39/NnRGRuxgsBjy2+TFkV2W3enxA4AB8PP9j6FQ6mTujriCsArUH8lGzOw+wCI6r9BCWykrkrXwKxmvXIGl84TXh/0EZ1Nf+CRIQuLA/fFNae9TtHkIInNy0Hgc+fNdhnZefPxb+8BeIHjjYYR3Zx5CO7HIQ6BgB3EJjwHJHtwq+JEn6L4Bnmj6shQeM5pSDnL+nkiT9CcAPWrxkRuPIyzdbrjSTJGkIgLdx9yjMcgDDhRAON1uQJGkRGve266wjQogb7TmhlRDyMyHEvaNVXYYhHRERERERtcZsNuPgwYM4dOgQbDab03qNRoPp06dj3LhxUCqVMnRIRK6WXZmNRzc/CqPV2OrxRwY+gp+n/FzmrqgrCSEgjFZIGiXHVXoIS3k5cleshOn6dUClhVfyi1CFD3F4jv+MOPg9GCfr3pPXjh3C1n/+FRaz/cFhSrUac77xXQyaOFW2vnoShnRkV1Og80c0rkg7CeBE038vAJgEYG+L8m4TfEmSNAPAzqYPLQB+iLuDqW7Tq9zk+j1t2u/uCoCW6+YXCSFaGz8KSZK8AOzG3UHdG0KIF5zcJwdAfEd6vMfTQoj32losNX4nfBfAyqaXTgJ4QAhR54Je7N2TIR0REREREdlVWlqKzZs3Iycnp031ERERSE1NRUxMjHsbIyK3+PTKp/htxm/tHv/btL/hwfgHZeyIiNrLXFKC3OXLYc7NAxQq6MY8DXXMOIfn+KREInBBf1nD2MKsq9jwp99AX13lsG7yoyuQvGiZrCFiT+BJIR3X5MpvFQB/IcRoIcRzQog3hRCnhRDmrm7MHkmSfNC4B9odfwVwVsb7BzaFKa641pim8MqV5Po9/RXuDujesxfQAYAQogHAUwBaviXj2aawr1tpCuj+gy8DunMAZrszoCMiIiIiInImLCwMK1euxOLFi+Hj4+O0vqioCG+//Ta2bNkCk8nl22oTkZs9PPBhTI+dbvf4r47+CkX1RTJ2RETtpQ4PR/x770EdEwPYLDCcfBumG3sdnlN/rBAVn1yBsDhfPe8qkYkD8fhv/4zg6FiHdYc+eR/bX/87rJZuGx9QJzGkk5kQolIIYejqPtrpfwD0bfr5DQCvyHVjSZICAGwHcECSpNGdvNb9AA4A2OTKoE6O39Omfpfe8/Jrzs4TQlwDsKHFSyoAjzs5bQSAIBf8+MhZfy38A8DzTT+/iMb98yracT4REREREZFbSJKEESNG4Fvf+hbuu+++Np1z/PhxvPXWWyguLnZzd0TkSpIk4dWJryLcO7zV49XGavzk4E9gtVll7oyI2kMdGYm4996DKjISgIDx/McwXra71gEA0HC+DGXvXYLNaJGnSQAB4RF47Dd/QtywkQ7rLu3bhXW//xUMdVzP0BMxpCOHJEmaCOCbLV56vmmFlhz39gOwDUAygGAAuyVJGtvBa00HsAWAD4AHAWyUJMmTdvudDcC7xcdHhRBX2njuvTuRLnZULISoEUJUueBHm942KknSP/Dl59glANOFEGVt/LURERERERHJwsvLC6mpqXj22WfRp08fp/WlpaV46623cPLkSXCrESLPEagLxB+m/AESWh8td7L4JP578b8yd0VE7aWJiUb8e+9CFRYGADBd3QzD2Q8ghP3VcsbsKpS+eQHWOvlWw+t8fLH4J69i2AOzHNblXzqPj3/xA1QVczVvT8OQjuySJEkL4B18+XmySgixS8YWTADKW3wcBGCXJEnJ7bmIJEkzAaTj7pCrFIAnrRGec8/H+9px7kE07iN4x2hJkpz/i1IGkiT9H4BvNX14J6Ar7cKWiIiIiIiIHIqNjcXXv/51zJ49G2q12mGtxWJBeno6PvvsMzQ0yPJ+VyJygXER4/C14V+ze/zfZ/+NsyVn5WuIiDpEEx+PuFXvQRkSAgAw5xyA4cSbEFb7j4XNt+tQ+vp5WCrkG4anVKkw6/lvY/JjKx3WVRTcwkc//z4KrrV17QZ5AoZ05MgrAAY2/bwUwPflvLkQwojGVV+bWrwcCGCnJEkpbbmGJEmzAWwE0HK85YcAlgshPGk2wbB7Pj7a1hOFEPUALtzzskv2+OsMSZL+DuDbTR/eCehKurAlIiIiIiKiNlEqlZgwYQK+9a1vYfDgwU7rL1++jNdffx35+fkydEdErvDiqBcxImxEq8eswoofH/wxak21MndFRO2lTUhA3DvvQBkYCACwFJxGw9H/gzDbD+EsZQ0oef0czEX1MnXZOG53/KJlSH3pZSgdvAmooaYaa379U1w9eki23si9GNJRqyRJGgPgBy1eekkIUW6v3l2aRiYuRWPQdoc/gB1NozjtkiRpPoAvALQca7kawAoPC+gA4N5/9WW38/zr93w8pBO9dJokSf8L4P81fZgJBnREREREROSBAgIC8Mgjj+Dxxx+Hv7+/w9rq6mq88847OHDgAGw2+6O2iKh7UCvUeG3Ka/BV+7Z6/Hbdbfzm6G84zpbIA+gGJiH2v29D0fS92lp2FfpDf4bNWGP3HFuNCSWvn4cxp1quNgEAAydMwcO//D28/Oz/vcJiNiH9b3/A8S/W8mtQD8CQjr5CkiQVGsdcqppe2iaE+Kir+mkR1G1o8bIfgO2SJE1p7RxJkhYAWA9A2+LlVQCeEo4GD3dDkiQFo3FPvpby2nmZe+sTO95R50iS9BqAl5o+LEPjarpwSZKGOfgR3VX9EhEREREROZOUlIQXXngBgwYNclgnhMCePXuwevVq1NZyBQ5RdxfjF4NfpPzC7vGtOVvxxfUvZOyIiDrKa+hQxL39FhQ+PgAAW3Ue9Af+CFt9md1zhMGC0rcvouGyvGtXopIG4/Hf/RXBUTEO6w5+9B52vPEPWC0Wh3XUvTGko9b8GMDIpp/XA3ixC3sBAAghzAAeRmPwdocvgK2SJN3fslaSpIcArAWgafHyuwCe8bSArkngPR/rm0ZYtse9q9QCOt5Opz3S4uehAHajcRynox+/k7lHIiIiIiKidvH29sYjjzyCefPmQalUOqy9efMm/vOf/yArK0um7oioo+YlzMPC/gvtHv99xu+RU50jX0NE1GFeI0Yg9q03IXl7AwBEfQn0B1+DtfqW/ZMsNpSvzkT9qWKZumwU2CcCj/3mz4gd2vrY3Tsu7t2B9f/zKxjq62TqjFyNIR3dRZKkIQB+3uKlXwghcrqonbs0BXWPoDGAu8MHwBZJkqYDgCRJSwF8BqDl4N63ATzroQEd0BhGttSR3cbvPcevg70QERERERGRHZIkITk5Gc899xxCQ0Md1ur1enz44YfYvn07LHwHPFG39tPxP0W8f3yrxxosDXj54MswW80yd0VEHeE9Zgxi//1vSNrGAWzCUA39oT/BUubgjTM2oHLNNdQecBDmuYHO1xdLfvoqht4/w2Fd3sVz+OSXP0J1ibxBIrkGQzpqJkmSAsB/8eWIyFMA/q/rOvoqIYQFwGNoDOLu8AaQLknS/wD4GF+O6QSANwF8XXj2cN57Qzr7u5rad29I1/pAdRkIIfoKIaR2/niqM/eUJOkpSZL23fsDjSssiYiIiIiIXCoiIgJf//rXMXr0aKe1R48exTvvvIPyctm3gSeiNvJWe+O1qa9BpVC1ejyzPBP/d6ZbPUIjIgd8UsYj5l//gqRuWudhbkDDkb/BUnjO4XnVW26iastNWfeBU6rUmP3idzD50RUO68pv5eGjn38fhdlXZeqMXIUhHbX0HQApTT+3APiaEMLahf20qimoexzApy1e9kLjmM6Wf1t6HcALHh7QtaYjv56e9v+gvfoCuL+VH+O6sCciIiIiIurBNBoNFi5ciCVLlkCj0TisLSgowBtvvIHz58/L1B0RtdfQkKH4zujv2D3+3qX3cOT2ERk7IqLO8J08CdH/93fgTlBnM6Ph+H9gzj3s8Ly6A7dQuTYLwirf41ZJkjD+oYcx///9EEq12m6dvroKn73yE1zLcPxroO6FIR0BACRJSgDw2xYv/VUIcbaL2nGqKTx8Ao0r51rzLwDf6CEB3b0Dhb06cI17z+ltQ4pzAOxv5ceJLuyJiIiIiIh6geHDh+OFF15AVFSUwzqTyYT169djw4YNMBqNMnVHRO2xYugKTIyaaPf4Tw/9FOUNXBVL5Cn8HngA0X/5M3BnL1lhg+HMKhivbXN4nv5UMcpXZ8Jmknd9y6BJ92PZz38HnZ+/3RqL2YRN//sHnNi4TtYVf9RxDOkIkiRJAN5C49hIALgB4JUua6iNmoK67a0dArC9hwR0AEO6ThNCvCeEmHbvDwBPd3VvRERERETU8wUHB+OZZ57BxIn2H+7fcfbsWbz55psoLCyUoTMiag+FpMDvJv8OwbrgVo+XG8rx88M/h03YZO6MiDrKf9YsRP3xNUDxZVRiylwPw8U1Ds8zXKlA2X8vwqaXdz/K6EFD8Phv/4ygyGj7RULgwIfvYtdb/4KV+952ewzpCACeAzC9xcfPCyHu3cOs25EkaSWAd1o7BGCtJEkLZW7JXarv+dhbkiSfdl4j/J6PqzreDhEREREREbWXSqXCrFmz8OSTT8LHx/E/6crLy/H2228jIyOD74In6mZCvULxm0m/sXv80O1D+PDyhzJ2RESdFTB/PiJ//ztAkppfM2fvRMOpdyBs9lfLmXJrUPLGeVhr5F0BHxQRhcd++2fEDBnmsO787m34/LVXYdTXy9QZdQRDOgKAV1v8fAuAbEmS+jr6ASDinmuoWqlzPHS/EyRJehqNAV3Lz+FjLX6uAbBGkqTF7upBLkKIcgCV97wc187LxN/zcVbHOyIiIiIiIqKOGjBgAF544QUkJCQ4rLNardi6dSs++eQT6PV6mbojoraYGjMVTw5+0u7x/z31v7hcflnGjoioswIXLULEq6/c9Zol/xgaMv4NYTHZPc9SrEfJv8/BXCrv92ovXz8s/dlvMGTqdId1uefP4ONf/BA1pSUydUbtxZCOgLtHIc4DcLMNP+7dCy66lZoh7mhWkqTnAPwXd3/+/lQIMQHA31q8pgbwqSRJy9zRh8zu/ZvdgHaef++//vg3RSIiIiIioi7i5+eHJ598EjNmzIBC4fjRzNWrV/H6668jJydHnuaIqE2+O/a7GBQ8qNVjZpsZPzrwI+jNDNiJPEnQww+jzy9+ftdr1uIL0B/5K4TJ/mo0a5URpa+fh+lWrbtbvItSpcacb3wXEx9+wmFd+a08/PMHz+Dc+YMydUbtwZCOPIokSS8AeAONIy3v+JEQ4n8AQAjxXQB/aXFMBeBjSZIela9Lt7h4z8cT2npi02jMEU6uR0RERERERDJSKBSYPHkynn76aQQGBjqsrampwapVq7Bv3z7YbNzriqg70Cg1eG3qa/BSebV6PKcmB6+deE3mroios4KfeALhL79812u2ihvQH/wTbA33DjtrUVNvRumbF2DItl/jakIIlDWUQaTEw/ehZAiFZLdWawB2//EvyDp+RLb+qG0Y0pHHkCTpmwD+jbsDuu8LIf7Usk4I8QMALf8WpATwgSRJjt9S0L1tu+fjae04dwoaw8o7zgghijvdEREREREREXVabGwsnn/+eQwZ4ngYjRAC+/btw6pVq1Bdfe/W5UTUFRICEvDyuJftHl+ftR7bc7bL2BERuULI008h7KWX7nrNVlsA/YHXYKstsnueMFlR9u4l6M+XurynenM9zpacxZpra/D7jN/j6W1PY+qnUzF9zXQ8v+t5/NO4BluTC2FQ299DT5gtKLh2xeW9UeeonJdQTyeECGzvOZIkTQOwt8VLuUKIvq7pqNX7/T8Af7/n5e8IIf6vtXohxI8lSbIA+FnTS0oA70uSpBRCvO+uPt1oO4AGfDmadIIkSYOEEG35qvrUPR9/7srGiIiIiIiIqHO8vLywbNkynD59Glu3boXFYrFbm5ubi9dffx0LFy7EoEGtj9ojIvksTlyMIwVHsCN3R6vHXz3yKoaHDkeUb5TMnRFRZ4S+8DyEyYSyf/+7+TXRUAH9wT/Ca8L/gzKob+snWgUqPr4Cm94M35T2/7k328zIqc5BVmUWsqqykF2ZjayqLNyuu+303JJgIzZPLMLME+Hw16u/ctyQ4Iepjz/V7p7IvRjSUbcnSdL3cPcISwHg20KIfzk6Twjxc0mSrAB+2fSSAsC7kiSphBDvuKdb9xBC6CVJWgtgeYuXXwbwtKPzJElKAvBQi5csAD5yfYdERERERETUGZIkYezYsYiNjcXatWtRUlJit7ahoQGffPIJkpOTMXPmTKjVX30QR0TykCQJv5r4K1wou4DC+sKvHK811+LHB3+Md2a/A5WCj2KJPEnot78FYTKi/O3/Nr8mTHXQH/4LvJJfhCrczip4AVRtuA5bnRl+D8ZBkr46hlIIgcL6QmRXZeNa5bXmUO5m9U1YbPbfrONMrY8FmycW4YFTYYio1DW/XhpgxLWxSkhO9sIl+fE7Qw8lSdIrAH7V4qX9QohpXdNNx0mS9EMAf2zxkgDwDSHE6205Xwjxq6YVdb9uekkB4O2mFXVvubZbt3sFwKMA7vzr6ylJkj4XQmxsrViSJB2AdwFoWrz8XyHEdbd2SURERERERB0WHh6O5557Dtu3b8fJkycd1h4/fhy5ublYtmwZQkNDZeqQiO7lr/HHa1Nfw1PbnoJNfHXfyDMlZ/Dm+TfxjVHf6ILuiKijJElC2Pe/D5vJhMr3V395wGJEw7F/Qjfmaahjxtk9v2ZXHqx1ZkizQ5Fd3bgiLquy8Ud2VTbqzHVu6duosWFHcjEmXQhB/wJf1HlZsOe+Epj0EkxWEzRKjfOLkGwY0nUBSZJi0Pr/+4h7PlZJktTXzmXqhBBlLm2sm5EkKRTAj1q8JAA8395wTQjxm6ag7vd3Lg3gF5IkfSyEcMlXQjl+T4UQNyRJ+juAH7R4eW3TSsM3hRCmFv0MBvA2gIktassBvGr/V0FERERERETdgVqtRmpqKhISErBx40YYDAa7tcXFxXjjjTcwb948jBo1qtV36xOR+40OH40XRryAf5/7d6vH3zj/BsZHjsfYPmNl7oyIOkOSJPT5yU8gTCZUffLplwdsFhhOvg1hqoMm4QG759cfK8T+S+n4S9QqmBUdXyHXXjYlcHhUJaQQX/gM6Y+nExYiMShRtvtT20lCiK7uodeRJCkHQHwnL7NKCPGUg3u8AjeupJNrTzpJkkYA2AMgCMBznRlT2WJV3i0ADwghsl3TpTy/p033UQLYBGDuPYdKAJwGUAsgAcAYNIaRd5gAzBBCHOxkjz2KJElDAVy88/HFixcxdOjQLuyIiIiIiIjoblVVVVi3bh3y8/Od1g4fPhzz58+HTqdzWktErmexWfDs9mdxuuR0q8cjfCKwNm0tArQBMndGRJ0lbDYU/vwXqF6//ivHNAPnQzt4ocPzT/tcxm+j30SD0uiW/sK8wpAYlIjEwEQMCBqAxKBE9A/oD52qd/6d4NKlSxg2bFjLl4YJIS51VT+OcCUddWtCiPOSJD2Ixj9EH3byWn+SJKkGwG5XBnRyEkJYJUl6GI2r5B5pcSgcwBw7p5UAWMmAjoiIiIiIyPMEBgbiqaeewr59+3DwoON/1l24cAG3bt3C0qVLER0dLVOHRHSHSqHCH6b8AUs2LUGtqfYrx4vqi/Dq0Vfxl/v/wlWvRB6ivKG8eUxl9lwFhuQEYcTpyrtqTFc3QxhroR35OCSp9T3fxtQPxh/yXsIvY/+FalXHh7t5q7wbQ7jARCQGJSIpKAmJgYkI1AV2+JrUtRjSUbcnhDgH4JyLrvWGK67TlZpGdD4qSdJaAN8HkGKntALApwB+JYQolas/IiIiIiIici2lUokHH3wQ/fr1w/r161FXZ//hXmVlJf773/9ixowZSElJgULR+sNCInKPSN9IvDLhFXx///dbPb4zdyfWZa3D0qSlMndGRI7ozXpcr7p+175xWVVZqDBU3FW3YabAd/QSJly5e0KhOecAhKkOurHPQlKqW71HkiEef879Pn4W+w+UaCparblDJanQN6Bvcxh350ekTyQUdoJA8kwcd0nk4SRJ6ofG8ZZRAHwAFAHIBXC45T519FUcd0lERERERJ6mvr4eGzZsQFZWltPaAQMGYNGiRfD19ZWhMyJq6ZUjr2Bd1rpWj+mUOnya+ikSAhNk7oqILDYL8mrycK3qWuPquMpsZFVl4VbtLQi0LStRWgW+97kN47K+Wq8MHQiv8d+EpLY/ZrJMVYWfx/4DubpCAECkT2TzqMrEoEQMCByAhIAEqO2EfeScJ427ZEhHRL0WQzoiIiIiIvJENpsNGRkZ2LlzJ2w2m8NaX19fLF68GAkJDAOI5KQ36/Ho5kdxs/pmq8cHBg3Eh/M/hFaplbkzot5BCIFifTGyq7LvWhl3o+oGTLbOr2tQWQR+uM6G0Te+mq8oAuLgNfH/QaH1t3u+RWND7UM+6Dt0IPw0fp3uh+7GkI6IyAMwpCMiIiIiIk9WUFCANWvWoLKy0mntlClTMG3aNCiVShk6IyIAuFpxFY9tfgxmm7nV408OfhIvJ78sc1dEPU+tqbY5jLtW2bRCriobNaYat95XbRZ4ea0NI3K+mrFIPuHwnvgSFD6h9i+gUiDkiUHwGhzixi57J4Z0REQegCEdERERERF5OoPBgM2bN+PChQtOa2NiYrBkyRIEBQXJ0BkRAcAHmR/gtROv2T0+KXoSJkROQEpkChKDErnXFJEDZqsZN6pvfGXfuKL6oi7pR4KEBG0Uvv1BNaKyq756XBcArwnfgTIgxv5FFEDQkiT4jO3jvkZ7IYZ0REQegCEdERERERH1BEIInDt3Dps3b4bZ3PqKnTu0Wi0WLFjAf/sQyUQIgW/u/iYO3j7otDZYF4zxkeOREpmClMgURPlGydAhUfdjEzYU1BU0h3B39o3Lqc6BRVi6pKdgXfBd+8YlBiaif2B/eKu9Ya2rR/6zz6Lh3Lmvnqj2gtf4b0EVmujw+gHz+sFvqoMwj9qFIR0RkQdgSEdERERERD1JWVkZ1q5di6Ii5ysKxo4dizlz5kCtVsvQGVHvVt5QjqWblqKsoaxd58X5xTUGdlEpSI5IRoA2wE0dEnUto9WIMyVncKTgCE4Xn0ZWZRb0Fn2X9OKl8kL/gP6NQVzQl4FciJfjkZTWmhrkPf0MDJdayYEUaniN+zpUkSMdXsN3agwC5vaFJEmd+SUQGNIREXkEhnRERERERNTTWCwW7Ny5ExkZGU5rw8PDsXTpUoSHh8vQGVHvdqTgCJ7f+XyHz5cgYUjIEKREpmB85HiMDh8NnUrnwg6J5COEwI3qGzhScASHCw7jVNEpGKwGWXtQSArE+8d/uTIuKBFJgUmI9ovu8NhZa1UVclc+BePVq189KCmgG7Uc6vhJDq/hPbYPghYnQlIyqOsMhnRERB6AIR0REREREfVUV69exYYNG9DQ0OCwTqVSYc6cORg7dizfuU/kZn879Tf89+J/XXItjUKD0X1GIyUyBRMiJ2BQ8CAoFUqXXJvIHSoNlcgozMDhgsM4UnAEJfoS2e4d7h3eHMINCBqAxMBEJAQmQKvUuvxelooK5K5YAVP29VaPa4YshjZpjsNr6AYHI/ixQVBo+Ge6oxjSERF5AIZ0RERERETUk1VXV2P9+vXIzc11WjtkyBCkpaXBy8tLhs6IeiebsOGt829hVeYq1JpqXXptf40/xkeOx/iI8UiJSkGcXxyDd+pSZqsZ50rP4UjBERwpOILM8kwIuDeL8FX7YkDggLvGVCYGJco+KtZcUoK85StgsvP9Vz1gJnTDljm8hqavP0JXDIHCm2OpO4IhHRGRB2BIR0REREREPZ3NZsOBAwewf/9+OHsGFBAQgKVLlyI2Nlam7oh6J7PNjEtll3Cs8BiOFR7DudJzsNgsLr1HpE9k4352kSlIjkxGqFeoS69PdC8hBPJq8xpDudtHcLzouNv2lVMpVOgX0K85hEsKSkJiYCIifCK6TThtLipC7pPLYb51q9XjqtgU6MY8BcnBaE11hDdCnxkGpb/rV/z1dAzpiIg8AEM6IiIiIiLqLXJycrB+/XrU1NQ4rJMkCdOnT8ekSZOgUHRsTx4icuzO89g7YYLerMep4lPNod21ymsuv2dSUFJzaDe2z1h4q71dfg/qfWpMNTheeByHCw7jaMFR3K677fJ7RPtGN4dxd1bJ9fXvC7Wy+68wM926jdwVy2EpKGz1uLLPcHilvAhJUtm9hjJIi9BnhkEdxj+z7cGQjojIAzCkIyIiIiKi3kSv1+OLL77A1atXndYmJCTgoYcegp+fnwydEfUOer0ehw4dwvnz52E0GjFgwADMnTsX/v7+d9WVN5TjeNFxHCs8hqMFR1FY3/oD/o5SSSqMCBuBlKjG/eyGhg6FWtH9Aw/qehabBRfLLuJowVEcLjiMC2UXYBM2l13fT+OHlMgUjI8Yj4HBAzEgcAB8Nb4uu35XMOXmInf5ClhKWt+DTxGcAJ8p3wMkjd1rKHzUCH16KDQx/J7cVgzpiIg8AEM6IiIiIiLqbYQQOHHiBLZv3w6r1eqw1tvbGw899BASExNl6o6oZ7JarTh16hT27t2LhoaGu475+/tj5cqVCAkJafVcIQTya/ObV9llFGagxuR4RWx7+ah9MK7POIyPHI+UyBT0D+zfbUYGUte7XXe7eYRlRmEGas2u209RKSkxPHQ4JkZNxMToiRgaMhQqhf1VZZ7KeOMGcleshLWsrNXjCr8o+Ex/GZDs7wsraZQIWTEEugGBbuqyZ2FIR0TkARjSERERERFRb1VUVIQ1a9agvLzcae3EiRMxffp0qFQ978EpkbtlZ2dj+/btKC0ttVvj6+uLFStWIDw83On1rDYrrlRcaQ7tThefhslmcmXLCPUKbR6NOT5yPCJ8Ilx6fere6s31OFF0AodvH8bRwqPIrcl16fWjfaMxMWoiJkVNwrjIcfDX+Ds/qQcwXLuGvBUrYa2qavW45BUM39m/AOBj/yJKCcGPDoT38DC39NiTMKQjIvIADOmIiIiIiKg3M5lM2LJlC86ePeu0NioqCkuXLkVwcLD7GyPqAUpLS7Fjxw5kZWW1qd7b2xvLly9HZGRku+5jsBhwtvQsjhU0hnaZ5ZkQcO3z3n4B/ZoDu3ERvSdU6S1swobL5ZdxpOAIDhccxrmSc7AIi8uu763yRnJkcnMwF+sX22tXahoyM5H71NOw2dkfVtL4wnf+K4Bw8GdMAgIX9odvSpR7muwhGNIREXkAhnRERERERETAhQsXsGnTJphMjlfjaDQapKWlYfjw4TJ1RuR59Ho99u/fjxMnTsBma99eXTqdDk8++SRiYmI6fP9qYzWOFx1HRmEGjhUec/kqKIWkwLCQYRgfOR4ToiZgZNhIaJT299Ki7qmovghHC47iSMERHCs8hipjlcuuLUHC0JChmBA1AZOiJ2FE2AjuedhCw4ULyHvqadjq61svUGnhv/DXENYgh9fxnxEHvwfjem3g6QxDOiIiD8CQjoiIiIiIqFFFRQXWrl2LgoICp7WjRo3CvHnzoNHwwTzRHY72nWsPjUaDJ554AvHx8S7pq6CuABmFGThaeBQZhRmoMFS45Lp36JQ6jO0ztnml3cDggVBICpfegzqvwdKAU8WnGkdYFhzF9errLr1+H+8+zfvKpUSkIFAX6NLr9zT602eQ97WvQej1rRcoVAhY+hvYTK3vVXmHT0okAhf0h6RgUHcvhnRERB6AIR0REREREdGXLBYL9uzZgyNHjjitDQ0NxdKlSxERwb2qiNqy71x7qNVqPPbYY0hISHDJ9e4QQiCrKqt5NObJ4pNosHQ8UGxNkDYIyZHJzXvaxfh1fFUgdZwQAtcqr+FwwWEcKTiC08WnYbaZXXZ9nVKH+yLuw6SoSZgYNRH9AvpxRVc71R8/jvyvPw9hMNipkBDw+G9h0zvef85rRCiCHx4IScVwvCWGdEREHoAhHRERERER0VdlZWXh888/h97eO/ybKJVKzJo1C8nJyXw4S71SWVkZtm/f3uZ95wBgxIgRuP/++7FlyxZcv25/NZNSqcQjjzyCpKQkV7TaKrPVjPNl53Gs8BiOFRzDhbILsAqrS+8R4xuDlKjGwC45IhlBOscj/KjjyhrKmkdYHi04inJDuUuvPyh4UOMIy6hJGB0+mmNOXaDu8GHcevEbEA7GTQc99VtYqsIdXkc7IBAhywdDoVW5ukWPxZCOiMgDMKQjIiIiIiJqXW1tLdavX4+bN286rR04cCAWLlwIb29vGToj6noNDQ3Yv38/jh8/3uZ952JiYjBnzpzm/eYsFgvWrFmDq1ev2j1HoVBg2bJlGDx4sEv6dqbOVIdTxacaQ7vCY8iuynbp9SVIGBQ8qHmV3eg+o+Gl8nLpPXoTo9WIMyVncOT2ERwpOIKrlfY/lzoiRBfy5QjLyBSEeoW69PrUqG7/fuR/69uA2f5Kx+Cv/w7m0jDAQZSjjvFF6FNDofRleAowpCMi8ggM6YiIiIiIiOyz2Ww4fPgw9uzZA2fPj/z9/bFkyRKX7aNF1B11ZN85f39/zJgxA8OHD//KilOr1Yp169YhMzPT7vmSJGHx4sUYPnx4p3rviBJ9CTIKM5pDuxJ9iUuvr1aoMTp8dHNoNyRkCJQKpUvv0ZMIIXCj+gYO3z6MI4VHcKroFAxWe6MS20+j0GBMnzGYFDUJE6ImICkoiaukZVK7axdufeclwGp/JWvIt38HU0E4YLX//VgV6oXQZ4dBFaRzQ5eehSEdEZEHYEhHRERERETkXH5+PtauXYvq6mqHdZIk4f7778fUqVOhUHBvHOpZ2rvvnEqlwuTJkzFx4kRoNPZXtlitVnzxxRc4f/68w+stXLgQo0ePblfPriSEwM2amzhWcAwZhRk4XnQcdeY6l97DT+2HcRHjmsdj9vXv2+tDokpDJTIKM5r3lnN1UDogcEDjarmoiRjTZwxXNnahmq1bcfv7PwAcrM4N+8H/wJgXDmGyH+Yp/DUIe2YY1BE+7mjTYzCkIyLyAAzpiIiIiIiI2qahoQGbNm1yuOLnjvj4eCxevBgBAQEydEbkXh3Zd2748OGYMWNGm/8M2Gw2pKen4/Tp0w7r5s+fj3HjxrW5D3ey2CzILM9sXmV3tuQszDb74/o6oo93n8ZVdk2hXW8Yt2i2mnGu9ByOFDSOsMwsz4RwNOOwnYK0QUiJSsHEqImYEDkBfXz6uOza1HnVGzei4OUfA/YyG0lC+M9eg/FmGGz19v+8SToVQp8aAm3f3vt9mCEdEZEHYEhHRERERETUdkIInDp1Ctu2bYPFYnFY6+XlhUWLFmHgwIEydUfkWh3Zdy46Ohpz5sxBbGxsu+8nhMC2bduQkZHhsG727NmYMGFCu6/vbnqzHmdKzuBYYeNKu8sVl11+jwGBA5pHY94XcR981J6/UkgIgbzaPBy+fRhHC47ieNFx6C16l11fJakwKnwUJkU3jrAcHDwYCokrnbuzqrVrUfjzX9gvUCgQ8es/w3A9BNZKo90ySa1A8BOD4TUo2A1ddn8M6YiIPABDOiIiIiIiovYrKSnBmjVr2jT2b/z48Zg5cyZUKpUMnRF1Xkf2nfPz88PMmTMxbNiwTo16FUJg165dOHz4sMO66dOnY+rUqR2+jxwqDBU4XnQcxwoaV9rdrrvt0uurJBWGhw1vDu2Ghw2HWqF26T3cpcZUg+OFx3G4oDGYc/X/m77+fZtHWPaUMLO3qfjoIxT/+jf2C5RKRP7hrzBkh8BS7CDUVQBBS5LgM7b3rZhkSEdE5AEY0hEREREREXWMyWTC9u3bcerUKae1ERERWLp0KUJDe/6oOvJs7tp3rj2EENi/fz/27dvnsG7q1Kl44IEHPGbPtvza/MbRmAXHkFGUgWqj4z0u28tL5YX7+tzXPB4zMTCx2/y/sdgsuFh2sXmE5YWyC7CJtq3ObAs/jR9SIptGWEZNQLRvtMuuTV2n/L33UPKH1+wXqNWI/svfYbgRDFNOjcNrBczrB7+pMS7usHtjSEdE5AEY0hEREREREXXOpUuXsHHjRhiN9kduAYBarcb8+fMxatQoeRojaoeysjLs2LED165da/M57d13rr0OHTqEXbt2OayZMGECZs2a1W3CqLayCRuuVlxt3s/uVPEpGK2Ov4a0V4guBOMjxzevtIv0jXTp9Z25XXe7eYRlRmEGas21Lru2UlJieOhwTIxuXC03NGQoVAquVu6Jyt58C6V//avd45Jajeh//hvGnCAYLlc4vJbv/TEImNPX475edBRDOiIiD8CQjoiIiIiIqPMqKyuxbt063Lp1y2ntiBEjMH/+fGi1Whk6I3JM7n3n2isjIwNbt251WDNu3DjMnTu3U2M2u5rRasS5knPN+9ldLL/o0pVmABDvH98c2I2LGIcArWvD1XpzPU4UnWgM5gqPIrcm16XXj/aNxqSoSZgYNRHjIsfBX+Pv0utT91X6j3+i7F//sntc0moR8/obMN0Kgv5UscNreY/tg6DFiZCUPT+oY0hHROQBGNIRERERERG5htVqxb59+3Dw4EGntcHBwVi6dCmioqJk6Izoq7py37n2OnXqFDZt2uSwZvTo0UhLS/PooK6lGlMNThSdaN7PLqcmx6XXV0gKDAkegpSoxtBuVPgoaJXte+OATdhwufwyDhccxpGCIzhXcg4WYXFZj94qbyRHJjcHc7F+sb1mBRTdTQiB0r/+L8rfestujeTtjdi33oS5JBh1+x2/YcZ7TDiCHx7o6ja7HYZ0REQegCEdERERERGRa924cQPr169HXV2dwzqFQoEZM2YgJSWlxwQL5Bm6w75z7XXu3Dls2LABjp7jDh8+HIsWLYJSqZSxM3kU1Rc1j8bMKMxAWUOZS6+vVWoxOnx08352g4IGQan46v/HovoiHC04iiMFR3Cs8BiqjFUu60GChKEhQ5tHWI4IGwG1Qu2y65NnE0Kg5A9/QMWq9+3WKHx8EPfuO7BUBaN6y81WayS1AqFfGw5tfM9ficmQjojIAzCkIyIiIiIicr26ujps2LAB2dnZTmsTExOxaNF88DelAABYDUlEQVQi+Pj4yNAZ9Wbdcd+59rh06RLWrVvncCzn4MGDsWTJEqhUPXd/MiEErlddbw7tThSdgN6id+k9ArQBSI5IRkpkCvp498GxwmM4WnAU16uvu/Q+fbz7YFL0JEyImoCUiBQE6gJden3qWYQQKPr1r1H18Sd2axT+/oh79x3YDKGoXHcNaPnlQgGErBgKr0HB7m+2G2BIR0TkARjSERERERERuYfNZsOxY8ewa9cup3t9+fr6YvHixUhISJCpO+pNuvu+c+1x5coVrFmzBlar1W5NYmIiHn74YajVvWMVltlmxsWyi82jMc+Xnnfp2ElX8lJ54b4+92FiVONquX4B/TjCktpF2Gwo/OUvUb12nd0aZWAg4latghChqPjwMoS58ete0MNJ8BnTR65WuxxDOiIiD8CQjoiIiIiIyL1u376NtWvXorKy0mntlClTMG3atB45ro/k50n7zrVHdnY2PvnkE1gs9oOohIQEPProo102nrMr1Zvrcar4VPNKu6zKrC7tZ1DwoOZQbnT4aGiUve/3hFxLWK0o+MlPULPR/l6VypAQxK9+H1CEoOy9TPg/GAe/KdEydtn1GNIREXkAhnRERERERETuZzAYsHnzZly4cMFpbWxsLJYsWYLAwED3N0Y91vXr17Ft27Z27Ts3adIkTJo0ySOCrZs3b+Kjjz6C2Wy2WxMXF4cnnngCWq1Wxs66n7KGMmQUZjSHdkX1RW69X6hXKCZGTWwcYRmZglCvULfej3onYbHg9g9+iNpt2+zWqMLCEP/BaiiDI6H06/5f11yNIR0RkQdgSEdERERERCQPIQTOnj2LLVu2OAwWAECn02HBggUYMmSITN1RT+Hp+861R15eHj788EMYjUa7NdHR0XjyySfh5eUlY2fdlxACebV5zaMxM4oyUGuq7dQ1NQoNxvYZ2xzMJQUlcYQlyUKYzbj10ndRt3u33RpVZCTiV6+GJqZ3raIDGNIREXkEhnRERERERETyKisrw5o1a1BcXOy09r777sPs2bN7zd5a1HE9ad+59igoKMDq1asdjvOMiIjA8uXL4ePjI2NnnsFqs+JyxeXGVXYFx3C65DTMNsdvIgCAAYEDmkdYjukzBl4qhqDUNWwmE259+9uo33/Abo06Jgbxq9+HOjJSxs66HkM6IiIPwJCOiIiIiIhIfmazGTt37sTx48ed1oaHh2Pp0qUIDw+XoTPyND1137n2KCoqwvvvvw+9Xm+3JiwsDCtWrICfn5+MnXmeBksDzpScaR6Pebn8MgQEgrRBSIlKaVwtFzkBfXz6dHWrRM1sRiNuvfgi6o8ctVujiY9H3Or3oe5F30sZ0hEReQCGdERERERERF3nypUr+OKLL5yGKyqVCnPnzsWYMWM4Ro6a9fR959qjtLQU77//Pmpr7Y9uDAkJwYoVKzxurGdXMlgMsAkbdCodFJLnB7rUc9kaGpD/3NehP3nSbo2mf3/Ev78KqpAQGTvrOgzpiIg8AEM6IiIiIiKirlVdXY3169cjNzfXae3QoUORlpYGnU4nQ2fUXfWmfefao6KiAqtWrUJ1dbXdmsDAQKxcuRJBQUEydkZEcrDW1SP/a19Dw9mzdmu0SUmIW/UeVL3gawBDOiIiD8CQjoiIiIiIqOvZbDYcOHAA+/fvh7PnVIGBgViyZIlH7yNGHdNb951rj6qqKqxatQqVlZV2a/z9/bFy5UqE9JLVNES9ibW2FnlPPwPDxYt2a/xmzkDMP/4hY1ddgyEdEZEHYEhHRERERETUfeTk5GDdunUOR/YBgEKhwPTp0zFx4sQesacYOWa1WnH69Gns2bOn1+471x41NTV4//33UVZWZrfG19cXK1as4F6PRD2QtaoKuU89DeOVK185po6LQ/y770AdHd0FncmLIR0RkQdgSEdERERERNS96PV6bNiwoU2jDBMSEvDQQw/Bz89Phs6oK3DfuY6pq6vD6tWrUVxcbLfG29sby5cvR2RkpIydEZEcLBUVyFu5Esas7ObXNP36Ie69d6Hu06cLO5MPQzoiIg/AkI6IiIiIiKj7EULg+PHj2LFjB6xWq8NaHx8fPPTQQxgwYIBM3ZEcuO9c5+n1eqxevRqFhYV2a3Q6HZ588knExMTI2BkRycFSWorc5StgysmBNnEA4t55B6qwsK5uSzYM6YiIPABDOiIiIiIiou6rsLAQa9euRXl5udPaiRMnYvr06VCpVDJ0Ru7Cfedcy2Aw4MMPP0R+fr7dGo1GgyeeeALx8fEydkZEcjAXF6P4t79DxKuvQBUc3NXtyIohHRGRB2BIR0RERERE1L0ZjUZs3boVZ8+edVobHR2NJUuWILiXPYjsCTq679yMGTMwfPjwXrfvXHsYjUZ8/PHHyMnJsVujVqvx2GOPISEhQb7GiIjciCEdEZEHYEhHRERERETkGc6fP4/09HSYTCaHdVqtFqmpqRg+fLhMnVFnXb9+Hdu3b0dJSUmb6rnvXPuZTCZ8+umnuH79ut0apVKJRx55BElJSTJ2RkTkHgzpiIg8AEM6IiIiIiIiz1FeXo61a9c63GPrjtGjR2Pu3LkMcbqx8vJybN++nfvOycRisWDNmjW4evWq3RqFQoFly5Zh8ODBMnZGROR6DOmIiDwAQzoiIiIiIiLPYrFYsHv3bhw9etRpbWhoKJYuXYqIiAgZOqO2amhowIEDB5CRkcF952RmtVqxbt06ZGZm2q2RJAmLFy/malQi8mieFNJxN10iIiIiIiIiIvIIKpUKs2fPRkJCAj7//HPo9Xq7tWVlZXjrrbcwe/ZsjBs3DpIkydgp3evOvnN79+51+PvWEvedcy2lUoklS5ZApVLh/PnzrdYIIbBu3TpYLBaMHj1a5g6JiHofhnRERERERERERORREhMT8eKLL2L9+vW4efOm3Tqr1YotW7bgxo0bWLBgAby9vWXsku7gvnPdh1KpxKJFi6BWq3Hq1Cm7dV988QUsFgvGjRsnY3dERL0PQzoiIiIiIiIiIvI4fn5+WL58OQ4fPow9e/bA0ZYuV65cQUFBAZYsWYL4+HgZu+zdOrrv3IMPPojAwED3NdbLKRQKpKamQqVSISMjw27d5s2bYbFYMGHCBBm7IyLqXRjSERERERERERGRR1IoFJgyZQri4+Oxbt06VFdX262tqanBe++9h2nTpmHKlCkcn+hG3Heu+5MkCXPmzIFKpcLhw4ft1m3fvh1msxlTp06VsTsiot6DIR0REREREREREXm0uLg4vPDCC9i4cSMuX75st04Igb179+LmzZtYvHgx/P39Zeyy5+O+c55FkiTMmDEDarUa+/bts1u3Z88eWCwWPPDAA9zbkYjIxSRHowCIiHoySZKGArh45+OLFy9i6NChXdgRERERERERdYYQAqdOncK2bdtgsVgc1np5eWHRokUYOHCgTN31bNx3zrMdOnQIu3btclgzYcIEzJo1i0EdEXV7ly5dwrBhw1q+NEwIcamr+nGEK+mIiIiIiIiIiKhHkCQJ9913H2JjY7F27VqUlpbarW1oaMDHH3+M8ePHY+bMmVCp+JisI8rLy7Fjxw5cvXq1zedw37nuZ/LkyVCr1di6davdmqNHj8JisWDu3Llc9UhE5CL82wcREREREREREfUoffr0wXPPPYft27fj1KlTDmszMjKQm5uLpUuXIjQ0VKYOPR/3net5xo8fD5VKhU2bNtmtOXHiBCwWC9LS0hjUERG5AEM6IiIiIiIiIiLqcTQaDdLS0pCQkICNGzfCaDTarS0qKsIbb7yB+fPnY9SoUfI16YG471zPNnbsWKhUKmzYsAH2tkk6c+YMLBYLFi1aBKVSKXOHREQ9C0M6IiIiIiIiIiLqsYYOHYqoqCisW7cOt27dsltnNpuxYcMG3LhxA/Pnz4dWq5WxS89w48YNbNu2jfvO9XAjR46ESqXCunXr7K6SvHDhAiwWC5YsWcJRsUREnSDZe0cEEVFPJ0nSUAAX73x88eJFDB06tAs7IiIiIiIiInexWq3Yu3cvDh065LQ2ODgYS5cuRVRUlAyddX8d2Xdu2LBhmDFjBved82BXr17FZ599BqvVarcmMTERDz/8MNRqtYydERE5dunSJQwbNqzlS8OEEJe6qh9HGNIRUa/FkI6IiIiIiKj3uX79Oj7//HPU1dU5rFMoFJg5cyZSUlIgSZJM3XUvHdl3LioqCnPmzEFcXJybuyM5ZGdn45NPPoHFYrFbk5CQgEcffZSrJYmo22BIR0TkARjSERERERER9U51dXXYsGEDsrOzndYmJiZi0aJF8PHxkaGz7oH7zlFLOTk5+PDDD2E2m+3WxMXF4fHHH4dOp5OxMyKi1jGkIyLyAAzpiIiIiIiIei+bzYZjx45h165dTleJ+fr6YsmSJejXr59M3XUd7jtHrcnPz8cHH3wAo9FotyY6OhpPPvkkvLy8ZOyMiOirGNIREXkAhnRERERERER0+/ZtrF27FpWVlU5rp06divvvvx9KpVKGzuTFfefImYKCAqxevRoNDQ12ayIiIrB8+fJetfKUiLofhnRERB6AIR0REREREREBgMFgQHp6Oi5evOi0NjY2FkuWLOkxwRT3naP2KCoqwurVq1FfX2+3JiwsDCtWrICfn5+MnRERfYkhHRGRB2BIR0RERERERHcIIXD27Fls2bLF4d5bAKDT6bBw4UIMHjxYpu5cz2q14syZM9izZw/3naN2KS0txfvvv4/a2lq7NSEhIVixYgUCAgJk7IyIqBFDOiIiD8CQjoiIiIiIiO5VWlqKtWvXori42GntuHHjMGvWLKjVahk6cx3uO0edVVFRgVWrVqG6utpuTWBgIFauXImgoCAZOyMiYkhHROQRGNIRERERERFRa8xmM3bu3Injx487rQ0PD8eyZcsQFhYmQ2edw33nyJWqqqqwatUqh/s5+vv7Y8WKFQgNDZWxMyLq7RjSERF5AIZ0RERERERE5MiVK1ewYcMGGAwGh3UqlQpz587FmDFjIEmSTN21ncFgwP79+7nvHLlcTU0N3n//fZSVldmt8fX1xYoVKxAeHi5jZ0TUmzGkIyLyAAzpiIiIiIiIyJnq6mqsW7cOeXl5TmuHDh2KtLQ06HQ6GTpzzmaz4fTp09x3jtyqrq4Oq1evdjgi1tvbG8uXL0dkZKSMnZGnsVgsKC4uhpeXF4KCgrrlmx7IMzCkIyLyAAzpiIiIiIiIqC2sVisOHDiAAwcOwNmztMDAQCxduhQxMTEydde6juw7N3HiREyaNAlardbN3VFPo9fr8cEHH6CgoMBujU6nw5NPPtnlfzao+6mvr0dGRgaOHz/evHI5LCwM48ePx4gRI7gXJrUbQzoiIg/AkI6IiIiIiIjaIycnB+vWrUNtba3DOoVCgenTp2PixImyr0bjvnPUVQwGAz788EPk5+fbrdFoNHjiiScQHx8vY2fUXVVXV+PIkSM4deoULBZLqzU6nQ6jR49GcnIygoKCZO6QPBVDOiIiD8CQjoiIiIiIiNqrvr4eX3zxBa5du+a0tn///njooYfg6+vr9r4MBgMOHDiAY8eOcd856jJGoxEff/wxcnJy7Nao1Wo89thjSEhIkK8x6lbKy8tx6NAhnDt3rs1frwBg4MCBSE5ORkJCAkdhkkMM6YiIPABDOiIiIiIiIuoIIQQyMjKwc+dOWK1Wh7U+Pj546KGHMGDAALf0wn3nqLsxm8345JNPcP36dbs1SqUSjzzyCJKSkmTsjLpaYWEhDh06hMzMTKejgx0JCwtDcnIyRowYwfG81CqGdEREHoAhHREREREREXVGYWEh1qxZg4qKCqe1kyZNwvTp06FUKl12f+47R92VxWLBmjVrHI5dVSgUWLZsGQYPHixjZ9QV8vLycPDgQWRlZbn0ulqttnkUZnBwsEuvTZ6NIR0RkQdgSEdERERERESdZTQasWXLFpw7d85pbXR0NJYuXdrpfZW47xx5AqvVivXr1+PSJfvPxSVJwuLFizF8+HAZOyM5CCFw/fp1HDx4ELm5uW6/X1JSEpKTk9G/f3+OwiSGdEREnoAhHREREREREbnKuXPnsHnzZphMJod1Wq0WaWlp9z48bBPuO0eexmq1YuPGjU5D7IULF2L06NEydUXuZLPZcPnyZRw8eBBFRUXtOtfHxwcTJkyAXq/H6dOnYTAY2n3/0NBQJCcnY+TIkVwx3IsxpCMi8gAM6YiIiIiIiMiVysvLsXbtWhQWFjqtHTNmDObMmQONRuO0tiP7zvn6+mLGjBkYMWIE952jLmWz2bB582acOnXKYd38+fMxbtw4mboiV7NarTh//jwOHTqE8vLydp0bGBiISZMmYdSoUVCr1QAAk8mE8+fP4/jx420e6duSVqvFqFGjkJycjJCQkHafT56NIR0RkQdgSEdERERERESuZrFYsHv3bhw9etRpbWhoKJYtW4Y+ffrYrblx4wa2b9+O4uLiNt2f+85RdySEwLZt25CRkeGwbvbs2ZgwYYJMXZErmEwmnDlzBkeOHEF1dXW7zg0LC8PkyZMxbNgwu/t1CiGQk5ODjIwMXL16FR3JMxITEzF+/HgkJCTwTQu9BEM6IiIPwJCOiIiIiIiI3OXatWvYsGGD05VvSqUSc+bMwX333XfXPkrl5eXYuXMnrly50uZ7ct856s6EENi9ezcOHTrksG769OmYOnWqTF1RRxkMBpw4cQJHjx5t8wrfO6KiojBlyhQMHDiwXaFZVVUVTpw4gdOnT6OhoaG9LSMkJATJyckYNWoU38TQwzGkIyLyAAzpiIiIiIiIyJ1qamqwfv165OTkOK0dNGgQFi5cCEmSuO8c9VhCCOzfvx/79u1zWDdlyhRMnz79ruCauoe6ujpkZGTg+PHjMBqN7Tq3X79+mDx5MhISEjr1e2symXDhwgUcP368zauMW9JoNBg9ejRHYfZgDOmIiDwAQzoiIiIiIiJyN5vNhkOHDmHv3r1Ox7T5+/vDYrFw3znq8Q4fPoydO3c6rJkwYQJmzZrFoK6bqKqqwpEjR3D69GlYLJZ2nTtw4EBMnjwZsbGxLu1JCIHc3FxkZGTgypUrHRqFOWDAAIwfPx79+/fn19EexJNCOlVXN0BERERERERERNRTKRQKTJ06FX379sW6desc7tlUU1PTpmty3znydJMmTYJKpcLWrVvt1hw9ehQWiwVz585leNKFysrKcOjQIZw/f77Nq3sBQJIkDBs2DJMnT3a472ZnSJKEvn37om/fvqiqqsLJkydx6tSpdo3CzM7ORnZ2NoKDg5tHYep0Orf0S9QarqQjol6LK+mIiIiIiIhITg0NDdi4cSMuX77c4Wtw3znqSU6dOoVNmzY5rBk1ahQWLFjAoE5mhYWFOHjwIDIzM9t1nlKpxKhRozBp0iQEBwe7qTv7zGYzLly4gIyMjA6Pwhw1ahSSk5MRGhrqhg5JDp60ko4hHRH1WgzpiIiIiIiISG5CCJw8eRLbtm2D1Wpt83ncd456qnPnzmHDhg0ORxUOGzYMDz30EJRKpYyd9U65ubk4ePAgsrOz23WeWq3GuHHjkJKSAn9/fzd113ZCCOTl5SEjIwOXL1/u0CjM/v37Y/z48RgwYABDYg/jSSEdx10SERERERERERHJRJIkjBs3DnFxcVizZg3Kysoc1nPfOerpRo4cCZVKhXXr1tkdp3jx4kVYLBYsXboUKhUfabuaEALZ2dk4ePAg8vLy2nWuTqdDSkoKkpOT4e3t7aYO20+SJMTHxyM+Ph7V1dU4efIkTp482a5RmNevX8f169cRHByMcePGYfTo0RyFSS7HlXRE1GtxJR0RERERERF1JZPJhG3btuH06dNfOcZ956i3uXr1Kj777DOHK0wTExPx8MMPQ61Wy9hZz2Wz2ZCZmYlDhw6hqKioXef6+vpi4sSJGDt2rMd8jTKbzbh48SIyMjLa/esFGlcL3hmFGRYW5oYOyVU8aSUdQzoi6rUY0hEREREREVF3cOnSJWzfvh01NTVQKBQYMmQI952jXik7OxuffPIJLBaL3Zp+/frhscceg0ajkbGznsViseD8+fM4dOgQKioq2nVuUFAQJk2ahJEjR3psWCqEQH5+PjIyMpCZmdmhUZgJCQkYP348EhMTucq5G2JIR0TkARjSERERERERUXdhtVpRVVUFLy+vbjUyjkhuOTk5+PDDD2E2m+3WxMXF4fHHH+fowXYymUw4ffo0jhw5gpqamnadGx4ejsmTJ2Po0KE9am/AO6MwT506Bb1e3+7zg4KCmkdhenl5uaFD6giGdEREHoAhHRERERERERFR95Ofn48PPvgARqPRbk10dDSefPJJBiNt0NDQgBMnTuDYsWPtDqKio6MxZcoUJCUl9egVY2azGZcuXUJGRgYKCwvbfb5arcbIkSORnJyM8PBwN3RI7cGQjojIAzCkIyIiIiIiIiLqngoKCrB69Wo0NDTYrYmIiMDy5cvh4+MjY2eeo66uDkePHsWJEydgMpnadW5CQgKmTJmCvn37QpIkN3XY/QghcOvWreZRmDabrd3X6NevH8aPH9/jg83ujCEdEZEHYEhHRERERERERNR9FRcX4/3330d9fb3dmrCwMKxYsQJ+fn4ydta9VVVV4fDhwzhz5ozD/f1aM2jQIEyePBkxMTFu6s5z1NTUNI/CdPQ5aE9gYCCSk5M5CrMLMKQjIvIADOmIiIiIiIiIiLq30tJSvP/++/j/7d15nF11efjxz5OZBEgI+76GRSBAwARCWBJZxI1WcAHBFahbXaqt1qqtrdhf1Vqr1VZrtVZABYqiFRcUFUEJS0LYBGQRkCg7QoCELZnw/P44d8iZmzsz987ce+5M5vN+veY1c773+z3nmXvvnDP3Pvf7fJcvXz5on80224yTTz6ZjTfeuMLIxp6HHnqIhQsXcsMNN7Q0AywimDVrFvPnz7dUYwN9fX3PlcK89957Wx4/efJk9ttvP+bNm+f9WxGTdJI0DpikkyRJkiRJGvseeeQRzjzzTB577LFB+2yyySacfPLJbLrpphVGNjbcc889LFy4kJtvvrmlcT09PcyePZvDDjtsQt5vreovhbl48WJuuummEZfCPOigg9hzzz0thdlBJukkaRwwSSdJkiRJkjQ+PProo5x55pksW7Zs0D4bbbQRb3rTm9hiiy0qjKw7MpOlS5dy6aWXcscdd7Q0dsqUKRx44IEccsghlgkdoeXLl7NkyRKWLFky4lKYc+fOZfbs2UydOrUDEU5sJukkaRwwSSdJkiRJkjR+PP7443z961/nj3/846B9pk2bxsknn7zOlhXMTG677TYuvfRS7r777pbGbrDBBhx88MEcdNBBrpHWJn19ffzmN79h0aJF3HPPPS2P7+3tfa4U5tZbb92BCCcmk3SSNA6YpJMkSZIkSRpfVqxYwTe+8Q0eeOCBQftMnTqVN77xjWy77bYVRtZZzz77LDfddBMLFy4c8ndvZPr06Rx66KHMmTOH9dZbr0MRqr8U5o033jiiUpgzZsx4rhRmT09PByKcOEzSSdI4YJJOkiRJkiRp/HnyySf55je/yb333jton/XXX583vOEN7LDDDhVG1n59fX1cf/31XHbZZTzyyCMtjd10002ZP38++++/P729vR2KUPWWL1/O1VdfzZIlS1ixYkXL4zfeeGPmzp3LnDlzLIU5QibpJGkcMEknSZIkSZI0Pj399NOcddZZ/OEPfxi0z5QpU3j961/PzjvvXGFk7bFy5UquvvpqLr/8cpYvX97S2K222ooFCxaw9957OyOri/r6+rj55ptZtGhRy6VJoSiFOWvWLObNm8c222zTgQjXXSbpJGkcMEknSZIkSZI0fj3zzDOcc8453HXXXYP2mTx5Mq997WvZddddqwtsFJ566ikWL17MlVdeyVNPPdXS2B122IEFCxawxx57EBEdilAjcc8997Bo0SJuuukmVq9e3fL4nXfemXnz5lkKs0km6SRpHDBJJ0mSJEmSNL6tWrWKc889l9tvv33QPj09PZx44onsscceFUbWmuXLl3PFFVewZMkSVq5c2dLY3XbbjQULFrDzzjubnBvjVqxYwdVXX81VV101olKYG2200XOlMKdNm9aBCNcNJukkaRwwSSdJkiRJkjT+9fX18e1vf5tbb7110D6TJk3ihBNOYObMmRVGNrxly5Zx2WWXce2117Y8w2rmzJnMnz+f7bffvkPRqVNWr179XCnMoUq2Dqanp4f99tuPgw46iG233bYDEY5vJukkaRwwSSdJkiRJkrRuWL16Nd/97ne56abB34ePCF71qlcxa9asCiNr7MEHH2ThwoXccMMNtPIefUSw3377MX/+fLbccssORqiq3HvvvSxatIgbb7xxRKUwd9ppJ+bNm8dee+1lKcwak3SSNA6YpJMkSZIkSVp3PPvss5x//vlcf/31Q/Y77rjjmD17dkVRDXTPPfdw6aWXcsstt7Q0rqenhzlz5nDooYey6aabdig6ddOKFSu45ppruOqqq1i+fHnL46dPn87cuXM54IADJnwpTJN0kjQOmKSTJEmSJElatzz77LP86Ec/4uqrrx6y3zHHHMNBBx1USUyZyV133cWll17KnXfe2dLYKVOmMHfuXA4++GCmT5/eoQg1lvSXwly8eDG///3vWx7f09PDrFmzOOigg9huu+06EOHYN56SdL3dDkCSJEmSJEmSpHaYNGkSf/qnf0pvby+LFi0atN8FF1xAX18fhx56aMdiefbZZ7nttttYuHAhd999d0tjp06dysEHH8zcuXPZYIMNOhShxqKenh723Xdf9t13X+677z4WLVrEDTfc0HQpzNWrV3Pddddx3XXXseOOOzJv3jxmzpxpKcwxypl0kiYsZ9JJkiRJkiStmzKTiy66iIULFw7Z76ijjuIFL3hBW4+9evVqbrrpJhYuXMiDDz7Y0tjp06dz2GGHMWfOHKZMmdLWuDR+PfHEE8+Vwnz88cdbHj99+nQOPPBADjjgADbccMMORDi2OJNOkiRJkiRJkqQuiQhe+MIXMnnyZC6++OJB+/3iF79g1apVHHXUUUTEqI7Z19fHddddx2WXXcayZctaGrvZZpsxf/589ttvP3p7fdteA02bNo0FCxZw6KGHcsstt7Bo0aKWSmEuX76ciy++mGnTpnHggQd2MFK1yr92SZIkSZIkSdI6JyI4/PDD6e3t5Wc/+9mg/S699FL6+vp48YtfPKJE3TPPPMPVV1/N5ZdfzooVK1oau/XWW7NgwQL23ntvJk2a1PKxNbH09PSwzz77sM8++3DfffexePFibrjhBvr6+oYdu/7667PffvtVEKVaYZJOkiRJkiRJkrTOOuyww+jt7eXHP/7xoH2uuOIKVq1axTHHHNN0suzJJ59k8eLFLFq0iKeeeqqlmHbccUcWLFjA8573vFHP4NPEtO2223Lcccfxohe9iGuuuYbFixcPWQpz9uzZllAdg0zSSZIkSZIkSZLWafPmzaO3t5cf/OAHg/ZZsmQJfX19HHvssUMm6h5//HGuuOIKlixZwqpVq1qKY/fdd2fBggXsvPPOLY2TBjN16lTmz5/PIYccwq233sqiRYtYunTpWv0OOuigLkSn4ZikkyRJkiRJkiSt8w444AB6e3v53ve+R2Y27HPdddfR19fHK1/5Snp6egbc9sgjj3DZZZdx3XXXsXr16paOvffeezN//ny22267EccvDaWnp4e9996bvffem/vvv5/Fixfz61//mr6+Pvbcc0823XTTboeoBkzSSZIkSZIkSZImhP3335/e3l6+853v8Oyzzzbsc+ONN9LX18fxxx9Pb28vDzzwAAsXLuTGG28cNLnXyKRJk9hvv/047LDD2HLLLdv1K0jD2mabbTj22GM5+uijueaaa9hxxx27HZIGYZJOkiRJkiRJkjRh7LPPPvT29vKtb31r0Blxt9xyC+eccw69vb3ceuutLe2/t7eXOXPmcOihh7LJJpu0IWJpZPpLYWrsMkknSZIkSZIkSZpQ9txzT173utdxzjnn0NfX17DPHXfc0dI+11tvPebOncvBBx/Mhhtu2I4wJa3jTNJJkiRJkiRJkiac3XbbjTe84Q2cffbZrFy5csT7mTp1Kocccghz585l/fXXb2OEktZ1JukkSZIkSZIkSRPSjBkzeOMb38g3v/lNnnnmmZbGbrTRRhx22GHMnj2bKVOmdChCSesyk3SSJEmSJEmSpAlrxx135OSTT+Yb3/gGTz311LD9N998c+bPn8+sWbPo7fUtdkkj5xlEkiRJkiRJkjShbbfddpxyyil8/etf54knnmjYZ5tttmHBggXMnDmTSZMmVRyhpHWRSTpJkiRJkiRJ0oS39dZbc+qpp3L22WfzyCOPPNe+8847M3/+fHbffXcioosRSlrXmKSTJEmSJEmSJAnYYosteMc73sEtt9zCE088wU477cR2223X7bAkraNM0kmSJEmSJEmSVDN58mRmzZrV7TAkTQAWzpUkSZIkSZIkSZIqZpJOkiRJkiRJkiRJqphJOkmSJEmSJEmSJKliJukkSZIkSZIkSZKkipmkkyRJkiRJkiRJkipmkk6SJEmSJEmSJEmqmEk6SZIkSZIkSZIkqWIm6SRJkiRJkiRJkqSKmaSTJEmSJEmSJEmSKmaSTpIkSZIkSZIkSaqYSTpJkiRJkiRJkiSpYibpJEmSJEmSJEmSpIqZpJMkSZIkSZIkSZIqZpJOkiRJkiRJkiRJqphJOkmSJEmSJEmSJKliJukkSZIkSZIkSZKkipmkkyRJkiRJkiRJkipmkk6SJEmSJEmSJEmqmEk6SZIkSZIkSZIkqWIm6SRJkiRJkiRJkqSKmaSTJEmSJEmSJEmSKmaSTpIkSZIkSZIkSaqYSTpJkiRJkiRJkiSpYibpJEmSJEmSJEmSpIqZpJMkSZIkSZIkSZIqZpJOkiRJkiRJkiRJqphJOkmSJEmSJEmSJKliJukkSZIkSZIkSZKkipmkkyRJkiRJkiRJkipmkk6SJEmSJEmSJEmqmEk6SZIkSZIkSZIkqWIm6SRJkiRJkiRJkqSKmaSTJEmSJEmSJEmSKmaSTpIkSZIkSZIkSaqYSTpJkiRJkiRJkiSpYibpJEmSJEmSJEmSpIqZpJMkSZIkSZIkSZIqZpJOkiRJkiRJkiRJqphJOkmSJEmSJEmSJKliJukkSZIkSZIkSZKkipmkkyRJkiRJkiRJkipmkk6SJEmSJEmSJEmqmEk6SZIkSZIkSZIkqWIm6SRJkiRJkiRJkqSKmaSTJEmSJEmSJEmSKmaSTpIkSZIkSZIkSaqYSTpJkiRJkiRJkiSpYibpJEmSJEmSJEmSpIqZpJMkSZIkSZIkSZIq1tvtACSpi6aUN26//fZuxSFJkiRJkiRJaoMG7/NOadRvLIjM7HYMktQVEXEscH6345AkSZIkSZIkdcxxmfn9bgfRiOUuJUmSJEmSJEmSpIqZpJMkSZIkSZIkSZIqZrlLSRNWRGwMHF5q+gOwskvhdMvpwFzgKuDULscijYTPYY2Wz6GJw8d6fPBxqp73efvsxsBy+scBd3QpFkmSJJi4/+tNAXYsbf8yMx/rVjBD6e12AJLULbUT85isRVyViHiy9uOTmXlTV4ORRsDnsEbL59DE4WM9Pvg4Vc/7vH0ior7pDu9TSZLUTRP8f71rux1AMyx3KUmSJEmSJEmSJFXMJJ0kSZIkSZIkSZJUMZN0kiRJkiRJkiRJUsVM0kmSJEmSJEmSJEkV6+12AJKkrjoDuAS4q6tRSCN3Bj6HNTpn4HNoojgDH+vx4Ax8nKp2Bt7nkiRJ66oz8H+9MS0ys9sxSJIkSZIkjWsRsQ9wY6lp38y8qVvxSJIkaeyz3KUkSZIkSZIkSZJUMZN0kiRJkiRJkiRJUsVM0kmSJEmSJEmSJEkVM0knSZIkSZIkSZIkVcwknSRJkiRJkiRJklSx3m4HIEmSJEmStA54CPhY3bYkSZI0qMjMbscgSZIkSZIkSZIkTSjOpJMkTRgRMRl4GXBA7WtXYHNgU+AZ4F5gCXAu8IP0kyyS1jGeByVJkiRJE1FE7ALcAEyrNS3NzBndi6jgTDpJ0oQRETsAf2iy++XA8Zl5XwdDkqRKeR6UJEmSJE00ERHAL4AjSs1jIknnTDpJ0kTzCPBLipkivwPuB5YBGwP7A28BZgGHAhdFxJzMfLpLsUpSJ3gelCRJkiRNJO+iSNA9AGzd3VAGciadJGnCiIhJAJn57BB9eoHvAi+vNb0rM/+zgvAkqeM8D0qSJEmSJpKI2BX4NUWZy+OB82o3jYmZdJO6HYAkSVXJzGeHemO61qcP+GSp6cjORiVJ1fE8KEmSJEmaKGplLr9GkaD7bmZ+p8shrcVyl5Ikre3x0s8bdS0KSeoez4OSJEmSxq2I6AF2B/YGtqMo7/8MRan/O4AlmflE9yJUv4jYBXg+xeO0IXAfsBS4PDNXjXL37wYOp3jc3zXKfXWESTpJ0pjW4Qv1YN5Q+vmWDh1DGnMiYi+KNcl2ADYAngYeBG4HrvcFTHd4HpQkSZKk4UXETsCrgKOBBQz9gcPVEfEz4AuZ+aMq4hvramUh5wIH1r7PAaaXurS1PGREHA+8DzhkkC6PRMS5wD9k5h9HsP/dWFMl5n2Zef/IIu0s16STpDoR8b/AiXXNY6JGcTeNxQs1MOILdd2xJgFbAXsBbwVeV7tpJfD8zLx5NPuXxrKI2AR4L/BnwE5DdF0NXAecl5n/3PnIxh7Pg5Kksab2QY4bKEo4ga9bJEkTVEScDbx2hMN/CLwlMx9oY0jjQkQcAXyY4nXuZsN0b8v/GRGxIfDfwElNDnkAODkzL2zhGAFcArwA+GlmvqR0W39SbEz832SSTpJKIuJY4PwGN42Jk3bV1tULde04WwAPDdHlMeB1mXlBK/uVxpOIOAH4ErB5C8MeyMxtOhTSmON50POgJI1VtTeffgEcUWqekK9bJEmKiCXAAQ1uugf4LcXrpl5gV4oKMpPq+t0GHD5WZ1t1SkT8JfBvTXYf9f8ZtTKk3weOqbvpIeBaitehuwGzgSjd/gxwdGYubPI47wE+D6wA9s3MpaXbxlSSznKXklRTm03ypW7HMcY8H3hxVQerXajPpbUL9dbA+RHR9IV6GEnxz8m/TMRPUGniiIiPAqc1uOn3FC9OHgLWB7YFZrHmE/oTzfPxPChJGpveRZGge4DiWiBJkgrXAl8DfpyZd9TfGBHbA/8AvK3UvAfw7Yh4QTqzCYqk2N0Urz/b6Z8Z+Hp3FUUFma9k5sr+xojYG/gqayrLrAd8LyJmZeZ9Qx2grszlh8sJurHIJJ0krfEZivV+AJYzsISZBhq3F+qSZRSJB4AeiplEh1D8g/ZeYM+I+PPMvHukv4w0VkXE+1k7QXcO8MnMvKFB/0kUfx+vBl5Sf/sE5XlQktQ1tRLM/eWn3wWc18VwJEkaCxL4EXBaZi4ZsmPmPcDbI+J64Iulm+ZTLIHzvx2LcmxaBdwELAGuqn2/ATgMuLhdB6n9//LeuuYTMnOtqmaZ+ZuIeCFwEWte924OfBT48yGOEcDpwFRgIQMf3zHJcpeSBETE0cDPapt9wAcYONV7TEx/rlptyvu/0NyFelT3Ue1CfQswudT8ikYX6lr/DRh4oQb4cmYOeqFuMo4Nge9QzJx5ADjStZi0LomI/Sn+jvs/rLWKoqRhU2/uRURvZvZ1Kr6xxvOg50FJGmtqbz5dDBwOfDczXz3WyjZJklS1iJiRmXeNYNx5FB9I7XdBZv5JG+PaBNg+M29qw77mADdn5lOjDmzNPjcFnsrMpxvcdgTtfc17JvCmUtMZmXnqMGP2oHj9PaXW1AfsmZl3DtL/vcDngKcp1le/tUGfMfV/U33dVUmacCJiGsXaP/0+C1xX0bE3iYh92rSvObU3bNvpTGCjzJydmW/NzK9k5jWZuarNx4HikzDlN6bPGOyNaYDaPySnACtLzW+uvck9Ypm5Angj8BRF2SBLoGqdERG9FCU/ytUU3t5sgg6g3Qk6z4MDeB6UpHVEROwSEa+MiHdFxAcj4k0RcXhETB5+9LDeTZGgW0Yxi06SpAlvJAm6mvqZVkeOMpTnRMTGwIXAryJi9ij3dTjwK+AH7Xzdm5nLGiXo2q0W8/F1zZ8ablxm3gZ8r9TUC7xukGPsDnyitvmxRgm6scgknSQVNYpn1H6+k8ZrNLWdF+o1qrhQtyIzH6SYEg9weERsO9p9SmPECcCc0vZFmXl6t4LxPLiG50FJ6pyI2DUiToyIT0fEJRHxeERk6euuNh7r+Ii4nOJ1xXeBL1CUpTwTuAS4PyL+MyK2GOH+y2usvC8z7x991JIkTWjX1m1vUJv9NioRMR34CXAQsBlwUUQcMMJ9HQVcQLFW/AuB70fE+qONsWIvoShB2e+KzLylybH171u8apB+H6gd4zHg7og4qdFXqf+0UvvLm4yl7UzSSZrQIuJQBn769O3tnDI+xHG9UA9UxYW6VX8s/TyjTfuUuu3tddufaNirAp4H1+J5UJLaKCKOiIgLI+Jh4A6KtWX+mmIGWtvXno6IDSPiHODbDCxDXG8z4B3AjRHR0jqvtTKXX6O43v00M88YYbiSJGmNRtVipjRoa9VK4OHS9qbAzyPioFZ2EhEvAn7IwNeLD1EsXTGevLRu+5IWxl7KwMdpdkRs3aDferXvGwPfAM4Z5KvfFqW2/2ghnrYySSdpwoqI9She5PafC8/MzJ9XdHgv1ANVcaFu1faln5e3YX9SV9XKPhxearqLNi4APQKeBwfyPChJ7fV8irU1N+v0gSKiBzgXOKnupoeAn1Ik7q4BsnTb1sD5ETG/hUP9BfACYAXwthEHLEmSynav2+5j4AcWRyQzn6H4AOUPSs2bAD+LiIOb2UftAz3fB8rVYs4C3piZq0cbY8X2rdu+otmBmfkExbp0ZW1ZNmMsMEknaSI7Ddiz9vNDwPurOrAX6rWMqQt1RMxgzSegn6T49LU03tXX1b8oM7Nhzwp4HlyL50FJqsYztP+c9s/AMaXtVRQJtR0y8yWZ+ZrMPIDiXF8+v68HfK+ZksJ1ZS4/nJlL2xO6JEkTXv2yA0sy89l27DgzV9b2//1S80bAT2vVvQYVEX8CnA+Uq8R8A3jTOHy9CzCzbvv2FsfX//+2d32HzDwlM2O4r9KQpaX2GS3G0zYm6SRNSBExh6LcTb+/zMyHB+vfCV6oB+j4hRogIl4fEZsPtaOI2BL4FjC51nROFSVQpQrUz1C7AorSWRFxdEScHhG/iYjHIuKJiFgaET+PiA/VEjZt53lwAM+DktR+q4DrgK9SlHw+gKLU5VvadYCI2BV4b13zCZn5hdp17jmZ+RuKsszlRN3mwEeHOUZQlDaeSrFe6BdHG7ckSSrKVQNvrmv+v3Yeo/S693ul5unAhRGxYJC4jqVY23a9UvOZwCntSiBWKSI2Y+3qBr9vcTf1/Z838ojGFpN0kiaciOilKHPZW2v6SWae3Y1YvFBXfqF+K8XCsedFxLsj4qiImB0RcyPiFRHxb8CtwNxa/9uBD7UYizRWHVi3fXMt+fZz4GfAKRSJoo0o3gTcieKNxE8Ct0XEFyNiKm3medDzoCR1yJnARpk5OzPfmplfycxrMrPd5ZA/ypoPNQCckZnnD9a59qGHUyjKPvd7cy3ZN5j3AAuAp4G3dHMmvCRJ65hPAtuUth+l+HBPW9X+/3gNxevYfhsCP46I8rIURMQrgfMYuC7e6cCfjcfXuzWb1G0/WasI04oH67Y3Hnk4Y4tJOkkT0YeA/Ws/P0GxcHvXeKGu/EK9PvBqigVhL6JYH2QxxSel/pJiXSyAC4AFmTnqOuTSGFFfSmsqcBVwVBNjJwPvBBY2U5KrVZ4HPQ9KUrtl5rLMfLqTx4iIDVi7RNanhhuXmbcx8MMpvcDrBjnG7sAnapsfy8xbW49UkiTVq722fHdd899l5iOdOF7tde+JFK9n+00DLoiIo2oxHc/AqiZQJA3fPI5f70Lx+r5sJJVa6sdMH2EsY07v8F0kad0REXsDHyk1/X1m3tWlcJ6Tmasi4kTgHNa80O+/UL88M39Ru1Cfw8Bz91eBt43zT9NWeaF+PXAEcDjwfGBrYEugB3iMYsbIIuB/M3PRCOKQxrJN6rZPB7ao/fwE8F/Aj4G7Kc4/+wN/BswvjZkNfCciDm/3TATPgwN4HpSk8eElFB966XdFZt7S5NjTKT6g0u9VwD816PeB2jEeo5gJfdIw+51W6vNEZv5gyN6SJE1AEbE/8PW65p8CX+rkcTOzLyJeCzzLmv8DpgI/jIjPUyzNU369+xXgz8f5611Y+zXvSD5IVf+at36f45ZJOkkTRkRMAv6HNeXRrgb+vXsRDeSF+jkdu1Bn5j3AWbUvacKIiPUYWBoSYIfa998AL83MP9Tdfg1wekS8H/jXUvshwAdp/EbiqHgefI7nQUkaH15at31JC2MvBfpYc12bHRFbZ+YDdf36r98bU6y9OpwtKD7QArAUMEknSVJJROwE/IiBr5mWAm+o4rVl7XXv64CkmFkHsAFrLzPwX8A714HXu42M5Hdq2/2QmdGufbWD5S4lTSTvBQ6u/dxHsZ7D6i7Gs5bM7KModXNuqbn/Ql1+Y/q/WDfemG6kqxdqaR3VM0j7YzRO0D0nMz8D/Ftd81/VFthuO8+DgOdBSRov9q3bvqLZgbWyxjfUNe8z6ogkSdKgImIrijXZty813w+8KDMfqiqO2vuRr2fNB2vqfZF1K0G3om57gxHso35M/T7HLZN0kiaE2kLs5Vkfn83M67oUzpC8UHuhltotM5+kmJ1W77NDJehK/p4ioddvM+Bl7YitEc+DngclaZyYWbd9e4vj76jb3ru+Q2aekpkx3FdpyNJS+4wW45EkaZ0VEZsBPwf2KDX/ETg6M39bdTy1170XNroJuHAder0LvuYdkkk6Seu8iAjgv1mzXsSdwGldC6gJXqhbts5eqKU2eqJBW30N/oZqn/b/bl3zEaMNaJhjeh5sjedBSapQ7Y2+zeqaf9/ibur7P2/kEUmSpMFExMYUa87NKjUvo5hBd1OXYjoZ+Fqjm4DzIuK4ikPqpMfqtqdGxLQW97FV3fajIw9nbDFJJ2kieCtwVGn77ZlZv3bPmOKF2gu11AGP1m0/kJl3tTD+yrrt+tkDbeV50POgJI1xm9RtP1n7UEsrHqzb3njk4UiSpEYiYjrwE+CAUvPjFEs/XNelmE6leL1bzs+UX3NPAb4dEa+qNLAOycyHKZKiZTu1uJud67Yrn/3YKSbpJE0EHyv9fAFwe0TMGOoL2KZuH70N+k3pRLBeqAEv1FIn3Fa3fV+L4++t2958FLEMyfMg4HlQksa6+rVZR/IhwPox00cYiyRJaqD24ccLgINLzSuAl2Xm4i7F9Fbgfxj4evdvM/MQ4HOltsnAuRFxQoXhddLNddu7tzh+12H2N26ZpJM0EZRLgB0D/K6Jr/p1kLZv0GetNSNGywv1c7xQS+1XX8LjmRbH1/dffxSxDMrz4HM8D0rS2FafpHt6BPuoT9LV71OSJI1QRGwA/BCYX2p+EviTzLy8SzH9OfBligox/f4mMz8JkJl/BXymdFsvcE5EnFRdlB1zY932Ic0OrCVb9xtmf+OWSTpJGiO8UA/ghVpqv1/XbW/S4vj6/g+POJJBeB4cwPOgJI0vI1krtW3rq2Zm1L5mtGufkiSNVxGxPvB9Bq6l/jRwbGb+qksxvQv4Twa+3n1/Zn663C8z/xr4VKmpB/hmRLy+81F21E/qto9oYewCitf//a7NzAdGHdEYYZJOksYAL9ReqKUK/JiBbwbuWnvh0qx967bvHn1Ia3ge9DwoSePMirrtDRr2Glr9mPp9SpKkFtWWp/kucHSp+RngFZl5UZdieg/wBQa+3n1vZn62Uf/M/BDw8VJTD/D1iHhT56LsuAsZWEXgkIjYq8mxp9Rt/19bIhojTNJJWudl5ialT5Y29QUcWbebpQ36XdeO+LxQA16opY7LzHuBK0pNk4EXtrCLl9ZtXzrqoGo8DwKeByVpvDFJJ0nSGBMRvcC3gJeVmlcBx2fmhV2K6X3A50tNCbw7M/99qHGZ+RHgH0tNk4DTI+LP2h9l52Xmk8B5dc0fHG5cROwBvLLU1Aec3cbQus4knSR1kRfqghdqqTKn122/r5lBEbEAOKjU9CzF4tuj5nmw4HlQksadx+q2p9bKD7diq7rtR0cejiRJE1tE9ABnAceVmvuAEzPzh12K6QMMXLIhgXdm5hebGZ+ZHwX+odQ0CfhqbS338eg0iqRpv1Mi4tjBOteq/5wOTCk1/09m3tGZ8LrDJJ0kdYkX6rWchhdqqdNOB24ubR9VS5INKiK2Yu3k3rfa8bfmeXAtp+F5UJLGhcx8GFhW17xTi7vZuW77tyOPSJKkCe9rwGvq2v4WuDYiZrT41crSEA1FxBbA35SaEnh7Zv5XK/vJzP9X+z2e2zXw9xGx4WhjfG6HETs0uh+Abeq69g5xn23RxO9yJwM/pAtwXkS8u1amtBzTTOAi4NBS88PAx1r+Bce4yGzbOsWStM6IiCOAi0tNS9u5CHvtwnUz0H8B679Q//cI9vVh4BOlpj8Ae2dmW8rlRMQODFzrqN/BwDml7XuA+YPsZkVm/rGJY30a+OtS0yqKmT5fycyVpX4zga+y9oV6VmbeN9xxpIksIl5Esf5Z+cNa/w6clpnL6voeDXwJ2L3UvAw4IDN/N8o4PA82PpbnQUnqsHb9rx8RlzHwPHxsZv6ghfFXA3NKTUd3a60cSZLGu4hoZ6LjyMy8ZLQ7iYj9gF8AmwJvzcyvjWJfHwD+hWJ9+CMz8/bRxlfa912s/eGhVp2Zmac0cawe4AcMLEkK8CBwDbAc2JXif6TykhgrKf5XatvSG2OFSTpJaqDTSbraMbxQr30sL9RSBSLi3cB/1DWvAq6kSDRtADyftf/2V1K8AdmWWv6eBxsey/OgJHVYG5N0XwbeVmr6ZGb+7WD968ZOoyhvWf4QyDaZ+UCrcUiSpLGZpAOIiP2BfTPzrDbs6+3ARe18vVvb711U9Jq3drwNKT50emKT+34QODkzfzLC2Ma0Rp8IliRVIDN/HREvpA0X6sz8dEQ8Tgcu1FXKzNUR8RrWvlBvBbx0kGH9F2rfmJaalJlfiIjVwL8CU2vNk4EFQwx7AHhVZl7exjg8D9bxPChJ48pPGJikO6KFsQsY+J7MtSboJEla92Tm9cD1bdrXl9uxn26rVb05KSLOA95PUaWmkUeAc4GPZuZDVcVXNZN0ktRFXqjX5oVaqkZmfikifkqxDtpxwPRBut4P/Bfwucx8rANxeB6s43lQksaNC4GnKGagAxwSEXtl5i1NjD2lbvv/2hmYJEkTTWbG8L3USLurh7Vw3PMo1qTbhaJazHbANIr3IZYCl5WXfVhXWe5SkjSmTfQLtVSFiNgAOAzYgWJh6JXAQ8D1mfnrbsYmz4OS1G7tLG0fEV8H3lhqOiMzTx1mzB7ADcCUWlMfsFdm3jGSGCRJkjR+maSTJEmSJEkTRpuTdLsCt1CUje53XGZ+f5D+6wMXAYeWmr+cmX8+kuNLkiRpfLPcpSRJkiRJWudExA40ft9jm7rt3oiYMchuVmTmHwc7RmbeGRGfB/661HxeRLwP+Ep5xnNEzKRYc7ScoHsY+Njgv4UkSZLWZc6kkyRJkiRJ65yIuAvYeZS7OTMzTxnmOD3AD4CX1d30IHANsBzYlaJ0cXm9nJXA0Zl56ShjlCRJ0jjlTDpJkiRJkqQRyszVEfEaillyJ5Zu2gp46SDDHgRONkEnSZI0sU3qdgCSJEmSJEnjWWauyMyTgBOAK4fo+gjwJWDfzPxJJcFJkiRpzLLcpSRJkiRJUhtFxC4U5S23A6YB9wNLgcvK69RJkiRpYjNJJ0mSJEmSJEmSJFXMcpeSJEmSJEmSJElSxUzSSZIkSZIkSZIkSRUzSSdJkiRJkiRJkiRVzCSdJEmSJEmSJEmSVDGTdJIkSZIkSZIkSVLFTNJJkiRJkiRJkiRJFTNJJ0mSJEmSJEmSJFXMJJ0kSZIkSZIkSZJUMZN0kiRJkiRJkiRJUsVM0kmSJEmSJEmSJEkVM0knSZIkSZIkSZIkVcwknSRJkiRJkiRJklQxk3SSJEmSJEmSJElSxUzSSZIkSZIkSZIkSRUzSSdJkiRJkiRJkiRVzCSdJEmSJEmSJEmSVDGTdJIkSZIkSZIkSVLFTNJJkiRJkiRJkiRJFTNJJ0mSJEmSJEmSJFXMJJ0kSZIkSZIkSZJUMZN0kiRJkiRJkiRJUsVM0kmSJEmSJEmSJEkVM0knSZIkSZIkSZIkVcwknSRJkiRJkiRJklSx3m4HIEmSJEnqrIiYAfyu1HRmZp7SnWjWiIgjgItLTR/LzNO6Esw4FxGTgD2B/YHNgY2BZ4EngAeBu4DbMnNZt2KUJEmSNJBJOkmSJEnrtAYJqk4ZE4kvTSwRsR/wTuAkisTccP3vBBYBPwUuzMz7OhuhJEmSpMFY7lKSJEmSpHEmItaLiM8C1wJvp4kEXc2uwGuB04EvDXOMIyIiS1+njSbmiSwiTqu7L4/odkySJEnqPmfSSZIkSZI0jkTEesD/AS9rcPODwE3AH4FVwGbA9sBMfA9AkiRJGlP8B12SJEnSuu5uYJcm+x4PfLq0vYiijGAzVrQSlDQK/8zaCbrvAp/KzMWNBkTEBsA84BXAq4EdOhmgJEmSpOGZpJMkSZK0TsvMPuCuZvpGxB/rmp7OzKbGjmW13yG6HUe9zLyEMRjXWBYRzwPeXdf8/sz87FDjMvMp4BLgkoh4P3Ac8LyOBClJkiSpKSbpJEmSJEkaP05k4Gv5nw+XoKuXmaspZt5JkiRJ6qJJ3Q5AkiRJkiQ1bUHd9jldiUKSJEnSqDmTTpIkSZI6JCK2olgHbFtgC4p1636SmbcNMWYzYG+KUoSbAesDjwMPA9cBN2dmdjbyoUXEdIpk0Y4UMS4D7gAW1soqdiuuLYBDKdZb25jiPrsFuCIzV41y3xsDh9f2vRFwH/B7it95VPtu0XZ123dXeOy2i4idgdkUfyObAY8C/5eZ93YzrrKImAQ8H9iT4u94I+BJ4H7gZuCG2uzECWEk57XauPWBQ4CdgC0pSt0+BNwOXFkrTTzSmNYH9qM4d24GTAOeAZYDfwB+C9zW7XOnJElSPZN0kiRJkjRCEXEXsHNtc2lmzqi1LwD+ATgS6Kkb9lfAgDezI2Ie8BrgRcC+DL1O28MR8VXgc5l5f5NxzgB+V2o6MzNPGaL/JRQJKQAyM2rt2wOfAI4HpjYY+nREfBn4WGYuayKuI4CLS00fy8zThuh/F43v75m1uP4EmNxg6OMR8Rng060mESNiV+DTwMsH2fcfI+LrwD9k5hMRcQpweun2UzPzjFaOOYz61/GbtXHfAETEYImMj0bER4cYemRtncHB9vXLzDyi1v4K4G+Ag1n7+X4P8L1avxm08NytN9hzucmxewEfpnjsNx2i62MR8VPg9Mz8cWn8EQx8fpddHDF4KPVxRsRpQPm+X+u+HswI/v7vog3ntdL+5gF/BxwNbDDIYR+PiHOAf2wlQRsRuwF/D7wKmD5M98dqz4fTM/P8Zo8hSZLUSZa7lCRJkqQ2ioh/BC6heEO6/o3sRv1fBVwJvA+YxdAJOoDNgQ8CN0bEi0YVbAsi4hjgeuBNNE7QQTHr773A5RGxY0VxnQosAV5B4yQaFDOfPgb8LCI2aWHfJwE3USQABtv3FhSP3VURsUez+x6FB+u2j6vgmG0TEb21JPP/UcyqajppVpWImBwR/0Hx2L+JoRN0UMzaPAG4oNOxdUur57XamKkRcTbF+e3lDJ6gg+Jv9O3AbyPi+Cb3/3qKx+hkhk/QQfE4HQd8oJn9S5IkVcGZdJIkSZLUJhHxlxSzOvotBW6kKFe5NUVZv3r1H55cTVGa7Xe1cUmRmJsFbFPqtznwo4g4PDOvaEf8g4mIw4DvAuvVmh4ArqEoc7kJRem7zUtD9gLOjYj5mflsB+M6Afgf1iR6+u/vxyjK6R3MwDfvDwO+DJzYxL5fA3yTtRMSdwK/AZ6gKD15EMX9MhP4PvDFkf02TbsCeEFp+8SIWJiZnT5uu/wr8ObS9q0UM7CeoCifOLcbQfWrlXK9AJjf4Oa7KMqnPkKRqN6e4u9y/ari64aRnNciYkvgJ8CcupueAq4F7qU41+1I8Zj3J8GnAt+KiLdk5teGiGkB8A3WTvLeRnH+fIzib3cjYHdgN/yguiRJGoNM0kmSJElSe2xFURYRikTKX2XmonKHiFiPgcmsfo9SJIR+APwqM59udICIOAT4OEW5OSje2P7fiHheZq4c9W8wuO9RJKJ+Dbw/M39eF1cv8G6KBEx/UusQ4PUUb6R3whbAmRRv0v8K+OvMvKourg0oyvN9qNT8moj4QmZeOtiOa7MA/5uBCbrrgXdm5uV1fadTzGz8IMWaZf8w4t+oOWdRlInsT04E8IWI+DOKhOWPMnPpKI+xS+37wcA5pfbPA58bYtxw5VfnsKb05I+AD2TmzeUOtftzvfqBVYii/uTXWTtB903gE/Wx1sb0Uvw9vhZ4Xd3NV7LmvvxLilmm/V5bu32sa/m8VlvD7xwGJujuBT4CnJ2Zz9SN34TiOf1BikRaAF+MiKsz8/pB4voXBibovgV8ODPvbNQ5IjYEXkgx43HbwX5ZSZKkqpmkkyRJkqT26C/l9kPg1Y2SZrU3p+vXW/olsH1mPjncATLzioh4IUUy5tRa804UyYEzRhh3M7agWFvr5Zn5RIO4+oDP1dbY+rfSTW+jc0m6abXvZwMn12Koj+sp4MO1ZF05QfI2YNAkHUVSYqPS9hXAizNzRYNjLAc+EhE3UCQmtmjpt2hRZt4QEV+hKA1YNqf29cWIuBdYDFxV+35lo9iHOMZd8NxaZmWP9t82Qv2zGr8MvCMz11r7rnZ/Lh/FMUbjrRRlU/utolhT8KzBBtSedz+jKKX6t3W3PU0x+46IeLRu6P2jvC+rMpLz2vspEmL9rqH4+3m40QEy81HgbyPiGopkW1DMTvw34Kj6/hGxFUUCud8lwEmNnk+lY6wAzgfOr50PJEmSxgSn+kuSJElS+zxEkTBqelZbZj7UTIKu1D8pZq2V1yZ7ffMhjsijFG+Cr5Wgq/MFBs6mOrjDb4jfDrylUYKuzj8C5cdkrTf++0XEdsCrS01PAq8bLsmVmecCXx0mjnb5C4ryo4PZjiLZ9HGKBNKyiLgyIt4XEdsMMa4KNwPvGSqh0g21GXEfrmv++6ESdPUyc7iZhONV0+e12t97ec23x4A/HSxBV5aZ5wFfKjUdGRH15TIBdq7b/k4rz6da8l6SJGlMMEknSZIkSe3zlcx8pNMHqSX1flxqmlcrMdcpX87MB4frVEuW/aTU1EuxZlenfKaZN9xrj0m5TOV2tdk4jRzPwKozZ7Uw4+mfgI6twdcvM1dl5qspZtPd08SQXop1Az8D/C4iPlUr/9cNn+1wadaROgaYUdq+kzVlHie6Vs5rr6VYD7Lf5zLzvhaO9Zm67WObGDPY37IkSdKYZ5JOkiRJktrne+3cWUSsHxFbRsTOETGj/MXAkoDTgR3aeew6P2qhb/26XZ18A70TcR1at/2tZg+Qmb8HFg3bsU0y8yvAbsBJFKX8mikTuT7F+l9XNChnWYXzu3DMZrywbvu/M7PjCddx4nst9H1R3fa5rRyotqbc70tNCxp0+y2wurT9jojYq5XjSJIkjRWuSSdJkiRJ7bEa+PVodhAR84ATgEOAfYCNWxi+KQPf3G6n37TQd1nddiu/QytWZOYfWujfbFz7120vaeEYUKwDd0iLY0asth7YucC5EdFDEf9BFOvTHVDb7mkwdF/ggoiYV1sHrgp3Z+ZDFR2rVfXJ2Uu6EcQY1Op5bX7p55XAMyNIBj9CsdYmFEnoATLz0Yj4MfCntaYtgGsj4iyKpPqvausBSpIkjXkm6SRJkiSpPR4baRm/iNgX+CLwglEcv1PJMFg7wTWUVXXbk9sZSEkrMUHzcW1R+vnxzHy0xeN0KlE6rMxcDVxT+wIgIqYDfwK8g7WfXzOBvwM+VFGIw5ZM7aJt67Zv6koUY0/T57Vayd3tSk1TgDtGefzNBmn/S+Bg1vy9rg+8ufb1TEQsAa4ELgUuraIMsSRJ0khY7lKSJEmS2mNEs5EiYj7FemmjSdBBB1/fjdGyf52KaZPSzyN5TB9vUxxtkZnLM/N/M/Nw4A2snax8V0SsV1E4Vc3YG4nNSz/3VTi7cKxr5X7YlPafh6Y3aszMOyjWWby4wc3rAYcB76co1flgRPwsIl4dEdHm+CRJkkbFJJ0kSZIkdUlEbERRnq38RvRjwJeB1wKzga2BaUBPZkb/F/CxquOdIJ4p/TxlBONHMqYSmXkW8MG65g1Zu9TjRJTdDmAdUOlzPzPvzMyjKMrLfpFirbpGeoCjgfOAyyNi54pClCRJGpblLiVJkiSpe/6cgWX2FgEvb3Ldro06E9KEt4wiKQqwcURManEm4WDl+caKLwH/BEwtte1F4xlJ64JG6/E18jCwQ+3n3oiYvg7Npmv2Phith+u2b8vMPTt90My8kqK0JRGxFUUZzPnAkRTrMpZnzx0M/CIiDhhBKVtJkqS2cyadJEmSJHXPcaWfE3hdkwk6GLj2k9pnaennKcAeLY6f1cZY2i4znwZuqWvetBuxNKmvbrvVDxs3+7vdV7e9d4vH6bTR3A+VPL61tevKa0XuEhGdWpNysBgezMzvZ+bfZOZcYEfg4wycIbsr8NdVxiVJkjQYk3SSJEmS1D3PK/18c2be2cLYQ9odjIDajJySI5sdGBE9wIL2htMRq+u2Hxuk31goAVm/xt8mzQ6srbW3W5PdL6vbPqLZ4zRptPfliO8HYJ9RHrsVl5d+nkz778eWZOY9mfkR4I11N72yG/FIkiTVM0knSZIkSd2zSennwRIla4mIo4Cd2h6NAH5et/2WiIiGPdd2LLBNm+Npq4joBXava75nkO7P1G2v1/6IhrUceKq03coMtxcC6zfZt/5xf2tEtPM9k9Helw/WbbdyP/xJi8cajQvrtt9a4bGHch4Dz7G7dCsQSZKkMpN0kiRJktQ95dJwz2smKVArH/fJzoU04f0U+F1pew5NJBoiYkPgM50KqnSc57eQNGzkRAaWP1wN/GqQvo/WbW/bqFMnZWYC15WadomIYWeG1f6W/q6FQ/0EuKO0vRvwvhbGD+fRuu1W78tr6rZf3syg2n31qhaPNRrfYODvekJEvLDC4zdUex6V15asT5pKkiR1hUk6SZIkSeqe60s/bwG8ZajOtXKKXwYO6mRQE1lmPkuxhlXZFyLipMHGRMQWwAVUMzvnc8C1EXF8bVZc0yLiQODf65p/kpmPDDLkTmBlafvIqtcYq7mobvufmhjzL8ChzR4gM1ez9uP+iaEe93oRMdQsypvrtl/c7H5rfgv8obR9YES8Yph4tga+DfS0eKwRy8xHgU/XNZ8XEfNb2U9E9ETEqyJiswa3HdFq4i8i/pSByen6x0OSJKkrTNJJkiRJUvecW7f9hYh4T0RMqe8YEXOBXwCn1poe6nRwE1Vm/g8Dy/ZNBs6JiAsj4g0RMTsi9qwlCz4O3EqxFl0C36ogxP0pki/3RsTna3FMG6xzROwaEZ8CFgLlpMdK4P2DjcvMlQxcq20n4PsRcVxE7B0RM+q+mi0t2aqvMXAW1Csi4msRsWl9x4iYGRHns+b3erTZg2Tm6RRlEftNBs6OiDMiYs9GYyKiNyKOjoivAXcNsfslwIrS9kkR8R8RcVRE7FF/XzaILYGv1jV/MyJe2yCmnog4AVgMzGTgjN0q/AvFjNR+mwCXRMQXB7sfoZglHBGH1p6rdwDfATZq0PX5wM8j4jcRcVrt77FhIjIi1ouItwFn1910ZvO/jiRJUue09Kk7SZIkSVJbnQm8B9ivtj0Z+DxwWkQsAh4GNgb2BWaUxv2SIuHSSjk/teYkihKI80ptL2boGVD/j6JU5mtKbdn+0J6zJcXz5z3A6oi4GbiP4nkDxcyhvYCdG4xdBbwuM28d5hifBY4sbb+09tXIkcAlTUXegsz8XUR8BvhAqflU4LURcQXF7zwN2JPi9+33CeAw4PAWDncqsBXwgtp2ACcDJ0fE74BbgEeAqcD2FH+7wyYnM/OJiPgya5KHAby79tVIo5KmnwVOYc2MzWkUScRPUyQBn6CYkTuXNbPGVlDM0P3OcDG2S2b2RcRrKGaX9s9m7AHeCbwzIu4BbqS4HydRJOJ2oHjsWpmpORP4aO3ryYi4EXiAYu25ybV9zqZ4rMp+Cfx367+ZJElS+5mkkyRJkqQuqb2Z/XKKGXK7lW7alMETIT8HXk1718tSncx8NCJeRJEYeTONkyb9ngL+IjP/JyLeUXfb8jaHdhvFrL36yjg9FMncfZvYx63A2zPzl8N1zMwfRsRHgI9RYdnEBj4C7A68stS2PgMTiGWfysy/i4hLWjlIZq6IiKMpyoK+nYGP+y6MrqTp3wHPA44dyeBabK+gmOVZLq25fe2r3h8p7q+7R3K80cjMxyLiCOCfgfcy8LkzWLz1nqT5teOm0lwZ4O8Bb6iVtZUkSeo6y11KkiRJUhdl5u+BORRJgSeH6HotRdLgJZn5eBWxTXSZuTwz30oxG+dfKdYQfJgicbAU+BXFzKhdaiUyoSjtV/ZYm2N6G0WC5s3AWRQz95qxiqIE4cnArGYSdKVjfpxixtg/U/zO91MkJitTK715PPAXteM37EYxS+pFmfmhURxrVWa+g6Ks6LkMLFPZyCMU5RSPHma/z2TmcbV+XwGupng+rRxqXN0+fg0cSDELt2+Qbo9TrF05KzMXNrvvdqvdj+8H9gC+RDHLbTgPA9+lmDG4dWbe16DP6cDrKZ7/f2hwe70+ilmxx2TmKzPziSbGSJIkVSKKsuaSJEmSpG6rrSt2KEXZvo0oEjz3A9dn5u3djE3NiYhvAG8oNe2bmTd1+JibUyRCdqNYc246sJoiWbOMokTjbzKz2VlJY1pETKJIVO1HUd7xKeAe4KrMXNqB402mmKW1W+14G1CUlrwXuBm4qRszsyJiI4qynDMozhcPAb8HfpWZlSZRmxURMyket80pEtp9FM/TP1A8T+/MFt+oiohtKUpfzqB4/m9A8Zx4lGLW6PV+sEGSJI1VJukkSZIkSWqDiAiKmW39a8A9AWycmau7F5UkSZKkscpyl5IkSZIktcfLWJOgg2Jmlwk6SZIkSQ2ZpJMkSZIkaZQiYjrFuoJlX+9GLJIkSZLGB5N0kiRJkiTViYhDIuI/I2KnJvruAlxCsWZZvweA/+1QeJIkSZLWAa5JJ0mSJElSnYg4ArgYeJYiAXchcA1wP/A0sCkwE3gJcAIwuW4Xx2Tmj6uJVpIkSdJ41NvtACRJkiRJGsMmAUfVvpqxGnifCTpJkiRJw7HcpSRJkiRJa1sBPNPimBuAl2Vm/dp0kiRJkrQWy11KkiRJktRAREwHXgQsAPYHZgBbAhtQJPCWAXcDlwE/BX6avsiWJEmS1CSTdJIkSZIkSZIkSVLFLHcpSZIkSZIkSZIkVcwknSRJkiRJkiRJklQxk3SSJEmSJEmSJElSxUzSSZIkSZIkSZIkSRUzSSdJkiRJkiRJkiRVzCSdJEmSJEmSJEmSVDGTdJIkSZIkSZIkSVLFTNJJkiRJkiRJkiRJFTNJJ0mSJEmSJEmSJFXMJJ0kSZIkSZIkSZJUMZN0kiRJkiRJkiRJUsVM0kmSJEmSJEmSJEkVM0knSZIkSZIkSZIkVcwknSRJkiRJkiRJklQxk3SSJEmSJEmSJElSxUzSSZIkSZIkSZIkSRUzSSdJkiRJkiRJkiRVzCSdJEmSJEmSJEmSVDGTdJIkSZIkSZIkSVLFTNJJkiRJkiRJkiRJFTNJJ0mSJEmSJEmSJFXMJJ0kSZIkSZIkSZJUMZN0kiRJkiRJkiRJUsVM0kmSJEmSJEmSJEkVM0knSZIkSZIkSZIkVcwknSRJkiRJkiRJklQxk3SSJEmSJEmSJElSxUzSSZIkSZIkSZIkSRUzSSdJkiRJkiRJkiRV7P8DxT7up61Lh1cAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1800x1500 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "reg=0.02\n",
    "plt.figure(figsize=(6,5), dpi=300)\n",
    "for i in range(500, 4001, 500):\n",
    "    to_plot = np.load(f'learning_curve_data/rmses/{str(reg)}_reg/RMSEs_{str(i)}_sparse_points.npy')\n",
    "    plt.plot(to_plot[1:, 0], to_plot[1:,1], label=f'{i}')\n",
    "plt.xscale('log')\n",
    "plt.xlabel('Training Structures')\n",
    "plt.yscale('log')\n",
    "plt.ylabel('Energy RMSE (ev/atom)')\n",
    "plt.title('Learning Curves Using Various $N_{sparse}$ per species')\n",
    "plt.legend()\n",
    "plt.tight_layout()\n",
    "plt.savefig('figures/learning_curve_n_sparse.png')\n",
    "plt.savefig('figures/learning_curve_n_sparse.pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "485fff98",
   "metadata": {},
   "source": [
    "Seems like s=500, s=1000 and s=1500 (later on) saturate at before 20000 training points, but the others don't. This likely means that the model is hitting a wall due to the lack of (good) training data, not due to the sparse points. It does, however, suggest that having used 2000 sparse points for your model was a good shout, at least for the training data you have.\n",
    "\n",
    "**REMEMBER AND MENTION THE FOLLOWING:**\n",
    "- This was only done using energies, and so model complexity will probably saturate with a greater number of structures when forces are included\n",
    "- As the structures used are likely to be correlted for the same crystal, the saturation accuracy for all models may be reduced further when using structures from extended simulations"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3417b58d",
   "metadata": {},
   "source": [
    "# Get step timings for different numbers of sparse points"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "c5aa0cfd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training GAP model using N_sparse=500 and N_train=2000...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Making predictions for test set...:   0%|          | 0/250 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time taken: 42.379735 s\n",
      "\n",
      "Training GAP model using N_sparse=1000 and N_train=2000...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Making predictions for test set...:   0%|          | 0/250 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time taken: 91.114511 s\n",
      "\n",
      "Training GAP model using N_sparse=1500 and N_train=2000...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Making predictions for test set...:   0%|          | 0/250 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time taken: 155.36021 s\n",
      "\n",
      "Training GAP model using N_sparse=2000 and N_train=2000...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Making predictions for test set...:   0%|          | 0/250 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time taken: 202.664025 s\n",
      "\n",
      "Training GAP model using N_sparse=2500 and N_train=2000...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Making predictions for test set...:   0%|          | 0/250 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time taken: 234.288461 s\n",
      "\n",
      "Training GAP model using N_sparse=3000 and N_train=2000...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Making predictions for test set...:   0%|          | 0/250 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time taken: 288.775132 s\n",
      "\n",
      "Training GAP model using N_sparse=3500 and N_train=2000...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Making predictions for test set...:   0%|          | 0/250 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time taken: 336.621913 s\n",
      "\n",
      "Training GAP model using N_sparse=4000 and N_train=2000...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Making predictions for test set...:   0%|          | 0/250 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time taken: 375.142888 s\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#Train and test GAP model using various slices of training frames and testing on the first 100 CSD-1k structures\n",
    "spar_low=500; spar_high=4001; spar_gap=500\n",
    "ntrain_low=2000; ntrain_high=20001; ntrain_gap=2000\n",
    "results=np.zeros((len(range(spar_low, spar_high, spar_gap)), 2), dtype=float)\n",
    "\n",
    "for ni, i in enumerate(range(spar_low, spar_high, spar_gap)):\n",
    "    K_NM_total = np.load(f'learning_curve_data/Knm_learning_curve_{i}.npy') #import full KNM for this number of sparse points\n",
    "    X_sparse = load_obj(f'learning_curve_data/X_sparse_PCovFPS_{i}_696.json') #Import X_sparse for this number of sparse points\n",
    "    \n",
    "    \n",
    "    j=2000\n",
    "    \n",
    "    K_NM = K_NM_total[:j] #get correct slice of KNM\n",
    "    print(f\"Training GAP model using N_sparse={i} and N_train={j}...\")\n",
    "\n",
    "    model = train_gap_model(kernel, train_frames[:j], K_NM, X_sparse, y_train[:j], y_dressed, \n",
    "                            grad_train=None, lambdas=[0.02], jitter=1e-9, solver = 'RKHS') #train model\n",
    "\n",
    "    #test model on full CSD-1k set (only on first 250 structures)\n",
    "    npred=250\n",
    "    start = time()\n",
    "    ytp = []\n",
    "    for frm in tqdm(csd_1k[:npred], desc = \"Making predictions for test set...\", leave=False):\n",
    "        manager_test = soap.transform(frm)\n",
    "        ytp.append(model.predict(manager_test))\n",
    "    \n",
    "    time_taken = time()-start\n",
    "    \n",
    "    print(\"Time taken:\", np.round(time_taken, 6), \"s\")\n",
    "    print(\"\")\n",
    "    results[ni] = [float(i), time_taken]\n",
    "\n",
    "np.save(f'learning_curve_data/prediction_time_test_n_sparse', results)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "id": "7668e55c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Prediction times')"
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot results\n",
    "plt.figure(figsize=(6,3), dpi=200)\n",
    "plt.plot(results[:,0], results[:,1]/250)\n",
    "plt.xlabel('$N_{sparse}$ per species')\n",
    "plt.ylabel('Prediction time (s/struct)')\n",
    "plt.title('Prediction times')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "cb522c7e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Computing representations...:   0%|          | 0/250 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time taken to compute the first 250 representations of the test set: \n",
      "  7.017690658569336 s\n"
     ]
    }
   ],
   "source": [
    "#determine time taken to compute the representations\n",
    "start = time()\n",
    "for frm in tqdm(csd_1k[:npred], desc = \"Computing representations...\", leave=False):\n",
    "    manager_test = soap.transform(frm)\n",
    "print(f\"Time taken to compute the first 250 representations of the test set: \\n  {time()-start} s\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "c6db7a22",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Computing representations...:   0%|          | 0/250 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time taken to compute the first 100 representations of the test set WITH FORCES: \n",
      "  424.49444484710693 s\n"
     ]
    }
   ],
   "source": [
    "#determine time take to compute representations WITH gradients too\n",
    "#this will give an idea of what proportion of the predictions will be taken up by computing the representation\n",
    "#for forces as well\n",
    "hypers[\"compute_gradients\"] = True\n",
    "soap = SphericalInvariants(**hypers)\n",
    "\n",
    "start = time()\n",
    "for frm in tqdm(csd_1k[:npred], desc = \"Computing representations...\", leave=False):\n",
    "    manager_test = soap.transform(frm)\n",
    "print(f\"Time taken to compute the first 250 representations of the test set WITH FORCES: \\n  {time()-start} s\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29f41623",
   "metadata": {},
   "source": [
    "For the output here above ^^ this was actually the time taken to compute **250** representations, not 100 (npred was set to 250, just forgot to change the print function)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "96d3e8f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Making energy predictions for test set...:   0%|          | 0/250 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time taken to make ONLY ENERGY predictions WITH GRADIENTS: 571.9197833538055\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Making energy predictions for test set...:   0%|          | 0/250 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time taken to make ONLY FORCE predictions WITH GRADIENTS: 784.9188103675842\n"
     ]
    }
   ],
   "source": [
    "## Test how long it takes to compute energies cf. forces using an old model (that has the same hypers)\n",
    "model = load_obj('model_data/full_GAP_model_FPS_0.055_3.25.json')\n",
    "npred=250\n",
    "start = time()\n",
    "ytp = []\n",
    "ftp = []\n",
    "for frm in tqdm(csd_1k[:npred], desc = \"Making energy predictions for test set...\", leave=False):\n",
    "    manager_test = soap.transform(frm)\n",
    "    ytp.append(model.predict(manager_test))\n",
    "time_taken = time()-start\n",
    "print(f\"Time taken to make ONLY ENERGY predictions WITH GRADIENTS: {time_taken}\")\n",
    "\n",
    "start = time()\n",
    "for frm in tqdm(csd_1k[:npred], desc = \"Making energy predictions for test set...\", leave=False):\n",
    "    manager_test = soap.transform(frm)\n",
    "    ftp.extend(model.predict_forces(manager_test))\n",
    "time_taken = time()-start\n",
    "print(f\"Time taken to make ONLY FORCE predictions WITH GRADIENTS: {time_taken}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "5f8f8888",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time increase in computing forces instead of energies, INCLUDING representations: 1.372\n"
     ]
    }
   ],
   "source": [
    "print(f\"Time increase in computing forces instead of energies, INCLUDING representations: \"\n",
    "      f\"{np.round(784.9188103675842/571.9197833538055, 3)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "17ff39c1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time increase in computing forces instead of energies, EXCLUDING representations: 2.445\n"
     ]
    }
   ],
   "source": [
    "print(f\"Time increase in computing forces instead of energies, EXCLUDING representations: \"\n",
    "      f\"{np.round((784.9188103675842-424.49444484710693)/(571.9197833538055-424.49444484710693), 3)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "id": "925ff631",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Number of sparse points per species for this model: 1622.5\n"
     ]
    }
   ],
   "source": [
    "print(f\" Number of sparse points per species for this model: {len(model.get_weights())/4}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "id": "82230c78",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time taken to make only energy predictions (excluding representation w/ gradients computation time) using 1622.5 sparse points per species: 147.4253\n"
     ]
    }
   ],
   "source": [
    "print(f\"Time taken to make only energy predictions (excluding representation w/ gradients computation time)\"\n",
    "     f\" using {len(model.get_weights())/4} sparse points per species: {np.round((571.9197-424.4944), 5)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc70ec36",
   "metadata": {},
   "source": [
    "The above result makes sense, as only slightly more sparse points per species were used than for the N Sparse = 1500 model trained on only energies, and that model had a prediction time of 155s (including the 7s required for computing representations without gradients). Discrepancy may be due to the use of a different number of sparse points for different species..."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79dc0e86",
   "metadata": {},
   "source": [
    "**Results:**\n",
    "- Computing the representation without energies took only around 7.02 seconds for all 250 points, and so increasing the number of sparse points is the primary driver or the increased prediction time experienced\n",
    "- The dependency of the number of sparse points on prediction time is the same for a model with or without forces used for training\n",
    "- However, the amount of time taken to compute just the representation with gradients increases by a factor of about 60, whereas the energy prediction time stays more or less constant\n",
    "- This means that the proportion of time taken to compute the representation will be far larger when gradients are included (as we already knew), and so the benefit of using a larger number of training structures will be greater when forces are used in training as well"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "d8fb439d",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Test how long it takes to compute energies cf. forces using an old model (that has the same hypers)\n",
    "model = load_obj('model_data/full_GAP_model_FPS_0.055_3.25.json')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "fd9d0442",
   "metadata": {},
   "outputs": [],
   "source": [
    "hypers[\"compute_gradients\"] = True\n",
    "soap = SphericalInvariants(**hypers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "0052caae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Making energy and force predictions for test set...:   0%|          | 0/250 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time taken to make ENERGY AND FORCE predictions WITH GRADIENTS: 971.5557570457458\n"
     ]
    }
   ],
   "source": [
    "npred=250\n",
    "start = time()\n",
    "ytp = []\n",
    "ftp = []\n",
    "for frm in tqdm(csd_1k[:npred], desc = \"Making energy and force predictions for test set...\", leave=False):\n",
    "    manager_test = soap.transform(frm)\n",
    "    ytp.append(model.predict(manager_test))\n",
    "    ftp.extend(model.predict_forces(manager_test))\n",
    "time_taken = time()-start\n",
    "print(f\"Time taken to make ENERGY AND FORCE predictions WITH GRADIENTS: {time_taken}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fc58cb2c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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