{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import os\n", "import sys\n", "import numpy as np\n", "import pandas as pd\n", "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", "import matplotlib.patches as mpatches\n", "import gc\n", "import time\n", "import subprocess\n", "from concurrent.futures import ProcessPoolExecutor, as_completed" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "from rdkit import Chem\n", "from rdkit.Chem import AllChem, DataStructs, Draw\n", "from rdkit import RDConfig\n", "from rdkit.Chem import Descriptors, rdMolDescriptors, Lipinski, rdDistGeom, rdPartialCharges\n", "from rdkit.Chem.AllChem import GetMorganGenerator\n", "from rdkit.DataStructs.cDataStructs import ConvertToNumpyArray\n", "from rdkit.Avalon.pyAvalonTools import GetAvalonFP" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2024-10-22 16:15:47.353235: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:485] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", "2024-10-22 16:15:47.410527: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:8454] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", "2024-10-22 16:15:47.428538: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1452] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", "2024-10-22 16:15:47.516235: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n", "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", "2024-10-22 16:15:48.857768: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n" ] } ], "source": [ "import tensorflow as tf\n", "from tensorflow import keras\n", "from tensorflow.keras import layers\n", "from tensorflow.keras.models import Sequential\n", "from tensorflow.keras.layers import Dense, Dropout, Activation\n", "from tensorflow.keras.regularizers import l2\n", "from tensorflow.keras.optimizers import Adam\n", "from tensorflow.keras import regularizers\n", "from tensorflow.keras.models import model_from_json" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "from sklearn.model_selection import train_test_split\n", "from sklearn.linear_model import Ridge\n", "from sklearn.ensemble import RandomForestRegressor\n", "from sklearn.neural_network import MLPRegressor\n", "from sklearn.svm import SVR\n", "from sklearn.metrics import r2_score, mean_absolute_error, root_mean_squared_error,mean_squared_error" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", "I0000 00:00:1729581352.212913 54798 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581352.391711 54798 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581352.391825 54798 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n" ] } ], "source": [ "tf.keras.backend.clear_session()\n", "gpus = tf.config.experimental.list_physical_devices('GPU')\n", "if gpus:\n", " try:\n", " for gpu in gpus:\n", " tf.config.experimental.set_memory_growth(gpu, True)\n", " except RuntimeError as e:\n", " print(e)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "target_path = \"result/2_solubility_fingerprint_compare\"\n", "os.makedirs(target_path, exist_ok=True)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "data_ws = pd.read_csv('./data/ws496_logS.csv', dtype={'SMILES': 'string'})\n", "smiles_ws = data_ws['SMILES']\n", "y_ws = data_ws.iloc[:, 2]\n", "\n", "data_delaney = pd.read_csv('./data/delaney-processed.csv', dtype={'smiles': 'string'})\n", "smiles_de = data_delaney['smiles']\n", "y_de = data_delaney.iloc[:, 1]\n", "\n", "data_lovric2020 = pd.read_csv('./data/Lovric2020_logS0.csv', dtype={'isomeric_smiles': 'string'})\n", "smiles_lo = data_lovric2020['isomeric_smiles']\n", "y_lo = data_lovric2020.iloc[:, 1]\n", "\n", "data_huuskonen = pd.read_csv('./data/huusk.csv', dtype={'SMILES': 'string'})\n", "smiles_hu = data_huuskonen['SMILES']\n", "y_hu = data_huuskonen.iloc[:, -1].astype('float')" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "def mol3d(mol):\n", " mol = Chem.AddHs(mol)\n", " optimization_methods = [\n", " (AllChem.EmbedMolecule, (mol, AllChem.ETKDGv3()), {}),\n", " (AllChem.UFFOptimizeMolecule, (mol,), {'maxIters': 200}),\n", " (AllChem.MMFFOptimizeMolecule, (mol,), {'maxIters': 200})\n", " ]\n", "\n", " for method, args, kwargs in optimization_methods:\n", " try:\n", " method(*args, **kwargs)\n", " if mol.GetNumConformers() > 0:\n", " return mol\n", " except ValueError as e:\n", " print(f\"Error: {e} - Trying next optimization method [{method}]\")\n", "\n", " print(f\"Invalid mol for 3d {'\\033[94m'}{Chem.MolToSmiles(mol)}{'\\033[0m'} - No conformer generated\")\n", " return None" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "def convert_smiles_to_mol(smiles, fail_folder=None, index=None, yvalue=None):\n", " mol = Chem.MolFromSmiles(smiles)\n", " if mol is None:\n", " print(f\"[convert_smiles_to_mol] Cannot convert {smiles} to Mols\")\n", " return None, {\"smiles\": smiles, \"y_value\": yvalue, \"error\": \"Invalid SMILES\"}\n", "\n", " try:\n", " Chem.Kekulize(mol, clearAromaticFlags=True)\n", " isomeric_smiles = Chem.MolToSmiles(mol, isomericSmiles=True)\n", " mol = Chem.MolFromSmiles(isomeric_smiles)\n", " except Exception as e:\n", " print(f\"[convert_smiles_to_mol] failed {smiles} isomeric_smiles by {e}\")\n", " if fail_folder and index is not None:\n", " img_path = os.path.join(fail_folder, f\"mol_{index}.png\")\n", " img = Draw.MolToImage(mol)\n", " img.save(img_path)\n", " return None, {\"smiles\": smiles, \"y_value\": yvalue, \"error\": f\"Isomeric SMILES error: {e}\"}\n", "\n", " try:\n", " Chem.SanitizeMol(mol)\n", " except Exception as e:\n", " print(f\"[convert_smiles_to_mol] failed {smiles} SanitizeMol by {e}\")\n", " if fail_folder and index is not None:\n", " img_path = os.path.join(fail_folder, f\"mol_{index}.png\")\n", " img = Draw.MolToImage(mol)\n", " img.save(img_path)\n", " return None, {\"smiles\": smiles, \"y_value\": yvalue, \"error\": f\"SanitizeMol error: {e}\"}\n", "\n", " return mol, None" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "def process_smiles(smiles, yvalue, fail_folder, index):\n", " mol, error = convert_smiles_to_mol(smiles, fail_folder, index, yvalue)\n", " if error:\n", " return None, None, error\n", "\n", " mol_3d = mol3d(mol)\n", " if mol_3d:\n", " return smiles, yvalue, None\n", " else:\n", " img_path = os.path.join(fail_folder, f\"mol_{index}.png\")\n", " img = Draw.MolToImage(mol)\n", " img.save(img_path)\n", " return None, None, {\"smiles\": smiles, \"y_value\": yvalue}\n", "\n", "def process_dataset(smiles_list, y_values, dataset_name, target_path=\"result\", max_workers=None):\n", " start = time.time()\n", " valid_smiles, valid_y = [], []\n", " error_smiles_list = []\n", " fail_folder = f\"{target_path}/failed/{dataset_name}\"\n", " os.makedirs(fail_folder, exist_ok=True)\n", "\n", " with ProcessPoolExecutor(max_workers=max_workers) as executor:\n", " futures = [\n", " executor.submit(process_smiles, smiles, yvalue, fail_folder, i)\n", " for i, (smiles, yvalue) in enumerate(zip(smiles_list, y_values))\n", " ]\n", " for future in as_completed(futures):\n", " smiles, yvalue, error = future.result()\n", " if error:\n", " error_smiles_list.append(error)\n", " elif smiles is not None and yvalue is not None:\n", " valid_smiles.append(smiles)\n", " valid_y.append(yvalue)\n", "\n", " if error_smiles_list:\n", " error_df = pd.DataFrame(error_smiles_list)\n", " error_df.to_csv(os.path.join(fail_folder, \"failed_smiles.csv\"), index=False)\n", " print(f\" [{dataset_name:<10}] : {time.time()-start:.4f} sec\")\n", " return valid_smiles, valid_y" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " [ws496 ] : 0.9100 sec\n", " [delaney ] : 1.5038 sec\n", "Error: Bad Conformer Id - Trying next optimization method []\n", "Error: Bad Conformer Id - Trying next optimization method []\n", "Invalid mol for 3d \u001b[94m[H]O[C@]([H])(c1c([H])c([H])nc2c([H])c([H])c(OC([H])([H])[H])c([H])c12)[C@@]1([H])[N@]2C([H])([H])C([H])([H])[C@@]([H])(C1([H])[H])[C@@]([H])(C([H])=C([H])[H])C2([H])[H]\u001b[0m - No conformer generated\n", "Error: Bad Conformer Id - Trying next optimization method []\n", "Error: Bad Conformer Id - Trying next optimization method []\n", "Invalid mol for 3d \u001b[94m[H]O[C@@]([H])(c1c([H])c([H])nc2c([H])c([H])c(OC([H])([H])[H])c([H])c12)[C@]1([H])[N@]2C([H])([H])C([H])([H])[C@@]([H])(C1([H])[H])[C@@]([H])(C([H])=C([H])[H])C2([H])[H]\u001b[0m - No conformer generated\n", " [Lovric2020_logS0] : 9.3446 sec\n", " [huusk ] : -0.2241 sec\n" ] } ], "source": [ "smiles_ws, y_ws = process_dataset(smiles_ws, y_ws, \"ws496\", target_path)\n", "smiles_de, y_de = process_dataset(smiles_de, y_de, \"delaney\", target_path)\n", "smiles_lo, y_lo = process_dataset(smiles_lo, y_lo, \"Lovric2020_logS0\", target_path)\n", "smiles_hu, y_hu = process_dataset(smiles_hu, y_hu, \"huusk\", target_path)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "LEN_OF_FF = 2048\n", "LEN_OF_MA = 167\n", "LEN_OF_AV = 512" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "def get_fingerprints(mol):\n", " if mol is None:\n", " return None, None, None\n", " \n", " morgan_generator = GetMorganGenerator(radius=2, fpSize=LEN_OF_FF)\n", " ecfp = morgan_generator.GetFingerprint(mol)\n", " ecfp_array = np.zeros((LEN_OF_FF,),dtype=int)\n", " DataStructs.ConvertToNumpyArray(ecfp, ecfp_array)\n", " \n", " maccs = Chem.rdMolDescriptors.GetMACCSKeysFingerprint(mol)\n", "\n", " avalon_fp = GetAvalonFP(mol)\n", " avalon_array = np.zeros((LEN_OF_AV,),dtype=int)\n", " DataStructs.ConvertToNumpyArray(avalon_fp, avalon_array)\n", " \n", " return ecfp_array, maccs, avalon_array\n", "\n", "def fp_converter(data, use_parallel=True):\n", " mols = [Chem.MolFromSmiles(smi) for smi in data]\n", " \n", " if use_parallel:\n", " try: \n", " with ProcessPoolExecutor() as executor:\n", " results = list(executor.map(get_fingerprints, mols))\n", " except Exception as e:\n", " print(f\"Parallel processing failed due to: {e}. Falling back to sequential processing.\")\n", " use_parallel = False\n", " \n", " if not use_parallel:\n", " results = [get_fingerprints(mol) for mol in mols]\n", " \n", " ECFP, MACCS, AvalonFP = zip(*results)\n", " \n", " ECFP_container = np.vstack([arr for arr in ECFP if arr is not None])\n", " MACCS_container = np.zeros((len(MACCS), LEN_OF_MA), dtype=int)\n", " AvalonFP_container = np.vstack([arr for arr in AvalonFP if arr is not None])\n", "\n", " for i, fp in enumerate(MACCS):\n", " if fp is not None:\n", " DataStructs.ConvertToNumpyArray(fp, MACCS_container[i])\n", " \n", " return mols, ECFP_container, MACCS_container, AvalonFP_container" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "mol_ws, x_ws, MACCS_ws, AvalonFP_ws = fp_converter(smiles_ws)\n", "mol_de, x_de, MACCS_de, AvalonFP_de = fp_converter(smiles_de)\n", "mol_lo, x_lo, MACCS_lo, AvalonFP_lo = fp_converter(smiles_lo)\n", "mol_hu, x_hu, MACCS_hu, AvalonFP_hu = fp_converter(smiles_hu)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "#Morgan Fingerprint (ECFP)\n", "xtr_ws1, xte_ws1, ytr_ws1, yte_ws1 = train_test_split(x_ws, y_ws, test_size=0.2,random_state=42)\n", "xtr_de1, xte_de1, ytr_de1, yte_de1 = train_test_split(x_de, y_de, test_size=0.2,random_state=42)\n", "xtr_lo1, xte_lo1, ytr_lo1, yte_lo1 = train_test_split(x_lo, y_lo, test_size=0.2,random_state=42)\n", "xtr_hu1, xte_hu1, ytr_hu1, yte_hu1 = train_test_split(x_hu, y_hu, test_size=0.2,random_state=42)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "#MACCS Fingerprint\n", "xtr_ws2, xte_ws2, ytr_ws2, yte_ws2 = train_test_split(MACCS_ws, y_ws, test_size=0.2,random_state=42)\n", "xtr_de2, xte_de2, ytr_de2, yte_de2 = train_test_split(MACCS_de, y_de, test_size=0.2,random_state=42)\n", "xtr_lo2, xte_lo2, ytr_lo2, yte_lo2 = train_test_split(MACCS_lo, y_lo, test_size=0.2,random_state=42)\n", "xtr_hu2, xte_hu2, ytr_hu2, yte_hu2 = train_test_split(MACCS_hu, y_hu, test_size=0.2,random_state=42)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "#Avalon Fingerprint\n", "xtr_ws3, xte_ws3, ytr_ws3, yte_ws3 = train_test_split(AvalonFP_ws, y_ws, test_size=0.2,random_state=42)\n", "xtr_de3, xte_de3, ytr_de3, yte_de3 = train_test_split(AvalonFP_de, y_de, test_size=0.2,random_state=42)\n", "xtr_lo3, xte_lo3, ytr_lo3, yte_lo3 = train_test_split(AvalonFP_lo, y_lo, test_size=0.2,random_state=42)\n", "xtr_hu3, xte_hu3, ytr_hu3, yte_hu3 = train_test_split(AvalonFP_hu, y_hu, test_size=0.2,random_state=42)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "def concatenate_to_numpy(*dataframes):\n", " numpy_arrays = [df.to_numpy() if isinstance(df, pd.DataFrame) else df for df in dataframes]\n", " if not all(isinstance(arr, np.ndarray) for arr in numpy_arrays):\n", " raise ValueError(\"All inputs must be either pandas DataFrame or numpy array\")\n", " return np.concatenate(numpy_arrays, axis=1)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "#ECFP + MACCS\n", "group_nws1 = concatenate_to_numpy(x_ws, MACCS_ws)\n", "group_nde1 = concatenate_to_numpy(x_de, MACCS_de)\n", "group_nlo1 = concatenate_to_numpy(x_lo, MACCS_lo)\n", "group_nhu1 = concatenate_to_numpy(x_hu, MACCS_hu)\n", "\n", "xtr_ws12, xte_ws12, ytr_ws12, yte_ws12 = train_test_split(group_nws1, y_ws, test_size=0.2,random_state=42)\n", "xtr_de12, xte_de12, ytr_de12, yte_de12 = train_test_split(group_nde1, y_de, test_size=0.2,random_state=42)\n", "xtr_lo12, xte_lo12, ytr_lo12, yte_lo12 = train_test_split(group_nlo1, y_lo, test_size=0.2,random_state=42)\n", "xtr_hu12, xte_hu12, ytr_hu12, yte_hu12 = train_test_split(group_nhu1, y_hu, test_size=0.2,random_state=42)" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "#ECFP + Avalon\n", "group_nws2 = concatenate_to_numpy(x_ws, AvalonFP_ws)\n", "group_nde2 = concatenate_to_numpy(x_de, AvalonFP_de)\n", "group_nlo2 = concatenate_to_numpy(x_lo, AvalonFP_lo)\n", "group_nhu2 = concatenate_to_numpy(x_hu, AvalonFP_hu)\n", "\n", "xtr_ws13, xte_ws13, ytr_ws13, yte_ws13 = train_test_split(group_nws2, y_ws, test_size=0.2,random_state=42)\n", "xtr_de13, xte_de13, ytr_de13, yte_de13 = train_test_split(group_nde2, y_de, test_size=0.2,random_state=42)\n", "xtr_lo13, xte_lo13, ytr_lo13, yte_lo13 = train_test_split(group_nlo2, y_lo, test_size=0.2,random_state=42)\n", "xtr_hu13, xte_hu13, ytr_hu13, yte_hu13 = train_test_split(group_nhu2, y_hu, test_size=0.2,random_state=42)" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "#MACCS + Avalon\n", "group_nws3 = concatenate_to_numpy(MACCS_ws, AvalonFP_ws)\n", "group_nde3 = concatenate_to_numpy(MACCS_de, AvalonFP_de)\n", "group_nlo3 = concatenate_to_numpy(MACCS_lo, AvalonFP_lo)\n", "group_nhu3 = concatenate_to_numpy(MACCS_hu, AvalonFP_hu)\n", "\n", "xtr_ws23, xte_ws23, ytr_ws23, yte_ws23 = train_test_split(group_nws3, y_ws, test_size=0.2,random_state=42)\n", "xtr_de23, xte_de23, ytr_de23, yte_de23 = train_test_split(group_nde3, y_de, test_size=0.2,random_state=42)\n", "xtr_lo23, xte_lo23, ytr_lo23, yte_lo23 = train_test_split(group_nlo3, y_lo, test_size=0.2,random_state=42)\n", "xtr_hu23, xte_hu23, ytr_hu23, yte_hu23 = train_test_split(group_nhu3, y_hu, test_size=0.2,random_state=42)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [], "source": [ "#ECFP + MACCS + Avalon\n", "fgroup_nws = concatenate_to_numpy(x_ws, MACCS_ws, AvalonFP_ws)\n", "fgroup_nde = concatenate_to_numpy(x_de, MACCS_de, AvalonFP_de)\n", "fgroup_nlo = concatenate_to_numpy(x_lo, MACCS_lo, AvalonFP_lo)\n", "fgroup_nhu = concatenate_to_numpy(x_hu, MACCS_hu, AvalonFP_hu)\n", "\n", "xtr_wsf, xte_wsf, ytr_wsf, yte_wsf = train_test_split(fgroup_nws, y_ws, test_size=0.2,random_state=42)\n", "xtr_def, xte_def, ytr_def, yte_def = train_test_split(fgroup_nde, y_de, test_size=0.2,random_state=42)\n", "xtr_lof, xte_lof, ytr_lof, yte_lof = train_test_split(fgroup_nlo, y_lo, test_size=0.2,random_state=42)\n", "xtr_huf, xte_huf, ytr_huf, yte_huf = train_test_split(fgroup_nhu, y_hu, test_size=0.2,random_state=42)" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "BATCHSIZE = 32\n", "EPOCHS = 100\n", "lr = 0.0001\n", "decay = 1e-4" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [], "source": [ "def new_inference_model():\n", " model = tf.keras.Sequential([\n", " tf.keras.layers.Dense(\n", " units=1024,\n", " activation='relu',\n", " kernel_initializer='glorot_uniform',\n", " kernel_regularizer=tf.keras.regularizers.l2(decay)),\n", " tf.keras.layers.Dropout(0.2),\n", " tf.keras.layers.Dense(\n", " units=469,\n", " activation='relu',\n", " kernel_initializer='glorot_uniform',\n", " kernel_regularizer=tf.keras.regularizers.l2(decay)),\n", " tf.keras.layers.Dropout(0.2),\n", " tf.keras.layers.Dense(units=1)\n", " ])\n", " \n", " return model\n", "\n", "def save_model():\n", " model = new_inference_model()\n", " model_json = model.to_json()\n", " os.makedirs(\"save_model\", exist_ok=True) # Ensure the directory exists\n", " with open(\"save_model/model_config.json\", \"w\") as json_file:\n", " json_file.write(model_json)\n", " model.save_weights(\"save_model/model_weights.weights.h5\")" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [], "source": [ "# Environment settings for optimal performance\n", "os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' # Suppress TensorFlow INFO and WARNING messages\n", "os.environ['TF_GPU_ALLOCATOR'] = 'cuda_malloc_async'\n", "os.environ['CUDA_DEVICE_ORDER'] = 'PCI_BUS_ID'\n", "os.environ['TF_XLA_FLAGS'] = '--tf_xla_auto_jit=2 --tf_xla_enable_xla_devices'\n", "os.environ['XLA_FLAGS'] = '--xla_gpu_cuda_data_dir=/usr/local/cuda --xla_gpu_force_compilation_parallelism=1'\n", "\n", "import logging\n", "class FilterNUMA(logging.Filter):\n", " def filter(self, record):\n", " return \"NUMA\" not in record.getMessage() and \"XLA service\" not in record.getMessage()\n", "\n", "logger = logging.getLogger()\n", "logger.setLevel(logging.ERROR)\n", "for handler in logger.handlers:\n", " handler.addFilter(FilterNUMA())" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [], "source": [ "import re\n", "import subprocess\n", "from tensorflow.keras.models import load_model" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [], "source": [ "def model_study_datasets(xtr_list, ytr_list):\n", " models = []\n", " \n", " fps_file = 'new_fps.npy'\n", " y_true_file = 'y_true.npy'\n", " \n", " for i in range(len(xtr_list)):\n", " np.save(fps_file, xtr_list[i])\n", " np.save(y_true_file, ytr_list[i])\n", " \n", " save_model()\n", " \n", " result = subprocess.run(['python3', './extra_code/basic_model.py', \n", " str(BATCHSIZE), str(EPOCHS),\n", " fps_file, y_true_file],\n", " stdout=subprocess.PIPE, \n", " stderr=subprocess.PIPE, \n", " text=True)\n", " \n", " if result.stderr:\n", " sys.stderr.write(result.stderr)\n", " \n", " warning_patterns = [\n", " r'WARNING: All log messages before absl::InitializeLog\\(\\) is called are written to STDERR',\n", " r'could not open file to read NUMA node',\n", " r'Your kernel may have been built without NUMA support',\n", " r'XLA service .* initialized for platform (CUDA|Host)',\n", " r'StreamExecutor device \\(0\\):',\n", " r'Created device /job:localhost/replica:0/task:0/device:GPU:0',\n", " r'Using CUDA malloc Async allocator for GPU',\n", " r'Could not identify NUMA node of platform GPU',\n", " ]\n", "\n", " filtered_stderr = \"\\n\".join([\n", " line for line in result.stderr.splitlines()\n", " if not any(re.search(pattern, line) for pattern in warning_patterns)\n", " ])\n", " \n", " if filtered_stderr:\n", " print(f\"[{i}] Filtered stderr:\")\n", " print(filtered_stderr)\n", " print(f\"Return code: {result.returncode}\")\n", "\n", " if result.returncode != 0 and filtered_stderr and not all('Your kernel may have been built without NUMA support' in line for line in filtered_stderr.split('\\n')):\n", " raise ValueError(f\"[{i}] Error during learning result process: {filtered_stderr}\")\n", " \n", " try:\n", " trained_model = tf.keras.models.load_model('save_model/trained_model.keras')\n", " models.append(trained_model)\n", " print(f\"Model {i+1} loaded successfully\")\n", " except Exception as e:\n", " print(f\"[{i}] Error loading model: {str(e)}\")\n", " continue\n", "\n", " os.remove(fps_file)\n", " os.remove(y_true_file)\n", " \n", " return models" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "I0000 00:00:1729581366.770352 54798 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581366.770474 54798 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581366.770536 54798 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581367.803838 54798 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581367.804076 54798 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "2024-10-22 16:16:07.804094: I tensorflow/core/common_runtime/gpu/gpu_device.cc:2112] Could not identify NUMA node of platform GPU id 0, defaulting to 0. Your kernel may not have been built with NUMA support.\n", "2024-10-22 16:16:07.804125: I tensorflow/core/common_runtime/gpu/gpu_process_state.cc:198] Using CUDA malloc Async allocator for GPU: 0\n", "I0000 00:00:1729581367.804432 54798 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "2024-10-22 16:16:07.804469: I tensorflow/core/common_runtime/gpu/gpu_device.cc:2021] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 3586 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Laptop GPU, pci bus id: 0000:01:00.0, compute capability: 8.6\n", "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", "I0000 00:00:1729581370.515355 55206 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581370.563787 55206 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581370.563949 55206 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", "I0000 00:00:1729581370.578120 55206 service.cc:146] XLA service 0x55f5295b8ed0 initialized for platform Host (this does not guarantee that XLA will be used). Devices:\n", "I0000 00:00:1729581370.578297 55206 service.cc:154] StreamExecutor device (0): Host, Default Version\n", "I0000 00:00:1729581370.744565 55291 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581370.744733 55206 service.cc:146] XLA service 0x55f529312080 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", "I0000 00:00:1729581370.744777 55206 service.cc:154] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Laptop GPU, Compute Capability 8.6\n", "I0000 00:00:1729581370.745041 55206 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581370.745162 55206 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581370.745246 55206 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581370.750730 55206 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581370.751044 55206 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581371.519288 55311 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n", "GPU memory cleared.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[0] Filtered stderr:\n", "I0000 00:00:1729581371.519288 55311 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n", "GPU memory cleared.\n", "Return code: 0\n", "Model 1 loaded successfully\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", "I0000 00:00:1729581381.429190 57193 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581381.465417 57193 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581381.465517 57193 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", "I0000 00:00:1729581381.478905 57193 service.cc:146] XLA service 0x5651061d6120 initialized for platform Host (this does not guarantee that XLA will be used). Devices:\n", "I0000 00:00:1729581381.478971 57193 service.cc:154] StreamExecutor device (0): Host, Default Version\n", "I0000 00:00:1729581381.614689 57285 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581381.614835 57193 service.cc:146] XLA service 0x565105f2f2d0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", "I0000 00:00:1729581381.614874 57193 service.cc:154] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Laptop GPU, Compute Capability 8.6\n", "I0000 00:00:1729581381.615072 57193 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581381.615177 57193 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581381.615249 57193 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581381.620486 57193 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581381.620866 57193 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581382.245162 57304 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n", "GPU memory cleared.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] Filtered stderr:\n", "I0000 00:00:1729581382.245162 57304 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n", "GPU memory cleared.\n", "Return code: 0\n", "Model 2 loaded successfully\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", "I0000 00:00:1729581390.732413 59154 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581390.772435 59154 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581390.772550 59154 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", "I0000 00:00:1729581390.785458 59154 service.cc:146] XLA service 0x5561280b68a0 initialized for platform Host (this does not guarantee that XLA will be used). Devices:\n", "I0000 00:00:1729581390.785504 59154 service.cc:154] StreamExecutor device (0): Host, Default Version\n", "I0000 00:00:1729581390.945947 59239 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581390.946087 59154 service.cc:146] XLA service 0x556127e2cb50 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", "I0000 00:00:1729581390.946125 59154 service.cc:154] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Laptop GPU, Compute Capability 8.6\n", "I0000 00:00:1729581390.946360 59154 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581390.946460 59154 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581390.946540 59154 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581390.952307 59154 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581390.952587 59154 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581391.605462 59258 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n", "GPU memory cleared.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[2] Filtered stderr:\n", "I0000 00:00:1729581391.605462 59258 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n", "GPU memory cleared.\n", "Return code: 0\n", "Model 3 loaded successfully\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", "I0000 00:00:1729581398.027167 61114 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581398.066263 61114 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581398.066376 61114 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", "I0000 00:00:1729581398.078319 61114 service.cc:146] XLA service 0x5599f1579260 initialized for platform Host (this does not guarantee that XLA will be used). Devices:\n", "I0000 00:00:1729581398.078364 61114 service.cc:154] StreamExecutor device (0): Host, Default Version\n", "I0000 00:00:1729581398.208097 61199 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581398.208246 61114 service.cc:146] XLA service 0x5599f12d2410 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", "I0000 00:00:1729581398.208282 61114 service.cc:154] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Laptop GPU, Compute Capability 8.6\n", "I0000 00:00:1729581398.208627 61114 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581398.208713 61114 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581398.208774 61114 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581398.213512 61114 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581398.213797 61114 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581398.907187 61219 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n", "GPU memory cleared.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[3] Filtered stderr:\n", "I0000 00:00:1729581398.907187 61219 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n", "GPU memory cleared.\n", "Return code: 0\n", "Model 4 loaded successfully\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", "I0000 00:00:1729581407.645869 63069 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581407.672088 63069 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581407.672193 63069 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", "I0000 00:00:1729581407.682344 63069 service.cc:146] XLA service 0x55701f08b260 initialized for platform Host (this does not guarantee that XLA will be used). Devices:\n", "I0000 00:00:1729581407.682402 63069 service.cc:154] StreamExecutor device (0): Host, Default Version\n", "I0000 00:00:1729581407.807108 63154 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581407.807303 63069 service.cc:146] XLA service 0x55701ede4410 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", "I0000 00:00:1729581407.807340 63069 service.cc:154] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Laptop GPU, Compute Capability 8.6\n", "I0000 00:00:1729581407.807524 63069 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581407.807617 63069 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581407.807686 63069 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581407.812535 63069 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581407.812830 63069 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581408.506592 63173 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n", "GPU memory cleared.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[4] Filtered stderr:\n", "I0000 00:00:1729581408.506592 63173 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n", "GPU memory cleared.\n", "Return code: 0\n", "Model 5 loaded successfully\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", "I0000 00:00:1729581417.297200 65029 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581417.326699 65029 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581417.326800 65029 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", "I0000 00:00:1729581417.337750 65029 service.cc:146] XLA service 0x55f599448f20 initialized for platform Host (this does not guarantee that XLA will be used). Devices:\n", "I0000 00:00:1729581417.337820 65029 service.cc:154] StreamExecutor device (0): Host, Default Version\n", "I0000 00:00:1729581417.459708 65114 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581417.459835 65029 service.cc:146] XLA service 0x55f5991a20d0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", "I0000 00:00:1729581417.459869 65029 service.cc:154] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Laptop GPU, Compute Capability 8.6\n", "I0000 00:00:1729581417.460048 65029 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581417.460141 65029 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581417.460216 65029 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581417.464929 65029 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581417.465173 65029 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581418.105614 65133 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n", "GPU memory cleared.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[5] Filtered stderr:\n", "I0000 00:00:1729581418.105614 65133 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n", "GPU memory cleared.\n", "Return code: 0\n", "Model 6 loaded successfully\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", "I0000 00:00:1729581426.748701 66989 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581426.786880 66989 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581426.786985 66989 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", "I0000 00:00:1729581426.800483 66989 service.cc:146] XLA service 0x556fdfb8dde0 initialized for platform Host (this does not guarantee that XLA will be used). Devices:\n", "I0000 00:00:1729581426.800538 66989 service.cc:154] StreamExecutor device (0): Host, Default Version\n", "I0000 00:00:1729581426.938334 67076 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581426.938460 66989 service.cc:146] XLA service 0x556fdf8e6f90 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", "I0000 00:00:1729581426.938492 66989 service.cc:154] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Laptop GPU, Compute Capability 8.6\n", "I0000 00:00:1729581426.938657 66989 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581426.938735 66989 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581426.938792 66989 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581426.947040 66989 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581426.947286 66989 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581425.793397 67096 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n", "GPU memory cleared.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[6] Filtered stderr:\n", "I0000 00:00:1729581425.793397 67096 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n", "GPU memory cleared.\n", "Return code: 0\n", "Model 7 loaded successfully\n" ] } ], "source": [ "xtr_ws=[xtr_ws1, xtr_ws2, xtr_ws3, xtr_ws12, xtr_ws13, xtr_ws23, xtr_wsf]\n", "ytr_ws=[ytr_ws1, ytr_ws2, ytr_ws3, ytr_ws12, ytr_ws13, ytr_ws23, ytr_wsf]\n", "xte_ws=[xte_ws1, xte_ws2, xte_ws3, xte_ws12, xte_ws13, xte_ws23, xte_wsf]\n", "yte_ws=[yte_ws1, yte_ws2, yte_ws3, yte_ws12, yte_ws13, yte_ws23, yte_wsf]\n", "res_ws = model_study_datasets(xtr_ws,ytr_ws)\n", "# 7m 30s" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", "I0000 00:00:1729581434.838069 68946 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581434.875276 68946 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581434.875389 68946 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", "I0000 00:00:1729581434.888378 68946 service.cc:146] XLA service 0x55943402d1b0 initialized for platform Host (this does not guarantee that XLA will be used). Devices:\n", "I0000 00:00:1729581434.888422 68946 service.cc:154] StreamExecutor device (0): Host, Default Version\n", "I0000 00:00:1729581435.019892 69037 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581435.020060 68946 service.cc:146] XLA service 0x559433d86360 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", "I0000 00:00:1729581435.020110 68946 service.cc:154] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Laptop GPU, Compute Capability 8.6\n", "I0000 00:00:1729581435.020329 68946 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581435.020443 68946 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581435.020507 68946 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581435.025356 68946 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581435.025667 68946 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581435.710918 69056 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n", "GPU memory cleared.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[0] Filtered stderr:\n", "I0000 00:00:1729581435.710918 69056 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n", "GPU memory cleared.\n", "Return code: 0\n", "Model 1 loaded successfully\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", "I0000 00:00:1729581446.100762 70915 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581446.128292 70915 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581446.128434 70915 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", "I0000 00:00:1729581446.138887 70915 service.cc:146] XLA service 0x55b646421050 initialized for platform Host (this does not guarantee that XLA will be used). Devices:\n", "I0000 00:00:1729581446.138957 70915 service.cc:154] StreamExecutor device (0): Host, Default Version\n", "I0000 00:00:1729581446.268979 71000 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581446.269114 70915 service.cc:146] XLA service 0x55b64617a200 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", "I0000 00:00:1729581446.269152 70915 service.cc:154] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Laptop GPU, Compute Capability 8.6\n", "I0000 00:00:1729581446.269328 70915 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581446.269425 70915 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581446.269494 70915 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581446.274256 70915 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581446.274490 70915 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581446.886196 71019 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n", "GPU memory cleared.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] Filtered stderr:\n", "I0000 00:00:1729581446.886196 71019 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n", "GPU memory cleared.\n", "Return code: 0\n", "Model 2 loaded successfully\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", "I0000 00:00:1729581456.968881 72874 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581457.008087 72874 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581457.008217 72874 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", "I0000 00:00:1729581457.025988 72874 service.cc:146] XLA service 0x5588183f5a80 initialized for platform Host (this does not guarantee that XLA will be used). Devices:\n", "I0000 00:00:1729581457.026038 72874 service.cc:154] StreamExecutor device (0): Host, Default Version\n", "I0000 00:00:1729581457.174270 72962 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581457.174408 72874 service.cc:146] XLA service 0x558818181f90 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", "I0000 00:00:1729581457.174439 72874 service.cc:154] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Laptop GPU, Compute Capability 8.6\n", "I0000 00:00:1729581457.174628 72874 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581457.174712 72874 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581457.174776 72874 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581457.179864 72874 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581457.180159 72874 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581455.901401 72984 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n", "GPU memory cleared.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[2] Filtered stderr:\n", "I0000 00:00:1729581455.901401 72984 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n", "GPU memory cleared.\n", "Return code: 0\n", "Model 3 loaded successfully\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", "I0000 00:00:1729581466.055888 74839 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581466.095107 74839 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581466.095223 74839 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", "I0000 00:00:1729581466.107885 74839 service.cc:146] XLA service 0x56093d71a050 initialized for platform Host (this does not guarantee that XLA will be used). Devices:\n", "I0000 00:00:1729581466.107937 74839 service.cc:154] StreamExecutor device (0): Host, Default Version\n", "I0000 00:00:1729581466.245314 74924 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581466.245441 74839 service.cc:146] XLA service 0x56093d473200 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", "I0000 00:00:1729581466.245474 74839 service.cc:154] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Laptop GPU, Compute Capability 8.6\n", "I0000 00:00:1729581466.245679 74839 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581466.245771 74839 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581466.245849 74839 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581466.251509 74839 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581466.251811 74839 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581466.945240 74943 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n", "GPU memory cleared.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[3] Filtered stderr:\n", "I0000 00:00:1729581466.945240 74943 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n", "GPU memory cleared.\n", "Return code: 0\n", "Model 4 loaded successfully\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", "I0000 00:00:1729581477.733994 76803 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581477.762357 76803 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581477.762461 76803 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", "I0000 00:00:1729581477.771799 76803 service.cc:146] XLA service 0x562b4feb2f60 initialized for platform Host (this does not guarantee that XLA will be used). Devices:\n", "I0000 00:00:1729581477.771848 76803 service.cc:154] StreamExecutor device (0): Host, Default Version\n", "I0000 00:00:1729581477.902469 76894 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581477.902598 76803 service.cc:146] XLA service 0x562b4fc0c110 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", "I0000 00:00:1729581477.902631 76803 service.cc:154] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Laptop GPU, Compute Capability 8.6\n", "I0000 00:00:1729581477.902803 76803 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581477.902896 76803 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581477.902965 76803 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581477.907696 76803 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581477.907916 76803 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581478.622371 76914 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n", "GPU memory cleared.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[4] Filtered stderr:\n", "I0000 00:00:1729581478.622371 76914 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n", "GPU memory cleared.\n", "Return code: 0\n", "Model 5 loaded successfully\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", "I0000 00:00:1729581487.303159 78774 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581487.332123 78774 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581487.332231 78774 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", "I0000 00:00:1729581487.343872 78774 service.cc:146] XLA service 0x564e0ada4eb0 initialized for platform Host (this does not guarantee that XLA will be used). Devices:\n", "I0000 00:00:1729581487.343922 78774 service.cc:154] StreamExecutor device (0): Host, Default Version\n", "I0000 00:00:1729581487.482320 78859 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581487.482480 78774 service.cc:146] XLA service 0x564e0aa26e50 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", "I0000 00:00:1729581487.482522 78774 service.cc:154] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Laptop GPU, Compute Capability 8.6\n", "I0000 00:00:1729581487.482832 78774 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581487.482921 78774 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581487.482985 78774 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581487.488130 78774 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581487.488390 78774 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581488.142089 78878 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n", "GPU memory cleared.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[5] Filtered stderr:\n", "I0000 00:00:1729581488.142089 78878 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n", "GPU memory cleared.\n", "Return code: 0\n", "Model 6 loaded successfully\n", "[6] Filtered stderr:\n", "I0000 00:00:1729581499.502982 80842 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n", "GPU memory cleared.\n", "Return code: 0\n", "Model 7 loaded successfully\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", "I0000 00:00:1729581498.448899 80732 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581498.486189 80732 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581498.486285 80732 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", "I0000 00:00:1729581498.500002 80732 service.cc:146] XLA service 0x5596e161b000 initialized for platform Host (this does not guarantee that XLA will be used). Devices:\n", "I0000 00:00:1729581498.500064 80732 service.cc:154] StreamExecutor device (0): Host, Default Version\n", "I0000 00:00:1729581498.649008 80823 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581498.649232 80732 service.cc:146] XLA service 0x5596e13741b0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", "I0000 00:00:1729581498.649283 80732 service.cc:154] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Laptop GPU, Compute Capability 8.6\n", "I0000 00:00:1729581498.649578 80732 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581498.649666 80732 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581498.649731 80732 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581498.659566 80732 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581498.660117 80732 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581499.502982 80842 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n", "GPU memory cleared.\n" ] } ], "source": [ "xtr_de=[xtr_de1, xtr_de2, xtr_de3, xtr_de12, xtr_de13, xtr_de23, xtr_def]\n", "ytr_de=[ytr_de1, ytr_de2, ytr_de3, ytr_de12, ytr_de13, ytr_de23, ytr_def]\n", "xte_de=[xte_de1, xte_de2, xte_de3, xte_de12, xte_de13, xte_de23, xte_def]\n", "yte_de=[yte_de1, yte_de2, yte_de3, yte_de12, yte_de13, yte_de23, yte_def]\n", "res_de = model_study_datasets(xtr_de,ytr_de)\n", "#12m 43s" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", "I0000 00:00:1729581510.471303 82705 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581510.510176 82705 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581510.510285 82705 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", "I0000 00:00:1729581510.523561 82705 service.cc:146] XLA service 0x56481dff04f0 initialized for platform Host (this does not guarantee that XLA will be used). Devices:\n", "I0000 00:00:1729581510.523614 82705 service.cc:154] StreamExecutor device (0): Host, Default Version\n", "I0000 00:00:1729581510.665334 82796 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581510.665505 82705 service.cc:146] XLA service 0x56481ddb0dd0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", "I0000 00:00:1729581510.665545 82705 service.cc:154] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Laptop GPU, Compute Capability 8.6\n", "I0000 00:00:1729581510.665868 82705 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581510.666034 82705 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581510.666131 82705 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581510.671972 82705 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581510.672399 82705 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581511.384943 82815 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n", "GPU memory cleared.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[0] Filtered stderr:\n", "I0000 00:00:1729581511.384943 82815 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n", "GPU memory cleared.\n", "Return code: 0\n", "Model 1 loaded successfully\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", "I0000 00:00:1729581519.214259 84670 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581519.240393 84670 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581519.240503 84670 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", "I0000 00:00:1729581519.250564 84670 service.cc:146] XLA service 0x55bdd6c5e3b0 initialized for platform Host (this does not guarantee that XLA will be used). Devices:\n", "I0000 00:00:1729581519.250608 84670 service.cc:154] StreamExecutor device (0): Host, Default Version\n", "I0000 00:00:1729581519.387208 84755 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581519.387362 84670 service.cc:146] XLA service 0x55bdd6ba9ef0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", "I0000 00:00:1729581519.387397 84670 service.cc:154] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Laptop GPU, Compute Capability 8.6\n", "I0000 00:00:1729581519.387606 84670 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581519.387714 84670 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581519.387782 84670 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581519.393466 84670 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581519.393798 84670 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581520.006096 84774 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n", "GPU memory cleared.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] Filtered stderr:\n", "I0000 00:00:1729581520.006096 84774 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n", "GPU memory cleared.\n", "Return code: 0\n", "Model 2 loaded successfully\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", "I0000 00:00:1729581529.386217 86625 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581529.423024 86625 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581529.423130 86625 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", "I0000 00:00:1729581529.436833 86625 service.cc:146] XLA service 0x563fe18400d0 initialized for platform Host (this does not guarantee that XLA will be used). Devices:\n", "I0000 00:00:1729581529.436872 86625 service.cc:154] StreamExecutor device (0): Host, Default Version\n", "I0000 00:00:1729581529.575150 86716 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581529.575284 86625 service.cc:146] XLA service 0x563fdfc68840 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", "I0000 00:00:1729581529.575315 86625 service.cc:154] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Laptop GPU, Compute Capability 8.6\n", "I0000 00:00:1729581529.575479 86625 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581529.575564 86625 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581529.575644 86625 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581529.580281 86625 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581529.580511 86625 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581530.199878 86734 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n", "GPU memory cleared.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[2] Filtered stderr:\n", "I0000 00:00:1729581530.199878 86734 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n", "GPU memory cleared.\n", "Return code: 0\n", "Model 3 loaded successfully\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", "I0000 00:00:1729581539.616280 88584 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581539.654333 88584 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581539.654467 88584 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", "I0000 00:00:1729581539.667632 88584 service.cc:146] XLA service 0x557a02649f20 initialized for platform Host (this does not guarantee that XLA will be used). Devices:\n", "I0000 00:00:1729581539.667674 88584 service.cc:154] StreamExecutor device (0): Host, Default Version\n", "I0000 00:00:1729581539.820620 88675 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581539.820781 88584 service.cc:146] XLA service 0x557a023a30d0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", "I0000 00:00:1729581539.820823 88584 service.cc:154] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Laptop GPU, Compute Capability 8.6\n", "I0000 00:00:1729581539.821008 88584 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581539.821100 88584 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581539.821171 88584 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581539.826427 88584 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581539.826699 88584 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581540.546660 88695 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n", "GPU memory cleared.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[3] Filtered stderr:\n", "I0000 00:00:1729581540.546660 88695 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n", "GPU memory cleared.\n", "Return code: 0\n", "Model 4 loaded successfully\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", "I0000 00:00:1729581548.461017 90552 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581548.494287 90552 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581548.494436 90552 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", "I0000 00:00:1729581548.507492 90552 service.cc:146] XLA service 0x564a54dd0100 initialized for platform Host (this does not guarantee that XLA will be used). Devices:\n", "I0000 00:00:1729581548.507533 90552 service.cc:154] StreamExecutor device (0): Host, Default Version\n", "I0000 00:00:1729581548.645477 90637 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581548.645601 90552 service.cc:146] XLA service 0x564a533c9220 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", "I0000 00:00:1729581548.645637 90552 service.cc:154] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Laptop GPU, Compute Capability 8.6\n", "I0000 00:00:1729581548.645914 90552 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581548.646011 90552 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581548.646089 90552 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581548.650883 90552 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581548.651209 90552 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581549.454003 90656 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n", "GPU memory cleared.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[4] Filtered stderr:\n", "I0000 00:00:1729581549.454003 90656 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n", "GPU memory cleared.\n", "Return code: 0\n", "Model 5 loaded successfully\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", "I0000 00:00:1729581559.168176 92517 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581559.195339 92517 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581559.195458 92517 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", "I0000 00:00:1729581559.205123 92517 service.cc:146] XLA service 0x55f572a490c0 initialized for platform Host (this does not guarantee that XLA will be used). Devices:\n", "I0000 00:00:1729581559.205168 92517 service.cc:154] StreamExecutor device (0): Host, Default Version\n", "I0000 00:00:1729581559.337704 92603 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581559.337840 92517 service.cc:146] XLA service 0x55f5726cb060 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", "I0000 00:00:1729581559.337878 92517 service.cc:154] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Laptop GPU, Compute Capability 8.6\n", "I0000 00:00:1729581559.338040 92517 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581559.338134 92517 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581559.338215 92517 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581559.342813 92517 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581559.343056 92517 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581560.010274 92623 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n", "GPU memory cleared.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[5] Filtered stderr:\n", "I0000 00:00:1729581560.010274 92623 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n", "GPU memory cleared.\n", "Return code: 0\n", "Model 6 loaded successfully\n", "[6] Filtered stderr:\n", "I0000 00:00:1729581570.612888 94582 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n", "GPU memory cleared.\n", "Return code: 0\n", "Model 7 loaded successfully\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", "I0000 00:00:1729581569.581321 94477 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581569.623172 94477 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581569.623363 94477 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", "I0000 00:00:1729581569.636189 94477 service.cc:146] XLA service 0x56352683bf20 initialized for platform Host (this does not guarantee that XLA will be used). Devices:\n", "I0000 00:00:1729581569.636257 94477 service.cc:154] StreamExecutor device (0): Host, Default Version\n", "I0000 00:00:1729581569.791304 94562 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581569.791444 94477 service.cc:146] XLA service 0x5635265950d0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", "I0000 00:00:1729581569.791476 94477 service.cc:154] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Laptop GPU, Compute Capability 8.6\n", "I0000 00:00:1729581569.791754 94477 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581569.791837 94477 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581569.791900 94477 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581569.796573 94477 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581569.796833 94477 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581570.612888 94582 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n", "GPU memory cleared.\n" ] } ], "source": [ "xtr_lo=[xtr_lo1, xtr_lo2, xtr_lo3, xtr_lo12, xtr_lo13, xtr_lo23, xtr_lof]\n", "ytr_lo=[ytr_lo1, ytr_lo2, ytr_lo3, ytr_lo12, ytr_lo13, ytr_lo23, ytr_lof]\n", "xte_lo=[xte_lo1, xte_lo2, xte_lo3, xte_lo12, xte_lo13, xte_lo23, xte_lof]\n", "yte_lo=[yte_lo1, yte_lo2, yte_lo3, yte_lo12, yte_lo13, yte_lo23, yte_lof]\n", "res_lo = model_study_datasets(xtr_lo,ytr_lo)\n", "#10m 9.2s" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", "I0000 00:00:1729581578.805726 96445 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581578.837320 96445 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581578.837496 96445 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", "I0000 00:00:1729581578.850697 96445 service.cc:146] XLA service 0x559baec64350 initialized for platform Host (this does not guarantee that XLA will be used). Devices:\n", "I0000 00:00:1729581578.850737 96445 service.cc:154] StreamExecutor device (0): Host, Default Version\n", "I0000 00:00:1729581578.980532 96530 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581578.980683 96445 service.cc:146] XLA service 0x559baec55040 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", "I0000 00:00:1729581578.980715 96445 service.cc:154] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Laptop GPU, Compute Capability 8.6\n", "I0000 00:00:1729581578.980880 96445 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581578.980961 96445 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581578.981026 96445 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581578.988729 96445 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581578.989009 96445 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581579.684161 96550 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n", "GPU memory cleared.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[0] Filtered stderr:\n", "I0000 00:00:1729581579.684161 96550 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n", "GPU memory cleared.\n", "Return code: 0\n", "Model 1 loaded successfully\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", "I0000 00:00:1729581590.703218 98408 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581590.737480 98408 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581590.737578 98408 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", "I0000 00:00:1729581590.750895 98408 service.cc:146] XLA service 0x55f63480f590 initialized for platform Host (this does not guarantee that XLA will be used). Devices:\n", "I0000 00:00:1729581590.750951 98408 service.cc:154] StreamExecutor device (0): Host, Default Version\n", "I0000 00:00:1729581590.881812 98499 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581590.881936 98408 service.cc:146] XLA service 0x55f63290ba90 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", "I0000 00:00:1729581590.881965 98408 service.cc:154] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Laptop GPU, Compute Capability 8.6\n", "I0000 00:00:1729581590.882113 98408 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581590.882190 98408 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581590.882251 98408 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581590.887441 98408 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581590.887743 98408 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581591.501874 98518 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n", "GPU memory cleared.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] Filtered stderr:\n", "I0000 00:00:1729581591.501874 98518 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n", "GPU memory cleared.\n", "Return code: 0\n", "Model 2 loaded successfully\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", "I0000 00:00:1729581602.121342 100372 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581602.158634 100372 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581602.158735 100372 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", "I0000 00:00:1729581602.172526 100372 service.cc:146] XLA service 0x5580df908f90 initialized for platform Host (this does not guarantee that XLA will be used). Devices:\n", "I0000 00:00:1729581602.172572 100372 service.cc:154] StreamExecutor device (0): Host, Default Version\n", "I0000 00:00:1729581602.319493 100457 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581602.319620 100372 service.cc:146] XLA service 0x5580df68f3e0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", "I0000 00:00:1729581602.319653 100372 service.cc:154] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Laptop GPU, Compute Capability 8.6\n", "I0000 00:00:1729581602.319840 100372 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581602.319938 100372 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581602.320012 100372 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581602.325549 100372 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581602.325831 100372 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581602.990520 100476 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n", "GPU memory cleared.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[2] Filtered stderr:\n", "I0000 00:00:1729581602.990520 100476 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n", "GPU memory cleared.\n", "Return code: 0\n", "Model 3 loaded successfully\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", "I0000 00:00:1729581611.816786 102337 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581611.857822 102337 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581611.857919 102337 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", "I0000 00:00:1729581611.871636 102337 service.cc:146] XLA service 0x560390707a80 initialized for platform Host (this does not guarantee that XLA will be used). Devices:\n", "I0000 00:00:1729581611.871674 102337 service.cc:154] StreamExecutor device (0): Host, Default Version\n", "I0000 00:00:1729581612.014939 102422 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581612.015068 102337 service.cc:146] XLA service 0x560390493f90 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", "I0000 00:00:1729581612.015104 102337 service.cc:154] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Laptop GPU, Compute Capability 8.6\n", "I0000 00:00:1729581612.015276 102337 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581612.015369 102337 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581612.015448 102337 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581612.020072 102337 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581612.020327 102337 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581612.721122 102442 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n", "GPU memory cleared.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[3] Filtered stderr:\n", "I0000 00:00:1729581612.721122 102442 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n", "GPU memory cleared.\n", "Return code: 0\n", "Model 4 loaded successfully\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", "I0000 00:00:1729581624.182912 104306 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581624.221140 104306 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581624.221260 104306 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", "I0000 00:00:1729581624.234705 104306 service.cc:146] XLA service 0x5608e20ab080 initialized for platform Host (this does not guarantee that XLA will be used). Devices:\n", "I0000 00:00:1729581624.234770 104306 service.cc:154] StreamExecutor device (0): Host, Default Version\n", "I0000 00:00:1729581624.376020 104391 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581624.376166 104306 service.cc:146] XLA service 0x5608e06a4310 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", "I0000 00:00:1729581624.376210 104306 service.cc:154] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Laptop GPU, Compute Capability 8.6\n", "I0000 00:00:1729581624.376394 104306 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581624.376491 104306 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581624.376572 104306 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581624.381789 104306 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581624.382107 104306 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581625.109021 104410 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n", "GPU memory cleared.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[4] Filtered stderr:\n", "I0000 00:00:1729581625.109021 104410 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n", "GPU memory cleared.\n", "Return code: 0\n", "Model 5 loaded successfully\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", "I0000 00:00:1729581636.750135 106274 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581636.777522 106274 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581636.777632 106274 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", "I0000 00:00:1729581636.788110 106274 service.cc:146] XLA service 0x563d092c0260 initialized for platform Host (this does not guarantee that XLA will be used). Devices:\n", "I0000 00:00:1729581636.788153 106274 service.cc:154] StreamExecutor device (0): Host, Default Version\n", "I0000 00:00:1729581636.935448 106359 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581636.935645 106274 service.cc:146] XLA service 0x563d09019410 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", "I0000 00:00:1729581636.935680 106274 service.cc:154] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Laptop GPU, Compute Capability 8.6\n", "I0000 00:00:1729581636.936007 106274 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581636.936127 106274 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581636.936202 106274 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581636.941601 106274 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581636.941824 106274 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581637.611218 106378 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n", "GPU memory cleared.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[5] Filtered stderr:\n", "I0000 00:00:1729581637.611218 106378 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n", "GPU memory cleared.\n", "Return code: 0\n", "Model 6 loaded successfully\n", "[6] Filtered stderr:\n", "I0000 00:00:1729581647.823414 108343 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n", "GPU memory cleared.\n", "Return code: 0\n", "Model 7 loaded successfully\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", "I0000 00:00:1729581646.740451 108239 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581646.779158 108239 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581646.779301 108239 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", "I0000 00:00:1729581646.792678 108239 service.cc:146] XLA service 0x55995e02ef60 initialized for platform Host (this does not guarantee that XLA will be used). Devices:\n", "I0000 00:00:1729581646.792733 108239 service.cc:154] StreamExecutor device (0): Host, Default Version\n", "I0000 00:00:1729581646.969233 108324 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581646.969360 108239 service.cc:146] XLA service 0x55995dd88110 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", "I0000 00:00:1729581646.969390 108239 service.cc:154] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Laptop GPU, Compute Capability 8.6\n", "I0000 00:00:1729581646.969627 108239 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581646.969720 108239 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581646.969783 108239 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581646.974690 108239 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581646.974951 108239 cuda_executor.cc:1001] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", "I0000 00:00:1729581647.823414 108343 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n", "GPU memory cleared.\n" ] } ], "source": [ "xtr_hu=[xtr_hu1, xtr_hu2, xtr_hu3, xtr_hu12, xtr_hu13, xtr_hu23, xtr_huf]\n", "ytr_hu=[ytr_hu1, ytr_hu2, ytr_hu3, ytr_hu12, ytr_hu13, ytr_hu23, ytr_huf]\n", "xte_hu=[xte_hu1, xte_hu2, xte_hu3, xte_hu12, xte_hu13, xte_hu23, xte_huf]\n", "yte_hu=[yte_hu1, yte_hu2, yte_hu3, yte_hu12, yte_hu13, yte_hu23, yte_huf]\n", "res_hu = model_study_datasets(xtr_hu,ytr_hu)\n", "#13m 24s" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [], "source": [ "def print_metrics(dataset, model_name, ypre,yte):\n", " r2 = r2_score(yte,ypre)\n", " mae = mean_absolute_error(yte,ypre)\n", " mse = mean_squared_error(yte,ypre)\n", " rmse = root_mean_squared_error(yte,ypre)\n", " print(f\"[data : {dataset} ][model : {model_name} ] = r2 : {r2:.5f}, mae : {mae:.5f}, mse : {mse:.5f}, rmse : {rmse:.5f}\")" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", "I0000 00:00:1729581657.662945 55205 service.cc:146] XLA service 0x7f8bf0002f40 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", "I0000 00:00:1729581657.662992 55205 service.cc:154] StreamExecutor device (0): NVIDIA GeForce RTX 3060 Laptop GPU, Compute Capability 8.6\n", "2024-10-22 16:20:57.668163: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:268] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.\n", "2024-10-22 16:20:57.696340: I external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:531] Loaded cuDNN version 8907\n", "2024-10-22 16:20:57.701263: W external/local_xla/xla/service/gpu/gemm_fusion_autotuner.cc:806] Compiling 29 configs for gemm_fusion_dot.17 on a single thread.\n", "2024-10-22 16:20:58.163105: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:393] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_17', 20 bytes spill stores, 20 bytes spill loads\n", "\n", "2024-10-22 16:21:00.217402: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:393] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_17', 16 bytes spill stores, 16 bytes spill loads\n", "\n", "2024-10-22 16:21:00.756085: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:393] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_17', 252 bytes spill stores, 252 bytes spill loads\n", "\n", "I0000 00:00:1729581664.543501 55205 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n", "2024-10-22 16:21:04.595761: W external/local_xla/xla/service/gpu/gemm_fusion_autotuner.cc:806] Compiling 28 configs for gemm_fusion_dot.17 on a single thread.\n", "2024-10-22 16:21:04.980122: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:393] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_17', 252 bytes spill stores, 252 bytes spill loads\n", "\n", "2024-10-22 16:21:07.009148: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:393] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_17', 8 bytes spill stores, 8 bytes spill loads\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[data : ws ][model : ECEP ] = r2 : 0.61105, mae : 0.92837, mse : 1.38107, rmse : 1.17519\n", "[data : ws ][model : MACCS ] = r2 : 0.64861, mae : 0.87559, mse : 1.24771, rmse : 1.11701\n", "WARNING:tensorflow:5 out of the last 9 calls to .one_step_on_data_distributed at 0x7f8c5067f060> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", "WARNING:tensorflow:6 out of the last 12 calls to .one_step_on_data_distributed at 0x7f8c5067f060> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", "[data : ws ][model : Avalon ] = r2 : 0.70445, mae : 0.79791, mse : 1.04945, rmse : 1.02443\n", "[data : ws ][model : ECEP_MACCS ] = r2 : 0.68874, mae : 0.82767, mse : 1.10520, rmse : 1.05129\n", "[data : ws ][model : ECEP_Ava. ] = r2 : 0.71958, mae : 0.75887, mse : 0.99571, rmse : 0.99785\n", "[data : ws ][model : MACCS_Ava. ] = r2 : 0.74675, mae : 0.73342, mse : 0.89924, rmse : 0.94828\n", "[data : ws ][model : ECFP+MACCS+Ava. ] = r2 : 0.76254, mae : 0.72435, mse : 0.84317, rmse : 0.91824\n" ] } ], "source": [ "print_metrics('ws','ECEP ', res_ws[0].predict(xte_ws[0], verbose=0), yte_ws[0])\n", "print_metrics('ws','MACCS ', res_ws[1].predict(xte_ws[1], verbose=0), yte_ws[1])\n", "print_metrics('ws','Avalon ', res_ws[2].predict(xte_ws[2], verbose=0), yte_ws[2])\n", "print_metrics('ws','ECEP_MACCS', res_ws[3].predict(xte_ws[3], verbose=0), yte_ws[3])\n", "print_metrics('ws','ECEP_Ava. ', res_ws[4].predict(xte_ws[4], verbose=0), yte_ws[4])\n", "print_metrics('ws','MACCS_Ava.', res_ws[5].predict(xte_ws[5], verbose=0), yte_ws[5])\n", "print_metrics('ws','ECFP+MACCS+Ava.', res_ws[6].predict(xte_ws[6], verbose=0), yte_ws[6])\n", "\n", "# [data : ws ][model : ECEP ] = r2 : 0.66232, mae : 0.83593, mse : 1.10171, rmse : 1.04962\n", "# [data : ws ][model : MACCS ] = r2 : 0.74583, mae : 0.71737, mse : 0.82926, rmse : 0.91064\n", "# [data : ws ][model : Avalon ] = r2 : 0.71877, mae : 0.75613, mse : 0.91754, rmse : 0.95788\n", "# [data : ws ][model : ECEP_MACCS ] = r2 : 0.77450, mae : 0.63215, mse : 0.73572, rmse : 0.85774\n", "# [data : ws ][model : ECEP_Ava. ] = r2 : 0.74313, mae : 0.67417, mse : 0.83806, rmse : 0.91546\n", "# [data : ws ][model : MACCS_Ava. ] = r2 : 0.77575, mae : 0.65553, mse : 0.73164, rmse : 0.85536\n", "# [data : ws ][model : ECFP+MACCS+Ava. ] = r2 : 0.78273, mae : 0.62530, mse : 0.70886, rmse : 0.84194" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2024-10-22 16:21:10.619072: W external/local_xla/xla/service/gpu/gemm_fusion_autotuner.cc:806] Compiling 28 configs for gemm_fusion_dot.17 on a single thread.\n", "2024-10-22 16:21:10.993617: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:393] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_17', 236 bytes spill stores, 236 bytes spill loads\n", "\n", "2024-10-22 16:21:13.017899: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:393] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_17', 8 bytes spill stores, 8 bytes spill loads\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[data : de ][model : ECEP ] = r2 : 0.73302, mae : 0.62410, mse : 0.72927, rmse : 0.85397\n", "[data : de ][model : MACCS ] = r2 : 0.81337, mae : 0.51370, mse : 0.50978, rmse : 0.71399\n", "[data : de ][model : Avalon ] = r2 : 0.81189, mae : 0.49952, mse : 0.51382, rmse : 0.71682\n", "[data : de ][model : ECEP_MACCS ] = r2 : 0.85766, mae : 0.43006, mse : 0.38881, rmse : 0.62354\n", "[data : de ][model : ECEP_Ava. ] = r2 : 0.82396, mae : 0.48403, mse : 0.48087, rmse : 0.69345\n", "[data : de ][model : MACCS_Ava. ] = r2 : 0.84670, mae : 0.44083, mse : 0.41874, rmse : 0.64710\n", "[data : de ][model : ECFP+MACCS+Ava. ] = r2 : 0.85978, mae : 0.42124, mse : 0.38301, rmse : 0.61888\n" ] } ], "source": [ "print_metrics('de','ECEP ', res_de[0].predict(xte_de[0], verbose=0), yte_de[0])\n", "print_metrics('de','MACCS ', res_de[1].predict(xte_de[1], verbose=0), yte_de[1])\n", "print_metrics('de','Avalon ', res_de[2].predict(xte_de[2], verbose=0), yte_de[2])\n", "print_metrics('de','ECEP_MACCS', res_de[3].predict(xte_de[3], verbose=0), yte_de[3])\n", "print_metrics('de','ECEP_Ava. ', res_de[4].predict(xte_de[4], verbose=0), yte_de[4])\n", "print_metrics('de','MACCS_Ava.', res_de[5].predict(xte_de[5], verbose=0), yte_de[5])\n", "print_metrics('de','ECFP+MACCS+Ava.', res_de[6].predict(xte_de[6], verbose=0), yte_de[6])\n", "\n", "# [data : de ][model : ECEP ] = r2 : 0.83667, mae : 0.46861, mse : 0.48737, rmse : 0.69812\n", "# [data : de ][model : MACCS ] = r2 : 0.79751, mae : 0.49282, mse : 0.60425, rmse : 0.77734\n", "# [data : de ][model : Avalon ] = r2 : 0.85147, mae : 0.41674, mse : 0.44323, rmse : 0.66576\n", "# [data : de ][model : ECEP_MACCS ] = r2 : 0.85653, mae : 0.38113, mse : 0.42812, rmse : 0.65431\n", "# [data : de ][model : ECEP_Ava. ] = r2 : 0.88713, mae : 0.35384, mse : 0.33680, rmse : 0.58035\n", "# [data : de ][model : MACCS_Ava. ] = r2 : 0.88132, mae : 0.34731, mse : 0.35414, rmse : 0.59509\n", "# [data : de ][model : ECFP+MACCS+Ava. ] = r2 : 0.88028, mae : 0.34027, mse : 0.35725, rmse : 0.59770\n", "\n", "# [data : de ][model : ECEP ] = r2 : 0.83681, mae : 0.48289, mse : 0.48696, rmse : 0.69783\n", "# [data : de ][model : MACCS ] = r2 : 0.81288, mae : 0.46656, mse : 0.55837, rmse : 0.74724\n", "# [data : de ][model : Avalon ] = r2 : 0.86393, mae : 0.38842, mse : 0.40602, rmse : 0.63720\n", "# [data : de ][model : ECEP_MACCS ] = r2 : 0.86069, mae : 0.38990, mse : 0.41570, rmse : 0.64475\n", "# [data : de ][model : ECEP_Ava. ] = r2 : 0.86526, mae : 0.39938, mse : 0.40208, rmse : 0.63410\n", "# [data : de ][model : MACCS_Ava. ] = r2 : 0.87254, mae : 0.36285, mse : 0.38035, rmse : 0.61673\n", "# [data : de ][model : ECFP+MACCS+Ava. ] = r2 : 0.89274, mae : 0.32713, mse : 0.32007, rmse : 0.56575" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2024-10-22 16:21:18.631787: W external/local_xla/xla/service/gpu/gemm_fusion_autotuner.cc:806] Compiling 28 configs for gemm_fusion_dot.17 on a single thread.\n", "2024-10-22 16:21:19.005581: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:393] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_17', 268 bytes spill stores, 268 bytes spill loads\n", "\n", "2024-10-22 16:21:21.089454: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:393] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_17', 12 bytes spill stores, 12 bytes spill loads\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[data : lo ][model : ECEP ] = r2 : 0.52569, mae : 0.81087, mse : 1.53130, rmse : 1.23746\n", "[data : lo ][model : MACCS ] = r2 : 0.59064, mae : 0.78713, mse : 1.32162, rmse : 1.14962\n", "[data : lo ][model : Avalon ] = r2 : 0.61865, mae : 0.76969, mse : 1.23118, rmse : 1.10958\n", "[data : lo ][model : ECEP_MACCS ] = r2 : 0.62562, mae : 0.72673, mse : 1.20867, rmse : 1.09939\n", "[data : lo ][model : ECEP_Ava. ] = r2 : 0.62979, mae : 0.74040, mse : 1.19521, rmse : 1.09325\n", "[data : lo ][model : MACCS_Ava. ] = r2 : 0.58346, mae : 0.75899, mse : 1.34479, rmse : 1.15965\n", "[data : lo ][model : ECFP+MACCS+Ava. ] = r2 : 0.68656, mae : 0.71189, mse : 1.01192, rmse : 1.00594\n" ] } ], "source": [ "print_metrics('lo','ECEP ', res_lo[0].predict(xte_lo[0], verbose=0), yte_lo[0])\n", "print_metrics('lo','MACCS ', res_lo[1].predict(xte_lo[1], verbose=0), yte_lo[1])\n", "print_metrics('lo','Avalon ', res_lo[2].predict(xte_lo[2], verbose=0), yte_lo[2])\n", "print_metrics('lo','ECEP_MACCS', res_lo[3].predict(xte_lo[3], verbose=0), yte_lo[3])\n", "print_metrics('lo','ECEP_Ava. ', res_lo[4].predict(xte_lo[4], verbose=0), yte_lo[4])\n", "print_metrics('lo','MACCS_Ava.', res_lo[5].predict(xte_lo[5], verbose=0), yte_lo[5])\n", "print_metrics('lo','ECFP+MACCS+Ava.', res_lo[6].predict(xte_lo[6], verbose=0), yte_lo[6])\n", "\n", "# [data : lo ][model : ECEP ] = r2 : 0.62102, mae : 0.73440, mse : 1.00307, rmse : 1.00153\n", "# [data : lo ][model : MACCS ] = r2 : 0.59578, mae : 0.68067, mse : 1.06987, rmse : 1.03435\n", "# [data : lo ][model : Avalon ] = r2 : 0.68422, mae : 0.67421, mse : 0.83580, rmse : 0.91422\n", "# [data : lo ][model : ECEP_MACCS ] = r2 : 0.68492, mae : 0.64230, mse : 0.83395, rmse : 0.91321\n", "# [data : lo ][model : ECEP_Ava. ] = r2 : 0.69790, mae : 0.64407, mse : 0.79957, rmse : 0.89419\n", "# [data : lo ][model : MACCS_Ava. ] = r2 : 0.70558, mae : 0.61507, mse : 0.77926, rmse : 0.88276\n", "# [data : lo ][model : ECFP+MACCS+Ava. ] = r2 : 0.71277, mae : 0.62492, mse : 0.76024, rmse : 0.87192" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2024-10-22 16:21:26.633093: W external/local_xla/xla/service/gpu/gemm_fusion_autotuner.cc:806] Compiling 28 configs for gemm_fusion_dot.17 on a single thread.\n", "2024-10-22 16:21:27.004492: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:393] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_17', 252 bytes spill stores, 252 bytes spill loads\n", "\n", "2024-10-22 16:21:29.032220: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:393] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_17', 8 bytes spill stores, 8 bytes spill loads\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[data : hu ][model : ECEP ] = r2 : 0.79756, mae : 0.74109, mse : 0.98363, rmse : 0.99178\n", "[data : hu ][model : MACCS ] = r2 : 0.81545, mae : 0.70877, mse : 0.89674, rmse : 0.94696\n", "[data : hu ][model : Avalon ] = r2 : 0.85838, mae : 0.62720, mse : 0.68814, rmse : 0.82954\n", "[data : hu ][model : ECEP_MACCS ] = r2 : 0.85673, mae : 0.60539, mse : 0.69614, rmse : 0.83435\n", "[data : hu ][model : ECEP_Ava. ] = r2 : 0.88684, mae : 0.56374, mse : 0.54984, rmse : 0.74151\n", "[data : hu ][model : MACCS_Ava. ] = r2 : 0.88979, mae : 0.55776, mse : 0.53551, rmse : 0.73178\n", "[data : hu ][model : ECFP+MACCS+Ava. ] = r2 : 0.89083, mae : 0.55597, mse : 0.53045, rmse : 0.72832\n" ] } ], "source": [ "print_metrics('hu','ECEP ', res_hu[0].predict(xte_hu[0], verbose=0), yte_hu[0])\n", "print_metrics('hu','MACCS ', res_hu[1].predict(xte_hu[1], verbose=0), yte_hu[1])\n", "print_metrics('hu','Avalon ', res_hu[2].predict(xte_hu[2], verbose=0), yte_hu[2])\n", "print_metrics('hu','ECEP_MACCS', res_hu[3].predict(xte_hu[3], verbose=0), yte_hu[3])\n", "print_metrics('hu','ECEP_Ava. ', res_hu[4].predict(xte_hu[4], verbose=0), yte_hu[4])\n", "print_metrics('hu','MACCS_Ava.', res_hu[5].predict(xte_hu[5], verbose=0), yte_hu[5])\n", "print_metrics('hu','ECFP+MACCS+Ava.', res_hu[6].predict(xte_hu[6], verbose=0), yte_hu[6])\n", "\n", "# [data : hu ][model : ECEP ] = r2 : 0.74465, mae : 0.79418, mse : 1.13364, rmse : 1.06472\n", "# [data : hu ][model : MACCS ] = r2 : 0.81311, mae : 0.67737, mse : 0.82969, rmse : 0.91088\n", "# [data : hu ][model : Avalon ] = r2 : 0.83078, mae : 0.64302, mse : 0.75128, rmse : 0.86676\n", "# [data : hu ][model : ECEP_MACCS ] = r2 : 0.84665, mae : 0.61117, mse : 0.68083, rmse : 0.82512\n", "# [data : hu ][model : ECEP_Ava. ] = r2 : 0.85118, mae : 0.58893, mse : 0.66071, rmse : 0.81284\n", "# [data : hu ][model : MACCS_Ava. ] = r2 : 0.85636, mae : 0.57487, mse : 0.63769, rmse : 0.79856\n", "# [data : hu ][model : ECFP+MACCS+Ava. ] = r2 : 0.86441, mae : 0.57482, mse : 0.60195, rmse : 0.77585" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [], "source": [ "ypred_de = [res_de[0].predict(xte_de[0], verbose=0),\n", " res_de[1].predict(xte_de[1], verbose=0),\n", " res_de[2].predict(xte_de[2], verbose=0),\n", " res_de[3].predict(xte_de[3], verbose=0),\n", " res_de[4].predict(xte_de[4], verbose=0),\n", " res_de[5].predict(xte_de[5], verbose=0),\n", " res_de[6].predict(xte_de[6], verbose=0)]" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [], "source": [ "ypred_ws = [res_ws[0].predict(xte_ws[0], verbose=0),\n", " res_ws[1].predict(xte_ws[1], verbose=0),\n", " res_ws[2].predict(xte_ws[2], verbose=0),\n", " res_ws[3].predict(xte_ws[3], verbose=0),\n", " res_ws[4].predict(xte_ws[4], verbose=0),\n", " res_ws[5].predict(xte_ws[5], verbose=0),\n", " res_ws[6].predict(xte_ws[6], verbose=0)]\n", "\n", "ypred_de = [res_de[0].predict(xte_de[0], verbose=0),\n", " res_de[1].predict(xte_de[1], verbose=0),\n", " res_de[2].predict(xte_de[2], verbose=0),\n", " res_de[3].predict(xte_de[3], verbose=0),\n", " res_de[4].predict(xte_de[4], verbose=0),\n", " res_de[5].predict(xte_de[5], verbose=0),\n", " res_de[6].predict(xte_de[6], verbose=0)]\n", "\n", "ypred_lo = [res_lo[0].predict(xte_lo[0], verbose=0),\n", " res_lo[1].predict(xte_lo[1], verbose=0),\n", " res_lo[2].predict(xte_lo[2], verbose=0),\n", " res_lo[3].predict(xte_lo[3], verbose=0),\n", " res_lo[4].predict(xte_lo[4], verbose=0),\n", " res_lo[5].predict(xte_lo[5], verbose=0),\n", " res_lo[6].predict(xte_lo[6], verbose=0)]\n", "\n", "ypred_hu = [res_hu[0].predict(xte_hu[0], verbose=0),\n", " res_hu[1].predict(xte_hu[1], verbose=0),\n", " res_hu[2].predict(xte_hu[2], verbose=0),\n", " res_hu[3].predict(xte_hu[3], verbose=0),\n", " res_hu[4].predict(xte_hu[4], verbose=0),\n", " res_hu[5].predict(xte_hu[5], verbose=0),\n", " res_hu[6].predict(xte_hu[6], verbose=0)]" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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VqyY3fXFDn7gROb+8jbvv37+XapwvzrFLTEwEACQlJeH8+fNy8wQAqampMsO/1s+qOPkSNwjLa0iVt0x8/AAUevwA2cewKD3F5Z0LQO758PDhQ6HneXHTLkhZfrbm5uYAgOjo6GKvK/7OGBoaQiQSyYwj/t7IOk5Abu/6/PKmVdByecehsM8tf37OnDmDGTNmQFFREQsXLkTnzp1haWkJDQ0NiEQi/Pzzz1iwYAEyMzNlpinv8xKfpwkJCUhISJCbJ0D+95wxxhhj7EfDw9owxhhjjBVBYQ1k8nrifup6ANCoUSMcO3YMb9++xdGjRzFt2jRUqFABx48fR5s2bYReqHl169YNEyZMwJs3b9C6dWuZw5AAQLly5QAAL1++lLn8xYsXwv8FNSYXhXhbzs7OoNw5j+S+xD3Ev0fiRs2CeoLLatQVHz8AyMjIKPQYioeDKa6CGlTF50lJz4UvzcnJCQDkDv9TEPHnkJCQIPf7LP7elOVxKu7nFhwcDACYMmUKpk+fjpo1a0JTU1O4Vj158uST8iE+Ph4eHoWeo2FhYZ+0DcYYY4yx7w03zjPGGGOMFYG4YVVeQ9ijR49Kdb28ypUrBzc3NyxatAj37t1D5cqV8ezZM/z9998y469YsQI+Pj548eIFWrZsKXMbNjY2AIC7d+/KTOPOnTsAcnve5u81X1zioXOioqIKHEbje1elShUoKCjg1atXeP78ucw44qFB8tLR0RGGwRF/Lp9DVFSUzPCUlBQ8fvwYwH/nzZckr9d6UbRv3x7lypXDy5cvsWvXrmKtK/780tPT5Y7/Lv58yvI4iYd1kkX8mebNj/gGmPhGRX7yxpovjPh7fvv27U9anzHGGGPsR8SN84wxxhhjRaCvrw8dHR2kpqbKbCDduHFjqa4nj4aGBmxtbQEAcXFxcuOtXr0aQ4cORVxcHFq1aoXY2FiJ5W5ubgCANWvWyFz/999/l4hXElWrVkXt2rXx5s0bBAYGlji9b5WWlhYaNmwIANi0aZPU8sTEROzbt0/mut27dwcArFq16jPlLrc3+fXr16XC/f39kZaWBktLywKHSior6urqAD5taJTy5csLk7mOHz++0Cc1zp8/jwsXLgDIvUkmbtBevXq1VNzU1FTh+1wa35uiysjIgJ+fn1T47du3cfbsWWEoLDHx8cv7dIzY8ePHP7lxvmnTpjAwMMCNGze4ZzxjjDHGWBFx4zxjjDHGWBGIRCKhwW3ixIkSPVU3b94Mf3//Ul1v5MiR2L59O1JSUiTCz5w5g5MnTwIA7O3tC8zv+vXr4enpicePH6Nly5Z4+vSpRPra2tq4fv06JkyYgIyMDABATk4OlixZgsOHD0NZWRmTJk2Su43iWLx4MUQiEXx8fLBx40apIUX+/fdfLFiwAHv27CmV7X2tJk6cCACYP38+Dh48KIS/ffsW7u7ucntAT5s2DXp6eti8eTMmTpwoNaTRmzdv4O/vj19++eWT86akpISBAwdK3Mg5d+4cZs+eDQCYPHlyiXqtlxbxZKynT5/+pPXnzJmDxo0b48WLF2jcuDG2bNkiTEgs9uDBA/j4+KB58+YSQz9NmzYNALB27VqEhIQI4e/fv4enpycSEhJgZWUFd3f3T8rbp1BSUoKvr6/E8Xj69Ck8PT0B5N7YyTvRrHiy1kWLFkmMvR8REQFvb2+oqal9Uj7U1NQwb948AECvXr2wd+9eqeF/bt++jWnTpknNneDu7g4rK6vPevOJMcYYY+xrxI3zjDHGGGNFNHfuXJQrVw7Hjx+HiYkJHBwcYGZmhoEDB2L58uWlul54eDjc3d2ho6ODmjVromHDhrCysoKLiwvev3+P/v37o0WLFgXmV0FBAf7+/nB3d8e///6Lli1bIj4+HgBgZmaGLVu2QEVFBatWrYKJiQkaNGgAU1NTTJs2DQoKClizZg3q1Knz6Qcsj/bt22P16tVIT0/H0KFDoaenB0dHR9SvXx8mJiaoXLkyfv75Z7lj4H8vevbsCW9vb6SmpqJz586oXLky6tevD3Nzc1y6dAlTp04FACgqKkqsV6FCBRw4cAAGBgZYuXIljIyMUKdOHTRq1AiVK1eGgYEBBg8eXKIhRYYPH443b96gSpUqqFevHqpXr46mTZvi7du36NSpE0aNGlWifS8tffr0AZB7w6datWpwcXFB8+bNcfTo0SKtr6KiguPHj6NHjx6Ij4+Hp6cn9PT0YGtriwYNGqBChQqoVq0a1q5dCxMTE1SpUkVYt2PHjpg+fToyMzPh4eGBihUron79+jA1NcWuXbugq6uLHTt2CL3Ty4KTkxOaNGmC5s2bo1q1arC3t0elSpVw7do1WFtbSz0dM2zYMFhbW+Off/5B9erVUadOHVSvXh0NGjSAjo5OiT7nkSNHYvr06Xj16hW6d+8OAwMDNGjQAA4ODtDX14etrS2WLFkiNbdCfHw8YmNjZc6jwRhjjDH2PePGecYYY4yxIqpevTrOnDmDtm3bQkFBAffv30elSpVw8OBBjBgxolTXW7lyJcaNG4c6derg1atXwnAjbm5uOHDgQJGHh1FUVMSWLVvQo0cPPHz4EK1atRLGv+/cuTMiIyPh4eEBNTU1XL9+HUSEbt264dy5cxg2bFjxDlAhfHx8cP36dQwZMgSGhoa4c+cOHj58CAMDA/Tt2xc7d+4Uevt+z/766y+sXbsWtra2ePbsGWJjY9GpUydcvnwZpqamAGRPKOrs7Iy7d+9i5syZqFmzJqKjo3Hz5k0oKCigbdu2WLt2LX777bdPzpeBgQEuX74MT09PvHjxAtHR0ahWrRoWL16MPXv2QEHh6/jp0LRpU4SEhKBBgwZ49uwZzpw5g9OnTws3noqiXLly2LVrF86cOYPBgwfDwsICMTExuHHjBogIHTp0gJ+fHx48eIDatWtLrLtw4UIcPHgQbdq0wYcPH3Dz5k0YGBhgxIgRuHHjBurXr1/au1wgkUiEvXv3Ys6cOcjJycHdu3dhaGiIkSNH4tKlSzAxMZGIr62tjXPnzsHT0xPa2tq4f/8+MjIyMHHiRISHh5d4MtuFCxfi/Pnz6NevHzQ1NXHjxg3ExMSgQoUK8Pb2xuHDh9GqVasSbYMxxhhj7HshovzPGjLGGGOMMca+iDFjxmDNmjVYuXIlxo8fXybbnDNnDubOnQtfX1/MmTOnTLbJSi4sLAwtWrSAi4sLj/HOGGOMMfaN+jq6vzDGGGOMMfaD+/DhA3bt2gUgt5c8Y4wxxhhj7PvGjfOMMcYYY4yVoVWrVgnDFIk9e/ZMGAPd3t6+zIdGYYwxxhhjjJU9pS+dAcYYY4wxxn4k+/btw4QJE6CjowNra2ukp6fj3r17yMnJgYGBATZv3vyls8gYY4wxxhgrA9w4zxhjjDHGWBkaN24cdHV1cfXqVdy7dw9EhMqVK6Ndu3aYOnUqzM3Nv3QWGWOMMcYYY2WAJ4RljDHGGGOMMcYYY4wxxsoYjznPGGOMMcYYY4wxxhhjjJUxbpxnjDHGGGOMMcYYY4wxxsoYN84zxhhjjDHGGGOMMcYYY2WMG+cZY4wxxhhjjDHGGGOMsTLGjfOMMcYYY4wxxhhjjDHGWBnjxnnGGGOMMcYYY4wxxhhjrIxx4zxjjDHGGGOMMcYYY4wxVsa4cZ6xPDZt2gSRSCT3FRYWJhE/PT0da9asQZMmTaCrqwsVFRWYm5ujd+/eOH36tBAvLCxMbpo9e/YU4jVv3lximbq6Ouzs7LBq1Srk5OR80j6J0xo4cKDM5fPmzRPixMTEyIzTvXt3iEQijB49usBtnT17Fr1794a5uTlUVFSgo6MDJycnrFu3DsnJyRJxi3rsACAqKgoDBgyAtbU11NTUYGBgAHt7e4wePRpJSUlFPhayvHr1CqqqqhCJRLhy5UqJ0iqKOXPmQCQSffbtMMa+jO+xHMlLnL61tTWISGr5mTNnhG1v2rRJZhq///47RCIRateuXeC2/v33X4wePRo2NjZQV1eHhoYGatWqhZ9//hnPnj2Tin/w4EF06tQJxsbGUFFRgZ6eHlq1aoXg4GBkZmYK8V6/fo2ffvoJNWvWhKamJnR0dFC9enUMGDAAN2/eLN4B+Sj/cc/7srKy+uR9GzhwoNx0Dx06BACIiYmRCFdQUIC+vj7at2+P8PDwT9ofeW7dugWRSARlZWU8f/68VNOWZeDAgTKP35ci/jy0tLTw4cMHqeWxsbFQUFCASCTCnDlzZKZx4MABiEQi6OvrIz09Xe62Xrx4genTp8PW1hblypWDmpoaqlatinHjxuHhw4dS8YtaB0tOTsbixYthZ2cHbW1taGlpoXLlyjLrXyVV1O96abGyspJb32Xse8f1jx+z/iHPxIkTIRKJ0LFjx1JNV56Cyr3PqaDzfvLkyUIdSd458T1q3rw5mjdv/knrHjly5It8jgxQ+tIZYOxrFBAQgOrVq0uF16xZU/j/1atXaNu2LW7evAlvb29MmTIFenp6ePbsGfbv349WrVohMjISdnZ2wjq//vorWrRoIZGmvr6+xHtra2sEBwcDAF6+fIn169djwoQJeP78ORYvXvxJ+6OlpYWdO3di9erV0NLSEsKJCJs2bYK2trbcRu6XL18KDQDBwcFYtmwZ1NTUpOL5+vpi3rx5cHJywvz581G5cmWkpKTgwoULmDNnDh48eICVK1cCKN6xu3btGpydnVGjRg3Mnj0bVlZWePXqFW7cuIFt27Zh8uTJ0NbW/qTjAgBbtmxBRkYGAMDPzw+Ojo6fnBZjjIl9b+VIXlpaWoiOjsapU6fQqlUriWX+/v4FliniOABw584dXLp0CQ0bNpSKc+jQIbi7u8PAwACjR49GvXr1IBKJcOvWLfj7++Pw4cO4du0agNyyzNvbG5s2bUL79u2xYsUKWFhYIDExEaGhoRg1ahRevXqFcePG4cOHD2jUqBE+fPiAKVOmwM7ODqmpqXjw4AH27NmD69evo06dOp90XPIe97xUVVU/ed8AQF1dHadOnZJKN//5NWbMGPTr1w/Z2dm4c+cO5s6dixYtWiA8PBz16tX7pH3Kb+PGjQCArKwsBAYGYtq0aaWS7rdEWVkZWVlZ2L59OwYPHiyxLCAgAFpaWgWe/35+fgCAN2/eYN++fejTp49UnMuXL6Njx44gIowePRqNGzeGiooK7t+/j6CgIDRo0ABv374V4he1DpadnQ1XV1fcunULU6ZMQYMGDQAADx8+xMGDB3H27Fm4uLiUxmECULTvOmOsdHH948erf+SXmZmJoKAgAMDRo0fx7NkzmJubl0raXytZ572ZmRmMjY0RHh6OypUrf6Gclb21a9d+8rpHjhzBH3/8wQ30XwIxxgQBAQEEgCIiIgqN265dO1JSUqKTJ0/KXH758mWKjY0lIqLQ0FACQDt37iwwTRcXF6pVq5ZEWEZGBllbW5OGhgZlZGQUcU/+A4D69+9P6urq9Oeff0os+9///kcAaOjQoQSAoqOjpdZfunQpAaAOHToQAAoODpaKs2PHDgJAgwcPppycHKnlSUlJdOzYMeF9cY6dp6cnaWpqUlJSksy4srZXHLVr1yYjIyOqX78+6ejoUEpKSonSK4yvry/xpZex79e3XI4AoICAgCKl36hRI+rXr5/EsqSkJNLQ0BDKFFlpRURESJQpQ4cOlYrz77//kqamJtWrV4/evXsntTwnJ4d2794tvF+8eDEBoLlz58rM8/Pnz+ns2bNEROTv708A6NSpUzLjZmdny933gsg67rIUd9+8vLxIU1OzwDSjo6MJAC1dulQi/OTJkwSAhgwZIndd8flaFGlpaaSvr092dnZkbm5ONjY2RVqvJLy8vMjS0vKzb6eoxJ+Hu7s7OTk5SSzLyckhS0tL4fz39fWVWv/58+ekpKRELVu2JDU1NWrTpo1UnMTERDIxMSELCwt68uSJzHzkvQ4Upw526tQpAkD+/v4y0y3s/Pfy8iIXF5cC44gV5bte2iwtLcnLy+uzb4exrxHXP77v+oeLi0uRr287d+6U2NcFCxYUab2SkFfufW7FOe+/VhkZGZSZmVnidJKTk0ucho+PD7dVfCE8rA1jnyAyMhJ///03Bg8ejJYtW8qMU79+fVSsWLHE21JWVoaDgwNSUlKQkJDwSWno6OigW7duQm8BMX9/fzg7O8PGxkbuuv7+/jA2NsbmzZuhrq4ulQaQOzSOrq6u8KhgflpaWnB1dQVQ/GP3+vVraGtro1y5cjLjlmSImEuXLuH27dsYMGAAhg4disTEROzevVtYPn78eGhqasrsgdGnTx8YGxsLjypu374drq6uMDU1hbq6OmrUqIHp06dLDecjS05ODpYsWYLq1atDVVUVRkZG8PT0xNOnTyXiNW/eHLVr10ZERASaNm0KDQ0NWFtbY9GiRaXyuChjrOx8a+VIft7e3tizZw/evXsnhG3btg0A4O7uLnc9ca/hRYsWwcnJCdu2bUNKSopEnBUrViA5ORlr166Fjo6OVBoikQjdu3cHkNs7bPHixahevTpmzZolc5smJiZo0qQJgNwyBQBMTU1lxlVQ+LxV4+LsW0k1atQIQO5QK6Vh3759eP36NYYMGQIvLy88ePAA586dE5Z37doVlpaWMsujhg0bwt7eXnj/xx9/oFmzZjAyMoKmpiZsbW2xZMkSicf/5UlLS8NPP/2ESpUqCcMw+Pj4SJyLQO4QJx07dsTRo0dhb28PdXV1VK9eXWY9pri8vb1x4cIF3L9/Xwj73//+h9jYWAwaNEjueps3b0ZWVhYmTJiA7t274+TJk1Kfz19//YX4+HgsWbIEFSpUkJlO3qEkilMHK8vzv6DvemZmJoyMjDBgwACp9d69ewd1dXVMnDgRQO7nPWnSJNStWxc6OjrQ09ND48aNsX///iLl4/Hjx+jfvz+MjIygqqqKGjVqYPny5RLnqXjYg2XLlmHFihWoVKkSypUrh8aNG+PixYslPRSMfVW4/vH91z/8/PygoqKCgIAAWFhYICAgQBgKKCEhASoqKjLzfO/ePYhEIvz+++9C3FGjRqFmzZooV64cjIyM0LJlS5w9e7ZI+bh9+za6dOkCXV1dqKmpoW7duti8ebNEHPGwSVu3bsXMmTNhZmYGbW1ttG7dWqKM/VSyhrURDzN7584d9O3bFzo6OjA2Noa3tzcSExMl1n/37h0GDx4MPT09lCtXDh06dMC///4rcxifhw8fol+/fhLlzR9//CFzf7ds2YJJkybB3NwcqqqqePTokTBEz4kTJzBo0CDo6elBU1MTnTp1wr///iuRjrhd4MyZM3BycoKGhga8vb2FZXmHtSlqGTdw4EAhv3mHBxIPfbxz5040bNgQOjo6QjuEeJus5LhxnjEZsrOzkZWVJfHKzs4Wlh8/fhxA7g/R4sjJyZFKtyj++ecfKCkpQVdXt1jby2vw4MG4ePEioqKiAOQWNHv27JF6JDuvCxcuICoqCp6entDX10ePHj1w6tQpREdHC3GeP3+O27dvw9XVFRoaGoXmo7jHrnHjxnj+/Dk8PDxw+vRppKamFmm9ohBX0ry9veHu7g4NDQ0hTByekpKCHTt2SKz37t077N+/H/3794eysjKA3MK4ffv28PPzw9GjRzF+/Hjs2LEDnTp1KjQfI0eOxLRp09CmTRscOHAA8+fPx9GjR+Hk5IRXr15JxI2Pj4eHhwf69++PAwcOoF27dvjpp5+ERxcZY1+H77Ecycvd3R2KiorYunWrEObn54eePXvKHWosNTUVW7duRf369VG7dm14e3vj/fv32Llzp0S848ePw9jYWGhcLsiVK1fw5s0bdOnSpUg3axs3bgwA8PT0FBqbS1P+zyYrK0uiEbA4+1ZQunnPJXkePXoEADA0NCzeTsjh5+cHVVVVeHh4wNvbGyKRSKrMfPz4sdQQPPfu3cPly5clGq3/+ecf9OvXD1u2bMGhQ4cwePBgLF26FMOHDy8wD0SErl27YtmyZRgwYAAOHz6MiRMnYvPmzWjZsqXUGO43btzApEmTMGHCBOzfvx916tTB4MGDcebMmRIdi9atW8PS0lKiod/Pzw/NmjVD1apV5a7n7+8PU1NTtGvXDt7e3sjJyZEaB/f48eNQVFQsUv2huHUwR0dHKCsrY9y4cQgODv5s8wYU9l1XVlZG//79sXv3bqkOEFu3bkVaWppwvqSnp+PNmzeYPHky9u3bh61bt6JJkybo3r07AgMDC8xHQkICnJyccPz4ccyfPx8HDhxA69atMXnyZJnzKP3xxx84ceIEVq1aheDgYCQnJ6N9+/ZSjTWMfc24/iHtR6h/iD19+hTHjx9Hly5dYGhoCC8vLzx69Ego9wwNDdGxY0ds3rxZ6mZ6QEAAVFRU4OHhASB3+DUgd+i0w4cPIyAgANbW1mjevLnUHAb53b9/H05OTrhz5w5+//137NmzBzVr1sTAgQOxZMkSqfgzZsxAbGwsNm7ciD///BMPHz5Ep06dilTfAWSf94Xp0aMHbGxssHv3bkyfPh0hISGYMGGCsDwnJwedOnVCSEgIpk2bhr1796Jhw4Zo27atVFp3795F/fr1cfv2bSxfvhyHDh1Chw4dMHbsWMydO1cq/k8//YTHjx9j/fr1OHjwIIyMjIRlgwcPhoKCAkJCQrBq1SpcvnwZzZs3l+qE8Pz5c/Tv3x/9+vXDkSNHMGrUqAL3t7AybtasWcLN//DwcOFlamqK8PBw9OnTB9bW1ti2bRsOHz6M2bNnF/k6wIrgS3fdZ+xrIn4sStZLUVFRiDdixAgCQPfu3StSuuLHAWW9Hj58KMQTP66XmZlJmZmZFBcXR9OnTycA1KtXr0/aJwDk4+NDOTk5VKlSJZo8eTIREf3xxx9Urlw5ev/+vTB0Tf5hbby9vQkARUVFSezHrFmzhDgXL14kADR9+vQi5ae4xy4tLY26du0q8TnUq1ePZs6cSS9fvixSGrIkJyeTtrY2NWrUSAjz8vIikUhEjx49EsLs7e2lHl1fu3YtAaBbt27JTDsnJ4cyMzPp9OnTBIBu3LghLMs/rE1UVBQBoFGjRkmkcenSJQJAM2bMEMJcXFwIAF26dEkibs2aNcnNza0Ye88Y+1y+lXIkOztbiCN+ASA/Pz+JsKysLIl85H1s3cvLixwdHYmI6M6dOwSAwsLChEfH8z9WHhgYSABo/fr1RET0/v17KleuHDVt2lQinpqamsS1uSDbtm2TSLMo5s2bRyoqKsLxq1SpEo0YMULiWl1c4uuzrNfgwYOFeMXZN6LcYywrTWdnZyGOeFibxYsXU2ZmJqWlpVFkZCTVr1+fANDhw4eFuFlZWRKfr5+fHwGQOhfyP14fExNDCgoK5O7uLrHPeYedy8zMJGNjY6nhBqZOnUoqKir06tUrmfsoPhcDAwNJUVGR3rx5I7H/eYe1OXr0KAGgJUuWSKSxfft2AiAxfJ+lpSWpqakJQzMQEaWmppKenh4NHz5c7jEvSN5hhnx9fcnExIQyMzPp9evXpKqqSps2baKEhASZj/efOXNGor4krpdZWlpKDEdTvXp1MjExKVJ+ilsHIyLy8/OjcuXKCeeSqakpeXp60pkzZ6Ti5j8vPD09qVmzZlLh+YfTKcp3/ebNm1KfGRFRgwYNyMHBQW7+xefw4MGDqV69ehLL8g9rI7725a83jRw5kkQiEd2/f5+I/vsO2draSlzzLl++TABo69atcvPD2NeC6x/fT/1D/Fsy76tZs2bk6ekpFS5rGwDo6NGjRJQ7VI9IJKIBAwYIcQ4cOEAA6Pjx40JYVlYWmZmZUY8ePeTmX3z9bdWqFXXr1k1iWf5yz93dnVRVVenx48cS8dq1a0caGhrCsEHi86t9+/YS8cRDtoWHh8vND1HB531mZqZwfc97Toh/j+evS4waNYrU1NSEMu3w4cMEgNatWycRb+HChVL76+bmRhUqVKDExESJuKNHjyY1NTWhbiPe32bNmsndl/zH9vz58wSAfvnlFyFMXO+UNTSVi4uLxBB0xSnj5A1rs2zZMgIgc7gnVjq45zxjMgQGBiIiIkLidenSpRKnu3jxYql0LSwsJOLcuXMHysrKUFZWhpmZGZYvXw4PDw/89ddfJdq2SCTCwIEDsWXLFmRlZcHPzw+9e/eWO1zMhw8fsGPHDjg5OQmTq7i4uKBy5crYtGlTmQ2joqqqir179+Lu3btYuXIl3N3dkZCQgAULFqBGjRqf/Ljbjh07kJSUJPEolre3N4gIAQEBQtigQYOkHl0PCAgQel6I/fvvv+jXrx9MTEygqKgIZWVlYVI18dMKsoSGhgLIfYwsrwYNGqBGjRo4efKkRLiJiYkwgZtYnTp1Sm3YAsZY6fjay5F58+YJccQvILe3Tt6wgibQ8vb2xpUrV3Dr1i34+fmhcuXKaNasmdz4fn5+UFdXFx47L1euHHr16oWzZ8/i4cOHJTksxTJr1iw8fvwY/v7+GD58OMqVK4f169fDwcFBoidecVWuXFnqs4mIiJD7uHtRqaurS6WZt8e62LRp06CsrAw1NTU4ODjg8ePH2LBhA9q3by+Rx7yfr/jpufznwrx58yTSDggIQE5OjlSZmZycjO3btwMAlJSU0L9/f+zZs0fohZWdnY0tW7agS5cuEhMHXrt2DZ07d4a+vr5QZnp6eiI7OxsPHjyQeyzEvfLzl5m9evWCpqamVJlZt25diaEZ1NTUYGNjUypl5qBBg/DixQv8/fffCA4OhoqKCnr16iU3ft6n9YD/6mWxsbFS+f6cvL298fTpU4SEhGDs2LGwsLBAUFAQXFxcsHTpUiFeTEyM1HkRGBiIM2fOSIWfPn1aal8L+67b2trCwcFBos4VFRWFy5cvSz0mv3PnTjg7O6NcuXJQUlKCsrIy/Pz8CqxfAbnnS82aNaXqTQMHDgQRST3l0aFDBygqKgrvxZMzch2LfUu4/iHtW6t/nD59WuoYnTlzBoGBgVLh4iFHAAi/Yy0sLNCmTRsAQKVKldC8eXOJJ5XatWsHExMTievvsWPHEBcXJ3X9Xb9+Pezt7aGmpiZcf0+ePFmk62+rVq2kzpGBAwciJSUF4eHhEuGdO3eWeF/c66+s815JSanAdWRtMy0tDS9fvgQAoWzr3bu3RLy+fftKvE9LS8PJkyfRrVs3aGhoSPTeb9++PdLS0qSGSOvRo4fcfImfXBBzcnKCpaWl0G4gpqurK3doKllKUsbVr18fQO6x2LFjB549e1bk7bKiKfhsZewHVaNGDTg6OspdLv6hFx0djWrVqhU5XWtr6wLTBXJ/OG/btg0ikQhqamqoVKlSkR5VLopBgwZh7ty5+PXXX3H16lWsXr1abtzt27fjw4cP6N27t8QjVL1798bChQtx4sQJuLm5SRyLovjUY1ejRg3UqFEDQG7FY9WqVZg4cSJmzZolNexMUfj5+UFNTQ1t27YV9q9OnTqwsrLCpk2bMHfuXCgqKsLDwwOTJ0/Gpk2bsHDhQty9excRERESs6B/+PABTZs2hZqaGn755RfY2NhAQ0MDT548Qffu3Qsciqeg8QfNzMykCsu8jRtiqqqqpTrcD2Os5L72cmTYsGHo2LGjRFj9+vXh6+srEa6qqip3O+IhPDZs2IAdO3Zg/Pjxch/tFj9S3aNHDxCRcN3t2bMnAgIC4O/vj4ULFwLIPTafUqYUh7GxMQYNGiQMnXHmzBm0a9cO48aNk/rRVVRqamqFfjbF2TcxBQWFQtMFgHHjxqF///5QUFBA+fLlUalSJanP4+DBgxJDvxw6dAhz585FRESERDwzMzPhf/HQK2ZmZnBwcBA+u9atW0NTUxN+fn4YMmQIgNwGk+XLl2Pbtm0YPnw4jh07hufPn0sMafP48WM0bdoU1apVw2+//QYrKyuoqanh8uXL8PHxKbTMVFJSkhqqRyQSwcTERGqYgM9ZZlpaWqJVq1bw9/dHTEyMMDxe/jGMAQjDJzRo0ACGhobCMezWrRvmzJkDPz8/tG7dGkDuOfLw4UMkJydDU1OzwDx86vmvo6ODvn37Cuf6nTt30Lp1a8ycORNDhw5F+fLlYWZmJnVezJ07F3FxcdiwYYNEeN5rWHG+697e3vDx8cG9e/dQvXp1BAQEQFVVVeI7uGfPHvTu3Ru9evXClClTYGJiAiUlJaxbt67Q+QNev34NKysrqXDx+V3Y+SK+/nEdi31LuP4h6Vusfzg4OEhdf4cPHw4zMzP4+vpKhOctr8XDz06cOFFiyLDevXsjNDQUW7duxfDhw6GkpIQBAwZg9erVePfuHcqXL49NmzbB1NQUbm5uwnorVqzApEmTMGLECMyfPx8GBgZQVFTErFmzCm2cf/36tdzft+LleZX0+lvYeS9LYdsU1zn09PQk4hkbG0u8f/36NbKysrB69Wq57Sv5h6qVN/cAkNsZT1ZY/mNWUBqylOQYN2vWDPv27cPvv/8OT09PpKeno1atWpg5c+Yn15uZJO45z9gnEBda+/btK/W0xT/uHRwcUKtWrVJrmAcACwsLtG7dGnPnzkW1atXg5OQkN664h9f48eOhq6srvMSVF/FyU1NT2Nra4vjx4zJ/kOZXGsdOJBJhwoQJKF++PG7fvl3s9cWT2KWlpaFixYoS+xcTE4Nnz57h2LFjAHLvSHfp0gWBgYHIzs5GQEAA1NTUJAqhU6dOIS4uDv7+/hgyZAiaNWsGR0dHaGlpFZoXcSEpa9zXuLg4GBgYFHv/GGNfvy9djpiZmcHR0VHiBeROopk3zNbWtsBtDRo0COvWrcObN2/g5eUlN56/vz+ICLt27ZK45nbo0AFA7kSZ4nFF3dzc8OLFiyJNxOjo6Ag9PT3s379fmOzsUzRr1gyurq5ISEgQekx9DsXZt+KqUKECHB0dYW9vD2tra5kNFba2thKfr7jhMv+5kPfHvnii07i4OOjr6wufnbm5OZKTk3Hx4kXcvXsXAIReyuLeeAEBATAzMxMmJAVyz/nk5GTs2bMH/fv3R5MmTeDo6AgVFZVC91FfXx9ZWVlSEwsSEeLj48u8zPT29saBAwdw/fr1AidF27p1K1JSUnD58mWJ879OnTogIuzduxdv374FkHuOZGdn4+DBg4Vuv7h1MHlq1aoFd3d3ZGZmCk8uqKioSJ0X+vr60NLSkgrPW98pzne9b9++UFVVxaZNm4SnLLp27SoxNnVQUBAqVaqE7du3o2vXrmjUqBEcHR2l5heQRV9fX279CgDXsdgPiesfX3/9Q951Vl9fXyo8b9kp/n2+YsUKiX0dOXKkxHIg9/ilpaVh27ZtePv2LQ4cOABPT0+JntVBQUFo3rw51q1bhw4dOqBhw4ZwdHTE+/fvC92v7+H6K65ziMfeF4uPj5d4r6urC0VFRQwcOFDmE5QRERESTzECKHCegvzpi8PyN64XZa6D0tSlSxecPHkSiYmJCAsLQ4UKFdCvXz+ppyDYp+HGecY+gb29Pdq1awc/Pz+pR2LFrly5gsePH5dxzgo3adIkdOrUqcDH7KOiohAeHo4ePXogNDRU6tWqVSvs379fuHs7a9YsvH37FmPHjpVZOfnw4YMw+VBxj528ycri4uKQlJQk0YBQVOKKyV9//SW1b0eOHIGysrJEb6xBgwYhLi4OR44cQVBQELp164by5csLy8UFY/4eHvl7lskifhQt/4SuERERiIqKQqtWrYq9f4yxr9+3XI7k5eXlhU6dOmHKlCkwNzeXGSc7OxubN29G5cqVZZYpkyZNwvPnz/H3338DACZMmABNTU2MGjVK5kSM4sZMIHc4lmnTpuHevXuYP3++zO2/fPkS58+fBwC8ePFC5rBs2dnZePjwITQ0NCSu76WtOPv2tfDz84OCggL27dsn9dlt2bIFAKTKzEuXLuHcuXM4ePAgvLy8JH7syyoziahIw/eJy8T8Zebu3buRnJxc5mVmt27d0K1bN3h7exc4gaCfnx+0tLRw8uRJqWO4dOlSpKenIzg4GEDu0A4mJiaYOnWq3MfG9+zZI/xfnDrY69evkZGRITPNe/fuAcAn1avEivtd19XVRdeuXREYGIhDhw4hPj5e6iaHSCSCioqKRCNEfHw89u/fX2h+WrVqhbt37+Lq1asS4YGBgRCJRGjRosUn7ytj3yquf3yf9Y+3b99i7969cHZ2lrmvHh4eiIiIEDq21ahRAw0bNkRAQABCQkKQnp4u8ZQbkHv9zf/79ubNm0VqjG3VqpXQgS2vwMBAaGhoFGnS3S9NPEStePg+sW3btkm819DQQIsWLXDt2jXUqVNH6gaK+OZ2UYnrA2IXLlxAbGwsmjdv/mk7UgxF6U2vqqoKFxcXLF68GEDuUIWs5HhYG8ZkuH37tsyZpytXriw8Sh0YGIi2bduiXbt28Pb2Rrt27aCrq4vnz5/j4MGD2Lp1KyIjIyXGOi0tYWFhaNGiBXx9fTFnzpxirevq6irRg00WceP11KlTpcbpBHIfzz558iSCgoIwbtw49OrVC7NmzcL8+fNx7949DB48GJUrV0ZKSgouXbqEDRs2oE+fPsJ2i3Pshg0bhnfv3qFHjx6oXbs2FBUVce/ePaxcuRIKCgqYNm1asY5LVlYWAgMDUaNGDeEx/Pw6deqEAwcOICEhAYaGhnB1dUWFChUwatQoxMfHS1VcnJycoKurixEjRsDX1xfKysoIDg7GjRs3CjzOQO6j4MOGDcPq1auhoKCAdu3aISYmBrNmzYKFhYXEjPGMsW/H116OlBYzM7NCe9/9/fffiIuLw+LFi2X+sKhduzbWrFkDPz8/dOzYEZUqVcK2bdvQp08f1K1bF6NHj0a9evUAAHfv3hV6wXXr1g0AMGXKFERFRcHX1xeXL19Gv379YGFhgcTERJw5cwZ//vkn5s6dC2dnZ2zZsgUbNmxAv379UL9+fejo6ODp06fYuHEj7ty5g9mzZ0v0QhOJRHBxcUFYWFihxyI1NVVubzvxj9Di7tuX9vr1a+zfvx9ubm7o0qWLzDgrV65EYGAgFi5cCGVlZfTt2xcTJ05E3759kZ6eLjU+fJs2baCiooK+ffti6tSpSEtLw7p164Se4wVp06YN3NzcMG3aNCQlJcHZ2Rk3b96Er68v6tWrhwEDBnzSfoqfIMg7dm9RqKmpYdeuXQXGuX37Ni5fvoyRI0fKHBvW2dkZy5cvh5+fH0aPHg0dHR3s378fHTt2RL169TB69Gg0btwYKioqePjwIYKCgnDjxg10794dAIpVBwsNDcW4cePg4eEBJycn6Ovr4+XLl9i6dSuOHj0KT09PVKhQoVjHIK/ifteB3KcPtm/fjtGjR6NChQrC8D5iHTt2xJ49ezBq1Cj07NkTT548wfz582FqalroWNETJkxAYGAgOnTogHnz5sHS0hKHDx/G2rVrMXLkSNjY2HzyvjL2teL6x3++5fpHcQUHByMtLQ1jx46Vua/6+voIDg6Gn58fVq5cCSD3+jt8+HDExcXByclJapijjh07Yv78+fD19YWLiwvu37+PefPmoVKlSjLPsbx8fX1x6NAhtGjRArNnz4aenh6Cg4Nx+PBhLFmyBDo6Op+8r2Wlbdu2cHZ2xqRJk5CUlAQHBweEh4cjMDAQQO6wg2K//fYbmjRpgqZNm2LkyJGwsrLC+/fv8ejRIxw8eFDujTBZrly5giFDhqBXr1548uQJZs6cCXNzc4waNarU9zE/8RMrixcvRrt27aCoqIg6dergl19+wdOnT9GqVStUqFAB7969w2+//SYxzx4roTKcfJaxr15Bs30DoL/++ksifmpqKv3+++/UuHFj0tbWJiUlJTIzM6Pu3bvT4cOHhXjiWbl37txZ4PbzzkJfkIMHDxZ5dngA5OPjU2CcpUuXEgCKjo6mjIwMMjIyorp168qNn5WVRRUqVCBbW1uJ8NOnT1PPnj3J1NSUlJWVSVtbmxo3bkxLly6lpKQkibhFPXbHjh0jb29vqlmzJuno6JCSkhKZmppS9+7dpWZvL8px2bdvHwGgVatWyY1z9OhRAkDLly8XwmbMmEEAyMLCgrKzs6XWuXDhAjVu3Jg0NDTI0NCQhgwZQlevXpU7O3xe2dnZtHjxYrKxsSFlZWUyMDCg/v3705MnTyTiyTs/vLy8yNLSUu7+MMbKzrdSjsiS/3r1qelHRERIpNW1a1dSUVGhly9fyl3H3d2dlJSUKD4+Xgj7559/aNSoUVSlShVSVVUldXV1qlmzJk2cOJGio6Ol0ti/fz916NCBDA0NSUlJiXR1dalFixa0fv16Sk9PJyKiu3fv0qRJk8jR0VEinouLC23ZskUivffv3xMAcnd3L3B/xceloM89MzNTIn5R983Ly4s0NTUL3HZ0dDQBoKVLlxaaz/zE56s8q1atIgC0b98+uXHWr19PAGj37t1CWL9+/QgAOTs7y1zn4MGDZGdnR2pqamRubk5Tpkyhv//+mwBQaGioEE9W+ZaamkrTpk0jS0tLUlZWJlNTUxo5ciS9fftWIp6lpSV16NBBatsuLi7k4uIiEWZgYECNGjWSu49581PY55GQkEAAyNfXl4iIxo8fTwDo+vXrcteZPn06AaDIyEghLD4+nqZNm0a1atUiDQ0NUlVVpSpVqtDw4cPp1q1bUmkUpQ725MkT+vnnn8nZ2ZlMTExISUmJtLS0qGHDhrR69WrKysoqdP/zH7u8PuW7np2dTRYWFgSAZs6cKXOdRYsWkZWVFamqqlKNGjXor7/+klmfsrS0JC8vL4mw2NhY6tevH+nr65OysjJVq1aNli5dKlGXK+g7lPezZOxrxvWP76f+IW//81/f8qpbty4ZGRkJ25OlUaNGZGBgIMRJTEwkdXV1mecHEVF6ejpNnjyZzM3NSU1Njezt7Wnfvn0yy2ZZ18pbt25Rp06dSEdHh1RUVMjOzk7qc5Z3fomvy4WdF+LzPiIiQuZyWemIy4+EhASZaeX9jN+8eUODBg2i8uXLk4aGBrVp04YuXrxIAOi3336T2pa3tzeZm5uTsrIyGRoakpOTE/3yyy+F7m/e7R8/fpwGDBhA5cuXJ3V1dWrfvj09fPhQIm5B34f89ZzilHHp6ek0ZMgQMjQ0JJFIJByPQ4cOUbt27cjc3JxUVFTIyMiI2rdvT2fPnpWZB1Z8IqISDJDFGPsipk6diq1bt+Lhw4dQU1P70tn5avBxYYwxVlqOHDmCjh074saNG4WOfcu+XXfv3kWtWrVw6NAhYQxixhhjjDFZQkJC4OHhgfPnzxc4h19xbdq0CYMGDUJERESxJ7dl3z4e1oaxb1BoaChmzZrFDdD58HFhjDFWWkJDQ+Hu7s4N89+50NBQNG7cmBvmGWOMMSZh69atePbsGWxtbaGgoICLFy9i6dKlaNasWak2zDPGPecZY4wxxhhjjDHGGGPso0OHDmHOnDl49OgRkpOTYWpqiq5du+KXX36BtrZ2qW6Le87/2LhxnjHGGGOMMcYYY4wxxhgrYwqFR2GMMcYYY4wxxhhjjDHGWGnixnnGGGOMMcYYY4wxxhhjrIxx4zxjjDHGGGOMMcYYY4wxVsaUvnQGvjY5OTmIi4uDlpYWRCLRl84OY4wxViREhPfv38PMzAwKCnzvnctzxhhj3yIuz6Vxmc4YY+xbVNQynRvn84mLi4OFhcWXzgZjjDH2SZ48eYIKFSp86Wx8cVyeM8YY+5Zxef4fLtMZY4x9ywor07+rxvmFCxdiz549uHfvHtTV1eHk5ITFixejWrVqRU5DS0sLQO6B09bW/lxZZYwxxkpVUlISLCwshHLsR8flOWOMsW8Rl+fSuExnjDH2LSpqmf5dNc6fPn0aPj4+qF+/PrKysjBz5ky4urri7t270NTULFIa4sfktLW1ueBnjDH2zeHHvXNxec4YY+xbxuX5f7hMZ4wx9i0rrEz/rhrnjx49KvE+ICAARkZGiIyMRLNmzb5QrhhjjDHGGGOMMcYYY4wxSd9V43x+iYmJAAA9PT25cdLT05Geni68T0pK+uz5YowxxhhjjDHGGGOMMfZj+26nfyciTJw4EU2aNEHt2rXlxlu4cCF0dHSEF080wxhjjDHGGGOMMcYYY+xz+257zo8ePRo3b97EuXPnCoz3008/YeLEicJ78WD9BcnJyUFGRkap5JOxH4WysjIUFRW/dDYYY0xCdnY2MjMzv3Q2GGNljOsljH1f+Dc6Y+xHoaKiAgWF77av9Q/pu2ycHzNmDA4cOIAzZ86gQoUKBcZVVVWFqqpqkdPOyMhAdHQ0cnJySppNxn445cuXh4mJCU9wxRj74ogI8fHxePfu3ZfOCmPsC+F6CWPfB/6Nzhj7kSgoKKBSpUpQUVH50llhpeS7apwnIowZMwZ79+5FWFgYKlWqVOrpP3/+HIqKirCwsOA7VYwVEREhJSUFL1++BACYmpp+4Rwxxn504oZ5IyMjaGhocOMcYz8Qrpcw9v3g3+iMsR9JTk4O4uLi8Pz5c1SsWJF/w3wnvqvGeR8fH4SEhGD//v3Q0tJCfHw8AEBHRwfq6uolTj8rKwspKSkwMzODhoZGidNj7Eci/g6+fPkSRkZG/Cg5Y+yLyc7OFhrm9fX1v3R2GGNfANdLGPs+8G90xtiPxtDQEHFxccjKyoKysvKXzg4rBd/VbeV169YhMTERzZs3h6mpqfDavn17qaSfnZ0NAPzoCGOfSFxh5vGdGWNfkvgaxD/iGfuxcb2EsW8f/0ZnjP1oxNc78fWPffu+q57zRFQm2+HHRhj7NPzdYYx9TfiaxNiPja8BjH0/+PvMGPtR8PXu+/Nd9ZxnjDHGGGOMMcYYY4wxxr4F3DjPGGOMMcYYY4wxxtgXEhYWBpFIhHfv3n3prDDGyhg3zv8ABg4cCJFIJPVq27atRLxr166hV69eMDY2hpqaGmxsbDB06FA8ePAAABATEyMznf79+8tcrquri2bNmuH06dNlvs+bNm2SyIuxsTE6deqEO3fuSMRbuHAh6tevDy0tLRgZGaFr1664f//+Z8/frVu34OLiAnV1dZibm2PevHkFDsskLqhlvSIiIoR4J0+ehJOTE7S0tGBqaopp06YhKytLIq1jx46hUaNG0NLSgqGhIXr06IHo6Gi5205PT8eAAQOgra2NatWq4dSpUxLLlyxZgjFjxnzikWDsx3PlCtCyZe5fxorrc5fpIpEIFy9eBCBdlpqamqJ3794Flhl5WVlZQSQSYdu2bVLLatWqBZFIhE2bNkkt+/XXX6GoqIhFixbJTDc+Ph5jxoyBtbU1VFVVYWFhgU6dOuHkyZPFOgYAsHv3bjRs2BA6OjrQ0tJCrVq1MGnSpCLtX3GIj4VIJIK6ujqqV6+OpUuXSpT9N27cQN++fWFhYQF1dXXUqFEDv/32W6nnJT8iwpw5c2BmZgZ1dXU0b95cqr4ky6pVq1CtWjWoq6vDwsICEyZMQFpamrB8zpw5UueWiYmJRBryzsGlS5fK3e6JEydgY2MDHR0deHl5ISMjQ1iWmJgIGxsbPH78+BOOBGOMlZ1v+Tf6j1y+X7hwAYqKilKf0/cub5muoKAAMzMzeHh44MmTJ0KczMxMTJs2Dba2ttDU1ISZmRk8PT0RFxf32fN3+vRpODg4QE1NDdbW1li/fn2h60RERKBVq1YoX748dHV14erqiuvXr0vE2bFjB+rWrQsNDQ1YWlpK1U+eP3+Ofv36oVq1alBQUMD48eML3e6bN2/QqVMnlCtXDvb29rhx44bE8lGjRmH58uWFpsO+L9w4/4WUdeNM27Zt8fz5c4nX1q1bheWHDh1Co0aNkJ6ejuDgYERFRWHLli3Q0dHBrFmzJNL63//+J5HOH3/8IXP56dOnoa2tjfbt2xf5h3xp0tbWxvPnzxEXF4fDhw8jOTkZHTp0kPgRd/r0afj4+ODixYs4ceIEsrKy4OrqiuTk5M+Wr6SkJLRp0wZmZmaIiIjA6tWrsWzZMqxYsULuOk5OTlKf35AhQ2BlZQVHR0cAwM2bN9G+fXu0bdsW165dw7Zt23DgwAFMnz5dSOfff/9Fly5d0LJlS1y/fh3Hjh3Dq1ev0L17d7nb/vPPPxEZGYnw8HAMHToUffv2FRoToqOjsXHjRixYsKCUjg5j37/AQCA0FNiy5UvnhJWW76lMf/78ORwcHITlecvSkJAQXL9+HZ07dy7yBFQWFhYICAiQCLt48SLi4+Ohqakpc52AgABMnToV/v7+UstiYmLg4OCAU6dOYcmSJbh16xaOHj2KFi1awMfHp1jH4H//+x/c3d3Rs2dPXL58GZGRkViwYIFEPaE0zZs3D8+fP0dUVBQmT56MGTNm4M8//xSWR0ZGwtDQEEFBQbhz5w5mzpyJn376CWvWrPks+RFbsmQJVqxYgTVr1iAiIgImJiZo06YN3r9/L3ed4OBgTJ8+Hb6+voiKioKfnx+2b9+On376SSJerVq1JM6tW7duSSzPf+75+/tDJBKhR48eMrebk5MDDw8PjBgxAhcuXMDly5fx119/CcunTZuGESNGoGLFiiU4IowVEd9t/+58T+V5SX6jN2/eXGbjel7fW/k+Z84cDBw4sMB9BgB/f3+MGTMG586d++FuBIvL9KdPn2L79u24desWevfuLSxPSUnB1atXMWvWLFy9ehV79uzBgwcP0Llz58+ar+joaLRv3x5NmzbFtWvXMGPGDIwdOxa7d++Wu8779+/h5uaGihUr4tKlSzh37hy0tbXh5uYmTBL/999/C3WO27dvY+3atUJ9SSw9PR2GhoaYOXMm7OzsipTfBQsW4P3797h69SpcXFwwZMgQYVl4eDguX75cpEZ+9p0hJiExMZEAUGJiotSy1NRUunv3LqWmppZ4O2PGEAFEY8eWOKlCeXl5UZcuXeQuT05OJgMDA+ratavM5W/fviUioujoaAJA165dkxlP1vKnT58SAFq/fv0n5v7TBAQEkI6OjkTYgQMHCADdvHlT7novX74kAHT69OnPlre1a9eSjo4OpaWlCWELFy4kMzMzysnJKVIaGRkZZGRkRPPmzRPCfvrpJ3J0dJSIt3fvXlJTU6OkpCQiItq5cycpKSlRdna2EOfAgQMkEokoIyND5rZGjhxJ06ZNIyKilJQUAkAvX74kIiI3Nzfas2dPkfJMVLrfIca+JTExRFeuEEVGEhkZ5V7/jYxy31+5kru8pAoqv35EZVWeE32fZTqR7LI0KCiIANC9e/cKzaulpSVNnz6dVFVV6fHjx0L40KFDacyYMaSjo0MBAQES64SFhZG5uTllZGSQmZmZVHncrl07Mjc3pw8fPsjdt6Ieg3HjxlHz5s0L3Y/SYGlpSStXrpQIs7e3p+7duxe43qhRo6hFixafLV85OTlkYmJCixYtEsLS0tJIR0enwLqbj48PtWzZUiJs4sSJ1KRJE+G9r68v2dnZFSs/Xbp0kUo3rxcvXhAA4bs7depUGjVqFBERnTt3jhwcHCgrK6vI2+N6CSuRPBf/otbhi4LLc2n8G13a5/yN7uLiIlU+5/U9lu++vr7k5eVVYJwPHz6QlpYW3bt3j/r06UNz584VljVq1Ej4zSz28uVLUlJSolOnThER0ZYtW8jBwYHKlStHxsbG1LdvX3rx4oUQPzQ0lAAI+0FEtGvXLqpZsyapqKiQpaUlLVu2TGIblpaWtGDBAho0aBCVK1eOLCwsaMOGDcXa96KQVab//vvvhV6rLl++TAAoNja21PMkNnXqVKpevbpE2PDhw6lRo0Zy14mIiCAAEufvzZs3CQA9evSIiIj69u1LPXv2lFhv5cqVVKFCBZnXfBcXFxo3blyh+W3Xrh2tW7eOiIju3r1LGhoaRJTbxmNnZ0cRERGFpsH1l8/rS5Tp3HO+DMXGApGRwNWrwPbtuWHbtuW+j4zMXf4liHtPT506Veby8uXLf3LaGhoaACDcffxS3r17h5CQEACAsrKy3HiJiYkAAD09Pblxzp49i3LlyhX4+vXXX+WuHx4eDhcXF6iqqgphbm5uiIuLQ0xMTJH258CBA3j16pXE3f309HSoqalJxFNXV0daWhoiIyMBAI6OjlBUVERAQACys7ORmJiILVu2wNXVVe5xsbOzw7lz55Camopjx47B1NQUBgYGCAoKgpqaGrp161akPDP2I7OyAhwdAQcHICEhNywhIfe9o2PucvZt+RHLdCC3XAGKXq4bGxvDzc0NmzdvBpDbq2r79u3w9vaWGd/Pzw99+/aFsrIy+vbtCz8/P2HZmzdvcPToUfj4+MjslSfet6IeAxMTE9y5cwe3b98u0r6UFiJCWFgYoqKiCqyTALn1koLqJADQrl27Qusl8kRHRyM+Ph6urq5CmKqqKlxcXHDhwgW56zVp0gSRkZG4fPkygNwn844cOYIOHTpIxHv48CHMzMxQqVIluLu7499//5Wb5osXL3D48GEMHjxYbhxDQ0OYmpri+PHjSE1NxdmzZ1GnTh1kZGRg5MiRWL9+PRQVFeWuz1hJXXnwAC3PnsWViAhkBuVe/BNWr4eDggIur1v35S7+7JP9iOV5afxG/xHL9+3bt6NatWqoVq0a+vfvj4CAAOGJcg8PD2zdulViuLrt27fD2NgYLi4uAICMjAzMnz8fN27cwL59+xAdHV1gb/3IyEj07t0b7u7uuHXrFubMmYNZs2ZJPdWwfPlyODo64tq1axg1ahRGjhyJe/fuleq+5xcfH489e/ZAUVGxwHI3MTERIpGowPM1ODi40HpMcHCw3PXDw8Ml6jFAbvvKlStX5J7j1apVg4GBAfz8/JCRkYHU1FT4+fmhVq1asLS0BCC/feXp06eILcGFwc7ODqdOnUJWVhaOHTuGOnXqAAAWL16M5s2bCyMjsLIXEREBkUiEpk2bFjjs9GdRarcDvhOf86488N9LJJL8K359Dl5eXqSoqEiampoSL3Gv68WLFxMAevPmTYHpiO+6q6urS6Rz9epVieXiu/IfPnyg4cOHk6KiYoG91T+HgIAAAkCampqkoaFBAAgAde7cWe46OTk51KlTJ4leX7KkpKTQw4cPC3y9fv1a7vpt2rShoUOHSoQ9e/aMANCFCxeKtH/t2rWjdu3aSYQdO3aMFBQUKCQkhLKysujp06fUpEkTAkAhISFCvNOnT5ORkREpKioSAGrcuLHE3fn8MjIyaNSoUWRlZUWOjo509uxZev36NVlbW1NsbCzNnDmTKleuTK6urvT06dMC8813eNmPKiiISElJ8novfikp5S4vKe5pJ+lz97L7Xst0TU1Noedx/p7zT548oUaNGlGFChUoPT290LyKe4vv27ePKleuTDk5ObR582aqV68eEZFUz7rExETS0NCg69evExHRtWvXSENDQ/gML126RAAKfWKrqMfgw4cP1L59ewJAlpaW1KdPH/Lz85N4sq20WFpakoqKCmlqapKysjIBIDU1NTp//rzcdS5cuEDKysp0/PjxAtN++vRpofUSec6fP08A6NmzZxLhQ4cOJVdX1wK3+/vvv5OysjIpKSkRABo5cqTE8iNHjtCuXbvo5s2bdOLECXJxcSFjY2N69eqVzPQWL15Murq6hX4vz549S46OjmRlZUWjRo2ijIwMmjt3Lo0fP55u375NTk5OZGNjQ6tXry4wHSKul7DiGzNmDCE0lMaOHk05yL3oZ+cv2EuIy3Np/Btdvs/xG70oPee/t/K9KD3nnZycaNWqVURElJmZSQYGBnTixAki+q+X/JkzZ4T4jRs3pilTpshNT9yr/P3790Qk3XO+X79+1KZNG4l1pkyZQjVr1hTeW1paUv/+/YX3OTk5ZGRkJPTMLi2+vr6koKBAmpqapK6uLrSvjC3gMZPU1FRycHAgDw+PAtNOSkoqtB4jHglAlqpVq9KCBQskwsT1m7i4OLnr3b59mypXrkwKCgqkoKBA1atXl+jhv2HDBtLQ0KD//e9/lJ2dTffv36fq1avLbbcpas/5d+/eUd++falixYrUrFkzunPnDj148ICqVq1Kr169ouHDh1OlSpWoV69e9O7dO5lpcP2l9K1ceZaAkwQ4EIBiPYlZkKKW6Uqft+mf5RUUBAwcCGRl5RbzwH9/lZSAQoZ1K5EWLVpg3bp1EmHinlhUzDtC27dvR40aNYT3FhYWEsudnJygoKCAlJQUmJqaYtOmTbC1tf3EnH86LS0tXL16FVlZWTh9+jSWLl1a4MQgo0ePxs2bN3Hu3LkC01VXV0eVKlVKlDeRSCTxXvwZ5A+X5enTpzh27Bh27NghEe7q6oqlS5dixIgRGDBgAFRVVTFr1iycO3dOuJsdHx+PIUOGwMvLC3379sX79+8xe/Zs9OzZEydOnJC5fWVlZakxCwcOHIixY8fi+vXr2LdvH27cuIElS5YUOrYbYz8qDw+gRo3cnvL5XboE2NuXfZ5YyXyvZToAiR5QiYmJKFeuHIgIKSkpsLe3x549e6CiolLkbXTo0AHDhw/HmTNn4O/vL7dXXUhICKytrYUxO+vWrQtra2ts27YNw4YNK3JZWdRjoKmpicOHD+Off/5BaGgoLl68iEmTJuG3335DeHi40LOwtEyZMgUDBw5EQkICZs6ciZYtW8LJyUlm3Dt37qBLly6YPXs22rRpU2C65ubmJc6brHpJQcc5LCwMCxYswNq1a9GwYUM8evQI48aNg6mpqTDmb7t27YT4tra2aNy4MSpXrozNmzdj4sSJUmn6+/vDw8NDqpdafk2aNEFERITw/sGDB9iyZQuuXbuGZs2aYfz48Wjbti1q166NZs2aCT3SGPtUsWlpeJWZCRGArZ26AAC2tWwJr2PHQCIRDBITYfniBUhRCa+Xb4LBl80uK6bvtTwvzm/0X3/9VeLJ79TUVFy8eBGjR48Wwv7++280bdpUIs1vuXw/e/asRDmVkZEBIsKuXbuEsBkzZmDGjBkAgPv37+Py5cvYs2cPAEBJSQl9+vSBv78/WrduDUNDQ7Rp0wbBwcFo2rQpoqOjER4eLvH5Xrt2DXPmzMH169fx5s0b5OTkAAAeP36MmjVrSu1HVFQUunTpIhHm7OyMVatWITs7W6iv5S3nxJOvv3z5skjHqjiqVauGAwcOID09Hfv378fOnTvlzj2XmZkJd3d35OTkYO3atQWmq6WlBS0trRLlrbjtK6mpqfD29oazszO2bt2K7OxsLFu2DO3bt0dERATU1dUxdOhQ/PPPP+jYsSMyMzOhra2NcePGYc6cOSV6Sk9HR0cY1UGsZcuWWLp0KYKDg/Hvv//i/v37GDp0KObNm8eTw5aBv/76CxMmpAIYC2AAQkOXlfmTmNw4X4a+ZOOMpqam3AZlGxsbAMC9e/fQuHHjQtOysLAosHF6+/btqFmzJsqXLw99ff1Py3ApUFBQEPJZvXp1xMfHo0+fPjhz5oxU3DFjxuDAgQM4c+YMKlSoUGC6+QtyWfIW5PmZmJggPj5eIkxceBobGxeYLpA7kY6+vr7MiVUmTpyICRMm4Pnz59DV1UVMTAx++uknVKpUCQDwxx9/QFtbG0uWLBHWCQoKgoWFBS5duoRGjRoVuv1Tp07h7t278PPzw5QpU9C+fXtoamqid+/en33SOsa+BwoKQE7Of3/Zt+lHKdPFN7oVFBRgbGwsd5K3gigpKWHAgAHw9fXFpUuXsHfvXpnx/P39cefOHSgp/Vc9zcnJgZ+fH4YNG4aqVatCJBIhKioKXbt2lbu94h6DypUro3LlyhgyZAhmzpwJGxsbbN++HYMGDSrejhbCwMAAVapUQZUqVbB7925UqVIFjRo1QuvWrSXi3b17Fy1btsTQoUPx888/F5puu3btcPbs2QLjfPjwQWa4iYkJgNyb96ampkL4y5cvC6yTzJo1CwMGDBAmMbO1tUVycjKGDRuGmTNnQkFBeuRMTU1N2Nra4uHDh1LLzp49i/v372O7eEyJIiIiDBs2DMuXL0dOTg6uXbuGnj17QkNDAy4uLjh9+jQ3zrMSs7p48b83Srk/1hPKl4dDngmdqUULOGRfwrXx9qBxZZ1DVhI/Snle0G/0ESNGSEzs6eHhgR49eqB79+5CmKwbwd9y+e7o6Ijr168LcX///Xc8e/YMixcvFsLyDivn5+eHrKwsieNARFBWVsbbt2+hq6sLDw8PjBs3DqtXr0ZISAhq1aol3JBITk6Gq6srXF1dERQUBENDQzx+/Bhubm5yJ6qVdaNc1g2K/EPkiUQioeG/NKmoqAjnWK1atfDw4UOMHDkSW7ZskYiXmZmJ3r17Izo6GqdOnYK2tnaB6QYHB2P48OEFxtmwYQM8PDxkLpPXvqKkpCS3PSokJAQxMTEIDw8X6iwhISHQ1dXF/v374e7uDpFIhMWLF+PXX39FfHw8DA0NcfLkSQCAVSmOServ74/y5cujS5cu6N69O7p27QplZWX06tULs2fPLrXtMGmxscAvv6zDxo1/AfgbAKCn5wNtbSVERgIGBsDHUY4+O26c/0K+psYZV1dXGBgYYMmSJTIL1Hfv3hVrTDsLCwtUrly5FHNYOiZMmIAVK1Zg7969wjjpRIQxY8Zg7969CAsLExqxC5K/IJeloPFhGzdujBkzZiAjI0PoeXj8+HGYmZkVepEnIgQEBMDT01PuOLUikQhmZmYAgK1bt8LCwgL2H2uVKSkpUncAxe+LUoCnpaXBx8cHISEhUFRURHZ2tlBByMzMRHZ2dqFpMPajMjICTEwACwtg8GDAzw948iQ3nH3bvucyPe+N7pLw9vbGsmXL0KdPH+jq6kotv3XrFq5cuYKwsDCJMvTdu3do1qwZbt++jdq1a8PNzQ1//PEHxo4dK3WjQLxvJTkGVlZW0NDQQHJycsl2uBC6uroYM2YMJk+ejGvXrgk/vu/cuYOWLVvCy8tLbm+0/DZu3IjU1NRPykelSpVgYmKCEydOoF69egByew+ePn1aooEiv5SUFKkGeEVFRRCR3J6N6enpiIqKkup5CeQ2ejg4OAiNGEXl5+cndFh4+/YtgP/GUOZ6CSstQTVqYOC9e8giAj62k9HH76xSVhb8F+d2elFSBII2f6lcstLwPZfnBf1G19PTkyh71dXVYWRkVKTy/1st3/M/Da+np4ekpCSZ+5yVlYXAwEAsX75camzzHj16IDg4GKNHj0bXrl0xfPhwHD16FCEhIRgwYIAQ7969e3j16hUWLVokPNVw5coVmXkVq1mzptRT/RcuXICNjc1XMb/KrFmzYGNjgwkTJgjtDeKG+YcPHyI0NLRInTU7d+6Mhg0bFhinoA4DjRs3xsGDByXCjh8/DkdHR7ltJuJ6TN6bH+L3+dtFFBUVhZsyW7duRePGjWFUSj/gEhISMH/+fOFzzs7O5npMGcptfhv58ZX7ub99qyRxs7ashp7nxvky9qUaZ9LT06XuJiopKcHAwACamprYuHEjevXqhc6dO2Ps2LGoUqUKXr16hR07duDx48fYtm3b581gGdDW1saQIUPg6+uLrl27QiQSCQ3N+/fvh5aWlnCMdHR0hEnv8ivpsDb9+vXD3LlzMXDgQMyYMQMPHz7Er7/+itmzZwuFw+XLl+Hp6YmTJ09K3J0/deoUoqOj5U6WtnTpUrRt2xYKCgrYs2cPFi1ahB07dgiFd4cOHbBy5UrMmzdPGNZmxowZsLS0FH6UF2TevHno0KGDENfZ2RlTpkzBoEGDsGbNGjg7O3/ycWHse1ehAhATA6ioACIRMGwYkJEB5Jkbmn1jvocy/fXr11JplS9fvtChRYqrRo0aePXqldyhYvz8/NCgQQM0a9ZMalnjxo3h5+eHlStXYu3atXByckKDBg0wb9481KlTB1lZWThx4gTWrVuHqKioIh+DOXPmICUlBe3bt4elpSXevXuH33//HZmZmYUOJVMafHx8sHjxYuzevRs9e/bEnTt30KJFC7i6umLixInC56KoqAhDQ0O56ZRkWBuRSITx48fj119/RdWqVVG1alX8+uuv0NDQQL9+/YR4np6eMDc3x8KFCwEAnTp1wooVK1CvXj1hWJtZs2ahc+fOQp1j8uTJ6NSpEypWrIiXL1/il19+QVJSEry8vCTykJSUhJ07dxb7sW1xmufPnweQe8OjRo0aWLVqFVxdXXHy5Em5TzEyVhwexsaokZgIh7g4qWXDRl2B/cNneAlD+B00gm3BD9eyr9T3UJ5/KT9C+X7o0CG8ffsWgwcPho6OjsSynj17ws/PD6NHj4ampia6dOmCWbNmISoqSqIcrVixIlRUVLB69WqMGDECt2/fxvz58wvc7qRJk1C/fn3Mnz8fffr0QXh4ONasWVPoMDH5tWrVCt26dZMYpqg0WFtbC0PwHTp0CFlZWejZsyeuXr2KQ4cOITs7Wzi/9fT05A6JWNJhbUaMGIE1a9Zg4sSJGDp0KMLDw+Hn54etW7cKcfbu3YuffvpJmCi3TZs2mDJlCnx8fDBmzBjk5ORg0aJFUFJSQosWLQAAr169wq5du9C8eXOkpaUhICAAO3fuxOnTpyW2L+64+eHDByQkJOD69etQUVGROVRRfuPGjcOkSZOEupyzszO2bNkCV1dX/Pnnn9y+8hnlPn1jDWATAGUAuZ1OympYMymlMsL9d+RzTyBHRJSWRpSTk/t/Tk7u+8/Jy8tLmLAj76tatWoS8SIiIqh79+5kaGhIqqqqVKVKFRo2bJgwkVj+yWTyK2x5Wco/iZ1YbGwsKSkp0fbt24mIZB4XAAVOgFMabt68SU2bNiVVVVUyMTGhOXPmUI74pKD/JoOJjo6WWK9v377k5OQkN90WLVqQjo4OqampUcOGDenIkSNScbZu3Ur16tUjTU1NMjQ0pM6dO1NUVFSheb516xZVqVKFPnz4IIRlZ2fTyJEjSVtbm+rXr1/gpHNEPHEJY58TTyAnqSzKc6Jvv0yX9dq6dSsRyS9Li0o8YZw84gnj0tPTSV9fn5YsWSIz3vLly8nAwECYhDYuLo58fHyESVbNzc2pc+fOFBoaWqxjcOrUKerRowdZWFiQiooKGRsbU9u2bens2bOfvM/yyDsWQ4cOpVq1alF2djb5+vrK/DwsLS1LPT955eTkkK+vL5mYmJCqqio1a9aMbt26JRHHxcVFYqK8zMxMmjNnDlWuXJnU1NTIwsKCRo0aJTHBfJ8+fcjU1JSUlZXJzMyMunfvTnfu3JHa/oYNG0hdXV3upGfyuLu7S036eunSJapevTrp6enR3LlzC02D6yWsqCKrViWEhpLCyZMSfx3XraMIGxsigCIjS2dbXJ5L49/oZfsbvagTwsrzLZbvBU0I27FjR2rfvr3MZZGRkQSAIj9eAA4fPkwAqFmzZlJxQ0JCyMrKilRVValx48Z04MABic8m/4SwRES7du2imjVrkrKyMlWsWJGWLl0qkaasz8LOzo58fX0l4uR9/yl8fX3Jzs5OKlw88erFixcLrFvm/wxLW1hYGNWrV49UVFTIyspKakLcgIAAyt/8efz4cXJ2diYdHR3S1dWlli1bUnh4uLA8ISGBGjVqRJqamqShoUGtWrWiixcvSm37U+tuR48epQYNGlB2drYQlpycTL169SItLS1q1aoVvXjxQua6XH/5dDk5OaSrq5vn86onMQG4+FXWZbqIqKw66X8bkpKSoKOjg8TERKmxsdLS0hAdHY1KlSqVeo8yxn4E/B1i7PMpqPz6EXF5zhgrDF8LWFFs3LgRh3/6CeHr16NiQgIGHzkCv/btccfSEinq6hi9ey9ytjTAT7c9UMjUVUXC5bk0LtMZY+w/fN37NNnZ2RJzXwBAREQ26tdXkBrWLDKydOYcKWqZzsPaMMYYY4wxxhhj+fj6+mLevHkAALu+fbFDTw+vdXTgeO8e2i1ejBR1dezo2R3759jjhcp7ZKcpw5IbShhjjLGvSmpqqsTwW6ampoiLi8PTp1/HvHDcOM8YY4wxxoolODgYw4cPl7nM0tISd+7cKeMcMcZY6fLy8kJgYCCAigAM8MfyRahk+9/kgqKPD6An5BAa34oUwql587LNKGOMMcbkevPmjcTkxC1atMCpU6cAAPHxQLVqwNKlQP36X25eOG6cZ4wxxhhjxdK5c2c0bNhQ5jJlZWWZ4Ywx9q1wdnbGhQsXPr6LBQD0GfsUfq2HY/i0CchSUgKJRAAAyv0DJZEIm6pX/wK5ZYwxxpgssbGxsLKyEt4PHToUf/75p/A+MBA4fRoICsptnI+MBKZOBZYsARwdyy6f3DjPGGOMMcaKRUtLC1paWl86G4wxVqqICAYGBnjz5o0QtmPpIyybnojMbBE6/i8Cl2JHwSHPD3uxS/b2sOfrImOMMfZVuH79OurVqye8/+WXXzBz5kzExgKvXgFRUcD69bnLtm0DvLyAZcuA0FBgyxZunGeMMca+L1eufJlb8IwxxhgrElkTxWVnZ0NBURG9Pr7PgQhPUR4AoJCTgxwFBeEv+7Lo4zBDjDH2vePrXeFOnDgBV1dX4f3mzZvh6ekJAMjTkV7w8iXg4PDfe3FjPRFgYABYWn7e/HItgjHGGPvcAgP/uwUPID4+Hs7Ozvjtt9++cMYYY4yxr9+VS5fQMiAAVy5d+izpp6WlSTTMm5qagoigoKAABAWBFHOXKYDwXFcXKpmZqP74MdatXIXq2dlQEYnwPD39s+SNFUxRUREAkJGR8YVzwhhjZUN8vRNf/5ikLVu2SDTMHzt2TGiYj40F5s8HCjt0CQm5jfWOjrIb80sb95xnjDHGPgfx83IiEbB9e27YunWIMTREj1mz8ArA+AsXMG7cuC+aTcYYY+xrF3jrFkKrVMGWW7fgKGe+i0+Vf6K45s2bIzQ09L8IHh54qVcDxu1zu9Qda9gQGcrKaBUZiW4jZuOOZTncjYvD8bdv0cHAoFTzxgqnpKQEDQ0NJCQkQFlZOfeGCmOMfadycnKQkJAADQ0Nqae9GLBw4ULMmDFDeH/16lWJoW2K2tAufjhBSQnYtKn08icPf5KMMcbY55C35P84aRwyM2E1axYiPwb/sWZNWeeKMcYY+ybEPnqEV2/eQCQSYbuhIQBgm6EhvCIicseG19ODZZUqJdtGvonihgwZgr/++ksqXpphDiJtbACRCNtbtAAABLVpg4a6GQiMiwMAbHn2DF4mJiAABsrKsFRTK1HeWNGIRCKYmpoiOjoasbGxXzo7jDH22SkoKKBixYoQiX9jMgDA8OHDJSZ7jY6OlijjgdyJXwcOBLKyipbmpUuAvX3p5VEebpxnjDHGPoe8JX++cQGzAER7ecGnY8cvkjXGGGPsa2f19Knwv0hbGwCQoK0Nh+Tk3MCUFFAhjfNXkpIw9d9/scTaGo4f0xC7ceMG6tatK7wXTxQnMy8fPgAbNuTm5WOZ/lZbG/3T0oQ4b4ngEBkpvKfmzQveQVZqVFRUULVqVR7ahjH2Q1BRUeGnhPJp3bo1Tp48Kbw/ceItvL3LS0355uEB1KghOb68LAoKQE7OZ8qsDNw4zxhjjH0OHh5A//4yFykBqLp5M7B5s1TDPWOs6ObMmYN9+/bh+vXrXzorjLFSFvTmDQZqayNLSQn0sXeg+K9SVhY2JSUVmkbgixcIffcOW168kGic/9///oc2bdoI7zdt2gQvLy/5ealRAwPv3UMWkZAHKR/DFQEs19MrNG+sdCkoKECNn1ZgjLEfjoWFBZ7muaGfkpKCadPUhSnf8jbO5yVugBeJJH+SKysDVasCL14AZXXPl2+1/AAGDhwIkUiEESNGSC0bNWoURCIRBg4cKLXswoULUFRURNu2bWWmm5GRgSVLlsDOzg4aGhowMDCAs7MzAgICkJmZKcSLj4/HmDFjYG1tDVVVVVhYWKBTp04Sd7WuXbuGjh07wsjICGpqarCyskKfPn3w6tWrEu27SCQSXuXKlYOdnR025RswKiwsDF26dIGpqSk0NTVRt25dBAcHl2i7RZGeno4xY8bAwMAAmpqa6Ny5s8QFRZasrCz8/PPPqFSpEtTV1WFtbY158+YhJ88tvT179sDNzQ0GBgYQiUQyGyyaN28ucWxEIhHc3d0L3HZwcDAsLCygp6eHKVOmSCyLiYmBjY0NkorwI4mxH8r8+QCAKzY2aLl8Oa7Y2Py3TFERqFkTuHLlC2WOfYvEZXr+V/6y+tq1a+jVqxeMjY2hpqYGGxsbDB06FA8ePACQe92WlY5IJMLFixcB5DZW5Q03NTVF7969ER0dXaw8//rrr1BUVMSiRYtK5yB8o/IfT2NjY3Tq1Al37tyRiLdw4ULUr18fWlpaMDIyQteuXXH//v3Pnr9bt27BxcUF6urqMDc3x7x580CF3Dx88OABunTpAgMDA2hra8PZ2VlyrG4AERERaNWqFcqXLw9dXV24urpK1E3mzJkj8zzU1NSUu9309HQMGDAA2traqFatGk6dOiWxfMmSJRgzZkzxDwL7qnh0745LOjoyl13S0YFH9+4yl8WmpSHy/Xtcff8e21++BABse/kSV9+/R+T791i1datEw/yxY8cKbJgHAA9jY1wq4nPt2QDGv3lTpLiMMcYY+zQ5OTkQiUR52tEq4tKlLERFqQtTvm3bBly9CkRG5k4JBwBGRoCJyX+95/NXdzMzgbt3gdevgcaNy2RXuHH+i7lyBWjZsswaZSwsLLBt2zakpqYKYWlpadi6dSsqVqwocx1/f3+MGTMG586dw+PHjyWWZWRkwM3NDYsWLcKwYcNw4cIFXL58GT4+Pli9erXwQzMmJgYODg44deoUlixZglu3buHo0aNo0aIFfHx8AAAvX75E69atYWBggGPHjiEqKgr+/v4wNTVFSkpKifc9ICAAz58/x40bN9CnTx8MGjQIx44dE5ZfuHABderUwe7du3Hz5k14e3vD09MTBw8eLPG2CzJ+/Hjs3bsX27Ztw7lz5/Dhwwd07NgR2dnZctdZvHgx1q9fjzVr1iAqKgpLlizB0qVLsXr1aiFOcnIynJ2dC20EGTp0KJ4/fy68Nnx8VFeWV69eYciQIVi2bBmOHTuGzZs34/Dhw8LykSNHYtGiRdDO97gwYz+yrKwsVPg48WugmxtC7e2xJc+s8ejZM7fU37Lli+WRlZIyLtPbtm0rcf1+/vw5tm7dKiw/dOgQGjVqhPT0dAQHByMqKgpbtmyBjo4OZs2aJZHW//73P6m0HPI856mtrY3nz58jLi4OISEhuH79Ojp37lxgWZVfQEAApk6dCn9//5Lv/Dcu7/E8fPgwkpOT0aFDB4mhGE6fPg0fHx9cvHgRJ06cQFZWFlxdXZEsHsrjM0hKSkKbNm1gZmaGiIgIrF69GsuWLcOKFSsKXK9Dhw7IysrCqVOnEBkZibp166Jjx46Ij48HALx//x5ubm6oWLEiLl26hHPnzkFbWxtubm5CR47JkydLnYM1a9ZEr1695G73zz//RGRkJMLDwzF06FD07dtXuJEQHR2NjRs3YsGCBaV0dNjXQOFjRxSFIjxjbnXxIhwjI+EQGYmEj+dZQmYmHCIj4RgZiQmmpkLcq1evwvVj2XwlKQktr1/HlUI6mwj95uXcvFLMykIQN84zxhhjMpXGT6eMjAwoKioK78uXLw8gFg0bKsLBAUhIyA1PSMhthHd0/G9KuAoVgJiY3PHkvxrEJCQmJhIASkxMlFqWmppKd+/epdTU1JJvaMwYIoBo7NiSp1UILy8v6tKlC9na2lJQUJAQHhwcTLa2ttSlSxfy8vKSWOfDhw+kpaVF9+7doz59+tDcuXMlli9evJgUFBTo6tWrUtvLyMigDx8+EBFRu3btyNzcXHif19u3b4mIaO/evaSkpESZmZkl3FNpAGjv3r0SYXp6ejRx4sQC12vfvj0NGjSo1PMj9u7dO1JWVqZt27YJYc+ePSMFBQU6evSo3PU6dOhA3t7eEmHdu3en/v37S8WNjo4mAHTt2jWpZS4uLjRu3Lgi5/fSpUtkbGwsvO/duzctWbKEiHLPo86dOxcpnVL9DjH2FXv79i3B2JhgY0O1qlYloz17CKGhZLRnD0VWrUpXbGwoxsYmtxwwMiKKjCS6coUoJuaTt1lQ+fUjKrPynOiLlOnyJCcnk4GBAXXt2lXmcnHZW1AZIRYQEEA6OjoSYUFBQQSA7t27V6T8hoWFkbm5OWVkZJCZmRmdPn2aiHLLQTU1Nfr7778l4u/evZs0NDTo/fv3REQ0depUqlq1Kqmrq1OlSpXo559/poyMDCG+r68v2dnZCe+zs7Np7ty5ZG5uTioqKmRnZyexDfF+7969m5o3b07q6upUp04dunDhQpH2pyRkHc8DBw4QALp586bc9V6+fEkAhGP3Oaxdu5Z0dHQoLS1NCFu4cCGZmZlRTk6OzHUSEhIIAJ05c0YIS0pKIgD0v//9j4iIIiIiCAA9fvxYiHPz5k0CQI8ePZKZ7vXr16XSzW/kyJE0bdo0IiJKSUkhAPTy5UsiInJzc6M9e/YUab+5XvL1exIdTSZ791L9wEBav3Ur1Q8MJJO9e+lJdLTcdYLi40kpLIwQGir9OnGC0Lo1AaDofGmMefCAEBpKYx88kJ2X1FQyOXeO9PdeJgz+h3T2y0g/NJQ2hYeXaJ+5PJfGx4Qxxr4fJf3p9O7dOwIgvBo0aEBEREFBREpKuWnnfykp5S7PLyiISFFR9jqKirLXKY6ill/cc74sxcbmPktx9SoKfMbiMxk0aBACAgKE9/7+/vD29pYZd/v27ahWrRqqVauG/v37IyAgQOLR5uDgYLRu3Rr16tWTWldZWRmampp48+YNjh49Ch8fH5mPJufe2QJMTEyQlZWFvXv3Fvr4dElkZ2djx44dePPmDZSVlQuMm5iYCL1CxoqsVasWypUrJ/dVq1YtuetGRkYiMzNT6KkDAGZmZqhduzYuXLggd70mTZrg5MmTwrAEN27cwLlz59C+ffsC8ypLcHAwDAwMUKtWLUyePBnv37+XG7dq1apISUnBtWvX8ObNG0RERKBOnTp48+YNZs+ejTVr1hR7+4x9rx49egRdXd3c6/uGDbjz559I+Hi9SyhfHg5//gnHDRtgJX5aRdbtfPb1+8JlujzHjh3Dq1evMHXqVJnLxWXvp1JXVwcAieHrCuLn54e+fftCWVkZffv2hZ+fHwBAR0cHHTp0kBpGLiQkBF26dEG5cuUAAFpaWti0aRPu3r2L3377DX/99RdWrlwpd3u//fYbli9fjmXLluHmzZtwc3ND586d8fDhQ4l4M2fOxOTJk3H9+nXY2Nigb9++yMrKKvJxKA3v3r1DSEgIABRYL0lMTASAAuslZ8+eLbBOUq5cOfz6669y1w8PD4eLiwtUVVWFMDc3N8TFxSEmJkbmOvr6+qhRowYCAwORnJyMrKwsbNiwAcbGxsLTF9WqVYOBgQH8/PyQkZGB1NRU+Pn5oVatWrC0tJSZ7saNG2FjY4OmTZvKza+dnR3OnTuH1NRUHDt2DKampjAwMEBQUBDU1NTQrVs3ueuyb0sFKyvEuLnhkocHhru745KHB2Lc3FChgPKywCFoRo0C/vc/vHr1ClZWVoUOgRP7ccLX2FjgxR017FVrjNflk4H+j5Eo54HRgXkmiWWMMcZY6f10evbsmcTvGQ8PD1z62AXew0N+b/hLl3KX5+fhkTsNnCybN8te53PgCWHLUt5KpHgiIXGjjNhnbJweMGAAfvrpJ2Gc2fPnz2Pbtm0ICwuTiuvn54f+HycybNu2LT58+ICTJ0+idevWAICHDx+iefPmBW7v0aNHICJUr169wHiNGjXCjBkz0K9fP4wYMQINGjRAy5Yt4enpCWNj40/a17z69u0LRUVFpKWlITs7G3p6ehgyZIjc+Lt27UJERESBw7wAwJEjRwpsnCjoh3Z8fDxUVFRyG/DyMDY2Fh4Fl2XatGlITExE9erVoaioiOzsbCxYsAB9+/YtMK/5eXh4oFKlSjAxMcHt27fx008/4caNGzhx4oTM+Lq6uti8eTM8PT2RmpoKT09PuLm5wdvbG2PGjEF0dDQ6d+6MzMxMzJkzBz179ixWfhj7XoSFhaFFixa5bxYsgMJPPyFHQUFqIjsAcL18Ofcf8XVfSQnINycG+4p9wTL90KFDQuO12LRp0zBr1iyhEbqwslfMyckJCgqSfTUSExMlHhMVe/r0KZYuXYoKFSrAJu/8CXIkJSVh9+7dwk3n/v37w9nZGatXr4a2tjY8PDzg6emJlJQUaGhoICkpCYcPH8bu3buFNH7++WfhfysrK0yaNAnbt2+Xe/Nh2bJlmDZtmjCPyuLFixEaGopVq1bhjz/+EOJNnjwZHTp0AADMnTsXtWrVwqNHj4p83D5VYmIiypUrByIShu7r3Lmz3O0SESZOnIgmTZqgdu3actN1dHQsdFLcghr34+PjYZWvsVNcB4uPj0elSpWk1hGJRDhx4gS6dOkCLS0tKCgowNjYGEePHhV+NGlpaQlz+8z/OAeHjY0Njh07BiUl6Z8h4qGYpk+fXuC+eHt74+bNm6hZsyYMDAywY8cOvH37Fr6+vggNDcXPP/+Mbdu2oXLlyvD394e5uXmB6bGvm+rHm4IAIFJQkHhfZDk5uTO/IXeiOPGNRquPc2wA/w1XIx4CR4yaN89zyVcAWtcApkXJ/iVNhN8yMnJbGeTcgGKMMcZ+NKXx0+n27duwtbUV3v/8889C/TI/8WSv4r8FkVdFLsu53blxviwFBQEDBwJZWf+ddWXYKGNgYIAOHTpg8+bNICJ06NABBgYGUvHu37+Py5cvY8+ePR+zpoQ+ffrA399faJwnIojyNDLJIu4FX1g8AFiwYAEmTpyIU6dO4eLFi1i/fj1+/fVXnDlzRuLL9ylWrlyJ1q1b48mTJ5g4cSImTJiAKlWqyIwbFhaGgQMH4q+//iqw5zsAuT2+SqKw47p9+3YEBQUhJCQEtWrVwvXr1zF+/HiYmZkVOpFVXkOHDhX+r127NqpWrQpHR0dcvXoV9nJ6GnXr1k2iJ1pYWBhu3bqFNWvWoEqVKti6dStMTEzQoEEDNGvWDEZGRkXOD2Ofy5WkJEz9918ssbaG42eeE2Hjxo0S361tQ4agav36Ej/w87petSquVq0KEolgkJgIyyNHgCJONse+Al+wTG/RogXWrVsnESZueC3uE2jbt29HjRo1JMLyNsznb0y2t7fHnj17oKKiUmjaISEhsLa2hp2dHQCgbt26sLa2xrZt2zBs2DB06NABSkpKOHDgANzd3bF7925oaWlJPFW2a9curFq1Co8ePcKHDx+QlZUld36TpKQkxMXFwdnZWSLc2dkZN27ckAirU6eO8L/px/GnX758+dkb57W0tHD16lVkZWXh9OnTWLp0KdavXy83/ujRo3Hz5k2cO3euwHTV1dXl1m2KKn/9o7B6HBFh1KhRMDIywtmzZ6Guro6NGzeiY8eOiIiIgKmpKVJTU+Ht7Q1nZ2ds3boV2dnZWLZsGdq3b4+IiAihgVRsz549eP/+PTw9PQvMq7KyssTNFiB3suSxY8fi+vXr2LdvH27cuIElS5Zg7NixEjd82PctNi0NrzIz8TIjAyLkPu8OAPj4hOi5S5ckzrugGjUw8N49ZBEJccV/lUQibPp4Tch7ycf/jBEZ2xQOf/4ptf1wHx80ior6mNDn63TFGGOMfUtK+tPpzJkzcHFxEd5v2LABw4YNk4onnuzVwgIYPBjw8wOePMkNl8fWFjA2zl1nyBBg48bcdUrYFFks3Dhfljw8gBo1JG8NiV26VCaNMt7e3hg9ejQASP2oEfPz80NWVpZELyMigrKyMt6+fQtdXV3Y2NggSlzxlKNq1aoQiUSIiopC165dC82bvr4+evXqhV69emHhwoWoV68eli1bhs3ynjEpIhMTE1SpUgVVqlTBzp07Ua9ePTg6OqJmzZoS8U6fPo1OnTphxYoVhf4oBHKHtYkt4NkbS0tLYWJcWXnKyMgQjqfYy5cv4eTkJDfNKVOmYPr06UKPQFtbW8TGxmLhwoXFapzPz97eHsrKynj48KHcxvm80tPTMWrUKAQFBeHRo0fIysoSLpQ2Nja4dOkSOnXq9Mn5Yay0BL54gdB377DlxYvP2jg/fvx4/Pbbb8L7S5cuoUGDBrhawHBR4iFuxOj588+WP/YZfMEyXVNTU25DrLhH+71799C4ceNC07KwsCiwUVfcmCzuFS1rmDp5/P39cefOHYke0jk5OfDz88OwYcOgoqKCnj17IiQkBO7u7ggJCUGfPn2E+BcvXoS7uzvmzp0LNzc36OjoYNu2bVi+fHmB25XVyJw/LO/TbeJlOUWYaLKkFBQUhONdvXp1xMfHo0+fPjhz5oxU3DFjxuDAgQM4c+YMKlSoUGC6Z8+eRbt27QqMM2PGDMyYMUPmMhMTE6kn915+HOJD3lOMp06dwqFDh/D27VvhhsnatWtx4sQJbN68GdOnT0dISAhiYmIQHh4uPKEREhICXV1d7N+/X6jPiIkb901MTArcF1l5uXv3Lvz8/DBlyhS0b98empqa6N27Nw+99z25cgWYOhVYsiR3KDgZ8vaEB9F/3fN0dAAATW7cQEyjRrBUUwOQOwRODQ0NmTfSL9nbw15LKzdevkv+TPwCIHeC2hwFBeGvSlYWSFEJr5dvgnQXKMYYY+zHVJKfTjt27ECfPn2E9wcPHkTHjh1lxhVP9qqiklsFGDYMyMgA8ozcKHOd2NjirVPaeMz5L0X8CLlC2X4Ebdu2RUZGBjIyMuDm5ia1PCsrC4GBgVi+fDmuX78uvG7cuAFLS0thbNh+/frhf//7H65duyYzjeTkZOjp6cHNzQ1//PEHkpOTpeK9e/dObj5VVFRQuXJlmeuVRJUqVdCjRw/89NNPEuFhYWHo0KEDFi1aJPPumyxHjhyROEb5X0eOHJG7roODA5SVlSWGkXn+/Dlu375dYON8SkqK1PADioqKJW5QuHPnDjIzM4Xeg4WZP38+2rVrB3t7e2RnZ0uM05uZmYns7OwS5Yexkijq+LGlxdnZWaJh/vHjx2jQoAFiY4H4e8rQFylDIV4NCvm+F+IhbpSyshC0YAEgp4LBvgFfqEyXxdXVFQYGBliyZInM5QWVvbKIG5Otra2L1TB/69YtXLlyBWFhYRJl45kzZxAREYHbt28DyB1m7ejRo7hz5w5CQ0PhkWdgx/Pnz8PS0hIzZ86Eo6MjqlatWuBNcW1tbZiZmUn1Mr9w4YLU0wFfiwkTJuDGjRvYu3evEEZEGD16NPbs2YNTp07JHFImP/GwNgW9RowYIXf9xo0b48yZM8jIyBDCjh8/DjMzM6nhbsTEw/Lkr5coKCgI9RJxvSXvzRHx+/x1l+joaISGhmLw4MGF7m9eaWlp8PHxwYYNG4Qh/8TDDnKd5Nt35QrQsiVwN/AK0LUrEBoKbNkiN35Q3u+6nKc+JBrw81DI91ceBQXg6NvOUHotgsODB1i/YgUcHjyAyevXMHr7Fg7Zl2A4vowGqWWMMca+McX56bRy5Ur06bMEwEkADrh06ZLchnkxVdX/qgAiUdEa2T9lndLEPefL2qc8Y1GKFBUVhR7vssaTFfeAGjx4MHQ+9jAR69mzJ/z8/DB69GiMHz8ehw8fRqtWrTB//nw0adIEWlpauHLlChYvXgw/Pz/UrVsXa9euhZOTExo0aIB58+ahTp06yMrKwokTJ7Bu3TpERUXh0KFD2LZtG9zd3WFjYwMiwsGDB3HkyBGJCWxLy6RJk2BnZ4crV67A0dFRaJgfN24cevToIfQcU1FRKXB81pIMa6Ojo4PBgwdj0qRJ0NfXh56eHiZPngxbW1th6CAAaNWqFbp16yY87dCpUycsWLAAFStWRK1atXDt2jWsWLFCYmLfN2/e4PHjx4iLiwOQO0wRkNsrzsTEBP/88w+Cg4PRvn17GBgY4O7du5g0aRLq1asnNRSALHfu3MH27duFsW2rV68OBQUF+Pn5wcTEBPfu3UP9+vU/+dgwVlJFHT+2pIhIqlHq/fv3wljgue1ZaoByYyBLhLZVduLon9LX+oBFi+ARFpb7rB/7tnyhMj09PV2ql7OSkhIMDAygqamJjRs3olevXujcuTPGjh2LKlWq4NWrV9ixYwceP36Mbdu2Ceu9fv1aKq3y5ctD7WOv0k/l5+cnDHOWX+PGjeHn54eVK1fCxcUFxsbG8PDwgJWVFRo1aiTEq1KlipDf+vXr4/DhwxKN2LJMmTIFvr6+qFy5MurWrYuAgABcv35dauLZr4W2tjaGDBkCX19fdO3aFSKRCD4+PggJCcH+/fuhpaUlfD46OjpSw8CIlXRYm379+mHu3LkYOHAgZsyYgYcPH+LXX3/F7NmzhYb1y5cvw9PTEydPnoS5uTkaN24MXV1deHl5Yfbs2VBXV8dff/2F6OhoYTz/Nm3aYMqUKfDx8cGYMWOQk5ODRYsWQUlJ6b/5OT7y9/eHqalpoU8A5Ddv3jx06NAB9erVA5B7w3TKlCkYNGgQ1qxZU6S6Dft6HVwTi8TQV0h9ugx49gwAkLllG5S9vHJ7xhsYSIzt3jwrK/d5dDnzS+UdqkbMSFkZJsrKsHjxAoM3bYLfoEF4YmQEo3zzR4kv+fWNYuFg+Qp3+z7FtsyREAEYdvAg0pRVoJ6ZgQoKzzEpsHSPA2OMMfatK+5Pp/+eTv8NQEt4ev4PDRqUL8MclyFiEhITEwkAJSYmSi1LTU2lu3fvUmpqask2kpZGlJOT+39OTu77z8jLy4u6dOkid3mXLl3Iy8uLiIg6duxI7du3lxkvMjKSAFBkZCQREaWlpdHChQvJ1taW1NTUSE9Pj5ydnWnTpk2UmZkprBcXF0c+Pj5kaWlJKioqZG5uTp07d6bQ0FAiIvrnn39o6NChZGNjQ+rq6lS+fHmqX78+BQQElHjfAdDevXulwtu0aUPt2rUjotzjg9zhJSVeLi4uJd5+QVJTU2n06NGkp6dH6urq1LFjR3r8+LFEHEtLS/L19RXeJyUl0bhx46hixYqkpqZG1tbWNHPmTEpPTxfiBAQEyNwfcTqPHz+mZs2akZ6eHqmoqFDlypVp7Nix9Pr160LznJOTQ05OTnTw4EGJ8IMHD1LFihXJ2NiY/vrrrwL3uVS+Q4wVICg+npTCwgihoVIvpbAwCoqPl79yRARF9OtHLc6coQgZ5YBYSkoKASAHgE5+/JudnS2ZjyAiJSUigKgiYsi6YTghNJREJ09K5Ml18WK6cuQIxZTwe1FQ+fUjKpPynOiLlOmyrvHVqlWTiBcREUHdu3cnQ0NDUlVVpSpVqtCwYcPo4cOHREQUHR0tMx0AtHXrViLKLU90dHSKncf09HTS19enJUuWyFy+fPlyMjAwEMquKVOmEACaPXu2VNwpU6aQvr4+lStXjvr06UMrV66UyJOvry/Z2dkJ77Ozs2nu3Llkbm5OysrKZGdnR3///bewXLzf165dE8Levn1LAIR6yeci73jGxsaSkpISbd++nYhI7udSGvWigty8eZOaNm1KqqqqZGJiQnPmzKEc8blNRKGhoQSAoqOjhbCIiAhydXUlPT090tLSokaNGtGRI0ck0j1+/Dg5OzuTjo4O6erqUsuWLSk8PFwiTnZ2NlWoUIFmzJhRrDzfunWLqlSpQh8+fJBIa+TIkaStrU3169cXznlZuF7ydYqJIbpyhejWoZjcQjTfKyd/2Ee3b9/O/b5UrSqzDoDQUIpMSpK5sbQrVyjHyCg3fSMjSrtyJTcTMTES0dPPR8jMS7acPH0KLs+l8TFhjLHvQ1F/OrVuPZgAewLqERBPAJGREVFkpMzi+atV1PJLRMQz1eSVlJQEHR0dJCYmSk04lpaWhujoaFSqVKnEPcoY+xHxd4iVlavv38scPzbSwUEYP1amsWMxFsDq7t0x1twcv1WtKhXl+fPnMDMzA5B7D3/sx/WQZ2gbIR9Xc8fVI4ggCg39b0HecXDzyDsObnEVVH79iLg8Z4wVhq8FXydx8Uj4r5y8YmODqcOHY8mGDXB88EAIf7UqCAbjPCQnijMwgObWrUhWUpKcFBYy6gF5ymKCCCKQ8Pe/Bf/9f2X2bEwtXx5L/voLjvfuSeU9E4p4On8zKv386cPacHkujY8JY4z9OKpXr4779/8rY0UiApEIIpHkXOvfQmt2UcuvLz84KmOMMfaZFGn82NhYxEZEIDIiAlcvX8b2j0MtbIuLw9XLlxH54IEwTv21a9fQyMwM9gDqARgobszZti23JT4yMnc2mXwGiIKwacHC/wKKOQ4uY4wx9qMICgKUlAAPBCHz4yisgW5uCLW3xxZXVyHeTMyH4XgP7Ny587+GeQAHAwJww9kZJsrKcFRQwLpDh+CgoAATZWWpoWqEjQFCg7zQMK+klLs8Nja3fL96FYHp6bn5+Dh0U34d9C9DeSCPN88YY4wVFxFBJBJ9HJrZA0Dmx3DRx7+58cTF8/eEx5xnjDH23RHGj1VTw2BTU/g9f44naWnSP8oBwMoKVuJe7YsWQfSx1E/IyYFDSgqQkgLExWH3mzfo0aOHRA88pKfn/k1IkJx6/mMa4nH17lt44P/snXlcVFX/x993mGFREVQ2FRVTSCxbGMysnlIqTdt7nhYD07Jc02zT9noyc0kts9yeTNOx8KmsX5ZZWtpmogz6ZIqhGbizCYILMDDn98edO/sMg2Klnvfrxesydzn33DvK95zv+X4/39suS2bCOzN4vgE6uBLJ35WlS5cybNgwr8c6dOjAtm3b/uQeSSSSs4X0dEhOBmP/f2KOSOJ9ke5YOE9LY9BXXyEUhc8rbyDj2i+566677Ndu2LCBHj16AJDfsyfBY8eizJrFMIuFmtdfJ8StVkzBVekcW5RM1wwj7mxflEXTq1KgRxwlERHktm/P3HHj1H5ceSWDPvsMoShEHTlCu8JidFj5/HMIjj9db0YikUgkkrMTi8VCcHCw/XNQ0DKyskykpnqem5UFKSl/Yuf+BKRzXiKRSCRnHfGhoeqkXFFQFIWhrVtTI4THpBwAkwnTpEkMfvJJavV6hC2qXdvqheD2nTv5p80RmQ4sCQpCV1fnWL53XsZftMjRj3jIz4fgYFA2Q849G30657NSUvxL7kgkfyNuueUWuwPMHYO3RTCJRHJOkF1Rwbjdu5l63nmknqr8SOYGfgOMzHcsnEdGYpw/33bCUX7p3d9+el5eHomJiWqke0kJIbm5ZH/zDeOmT2fqhx+SumWLRxHZhAQ1Ey4HqENHEFb7NiMDNgOsdRTR1nDtB+RcP44LI/ay43AMEQUuNWolEolEIpHYyM6GceNg6lTsjvejR48S7jQPvvDCC9m6dSs5OepnnQ6sVse2IW2fKUhZG4lEIpGclYTodCg2B7uiKN4d8wDp6aRPmkTWyJFeD/f68EM+dIoQfj43F93Gjd7byspSQ/6c+xFiU7GJiSHGYKDV0aNqn84EkTyJxAfh4eF07tzZ608H6ZWSSP6eZGdDWpq6PU0sLixkbXk5SwoLT6mdmBiIeCsZpU797L5wTi0w8VugPQCFhYWqYx5Uj3tqKgwcyOJrr1VlaFJT1Qy31FT1uA2TCQ4mN+eq6bMwJd3KMOZixshB4jgcFMMbH1QxISGBILf+ufYjGWNtFs1K87n4xnjn5iUSiUQikTixeDGsXQtLlqifCwsLXRzzd9xxB1u3bgUcWehGI8ydq27j4tT9gbR9JiGd8xKJRCI55ymwWsltr07w3aPh1+TlQWwsACUlJXRxkp7J7tKFtOnTyQ5Ajia7eXPuW7KEdy67TNXBbd6cOR07YmzWzLsOroTZs2fbCzUajUZ++OGHv7pLEolEcuZymmatBVVVmCsryamsZFlREQCZRUXkVFZirqy0121pCPHxULg0FjHCU24GgJFGWPMyUMDRo0eJ0WbqBQUUTJ2KuUsXchITXeRwchITMScnU/DBB/Zm0tPhmncEP6VcyOA+U5jPMHqQRQL5LN8Yz9i4DTyfn0+dr47qgTWxCBRqCDkrdXAbC2nTJRKJ5NzEqXQLy5ap+zIz4f33dxAX1x9tof3xxx/n448/tl+nZaFnZcGwYeo2P1/dX1/bfsrB2fkTYhYCRsraSCQSieScJ+HoUXjuOfWDe7FW2/7qK65w6ODZlvEXp6erEXnp6aS+/bbvZXxs0YQVFXQ7coSPL7iAZ//4g9QWLRjWvr1vyZ1zmGXLljF27Fhmz57NlVdeybx58+jXrx/bt2+nvbaQIpFIJBL/2CReUBTXWeugQR4SLyeLczFzzYIWWywYzWb7ftGrV4PbDQmBx1+tZjqAFdewsvbHQYGrugXRtGlTp87Y6sh07672x6scDuRXVVFisaAAX1vUBQXSiuCrOBQd1JSrC+am5GQytu4Avfdst/B3WlLp9Pls1MFtDKRNl0gkknMX54wybapdVCRIT+8CqGOFmTPfZMyYMR7XhoS4Xuv82VfbPsrB2dHkb6KjHTELf7UMjvQESCQSieScpqCqigk6HUHuVluTxKmrY4lOZ3fMF1RVYd6/n5xevVh21VUAZPbuTc6vv2KOiHCJEPQVTfjm/v2sO3KEtw8c8C+5cw4zY8YMhgwZwoMPPkhycjJvvPEG7dq1Y86cOX911yQSieTMQZN4MRrV2So4Zq1uEi8niyk5Gb0mO2Pbp231ioIpOfmk254e9av6i86t8edyYZ6ZHx/e6BodZ6sjo6+tVU91ryNjtWJKTiZhwwZSzWaMZjPlwqK2G2mB+WbEXDNkbiAmBtJjY5lw2Ie3vVxP5VdJJ/1s5xLSpkskEsm5i8mklmYDZ0e5tpxv4aGH1nl1zJ9s287l4Nyz2QoKYNo01Sm/YoW6L9BI+9OJ9AZIJBKJ5KxlcVYFLRZuYXFWhc9zEjZs4HmrlTr3iHkbIiiIjGuucTk/9cQJjMOGUaxF5FksGLduJdVsdokgdJ78F1nUyX+RxcIym4Nk0aFDp5T2f7ZSU1OD2WymT58+Lvv79OnD+vXrvV5TXV1NRUWFy49EIpGc8zR01noSpMfGkuUjXDwrJYV0mzTcyWBKTiZIm8grrtug2jo6TgxxXWeop45MVvv2pMfGel1Q0NrVKwoLOyfb0+b797Mdt7ptn+lGx/BQNKlcnQ4KC//ayf3fEWnTJRKJ5NwmPV3NLPPGK6+sYf78XgG1s3gxtGihbgNp27kcnCZ/k5DgSCQ8cULdFhU1aszCSSGd8xKJRCI5a5m+tZDyjuXM2Oq7MJ0pORm9n7LvExYsgIwMu0U3hYV5RuTZznWPEHSe/PvCaDZ7OPXPdUpKSqirqyPWzaETGxvLoUOHvF4zadIkIiIi7D/t2rX7M7oqkUgkf28CnbU2Ejq37alyzc8/89awUV6PbRw5gkfWZAKOdYaCAtie69Qfm33Xtgetql2pb0FhcLzD/sQYDLRSDJAXjvJ6EuSFQ6kBikP44w+w1XlHCOjf/6+d3P8dkTZdIpFIJA60Ki7qDLpfv36+T3Vj+nQoL4cZM7wf15LRvSWla4mE/vgr68ZI57ykUVi3bh2KolBeXv5Xd0UikZzj/LSrCtPGSqZ+/DPbO+wB4JfoIpZuqsS0sZKfdrlGqKfHxpI1fLjXtlodOcLgVatUK22z6OmXX07WKO+OAvcIwfPDwrjEWQvXB6ea9n+2orgtbAghPPZpPP300xw5csT+s3fv3j+ji5IzlF69ejF27Ni/uhsSyZ+Lv1nrKRJjMBBnMGAMD2duUhLG8PBTLnb+888/88b48Sx4bLS6wz1yHbiHTC4lh18WmUm/qoCEBLg/o4agMmhVeoSL83Yxd8YMUvLyCC4VPHXlDo+wdrsD30c/4kNDyTqvJ32es5K1NoMP91jp+ExPKAkFTltCwlmHtOkSiURy7tK9ewfgIKrG/LeAQpMmfku2AfDTT6pNXboUtm5V9/3yi/rZZFKP28rBYTTC3LnqNi7OtW3nREJfnIaYhYCRzvlziPXr1xMUFMQNN9zwV3fltJKQkICiKCiKQlhYGF26dOG1115DOOlJ/+9//2PAgAG0a9eOsLAwkpOTmTlz5mnvmxCCl156iTZt2hAWFkavXr3Ytm1bvde98cYbnH/++YSFhdGuXTseffRRqpwkMF566SX7M2s/cXFxLm0cPXqUhx9+mPj4ePsz16fzuHr1apKSkoiIiGDQoEHU1NTYjx05coSkpCT27NnTwLcgkZxertq3gYHHzYxvVU2tIQgA0dxCxjEzA4+buWqfa4T6I488wrO23xUtgt729+Lzp58m/vBh1Zp7kQZwj8hzZ3FhIdm2kDp/BvdU0/7PNqKioggKCvKIqCsqKvKIvNMICQmhefPmLj9nI4MHD0ZRFIZ7WVAaOXIkiqIwePBgj2P1jQFqamqYOnUqF198MU2aNCEqKoorr7yShQsXYrFJMgEcOnSI0aNHc9555xESEkK7du24+eab+eabb+znbN68mZtuuomYmBhCQ0NJSEjg7rvvpqSkpEHP2qdPH4KCgthwFmWV9OrVy26ng4OD6dSpE08//TTV1dX2c/Lz8xkyZAgdO3YkLCyMTp068eKLL7rY4NPF7Nmz6dixI6GhoRiNRn744Yd6r1m6dKn9303r1q25//77KS0ttR+3WCy8/PLLdOrUidDQUC6++GJWrVp1yvfevHkzl156Kc2aNeOWW26hrKzMfqy2tpaUlBQ2bdrUgKc/iwlk1nqKxIeGkt+zJ1kpKQxr04aslBTye/YkPjS04Y0VFLBmyhTuHzyY9ydMILtLF8JOVNE9bwdzZ8yge94OYktLiSkrI5picjCSnKGGq5tMkEVPLi7Zyf4Bd7JpxHCGrVjBhhEjqRjQl60ll6mL7WYzMZ9/Ttzhwxjz8pg7cybGmhriFIUYW40YgOyKCtK2bKEs4ihf3L2E7pVr+dcxE7/v0PHzz967/1dO7v+OSJsukUgk5zaKoqBOl28HhhMV1QuApk1VSRl/cnBXXQUDB6qJ7M6L4RkZ6v6rroL4eMjPV+3vsGHqNj8fuzwd+E8krCfR/U9BOuf/IrSBXvafqJ/37rvvMnr0aH788cez3qH68ssvc/DgQXJzc3niiSd45plnmD9/vv242WwmOjoak8nEtm3bePbZZ3n66ad56623Tmu/pk6dyowZM3jrrbfYtGkTcXFxXH/99VRWVvq8ZunSpTz11FO8+OKL5ObmsmDBApYtW8bTTz/tct4FF1zAwYMH7T9btWVFG48++iirVq3CZDKRm5vLo48+yujRo/m///s/r/e1Wq2kp6czfPhw1q9fz8aNG/nPf/5jPz5+/HiGDx9O+/btT+GNSM5FsrMhLU3dNjYFu3YxMD/YkS2noRncWhhx2BGhfuWVV/Lmm2+yqqyM0NJSUvPy6L15MygKTU6cIL64GBYtUq25k0WPKSsjrrRUndDPmIFRr7dHCHorAqsDujRpwv1ui2bSCHsnODgYo9HI6tWrXfavXr2aK6644i/qlW/+bJverl07MjMzOaEJJQJVVVV88MEHPv8m+xsD1NTU0LdvXyZPnszQoUPtf/NHjRrFrFmz7IvI+fn5GI1Gvv32W6ZOncrWrVtZtWoVvXv3ZpQtm6SoqIjrrruOqKgovvrqK3Jzc3n33Xdp3bo1x48fD/gZ9+zZw88//8zDDz/MggULGvqK/tY89NBDHDx4kF27djF16lTefvttXnrpJfvxHTt2YLVamTdvHtu2beP1119n7ty5PPPMM6e1X8uWLWPs2LE8++yzbN68mX/84x/069fP75jxxx9/5L777mPIkCFs27aNDz/8kE2bNvHggw/az3nuueeYN28es2bNYvv27QwfPpzbb7+dzZs3n9K9H3zwQdLS0sjJyaG8vJxXX33VfmzatGlcddVVdO/e/RTfyllCILPWRiBEp0MxmyEtDcVsPuli5wU9etDi3Xe55O67OWhbQGhWdYI5M2aQ+ttv/PellygYMID4khJ0trR4YQtXT0+HPyaYsKAnxGIhyHY8CEGIxeJYZE9NJf7ee8m/5x6yRoxg2KefktW3L/nXX0985872vizetYu15eUs+fVX9B+pIrV1SzPJfT+HsjVm2uPwJmiT+4MHT+qxz1rONJsukUgkksbBNUOqANgA5FBaqo4PSkrq13ofMcL/PbTjISEOO6wo6mdnsrPVIZB23Jnk5EaPWWg4QuLCkSNHBCCOHDnicezEiRNi+/bt4sSJE6d8n9F5eYK1a8WYvLxTbisQjh49KsLDw8WOHTvE3XffLf7973/bj11++eVi/PjxLucXFRUJvV4vvv32WyGEEEuWLBFGo1E0a9ZMxMbGigEDBojCwkL7+WvXrhWAKCsrs+/76KOPRNeuXUVwcLDo0KGDmDZtmss9OnToICZOnCjuv/9+0axZM9GuXTsxb968U37WDh06iNdff91lX0pKirjjjjv8Xjdy5EjRu3fvU76/L6xWq4iLixOTJ0+276uqqhIRERFi7ty5Pq8bNWqUSEtLc9n32GOPiauuusr++cUXXxQXX3yx3/tfcMEF4uWXX3bZl5KSIp577jmv5xcWFgrA/u993LhxYuTIkUIIIX788UdhNBpFbW2t33u605j/hyRnLqNHCwFCjBnT+G2zdm29P0IIUVdXJ1CF7tSf2Fix9vvvRXZioohZvlywdq2IXr5cmBMTRfbGjSJf+zdrNqud1+lElcEgrDqdECCs2dmiqq7Oow+K21b7MW7aJObu3y+6Z2eLuB9/FHsb4f+EP/t1JpKZmSkMBoNYsGCB2L59uxg7dqxo2rSpyM/PD+j6P8ueC/Hn2vRBgwaJW2+9VXTr1k2YTCb7/qVLl4pu3bqJW2+9VQwaNMjlGn9jACGEmDJlitDpdCInJ8fjfjU1NeLo0aNCCCH69esn2rZta//sjGb/P/nkE6HX64XFYjml53zppZfEPffcI3Jzc0V4eLj9nqtWrRIhISEu4w0hhBg9erS4+uqrhRBClJSUiHvuuUe0bdtWhIWFiQsvvFC8//77Ludfc8014pFHHrF/Pnz4sBg4cKCIjIwUYWFh4oYbbhB5Tt/nwoULRUREhFi1apXo0qWLaNq0qejbt684cOBAg57L/b5CCHHHHXeIlJQUv9dNnTpVdOzYsUH3aiiXXXaZGD58uMu+Ll26iKeeesrnNa+99po477zzXPa9+eabIj4+3v65devW4q233nI559ZbbxXp6emndO+wsDCRm5srhBBi9uzZon///kIIIX7//XeRmJgoKioqfF6rIcclp4FTNPJ3jRnj3X5/+63L597Tp4tNSUnqvUC1zzbMZiEuxew45vzz+edCTJggRFCQ9+M6ncifOlVk//abMFdU2McEMcuXC3NSkshOShK7Y+NcrvHWzKlwttlzIU6vTZdIJJJzkU2bhOjdW93+HampqRFgFPCNbbvKq70EIfR6IZymNR6YTN6v83eNO9rwpEkTIbp3F2LWLCFSU4WIixNizx4hqqpO/Zm9Eaj9kkF7fyLeoikzi4rIqazEXFlJQVVVPS2cPMuWLeP888/n/PPPJyMjg4ULF9plXtLT0/nggw9cZF+WLVtGbGws11xzDaBG1U2YMIH//e9/fPrpp/zxxx9e0+Y1zGYzd911F/fccw9bt27lpZde4vnnn2fRokUu502fPp3U1FQ2b97MyJEjGTFiBDt27Gi05xZCsG7dOnJzczHUo3l55MgRWrZs6fecfv360axZM78/vvjjjz84dOgQffr0se8LCQnhmmuuYf369T6vu+qqqzCbzWzcuBGA3bt3s3LlSm688UaX83bu3EmbNm3o2LEj99xzD7t37/Zo57PPPmP//v0IIVi7di15eXn07dvX632jo6Np3bo1X3/9NSdOnOCHH37goosuoqamhhEjRjB37lyCgoL8vi+JREOrjp6T46iOnpmpfvaXxtZQTIcP24u1euOhX46wY8cJz3+7mZn0rqsjdf58iiMjASiJjMQ4fz6px445irU6SQOEzJqFYpMGUGJj7RGCzkVgtb+qzgVjF55/PpuMxlNP+z/Lufvuu3njjTd4+eWXueSSS/j+++9ZuXIlHTp0+Ku7Bvy1Nh3g/vvvZ+HChfbP7777Lg888IDXc/2NAUDN0Lruuuu49NJLPa41GAw0bdqUw4cPs2rVKkaNGkVTL3UUIm3/b+Li4qitreWTTz5xuUdDEEKwcOFCMjIy6NKlC0lJSfz3v/8F4LrrriMyMpKPP/7Yfn5dXR3//e9/SbfpSFRVVWE0Gvn888/59ddfGTp0KAMHDiTLVy4rqlxQdnY2n332GT///DNCCPr37+8i6XP8+HGmTZvGkiVL+P7779mzZw9PPPHEST2jxv/+9z9++umnRhmjDB8+vN4xiq9I9JqaGsxms8sYBVRpIX9jlCuuuIJ9+/axcuVKhBAUFhby0UcfuYxRqqurCXX7GxcWFsaPP/54Sve++OKLWb16NbW1tXzzzTdcdNFF9vcwdepUwsPDfV4r8eRkstq0a35Z0ThGvkePHvz39tu9H3QKc+uzaRNrU1JY0qeP13D1mBiIaqX+LhTVNtdpuWo33QTPPw91aopddlISadOnk52UpB63Wkno3p3UAwcwms32MUFxZCTGefNInTeP8zI/AMCCnnRcxeWDgqTevDf+7jZdIpFIzjQWL4a1a2HJkr+6J55UVFQQHBwMjALSgMeIifHudwJYuBAWLGj8zHpvPoimTWHOHOjZEz78UE0kbNfOM9L+T+f0rA2cuZzOSLtAoilPF1dccYV44403hBBCWCwWERUVJVavXi2EcETJf//99/bze/bsKZ588kmf7W3cuFEAorKyUgjhGTl/7733iuuvv97lmieffFJ07drV/rlDhw4iIyPD/tlqtYqYmBgxZ86cU3rWDh06iODgYNG0aVNhMBgEIEJDQ8VPP/3k85r169cLg8Egvv76a79t79u3T+zcudPvjy9++uknAYj9+/e77H/ooYdEnz59/N73zTffFAaDQej1egGIESNGuBxfuXKl+Oijj8Qvv/wiVq9eLa655hoRGxsrSkpK7OdUV1eL++67TwBCr9eL4OBgsXjxYr/3/eGHH0RqaqpISEgQI0eOFDU1NeLf//63GDt2rPj111/FFVdcIZKSksSsWbP8tqMhI9TOXZxXuBXFddsYUWbOnJ+40WvUXdCnawVRJ2z3UyPmjUajEEII06FDQr9undfr9OvWCdOhQ44bVFUJYbWqv1utXpfZzRUVXtsyBxDJebLIqDJXTnfk/F9l07XI+eLiYhESEiL++OMPkZ+fL0JDQ0VxcbHXyHl/YwAh1AjkMfVEuWZlZQlALF++vN4+PvPMM0Kv14uWLVuKG264QUydOlUccv4/VA9ff/21iI6Otkffv/766+LKK6+0Hx8zZoxLRtlXX30lgoODxeHDh3222b9/f/H444/bPztHsOfl5QnAZZxQUlIiwsLCxH//+18hhBo5D4hdu3bZz3n77bdFbGxswM+l3ddgMIimTZuK4OBgAQidTic++ugjn9fs2rVLNG/eXPznP//x23ZhYWG9YxRfGQ379+/3eAdCCDFx4kSRlJTk974ffvihaNasmX2Mcsstt4iamhr78QEDBoiuXbuKvLw8UVdXJ77++msRFhYmgoODT+nev/76q7j66qtF+/btxYABA8SRI0fEe++9J2699Vaxb98+0adPH9GpUyfx7LPP+mxDjkscnEzA+13PHxFM3+wawX6SRl4bszNwnOCbb73aUNauFSPGjHGNZk9MFNlJSSLf7f9i1a69whoXJ0T37sI6Z66oM3ZXw+NmzlRD9Gz9Gj16tJr59PDD9n1Lru8j9N9674N+9Wphuu46IUBcitkjis8pgP+kkfbcE/lOJBKJRIj8fCGys1VbExOj2p2YGPVzdrZ6XIiGR9U3ZhT++vX7BaQIuNRnpLz7z913+x+DbNwohE6nRr3fe6+61enU/f74M30QvgjUftVTq1bSmJiSkxm8Ywe1QniNplzUpctpue9vv/3Gxo0bWb58uXovvZ67776bd999l+uuu47o6Giuv/56li5dyj/+8Q/++OMPfv75Z5dioZs3b+all15iy5YtHD58GKut+OGePXvo2rWrxz1zc3O59dZbXfZdeeWVvPHGG9TV1dmjVrUoJ8BexLTIqQjTyfLkk08yePBgiouLefbZZ0lLS/Opabht2zZuvfVWXnjhBa6//nq/7bZt2/aU+6a4CVwJITz2ObNu3TomTpzI7Nmz6dGjB7t27eKRRx6hdevWPP/884Aa0a/RrVs3evbsSadOnXjvvfd47LHHAHjzzTfZsGEDn332GR06dOD7779n5MiRtG7dmuuuu87rva+66iqXYmp5eXksWbKEzZs3c/XVVzN27FhuuOEGLrzwQq6++mqX71MiccZkgsGDobbWtZALqPKvbkk1DaagQNWsUxRoEqbuU6xWhE6HzmrFqtNx3tNN2FkSBKjRtcOHD7f/nUuPjSW5SROMZrNH21kpKaQ4R2A6L6v7ErSbMQOGDkUHeC8Vq542bhxMnarq7EnOLP4qm64RFRXFjTfeyHvvvYcQghtvvJGoqCiP8+obA0D9dkg7BzxtmDcmTpzIY489xrfffsuGDRuYO3cur776Kt9//z3dunWr9/oFCxZw9913o7dpQw8YMIAnn3yS3377jfPPP5/09HR69uzJgQMHaNOmDUuXLqV///60aNECUCPpJ0+ezLJly9i/fz/V1dVUV1d7jfgHdcyi1+vp0aOHfV+rVq04//zzyc3Nte9r0qQJnTp1sn9u3br1SY1Z0tPTefbZZ6moqGDKlCk0b96cf/7zn17PPXDgADfccAN33nmni467N2JiYog5RbHMho5Rtm/fzpgxY3jhhRfo27cvBw8e5Mknn2T48OH2WgEzZ87koYceokuXLiiKQqdOnTwyP07m3hdccAHfffed/XNpaSkvvfQS33//PaNHj+bKK69k+fLldO/enR49enDzzTcH/B7OFZxtp3PA+6BBqo2OigL3wGbna1ZUF0JKOQ/cNJ3/zbwdpc7TyGd37cq46dOZWlFBqo+ini7fc2R30Pn+3ufcfjuKre1iW4abhnOuTkgnm85+cLBahHnYUKipgZAQCq64gpL0dBQhWNa7t/rcaWkM+uorhKIw+ZeJ1A47H+Z7GROMHEnK77/bP+t0YLU6thKJRCKRnC6cddk101lcrOq2awjhGlUfyDyzoef7wmw2c8UVRkCzn1Z8VVlTFLWvoaGwZo26z9cYpHt3KCuDZs0c9vboUaivVvjp9kE0JmelrM3s2bPp2LEjoaGhGI1Gfvjhh7+6S4Dq/MlKSfF6LCslhXQf1epPlQULFlBbW0vbtm3R6/Xo9XrmzJnD8uXLKSsrU/uWns5HH32ExWLh/fff54ILLuDiiy8G4NixY/Tp04dmzZphMpnYtGkTn3zyCaCmInvD24RKm9g7457GrVZxPvWRbVRUFJ07d6Znz558/PHHvP7666zR/sc7sX37dtLS0njooYd47rnn6m33VGRt4myFIA8dOuSyv6ioiFg/3/3zzz/PwIEDefDBB+nWrRu33347r776KpMmTfL5rpo2bUq3bt3YuXMnACdOnOCZZ55hxowZ3HzzzVx00UU8/PDD3H333UybNq3e5wb1+xs6dCjTp0/HarWyefNm/vWvfxETE8M111zjMkGWSNzxVx09K0s9fiokJKgDCaMRDh6IJrq0nC55+xk743OMeXnElh6msjgGGAwMYfz4D10WIJ3RuW0bzOLF1GzZQquqKrrodITbinYqViuF27dj3riRgl27/tapiJL6+atsujMPPPAAixYt4r333vMpaRPIGCApKcnFCe2NxMREFEWp9zyNVq1aceeddzJ9+nRyc3Np06ZNQPbm8OHDfPrpp8yePdve37Zt21JbW8u7774LwGWXXUanTp3sRXE/+eQTMjIy7G1Mnz6d119/nXHjxvHtt9+yZcsW+vbt63fM4mu/81jG25jF17X+iIiIoHPnzqSkpGAymfjuu++8Fr09cOAAvXv3pmfPni5F7X1xKrI2UVFRBAUFNXiMMmnSJK688kqefPJJLrroIvr27cvs2bN59913OWiTGYmOjubTTz/l2LFjFBQUsGPHDpo1a0bHjh1P6d7uPProo4wdO5b4+HjWrVvHv/71L5o2bcqNN97IunXrAm7nXMLZdha1qIDpWyhqUeG3OFtCjypS763EeE8lJy5XF6e2XtWC5PPWY05KosDtO1v8n/+wNjSUJYWFHm3Z/4/FXgpJt0DiTSjXtva8qTbc1SbWmnScbatHXTD1ICSEbLOiyvWYHYvpCUePkjpvHkYnKTvN0Z86bx7bMo8y4RW1Ce0vgE4bcz/zDBiN1EXHQXQMRiPMnau+w7+8mJxEIpFIzmpMJkddc2/O5gkTAleYa2zZ2RUrVpCamooaCKfJQvqeUWv9rqqC0lL196Ii3wVimzdXHfOgbutzzMPp90E0Jmdd5PyyZcsYO3Yss2fP5sorr2TevHn069eP7du30759+7+6e3a0aEp/UZWNQW1tLYsXL2b69OkeWp7//Oc/Wbp0KQ8//DC33XYbw4YNY9WqVbz//vsMHDjQft6OHTsoKSlh8uTJtGvXDoDsesSgunbtatcS1Vi/fj1JSUl/uk55ixYtGD16NE888QSbN2+2T7S3bdtGWloagwYNYuLEiQG19c4773DC5mhrKB07diQuLo7Vq1fbdX1ramr47rvvmDJlis/rjh8/jk7n+kctKCgIIYRPp0B1dTW5ubn84x//AMBisWCxWLy2E+hiyIIFC2jVqhW33HKL3aGjafFaLBbqbNqdEkl9nI4os5eXVfDC/t10mVvNgrxHmTPgQX61XMRTPEHMiiL2GVpzq+Vz2tCVEjpx4kSaRxsxBgNxBgPtQkMZ0ro1Cw4eZG9VFTH1aEEDHuGHPW0jnFKrFcWmtSwUhf7a34/jxzHM7QzUH6Uo+fvzZ9l0d2644Qa7w9lb/ZBAxwD33nsvzzzzDJs3b/bQna+traW6upqWLVvSt29f3n77bcaMGeMRhV5eXm7XnXcnODiYTp06cezYsXqfaenSpcTHx/Ppp5+67P/mm2+YNGkSEydORK/Xc++999rP1el0LhrnP/zwA7feeqvdYW+1Wtm5cyfJ3px3qGOW2tpasrKy7Fl2paWl5OXl+bymsTAYDDzzzDM8/fTTDBgwgCZNmgCwf/9+evfujdFoZOHChR722xsvv/xyvRr4bdq08bo/ODgYo9HI6tWrud1J83v16tUemZDOHD9+3J7hoKGN89zHKKGhobRt2xaLxcLHH3/MXXfddUr3duabb75hx44d9tpGdXV1LmMUiXecI8roo0bBc30h/Nbca0RZdkUFZOY4dmhfcaSF3+ZbSGUeAPn33ENJZCSK1cqyEycgKIjMAwcYFBeHAA5UVzNj717W3XOPen3mDEeTwuJo2+4ZhyhFzyu/HmT4hdEez/FBUVO6d/S+kOMtGtAUG8vg/fup1es9Hf21tUywtiXjFgNvmw1YC0MpWtSa6MH7UWJriLnnHhg0iKCaGn4mhOBg1fQPdQTmSyQSiURyWkhPh+Rk10h5jdpataTK88/7j6rXCDQK3xlfmd9vvfUWo0ePtn16n/vv78nChQ839PHsnI6o9r99pttpktX5y7jsssvE8OHDXfZ16dJFPPXUU17Pr6qqEkeOHLH/7N2797Rq1O49cULE/fij6J6dLebu3y+6Z2eLuB9/FHtPk9blJ598IoKDg0V5ebnHsWeeeUZccskl9s/33nuvuPjii4WiKKKgoMC+v6ioSAQHB4snn3xS/P777+L//u//RFJSkgDE5s2bhRCemvNms1nodDrx8ssvi99++00sWrRIhIWFiYULF9rb7dChg3j99ddd+nTxxReLF1988ZSe2Vu7RUVFIjQ0VHz44YdCCFWnNDo6WqSnp4uDBw/af4qKik7p3vUxefJkERERIZYvXy62bt0qBgwYIFq3bi0qnHSoBw4c6PLv9cUXXxTh4eHigw8+ELt37xZff/216NSpk7jrrrvs5zz++ONi3bp1Yvfu3WLDhg3ipptuEuHh4SJfEx0Tqs7tBRdcINauXSt2794tFi5cKEJDQ8Xs2bPr7XdhYaHo0KGD2Ldvn31fcnKyeOmll8T69etFs2bNRFZWVr3tSG3Xc5u9e1W51+7dhZg7V93Gxan7T5XReXmCtWvF1Q//x0VErk5NfrdvtR9v2nxCCFFVVyesNj15q9UqqurqAuuAm6Cd6brrhH71ap96tT2v+9hD/+5Ude+kHqsrp1tzXog/36YL4dCc19DGLxrOmvOBjgGqqqrEP/7xD9GiRQvx1ltviS1btojff/9dLFu2TKSkpNht/e7du0VcXJzo2rWr+Oijj0ReXp7Yvn27mDlzpujSpYsQQogVK1aI9PR0sWLFCvHbb7+JHTt2iNdee00EBQXVW+dECHUcMH78eI/9FRUVIiQkRHz66adCCIdO/EUXXSSGDBnicu7YsWNFu3btxE8//SS2b98uHnzwQdG8eXOX9+asOa+9t65du4offvhBbNmyRdxwww2ic+fOdu30hQsXioiICJf7fPLJJ6KhQ2n3+wqh1oRp3bq1eO2114QQqgZ7586dRVpamti3b5/LOOV0kpmZKQwGg1iwYIHYvn27GDt2rGjatKnLWOKpp54SAwcOtH9euHCh0Ov1Yvbs2eL3338XP/74o0hNTRWXXXaZ/ZwNGzaIjz/+WPz+++/i+++/F2lpaaJjx472MWOg9/bF8ePHxfnnn2//dyqEEP369RMPPfSQ2LJli4iPj7fXDnDnTBmXbDpyRPTevFlsauS/7/knTgjTxgpBYoVg+Y+qnVr+oyCxQpg2Voh8t/ei2Vr3+houeuyDBrnW5LBptyveNNwfflgAIuG6R0XQ6jXedeZXrxPDPz4oMNSJFxIfV9v65huXbXTiThf7GYgmr7mkxPszJJaIzz9Xz/s5u05Ex1gFCBEdYxU/Z9d5jBtOB9KeeyLfiUQikaiYzapd0+lctxMmuJRUcfnR64UwmVzbMZkadr4Q3uvTjB49Wq0ZY/t55513PPrY0J/GqN+icTp9EIEQqP06q5zz1dXVIigoyKNY2ZgxY8TVV1/t9ZoXX3zR5R+S9nM6J/Mn7fw5CW666SbRv39/r8fMZrMAhNn2L/+LL74QgNd39f7774uEhAQREhIievbsKT777DO/znkhhPjoo49E165dhcFgEO3bt7dPOjX+TOe8EGrh1QsuuEDU1dX5/N47dOhwSveuD6vVKl588UURFxcnQkJCxNVXXy22bt3qcs4111zjUszPYrGIl156SXTq1EmEhoaKdu3aiZEjR7q867vvvlu0bt1aGAwG0aZNG3HHHXeIbdu2ubR78OBBMXjwYNGmTRsRGhoqzj//fDF9+nT7v0V/3HPPPR5FX7OyskSXLl1Ey5Ytxb///e+Anv9MmQRLTh8B1FINjE2bRP5tt4nsDRuE+bffRIytmGvU8k/FkuuuE6lz5ojPevb0sPQ16MW9mDwGAA26tZOTRCue8/vLnqMbc2Ki14l/8vkbGzQIChQ5cXXlz3DOC/Hn2nQhPJ3z7jg75xsyBqiqqhKTJk0S3bp1E6GhoaJly5biyiuvFIsWLXIpInrgwAExatQoe/H1tm3biltuuUWstRXA/f3338VDDz0kkpKSRFhYmIiMjBTdu3d3WZz3RXZ2tgDERh/VnW6++WZx88032z93795dAOLbb791Oa+0tFTceuutolmzZiImJkY899xz4r777vPrnD98+LAYOHCgiIiIEGFhYaJv374iLy/Pfvx0OueFUIufRkdHi8rKSnvxWW8/p5u3337b/t2mpKSI7777zuX4oEGDxDXXXOOy78033xRdu3YVYWFhonXr1iI9Pd1lMX/dunUiOTlZhISEiFatWomBAweK/fv3N/jevnjqqadciv0KIcTOnTtF9+7dRfPmzcXw4cNFnY//l2fKuERzio9x+jfZGLjYp2/dtraf/BMnRHZFhTBXVIiYH1UHfvjaH7wXPU9MFGL0aGH69Veh9+HAV9asFcrnn9sWApYLEhNF9vPPi8979PB6/rKVvwmRnS2+mGAWm6O6iLiPPhKXzPmPiB3yk9B9vlboPl0nguJOuNhPt/Vyrwvh9sLt37g+d6vLKwK6/nQi7bkn8p1IJBKJij9ns+YUD9TZHcj5/ha8k5MnCvhRgFEA4uuvv3bp40UXCfH000J06eJoOzLSt1Nec+b76u/JFq5tNB/ESRCo/VKEEOL0xOT/+Rw4cIC2bdvy008/uRT/fPXVV3nvvff47bffPK7RioRpVFRU0K5dO44cOUJzNxGjqqoq/vjjD7uevUQiaRjy/9A5yumofDpmDModd9g/KkIgFMW+1RC2Qm8aKZjZjEMnXEuZa4je3JidO5m1fz9j2rZFzEpk1iwYMwZmDspxyQPMSUzEOH++vSCttj1/aFN+29nda9unYpErKiqIiIjwar/ORfy9D/m3SCKRwN/7b0FBVRUlFgsK0O+XXyiyWIgxGPjyoosQQJTBQIdT7PPSwkIG5+6gFk/jo0dhUXIXMpzqTCjg5UzbTgVMr7zCVQcP0mH7drsN9IbdVgvhyKXXjlkFQqfYt+ahQ0mx1VASKNQY9OgttTw6+mFm3XEHIz/5hCGDZuJcAmTpUie5Hvfnstn9a/5ZRXezmbrCUIoXtSbm/oPoYqr4sZuRDStCfV4P0KcPfPWV92ONgbTnnsh3IpFIJA6qq7HLqgnhkFXLsU1H3SVczGZwL5WVnQ0jRqhbf+c7m2ln0+06b53J1q3XcuGFF7r0sSHDlGeegdWrYe9e2LQJ4uM9zxkzBsfce2bgbf+VBGq/zsqCsN4Kkbrv0wgJCaF58+YuPxKJRCJpRNwrn2Zno1Zn8167IruigrQtW1RtW2fcqtaYJk5Eb5s5u2vGAjQ/doycxESXAnXHk+pg+hZIUtsOtBBMQVUV5spKcioref+gWgBvyYEiTBsrIamSpWuq0PwXWh9iysuJKy3FmJfH3Lff5uI/9hBTWkZYmffZ/oQJ9fdDIpFIJOcGCRs2kGo2YzSbKbbp5hdbLBjNZlLNZhI2bDjle6THxpJl9FHY2qgWtjYlJ6PXbKz7SfaZubrNeO45Et5+2+UUrZCq4iTwarfVTjrvb86cSWxpKal5vzF3xgxS834juEwh5O4HwVbHYE9sDL927Mj/EjuzzLb4vqDXLeTWVWKurKSgqkp9LqcCcEay+YY0jKhjjqwsuOoqKNwWyquGCyk/oIPfmiGGp/BJaE/Kd4Zy1VW+C8gBbNly8gXzJBKJRCI5VUJCHE5zxVHvnJgYtTh5IMXKFy9Wp+NNmqjnPf00NG0KrVq5nu+vCK1m/6OiHqam5kIXuxgS4nqtN7TSTk2aqAsFWVmQn+/qmA+0cO3ixdCihbo9EzmrCsJGRUURFBTEoUOHXPYXFRURG+u9SJBEIpFITgNuBVIBR+XTadM8q7M5sbiwkLXl5SwpLCTVecHUrWpN+po1JO/Zg3HePK9dqGzSxCVq70DvAezrq1cL3vUthLzAF2NdnCC2CMEyqwUmmwEoBa7v3ZlNxNFaqDYovriY/AEDCLZYAIWhH31EjcFAiMWC4ubiaNVKjfKTSM52hg8fjslk8nosIyODuXPn/sk9kkj+npiSkxm8Ywe1QuDqAge9orCoS5dGvZ+vwtbpsbEkN2mC0Wz2vMiLk33RlCkAxFRUEFdaSrviYoasXMmC/v3ZHRdHqZfC0VkjR5KycydDv/iCYFu2wEMrVmCx2UyNhMxMx61tnoHqyCAyjpnB1j3Rq5dL24NYTBpruY8lmFHHHPbhxOgiuOMI9Cmk5O3m9Ex1LPB7e1yN+grmSSQSiUTyVxAfrzq3fRUr9zZFb9oU5syB116DykoYNcrVOe6vCK1GaWmQV7vo71qTCR57zNGHoiIoLISoKOjQwXFeoIVrp0+H8nKYMQPuu8/PS/qbclY554ODgzEajaxevZrbb7/dvn/16tXceuutf2HPJBKJ5BzDmxUtKnKxopYlmTy8fhCPjBE07RlBSWwsCrCsSI1MzywqYlBcnCN932Ry5Km7LdsrVitC55oMZo/MqwXeS6B94q/U9t4BgP76IiI2xXGoORRU1S8N4OwkQZu7a9taBaZ0YT+xdA7K5+uHPuTKd+6H2lonp4Laz2CrIB2TPRVQSx38/HPvqXsSydnGyy+/zBNPPOH1mMxelEgc+HOKZ6WkkBIe3ij3iTEYiDMYaBcaypDWrVlw8CB7q6qIMRg8ztUc977kbS7ZtYvz9+wBIP7pp8kfMAC9xUIQqrN9Y3IyPWfPBqsAnYJOCKxOGW/Ojnid7bMFPV9d/iI3bXge06uvMmjcOOr0eqfoe3XjsmBRUEDbQyVc30oh/cgyqIUMfSafRwzCWljHtHkRjH+9NXW91fEGaUWIr+JAgaCjBt6bGmqPPgwLU50ZToH/9iGIJpEjkUgkEsnfBc0RD65R9eDb0e0cL/fhh3D55apszIAB6jz1gQe0o3VAEPZoNZsRDsQuavNejYwMRx9KSnwvevtxARAUBA89pMrZbd2q7vvlF/WzENCxI1x5pff+/N04q5zzAI899hgDBw4kNTWVnj17Mn/+fPbs2cPw4cP/6q5JJBLJuYM3K+qGvqyYedlGuA+UtWth3z7A4fPW0vc1hJel95iyMuJKSzleXMz92dnMzMjwciNgSD61Q7B7E+rCLZROMnPjPmCfZ6SdO34jB0emwE7VSfLDxhBSUjI4eFNXWt/kGSKw/vUsvvp3Ct3awsiRsGCBqqsnHfOSc4WYmBhivOXWSiQSn/iKam8o2RUVjNu9m6nnnWfPTIsPDSW/Z0+CFQVFURjaujU1QhDitODt7sB/c98+th8/bl8Y12qqZHfpwpI+fUjNy4OxY3HyB6CgZpTFlpbSqrKSWp0OvdXK4fBwYo4c8dnnHmRh3RlD31Zvc9v+cm4pO84n0Z4LeS4LFgkJxAJf41iob1FXzNelRpSwtZB0DOYccKwwRFpgvmrf64B025hAiz7cvNl71F9WlqeGr0QikUgkf1cCmKJTXAwDB6q/ayq08fEW4AhgAN4GbgEu9LjWm13UFrvbtYMhQ2DKFPjjD/WYu7Pdm3PfX/R9XZ0q3+OceCuE6vh3/hwop6NUXqCcdZrzd999N2+88QYvv/wyl1xyCd9//z0rV66kg3NexClyFtXQlUj+VOT/nbMQX/rxzoKvPtCkXSzomfHbYYK0lXfbcef0fZMmSKdhcxq0KSkhf8AAzhsxgg+//149ZFuS17YTWrS06+Vqnn+/bdeD3XA6eUjcgva58SZ1W2c7W9uOHgOlpeqK/rBh3nX1JBKJRCIBh1PcGB7O3KQkjOHhxBkMXqPaA2HaVlU2btrWQpf9ITqdvT6XoigujnlwOPCzUlIwHj1KZFUVLS0WLty9m6dNJroUFNg15TPT0shJTGTwRRfxz9hYtFh4BYgvKaFgwADScnLIa9+etP/9Qv6oURxKSSFt2TKyk5Ls9rnOaZr6P0MUTVr9SjPLGj4JaenSN69VxZxEbjX5G21rmjQJvTYe9ZIJNyHMMSZw1vQFh613t/kSiUQikZwJBDBFd3FmazZwyZIKYAMQAcSRna065gOxi5rUTlaWOv/9/Xf4+Wfv59ZXD879fv/6l/9nGTHC/3F33Evl/ZmclUOLkSNHkp+fT3V1NWazmauvvrpR2g2yFSOqqalplPYkknON48ePA2A4yUml5BSppxDrSRGIBdOsp4/C3JeTxdLh7XlrqHft+KwUtSgd4FLlZhiQDRy2WCgGTMuXq06MiAjViRERQZzBwODzk8jyEdrm0nY92J0kOh2zPv2c5rt1GCoMTBpv8Ci289zMGA4Rhxkjw5iLGSMHiaOIGPR6ePll9aswm11TDSV/LlbrqcSgSiSSM52/+98AZ6f4sDZtyEpJIb9nT+LrkWJzxrmg+WeVqozLZ5VF5FS6FlGtD82Bv/itt1hvtTLg88/Z2rkzkzIy2N6xo93GF0dGYpw/n/dmzmR5ZiaDSVD7ERuLOSmJbQkJ/NdWzPW/V/+Dbc2bM617d9bGxLDktts43NHISJ2r3eSDDdTO2wrzcyDctai65kNwWbDw431InzSJLB/hcK2eT2FwW7cxQXY2XUen0bdVdkAF9iQSiUQiOROoZ4oOOAdXtgJs0Wc8QFGRWjPtwgsDs4vuBWyDg137UN+it69Ct6+/rq7He8Nkgtmz/bcLgRecPd2cdbI2pxO9Xk+TJk0oLi7GYDCgk2ETEklACCE4fvw4RUVFREZG2he6JH8yzo70U8nT8lfsVQhHFRf3HLY334Tt27GioENQh44grAigd9JnLBj7D8ApbV/L4QcWHzzII7t2MbNzZzJ27yaoSROMwN3AHcCvZWVERkaSb7V6Tc1ft3kz4NDJPRlpAHvq/9ixKLNmMWyUhbqZrxMapGP8A67Fdu4YE8/my/Lp0TMYUJjPUIKpoYYQzFlqul5jfBWSkyM4OBidTseBAweIjo4mODjYHjUqkUjOfoQQ1NTUUFxcjE6nI1ibJf4NcY5iVxSFkHr+VrlL17gUNLfZqBMhbrJx9Ui7FVRVUWIr1LrM5lj/sFcvJixYwEuDBrlowDvqvaj1WMZ2ugx+z/dazLXI5sjXWNLvZgY9+yxX/qbQ4+b7uLxwM4u5jyETJ5I/vgb0wiNUXq8o/CcpyfeChSZy6y52i6dc0OefQ3wrt+sXLyZ0/Vo+H7WEoFmpXgvsSSQSiURypuBjiu4D72OO/v3VbWmpGg3fULvo3gdN6tWXc7++QrenQqAFZ0830jnfABRFoXXr1vzxxx8U/FnLJxLJWURkZCRxcXF/dTfOLQJ1pPvBQ3stUAvmbkX794fu3TnRqj2PbR/CSGbTlv3EUEh532qyk5NpUlXFebom1L5rYHcvgSVWUPibgVeO76PcWsdre/YwqE0bAO4D0oD1o0ejREYCXpwYZjOMG8e3d94JyclEV1Xx8kUX+S145+89hji9R8OHmRgeUN+jEhVFiNt7FMHqaEH1ByhYlBAQkJvrWOFfsqRBX4WkkdDpdHTs2JGDBw9y4MCBv7o7EonkL6JJkya0b9/+7xVwc4qCp4sLVemaJYWFqq78xGQYv8PVse1W0Jxe/u/r7ODX7G1xZCTPDxnisx9dRobRcu8+zqv+BoBFEyfxwPjxWPU6p2KuTlpzCpSJWow5Oeq+TBjQezmdYrfzrz2rmDZ5JDyX63GfTy+8kBtbuXvU8Tvz91UEN76pbUzgZeyk/zAT/Nh8iUQikUjOBHxM0WnfHq6/Hl59NbB2nPXh3QvPBtqHX36B8ePh7bfhoov8t+Gr0G1SkjrfDg2F226DTz+Fqip1fyD4Kzj7ZxZ+V4QUgXahoqKCiIgIjhw5QvPmnsWGQE2BldI2EknDMBgMMmL+r8A5wk5RVEujbTXqMQNjxsCsWep25kzU8ueaBXNHs2C+xOKqq8n5NRhjqkJ+bBwlEREoQtBvyhSKWrQguqyML8ePB0Xhj5gYnmg6hXtrP2DSM/+0hbZZSXj1VYSiMOvgQW7etk2dgH/5pYeHu2DXLkreeAPl66/pN3UqRZGRxBw5wpdt2mC1WmneqhVJnTuftve4b5860NH8As51yY1kM5VxjGMqZlJ9NdEgArFf5xKBvA8hBLW1tdTV1f3JvZNIJH81QUFB6PX6v1/WjIfRrR/nyPZ+v/xCkcVCjMHAlxddxMov4cWZNVgnbvW4LmiEkfeeC1dNtp/7Lr3+egaPH0+t3jOuK6iujrqgIHtRWC0U3Tx0KCk7d9qz5awobEns7BIp74sgK7SbFMaGNbcQt3ZZvef7jPyvrnZ4H4RwCbOrdsq0E0K4FsFthLHTqSDtuSfynUgkEsnpQzOXngXQbavnXjCbT70o+kkMebxSUQHNmjmS5I4ehYaYipwc7wVnG+MZA7Vf0jnvhjT8EonkrOIkHenOQWP9+kFRkasPvM2hHFrf1HALVlAA27bBffdB6Ufr7PsVIRCKYt964Dwxdjouevf2nDDn50NJCUplZb3tu0/ofQYOur3H7KQkxg0bxtR580jdvdvne1y/Hp57Tm3vxx/hiSfUqvIfcA/3sIwPuJt7ySQoCKZPh0ce8fnq6kXaL1fk+5BIJGcM9RndelKrlHXrHL/jmEp7TPKcNVx0sOxEG+6Kqaz/vkuXkvPKKxjnzPG498px43hg/HjaFRczZOVKFvTvz/bo89g+PIP2JcX287KTkhjx6KNkd+mCYrUinOVm3HB27H9w3bU+FwYAZoaEMKZNm8ZNPWvI2OkUMx28Ie2XJ/KdSCQSyemjoAC+/x4efvgPKio6oGnL6nRWgoN1OJen0cx3airMmdNw03eKQ57Tguacd1fC+zOd81LWRiKRSM5m0tMhOdn7UnBWlk9rU59yzaVADvjVcq2vXTXVPhf0eGjVKrWCplv0HDXWqh3wSIEXjPi//7P/DjgmzLabmK67zj6hd29fX1vLoooKj/75lOV3e4+L+/ZlbUoKS/r0IfX++32+x8xMR3ufvlnAxZQgULiFzwC4lc+4lByUOsGMsVE88ohMk5dIJJJzjlMUPDUlJzN4xw5qhbA75LWtXlEYG9KJafsLoDgUVraG/gchuoorhl+pzpD93PfgZ5s40CWd/H8nAcfsEfLa9oX77+fjF16g5/btKED7FdXkGpJpb3E45kG1m9lduhB0wkq3gt0MX/kZM26/k7yO7TwWDWpRMy11CNLXrCG5oMBrxP3PI0dyeW5uve+nwTRk7NRY9XwkEolEctYT6Hqu83nQ6GvAHjiGIR1xXtq3Wh2O+W7dYNQoVSVu2za1jydj+v4uGu/ONFQD/3TwNxJYlEgkEslpJdBy6Kjaa1qQmjfttedm+iiZXo8Fc26XPWGE7fLel+yRw6h88mqWTJzovZ2JE5ntlvs27JIsss9Pt98kfc0askaO9Hp9VkQE6XfcAQReob3AasWclIQ56Xx7QbzMtDRy6uowV1ZSYBu5+GqvgATMpJKDkTBOABDGCXIwYiaVAhL8vjuJRCKRnKXUZ3S1QiU+SI+NJcvHInFWSgqPxMcTO7Ynqe+mMPfGNqS+m0Ls2J6EvfCG7/vaZsz/vWUJqanw4iiFuNJSjHl5zJ0xA2NeHk2qqshOTubNO+7g2unTyU5K4jI2MciyGICC2Fg2JSWRk5hot5vhlqPoqaVJVRULX5tM7OHDdM/bwdwZMyAvHEoNPFC2xOuz6GxBANo2uLY2oPdzSngbOwU6cJBIJBKJxAnn9dxAzwv0mpNhxQpVhhVmO+31LmOzdasjWr5pU3XfyZi+UxzynBY0DfysLLXAbVaW+jk+/s/rg5S1cUOmzEkkkrMOd/FzbSl40ya/Fqde7TU/Wq4+yc6mcsQ4emdPxTw6Au7YD2BPcdci8bKGjuCynTt4JSNDLTjnJmszYcECvr30UlVWZtcusFpJwcw/xqSoenW2zuckJmKcP98j0s/ctCkp6kgkYGlZZ9kA7SRvMjm+2rOKAHSNT8EkS/vlinwfEonkjOIUBU9z8vIwHjhgjz53sXc6HdXhUQQndvA02b7u26IFlJVR1awVQ4/P5ALrZh4xvMmhli3ZGxFLE3GCflOmUNyiBWFVVZwIDeWeb75h8n/+Q4fCQgCUtWvtzdntpZs8XVWfPigWwf0s4gPdvYggwaWWLeRgpA4dQVgpiIrh8rmzXaRz9kZHs2n4cOK/+urUc8694W/s1K6d47zToEkv7Zcn8p1IJJIzlUBlXJzP69MHSktVUwxQVgatWsHXXzeu9IurmqxvfXm93lXpzZfp27QpsCj/06nx/ndDytpIJBKJRMW9JPvQoYE50m34VK4JCXFKuVNITa2/vYIPP6SkYj+9Ej/D3LuvutMKoqAZ958fhdlQSmF1DQ+UlfArsLVjRwD0dXXctXYtn151FVUhIWzt2JG1KSm8/cxk3po6iaO5eykSMSzZWMH6H3bz5ME67gJiysuJKy0lvriYB1euZP6NN3MgNpoYp3y6QCq0FxTAhNBkXqraQR0Ox4JdJgeFRcld/LaXjolFDMKAZ/FRoQtCWfxe/V+GRCKRSM5uGigXp03mY2+4kri5c2lXXMz9K1ey0ObAjrnzTigpIQTsRklRvAwB3O6XHR3NuOeeY+q8eSzOy1B3WqBjZqbjGlt7J2yNZV57LZnXXkv2sGFEHTmCaeJED3k5Z3m6Ce++y68dO/JKzFJuyEhl5wLIz1eoq4uhpDIOa5t2LGAIN+xfwO/DRxFWUoii0zF0xQpqQkIIqa5u6NsNHH9jp0AGDhKJRCKRELiMi4v8q42yMsfvpaWNI/2ijRuEsAKVQITWO5/XZGVBbm79pq+hSm8NHPKc1UhZG4lEIjnLyM6GtDR1ayckxFW33XlW7vUCh/Zal1srSPp8C11urfBQrgkozc4p/TuhXz9S581j+vw0iLTY+gN0PM7Cmj38cuwYhffcTW3LluQkJrL20ksBaFFZyb3ffMOcGTP44N//Zt011wCwqHkMzSxraJf4K/tjoyjrXkh2XTkP/KCniGhaNYvm58+z+WnEaIatWMFXI57mqyOxVP5Yyr6f1Ny79HR1wOGNrCz1eEICPN8zlrqh3pfya4emkB4b67e990ln25PeHfCH33jPa0FZiUQikZwjxJycXFxCgjoBHl/yBrsGZJA1YgQjVqwga8QI8gcMIL6kxH+euO2+2bfeStr775OdlAS41lZxxjRxInotfM6Lwx0gdd48EjIz/crLATw/ZAip8+bx6YSjGI1qmvynn8LGA/G0qsgn5o8snvpjGF0rs2iy4ScU2/tR5s4l5KKLAno/p4SvsZOToc9OSiLNJukDOAYOEolEIpEQuIyLi/yrH05V+kUbN3TvrgPCA77O35x50SK1VEugSm8nOeQ5q5GR8xKJRHKW0ZAV6+xs2HPbYu7Y73mBFjT2xJ5C3tpfzugphbzWvjmHDqmGVlFcDfCgQbCttoI51t282fU8UrW0LacwgCXXXcfgp56iLijIsThv2+qB2okTwWTiN8CImgYPUBIZyU2TJ9vb0fYTaYH5OVi0A2UGAI6lHWffV5EIpZaoLxeit0WrtxSHiRp8heMFuIUc+Fq9twfJ2a+z9dsmHzDhFe/v1729dz9pyZtOl2vbVp1bem9AIpFIJOcG/iK1/VSQ0+zT+7Xp3GRZwQBUw6wAIRabdfRTAF677+I9e1i7fz/TFyzgyQcecKmtMuirrxCKQtSRI34LtGqObH1tLYumTKHOOQ7MPVve+dy2bVk8B9atU8cVV1wBqLH+6nOEKtCp0yllAXoj0MJ8/rAvYvTtS2pe3kn3RSKRSCRnJ4HWGPd3nq9rToannjrG5MlhqLHavuO1n35aldHZv9/Taa7NbTVZmwxbgl2gBV5PMbH/rERGzkskEslZgEdtsqQK5iRuwbSpwvuKte2Cb6flcOV+zyXugl27MFdWsq2mkv8WFQGwrKiIbTWVJPSpJPXGKoxGVTMPHAb4PlMhP1eVs8SmNwu4hAFkrFlDmtns9Rlqhw6FNWtg4kS7qJ1wk4/RLLsjPd6tES0av4UF4/z59ug9na3qvLa1oOenEY6Qg/pW7+2RAmUGlBPqfZvlBEFeOK0UA4NvMbh0w1t7TZrA8l3dqAiLRUlNhblz1W1sLHTr5vWdSCQSiaRx8JEk9vfCS6R2djYsv813mlr6VQX8ssjMpeTQh68B0ObAQnE3kpBdUUHali1kV1RQUFWFubKSnJoaltkMemZtLcb58ymKjASgODLSxZ66YPWeU/+fydN4pt/DmJJuRZQZaHIsiNDCJl7PXRR2KcknkgOLtvOXBegDf9/7yRbZK6iqwty8OTmXX86y669X+3399eRcfjnm5s3tBeIlEolEInHGW41xf+fVt6+h5OXlMXnyAny5gp95xjEPHjlS1ZD/+GO47z7VjrrPcaOj1evcpuoBFXg9CZN+ViMj5yUSieQswEPL7u5CLBeWM3BxIbylRrBrRrKgADrYLkhLSmLA9PFMnTcPY95OFNsSd8LatWoxNBz+72KLBaPZDPNsO3r3UrexVYgIi+oN6G2b3BcV0f1wHDPfhOED/kmHJefT4sV0FCH4/uKLsXdIrZQKOgVIBuZyWcXHzOnRQ72XG6aJE8l47jmP/bo6K9YgnYezPkjAi+8uIG36dLV4rC2qbZcpiyvTHSEH9a3eF1RVkVtngRbQtKqKo2Gh0PEYjL+YaSbhoSKvtXfwoKoPmJqqFgDafzyeC5oV8H9zgxEoRPUdSofW53iYgEQikfwJNFQH9S/HJgr77TSFQfvd0tScq8ElJJAM5KAmczljzzJzCnlbXFjI2nJ1Ef3N/fsd57rf3722ii0aHiCmrIzYsjJaxsSQa7G4X8kDV7yGSCllcJ8pmO9tz+EeBrZWHqX75hwUq0DoFPs24/5g2Bl4tF1Dcf/enQvuuWf/BVpkL2HDBvWXSZMcY6TwcIyTJqljp337EL16nVrHJRKJRHLWoDm13WuMu0ekO5932WUwe7bqlH/lFVi+3Ps1gVBQAKtXb+ahh4YAX9r3uxd2/ec/1Xs5z4MzMx12dOZM+O47qKhwXSwICnItGKtxqlH+5xLSOS+RSCRnASYTDBpXRV0zizqRtTnJSSsiaE0cL/0bCqoMdAgNJSEB7sXEIga7aMpqjutagjAdPszgVq2oFcIRgWfbBqEQ8kYXjms3z9zg0Z9ii4WBFjM8BA8CNAPm2bz67jNtnW1qW5YOpHH55WmoxWnUNX0roLNasep09tm7tl8RIBSYPeUthj8zxqMfy4qb8l2LFvZnvDRvF0Ee7gsVZ/+4++q9fSI+H44J9cCxyBCYn8P9x4ENeEzEQ0LAVs/W3ibA/pIQjHbHkIIQ0jEvkUgkp4PGcMT+FTgvoo8DrDYXsCgqti+iqzsEmEyIwYNRamvtcXCaw1gEBaG88w4FUVGUVFaigCNCvqiICQkJvJSfTx0OG++L9SNH0X1nHnXoiC8p4esjcRzrnczNv/5KbHAwu4+foAqhFnnvXq5edF0hufo4Dq0v4Mj+MqKiFOJ37mL4ys+Y2/8WdndIpOORHZQRwx7Rwf5IcGp1VZ2/dy1ib8kS9Xt3fn0nuyBgSk5m8I4dXsdIekVhUZcuJ9dxiUQikZyVBCrj4nzeI4+o9mjoUHjqKRg//uSlX9QhxaW4L+U7R7prWeOKAocO+R4/ebOj7o55WeC14UjnvEQikZwFpKdDRlsnJ7k2S4y0UDfHzPPA8zYH8hsfVPH4y7dgrl1PWW81Ot5ZU/bEeVeTfscdJFdWeo1erxuawvGdTsVjJibD+B2gd8xm7b/VKoQs60B1er7jfPcCcjZSSjpRQgGZmR24YaCBVoqBeHSM+M/bLOjbl72tW5MUHk5ceTntYmMZMmkSC/r3Z290NO2OqNF/mhNfsVoROh0H45vyQe80AJbccBNXfbePyJpCIju3aND7NU2cyODx46nV6z2kduzRhF6i5Oxa9X6q2kskEonk9OCRVUbjR2afDpwX0Q3U2iXZFCdpNoNpkXpyejqKD5Ha3EUb6ZqRQsK6dfZ9ztlwz+fn19sXza7uIJn9NzxGx28XEFuzl2KlHddt2QJAqfOsXAeE2/LJImrJOGYbR8RCVZ8+6C21BCF4cMXn1Br0dl18xW154FSi7Zy/d42yMs9XdLJ2OT02luQmTbyOkbJSUkgJD7zAnkQikUjODfwFgml4Cyr4+GN48MGTDyp45ZVXgFxgEWDAXdJGr4eFC+HOOx19CmT8BN7HULfcoi4wFBWd2wVeG4p0zkskEslZwoSwZJ6vtDnJ3YutOkVyjY3bALPhN0ARkYBDU1ZDfPEFHD0KsbH2ibm27UIux4llD7aRwZpYKGgC871oyY9MofqGQ+rvtuKpHlitmCZNIp01AOiKBf27h4KhJ6UWhWFVKQw1GKgBQq69lvyqKoJDQ1HuvpuhgwdToygUR0QQV1pKu+JihqxcyfDHHgNgdE0RSmQEAOVNgrnrzUfUe54oQNDRS2e8kz54MMmjR2OcM8fjWNbo0aQ4Se04F5gLtACQRCKRSBqfM3WBVO13Orm1yeTgaUBWT8iiv5M0G9u3A5B9/vmMGzqUQau+4r0b+nJhVh1vZviP9HbPRNO2AOHHjtGuqIiDLVvxiv5pmq3SU0JfDtGa4TkhmIo+YPCll1Kr9z+lDLIKFkya7ChQCwQhCLJYsKBnMIs8Cqg32Z4NT3hWaw2kiOsbb8Djj0Odu+Ycaur92LEwfbrnsZOxy/YMPzxlhSQSiUQiaQiNHVTQr9/zrFrVG3Xmvxm4zOMcb7avvvGTNwkbjc8+U7dVVVK5tSHIgrASiUTyF+FclK0xGNw2llbPe59VZqWkkB4bC6iT9CBco7+do8BNEyfCTTcR8/DDxJWWYszLY+6MGRjz8ogrLWV12Y0UkODSvpY2rmiDBW2G2v64Q2LHx6w1/PhxkgsKyEpK5rbYDxyGX+gwmdSwAkWnI8QmaxMSFoaiKGq0YFYWIRYL8SUl5A8YQNaIEQxbsYLLplWh2CblHs8ImLTqNYGSnq4K16JGETpvWbxYPY7jo7cCc4EWAJJIJBJJ42Av5u2FrCyXP91/K9z7XWebsmnb/v3dLvj2WwAW3Xwra1NSePXuQaxNSeGDUD05OdBlXyyftvY+Prjxp25QarA75IWCfQZ+tEkTtnfsSFlEc/Jmn8BMKgV05EJ+ZduSHO6aNI2skSPrfZ5PthTRMf4er8c+fjKLr1qlc+GFrgXZ237r3ZgGUsR17FjvjnlQ92uO+VOxyzEGA3EGA8bwcOYmJWEMDyfOYCDGYKj/YolEIpFIvGAyqYvI0LDiqt64+OKLWbUqGkgDHkdzzLvbvmHDPIum1zd+mjDB9321fkrHfMOQkfMSiUTyF+FclC21efNTbi8+Hj7/HHpu9RLBtX079OgBqOnYLSub0H+fl3TskSNJ2blTbc/m8A62WFCAoStWUGMwoLNYGcsbgCPCraVOnaS2Cw3lektrXq1S9et5LtdJiNXtZrbqM5VNm7pE7dPb1pcGRLDVoSPEYqEOHUFYyfqiHzl5ia7tas84dKj6jA3UMohRFJfofE1SJ6ZZM7+6xocOqZXsExL8FwCSSCQSyenDPTL7TKCIGA4Sx17asVAZwv1iAZe22oshJsae+14gBCXbt3OoRw+W/uMqAPI6tgGg5B/HMI4shbJgaFENk50i5VHN848rguHfPbmv92u8/6RRjYL3UwwWwEwqlNm08KPU7DR7bRgnNIm5l8MOM2f9GwB2O61tp74GpajF04fdUMBQYwmWWoVfHt3EuOnTmfrhh7T9IofyMkFdiyiWLVOz9vzVDjCZ1GO+Iudffx1efbX+wnz+iA8NJb9nT4IVBUVRGNq6NTVCqIEEEolEIpGcBOnpsGKFYz7pTKBz44ICSEgwAvGAGoWgKBfbC7+ed54qYfPFF7Brl+qY14qmO7eRm+u9/dxcuO46NUuttDSwfgaS9XauI53zEolE8idSUFVFic3Z7VyUbVBcHAKIMqhFW0+W+KYOJ3mzoCDWlpfT5MQJYr780u6cB4iNBfZ5Flwd9uijzHnjDXtxWOcUdMXp8xuMJXnuI/YJbbe4UPLbqJPUzZsVXn1eZ9OhxyGxo6GNDNy057XJ/326XlxqzSZx2DiYU48Fj4nhWHgc2yvb8Q5DGMICEshnIYPozQrAi8NAp2tY2IH2buPiyL/9doJbt0YZMoShCxZQc/AgIT//jNLOcZ6vFMTCQv8FgCQSiUTS+MTEqJHYp+KI/SuIiYG6uHj+1Taf+x4Mxvyuwqd7hrLppxri40PsxiZh7VqYPNn1YkUACjSvhclb7buDKw10jghl+7TWiP4H0UUf59LfvyfOUs6Mr6fwyB9R3he1bQv3v9ORImLoiRpOl5OUyCMPP0yr8nLOO3SI27//nhcfeIBanQ6h0xFdXs7RJk3ITk5m8r/GMOvVV9hLOxbY7HU79lJEjENiKCEBBQgGFo8erRZz/+MPZt5kpLWtL8W2FD1/af7p6dCypZcMA1SnR79+qi2urzBffTg74hVFIURxH/BIJBKJRFI/zoFeX3/tekxRvMeU+XJ4q9I4rkF4wpYeJ4TqkJ80Sd0fHQ3Hj3sueHur3aKRkaFuf/4ZevZ0BD346ie4Zr1J57x35NK+RCKR/IkkbNhAqtmM0Wym2OboLrZYMJrNpJrNJGzYUE8L/qkDlkdFMae2lm1HjgDQtKqKou++w7xpEwWbNkFBgSMd+/Bhu2RNk6oqspOTWdKnj9qYj0mmCApCLDExbJi6Mp6fr0bth+h0KIqiOkJ+jfO9/Ouj3Z9GjiZ//2CMRhjeZDHh2fXkrQPEx9OyMp/LyGI+w+hBFjEUM55pWIMUWpWX06WggPDjxwHVUV/YvDnmW26hoKqqnrfpea+QXbtQsrJg2DBVUmfXLoiPx2RSU/gARGIFTN+ibnGk9jmvRUjHvEQikfw5xMerdirr7WyGLUsj6+1su936O6P1+8dNIQwbrqj2tkAhvpPNgNgMj2niRPTu4q9udjYIhT6bkqn5Z0+u/TgF091tCBqdwvEB1/JNyU0sJYNWlNnP95Bvs9GJP7iYX+yfF/fty/pu3bhn7Vr++9JLtI/szndjxtDMZnOPBwcTXFMDwDd9Lubrdb/SI2kN82MH0YMsEshnP/F2iaGCDz7AnJxMTmIiy3qraXSZaWnkJCZ6SN/Vl+ZvU/LzSN/X9oeESLsskUgkktNHdjakpXlKxngjIUF1WhuNagFzZzR75x5U4C7zJoRQpV/5KuA+Fhc7tkaj2oeEBDUqXpPXcScoSD0eH68GPxiNqixdaqr6WetnQQGYzZCT45pZnpOj7i8oCLib5wSKEA3M6z/LqaioICIigiNHjtC8EWQmJBKJxJmlhYX2omzuaEVbNW34k0FZt87xuxAIRbFvNUTv3pCdTTVw8L77KLVN6vu99hrFzZsTU17Ol+PGIS6/nKivv6ZDYaHrTVauVMPO8L1iX10Nrx3M5/n8fN99taW7a5HtVw49QVhJDDNnKXQe3Y/gMluJ9y+/9FuefulS1/T1e1nKIgYTvHZ1/e+iVy//L7QB5OTYovhG74Q79sPHbeGtRMzmP6fwq7Rfrsj3IZFIXBgzBmbNUrczZ/7VvWkYvoytzfDkJHqXcdN4PM/IuwsFZXfupsWH57Hm7ebk5sLKjKW8x33obUJ4+6Ki6D53Lu2Ki7nObOat225DZ7VyQX4+M996i9S8PPJjYymNiEARgn5TplDUogUxZWUUtWjRsGfq3csebafZyUDGMPTu5dKMLxu7bx907+6ZMbFp099/YUbaL0/kO5FIJGcaDRl2LF3qKMDqTlAQvPOOetw5wr5fPyiyTZfnzbNw++2DgXLgXeDk/AlaJpsWHe8PIdQ5v5aFJoRrFpqz6daOu0fXnwve6EDtl5S1kUgkkj+R9NhYkps0wWj2oveekkJKePgptW9KTmbw9u3UKorXYq+LXntNPTE1lRCg49q19msVm3UsjohwTPIfeACRlqZaTm0W7bR44CtFLSQEVj36KDzwAHh5Ji0FfsjKlbzTvz/7oqPJLBtOfFkJZNh0bMEjb337e5voep9rLpx7+vr7pJNLMqaJ4xk8fjy1er3nu7AthDQWBVVV5NZZIBFHAdy0Ivgqjtw6aFV1anJFEolEIjkJ/BUE8bPoWx9/unaqF2ObXVHBuGPHmJqUZE9X1xa97TNgK6CD6dOAGwohpZyyPwoxGrXJYTpQx1IGAa61Zh4ZPZrKpk3p9vvvrO/WjcV9+pCal0fHzEx7t+zjhsjIwJ+lVuG8D7swbq6nxJApOZnBubnU4lnMPUhA3avJAdcO0DIPTlW6RiKRSCSSQDnZYUd6OiQnu8q1aWzc6FiEdpabcUipCm6/3QAstR3x7fHWnOPh4VBZ6XncWS/eX+2W995Tf3e2qe5ZaCaTY8HBW9bbokU+u3lOIp3zEolE8hdh13vHrXjrKeDX+T9yJCmZmXDffXZLaZo40acDG6uVlxctclhRoxH27mVfTQyFZtUAmzZVwPTdLPnwPAblNHfSqbO1cdFFYEtNB4fj4LW5cxn81VcowEMrVmAxGFz07XW4WnArCjoExTOWgM057+wccU5f1ybr6WvWcH7BXrrPn+v5LhphIcQZuxzRfBzjoUgLzDeTcQzY0LhR+hKJRCIJAO+zWN9i5QFyWrRT3T3+9czwFysKa+vqWHLDDdy/Kpuw0loSyvayv3UMVbpgaupCoCQEWlggsgZ97/3UAkHX76eu6xFY3hq2BnNT4Qf2LhTExlISEcGhFi0wXXcdAL927AiA6frr6btpEz22bSPrggsATwd6IPx8aQo9rg336jBPj40lubIS4759Htd9ERbP4F9jaWcMvHaAP6eBRCKRSCSNTSDDjt691an4okXeF/n9LUK//DK88IL6u8Ph7W6Dfdtk7RpvjnlnAqndUh/+FhwCLW57LiE15yUSicQXDRGKawB2vffwcOYmJWEMDyfOYCDGYDi5blZUkLZlC9kVFS77fWnGkp6uWkRUB3bWyJHeG9bpKImIUJfHJ0yA//4X8vNp1zPeoYnX3RaJd/1ejOu2kHpvBQk9qiDpG0i8BS69FFBXgp/55hsijx4F4IeLLsKclETa9OnkJCURYrFgQc+zTPDalSNEAtD1l0xyl+aQazLzweQCu3NEK/inad7FdYuhSBdHXVKy67to0JsNHFNyMnq7eC0uW72iYEpOPk13lkgkEolPXAqCBChW7gNv2qlz5qhN2LVTT2Xc4C4e6yRAK4pUUdh8RYd5wAByBgxgmU2sdc5NN/HfbmnEv9Cc956bRtHtt5PfP4OL72gKCScgohambqU2Un3uunAByUfh2Z2QuY3r2KS+FiAhM5PUefO4afJkymyp15rjvax5c26aPNnumPeGPxurmUYtkh18OMxtq+1aW9o2+oJYtXaAWvbFpeaNRCKRSM4dGnuK3pjt1Tfs6NNHNfUzZriafPCczxqNrhru4NCI90Zk5FKfx3Q6dUFc58dQN20Ko0c73kN9tVsagnsbEk/kq5FIJBJfuE+UTxHN8B/6NZT8nj3JSklhWJs2ZKWkkN+zJ/EnKXuyuLCQteXlLLFpw8cYDLSyBnHxzj+Y+cHHGKuriauoUJ3/7iFmThZSsTmwFSdnfmZaGjnnnYd5yRIKLr8cQkJ444MqgpIrIbHSIeFyRSmklMNdeyBzA8zTwfxHUWyp7rVC8Oq119on+6brr+eZBx9kbUoKb992GwCDWcit/J/XZ4y0FaprJYpJzjCSPDCV6R8nqH3MVPX2li+Hf7+QxbLghfx7/n4iyvJp+9EH6kJIRARzzzsvoIWQkxmgpcfGkuVj+T8rJeWU6ghIJBKJ5CRxWoz2QKtCGiDOxdqKi8FINqssabwxMNteQK3B4wZ/1dImTLBXY1NsKVkdMz8gdd48jPPnU2SzrxaDgUkPX8vOt49zWebbhFgsxFHEFksqpokTHTbd2SMOIAQT3nmH/KRWFMTGIiCg4rL+Mg1eiursdf/g5m1IDTAQwVcAw/fbqol7fwtLNlbYuyUj4SUSieTco5Gn6I3anr9hx7Rpat0TgF9stdWXLIEvvlCHAnV1eF2ErqtzDBXef9+5ReGyffxx32Oa996DefPgzju9H8/OVhPz1q93vIdAFgvqozHaOFeQBWHdkMVmJJJzHF9VVuopShoIjVmLrqCqihKLBQXo98svFFksxBgMfHnRRQhg5qtBZE7WMWJ0CG/MhBqrla3l5Yzbu5ep551HakUFdO9OdUw7Nt8xjNsuiaYwwulvnk2v1lsBVeeCbQjUcDhtq1EHeKvwrungumEeOpQPEm7kp1s68casWVyWl8cJQgmlymtingU9g1nE+6S7FJa5evQ7fH9HZ8bs2sXMBx8EoNpqJVhRUBQFIQQ1QhDiZ9n+ZL+nnMpKjGazh1yR2WhsVAkdX0j75Yp8HxKJBLAXTc3u0oVxDz3E1P/8h9QdO3xXEvWBe/HxmYxhDLN4l8HM1T3M0GEK9/+3H0GlDRg3BFItzYm+U6fydffuXo8F1dXx0qJFDFytFkMvsRVtvW7aNPvCuAtu9jj/nnsoiYhgR7t2ZDz3XP0vxIYO6BAaygmLhXcNBvpXVdkLvWtbc5MmXBoTQ0379n7tr4Y3u33Zu7/zS+f9XLyrLVseTAy4f2ci0n55It+JRHJu09hTdH/tbdumZsa9+ebJSdfZhh311kbxhjfz3wDlOJdrnNvq0UMdv/z2m3dJG5MJHnvM872Gh0NioveCr4Hir2jsuUCg9ks6592Qhl8iOcdp5LLip8vX7+Igt/cLVwd5714u93pL2cmiiv2MaduWmYmJUF2NEhqsXmSoI/qabZSNL6JW71mORA8sSk4mPTaWCfn5vJCf771jPpzvfnG75q5vvmHZK694PI4zKZjZjOpUaRO7i+YRh9EpCsWTD1AcGUHMkSN82aYNQgiiWrakQ2fv0XwajfE97auqorvZTLvQUIa0bs2CgwfZW1XFJqPxpLMiGoK0X67I9yGRSADYtw+6d2fMqFHMuuoqxvz4IzPfflsNX2ugJkoHpYAoShAofEk/YilyOa4tzAY8bli61FEtzQ0Lel7iRYbGvkVhRCR6YaXflCkUtWjRoD67jGO82OegujqS9uwh16Yt31DMRiOXNmtGTUgIxRERdJ87l3bFxQxZuZIF/fuzNzqaTcOHE19S0mB9/592VfHHYQuKAgMP/IKIsKAcMbCkzUUIAR1bGriy89lXbF3aL0/kO5FIzm0aeYoeUHsnG1BnG3bQrp1aI2XKFPjjD//XaAVSvSX0+RkqeLQxZQpMmqTOY2+9VdW091bU1RuN8V4lnkjn/EkiDb9Eco7jz/rZrGb2+ekuNdv80dgDCXs3CwsZvGMHtd4uroWnpyzn6zX3YY67EJpbVMf9lF+ghVOE/fbtWJ+YzOifnyWrTn2QCxLXs21+jUeT5qZNSbFF643ZuZNZ+/f776DNs64VgA0I28sJq6rixzFjEIpC1JEjdCgstBeErUNHEFYX5zxr19mb0CL9vUX8+6Oh35N73T6NhkbpNybSfrki34dEIrFnmdXU0G/HDkeWWZcuiOBgogwGOjRk8dTJWGh2yd9Cst/ZtoYWYufGpH+ZefujGPav3eW4vZtt80aLI0d49OOPefm++7wutrtz17ff8t+0NPps3MTqVGMDbDaggKmJkatiw+nwozp+qlYUgm2ZfQKoMRgIEcLlPXizodkVFYzbvVvN7rP9zQ4kU+9sLLYu7Zcn8p1IJOc2AUzRG6JUF5DDu0ULWLPm5ALq3KPFs7KgZ0/f53tL5nO2lTqd98Kq3tpoSJycZvK9RfifzHuVeBKo/ZKa8xKJROJMAPq0DdGla8RadC5cVRXLojDv6fhPjPyWV9fMYiBL4IMNMM8M880QaQGguKYGo9lM6okTXDbhET670/EgRagCcO76898fPoy5spIvSkow2bTt/aM+pDbJb1tU5O9kF06EhGCcP5/UefNIyMykmFZkk8ow5vJrsJFCJc7eT4ArJh62a+RqTgttq6+txXT4cL33bOj35OvfQIhOh2K7t6Iof5pjXiKRSCSeJGzYQKrZjHHrVootNhtosWDcupVUs5mEDRsa1F7pmyYsqMZCZ7NzfufADdG1d6uW9vTT8OQb8cROjASb86A+xzxAWUQE2zt0YNHkyd5PcLPvq2wL7xu6dKXp8ROB9RWgoAmUGsi40aDq7dvGTyEWi73gu9lW8N39PXizoe71cwBGHE6GWu/F1qlV1OMSiUQiOetpxBIy9banUVamOsTtdWUaQEiIa6mX4GD/52dkqDbRVusdgNdeU23ltGlwxRVX+LxWGz5s367WTXv5Zce8tj42bXLo4LtzMu9VcvJIr4FEIpH4wGr7E6ltt+d6r9lmNrsaUmcaeyChkZCgGnFbB122ffgagHvIpOfEUoJqbblsWg04Z8f1xIm0+CqTS8khVTFjKFOg1IDIi+CZ0CSia9Qo+kdDQkg1m7np118p00IM/IX8aw7qWoFp4kTWjxpFyyNH/D+Ue7E6GzfxOT2S1jB/eg8uSVhDe5HPfhxSBOvX3EGnkRFem8yKiCD9jjscO3xUew3ke/JXt8/fvwFvZFdUkLZlC9kVFYFfJJFIJJIGYUpORq8t2tr2aVu9omBKbphzt9XodHIX+Z7Nn1Q6slO1tPyn57KjqZGaVmq1tBljCzi05lLMI4c2qMnMa6+168a7L7Z3tRkrbfG8wlYTpaJ5U442a1p/49qY49UuMKAnlITy8suupyzu25e1KSks6dvXvs/Zhmb9nMUl0xfy3fafWbqpkqUbK3n/oLqIn1lURE5lJebKSsZfVMlHh8O8duOjw2HMNlbV31+JRCKRnFW4rWU3Wnu+CAqCN944tXtopt4Xublw333qHF+zlStWqMeWLauiuvoWnEcyOp3ar2HDHEVWv/1WdeaXlNS/8KBx8KDj98Z+r5KGIV+7RCKROFFQAL8cisHSKo7/6Y0MYy7/0xs5SBx9MmIwGqG4WD23uLhhq+ndlWy+IY3uSnb9J9eDyQRBlQYoNUBeOHNnzKB73g7iSks5v2w3ADEUsX7Nv9g4coTXNrJGjiR9zRr0ZUXkYGSTSGV/SWd6z46hx7u19Crfj6iuBqD58eOe9V0DiODLHjmM9DVraFdSwhdPP61epuXN1aPnE1RXx5zJbzF6UjwtBxRCSjlK30Jq8KwgE6QNJmxt63xV3wkg7cHXwCQhQf2uT+XfgL0bXiIEJRKJRNK4pMfGkuWj6GtWSgrpsbGBN2bzLretyPV5iotVDA9XZ+P1ER8P+fmQlcWMo8NIrsziqbvzIT6e72N7YE5KIrd9e9drbPazZXl5vc0LnY77Vn7JpXm7iD18mFXjxmGaOJGg+sRrne5j35raQ164OvYoCwaLaihLSlQJIXPz5uRcfjmZ110PwAfXXU/O5Zdjbt6chB5Vdhsa2nMrW1I68r9nq8k4ZibjuJnSOqfMBrNZzWzYt4+Oz6mRCO72veNzGQ0PZZRIJBLJGYvTWjZz5zqc0oGY2vra80ddHYwd27C23ePBNFO/ZEn9Ue2arTxxQpsrhwJP4ZxCZrWq/Zo3D2bPhk8+gS++UI9mZjp+r4+bbmr89+ojFk5SDwEmO0gkEsm5gTrPiyeYfCwEI1D4T+1QDNTYncLeZE8WLfLdpmbwHrcuJq1oLY9FL+FRXepJGzxQI7mTk0MxXt4TLArfczc/rhiMMChqCjmeqfY6qxWrTmffamjnCUWHsmQxa9uqevJ9ACVE1UWrDAsLKCJQ05jXdHG1+yhAfHExcaWltCsuJuOr1bxx5zBOtAvj6WPHeMSL5u+Uagup015C1yGCEz+pkX7BNxRRvSpObfCIAQrV6954PZr7yvfTrrKSIQYDCywW9oaHE5OQ4Frt1TnkfdAgFxFB7XvSivcsWAB79zoGJiaTQ5uwof8GwEn7GFhW5IgQHBQXh4CGax9LJBKJJGB0OAq2+li+9Y/NEdwKz/rrXqmsDKjgrGqiQpxMlMLSj0LIGALGzEzHic4FXW3bw5GRAXV9U9dktt1/v6r/brGQvmYNXQoKSJ0/3+91yfn5jPzsMxbdcAN72nWg+P/awLsdQS9QanX2cUFmJrx5u00iaNIke3BfcXg4xkmT1Op4mfu4YEQTausUtveOBqDZseMcDW0CQTiy+2xt6hWFRaWlRFdW2scOzkVmoysrT14bUCKRSCRnHJqDW9NyHzoUampUCZlTbe/999XIdW/xXUFB8N57DWvbOR5Mq60SEgL/+Ic6Z7RnwPslMPF4mzqdfYhQXAwvvFD/ddr8tbHfq7dnd8dX/bZzGVkQ1g1ZbEYiObfxVxwmKMh7tXNvBVzs2BzDNRYFpX8/DGVFWFrEIFZ+SbDhJKrLOKHVkNPp1IFECjmY8Vz63xcVRfe5cz0mtpuGDye+pMRxoq3qzdJNmxjcqVNAheR80X3HDvZGRzN7wpfc/r8F9v3VBgPBFgtGzOyLuZQfDe1Y1b07jzzyiGsVVveofB+F4HTX9sJqVb+DC5JPEBwSgqLTIaxWaqqrCQkLa1C1V/fiPe4DEx91+/z/G9Bu7VTYzvlRnI3wqRS2k/bLFfk+JBIJwL6qKrqbzbQLDWVI69YsOHiQvVVVbDIaiW/Igmgg1eNsWAhiMO+xVNSvXefXRF1XCM/6jtRvCBPeeYdmJ06Q+ttvXLVtGzmJiRjnz3esWDihLeJnDxuGMS8PAZyIbcNttSsoLRWUEMUeHGMXRQFxbSGM3wF6z6llEAp1TtbOV/F2Z8xGIynh4ZCTQ/Xll3sWmd2woX7De4Yi7Zcn8p1IJJLTzZdfQv/+nvtXroR+/eq/3jkerF8/KCpSg7y+/NIRD9bYCV///Cf83//5Hpo0a6b6MLypywYyfw2UQJ7d2eUxZgzMmqVuZ85snD78XQnUfsnIeYlEInFCjUj37oB97z11lVtzhmtbv9gscDAgbF5lfVkxSk+nG5zkGmlMDLS6ooKa+3fzsP48Vt3v/bz4khLyBwywT2yHrlhhj54DyE5KYtywYUydN49Uo5F0IFmbtLsRVF6Ooao5VbGKV1mboNpaXly8mG8vuYSPX3iBC7bvBcCKgg6B3lJn962XFCuc/22AUW9eCsExpQtGoyO6PSTMoUur6HSOzw0IeXd2xCuK74iBBv0bsGFKTmbwjh3UCuFV+3hRly6BNSSRSCSSgIkPDSW/Z0+CFQVFURjaujU1QjS8YLe/AYIbt+tWMGBxADN5VBM1a1A2r9aNY5yYiplUh4laF0ufG3az0ljdsL564fkHH7T/Lnr3JrqsjKal1YjiMAzfN6fygcPosPL8giV8fk1P9kZHE2srqK4ATYoO8rVwPLvi5GwXAlgTqxaJnW/2uHfd0BR6dljNpvHNqdXrPYq3g8Nh7y2zIcRisRtcRaezj18kEolEImkstDmdewzX4cOqI7u+mDpnx7tzFLvzsMFkUpO3vQX8nQx6vaov72tocuyYp6vB/fkag0CePT8/oGT2cxapOS+RSCQ+cNceb9my4XpsJW+YEEHqOqg2kdW2IkhPyRsnn5IdHw93LyiksnM5x64sZOKLNVTSzKv8TIjNMa/eH5eJrb1wW58+6o6gIPjXvwBPjdc7p03j+IBr+XnUKK992jhyJMWRkaxLSWFZWhrNqVSvt/XKjKrfX0QMQkDPiYfRa0v9bun6+tparnkn3+t9uj4cxic3VZGVpRp6X8oB2dmQtiCd7QsbpyrvqWjyNar2sUQikUgCJkSnQ9EKlStKwx3z7tiu15zL2hxbW35+bXFswKYlPR1eHvEZr0y/iVeSRrOR7hhRhVqzsmDMldH1NxLoKrEQZHz1FaAu3JcOuJmjI66h/INL2d1/IJX9+/P0qi/JKi4mPyPDJbsuOzGRtOnTye7alT8meB+72F+rW6H6oCAYNfgOVtZ6L97eQkBqWRlzZ8zAWFZGnMFAjMGgHmxsMVyJRCKRSLxw003q1t1xnZERWH0xk8mhJ+8tHsxkUm3+xo2N1mU++0wtJusL52fp2FE1o6mpjWtGs7PV2IWgINd7uj97Y9ZvOxuRznmJRCJxw3ke+NHT2WxomkbfVtl062av2cawYdTrGAaIHpuOsc67Y9hYl0X02PQGF0wpqKrCXFlJTmUlH5U6tMtj37+PvKQ27HFy8mYnJamT6aQkdUfnzvD00xRccQXZSUnkJCayrHdvtY20NHISEzF36kTNp58SV1pKSl6eOlnOyyOutJTXfvsNBTDUuTrttSKvue3bu7S3OTERc1IS+bGxPMsERjCbkbzFYu7DSDY/r7mD60Ye9vqcWSNHMmPjq+oHNyu/xJrBbWMT/Ea3g0Pzzl4U5xTL0DvV7Qv434A3dG5biUQikZwBuDmKT3S+iDp0bOVihjEXU9JtXDV9Fv9rFYDshq3ALDk5fBZazdqUFL7sk0p3snmc6bSngNxcsOrjCa6ox/keqE1TFEx9+6q/4rpw385ykFCLBX15CcoLLxBSVeVyqX0h/z//oax/usttte3rLzoK1TMjyV48dsUSA+np0KqV7Xy3hf+VxUVkjRjBsBUryBo+nPzQUOK3bVPfUWMZXolEIpFI/GAyORzM7mgOZn+kp6smyhsNjAez88wzcNFFvo+fOOGqX//MM97P+/ln+P3302NGFy9WFwjuvNP7ce3ZA1m8OJeRsjYSiUTihktRlEcWQ+VaPh+1BH28a7WS+hzDoBqZmYOAOshK6sLTwx5i0rz/0CNvB0E6aBnpo2CKnyopCRs2OPpg2xZbLC4yNMLmIHeOik89cAB27YJJk0hYu9bRhs0qFkdGurRR1aePTymc2MOlLgXahj/2GAAZzz3nsz3RuzcTed7++T6WYCaVI9icGG6a8/3Hvca4lSZaHTnCkaZNqdXr0dfVEXHsGAdbRVHw8st4y3zzVv916eoYxraKoya2HVXpQ2j1qVu11wYQqPSNN2IMBuIMBg/tY3uEoEQikUj+vrhVTTvcfyi9Uytp0T6cIQ8qjCtOoyhlP+dHCO6xXZJdUcG43buZet55pDppjRb06EFJRASKEHw4ZQqgLmoP+uorkpQcvj/Sg5EZ7/Ar3Rjf+mumv9uL46GhDk21k0BXV8fiyZNd9tURRBB15CQlOiTu8vIgKIiCqChKIiNRrFbHwrvFwg0xlbTqCW2bGRj5z1B7AfXEyFC4tSdKnYKwKihftEYECWI3qP2NiY4mbr9T8fbycvZGRxM/ahRKaSkASnExIc7jHiFOzfBKJBKJRBIA/tTr84PWawABAABJREFUsrIaT59dW+cPC1OHFP4kZv75T3jlFbXGfIT35DOP81991VOCVaupBo1jRr3Nt22JefbpvLv865/1fs9UpHNeIpFI3CkoIMTN2ug/zIQHGi6Ilp4OF7WM4WD/OGb0TWdtSgoz+qTzRt7bJF4ZQ/YP6nkeemt+ypz71S4XMO2tWZiTklCEsE+mP0i7lkEjR5L74VYun/0opokTGTx+vFftV31tLYumTHGRvnGRwtHpaOumY9/0xAnur6c9jaO65jSzVpChz+Srtn0JanGIFuVRVDZrYnfAB9XVUdg5gsfH2ORzbKOW2qAgSiMiuGnSqy7P7Yw3zbtfDsfTTORTUxoMzyoI6ymWoT9JGk37WCKRSCR/DTa7kV1RwbjS3UzeEk+HpkfRKcAvRWCBz48XkVMZhwAmfv8La5vVMvO7rSy5+Up7MwmZmfbffS1q599zDyURERwWLZh7ogfHQ0MJOWqluvnJ2YwXFy3i/D17KIiNpUNxMVit6ExqQR2Xxfy8PFixggTnWi5aH4Wg/z4zvAqlwLBevRhqM6nFxRDXSke7djBkCCxYoLB3r2JfB49PSCA/NtZevH2oyUTNwIGEnDjh6KSPmjASiUQikZwOnGPinDPCGlpfrKAADh2C0FDQks/0etWpfuiQerxDB9d1/oyM6bz//uMebXXtqmrdx8So89nmzdWgP+coeWeCgtT6eJrj32GHTzoezS/e5ttlZepWM+POteHcOZn3e7ajCNHYpQDObGQleIlE4lLo1CmS22VZO8A/nQVVVfy41cLg9Bpqp+yAFhYoM6Af34VaJRiOGKAwFID2FBBFCQKFnBg/Zc4LCsgpLMR4/LjH/cxFRRidLKBWYA2BI8we2DRsGDohvBZ9XTRxEnlr/uES5e6CyQSjRnmUfc/xUUTWPHQoKTt32j8LFBRshd++/dZxolvkvD/0wKLkZK867UuXOuq/elxnm+ufTFrh3x1pv1yR70MikZxOxuzcyaz9+132KXhfNAaIrKzim15XIoAog4FPfz/CEwe3Uqv3jJXSFrUznn3W0bbdntdvIwNBjBpFQUUFJQ89hGIy0W/KFIpatCCmrIwvx49HLF3Kj9HRPFFQQK2XMY9WyNzdDldXOyL0hAhgHTwnx3sYndl8zobRSfvliXwnEonkdDFmDMyapW6ffBK6d/d0bm/a5F8GpqAgMM10Z3Par18/Vq0qBHJQq9cE2W3n4sXqvadNg+hoR5R6r15qFL07mp49nIQdPgn8zbe1hYJ77/W89759J/d+z2QCtV8ycl4ikUjcMZkc1sabIFoDIrnsEjTzcczYIy3Uzt/qOKl3LwAKSHDsK/ZR5lwI1fInJsL8+eisVqw6nX3Lc89h6tCBQeOfok4fZI9e1xzzilUgdApL+vQhJXeHo02nCf/Yy5/HsCeO74dF8Pq8t0jJ22kv6Ar4XrK34dEnN+yFcYVwieB3Lwjr0jc3suLjSfFRQFWmzEkkEonkdFBQVUWJLWNsWZFa86V5UBDHLBbqdDrvjnmbHTvSLASj2ezY37sXDyS+z7vzL/W4ZOGUKaSvWUMdMMQtKy1gx7wP+xlUZ+W9Cy+AtDQS/vlPdefVV3tG7x87BseOYTYaXfttIyslhZTwcI/9J61AI8PoJBKJRPIn4U2WRctkX74cWrdWp9xDA0y2DsQxr2mqL14MgweXI0QUsBU4yAUXRDB6dBO7s3rtWvjuOzWJ/s0362+7ZUvH742tBOdNbdfffHvjRsd82/3ebuqAAb/fcwGZRy+RSCTuNGI1F1NyMno3B7l9W6vAxGTHbTFRpwugSorJRExlJXGlpRjdCrbGVFbS8bx0Hh+1zmt/mh5XU8cX9u3L2IfH2PqjdkibmFdcdoyySfv4MaUb02+7m2xSGcYcKqMSfEYEAsSUlRHrrU+2HLcyPIXy0tes4as/vBeEde6bht1o+XDMu3OK9V8lEolEIrGTsGEDqWYzRrOZYpvUW2VdHXX+jIwXqTfTxImYTPA5NwOOwqiazf/i8ssBWLVmMFkjR3pvt85/X+e+9prX/d89Mo70zExYvBjTxInobWFvHpJ0ioIp2TFGaWgh8+xsAit471ZkF6NR/dzYOfgSiUQikdhISFAdzUajGgsHjpi4K66Ajh1V+xWoc9u52Kk3JkxwuBAGDdqCEJHAY8B+fv/dytatTbjhBpg9W9WM16b9mZnqtb4K1YIqedOtW/19PFmc1Xa90dD5dkhI4+rfny1Id4VEIpF4Y/t2datZjpP07qbHxpLlK1R7ZAqscTiZ3yedvMUBLAqkpxP/xRfkDxhA1ogRDFuxgqwRI8gfMID4kSPpeMtF3NZktdp9t+izo01V/djKpk0pj3SNeNMm5NZmIdTaVt8z+6XRI3EN85PSaR60ncv52Wv3hC6I+JISCtz69PuAdKpLmrMq6QoSp39EdlISdbha8Eibz17nJ1Kua5MmzE1KwhgeTpzBUG8BVTnXP3PJz89nyJAhdOzYkbCwMDp16sSLL75ITU3NX901iURyjuO84O5e80X9oH7yZ89+GjmahMTBpKdDUVk8wWVWuhQUMHjlSvv1q41G1iam8EdSKw5q4XDu0jJ+JuoAZTXRLn3RtmE1FTB2LFRUkL5mjU/n/8LQFBYMiOVgrlrI3Bge3iA7/NpnFay9aQvTVlT476gWRpeVBcOGqdv8/LM3v/0cQ9p0iUTyd8TZme4eE6dN/305o71x1VX+k+tDQtR7Ksq9gOZJv4h33jnO+vVtefdddcGge3d44AHQSr0VF8Pzz0OdjwX57GxVCbc+kxnwgrmNggJVXS4nx5FZMGeO+gxms3pczrcbFylrI5FIzl685WAFyrffkp2UxLjRo5narBmps2efcjUVHWDFU5NWU5Rp1copJS2A9O4QiwWh6EBY7Z954QVa8wKWqGjiSkuJLy6m44EDfNi7t3ojf2nx7hH+oHZsfo79Y2LafpfOC0VBEYL80dPpOHOsRxHZqyzr2cyl6Pr+hjXlEHP73MbQvLXEPTOE9qvVvL2YDh2IO3iQdpWVXB8WxqtRUfZ0fO2dLe7SBWPz5gEXUJUpc2cuO3bswGq1Mm/ePDp37syvv/7KQw89xLFjx5g2bdpf3T2JRHIW0dBhQnpsLMlNmniVeQHQ19UxLjOTT668kn1xbagMC0GxWhE6nX279V//pv8VnaCggAmPdOD5Fjq2t+jI9o4d7e0cjoggbf50AG5iMqEnqqgKC8WYs4viuHD2tI51tdVORBWVowTBjL3PEVq6hwuLd/LgypUs6N+fvdHR9mw2d9wl6T7/Qo2U67Y0lPzXAytk7iwTsKK6EFLK+WxlITk5zV1K53jQ2Dn4kr8N0qZLJJK/I/5kWSIj1eKmmsyNX/tloz5Zm3HjtN/exzGZ1vHgg2HeL7ChLRi4uwa0rWYy6xvPOEe/BzLe8Vbw1WKBgQNd+ybn242HjJyXSCRnL/XlYLnjvET8xRcs7tuXtRdeyJKkJDXH7LvvTiqSK8bgGnV2UUg4unIDt7fKZV9SGgOTs4mLU4PFYrvFkH3FFaTNm0f2woW+l6BjYiA6mkORXZjIM1gV17XW9iXF5A8YwOzXX+fDtLSTLx7ndJ1p4kRMIsM+nLDikMLpOHMsAMI2WReKjoLYWKKuraPpJUexppUC8F6fm+nfYw2/3n8vBd99B/n5xPfoQX7fvmSlpzPippuI1esxNm3qEqEXGxxs645Sr2NeIyQEzJUVpG3ZgrmyQg4UzhBuuOEGFi5cSJ8+fTjvvPO45ZZbeOKJJ1i+fPlf3TWJRHKW0ZBhghZ1piXWeaM2KIhXMzLI7diRyrAQ4kpLiS4vByC6vJy40lL6/mcUrW9OhYQErkuvInRpArgVVLPry9fWoluwgKoqNWTO3DGZAy9dR8spUV7vH1QOsaNa03xAMsV5iVQN6M2jI9YxbMUK3n79dc7fu5dDzsK0qJJ0zjJ5XWv1tFIMHPggl29II3dJNtu26MjJUSgo8G+HE3pUkXpvJcZ7KjlxuarJf6JnEcZ7Kkm9t5KEHlV+37Hk7EPadIlE8nfH3aRpa9iazE2qarL9Rp/7k7XR6QSwGodT3l3rtn6sVv9R6t7GM96i3zMz1c9a9LsvvGUWaAQFqVI7BQVSoqYxUYRwf9XnNrISvERyZuBzddg5bKtfPzXPKyYGvvyy/mVvRaEgNpaSiAgUoN/kyRS1aEFMWRlfjh+PUBSi/vc/OoSGNri/1VarPepMCEFllSB83FiUt2YhRo+h5rWZdmM2ZscOZh06xJi2bZnZubPHEnRBAXxfWMHCH99h6rx5pOblYUVdbRW4mvkxo0cz6447HEvsJ8m/33mXZ5YuRY8apZ+dlMS4YcOYOm8eF+f9zpaBM+i+ehLHWrbj06PXcXfJWxi++Mx+vZYt4J41IHr1qvddBRIp74sxO3cya/9+9V0mJp5UG2cKZ7P9eu6551i1ahXZfnIxq6urqa6utn+uqKigXbt2Z+X7kEgkJ8/JDhPGjIH/m1VAxpAi3h14nLCDBymIjsbqRQhWZxXcuPAXnjbP49aJEylu0YLosjI+e/ZZDHV1tKyopOO011Datg2s03YD6lrk1T3a/eeRI7k8NxcAI9kIFFZzHa0oY/To0bx1xx2M/vhj3nzrLawo6BAIoMZgwGCpRYfgUrLZYriUNy1jGc0s3mQMjzDT0RU/M0dl3TovfcZlYOLN7ktcOZvtOUibLpFI/h7s26fKyLRrBxdeCAsXej9Pr1cla7KyYNYsNVbvk088I9BzcrxH4kMKsBkYgBo533AmTIAnn3REqQsBO3dCZaXreKZFC+jUSR2z3Hef43rtGm2r4demB7B2IL3J9ROoTZeyNhKJ5IzEZ2qWtxwsbdlbw5cVMZlIcJooa1HhxZGRGOfPV3du2HBSE0u7c7mgAKWkhOaKwuKDB3jks8+Y+d57XLM1mxKrFSUykmWlapR5ZlERg+LiEECUEPZFgYQEYHQh3JHCkj59SM3Ls6dBKUBBbCzFERHohGBZ794AND92nIrwZg3ut8b7G0fzKWPJQX2Pi/v2ZW2Kev+FLe/n7cUpUD2Mp58IpvNbj6CnElNWFoMvv5xaITy0efWKwqIuXfy/K2wReg2M+i+oqqLEYkEBlhWpkXsu79JgOKkFFslfw++//86sWbOYPn263/MmTZrEv//97z+pVxKJ5EylIcMEZ0e+aVMFl0z/mNvnzeOFxX+wKSmJdZdcwvMPPuhxD6tOYcWQi1kxZLZ9LFESGUnP2bMd9+jVixd+2ccrpbuw+jJz2sK6PchOi6gXRFQdI2nfPoY4SdbEFxdjQY+BWsykUhAby+6IaApEFP+1jQeWpaUx+Kuv2KV0JPnI73QsPEidRc92OtKWA8TrClEsW7gLNczubjJZxCAMQYKnp0cBvvP6TcnJDMrdQR3CIzAwCIX3kr3bfcm5g7TpEonk74K7DOqoUd4lXxYtUiVwHntM/bxvH0yfDk884bqgf/CgunV3gEMXQCE4uC0nU24jNBQGD/ZUgDv/fNfPoEb9Z2c7HPN6PdTWeurqawsO/pgwQdW790Yg10sahoycd+Nsj1SQSM5kAop2+3Gpar1qaz0b0KyIVljVC0uzshhcWUmtl7w0zaGcHhvr5coAcXI0Jy5ezK527Ujcu5ed7do5TsF7lHn+5ZdTsmcP678o57GEE9S2EK5R/UeO0KGwEGXtWkdbQqjp8W7Rdh4h9nW2fXrs52r6uK3Ky4l7KB6C4L2IdKxC4aYpk+xZBdHPxHP/oxG0OnKExROD+OBIP2IpwtIihv+b+zl3xhzzeA1mo5GU8HCP/b5oiC6wc+ReQyL2z3T+7vbrpZdeqneivWnTJlKdvuADBw5wzTXXcM011/DOO+/4vVZG2UkkkkBYahsmXFybzVTGMY6pmFH/7rgPE5zNZvTonyi+w8LDHy9n1luzXGytSzhaPQvK+tpaFv3+O+nDhrlGmjeA84c25ZL8PD6wZNjtW43BQIjFQgpmksllEYMJXrvafo02HrCPCzR6X00wFiwEY3VSPNUi67Wty7P6YemmSjKOeWrym5oaSe8euN0/l/m723OQNl0ikZx9aJHvAZR+cyE/X3XQu5p/7+ljmvRLr16walVg/WrRAtascfg7iovVeXHv3vDyy97dHqA61zt2hIwMz2NmM6Sk+L/vvn1wySVgixts8PUSlUBtutScl0gkfz98CLolJKiOWaNRNUrgqQVHerqac+aNrCy/jnmAdIOBrJEjvV+ekhKQY17r/uLFno/x07JlmPr0Yem117LLpl+/Mz6eEZ9+is5Wht1blLkpOZmEDRtIPXCAMZcepy5SHSkU2aL6U+fNIyEzE1C14YNsVto+Ade2QqDUWKFGAW2wYYVWz0eT8GIIhnJBh4OHCKuqov2hIlqVHmH6a3PZ8/QxtmUeJXXePC6bP5eiyEhAzSrYNucYTyQd4P7ux/j2iJEY1Gj1oLJizntOfd8628hGMzrDhgVeLR4apgtsSk5Gb3te93cZhPouJX8+Dz/8MLm5uX5/LrzwQvv5Bw4coHfv3vTs2ZP5WuaKH0JCQmjevLnLj0QikbiQnU36gjS2LszmPhaTxloG4jAs7sOENz6oIii5EhIrOdG7EoDFaTeQk5jIv9audTiqvdjam8uPe+3C+vFPk37jjQCMOJysLo57wwoTbA5Muw21bd8nnUybY74OHQoQXOto6EeuYjCLME2ciN5tPODQs1dgYjKgo4YQBAqL+5gQQWpwguaQ17YiSK+K0NZDyxaO/jtv7fslZwXSpkskkjMVX9rxMTEOXfenn4amTdWf+tAy8kwmVY9dxTV9TK9XHelCQFVV4I55gPJyV3/Hbbep8+KSEt9uD1Cj3jXHvJaY3hCl2Ph4+Pxz1+tOtoydpH6krI1EIvn74UOzxmRyBMUHlJrV0GVvUK1yq1bq5UJgVRS7lmtDu79vn6oFN22aWnwF4KqYGNXaO3cemHPbbT7by0pJsUeZD96+nVpF8XC662treXfKVCwtoklfs4YVl1/Osmuv9WxMURDBtlV8p5o0pa8WU2r70LzkGAVtWhNx/Ci/Db6PJ4cPpzJFwMYWkFIOekdkoGOSL+iyr4DspCRS8/LU94ca2R97+DDtmzVjyIUXsuDgQbYVV5G92sCSaP9R8M6ZEs5FbAYNUl/dgfAKXj++m6nnnUeq06QtPTaW5CZNMJo9I/fu/DaF9F4ycu+vICoqiqgo70UM3dm/fz+9e/fGaDSycOFCdKdQL0EikUjsvPUWrF1LjGE6d/MtAMOZQzbd2U4yBd9H8cQTHexZWmPjNoBNieaYUPPJKyPDHFJ3PjBNnEjynj2smD/fnoWmEfT4o2BbjJ99Ryzn/VDHk3V5Hm00E2C8/HJalZfTtqSEkZ99ZpeviSkrw0IQ1YQCghKiCY9pSk3hYYqIYR+2bLw10KWggFRv/Z16PvQ7CHvD4DfVhj65JZ1Orydz5RhP0dyiFVnE9qs/TK5bvIHYQgPtQkN5sHVr3jl4kL1VVXSLN9R7reTMQdp0iURypuJLHtdZ5uaRR1Q991GjVId9IPIu6emwbt0c3nlnhMd5WVmO8zxlb/yjnRsUpPZXc8hnZsJll/m/Vq9XFxiSkmDIEFiwAPbudRSSrY/4ePX527U7ueslgSNlbdw4E9IIJZKzkgArtPkqtOKSWuVc3cXZimzapFqYethXUUH3rVtpFxrKkLg4Fhw4wN6aGjYZjcT70CrXun/oEAwc6KjyDmrq2scfq4YttXJdvffXFgUUq0DoFBcJmC9KSrjp1189rjG98grTdBMZ/HAzzsv7H4OSmlHWvLmHRE3igmp2DgrxvjRrtTLi//6PubfeitDpUKxWXn73Xabecw+VzZpBmQHe7gzP5Xpc2uIbPWXX1jLm44+Z+dZbLseqP/+cgxf0p7RUXRTod4ug+ICu3gJ8zivzXovYjN4Jd3gv9ppTWYnRbHbI2dgq5rZ4ysia2eH11gY+Ezlb7JeW9t6+fXsWL15MkFOxxbi4uIDbOVveh0QiOUWcxxdOAwh3dTcNBcGYMTBzJiy9/noGjx/vXequtpaHPv9cXVx3k7UxvfIKnfbv56ZJkxCKwuGICLVtq5UvnnqKmCNHiPrf/wg9coS4fzwI8x93NOyj+Kvo3dtDvmYICxhlWz2YyWjG8Ro1hPBK8lLG5w5GTy05iYkY58/3KB7LN9FwbTF8E02zFoeZOm8u7+Y9Sh06cjBSh44grPbxQENy2BuzsPu5yNlkv6RNl0gkfwb1SaAG4moA7+fMmOFdGgZcTeO//vUvPv54N5CDmhYXZB8amEyqZr1WtNXZV3A6MZvhggtcC8nW1Ljq19dHdfWpXX+uE6j9Omuc8/n5+UyYMIFvv/2WQ4cO0aZNGzIyMnj22WcJDg4OuB1p+CWSv4h6PbGAED614DzmjKdoRRo6sQw4xeu6QpRnc/H6h1cImgiFTnvyGbV8OXP738L22ERWVTUhKjyG5t06kPDHuoBu40trfu+dd3LR8OWUXesrjx7fmrnePBma07uigrLmzb1q4IPq7LD3LcBq8Uu9lQ+IrYIIC0E6+H/2zjw8iirrw2/1kg5LSIAkHSCBgCYQFEfTQUT9RokIisuoswgTHKIoCCO4jTAuuAyigqKyKIuiAYIGZxwdUXCJ4jjOQCQdXNhBSCAI2UhIWJJ0p+v7o7p6re50Nghw3+fhqXR1LbduN33uPfec3+m08Eeq9TZijUbWXXSRV7HX4tpaEj6xQlk4rO0Bow5BTC1Msij7AtzzTOZssV9ZWVnceeedmu81ZchytvSHQCBoWt0RP0I00A6dntkdnmbx8Tuoje3DunWw//lV9PzhWYYsXeR3/HcTJoAkMWTRYkw6iS5HjlDdqRP1RiMfPP44v3n+ef+b+NrX0aOhTyo8NR06yJqrBQa7nSeXL2f9JZcwZ8kSLLt2IQGPM5OHeJnuKDP8CroylQVEU0qG4e/Y7p7MFYvvoDg6msGLF5NQVsYt//kPb2VOoLKDnvo6OKa3Q60Owh2M/vJLkt+wsazkPvIZjKFvAtHTmx7gIGg5Z5P9EjZdIBCcCqZOhQULcC2u+xKKq8H3+FB+ol59VZGZufbaZHbv3g30AjaRkhLB/fd35t57m37N5vC3v8GTT7rvEdBHIjjlnHPO+U8//ZTVq1czZswYzj//fLZs2cI999zDHXfcwUsvvRTydYThFwhOE5qeWCceFdpaGBTfZgRrvh/rv270EHnYMByAzRkhB4qDe/6BYqbu2aN5jg5wNMig95/d6+12Hl+5kroNvXjtpf/jWJcOHjdDM0ovcOOAQ+HQs9ZjnzNC36fQnGPYMKT581nVbUqz6vT6ZUp49F1jxV6zVjm4O1Oiwe48wiiDTdfoPc9UhP3yRvSHQHD20NikOyhNMtAKOkl2TaCvS1rNp0vNfpHn/570F369w0px195MfeE1PkjuzJ/ff5/n3nqLyE8+afwmC86HKdr23BPrhAlkXXcdC267TTM7TSU/OZlpEycyZ8kS0nbt4h1G80dyaECH3ajn+77ncdkS/0UG34X3/hM78doTNq75v56KuK0IkzvlCPvlj+gTgUDgS4iJ90BorgZo8nDBA7ch3bmzkKSkPkiSEjF/552Br/n73ysBCPv2+b/Xty/cfTe89hr88kvgO8+cqbS7PfpIBOegc16LF198kUWLFrF3796QzxGGXyA4jYSkWdN+U6sCNV9Fp4MBA6D/kh18YD+seYzBbidr9mz679/vmmT/atdexuuyGLkig4wM2Hj0KEM3b/Y712qxYLE8DktvC9rOQFH1ofKvXpdwz/91odPNpRSO2Y4cINIva/ZsMmbOhMsuA0L+eL3wzZSQri1BnrZD0b33vackkTVggFfR3ubc80xF2C9vRH8IBGc2TZl0Nxpa35iBdmLDQCZZvIN75XZB9HhmLR5FfFkZd69d69J9f3/GDEwNDcgy3DD7BUq7diW2spIrpx8lesgOlo6/rvGHnDUL6dFHvTTpfcmeNYuHJk92Xf/O6Zu4VfoncUerXNlpAFOnTGHBbbcx5f33mb9wIWV0RwL0Pc10/XMGUxwOFl55pbKQr3UjDRmdsyq97AxC2C9/RJ8IBAJfQky8dxHKvDDE4YIHdmAc8A4AlZWVREVFeR0R6Jr5+aFlA953n1IyJxBq+9urj+RcJ1T7dVaL/x09epRu3boFPaauro7q6mqvfwKB4DTTSDlxk8ltjCWpbY1OoGruwZAksJDPl6RjwX3iNdfAtm0QtSaRrh6am57kTZ5MRm4uK0aOZH1qKitHjCCTLKZ9dKUryjtM7Rd1du0cdFgsFkAp/a5zFsBVtzPffBODc8net5hsQHyr7jrRd7Xx4dZj/P2ljkQZteuK5913H/337yfdZiPf53e1KdXiY2MVrX6LBRYvhrQqM91naHvV81JTvRzzzb1ncz5zgUAgELQuiYnKpNVigbIyZV9ZmfI6LU1534VndTcfiopgm7NUSoNz6hPI5Two+X+8M/cCzMm7SMXKG5MKGF3+LwrHjOG/k6ZgXgMbJk2icMwYrnj9ddKWLGHw0iWUOSfipVFR/HNpYuOOeTswqycPXZTJys6Dgx469vHHXdcvi4pi9tIRXLZkMYk5ORSZzViTkylISmL1sGEArE5PpyApicLk7uy8II69ne0UZGXxnvP9SA39fMCrwPzK559Xwv1agjCmAoFAIGgGoZqP7Gwl6h38p60GQ2AzpjUvVO+5bVvgY7S5FNUxX1dX5+eYD3ZfNbI+kFnW65Wo+PfeC3z37t3dxVlPpY9E0Pqctc75n3/+mQULFnCvp8iTBs8//zyRkZGufwkJCaeohQKBwA9fT6zForxujXLgzZwkBpnv+6E2//zz4U+sIJ313IH7xP+UVsPc7/nHV/W8YrrI+2TnSGJ7795YPSbZOenpPJz0DHUPjeC/e2r5+GP49mMjUqURdkXAy8no90RAxQmovAQqO6KrqOOiXXtZ/PLLDNi/nzCbjUt27yZv8mTNdktOB77b2e8c1Wg48SP1em7csoXLf7KSVmClMlCOXkoKK269lfUNDax0RvaF9PH6fE7x8VBYqFSlnzhR2X78sXKoasCCGbLmfKWa8pkLBAKBoG1obNL9watFSrhYQQG27NUA2FbmKCFqVqvilUdx4o8YG8sh4rBiYSKL2UYKAA3ONHRZUizJkZE2SK3i9hELsJLG3YssdOcIJpsNI3ZuYQ16wGSzkT1rVuBFb7tE5LKogM923mPh9M4N4+WXM1xF5lRbplrcv/Xpg7qMr15f3ertDWTPmkViTg5pS5ZgWbrUy4FvWbqUS5cs4cqFC93vOyXyAtptJ3mTJzP2+edbrvsmjKlAIBAImkGo5iMjQ5kbapGXB/37e0//PeeFjz4KnTq5ndvqPdev9587BvG3AylAbxwOR8Bal8Hmo8GeoaEBZsxwByj4YjYr5wrZmrODdi9r8/TTT/PMM88EPWbTpk2keeSDqFXhr7rqKt58882g59bV1VFXV+d6XV1dTUJCgkiZEwhOF22Vj9UEwdompdL7nFj/SzlHKiX0N15PjFxKCbFczzokZLbPcHAy/Tj8rzv8qyc8th062cEABlnG7hnJHkDDnWFXK1ujg1SblReZziPMpsBocemp79nv4N4L8vii5nKmTJnCQqdO7bjPPsOydKlLL1dyOJB1Ogbu20fmB7k8NWoijlgb77/wKDfOmeP3eF3Q89klF5FbWckzRUXYA5iPzjod711wAZnbt1Nqt3sVbI2QjSRFhAf+eEP4nIpraxlstZIQHs74Hj1YdugQB2pr2WSxEB8e7nd8KF8pz898+HCorISuXSE3t5HPvB0hUr69Ef0hEJz5BE1Bt3hIsCAhIbu27jdkl86szl5HPWGARC8O8Gby7Twx8S4u2RjO+BPvc2zPYW54cQ71EQ10rG5g/V/uQy85vIqbAziQ0CHTgI4fks7DsnSpfwMnWDhQ2Y+L33yTishIdwV7p223TphA6u7dSMjo42rpnG0lOSqc4fU9WLj3EGHxtXw/xELpRRdpXn/ls88y9ssvWTV8OJnTp2PXCLsz2O3c8/HHLP3NLTQES5RTxxvOMYF1wgRSc3Kap/3W7AGUwBNhv/wRfSIQnN0013z4SqB6FkLNyvKfVqrzwvvvV977/e/hr3/1vucLLyg67089BTfe6Bs971nxzGMc0ohXNdh8NNBYR30WXwwGePttpe0iOr79c9ZozpeXl1NeXh70mMTERMKdDplffvmFYcOGMWTIELKystCFomHggTD8AsHZQ/6uXUwrKWFOeDhpN94YspVvqn6d1omqg2CfOY4jkV2QZJkr58/npK/z2KcQWyAMwH1FMcy/6wKXkX44+UkKJkZxyZKjvLzrGaCB7Gw9V/62lm9zi4l9KIOM2X+lrGtXulce5e3nn+fOxx+lV1kZkz/6iGWjRrE/JoY358zh5dtvZ/aSJVy0bx9bExO9nQGuDgitra7ugKAFW100YzRW53AQJklIkoQsy9TLMqYm/t57tTWUOrjt2loK++WL6A+B4MxHnbBKA6qR79mL9EY/5B1dsFqh939W0f3hTKQG/0hwWW9g8/1Z/GVzBnPmKBNc34mvbsoOHLd5138JaLec2WwAR6VI6mUDXyUP5oEHJ3N4QITLsY0D0EHPrAHc9P6XPND5r1y98FV6l5Vx19q1vHnDDRyMjmbDvX/m8fK5vEMGVitccLFi0+6/X2LBApnJD8i89sABCv75TyyXXOJaVFft8egvv+TdZ58FIHv4cO54/HG/Ptg04V7Sdu/k/vu+Y/5vj/u9/8mFFzJ++3bid+1i3I8/suK66zhgs7Hp0UeJ/+yz5oXiNXsAJfBE2C9/RJ8IBGc3zTUfxcXehVBffx0OHlSi7jMzvaeValHVnj3dU06tdvjec9UqGDdOiWT3RS0m29xks6Ii2LoV/vQnOHpUKRyrtiEyUtnnS2O10xorwyM4tYRqvwKoG7UfoqOjiY6ODunYgwcPMmzYMCwWC2+//XaTHfMCgeDsYsXChay/7TZWrlhBmq9grYqGlc/OhnHPVdMwfi/ykn6wq4tXKr1azV3zRGeJdzVyr1/Ou/738iy65uMU1jU04NDQo//wr3/lhrw8xn9cyLgby5GRqBpZx/rUVAaO+ICRrKZyXk/69zOTuPEX6Awsne3yLlRERXLznBeUvyMjuXvNx0xYs4Z6o5FH7r2X9ampZI8YwbyFC4murCTy2DGOduqktNOnrZIMf+ubyIzCQldhOXU7MzHRFVWv9qy6VQu2+uEpHKzeq5HPydMRL0kSpmYUtvXk1Vfh4Ye1B116Pcyd26LLCwQCgSAIgSaSaiq4I6OE0tQqYjJK0L3WhdhYiHkgg0tIoQD/cDNLQx6bX1ZmritXwuCx1TB3L/wjHipMIINjWIVy8HE9mBrAgL/dchY3V/dJQBf5KBLw35FJHB4QASd1UNQJ1vaCzH3QzcalGcU8fusApt8ynaIxYwiz2ZCAiU67e9TWhd1Sf5Dh0CGQZR2SBKtXA8k1vHHeXu4ccTtxR44Q88YbxFRVccu33zJn9GjsBgNfWCwUJCUhSxLvqQvezrGF6sgv0iXSh3I2/c8Gv3UvOKj2Os5kovCKKwgbPBjprrv4M1DvcGAaObL5oXge4yBNLaKAAyiBQCAQnMs0x3yoY4f334ehQ5VppKpqPWpU4GklBA7M8pxydumiBAkMGAALFx5g0iR/Cey8vOYlmql41c/xaYPqmFed9YEi6X3xlAUSzvkzh3YfOR8qqpRN7969WbFiBXoP51ZcXFzI1xGr8gLBmU1RbS3lzknw9d99R6leT2xlJeumT0eWJHd6eiPL3KP/t5vV9Qfh/V6wMMm1v7GVat+8NGn9+tZ5MEA+eJCihx+mPDISSZa5fvZsSrt2JbaykmGbN7M6PZ3MdesY/tBD3LF1B7JO4+fdLsHs/sz76QmuiPwP23v35q5p07AZjV79FHH8ODUdO5KmkU4fcbye1ZdZuGP7dnqZTEzu1ctLWqbUZsNitfqdZ7VYSI2I8G+TqjmgpYPb0nCEEBGR82cfoj8EgjMHLUUz1Z7b6uE3O36k1GYj1mjkXwMuwhgG335iZNXYbXzXYKEBHXocru1gnZVdnVOprlYc/Jet2s1HhoPeN9XOTPfi6wkP8X+7N7v04IvMZsojI9mekMBd06djMxqRagz0+eh8hg2D5RF7cETYtccdPqyNHM08w1/YURHNfpTsMEkC+b7dcNtBkt8/xpaFtxK2/guPNsveIYUeGOx2HsnJ4V9XXEFpVBSbJ0wgvryc4uhoEhZ/CGXhsLYHg58KLgXXYoJqEbXAg3EOIeyXP6JPBIKzn6aaD62xQ7BppRrbFYqDG/yj6BUaAL2XfE5LTFuw9vpy0UVKtP+mTf7JbUJVrv1y1sjahEpWVhZ33nmn5ntNeURh+AWC009+dTXT9u5lTr9+pDXx/6H09dfuv3HOuX102+VhwzQtqadjf3jBj1TKNgyVEgumL2GBdD/bjv4K69rwkJzzsqRDkh2sGj6ccdOn0xCgDLtvKrwmssxfTCZ+bUjl5hMb3eeqz+UzSf/PiwuZZHiYLQ/W+V1qwISOdNhtYPP6av+2+PSTdcKEAPI23veTO3dGtlhc0jIFNTVeznk1Si+gcx4aHY21dXpesHRFvR6WL3evD7TXVEFhv7wR/SEQtG8am0im1XztOjaQ3MyPJ88netRgDpDAMsYznmUkcIDBbOJgXDR0UYqg8sKP0NWmRLl3CDIrV6PPUeyWYcIgRuzewCfcpLQj1AV3XzvpIYujRe8euzkQYVYebrazrZVG+k8PY+yQ+Tx5553KWCHIvbTGOjYMZJLFO8Yx6GWJ5VkSf/xjy6XgghJMAFg450NC2C9/RJ8IBGc/oZiPUJzQFRWBp5Wg/V5wbMBDwGOcd56JRx7pxrJlcOCAtqNcC8/5I3jPJQNNg7WordVObhOqcu2XUO3XWaP7kpmZiSzLmv8EAsGZxYqSEtZXVbFSI9KsMbJTUjA4rZMrPd352mC3kz1rVsBzEzduJM1qxWK1UiUrE3p7lINJSyewbclJyNnIoUPeVd/9qK+H7t2pTRxANRFk5Oay/IUXNA/tUlND1112eDkZdgVwWgNIEi/V13PziY0kzgrH4Fxad03CfQrJ/t8j9/k75p2+iJe6TeUD8yieWbYMvc8SvXo9vbOfYqqO0vn4cbcl97mfqz9XrlSkZZwT/VijkY7Ov4dFRWGJiCDOaCTWaAz8jCqqs8DHaeCZntcWZGTAmjXa761Z4x2439ZtEQgEgnOBxERlUmqxKCnn4E49T0sDZmnYc+fWIElkp6RgM8eTSCGX9c9l6dwhDEnOJZFCDhIP726EJVblX5TTSR8ePFzO0NDAY9nZDCgsJKaqCntlJ5djvshsZuayZei1VnF9CXHcoXLgnWKlnUs92hplY+fS48wYPz6wY97jXlpjnVv4kHfIAJuO7/IkMjLwstdtgqpFZLHA4sXKNi5O2S8QCAQCQQBCMR+NjR08ZWICTCs192moynowBFjIV1/tY/fubkycqMjZFBbC4cON+AacqPPHl16CW29t+lzSYFCkfwKpzmVnK8eAtixQdnbo9xKcHs4a57xAIDizKaqtxVpTQ0FNDaud1VlySkspqKnBWlNDUW1tSNfJMJvJCxCZlTd5Mhk//hhwkpidkoIeb0eA5wQ7y9iBbSutbDu4i1v3fk9+tRJ9np/vYZSHDoWKCjrs20ZnjgHQ1Xmcr6Xs/nwMRyZdi+6TnjAplTHGXoEfrKGBVxcsYF/uUPImTw58nJej3mPr/LW/8YUXSMzJ4anx4wNG8zcYDGzJHUZ052ju+CI3oObLxsmTycjNhZwcKCigaNMmrLt2UWqz0ck54tl6/DiLkpL454UXEtSd4RyNHR9g4eXkxRwfYMEeE8ePh2MpKHDq8OK6FVarEjnRmpjNytZ3zcNsVu5ltXLK2iIQCARnO41OJDOD2PPUVDLMZmJjoVuciZixJZBaRewdJUR0d85cZ6UoUm7glq1pRMLMrtfz3NixbEtMpCwqijhDOBvCfg1AYk4OM8aPpyH4DN67nZMnMzr3q+AHGQy8vPNI8LY2Idjo4j176L9/PwCfcKOmU6JNiY9XPBZ5eXh5MJpTYFYgEAgEZx1ec2cPQjEfoTihgzn5Pd/zpDGpm+3btzNs2OVYrUrbrVbFUR4oaCs/Hy6/XNlfUADvvKPs//BDpYgtKJnbBQWKgz8mBgYODHz/vLzgKq8ZGcoxzTlX0D4QznmBQNAu8IxaL7MpUWNlTu3yNKuVxI0bG7mCPzqfrYsPPoCSEj+PaobZzO++0nYEvPXCbMZdeRmPrE7jDyMXUhxbxdwtJVitsHChh1H2GDHokCkym0GS6FZV5Yq20zc00K2qij69JGa9WeccMEi8azuoeW/lJD0PTJmCzaOOt66xUYTPBF+PxOwjHcieNcsVfa/Fwzk5/C55OU8abuP9q37tvpzzfpLvfZ3hConHj5P2yy9YrFbKndcvs9lIKyjg8s2bg3+GztHYo+l5PLxrIo9dk0enskJ+dUN8SJERrYE6WEtLUwZyaWnugVxTojQEAoFA0DhNmUhq2fOi2lpKImv4YEsNtqsOA2C76jBzP6qB5BqkLZEwuYkyKuqCvCwzuyKcG3sWUJdcT/rcufztrbdCv47TTJbRHSsWZvFY4GPz8vj9DbfRfUaQtmoskqtLBOpYQN3mDxjAyhEjkIHPbpjnckr0OhTAG9IWmEzeK93NLTArEAgEgrOOYFnIjZmPYGOHt9+GZcsUZ3cgJ39DA/zzn7BokVLwVUVbW/5n4BD5+esYMGCAV9vnzg0etLViBWzYAH/6kzJfrHDWoK/zSG5X5XduuEGZV376KXTv7n72QARa3FAJljEgaL+Ij0sgELQLNOVonFs1fb1RS+Qk1mgkzmjEsmMHi19+GcuOHcRVVBBbWalYvqFDvTyqnlHRn3+uXMPl+HZa6r8PuxprcjLLrxnJwptuBeDTkp3cMSaP5RtqwFzLqlWwigzWPuUeMSTm5HDD7NkciYrC4Yy2c+j1HImK4us/n+Tx8zaSlwf//jfOKL8AD2VX3h9CHrGVlcRVVGDZtYvH1Bw1Zzv9HOceNExIJee3RjJyc9kYIPq+e1UVc0ePJm3JEl5aeg1lUVGu66tp9bJOR1xFBebKSq97Zz//PAbn3wE/Qw1c/b/VxOr3lO9AzmqJGTNNrhTDU5GeFyxaQ6QKCgQCQdsRaCLpsudHjij2vLLSJZOmLuoP/clKpXPxu7Khgcx6RcpGfncjf57hnAUHCDyXAuzPmziRab8byhsFFt4fNpj1qamUR0Yyc9mywA+hXqsgSpGqqzByU+VnDCGPRUyitmM3ABwa4fvx8fDxx84Xqhl3BI+WV7PR9Ad1TP7gAwYUFbnGADnp6QxK+i/X7b6TJz4po/8n33Mg7yOhySYQCASC00JbZCH7jh0+/tht5nyd/D/9pLgREhOViPa0NFCT27UZDJxPWVlnoqPNmm23WBTNe/AP2lKPC5XsbEhIUOafarDYokXa0j6BFjeEqtyZzVlTELa1EMVmBILTh28hURVXIVGtkuwBqHM4CHv3XaTMTGS7nXqjEZMzIh9QhOWSk2HFCqTBHlU9Y2phqZXwqgYe+nYFc0aPxh6omKtP8TWGXQ3AJRRQgIUGdOQMTydz+nTNaxiARe/8k/yCP7HE6mzD4AqY85P/zaYNgk3dXdc+YTTSwWajODqawYsXU6Ius4NfITrXI0+yELfjKDvpz86kXliWLkXncODQ6VzbDZMn83OvXoHbbLcz++Vs/pz7Dj/17cu0iROZs2QJabt2gdVKQVJS8M9Qqx9DKGDjd73TUFeukZq1px1hv7wR/SEQnB6aUjS7uBgGD4YR3fJ5zj6Nxwxz+PxImlJgrUGp+lYHhI0ahVRaihwbS/3atZiAeUYjDx85oimZpgdeOu98Hvx5T0htVouzq9sb/rqPGZUvYpTtXD97NqVdu9K1upq4igq29+2rfZFqA3Sxg02C2clwqCNUmDBUhPP22zB26M/IV1yBHN8b3d13wZtvwsGDrmpyxbW1pG7cRFSNzO6IRjLjAj2Hc1ziNz4B7vt4LQvmvoitayzGXI/KeX36NOtegrZD2C9/RJ8IBGc2rVmwVB07JCTALbco8jBqqbqKCjAa4a23ICXFbeZUN0LXrlBVFexeDpQY5lTq6/MwGo2abQ/leUN9npkz4Ykn3K/r6iAszH2N+nolG6CxQrh9+mifK5LXTi/nXEFYgUBw9uCXvr59e5OX2U06HZIz700Cl2M+PzmZ9LlzyZ80SbnuypVeUdGUhUOUjdpEB8+NHYvdN3TbA9fE1w6Js8LpjdKWUmI5RBxWLHyT+zuWTX5Ns415X33F3W8sIMW6khEjnG2oCnNeHO+tc38pytJ3R5sNCYgvL6dozBhWzJrlLvCq4ZjvDKx8ZhuxlPLf5EHcN2UKXY9WY9m1i9dffhnLrl2YK44QX1bGH3LX83aAIrZ5R47w4N+mYLLZWDFyJOtTU1k5cqTfcQElhTQIFpWudn97Ss9rT20RCASC9kZTimarGUtZ6SvotWs9WdesdOvLOvXETGlpSE49MamsDFNaGqSl8UAAxzwoUeUP/ryHbL3eldGlRc93JagwIu+KhJeTlW2FkU9e6MtlS17HsnSpK4OsMiLC2zHv6z+PcNpgowxP7ITXNkOOkh03dixw3nlIRUXoNuXBvfcqTnkPMd348HA+/PO9xO/O52/LlgWVn/PDGTGvjktcBd4bGuhy/DgA710xlIKkJH6IiaJo1CivDML86mrSv3fX0hEIBAKBoDVpzSzkw4ehf3947TV4/HHYtk1xyh85orxvs8Edd7jNnKfmu5r4rc0sIB84xP79VoxGY8C2ByKI6yAgo0Z5v9aS9glVYlWoyp25CNeCQCBoN7jS1yMiWJycjCUiQpGjueGGlot9Oz2pLoey01JVLsrhErmAH7OsLue6l7yMb3VQLQywL3coRShtOZjchV5zP2Fc8nxuZzUvMF1pgtNK65yp6va1iobOaHJo2FTAG89tIqZrMRw1uPPVG1BeD6qCBQUcTO7CON52ac+rCw935OYGLPAKcAz4Y+xJCrCwZuSlbBg0iDFffUnepElMWrOGvEmTWHfXi3StM/Bw0ho+vvxy5URXm5WJ/6FrrqEgKpKCyy5j9bXXApBz7bUUXHYZ1i5dqHc4/D9DpwRBIIJpB65Z037S80SqoEAgEGjTrHR150mmrQVI7yknSatzMG11nvTqq0Fn8tlHjrh0133Ro8jlXfn735M1a5bmMZE1Nbz/zb0wZihMSoU1PUmd5GDdmKdJnGVyjQNkjXGAwWZjYNE+fvfVV+5FdN9hQgOwLJFDDo+C9o3MmhdOeEyR0ImKCl783Zdj2ja2Qa+npmNHAMqiorAsXcrgJUtIzMlB1hsofzWb/Opqbt2yhfVVVaxUQw8FAoFAIGhFWrNg6YoViiRsdnZojnNPzfdgx8H79Ov3R2pre5CQ4DbqwdqumnQ1aGv58pAfA1A05kOZS4a6uBGiCrCgHSJkbXwQKXMCwemlzuEgTJKQJAlZlql/5x1MmZmgFUFmMEBWVnBrXlxM0fXXU37++Uhbt7rS02MrK1k3fToNkkTs0aP0cU5IdXEnkbvYoPcJeHx7SG3+7fr1FPboSacV3flmw+9gym647SCXfHmAqK5lpK0+zNvP3UDffbsZ/9FHLBs1igMxMeTdey+9y8txIKFDRlq/3n1RGWWir25V3u8FC5Nc8jaePJuRwYy779Zso0GWeTIrixs2bPDrA4deT9RjT2H47W+oOHkCft7LyF8OUhEVBQ4Hd372GXkpKZT17UuZh8nwbJ6nIan99a+9P0NZxtRImLkqGaPTKQGA6tZqhQsuaD/pee05VVDYL29EfwgEp45mpauHcpLV6qcnlp+czLQ332TOr35Fmc3GqJ/8peDWDhrE9d27I339tXcDPO+jUhAFS/rBri7MZypTWMA8pjIraQplS4tD7oNgyFdf7bdvRV4192/by4wB8Vx1oQkJuP7bbynt0IHYykpefu01xj7xhEtqJ5BknXIDvMcKvq89MNjtZM2ezfM/Pc/WyP7c/s/9rHYGP8Qajay76CJk4Je6Ol4pLmZOv36kid/QU4qwX/6IPhEIznyCzfcakwgtKgos66IlO9o0ZKCKq6+eyvr17rQ/T5k+nc677epwYuBARTJn2TI4cECR0xk1yn1cMCIjlWc/77zQWhmKxGoTVIAFp4hQ7ZdwzvsgDL/gnKEporCnm+aIfXs8n3TsmGt3ID3WhuHXMrlDFkvW9Gpa23wmy+c/2JOipw5hi5JdOu6xR46w6JVXuOXbb9GhZMLbfDXwgeXDR3Dn9L8iGzRm1OpEu9II0y+iv7SDz47exOHIrjw45c9M/NcaYo9WMmr2nIAT8sb6QOsYX7JTUsjcsQO7hukwSBJZAwaQYTYHb4AGqnZgt6HV2MfvxbCsH0c2dFGleAUhIOyXN6I/BIJTx6pV0OR19FBOSknxm8lP/fOfWXDbbUzt1YtxcXFYrFaXbVW3aUstLHoogp0JJYzbto0GLVtnl2FzNxhcScz7RuIXdmAd12OmlBJiuSrpY3YuPd6iftEj8ZRpAOsfN5P5fDVZpr0uZ/ev3tzNj+cf9Do+kH1eMG8eb9xwA1v69cNR2gFi6giUNnCnOY4fjx2nsO4kFRp9+92Ee4mtriIxJyekZ5jaqxfzkpJCf2hBixH2yx/RJwJB+yVUt4KnVvz48W6HdijzvebUKPM8tjEmTXqQ119/xWufp6P7kUfcbR8+HBYuVLTtv/9e0bufNg2efRZ69/Z+xvnzFdkdX/LzlaCvJ54I3R0TaHHj44+VbO7G9OgFpwehOS8QCILTFFHY9kJTxL49ni87JQWDjw6ra9LrcPC3t94irSGPJccyYJb7WE18rbvPsXte+QVbpLJM7nC2s7RbN347cyabk5MpMpvRoaGBn5zMvNznkSc3YpmjbLDUys4lx0nMyeGJ2yey4cILyXz8UUbNmRPwtJmJiaiiN36atChOd0XOx6d/VJzp+VdGRpIXYEEkLzW1WY55cOsOp88uYVenKq6ZXeIpxSsQCASCdkyz0tVDOcmpJ1Z0zTWsXLWKwVlZZI8YAUDOL79Q8tBDdAcu7NTJJaXWsdZI/hdGVq6EDLOZ71RJPB+GvlAJ/RWNdfu1FbyZNJr9yVEUmc3EUEZu5SjiKiqICSZQa5dg/vmK/JwGDRNSKXvXzPr18PKWEtZXVfH4j8Ws2lTDTzGlAHQ5ftxVM8bPPtvtrJw1i/s+/JBN99zLbTcdgjGXkd1FO0wwPzWVt1IGsCktlY8HDQJAcg5bJGcI33ZSQnLMd3EK5+aUllJQU4O1poai2tpGzhIIBALBuUaobgV1vpeXBxMnKttQ53vBZF10usCZzK+8or3fkxEjvnE55gPJ9JWWwj//Ce+9B8eOQU0NjB6tOOHV51+92v8ZV6xwt9FzK0nKdZvijgkksXrjjaHp0QvaN4EFigUCwdmHZz6Yp7UZN+60Lqs2utquWiLfZXZfgbYAz5cxbhwpHTpgOXHC/9o6HeWRkYAzUC/TzFg5iJxNY457SQp4TNqSJQAUjh5Nn5ISGvDQwB8xAnZ5HKyGAKoR8+ol1a0ddMvjyc1wxgm67u1/30i9nuENcSSdtDO6g3+K/k3f/UB6TWeyM9MYawjw7HpgfCGJGwuxOrMYfCMVm0tRbS3lzgK375UqzorVpaVkxsUh10O00Uif8PAW3EEgEAjObvKrq5m2d2+7kCDxjehq0UnOWW7ihg1+p5Q5HIxyev0rjh8n7VBP0ujB9eNlTpTryMmBiy+GvTt7w3W1rmw2lQ1PdANZKfBSGRGBZelS13vysGHEl5ezd8wYrnzsbcqu7qrZ7JlHUnlmTQT2HRHw+maXUVSlaMZOrSNr/XFYVMwP5pMAfG4v5XN7KTg/puqOHQOOG76bPJnU3bsBuIzv+KHWe3Hc1w5LzutIkkS8yUSc0Ui3hnB2vhRDw6gyiKkls+pd9LPK4fGfaSBwOGFNg9I3ZTYbFqvV3TcaEj0CgUAgOLdorlvB04kuSfDTT6FF3WdkuJPpfHE4lCh0LR54QNF199Sc96Rz5zqWLfu167WnI1s1zaqjW0V1Qbz7Llx+uVvvXev5zWZvN8brr8PBg0q0fVPdMarjX5VYnTBBkVj9xz/ciYhaevRZWdrXE7QvhHNeIDiXCMXanAalK8/Vdk2jHMgS+S6RB3u+pCRYutStj646AYB30ocjfdaFrFU1XDnIyMzDicwoLNRsq76hgQa93ut8v3s2QmJODvkTJ3JUjmD1sGEAvJueTtJnJdC1N1QaoKQDrO0BtxZDX41FBQM4xhcTZF4NwMoBA7i0Sxf6dwqHJDMsLfZz/P97QF/mPvwRY6z9GftTPNn12hq7qmyNWrg3ITyc8T16sOzQIQ7U1gYt+hq0PzZudP2t9qBwBggEAkHorCgpcRX0PF3O+VDX0Ztykrp4O2RrInkD93nZWTW63GC302t2BBNyrZQTTbmkzGrLyuCuu4Doi5EG5+MoC4e1PSGjEGLrfRa+3ZHqcxctoshs5ptBg5h/221sS4hxt9dHy33U9TAqD24aWc6Rigb6lh1k/Nq1rB51NYUxZrIv3AIXepzreQ2fe4Pbqa8uJEx86CEWvjKPIbt28PhjMPsL6LY3nyFPziTurw+R0LlzQDscHx5O4dChlB6UuHSTRPzhBMbdLbMiUceBLfG8FR/JqGK3nfVF9tmqYwCBQCAQCFrLrdCoH0ADX933YMEA8+YpUjO/+x0oa87eE+EvvjB5Re5nZwd2dKuo0emVlTB2rPd+3+f3dWPce6/y3qhRzes338UNkyn4wkVeXuN6/oL2gdCc90Ho2QnOajz0XfOTk5k2cSJzliwhbdeu0IqrtiLBiro0O4jfR7/W8xnjqqsZnJ3NYb2HSGuAoqsHLruMi/PzNbVa106bxl3Tp5NQVsa1+fk852mRQ0Bnd+AwuJ36AfXfh12lNCqpGpYWuM/3ifwLlb+VpvLM/WE0vOt2hAcqjqc7qsMR6T/CsVospEZEABqFe0Mo+hqIVSUlbaJjf64h7Jc3oj8Ep5NTUdbFM+vo+h9/pNRm8yroeTqyjppVNDvASUVFkLjva/dxAQqiWidOJHWXO/VMUt3JydUwca9S7HVfZ7A5PeLrv/a7RlAC3DfOaGSTxULptnBSLRJ1RiNhTsm6IrOZg927sz41lRl33RXS4n33qir6HT7M+LVrWTZqFFv79OFEhw5M+c+3zHv9NaRNm5B7xdNw31QMry+g7oEHCHv55ZDssFYXb62v8VoEV4dDkXo9R51R8554jgEEbYuwX/6IPhEI2hfBysYAjBgBn32m/V5z/QDFxUpGXH09/Pa3oUWEy7JbO17hBNCRrl1tmExGTb37QOXu9HrVwR8cT7eK73iwWTV6QqAlxXYFbYvQnBcIBP546Lt6yalAEFHYtiExsQ200Xz0az2fMf6TTygcNoyVAwZgUD3xPlIxBiSyU1KIDw93abXqvA/BXFVF4Zgx5E2axG+/+UY5xrlUL4WQv79p8r1kz5qFIYC+rMFuJ3vWLHqaf4bkGqKjDro0YzofP8mAoiI6nziBLpSRgQfll5Tw3SfhdHsh2qVt63IW+Nz7v48qS/qqTq2WZo1Jp/NKn2+uYx4UTeC20LEXCASC08WpKOuSuHEjaVYrFquVMqdTWM06SrNavbKSThUmk7dpCeSYz8+H9HRlG+ikxES87KWfg9snpE3WG8ggGwv5fEk6MSN/gtQqLnuqBGw6VEvefVaMUkdFA31DAzOXLWPmm2+6r69x398aevCQPpni/GJ6HbYyq8NMwpwLJRLQNyeHK197jRnjxwfuLJ8F6Y8fe4z3nn6atNtuY9G6dXSqrwdgdfowNn/xBdZffmG/NR/DP5Q8eNM77yBt3gxWK9L+/UHtsMkE1ppq0r//HmtNNSYTriw4S+fOLEpKIi0igjijkRXO6Hj1amKyKBAIBAJfgpWNAaVQakGB4hwuKvJ+r7l+gPh4uP12Re9982ZlX7C177FjFWf44sWeWejKGMNuN7JokSIv49s+FV+d+OXLA9/LE0+3iu94sFk1ekIgkB590OxFQbtCjLcEgnOIotparA0NFCQlueRUctLTKUhKwtrQ0GqFvrwm3RqvIXhRF4PBrd3WVIocDqzJyRT07+/9jA0NbDl+nP+LiiLPEsARbHE7guNNJuIkCcvBgyw2mUgzmTAfqSS2shKjzY4ExFRWEldRgWXXLha//DJpu3bRrapK+6Gcm8fpwe25ueRNnqzdhsmTycjN5ZecYlhipXxOCZLTS368Yzjb+vblWMeOODwzAILQ1dnJOaWlbG+o4fN9v+WlRYuC3tv86AzijEbS9DoWffwxFr2OOKPRlS6fX61M8POrq0NqQ1MQzgCBQHCmEqiIWKDJaUvxKnbu3OcpQZKdktK6N2xFXvyomvU3fs9LawLbkexs+OyrTP47eYrm+wMLC4mrqCDWWbD1wb4f8o75twxL/oiopGJODqsB4KfoUkiqgeQaMNdSnnshax+dpnnNBr2eGePHM+PuuwPP+CWJ9+2HmFa7laH2Ysw3pPH4yRnebQ+2qOBxHQBkmchjx+hVWUnfnBzSEhNJe/BBVz2cMpsNS3k5aSdOkHjsWLMjGjylj8Ate7PJYuHeXr3IS02lcOhQUp1OektEhKvIrucYQCAQCARth9a8+UwkmIlqqh+gqEhxbl90kdvJ/eOPyjZYQld2tuKgt9k6euxV5tA1NXDrrdrtC+To7tZNed+zqKsn6v5Dh4KPBw8d8j6+BTFuLlpSbFfQPhCa8wLBOYQrim7pUiSnBSyLilKKoB0/Dhs3toq2t692nJaWXFtpoyUeOwbOwqtez3j8uGINgbSlFpjgX0ht4kRY9JDSxvjwcPKWfEDCu/NYHTuV1z5+lYsu+hmdQ08+aSzjLiaVv07hmDGuaLkJa9awt0cPrlywgJjKSgrj4mjQ66l3mLAf6oyh6wkyK6N4mFcYx+uAW6bGU67Ghp6hs46wYXp3MMia0fUP/P3vvDRmjJ80japXq1LpdA6U2myMtVldfaNcUDnHSypn5kz63jGGQoeDsAceQFqwgIk2G/WvvOKKymsLbePW1rEXCASCU82pLuuSYTaT0rGjlzSJSl5qaruQIPEsVBtT2cWVxr6mrgRSq/hobQkFBV0009gzMmDs2Ay+Ixk47rJVqp376zvvsGzUKA5360Z8eTmv7rmReevX8xLpvES6awxw3FgPS919dNfIbO6tehbwt8EPrV7Nq7/7PQ594zNlXUMDK154AVlvQGqwe9Vjz8jNZUBREWkeRWYD9tHEiVxYWIhp40aye/Vyybz5LbjY7WTNnt2kam+e0kernQXXc0pLGRcX55Y+2rIFpk1DmjMHU1qay2mvStdN6NGjRdJ1AoFAIAid5miwny5UJ3aHDooD3TOJPJiJ6t9fkafRWoDQ8gNorT+r1/eNFVPlXJ5+Gp5+ugHVGR8IrfbFx8P778MTTyhjOLXcXVmZd5mcxx9XCs127w6zZrnL5tx4o/tagcaDTa7REwJaevSCMwfhnBcIziGyU1Lckz5fh28LC31pVWxfuRIuvTRwBXMVX220lhDsGSXg0q2J5H1hpOOfjFwQ43YEby2rJf8LIx+biki7T3mQrh++iwQMK81hxcvjGPwXmS1vfsOQ25LQ6SSWOiYy3raMRUzCiDIxP+/QIZfDvt5o5Gnbk7xgfAxsErOND3K77SNu5yOKK6OJq6ggoazMpS97ICZGicyngQ25t5FSlMf2pSf9njFv8mRiKytZ+Yc/kLB7N3evW8frN93Eln79GLR3L6kpKbztlDjwRQ+YHHBCB4aGBqbl5PDhFVdQFhWlRB9ecglYrZg8PkgpJ4fDd9xBucOBFBXFamfUnt8EPzyc/Lw8pm3bxpyBA0kbMiTkz004AwQCwZlOsCJiQfynrYLvYnN7wXMxd/7/hUGkTfE2z1YcxSeHlmIZHacY6KNG5MPeGvkzZ0LOPIm4igriy8q428Nerr/kEv59ySWsHDFCqZ2DErGeOX06doPBXcdF3Tqgr74j95beROT3/YkrL/KzwS/ffnvIz7Zh0n3M2/0Ec5lNAf6RBj/TT7k93iVuVFyLDTodJqfNzjCbkQs7csdJ/wWXdysi+V1urn9DgkQ0hFRw/Z//9PMEedpeSZKUMYFAIBAI2gStebTvvLnJtdhOAZ7FTjdvDj3obsUKt2Pe1w+wbRv85S+KRntMDHzzjeKcLyxsvD133qncb/dumaefvgE4DBQEPSeQCc3J8TaNJpPyvP/+t7IgoNN5R74PHqw8f5cusGlT4+PB3/3OXQtGdf4LZ/q5jSgI64MoNiM42ymoqdGMsmtpoS/PeZsayK11jOf+AwcUQ+a7aqxVmKUpBHpGgA5re3HyxSRiejp4a6lEVaVEVFeZuybIlP2iQ/aIfXMgoUN2bVWujdrEcw3T2H/fHGbnptFhewH/PqYxGgFu4GMOE4eMxDqux0wpDR07oz9xzFU8Tp2w1xuNmGw2nu80k3+mPMFfkp5k9IR0JIeMrHNHuFvvvZfUnTupW7uWsLvuQkpIYN/w8VS8uZJBjp8xfb+Jj00mbtq6VbuDnF4C3yK09tt+h76ywn2cZ0T+V1+5d6PtaJCvvpqpb77JgvPPZ+qePcy7+27t+wvaBGG/vBH9ITgdBCoi1lYFuYpraxlstZJQUsL4rCyW3XknB2Jj2WSxEN8WxWBDqHQbqFBtqeeicYCC7L7Ze8XFcP2gYvKP9yPMZmO/2UxZZCR2vZ7fzJpFadeuxFZWsm76dGySgbijR6jo0kXJCPQh6isDVel2bjf2IqdqF3W33kqY3a7YQhQbPPGqq8iePp0GQ+PxS/0ndOLnfYMZZC+gAAsN6NDjcG0vjP6OrYvroSycxTf3YNHBg/x0/DiDwsKY9PrrLBs5kgM9erBp9mzit2xxDX5GP1nD6nSrX99M/Ucn5r12KQ506Ait2lvQgutAVocOZNx8c+jV+ASnBGG//BF9Ijib0ZpH+86b27vXrrGCpFpFYCVJcbw7HGA0wrFjyntvv60UcZ0/P/T75+cr95882c6iRQZgHrCcQM55tX89TWgohWp9syQDfVanejwoaL+Ear9E5LxAcI4SSpRdCHNwF1oRg774rhh7rrYHWzUOtR2qQd2hPpDnAzpXtk8OLYWkOMokuGm8EUrCAclV3DSDbLLIxIjd5ZBXtzYMZJLFTVUrGMx6Bh9fyW15adjygKHuebTqzC+jO5/gzmtTu0V34jiAK1IO53kmmw17lygeXpHKX+MLODj278RV/Ir4ykru/vJLZSLfsyexCQlw9CimQYNcHfjK/RILyibw4OR6Xo43cdPXXwfuKFXmVs2aaGjg7ddfR78pDzZu1Fzq94pG9HkeA/BUWBgFmzaxOiYGgJyYGMZt2oQsy0R360af888P3B6BQCA4y2jNjLCAFBURX15OYXg4YZMmIZWWMiEvj/q1azFt3do2DtYQ8u0DRWt74VOQXY/E8hT/7L34eHhjXTwThi7lTe4hMSfHfQlfeT4n1gkTAPxkcKS0I0AXPjxaSsGDDyL37Uv00aP0LilBAsJsNp756SdSX3uN+++/368tXaur6VhbS0m3bnStqeG+2ztz+a1wsyWWQ8RxgARyGc59LKSeMKrKe6C/oydZb0iM7alkhNU0NBCh1yNZLEwwGqkHTNdcQ9HuespLTBw+XMunG+vBYoBOdjCAToZOGPhoZwRjzRdiqOhA92nj6f7hMkxlBzAEyYMPKn00YQKpu3efGg0mgUAgEATkdGbetRaqxE0gqRYt6T9Zhn373Ps3bIDf/Eb5e+VKmDQJApRK87qWLMOOHVBTU82iRScBMzCaQYMOcuCAoi/fowf88osyJuvcGc47TykG62lCQ5EnbOpndUrGg4KzAhE574NYlRec7bii7Hy0vbWi7KZOhQULlO28eY1fO9AKsS9NXTEOtR2uqIPoWlhshe6NR+kx7Gq/61xCgWaKegbZ7JRS+DbiesKrPZbSDx/GlpHJD1W9eZO7uYu3SGA/V/Bf/sgb/Cb5A6ZPnMicJUtcqfe+zfJYP/B6oDqDwS+yz1RXB/X1FB02BVzdf7eumJfq9gTuLA+sqamkmkzuVZEAH2TBd98p2v1BUKPxfaPyW6OWgSA4wn55I/pDcDooLm6bjDBNPH9jkZCQXVv3G60wzA8llMxjESBYtHaggIDsThYyBmtn76l9OrxbAdf1nO5aKPbDDtmzZ/Hr77/n0sWLSSgrY5OnXJ8rvA3vaH1n8XgAaf16jeNlr77+ZsSjXNy1mAjrBgpK47FYwEQddYQxj/uZygIWMpkpvBbyeMd1+fVfe9wfzXGLNOwqZ5afTBj11MnB8+DVbELfoAzrpEmk7tjhf4LqXcjIaLzhgjZB2C9/RJ8IznbOhkjrujp30J0sewfdrVqlyPQ0NLTOvcLClGv16we7d3u+o1o6b+PpcCjtk2XFUR4W5h8UuGqV2/Hui6dpDOWzOqXjQUG7JlT7JcR8BYJzDFXbOy81lYk9e5KXmkrh0KEux3xRUfDq4kVFjd/Dt4K5um2KfHhz2uGq/F4eDmOGwqwBYFcbgffWLsGsFL9q8J40OH8i1e0qxpIvWwivUTTXXUvpN9yAsaqMm8ybeLfLOIaQx695n67s4Y8XfcPKkSNZn5rKyhEjvK5fSSR2ZwJTAwbK6c7WUX8BvbNwjSxjstlc0YHWgQO5/qOPyK+pAZOJxESYmJZPlSWd3qWKcF+prhbLmBpeGhcJ1doFcNQucH0cgSrGBCghr/PZzjx6FINzFKNVvDb7yBHNdggEAsHZhpoRlpenFBnPy1Net8lEzGX0cDnkXY55g4GgBq4pJCYqUfIWi2L3wG3/0tL8KrVlmM3kBfAkvBqbovzhmeEGdOsa+PZqn65YoRRa3XjffZrHLZv8GiNzN3O0PJbCMWPImzSJ7Fmz0KuzbMlnPGBXNOo90T5e2ertdrJnzeLXtv+x5197ID6e2FhIiynipr5buITN3I4yYPk9/+ASCuiw3T1gyc+H9HTtAniuj3JWSqPjFlV+z2CQeCu7cYFateC6JSKCxcnJWCIiiDMaiV25UvuEvDzhmBcIBILTRIDp1xmByeRtOj2nlxkZis56a3HrrYpz/rrr4JlnigA1KE/tONVWKjZWkiA8XCleq7bTd/qbkaGYQC20TGOwz+qUjgcFZwVn4H95geDMINgk7HRj0ulcMi6SJHkV/mriHNwLNZ3NYoHFi+GiixRjddFFymuLRXk/lErkzWlHRobHSrdNB7lxMDlAqMHkVMg1u156GtVSlBT17OTf0Gvux2Qn/4ZKIrE5Hen5SUmkz51LflISALLBwL6Z2Yy8bjHV1dX0Zj+55t+yNHkqtSfLWe2MystJT6cgKQlrcjJFZjNdOYoeJXxATwPRVHDB2pcChhSseOMN1oeHs7KkBHCm1UkrSGc9Y3FOst/dCEussNQKEdrXSZEkFn/8MRadTpmgG43eB/h+kM4PLjYmRnOCnzlyJHmRkZr3youMJOO227Q/A4FAIDgLCTY5bU2KrsxgW5b2LHJbVh5FV7aSg9VjEUAzhzvIIoDvYu75ZgNmo5G0SMWOpEVGYDYaGRRvDHQJwNmnZsU2SSmKg1+n5oc7NxfzA1/FjuahbiuUhW2djozcXDZM1nbm95/ckYzcXK+F+IzcXP4X4PjvJk+m33c64uIkYuKVDzU+HjaVJfL3fWkUYCEWZcASSxkFWEgZ6x6weCoC+eJyCOSaSZkcoFaAz7glVB96wKAMX6/CmegJEggEgrOEANOvkObN7RnPgLvPP2/59fr2he7dQa2PvnJlPU89dSuQqXl8U9ebDx1Str510LdvdwcIhvpZnarxoODsQGjOCwRtRAiyrO2SlmjeaWnI19RARETTK5E3uR1OYfo3753D3YudHW6uhd5OGRbf9PDex6HaSHJyuJc+3vz5sG1bPIkUUj9yP6QeJHPEbDrs6c3QLlv5ssrCCo9I+LRdu7DY89g8IxWSq2Huz7y/ZDSJSxrXxZWHDdPUtTfOfApmzACdjqKYGMqjopAcDlY7NXtzDh1iXGUlA5IlIpO+hF0wmhyWM47KWeEUTq9Vft19BhUAkXo9n378MQlz5zLBZqP+lVe8FmcCfpD19cSbTBT27k2YpGj0T+jRg3pZxqTTUeo8VdX4VbcCgUAgaBsSE+ESlFJnvsVIx46FzbSSbHhGBqSkaOdw5+Vp5tur0dq+EnqDOnWiaOhQTTvSKE7bFCvLdM+zIh+UmBLu4ONYBwePVBBbWcklur9zy1s3wPju0KsXB//vdvZ9/g3gr0HfjVf4PHkIj028m95LDPx11+skcIBfHnnV63hPe3ZZ+PcUfVBAWIkMDU45H48Bi28Gg2wwUPhUFpUFsCM7ny+Zxgsr51AwLi1g3dUb+YTtuIvBSzLIEkg6ZQjTHN1az/6VJAmTJDUuECwQCASCU0aotdjONLR03IPhW1zVF1WjXr1WVZUBz6KvoWi8B6tnd6OzXJxvG8aOdf8ty2fnZyU4vQjnvEDQinjKsnpKsYwbpynL2nKaUrE1RJoxB/fC0yhJEnjKajVlxbh/f7j4Yu3MA812OFdD/tB3JdO7p1FRAeRs9D9Z5QlFZ3Xw4Ku9jGuv1FpuGmvDJgHDnC7n9FL+dns3oqnnk78M8YqEH/fZZ9jDlkD5SzCyBFKPkjFiLlmznufu6Y8oBVR9pF50dgeJs7U74ouZefS6JJb4sNcIPz+BxNfmuN5zOfkbGrCcOKHsXPIa8rBhxDij9MiFgqIkr0UAlfwOHbjQYMDkXNmQcnIwBfpy+n6QzteaE3xQouoPHiShpobxRiPLbDYOREQQGyzVQiAQCATNJjsbHh8Xy6EGpRjpMsYznmUkcIAj+liyl7fBTUOsbKZGazfmhPe0IyFhMhEPXP3VBVREvsWNS5bw1K5dSj0W5wJ22C2jlGMrKuj1449cHh1NXEUFCWVljF+7ljdHjaI4Job3KnOZM2YM1tTzOTDCyNDdeRjkej7t/BNxFUWu45eNGsWBmBhiKyuRKioIG+pTNDXIwMm1eD8D5qNkum2pXInFkuZ1CYqK6HW4nGu7S9xR83dWVvyKHmWV1P77QnSZ9ew8IXFBgpFJD7aiD/1s9QQJBALBGUqA6dcZjVbAnRaqU76xoAKDwfdayrhCr5fp3FkiObnx9eZgQZTZ2YG18T0DBM/Gz0pwehHOeYGgFQmlwncgg9MsP3sbh+efzuriK1a4HfPSgGrke/YivdEPeYeHt99jNWTjhk08Nncuz737d76aW8CdmTL7Z8VQPr0cDLKfdqtOllgxUCkU52lMbzq+EZYo0WmumnpRNh6x/6T8/cILASLhrVCppOXvTY9A+qwDWS+8wNgnnvB7tiV7I3g9V9GD9412BHhicTyf1xdy7zVhZKeUugrrqc3x1HPPmj1beR6PyPvHeVbZp0YJOh9FuuMOTLt3h/zlbMp3Mj4xkUKzmTCTCUmnY4LDQX1dHaYOHYKfKBAIBIJmofiE40m0FFJPGCCxlAmEUc+G70ytW0CuGVHWLkd8fj7StGmYnMakuXEFngEQa2vLODkslRXODDbVMe/CY+ASX15O4ZgxhNlsSMDI777jUPfulHbtysphIwGoG17DgOJjlJRA537dKfzdrYSFhSEVFjJhzRov5z8QOJXQZ+D00sOH+OsrVuwOiT849ehvJ4csxmHUyzw6NxroA4mJmIHPUWx84ZgxGO12dLKMnKMUgw+rq299H7rwLggEAoGgDQkW+Ne9u1LQVR1W7NwJx49rO8b1enjzTUUuV+ta330nccEFgdebQw2ibGmgokDQXIRzXiBoRVoiCROyn/0UhOefrkxnrUcD0F9fgj21im6jSzAu7uJuh8dqyKopU1ifmso7+/YxP9OCFSAXYk9WU/as1e9er9alkmGO8NufbTKReeIEdr3evxibE99IeBdRysTdHuVg3NIHXLt9U+Mfe6krYYRzCP9oxwkzYjkIgImc1TAu00xWh46MPeH/DHmTJ5PqXZ6ey8ijoHIgERVfM6CskMS1J/n7qF8Tl1RFbE2N8wFC+3I2de3H0xEv6XTCMS8QCASngHpMHj5hiXpHGzhYWxJl7WNMQrItGh78xCG1EGkjTj6EbbYiCvtu+jVkfvYZsiQRffQofZw1WYiMhIcegqefhoYGL8d63xx/2bnqLiaO3q/Y2UtPgrxnj/KsmzcjWSz+zn/fGXqAgVP63Bv5znmIwzmYcGW6NQAPAPfL3tI4zmLwrjYaDJjefts1FhE+dIFAIBCcifgG/n38MQwZ4j2s2Lo1kPNdMbtDhkwCFqEYUT2SJCPLioEMtt7cnCDK0xmoKDj3EM55gaAVaepKa7P87C0Jzw+R05Xp7KWAElcLyTaQwf5/irxMxUWlpF4dx/qD8OtoI0dee5eGBX9D12DnPafUzOr0dDI/+4wGvQFrj0z+VjyRSUxwa7c6t+efr92GjHffJeXTTzVlYbKffVYzEl7f0ECDlzPfHdkefrKeFIeN8Z9+yrK0NA4kJTHpwXiemhyu6Nr7RDvW4+5k18eaBCxVkvYcuLeAa7SgRt4PYDtyOXw4ZjwJtl8oJZZxl/6a4WmRmN580y2k54n65czP5+TUaRROmsPJC9JOjTSTQCAQCJrNKV1Mb0qUtcYAx7Yyhz2XjqMhayvfsohnVs4PrL2u5cF3StUdRnWqS1RERfrVcgGgslKp3aJB9qxZZE6frik758pI++IL75Mam6H7DJyKRk6g4lA9Xb/8B/EzMjFi96sxI+sNSMuzlPNFqJ5AIBCcUbSBuuxZS6CxSnx84IKpWma3T58+7N/fADwJHGD+/ItZuTIspHFPU4IoRUkWwelAOOcFgjYilJXWZvnZWxKe3wRMJsivrmba3r3M6dePNE/x+DbC89F410Mv3kNepmCilT+dBDYCA+Ng0etA4KKrB37/Io8eGct5Fb9wz5qPePPmmzlwfhKO8q2kl8OcgQNJq6z0diJERgIeznBZxuEZJS/LIEmuSPhLnz/Khie6+T3Phsl/ZlDhPlcq/YSTJ6kPC8P0ax0jLoGhQz0dG5KXY169DYC+xkjnBiPJUR6F9U6cINZoVL4s48dTO38ZnbZtYhVKtRqHTUICYinjhhlp4OmjCPTlXLGCDhvW8/mGlTxAWlut/QgEAoGglWi3suEaAxx9ZRkpYy285tx9o6/2emHwiIXsmBj+VFKOQycHdqr7YAOMuGvBI0lk5OYyoKiINI1F+P9OnoJtuMcifFNm6B6dnthXAkxABpeQokTKA/nJyUybOJE5S5aQ8Oq7mK/XcLqLUD2BQCBo97SxuuxZRShjFXWx4+GH/c3u/v0yFksCOPPLIRGb7QQGg5777gtt3NOUNfB2O7YSnNXoGj9EIBA0BXUeZ7HA4sXKNi5Oex6Xna3400Hbz56drXGDjAzFgmiRl6e830qsKClhfVUVK9U08WaSX11N+vffk19dHfQ4r0eblQJ2dSkd761dYmaHFH63MwXsyi7fibpkl8meNYv48nIOj76FvAn3MHHNGvLuuYeiEdfy2TvvsL62Vnm2xERlVGWxkN+1K1OmTKF7VRWWHTtY/PLLWHbupGu9RPKBA3Q8eRIkiWEFBVh27cJQIbHBMFS5p88kemfvBLb07cv+Hj0gOxspPNylwRsWFnr/ffdJOCXDhpKXmsrEnj3JS02l8IoriC8oUDps4kR2rshjHG9jc665qpF5Eh5fqPnz/b+cMTFw+DAUFLicIaPJ4RIKuES20puixr+TAoFAIGiU/HxIT9cudN4STKbAkWdtQUg2XWOA44oYdx5yu9PWXKq38sGrRV62WC4tU44tLVVsVVoaGRdeyHvl2nJpeZMnk5Gb67f/JuAQcGLAAFi0SLm+2Uzxgy8rbXLabXV74NkVXPG6xzhKnaE7bS15ecrr+PigfeT5+CoN6FgxciTrU1NZOWIEZrPPSU0ZQLYibfW9FAgEgrONoiKwWr2mTeTkKK+tVuX9M4VT/dvf2FhFXez4/HNvs/uf/9RTUhKO6piPiIjA4ajFYNAHvFZjqCVxdEG8oad6bCUQiMh5gaCVacpKa4uzmNsguqqotpZyZ6T36lJFTiantJRxcXHIQLTRSJ/w8CZd86WfSlhvq+Kln0rIuSLECPxcMxR1hKX+WutMTmXGbkUvPiaphrKlxX6H5E+eyCVOPXZVu7XIbKY8MhJJllntTH3PKS1l3OrVyE8/TfSRI6wYMYL/DRrEnz/4gAXz5yMZDIz84x959v130MsynWprOdGhA1v79mXt9OmMMWaz+7WdAMg6nSuqHln2ksCRr77aq32xsYpfvLISevVS9hUXKwVw1Gr1nh+ryWP0IEkSJp9RQqxZ4vO4TO7qdhErtwX5Qk2Y4P3l1OnghhvUCyvXUvVw1fs5XSkis14gEAiaT3Oj7Npb6rznwn3ArLpgAxwnftrrnrrrrgVmhQYk9Mj0fWIsLF3qV8sFn+PUbSkD2PvlGq4Ydp5i4yZOhPp6uvzzJ+IqiogvK+PutWt5c9QoimNi6NbQ2b+hTlvrlU3YyCzd8/F/MXfls8ihlMhm3hh2MwA5w4czrksX5Joa97jqNIXqiehPgUAgCI1ToC57ymgPv/2NSfyaTDUMGuQeZ6SlpbFp06Zm30/I1QjaM8I5LxC0AU2RZVVpkp+9hZYlmFxN4ka3nIw6KS6z2bBY3U5yX0ezFqqTf/vx4/y97hfQwUc1pRTUBHfyq4/WrRtsU+uheQqte6xwGwzwu0lmFlHscYwMOsmr/a5n0ygCV2azYYmNhdcVeZzYykoA/n711dy1bh3yqlWkHT8OY8awbMwY14irNCrKmRJ/QrmgHeUX1XOJ3cmIggI4eFCRyxk0CPr0IT5e+ciUgYdy3M8/w5VXQu/eTf9YXXP6LUCaslAgeWjRu/D9cmrIJKlOERsGMskSmfUCgUDQTFqjhnu7mEBrLNyvLCnhf0ePMjU+nl9HRblsuvrMHXdACrjskCoto1pHX+31oiszOJ6VwsCx/g59B3qyTHcz4ugHxFVUkFBWxvi1a1k2ahQHYmKIqazEgcSPDGIxkxnPmyRQTClfcOU18W5niXNQNuD8OL77v1s5qu/BkVvG8/bDy4hsOIThPxsC9kFIixIalOQUcR3PKS+c7SiLjMRSXKysyuMxrmrOALIZtMb3UiAQCM41TpG6bJvRHn77PQMOBg/2f997sSPCtX/MmDG88847Lbq3kKsRtGckWT5T1vZODdXV1URGRnL06FG6nAKNbUFw2lu0WFtQXKwYJl8/+6ZNjWRN19W5LYssN8myTN29mwUHDxKf14sPrkny6ttVJSVk7tiBXeOnwSBJZA0YQIZfLrY3+dXVDC4o8H/DJfrqfBnAyV9XB1u2QNp1tbDECqXhsLYH3FYMPU/CjAvgu2isVmjoVstl31txHHYeM+oQkTElbLl3HPHl5V7XXTV8uKsIXCAkWdGyVbcu7Hb/HHUAu8TTcefRJ8rInXu2a16z+9GjfP7II8iSRPTRo/Q5fDjg/VvwsQJQvLEY842DqTcn8NSB8fyhZhl9pAOUfrKJ+th47UFXQYFmdOMdA61cOTU19O+k4LQi7Jc3oj8E7QFPM6L+rqtbFa2RuOcE+vrrobRUWahdt+70OE+lr792/41bmsYT1aarz9yLYjYxmAMksIzxTGE+F7LN77yStVbM16ciSXAJBRRgcWmzz16yhMG7dnkdX2c0umq5bEpO5pFJkyhdfDkHdg7gGBGuFoZRj8NgIitLW/GvrrqOsM5hSDoJ2SFTf6weUxdvg+u5KHH9jz9SarMRazSy7qKLGs0mVMd3HW8uoXD0DhxS88dVrU1zv5eCU4OwX/6IPhG0FwJMm7Ba23+GcWv89rfEP5OfD7fcosSsTZ0Kl14Kd9wR6J42IBN4h8cff5xnn322aTcTCNoJodovETkvaNe0h2ixtqbZK7gmU5MKtmpFvRUnlzL3ozj+0t89wcwwm0np2NErUl4lLzWV1IgIv/2+vPbLL8ofPpHunprxzB4AVwd8NMxmiDOE0+vFoYxIl3h+jQSJxyHxBAyphO+iARicGE6leSiTRuxnx7eHcKyBT4yZ9LSVu9YC1G1Gbi4pRUVeBWNV9HY7DQaDZpG50bNnk/PLL9hfe83vvA2XpDL0JyuU+73loiIy0uuevuOPQJ9jc4LmEobGE0Yh9RVhSJLEXCZgkuupG+W+UMBBl0/6xooVIFlEVIFAIBA0l+ZG2bWX1PltK/KpvX8as9+YxeMx9dhlWdMxPzMxEatToiU7O5zMTDhojyeRQuI4RDQVLGUC83nA71zzrv9ArMwHr0bzwMOxVDZEstypzZ49YoSXc96ODpPNhgMJCZkVI0fy74svJubajzm281KPqypF1q1B5Ng8HfGSTvJzzEPLsgnd4zszm4+1bFzV2pzp0Z8CgUBwujkTa3e3xm9/U/0z+fmKI37SJPjkE8UxD8r5l14KnTtDTY3WmUOAzSxevJiJEyc2fiOB4AxHOOcF7Y72kG51qmluFnNTUqw9J5guomzkpFvJcc4XC/te7dW3nkoyDmDizp0sSk7WvJen8z9LjQwPVGTFIJOdGTxKTJ3UHnLUs+2gje5fwdFrSxX1mGtLGbzhCDXPzCf39vvpNuBSVn3b13Wuw+Z9LV95G8BPr3b5Cy94acSr5E2eTN/CQh7873+xHD/u1ydhYZCdksK47dtpCPpEyg9uVkqK3/7mpsproQy6TOAadEnUoXyhAg66AsgkSWZFT0cUwREIBILm0dzaMu3FeVo2dwVXVa2n5tkc3v70Ue7YscP/IBlmFBYyo7BQeZlxNVFRcOONUI+JIvpqneK2zQ88QH5yMnOmTGH8t78j9k8/023YNwDkpKcz7rPP3JlnJSWu+jHPyY+yflh3ACrSL4PPakBqgKPh6MrCW8VZkp2S4somVBcl1K0a9R4MX9vpO4Y4XbS45pFAIBCco5zJuuXN/e1viX9m4ULYsEH550llJYwdG7y9H330ETfddFPwgwSCswThnBe0O9pLtFi7wSd3rLkFW7NTUhj70w4weHSeTyR7Yq7St7FGI3FGIwnh4Yzv0YNlhw6x9fhx8o8dC+hA1nT+B2CSKVEzxdwXkwn6fu287vMeheEibGx40cbV3AOcAAtkkM3bZGLE7loTUI+3oWcmT/A3nqF75VHiKipcReBUvdpu1dWAv9MeoKvdzvFRo4hbvFjRub35ZpYdOsSB2lpijUZSg2QbeJLXqROpztT1UD7Hsi3hTU4bzOifz80XT2NY/hyseJ8UcNDV1PSNc0FvSiAQCFoZaUA18j17kd7oh7wj+ELs6XSeFv+3iJp9yix84E/KLHzgjznM+M/vIQY/iTr1bz0Sc2MUZ/WNN7rfziCbLKd99jnFxYrrrmPDhReyoXYHLIVSOQqAsqgo78yzYcO86seonnJHlHcB+U57ogh7ux+xsS1b8G5ONqFWNpzWuEodQ5xuzsToT4FAIDhdnC265U357W+qf8bTmb98eVNbJgNH+eijZdx00yVNPbndIKbLgqYinPOCdkd7iRZrN/jkjjU3xTrDbGbfvzsyI1bDgTw5FcO+CLKylZfx4eEUDh3Kobo6Kux20jp35vqffuKEwxFwIcAzuiwoRw3c1Csu5McPFrUGcPV333OULmxnAPewlCzu8rvGOJZTSVdKiaGuvAvrx0yjT4xMh0OFTFizhnqjkbLISM0ic7GVlSDLxJeXU3jHHRQ/+gb3jO3Ja7N7cNFQGZPOOz1AJ8s4JB+3gyro17Wra5fXYobz/VKfz3HKP692f/SEaOFXrCAifz13sJLNurTQJ9xNSd84F/SmBAKBoJVQo+wcGSWUplYRk1GC7rUuIUfZtYrztAmzxPgrE11/7zPHsT8yGVmGbcZyIAp9gyID50vDhFQe2B3B/TLMmwf336/sf4cMtpNCAd4rDWoEvCTLrL7tNu+ZvYbEXNbs2QAMnXWEDY92U8LPVXPr3BokifSoKD4/v5I/LyshPr71tKlDjXrXyoZTx1VhkoQkSUzo0YN62X8McSo5k6M/BQKB4HRyimp3twnN+e0P5p8BGDHC+3hPZ37TeZctWy7lggvOXMc8iOmyoOmIgrA+iGIz7YMzudBKqxCkEtyq+noya2s94s/cNFZYrKCmRnH+es4udcAEC9acCL++1SoA51sIznMhwHV9DSQZZAkW3f8mtz73AOYbglspz8gznSQFvG5sZSXrpk/3SnkPGZ8KOJ5F5mSg3mjEZPPQyLFaGT0nldWr4fbblXQ+leLaWgZbrcSWlfHrL79k0a230uCcdBuASIOBFQMGcEHnzvQJD1cK727dil1jYq5H4ukOA3j5gQ5U/n4vXf/ejx19niB29QKqM6fS5e153idofF/KdLE8NGAdt/xGZvkn0Wwq7dOygq7trTqhwA9hv7wR/SFoD6hZUrZ6+M0Od0HRfw24CGNYaAVFm1wwXoupU2HBAmU7b17QQ/87eRWXLlIi3aX16137XYXS1QVndb9DRtZJ6CdZWP5EhCszbuNGGDpU+Vst9NqAhN45ivC6NtpFZlWsEybQrbqaCqczf/hLL1Gp8f86OyWFh/bsaVLh1sZQ7btv1Psmi4V45zVbUjj2dNLSIvSCtkHYL39EnwgErUdzfvsD+WfAf0r47bduZ37jqA4JxdMQFdXAl1/qz8jppZguC7QI1X4J57wPwvC3D9Qff99osXPGOd9IKfWCpCTNoqZWiyVoYbHi2louzrNSsT0caV0P5OsPQWwtTLRg/Szcr29XlZQEjIbXWghQnfO+UWWPJvQmt6qSwsNlFGTegfGaP2LOCe4cmLp7NwsOHmRqr16Mi4sL4vSXXZF1AI5hwzQ15u3oKbjpaQZ+8iKdHdWa12oACoE+UVEYqqpcXzxZ0iHJDrZnW7Hck8rJk9ChgzLwkGUw1xYRH15OHRCuUdHGb0EjIoKikhK+XbKEsQ8+6Hd84rPh1G+JpuYP1dTcVk3M+2H8tPBWzJRSQixmq9vC55f1IW2w//dFliQkz4WHWrllE+5GvpPKgwlzcjoR9ssb0R+C9kBTF7l9aZHz1GOWaBt+PcbKUmxdYzHmNj5L3L6qgJSxFlYNH07m9OnYNSLlkWXuXLeOzf2S2RKTxOf1HRl2eayS6z5tGjvumsPYO2AO03iJh1nG3RzGzEWd9qGX7ay65hoy778fu16veW1PW5P97LPedWFc7+OnkdOcfm6MOofDFfUuy7Jf1HtLP2eV93LyeLF0G4/EDuQPo4c0u72CMxthv/wRfSIQtD5qUl1mpqJOECy5LphzXmtK2JTjnWehVSnuTJpeiumyQItQ7ZeQtRG0S875VNvGtH2efRZoemGx+PBw8voN5YrREr0TJO6SevDmizIHDTrNvm2q1qqvpuqre36h8OgJhudVMKu/joox44muKafmoxwo8K8go6XDvvKXUr6e043IDANdTHoO1tV5PatvyruWYx7AQAOXrpnh5cj3ZTDwdVUVhvJyuPJK1xdw073LSOAA146N5aTz2JMn3QMOmUQATEC2hiPDq3jcs89Cbi6J69eD6pj3cUIUPlELFBNbWUkNXZHSSzjwWRTFUhTdj1Z7jXRWTJF5hWxW6DLRO9zfF8nz+5KV1fJIOKE3JRAIBE2mNQuKNjl13iOv3OC0jobKphXxGZ37FSlFRZoBAZsmTiRt924aALtnptmUKbB+Pb36ruTejjLpJ9YTM2wQv6suZG9xGJv+W098XyMZNhspdXVYCgr8b+xjq1XHvMFuV+yr+r6GSW9OPzeGlyNekjD5tK+ln7PK6p0/kX/V+bz375/4A8I5LxAIBIK2Q5VeOXIEfvghuASL6p/p2FHR3PeU2Qs2JfSV5Hv0UfjiC5n8/MPAS8ALgBFfg34mTi/FdFnQEkTkvA9iVb79cM6n2gbR9ikeOLDRFOtgNKVvfaPh1W2gKH3P6LIpU2VeWmxyTdi/S+7PXydO4IUlS7l01073SapDWSPyzHcR3dqpE5bjx/3um19aiuX22/0fwPmQ+SkpTLv7buYsWULarl1eh6jPZc/Lw3DppX6dJEkyYdRTj3Yn/ZFVrDJkUtS9O+WRkexISPCO8HPy8aBB3PDpp5CZyaqrrw4cjajidNb7ZgfIw4Yh6w0UPp3FZQsyKC2Fa7oWkFvZxlpQ57zeVPtG2C9vRH8I2guBJN8ay3ZrKeXzVtH94UykBv+8cllvoGJuFtH3a1dnP7SpGP1lgykLT+DT30/mL5mJfgXTrRMmkLp7t8c19UhPPw2vvgoVFdC1q2LCKyuhe3fkzz7HVi8T1tO9KB8o484Xvd1OcnExj61axR2PP+73fn5qKjZZZujmzX7vtXU/qzT3c9747R6KDx5BkiTuNf5CeddIoiuPstjWE1mWie/VjcuuPL8tmy5oZwj75Y/oE4GgdVCT6g4fhjvugMpKt0+ga1fFQR8Xp8TPORPhXBH16vR48+bGp4S+0nxvvKHs27jRTt++RiAMqAdSAQ3beYZOL8V0WeCLiJwXnPGcyYVWWhWNSnAtLSzWlL71jYZXFwJijUbN4w8f0Lm01t5bLXHE9jZZKNq12SNHsD41lVUjruXSXTuxYSCTLFY5z9WKPHM55u0SMyMGsLu8Bjoc93MSHGlQfuhUORfVp38yMQVD9REWvzCf9V0MrBwxwss5P4vxXMuPDOh4gC49e3p1kprq97e/STzzjEnRvfFBr4dRyzMgJYXEam25HJUbf/oJOUM5NsNiCRiNqJehQSJoQTxLQx6bZ6S6ggcrK5VtAzr0tLRyYCO0SnVCgUAgOLdQC4ZrFg5vA2IeyOASjUKs4LQhD6Qi3699bo/B8dRVFhLTOYz9BXWEF/2XC8p2c49vwXQPpIYGmDHDvaOy0r22XlGBlGYhTH3tXJT3HWPMLy5m24kTfu0Z+O9/89M11/DPyy4D/DMHJUkizNmnTc0qbG2aev+h9mJwKgRKsjKWqYjqwu8kZyCC/QQywjkvEAgEZzpNqM3eZvcfPNh/vxquW1kJN97o3u9MhHNF1Pv6DAJJt4BSG6ewEH78EaZPh0WLoF+/Y0RHq4vV9QwcOJCVK62acsZnOmfb8wjantA8eQKB4NTjzB07PsDCy8mLOT7AoixjO/VnTDodknMiKklSyI55QLHM6enKthHUhYC81FQm9uxJXmoqhUOHBozQT0xUjLfFoqy2v2P+LUnD/8Pg118ne/hwAHLS0ylISuLiAf/j0nd/6zo3w2wmL9CS8p9TmXGZmdF/iocKI5bISBYnJ2OJjCTOaORPUwdwiDg2yWncxwI2kcYm84X0Mf6Ljt22sLxBWUxYee21DH79dZYPH06R2cz73MsQ8uibsoVf7Shn5XfVFBUpt1RT/crLYfly7WYtX46r+F32rFkYglS+mXe+z+RadSQ4Lba6nV+v3bd5kyeT8dVXABicEr3qQKiEWA4RRwEW8u5crHwAHt+XVqGR76RAIBAIfCgqInbHDuIkCcvPP7P45Zex/PwzcZJE7I4duAxOG5Cd7bYVDc4hv7o16JX3g2HqYkLSSQyKCyfygWEMWDCYiWvWkDdpEoVjxhBfXq4cqC40aGnHA/nJyaTPnUt+crKS1+1xY3WM8fZWB4MvGMvTPzvtoXqA0y7+7PQmfHPFFXQ3GBjQsSOz+vbFEhFBnNFIrPNfnNGIJSJCGR94vHcqaO79n9x4xDV2kDUW5Z/ceKRtGy4QCASCU4I6t1y58vTdPxT0epg5E1avVl7n5CgR4VarMmxRJW5iYpT3Y2K0p4Qmk/Ks69fD0qUnPBzzcMstt7B161bXtSwWWNxGU9hTydn2PIJTh5C18UGkzAnaFXV1TP1LGAsWSkydIjPvxVbS9pk6FRYsULbzghdmbSqrVvlUZ1//tfvNQDItHkXSXGnhnmFnOmCCBXZHYDDAG1kOxiVbkaZPR549m3qLhX+8q2PCuDpONIThEsVZ/2+Pe6u7ZS8t25iJ8ZRNPAZVRkgvg/d7wcIkrFbvKutPPKF0ly9r1yrHqbl7BZddhuV+/1DEDZdcwmWRkcoL57HFyckMfuABEsrKGJ+by7Ibb+RAly68lZLCqEOH3I+vSggUFpL6+utw4AA/vbWJi0bFe90jjDo25IeRamlDLai2+k4KWoywX96I/hC0C5z2ps5oJMxud9m/eoPBrdHehkPxn9YVEz1qMAdIYBnjGY9SQ6V87SYGXR/f6Pn51dVM27uXZxP6MXTDf5BuutG/ZFvPnkqu+0svwZ13+l1j6pQpLLjtNqa+/z7z7rxTM6/737+aylU/LuDFPtOYvfxm+nUOZ5NngfUA4wfHVVd5ZQ42Vri1rWnu/d9/bxO/i/WX7PtHaSd++weNMEfBWY2wX/6IPhGcqXjUZveaW65rvDZ7m90/VAJFxufng80Gv/mN+3n+9S8wGpXnAa17lgDXAxJ3330rb7zhloA92+SMz7bnEbSMUO2XcM77IAy/oD3QJkb8FI4MCgrAMqoWIm0wuALGF2oWbdPLsHxgChlms2tfcW0tg61WujWEs+2lHjDqEMTUwr0WKA936bXlP/kk06KimHP0KGnPPOO+r2f2/vASmL4DDBo/czJIq+OQz6uFwVVQq4NwB1QaYfpFSnurjXA43G9AsngxvPmmUqQ4P19J2wOgro4CZ3G7RjX6nVa7rraWsLAwJLsd2Wikvq6OMknyrilw8CAH6uuVmgImE9TXU7DVpJkC2FZ6dqd7YCloHGG/vBH9IWgX+K1Ye6BWB8vQ1n1vDQoKYKilDrsUhkOW0EkyBrmeDVZTSLZi6u7dLDh4kPi8Xhz4a3JI98wfMICpf/4zk/71Ly4oKuL62bMp7dqV2MpK1sXHIw8YQLTRiN5aQs0+xbBE33E9MXIp9Rh57fJlrPzfczw8PJ5xf/0rDRoR+arMW8YXXzS1S9olqnPeV7JPOOfPTYT98kf0ieBMxVPFLpCzuy09ck1R0VPbpddDg4acq8HgPZwJJmvjRl3SV2fGzr3CCyk4RxCa8wLBGUxiovtv1aCWlXk7npts0NrkokHI2ehxXe1D1tV25NraWq99aor7F+skblojwcc9FOe6TUdviihZW8627RLLa+tYn5rKyrVrSSsogK1bSX5xERbms1mXpjisvzLjKOoIS/2LzADIow+7X5icYnBRNu/jh13tVWX97bdh7FiYMEFjFdxkIlaWQ9Pod55o6tBBea3XIzlfx0PwmgImkytlTi2ys2yZsljQVilzp/rrIxAA1NXVMWTIEH744Qc2b97MxRdffLqbJGgBp1tr9bTgrDOiWR0sL6/Nq4PFxkK3OJOHrZA4cMAU1FYU1dZSbrMhAaudIXbFyaW8/393kFiSR/TRo/QpKfE+Sa+HBx+E5ctZkZHBhgsvZMOFFwIgOY1DWVSUUtDdWTRVHjbMdbrDuYIfho0H//cnHgTI3cFHQ4fyXnq6Xxsv3rOH/vfc07xOaYck9okh9uBBYspruK7EyKdmG2XREST2STzdTRMIWg1h0wXnItnZ7jV6da7kObfMymr6NZsynsrOVuaujdGlC/TtCyUl8NZbMGqU/zF5ebB9e2jP445LUFcHlHmsJIEzrk4gEHjQZOd8SUkJf/nLX/jyyy8pLS3FN/C+QWuJTSAQNIm2MOJtc1FtYmMh8rkUjk7aoYTH+67YO5fYu98/Fnbvhk2bvEYYJp2Om9RiNLIENuUC35iHUL4yklpZ5j1nYdScoUMZN3o0W/v0YdGEP3LRp1+z6xYDv+1o5v0TJRx7r5OyNuAjZ+Nqk7qYL/nst0swe4BXsz39KIEK6ba0WK+K5/GSJGHyCXtwFdmpq2b63r28NqYfF5m6tFnK3Cn8+ghOEWeCPZ82bRo9e/bkhx9+ON1NEbQCnlqr54xz3pPTUB1MtRVqerXmwrIPiRt9FtclIMrG7/52F3CXstvDsQ5QFB1N+UcfIUVFsXrYMCXfXb2Ej446dokh/x7Afydlc+kipWC8zmMVv8hs5nBkV5bKE/hn2q+cF3HK2jgcyDod+QMGsLJXL86Wr5FlSCK7q8x0jjCh0+uY0+DgWE0dXaI6nO6mCc4QhE0XCNonbbFG35TxVEYG7NvnXa/dlw4dIClJkXC97DI4dkzZrzVsCeV58vOhV69Sior8IwFkWcnGFggE3jTZOZ+Zmcn+/fuZMWMGPXr0cBWkFAgErYeX0Uuuhol7YUk/2NWl+YF2IVjS1opqjI+HklVm3jsi86edO/zeH1hYyJEuXYitqVG8vhojDE9nsEpiTo7rb69IvKVLXfuPdy6kJrGKAukoNbIMF+dAxWCorlbCAXwJ9BM2ORXdzxFKAl4T/SiNOdZbC5MJVu4vYX1VFdklJcxLars039Mc/CloA9q7PV+3bh2ff/4577//PuvWrTvdzRE0E09JLM/CYuPGnUOSWCGmOqn67nP69SOtFWUbPB3xgRaWPclOSWHsT05JOJ+Fa1VOxhcv++zhmNdkdn823XCIfTffxBWL/OV+PK/lWgVWHfwe9jWntJRxcXHIQLTRSJ8AheoD0Vb93Vw8HfE6vU445gVNQth0gaD905I1+paMpzwzoLU4eVJJahs3Tnl94ID2sOXQIUhPh7uUdXpNWZv8fEhPr6KmRh3jNAB6r2POuXGgQBACTXbOf/vtt/znP/8RaWgCwaliZAmkVinbXa00eQwwMmjNqEaTCT45UuG1T414W/H881xYWIjp7bcVj+9DDykHeFjqjCujScnr4+0MnpUC07eDQSMSzzky+LF3bwB+dDiUkcKVl2N85RUef+stni4qcrcFbbUddX/37tAv6tRIxmgSZKVES3KgpU6KpnAagj8FbUB7tuclJSXcc889fPjhh3Ts2DGkc+rq6qirq3O9rq6ubqvmCZqAkMQi5PD1FSXKYuvKkpLT6izOMJvZ9++OzIj1l4TLmzyZ1N27yU9OZtrEicxZsoS0XbvInjWLzMcfx46GbfWVm530M45uNu5YUcJ2ZjIL73C+7FmzyJw+HbvBoC2W67T3pTabUkBe3X311U1yuJ+y/j4n9ZwEpxph0wWC9ktryJG2ZDz11Veh32fmTEXa5t//VqLpPYctjzyi+AoSE5XncTiUOmQxMcq9Dx+GSZP+R03N5c6rKY5537adc+NAgSAEmqazACQkJPilyQkEgtalqLaWw11q6H5ZDYZrFeer4dpSul9Ww+EuNRT56LSHjDoysFiUqqYWC/aYOH48HEtBgfcqfEGBsoLu4c8Oue3WmhoKamrIraxU2g48lp1NSlERMZWVmI8exWSzKQJ4FotiocFtqdPS3COQ5Gp4+XtIrkb3lZn+kztp31gdpahRderrLl2wPfWUyzEfazSyODmZlACTg5SOHYkzGsn7zEheHkycqESGFxZ6FH49Fbz4ojL6eeklv7cSN24kzWrFYrVS5oxQLHM6KdKsVm9JglZE4+tDXNwpXrQQtBrt1Z7LskxmZib33nsvaU1wZD3//PNERka6/iUkJLRhKwWhkp2tSF+BtiRWdvbpadcpx2Ry2yWP8HVPm+m52FpQU4O1pnF7n19dTfr335Pfyo6rUdc7/3AuwOp8VmJXjByp1H0ZMQIkiYzcXPI6BbDPhR3hjb5Q1BHqJOjmjKxPL+WNpNF8mTyYNUOHkj53LvnJyWTk5iqO+UD4OOwNkkR2SorSLg+HuxYt7e9m4Rn5IBC0EcKmCwTtF3WNviVzy6aOp4qKlLl8QQF88omyT5Jg4EC4887A95kxQ5mK9++vHF9UpFxj61a3r+DDD+H55xWHPSiZ7mVlcMMNsH//5R5X03Y3npPjQIGgESS5iVb8888/Z+7cuSxZsoTExvJjzkBEJXhBe0D6+mv337iDzjz/s8pXX928i9fVuaP3ZBmTrp56FCdBa1SQD6nt06bBrl2KoF2gUvBZWRRflUH/hbs5cd1BhlX24tjzSVRLe9g5uxidw4FDp/PXkm+EWKORdRddxKG6OjK3b6dvx478yWxm+eHDFNfV8V1qKrEmU5M14lsFz3zFK69Ucgw7dIBvv/XK+1tVUkLmjh3YNT4YgySRNWAAGWZzmzTR5+vTqHax4NTRVPt1qu35008/zTONVIDatGkT//vf/1i9ejXffPMNer2ewsJC+vbt22jxOK0ou4SEBGHP2wEFBdqSWFarkMRqqb2funs3Cw4eZGqvXsxLSmq1dhXX1nJxnpWK7eH0XmvDPOp79pnjWP7C88RWVnHD7Bco7dqV7nV2/vXKaxgqSyhf/hY3Hj6MDneQvAN4qPwCXo7e6n8T9WE9n+f993ll4UKeGzuWGePHh9TWV8IHcX63MHr2gOt//JFSm81l632zydp0fOWJpz2//noltDA2FtatE3n8gkZpznxU2HSB4OynKeMpr1JrUtPm82o9sYyMll2nMcQ4UHAuEKpND8k537VrVy/duuPHj2O32+nYsSNGo9Hr2CNHjrSg2acf4ZwXtAdOpfN11Sp/bXfXvTwMc8jXC6XtsbGKV3frVs0RRtGmTZT3748EXPfDj5TZlYn22kEX8b21mEccJfRq0DFu4RLevnU427S05APgOwl3XHUVkiQhy3KTCre2iVZtKIsMzn4tqKnxSudXsVospEZEtE57BGcUodiv02nPy8vLKW+kAlRiYiKjR49mzZo1Xu1saGhAr9eTkZHB8uXLQ7qfsOftB3Uy6SuJJSZlzbP3ntJmjTmjW8KHnzj4w20SV/Y5SPbuwfRa/67rPUmWFVk5Hwd7nNFIQng443v0YNmhQxyoreVwYxr0QMSJE9R07EhsZSXrpk/nUNeu3PHEE1Rq2TNPz7+PyW7M4X7Kxlda3oyWRD4IzilCtV/CpgsE5xZNGU8Fm+Pr9cp5gcyQ5/WCXSdUJk2CRYvOjnGgUKoTNJVQ7VdImvOvvvpqa7XrlFBXV8eQIUP44YcfGl2VFwjaDR6/9BlpaaR07KjpfM1LTW1V52trF/rMMJtDa7tnuLWPpU48dkyx1rjn/GU2G2kFVmWHHiqGX4WMxLAdm0hbutR9fiOoYxB1Ei5ZrTBtGtKcOZiaYGHbizawb4SiQBCM02nPo6OjiY6ObvS4+fPn8+yzz7pe//LLL4wcOZLVq1czZMiQtmyioI1oDa3Vs5WQbaYHntJlnjbSV3+9pXz1mQ5bPVx4XTz/fqwQZlXC9J1gkN31XlRFOVlixcAB/C4mhjBJQpIkJvToQb0s84+yMsZt20GDFNgZfcwpNedb5B3c9WpwyKCTMBdV88wHbzJr1J0ciIlFercP0p9/xiHJLhvva+tVmtPfzcKzqr2W/kBWVuvcR3BOI2y6QHBu0ZTxVLA5fkND6Innwa4TKjfdBB98oN3uM83Z3Zo1+gQCT0Jyzo9TyzafIUybNo2ePXvyww8/nO6mCAShE+CX/lQ6X1uj0GdREWxXpV59ots+2Xqc+/iZ+QOd0eYBRhjZZjOZpaXYZf+Jtt4hs+z5F5AACRlzZSVxFRWYKyv54fzzQ26naxI+a1bIFrbNC7FmZ8O4ceSfd55XoT0AO3qmdlnO3QXK/N4WZdSMUIz1iZQSCDw5E+x5b2dRZ5XOnTsDcN555xF/Sgs/CFqLEOuhnvNIMsiSe+vCZ+aanZLiiv5uzBndVDzVWFRt2exsGDLEREReHDWTO8FSf8f2pjR/x7YkSZgkiQyzGfvPHcms9z9PxfUcTm+BwW7n5dde47mxY3FIEqXdumGokpBlWDttOqnlO/jNmjyuM67l3bdLONmrB5ZffvG7rq/D3XOMIjlkZJ3k2m7fDt3NraQ209qRDwKBBsKmCwTnFs0dT6lze88ELt+oeZ1OsX8nTwYOnvC/jlrw1V34VUU9pnt3GDQocLvnzGn/zm6tsVFODowbJ5TqBK1Hk0WV9Xo9pU6nlCcVFRXo9XqNM04t69at4/PPP+cljSKKAkG7w7NSi0811tgdO4jT67FERLA4ORlLRARxRmObOF9bs9BnYiKMvcEIFUbYFQEvJyvbCiNPflDFhlqPQm0BquNkpKSQF2Di+t3gNC7NfN71Or68nMIxY9h8zz3EvRUWuGHOEYhOHYls367Z78Gq4LZ5IdaMDFi+3LvQnpMdJLOpur+rXu7Q88MpHDqUvNRUJvbsSV5qKoVDhxLfClIGgnOD9m7PBWcXAeqhnvMUFcHhHUa6S0b67D7C4pdfpvfuSrpLRg7vMCrmyKegaIbZHNBG5qWmtkiWJTFRsTGetdorK5X67TU1Hgc6vLfbtwcuIF+0Zw9PP+nUnPfxCkjHtc95/4knuXzbNv4x4ynXF6ebVMm3MybT0M1BkdlMLGV8b0sjZWwa3Hgj4J7YBJrg/DqxiBdu2IGhQuJXu35m8csvk7S/GOrhqSlb+XVigIdoCWpW3+moZSM4ZxA2XSBoX+TnQ3q6sm1NmjKe8p3jp6UpznItUlOVabFWoVrf6/TvX4fikP8RmEhMzAFiYiAmRjlm0SLlXnFxyvQ+Pt673fv3w5YtTZ6KB6Wt+hu0x0ZlZbjm5WdhKU7BaSCkyHlPAknU19XVERYWxDF2CigpKeGee+7hww8/pKMzPbYxtIrNCASnDM9fctVaOX/p44FCo5Gwujqv9PBGNdGbkRvWmlGNSiZ3OPYxQ8EmgbmO/jv382f9Ih6adTd2NKLNPUcYP/2ktP/55wPew2ZXHOMN6NicfD6PTLyXF5cspvu6MA7f5oAof1G8gcXFTP3731l2880c6NqV2N//3r0EDm4Lq6LxW9em0YpqVH737qweNgyAnPR0/vTZZyBJRB89wtiSleST5sqI9/wuqBGKAkGotGd77kliYmLAtgoEZzq/TiwimnLijeF8aptEHKX8Zk0emca3WGX7B1V0Jdu4mq7gHabljAZvbnZdoLopWmosFvKZwzSmMQdr5YXK4ntZOKztAaMOQUwtY39vhHJv06kOR9Y/WQxPaLdD7qRsXVkDKHb1Ny885zpGcl60LCqKoa+/7j7XaSvtkoGT458JKZusiEQoh7oxRgw2O3pktiQmsisxgRsuXcW87xbirVbfAjSyA+v3HmDsfbFMm99+IwQFZybCpgsE7Yv2IH+iNcfPy4OhQ/0z5vPzlTHAvHnBr1NQYGXHjjSgM3CMF154gWnTEqmvV45V7zVxYmB/QhAXiAut8UQw90Zb9rdQqhOcCkJ2zs+fPx9QHEBvvvmmKyUNlKIu33zzDQNa4JhqKbIsk5mZyb333ktaWhqFhYUhnff88883WmleIGgzGvmlN739tstihex8baZl8jScLYlqzMgAi5zPL3c4J/I5x9gJTOVu5wRbCq6Nu2IFRdu2cTg3l+5XXcXRhgbssoxBkojU69lRWspD0+9mMzEcIJp7bxzP5tRLWDLiFqoWxsFjEfD6Zpe3Qudw4NDpWPHss1h27WLCxx9TbzBgUgvUNcHCtqVWrSvqPjwcydn5ZVFRip6+k8PDbmc541iVJZNyZTQg8ucETae923OBoL3RlnqoRSQqf9jA4RRwj6WMT203uo5x2PxnrrHR0cR9+GFAZ3RjbQ5UN0VLjeVPrCCd9dzBSqzdZsL+jrC4H+yKhDU9wChjkHVkZfvcwzkcGar/mk3Tr8RuMHiH+wF6u52O6BjQNZLxPXrw5L59lNps/Doykm+rqhUdeeexnpI3WbNnu+5zqZzH//2SSuFQh5fe/YZ8mT+N0nn3QXY28rhMDnfrRnlkJJIs857Hgvi4//s/5Jqa1imqq+EV+et99fz9dRM92nH6vuDMQth0gaBtacoYoD3Kn/jO8ePj3RnyN9wA//qXkv0my8HbajLBZ599xnXXXefcc4zs7GwyMjL87qPeK5A/oanO7kDujVPV30KpTnAqCNk5/8orrwCKE3zx4sVe6XFhYWEkJiayePHiVm/g008/3ajzfNOmTfzvf/+jurqaRx99tEnXf/TRR3nooYdcr6urq0lISGhWWwWCJtNav/RtZZma6JFQm9FppTKRX8B9bJkVw+Tp92M3GNwTbOfxrmhzn/Ynqs9gt7uK3TXIMhV2OxkHDkDOAnpP/DMjrh/BwdsuA5uN9397E5WfRUKXeqgywuFwBq/djzyqhOKYGMxHjgBKBJ7JZlPK1Dc0+D9EiP2uk2UckuTathSvqPwAjogYyijAAmOdJ4nII0EzOF32XCA4U2nT6Dens1hqsKNzWkd1qyxnu1+rv/mywUD8q69SOHSoX/FVNaNKq81NqZvSmyJipXIaZInbUWzyHazkfyNjeO+SK+k84meO7UpVWmiTyLMqptPTnOfkKE+wIXcS/YvGsnPp436Pf/7kSF5YdQHxSXZ0kuSKqN1x4gQP1w/gRdN2v3M2/vk+LLt2Iks6JFnJF1CGPDqPIY/EvDU1rL9xLy+t6UdOmnMBIiODHaQwsJc7U1aNzC+N6opFwlWQvjWK6mIyefSJxKp/mDzaK7RqBS1H2HSBoG1pyhigqRHhpwN13Tg8HH78UdkXSluzsrK48847Xa9zc3O55pprmnz//HylzNzbb8Mdd/i/r07FQ3FvnI7+bo0afQKBFiE75/ft2wfAsGHD+Oc//0nXrl3brFGe3HfffYwePTroMYmJiTz77LNs3LgRk8/yXFpaGhkZGSxfvlzzXJPJ5HeOQHBaaMkvfVtZpqaMRoqKuC2xHBmJdc6J/FDyGJoLqUU7vSLAVVzR5p7ObUkie9YsMqdPVxz6avPV9+12MBiwL3mNtYDkjICvwu5VpC7tucs5cMDC5klWuv5mqDtSXmX5ckVEtyn9XlREbGkpcZJEwp49jP/oI0UmJymJ2B07lBCEZs6wg0blT55M6u7drteywYAk8ucEzeR02XOB4EyixWveoS5uO53FKWP9F+kzyOYd12qsG4s9j4KMVDxHr5IkcXi/FLTNaTXuuiiq1dXKZIuNdUb0Ow3vPnMc1shkkGW+HnYBAB3Tizj2WZJyoaNGQHHqe+uuqssLMcBzwHFXNpu6jYqEW49/BwX+7fJ1zKuFW7fKKSzlQcbLy0jgAKXE+g15rFZYU1cCqVV8tLaEgoIurs+tWze8xxmuaH5l01KZOl/OBGeN4MxF2HSBoPVp7hjgTJE/MZma1taZM2fy5JNPul5///33/OpXv2rWvVX3gloeJ9BUPBTbeSr7W0OpjgMHmlejTyDQosma8+vXr2+LdgQkOjqa6OjoRo+bP38+zz77rOv1L7/8wsiRI1m9ejVDhgxpyyYKBC2jNX7pW9MyNXc0kpiIOr33dKhLuCfbvpPy7duhuxn6+LQ/IzeXlKIiLBoOfSZP5uH585nXoUNQ7fc/5kmKzt1WCWw2f8vfrVvT+z0x0V0LwG5HkuXAMjktoDENYUnkzwlagVNtzwWCM4kWO1SbsLjdrZuydaBDh8O1BchPTnbVVblk1x70OHh2ZjPbPDwFwxON102Jjwfb29kY7slEstvpl/Ou30OXRkV5LYjHxl4NqMMRGbtdguRjMHEvLOnH0SMxxFQcpNfRGiZ2NPKWzcaBiAhWZSeyMap3wHouAH1LSpi+ahVv3nwzB84/H2n8o+Q+bmZpwwTCqKcek/sEcy1E2rCMBmYr2QEnh5ZiGR3nWkiQ82P5409bGLDwNdIeuN+vH1sqU+fLmeKsEZzZCJsuELQezR0DBEuKf/ttZbrZv3/rZuE1V3ovI0N5hmDR6wB33303y5YtAyzAHNasGcivfhXXpDZquRe++EIpTms2K2358EPvqXgotvNUys20Zo0+gUCLJjvn77rrrqDvv/XWW81uTEvo3bu312tVb++8884j3rfctEDQnmiNX/rWtEzNHY1kZyvWXZZdznh1G1tZSVxFBQllZQy3Wpl9SwbY9B4F5IK039OhrtORs3o1t19yCX+sqQmo/e6QZa754Xul0F2gxY9Bg/z6fftbGzg56E+Ez5vDwD9pjG6cowTPKHyXTE4rzLBjjUbvgnZ79nCgrIzYo0eVA0T+nKAVaa/2XCBoDzTLodrMxW3zoFhkcxxSQgLcPR7pTaVw6O4jySwaeStfp17CnSNeYNmu57mk+wFGZXosInvMyrOz04K3OdNMSmpodVOMmRlwkWKXPaPMfTXjDXY7bwwYhDrUTkkpwG6/GyiAkUrkOiNKOLwwiY09zVx6qwlJp2Oiw0F9XR2mDh04DwJmjm2YPJkhO3b4LYbfAUjIimPekxx3doDLYR9l81pIIP5qpKJCpLo6KChodlHdUBFatYJTgbDpAkHr0RqLqr5xYR9/3DYSeS2R3lOHKr6oGvR//vPt5OW959z7JyCdL76AG2/UPi8QWu6FI0eUe1RUwOOPK33k6QJpqu08FXIzrVWjTyDQosnO+crKSq/XNpuNLVu2UFVVRXp6eqs1TCA4p2jhL31RERzfDgOBBnTocbii1rdth07dm6C20tTRiOqMSEmBqCjw+Y0AiC8vp3DMGH7o25ffzpyJoxPwgRlDVThZ2drt7155lLiKCo6W1XBy7XoYNZTYCy6gT79+WGtq2HHihNc9JJR5+KG6Oj6rrHQXuktKCm3xQ5IonZ/DVVXr+ffLK0HLOd/GM+z48HBvDeGGBuozMjD16QN/+5vInxO0KsKeCwSBadbPfXMXt+PjkYoKXXZq/5/+xLebT5CfGUb+sBsBO1vSYxnyWS7ZWTaujO7kLgfuMSvPmJfWaJsLapzNw53dFjABYNs2AK786SeyXniBsU884XfIMz9VM+zKSMBZKG7cOEi+FOQaGKZErpNeCp/FsccAcfUSfcLDkXQ6TB06BLqzq56LmqUG3ovh+57Kghn+k/GZHVJ4+uQOGpDxjRTQI7E8xSlXYzIRK8veC+I+RXXbAqFVK2grhE0XCFqPlkz5POPCbrkFVq2CkhLIzVXeb42aIy2R3vM89+OPtY8Z61LUWw3sAWRiYqZQVta89ofiXgjmAglmO4XcjOBsocnO+Q8++MBvn8PhYPLkyfTr169VGtUaJCYmuopKCQRnO4mJ0ItYNhHHARI4RmfSWc9xOjJibCwHUfzTIRn/YKORiy9WcvF8b+6DOuFXKTKbKY+M5G9/uIti1VJeXUbW2B4MGACJI2oJ+72NfyVfTrddNpYxnqnl89g5Zhy321bzKcs5sfokOpOJ8G++0Wy2+r/9xi1bXBNrr0J3sqwUuvOx/MX/LaJmnzJCGfiTMroZ+GMO21cpo46IvtHEX6HRcW00w1aL+QFICQmY9uwR+XOCNuFMsecCwekm5J/7loTaefyuJ+blKX8sxSf6u4Cx9cBGkNV6LT6z8g7bZXoTTbGuj2ab1QwtR0k4pVk9iLnzELrYAA7pr75S2pOT494ny8p9ndvHL+nG4xs3klVURGZmprJQAIDVL3J97HFn2z0KrapFakvr610LBRF1dSSEh1Miy9R7FLd0kZeHMTaVuNf8J+OZvcz0PdSRscf9o/CXd0olw+zODvBbEPcpqtuaCOeBoK0RNl0gaBuaOuXzTIr3NCetWXOkJdJ7GlP3ANgAIziFa8vLm3YfT5q72BGK7RRyM4KzBUluJQ/2zp07ufrqqzl06FBrXO60UV1dTWRkJEePHqVLly6nuzkCQUisWgWPjS2iB79QTxjrGIWZUkqI4Xo+RUKmnGiK5BCX5gsKFOupNRqZOhXmzfO+ueqM8EEG9pvN3hN7zzcl71269+NwLOwPSMxjClNZyDzuo/vK+Ywdqxy8qqTEpU0biEARgZ4OAffB7kY4kNAhu7buEz3+Li6GwYP9RwmbNoGQ0BKcRlrLfgl7LhAoNOvnXrWfvlitIWdXBbNzBkki69lnyVBD8Dwc5Z626tLBsleb3/xPNbPK9zJJ148kfWduHiVRVioREyvz0VoZIzolAg6PkLrrr4fSUlbdfDOZU6Yosja+7XE4uGXPHv4xcaKyY/hw9I8/ToPGc6m69hlqFThA+vpr9984bbcsu4u0AvI113iPRZx9WVfnnozLsnsyvm5PDaOKrd5aNTpYG2/h+vNbT0u+qQRqr0DgS2vaL2HTBYLm0RpTviDTZC/d9KbSkusGO9ebVDp3vpTa2sWt0v5A7oXGhkfCdgrOdEK1X02OnA/Ezz//jL3x/+ECgaANyMiAjLGJrtcOp9c7hnIK8HQShLgWpy5Tx8bCDTfAv/7lFp/zzWULshR+EpO2Yx68HPNd9HqqGxogvZQBn1UjSxKXH/0KSmBy1/cwDrwTrMr9Mvr0CahNqwcaPJ5Sq9CdL/+dlM2lizIxYnc55NWtDQPfTcriCs8TxPK84CxH2HOBQKFFP/ctyK7KMJsD2rm81FRSMzPh668DRujb3sgib5x3mx/ZX8KG2io2vF8CC7u41qXLyySGpnk4wkl038x5UMZHH5GyfbtmkfbcKQ/y4LYfSQXKgf8sW0Z5164h6doDZKekkLl9O3Y8bLeqZy/LZM2fDxdeCJMn+4XMBVIEHBRvxFxiJKGkhLuzsngz804OmGMZFN92cjWhILRqBacDYdMFgubR3srCtdZ1g52r0ADo+dWvLub77xcHjDlo7D6+hWqbm0EmbKfgXKHJzvmHHnrI67Usyxw6dIhPPvmEcePGtVrDBAJB09g3M5v4GdpOZllvQFqeFfrF1NFIeDj8+KNyDSQlqq20DClQLpvqhHAubY+9KIukfQfY27snDVqp6U5qGpQYOzmqgR1LFS35wbyGPGwYhsrAuXO+xduWp6Qwdvt2v+trOQRUrng9g+1XpJAy1n/UsSc7jysyNEYdYpQgOAsQ9lwgaJwm/9y3hn5JUZEiUAvoHA4cOp1ry/btcOWVyqw4wGzZ6Jwtq5IxErC61Fv/XZaAo0bkknDAU3XHX5qnyGxme+/eyvk+sjaTHnmQ2UuXctOGDa73y512WLPtZrOXxl6G2UzK//2fpuM/b+JEUnfvVl5MnBiaZ6SoiPjycorCwwmbNAmptJQJeXnUr12LaevW5gv8CgTtHGHTBYLWpzWnfG1Vc6R1rqvmnM8CriU8PJmPP/YuIt3U+/gWqhXxbQJBcJrsnN+8ebPXa51OR0xMDHPnzm20SrxAIGg7jJkZjHwphTnmMUybOJE5S5aQtmsXADuW55Gi5WQOhsnkpZ8rOR396tZLP9fXGXHvvQDEX/U/Puh7G9d+9x1fXHppwFv5RsvhcPA357Ulz2j/v/0NgHqHg+4GA+awMA7U1VHT0IAOOGKzOc9RrunrvG8MtRituhUIzmaEPRcI2oDWmH0mJhIbHU3c4sUklJUxfu1alo0axYGYGGJ//3tFdkaNTA8yW07cuNH1tys2vqui/+5i2NWAZwScf0hdwAw4YHtiIjc/9xwNw6/lyNwsokNpu69cz7PPKo/i68wH77FGKJ4Rp5iuST0ekMrKMKV5FHk/xTWp8qurmbZ3L3P69SNNyHEI2ghh0wWC9klb1RxpznXVYrClpe519o4dGzhxYgcQC3zEk0/aeOyxS13mtin3aUqhWhHfJhB40yTnvCzLZGVlERMTQ8eOHduqTQKBoBnEx8Py5fDilyNZn5rKyhEjSN21Bx0OunVr3jWLrszgeFYKAzWiyrdl5dHpylT6qDd3OiOK6uooj4pCeuopVg8bBsA3v/qVcpIz0k7Vk43U6znaoKFMq9NRHhnpv99ZiWaocwJSYbe7HA4yMHXPHtffi5OTWXboEAdqAxS68yAqOZZSXRxl4QlU3DKe7h8uI6b2AFHJolKb4OxE2HOBoA1paahddjbxmZkUjhlDmDPyfcKaNdQbjZhkWVk4D2G2nJ2S4tKu93NH2yWYPSB4BJzzzexZs8icPl3RnFcX0T004bscP87Afv9j5+sDKHz3XfrccUfwtvsQe/PNxOXlkbBrl7czv7Ky6Xn/LSnK20asKClhfVUVK0tKhHNe0CYImy4QtF/aKmK8OdfVKiR74oQEXODcm8czzzT/Pi0pVCsQnOs0qSCsw+EgPDycrVu3kpSU1JbtOm2IYjOCMxFX6npJCddv3UppZCSxdjtrF76GXFJCzDur6NO3b5OvK0kwik/4hBv9iqT+kWx2MgBroXeKuGdxN9/0d5WBkkSZJNHz3Xf54fbb3VF/HsRWVrJu+nRkSeKXbt145fe/Z87f/07aa6+xqr6ezNpatBQ0DZLE2/37M6A4jkemycyaLXP5YJ3Gkd7UVdfxo1zH9H17md23HxdJJkxdxHK+4MyhKfZL2HOBoJ0TSmHZEKqkFdTUaOq/D5xnYep1EdoF7jSq4BXIMpYXX9Rsql8B1y5dmlwUt85qJSwtDUmnQ3Y4qDeZMNXVNamQrvuhW16Ut6V4Sgpd/+OPlNpsxBqNrLvoImQg2mikT3j4KWmL4MyjqfZL2HSBQBAKwYrB6vUOli/XNatAbSjXb0kBXIHgTKZNCsLqdDqSkpKoqKg4aw2/QHAm4pW67ow4LzMYSHvgfmVnURFyM5zz2dmQMfZGQNGvd6BIxcjAO4x13hyvJXDPSD2/CDvnvm2yDLJM2T/+AcOHQ/fufvcui4ry06BduW8faRYLGUBKUpK2Rq1TX37qc/D1eonV2RKXD3a/71ucRsXUxcTK3ftZX1VFdmkJ88RvnOAsRthzgeAMIZjIaxMi9FWZN3W7YjlYugSIgNMIkzuem6vcxuFA9llMdxVwVYuvHzzYeNt9MJnNrkwAafx4TK2R999WAr8hoCUpVGazeS2UyFdffUrbJDh7ETZdIBCEQrBisN99p2vx+nVbFcAVCM4FGg8n9WHOnDk88sgjbNmypS3aIxAImkF2SgoG5+TYpd/u3BokieyUlGZdNyNDKTRrQynmqv5guOLjDAavFPX8fFg2xszb4QEsrzqBt9sZOmuWIlMzZgzLk5Nd7VdRJ/v6hga6HD8OQE56OgVJSVhTUjj03HNebVK327crQXOeOncFBUrAXFGRd3EaUKLbrDU1FNTUuArm5ZSWUlBTg7WmhqLaWu9nyM+H9HRlKxCcwQh7LhC0Y1TZGosFFi9WtnFxTXZWxxqNxBmNWCIiWJycjCUigjijEfOO7ZCejmTN1/bpm0wum1188CB/+OMfiamoYMCuPaRml2reKy81lQyzuXltVxcE8vKU4q95ecprVzh/Ux66dfquJbTVuEwgCISw6QKBIBRmz57t/EuRlpWkttGZUdfxdU32OAoE5yYhy9qsWLGCP/zhD/To0YMTJ05gt9sJCwujQ4cOXscdOXKkTRp6qhApc4IzlUCp61aLhdSIiOZftwDuthRQQOMp4lOnwoIFMHpGDTnpVldhVl/6T3ienbs/JyUlhW3btgVtP7hT5n1T5+OMRhLCwxnfowfLDh1iU2Et3GuB8nAvNR3PX7nYWKUITmwsrFsHluqv3ffBXaves91e0W3qQ06dCvPmBeg1geDUE6r9EvZcIDhDcMrW5NfUMO3nn5nTuzdpGplmjV7G4SBMkpAkCVmWqZdlTA88EJIt++mnn7jooosA0A8cSI+nn6dm9aUcvW8HkgyyhCsk32u8EYLkTptyuu9P243LBGc/TbFfwqYLBIJQGTVqFOvW/QhsAg7w0ksDWb26s7+8XQvQUMZr1esLBGcarS5rc+edd3LdddfxyiuvIPlEuAoEgvaDmrLeWsTGQnR3oAJkSYckO2hAh955F62q7F/83Uj3a4xE1Tfws9HhLgTrSoe/gszMnrz99tsB2+/pIFcd8q7UeSArJYXfxcS4HA4TevRg+Q6Ze6p02PGvA6dSVubeWizA8BSYvgMMsmZ0W9aAAU0rPS8QtHOEPRcIzhCczuQVJSWsP3qUlUeONMs5b1LD1oqKkMrLMYVoy77++muGOQu7A1w1axZfRXVh3DNVfFZlpFtDOPaPeqC76RBVBp/i6y0tittSTvf9PfCVFBIIWhNh0wWCs4dA8qutwfnnn8/PP//sfJXIsWOVdOrUkYceatn6tW+b26oArkBwthOyc14NsM/MzGyrtggEghZQ73DQ3WDAHBbGgdpaahwOJKCkvh5rTY1m8bH86mqm7d3LnH79SAuwihcfD2vyYpGvjENKSEC+azzSm8vgoKIFm5jgPladExzZGY587VAMkUXELP6BHker6Lzm31SOHEZ5TBfqa//AlCkDsFrdvgA19d4zEn7v8eNUaOjE5sXHk2o2e+2TJInMDImLAujc6fXQ0ODvtDd8beapezoyI9Y/uk3Vr/csZitKzwvOdIQ9FwjaP54FRT0l18bFxTW/oGhiovvvRmxZTk4OY8aMAbMZIiOZ9+qrzOrSBWw21lVV8K8LL+SVuRLvZYcxxdaDF1+R3YsAAkB7XHOg1mcRQyBoIcKmCwRnD57yq63lnJdlGZ2PfbbbT6DXK7K1LV2/1mpzO1ofFwjOGEKWtdHpdJSUlBATE9PWbTqtiJQ5wZmK9PXX7r/RlpPxLT42dfduFhw8yNRevRovgBogRTxQVXYL+bzINP5qnMl3tqEoMWN2jEYbdnsHL1+2+rdv6n1edTVDN2/2izoLlhJeUKD4GXzrwGVnw9ix/sdbrUCSknoe8D6i9LzgDCBU+yXsuUDQ/tGy6UEl10IhRFv20ksv8cgjjyj716////buPLyJcv0b+HfSpilIW5au2EpFQILIgaSAdQVkEUFxFy1IPWjBDf254OHVo7jggugRRQUUZakKR3E54opQN5TQpqCgIgi0FChdoJtg26SZ949pplnbJM2e7+e6uIbJMvMkhd4z97Pc9u2wWWIu+fpR+Pxz300kc2UgQbByuKQQOzGoA+7EL8Z0otBmOUF74kTr5Vc7G1cNBgNiYmLk/djYWJw8ebLTs2x82WaicOP1ZW0AqUde1UG31wcffODOIYnIS/LVauTu3g2jKNol5uXlWdCJ0XhOusCdVWUfNeB/eGLWZJy/7DFs2/MppJR3NAyGtl875lyAfAqLG1ZBEJCuUnU86sxmLp25DpztOnc9e0ovt03aAy6MbrP4kEUDBmDurFlYuGwZsvbsYel5CkmM50TBzVFMt1tyzV3OAjYgx7K77roLS5YskR9+vmdPPFhTY90Oy5v6bT3cnkjmbrJ9dUUFCmprsaaiIuSS87bXNSouO0I+wJhOFLrcmNTmFnNC0Gzo0KHYvn27Z4204as2E0Uyt5LzcXFxdsVliCg45KSkQN21q8PiY/LyLAAyt26VHzffIlYZDFbvc3s0XqtMoRQ9xWoIgoDaCU0o0GhwxvgDGLbnbAh4G9VIxEG0daN3lNdOj41FSXa21brydqPObObSOVvnrqrKcdI+OdnF85hPN2ECCjQarJkwQUrOE4UgxnOi4OZqTPeYg55qqVDc5/JLqqqqkJiYiFF79kB75IjDw0T3r0Vivz04KqQh6i8lVi3seKkdV5LtPlnWhyhMMaYTha78/LZJbXbLr9oMZHNVeXk5evfuLe9fd911WGeuNeMFvmgzUaRzKzn/0ksvITk52VdtISIvaa/4mC9G45lHq38jjkR1QgIEUcTE0c8CAP43Zgw+//JLiMKNSKyrQ2bFUatR6x1xOOqsgwKtKou5dOZB/h0Vp2lvdFtpYyOq4+MhnHMO1o0bJ51u3DjMKCyEGB+PxMZGJgkopDCeE4UOrxYUdTK97PyrrsKW0lL5ZSdOnEDXrl2lncmTgeXLHR6upbsJR5cfAXAELQBynHTuu5psN0+GK3jEdwMJiMINYzpR6HJhUptbdu/eDbVaLe8/+OCDeOaZZzrZSmvebjMRuZGcZ/V3ouDnSvExX4zGMye+Y39aKz8mtHafV3XvDq3FTf3SP6xHrXvEw7l0nhankWcbPP10W5IgLg7ap58GDh0CDh1ikoBCBuM5UWjwSUFRm55q8dZbERsVhWaLlxiNRrlQHAAkP/YYUo8fR9fGRpSkpMBk8Zy8xI1RwBNxzjv3XZ21Z54MN36iGptHeHlZH6IwxJhOFD4cLb/qji1btuD888+X91955RXcfvvtXmyhvc62mYgkLlckcrFuLBEFkHl5Fp1Gg1m9e0On0aAkOxvpNqO6f/tN2pov571RmuzIkQPAggVyoTnzDbt5Gw1gzUA1Zs2SetRLSqQcgUfy86U5c4DjuXT5+R4e2Mnp1GpEmz9P62OWSYJ8i9EJXlFUBIwZI22JvIzxnCg0uBrT3aZSAYIAg8EAhUViPjY2FiaTySoxDwBHJ03CmUlJWPvYYyi87TaHh+z1bw1yT01xesp24ygEPBGrRnFx22S4HYtSsLKr46F3Oo0GOSnOz0UUSRjTiUKfeVKbVgssXSptU1PdG8j2wQcfWCXmP/zwQ58m5r3RZiJq43JOrqCgAD3NFRWJKGipFAp5FI0gCA7XTd/8gRI4pkRSTRyWDhgAbVwcUpVKj0fj/fDDD+jbty/w9dc408lFgO6UUzAtNaW1Xa6PWncoJ0fK8Ds8kU563otyUlKgczI/zydJAst19Im8jPGcAoYdj25zJaZ7oqGhATExMfL+0KFD8ffffzschbu6ogLftrQgf/x4aWgcAEXr8DhzazZsaL/Dvb04aszT4N/ZKdBqpUlwgLSdZg7lrSPxvPPJicILYzpR6DNPatPp4NFAtiVLluDqq6+W97ds2YIrrrjCF02VdbbNRGTN5evciy66CNHRbi1RT0RBpLQU0OuB4mLg05WxwA3ZEGdrMPxob7wqavDtqZ6Nxlu1ahUuuOACef+WBx4AYHHjbp7f1qNH5z+EI+ZERQcJC2/lhBQ2W6+x/AFZrqNfXCw9brEWMFFnMJ5TwERQx2NRfT3G7NiBovp6357Hg9hWXl6OeItCrNdddx22b99u9ZrSxkboGxpQ3NDQtkb82LGoGD4cvYxGDD56FEvfeANalQqpSiXST3G9c982jj7xpJPJcDVK4LgSpxu9M5CAKBwxphOFh9ZJbQDcG8g2d+5c3HXXXfL+7t27ce655/qghfY8bTMR2WMkJ4oQdsu0iwpUy8u0CwAER8u0t+vBBx/EwoUL5f0ff/wRGcOG4Xm93n6NXG93ozspaudsLp1lTigry4PT+WLtX0serqNPRBTUOijgDYsC3uFkdUUFCmprsaaiAlkWiXCvn8fN2PbHH39goMV67c4KxVmuEW9WlZCAS2fOBAAc690bs1asQF5MDJpF0aUR/c7iaO7lSlyqc1BYrjoWP/XJxkiNAEEQkJeW5vK5iIiIwt0111yD9evXy/vl5eVITU0NYIuIyFMuJ+cPHTqEdM5RIQpZ+flAbq60JLyjZdpXrnTveGPHjsWmTZvk/ZKSEvRpTbCUZGcjRnB8M11UBMydCyxc6FmSXGZT1A55eUBzs1WXvTdzQua1f519rk7z9g+IyAnGc/KrCOp4LG1sRLXBAAFoG21eWYkZqakQASQqlejT2fXi4Xlssy0Ut3j60zh/2Vf4bVARBt1kHZDz1WpM//13WP5k7AqyqlQQAKhcLEgpx1G9HsK0ach79lk0Z2dDpVCgsvU1toXlYgSFxag8weVzEUUKxnSiyPSPf/wDv/zyi7xfX1+PuLi4ALaIiDrD5azS4MGDsSYCpiEThaWiIuSsGIOdbzme9+7OMu2iKCI2NtYqMV9fXy8n5oH218j16qoGHcyly8yUOgBs17HVaqXHLXNGLp3OR2v/AvD7OvoUuRjPya/8XMA7kDK3bkWWXg+tXo8qgwEAUGUwQKvXI0uvdzga3aPzZLof2z788EO7QnH/+PkINLUFqHqh7feBeTkbddeu6O5kqYzO1FpRKRQQ1qwBCgog5OfLcZSF5Yg8w5hOFFlEUYRSqbRKzDc3NzMxTxTiXM4sPfXUU7jjjjtw9dVX49ixY75sE1F48GXhO3eP3ZoR7/mpdPHu4jLtdhobG6FQKNDU1CQ/ZjQaO7wY6PRy6u183va+ipDNCXn6AyJyAeM5+VUEdTzmq9WIbu3ANY8ytxxtnq9We+U8jz/e9ndXYtuSJUtw1VVXAQBOQ2989O93cOaJ0zBopxSQB/2yFr+/XYzf8/VWHQw1RqNX2gugwwuB9JZSFpYj8gBjOlHkMBqNUCgUMFrEZ5PJBCVrsRCFPJczP7fffjt+/vln1NTU4KyzzsL//vc/X7aLKPT5svCdK8d2cCPca+NajOtVjJyBeuQvKHVrZFpFRQW6dDkfwCYAWgwaNAiiKCIqKqrD97o7ys8u4d7O523vqwi5nBCHDpIfMJ5TwIR5x2NOSgp0Go3D5zoz2tyWOY46PI9NbLMtFFeKI5jyxI1QT9OilygdqJdYBfU0LdTTs5C/YIHcweBIQlSUZ7VWXLgQYGE5IvcxphNFhhMnTlgl4fv37w9RFOVZ3b4cF0hEvudWQdjTTz8dmzdvxpIlS3D11VdDrVbbVYcvLi72agMpshXV12Pu/v1Y2LevTwuqeY0vC9+5e2yLjLcIAQIAxbEqfAUtcAzAQ8CNJtF2mXaHdu7ciSFDhgBYDGAM1Oqn8euv41xuurvLqa9eDewrKEXB89XIesD+85YfEVGNRBh693H5a7ZdxzYoubCOPpE3MJ6TX7lZwDscKACYLLad5egSAJD2RbFta8lRobgtj2/CiNdyoYQRitZx/eatAdHI7J8LnUYDrV5v14YijQaDu3XzbEk31lUh8hnGdPI3r9UQI5dUVVUh2eKaadKkSdiwYYPVa9wtEE9EwcWt5DwAlJaWYv369ejZsyemTJliF/iJvGl1RQUKamuxpqIiNJLzvix85+6xLW6EhdYbb/PWfCPsysi0FSu+xi23PAhgGIDrAQDHjo1DcbHr/Q05OYBabd1UM50O0GjsEw8VyATWAljb1rlg/rxpANJaP09HX0XI5YQsfyAcOkg+xHhOfhNBHY/JSiVSlUpkxMZiZloaVpSXo6yx0bPR5hYsLwGyUIR3MRdzsRB6UboDF0XriVbOCsWlvpqD389TQz3NPiD/ma/DeTkaFDc0ALDvYOhUrRVXLgSIyGOM6eRPTAT7z59//on+/fvL+3PmzMHixYsB+HZcIBH5l1tR+/XXX8d9992HsWPHYteuXUhKSvJVuyiClTY2otpggABgXWUlAGBtZSVmpKZCBJCoVKJPbGxA2+iUL0eGuXns0vNzcGKlGoOmaVE0YADmzpqFhcuWIWvPHvy2UodTztego1i9aNEiPPDA/QDMI+ik83Wmv8HZCHbbvocc5GMlpNF9cqdC64lMimjkYiVg6viriKCcEJHLGM/J7yKk4zE9NhYl2dmIEQQIgoC8tDQ0i2KnC4hbXgJMx2qMQQGmYw30yEJ0NPDWW8C11wIxMSJiYlQwtBakBaRCcY7Wo22BAlEwyVszX3UwyEJiKhtR6GBMJ39gItj/CgsLMWLECHn/+eefx7333ivv+3JcIBH5l8vJ+UsuuQTbtm3DkiVLcNNNN/myTRThMrdulf9uXvW0ymCwmmItjhrl30a5qhMjwzpcwsfNY2dmSmPdiwGsmjABBRoNVo+fgKw9ezBtGrAd7Qfr6dOnIz8/H9IrVwJQwvwT8aS/oaMR7LZ9D+8gB79DjWLYf15FoQ73QIM1Ln7NEZITInIJ4zmRb1km4gVBgKqdNdxdlXN+KTQrq5EzTcD1kLIiU7EWqzADb68UoT4/EcaoU6FQWCfQTSaTvB6tWfcByahUpKIqNgPHrpiJXh+tQFJjGboPkAKyrzoYQm8qG1HwY0wnf2Ei2DecLRH06aefYvLkyfL+u+++i6lTp1q9lyvGEYUPl5PzLS0t+OWXX5Cenu7L9hAhX61G7u7dMIqieby0vI0WBKwcODBQTXOPmyPD3FrCx4Vjv/huI555LB5ftpyDt0dL68PnjxmHSV8WojwqHi8+2gjA8QyEAQMGYO/eva177+Drr5dg7Ngedq9zZyZ6RyPY2+t7EAUFBNHx5+UAPCL3MJ4ThaDMTKghdbibWjvKk1AldWBPk15imZbv378/9uzZ4/BQacPT0VRTgqRuMRAUAkRTHpr/aoYqvq3n2hcdDJzKRuR9jOnkL0wE+4ajJYJef/115OXlya8pKCjAKAeDE7liHFH4cHkIzMaNGxn0yS9yUlKgcxJJdBoNclJS/NwiN5lHhmm1wNKl0tZyIVgLpY2N0Dc0oLihwWoJn+KGBugbGlDa2Ojxse9J3Yqjrx3CJcufRk33OABATfc4XLL8aRx97RDuSd1q956WlhYIgmCRmAcaGxvRo4eUmDffqzsbPFdUX48xO3agqL7e4fMqVdtIi/ZGsJuPXy0koxypOKm2/7xufBVEZIHxnCgE5edDbF1D2raQqxgVjRyLl06aNAl73nkHGDNGGpLngCpeBUEhBWRBIVgl5n2qnQuBjq4hiMgeYzr5S06OlPB1RKeTnifXlJYCej1QXGy9RFBxMXDrra8hL+9J+bW7du1ymJi31dF9OhEFN1aKoaBmW4wsJLgxMsztJXzcOHa+Wo0Zv+9GC8S2g7duoyBgldp6BkJdXR26d+8u7yckJKCmpgaCILg8E93p6H9n8/Vs2J8nHeccLMGWL2JwSob1500HB+AREVGEyMmB4GR4nLbFiO2tf5cLxc2Z02G1vg6X0/Mzt2YQEhFRwHDmcue0v0TQba1/BJSVlXXY+cYV44jCA/vVKCiZi5Fpjx/H0hdegLamBqlKpfeKkfmai0PE89VqRLe+ztESPvlqdbvHLtILGDNR5XBgXE5KCj5JdzwD4ZN06xkI+/fvt0rMT5gwAbW1tfI6teY+gVe+rse6kTvwytf1KCmRHndp9L/lfL12mM+j0wGzZknbPaUqpGc4/i6dfs1FRe2OGCQiIgpZrcPiRIulZrQADvXpg8VZWY6H4un10lA9C5bJ8ECxuoY4fBgAsPbIEeczCImIKGA4c9k78vOlpYAA+yWCAAOAHNTW1ro0K8bR/bP5Pj2QeDtO5B6OnKfgU1qK9OpqlMTGIua22yBUViJPp0PzZ59B9euvYVUKPiclBequXa1GypvpNBpo4uIcvs88ED05uf2BcSkpAA7Zz0CwXBno+++/x4UXXijvz5s3D0899ZTdsVQqYM1B6UY+v6ICi/tLo9pcGv1vmSSYMUO6+nDyc/RK8VZHi/cRERGFMovhcTtHjMDfr7yCDACVAAouuQSnfvEFYC4I6aRaX+nff6PaYIAAWHWoz0hNhQggUalEn1jH9Wh8weoaQhQBQUCVyeR8BiEREQUMS4d4R3trxQMj0dj4E1SWy751MAndK/fPXsbbcSL3MDlPwad1npcKkG8uhaoqqCx/q4dhKXhXl/ApLQUWLZKCXZcu0mPOct7mGQgZsbGYmZaGFeXlKGtslGcgrFy5EjfffLN87Pz8fOTYLBhY2tjY7o38i/364f59+xwX8DUasfLZZ6XkAABUVlpfhXjz51haClRXS/9m3OgMICIiCgmtWZE3Vq/GrXl5OA1AGoCPli1D/3//2/q1Tqr1ub2cno/lJyUht6oKRrTNBDBvowGsTEryW1uIiKhjwZgIDm0tAKLkbWFhEVQq6wUuQiXRzdtxIs8xOU/BJ8JKwXeUQDczBzvLgPz339LWWc47PTYWJdnZiBEECIKAvLQ0NIsiVAoFHnjgASxatEh+z08//YRzzjnHrn2u3MjrtVrHo//vugua3bvtP7QgdLjEjczF9eo7WLxPEoadOkREFDkefeopPP744wAAeZGaWbPa4p4zOh2g0SC/ogK5u3c77lAXBKwcONDZEXwiZ/BgqPv3h3b5crvndHl50Ozdy9hNRERhJy7ubwC1AMoArIBSeRt69RqK1FQpMR+KiW7ejhN5jmvOU/CJsFLw5gS6TqPBrN69odNoUJKdjXSbaeWZmR33lEdHS30bllQKhbx2vCAIUCkUGD16tFVivqSkxGFiHnBvXXyFzRarVztuaPfu0lw+B2vg2nFxvfp2F+9z9MUQEREFAVfXZb3pppvkxDwAHHvpJfu4Z0thfamfk5ICncZxPRqdxroejV/k5wNRUQAARWtVQfMWUVGM3UREFHaOHz+OAQO6AsgEMBKjR+9FU9NQq7Xizff+Wm3bJHRzojsryzoRHix4O07kOSbnKbiZbyoV4f1P1VEC3ZZlsHOmo74LURQRExODb775Rn6svr4efdrpdnflRl4u4BsXh6UDBkAbFycV8HU2kq+mpv0ri9JSKXHfTlE7u2RGhHXqEBFReHClD/rcc8/FGosX1NbWotdddzmPe4MGdVitz65DPRBycpC8Zg1Sjx2Dds8eLH3hBWj37EHqsWNIXrOGsZuIiMJKaWkpevXq1brXjFtvvRWbN2+2WyIoFBPdvB0n8hyXtaHgZFH0DDNnAitWAGVlEV0Kvr3CMYLQ8RSxxsZGdDEvUt/KaDQiqnXEmiucrYvvdPmcI0eAhASgrs7xAZ0tU+TCnLjVd4nO195TKACTqW1LREQURFydri6KIhITE3H8+HH5vY2NjVaF4gDYx73Vq6WY6aBan6vL6flLukKBkhtuQExLCwSTCXmfformqCioLJbVIyIiCnU7duzAsGHD5P0nn3wSDz30kMPXtnfv37pSXVDj7TiRe5icp+DEUvDtsk3Gq9XA8ePO+y4qKiqQmpoq7w8ePBg7d+50+Xyu3MhbjvYXBAEqQZB+jhUVwK5djtfkcXZl0U7dATEqGiXzV2Ldy9LDlsmMFEMy0tmpQ0REQc6VdVmNxhZE20yZa2lpgcJydp2zwQzm5WkcVOtrrx5NQCQnQ9Wrl/wZhBUroGLsJiKiMLJx40aMHz9e3l+1ahVuuukml94bSolujrEk8owgiizJYKm+vh4JCQmoq6tDfHx8oJtDZOXQIWD4cCnY3XQTsGqV9Ni2bVLAc9R38fPPP2Po0KHy/j//+U+sWLHC7XM3mUzyjbwoiu7dyBcXSxkH2ysLvd55t7/5PTY00GM7NHIHhW1HhdjY1NapI4rs1KGIwfhljd8HBbO3327rg7YVHQ0sX96Mf/6zLXalpaXhyJEjjg/WFAZxLxw+A5GXMH7Z43dCoWzNmjVWifivvvoK48aN6/B9lvf+lonuwsK2temDEUM6URtX41d4L+RNFGbMEwp0OuDOO6WkfEmJFLAdBbz//e9/Von5F1980aPEPODauvhOmbvQtdoO18C1Y1N3ILp1FR6na++pVG3DEB2MGCQiIgq09tZl/eqrOqvE/OjRo50n5oHwiHvh8BmIiIhsPP3001aJ+eLi4g4T8+baakePtt37z5olbS2LxgYrhnQi9zE5TxRiXA12CxcuxJQpU+T9zz77DHfffbcfWuiAZa+Cq1cWThL6Kz5xnNB/6y0WmSEiotBj0weNMWNGy8/l5eVh8+bNAWgVERERdcasWbPw//7f/5P3Dxw4YLXmvDOWheKZ6CaKDFxznigM5eTk4J133pH3d+3ahbPOOiuALYL1lYQrVxZO6g4YfpXeZ7vm3qefAtOmeb/ZREREvmC7LuvLL5/Er7/WAagEACxYsMDqpp6IiIhCw8UXX2zVuV5dXY1evXo5fb2rheKJKDwxOU8UZvr164d9+/bJ+1VVVUhMTAxgizrBQUI/ORlISpL+XHEFsHChtGbvxo3SMvW8eCEiolBg2Qe9adPX+PXXcQBiADS7VSguXBQVAXPnSnHdUQ15IiKiUJCeno7Dhw/L+ydPnkSXLl3afY8rheJZLZIofDE5TxQmjEYjlEql1WONjY1Qhdnct/R06UKlqgr47be2i5fjx3nxQkREoUWlAvLz8zF9+vTWR5o7LBRXVF+Pufv3Y2HfvsgKo8KIltP4mZwnIqJQYzKZEBUVZfWY0Wi0e8yR/Py2QvGOaqutXOndthJRcOGa80RhoK6uzioxn5CQAJPJFB6JeXNFnKIi+aH8fOkiBWinMCwREVGQe/rppy0S864ViltdUYGC2lqsqajwdfN8rrQU0OulmW+W0/iLi6XHS0sD2z4iIiJXNDU1WSXhu3fv7jBZ70x7heJ1OtZWIwp3TM4Thbh9+/ahe/fu8v7EiRNRW1sLwTykPNRZDqVrxYsXovD36aefYuTIkejSpQsSExNx1VVXBbpJRF41e/ZslwvFlTY2Qt/QgOKGBqyrlNakX1tZieKGBugbGvBJVRXG7NiBovp6v7TdWzIzpVHyWq00Iw5om8aflWU9zZ+IQhdjOoWzuro6xMbGyvsjRoxATU2Nx/fjtoXiiSj8cVkbohD23Xff4aKLLpL3H3roITz55JMBbJGXuFIRB9Ki8ubCsLYFYokodK1fvx633nornnrqKYwZMwaiKGLnzp2BbhaR14wdOxabNm2S9zsqFJe5dav8d/OtfpXBAK1eb/W6NRUVIbXUDafxE4U/xnQKZ4cPH0Z6erq8n5OTg3wPp3HbFopfsQIoK5MeJ6LwJogiV2a2VF9fj4SEBNTV1SE+hG5uKPK89dZb+Oc//ynvv/3227jxxhsD2CIvshxlIAjSnbp52+pQmYjhw+0vXgoLpXXpiSJNuMQvo9GIzMxMPPbYY5g5c6bHxwmX74PCT0ZGBg4dOiTvu1Io7u2KCuTu3g2jg8v2KACnREWhvqUFyUolPh8yBCKARKUSfSxG8gWr4mLrmjFmej2g0fi/PUSBFk7xizGdwtmuXbtw9tlny/sPP/wwnnjiiU4ds6lJKhRvvvVtbpbq0xBRaHI1fnHkPFEIuu+++/DCCy/I+z/99BPOOeecALbIy1wYSpeeDpSUtF285OXx4oUoHBQXF+Pw4cNQKBQYNmwYjh49iqFDh2LRokU466yznL6vqakJTU1N8n59iC3vQeGvM4XiclJSoO7a1W6kPAC0AGhoaQFgP5peHDWqU232J86EIwo/jOkUrmxnsC9btgx5eXmdPq7lvawg8N6WKFKE3SpWXM+Ogo2DeqYuPefMRRddZJWYLy0t9Tgx78n5/cLFReVVqrZB9rx4IQoP+/fvBwDMnz8fDz/8MDZs2IAePXrgoosuwvHjx52+7+mnn0ZCQoL8JyMjw19NJgLQfkxtbm7uVKE4S+aLd8uVbEWbbbQgIF+tdvvYgWCexq/VAkuXStvUVE7jJwoHjOkUjv773/9aJeY/+eQTryTmiShyhVVyfv369Zg+fTpuvvlm/Pzzz9iyZUv4LPNBIctBPVOXnrMliiKio6Px3XffyY81NDTgtNNO80nbggYr4hCFhfnz50MQhHb/FBUVwdQ6ZPahhx7C1VdfDa1Wi7feeguCIOC9995zevx58+ahrq5O/lNWVuavj0YEwHlMraurg8qi93jkyJEeFYpLViqRqlRCGxeHpQMGICsuDr2iHU+C1Wk0yElJcfszBIJ5JpxOB8yaJW1LSrhEHVEwY0ynSPWf//wH119/vbyv0+kwefLkALaIiMJB2CxrYzQacffdd+O5556zWs/uzDPPDGCrKFK1V8/0yBFpv3dv57VO+/SxPt7ff/+Nrl27Wj3m6lR4d9rm7PwBwYo4RGHlzjvvxNSpU9t9TWZmJhoaGgAAgwYNkh9XqVTo27cvDh486PS9KpXKKgFKkauoCJg7F1i4EMjK8u25OoqpRuNRnHNOmvz6zhSKS4+NRUl2NmJaE195aWnQ1dcje/t2KACYAHkbajiNnyi0MKZTJLrnnnuwePFieX/v3r3o169fAFtEROEibJLzXM+OgklmZtvfzQPjqqrsC545e86y3tvRo0eRltZ2Yz9kyBD8/PPPPm1bUJSJ5qLyRGElMTERiYmJHb5Oq9VCpVLhjz/+wPnnnw8AMBgMKCkpQZ+g6DmkYGc5gt3XyfmOY2qq/Lw3CsWpLGaRCYKAdJUKqUolMmJjMTMtDSvKy1HW2IhkpbJT5yEiag9jOkWayy+/HJ988om8X1FRgWQOGiMiLwmbdSK4nh0Fk/x8qW4pYF/PVKFoW6HFUa1TywF1O3bssErM33LLLZ1KzHfUNtvzBxwXlSeKOPHx8Zg9ezYeffRRfPXVV/jjjz9w2223AQCuvfbaALeO3OWv2ialpYBeDxQXW49gLy6WHi8t9c1524upgAGAVCNl2bJlnU7MO2IeTa/TaDCrd2/oNBqUZGcjPTbW6+ciInIXYzqFg4EDB1ol5v/66y8m5onIq4J+5Pz8+fPx2GOPtfuawsJCu/XsAOCtt95Ceno63nvvPcyaNcvhe+fNm4d7771X3q+vr2eCnjotJwdQq+1HygNAYaG0dfScTgdoNNLfP/roI1x55ZXyc4sXL8acOXN82ra3fqzHCuV+nFnfF1nx8Z0+FxGRJ5577jlER0dj+vTp+PvvvzFy5Ehs3rwZPXr0CHTTyE3+GsUeqFlh7cVUYCSA7fjkk098uh6t7Wh6lZtr2RMR+RJjOoUqURShsKl7ZjAYEO2k3gsRkaeC/rcK17OjUKdQACZT29aV55599ln861//kvc///xzXHLJJT5v2wZDBQpO1GJNRQWT80QUMEqlEosWLcKiRYsC3RTyQCBqm+TnA7m5gNHoeFbYypXePZ8jbbG8BYBUE0an02HEiBG+PzkRUZBiTKdQZDAYEBMTI+9HRUXBYDC4XcydiMgVQZ+c53p2FKo6qmeamgr0zK6HceZ+RK/oi+M/xSM5Gbjxxhvx7rvvysf59ddfrTqdvN22K2Y14u0NBlQcBb5uqQQArK2sxIzUVIgAEpVK9OH0eCIiclEgRrG3N4LdclaaL5hjqigeREXFAgAzAWTgu+/WY8SI0313YiIiIvK6v/76C3FxcfL+4MGDsXPnzgC2iIjCXdAn511luZ5dRkYG+vTpg+eeew4A17OjwOionmlJCXD/wQosOVyLu56twHOnxUOt7osDBw7Ix6iqqnKpc6ozbVN8uxW4W3pcaGk9r8EArV4vv14cNcrrbSAiovAU6FHs7c1Y84X0dECrvRqffvpB6yPLcfBgBTIymJgnIiIKJUePHrWq+XbVVVdh/fr1AWwREUWCsCkIC0jr2U2dOhXTp0/H8OHDUVpayvXsyHU+qFrnqJ5paWMj9A0N+LW5Af+tbBupHnv2ABxQKoGUFABAU1OTTxLztm3LV6sR3dpI80BG8zZaEJCvVvusDUREFH5ycqTR6o7odNLzvmAewa7VAkuXStvU1LYZa76iVqstEvPSiLuMDBaKIyIiCiV79uyxSszff//9TMwTkV+Ezch5gOvZUSf5qWpd5tat8t/NK9ZVNTcDy5fLj5suushv69nlpKRA3bWr1Uh5M51GA43FlD4iIiJ3+HMUe0cz1ryNheKIiIjCw08//YRzzz1X3n/ppZdw1113BbBFRBRJwmrkPIUHHwxgd660FNDrgeJi66p1xcXS46WlXj+lo5Hq5uH1QksL8tXqgBWaUdhsiYiIPBGoUeyOZqx1xJPrDoPBYJWYj46OhslkYmKeiIgoxHz88cdWifn333+fiXki8iveQVDQ8dMAdkkAqta1N1K9aMSIgIxUT1YqkapUIiM2FjPT0rCivBxljY1IVir93hYiIgp9/h7F3hnuXnfYFoo7++yz8csvv/iwhe4rqq/H3P37sbBvX2TFxwe6OUREREHptddew+233y7vf//99zj//PMD2CIiikRMzpPfObphLC0FqqulG3jLAewzZki58cREoE8fHzQmQFXrPv74Yylz0TrXXxBFiAEaLQ8A6bGxKMnORowgQBAE5KWloVkUoVJwDD0REXnGMhHv6ih2f/H0uqOiogKpqanyfrAWiltdUYGC2lqsqahgcp6IiMiBefPm4ZlnnpH3f/vtN6hZb42IAoDJefI7RzeMlgPYcWY98OB+VC7vC6227YbSywPYJTk5gFptPVLeTKcDNBqnb/V0VNq9996L/6xZI83zr6rCg2efjc2xsQEfqW6ZiBcEAaoAdhYQERH5kicT5/bs2YMzzzxT3r///vvx3HPP+a6RbiptbES1wQABwDqLgvMzUlMhAkhUKtEnNjagbSQiIgoGU6dOxTpz7zyAw4cPo3fv3gFsERFFMibnyS86umF88V0l7p8eC6MRwPgKQFMLjKsA/oj35QB2a25WrfNkVNoFF1yAH374Qdq54QaU7N2LPn36QBRFjlQnIqKwFkxLrbg7cS4UCsU5LDhvMFgtoyeOGuXfRhEREQUZjUaD7du3y/t1dXWI5ywzIgogJufJLzq8YUwF8n/UYloOgNFS8h5jKoEvU7HybeD8s5UAfDTay1y1LiMDmDkTWLECKCtzWLXO01FpoigiKioKosUwvIbjx9GtWzcAHKlOREThL5iWWnFn4tzHH3+MK664Qt5///33cfXVV9u9r0inw9zffsPCQYOQNXKkD1rdvny1Grm7d8MoinLBefM2WhCwcuBAv7eJiIgoWIiiiC5dLkBT0yIAcwHo0dTUhJiYmEA3jYgiHJPz5Bcd3TAaRRHTTuiB5RZPdDcAy/WYdgLAVh+O9nKjap0no9L+/vtvdO3a1eqxlpYWKIJhlHxRETB3LrBwoR+q7xIRUaQJhaVW2ps4t3TpUrxx223YBOk2/sV2CsWt3rkTBf36Yc3OnQFJzrdXcF6n0QSk4DwREVEwaGlpQXR0NIDFAMYAmA6TqRACB8gRURAIguwghbyiImDMGGnrRE5KCnRO1m/XaTTIV6sRbU53m+Nj6zYaAvJ9XZhFpcLqbQ3o8dYOrN7W4LRqXb5ajejWAO6ok8G2nUePHrVKzA8dOhSiKAZHYh4AVq8GCgqANWukfRd+lkRERK7K3LoVWXo9tHo9qgwGAG2d2ll6vVWnt7+ZJ85ptVIZGK1W2jdPnJs3bx5uu+023ATpNv6radPsEvOlf/4J/bZtKC4sxLqkJADA2qQkFBcWQr9tG0r//NO/H6qVwmZLREQUqXbv/hvR0SMADANwPQAgOflubN8uQK+XisQTEQUSR85T51kmeF0Yfa0AYLLYAh2M9tL6Z7TX8zsrUNuvFi/srMBNIx1Pt3dnVNqOHTswbNgwef/WW2/F8uXLvd9wd5WWAtXV0iwBcxGctWuBGTOARYvc+lkSERG1J5iXWmlv4txdl1+OHz/5xOI2Huj51VdAcbG0OH1iItCnDzIPHZKPJ7Qu1VMVHw/tiRPSgydPQuzXz2+fKVmpRKpSiYzYWMxMS8OK8vKAF5wnIiIKlOrqaqjViQDM9+/SVUhHReCJiPyJyXnyTHsJXoubVkuu3jA6St77ypY/G3HguAGCAOxMkqbb/5JUibcLUyGKwOk9lTivn+Pp9u2188MPP8RVV10l77/88su48847ffIZ3JaZ2fZ38zS+ykrrq5MOfpZERESuCMRSK+4UnrWcKCcI0n5WVhaKLJerEwQpHjq4k88/fhy58fEwRkdLrzO/HkC00YiV9fXe+2AuSI+NRUl2NmIEAYIgIC8tjQXniYgoIu3fvx9nnHEGgBsBrASghHl6fntF4ImI/I3JefKMowRvB93PHd0wBmK01/mHLKbTt96/i/EGaf17ADgJiP1GWb2no3Y+88wzmDdvnvz6L7/8EuPHj/fZZ3Bbfj6QmwsYjc6HCHAoAREReZm/Ot89LTwriiK6deuGkydPIgcWt/HmGOjgTj7nqqugLixsGylvQZeQAM3YsZ35KB6xTMSz4DwREUWioqIiDB8+vHXvHcyZMx4vvTTD7nW2ReA7d06WcyMiz3AYDXkmP1+6OQXablYtb1rz8x2+TaVQQNDrgTFjIOj1VjeQ5uS9zmTCrGnToDOZUJKdjXQfFoq77bgaMDpe6x5GQXrehtxOjQazeveGTqOR2zl16lSrxPxvv/0WXIl5AMjJka5C2uPCz5KIiMgV5k5tbVwclg4YAG1cHFKVSq92vpc2NkLf0IDihgarwrPFDQ3QNzSgtLGx3febC7WfPHkSAPAOgGhn9Vd0OimW2lC0VpNV2FaVJSIiIr/5/PPPLRLzwDvvvIMZM6TEvDn94IsJZbbl3IiIXMWR8+SZnBxArbYeXW3WUfdzO2vUqxQK6fGCAgj5+VBZBFVfePWqFJxX2LVtpLyF/AQNcsY6nm7vaFRaZmYmSi2qyVRXV6NXr17eb7Q3KRSAySTNfnA0Ot6bQwl8hUMUiIiCmj+WWrEsLGvuYzcXnjUTR41y+N6TJ0/ilFNOkfdPP/107N+/X1pfHmiLleatjeSkJKQePoyMhgbMVCqxwmBAWVwcki1nGRIREZHb3L3Ve/PNNzFz5kx5f9OmTRgzZgwOHZKKvmdkADNnAitWAGVlbUXgPeXBar9ERHaYnKfO6+CmFUDHUauxEYiNDWxUs5xr70a+wGg0Qmkz+q+pqQkxMTHebJ13JSdbX5289BLw229tSfr2fpbBxs2CxERE5IQPOzt9vdSKp4Vnq6urkZSUJO9fcskl+Pzzz6Ud21jp5E4+PTMTJSkpiFGpICgUyDOZ0NzUBFWXLl79jERERJHGnVu9xx9/HI8++qi8//PPP2PIkCEA2i8C3xkerPZLRGSHyXnynIs3rQBci1odPe+jqDYgSQnFYSViG2JxhTINHxnK0RjXiAGZHU+3r62tRY8ePeT9Xr16oaqqCoKP13d1p9idQ7ZXJ5deCgwfDpx2mneHEvgKhygQEXlfCHd2elJ4tq1QnOSOO+7AkiVL2l7gxp28ZSJeUCiYmCciIvKQJ7d6N998M1ZaVHY9ePAgMjIyrF7jqAi8I+6MVXBUzo3FZonIXUzOk+fc6X7uKGrdeivw+usBiWrDM2NRk5KNbioBCoUAkykNfzWJiO/S/vD5P//8E/3795f3J0+ejE8++cRn7bTkabE7K5Y/p4wM6SrI20MJfIVDFIiIvCMMOztdKTyr1+uRZb7jHjAAZyxciNzRo+1f6OqdPBEREXmFu7d6F154Ib7//nt5v6amBt27d/f4/O6MVejMar9ERGYsCEudo1KhqKEBY3bsQFFDg/Ob1vaKkOp0wKuvtv+8g8Jr3hTfRQGFQor8CoXQYWL+m2++sUrMP/LIIz5PzHe22F2HVKq2q59gT0B4WJCYiIhsZGZKd55arXTnC7TdAWdlWd8hBzm58KxCgaUbNkCrUDgsPPvFF1+0JeYBTHjhBexLSMCaigp/N5mIiIhsuHOrl5ycbJWY//vvvz1KzJeWAnq9VG7GcqxCcbH0uEVpOad8WWyWiMIbf21Qp1mO4nZJR1EryKPaG2+8gdEWo+vWrl2Lxx57zOfnzdy6FVl6PbR6PaoMBgBtxe6y9HqrYnhhr6POHh935hARhY0Q7uwsKgLGjJG2QFvhWd3772PW889Dt349SrKzkR4bK7/nrbfewsSJE4GUFGDAACzdvBnbW4vBrj1ypN0Ob9vzERERBatQjlmu3OqZTCYIgoAq88ACAC0tLYi1iPnu6MxYBfNqv1otsHSptE1NDd4VYoko+HBZG/JIaWMjqg0GCIDVKO4ZqakQASQqlehjGxg7WqPenTXsA+See+7B4sWL5X2dTocRI0b45dyeFrsLe64UJCYiIsdCeD621bTzJGl5HpXF8jzC2rVQWSzP88Tq1XjkkUekN69dCwCYDUAQRUAQUGUyWa1ZL44a5fx8obUkPxERRZhwiVmObvUaGxvRxaK2S3JyMio6OfutM2vH+6rYLBFFDibnySOWo7TNpU/No7jNbG9qO4xaQR7VzjvvPPz444/yvqMiM77kSbG7sBYCnTlERCElBDo7nS2Rv/ilzLYXOVmg9hGL47wcHY3/MxphBCC2vt68jQawMimp3fOF8JL8REQUpsIpZjm71YuJqUWXLj3k111wwQX47rvvOn2+zo5VYIkaIuoMJucjkDvVx53xeBR3R1ErCKOaKIpQ2Cyx89dff+GU1mnwgeBKsbuwF+SdOUREISOEOjudFYnLQT5WIhdK2A95MwoCZlhUjqupqUH3Hj1wbv/+0C5fbncOXV4eNHv3AqLI+uNERBQywilmObrV+/PPgxgwoK134eabb8abb77p9XOHwFgFIgozwbmoN/mU5RQ3T+WkpEDnpPtYp9EgJyXF84MHkb///tsuMd/S0hKwxLxc7C4uDksHDIA2Ls5hsbuIEUpFbImIgpX5DlinA2bNkrYlJdLjQaSoCBg0CIiKkvYtc/DvIAfnRTleoHaEKOKd1r83NjZKheLy8+UDKVrvvM1bREXJa+2H8JL8REQUYcItZlne6u3c+YtVYv7xxx/3emKea8cTUaBw5HyE8OUUt3AdxV1eXo7evXvL+xqNBnoHS8r4k7nYXYwgQBAE5KWloVkUoQrS4rlERBQiVCoU1ddj7v79WNi3L7Li4wPdIjurVwO//QZMnSovGW9l1SoA0yAPdWsBEGXxfEtLS1uHe04Oks88E6mlpcioqsLMzz7DiksvRVlSEpItFuj1y5L83pjSSEREES+Ey8i0a/Pmzbj44ovl/TfffBM333yz18/DidlEFCjM6EWIzlQfdyacR3EXFxdbJeZnzZpll5gvKgLGjJG2jhTV12PMjh0oqq/3attUCgWE1iEEgiAwMU9ERF6xuqICBbW1WNPJomreVFoK6PVAcXHb4IIvv5S25tF05jBo7CkNeTMNG4ZZAPQAygGYevVyuERdukKBkhtugO6OOzDrk0+gu+MOlNxwA9KdxNXhQhE2YQyGC04Cv6e8MaWRiIjIgjmUhfqt4jvvvGOVmP/ss898kpg348RsIgqEEP9VTa7yxRQ38yhunUaDWb17Q6fRoCQ7G+mxsd5ptI90lFT/4IMPoLUYbrBkyRIsXbrU7nUd3UsHY5KDiIjIUmljI/QNDShuaMC6ykoAwNrKShQ3NEDf0IDSxsaAts/R4ILaWmlrvo4xTzvvcXY6arZvR5Rej+UARgKYfv752FFd7fjgyclQ9eoFoXX+uqDVQtWrl9389fJyaRTdnB6rMQYFuDdpTeenuVv0OhQVFmLM88+jaNs2qRdCr5eeJyIiclM4Lc3y7LPPIicnR94vKirCxIkTA9giIiLf4LI2EcJXU9wsR20LggCVuZs5iFkm1W1njz/11FN46KGH5P2vvvoK48aNk/c7Wh6oMaERsSkGCIBVkmNGaipEAIlKJfoEeecFERFFjsytW+W/myN4lcEArcVsMXHUKP82ykJ+PpCbCxjt67wiOhpYuRK48UZp2nllZRl6pp0mv7fDQnGuzF8vLcVva6pxVrOAKX9Lgf96rMVVH85ATIUItHi4LqDFlMXVd92FAo0Gaw4cQFYoVu0jIqKgES5Ls9xxxx149dVX5f39+/fj9NNPD2CLiIh8h8n5CBTs1cd9sfSqK2vuP/DAdXjvvffk9/z+++8YOHCg1XEsl/8x90OYlwcCABRsBQ5Zn7syiJIcRERElvLVauTu3g2jKMKcCjZvowUBK23ioDu8Ec9dHVzwxx+/4B//+If83OOPP45///vfHZ/AMlthMX/dfN2gzcrEAwAeAGD6uzXwV1YhJrtzSfTSd99F9eOPQzAasW70aADA2jFjMOPLLyFGRyPxkUfgYSkgIiKKcE5CW8i45JJL8KV5DTsAVVVVSExMDGCLiIh8i8n5CGKe4paRAcycCaxYAZSVBd8Ut/ZGtnuqw6Q6AKAtMV9dXY1evXrZHaejEXy3Hlfj9V5SksNWZ5McRERE3paTkgJ1165WnchmOo0Gmrg4j4/t7XjubHCBLwrFma8bbkQ+ViIXShihaO22EGAzdN+T46emAq0jAoXWa4aq7t2hXb5cfg3HzRMRUbDwV+3y008/HSUlJfL+iRMn0LVrV5ffzxrrRBSKuOZ8BDFPcdPpgFmzpG1JifR4oDkq+LZ2rfeWXm1vzX3AAKBtLbumpiaHiXlAGsGn0zk+h04HPHhpAlaeeabD51eeeSbOT0hwv/FERER+oLDZesIX8by99XPfffddq8T8559/7pVCcebrhneQg5FoJ/BbrIXr1vHVanmEjNg6asC8jW59noiIKFj4una5KIoQBMEqMW80Gt1KzAOssU5EoYnJeV/qqPJoAKhUgKAvArKyIIwYDtVOH7bNjc9vWfDttMoibMIYnFZZBK1Wejwz08nxXDlHURFyVozBzrek12ghHV8L83tGAngHycnJMJlMiImJcem4wwXpOMOFttdkbt2Kabt3W7+wtRdg2u7dVmv7utz+cBOJn5mIKIglK5VIVSqhjYvD0gEDoI2LQ6pSiWSlEoB7v7YdFXA1z1ST47mb5MEFrxRh1rox0L1ShJIS4N13n8ONN94ov66oqAiXXHKJ+ydwwFFnfEvrZbModP7yOSclBTonoyN06enISUnp9DmIiIg6w9cD6Myam5uhsKhld8opp8BkMiEqKspn7eQtKREFEybnfSlYu21Xr5aiVFGRb9vmxue3HNk+HasxBgWYBul90dHS8w6P58o5Wl/T81PpNTNajz8dq+WXTJkyBRUVFRDMa960c1zzCL77kqTj3Ju0Rh7Bl69WI9q2KK55JJzJZD8SLlj/jfhSJH5mIqIglh4bi5LsbOg0Gszq3Rs6jQYl2dlIby1g3t6vbdub2/Zmqsnx3AMqFSCskRoi5K/Bvffegblz58rP79+/H1pHC9N7QW+Uowkx2I2BmIWlOKm2GLrfGa0JeLsZC0zMExFREPBFh7ut+vp6qCwWxddoNPjrr7/a7st91E7ekhJRMBFE0YMqVmGsvr4eCQkJqKurQ3x8vPsHsKw8OnEiUFkp3bx9/nlb5dE+ASjxVVoK7NwJ1NUBd90F1NRIj/foAbz8MpCQAJx9dufb5unnLy3F7z9UI2eagM8xESmoRAWS8U+8iSVP1OD0YT2Af/5TOl6vXsDixdL77r4bOHbM/hyAXTtaEnrgLryMpxruRHdTLSrQAxPxLqbd+AXufeoeh+9xdtxmgwDllIkQKishJifD8PHniFGKQGMjipVKaE+etPuI+vvvh+axx9qyFL17B9e/EV8K1v8XRGGk0/ErzPD76BxXf23PmSNdRsyZ0xaai4sdF3DV69sKuHamITUxMbi4uRkCgGoAeh8Vijt0CBg+HHjRNAfXV76MtUl34f+iXkLhNhHpyc2drrB3qLERw/V6ZMTGYmZaGlaUl6OssRGFWq3cMUJEkYfxyx6/k8B4++22emu2zGVXPFzdDQBw5MgRnHrqqfL+9ddfj7Vr1/qsnaWlwHffAS+9BOzbJ6VEeEtKRL7kavxict5GpwO/ZQ+vIEi/5c1bs0B85a72PHe2bZ5+fov3mSBAAVHe2h3P2XndfM6EdqaOOGu7C68p7t8f2uXLoTCZYFIo5K0+Lw+avXtdO0+4/bcM1v8XRGGEN67W+H10jiu/tvV6x4n7o0eByZPtC7h6lJx30BC7+O2L+NHaKeC0M95Ld/BNJhNiBAGCIEAURTSLIlQKTmwlimSMX/b4nQSOVzvcLfz+++8YNGiQvD9v3jw89dRTHh/PlXY6SonwlpSIfMnV+MWrf2/z1Xxub7SrvZs9hcI7bfP08+fnQ2x9nzkhb97K8bG9SGl7DkftsCF/G+29x5XjWr7mttuQ3NCA1GPHoN2zB0tfeAHaPXuQeuwYks2zFRSKtp9FMP0b8aVg/X9BREQOtV9IXeJs+vjkyc4LuHqjIeb4LfoyfrTOkY/J1kJo/ZBCVRVisr04lx+ASqGQp+4LgsDEPBERBSVzePJGmPrhhx+sEvOvvvpqpxLzlhy107wm/RNP2L+et6REFBREslJXVycCEOvq6jw/iF4vitLvees/er33GurNdnm7bZ5+fmfvy88XRUAsHDBAHP3882LhgAGufY72Pq+773HnNXq92KhUiqbWx02A2KhUWr8uWP+N+FIkfmYiP/JK/Aoj/D7cV1goiqNHS1tRdP5r+4knRDE62vFz0dFS2G5sFEWTSTqoafRosWlLocftat661f/xIz9f/pB21x/mD0lE5AOMX/b4nQROWZkopqaK4vDhorh0qbRNTZUe98R7770nQhp/JwIQP/roI5+3092UABGRt7gavzg8x5e82b0cijz9/E7et3rCBBRoNFgzYYJb59ANGIAxzz+PbQMGAABE83y2jmYSdOI1KoOhbSRc677DeXSR+G8kEj8zEVEIcFYczfbX9qWXAjqd42PodNK6ripVa9hbvRpCQQFi1nlWca2+vh4jzzkHANBi2yBfysmRP6R8/TF+vPSc+UMSERGFCdsC72bp6UBJiRT6Zs2StiUl0uPueumll3DttdfK+z/99BOmTJnSqXa70k7LSXhERMGI2TFfSE724nxuL7crKQlQKqV1Uvv0kaJUUpJ32+bp53fwvtLBg6E/4wwUn3MO1o0bBwBYO24cikeMgH7ECJRefLHTc/wdF4dyAC+03lS/OH48RIUCwpAhztvlSts7eo35+SFDgPh4oGtXaTtkSNvrgvXfiC9F4mcmIgpy5qnexcXAunXSY2vXSvtHj0qXCO392nbY39reQfV66XkXlJeXIyEhAZUAygEc6NHDb/GjtLER+pYWFPfvj3WjR0sfYcwYFPfvD31LC0obG312biIiIn9z1kEPWHS4Q9p6Ug/9/vvvx9133y3v//HHHzintfPdW5y106K/3c6gQbwlJaLAY0FYG14rNtPUBMTEtFUYaW72LIp5W1OT1B5zW5qaPI+wrYqKgLlzgYULpWVY5eN68vlt3id8+638lABp7pt5ayaOGmV3jm2lpRg5fjyiRRHGZ58FevRAslKJz884A2LXrkiMiUEflcpxu1xpe0evMT/f3Cx1hhgMbfuW330w/hvxpUj8zER+wmJp1vh9uMaVwq8mk/2v7UOHgOHDgYwMYOZMYMUKoKwMKCwE0jM6XwTctlDcww88gCeefdZv8UP45pu2v4siREGQt2biqFE+Oz8RRS7GL3v8TnyjtfY5BMFxgXcv1T7HVVddhQ8//FDeP3r0KFJSUjp/YDeYC8aai9SbLyeKioDBg3lLSkS+4Wr84uQeX7H87d7J5LdX2bYjNrbTh7TsZZeT855+fpv35avVyN29G0ZRlBPy5m20IGDlwIF25yguLsbI+npg2TIYATkBUGUwQLt7t3x4cdQox+1ype0dvca872zr6nnCTSR+ZiKiIJafD+TmAkZjW77csjjaypWOR6GZp4//0lSPB/fvxys39MUQVbz0vCsHbceWLVtw/vnny/uvvfYaZs+e3fYCP8QPq+uP1i/AvLW6/iAiIgpRlrXNzbHeXODdrLNDOc8++2zs2rVL3m9oaEC3bt06d1APmCdx2w4qSEnhLSkRBR6XtSGPeGnGeodyUlKg02gcPqfTaJBj0+P+wQcfQKvVAgsWSEkBQL7SsEzq56vV3mlgO8zr9q3W1WPMjh0oqq/3+TmJiIjc0d5U746WVlepgDUVFSiorUV+RUXbzW0nDvrBBx9YJeY/+ugj68S8n7h7/RFoztYKJiIicsZyLXZHfen5+Z4fWxRFREVFWSXmm5ubA5KYB7y7dj4RkbcxOU8eycyURslrtVLvOtDWy56VZd0Lb8vTG0iFzdbWU089hauvvlra+fprvOrkdW+deSZWlJf7PFlunlHwwi4pcbGmosKn5yMiIuoMV+t1lzY2Qt/QgOKGBqyrrAQArK2sRHFDA/QNDdbrsbtRBPyBB9bh6qu7A5CG7HmzUFxndHT9EQzaWyuYiIjIkc500LfHaDRCoVDAZDLJj5lMJiiVSs8O6CXeWDufiMgXgvk+g4JYZ3rZ3b2BTFYqkapUQhsXh6UDBkAbF4dUpRLJFsH92muvxUMPPSTv7969GyNHjgRgf1O94dgxnyXLzTMKPi1qRP62BqB/A35JlBIXa45U4tM9DhIXREREAdRhvW6bXvXMrVuRpddDq9ejymAA0Lp0nF6PLL0emVu3ul0E/P7778eiRRUAxgCY7pNCce6yvf4Y2LUrYgQB5U1NAW2Xmb9mMRIRUfhzoy+9XSdOnLBKwg8cOBCiKEKwLHJDRERWWBDWBovNuM5cVMWWXg/YzgTvbLGZJpMJMYIAQRAgiiKaRREqhQKiKCIjIwOHDx+WX3vs2DH07NkThxobMVyvR0ZsLK5ITMTbFRWoaG6WXmM0SgVihwyBCCBRqUQfi/X3i+rrMXf/fizs2xdZbvw7kK85Cr5pe9Cyiq3FNQkLyRGRNzF+WeP34Z5263XPmQO8/LK0XbwYb1dUyOux2zKvx56TkuJSEfDSUuDmmx9AQcEmAJ8DSEFiYgu+/DLKq8XoPNVkMuGXhgY8eOAAkpRK/LeqCnNOPRWL+/cPXKNauVLMl1f5RKGH8csevxPfabfAu5tLvlRWVloVer388svx8ccfO3xtUREwdy6wcKFF3ToiojDDgrDkN+aK5+atI50tNqOy6MIXBAEqQYDBYEBMTIzV65qbm+We+vTYWJRkZyNGEKD49tu297duzaP85PNbJMtXV7QtReNOcl6ugbdADTy4G4gW207YumUhOSIiCjZ29bqPWvSqWw7LnjEDOaIIdVoatEeO2B1Hp9FAExfn5KD288el64PnAABabMNC3IAHqxdCq227U3d6feCHO/ujzc14/tAhFNTWokvrtcjaykrMSE112LnvT52su0tERCSvxW7uS8/Lc9iX3qE///wT/S06ru+55x785z//cfp6y9n0TM4TUaTjsjbkMXdmrHu72ExNTY1VYj41NdXhOnYqhQJCawHYaJvCsLYFYt1aQ9cJed2+r1OA20OnkBwREZGVjorLTJ4MAFC0BnOFm0O0zYXigBwA0tI4NyEfY1CAaZDWvevw+sCHC62brwkyt27FutbP/3frCIRK2yV8AsRXawUTEVFk6exa7Dqdziox/8ILLzhMzHM5NiIixzhyPkJ5umyLJXd62XNyALXa8TI4Op39Mjjt2bt3LwYMGCDvX3HFFfjwww/bfU9OSgrUXbtajZSXz986yk/45hv5MVdG13dEULR2AJgAKNpWtiEiIgp6HQzLTr7lFqQKAjL+/BMz//c/rLj8cpT174/k3bulXvrWtWgcXW8YjUa5M/00vINE6CHiHVwP6U59KtZiFWbg7ZUi1OcnArBY18ZinbyiwkLMff55LHzvPWQVF7u2Tp6LXEm6B9NMOFdmMRIREXnbhg0bcNlll8n7//3vf3Httdc6fG1nZ9MTEYUrJucjlKfLtthyYca6nc7cQG7evBkXX3yxvP/YY4/hkUcecesYCsj5cliePl+tltfQdTS63tUbcPOMgpQMJQ4YlTCWxyL6yzScfkc5KkyNVoVsiYiIglJ7vepGI9IffBAlSiVijEYIooi8DRvQHB0NVWuBWPPdte31xokTJ9CtWzf5UNIguT8AaGFq7RpPQhWKoQWmwepYAKzu7FffdRcKNBqsOXAAWV6+s7e8JnDGagmfADFfc9iuFeyk7i4REZHXLF++HLNmzZL3v/32W1x44YVOX8/l2IiIHGNyPoKUNjai2mCAAFgt2+KvdVM7ewO5bNkyzJ49W95vr1fe4fmVSqQqlciIjcXMtDSsKC9HWWNbstyV0fWuaJtREItmMRtKCDBcJyAmJk0uZEtERBQybHvVn3gCeOyxtkQ8AEEUpf3oaJSuWYPqhga7643LYmIwbvx4ICUFqKjA5ZdfDlx3HcTcXAhGIxStXeLmrRgdDcHmTr303XdR/fjjEIxGrBs9Wjr2mDGY8eWXEKOjkfjII+j8uPn2rwmCaSact9YKJiIicsfDDz+MBQsWyPu7du3CWWed1e57vDmbnogonDA5H0Esp2h7Y9kWd3XmBvKuu+7CkiVL5P3CwkJkuVk5xrJArCAIyEtzniw333h7egNu/kwqQWGxLxWyJSKi9u3ZswcPPPAAtmzZgubmZpx99tl48sknMbo1GUt+4qxXPTcXuPRSp3fXmfX10uKxaLveqDQYMO7AAWDZMgDAPR9/LK9HKzi5Uxcc3KlnpqYCr74qPd863K6qe3doly+XX+PtxLnttYC6a1ccNxiCZiacJ7MYiYj8hTE9/EybNg1vv/22vH/o0CGceuqpbh2Dy7EREbXhEN4I4kpRVG8qqq/HmB07UFRfLz/mSbGZ7Oxsq8T8oUOH5MS8o3O0x1wgVjq/YJeYN4+uTzoeB7wwAEk1cUhVKoPmBpyIKBJMmjQJRqMRmzdvhl6vx9ChQzF58mQcPXo00E2LLOZedZ0OmDVL2paUSI+bmeOoRTx1dL0hMxqRU1LisFCco2PZyler5ZElovkcrdvo1udtFRUBY8ZIW3eYrwmy4uLwcr9+yOrWDalKJb44+2yUZGcj3YezDYmIwgVjengZOXKkVWK+trbWrcS8ud9fqwWWLpW2qalcjo2IIpsgiuFTcsMbvfL19fVISEhAXV0d4juxFnuwKm5ocDhFW6/Ven3d1Dl79+Llw4cx59RTsdiierurTCYToqKirB47ceIEunbt6rVzWDLXmDPAhMsvFVBVKSApWcT/PhOhhMJbNeaIiHwiXOJXdXU1kpKS8N133+GCCy4AADQ0NCA+Ph5ff/21Vd2R9oTL9xG0Dh0Chg+3H1VfWAikpzu93nimpgYPXnmlW8eyVfznn9AeOmT3uD49HZp+/ewef+76Imj/OxfF1y/E/Wvdm3XXZDLJM+5EUeTydETkc+EUvxjTw4coiujevTvqLQbFNTU1ISYmxu1jNTW1zaYXRS7HRkThy9X4FVbL2kyaNAkDBgzA5s2b0aVLF7z44ouYPHky9u3bh9TU1EA3L6g4K4raWd5a1/7kyZM45ZRTrB5raWmBQqFw+xxFRcDcucDChUB7K+G01ZhTyKP7q6sEZGe1LUUTPl1ZRETBqVevXlCr1Vi9ejU0Gg1UKhWWLVuGlJQUaB0to9KqqakJTU1N8n69izOqyEOurlVnOWddocC4sWM9P5ZZSgpw6JD9tUxKivwSc4e7IAA9PlmNMSjAvv+tQXFxFkQRLne4WybiBYHL0xERuYMxPTy0tLQgOjra7jGFh53VXI6NiMha2CTnq6ur8eeff+LNN9/EkCFDAADPPPMMXn31Vfz6669MzrfqqChqZ3ljXfvDhw8j3WKk3PDhw7Ft2zaPz7F6NVBQAKxZ035yntXjiYgCTxAEbNy4EVOmTEFcXBwUCgVSUlLwxRdfoHv37k7f9/TTT+Oxxx7zX0Op3bvr1S+9BAwcCFRVAZ99hrPmzsUxQZCvN4rq6zF3/34s7NsXWfHxLt2pm99zX3p6h9cyF2aWIhHVECHgc6wDAFz+91pM1M6AABHVSESpyOlwRES+xJge+v7++2+rmevp6ekoKysLYIuIiMJP2MzLteyVP3HiBIxGo8u98vX19VZ/wpm5KKpOo8Gs3r2h02i8um5qZ9e11+v1Von5O+64wyox7+o5SkulWnTFxcA66Z4ca9dK+3q9NKLOVk6OtJyuIzqd9DwREXlm/vz5EFqXB3H2p6ioCKIo4vbbb0dycjK+//57bNu2DVOmTMHkyZNRXl7u9Pjz5s1DXV2d/CdSbhw9XU/dl6ZNm4bFDz8M3HADcNttOPTaa9h53nlW1xurKypQUFuLNRUVLh/X/J6vamo6vJYpRSb0yEIxtEhCFQAgCVUohhZ6ZKEUmV79zEREkYQxPTIcO3bMKjF/8cUX++xnEYzXM0RE/hJWa84fPnwYU6ZMQXFxsdwr/+mnn2Lo0KFO3zN//nyHvfJcz85znq5rv379elxzzTXy/tKlSzFr1iyPzmE569y8lp15a+boX35xsVSUxrZ6vF4PaDROm05EFHDBvh5rdXU1qqur231NZmYmtmzZgvHjx6Ompsbqc/Tv3x8zZ87Ev/71L5fOF+zfh7fMmQO8/LK0Xbw40K2RCsVZdqrX1tYiISEBgPXSdxN/+QWVBgOSlUp8PmSI06XvPHkPAODttyHOyIXQYrR7SoyKhrBqJXvdiSgohUL8YkwPfyUlJTj99NPl/by8PCxbtsxn5wu26xkiIm8ImzXnnSXPLRUWFkKr1Vr1ynfp0gVvvPEGJk+ejMLCQqSlpTl877x583DvvffK+/X19cjIyPDqZ4hU7qxr/+STT+Lf//63vO9qgSBn5/B0iRpz9XjbmnSsHk9E1DmJiYlITEzs8HUnT54EALt1TBUKBUwmb1ZJCV2W66lbzg6bMQNurafuTaIookePHqirq5Mfsy0U58nSdx4vl5eTg91QQz3Nfvbk7lU6qHPY405E5CnG9PC2fft2aCxGpj399NMud6S4IxivZ4iIAiHok/N33nknpk6d2u5rMjMzsXnzZmzYsMGqV/7VV1/Fxo0bsWrVKqfBRKVSQRWBFUhcLZLqCXfXtb/qqqvw4Ycfyvt//PEHBgwY4NI5erbEwvi/NERfXo7jUW3nyMkB1GppFLwtnc75KHh3a9IREZF3ZWdno0ePHpgxYwYeeeQRdOnSBa+//joOHDiASZMmBbp5QaGtgDnkmWJVVdYxz5/zIl0tFJevViN3924YRdHhsnQrBw60O7Yn7zHr2VPamqCAAiZ5a36ciIh8izE99Hz11VeYMGGCvL9mzRpMmzbNJ+cKtusZIqJACfrkPHvlfcPVIqmeMK9rH9O63mBeWhqaRREqm5+NKIo49dRTrdYbPHbsGPZHR2PMjh1tReLaOcf99whY8rKAuwxpeO4/9ucA7Jeo6QirxxMRBU5iYiK++OILPPTQQxgzZgwMBgPOOussfPzxx/jHP/4R6OYFhWAqYG5bKC4jIwMHDx50+NqclBSou3Z1uCydTqNxuPSdJ+8xSzk7GWJKKoSMDOCWmRDeWAGxrAwpZ3M6HBGRPzCmh5ZVq1YhNzdX3t+4cSPGjh3rs/MF0/UMEVEgBX1y3lXsle+YP6eNWSbJBUGAynIReAAGg8FqqjsANDc3Q6lUYv7evXKROEfJ+bbPocB/Wz/HurUCcmcIVp+DS9QQEYWmrKwsfPnll4FuRtDydHaYtx0/fhy9evWS98eOHYuNGze69F53lr7z+D3p6RBKS+TpcAKnwxER+R1jenBxNoN+wYIFePjhh+X97du3t1u7zxvcvZ7x5ex/IqJACpvkPHvlOxYs08ZqamrQ02JO+amnnoof9u7FL42NEBobsa6yEgCwtrISM1JT7Qq+ufo5uEQNERGFO3dnh3mLp4Xi3F36ztP3yDgdjoiISOZoBn1eXh5ef/11+TUlJSXo04lRe54k0V25nvHl7H8iokAKm+Q8wF75jgTDtLE9e/bgzDPPlPevuuoqrF+/HsI338iPdVTwzZ3PwXtyIiIKR4GcHdaZQnGuLn3X2fdYKqqvx9z9+9tdLo+IiChctTeDPi9vFvT6thzKsWPHrAbSecKdJHpH1zMsGktEkUAQRZbYsFRfX4+EhATU1dXJhWXDSXGx42ljer3vp8Fv2rTJas26J554Qp4693ZFhVzwzZa54FtOSor8WCA/BxFRMAr3+OWuSPg+mpraZoeJon9mh7lVKC5I5p/P2bsXLx8+jDmnnorF/fsHrB1ERK6IhPjlLn4nnWO5wqz5msG8tXgGf//9N2JbZ6u7yzKJPnEiUFkpJdg//7zjJHp71zOutJ0ZLSIKVq7Gr7AaOU+u8/c0+KVLl+K2226T99977z1cc8018r6nBd8CNZ2fiIgo0Pw9O2z16tWYMWOGvN9hobgAzj8vbWxEtcEAAehwuTwiIqJw1t7Mc8AAIBdGoxFRUVEen6MzS+i2dz0TDLP/ifZogqIAAC12SURBVIh8jcn5CBOIafB33nknXnnlFXm/qKgIWkfD3lu5UvCNxV6JiIj856mnnsJDDz0k7zstFBck888zt26V/97RcnlEREThrL3Cq/Hx41FbuxmC5RB1D/gqie5u0VgiolDE5HyE8XeR1JEjR2Lbtm3y/uHDh9G7d2+Hr3Wn4BuLvRIREfmHW4XigqT6fL5aLS+XZz6beWteLo+IiCjytACIkrcFBQXoZF4egH+S6Jw1T0Thisn5COSPafAmk8luWtyJEyfQtWtXp+9xt+Abi70SERH51sUXX4zNmzfL+8ePH0ePHj2cvyFI5p97ulweERFROGppKW/9WxmAFejV60EolX19MvPc20l0zpononDH5Dx53YkTJ9CtWzcAWgALAcxFS8s2KJwk2S1ZJuIFQYDKG934RERE5LZTTz0VR44ckfddKhQXhPPPXVkuj4iIKFzt2rULI0acDSAGQDMeeeQRzJ/f1+szz32VROeseSIKd0zOk1cdPnwY6enprXs3ARiDOXOK4EJenoiIiIKAo9lvHhWKC/D8c3eWyyMiIgpH33zzDUaPHt2614zXX38dt9xyCwDvJ7d9mUTnrHkiCmdMmZLXFBUVIT39XAAaAMPQpUsuAKkOXHExoNdLdeKIiIjIO4rq6zFmxw4U1dd75XhNTU1WSfhevXqhsK4O43budP0c5qFzWi2wdKm0TU3tcOictz+Lebk8nUaDWb17Q6fRoCQ7G+kdjf4nIiIKA+vWrbNIzAMbNmyQE/O+olK1lZxhEp2IyDVMzpNXvPfeexg+fDiAUgB6AMVobIwH0FYHLivLuk4cERERdc7qigoU1NZiTUVFp49VV1dntWxNdnY2qqur3T+HeeicTgfMmiVtS0qkx82KioAxY6StDz6LmUqhgNCaJRAEwWkdGyIionDy/PPPY+rUqfL+tm3bMGnSpAC2iIiInOEdCnXa448/juuuu651LwdRUdLUdUd14PLz/d8+IiKicFLa2Ah9QwOKGxqwrrISALC2shLFDQ3QNzSgtLHR7WMeOnQI3bt3l/evuu02vPzll56fo6Ohc6tXAwUFKH3/fa9/FiIiokg2Z84c3H///fL+n3/+2TqQjoiIghHXnKdOufLKK/HRRx/J+3v2zEdDgyKY6sARERGFlcytW+W/m8umVxkM0Or18uPiqFEuH2/Xrl04++yz5f1HH30Uj40ahQ9aj+eNcwCQ1rarrpaS9evWAQAyL7lEWvfOm+chIiKKUJMmTcJnn30m71dWViIpKSmALSIioo5w5HwE8saarqIoIjU11Soxf/z4cfTv31/eN88c5wxyIiIi78lXqxHdOiq9dXKavI0WBOSr1S4f69tvv7VKzL/xxhuYP3++V88hy8yU1rjTaqU17wDkL1iAaKPRu+chIiKKQP3797dKzJ84cYKJeSKiEMC0aQTq7JquBoMBCoUCFRbvb25uRo8ePQB4XAeuQw6WpyUiIoo4OSkp0DmZiqbTaJCTkuLScdatW4dRFqPSN2zYgJkzZ3r1HFby86U17gB5zbucr7+G7vbbvXseIiKiCCKKIgRBwJ9//ik/ZjAY0LVr1wC2ioiIXMXkfITw1vq0x48fR0xMjLyfkZEBURShVCrlx1ypA+eJ1uVpsWZN545DREQULhStSW7z1lXuFIpT2Gw9lpMjXRQ44bXzEBERRQjzwDmzmJgYmEwmREd7dwVjDpQjIvId3v9EiMytW5Gl10Or16PKYADQtqZrll5vtX6tM3/88Qd69eol719zzTU4ePCgw9d2VAfOVaWl0lK0xcXy8rRYu1ba1+ul54mIiCJKaSmSd+9GqiBAu28flr7wArT79iFVEJC8e3eHwfHuu+92qVBcslKJVKUS2rg4LB0wANq4OKQqlUi26JBvT3vL6JlaL0FNUCC5pkb6LB6eh4iIKBI1NDRYDZwbMmQImpqaIJhvxL2IA+WIiHyHBWEjRL5ajdzdu2EURYdruq4cOLDd93/99dcYN26cvP/kk0/ioYce8k1jLWRmtv3dfI1RVQWrgrNuDhYkIiIKbZmZSAdQolQixmiEIIrI27ABzdHRULV2wDsLjpdddhk2bNgg77dXKC49NhYl2dmIEQQIgoC8tDQ0iyJULhaTsVxGLys+HqWlQN3RZKh7pWJXXQaWGmdidvQKDBbL8MmJvkjsk47M3u6fh4iIKNIcPXoUaWlp8v4111yD9957z6vncFDHHWvXAjNmSJcZiYlAnz5ePSURUURicj5C5KSkQN21K7R6vd1zOo0Gmrg4p+997bXXcLvFerDvv/8+rr76ap+001Z+PpCbCxiNbXkG8zY6Gli50i/NICIiCh6twVFOxAMQRFHabyc4DhgwAHv37pX3T5w40eF6tJYJckEQoOpgNF5pYyOqDQYIgNUyejNSU6EdD6AuETHHSmBADEQIeN2YB+WxZjRPkqbYiaJr5yEiIopUf/zxBwZaDK574IEHsHDhQq+fhwPliIj8g8n5CKQAYLLYtuf222/Ha6+9Ju/r9XponBSI84WcHECttr4AMNPpAD82hYiIKDi4GRxFUbRajxaQ1qj19nq0AKyWyTOn183L6GGZtN88elRb2yCgGSp2uBMREbngxx9/xHnnnSfvL1myBHfccYdPzsWBckRE/sH5whHE3bVjhw8fbpWYP3z4sF8T87bMeQXOciciImrVQXD0V6E4s3y1GtGtw+scLaP3RBe1w/fpdFKfAxERETn20UcfWSXmP/jgA58l5oH267gzbhMReQ9HzkcQV9eONZlMiIqKsnrMlanvvpKcDKSmAhkZwMyZwIoVQFmZ9DgREVFEMgfHnj2lIW3R0cDx41bBsaGhAfHx8fL+kCFD8PPPP/u0WR0to4e9cfg3pL4Ek6ltS0RERM698soruPPOO+X9H374wSpR72uM20REvsMxyBFGpVDI1dsFQbBLzJ84ccIuMd/S0hKwxDwApKcDJSVS7/ysWdK2pER6nIiIKCKZg+OYMcCePcDFF1sFx6NHj1ol5q+55hqfJ+ZtKWy2QFufglYLLF0qbVNT2eFORETkzIMPPmiVmP/999/9lphn3CYi8j2OnCfZoUOHkJGRIe9nZ2fjxx9/DGCL2qhUbX8XBOt9IiKiiFJaClRXA4IAw9v/hRKAIX8dlLm5gChiX10d+l18sfzyuXPn4tlnn/Vb88zL6GXExmJmWhpWlJejrLERyUql3KcQEyPF87w8oLmZcZ2IiMiR6667Du+99568f+TIEaSlpfnt/K7E7aIiYO5cYOFCICvLb00jIgobTM4TAKCwsBAjRoyQ9+fMmYPFixcHsEVERETkUGam/Nfo1rKr0TVVcoHYMyxe6stCcc50tIweO9yJiIg6NnToUKtZb/X19YiLi/N7OzqK26tXAwUFwJo1TM4TEXmCyXnCf//7X1x//fXy/vLly3HrrbcGsEVERETkTPWL+eh1Xy6EFiOE1nKr5q0BQC56ATiGDz74AFdeeWVA2mi5bJ4gCFC1LqlHRERE7RNFEbGxsWhubpYfa25uhlKpDGCrrFlM4sO6ddJja9cCM2YAoggkJgJ9+gS2jUREoYLJ+Qj32GOPYf78+fL+5s2bMXr06MA1iIiIiNqVdE8OhkGNYmjtnhsJPbZDgy1bfsS5554bgNYRERGRp4xGo10S3mQyyXXjgoXFJD6Ym1bVNokPgJSkJyKijrEgbASbMmWKVWJ+z549TMwTEREFufx8ILq1dntL66Vci3xJZ8TChUeYmCciIgoxJ0+etErMn3HGGRBFMegS80DrtUjrUE9zEt68jY6WniciItdw5HwEEkURKSkpqKqqkh87fvw4evToEcBWERERkStycoAhPZNRfmkqypCBFZiJmViBDJThhTUqjJrWO9BNJCIiIjdUVVUhOTlZ3p84cSI+++yzALaofTk5gFptPVLeTKcDNBr/t4mIKFQxOR9hmpubobKp4GIwGBAdzX8KREREocKQko5MlKAZUQCisRwzEYMW/DSI1VWJiIhCyb59+9CvXz95/6677sJLL70UwBa5R6EATKa2LRERuYfL2kSQ48ePWyXmTzvtNIiiyMQ8ERFRCBFFEeee2w/NOA4MKASeX4eBU06gZ6oKFoPuiIiIKMgVFhZaJeYXLVoUMon55GQgNVUaPb90qbRNTQWvRYiI3MSsbITYvXs31Gq1vH/ttdfiv//9bwBbRERERO6yLhSXCUyYBWiuwrjLKvHcaQlQceA8ERFRSPjss88wadIkef+dd97BDTfcEMAWuSc9HSgpAWJipKKweXlAczN4LUJE5CaOnI8AGzdutErML1iwgIl5IiKiECMXiktJAQYMQPqY85F8/fUAgHWVlfi1uQH6hgaUNjYGuKVERETUnjfeeMMqMV9QUBBSiXkzlUpKzAPSlol5IiL3ceR8mHv11Vdxxx13yPvr16/HVVddFcAWERERkbuqq6uRlJQk7axdCwA4BEAwGAAAVQYDtHq9/Hpx1Cg/t5CIiIhcMX/+fDz22GPy/i+//IKzzz47gC0iIqJA4sj5MDZ79myrxHxxcTET80RERCFm3759bYl5AOMLCxHdOkxNbH3MvI0WBORbzJYjIiKi4JGbm2uVmD948CDOPvtsFBUBY8YARUUBbBwREQUER86HKa1Wi+LiYnn/yJEjSEtLC2CLiIiIyF2FhYUYMWKEvL9o0SLcd999KG5osBopb6bTaKCJi/NnE4mIiMgF5513Hn788Ud5v6amBt27dwcArF4NFBQAa9YAWVkBaiAREQUEk/NhxmQyISoqyuqxkydPokuXLgFqERERURgoKgLmzgUWLvTbXfPnn3+OSy+9VN5/9913MXXqVKvXKACYLLZEREQUfBITE3Hs2DF5v7GxEUePqrBvn7RW+7p10uNr1wIzZgCiCCQmAn36BKjBRETkN0zOh5ETJ06gW7duVo+ZTCYI5gotRERE5Bk/D2l78803MXPmTHm/oKAAoyzWkU9WKpGqVCIjNhYz09KworwcZY2NSFYqfd42IiIico2jwXMtLS1QKBTIzGx7zHzLXlUFaLVtj4siiIgozDE5HybKyspw2mmnyfvnnnsutmzZEsAWERERhbjSUqC62u9D2mwLxe3cuRODBw+2ek16bCxKsrMRIwgQBAF5aWloFkWoFCwnREREFAwaGxutZrCnpKTg6NGj8n5+PpCbCxiNbUl48zY6Gli50n9tJSKiwGFyPgxs27YNI0eOlPfvvvtuvPjii4FrEBERUTgIwJC23NxcrFq1St4vKytDenq6w9daJuIFQYCKM+WIiIiCQk1NDXr27Cnvjxo1CgUFBVavyckB1GrrywoznQ7QaHzdSiIiCgYcXhXi1q1bZ5WYf/3115mYJyIi8ob8fGnoGuB4SFt+vldPd8EFF1gl5mtra50m5omIiCg4HTx40CoxP3PmTLvEvC1OfCMiilwMASHs0UcftSoMV1BQgFtuuSWALSIiIgojOTnS0DVHdDrpeS9JSkrCDz/8IO83NjYiISHBa8cnIiIi3/v555/Rx2LJuyeeeAJvvPGG09c3NwO9egEDBwJxcdJjCgVQUQHo9dIKe0REFN64rE2Iuuyyy7BhwwZ5f+/evejXr18AW0RERBReSkuBE78DgwC0QIEomOTtb78Dp/Tq/JLz7RWKIyIiotDx9ddfY9y4cfL+ypUrMWPGjHbfk50tbY8da1tBTxSBSy9tew2LwhIRhTcm50OMKIpISkrCsWPH5MdqamrQvXv3wDWKiIgoDGVmAqciGYVIRRkysAIzMRMrkIEyjJ+WjMPo3A2zbaG4tLQ0HDlypNPtJiIiIv/Kz8/H9OnT5f0vv/wS48ePd+F9LApLRBTpmJwPIc3NzVCpVFaPGQwGREfzx0hERORt0g1zOjKNJWhGDAABy5GHGDTDFK1C/krPj+1KoTgiIiIKfs8++yz+9a9/yft6vR4aF6u5sigsERFxznSIqK6utkrMn3baaRBFkYl5IiIiHzEvOd8MFYDWueYQ0AxVp5acty0Ud8sttzAxT0REFIJmz55tlZjfv3+/y4l5W+YV7biyHRFRZOGvfR8qqq/HmB07UFRf36nj/P7770hKSpL3r7vuOpSyMgwREZHfeOuG2bZQ3JNPPonXX3+9cwclIiIivxs3bhyWLVsm71dXV+P00093+zjJyUBqqjR6fulSaZuaKj1OREThj8OufWh1RQUKamuxpqICWfHxHh3jq6++woQJE+T9p556CvPmzfNWE4mIiKgd5hvmjAxg5kxgxQqgrMyzG+ZNmzZh7Nix8r4rheKIiIgo+PTp0wcHDx6U90+ePGlVR8Yd6elASQkQEyMVhc3LA5qbAZsVbYmIKEwxOe9lpY2NqDYYIABYV1kJAFhbWYkZqakQASQqlegTG+vSsZYsWYK77rpL3v/ggw9w5ZVX+qDVRERE5Ii3bpjffvttTJs2Td53tVAcERERBQ+TyYSoqCirx4xGo91j7rK8rhAEJuaJiCIJk/Nelrl1q/x38+q0VQYDtHq9/Lg4alSHx5k1axaWL18u72/fvh1Dhw71UiuJiIjIVZ29YbYtFFdcXIxhw4Z5qXVERETkD83NzVZ14OLj41FbWwtBENp5FxERUfuYnPeyfLUaubt3wyiKEFsfM2+jBQErBw7s8BjDhg3Djh075P0jR44gLS3N620lIiIi37r99tvx2muvyfsHDhxAZmZm4BpEREREbquvr0dCQoK8P3z4cGzbti2ALSIionDB5LyX5aSkQN21q9VIeTOdRgNNXJzT9zqaIteZteuIiIgocMaPH4+NGzfK+9XV1ejVq1cAW0RERETuOnLkCE499VR5/4YbbsA777wTwBYREVE4UQS6Aa5asGABzj33XHTt2hXdu3d3+JqDBw/isssuwymnnILExETMmTMHzc3N/m2oBYXNtj1//fWXVWJeoVDAZDIxMU9ERBSC+vTpY5WYP3nyJBPzREREIebXX3+1Ssw/9NBDTMwTEZFXhUxyvrm5Gddeey1uu+02h8+3tLRg0qRJOHHiBH744QesXbsW69evx3333efnlgLJSiVSlUpo4+KwdMAAaOPikKpUIlmpdPj6srIyxFmMqL/gggvQ0tLCteuIiCgshWKHu6tEUYQgCDh48KD8mNFoZGc7ERGFnXCO5wDw/fffY/DgwfL+0qVL8eSTTwawRUREFI5CZlmbxx57DACwcuVKh89/9dVX+O2331BWVobevXsDAJ5//nnk5uZiwYIFiI+P91dTkR4bi5LsbMQIAgRBQF5aGppFESqFfV+ITqfDOeecI+//3//9H1544QW/tZWIiMjfzB3u2dnZWLFihd3z5g73pKQk/PDDDzh27BhmzJgBURTx8ssvB6DFrmGhOCIiiiThGs8B4L333sN1110n7//vf//DZZddFsAWERFRuAqZ5HxHfvrpJwwePFhOzAPAhAkT0NTUBL1ej9GjRzt8X1NTE5qamuT9+vp6r7THMhEvCAJUDm7M3333Xdx4443y/htvvIGZM2d65fxERETByhcd7r6K566yLRQ3YsQI6HQ6v7aBiIjIn3w1gC7QMf3FF1/E//3f/8n7W7duxciRI/3aBiIiihwhs6xNR44ePYqUlBSrx3r06IGYmBgcPXrU6fuefvppJCQkyH8yMjJ83VQAwL///W+rxPw333zDxDwRERE67nB3JFDxHJAKxVkm5m+88UYm5omIKOJ5Es+BwMb0e++91yoxv2fPHibmiYjIpwKanJ8/fz6E1qVfnP0pKipy+XiOpo2b1351Zt68eairq5P/lJWVefRZ3DF58mSrter+/PNPXHTRRT4/LxERUSjwpMM9EPEcAH777Te7QnFvv/22X85NREQUzDwdQBeomH7FFVfgP//5j7xfUVGB/v37++XcREQUuQK6rM2dd96JqVOntvuazMxMl46VmppqN0qtpqYGBoPB7oLAkkqlslof1pdEUUSvXr1QU1MjP1ZbW2s12o6IiCgUzZ8/X57e7kxhYSGysrJcOp67He7+jOdm33//PS688EJ5f+nSpZg1a5Zf20BERORNgY7nQGBi+qBBg/D777/L+w0NDejWrZtf20BERJEpoMn5xMREJCYmeuVY2dnZWLBgAcrLy5GWlgZAWuNOpVJBq9V65RydYVskDgAMBgOio8Nm2X8iIopgwdDh7k/vv/8+rr32Wnn/k08+weTJkwPYIiIios6LtHguiiIUCusFBXifTkRE/hQyEefgwYM4fvw4Dh48iJaWFuzYsQMA0K9fP3Tr1g3jx4/HoEGDMH36dDz33HM4fvw47r//ftx6661OC834S3V1NZKSkuT9zMxMHDhwIIAtIiIi8q5w73Avqq/H3P37sbBvX2x56y3cc8898nMsFEdEROEi7ON5ETB3LrBwITB0qBFKpVJ+TqFQwGg0tjuqn4iIyNtCpiDsI488gmHDhuHRRx/FX3/9hWHDhmHYsGHymvRRUVH49NNPERsbi/POOw/XXXcdrrjiCixatChgbS4qAkaOPIGkpEvkx6ZOncrEPBERRbSDBw9ix44dVh3uO3bswF9//QUAVh3u27dvx6ZNmwLe4b66ogIFtbXIe/99q8Q8C8UREVGkCsl4vhooKADefLPZKjE/aNAgtLS0MDFPRER+J4iiKAa6EcGkvr4eCQkJqKur6/QFw5VXHsRHH50GYDGAe/DMM8/gwQcf9Eo7iYiILHkzfvlabm4uVq1aZfd4QUEBRo0aBUC64b/99tuxefNmdOnSBTfeeCMWLVrk8hq03vg+ShsbUW0wQAAw8ZdfUGkwADU1wIMPAoKAok2boE1P9+jYREREjjCe2+vsd1JaClRXA4IATJwIVFYCQAWAiQAEjB+vxZdfLnf7uERERO1xNX4xOW/Dm4Ffq60AkAKgAs8//zsuumgUEhOBPn283mwiIopwoXQz7w/e+D6Eb75p2xFFKbibt+aHW5MPRERE3sB4bq+z34n1YHgRgADABMuFBJgVISIib3M1foXMmvOhwro2TrK8ve++toI3DPxERETBL1+tRu7u3TBaJuRbt9GCgJUDBwawdUREROSK/HwgNxcwGgEpMQ+YE/PR0cDKlYFpFxERERBCa86Hivx8KcBLBKttdLT0PBEREQW/nJQU6DQah8/pNBrkpKQ4fI6IiIiCR04OoNM5fk6nk54nIiIKFCbnvYyBn4iIKPwobLZEREQUehQK6y0REVGgMST5EAM/ERFRaEtWKpGqVEIbF4elAwZAGxeHVKUSyUploJtGRERELkpOBlJTAa0WWLpU2qamSo8TEREFEtec9wFz4M/IAGbOBFasAMrKGPiJiIhCTXpsLEqysxEjCBAEAXlpaWgWRajY805ERBQy0tOBkhIgJkYqH5OXBzQ3AypVoFtGRESRjsl5H2DgJyIiCh+WiXhBEKAyF4clIiKikGF5Py4IvD8nIqLgwOS8jzDwExEREREREREREZEznJNNRERERERERERERORnTM4TEREREREREREREfkZk/NERERERERERERERH7G5DwRERERERERERERkZ8xOU9ERERERERERERE5GdMzhMRERERERERERER+RmT80REREREREREREREfsbkPBERERERERERERGRnzE5T0RERERERERERETkZ0zOExERERERERERERH5GZPzRERERERERERERER+Fh3oBgQbURQBAPX19QFuCRERkevMccscxyId4zkREYUixnN7jOlERBSKXI3pTM7baGhoAABkZGQEuCVERETua2hoQEJCQqCbEXCM50REFMoYz9swphMRUSjrKKYLIrvkrZhMJhw5cgRxcXEQBMHn56uvr0dGRgbKysoQHx/v8/OFAn4n9vid2ON3Yo/fib1I+k5EUURDQwN69+4NhYKr1vk7ngOR9e/NVfxO7PE7scfvxB6/E3uR8p0wntvjPXrg8Tuxx+/EHr8Te/xO7EXSd+JqTOfIeRsKhQLp6el+P298fHzY/6N0F78Te/xO7PE7scfvxF6kfCccYdcmUPEciJx/b+7gd2KP34k9fif2+J3Yi4TvhPHcGu/Rgwe/E3v8TuzxO7HH78RepHwnrsR0dsUTEREREREREREREfkZk/NERERERERERERERH7G5HyAqVQqPProo1CpVIFuStDgd2KP34k9fif2+J3Y43dC/sR/b/b4ndjjd2KP34k9fif2+J2Qv/Dfmj1+J/b4ndjjd2KP34k9fif2WBCWiIiIiIiIiIiIiMjPOHKeiIiIiIiIiIiIiMjPmJwnIiIiIiIiIiIiIvIzJueJiIiIiIiIiIiIiPyMyXkiIiIiIiIiIiIiIj9jcj6AFixYgHPPPRddu3ZF9+7dHb7m4MGDuOyyy3DKKacgMTERc+bMQXNzs38bGmB79uzBlClTkJiYiPj4eJx33nkoKCgIdLMC7tNPP8XIkSPRpUsXJCYm4qqrrgp0k4JCU1MThg4dCkEQsGPHjkA3J2BKSkowc+ZMnH766ejSpQvOOOMMPProoxH3++PVV1/F6aefjtjYWGi1Wnz//feBbhKFKcb0jjGeO8Z47hjjuYTxXMJ4Tv7CeO4axnTHGNMdY0yXMKZLGNPtMTkfQM3Nzbj22mtx2223OXy+paUFkyZNwokTJ/DDDz9g7dq1WL9+Pe677z4/tzSwJk2aBKPRiM2bN0Ov12Po0KGYPHkyjh49GuimBcz69esxffp03Hzzzfj555+xZcsW3HjjjYFuVlCYO3cuevfuHehmBNzu3bthMpmwbNky/Prrr/jPf/6DpUuX4v/9v/8X6Kb5zbp163DPPffgoYcewvbt23HBBRdg4sSJOHjwYKCbRmGIMb1jjOf2GM+dYzyXMJ4znpN/MZ67hjHdHmO6c4zpEsZ0xnSnRAq4t956S0xISLB7/LPPPhMVCoV4+PBh+bF3331XVKlUYl1dnR9bGDhVVVUiAPG7776TH6uvrxcBiF9//XUAWxY4BoNBPPXUU8U33ngj0E0JOp999pk4cOBA8ddffxUBiNu3bw90k4LKwoULxdNPPz3QzfCbESNGiLNnz7Z6bODAgeK//vWvALWIIgFjumOM5/YYz51jPG8f4znjOfke47lzjOn2GNOdY0xvH2M6Y7ooiiJHzgexn376CYMHD7bqYZwwYQKampqg1+sD2DL/6dWrF9RqNVavXo0TJ07AaDRi2bJlSElJgVarDXTzAqK4uBiHDx+GQqHAsGHDkJaWhokTJ+LXX38NdNMCqqKiArfeeivWrFmDrl27Bro5Qamurg49e/YMdDP8orm5GXq9HuPHj7d6fPz48fjxxx8D1CqKZJEe0xnP7TGeO8Z43jHGc8ZzCpxIj+cAY7ojjOmOMaZ3jDGdMR3gsjZB7ejRo0hJSbF6rEePHoiJiYmY6WKCIGDjxo3Yvn074uLiEBsbi//85z/44osvnK4BGO72798PAJg/fz4efvhhbNiwAT169MBFF12E48ePB7h1gSGKInJzczF79mxkZWUFujlBad++fXj55Zcxe/bsQDfFL6qrq9HS0mL3OzQlJSVifn9ScIn0mM54bo/x3B7jeccYzyWM5xQokR7PAcZ0RxjT7TGmd4wxXcKYzuS8182fPx+CILT7p6ioyOXjCYJg95goig4fDyWufk+iKOL2229HcnIyvv/+e2zbtg1TpkzB5MmTUV5eHuiP4VWuficmkwkA8NBDD+Hqq6+GVqvFW2+9BUEQ8N577wX4U3iXq9/Jyy+/jPr6esybNy/QTfY5T37HHDlyBJdccgmuvfZa3HLLLQFqeWDY/q4Mh9+f5D+M6R1jPLfHeG6P8dwe47l7GM+pMxjPXcOYbo8x3R5juj3GdPcwptuLDnQDws2dd96JqVOntvuazMxMl46VmpoKnU5n9VhNTQ0MBoNdT1OocfV72rx5MzZs2ICamhrEx8cDkCo7b9y4EatWrcK//vUvfzTXL1z9ThoaGgAAgwYNkh9XqVTo27dv2BXRcPU7efLJJ7F161aoVCqr57KyspCTk4NVq1b5spl+5e7vmCNHjmD06NHIzs7G8uXLfdy64JGYmIioqCi7HvjKysqQ//1J/sOY3jHGc3uM5/YYz+0xnruG8Zy8gfHcNYzp9hjT7TGm22NMdw1junNMzntZYmIiEhMTvXKs7OxsLFiwAOXl5UhLSwMAfPXVV1CpVCG/lpur39PJkycBAAqF9SQPhUIh906HC1e/E61WC5VKhT/++APnn38+AMBgMKCkpAR9+vTxdTP9ytXv5KWXXsKTTz4p7x85cgQTJkzAunXrMHLkSF820e/c+R1z+PBhjB49Wh65Yfv/KJzFxMRAq9Vi48aNuPLKK+XHN27ciClTpgSwZRRKGNM7xnhuj/HcHuO5PcZz1zCekzcwnruGMd0eY7o9xnR7jOmuYUx3jsn5ADp48CCOHz+OgwcPoqWlBTt27AAA9OvXD926dcP48eMxaNAgTJ8+Hc899xyOHz+O+++/H7feeqvcQx3usrOz0aNHD8yYMQOPPPIIunTpgtdffx0HDhzApEmTAt28gIiPj8fs2bPx6KOPIiMjA3369MFzzz0HALj22msD3LrAOO2006z2u3XrBgA444wzkJ6eHogmBdyRI0cwatQonHbaaVi0aBGqqqrk51JTUwPYMv+59957MX36dGRlZcmjEg4ePBgxa/qRfzGmt4/x3B7juT3Gc3uM54zn5F+M5x1jTLfHmG6PMd0eYzpjulMiBcyMGTNEAHZ/CgoK5NeUlpaKkyZNErt06SL27NlTvPPOO8XGxsbANToACgsLxfHjx4s9e/YU4+LixHPOOUf87LPPAt2sgGpubhbvu+8+MTk5WYyLixPHjh0r7tq1K9DNChoHDhwQAYjbt28PdFMC5q233nL4+yXSfu2/8sorYp8+fcSYmBhRo9GI3377baCbRGGKMb1jjOf2GM/bx3jOeG7GeE7+wnjuGsZ0e4zp7WNMZ0w3Y0y3J4iiKPo2/U9ERERERERERERERJYiZ3EjIiIiIiIiIiIiIqIgweQ8EREREREREREREZGfMTlPRERERERERERERORnTM4TEREREREREREREfkZk/NERERERERERERERH7G5DwRERERERERERERkZ8xOU9ERERERERERERE5GdMzhMRERERERERERER+RmT80TkV6NGjcI999wj72dmZuLFF1/s1DG9cQwiIiJyHeM5ERFReGBMJwosJueJwogoihg7diwmTJhg99yrr76KhIQEHDx40O65zMxMCIIAQRDQtWtXDB48GMuWLfNHk1FYWIi8vDyXXrty5Up07969U8cgIiIKdoznRERE4YExnYg6wuQ8URgRBAFvvfUWdDqdVeA+cOAAHnzwQSxevBinnXaaw/c+/vjjKC8vxy+//IIrrrgCs2fPxrp16xy+trm52WttTkpKQteuXQN+DCIiomDBeE5ERBQeGNOJqCNMzhOFmYyMDCxevBj3338/Dhw4AFEUMXPmTFx88cXIzc11+r64uDikpqaiX79+ePLJJ9G/f3989NFHAKRpbnfeeSfuvfdeJCYmYty4cQCA3377DZdeeim6deuGlJQUTJ8+HdXV1fIxT5w4gZtuugndunVDWloann/+ebvz2k53q62tRV5eHlJSUhAbG4vBgwdjw4YN+Oabb3DzzTejrq5OHkEwf/58h8c4ePAgpkyZgm7duiE+Ph7XXXcdKioq5Ofnz5+PoUOHYs2aNcjMzERCQgKmTp2KhoYG979wIiIiH2A8ZzwnIqLwwJjOmE7UHibnicLQjBkzcPHFF+Pmm2/GkiVLsGvXLixfvtytY8TGxsJgMMj7q1atQnR0NLZs2YJly5ahvLwcF110EYYOHYqioiJ88cUXqKiowHXXXSe/54EHHkBBQQE+/PBDfPXVV/jmm2+g1+udntNkMmHixIn48ccfkZ+fj99++w3PPPMMoqKicO655+LFF19EfHw8ysvLUV5ejvvvv9/uGKIo4oorrsDx48fx7bffYuPGjdi3bx+uv/56q9ft27cPH330ETZs2IANGzbg22+/xTPPPOPWd0RERORLjOeM50REFB4Y0xnTiZyJDnQDiMg3li9fjsGDB+P777/H+++/j+TkZJfeZzQakZ+fj507d+K2226TH+/Xrx8WLlwo7z/yyCPQaDR46qmn5MfefPNNZGRkYM+ePejduzdWrFiB1atXy734q1atQnp6utNzf/3119i2bRt+//13DBgwAADQt29f+fmEhAQIgoDU1NR2j/HLL7/gwIEDyMjIAACsWbMGZ511FgoLCzF8+HAA0kXGypUrERcXBwCYPn06Nm3ahAULFrj0PREREfkD4znjORERhQfGdMZ0Ikc4cp4oTCUnJyMvLw9qtRpXXnllh69/8MEH0a1bN3Tp0gV33HEHHnjgAcyaNUt+Pisry+r1er0eBQUF6Natm/xn4MCBAKQe73379qG5uRnZ2dnye3r27IkzzzzTaRt27NiB9PR0Oeh74vfff0dGRoYc9AFg0KBB6N69O37//Xf5sczMTDnoA0BaWhoqKys9Pi8REZEvMJ4znhMRUXhgTGdMJ3KEI+eJwlh0dDSio137b/7AAw8gNzcXXbt2RVpaGgRBsHr+lFNOsdo3mUy47LLL8Oyzz9odKy0tDXv37nW7vV26dHH7PbZEUbRru6PHlUql1fOCIMBkMnX6/ERERN7GeO78ccZzIiIKJYzpzh9nTKdIxZHzRAQASExMRL9+/dC7d2+HgdOWRqPBr7/+iszMTPTr18/qzymnnIJ+/fpBqVRi69at8ntqamqwZ88ep8ccMmQIDh065PQ1MTExaGlpabddgwYNwsGDB1FWViY/9ttvv6Gurg5qtbrDz0VERBTKGM+JiIjCA2M6UWRgcp6IPHLHHXfg+PHjuOGGG7Bt2zbs378fX331Ff75z3+ipaUF3bp1w8yZM/HAAw9g06ZN2LVrF3Jzc6FQOP+1c9FFF+HCCy/E1VdfjY0bN+LAgQP4/PPP8cUXXwCQprn99ddf2LRpE6qrq3Hy5Em7Y4wdOxZDhgxBTk4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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "def vis_res_fingerprint(ypre,yte,name,target_path):\n", " fig = plt.figure(constrained_layout=True, figsize=(15, 5))\n", " fig.suptitle('{0} Fingerprint Compare.'.format(name), fontsize=16)\n", " ax0 = fig.add_subplot(131)\n", " ax0.set_title('ECFP, MACCS, Avalon')\n", " ax0.plot(yte[0],yte[0],'k-')\n", " ax0.plot(yte[0],ypre[0],'b*',label='%s R2 = %.3f %%' % ('ECFP ',r2_score(yte[0],ypre[0])))\n", " ax0.plot(yte[1],ypre[1],'r*',label='%s R2 = %.3f %%' % ('MACCS ',r2_score(yte[1],ypre[1])))\n", " ax0.plot(yte[2],ypre[2],'c*',label='%s R2 = %.3f %%' % ('Avalon ',r2_score(yte[2],ypre[2])))\n", " ax0.legend(loc='upper left')\n", " ax0.set_ylabel('Y Truth')\n", " ax0.set_xlabel('Y Prediction')\n", " \n", " ax1 = fig.add_subplot(132)\n", " ax1.set_title('ECFP+MACCS, ECFP+Avalon, MACCS+Avalon')\n", " ax1.plot(yte[0],yte[0],'k-')\n", " ax1.plot(yte[3],ypre[3],'b*',label='%s R2 = %.3f %%' % ('ECEP_MACCS ',r2_score(yte[3],ypre[3])))\n", " ax1.plot(yte[4],ypre[4],'r*',label='%s R2 = %.3f %%' % ('ECEP_Avalon ',r2_score(yte[4],ypre[4])))\n", " ax1.plot(yte[5],ypre[5],'c*',label='%s R2 = %.3f %%' % ('MACCS_Avalon ',r2_score(yte[5],ypre[5])))\n", " ax1.legend(loc='upper left')\n", " ax1.set_ylabel('Y Truth')\n", " ax1.set_xlabel('Y Prediction')\n", " \n", " ax2 = fig.add_subplot(133)\n", " ax2.set_title('ECFP+MACCS+Avalon Fingerprints')\n", " ax2.plot(yte[0],yte[0],'k-')\n", " ax2.plot(yte[6],ypre[6],'b*',label='%s R2 = %.3f %%' % ('ECFP+MACCS+Avalon. ',r2_score(yte[6],ypre[6])))\n", " ax2.legend(loc='upper left')\n", " ax2.set_ylabel('Y Truth')\n", " ax2.set_xlabel('Y Prediction')\n", " plt.show()\n", " fig.savefig(f\"{target_path}/fingerprint_compare_{name}.png\", dpi=300)\n", "vis_res_fingerprint(ypred_ws,yte_ws,'ws496',target_path)\n", "vis_res_fingerprint(ypred_de,yte_de,'delaney',target_path)\n", "vis_res_fingerprint(ypred_lo,yte_lo,'lovrics',target_path)\n", "vis_res_fingerprint(ypred_hu,yte_hu,'huuskonen',target_path)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "ai", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.2" }, "orig_nbformat": 4 }, "nbformat": 4, "nbformat_minor": 2 }