Attentive FP_Solubility_AqSolDB regression_.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3fcdc5a6-f9b5-4ca0-a809-d9ca364080f3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "use GPU\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/media/suhaib/intensobp/5-Python_envs/pythonvenv/dgl_venv/lib/python3.9/site-packages/torchdata/datapipes/__init__.py:18: UserWarning: \n",
      "################################################################################\n",
      "WARNING!\n",
      "The 'datapipes', 'dataloader2' modules are deprecated and will be removed in a\n",
      "future torchdata release! Please see https://github.com/pytorch/data/issues/1196\n",
      "to learn more and leave feedback.\n",
      "################################################################################\n",
      "\n",
      "  deprecation_warning()\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline \n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "from rdkit import Chem\n",
    "from rdkit import RDPaths\n",
    "from functools import partial\n",
    "from sklearn.metrics import roc_auc_score\n",
    "\n",
    "import numpy as np\n",
    "import random\n",
    "import torch\n",
    "if torch.cuda.is_available():\n",
    "    print('use GPU')\n",
    "    device='cuda'\n",
    "else:\n",
    "    print('use CPU')\n",
    "    device='cpu'\n",
    "    \n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "from torch.utils.data import DataLoader\n",
    "from torch.utils.data import Dataset\n",
    "\n",
    "import dgl\n",
    "from  dgl.data.utils import split_dataset\n",
    "from dgllife.model import model_zoo\n",
    "\n",
    "from dgllife.utils import mol_to_complete_graph, mol_to_bigraph\n",
    "from dgllife.utils import atom_type_one_hot\n",
    "from dgllife.utils import atom_degree_one_hot\n",
    "from dgllife.utils import atom_formal_charge\n",
    "from dgllife.utils import atom_num_radical_electrons\n",
    "from dgllife.utils import atom_hybridization_one_hot\n",
    "from dgllife.utils import atom_total_num_H_one_hot\n",
    "from dgllife.utils import one_hot_encoding\n",
    "from dgllife.utils import CanonicalAtomFeaturizer\n",
    "from dgllife.utils import CanonicalBondFeaturizer\n",
    "from dgllife.utils import ConcatFeaturizer\n",
    "from dgllife.utils import BaseAtomFeaturizer\n",
    "from dgllife.utils import BaseBondFeaturizer\n",
    "from dgllife.utils import one_hot_encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "792f6477-65dc-4984-a1dc-1d978c0c1c5e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def chirality(atom):\n",
    "    try:\n",
    "        return one_hot_encoding(atom.GetProp('_CIPCode'), ['R', 'S']) + \\\n",
    "               [atom.HasProp('_ChiralityPossible')]\n",
    "    except:\n",
    "        return [False, False] + [atom.HasProp('_ChiralityPossible')]\n",
    "     \n",
    "def collate_molgraphs(data):\n",
    "    \"\"\"Batching a list of datapoints for dataloader.\n",
    "    Parameters\n",
    "    ----------\n",
    "    data : list of 3-tuples or 4-tuples.\n",
    "        Each tuple is for a single datapoint, consisting of\n",
    "        a SMILES, a DGLGraph, all-task labels and optionally\n",
    "        a binary mask indicating the existence of labels.\n",
    "    Returns\n",
    "    -------\n",
    "    smiles : list\n",
    "        List of smiles\n",
    "    bg : BatchedDGLGraph\n",
    "        Batched DGLGraphs\n",
    "    labels : Tensor of dtype float32 and shape (B, T)\n",
    "        Batched datapoint labels. B is len(data) and\n",
    "        T is the number of total tasks.\n",
    "    masks : Tensor of dtype float32 and shape (B, T)\n",
    "        Batched datapoint binary mask, indicating the\n",
    "        existence of labels. If binary masks are not\n",
    "        provided, return a tensor with ones.\n",
    "    \"\"\"\n",
    "    assert len(data[0]) in [3, 4], \\\n",
    "        'Expect the tuple to be of length 3 or 4, got {:d}'.format(len(data[0]))\n",
    "    if len(data[0]) == 3:\n",
    "        smiles, graphs, labels = map(list, zip(*data))\n",
    "        masks = None\n",
    "    else:\n",
    "        smiles, graphs, labels, masks = map(list, zip(*data))\n",
    " \n",
    "    bg = dgl.batch(graphs)\n",
    "    bg.set_n_initializer(dgl.init.zero_initializer)\n",
    "    bg.set_e_initializer(dgl.init.zero_initializer)\n",
    "    labels = torch.stack(labels, dim=0)\n",
    "     \n",
    "    if masks is None:\n",
    "        masks = torch.ones(labels.shape)\n",
    "    else:\n",
    "        masks = torch.stack(masks, dim=0)\n",
    "    return smiles, bg, labels, masks\n",
    " \n",
    " \n",
    "def run_a_train_epoch(n_epochs, epoch, model, data_loader,loss_criterion, optimizer):\n",
    "    model.train()\n",
    "    total_loss = 0\n",
    "    losses = []\n",
    "     \n",
    "    for batch_id, batch_data in enumerate(data_loader):\n",
    "        batch_data\n",
    "        smiles, bg, labels, masks = batch_data\n",
    "        if torch.cuda.is_available():\n",
    "            bg.to(torch.device('cuda:0'))\n",
    "            labels = labels.to('cuda:0')\n",
    "            masks = masks.to('cuda:0')\n",
    "         \n",
    "        prediction = model(bg, bg.ndata['hv'], bg.edata['he'])\n",
    "        loss = (loss_criterion(prediction, labels)*(masks != 0).float()).mean()\n",
    "        #loss = loss_criterion(prediction, labels)\n",
    "        #print(loss.shape)\n",
    "        optimizer.zero_grad()\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "         \n",
    "        losses.append(loss.data.item())\n",
    "         \n",
    " \n",
    "    total_score = np.mean(losses)\n",
    "    print('epoch {:d}/{:d}, training {:.4f}'.format( epoch + 1, n_epochs,  total_score))\n",
    "    return total_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a6d59414-5f7c-4a1c-863c-9b9042dbfe80",
   "metadata": {},
   "outputs": [],
   "source": [
    "atom_featurizer = BaseAtomFeaturizer(\n",
    "                 {'hv': ConcatFeaturizer([\n",
    "                  partial(atom_type_one_hot, allowable_set=[\n",
    "                          'B', 'C', 'N', 'O', 'F', 'Si', 'P', 'S', 'Cl', 'As', 'Se', 'Br', 'Te', 'I', 'At'],\n",
    "                    encode_unknown=True),\n",
    "                  partial(atom_degree_one_hot, allowable_set=list(range(6))),\n",
    "                  atom_formal_charge, atom_num_radical_electrons,\n",
    "                  partial(atom_hybridization_one_hot, encode_unknown=True),\n",
    "                  lambda atom: [0], # A placeholder for aromatic information,\n",
    "                    atom_total_num_H_one_hot, chirality\n",
    "                 ],\n",
    "                )})\n",
    "bond_featurizer = BaseBondFeaturizer({\n",
    "                                     'he': lambda bond: [0 for _ in range(10)]\n",
    "    })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "25c22b95-d3db-4353-8cd1-3e4683f00685",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Invalid SMILES: nan, Solubility: -7.89163614\n",
      "Invalid SMILES: nan, Solubility: -8.217167979\n",
      "Invalid SMILES: nan, Solubility: -8.213807356\n",
      "Invalid SMILES: nan, Solubility: -7.461800322\n",
      "Invalid SMILES: nan, Solubility: -7.62669423\n",
      "Invalid SMILES: nan, Solubility: -8.479083332\n",
      "Invalid SMILES: nan, Solubility: 0.9621\n",
      "Invalid SMILES: nan, Solubility: -1.2983\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "train_csv = '../dataverse_solubility/AqSolDBc_smiles_solubility.csv'\n",
    "test_csv = '../dataverse_solubility/OChemUnseen.csv'\n",
    "\n",
    "# Load the training data into a Pandas DataFrame\n",
    "train_df = pd.read_csv(train_csv)\n",
    "test_df = pd.read_csv(test_csv)\n",
    "\n",
    "# Ensure SMILEScurated is of string type and drop any rows with missing or invalid SMILES\n",
    "train_df['SMILEScurated'] = train_df['SMILEScurated'].astype(str)\n",
    "test_df['SMILES'] = test_df['SMILES'].astype(str)\n",
    "\n",
    "# Function to filter and print invalid SMILES\n",
    "def is_valid_smiles(smi, solubility):\n",
    "    mol = Chem.MolFromSmiles(smi)\n",
    "    if mol is None:\n",
    "        print(f\"Invalid SMILES: {smi}, Solubility: {solubility}\")\n",
    "        return False\n",
    "    return True\n",
    "\n",
    "# Apply the filter and print invalid SMILES for the training data\n",
    "valid_train_df = train_df[train_df.apply(lambda row: is_valid_smiles(row['SMILEScurated'], row['Solubility']), axis=1)]\n",
    "\n",
    "# Apply the filter and print invalid SMILES for the test data\n",
    "valid_test_df = test_df[test_df.apply(lambda row: is_valid_smiles(row['SMILES'], row['LogS']), axis=1)]\n",
    "\n",
    "# Extract SMILES strings and solubility values from the training data\n",
    "train_smi = valid_train_df['SMILEScurated'].tolist()\n",
    "train_sol = torch.tensor(valid_train_df['Solubility'].values).reshape(-1, 1)\n",
    "\n",
    "# Extract SMILES strings and solubility values from the test data\n",
    "test_smi = valid_test_df['SMILES'].tolist()\n",
    "test_sol = torch.tensor(valid_test_df['LogS'].values).reshape(-1, 1)\n",
    "\n",
    "# Convert solubility values to float32\n",
    "train_sol = torch.tensor(valid_train_df['Solubility'].values, dtype=torch.float32).reshape(-1, 1)\n",
    "test_sol = torch.tensor(valid_test_df['LogS'].values, dtype=torch.float32).reshape(-1, 1)\n",
    "\n",
    "# Structures as RDkit Mols\n",
    "train_mols = [Chem.MolFromSmiles(smi) for smi in train_smi]\n",
    "test_mols = [Chem.MolFromSmiles(smi) for smi in test_smi]\n",
    "\n",
    "# Structures as Graphs\n",
    "train_graph =[mol_to_bigraph(mol,\n",
    "                           node_featurizer=atom_featurizer, \n",
    "                           edge_featurizer=bond_featurizer) for mol in train_mols]\n",
    " \n",
    "test_graph =[mol_to_bigraph(mol,\n",
    "                           node_featurizer=atom_featurizer, \n",
    "                           edge_featurizer=bond_featurizer) for mol in test_mols]\n",
    "\n",
    "train_loader = DataLoader(dataset=list(zip(train_smi, train_graph, train_sol)), batch_size=128, collate_fn=collate_molgraphs)\n",
    "test_loader = DataLoader(dataset=list(zip(test_smi, test_graph, test_sol)), batch_size=128, collate_fn=collate_molgraphs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9dbd9906-555d-475e-928e-81515a18bd10",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = model_zoo.attentivefp_predictor.AttentiveFPPredictor(node_feat_size=39,\n",
    "                                  edge_feat_size=10,\n",
    "                                  num_layers=2,\n",
    "                                  num_timesteps=2,\n",
    "                                  graph_feat_size=200,\n",
    "                                  dropout=0.2)\n",
    "model = model.to('cuda:0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "05c53596-1916-4143-a8bd-9b9c63a9fe76",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1000, Training Loss: 1.3449\n",
      "Epoch 2/1000, Training Loss: 1.2465\n",
      "Epoch 3/1000, Training Loss: 1.2663\n",
      "Epoch 4/1000, Training Loss: 1.2354\n",
      "Epoch 5/1000, Training Loss: 1.1987\n",
      "Epoch 6/1000, Training Loss: 1.3033\n",
      "Epoch 7/1000, Training Loss: 1.2833\n",
      "Epoch 8/1000, Training Loss: 1.2952\n",
      "Epoch 9/1000, Training Loss: 1.2489\n",
      "Epoch 10/1000, Training Loss: 1.2188\n",
      "Epoch 11/1000, Training Loss: 1.2293\n",
      "Epoch 12/1000, Training Loss: 1.1940\n",
      "Epoch 13/1000, Training Loss: 1.1907\n",
      "Epoch 14/1000, Training Loss: 1.1884\n",
      "Epoch 15/1000, Training Loss: 1.2206\n",
      "Epoch 16/1000, Training Loss: 1.2193\n",
      "Epoch 17/1000, Training Loss: 1.1991\n",
      "Epoch 18/1000, Training Loss: 1.2030\n",
      "Epoch 19/1000, Training Loss: 1.1920\n",
      "Epoch 20/1000, Training Loss: 1.1915\n",
      "Epoch 21/1000, Training Loss: 1.1863\n",
      "Epoch 22/1000, Training Loss: 1.1861\n",
      "Epoch 23/1000, Training Loss: 1.1617\n",
      "Epoch 24/1000, Training Loss: 1.1832\n",
      "Epoch 25/1000, Training Loss: 1.1763\n",
      "Epoch 26/1000, Training Loss: 1.2273\n",
      "Epoch 27/1000, Training Loss: 1.1422\n",
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      "Epoch 977/1000, Training Loss: 0.5504\n",
      "Epoch 978/1000, Training Loss: 0.5437\n",
      "Epoch 979/1000, Training Loss: 0.5663\n",
      "Epoch 980/1000, Training Loss: 0.6413\n",
      "Epoch 981/1000, Training Loss: 0.5544\n",
      "Epoch 982/1000, Training Loss: 0.5389\n",
      "Epoch 983/1000, Training Loss: 0.5387\n",
      "Epoch 984/1000, Training Loss: 0.5378\n",
      "Epoch 985/1000, Training Loss: 0.5375\n",
      "Epoch 986/1000, Training Loss: 0.5347\n",
      "Epoch 987/1000, Training Loss: 0.6494\n",
      "Epoch 988/1000, Training Loss: 0.5477\n",
      "Epoch 989/1000, Training Loss: 0.5354\n",
      "Epoch 990/1000, Training Loss: 0.5467\n",
      "Epoch 991/1000, Training Loss: 0.5324\n",
      "Epoch 992/1000, Training Loss: 0.5242\n",
      "Epoch 993/1000, Training Loss: 0.5198\n",
      "Epoch 994/1000, Training Loss: 0.5374\n",
      "Epoch 995/1000, Training Loss: 0.5280\n",
      "Epoch 996/1000, Training Loss: 0.5960\n",
      "Epoch 997/1000, Training Loss: 0.5493\n",
      "Epoch 998/1000, Training Loss: 0.5486\n",
      "Epoch 999/1000, Training Loss: 0.5345\n",
      "Epoch 1000/1000, Training Loss: 0.5336\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "AttentiveFPPredictor(\n",
       "  (gnn): AttentiveFPGNN(\n",
       "    (init_context): GetContext(\n",
       "      (project_node): Sequential(\n",
       "        (0): Linear(in_features=39, out_features=200, bias=True)\n",
       "        (1): LeakyReLU(negative_slope=0.01)\n",
       "      )\n",
       "      (project_edge1): Sequential(\n",
       "        (0): Linear(in_features=49, out_features=200, bias=True)\n",
       "        (1): LeakyReLU(negative_slope=0.01)\n",
       "      )\n",
       "      (project_edge2): Sequential(\n",
       "        (0): Dropout(p=0.2, inplace=False)\n",
       "        (1): Linear(in_features=400, out_features=1, bias=True)\n",
       "        (2): LeakyReLU(negative_slope=0.01)\n",
       "      )\n",
       "      (attentive_gru): AttentiveGRU1(\n",
       "        (edge_transform): Sequential(\n",
       "          (0): Dropout(p=0.2, inplace=False)\n",
       "          (1): Linear(in_features=200, out_features=200, bias=True)\n",
       "        )\n",
       "        (gru): GRUCell(200, 200)\n",
       "      )\n",
       "    )\n",
       "    (gnn_layers): ModuleList(\n",
       "      (0): GNNLayer(\n",
       "        (project_edge): Sequential(\n",
       "          (0): Dropout(p=0.2, inplace=False)\n",
       "          (1): Linear(in_features=400, out_features=1, bias=True)\n",
       "          (2): LeakyReLU(negative_slope=0.01)\n",
       "        )\n",
       "        (attentive_gru): AttentiveGRU2(\n",
       "          (project_node): Sequential(\n",
       "            (0): Dropout(p=0.2, inplace=False)\n",
       "            (1): Linear(in_features=200, out_features=200, bias=True)\n",
       "          )\n",
       "          (gru): GRUCell(200, 200)\n",
       "        )\n",
       "      )\n",
       "    )\n",
       "  )\n",
       "  (readout): AttentiveFPReadout(\n",
       "    (readouts): ModuleList(\n",
       "      (0-1): 2 x GlobalPool(\n",
       "        (compute_logits): Sequential(\n",
       "          (0): Linear(in_features=400, out_features=1, bias=True)\n",
       "          (1): LeakyReLU(negative_slope=0.01)\n",
       "        )\n",
       "        (project_nodes): Sequential(\n",
       "          (0): Dropout(p=0.2, inplace=False)\n",
       "          (1): Linear(in_features=200, out_features=200, bias=True)\n",
       "        )\n",
       "        (gru): GRUCell(200, 200)\n",
       "      )\n",
       "    )\n",
       "  )\n",
       "  (predict): Sequential(\n",
       "    (0): Dropout(p=0.2, inplace=False)\n",
       "    (1): Linear(in_features=200, out_features=1, bias=True)\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loss_fn = nn.MSELoss(reduction='none')\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=10 ** (-2.5), weight_decay=10 ** (-5.0),)\n",
    "n_epochs = 1000\n",
    "epochs = []\n",
    "scores = []\n",
    "\n",
    "# Ensure graph and target are on the same device\n",
    "def run_a_train_epoch(n_epochs, epoch, model, data_loader, loss_criterion, optimizer):\n",
    "    model.train()\n",
    "    total_loss = 0\n",
    "    losses = []\n",
    "     \n",
    "    for batch_id, batch_data in enumerate(data_loader):\n",
    "        smiles, bg, labels, masks = batch_data\n",
    "        \n",
    "        # Move batch graph (bg) and labels/masks to the GPU if available\n",
    "        if torch.cuda.is_available():\n",
    "            bg = bg.to(torch.device('cuda:0'))\n",
    "            labels = labels.to(torch.device('cuda:0'))\n",
    "            masks = masks.to(torch.device('cuda:0'))\n",
    "\n",
    "        # Extract node and edge features, ensuring they are on the correct device\n",
    "        node_feats = bg.ndata['hv'].to(torch.device('cuda:0'))\n",
    "        edge_feats = bg.edata['he'].to(torch.device('cuda:0'))\n",
    "        \n",
    "        # Forward pass\n",
    "        prediction = model(bg, node_feats, edge_feats)\n",
    "        \n",
    "        # Compute loss\n",
    "        loss = (loss_criterion(prediction, labels) * (masks != 0).float()).mean()\n",
    "        \n",
    "        # Backpropagation\n",
    "        optimizer.zero_grad()\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "         \n",
    "        # Track loss\n",
    "        losses.append(loss.item())\n",
    "         \n",
    "    total_score = np.mean(losses)\n",
    "    print(f'Epoch {epoch + 1}/{n_epochs}, Training Loss: {total_score:.4f}')\n",
    "    return total_score\n",
    "    \n",
    "\n",
    "# Training Loop\n",
    "for e in range(n_epochs):\n",
    "    score = run_a_train_epoch(n_epochs, e, model, train_loader, loss_fn, optimizer)\n",
    "    epochs.append(e)\n",
    "    scores.append(score)\n",
    "model.eval()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "8b659152-a92a-4a08-a00c-cafcda7d8b3d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x750f08a6ec70>]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(epochs, scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "de09631b-2c9f-48f8-9faa-6ac8a24e5201",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import torch\n",
    "\n",
    "# Plot the training loss (or scores)\n",
    "plt.plot(epochs, scores)\n",
    "\n",
    "# Set the model to evaluation mode\n",
    "model.eval()\n",
    "\n",
    "# Initialize a list to store all predictions\n",
    "all_pred = []\n",
    "\n",
    "# Loop through the test data\n",
    "for test_data in test_loader:\n",
    "    smi_lst, bg, labels, masks = test_data\n",
    "    \n",
    "    # Move everything to GPU if available\n",
    "    device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')\n",
    "    bg = bg.to(device)\n",
    "    labels = labels.to(device)\n",
    "    masks = masks.to(device)\n",
    "    \n",
    "    # Get node and edge features and move them to the correct device\n",
    "    node_feats = bg.ndata['hv'].to(device)\n",
    "    edge_feats = bg.edata['he'].to(device)\n",
    "    \n",
    "    # Perform the prediction\n",
    "    pred = model(bg, node_feats, edge_feats)\n",
    "    \n",
    "    # Store the predictions on the CPU\n",
    "    all_pred.append(pred.data.cpu().numpy())\n",
    "\n",
    "# Convert the predictions to a numpy array\n",
    "res = np.vstack(all_pred)\n",
    "\n",
    "# Plot the results\n",
    "plt.clf()\n",
    "plt.scatter(res, test_sol)\n",
    "plt.xlabel('Predicted')\n",
    "plt.ylabel('Experimental')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "0d11b798-3d30-4fc9-aca0-933f1d719207",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.279232919216156\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/media/suhaib/intensobp/5-Python_envs/pythonvenv/dgl_venv/lib/python3.9/site-packages/sklearn/base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return fit_method(estimator, *args, **kwargs)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x750f0326c190>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score\n",
    "print(r2_score(test_sol, res))\n",
    "\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from rdkit import Chem\n",
    "from rdkit.Chem import AllChem\n",
    "train_fp = [AllChem.GetMorganFingerprintAsBitVect(mol,2) for mol in train_mols]\n",
    "test_fp = [AllChem.GetMorganFingerprintAsBitVect(mol,2) for mol in test_mols]\n",
    "# make RF regressor and train it.\n",
    "rfr = RandomForestRegressor()\n",
    "rfr.fit(train_fp, train_sol)\n",
    "\n",
    "rfr_pred = rfr.predict(test_fp)\n",
    "r2_score(test_sol, rfr_pred)\n",
    "plt.clf()\n",
    "plt.scatter(rfr_pred, test_sol)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "6a0ee21a-7090-4216-8ba3-aaeab81d96ef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model saved after 1000 epochs.\n"
     ]
    }
   ],
   "source": [
    "# Specify the file path to save the model state\n",
    "save_path = 'AqSolDB_AttFPPmodel_checkpoint_1000e.pth'\n",
    "\n",
    "# Save the model state, optimizer state, and current epoch\n",
    "torch.save({\n",
    "    'epoch': n_epochs,\n",
    "    'model_state_dict': model.state_dict(),\n",
    "    'optimizer_state_dict': optimizer.state_dict(),\n",
    "    'loss': scores[-1]  # Save the last loss for reference\n",
    "}, save_path)\n",
    "\n",
    "print(f\"Model saved after {n_epochs} epochs.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "91d09313-cead-40f4-98b7-6dde79454df8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Resuming training from epoch 1001\n"
     ]
    }
   ],
   "source": [
    "# Specify the file path where the model was saved\n",
    "load_path = 'AqSolDB_AttFPPmodel_checkpoint_1000e.pth'\n",
    "\n",
    "# Load the checkpoint\n",
    "checkpoint = torch.load(load_path)\n",
    "\n",
    "# Load the model state, optimizer state, and the last completed epoch\n",
    "model.load_state_dict(checkpoint['model_state_dict'])\n",
    "optimizer.load_state_dict(checkpoint['optimizer_state_dict'])\n",
    "start_epoch = checkpoint['epoch']\n",
    "\n",
    "print(f\"Resuming training from epoch {start_epoch + 1}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "e0b740f6-e13a-4ad6-b2b3-eca270185284",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1001/2000, Training Loss: 0.5428\n",
      "Epoch 1002/2000, Training Loss: 0.5542\n",
      "Epoch 1003/2000, Training Loss: 0.5398\n",
      "Epoch 1004/2000, Training Loss: 0.5355\n",
      "Epoch 1005/2000, Training Loss: 0.5460\n",
      "Epoch 1006/2000, Training Loss: 0.5444\n",
      "Epoch 1007/2000, Training Loss: 0.5419\n",
      "Epoch 1008/2000, Training Loss: 0.5293\n",
      "Epoch 1009/2000, Training Loss: 0.5378\n",
      "Epoch 1010/2000, Training Loss: 0.5287\n",
      "Epoch 1011/2000, Training Loss: 0.5641\n",
      "Epoch 1012/2000, Training Loss: 0.5454\n",
      "Epoch 1013/2000, Training Loss: 0.5356\n",
      "Epoch 1014/2000, Training Loss: 0.5209\n",
      "Epoch 1015/2000, Training Loss: 0.5301\n",
      "Epoch 1016/2000, Training Loss: 0.5240\n",
      "Epoch 1017/2000, Training Loss: 0.5170\n",
      "Epoch 1018/2000, Training Loss: 0.5333\n",
      "Epoch 1019/2000, Training Loss: 0.5193\n",
      "Epoch 1020/2000, Training Loss: 0.5249\n",
      "Epoch 1021/2000, Training Loss: 0.5269\n",
      "Epoch 1022/2000, Training Loss: 0.5476\n",
      "Epoch 1023/2000, Training Loss: 0.5517\n",
      "Epoch 1024/2000, Training Loss: 0.5451\n",
      "Epoch 1025/2000, Training Loss: 0.5275\n",
      "Epoch 1026/2000, Training Loss: 0.5499\n",
      "Epoch 1027/2000, Training Loss: 0.5256\n",
      "Epoch 1028/2000, Training Loss: 0.5186\n",
      "Epoch 1029/2000, Training Loss: 0.5215\n",
      "Epoch 1030/2000, Training Loss: 0.5259\n",
      "Epoch 1031/2000, Training Loss: 0.5307\n",
      "Epoch 1032/2000, Training Loss: 0.5257\n",
      "Epoch 1033/2000, Training Loss: 0.5342\n",
      "Epoch 1034/2000, Training Loss: 0.5440\n",
      "Epoch 1035/2000, Training Loss: 0.5357\n",
      "Epoch 1036/2000, Training Loss: 0.5332\n",
      "Epoch 1037/2000, Training Loss: 0.5444\n",
      "Epoch 1038/2000, Training Loss: 0.5306\n",
      "Epoch 1039/2000, Training Loss: 0.5613\n",
      "Epoch 1040/2000, Training Loss: 0.5231\n",
      "Epoch 1041/2000, Training Loss: 0.5345\n",
      "Epoch 1042/2000, Training Loss: 0.5283\n",
      "Epoch 1043/2000, Training Loss: 0.5596\n",
      "Epoch 1044/2000, Training Loss: 0.5320\n",
      "Epoch 1045/2000, Training Loss: 0.5227\n",
      "Epoch 1046/2000, Training Loss: 0.5282\n",
      "Epoch 1047/2000, Training Loss: 0.5924\n",
      "Epoch 1048/2000, Training Loss: 0.9291\n",
      "Epoch 1049/2000, Training Loss: 0.7928\n",
      "Epoch 1050/2000, Training Loss: 0.6515\n",
      "Epoch 1051/2000, Training Loss: 0.5882\n",
      "Epoch 1052/2000, Training Loss: 0.5721\n",
      "Epoch 1053/2000, Training Loss: 0.5495\n",
      "Epoch 1054/2000, Training Loss: 0.5365\n",
      "Epoch 1055/2000, Training Loss: 0.5388\n",
      "Epoch 1056/2000, Training Loss: 0.5255\n",
      "Epoch 1057/2000, Training Loss: 0.5265\n",
      "Epoch 1058/2000, Training Loss: 0.5133\n",
      "Epoch 1059/2000, Training Loss: 0.5248\n",
      "Epoch 1060/2000, Training Loss: 0.5220\n",
      "Epoch 1061/2000, Training Loss: 0.5139\n",
      "Epoch 1062/2000, Training Loss: 0.5104\n",
      "Epoch 1063/2000, Training Loss: 0.5149\n",
      "Epoch 1064/2000, Training Loss: 0.5237\n",
      "Epoch 1065/2000, Training Loss: 0.5234\n",
      "Epoch 1066/2000, Training Loss: 0.5202\n",
      "Epoch 1067/2000, Training Loss: 0.5098\n",
      "Epoch 1068/2000, Training Loss: 0.5306\n",
      "Epoch 1069/2000, Training Loss: 0.5201\n",
      "Epoch 1070/2000, Training Loss: 0.5289\n",
      "Epoch 1071/2000, Training Loss: 0.5146\n",
      "Epoch 1072/2000, Training Loss: 0.5059\n",
      "Epoch 1073/2000, Training Loss: 0.5167\n",
      "Epoch 1074/2000, Training Loss: 0.5120\n",
      "Epoch 1075/2000, Training Loss: 0.5056\n",
      "Epoch 1076/2000, Training Loss: 0.5332\n",
      "Epoch 1077/2000, Training Loss: 0.5294\n",
      "Epoch 1078/2000, Training Loss: 0.5135\n",
      "Epoch 1079/2000, Training Loss: 0.5239\n",
      "Epoch 1080/2000, Training Loss: 0.5879\n",
      "Epoch 1081/2000, Training Loss: 0.6618\n",
      "Epoch 1082/2000, Training Loss: 0.5791\n",
      "Epoch 1083/2000, Training Loss: 0.5396\n",
      "Epoch 1084/2000, Training Loss: 0.5431\n",
      "Epoch 1085/2000, Training Loss: 0.5192\n",
      "Epoch 1086/2000, Training Loss: 0.5268\n",
      "Epoch 1087/2000, Training Loss: 0.5326\n",
      "Epoch 1088/2000, Training Loss: 0.5243\n",
      "Epoch 1089/2000, Training Loss: 0.5233\n",
      "Epoch 1090/2000, Training Loss: 0.5204\n",
      "Epoch 1091/2000, Training Loss: 0.5084\n",
      "Epoch 1092/2000, Training Loss: 0.5124\n",
      "Epoch 1093/2000, Training Loss: 0.5212\n",
      "Epoch 1094/2000, Training Loss: 0.5154\n",
      "Epoch 1095/2000, Training Loss: 0.5269\n",
      "Epoch 1096/2000, Training Loss: 0.5014\n",
      "Epoch 1097/2000, Training Loss: 0.5085\n",
      "Epoch 1098/2000, Training Loss: 0.5154\n",
      "Epoch 1099/2000, Training Loss: 0.5170\n",
      "Epoch 1100/2000, Training Loss: 0.5306\n",
      "Epoch 1101/2000, Training Loss: 0.5234\n",
      "Epoch 1102/2000, Training Loss: 0.5103\n",
      "Epoch 1103/2000, Training Loss: 0.5213\n",
      "Epoch 1104/2000, Training Loss: 0.5147\n",
      "Epoch 1105/2000, Training Loss: 0.5006\n",
      "Epoch 1106/2000, Training Loss: 0.5244\n",
      "Epoch 1107/2000, Training Loss: 0.5218\n",
      "Epoch 1108/2000, Training Loss: 0.5296\n",
      "Epoch 1109/2000, Training Loss: 0.5018\n",
      "Epoch 1110/2000, Training Loss: 0.5092\n",
      "Epoch 1111/2000, Training Loss: 0.5076\n",
      "Epoch 1112/2000, Training Loss: 0.5248\n",
      "Epoch 1113/2000, Training Loss: 0.5311\n",
      "Epoch 1114/2000, Training Loss: 0.5273\n",
      "Epoch 1115/2000, Training Loss: 0.5121\n",
      "Epoch 1116/2000, Training Loss: 0.5246\n",
      "Epoch 1117/2000, Training Loss: 0.5200\n",
      "Epoch 1118/2000, Training Loss: 0.5154\n",
      "Epoch 1119/2000, Training Loss: 0.5064\n",
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      "Epoch 1895/2000, Training Loss: 0.4685\n",
      "Epoch 1896/2000, Training Loss: 0.4546\n",
      "Epoch 1897/2000, Training Loss: 0.4596\n",
      "Epoch 1898/2000, Training Loss: 0.4565\n",
      "Epoch 1899/2000, Training Loss: 0.4640\n",
      "Epoch 1900/2000, Training Loss: 0.4595\n",
      "Epoch 1901/2000, Training Loss: 0.4593\n",
      "Epoch 1902/2000, Training Loss: 0.4492\n",
      "Epoch 1903/2000, Training Loss: 0.4620\n",
      "Epoch 1904/2000, Training Loss: 0.4628\n",
      "Epoch 1905/2000, Training Loss: 0.5866\n",
      "Epoch 1906/2000, Training Loss: 0.5225\n",
      "Epoch 1907/2000, Training Loss: 0.5083\n",
      "Epoch 1908/2000, Training Loss: 0.4947\n",
      "Epoch 1909/2000, Training Loss: 0.4823\n",
      "Epoch 1910/2000, Training Loss: 0.4958\n",
      "Epoch 1911/2000, Training Loss: 0.4814\n",
      "Epoch 1912/2000, Training Loss: 0.4755\n",
      "Epoch 1913/2000, Training Loss: 0.4733\n",
      "Epoch 1914/2000, Training Loss: 0.5269\n",
      "Epoch 1915/2000, Training Loss: 0.4779\n",
      "Epoch 1916/2000, Training Loss: 0.4733\n",
      "Epoch 1917/2000, Training Loss: 0.4753\n",
      "Epoch 1918/2000, Training Loss: 0.4694\n",
      "Epoch 1919/2000, Training Loss: 0.4703\n",
      "Epoch 1920/2000, Training Loss: 0.4689\n",
      "Epoch 1921/2000, Training Loss: 0.4543\n",
      "Epoch 1922/2000, Training Loss: 0.4610\n",
      "Epoch 1923/2000, Training Loss: 0.4602\n",
      "Epoch 1924/2000, Training Loss: 0.4784\n",
      "Epoch 1925/2000, Training Loss: 0.4528\n",
      "Epoch 1926/2000, Training Loss: 0.4449\n",
      "Epoch 1927/2000, Training Loss: 0.4557\n",
      "Epoch 1928/2000, Training Loss: 0.4586\n",
      "Epoch 1929/2000, Training Loss: 0.4606\n",
      "Epoch 1930/2000, Training Loss: 0.4636\n",
      "Epoch 1931/2000, Training Loss: 0.4626\n",
      "Epoch 1932/2000, Training Loss: 0.4500\n",
      "Epoch 1933/2000, Training Loss: 0.4543\n",
      "Epoch 1934/2000, Training Loss: 0.4626\n",
      "Epoch 1935/2000, Training Loss: 0.4473\n",
      "Epoch 1936/2000, Training Loss: 0.4613\n",
      "Epoch 1937/2000, Training Loss: 0.4551\n",
      "Epoch 1938/2000, Training Loss: 0.4498\n",
      "Epoch 1939/2000, Training Loss: 0.4489\n",
      "Epoch 1940/2000, Training Loss: 0.4542\n",
      "Epoch 1941/2000, Training Loss: 0.4420\n",
      "Epoch 1942/2000, Training Loss: 0.4545\n",
      "Epoch 1943/2000, Training Loss: 0.4595\n",
      "Epoch 1944/2000, Training Loss: 0.4736\n",
      "Epoch 1945/2000, Training Loss: 0.4777\n",
      "Epoch 1946/2000, Training Loss: 0.4807\n",
      "Epoch 1947/2000, Training Loss: 0.4580\n",
      "Epoch 1948/2000, Training Loss: 0.4649\n",
      "Epoch 1949/2000, Training Loss: 0.4469\n",
      "Epoch 1950/2000, Training Loss: 0.4576\n",
      "Epoch 1951/2000, Training Loss: 0.4512\n",
      "Epoch 1952/2000, Training Loss: 0.4621\n",
      "Epoch 1953/2000, Training Loss: 0.4485\n",
      "Epoch 1954/2000, Training Loss: 0.4552\n",
      "Epoch 1955/2000, Training Loss: 0.4672\n",
      "Epoch 1956/2000, Training Loss: 0.5664\n",
      "Epoch 1957/2000, Training Loss: 0.6674\n",
      "Epoch 1958/2000, Training Loss: 0.5544\n",
      "Epoch 1959/2000, Training Loss: 0.5036\n",
      "Epoch 1960/2000, Training Loss: 0.4743\n",
      "Epoch 1961/2000, Training Loss: 0.4719\n",
      "Epoch 1962/2000, Training Loss: 0.4550\n",
      "Epoch 1963/2000, Training Loss: 0.4669\n",
      "Epoch 1964/2000, Training Loss: 0.4510\n",
      "Epoch 1965/2000, Training Loss: 0.4733\n",
      "Epoch 1966/2000, Training Loss: 0.4718\n",
      "Epoch 1967/2000, Training Loss: 0.4646\n",
      "Epoch 1968/2000, Training Loss: 0.5124\n",
      "Epoch 1969/2000, Training Loss: 0.4824\n",
      "Epoch 1970/2000, Training Loss: 0.4653\n",
      "Epoch 1971/2000, Training Loss: 0.4569\n",
      "Epoch 1972/2000, Training Loss: 0.4534\n",
      "Epoch 1973/2000, Training Loss: 0.4714\n",
      "Epoch 1974/2000, Training Loss: 0.4622\n",
      "Epoch 1975/2000, Training Loss: 0.4664\n",
      "Epoch 1976/2000, Training Loss: 0.4802\n",
      "Epoch 1977/2000, Training Loss: 0.4958\n",
      "Epoch 1978/2000, Training Loss: 0.4709\n",
      "Epoch 1979/2000, Training Loss: 0.4663\n",
      "Epoch 1980/2000, Training Loss: 0.4577\n",
      "Epoch 1981/2000, Training Loss: 0.4630\n",
      "Epoch 1982/2000, Training Loss: 0.4594\n",
      "Epoch 1983/2000, Training Loss: 0.4585\n",
      "Epoch 1984/2000, Training Loss: 0.4812\n",
      "Epoch 1985/2000, Training Loss: 0.4989\n",
      "Epoch 1986/2000, Training Loss: 0.4909\n",
      "Epoch 1987/2000, Training Loss: 0.4737\n",
      "Epoch 1988/2000, Training Loss: 0.4710\n",
      "Epoch 1989/2000, Training Loss: 0.4637\n",
      "Epoch 1990/2000, Training Loss: 0.4533\n",
      "Epoch 1991/2000, Training Loss: 0.4471\n",
      "Epoch 1992/2000, Training Loss: 0.4593\n",
      "Epoch 1993/2000, Training Loss: 0.4586\n",
      "Epoch 1994/2000, Training Loss: 0.4468\n",
      "Epoch 1995/2000, Training Loss: 0.4514\n",
      "Epoch 1996/2000, Training Loss: 0.4545\n",
      "Epoch 1997/2000, Training Loss: 0.4621\n",
      "Epoch 1998/2000, Training Loss: 0.4544\n",
      "Epoch 1999/2000, Training Loss: 0.4883\n",
      "Epoch 2000/2000, Training Loss: 0.5038\n",
      "Model saved after additional 1000 epochs.\n"
     ]
    }
   ],
   "source": [
    "# Set the number of additional epochs to train\n",
    "additional_epochs = 1000\n",
    "\n",
    "save_path = 'AqSolDB_AttFPPmodel_checkpoint_2000e.pth'\n",
    "\n",
    "# Continue training from the saved epoch\n",
    "for e in range(start_epoch, start_epoch + additional_epochs):\n",
    "    score = run_a_train_epoch(start_epoch + additional_epochs, e, model, train_loader, loss_fn, optimizer)\n",
    "    epochs.append(e)\n",
    "    scores.append(score)\n",
    "\n",
    "# Save the model again after additional training if needed\n",
    "torch.save({\n",
    "    'epoch': start_epoch + additional_epochs,\n",
    "    'model_state_dict': model.state_dict(),\n",
    "    'optimizer_state_dict': optimizer.state_dict(),\n",
    "    'loss': scores[-1]\n",
    "}, save_path)\n",
    "\n",
    "print(f\"Model saved after additional {additional_epochs} epochs.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "9ba83155-7198-47ba-a1c9-804709dd77b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x750f02f73be0>]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(epochs, scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "efc90cb5-3504-430a-ba26-14922c5a22f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set the model to evaluation mode\n",
    "model.eval()\n",
    "\n",
    "# Initialize a list to store all predictions\n",
    "all_pred = []\n",
    "\n",
    "# Loop through the test data\n",
    "for test_data in test_loader:\n",
    "    smi_lst, bg, labels, masks = test_data\n",
    "    \n",
    "    # Move everything to GPU if available\n",
    "    device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')\n",
    "    bg = bg.to(device)\n",
    "    labels = labels.to(device)\n",
    "    masks = masks.to(device)\n",
    "    \n",
    "    # Get node and edge features and move them to the correct device\n",
    "    node_feats = bg.ndata['hv'].to(device)\n",
    "    edge_feats = bg.edata['he'].to(device)\n",
    "    \n",
    "    # Perform the prediction\n",
    "    pred = model(bg, node_feats, edge_feats)\n",
    "    \n",
    "    # Store the predictions on the CPU\n",
    "    all_pred.append(pred.data.cpu().numpy())\n",
    "\n",
    "# Convert the predictions to a numpy array\n",
    "res = np.vstack(all_pred)\n",
    "\n",
    "# Plot the results\n",
    "plt.clf()\n",
    "plt.scatter(res, test_sol)\n",
    "plt.xlabel('Predicted')\n",
    "plt.ylabel('Experimental')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "9183f3f2-8708-446c-919e-9d6747358ef8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Resuming training from epoch 2001\n"
     ]
    }
   ],
   "source": [
    "# Specify the file path where the model was saved\n",
    "load_path = 'AqSolDB_AttFPPmodel_checkpoint_2000e.pth'\n",
    "\n",
    "# Load the checkpoint\n",
    "checkpoint = torch.load(load_path)\n",
    "\n",
    "# Load the model state, optimizer state, and the last completed epoch\n",
    "model.load_state_dict(checkpoint['model_state_dict'])\n",
    "optimizer.load_state_dict(checkpoint['optimizer_state_dict'])\n",
    "start_epoch = checkpoint['epoch']\n",
    "\n",
    "print(f\"Resuming training from epoch {start_epoch + 1}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "572bfbf6-a401-4977-a77d-caf98be4e672",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2001/3000, Training Loss: 0.4618\n",
      "Epoch 2002/3000, Training Loss: 0.4682\n",
      "Epoch 2003/3000, Training Loss: 0.4510\n",
      "Epoch 2004/3000, Training Loss: 0.4641\n",
      "Epoch 2005/3000, Training Loss: 0.4560\n",
      "Epoch 2006/3000, Training Loss: 0.4574\n",
      "Epoch 2007/3000, Training Loss: 0.5663\n",
      "Epoch 2008/3000, Training Loss: 0.4825\n",
      "Epoch 2009/3000, Training Loss: 0.4727\n",
      "Epoch 2010/3000, Training Loss: 0.4552\n",
      "Epoch 2011/3000, Training Loss: 0.4506\n",
      "Epoch 2012/3000, Training Loss: 0.4540\n",
      "Epoch 2013/3000, Training Loss: 0.5040\n",
      "Epoch 2014/3000, Training Loss: 0.4565\n",
      "Epoch 2015/3000, Training Loss: 0.4569\n",
      "Epoch 2016/3000, Training Loss: 0.5273\n",
      "Epoch 2017/3000, Training Loss: 0.4697\n",
      "Epoch 2018/3000, Training Loss: 0.4646\n",
      "Epoch 2019/3000, Training Loss: 0.4679\n",
      "Epoch 2020/3000, Training Loss: 0.4646\n",
      "Epoch 2021/3000, Training Loss: 0.4565\n",
      "Epoch 2022/3000, Training Loss: 0.4576\n",
      "Epoch 2023/3000, Training Loss: 0.4580\n",
      "Epoch 2024/3000, Training Loss: 0.4564\n",
      "Epoch 2025/3000, Training Loss: 0.4472\n",
      "Epoch 2026/3000, Training Loss: 0.5245\n",
      "Epoch 2027/3000, Training Loss: 0.4457\n",
      "Epoch 2028/3000, Training Loss: 0.4718\n",
      "Epoch 2029/3000, Training Loss: 0.4553\n",
      "Epoch 2030/3000, Training Loss: 0.4521\n",
      "Epoch 2031/3000, Training Loss: 0.4583\n",
      "Epoch 2032/3000, Training Loss: 0.4758\n",
      "Epoch 2033/3000, Training Loss: 0.4574\n",
      "Epoch 2034/3000, Training Loss: 0.4797\n",
      "Epoch 2035/3000, Training Loss: 0.4716\n",
      "Epoch 2036/3000, Training Loss: 0.4751\n",
      "Epoch 2037/3000, Training Loss: 0.4856\n",
      "Epoch 2038/3000, Training Loss: 0.4695\n",
      "Epoch 2039/3000, Training Loss: 0.4597\n",
      "Epoch 2040/3000, Training Loss: 0.4713\n",
      "Epoch 2041/3000, Training Loss: 0.4631\n",
      "Epoch 2042/3000, Training Loss: 0.4773\n",
      "Epoch 2043/3000, Training Loss: 0.4589\n",
      "Epoch 2044/3000, Training Loss: 0.4582\n",
      "Epoch 2045/3000, Training Loss: 0.4557\n",
      "Epoch 2046/3000, Training Loss: 0.4510\n",
      "Epoch 2047/3000, Training Loss: 0.6983\n",
      "Epoch 2048/3000, Training Loss: 0.5446\n",
      "Epoch 2049/3000, Training Loss: 0.4835\n",
      "Epoch 2050/3000, Training Loss: 0.4680\n",
      "Epoch 2051/3000, Training Loss: 0.4622\n",
      "Epoch 2052/3000, Training Loss: 0.4621\n",
      "Epoch 2053/3000, Training Loss: 0.4581\n",
      "Epoch 2054/3000, Training Loss: 0.4422\n",
      "Epoch 2055/3000, Training Loss: 0.4462\n",
      "Epoch 2056/3000, Training Loss: 0.4572\n",
      "Epoch 2057/3000, Training Loss: 0.4401\n",
      "Epoch 2058/3000, Training Loss: 0.4483\n",
      "Epoch 2059/3000, Training Loss: 0.4496\n",
      "Epoch 2060/3000, Training Loss: 0.4520\n",
      "Epoch 2061/3000, Training Loss: 0.4531\n",
      "Epoch 2062/3000, Training Loss: 0.4579\n",
      "Epoch 2063/3000, Training Loss: 0.4725\n",
      "Epoch 2064/3000, Training Loss: 0.4886\n",
      "Epoch 2065/3000, Training Loss: 0.4718\n",
      "Epoch 2066/3000, Training Loss: 0.4802\n",
      "Epoch 2067/3000, Training Loss: 0.4663\n",
      "Epoch 2068/3000, Training Loss: 0.4751\n",
      "Epoch 2069/3000, Training Loss: 0.4594\n",
      "Epoch 2070/3000, Training Loss: 0.4560\n",
      "Epoch 2071/3000, Training Loss: 0.4634\n",
      "Epoch 2072/3000, Training Loss: 0.4464\n",
      "Epoch 2073/3000, Training Loss: 0.4460\n",
      "Epoch 2074/3000, Training Loss: 0.4520\n",
      "Epoch 2075/3000, Training Loss: 0.4555\n",
      "Epoch 2076/3000, Training Loss: 0.4879\n",
      "Epoch 2077/3000, Training Loss: 0.4819\n",
      "Epoch 2078/3000, Training Loss: 0.4588\n",
      "Epoch 2079/3000, Training Loss: 0.4561\n",
      "Epoch 2080/3000, Training Loss: 0.4551\n",
      "Epoch 2081/3000, Training Loss: 0.4566\n",
      "Epoch 2082/3000, Training Loss: 0.4682\n",
      "Epoch 2083/3000, Training Loss: 0.4546\n",
      "Epoch 2084/3000, Training Loss: 0.4631\n",
      "Epoch 2085/3000, Training Loss: 0.4764\n",
      "Epoch 2086/3000, Training Loss: 0.4537\n",
      "Epoch 2087/3000, Training Loss: 0.4552\n",
      "Epoch 2088/3000, Training Loss: 0.4665\n",
      "Epoch 2089/3000, Training Loss: 0.4928\n",
      "Epoch 2090/3000, Training Loss: 0.4796\n",
      "Epoch 2091/3000, Training Loss: 0.4739\n",
      "Epoch 2092/3000, Training Loss: 0.4520\n",
      "Epoch 2093/3000, Training Loss: 0.4659\n",
      "Epoch 2094/3000, Training Loss: 0.4550\n",
      "Epoch 2095/3000, Training Loss: 0.4751\n",
      "Epoch 2096/3000, Training Loss: 0.4667\n",
      "Epoch 2097/3000, Training Loss: 0.4814\n",
      "Epoch 2098/3000, Training Loss: 0.4768\n",
      "Epoch 2099/3000, Training Loss: 0.4628\n",
      "Epoch 2100/3000, Training Loss: 0.4628\n",
      "Epoch 2101/3000, Training Loss: 0.5038\n",
      "Epoch 2102/3000, Training Loss: 0.4767\n",
      "Epoch 2103/3000, Training Loss: 0.4985\n",
      "Epoch 2104/3000, Training Loss: 0.4610\n",
      "Epoch 2105/3000, Training Loss: 0.4590\n",
      "Epoch 2106/3000, Training Loss: 0.4444\n",
      "Epoch 2107/3000, Training Loss: 0.5212\n",
      "Epoch 2108/3000, Training Loss: 0.4594\n",
      "Epoch 2109/3000, Training Loss: 0.4712\n",
      "Epoch 2110/3000, Training Loss: 0.4548\n",
      "Epoch 2111/3000, Training Loss: 0.4601\n",
      "Epoch 2112/3000, Training Loss: 0.4466\n",
      "Epoch 2113/3000, Training Loss: 0.4546\n",
      "Epoch 2114/3000, Training Loss: 0.4629\n",
      "Epoch 2115/3000, Training Loss: 0.4596\n",
      "Epoch 2116/3000, Training Loss: 0.4541\n",
      "Epoch 2117/3000, Training Loss: 0.4542\n",
      "Epoch 2118/3000, Training Loss: 0.4521\n",
      "Epoch 2119/3000, Training Loss: 0.4717\n",
      "Epoch 2120/3000, Training Loss: 0.4581\n",
      "Epoch 2121/3000, Training Loss: 0.4698\n",
      "Epoch 2122/3000, Training Loss: 0.4685\n",
      "Epoch 2123/3000, Training Loss: 0.4515\n",
      "Epoch 2124/3000, Training Loss: 0.4550\n",
      "Epoch 2125/3000, Training Loss: 0.4538\n",
      "Epoch 2126/3000, Training Loss: 0.5364\n",
      "Epoch 2127/3000, Training Loss: 0.5336\n",
      "Epoch 2128/3000, Training Loss: 0.4785\n",
      "Epoch 2129/3000, Training Loss: 0.4753\n",
      "Epoch 2130/3000, Training Loss: 0.4462\n",
      "Epoch 2131/3000, Training Loss: 0.4441\n",
      "Epoch 2132/3000, Training Loss: 0.4507\n",
      "Epoch 2133/3000, Training Loss: 0.4637\n",
      "Epoch 2134/3000, Training Loss: 0.4507\n",
      "Epoch 2135/3000, Training Loss: 0.4619\n",
      "Epoch 2136/3000, Training Loss: 0.4572\n",
      "Epoch 2137/3000, Training Loss: 0.4472\n",
      "Epoch 2138/3000, Training Loss: 0.4507\n",
      "Epoch 2139/3000, Training Loss: 0.4507\n",
      "Epoch 2140/3000, Training Loss: 0.4516\n",
      "Epoch 2141/3000, Training Loss: 0.4678\n",
      "Epoch 2142/3000, Training Loss: 0.5441\n",
      "Epoch 2143/3000, Training Loss: 0.5056\n",
      "Epoch 2144/3000, Training Loss: 0.5115\n",
      "Epoch 2145/3000, Training Loss: 0.4631\n",
      "Epoch 2146/3000, Training Loss: 0.4508\n",
      "Epoch 2147/3000, Training Loss: 0.4683\n",
      "Epoch 2148/3000, Training Loss: 0.4728\n",
      "Epoch 2149/3000, Training Loss: 0.4778\n",
      "Epoch 2150/3000, Training Loss: 0.4658\n",
      "Epoch 2151/3000, Training Loss: 0.4694\n",
      "Epoch 2152/3000, Training Loss: 0.4662\n",
      "Epoch 2153/3000, Training Loss: 0.4641\n",
      "Epoch 2154/3000, Training Loss: 0.4609\n",
      "Epoch 2155/3000, Training Loss: 0.4764\n",
      "Epoch 2156/3000, Training Loss: 0.4691\n",
      "Epoch 2157/3000, Training Loss: 0.4524\n",
      "Epoch 2158/3000, Training Loss: 0.4563\n",
      "Epoch 2159/3000, Training Loss: 0.4603\n",
      "Epoch 2160/3000, Training Loss: 0.4531\n",
      "Epoch 2161/3000, Training Loss: 0.4497\n",
      "Epoch 2162/3000, Training Loss: 0.4417\n",
      "Epoch 2163/3000, Training Loss: 0.4496\n",
      "Epoch 2164/3000, Training Loss: 0.4521\n",
      "Epoch 2165/3000, Training Loss: 0.4592\n",
      "Epoch 2166/3000, Training Loss: 0.4494\n",
      "Epoch 2167/3000, Training Loss: 0.4717\n",
      "Epoch 2168/3000, Training Loss: 0.4620\n",
      "Epoch 2169/3000, Training Loss: 0.5382\n",
      "Epoch 2170/3000, Training Loss: 0.5481\n",
      "Epoch 2171/3000, Training Loss: 0.5027\n",
      "Epoch 2172/3000, Training Loss: 0.5056\n",
      "Epoch 2173/3000, Training Loss: 0.4976\n",
      "Epoch 2174/3000, Training Loss: 0.4944\n",
      "Epoch 2175/3000, Training Loss: 0.4732\n",
      "Epoch 2176/3000, Training Loss: 0.4697\n",
      "Epoch 2177/3000, Training Loss: 0.5023\n",
      "Epoch 2178/3000, Training Loss: 0.9071\n",
      "Epoch 2179/3000, Training Loss: 0.5682\n",
      "Epoch 2180/3000, Training Loss: 0.5181\n",
      "Epoch 2181/3000, Training Loss: 0.4846\n",
      "Epoch 2182/3000, Training Loss: 0.4660\n",
      "Epoch 2183/3000, Training Loss: 0.4694\n",
      "Epoch 2184/3000, Training Loss: 0.4600\n",
      "Epoch 2185/3000, Training Loss: 0.4531\n",
      "Epoch 2186/3000, Training Loss: 0.4669\n",
      "Epoch 2187/3000, Training Loss: 0.4590\n",
      "Epoch 2188/3000, Training Loss: 0.4615\n",
      "Epoch 2189/3000, Training Loss: 0.4626\n",
      "Epoch 2190/3000, Training Loss: 0.4535\n",
      "Epoch 2191/3000, Training Loss: 0.4668\n",
      "Epoch 2192/3000, Training Loss: 0.4739\n",
      "Epoch 2193/3000, Training Loss: 0.4548\n",
      "Epoch 2194/3000, Training Loss: 0.4631\n",
      "Epoch 2195/3000, Training Loss: 0.4686\n",
      "Epoch 2196/3000, Training Loss: 0.5157\n",
      "Epoch 2197/3000, Training Loss: 0.4764\n",
      "Epoch 2198/3000, Training Loss: 0.4835\n",
      "Epoch 2199/3000, Training Loss: 0.4824\n",
      "Epoch 2200/3000, Training Loss: 0.4762\n",
      "Epoch 2201/3000, Training Loss: 0.4618\n",
      "Epoch 2202/3000, Training Loss: 0.4594\n",
      "Epoch 2203/3000, Training Loss: 0.4597\n",
      "Epoch 2204/3000, Training Loss: 0.4431\n",
      "Epoch 2205/3000, Training Loss: 0.4469\n",
      "Epoch 2206/3000, Training Loss: 0.4660\n",
      "Epoch 2207/3000, Training Loss: 0.4515\n",
      "Epoch 2208/3000, Training Loss: 0.4594\n",
      "Epoch 2209/3000, Training Loss: 0.5834\n",
      "Epoch 2210/3000, Training Loss: 0.5013\n",
      "Epoch 2211/3000, Training Loss: 0.4920\n",
      "Epoch 2212/3000, Training Loss: 0.4856\n",
      "Epoch 2213/3000, Training Loss: 0.4673\n",
      "Epoch 2214/3000, Training Loss: 0.4721\n",
      "Epoch 2215/3000, Training Loss: 0.4534\n",
      "Epoch 2216/3000, Training Loss: 0.4637\n",
      "Epoch 2217/3000, Training Loss: 0.4461\n",
      "Epoch 2218/3000, Training Loss: 0.4528\n",
      "Epoch 2219/3000, Training Loss: 0.4503\n",
      "Epoch 2220/3000, Training Loss: 0.4711\n",
      "Epoch 2221/3000, Training Loss: 0.4525\n",
      "Epoch 2222/3000, Training Loss: 0.4508\n",
      "Epoch 2223/3000, Training Loss: 0.4616\n",
      "Epoch 2224/3000, Training Loss: 0.4559\n",
      "Epoch 2225/3000, Training Loss: 0.4863\n",
      "Epoch 2226/3000, Training Loss: 0.4723\n",
      "Epoch 2227/3000, Training Loss: 0.4720\n",
      "Epoch 2228/3000, Training Loss: 0.4579\n",
      "Epoch 2229/3000, Training Loss: 0.4732\n",
      "Epoch 2230/3000, Training Loss: 0.4763\n",
      "Epoch 2231/3000, Training Loss: 0.4638\n",
      "Epoch 2232/3000, Training Loss: 0.4498\n",
      "Epoch 2233/3000, Training Loss: 0.4594\n",
      "Epoch 2234/3000, Training Loss: 0.4627\n",
      "Epoch 2235/3000, Training Loss: 0.4747\n",
      "Epoch 2236/3000, Training Loss: 0.4584\n",
      "Epoch 2237/3000, Training Loss: 0.4569\n",
      "Epoch 2238/3000, Training Loss: 0.4613\n",
      "Epoch 2239/3000, Training Loss: 0.4409\n",
      "Epoch 2240/3000, Training Loss: 0.4529\n",
      "Epoch 2241/3000, Training Loss: 0.4616\n",
      "Epoch 2242/3000, Training Loss: 0.4445\n",
      "Epoch 2243/3000, Training Loss: 0.4529\n",
      "Epoch 2244/3000, Training Loss: 0.4660\n",
      "Epoch 2245/3000, Training Loss: 0.4674\n",
      "Epoch 2246/3000, Training Loss: 0.4631\n",
      "Epoch 2247/3000, Training Loss: 0.7432\n",
      "Epoch 2248/3000, Training Loss: 0.5293\n",
      "Epoch 2249/3000, Training Loss: 0.4988\n",
      "Epoch 2250/3000, Training Loss: 0.4960\n",
      "Epoch 2251/3000, Training Loss: 0.4852\n",
      "Epoch 2252/3000, Training Loss: 0.4755\n",
      "Epoch 2253/3000, Training Loss: 0.4699\n",
      "Epoch 2254/3000, Training Loss: 0.4710\n",
      "Epoch 2255/3000, Training Loss: 0.4673\n",
      "Epoch 2256/3000, Training Loss: 0.4587\n",
      "Epoch 2257/3000, Training Loss: 0.4642\n",
      "Epoch 2258/3000, Training Loss: 0.4608\n",
      "Epoch 2259/3000, Training Loss: 0.4721\n",
      "Epoch 2260/3000, Training Loss: 0.4625\n",
      "Epoch 2261/3000, Training Loss: 0.4836\n",
      "Epoch 2262/3000, Training Loss: 0.4594\n",
      "Epoch 2263/3000, Training Loss: 0.4492\n",
      "Epoch 2264/3000, Training Loss: 0.4642\n",
      "Epoch 2265/3000, Training Loss: 0.4593\n",
      "Epoch 2266/3000, Training Loss: 0.4469\n",
      "Epoch 2267/3000, Training Loss: 0.4565\n",
      "Epoch 2268/3000, Training Loss: 0.4727\n",
      "Epoch 2269/3000, Training Loss: 0.4636\n",
      "Epoch 2270/3000, Training Loss: 0.4494\n",
      "Epoch 2271/3000, Training Loss: 0.4474\n",
      "Epoch 2272/3000, Training Loss: 0.4657\n",
      "Epoch 2273/3000, Training Loss: 0.4514\n",
      "Epoch 2274/3000, Training Loss: 0.4479\n",
      "Epoch 2275/3000, Training Loss: 0.4332\n",
      "Epoch 2276/3000, Training Loss: 0.4358\n",
      "Epoch 2277/3000, Training Loss: 0.4661\n",
      "Epoch 2278/3000, Training Loss: 0.4642\n",
      "Epoch 2279/3000, Training Loss: 0.4628\n",
      "Epoch 2280/3000, Training Loss: 0.4518\n",
      "Epoch 2281/3000, Training Loss: 0.4574\n",
      "Epoch 2282/3000, Training Loss: 0.6035\n",
      "Epoch 2283/3000, Training Loss: 0.5279\n",
      "Epoch 2284/3000, Training Loss: 0.4810\n",
      "Epoch 2285/3000, Training Loss: 0.4679\n",
      "Epoch 2286/3000, Training Loss: 0.4530\n",
      "Epoch 2287/3000, Training Loss: 0.4573\n",
      "Epoch 2288/3000, Training Loss: 0.4657\n",
      "Epoch 2289/3000, Training Loss: 0.4627\n",
      "Epoch 2290/3000, Training Loss: 0.4625\n",
      "Epoch 2291/3000, Training Loss: 0.4535\n",
      "Epoch 2292/3000, Training Loss: 0.4532\n",
      "Epoch 2293/3000, Training Loss: 0.4470\n",
      "Epoch 2294/3000, Training Loss: 0.4438\n",
      "Epoch 2295/3000, Training Loss: 0.4458\n",
      "Epoch 2296/3000, Training Loss: 0.4416\n",
      "Epoch 2297/3000, Training Loss: 0.4455\n",
      "Epoch 2298/3000, Training Loss: 0.4557\n",
      "Epoch 2299/3000, Training Loss: 0.4575\n",
      "Epoch 2300/3000, Training Loss: 0.4524\n",
      "Epoch 2301/3000, Training Loss: 0.4511\n",
      "Epoch 2302/3000, Training Loss: 0.4575\n",
      "Epoch 2303/3000, Training Loss: 0.4674\n",
      "Epoch 2304/3000, Training Loss: 0.4601\n",
      "Epoch 2305/3000, Training Loss: 0.4679\n",
      "Epoch 2306/3000, Training Loss: 0.4694\n",
      "Epoch 2307/3000, Training Loss: 0.4558\n",
      "Epoch 2308/3000, Training Loss: 0.4822\n",
      "Epoch 2309/3000, Training Loss: 0.4719\n",
      "Epoch 2310/3000, Training Loss: 0.4619\n",
      "Epoch 2311/3000, Training Loss: 0.4627\n",
      "Epoch 2312/3000, Training Loss: 0.4708\n",
      "Epoch 2313/3000, Training Loss: 0.4679\n",
      "Epoch 2314/3000, Training Loss: 0.4627\n",
      "Epoch 2315/3000, Training Loss: 0.4534\n",
      "Epoch 2316/3000, Training Loss: 0.4586\n",
      "Epoch 2317/3000, Training Loss: 0.4478\n",
      "Epoch 2318/3000, Training Loss: 0.4416\n",
      "Epoch 2319/3000, Training Loss: 0.4523\n",
      "Epoch 2320/3000, Training Loss: 0.4452\n",
      "Epoch 2321/3000, Training Loss: 0.4423\n",
      "Epoch 2322/3000, Training Loss: 0.4525\n",
      "Epoch 2323/3000, Training Loss: 0.4544\n",
      "Epoch 2324/3000, Training Loss: 0.4534\n",
      "Epoch 2325/3000, Training Loss: 0.4566\n",
      "Epoch 2326/3000, Training Loss: 0.4637\n",
      "Epoch 2327/3000, Training Loss: 0.4450\n",
      "Epoch 2328/3000, Training Loss: 0.4314\n",
      "Epoch 2329/3000, Training Loss: 0.4421\n",
      "Epoch 2330/3000, Training Loss: 0.4486\n",
      "Epoch 2331/3000, Training Loss: 0.4445\n",
      "Epoch 2332/3000, Training Loss: 0.4511\n",
      "Epoch 2333/3000, Training Loss: 0.4587\n",
      "Epoch 2334/3000, Training Loss: 0.4566\n",
      "Epoch 2335/3000, Training Loss: 0.4479\n",
      "Epoch 2336/3000, Training Loss: 0.4528\n",
      "Epoch 2337/3000, Training Loss: 0.4660\n",
      "Epoch 2338/3000, Training Loss: 0.4612\n",
      "Epoch 2339/3000, Training Loss: 0.4539\n",
      "Epoch 2340/3000, Training Loss: 0.4450\n",
      "Epoch 2341/3000, Training Loss: 0.4436\n",
      "Epoch 2342/3000, Training Loss: 0.4441\n",
      "Epoch 2343/3000, Training Loss: 0.4556\n",
      "Epoch 2344/3000, Training Loss: 0.4500\n",
      "Epoch 2345/3000, Training Loss: 0.4679\n",
      "Epoch 2346/3000, Training Loss: 0.4615\n",
      "Epoch 2347/3000, Training Loss: 0.4568\n",
      "Epoch 2348/3000, Training Loss: 0.4473\n",
      "Epoch 2349/3000, Training Loss: 0.4657\n",
      "Epoch 2350/3000, Training Loss: 0.4779\n",
      "Epoch 2351/3000, Training Loss: 0.4614\n",
      "Epoch 2352/3000, Training Loss: 0.4837\n",
      "Epoch 2353/3000, Training Loss: 0.4690\n",
      "Epoch 2354/3000, Training Loss: 0.4780\n",
      "Epoch 2355/3000, Training Loss: 0.4545\n",
      "Epoch 2356/3000, Training Loss: 0.4533\n",
      "Epoch 2357/3000, Training Loss: 0.4566\n",
      "Epoch 2358/3000, Training Loss: 0.4407\n",
      "Epoch 2359/3000, Training Loss: 0.4511\n",
      "Epoch 2360/3000, Training Loss: 0.4450\n",
      "Epoch 2361/3000, Training Loss: 0.4514\n",
      "Epoch 2362/3000, Training Loss: 0.4709\n",
      "Epoch 2363/3000, Training Loss: 0.4743\n",
      "Epoch 2364/3000, Training Loss: 0.4765\n",
      "Epoch 2365/3000, Training Loss: 0.4394\n",
      "Epoch 2366/3000, Training Loss: 0.4706\n",
      "Epoch 2367/3000, Training Loss: 0.5742\n",
      "Epoch 2368/3000, Training Loss: 0.5083\n",
      "Epoch 2369/3000, Training Loss: 0.4868\n",
      "Epoch 2370/3000, Training Loss: 0.4818\n",
      "Epoch 2371/3000, Training Loss: 0.4667\n",
      "Epoch 2372/3000, Training Loss: 0.4741\n",
      "Epoch 2373/3000, Training Loss: 0.4618\n",
      "Epoch 2374/3000, Training Loss: 0.4596\n",
      "Epoch 2375/3000, Training Loss: 0.4526\n",
      "Epoch 2376/3000, Training Loss: 0.4557\n",
      "Epoch 2377/3000, Training Loss: 0.4611\n",
      "Epoch 2378/3000, Training Loss: 0.4435\n",
      "Epoch 2379/3000, Training Loss: 0.4574\n",
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      "Epoch 2769/3000, Training Loss: 0.5666\n",
      "Epoch 2770/3000, Training Loss: 0.5338\n",
      "Epoch 2771/3000, Training Loss: 0.5110\n",
      "Epoch 2772/3000, Training Loss: 0.4948\n",
      "Epoch 2773/3000, Training Loss: 0.4904\n",
      "Epoch 2774/3000, Training Loss: 0.4865\n",
      "Epoch 2775/3000, Training Loss: 0.4758\n",
      "Epoch 2776/3000, Training Loss: 0.4796\n",
      "Epoch 2777/3000, Training Loss: 0.4829\n",
      "Epoch 2778/3000, Training Loss: 0.4821\n",
      "Epoch 2779/3000, Training Loss: 0.5003\n",
      "Epoch 2780/3000, Training Loss: 0.5036\n",
      "Epoch 2781/3000, Training Loss: 0.4895\n",
      "Epoch 2782/3000, Training Loss: 0.4846\n",
      "Epoch 2783/3000, Training Loss: 0.4808\n",
      "Epoch 2784/3000, Training Loss: 0.4717\n",
      "Epoch 2785/3000, Training Loss: 0.4738\n",
      "Epoch 2786/3000, Training Loss: 0.4706\n",
      "Epoch 2787/3000, Training Loss: 0.4766\n",
      "Epoch 2788/3000, Training Loss: 0.4628\n",
      "Epoch 2789/3000, Training Loss: 0.4660\n",
      "Epoch 2790/3000, Training Loss: 0.4629\n",
      "Epoch 2791/3000, Training Loss: 0.4793\n",
      "Epoch 2792/3000, Training Loss: 0.4618\n",
      "Epoch 2793/3000, Training Loss: 0.4653\n",
      "Epoch 2794/3000, Training Loss: 0.4642\n",
      "Epoch 2795/3000, Training Loss: 0.4662\n",
      "Epoch 2796/3000, Training Loss: 0.7056\n",
      "Epoch 2797/3000, Training Loss: 0.5640\n",
      "Epoch 2798/3000, Training Loss: 0.5273\n",
      "Epoch 2799/3000, Training Loss: 0.4968\n",
      "Epoch 2800/3000, Training Loss: 0.4767\n",
      "Epoch 2801/3000, Training Loss: 0.4762\n",
      "Epoch 2802/3000, Training Loss: 0.4554\n",
      "Epoch 2803/3000, Training Loss: 0.4813\n",
      "Epoch 2804/3000, Training Loss: 0.4659\n",
      "Epoch 2805/3000, Training Loss: 0.4574\n",
      "Epoch 2806/3000, Training Loss: 0.4704\n",
      "Epoch 2807/3000, Training Loss: 0.4640\n",
      "Epoch 2808/3000, Training Loss: 0.4597\n",
      "Epoch 2809/3000, Training Loss: 0.4739\n",
      "Epoch 2810/3000, Training Loss: 0.4663\n",
      "Epoch 2811/3000, Training Loss: 0.4669\n",
      "Epoch 2812/3000, Training Loss: 0.5075\n",
      "Epoch 2813/3000, Training Loss: 0.4887\n",
      "Epoch 2814/3000, Training Loss: 0.4733\n",
      "Epoch 2815/3000, Training Loss: 0.4765\n",
      "Epoch 2816/3000, Training Loss: 0.5961\n",
      "Epoch 2817/3000, Training Loss: 0.5036\n",
      "Epoch 2818/3000, Training Loss: 0.4695\n",
      "Epoch 2819/3000, Training Loss: 0.4899\n",
      "Epoch 2820/3000, Training Loss: 0.4780\n",
      "Epoch 2821/3000, Training Loss: 0.4837\n",
      "Epoch 2822/3000, Training Loss: 0.4833\n",
      "Epoch 2823/3000, Training Loss: 0.4672\n",
      "Epoch 2824/3000, Training Loss: 0.4722\n",
      "Epoch 2825/3000, Training Loss: 0.4598\n",
      "Epoch 2826/3000, Training Loss: 0.4724\n",
      "Epoch 2827/3000, Training Loss: 0.5068\n",
      "Epoch 2828/3000, Training Loss: 0.4936\n",
      "Epoch 2829/3000, Training Loss: 0.4763\n",
      "Epoch 2830/3000, Training Loss: 0.4672\n",
      "Epoch 2831/3000, Training Loss: 0.4633\n",
      "Epoch 2832/3000, Training Loss: 0.4665\n",
      "Epoch 2833/3000, Training Loss: 0.5614\n",
      "Epoch 2834/3000, Training Loss: 0.5119\n",
      "Epoch 2835/3000, Training Loss: 0.4791\n",
      "Epoch 2836/3000, Training Loss: 0.4692\n",
      "Epoch 2837/3000, Training Loss: 0.4702\n",
      "Epoch 2838/3000, Training Loss: 0.4820\n",
      "Epoch 2839/3000, Training Loss: 0.4591\n",
      "Epoch 2840/3000, Training Loss: 0.4538\n",
      "Epoch 2841/3000, Training Loss: 0.4513\n",
      "Epoch 2842/3000, Training Loss: 0.4627\n",
      "Epoch 2843/3000, Training Loss: 0.4579\n",
      "Epoch 2844/3000, Training Loss: 0.4575\n",
      "Epoch 2845/3000, Training Loss: 0.4493\n",
      "Epoch 2846/3000, Training Loss: 0.4642\n",
      "Epoch 2847/3000, Training Loss: 0.4824\n",
      "Epoch 2848/3000, Training Loss: 0.4635\n",
      "Epoch 2849/3000, Training Loss: 0.4551\n",
      "Epoch 2850/3000, Training Loss: 0.4643\n",
      "Epoch 2851/3000, Training Loss: 0.4582\n",
      "Epoch 2852/3000, Training Loss: 0.4692\n",
      "Epoch 2853/3000, Training Loss: 0.4640\n",
      "Epoch 2854/3000, Training Loss: 0.4561\n",
      "Epoch 2855/3000, Training Loss: 0.4548\n",
      "Epoch 2856/3000, Training Loss: 0.4781\n",
      "Epoch 2857/3000, Training Loss: 0.4737\n",
      "Epoch 2858/3000, Training Loss: 0.4580\n",
      "Epoch 2859/3000, Training Loss: 0.4680\n",
      "Epoch 2860/3000, Training Loss: 0.4494\n",
      "Epoch 2861/3000, Training Loss: 0.4595\n",
      "Epoch 2862/3000, Training Loss: 0.4561\n",
      "Epoch 2863/3000, Training Loss: 0.4676\n",
      "Epoch 2864/3000, Training Loss: 0.4691\n",
      "Epoch 2865/3000, Training Loss: 0.4669\n",
      "Epoch 2866/3000, Training Loss: 0.4582\n",
      "Epoch 2867/3000, Training Loss: 0.4595\n",
      "Epoch 2868/3000, Training Loss: 0.4587\n",
      "Epoch 2869/3000, Training Loss: 0.4454\n",
      "Epoch 2870/3000, Training Loss: 0.4514\n",
      "Epoch 2871/3000, Training Loss: 0.4668\n",
      "Epoch 2872/3000, Training Loss: 0.4494\n",
      "Epoch 2873/3000, Training Loss: 0.4486\n",
      "Epoch 2874/3000, Training Loss: 0.4563\n",
      "Epoch 2875/3000, Training Loss: 0.4676\n",
      "Epoch 2876/3000, Training Loss: 0.5100\n",
      "Epoch 2877/3000, Training Loss: 0.5236\n",
      "Epoch 2878/3000, Training Loss: 0.5142\n",
      "Epoch 2879/3000, Training Loss: 0.4698\n",
      "Epoch 2880/3000, Training Loss: 0.4763\n",
      "Epoch 2881/3000, Training Loss: 0.4738\n",
      "Epoch 2882/3000, Training Loss: 0.4722\n",
      "Epoch 2883/3000, Training Loss: 0.4663\n",
      "Epoch 2884/3000, Training Loss: 0.4694\n",
      "Epoch 2885/3000, Training Loss: 0.4637\n",
      "Epoch 2886/3000, Training Loss: 0.4661\n",
      "Epoch 2887/3000, Training Loss: 0.4540\n",
      "Epoch 2888/3000, Training Loss: 0.4962\n",
      "Epoch 2889/3000, Training Loss: 0.4682\n",
      "Epoch 2890/3000, Training Loss: 0.4811\n",
      "Epoch 2891/3000, Training Loss: 0.4828\n",
      "Epoch 2892/3000, Training Loss: 0.4588\n",
      "Epoch 2893/3000, Training Loss: 0.4596\n",
      "Epoch 2894/3000, Training Loss: 0.4677\n",
      "Epoch 2895/3000, Training Loss: 0.4457\n",
      "Epoch 2896/3000, Training Loss: 0.4475\n",
      "Epoch 2897/3000, Training Loss: 1.1381\n",
      "Epoch 2898/3000, Training Loss: 0.6410\n",
      "Epoch 2899/3000, Training Loss: 0.5312\n",
      "Epoch 2900/3000, Training Loss: 0.5347\n",
      "Epoch 2901/3000, Training Loss: 0.5101\n",
      "Epoch 2902/3000, Training Loss: 0.4789\n",
      "Epoch 2903/3000, Training Loss: 0.4863\n",
      "Epoch 2904/3000, Training Loss: 0.4975\n",
      "Epoch 2905/3000, Training Loss: 0.4918\n",
      "Epoch 2906/3000, Training Loss: 0.5004\n",
      "Epoch 2907/3000, Training Loss: 0.4823\n",
      "Epoch 2908/3000, Training Loss: 0.4709\n",
      "Epoch 2909/3000, Training Loss: 0.4692\n",
      "Epoch 2910/3000, Training Loss: 0.4666\n",
      "Epoch 2911/3000, Training Loss: 0.4638\n",
      "Epoch 2912/3000, Training Loss: 0.4802\n",
      "Epoch 2913/3000, Training Loss: 0.4601\n",
      "Epoch 2914/3000, Training Loss: 0.4708\n",
      "Epoch 2915/3000, Training Loss: 0.4843\n",
      "Epoch 2916/3000, Training Loss: 0.4673\n",
      "Epoch 2917/3000, Training Loss: 0.4808\n",
      "Epoch 2918/3000, Training Loss: 0.4619\n",
      "Epoch 2919/3000, Training Loss: 0.4687\n",
      "Epoch 2920/3000, Training Loss: 0.4656\n",
      "Epoch 2921/3000, Training Loss: 0.4704\n",
      "Epoch 2922/3000, Training Loss: 0.4563\n",
      "Epoch 2923/3000, Training Loss: 0.4493\n",
      "Epoch 2924/3000, Training Loss: 0.4478\n",
      "Epoch 2925/3000, Training Loss: 0.4510\n",
      "Epoch 2926/3000, Training Loss: 0.4473\n",
      "Epoch 2927/3000, Training Loss: 0.4673\n",
      "Epoch 2928/3000, Training Loss: 0.4839\n",
      "Epoch 2929/3000, Training Loss: 0.4576\n",
      "Epoch 2930/3000, Training Loss: 0.4567\n",
      "Epoch 2931/3000, Training Loss: 0.4489\n",
      "Epoch 2932/3000, Training Loss: 0.4454\n",
      "Epoch 2933/3000, Training Loss: 0.4537\n",
      "Epoch 2934/3000, Training Loss: 0.4445\n",
      "Epoch 2935/3000, Training Loss: 0.4542\n",
      "Epoch 2936/3000, Training Loss: 0.4613\n",
      "Epoch 2937/3000, Training Loss: 0.4614\n",
      "Epoch 2938/3000, Training Loss: 0.4578\n",
      "Epoch 2939/3000, Training Loss: 0.4586\n",
      "Epoch 2940/3000, Training Loss: 0.4505\n",
      "Epoch 2941/3000, Training Loss: 0.4745\n",
      "Epoch 2942/3000, Training Loss: 0.4473\n",
      "Epoch 2943/3000, Training Loss: 0.4672\n",
      "Epoch 2944/3000, Training Loss: 0.4695\n",
      "Epoch 2945/3000, Training Loss: 0.4720\n",
      "Epoch 2946/3000, Training Loss: 0.4648\n",
      "Epoch 2947/3000, Training Loss: 0.4596\n",
      "Epoch 2948/3000, Training Loss: 0.4679\n",
      "Epoch 2949/3000, Training Loss: 0.4741\n",
      "Epoch 2950/3000, Training Loss: 0.4727\n",
      "Epoch 2951/3000, Training Loss: 0.4553\n",
      "Epoch 2952/3000, Training Loss: 0.4525\n",
      "Epoch 2953/3000, Training Loss: 0.4507\n",
      "Epoch 2954/3000, Training Loss: 0.4666\n",
      "Epoch 2955/3000, Training Loss: 0.4912\n",
      "Epoch 2956/3000, Training Loss: 0.4733\n",
      "Epoch 2957/3000, Training Loss: 0.4778\n",
      "Epoch 2958/3000, Training Loss: 1.0173\n",
      "Epoch 2959/3000, Training Loss: 1.1305\n",
      "Epoch 2960/3000, Training Loss: 0.7951\n",
      "Epoch 2961/3000, Training Loss: 0.6958\n",
      "Epoch 2962/3000, Training Loss: 0.6281\n",
      "Epoch 2963/3000, Training Loss: 0.5588\n",
      "Epoch 2964/3000, Training Loss: 0.5355\n",
      "Epoch 2965/3000, Training Loss: 0.5130\n",
      "Epoch 2966/3000, Training Loss: 0.5183\n",
      "Epoch 2967/3000, Training Loss: 0.5042\n",
      "Epoch 2968/3000, Training Loss: 0.4887\n",
      "Epoch 2969/3000, Training Loss: 0.4961\n",
      "Epoch 2970/3000, Training Loss: 0.4889\n",
      "Epoch 2971/3000, Training Loss: 0.4987\n",
      "Epoch 2972/3000, Training Loss: 0.4815\n",
      "Epoch 2973/3000, Training Loss: 0.4804\n",
      "Epoch 2974/3000, Training Loss: 0.4783\n",
      "Epoch 2975/3000, Training Loss: 0.4847\n",
      "Epoch 2976/3000, Training Loss: 0.4753\n",
      "Epoch 2977/3000, Training Loss: 0.4875\n",
      "Epoch 2978/3000, Training Loss: 0.4860\n",
      "Epoch 2979/3000, Training Loss: 0.4916\n",
      "Epoch 2980/3000, Training Loss: 0.4878\n",
      "Epoch 2981/3000, Training Loss: 0.4823\n",
      "Epoch 2982/3000, Training Loss: 0.4812\n",
      "Epoch 2983/3000, Training Loss: 0.4715\n",
      "Epoch 2984/3000, Training Loss: 0.4947\n",
      "Epoch 2985/3000, Training Loss: 0.4775\n",
      "Epoch 2986/3000, Training Loss: 0.4794\n",
      "Epoch 2987/3000, Training Loss: 0.4709\n",
      "Epoch 2988/3000, Training Loss: 0.4680\n",
      "Epoch 2989/3000, Training Loss: 0.4719\n",
      "Epoch 2990/3000, Training Loss: 0.4715\n",
      "Epoch 2991/3000, Training Loss: 0.4680\n",
      "Epoch 2992/3000, Training Loss: 0.4869\n",
      "Epoch 2993/3000, Training Loss: 0.4814\n",
      "Epoch 2994/3000, Training Loss: 0.4823\n",
      "Epoch 2995/3000, Training Loss: 0.4795\n",
      "Epoch 2996/3000, Training Loss: 0.4773\n",
      "Epoch 2997/3000, Training Loss: 0.4671\n",
      "Epoch 2998/3000, Training Loss: 0.4678\n",
      "Epoch 2999/3000, Training Loss: 0.4726\n",
      "Epoch 3000/3000, Training Loss: 0.4753\n",
      "Model saved after additional 1000 epochs.\n"
     ]
    }
   ],
   "source": [
    "# Set the number of additional epochs to train\n",
    "additional_epochs = 1000\n",
    "\n",
    "save_path = 'AqSolDB_AttFPPmodel_checkpoint_3000e.pth'\n",
    "\n",
    "# Continue training from the saved epoch\n",
    "for e in range(start_epoch, start_epoch + additional_epochs):\n",
    "    score = run_a_train_epoch(start_epoch + additional_epochs, e, model, train_loader, loss_fn, optimizer)\n",
    "    epochs.append(e)\n",
    "    scores.append(score)\n",
    "\n",
    "# Save the model again after additional training if needed\n",
    "torch.save({\n",
    "    'epoch': start_epoch + additional_epochs,\n",
    "    'model_state_dict': model.state_dict(),\n",
    "    'optimizer_state_dict': optimizer.state_dict(),\n",
    "    'loss': scores[-1]\n",
    "}, save_path)\n",
    "\n",
    "print(f\"Model saved after additional {additional_epochs} epochs.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "146b5a37-1cc4-4c06-9782-cab3506292af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x750f028df4c0>]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model.eval()\n",
    "plt.plot(epochs, scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "bd090418-4ace-4f6c-9692-12b2570afcc3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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C14dEayGt2XFccQHD8BAL6hrl3UrJcw6moCcxOgylldrXblq48QAiw0O8Bi9UvNC8AnKp6/XXX0dKSgoiIiIwYMAA7Nixw+ghkQChdHlH7HZSzLjVVSgpuby6Hq9kHkbG/iLJ+9Ai0XpUejK2zx2GNVMG4tU7e2HB6G4+3z5eWlknuURXVFGL8f07KJqJsso4Cd47qCPWTBmIgwv/hI/+OgDTru2k4BED24LR3fDMzVfAF7FFWWWdYC6eljWziLYCLvD5+OOPMXv2bDz55JPYtWsXevbsiZEjR+L06dNGD40ECKU5MkK3E6JFwT9fUJOU7EqrRGvXooZJMTbJ8auRGB2GxBZsj5GSFIX7B6fIfoyaeva5mD+lJ2NQ55bYevAUHv5kD9747pjsxwt0RRU1eHDtbp8ExGLvdypeaF4Bt9T18ssvY8qUKbjvvvsAAMuXL8fGjRvx7rvvYt68ec2Or62tRW3txWZ/FRUVuo2V+C+lOTKetysorsTSzCNMzTfNQssy/GI9uJQUdWSdHfN8ve2xNtQ0OCRbWTw7Nh0J0WyBT+uYCIxIs+PdrAKm4+Xig2IlbUOCyTvb83362gi936l4oXkFVOBTV1eHnTt3Yv78+c6fWa1WDB8+HNnZ2V5vs2jRIjz99NN6DZEEEKU5Mp63u8weo9mJXw9aX8lqlWgNsOVSJUaHIWvu9dj9+1m3x8vYXyQaQPz9mlTc2KMtGh2crDwvpbldUvjZMjkVrIORXpWzPd/vVLzQvAIq8CkuLkZjYyPatGnj9vM2bdrg4MGDXm8zf/58zJ492/nviooKtG/f3qfjJMSVlid+PfjiSlZtorXr/Qjt9uJfzedv7Y7I8JBmjyc0+9QyOhwLx6Y72xiwPIbrTB1/rFasFmDZ+N4YlZ6M7LySoKmrY3ae73e57xOin4AKfJSw2Wyw2XybF0CIFK1O/How+5WsmuUz1iBUzmOMSk/G365JxYof8jWZfVg2vo8zCGNJItfKNV2T8P2RYt0ezwhR4SGoklmVWuz9rvVSLtFGQAU+SUlJCAkJwalTp9x+furUKdjtdoNGRUhgMeOVrGeT0xFpdsWzaKxBKGuQtDm3EG99rz7PJD4qDIvHdXeeLDfnFvosf8hTclwEbu1zScAGPglRYVg0rjsOFZ2TbFPhiuX97m8zusEgoAKf8PBw9O3bF1u2bMEtt9wCAHA4HNiyZQtmzJhh7OAICSBmupI1suO9VJAkt4u8mNfH98HgrknO+5332T4N7lWaBU0n9rjIcMlj/dUTN1/hDFBW/lggmeTOY32/+9OMbjAIqMAHAGbPno2JEyeiX79+6N+/P1555RVUVlY6d3kRQrRhhitZqY73YuUFPGeJ5Iyd9bZadZFPjovAQJcT57KtR5lPzmrwMyGj0pMlk7r9Gd9zLcRqweJx3b2+p/i/7kPDL0VKUhTN3PixgAt87rjjDpw5cwZPPPEEioqK0KtXL2zevLlZwjMhRD0jr2TVVMZVM0sk57Za1GjhZ1wAIDuvBEXl1VjxQ57q+5Xy4LDOeGj4Zc7XznWJM1B4y88x02wm8Q0Lx3fVIwCadnXFxcWhvLwcsbGxRg+HECIgO68E41fkSB63ZspAt+BMaJaID43EZonk3pZ1jAAQbQuBBcD52ovJtXxABaDZidiXbuqejBFXtPE6q7Fp70k8sPoXXcahh+UCf281M4LEGKzn74Cb8SGEBAcl9YTUzBIpua2sHm0ccM/AjkhsEY6kmAjYY9nqC2nJYgEiw0KwYV8hNuxrasPgOZt1+NR5HUaij/ioMIxI877xJcRqQf/URGfwsyO/1Bn8UFDk3yjwIYT4JdY6QUkuLSZYq06vysrHpMGpbiczJRWr+eWhqQzLQ5V1jXjj+6aWE/wOrkYHh39+vk+XoKfnJbHY80dFs+3crvlSDgeHV7aw73oyu7NV9YIVxr0taSZGh6NX+zjs/r0cpZV1zp/rlUxPtBFwvboIIcFBqskpb85/dzubSLLOEi3ceABDlmx1az7JeltvtXWiwkOYbss7W1WPqR/uQp+FGT7pKu7Nnj+8t+vhLvz38Cd7AmqJi+ft7yrUhLe0sg5bD55xC3qAi8Ght2aljQ4O2XklWLf7BLLzSiR72BHfo8CHEOKXWDven6qodZ6U5FST9jyZsd723awC5234E6jconi887UNim7nC665R4HE8++qpASBULPSzbmFGLJkK8avyMHMtbsxfkVOs4Ca6I8CH0KI3+J34LSJFQ5KXE9KfTsmMM0Sed6u0cE5Z5ik8Lk+dQ0O6qNlYhZcbPTqSmkJAtelTkB41khsdojogwIfQohmjJjWH5WejH/d3lP0GP6ktPO3MqZZIs/b7cgvdZthYrnNB9kF1EfLpMQqLqstQXD6XI1kIjzQfHaI6IeSmwkhmvCWDBofGYb7BqdixrAuPt31UlxZy3Tc6XM1GNurndc6LWIy9xdhUOeWGJWejOsvb4UtB89I3ua30iqm+yb6E6vJI2c51JvWMRGKEuGJfmjGhxCimtC0/tnqeizNPIy+z2b4dGpfbsf4UenJ2D53GBaM7sZ0u893n0Cjg8Pm3EKmoAcAOiZGMR1H9BEbEYp//bkH1kwZiO1zhwnuwGJNmvfkunSmpNQC0Q8FPoQQVViSQfldSr4KfqROVt7yOUKsFkwanIrEaOkeVKWV9cjJK8HTX+6XPJZ/rHsGpSg6gRLfqKhpwHNfHUB5dZ3k7OOdV3aQlZvluXQmNxAn+qLAhxCiipxkUF/lNYjt8BLL5wixWnBLr7ZMj5F9rJjpeXIXHis81MqUE+QNv/2dgiZtlVbWiyYW87uwlmYelnW/9rgIt6rdSgJxoh8KfAghqsiZrnfd9aI1foeX3WPnledJyZNQ5d7m2MKQ+wenYESaHdl5JahtcOCh4V2REBXGdNtberXFR5MHYN9TI7Hcy3Mh6nHwHoALLdfyom3utZiiw0Pwp3Q7PvrrgGZLZ0oDcaIPSm4mhKgid7rel3kNSjrG81fnQic8vpHloM4tsWzbUckxxEWGYfDiLSiquJhw3bpFOFrYQkXr8iRGh+GFP/dEeKjV7bkszTiEZdt835Q0mHgmFkst11oAxEaE4a9DOuG9HwtwtroelXWN+Cq3CLt/P+s1UZqanZoXNSn1QE1KCZGn0cFhyJKtzMtdnk1DzYC/2gfgdvJzbT46Is2OIUu2CvbdsqCp1URZlXilZYvHY7jy1vpATqNTwu7+wSl44uYrAKh7jaWa21JfL/2wnr9pqYsQogo/rS/1VW7mvAaWZTL+eQoFLRwgGfREhYeIFlv0Vtyuf2oi81IZYbdu90nncpeaWUipujwhVgsGdW6Jsb3aYVDnlhT0mAAtdRFCVOMDh3mf7cNZLyd/f8hrULJMJldVXSPeGN8Hs/5vT7N+T8DFLu+PfZ6LytpGnK2qQ2J0OLq2boEdBWWajYMAJZV1zuUutburqC6Pf6HAhxCiCT5wWLb1CFZmNeVB8IzMa5Cz1MBfnQvdz7zP9qkez+d7TngNengcmk7Kcz7Zo/qxiLjtR88g62gxOI5DQlQYzlbVq2oxQnV5/AMFPoQQzYRYLZg5/FLMGNbVFHkN3qpJe8ujYZGTV+J1NksupQ1LifZeZ0gaF8vJ8kR1efwD5fgQQlTx1p/L6LyGRgeHVzMPY6qGTSKzjxVrMrYrU8yX40SE2eMi8J8JfWTX5TGibx1hQzM+hBDFtJxR0XJMT63fj6IK78sOfB7N01/ux4g0u4ygTH3wlhAVholXpeCd7cfctrsTc0mICsUTN6fDHntxttJqBaZ9uKvZDJC3/DUzfi7IRTTjQwjxSuqKVajgm9IZFS3Gyc/yCAU9PNdkVFZaJK1yALYePIXx/Tuovi/SXHJcBP4zoTeSVRZ+LKtqgD02wm22krVAplk+F0QYzfgQQpqRumIVK/imfEZFm3HKIScZdWCnloi/kACrVHlVU8uE+wenKL4PImxMz2Tc2KMtRqYnY0d+KbKOnlFc/NHbe0Nq559ZPhdEHM34EELcsFyxSvXnUjKjotU45XBNRpWa4QqxWrB4XHfR+/Nsa+CJv8dPdv6haLxE3Po9hW45Zl3bxCi+L6FEZbH8NTN8Log0mvEhhDixXrE+OupypvuTu72Xdes5S0d4MXwbCj4Z1dvMkT3WhvH9OyAlKdo5llHpyVh+dx/BHKLK2kbERITiXI1wawoOTZ3CifYKy2uQc6wEg7skAVC+y8oea1NUaJP1/U7b3o1FgQ8hxIn1irX0PFtirpwTj5yEUDkd4YUsGN0NO/JLkbm/CO9kFTT7fVFFLZZmHvE6lqZ6RUe9dvEWC3qI703/aBcW39Ydo9KTnX3YhNqMCHlqzBWKlqJY3++07d1YtNRFCHFivRJNjA6Xvb1XjNyEUDVXzPZYG/52TSoWbjyA8StyvAY93niOZe3PxxWPgfjO2ep6599JrEu6N/FRYVgu0HOLBR9oafW5IL5BMz6EECfWK1F7XCSevDmNeXuvGCUJoUqvmGcNvxRdW7fA9NW7ZC+TuY4lJiJM9YwT8R0OwFPrf8WINLtgl/TkuAjc0a89GhwcAA6DOiVh4IWcHaWNRflAS4vPBfEdCnwIIU5SSwOuuTEhVovXE4rc9hRyEkL5LeV9OyYgMTpctPWDK36Ziu+wrjQ3iB/L0oxDCu+B6KWoohbLth7FzOFdZfVhU1uDRyjQMrJtC3FHgQ8hxEnuFasWjT3lJoTyJyahoId/5IeGX4qUpCi3MWXnlWgyU/O/386qvg/ie0szD+MyewuMSk927sbiZ3M27D3Z7P3KL7l6Bsb8MucbjMtgI9LsiIkIQ3ZeCTxnk4jxKPAhhLiRe8Uq1tiThZyEUKETkyuxK2vaTRN8XJdJxWZzRqTZNanB4+0xPt11gmZ7TIQCH0JIM1rM5LBiXV7r2zEB1764TTToSYwOw3ePDEV4qPd9G7SbJvjwy6Tl1XWiszkPDe8qe8nVk1YzRsS3aFcXIcQrvRqNiu28cV1e2/lbmeQyVWllPXb+Vib4e6ldNyQwFVXUiM7mAMBKxt19QrOGUkn6QNOMETUrNR4FPoRozKiuzP7YDZofc22DAw8N74o2sTa337v2QdKiOBxLkHX/4BTMGn4p7LE0OxQoSs/XSs7mnK1ma0UiNGtIVZv9By11EaIho7oy+2M3aO/VkiMwy0tSMqBdcTjWHKZp13XGwg2/4oMcqtdjVglRobCFhgh2uueXSROjw5nuLzo8BJV1jaL3JVSDh6o2+w8KfAjRiFHr+/6YVyA05lMVNXgl8zDeuLtPszwKOVvtvfGszfLdI0Ox87cyrzlMapufEt/iZ+cWjesBAJj24S4AwrsQ4yLZAh+hoIe/b7EaPFS12X/QUhchGjBqfd8f8wqUjpk1F0ioNsuQJVsxfkUOZq7djfErcnDti9tQVlmL1jEROH2uaQmi0cFp0vyU+JbrEig/g2ePixA8hqWislQKW3xUGEak2QV/T1Wb/QfN+BCiASVF+Pz5cVkIVb+VO2bP+3l9Qm8s3HiAuTic0OxSYXkNHlj9i9vP7LE21DQ4FBc4JL4VHxmG1yf0aVYTR2wXIv/+uTHd7rU9CV+vipP4o5+tqhf9HFHVZv9BgQ8hGjBqfd+seQViOUe1DQ6m+zh9rkbwfhaMTkNCdLjmXdyFckWIOZytrsf/fivF4K5JzX7nrZ6Ut/eP1QK4Tiba4yLwp3Q73mXY1SX1OaKqzf6BAh9CNGDU+r4Z8wqkco5mXt+V6X4KiivxSuYRr/czfXVT7tLYXu1E70OLLu7EXJZmHsFl9hjJIELofcjP7Nw/OAUj0uzon5qIHfmlTIEPy+dIzxpYRBnK8SFEA0at75str0Aqf4cD8O+tR0Tvgx/zmh3HVecu0Q6awCT1t2fJI/ti9wn07ZiAEKtF88+RXjWwiDIU+BCiATWJt/74uEJYZljEYhV+lHde2UF02YnPA/rxaLHoY9EOmsAkVQ+H5X1YWlmPgYu2YHNuoek+R8S3KPAhRCMsu0sC6XGB5kUTiyrUzbDwY05JimI6/t53d2DRpv2Cv6dKzYFLbDaPdaavtLKpjcXm3EJDP0dEX5TjQ4iGjFrfN+JxvSWOJkaHqbrP+wenoLbBgeJzbEnGHIA3v8+HgwMeG53W7PeuO21IYOFn87ztHpQ708c3H6X8nOBg4TipTXzBpaKiAnFxcSgvL0dsbKzRwyHElFi6pKvluftGyrI7e+OmXm29/m7T3pOY+9lenKsRLlBH/ANfqHL73GHI2F8ksOuvGxZuPCBY7NKbNVMG6l7ygWiL9fwdMEtdBQUFmDx5MlJTUxEZGYnOnTvjySefRF1dndFDI0QxM/bfYt0irvYaWe5TnbH2F2zOLWz28825hVi48QAFPQHkyZvTkLG/yGuhyaZdf79gTE95S1NZR8+Y6nNGfCdglroOHjwIh8OBN998E126dEFubi6mTJmCyspKvPTSS0YPjxDZzNp/i3WLeEJ0OEorL154yJ3BUYJfsnBtPeHrmSmin/ioMCwe1x0j0uwYsmSr4K4tC4D1ewrx+oQ+eHzdPpRWSjcgXbYtz/n/ZvicEd8J6KWuF198EW+88QaOHTvGfBta6iJmIHTC5mdRjEy2XLf7BGau3S153NK/9IQ9LtKZK1FWWYvpFyol+/JLh1+yaHRwGLJkK9XxCSAfTR6AwV2TkJ1XgvErciSPXzNlIPp2TMDARVvcgnApSj9nQtXKiT5Yz98BM+PjTXl5ORITxesu1NbWorb2YiJlRUWFr4dFiCipGiQWNJ/Z8Ly9L798WRNH7XGRzXIm3rBafN78k9/RQ8ULA095ddPMjZyK5eGhVjx/a7rXRqZCWD5nnsw6Q0uaC9jA5+jRo3jttdckl7kWLVqEp59+WqdRESJNTf8tPb581XRJ99w1U3yuFgs3HtBkXDw+MKPihYFn4cb9GJlul12xXKiVhBg5fe6kqpWbZTs8zUg1MX1y87x582CxWET/O3jwoNttTpw4gVGjRuH222/HlClTRO9//vz5KC8vd/73+++/+/LpECJJaf8toa7i/Jevt8RfJdQWe3OtajtpcKpmdXY8q+tS8UL/8dD1XbFgdDfJ4/hAREml5VHpydg+dxjWTBmIV+/shRlDuzCNTerzyFIlmqXKuNB9a7W5YXNuIYYs2YrxK3Iwc+1ujF+RgyFLtmr2veDJjBszeKaf8ZkzZw4mTZokekynTp2c/3/y5EkMHToUV111Fd566y3J+7fZbLDZbGqHSYhmlPTfUrs85knqylCqGeOINDuy80okryzFOlrL4S3g4k+OtNxlbslxEXjw+q7YsPck0/Gnz9Uo7oTu2sg0O68Ey7YdlXw8qc+jmhlaMUJ1sp4dm44be3gv2yB2X3rOSJl92c/0gU+rVq3QqlUrpmNPnDiBoUOHom/fvli5ciWsVtNPaBGd+cNUr5KlJC2/fFm/tISKvWXsL2qWVCz2pScURCXHRWBMz2Ss31Po9vP4qKYiiWerLu7UEQq4FoxOwwOrqXihWVkALBidhh35pThy6hzTbaSWr1g7oatZsnWldIZWjFCgUlpZjwdW/4K//3EW829sXrDTG60viqT4w7Kf6QMfVidOnMB1112Hjh074qWXXsKZM2ecv7Pb7QaOjJiF2a9CeEquZrX68pX7peV6Ba3k9jyxirlzbrgcH2QX4LfSKnRMjEKbGBue3eSeF8RxHH45Xtbs7xsfGYabutuxKbfI51vpiTwWAMPTWmPhRra8G2+BiJpKy0pnjTwpmaEVw1In683v89HzkgTc2EP6e8tXM1Le6B1kKRUwgU9GRgaOHj2Ko0eP4pJLLnH7XQDv2CeM/OEqxJXcq1ktvny12E2m5vaeQRTgPVj1pqiiFm9+n9/s52er67FhXxGiwqyoqneI3gfRFwcgY/9pWcdLLV95Urtky/KdoNXMEY91N+KCdbkYmS4dQPhiRkqInkGWGgET+EyaNEkyF4gEJ3+5CvEk52pW6ssXaCogWCZSy0Ttl5bWX3paFh+koMf/JUSFYUQa++y92iVb1u8CrWaOeKwBSEllHdNnSesZKTF6BllqUBIMCXhyTshm47oDalDnlqJ1e/6UbhcNEhwcMH218O4utV9aWn7psbbFIMGjrKqe+TMqd4cjy+dMjJad3eUEICyfJSU74JTSM8hSI2BmfAgR4i9XIUqwLgW5EprdUvulpeWXHhUfJN5s2te086tvxwTs/K3M6wyNUTO8WnV275+aiMToMKY2GyyfJa1npMRoveznKxT4kIDnL1chcilZChJbblL7paXll54/BqHE9z7IOY4Pco436/vmuoRlZJ6JWL6RnPt4dmw6HrjQ3kWInFkaLXKZWOgZZKlBS10k4Ok51asXtUtB3gILLQoTqrm9K38LQom+PHfouS5hBcIM74092uLv16QK/t4Cts+SaxHBuMhwfPfIUGcBxzVTBmL73GGab+rQctnPV2jGhwQ8f7kKkUPtUpBQYKH2ylCrK0uWZG1CeK5LWC/d3pPpNmYPruffmIaelyTg8XW5bg1WWUtwiCV3j+3VzmfjBoSX/QAwFTb1tYDuzq4EdWcPXP5Sx4cFa4d0T/xy0/a5w0S/cNQWetSiUCS/lAdIV3T2XPogweujyQPw8P/tkVxylfoMmIWSz5LQMrjSrvNa0OP7l/X8TYGPBwp8Aps/VG5mkZ1XgvErcmTdxsgvPaWEviwXjE5DQnQ4MvcX4Z2sAuMGSEzn1Tt7wRZq9Ro0++NnQK5GB9escrorIwI/vQIx1vM3LXWRoKJF8qEZsNbtcZ0FUZvIqCZo9HZbAM6fJUXbAAtQfL7W7b5dp8yLyqtRWlmHxBY2JESHo1f7ePz1/Z8VPRcSuFrHRGBQ55a6JPOakdmKCJqxjhoFPsQUAmUmRi9SeUscgAeHdbnQEbkp2BvYSX59Ep6aaerNuYV4av1+FFWI99ty5XrfIVYLyqvr8MLXh9we32IBaL46sLWwheB8bSPTsZ67BrXaXu5vzJbcbbZADKDAh5hAIOXe6EkokTjuQlDx6paLnac/3fWH4tdTqt3H6xP6ICE63OvJZXNuIaZ+2LxJqFDA43nfb9zdBw4HvDYapaAn8IWFWAE0NgvuPQltUgiUGV45zFa+w2yBGEA5Ps1Qjo++zJiE529cZ8sKiqvwSuZhzV5PqXwBoPmSGh+0jkizo++zGZJBjhALgLjIUFTUNFDichCbNbwrVv5YIPo+amELxQu39WBq2umP5MyI859ZsyR3s+YjrpkyUHWQSjk+xPTMuPbrj/irWv4LT8vXk2XbvFBNlZnXd1Uc9ABNYz5b3aD49iQwtE+IQkRoCADh99L52gY8s+FXWK0IuAsluTPiZivfYcZqzlTAkBjGn3tomZEvXk8l08/8l9vb24/Jvi0hnp78MtctP0xIUUWt1z5c/kxuzzGemYoIalnYVCs040MMY8a1X3/mi9dTaR4ABzAnpfpSfFSYqlknooxUTo4c52rkvY8CZZZY7Yy4mZK79WqZwYoCH2IYsyXh+TtfvJ7+WkHZHmvDU2OuwIg0O55an4sPco4bPSTi4T8TeuPwqfN4ZcsRze7TiB1CvqLFbig5yd2+3llrpkCMAh9iGDOu/fqz/qmJojMcSl5PsXwBFhFhVtTUO2TeSp0el8Ti5h7tMOzyNgixWtCnQwIFPjqTep8kRIVhZHoybuxhwaVtYjD3s704V6NdPlcgzBLrOSOu185as+yyoxwfYhgzrv36s4z9RaLLOhyUvZ5C+QIsd6N30AMAe/+owHObDuDyBV9h0ab9aB1LM4ZmU1ZVjx35pdicW4iFG/drGvQAaCqI6ef0mhFXmkfkz2jGhxjKbGu//orPBxCTEBWGEWl2RffvOU2d1MKGHfklbrWCzMbBAW9+n4+VWflGD4V4kbm/CO9mFciaRYyJCGULkgLgWkmPGfFg3VlLgQ8xnJnWfv0Vy7Zz/ipb6VQzP029ObcQD3+yR1Z3eAuAxOhw3NwzGev3FDbrNn3nle2xNFO7XA9XdcbnWBMvPt99gjno4U/yj9xwGWZ/skfy+OLztarG5qnRwSEnrwTZx4qhRSV0FnpsSzdjVWU9UOBDTMEsa7/+Sq98AKGCk1I4ACWVdRh5RTIW3HSF175dUkXqSOBoYQtBaaW8v/WTN6chLjKc6VgtN0Rszi3EvM/2ub03l207ivioMCwe193rrLRWicK+nhHXe2etWVoTUeBDSADQIx9AbFqcVVFFjduVc1KLplwMqfwkElhiI8JklTv469WpGJWejEYHp+uGCKGWK0BT25WpH+7Cco+6OFonCvtyRlzPnbVmak1ELSs8UMsK4i9cr56Som2Y88kenKrwXZl61tLzYrw1nZRqWEpIYlQYXpvQB8Xna51tWQDvyz9aFehrdHAYvHgLiirEl82SXT5X/taCR6/2Fnq9LtSygpAA5u3qKT4qzJmQ6It8AC2mu71d5VPAQ6SUVtXjrrd/cv7bW7Cs9YaIHfmlkkEPcDEHpn9qot8lCuuRR2TGBGoKfAjxM0JXT+UXTgJxHrV8EqPDMbZXW8RFhqPRwSn+cqFCksEh1Ao06F+FQJbyqnpwaGpgmpIU7ZN8ETmB/ulzNX6bKOzrPCIzvi4U+BDiR1iuniJCrfjorwOw9cApfL77BEoq6/BuVgHezSpQtabOsr02PioMtlAr05UyMSdfBD3hIRbUNWqXVcG/19f+/HuzZRitEmjlBPqtYyL8ugWPL/OIzPi6UOBDiB9huXoqqqjF/wrKvNZI4YuSKVlTZ5kWXzSuO0ak2bFs6xGfbU8n/ichOgynKuqkD5TB20yBlgm0/VMTYY+1MeX49E9NZG7+a9aZU1/trDVjayKq3EyIH2G9KlqZlS84KwQ0rak3OuRfgbN2fV778++y75sELq2DHlf8Z0LrCsQhVgueGnOF5HF8Dgw/Iyo0R2LBxSApmJjxdaEZH0L8COtV0dlq8dYVheU1yDlWAqvFIntqW2panKWYojcJFxJWyyjZmcjQOibCZwm0o9KTsfzuPs3q+ABN79dFLnV8lCYKm6W2ja/okUAtF3Pg8+9//5v5Tv/xj38oGgwhRBxLnk1cZJho4MOb/tEut+PkLAmITYsrXav/c992+O/Pfyi6LQkMCZGhWHZXXxSfr2Uu0cAvM/kqgZYP9FkqN8tNFDZTbRtfBmBma03EXMcnNTWV7Q4tFhw7dkzVoIxEdXyI2fFT+oD3q6eHhndVlF+jVU2NrCPFuOudn6QPJH6Ff3/YQq2o8eG2rzVTBrrl7Ii91/n36rrdJzBz7W7J+548OAULbpZevlKLJYgwU80fvQIwX89uaV7HJz+fGv0RAhg/NS119eRwNHVOl5vCo0VNjc25hXhqvXizVGJu/HJEvEdZBHtcBO68sgN2HS/Fd4eLffb4rjOGrDMFrEvA72QV4MrURJ8HFFKJwnrWtpH6vhIKwNRshBBiltZElONDiAxmmZoWyrPJ2F+E6avl99LiCS0JqLmCJf4lMTocC8emY2T6xfdXQXEl1uw4jqUXKib7kmcQw7LVWmoJ2JXRRQQbHRxWZeXrUttG6vvKjMUF9aA48Pnjjz+wfv16HD9+HHV17hn7L7/8suqBEWI2el4ZsfC8etKilxbP9aqbJdjT8rGJsUoq67Bw435YrU1Bx+bcQrySeUSXv63Q7h6pmQI+gVaor5YrI4sIevssiVFT24bl+youMtx0xQX1oCjw2bJlC8aMGYNOnTrh4MGDSE9PR0FBATiOQ58+fbQeIyGG84crI6W7qbwpKK4CwB7sKX1sz10ewaptXAROavS30wL/9319Qm8s3HhAl7+RBep294xKT8b9g1PwblaB5LFGFBFUMiOqtLYN6/fVoyMvY7o/MxZdVENRHZ/58+fj4Ycfxr59+xAREYFPP/0Uv//+O6699lrcfvvtWo+REMPJ2TViFC2/nF7JPIxNe0+KfnkCF+sBKX1siwWYcnWqaJ2PYDDs8tZGD8EN//d9fF2urIC2hS0EHRMjESLyx4yPDEW0LcTtZ8kedaDkanRwyM4rQVQ427W83kUE5c6Iqq1tw/p9VVrJVl/JrEUXlVI043PgwAGsWbOm6Q5CQ1FdXY0WLVrgmWeewdixYzFt2jRNB0mI0cxYdt2T1l9Oj6/LRWmldD2gHfmlih/bwQErfsjHTT2SsWGvvAJzgSQ2Igz22AgUVZjnypoDRP/+rq69NAk7fzuL87UNOF9b7fx5eIgFvS6JR9uESLSNj8TgLkkY2KlpyUQqZ4x1E4Gc5SPXLfB6kjMjqkVtG9bvocQWNsnyGEa8Xr6mKPCJjo525vUkJycjLy8PV1zRtEWwuNh32f6EGMWMZdc9yUnwlCLnpJe5vwj/HJ2m6rE37C1EXGQo6hocqK43eYdMH/j5t1JU1Phv4UahXV51jRx2/FYG/FYGAPj8lxPO3DCxnBHWTQRylo+MKpYHyLsg0qK2Dev3kD02wnTFBfWgaKlr4MCB2L59OwDgxhtvxJw5c/Dcc8/h/vvvx8CBAzUdICFmYMay6574BE9+PHr5fPcJAHA+tlLl1Q1BGfQAwM8FZaiqazR6GF4lRodr9n4qLK/B1A934dXMI4ItU1hbT8hdPvJsq8Ivj63bfQLZeSWKWriwYg1EFozuhu1zh6neJCHn+4q1DU0gYS5g6OrYsWM4f/48evTogcrKSsyZMwc//vgjunbtipdffhkdO3b0xVh1QQUMiRDWYmpGk7tzRExMRCjO1TRIHscXnducW4h/fr6PebaImBe/zLFgdBqmr27+vlcrISoMt/ZuhxFpducyVqODw5AlWwXfu/yYts8dhh35pRi/IkfycWYM7YzBXVq5LZXpXZaCf15SS0qenebVkPt9ZXR9Mi2wnr8VBT6BjAIfIsYsdXykeH6JlV3Youw6brEih/wX8ah0O1Yy7JJ59c5eGNurHQCgrsGBgc9vQWmV7xpTEt/yPDk2Fab8VbJTuVL8ZyguMpwpmFkzZSBOn6thqtbs+t4EfF8xWSiAMOLCyV++r7SieeVmV506dcLPP/+Mli3d12jPnj2LPn36+HXLCkLEsBRTMwNvdU9GptuRc6wE2XklADhYLRb8e+vRZrd1XduPiwxnCnxcp/LDQ62YeFWKLsXuiG945pmMSk9GTEQY7nrbN61ICi8sY90/OIXpeP6zx8L1OF+XpZAKNPTuV+Uv31d6UxT4FBQUoLGx+Xp0bW0tTpw4oXpQhJiZWcquy5Wxv4hpCcz1i3jT3pOS9+uZ29To4FDfGJy5Ov7snoEdYLFY0DExCvcMSkF4qHsK6OlzvpntccXni0nhT+BydyT5spkpa80rvQMRf/2+8iVZgc/69eud///1118jLi7O+e/GxkZs2bIFKSkpmg2OEKINObtfFozu5ixnP//zfQzHp4nmThDzs1qAD3KOO//99vZ8t1mIzbmFWLjhV5+Ogd9J2MIWgvO13hO9XYMZPplfzo4kJWUpWHJf5M4kUSBiLFmBzy233AKgqQP7xIkT3X4XFhaGlJQU/Otf/9JscIQQ9eTsfrEAWLjxAEamJ2Pm2l9QXi2d2JwQHQ6AenX5M89cL9dZCgC6/l3Fgh7APZiRu3zEujzmWrmcJUfGlzNJRHuyAh+Ho2n6OjU1FT///DOSkpJ8Mii1amtrMWDAAOzZswe//PILevXqZfSQCDGMnOJp/Bf0a1uOMBcUPH2uhnp1BRjXWQqO40zxdxUKZuQsH7HWunol8zCq6urx1vf5TL35/KHAKblIUY5Pfn6+1uPQ1KOPPoq2bdtiz549Rg+FEMMp+bJ9ezv7BoXTFbXIySuh5a0AwwfBRouPDMPrd/XBwE4tBXNhWJeP5DQzXfFD86AH8L505Q8FTslFiruzb9myBVu2bMHp06edM0G8d999V/XAlPrqq6/wzTff4NNPP8VXX30leXxtbS1qay8m7VVUVPhyeIToTsmXrdBygzfPbTqA+Mgw2Y9BCIuz1fWwWiyaJQCPSk/GrOFdsTTziOAxHACxQi+eS1csM0kJUWEB1/rBXymq3Pz000/jhhtuwJYtW1BcXIyysjK3/4xy6tQpTJkyBR988AGioqKYbrNo0SLExcU5/2vfvr2PR0mIvqSquGrhbDUVLNRSoG82tsh8glovEaUkRWtyP/y4+JkkseWzsqp6ZOwv0uRxiTqKZnyWL1+OVatW4Z577tF6PIpxHIdJkyZh6tSp6NevHwoKCphuN3/+fMyePdv574qKCgp+iOG0rKIqtvuFmIM91oZ//aUXis/XovhcLRZuPGD0kJhYAMRFhjEHvjOGdsHgLkno2zEBO38rQ9bRM1i2LU/ydlovEWl1f673MyLNjvioMJyt8v5aqK0RRLSjKPCpq6vDVVddpfVYvJo3bx6WLFkiesyBAwfwzTff4Ny5c5g/f76s+7fZbLDZbGqGSIimfFFtVWj3CzGHp8ZcgcFdmjaLrGOsZWM0/tR93+AU0WUjXsvocMwacanzpM8vEX2664Tu3cFZagBZGCqbe9YIEgp6ANrZZSaKlrr++te/YvXq1VqPxas5c+bgwIEDov916tQJW7duRXZ2Nmw2G0JDQ9GlSxcAQL9+/ZptvSfErFgbNCoxKj0Z3z0yFC1sIWqHSTR0U49kt4DWiARYuUtPwMUmljOGdUVynPSYF45NbzbTIdZY15fdwVked8rVqU0BEOO4aGeX/1A041NTU4O33noLmZmZ6NGjB8LC3BMbX375ZU0GBwCtWrVCq1atJI/797//jWeffdb575MnT2LkyJH4+OOPMWDAAM3GQ4iv+KKcvueSmYPjmBOXZw3virU//04zRD62/WgxGh2c82/aPzVRdMlEifBQK+obHILLnHI6Nj5wXSdc3bW12/Irv5QqdDd/vyYVN/bwPltpRCsH1sft3SFB8xpBtLPLeIoCn7179zpr4+Tm5rr9zqLk0kEDHTp0cPt3ixYtAACdO3fGJZdcYsSQCJFF6yJo3pbMWHdfxUeGYcawrpgxrCtWZeUz55wkx0VgwehuWLjxgNvjxkWGYkS3NogMD3GrEEyAs1X1bn/TjP1FmgY9ABAdHoLn/tIT//wiV/V9r/7pd/S4JN4t+BYKIhKjw/Ds2HTc2KOt6H1K1eLxVedwqcfVskaQr5btiHyKAp9t27ZpPQ5Cgp6WU+VCVZRZk1AnXZXi/HKfNDgVb2/Plyz6BgB3XtkBI9OTMTI9udnJ4uvcQsxY8wvT4wcb/m/Kz/pprayqHnFR4YgIVZTd4OZsdX2zAn6A+oaYQrV4fN1hXKoGkNwaQXJaaBBjqPoUHD16FF9//TWqq6sBNO2sMouUlBRwHEdVm4nf0GqqXIsqymt/Pu7MJxLLh/C0NPMwhizZioz9RRjUuSXG9mqHQZ1bImN/ER5Y/Ytgsmiw4/+mcqpsy/XejwUoqtCu0ejTX+5Ho8cflA8S+L+7t5N8o4NDdl4J1u0+gey8kmb34cqXOW++wM982T1ynvh8KF8t24mR83oHC0UzPiUlJfjLX/6Cbdu2wWKx4MiRI+jUqRMmT56MhIQE6tdFiAJaTZVrcfI8VVHrvKofkWZHXGQ47hucgi92n0RpZZ3obV1L+o9IsyMnrwTzPpVudhqMPP+mvkx8/Wb/Kc3uS2rZVWhpSmj59b7BqZgxrItboOSLnDc9GNGBXYivZ8v8laLAZ9asWQgLC8Px48fRrVs358/vuOMOzJ49mwIfQhTQaqpci5Mnf2KZ99k+PLV+P4oq3PM2xvZsh3V7TqC0svnSGX/b+Z/tw1Prf9V0liEQuf5N/S3x1dt7TehkO6ZnstfeV2er67E08zBW/piPxeO6O0/I/tz40wwd2IWWu731Ggs2ipa6vvnmGyxZsqRZ0nDXrl3x22+/aTIwQoKRFlPlWp08OTQl3roGPQBQWlmPlT8WeA16XG9bVlVPQY+I2IhQvD7B/W/Kz/r5C8/3mtDSVGF5Dd70EvS4OltVj6kuy1e0PVw5qdkywPtSZbBQNONTWVnptSVEaWkpFQMkRCW1U+WsHaiJtqLCQ1BVx97jrKKmAc9s+BVWK5zBT4jVggWju+GB1eZOAve27KpFbhlwcfmKtocr58+zZXpQNONz9dVX4/3333f+22KxwOFw4IUXXsDQoUM1GxwhwYolSVTstlLF2Yi2buqRjBX39pN9u6KKWrdZDgBIiDb/xSOH5suuWiVm8ydkqR5zFjQtoXnmvFEyL82WSVE04/PCCy/g+uuvx//+9z/U1dXh0Ucfxa+//orS0lJkZWVpPUZCAoKvapF4I1ac7Y5+7fHKFukWA4Tdzt/K8PJfeiE5LkLRyX/eZ/ucSbr+cDK6f3BKs2VXLcd9+lyNopy3YE7mdf1+KT7HtsQcrLNligKf9PR0HD58GMuWLUNMTAzOnz+PcePGYfr06UhODuw3FyFKGPGF7LlkltTChh35pVj5Y4FPHi+YFZbXYOdvZZIVjIWcrapHTl4JBndN8ouT0Yg0e7OfJbXQbqaKfw3kVHUO5mReb98vVpm9xoKJosAHAOLi4vDYY49pORZCApKRX8j8ktnm3EJMX71L84rA5KLT52owtlc7xc1gs48VY3DXJFPnaAmdMDfnFuKp9b/65P5Zct78deu7FoS+X8SCHiC4iykqDnxqamqwd+9enD59Gg6Hw+13Y8aMUT0wQgKBUV/IrtPeBcWVTN2ziTqusxT8ibqovBr/t/MPZOWVSN6ef4+ILfEYSWx5SWqWiz/6b9ek4uP//eE1ABc7IUttDw/WZF6WhHLPmR9f90DzB4oCn82bN+Pee+9FcXFxs99ZLBY0NrLvbCAkkBnxhext2pv4VmJ0GPp2THD+2/VEXXy+jinwiY8MB9B0MpNTMFKpW3q1xRe7TzIf7+2EybqTy/W2j47qhmVbj2BlVoFbCxU1J2Q9k3n1zNWTwpJQ7uCABaO7ISnGZvh4zUJR4PPggw/i9ttvxxNPPIE2bdpoPSZCAobeuytYrr6J9kor63Hti9u8nriTWoQz3UdSi3DVQevdAzrgw5/YmsDe3q89fsovFV1SS4wOw4KbroA91vsJk3Un10t/7onBXZMANAWFM4dfihnDumoWQOi19d1sydOs3xtJMTaM7dXOx6PxH4q2s586dQqzZ8+moIcQCXrWItGqjoq/i48MhT1W/wRhof5R9rhIptsfL63yWvyPVXxUGL7cy9a7Kj4yDAM7tRQte2AB8Pyt3XFrb+GSCsyB/fnmu4y8lWxQuhW9f2oiosJDRI9JiApTlcxrxr5hVOtIGUWBz5///Gd8++23Gg+FkMCjtBaJEr5scOlPzlY3YHz/Ds6Tt16EKuKyVGOOiQjBRzkFqoLWs1X1KK9mS16/b3AKQqwW1ZXCWU+oCzf8KhkYbM4txJAlWzF+RQ5mrt2N8StyMGTJVqaA4uvcIsnikWpeW7NWQtbz+yWQWDgFLdWrqqpw++23o1WrVujevTvCwsLcfv+Pf/xDswHqraKiAnFxcSgvL0dsbKzRwyEBgL9SBLzXItFqV9e63Scwc+1u1fcTCF69sxdsoVbDcp08cyoy9hf5bAkyPMSCKFso8469hKgw/O/xEc12RSlZdmp0cBi8eAtTaxILhN/rQku0LJ+RRgeHK5/LZMqFWjNloKJcuuy8EoxfkeOz+1dDr+8Xf8B6/laU47NmzRp88803iIiIwLfffguL5eIHxGKx+HXgQ4jW5NQiUSMQp7PDQiyob5QfLhw5dR6DuyThnzd2w4Nr5LV/6J+SgB0FZbIf09XCjQec/8/ngCjd5i6lrpFDHWPQYwGwaFx32bumhGTsL0JNg0P6wAu87WBUu/NxR34pcwK40lw6M1dC1uv7JZAoCnwee+wxPP3005g3bx6sVkWrZYQEFX6Lc05eCbKPFQNoOtEM7KTd1aGZ678okRwXgYrqOkWBz7JtR7Fs21HIzZVtYQvFe/cPwKBFW9x2HKnhWq9p65zrkPbkZsifZ1cvPirMrfu5WnIT6YV2MKrd+Sgn2FB6cWD2XBq1/f2CjaLAp66uDnfccQcFPcRvGbElNWN/kdtV2bJtRzXdEWLW+i9K3XllByzNPKzqPuSmXJyvbcCgxVtwddeW+HJvkarH5rnOWtw/uMqQoAcAXh/fx7mzSi01ifSegQpr4FJUXu3156zBRmK08uRmqYsKM1RCVjprF4wURS4TJ07Exx9/rPVYCNGFmiRKNY+px44QoWRVf/OndDs6JLLthtLa2ap6fLm3CNESu4TkuDhroW4JTQk+wXWghidFNYn0noEKc4L0xgNePycsyeMA8OzYdMUXNyyNf4O5ErK/UZTc/I9//APvv/8+evbsiR49ejRLbn755Zc1G6DeKLk5MPEzPBn7i/BuVkGz3/syEbDRwWHIkq2CJwr+anH73GGafXG6zmgdOXUey7Yd1eR+9ZQYHe6z4n0s4qPCUF5Vr+nMWUSYBTX1+k35+Op9rSSRXuh9zn8+pJZoxZ6L1LLb369Jxfwb02SN1xuz1fEh7nya3Lxv3z707t0bAJCbm+v2O9dEZ0LMgKUonC/bRxhRvdl12js7r8QvAx8jgx6gaeZn1vBLsfLHfM16nOkZ9AC+S3CVm8si1Y6CX6IVI/YZFUrwbRkdjoVj03FjD22eP+XSBAZFgc+2bdu0HgchsrDm6MhJwPRVP5+iCmN3hPRPTfTJ7ElcZCjKqxsMzSeKDg/Bn9Lt+L9dJ3xy/x1aRmHn4yOwbOsRvPndMVTVm7cdjwVAm1gb/vWXXig+X+vTk7LcRHqpAIwPXP75+T6UVgoHmWKfUb2CEsql8X+Km5QSYhTW6ea6Bgf++fk+2SdlLQOQzbmFWLiBrWu11FW00oTsEKsFz45NxwOrxa+o5ejRNhYTB6fij7JqrNlxnDm4U8oWakWty7bp+Mgw3Dc4BdOu64KfC0qReeC0ZruwXJWer3W2WOiXkoi73v5J88eQQyjI5N8FT425AoO7aJPALEYskZ4fy0PDL0VKUhTze3VUejKq6x2Y9fFuyccX+oxSUEJYMAc+48aNw6pVqxAbG4tx48aJHvvZZ5+pHhgh3gjN4LhuGR6VnozNuYX45+e5olePQrTakso628SyI0RtbsGNPZLx9z9S8eb3+YyjF7f3ZAXmfLIHAGCPteGh67ti1Y8FPgk+ADiDnqaAJxUzhnVBxv4iXPviNllJtvGRYXhtfG9M/2gnKmqlZ28Soy/22RrYqaXh5QL4x40OD0GlS6ViI2q2+KJ+DGurkUCsWUX0wxz4xMXFOfN34uLifDYgQoSwFjpzOIDpq+VXydVySyrrdl+WHSGswZ7YWHbkl6KhkUNUuBVVdewF51gUVdTilS1HNL1PIeXV9Xgl8zCq6urx1vf5sv/GZ6vrERpixeSrO2FppvSYXftsmaVcgAVATEQo3rq3n8+XtKRovbzkD9vGif+TvauL4zj8/vvvaNWqFSIjjdlu6ku0q8u8WMvGK8ln0Xr3C+tYW0aH47lb0wUfs67BgYGLMgVnrqR2hKnt9m1WVov8Gj28V+/shZt6tEXfZzNEk5aTBV5Xb69pYnSYotlFNYxoj6AHasFAlGI9f8uu48NxHLp06YI//vhD1QAJkYs190ZJEi9rU0ZWrGN9fHQ3wcfcnFuIgYu2MCd7eru9mm7fZqamF2TrmAiEWC24o98loselJcdgR36ps/Ek3zm8tsGBl27viY8mD8Crd/bCmikDkTN/uGizSF8woj2CHtQ2TiXq8O/zdbtPIDuvRPfGq3qQndxstVrRtWtXlJSUoGvXrr4YEyFe+WJdf/LgFAxPs2u+VJAUbWM6Tug5yW0H4HkSVFNZN1Dxs2N9OyYg62gxPv5Z/OJty8Ez2HLwDJLjIjCmZzLW7yn0mmPFz7rovQwWaHkunsn73z0yFDt/K6Nt4zoKljpFinZ1LV68GI888gjeeOMNpKenaz0mQrxiWf9PYFxySIwOw/O3ate3yOtgGPxcUNqsjYCSoMXzJKimsq5a3nb5GB2A8WPo2zEB/Z/LlJWEXVhe4zUp3DPHSijZ11fKDK5zpCWxE+7YXu0MHFnwUJtL6E8Utay49957sWPHDvTs2RORkZFITEx0+48QX2ApG//s2HTJJYeW0eHImT/cpx/i4vO1TMe9suVIszL8coOWxOgwFFXUuE1LG7EMEhsRimV39vK6RDFrONvssFbX8573ExcVhqjwEGzYW6jZzjP+BPH0l/udr/uo9GRsnzsMa6YMxKt39sKs4V0RF+mbqiGPfbEvIJYh9GrnQoRJbRwB3N/n/k7RJ/KVV17ReBiEsGHZQmu1WkTrizx3azrCQ33bYFfOMoRnJVq5QUtpZb2z9gl/lWzEMkhFTQOe++ogFoxOQ0J0uNsSBQC8l10gORun1dfqzOu7YkCnljh9rgYFxVWqm50K8VZQz7OWzIxhXZFzrAQ/5hXjRFk1auobsPnX06ofu6yqHv/echizRlzW7HdGNOGVq9HBIedYCeb+317JnZpaV1Mn7oyoLm8kRYHPxIkTtR4HIcykttD6or6IXPyyHMvMjecXipqghb9Kfu3OXrBYoHsn8KLyGkxf3TQt7rlEMbZnO6z8sUDV/cdHhjHN2KS2isagzi2dfaB8TSxYDbFaMLhLEgZ3SWIaj5ylwX9vOYrL2sRiZPrFz0NBceWFopIXZx3NlqfButsw0E64ZsV6sRUoCfWK52Dz8vKwcuVK5OXl4dVXX0Xr1q3x1VdfoUOHDrjiiiu0HCMhzUhVaDW6pw6/LDdVov8Qz/ULhaUdgNDJkf/Zw5/uVRz08K0PAIvsisz8Qz72eS6GXd7GbWbtkgTl5S/iI8Pw+l19AICpejIfPOqV68QarLKMhwNwz8AO+CDnuOT9cQAeWL0L8VFholvzfZ2nIWeGSW7iPhA4J1yzYn3/BkpCvaL5/u+++w7du3fHTz/9hM8++wznz58HAOzZswdPPvmkpgMkxJWcrZZ8cDS2VzsM6txS96nyUenJmDX8UqZjXb9QxHKZeFInjZp6dUUKnxpzBZ4ao7ybdUllHQYuynTLz3CtgizX2ep6WC0WZ/VkodfFgqbZDX55zdcnTM/Hk8I6nhCrRVa+k1QTVV/maWzOLcSQJVsxfkUOZq7djfErcjBkyVavuTlKdxsGygnXrPiLLdbPlb9TFPjMmzcPzz77LDIyMhAefvHLbNiwYcjJkS7aRogScr5g1dKqlsWMYV1gjxXf2p4cFwGHg3N7LKFaJr4WHxXmtkvp2kuV930qrax3S051rYKsxOlzNUwJ7q5VsH15wmSpuu2JdTyrfvxN851wYjWflJKbmKxkBi6QTrhGE/pek/u58neKlrr27duH1atXN/t569atUVxcrHpQhHjSc6ullrUsQqwWjO3VVrRHVnl1Pe565+LyjetjjUizI+dYCaZ/tMtnfbBcvT6+j9v2+tcn9EX6U1+ruk8+ObV/aqLkkowYPmiQk8Mlt4u4N0J1fJTkjDEtY/o4N0urWTDWFjJqEveBwDrhGknqe80MuZF6URT4xMfHo7CwEKmpqW4//+WXX9CuHdVcINpS8gWrlNYBVqODw/o94jNSVXXuzTI9H8tqsegS9CREhWGgS97U5txCzPtsn6r75GcZco6V4FxNvaKgx1t/JtYcLrX9tVpGh+O7R4YiPNSKR0d1U50zJtXVnIPvE9Jbx0RosutLyU4guTNws4Z3DagTrlFYv9eMzo3Ui6LA584778TcuXPxySefwGKxwOFwICsrCw8//DDuvfdercdIgpxeWy1Zalk8tf5XWQGWkql9z2BOr8RO1+e9ObeQOTGbxQMf7oRFwZen2DS7VII7T+hK1rPDuTcllXXY+VuZM0dMzvtLKLgQu7Iur65vFghrhQ8gyyrrMGTJVtUzmkp2AsnZ7ZgcF4EZw6g7gFpyLxzlvs/9kaLA5/nnn8f06dPRvn17NDY2Ii0tDY2NjZgwYQIef/xxrcdITM7XNUP02mrJEqQUVdRi2dajmMlYkE/pmFyDuaQWbO0v1DpbVY8d+aXon5qIp7/cr+l9l9c0KLqdVtPs3q5kiypqnPWPxCj5G7IsK3iOx+Hg3JY8tcR/Gsf0TMb01drMaCrZCeQ64yU2sWUBLXFpJdhq9LBQFPiEh4djxYoVWLBgAXJzc3H+/Hn07t2bencFIW9f8PGRYbhvcCpmDOuiyReXXlstWU9wSzMP4zJ7C6YThBZjYu37pYXT52oMbXfBG3pZKwzpkoTEFjbERYaj0cGpfi95Xslm55Uw3U7u35B1WcFzPOt2n5D1OAAQbQtBZW2j5DKePS4CC0Z3w8KNBxQvGXte4PTtmCDZQsZziRIQnoHjma3mkJ58cREZbDV6WKiqpd6hQwe0b98eAGCxUGQebIS+4M9W12Np5mGs/DEfi8ep74fF0qPL2xesXGqqLQtRm1zbOiYCReXVCm6pTOuYCFN8AW47dAbbDp1x/tvzZKjFCcIX7ys1+WhKguQYWyhevK0nFm5sPrt055UdkJIU5Xx91Fz5C81gjemZjLe+zxeski40a+M641VUXo3SyjoktrDBHhuYOSUsfNUgNNhq9LBQXLf/nXfeQXp6OiIiIhAREYH09HS8/fbbWo6NmBhLPY6zVfWYqkGvHb22WvInQhas24JZavJ441o3o1SHZpSuj1dQXOnzx5PLdXu0VmUNfPG+khNceJKqpeJNUUUtEqLD3fqDrZkyENvnDsPM4V3dalgpvfIX27L+1vf5+Ns1qV77s0ktm/EzXrf2uQSTr+6EW3sbU2/LDHzZryzYavSwUBT4PPHEE5g5cyZuvvlmfPLJJ/jkk09w8803Y9asWXjiiSe0HiMxITnLIVoUTROqa8PyBcvK9UTIgvVEIjT2hKgwANIn3UQf5/jwV+t3Xtke6/ecxCqVbSV8gbvw3/zP9mGqhicIrd9XapYVlAbJfH0jqWKdSq78WRL+1+8pxHePDG0WeAXjUpUSvm4QGmw1elgoWup64403sGLFCowfP975szFjxqBHjx548MEH8cwzz2g2QGJOcpZDtEqc02OrZVO15a5YmnlE8ljPE4TYuITGnrG/qNn0dmJ0OBaOTXeeOOyx2kxBWwD87ZrUZvVo4i4EYCzPWe3jx0WFISI0RHYrDF6ZwHZ4z2UkAMzvEzXvK8+/O2s+llAQIpX/Iue+PClZ2mOdweJ3vhH59Eg+DqYaPSwUBT719fXo169fs5/37dsXDQ3Kdm9oZePGjXjmmWewd+9eRERE4Nprr8UXX3xh6JgCkdz1YK3yRvTYajljWFes2fG74MnZ8wTBujbvbeyj0pPhcHB4fF2us3N5SWUdFm7cD6u16ff9UxNhj41QHCzw4qLC0LtDgls9moLiKrySeVjzKsGe+BBi8bjuGJFmx6qsfCzceEDTx+BPEMu2HsHan3+XlSuh5H3l7e9uj42QTDS2WoC+HRMEf89auFJuDpLrjipvODTt+nIN+Cgx1vf0eo2DpUYPC0VLXffccw/eeOONZj9/6623cNddd6kelFKffvop7rnnHtx3333Ys2cPsrKyMGHCBMPGE8jk5MMA/pU4F2K14KkxabBAempY7dr85txCTF/9izPo8Xb7jP1FqKlXX9ulvKqphUTG/iIM6twSN/Voi7U/H1cc9FhwcblOiuvSUYjVgkmDU2Xns7BamnnEJ7kSrvg6R80ep0I6id3BATt/KxM9hu/ovvi27kzvQ1aj0pPxt2tSBX//1vf5bq+R0sRYrVq+BAM9k4+N7l9oFop3db3zzjv45ptvMHDgQADATz/9hOPHj+Pee+/F7Nmznce9/PLL6kfJoKGhATNnzsSLL76IyZMnO3+elqa80SIRJqcehxY7rvTGMjWstqI0y+3nfbZPcYsHqTGp2bbOP5tF47oDF+7TfebDhvH9OyAlKdrrlaXaispyaVnhu9HBqa5oLTc/TKslCpZK4q6vkZLlMV/tTgpUeu1aJRcpCnxyc3PRp08fAEBeXh4AICkpCUlJScjNzXUep+cW9127duHEiROwWq3o3bs3ioqK0KtXL7z44otIT08XvF1tbS1qa2ud/66oqNBjuAGB/1IWOjn7e+Kc1NSw2rV5lttrFfR4G5OaqfM2sTY8NeYK54lMyRS6knwWNbQq1LZs61HVf5cjp84jO6+E+XXSaolC7ntWqsUG4P751rOnXqCQ+xoT9RQFPtu2bdN6HKodO3YMAPDUU0/h5ZdfRkpKCv71r3/huuuuw+HDh5GY6D1aXrRoEZ5++mk9hxpQ+C/lZVuPYGVWgVs+gr8nzkklLKtdmzcyL4J/Tsopax/hyfWknnW0GMu2HVU8GtZZIzWve6ODw8os4YazrJZtO4pl244yz4Roldum5D3LOuukZ0+9QEPJx/pSFPicOXMGrVq18vq7ffv2oXv37qoG5WrevHlYsmSJ6DEHDhyAw+EAADz22GO47bbbAAArV67EJZdcgk8++QR///vfvd52/vz5bktzFRUVzqKMhE2I1YKZwy/FjGFdAyZxjmW6Xu3avJF5T/zfJzE6XFGdoFMV2l3B8yd1OQGJ1dKUK8OzXyjYtzTzsORt1bzuO/JLNW0Yq/dMCOtzT4q2ITuvxPlZHpFml5x1otYI6lDysX4UBT7du3fHO++8g9GjR7v9/KWXXsKCBQtQXa1dpdk5c+Zg0qRJosd06tQJhYVN69auOT02mw2dOnXC8ePHBW9rs9lgs+nXEiCQBUpzO9bpejVr840ODg6OQ3xkmOoTaYsLbQtYZjxcxxRiteCWXm3xblaB7Mf0xRW8nIBk2fjeSIi2uZ0gAGDtz8d9mivBGpxFh4cgJiIURRW1osfpPRPC8p6NiwrDnE/2uO0iZJmZoh1g6gXKd6jZKdrVNXv2bNx2222YNm0aqqurceLECVx//fV44YUXsHr1ak0H2KpVK1x++eWi/4WHh6Nv376w2Ww4dOiQ87b19fUoKChAx44dNR0T8R9yd5fIKSYmpzCY6zhezTyMwYu34K63fxLdqszquktbMx3nLV+Ar3mjhFgVYiVYKhdbLcB/JvTGjT3aNtudokehNtbgbNjlrZA173qsmTIQM4Z2Fj2Wfx1XZeX7fPeT1GvE55V5lk5g2RVHrRGIv1AU+Dz66KPIzs7GDz/8gB49eqBHjx6w2WzYu3cvbr31Vq3HyCQ2NhZTp07Fk08+iW+++QaHDh3CtGnTAAC33367IWMixlLS2oB1uj7nQoNLlsq/nuNYmnlEcibAHheB/0zog7hI6UnZncfL8NerUyF1PvdWjVhuWQJvlFzBewtIWSoXLxvfBzf2aCt4v76u8M3XVJLyc0HTdvVBnVuia5sYpvteuPGAotYbcgm9Rm1ibYgXKE/AUkGYWiMQf6F4O3uXLl2Qnp6OTz/9FABwxx13wG5XfvWohRdffBGhoaG45557UF1djQEDBmDr1q1ISBAuFkYCk9LdJawn8emrd2HxbU0NWMXW5oXGISQ+Kgyvj++DgRdmMY6cPidZUbmwvAYrfmiecMtfwd8/OAUj0uxe8wU8i9opbaQqxjNJvKyyFgs3HhDMn/KW5ClnO7QvcyVCrBaM7y+dS1RUUevMZZEzw6FXzo+318jBcbjr7Z8EbyOVo0O7k4i/UBT4ZGVl4e6770ZiYiL27t2LrKwsPPjgg9i0aROWL19uWKARFhaGl156CS+99JIhj0/MQY8O2Wer691OUN7W5lkauTa736p6WC8s2wBASlK0jFu745/rV7lFeGy0+wnHMxh5fUKfZh2+7bE21DQ4UF5VrzhnxluSuDeeJ3y1gYsvcyVSkqKYjuODaKm8Gld65vx4vkbrdp9gup3YxQHtTrpIalcoMY6iwGfYsGGYNWsWFi5ciLCwMHTr1g1Dhw7F3Xffje7du+OPP/7QepyEMFOzu0TOSQoQP0EpLRDoemJRmw/h7bkK7VhbMLpbs4ThFzYfwJvfC2/fFruClzPb5e2Eb9YkT7m5LHKLNRq1+0mrHB3anURFHM1OUY7PN998g8WLFyMs7OJ6cOfOnZGVlSW4bZwQvWjVIVuKVHKv0t0rricWloRfFvxYxFpsTF/9C8qr65wJwxn7i/CWSNDzt2tSBb/Elcx2aZ0s7Quuu/GEeMtlEcqrEaP37ictc3SCuTWC2jY2xPdkBT433ngjysvLce211wIAFi9ejLNnzzp/X1ZWhjVr1mg6QELkknPl6i3Jlj9JiZ3cXAmdoOTO1ng7sbDsVGLBP1fWHWssgcv6PYWCia5q2mG4vp5m6vnEJ6mz7MbzNhM2Kj0Z2+cOw4LR3ZgeT+/dT3rsigt0cj5jxDiylrq+/vprt/YOzz//PP7yl78gPj4eQFO/LNft5IToxXU9PamFDfZYG05V1IrmppRV1mHIkq2C09ExEWGiyZ48oROU3GUzDsCC0d28njCF8iYWjO6GhRsPMNWukbMEiAv/L0ZsOUbNbAX/erIsFyjJo1ByG9ZlO6lcFr5B69vb80VfX6N2P1GOjjpUxNE/yAp8OI4T/TchRvB2goyPCnPmjXjbXTKmZzKmrxbf9TUiza6qeaCSRpwLNx6A1WpxO8E0OjjERYbj0VGXo/R8LRKjw2GPi3SesK1WC9NOGl8UmNNqtgtwfz1ZduUBzZujSuVRKMm9YJn98tyNJybEasGYnsmiuVNjeiYbNrNCOTrKURFH/6Aox4cQsxBaTy+/0EQyzqMuiT0uAq9P6I31ewolp6MBqJ76F8rtSBCol+KZB+BaA2jWx7uxcOMBvPD1IZRX1zkfl6V2TaODQ/E58dpBvNYxEaoTXeXmJrm+ngAklwvmf7YPU2XmUSjNvWBZtvPcjSeGpUO62DKiHoI5R0cNKuLoH2TN+FgslmYd1/XswE6IK5Zt65FhIXh9ch8UV9Y6r1zlTEdrMfXveQWd1MKGOf/dLfjY/O4mhwOSs1L840vVEnpq/f5m1Xg9ec5g6Tnb5fp6ZueVSP59ygS6owttB1dT4kDrq3iWQIqWQ/yTmjY2RD+yl7omTZrk7G1VU1ODqVOnIjq6qdaIa/4PIb7GGsBYrRaM7dXO+XO5JzKt68pk55WIVm7mx/34ulxZJ2rPLeCNDg6vZh5hatzJ369rjpHaYnRixQgXjE5DQnS419dT7TKAtzwKNbkXWl/F03JI4KIijv5BVuAzceJEt3/ffffdzY6599571Y2IEEZKTyBKTmRK68p4S6RlHbdY13SpJMmmWZ5fJVtjeHLNMfLFbBdL0KjVMoDr65y5v0j2bXhaX8XTckhgowRx85MV+KxcudJX4yBENqUnENYTWd+OCcjOK2E6YXsLcDL2F3md7bjzyg6sT1GStxO13DYZrgovLKO9PqGp+3ltgwMv3d4T4OC2XOjLKspyd8MJcd0d9g5jB3pv7ymtr+JpOSTwUYK4uSnu1UWI0ZSeQFhOZGN6JuPaF7cx7f4R2lV21kseSlF5DV7JPCyZ98K6C8z1RN3o4JCTV4J5n+5TFTBwAGas+QWuubX8c9cj50TJbjhP/HZwPrdHilSwoeVVPC2HBAczVx8PdhaO9qS7qaioQFxcHMrLyxEbG2v0cIgEfnYD8H4CEWv2KLS1eUzPZLz1fX6zE67rffJXcxn7i/Au42yCXInR4SirrBMN6rbPHeZMYGbpiaWU0Ovpy35Eap7T8gvjzM4rwfgVObJuI0bL50ttDQjRFuv5mwIfDxT4+B81JxDPE1nfjgnNZnpcWdA0m2MLtcrOn5Hr/sEpWHkhqBIL6tQsbcnBEmxpfeJ2/fsUn2vq6i5l1vCumDn8UgBNjTdnrt0teZv7B6fgiZuvUDtc2aiRpW/R6xtcWM/ftNRF/J6a9XTP6Wg1W6m1NiLNjv6piaLLK0p6YinlmlBdXl0nWWBQi+DH9e/T6ODw9vZ80dyf5LgIzBjW1flv1jywEWl2tUNVhJZDfCdQZ9QomFOPAh9ieiwfdK1OIGbYQuyabxJitYgGdWp6YilVVF6NF74+pKgmjhpKcmMokTg4sVT+9sfgJ1CDOb1R5WZiaq6Vi2eu3Y3xK3IwZMlWn3U41mMLsQUXKzezVIQWq6JrRKBWWlknq+eXlliqVLuixpvBJ1AbhVLXd+3QjA8xLSOu2rTaSi2EP70uGtcdQPNeU3J3CckJ1FpGh2Nsr7aIiwzHK5mHZT8/fnYksYWN6XhfBWVylzaprkpwCcRGoWoqj5PmKPAhpqT0g652/VuLrdRiPE+2amt9sARq8ZFheP2uPhjY6eJs0WX2Fs0CgZbR4RjXpx3e/qGpeabQUtKhovNMY/Pl7JncpU2qqxI8ArEydiAGc0aiwIeYkpIPulbr30IzBGrMGNoFg7skNTvZqs1NYsl7WXxbdwzukuR2O7FAoG/HBMHZkRFpdjy1fqvkuOyxNtPlzVAicXAIxMrYgRjMGYkCH2JKcj/ocpfFpGaG+MBgVVY+0xZqIfzy0KwRl/psdkHpUo5QICAWFDX1GZP+24zv30HTmThCWAViQnsgBnNGosCHmJKcD7rcZTHWmaEQqwWTBqdKbqEWomfyrNZLOUJBEWtAmpIU7fx/2olC9BSIlbEDMZgzEu3qIqbEf9CFvposuNiWQM6y2ObcQkyVsTNCbFcQb9bwS/GfCX2QzLjTiFWjg0N2XgnW7T6B7LwSyV0oYru/tCL3ypN2ohAjyN39Z3a0O1FbNONDTEnOVRvrLERRRQ2e/vJXr78TS5gekWbHQ8MvxcqsfJytvli80HPWYmS6djMuZp0l4QNSqdynsspa2olCDBVoCe20O1E7FPgQ02L9oLPOQmQdOeO1cSiPNWE6PjIM9w1OwYxhXTVNVOaZufhaiNWCBaPT8MDqXaLHLdx4AHFR4bQThRgq0BLaAy2YMwoFPsTUWD7oLOvfbWJtyDhwmukxpRKmy6vr8UrmEVxmj9EkAOETf4vKq1F8vg7Lth019SxJQnS45DGF5TXIzithuj/aiUIIu0AL5oxAgQ8xPakPOsuy2Pj+HbA08wjT4ylJmFZKbgdyM8ySsAcqbOngtBOFEKInSm4mAUEqmdF1l5GY6PAQ2QnTSgkl/rIwcpaENVAZ1CmJOUGdEEL0QjM+JGBI1Z9hcuEsrUXBMLHaNWq7qhs5S8K6tXZg55YBt62YEOL/KPAhAUVoWax/aiISo8NRWlknevvK2kbsyC9VXDCMD3Yy9xfh890nUFrpfReY0q7qZqjXIWfHXbDtRKFCjYSYHwU+JCiEWC24pVdbvJtVIHns6XM1uKlHW9kFw6TydVx3ZdU2OGQ/ByNmSYRO5HICmmDZiWLWEgSEEHcU+JCgMSLNzhT4tI6JkF39VWgHmCv+d09/uR8v/bmn7PHHRYVh8bjuup1EpU7kcgKaQN+JYuYSBIQQd5TcTIIGn5sixDPZViphekSaHdl5Jfh81x/45+e5zPk6heU1gAWiY/EmMiwEI9Lssm4jRag6NGvFZT2qRZud1A5AoCnYlaq8TQjRB834kKDhOosDsCXbCs1qZOwvwpAlWxV3bz99rtY5FjkBk5bb2IVmdBaM7oaFGw+YupaQmcjZARjIs16E+Aua8SFBRUkPH89ZjYz9RYq3ofNKz9c6xyJn5kerbexiMzoPrP7F51v5A4kWOwAJIfqhGR8SdNQk26rdhs5LvFD9mB/Lqqx8LNx4QPJ2/C4yNbuHWJZmWNCJvInSHYCEEGNQ4EOCktJkW6Xb0D3Z4yLdxjJpcCre3p7PtItM7e4hrZ4DncibsNY1okKNhJgDLXURvySUlOvr22sxy+GtWjGffwSgWaVj1/wjoWU2z6RjMWqfA1Vcdsf6t6N8KELMgWZ8iN9RO+Oh5vZqZzksED4JStXGGZFmx5AlW1UnHct5DlRxmU2wFWokxJ9ZOI6jPZYuKioqEBcXh/LycsTGxho9HOJBqF4Kfwp+fUJvJETbBHNfpG4vVW+l0cFhyJKtgssaAJAYHYaxPdti3Z6TgpWbxQjl72TnlWD8ihzR2wLAmikDRZfxpJ4DvzSzYHQaFm6kgnxyUOVmQozDev6mwMcDBT7mxZ+wxfJTrBbAddXK9UQtdXv+hL997jDRkxUfPAHeZ0P44Enrk+C63Scwc+1uyeNevbMXxvZqJ3qMUc+BEEJ8hfX8TTk+xBBKcmxYknI978Y194W13srD/92Nl74+iKwjxV7HpWRLvBa03D3E+hyoQCEhJNBQjg/RndIcGyVJua65L4+OvIzpNp/vPgkAWLYtD/ECbSKktsT7om+T1ruHgqWHFiGEuKIZH6Ir1lYI3ihNLOZncrKOFsu+7dmqekwVGJfQbIia5yjGF7uHzDKjo3aXHiGEsKIcHw+U4+M7anNsWBKLxcRFhqG8ul76QC/ssTZkzbteNDBodHDIySvB9NW7cFbgcVjziMQEWhfwQHs+hBBjBGWOz+HDhzF27FgkJSUhNjYWQ4YMwbZt24weFrlATk8jb8RmPFgoDXoAoKiiVrRFw+bcQgxZshV3vfOTYNADaNPuYVR6MrbPHYY1Uwbi1Tt7Yc2Ugdg+d5hfBgm+mh0jhBAhARX43HTTTWhoaMDWrVuxc+dO9OzZEzfddBOKioqMHhqBNj2NhJJyxSZPLADiI8OYHlvJuIRO3krui5VZlqjUoK7mhBAjBExyc3FxMY4cOYJ33nkHPXr0AAAsXrwY//nPf5Cbmwu73e71drW1taitrXX+u6KiQpfxBiOtdiV5S8otq6zD9NXC27PvG5yCpZlHFIxafFxKe3dRuwfqak4IMUbAzPi0bNkSl112Gd5//31UVlaioaEBb775Jlq3bo2+ffsK3m7RokWIi4tz/te+fXsdRx1c+F1JQnMTcloheM543NhDfHv2jGFdZXVB92SPtXkdl9y+V9Tu4aKM/WwzsdQMlRCipYCZ8bFYLMjMzMQtt9yCmJgYWK1WtG7dGps3b0ZCQoLg7ebPn4/Zs2c7/11RUUHBj4/wOTrTPtzlk1YIUtuzn7w5DVMvFO2T66kxV3gdl5KTMrV7aFoefDergOlYmh0jhGjJ9DM+8+bNg8ViEf3v4MGD4DgO06dPR+vWrfHDDz9gx44duOWWW3DzzTejsFA4QdJmsyE2NtbtP+I7vi7+J5b7Mio9Gf+Z0EdWYnR8VBiWi4xLzkk5MTrMpwUO/QW/PMiCZscIIVoz/Xb2M2fOoKSkRPSYTp064YcffsANN9yAsrIyt+Cla9eumDx5MubNm8f0eLSdXR9GtkJ4en0uVv74m+Rxf7qiNZbd1U9yC3vfhRmiO7l4S+/ohVt7i7eSCAasPccAiAadhBDiivX8bfqlrlatWqFVq1aSx1VVVQEArFb3SSyr1QqHw+GTsRHl+JkZI9xwRTJT4PNTQZnkMSFWC3PitD2WlmwA9uXByYNTKOghhGjO9EtdrAYNGoSEhARMnDgRe/bsweHDh/HII48gPz8fo0ePNnp4xET6pyYiMTpc8rjSynqmejszhnVFfJTwdnlKaHbHujw4PM37TkxCCFEjYAKfpKQkbN68GefPn8ewYcPQr18/bN++HevWrUPPnj2NHh4xkGc7BAC4pVdbptuyzE6EWC1YPK6719whLZK2A42Wu/sIIUQu0y91ydGvXz98/fXXRg+DmIhQO4Q7r+zAdHvW2Qk+advzsezUeqEZX+/uI4QQMaZPbtYbJTcHDr6isucbnD+dxkWF4WyVtj21jEza9jfUo4sQoqWASW4mRAmpdggWXAyAtJx1MDJp299I1V0ihBBfoMCHBCSWdghlVfWYNbwr1v78Oy1PGYQCRUKI3ijwIaah5TIR65bplKRobJ87jGYdCCEkSFDgQ0xB63wPOQ1RadaBEEKCR8BsZyf+i09C9lyaKiqvwbQPd2FzrnDLESG0ZZoQQog3FPgQQ0klIXMAnv5yPxodzY/wrM/jegy/ZRpAs+An2LdMi71uhBAS6GipixhKKgkZAArLa7Bs6xHMHH6p82csS2NUW6c52kJOCAl2VMfHA9Xx0de63Scwc+1upmP5hpVS9Xk8O6BTbZ0mcl83QgjxJ6znb1rqIoZiTUIGmpa86hocoktj/HGey16DOrfE2F7tMKhzy6AMeqSWFAHhJUVCCAkkFPgQQ/FJyCwKy2vwQXaBZH2ewvIapuaiwYSlrhG9boSQYECBDzGUaxIyi99Kq5iOY63jAwB1DQ6888MxPLEuF+/8cAx1DQ7m2/oL1tdDzutGCCH+iJKbieFGpSdj1vBLsTTzsOSxHROjmO6TdQlt0ab9WPFDPlxXeJ7bdABTrk7F/BvZAzKzk1PXiBBCAhnN+BBTmDGsC+yxNsHf83V37hmUoll9nkWb9uPN792DHgBwcMCb3+dj0ab9zOPXi9Kt6FTXiBBCmlDgQ0xR1yXEasFTY65wax7Kc627Ex5q1aQ+T12DAyt+yBc9ZsUP+aZa9tqcW4ghS7Zi/IoczFy7G+NX5GDIkq1MBR6prhEhhDShwCfIbc4txODF7ifTwYvZTqZa4+vu2D2Sne1xEW5brVmPE/NBdkGzmR5PDq7pODPQorq1Fq8bIYT4O6rj4yGY6vhszi3E1A93Cf5+uUEnQ9a6O2rq8zyxLhfvZ/8medy9gzrimbHpsp+DlhodHIYs2Sq4K8uCpuBl+9xhTM+f6hoRQgIR6/mbkpuDVKODw7zP9okeM/+zfRiRZtf9pMjaNFRNc1HWJGnW43xJzlZ0X79uhBDi72ipK0jlHCvB2ap60WPKquqRc6xEpxHp655BKZCK56yWpuOMRlvRCSFEOxT4BKnsPLaAhvU4fxMeasWUq1NFj5lydSrCQ43/iNBWdEII0Y7x3+rEIKypXYGbAjb/xjT8/ZrUZjM/Vgvw92vMU8eHtqITQoh2KMcnSA3qlIRl2/KYjgtk829Mw5wbLscH2QX4rbQKHROjcM+gFNUzPVomEPNb0ad9uAsWuIeitBWdEELkocAnSA3s3BLxUWGieT7xUWEYGARJsOGhVky+upNm97c5txBPf7nfLSE5OS4CT96cpniXHL8V3fN+7SrvlxBCgg1tZ/dA29kvMmo7uz/j6+14fqj4uRi19XJoKzohhHjHev6mHJ8gNio9Gcvv7tOsVYQ91kZBjwKNDg5Pf7nfa1YU/7Onv9yvqjI2vxV9bK92GNS5JQU9hBAiEy11BblR6ckYkWanWQQNaF1vhxBCiPYo8CFU0E4jVG+HEELMj5a6CNEI1dshhBDzoxkfE+MTWYsqalB6vhaJ0eGwx0XSUpRJ8fV2isprvOb58D21qN4OIYQYhwIfk/K2JZqndms08Q2qt0MIIeZHS10mxG+JFkqULSyvwbQPd2FzbqHOIyNS+Ho79jj35Sx7XITqreyEEELUoxkfkxHbEu2KQ9PWaCO6pxNxtFOOEELMiwIfk5HaEu2KtkabF+2UI4QQc6KlLpORu9WZtkYTQggh7GjGx2TkbnWmrdGBj9pUEEKIdijwMRmpLdGukmlrdMDzRcNTQggJZrTUZTL8lmgpFtDW6EAntLuviHb1EUKIYhT4mBC/JTo5zvsyVjJtjQ54ejQ8JYSQYERLXSbluiWaKjcHH2p4SgghvkGBj4nRlujgRQ1PCSHEN2ipixATooanhBDiGzTjowPajkzkooanhBDiGxT4+BhtRyZCxAJianhKCCG+4TdLXc899xyuuuoqREVFIT4+3usxx48fx+jRoxEVFYXWrVvjkUceQUNDg74DdUHbkYmQzbmFGLJkK8avyMHMtbsxfkUOhizZ6vaeoIanhBCiPb+Z8amrq8Ptt9+OQYMG4Z133mn2+8bGRowePRp2ux0//vgjCgsLce+99yIsLAzPP/+87uOV2o5sATUZDVZ8QOz53uADYteghhqeEkKItiwcx/lVIZBVq1bhoYcewtmzZ91+/tVXX+Gmm27CyZMn0aZNGwDA8uXLMXfuXJw5cwbh4eFM919RUYG4uDiUl5cjNjZW8Tiz80owfkWO5HELRndDUoyNTmhBotHBYciSrYJb1fncne1zh9F7gRBCZGA9f/vNjI+U7OxsdO/e3Rn0AMDIkSMxbdo0/Prrr+jdu7fX29XW1qK2ttb574qKCk3Gw7rNeOHGA87/p9yfwEf1eQghxFh+k+MjpaioyC3oAeD8d1FRkeDtFi1ahLi4OOd/7du312Q8SrYZU+5P4KP6PIQQYixDA5958+bBYrGI/nfw4EGfjmH+/PkoLy93/vf7779rcr/8dmQ5ixVGtSJodHDIzivBut0nkJ1XQm0QfIjq8xBCiLEMXeqaM2cOJk2aJHpMp06dmO7Lbrdjx44dbj87deqU83dCbDYbbDYb02PIIbYdWYzeSx203V5fVJ+HEEKMZeiMT6tWrXD55ZeL/sealDxo0CDs27cPp0+fdv4sIyMDsbGxSEuT7nbuC0LbkVnosdRB2+31xwfEAJrNBlJ9HkII8T2/SW4+fvw4SktLcfz4cTQ2NmL37t0AgC5duqBFixa44YYbkJaWhnvuuQcvvPACioqK8Pjjj2P69Ok+mdFh5bkdufhcrVtCsxBfL3XQdnvj8AGx50ybnWbaCCHE5/wm8HniiSfw3nvvOf/N79Latm0brrvuOoSEhGDDhg2YNm0aBg0ahOjoaEycOBHPPPOMUUN2cm022ujg8Pb2fMOXOmh3kbGoPg8hhBjDbwKfVatWYdWqVaLHdOzYEZs2bdJnQAqZpRUB7S4yjmeript6tKWAhxBCdOI3gU8gMcNSB+0uMgYlkxNCiLEo8DGI0UsdtLtIf3JaVRBCCPGNgClg6I/43J+xvdphUOeWui530O4ifUklkwP6128ihJBgRIFPEKPu3/qRk0xOCCHEd2ipK8gZveQWLCiZnBBCzIECH+K23Z74BiWTE0KIOdBSFyE6kOrdZkHT7i5KJieEEN+iwIcQHVAyOSGEmAMFPoTohJLJCSHEeJTjQ4iOKJlce56VsOn1JISIocCHEJ1RMrl2qBI2IUQuWuoihPglvhK2Z30kvhL25txCg0ZGCDEzCnwIIX6HKmETQpSiwIcQ4neoEjYhRCkKfAghfocqYRNClKLkZh3QrhNCtEWVsAkhSlHg42O064QQ7fGVsIvKa7zm+VjQVB+JKmETQjzRUpcP0a4TQnyDKmETQpSiwMdHaNcJIb5FlbAJIUrQUpePyNl1QsXsCFGGKmETQuSiwMdHaNcJIfqgStiEEDloqctHaNcJIYQQYj4U+PgIv+tEaMLdgqbdXbTrhBBCCNEPBT4+QrtOCCGEEPOhwMeHaNcJIYQQYi6U3OxjtOuEEEIIMQ8KfHRAu04IIYQQc6ClLkIIIYQEDQp8CCGEEBI0KPAhhBBCSNCgwIcQQgghQYMCH0IIIYQEDQp8CCGEEBI0KPAhhBBCSNCgwIcQQgghQYMCH0IIIYQEDarc7IHjOABARUWFwSMhhBBCCCv+vM2fx4VQ4OPh3LlzAID27dsbPBJCCCGEyHXu3DnExcUJ/t7CSYVGQcbhcODkyZOIiYmBxaJfI9GKigq0b98ev//+O2JjY3V7XDOi1+Iiei3c0etxEb0WF9Fr4S5YXw+O43Du3Dm0bdsWVqtwJg/N+HiwWq245JJLDHv82NjYoHqjiqHX4iJ6LdzR63ERvRYX0WvhLhhfD7GZHh4lNxNCCCEkaFDgQwghhJCgQYGPSdhsNjz55JOw2WxGD8Vw9FpcRK+FO3o9LqLX4iJ6LdzR6yGOkpsJIYQQEjRoxocQQgghQYMCH0IIIYQEDQp8CCGEEBI0KPAhhBBCSNCgwMcEnnvuOVx11VWIiopCfHy812OOHz+O0aNHIyoqCq1bt8YjjzyChoYGfQdqgMOHD2Ps2LFISkpCbGwshgwZgm3bthk9LENt3LgRAwYMQGRkJBISEnDLLbcYPSRD1dbWolevXrBYLNi9e7fRw9FdQUEBJk+ejNTUVERGRqJz58548sknUVdXZ/TQdPP6668jJSUFERERGDBgAHbs2GH0kHS3aNEiXHnllYiJiUHr1q1xyy234NChQ0YPy5Qo8DGBuro63H777Zg2bZrX3zc2NmL06NGoq6vDjz/+iPfeew+rVq3CE088ofNI9XfTTTehoaEBW7duxc6dO9GzZ0/cdNNNKCoqMnpohvj0009xzz334L777sOePXuQlZWFCRMmGD0sQz366KNo27at0cMwzMGDB+FwOPDmm2/i119/xdKlS7F8+XL885//NHpouvj4448xe/ZsPPnkk9i1axd69uyJkSNH4vTp00YPTVffffcdpk+fjpycHGRkZKC+vh433HADKisrjR6a+XDENFauXMnFxcU1+/mmTZs4q9XKFRUVOX/2xhtvcLGxsVxtba2OI9TXmTNnOADc999/7/xZRUUFB4DLyMgwcGTGqK+v59q1a8e9/fbbRg/FNDZt2sRdfvnl3K+//soB4H755Rejh2QKL7zwApeammr0MHTRv39/bvr06c5/NzY2cm3btuUWLVpk4KiMd/r0aQ4A99133xk9FNOhGR8/kJ2dje7du6NNmzbOn40cORIVFRX49ddfDRyZb7Vs2RKXXXYZ3n//fVRWVqKhoQFvvvkmWrdujb59+xo9PN3t2rULJ06cgNVqRe/evZGcnIw//elPyM3NNXpohjh16hSmTJmCDz74AFFRUUYPx1TKy8uRmJho9DB8rq6uDjt37sTw4cOdP7NarRg+fDiys7MNHJnxysvLASAo3gdyUeDjB4qKityCHgDOfwfyko/FYkFmZiZ++eUXxMTEICIiAi+//DI2b96MhIQEo4enu2PHjgEAnnrqKTz++OPYsGEDEhIScN1116G0tNTg0emL4zhMmjQJU6dORb9+/YwejqkcPXoUr732Gv7+978bPRSfKy4uRmNjo9fvx0D+bpTicDjw0EMPYfDgwUhPTzd6OKZDgY+PzJs3DxaLRfS/gwcPGj1MQ7C+NhzHYfr06WjdujV++OEH7NixA7fccgtuvvlmFBYWGv00NMP6ejgcDgDAY489httuuw19+/bFypUrYbFY8Mknnxj8LLTB+lq89tprOHfuHObPn2/0kH1GyXfIiRMnMGrUKNx+++2YMmWKQSMnRps+fTpyc3Oxdu1ao4diSqFGDyBQzZkzB5MmTRI9plOnTkz3Zbfbm+1SOHXqlPN3/ob1tdm6dSs2bNiAsrIyxMbGAgD+85//ICMjA++99x7mzZunw2h9j/X14IO9tLQ0589tNhs6deqE48eP+3KIupHz3sjOzm7Wi6hfv36466678N577/lwlPqQ+x1y8uRJDB06FFdddRXeeustH4/OHJKSkhASEuL8PuSdOnXKL78btTBjxgxs2LAB33//PS655BKjh2NKFPj4SKtWrdCqVStN7mvQoEF47rnncPr0abRu3RoAkJGRgdjYWLeToL9gfW2qqqoANK3Zu7Jarc7Zj0DA+nr07dsXNpsNhw4dwpAhQwAA9fX1KCgoQMeOHX09TF2wvhb//ve/8eyzzzr/ffLkSYwcORIff/wxBgwY4Msh6kbOd8iJEycwdOhQ5yyg52cmUIWHh6Nv377YsmWLs6yDw+HAli1bMGPGDGMHpzOO4/Dggw/i888/x7fffovU1FSjh2RaFPiYwPHjx1FaWorjx4+jsbHRWYukS5cuaNGiBW644QakpaXhnnvuwQsvvICioiI8/vjjmD59ekB33x00aBASEhIwceJEPPHEE4iMjMSKFSuQn5+P0aNHGz083cXGxmLq1Kl48skn0b59e3Ts2BEvvvgiAOD22283eHT66tChg9u/W7RoAQDo3Llz0F3lnjhxAtdddx06duyIl156CWfOnHH+LhhmPWbPno2JEyeiX79+6N+/P1555RVUVlbivvvuM3poupo+fTpWr16NdevWISYmxpnjFBcXh8jISINHZzIG7yojHMdNnDiRA9Dsv23btjmPKSgo4P70pz9xkZGRXFJSEjdnzhyuvr7euEHr5Oeff+ZuuOEGLjExkYuJieEGDhzIbdq0yehhGaauro6bM2cO17p1ay4mJoYbPnw4l5uba/SwDJefnx+029lXrlzp9fsjmL7eX3vtNa5Dhw5ceHg4179/fy4nJ8foIelO6D2wcuVKo4dmOhaO4zi9gy1CCCGEECMEx0IwIYQQQggo8CGEEEJIEKHAhxBCCCFBgwIfQgghhAQNCnwIIYQQEjQo8CGEEEJI0KDAhxBCCCFBgwIfQgghhAQNCnwIIQFp0qRJzv5NAHDdddfhoYce0n0c3377LSwWC86ePav7YxNCmqPAhxCiq0mTJsFiscBisSA8PBxdunTBM888g4aGBp8+7meffYaFCxcyHUvBCiGBi5qUEkJ0N2rUKKxcuRK1tbXYtGkTpk+fjrCwMMyfP9/tuLq6OoSHh2vymImJiZrcDyHEv9GMDyFEdzabDXa7HR07dsS0adMwfPhwrF+/3rk89dxzz6Ft27a47LLLAAC///47/vKXvyA+Ph6JiYkYO3YsCgoKnPfX2NiI2bNnIz4+Hi1btsSjjz4KzzaEnktdtbW1mDt3Ltq3bw+bzYYuXbrgnXfeQUFBAYYOHQoASEhIgMViwaRJkwAADocDixYtQmpqKiIjI9GzZ0/83//9n9vjbNq0CZdeeikiIyMxdOhQt3ESQoxHgQ8hxHCRkZGoq6sDAGzZsgWHDh1CRkYGNmzYgPr6eowcORIxMTH44YcfkJWVhRYtWmDUqFHO2/zrX//CqlWr8O6772L79u0oLS3F559/LvqY9957L9asWYN///vfOHDgAN588020aNEC7du3x6effgoAOHToEAoLC/Hqq68CABYtWoT3338fy5cvx6+//opZs2bh7rvvxnfffQegKUAbN24cbr75ZuzevRt//etfMW/ePF+9bIQQJQzuDk8ICTITJ07kxo4dy3EcxzkcDi4jI4Oz2Wzcww8/zE2cOJFr06YNV1tb6zz+gw8+4C677DLO4XA4f1ZbW8tFRkZyX3/9NcdxHJecnMy98MILzt/X19dzl1xyifNxOI7jrr32Wm7mzJkcx3HcoUOHOABcRkaG1zFu27aNA8CVlZU5f1ZTU8NFRUVxP/74o9uxkydP5saPH89xHMfNnz+fS0tLc/v93Llzm90XIcQ4lONDCNHdhg0b0KJFC9TX18PhcGDChAl46qmnMH36dHTv3t0tr2fPnj04evQoYmJi3O6jpqYGeXl5KC8vR2FhIQYMGOD8XWhoKPr169dsuYu3e/duhISE4Nprr2Ue89GjR1FVVYURI0a4/byurg69e/cGABw4cMBtHAAwaNAg5scghPgeBT6EEN0NHToUb7zxBsLDw9G2bVuEhl78KoqOjnY79vz58+jbty8++uijZvfTqlUrRY8fGRkp+zbnz58HAGzcuBHt2rVz+53NZlM0DkKI/ijwIYToLjo6Gl26dGE6tk+fPvj444/RunVrxMbGej0mOTkZP/30E6655hoAQENDA3bu3Ik+ffp4Pb579+5wOBz47rvvMHz48Ga/52ecGhsbnT9LS0uDzWbD8ePHBWeKunXrhvXr17v9LCcnR/pJEkJ0Q8nNhBBTu+uuu5CUlISxY8fihx9+QH5+Pr799lv84x//wB9//AEAmDlzJhYvXowvvvgCBw8exAMPPCBagyclJQUTJ07E/fffjy+++MJ5n//9738BAB07doTFYsGGDRtw5swZnD9/HjExMXj44Ycxa9YsvPfee8jLy8OuXbvw2muv4b333gMATJ06FUeOHMEjjzyCQ4cOYfXq1Vi1apWvXyJCiAwU+BBCTC0qKgrff/89OnTogHHjxqFbt26YPHkyampqnDNAc+bMwT333IOJEydi0KBBiImJwa233ip6v2+88Qb+/Oc/44EHHsDll1+OKVOmoLKyEgDQrl07PP3005g3bx7atGmDGTNmAAAWLlyIBQsWYNGiRejWrRtGjRqFjRs3IjU1FQDQoUMHfPrpp/jiiy/Qs2dPLF++HM8//7wPXx1CiFwWTij7jxBCCCEkwNCMDyGEEEKCBgU+hBBCCAkaFPgQQgghJGhQ4EMIIYSQoEGBDyGEEEKCBgU+hBBCCAkaFPgQQgghJGhQ4EMIIYSQoEGBDyGEEEKCBgU+hBBCCAkaFPgQQgghJGj8P8W7N6e5rL+HAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Initialize a list to store all predictions\n",
    "all_pred = []\n",
    "\n",
    "# Loop through the test data\n",
    "for test_data in test_loader:\n",
    "    smi_lst, bg, labels, masks = test_data\n",
    "    \n",
    "    # Move everything to GPU if available\n",
    "    device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')\n",
    "    bg = bg.to(device)\n",
    "    labels = labels.to(device)\n",
    "    masks = masks.to(device)\n",
    "    \n",
    "    # Get node and edge features and move them to the correct device\n",
    "    node_feats = bg.ndata['hv'].to(device)\n",
    "    edge_feats = bg.edata['he'].to(device)\n",
    "    \n",
    "    # Perform the prediction\n",
    "    pred = model(bg, node_feats, edge_feats)\n",
    "    \n",
    "    # Store the predictions on the CPU\n",
    "    all_pred.append(pred.data.cpu().numpy())\n",
    "\n",
    "# Convert the predictions to a numpy array\n",
    "res = np.vstack(all_pred)\n",
    "\n",
    "# Plot the results\n",
    "plt.clf()\n",
    "plt.scatter(res, test_sol)\n",
    "plt.xlabel('Predicted')\n",
    "plt.ylabel('Experimental')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "e09b84b2-836a-47a7-b8d0-217e7828c695",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Assuming res and test_sol are NumPy arrays\n",
    "res_flat = res.flatten()\n",
    "test_sol_flat = test_sol.flatten()\n",
    "\n",
    "# Convert flattened arrays to lists\n",
    "res_list = res_flat.tolist()\n",
    "test_sol_list = test_sol_flat.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "aea9db71-762b-4d15-aa65-7f0466922d09",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "# Sample data (replace these with your actual predictions and SMILES)\n",
    "# Example definitions\n",
    "\n",
    "#res = np.array([0.5, 0.6, 0.7, 0.8, 0.9])  # Replace with your predicted values\n",
    "#sol = np.array([0.55, 0.65, 0.75, 0.85, 0.95])  # Replace with your actual values\n",
    "#smi = [\"C1CCCC1\", \"C1=CC=CC=C1\", \"CCO\", \"CCN\", \"CNC\"]  # Replace with your SMILES strings\n",
    "\n",
    "# Assuming `res_list` (predictions), `test_sol_list` (actual values), and `test_smi` (SMILES strings) are PyTorch tensors\n",
    "# Convert lists to NumPy arrays\n",
    "res_array = np.array(res_list).reshape(-1, 1)  # Reshape to (n_samples, 1)\n",
    "test_sol_array = np.array(test_sol_list).reshape(-1, 1)  # Reshape to (n_samples, 1)\n",
    "\n",
    "# Fit a linear regression model\n",
    "lin_reg = LinearRegression()\n",
    "lin_reg.fit(res_array, test_sol_array)\n",
    "\n",
    "# Get the trend line\n",
    "trend_line = lin_reg.predict(res_array)\n",
    "\n",
    "# Calculate the residuals\n",
    "residuals = test_sol_array - trend_line\n",
    "\n",
    "# Flatten arrays for std calculation\n",
    "residuals_flat = residuals.flatten()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "ac6efa8f-c426-4516-a683-e42b534c0bdc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Outlier SMILES strings and their corresponding actual and predicted values:\n",
      "SMILES: O=C([O-])C(=O)[O-], Actual: 0.3799999952316284, Predicted: -1.8076763153076172\n",
      "SMILES: CC1CC2C3CCC4=CC(=O)C=CC4(C)C3=CCC2(C)C1C(=O)CN1CCN(c2cc(N3CCCC3)nc(N3CCCC3)n2)CC1, Actual: -7.590000152587891, Predicted: -5.273151397705078\n",
      "SMILES: CC1(C)C(C=C(Cl)Cl)C1C(=O)OC(C#N)c1cccc(Oc2ccccc2)c1, Actual: -8.017000198364258, Predicted: -6.593557357788086\n",
      "SMILES: O=C(Cc1cccs1)NC1C(=O)N2C(C(=O)[O-])=C(C[n+]3ccccc3)CSC12, Actual: 1.3200000524520874, Predicted: -1.697324514389038\n",
      "SMILES: Brc1ccc(Oc2ccc(Br)c(Br)c2)cc1Br, Actual: -7.909999847412109, Predicted: -6.487215995788574\n",
      "SMILES: Brc1cc(Br)c(Oc2cc(Br)c(Br)cc2Br)cc1Br, Actual: -8.869999885559082, Predicted: -7.139585971832275\n",
      "SMILES: Brc1cc(Br)c(Oc2cc(Br)c(Br)cc2Br)c(Br)c1, Actual: -8.869999885559082, Predicted: -7.139118671417236\n",
      "SMILES: Cc1cc(O)ccc1O[P+](O)(Oc1cc(O)ccc1C)Oc1c(C)cccc1O, Actual: -6.699999809265137, Predicted: -3.5106303691864014\n",
      "SMILES: Cc1ccc(O[P+](O)(Oc2cccc(C)c2)Oc2ccccc2C)cc1, Actual: -6.010000228881836, Predicted: -1.4868799448013306\n",
      "SMILES: CCO[P+]([S-])(OCC)ON=C(C#N)c1ccccc1, Actual: -4.860000133514404, Predicted: -0.8017572164535522\n",
      "SMILES: Clc1cc2c(cc1Cl)Oc1cc(Cl)c(Cl)cc1O2, Actual: -10.220000267028809, Predicted: -9.028202056884766\n",
      "SMILES: CC(=O)OCC1=C(C(=O)[O-])N2C(=O)C(NC(=O)CC3CCCS3)C2SC1, Actual: -5.790500164031982, Predicted: -2.6802830696105957\n",
      "SMILES: O=C1NC2CC(Cl)CCC2O1, Actual: -4.698299884796143, Predicted: -0.9077130556106567\n",
      "SMILES: CN=C(NC#N)NCCSCC1NC[NH2+]C1C, Actual: -4.25570011138916, Predicted: -1.363468050956726\n",
      "SMILES: NC(CCSCC1CSC([NH+]=C(N)N)N1)=NS(N)(=O)=O, Actual: -5.455900192260742, Predicted: -2.112652540206909\n",
      "SMILES: O=C([O-])C1CC(Br)C(O)CC1O, Actual: -3.856100082397461, Predicted: -0.5642368793487549\n",
      "SMILES: NC1=NC2NCNC2C(=O)N1, Actual: -3.698199987411499, Predicted: -0.8646858930587769\n",
      "SMILES: CCN1C=C(C(=O)[O-])C(=O)C2CC(F)C(N3CC[NH2+]C(C)C3)C(F)C21, Actual: -4.4274001121521, Predicted: -1.7582483291625977\n",
      "SMILES: O=C([O-])C1CCCNC1[NH2+]C1CCCC(C(F)(F)F)C1, Actual: -5.06850004196167, Predicted: -2.5407729148864746\n",
      "SMILES: CC(=O)NC1NNC(S(N)(=O)=O)S1, Actual: -4.57889986038208, Predicted: -1.4026355743408203\n",
      "SMILES: CCCC[c-]1c(=O)n(C2CCCCC2)n(C2CCCCC2)c1=O, Actual: -1.6180000305175781, Predicted: -5.642910480499268\n",
      "SMILES: CC[NH+](CC)CCOC(=O)C1CCC([NH3+])CC1, Actual: -3.788100004196167, Predicted: -0.6741722822189331\n",
      "SMILES: CC(C)[NH2+]CC(O)COC1CCCC2CCCCC21, Actual: -4.8018999099731445, Predicted: -2.2683777809143066\n",
      "SMILES: [NH3+]CCC1C[NH2+]C2CCCCC12, Actual: -4.788899898529053, Predicted: 0.10476565361022949\n",
      "SMILES: CCO[P+]([S-])(OCC)Oc1nc(Cl)c(Cl)cc1Cl, Actual: -5.39900016784668, Predicted: -0.43942928314208984\n",
      "SMILES: N#CC(C#N)=N[N-]c1ccccc1, Actual: -0.3569999933242798, Predicted: -3.403752326965332\n",
      "SMILES: Cn1c(=O)[nH]c2c(=O)[nH]c(=O)[nH]c21, Actual: -3.937999963760376, Predicted: -1.1403366327285767\n",
      "SMILES: CO[P+]([S-])(OC)Oc1ccc([N+](=O)[O-])c(C)c1, Actual: -4.296999931335449, Predicted: -1.5330716371536255\n",
      "SMILES: O=C([O-])c1ccc([P+](O)(c2ccccc2)c2ccc(C(=O)[O-])cc2)cc1, Actual: -6.005000114440918, Predicted: -3.6655540466308594\n",
      "SMILES: CCO[P+]([S-])(OCC)Oc1ccc2c(C)c(Cl)c(=O)oc2c1, Actual: -6.236999988555908, Predicted: -1.2892338037490845\n",
      "SMILES: CCO[P+](O)(Cc1ccc(-c2nc3ccccc3s2)cc1)OCC, Actual: -4.001999855041504, Predicted: -0.4208199977874756\n",
      "SMILES: CCO[P+](O)(Cc1ccc(-c2nc3ccccc3s2)cc1)N1CCCC1=O, Actual: -3.7279999256134033, Predicted: -0.7455649375915527\n",
      "SMILES: [NH3+]Cc1cccc2c1C(=O)c1ccccc1C2=O, Actual: -6.300000190734863, Predicted: -2.42313551902771\n",
      "SMILES: O=C([O-])C=CC(=O)[O-], Actual: 0.5799999833106995, Predicted: -1.7214646339416504\n",
      "SMILES: COc1cc(N=Nc2c(Br)cc([N+](=O)[O-])cc2[N+](=O)[O-])c(NC(C)=O)cc1N(CCOC(C)=O)CCOC(C)=O, Actual: -8.079999923706055, Predicted: -6.563275337219238\n",
      "SMILES: O=C1c2ccccc2C(=O)[C-]1c1ccc2ccccc2n1, Actual: -6.209000110626221, Predicted: -2.8953161239624023\n",
      "SMILES: O=C(O[Na])c1ccccc1O, Actual: -0.10750000178813934, Predicted: -2.3059823513031006\n",
      "SMILES: CC(O)C(=O)O[Na], Actual: 0.9505000114440918, Predicted: -2.255603790283203\n",
      "SMILES: [Br-].[Br-].c1cc[n+]2c(c1)-c1cccc[n+]1CC2, Actual: 0.3133000135421753, Predicted: -1.9888781309127808\n",
      "SMILES: CCC(=O)O[Na], Actual: 0.7164000272750854, Predicted: -2.4337549209594727\n",
      "SMILES: O=CO[Na], Actual: 0.8048999905586243, Predicted: -2.824812889099121\n",
      "SMILES: CCC(C)C1(CC)C(=O)N=C(O[Na])NC1=O, Actual: 0.15279999375343323, Predicted: -3.197645664215088\n",
      "SMILES: O=C(CCC(=O)O[Na])O[Na], Actual: 0.09139999747276306, Predicted: -2.884335994720459\n",
      "SMILES: C#N.[K+], Actual: 1.0435999631881714, Predicted: -0.6400666236877441\n",
      "SMILES: O=C([O-])Cn1cc(I)c(=O)c(I)c1, Actual: -0.051100000739097595, Predicted: -2.3306665420532227\n",
      "SMILES: CCCCC(=O)O[Zn]OC(=O)CCCC, Actual: -1.2721999883651733, Predicted: -4.200969696044922\n",
      "SMILES: O=C(O[K])C(=O)O[K], Actual: 0.17730000615119934, Predicted: -2.486783504486084\n",
      "SMILES: O=CO[K], Actual: 0.9287999868392944, Predicted: -2.824812889099121\n",
      "SMILES: O=S1(=O)CS(=O)(=O)O[Ca]O1, Actual: 0.12549999356269836, Predicted: -2.153550386428833\n",
      "SMILES: O=C(CC(O)(CC(=O)O[K])C(=O)O[K])O[K], Actual: 0.2962000072002411, Predicted: -2.397393226623535\n",
      "SMILES: O=C[Mn+](C=O)C=O, Actual: -3.876199960708618, Predicted: -0.8369553089141846\n",
      "SMILES: O=S(=O)([O-])c1ccc(N=Nc2c(O)ccc3ccccc23)c2ccccc12, Actual: -1.1629999876022339, Predicted: -5.059283256530762\n",
      "SMILES: Clc1cc(Cl)c2c(c1)Oc1c(Cl)c(Cl)c(Cl)c(Cl)c1O2, Actual: -10.989999771118164, Predicted: -10.222105026245117\n",
      "SMILES: O=C(CNCP(=O)([O-])O)O[Na], Actual: 0.7188000082969666, Predicted: -1.7564170360565186\n",
      "SMILES: CC(C)(C)C([NH3+])C(=O)NC(CSSCC(NC(=O)C([NH3+])C(C)(C)C)C(=O)[O-])C(=O)[O-], Actual: -0.5135999917984009, Predicted: -3.6942553520202637\n",
      "SMILES: c1ncn([Sn](C2CCCCC2)(C2CCCCC2)C2CCCCC2)n1, Actual: -6.560500144958496, Predicted: -3.853011131286621\n",
      "SMILES: CON(C(=O)c1cc(C)oc1C)C1CCCCC1, Actual: -5.923099994659424, Predicted: -2.6850833892822266\n",
      "SMILES: CC1=C(c2ccccc2)C(=O)N(C(C)(C)c2cc(Cl)cc(Cl)c2)CO1, Actual: -6.320300102233887, Predicted: -4.388178825378418\n",
      "SMILES: O=C(OC1Cc2c(O)cc(O)cc2OC1c1cc(O)c(O)c(O)c1)c1cc(O)c(O)c(O)c1, Actual: 0.8173720240592957, Predicted: -1.6382439136505127\n",
      "SMILES: Cc1ccccc1NC(=[NH2+])Nc1ccccc1C, Actual: 0.20779499411582947, Predicted: -3.4300529956817627\n",
      "SMILES: CN(C(=O)Cc1ccccc1)C1CCC2(CCCO2)CC1[NH+]1CCCC1, Actual: 0.24029600620269775, Predicted: -3.5114824771881104\n",
      "SMILES: CSCCC(NC(=O)C(CC(C)C)NC(=O)C(Cc1c[nH]cn1)NC(=O)CNC(=O)C(NC(=O)C(C)NC(=O)C(Cc1c[nH]c2ccccc12)NC(=O)C(Cc1c[nH]cn1)NC(=O)C(CC(N)=O)NC(=O)CNC(=O)C(CCCNC(N)=[NH2+])NC(=O)C1CCCN1C(=O)C(Cc1ccc(O)cc1)NC(=O)C(CCSC)NC(=O)C(CCCC[NH3+])NC(=O)C(NC(=O)C(CC(C)C)NC(=O)C(NC(=O)C(NC(=O)CNC(=O)CNC(=O)CNC(=O)C(C)NC(=O)C1CCCN1C(=O)C(CC(C)C)NC(=O)C1CCCN1C(=O)C([NH3+])C(C)C)C(C)O)C(C)C)C(C)O)C(C)C)C(N)=O, Actual: 0.546658992767334, Predicted: -2.8817856311798096\n",
      "SMILES: CC(O)(CS(=O)(=O)c1ccc(F)cc1)C(=O)Nc1ccc(C#N)c(C(F)(F)F)c1, Actual: 0.6758700013160706, Predicted: -5.180774211883545\n",
      "SMILES: COc1cc(N)c(Cl)cc1C(=O)NC1C[NH+]2CCC1CC2, Actual: 0.512499988079071, Predicted: -2.9313061237335205\n",
      "SMILES: CCCCCCCCCCCCCCCCCC(=O)OCCO, Actual: 0.16571800410747528, Predicted: -6.166988372802734\n",
      "SMILES: O=C(NC(c1ccccc1)C1CC1)OCCCc1cnc[nH]1, Actual: 0.07298900187015533, Predicted: -2.576570987701416\n",
      "SMILES: O=C1CC2(CCCC2)CC(=O)N1CCCC[NH+]1CCN(c2nsc3ccccc23)CC1, Actual: 0.41360899806022644, Predicted: -3.1779632568359375\n",
      "SMILES: O=C(c1ccc(OCC[NH+]2CCCCC2)cc1)c1c(-c2ccc(O)cc2)sc2cc(O)ccc12, Actual: 0.5071439743041992, Predicted: -4.618062496185303\n",
      "SMILES: COc1cc(CC(C)[NH3+])c(OC)cc1I, Actual: 0.9928969740867615, Predicted: -1.608760952949524\n",
      "SMILES: Nc1ncnc2c1ncn2C1OC(COP(=O)([O-])OP(=O)([O-])OP(=O)([O-])O)C(OC(=O)c2ccc(C(=O)c3ccccc3)cc2)C1O, Actual: 0.6338899731636047, Predicted: -1.7499417066574097\n",
      "SMILES: COc1cc2nc(N(C)CCCNC(=O)C3CCCO3)[nH+]c(N)c2cc1OC, Actual: 0.79721999168396, Predicted: -3.055929660797119\n",
      "SMILES: O=C(CCC[NH+]1CCC(O)(c2cccc(C(F)(F)F)c2)CC1)c1ccc(F)cc1, Actual: 0.5276880264282227, Predicted: -3.4012184143066406\n",
      "SMILES: [NH3+]C(Cc1ccc(Cl)cc1)C(=O)[O-], Actual: 0.34071800112724304, Predicted: -2.1963140964508057\n",
      "SMILES: COc1cc2nc(N3CCN(C(=O)C4COc5ccccc5O4)CC3)[nH+]c(N)c2cc1OC, Actual: 0.9079629778862, Predicted: -4.495491027832031\n",
      "SMILES: Cc1cnc(NC(O)=C2C(=O)c3ccccc3S(=O)(=O)N2C)s1, Actual: 0.32189399003982544, Predicted: -3.8178415298461914\n",
      "SMILES: Cc1cccc2c1OP(=O)(OCC1OC(N3CC(C=CBr)C(=O)NC3=O)CC1O)OC2, Actual: 0.43062999844551086, Predicted: -2.8787682056427\n",
      "SMILES: CCCC1(C(O)CC=CC2C(O)CC(=O)C2CCCCCCC(=O)OC)CCC1, Actual: 0.20547500252723694, Predicted: -4.24744987487793\n",
      "SMILES: CCC(=C(c1ccc(O)cc1)c1ccc(OCC[NH+](C)C)cc1)c1ccccc1, Actual: 0.21884599328041077, Predicted: -6.317776679992676\n",
      "SMILES: COc1cc([N+](=O)[O-])c(OC)cc1CC(C)[NH3+], Actual: 0.9543859958648682, Predicted: -1.680776596069336\n",
      "SMILES: Nc1[nH+]ccc2c1ncn2C1OC(CO)C(O)C1O, Actual: 0.9946399927139282, Predicted: -1.9968280792236328\n",
      "SMILES: CCCN1CCOC2c3cc(O)ccc3SCC21, Actual: 0.9449729919433594, Predicted: -4.417067527770996\n",
      "SMILES: O=C([O-])c1cc(I)cc(I)c1O.[Li+], Actual: 0.5143669843673706, Predicted: -2.501492977142334\n",
      "SMILES: CCCCC(=O)Oc1ccccc1C(=O)[O-], Actual: 0.8067439794540405, Predicted: -2.222137451171875\n",
      "SMILES: Cc1cn(-c2cc(NC(=O)c3ccc(C)c(Nc4nccc(-c5cccnc5)n4)c3)cc(C(F)(F)F)c2)cn1, Actual: 0.7594410181045532, Predicted: -6.192736625671387\n",
      "SMILES: OCCN1CC[NH+](CCC=C2c3ccccc3Sc3ccc(Cl)cc32)CC1, Actual: 0.9798189997673035, Predicted: -3.6623449325561523\n",
      "SMILES: CCC[NH+](CCC)C1CCc2cc(O)c(O)cc2C1, Actual: 0.4873110055923462, Predicted: -2.9270455837249756\n",
      "SMILES: Brc1c(NC2=[NH+]CCN2)ccc2nccnc12, Actual: 0.5631729960441589, Predicted: -2.63535475730896\n",
      "SMILES: CCCCC(C)(O)CC=CC1C(O)CC(=O)C1CCCCCCC(=O)OC, Actual: 0.6248319745063782, Predicted: -3.950679302215576\n",
      "SMILES: CC(C)CC(NC(=O)C(Cc1ccccc1)NC(=O)CNC(=O)CNC(=O)C([NH3+])Cc1ccc(O)cc1)C(=O)[O-], Actual: 0.8909140229225159, Predicted: -2.558966875076294\n",
      "SMILES: CCc1[nH+]c(N)nc(N)c1-c1ccc(Cl)cc1, Actual: 0.37511900067329407, Predicted: -1.7118544578552246\n",
      "SMILES: C[NH2+]CCC(Oc1ccccc1OC)c1ccccc1, Actual: 0.3529509902000427, Predicted: -2.5186803340911865\n",
      "SMILES: C=CC[NH+]1CCC23c4c5ccc(O)c4OC2C(O)C=CC3C1C5, Actual: 0.3784019947052002, Predicted: -3.1434273719787598\n",
      "SMILES: CN(C)S(=O)(=O)c1ccc2c(c1)C(=CCCN1CC[NH+](C)CC1)c1ccccc1S2, Actual: 0.34625598788261414, Predicted: -3.6830568313598633\n",
      "SMILES: C1COC(NC(C2CC2)C2CC2)=[NH+]1, Actual: 0.3739669919013977, Predicted: -1.9853224754333496\n",
      "SMILES: C[NH2+]C1CCC(c2ccccc2)c2ccccc21, Actual: 0.8072419762611389, Predicted: -3.590045928955078\n",
      "SMILES: [NH3+]CCc1c[nH]c2cc(O)c(O)cc12, Actual: 0.31690099835395813, Predicted: -1.6781108379364014\n",
      "SMILES: C[NH+](C)CCc1c[nH]c2ccc(O)cc12, Actual: 0.051486000418663025, Predicted: -2.2073845863342285\n",
      "SMILES: OCC1OC(n2cnc3c(NC4CCCC4)ncnc32)C(O)C1O, Actual: 0.442577987909317, Predicted: -2.267909049987793\n",
      "SMILES: CCC(C)C(NC(=O)C(CO)NC(=O)C(CC(N)=O)NC(=O)C(CC(C)C)NC(=O)C(Cc1ccc(O)cc1)NC(=O)C(CCCC[NH3+])NC(=O)C(CCCC[NH3+])NC(=O)C(NC(=O)C(C)NC(=O)C(CCSC)NC(=O)C(CCC(N)=O)NC(=O)C(CCCC[NH3+])NC(=O)C(CCCNC(N)=[NH2+])NC(=O)C(CC(C)C)NC(=O)C(CCCNC(N)=[NH2+])NC(=O)C(NC(=O)C(Cc1ccc(O)cc1)NC(=O)C(CC(N)=O)NC(=O)C(CC(=O)[O-])NC(=O)C(NC(=O)C(Cc1ccccc1)NC(=O)C(NC(=O)C(C)NC(=O)C(CC(=O)[O-])NC(=O)C(CO)NC(=O)C(N)Cc1c[nH]cn1)C(C)C)C(C)O)C(C)O)C(C)C)C(=O)NC(CC(C)C)C(=O)NC(CC(N)=O)C(=O)[O-], Actual: 0.35431599617004395, Predicted: -1.8534027338027954\n",
      "SMILES: NC(=O)C(=Cc1ccc([N+](=O)[O-])o1)c1ccco1, Actual: 0.9906330108642578, Predicted: -3.551589250564575\n",
      "SMILES: Cn1c(-c2cscn2)nc2ccccc21, Actual: 0.7328760027885437, Predicted: -2.9861416816711426\n",
      "SMILES: CC[NH2+]C(C)Cc1cccc(C(F)(F)F)c1, Actual: 0.6998289823532104, Predicted: -2.396110773086548\n",
      "SMILES: COc1cc(CC(C)[NH3+])c(OC)cc1Br, Actual: 0.7356290221214294, Predicted: -1.1537302732467651\n",
      "SMILES: CSCCC(NC(=O)C(CC(C)C)NC(=O)C(Cc1cnc[nH]1)NC(=O)CNC(=O)C(NC(=O)C(C)NC(=O)C(Cc1c[nH]c2ccccc12)NC(=O)C(CCC(N)=O)NC(=O)C(NC(=O)CNC(=O)C(CC(C)C)NC(=O)C(CCC[NH+]=C(N)N)NC(=O)CNC(=O)C1CCC(=O)N1)C(C)O)C(C)C)C(N)=O, Actual: 0.8410990238189697, Predicted: -3.2807648181915283\n",
      "SMILES: CSCCC(NC(=O)CNC(=O)C(Cc1c[nH]c2ccccc12)NC(=O)C(CCSC)NC(=O)C(CC(=O)[O-])NC(=O)C(Cc1ccccc1)N[NH3+])C(=O)NC(Cc1ccc(O)cc1)C(=O)C(=O)C([NH3+])CC(=O)[O-], Actual: 0.5623530149459839, Predicted: -1.4464480876922607\n",
      "SMILES: CC(C)(C)c1cc(C=CC(=O)[O-])cc(C(C)(C)C)c1O, Actual: 0.38610199093818665, Predicted: -5.283780097961426\n",
      "SMILES: CCc1cc(OC)c(CC(C)[NH3+])cc1OC, Actual: 0.8668149709701538, Predicted: -1.1988227367401123\n",
      "SMILES: CC(C)C(CO)Nc1nc(Nc2cccc(Cl)c2)c2ncn(C(C)C)c2n1, Actual: 0.967278003692627, Predicted: -4.718585014343262\n",
      "SMILES: COc1ccc(OC)c(C(O)C(C)[NH2+]C(C)(C)C)c1, Actual: 0.8284980058670044, Predicted: -2.444380521774292\n",
      "SMILES: COC(=O)Nc1ccc(Cl)c(Cl)c1, Actual: 0.7815939784049988, Predicted: -3.243185520172119\n",
      "SMILES: Nc1[nH]c(=O)nc2c1ncn2C1OC(CO)C(O)C1O, Actual: 0.491690993309021, Predicted: -1.9166252613067627\n",
      "SMILES: CCN(CC)C(=O)NC1C=C2c3cccc4[nH]cc(c34)CC2N(C)C1, Actual: 0.2321760058403015, Predicted: -2.629277229309082\n",
      "SMILES: C[NH+]1C2CCC1C(C(=O)Oc1ccccc1)C(c1ccc(I)cc1)C2, Actual: 0.3613489866256714, Predicted: -4.731569290161133\n",
      "SMILES: CC(C=CC1(O)C(C)=CC(=O)CC1(C)C)=CC(=O)[O-], Actual: 0.23084299266338348, Predicted: -2.502340793609619\n",
      "SMILES: COC12CCC3(CC1C(C)(C)O)C1Cc4ccc(O)c5c4C3(CC[NH+]1CC1CC1)C2O5, Actual: 0.10470099747180939, Predicted: -3.6366543769836426\n",
      "SMILES: C=C(C)C1CCC(C)=CC1c1c(O)cc(CCCCC)cc1O, Actual: 0.7827500104904175, Predicted: -5.147570610046387\n",
      "SMILES: Nc1nc(=O)c2c(CCc3ccc(C(=O)NC(CCC(=O)[O-])C(=O)[O-])cc3)c[nH]c2[nH]1, Actual: 0.21137399971485138, Predicted: -3.547882318496704\n",
      "SMILES: Nc1[nH]c(CCNC(=O)C(Cc2cc(Br)c(OCCC[NH3+])c(Br)c2)=NO)c[nH+]1, Actual: 0.7960619926452637, Predicted: -1.4135011434555054\n",
      "SMILES: CC(C)OC(=O)CCCC=CCC1C(O)CC(O)C1CCC(O)CCc1ccccc1, Actual: 0.9845079779624939, Predicted: -3.0218544006347656\n",
      "SMILES: CCC(C)C(NC(=O)C(Cc1ccc(O)cc1)NC(=O)C([NH3+])C(C)C)C(=O)NC(Cc1cnc[nH]1)C(=O)N1CCCC1C(=O)NC(Cc1ccccc1)C(=O)[O-], Actual: 0.8469579815864563, Predicted: -3.119927406311035\n",
      "SMILES: Cc1nc(=O)c2cc(CN(C)c3ccc(C(=O)NC(CCC(=O)[O-])C(=O)[O-])s3)ccc2[nH]1, Actual: 0.8426520228385925, Predicted: -2.5979788303375244\n",
      "SMILES: CC(C)CC(NC(=O)C(CC(C)C)NC(=O)C(Cc1ccc(O)cc1)NC(=O)CNC(=O)C(C)NC(=O)C(CO)NC(=O)C(CC(N)=O)NC(=O)C(CC(C)C)NC(=O)C(NC(=O)C(Cc1c[nH]c2ccccc12)NC(=O)C[NH3+])C(C)O)C(=O)NCC(=O)N1CCCC1C(=O)NC(Cc1cnc[nH]1)C(=O)NC(C)C(=O)[O-], Actual: 0.7931129932403564, Predicted: -3.12984299659729\n",
      "SMILES: COC1=C(Br)C(O)C2(C=C1Br)CC(C(=O)NCCCOc1c(Br)cc(CC(=NO)C(=O)NCCc3c[nH+]c(N)[nH]3)cc1Br)=NO2, Actual: 0.017178000882267952, Predicted: -4.795261859893799\n",
      "SMILES: Cc1c(C(=O)NN2CCCCC2)nn(-c2ccc(Cl)cc2Cl)c1-c1ccc(Cl)cc1, Actual: 0.23027899861335754, Predicted: -6.7332234382629395\n",
      "SMILES: Clc1cccc(Cl)c1NC1=[NH+]CCN1, Actual: -0.10000000149011612, Predicted: -2.4534435272216797\n",
      "SMILES: CCCCCCCCC=CCCCCCCCC[NH3+], Actual: -1.0800000429153442, Predicted: -4.776608943939209\n",
      "SMILES: O=C(NCO)NCO, Actual: -3.4779999256134033, Predicted: 0.1717972755432129\n",
      "SMILES: Nc1c2ncn(C3OC(CO)C(O)C3O)c2nc[n+]1[O-], Actual: -4.611000061035156, Predicted: -1.7162516117095947\n",
      "SMILES: O=C([O-])C(=O)c1ccccc1C(=O)[O-], Actual: 0.5210000276565552, Predicted: -1.4962962865829468\n",
      "SMILES: O=S(=O)([O-])c1ccc(N=Nc2c(O)ccc3ccccc23)cc1, Actual: -0.2759999930858612, Predicted: -3.7963662147521973\n",
      "SMILES: Cc1ccc(C=CC(=O)[O-])cc1, Actual: -6.478000164031982, Predicted: -3.020616054534912\n",
      "SMILES: CCCCCCCCCCCCOc1ccc(C(=O)[O-])cc1, Actual: -2.444999933242798, Predicted: -5.725776672363281\n",
      "SMILES: COP(=S)(OC)Oc1ccc(S(C)=O)c(C)c1, Actual: -4.8979997634887695, Predicted: -2.4424877166748047\n",
      "SMILES: O=C1c2ccccc2C(=O)c2c(NCCO)ccc(NCCO)c21, Actual: -5.769999980926514, Predicted: -2.119455575942993\n",
      "SMILES: Oc1ccc(N=Nc2ccc(N=Nc3ccccc3)cc2)cc1, Actual: -9.699000358581543, Predicted: -5.942070960998535\n",
      "SMILES: O=C1c2ccccc2C(=O)[C-]1c1nc2ccccc2cc1O, Actual: -8.0, Predicted: -4.2601118087768555\n",
      "SMILES: O=[N+]([O-])c1ccccc1CNc1ccccc1, Actual: -0.7020000219345093, Predicted: -4.6201348304748535\n",
      "SMILES: O=c1oc2ccccc2c([O-])c1C1CC(c2ccc(-c3ccc(Br)cc3)cc2)Cc2ccccc21, Actual: -8.138999938964844, Predicted: -4.328943252563477\n",
      "SMILES: C1CN1c1nc(N2CC2)[nH+]c(N2CC2)n1, Actual: 0.2919999957084656, Predicted: -1.8821063041687012\n",
      "SMILES: O=C(C(Br)Br)C(Br)(Br)Br, Actual: -6.656000137329102, Predicted: -3.9040961265563965\n",
      "SMILES: O=C1c2ccc(O)c(O)c2C(=O)c2c(O)ccc(O)c21, Actual: -5.0229997634887695, Predicted: -2.2947075366973877\n",
      "SMILES: CCCCCCCCCCCCCCCCCC(=O)OCCCC, Actual: -2.302000045776367, Predicted: -7.296826362609863\n",
      "SMILES: CCCCCCC(O)CC=CCCCCCCCC(=O)[O-], Actual: -1.9359999895095825, Predicted: -4.784396171569824\n",
      "SMILES: CCOC(=O)C1(c2ccccc2)CC[NH+](CCC(C#N)(c2ccccc2)c2ccccc2)CC1, Actual: -2.753000020980835, Predicted: -8.050742149353027\n",
      "SMILES: CCC1(CC(C)C)C(=O)NC(=O)NC1=O, Actual: -5.142000198364258, Predicted: -2.578479766845703\n",
      "SMILES: CC1=CCS(=O)(=O)C1, Actual: -3.2260000705718994, Predicted: -0.22872662544250488\n",
      "SMILES: COc1ccccc1N=Nc1c(O)ccc2ccccc12, Actual: -8.925999641418457, Predicted: -5.876167297363281\n",
      "SMILES: CCC1OC(=O)CC(O)C(C)C(OC2OC(C)C(OC3CC(C)(O)C(O)C(C)O3)C([NH+](C)C)C2O)C(CC=O)CC(C)C(=O)/C=C\\C(C)=C/C1COC1OC(C)C(O)C(OC)C1OC, Actual: -5.224999904632568, Predicted: -2.2552642822265625\n",
      "SMILES: FC(F)Br, Actual: 0.5820000171661377, Predicted: -1.2735902070999146\n",
      "SMILES: CCCCCc1cc(O)c2c(c1)OC(C)(C)C1CCC(C)=CC21, Actual: -2.049999952316284, Predicted: -5.988750457763672\n",
      "SMILES: CC1(C)CC(=O)C(CC2C(=O)CC(C)(C)CC2=O)C(=O)C1, Actual: -1.590999960899353, Predicted: -4.310279369354248\n",
      "SMILES: CNc1ccc(NC)c2c1C(=O)c1ccccc1C2=O, Actual: -6.85699987411499, Predicted: -4.788489818572998\n",
      "SMILES: CN(C)c1ccc(N=Nc2ccc([N+](=O)[O-])cc2)cc1, Actual: -8.699000358581543, Predicted: -5.3694658279418945\n",
      "SMILES: c1ccc2oc(-c3ccc(-c4nc5ccccc5o4)s3)nc2c1, Actual: -7.5229997634887695, Predicted: -5.027779579162598\n",
      "SMILES: O=C([O-])CCC(C(=O)[O-])(c1ccccc1)c1ccccc1, Actual: 0.5460000038146973, Predicted: -3.3905489444732666\n",
      "SMILES: CCN(CC)c1ccc(N=Nc2ccc([N+](=O)[O-])cc2)cc1, Actual: -10.39900016784668, Predicted: -6.306523323059082\n",
      "SMILES: CNc1ccc(O)c2c1C(=O)c1c(O)ccc(NC)c1C2=O, Actual: -7.166999816894531, Predicted: -4.788827896118164\n",
      "SMILES: CC(C)(C)C(=O)Oc1ccc([N+](=O)[O-])cc1, Actual: -6.571000099182129, Predicted: -3.448747396469116\n",
      "SMILES: Nc1ccc(Nc2ccccc2)c2c1C(=O)c1ccccc1C2=O, Actual: -9.21399974822998, Predicted: -6.509732246398926\n",
      "SMILES: Nc1ccc(N=Nc2ccc(N)c3ccccc23)cc1, Actual: -6.520999908447266, Predicted: -4.094579696655273\n",
      "SMILES: Oc1cccc2scnc12, Actual: 0.7649999856948853, Predicted: -2.1072916984558105\n",
      "SMILES: O=C1OC(C(O)CO)C([O-])=C1Nc1ccc(S(=O)(=O)c2ccc(NC3=C([O-])C(C(O)CO)OC3=O)cc2)cc1, Actual: -5.150000095367432, Predicted: -2.4539825916290283\n",
      "SMILES: CCCOC(=O)CCOCC, Actual: -4.017000198364258, Predicted: -0.8570061922073364\n",
      "SMILES: NS(=O)(=O)CC([NH3+])C(=O)[O-], Actual: -3.8459999561309814, Predicted: -0.9217737913131714\n",
      "SMILES: O=[N+]([O-])c1c([O-])c([N+](=O)[O-])c(Cl)c([N+](=O)[O-])c1Cl, Actual: -0.6050000190734863, Predicted: -3.246267080307007\n",
      "SMILES: CC1CCC/C=C\\C2CC(O)CC2C(O)/C=C\\C(=O)O1, Actual: -5.670000076293945, Predicted: -2.236377716064453\n",
      "SMILES: CC(=N[N-]c1ccc([N+](=O)[O-])cc1[N+](=O)[O-])C(=O)[O-], Actual: -1.0169999599456787, Predicted: -3.5780303478240967\n",
      "SMILES: O=S(=O)([O-])c1cc(S(=O)(=O)[O-])c2c(N=Nc3cccc4ccccc34)c(O)ccc2c1, Actual: -0.33899998664855957, Predicted: -2.9458885192871094\n",
      "SMILES: CC(C)CCCCCCCOC(=O)CCCCC(=O)OCCCCCCCC(C)C, Actual: -9.987000465393066, Predicted: -6.247603416442871\n",
      "SMILES: Nc1ccc(O)c2c1C(=O)c1c(O)cc(Br)c(N)c1C2=O, Actual: -10.772000312805176, Predicted: -3.812587261199951\n",
      "SMILES: CN(C)C(=O)Nc1ccc(SC(F)(F)Cl)c(Cl)c1, Actual: -0.6349999904632568, Predicted: -4.674084186553955\n",
      "SMILES: O=S1(=O)c2ccccc2S(=O)(=O)c2cc([O-])ccc21, Actual: 0.41600000858306885, Predicted: -1.8504167795181274\n",
      "SMILES: O=c1oc2ccccc2c([O-])c1C1CC(c2ccc(-c3ccccc3)cc2)Cc2ccccc21, Actual: -7.1570000648498535, Predicted: -4.482651710510254\n",
      "SMILES: Cn1nnnc1SSc1nnnn1C, Actual: -1.7599999904632568, Predicted: -4.5968804359436035\n",
      "SMILES: CCCCN(CCC#N)c1ccc(N=Nc2ccc([N+](=O)[O-])cc2)cc1, Actual: -7.698999881744385, Predicted: -5.789750099182129\n",
      "SMILES: CCOC(=O)C1CC(=O)C(=C([O-])C2CC2)C(=O)C1, Actual: -4.954999923706055, Predicted: -0.9713386297225952\n",
      "SMILES: O=[N+]([O-])c1ccc(NS(=O)(=O)c2ccc(Cl)c(C(F)(F)F)c2)c(Cl)c1, Actual: -2.1559998989105225, Predicted: -5.581071853637695\n",
      "SMILES: [C]CN(C[C])C(=O)c1cccnc1, Actual: 0.7490000128746033, Predicted: -1.6387816667556763\n",
      "SMILES: [N]c1ccc2cc3ccc([N])cc3nc2c1, Actual: 0.3783000111579895, Predicted: -2.527482271194458\n",
      "SMILES: [N]c1nc([N])nc([N])n1, Actual: -1.591599941253662, Predicted: -6.54435920715332\n",
      "SMILES: [C]COc1ccc2nc(S([N])(=O)=O)sc2c1, Actual: -3.8101000785827637, Predicted: 0.39336538314819336\n",
      "SMILES: [C]c1cc([N-]S(=O)(=O)c2ccc([N])cc2)nc([C])n1, Actual: -2.2372000217437744, Predicted: 1.6333460807800293\n",
      "SMILES: [C]CN(C[C])c1nc(Cl)nc(N(C[C])C[C])n1, Actual: -4.411200046539307, Predicted: -1.510947346687317\n",
      "SMILES: [C]c1cc([C])nc([N])n1, Actual: -0.48840001225471497, Predicted: -3.289175510406494\n",
      "SMILES: [C]c1cnc2c(ccc3c([C])c([C])cnc32)c1[C], Actual: -5.1944499015808105, Predicted: -2.164106845855713\n",
      "SMILES: [C]c1ccnc2c1ccc1c([C])ccnc12, Actual: -3.968600034713745, Predicted: 0.7125298976898193\n",
      "SMILES: [C]Oc1cc([N-]S(=O)(=O)c2ccc([N])cc2)nc([C])n1, Actual: -2.5429999828338623, Predicted: 2.0851612091064453\n",
      "SMILES: [C]c1cnc([N-]S(=O)(=O)c2ccc([N])cc2)nc1[C], Actual: -3.1435000896453857, Predicted: 1.0869519710540771\n",
      "SMILES: [C]CCCNC(=O)n1c(NC(=O)O[C])nc2ccccc21, Actual: -4.8831000328063965, Predicted: -0.3899564743041992\n",
      "SMILES: [C]NC(=O)N([C])c1nc2ccccc2s1, Actual: -3.5741000175476074, Predicted: 1.3049790859222412\n",
      "SMILES: [C]CC(=O)N(c1ccccc1)C1(CO[C])CC[NH+](CCc2cccs2)CC1, Actual: -3.706399917602539, Predicted: 0.028693199157714844\n",
      "SMILES: [C]C([C])C(O)(c1ccc(OC(F)(F)F)cc1)c1cncnc1, Actual: -3.437700033187866, Predicted: 1.4052929878234863\n",
      "SMILES: [C]c1cc([C])nc(C([N])=O)n1, Actual: 0.12160000205039978, Predicted: -3.2070860862731934\n",
      "SMILES: [C]OC(=O)c1ccccc1S(=O)(=O)[N-]C(=O)Nc1nc([C])cc([C])n1, Actual: -3.1291000843048096, Predicted: 3.1661858558654785\n",
      "SMILES: [C]Oc1nc([C])nc(NC(=O)[N-]S(=O)(=O)c2ccccc2OCCCl)n1, Actual: -4.098899841308594, Predicted: 1.0770585536956787\n",
      "SMILES: [C]COCCOC(=O)C([C])Oc1ccc(Oc2ncc(C(F)(F)F)cc2Cl)cc1, Actual: -5.873899936676025, Predicted: -1.1498099565505981\n",
      "SMILES: [C]C(Oc1ccc(Oc2ncc(Cl)cc2Cl)cc1)C(=O)N1CCCO1, Actual: -4.592199802398682, Predicted: -2.0397520065307617\n",
      "SMILES: [C]Oc1cccc(N([C])C(=S)Oc2cccc(C([C])([C])[C])c2)n1, Actual: -6.013999938964844, Predicted: -2.903871536254883\n",
      "SMILES: [C]SC(=O)c1c(C(F)F)nc(C(F)(F)F)c(C(=O)S[C])c1CC([C])[C], Actual: -5.457499980926514, Predicted: -2.4326584339141846\n",
      "SMILES: [C]c1nn([C])c(Oc2ccccc2)c1C=NOCc1ccc(C(=O)OC([C])([C])[C])cc1, Actual: -7.460400104522705, Predicted: -2.7617883682250977\n",
      "SMILES: [C]CN(Cc1ccc(Cl)nc1)C(=C[N+](=O)[O-])N[C], Actual: 0.4918000102043152, Predicted: -1.889836311340332\n",
      "SMILES: [C]CN(C[C])C(=O)n1cnc(S(=O)(=O)c2c([C])cc([C])cc2[C])n1, Actual: -5.146699905395508, Predicted: -2.600111484527588\n",
      "SMILES: [C]CC(Nc1ncnc([C])c1Cl)c1ccc(OC(F)F)cc1, Actual: -3.996999979019165, Predicted: 0.4551734924316406\n",
      "SMILES: [C]c1cccc(C(=O)[O-])c1S(=O)(=O)[N-]C(=O)Nc1nc(OCC(F)(F)F)nc(N([C])[C])n1, Actual: -5.679800033569336, Predicted: 2.2295374870300293\n",
      "SMILES: [C]Oc1ccc2c(c1)c(CC(=O)[O-])c([C])n2C(=O)c1ccc(Cl)cc1, Actual: -5.581699848175049, Predicted: -2.221191167831421\n",
      "SMILES: [C]Oc1ccc2nccc(C(O)C3CC4CC[NH+]3CC4C=C)c2c1, Actual: -3.088599920272827, Predicted: 0.7176122665405273\n",
      "SMILES: [C]c1cc([C])nc([N-]S(=O)(=O)c2ccc([N])cc2)n1, Actual: -2.2685000896453857, Predicted: 1.651956558227539\n",
      "SMILES: [C]Oc1ccc2nccc(C(=O)C3CC4CC[NH+]3CC4C=C)c2c1, Actual: -5.031300067901611, Predicted: -0.3175680637359619\n",
      "SMILES: [C]c1cc(OC(=O)N([C])[C])n(C([C])[C])n1, Actual: 0.6751999855041504, Predicted: -1.2421098947525024\n",
      "SMILES: [C]Oc1cc([N-]S(=O)(=O)c2ccc([N])cc2)nc(O[C])n1, Actual: -2.9565000534057617, Predicted: 2.4238533973693848\n",
      "SMILES: [C]CNc1nc(Cl)nc(NC[C])n1, Actual: -4.337299823760986, Predicted: -0.22000861167907715\n",
      "SMILES: CCC(C)C1(CC)C(=O)N=C([O-])NC1=O, Actual: 0.15279999375343323, Predicted: -2.4879701137542725\n",
      "SMILES: [C]c1nn(-c2ccccc2)c([C])c1N=O, Actual: -3.7355000972747803, Predicted: -0.31226205825805664\n",
      "SMILES: [C]c1nc([N-]S(=O)(=O)c2ccc([N])cc2)oc1[C], Actual: -2.202500104904175, Predicted: 2.2032690048217773\n",
      "SMILES: [C]OCCCNc1nc(NCCCO[C])nc(S[C])n1, Actual: -3.4791998863220215, Predicted: 2.431363582611084\n",
      "SMILES: [C]N([C])c1cc[nH+]cc1, Actual: -0.2062000036239624, Predicted: -2.5506656169891357\n",
      "SMILES: [C]c1ccc2nc3sc(=O)sc3nc2c1, Actual: -5.369800090789795, Predicted: -2.137885093688965\n",
      "SMILES: [C]c1nc([C])c(C(=O)[O-])cc1C(=O)[O-], Actual: -2.809000015258789, Predicted: 1.6715703010559082\n",
      "SMILES: [C]CN(C[C])c1nc(NC([C])[C])nc(O[C])n1, Actual: -4.888999938964844, Predicted: -2.0522408485412598\n",
      "SMILES: [N]c1nc([N])nc(Cl)n1, Actual: -2.3849000930786133, Predicted: -5.547029495239258\n",
      "SMILES: [C]c1cc([C])c[n+]([O-])c1, Actual: 0.909500002861023, Predicted: -1.0965723991394043\n",
      "SMILES: [C]COC(=O)c1cn2nc(OP(=S)(OC[C])OC[C])cc2nc1[C], Actual: -4.94890022277832, Predicted: -1.9957603216171265\n",
      "SMILES: [C]c1nnc([N])nc1[C], Actual: -1.0938999652862549, Predicted: -3.671440601348877\n",
      "SMILES: [C]Oc1cc(NC(=O)c2cccnc2)c2ncccc2c1, Actual: -4.446100234985352, Predicted: -1.2631951570510864\n",
      "SMILES: [C]Oc1ccc2c(c1)c(CC(=O)[O-])c([C])n2C(=O)C=Cc1ccccc1, Actual: -5.543300151824951, Predicted: -1.7495543956756592\n",
      "SMILES: [C]c1ncc2nc([C])c([C])nc2n1, Actual: -1.1441999673843384, Predicted: -3.8103206157684326\n",
      "SMILES: [C]N1C(C(=O)Nc2ccccn2)=C([O-])c2ccccc2S1(=O)=O, Actual: -4.158599853515625, Predicted: -1.2752054929733276\n",
      "SMILES: [C]CCCSC(=Nc1cccnc1)SCc1ccc(C([C])([C])[C])cc1, Actual: -5.571199893951416, Predicted: -1.8761539459228516\n",
      "SMILES: [C]C([C])([C])C(O)C(Oc1ccc(-c2ccccc2)cc1)n1cncn1, Actual: -4.82919979095459, Predicted: -1.834823727607727\n",
      "SMILES: [C]c1ccc(S(=O)(=O)Oc2c(C(=O)c3ccc(Cl)cc3Cl)c([C])nn2[C])cc1, Actual: -6.894599914550781, Predicted: 0.20207548141479492\n",
      "SMILES: [C]c1c(Cl)cc(-c2ccc(O)nn2)cc1Cl, Actual: -5.537499904632568, Predicted: -2.0605173110961914\n",
      "SMILES: [C]COC(=O)C([C])Oc1ccc(Oc2nc3ccc(Cl)cc3o2)cc1, Actual: -5.586599826812744, Predicted: -0.9190230369567871\n",
      "SMILES: [C]OC(=O)C([C])Oc1ccc(Oc2ncc(C(F)(F)F)cc2Cl)cc1, Actual: -4.606400012969971, Predicted: 0.0971672534942627\n",
      "SMILES: [C]CCCOC(=O)C([C])Oc1ccc(Oc2ccc(C(F)(F)F)cn2)cc1, Actual: -5.50855016708374, Predicted: -0.537055253982544\n",
      "SMILES: [C]N(C(=O)COc1nc2ccccc2s1)c1ccccc1, Actual: -4.872700214385986, Predicted: -2.269279956817627\n",
      "SMILES: [C]COP(=O)(Cc1ccc(-c2nc3ccccc3s2)cc1)OC[C], Actual: -4.001699924468994, Predicted: -0.8347152471542358\n",
      "SMILES: [C]C(Oc1ccc(Oc2cnc3cc(Cl)ccc3n2)cc1)C(=O)[O-], Actual: -6.060400009155273, Predicted: -2.9174184799194336\n",
      "SMILES: [C]COC(=O)C([C])Oc1ccc(Oc2cnc3cc(Cl)ccc3n2)cc1, Actual: -6.062950134277344, Predicted: -0.7995693683624268\n",
      "SMILES: [C]OC(=O)c1sccc1S(=O)(=O)[N-]C(=O)Nc1nc([C])nc(O[C])n1, Actual: -2.2379000186920166, Predicted: 3.0241312980651855\n",
      "SMILES: [C]CC([C])(C[C])c1cc(NC(=O)c2c(O[C])cccc2O[C])on1, Actual: -5.3694000244140625, Predicted: -3.045847177505493\n",
      "SMILES: [C]c1ccc(C(=O)COc2c(C(=O)c3ccc(Cl)c([C])c3Cl)c([C])nn2[C])cc1, Actual: -6.520899772644043, Predicted: -2.417483329772949\n",
      "SMILES: [C]OC(=O)c1ccccc1CS(=O)(=O)[N-]C(=O)Nc1nc(O[C])cc(O[C])n1, Actual: -3.5339999198913574, Predicted: 2.1422042846679688\n",
      "SMILES: [C]C([C])([C])C(O)C(=Cc1ccc(Cl)cc1Cl)n1cncn1, Actual: -4.911499977111816, Predicted: -2.436383008956909\n",
      "SMILES: Cc1c(Cl)ccc(OC(C)C([O-])=Nc2ccccc2)c1Cl, Actual: -7.00570011138916, Predicted: -4.831305503845215\n",
      "SMILES: [C]OC(=O)c1ccccc1S(=O)(=O)[N-]C(=O)Nc1nc(OC(F)F)cc(OC(F)F)n1, Actual: -5.1519999504089355, Predicted: -2.6227972507476807\n",
      "SMILES: [C]COC(=O)c1cnn([C])c1S(=O)(=O)[N-]C(=O)Nc1nc(O[C])cc(O[C])n1, Actual: -4.456099987030029, Predicted: -1.8666423559188843\n",
      "SMILES: [C]COc1nc(N[C])nc(NC(=O)[N-]S(=O)(=O)c2ccccc2C(=O)O[C])n1, Actual: -5.382800102233887, Predicted: 2.473336696624756\n",
      "SMILES: [C]COc1nsc([N])n1, Actual: -0.6514000296592712, Predicted: -2.9518325328826904\n",
      "SMILES: [C]c1ccn2nc(S(=O)(=O)Nc3c(F)cccc3F)nc2n1, Actual: -3.8248000144958496, Predicted: -0.6884294748306274\n",
      "SMILES: [C]CCCCC([C])OC(=O)COc1ccc(Cl)c2cccnc12, Actual: -5.755300045013428, Predicted: -1.5173194408416748\n",
      "SMILES: [C]COC(=O)c1nc(C(Cl)(Cl)Cl)n(-c2ccc(Cl)cc2Cl)n1, Actual: -5.651599884033203, Predicted: -3.3083343505859375\n",
      "SMILES: [C]C#Cc1cc([C])nc(Nc2ccccc2)n1, Actual: -4.857500076293945, Predicted: -2.272218704223633\n",
      "SMILES: [C]C([C])=NOCCOC(=O)C([C])Oc1ccc(Oc2cnc3cc(Cl)ccc3n2)cc1, Actual: -5.847899913787842, Predicted: 1.4914121627807617\n",
      "SMILES: [C]OC(=O)c1c(C(F)F)nc(C(F)(F)F)c(C2=NCCS2)c1CC([C])[C], Actual: -5.200200080871582, Predicted: -2.2682790756225586\n",
      "SMILES: [C]Oc1ccc(C(=O)c2csc(S([N])(=O)=O)c2)cc1, Actual: -4.473299980163574, Predicted: -0.532630205154419\n",
      "SMILES: [C]c1ccc(C(=O)c2csc(S([N])(=O)=O)c2)cc1, Actual: -4.44920015335083, Predicted: -0.8013033866882324\n",
      "SMILES: [C]Cc1nn([C])c(C(=O)NCc2ccc(C([C])([C])[C])cc2)c1Cl, Actual: -5.10860013961792, Predicted: -2.7506279945373535\n",
      "SMILES: [C]C(Oc1ccc(Oc2cnc3cc(Cl)ccc3n2)cc1)C(=O)OCC1CCCO1, Actual: -5.030300140380859, Predicted: -2.3763716220855713\n",
      "SMILES: [C]Oc1cc(O[C])nc(NC(=O)[N-]S(=O)(=O)c2c(-c3nnn([C])n3)cnn2[C])n1, Actual: -2.606600046157837, Predicted: 3.167713165283203\n",
      "SMILES: [C]C([C])([C])c1ccc(CCOc2ncnc3ccccc23)cc1, Actual: -6.143899917602539, Predicted: -2.5349133014678955\n",
      "SMILES: [C]Oc1cc(O[C])nc(N=C([O-])NS(=O)(=O)c2c(Cl)nc3ccccn23)n1, Actual: -4.788599967956543, Predicted: 0.8133933544158936\n",
      "SMILES: [C]CS(=O)(=O)c1cccnc1S(=O)(=O)[N-]C(=O)Nc1nc(O[C])cc(O[C])n1, Actual: -4.634900093078613, Predicted: 1.9640083312988281\n",
      "SMILES: [C]C1([C])CCC(Cc2ccc(Cl)cc2)C1(O)Cn1cncn1, Actual: -4.328800201416016, Predicted: -1.2074038982391357\n",
      "SMILES: [C]C([C])C1CCC(Cc2ccc(Cl)cc2)C1(O)Cn1cncn1, Actual: -4.682799816131592, Predicted: -1.7285399436950684\n",
      "SMILES: [C]c1nc(C(F)(F)F)c(C([O-])=Nc2c(Br)cc(OC(F)(F)F)cc2Br)s1, Actual: -5.518599987030029, Predicted: -0.49997782707214355\n",
      "SMILES: [C]OC=C(C(=O)O[C])c1ccccc1Oc1cc(Oc2ccccc2C#N)ncn1, Actual: -4.827600002288818, Predicted: 0.07690668106079102\n",
      "SMILES: [C]SC(=O)c1cccc2nnsc12, Actual: -4.436299800872803, Predicted: -1.673431158065796\n",
      "SMILES: [C]Oc1cc(O[C])nc(N=C([O-])NS(=O)(=O)Nc2ccccc2C(=O)C2CC2)n1, Actual: -6.3942999839782715, Predicted: 1.1045527458190918\n",
      "SMILES: [C]Oc1cc(O[C])n2nc(S(=O)(=O)Nc3c(Cl)ccc([C])c3Cl)nc2n1, Actual: -3.3203999996185303, Predicted: 0.1034994125366211\n",
      "SMILES: [C]S(=O)(=O)c1cc(C(F)(F)F)ccc1C(=O)c1cnoc1C1CC1, Actual: -4.7631001472473145, Predicted: -2.107618570327759\n",
      "SMILES: [C]CS(=O)(=O)c1nc2ccccn2c1S(=O)(=O)[N-]C(=O)Nc1nc(O[C])cc(O[C])n1, Actual: -4.419099807739258, Predicted: 2.1187095642089844\n",
      "SMILES: [C]c1cc([C])nc(NC(=O)[N-]S(=O)(=O)c2ccccc2C(=O)OC2COC2)n1, Actual: -3.8929998874664307, Predicted: 1.0731830596923828\n",
      "SMILES: [C]COc1nc(F)cc2nc(S(=O)(=O)Nc3c(Cl)cccc3C(=O)O[C])nn12, Actual: -4.429200172424316, Predicted: -0.6050324440002441\n",
      "SMILES: [C]C([C])OC(=O)c1cc(-c2nn([C])c(C(F)(F)F)c2Br)c(F)cc1Cl, Actual: -6.922699928283691, Predicted: 1.7833995819091797\n",
      "SMILES: COCCOC(=O)CSc1ccccc1, Actual: -0.5, Predicted: -2.884939193725586\n",
      "SMILES: CC(=O)F, Actual: -3.0940001010894775, Predicted: 0.7675259113311768\n",
      "Outliers have been saved to 'outliers.csv'.\n"
     ]
    },
    {
     "data": {
      "image/png": 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4+luh1xfhcPVZKKrSI11bDBUj1mXG4FRFJgr+Gip9wSGCUNsz1N8v/eBBoFcvx+1TpgD/+1/ICh4BMjAkiACyZ+NmlKzNRp/yHAzGJPQpz0HJ2mzs2ehfD3uhHkSvtY0SpMcXYkDLeKfrUdoLxpvreXouyzIorMrE2eq1cFYTpIQ4YRjfiRxr+NeThYZKE3rUzZR0DsfxH0mxJpvt6dpTSI4tl5QyFLAWnrGxRdDriwBwYDkVHvv4Scsa7dcMBpj59iqwXGg/bO1/BoTPV63yo8747Tf+xvai5847+fzk66+HvOgBSPgQRMAQxEa6h2JDacytZnSvvEu0CNbdbKdeI4aiqCrT4YHmKcJDW6irkUP7WqQdzzAc/jHnwpAgXizckRAExKHmGzHIPBHJsdLSYsL326GuSMbXo6hSj1OneKflPn046HTCtHQG6766E+NXbUJhZYbNOacqMjF+1SZ89FOgq3p9A8PwI6w++ADIsH3pyMz045iKX37hF3PBBbbbp03jBc/atXw+tZNAqS6CCABui4IVKPaVyjevLEZOsnOrX1f1N+pwNU6krkZ6y3iwLGPjGcOyDBiGA8e5/psqPLRPpq5ChpvX6iot2L4WaaYpu0pmQpMu0sHSATHWZOJ48kr00MyClI42a7yNRnEcUFSlx8ynz0L666144AEOl10WgblzbZ2WP/ppHLbsG4Oh5xRAn2CEsUqPgr+GhmykxzqiM24c/+H3qew//QRcdJHj9gce4Bfmj1BkENJ5JB5BBBEHdha4jDQIYuPAzgKfrsPcasYFmtWSjnVWQzNowjj8ELEJxTW2b2mNNZnYVToXYBiXESFjTaakVnYpacFBE8ZhV8VCSa8n7pzRaK0tkXRsMJNnehTpuUcRnZASkOgVwwDv/TQRCYk1aG2twpNPmrF7tw4332zAnDm2x7KcGrv+HI73vr8Ju/4c3uFFD8Pw1jYLF/IRHGvsIzpqNW9OeNNN/L8+FT179vCLsxc9s2fz1t2rV3da0QNQxIcgAkKwDJI8sLMAfWKldS25qqHhTQrHYP/paIwmQQckABFVpdhVvsDBOdlUm4rf629Gwvlj0GvCULeRHovHjV0Bslg79aXT5qNo7VqXfjcVDUnQV02GIaFQ0muXA8sCHFRQMaxPnzGJ54yEOlyNOg8LmpWktCENKlUbKisTEBHRhrlzgWuvBTZsCMx6/AXHAZ99xn9kZPACqHt3P0Z07PnuO2DIEMft8+YBTz/dqcWONSR8CCIABMsgSanCqqIuGb0muK6/UYer0WfUcOzZuBn6Y1PahU4yUFSVgbzyhdAkd+dTVBOGYrjEFJ6naUHb9Bsc0m9gOCTHOH7x7d2dvYFlATCAChILjmTdgzcfPG/cYOz7Ygcii94EMtyf5wsOF3VDWVkqwsJ4L55Tp4CXXvJuaGhHo6iIH5S6aVMAxk588w0wbJjj9kcf5UfGk+CxgVJdBBEA3BUFK1HsKwWpwurX5gck1Ro5L9guwrCkBVCFa9Bn1HCP6pbkpAWdp98yUN8cC8B5l401ctvZWU6FqvpkcE6u6y3Wxcy1bxnQr+oy9M/4yqNrKOHZz3IMCisN2PvvQId9X37p/fU7EsLXc+ZMvl7YHrMZyM/no2D5+eLHeMzOnfwPmL3oWbiQX9BTT5HoEYGED0EEACEqAcZ5i+/J1FU+LWw2t5phNptRUZfktBOK44CyumRcOm2+pOudYZoBseJad91hrpCbFhw0YRzSco9hf3IedmM99ifnwXjGOsRF1kl+Fsh9ZoSpWSR52A7ujNrGWNQ02trvG2sy8U3JgximW4YkkeiVO5SIbrGnH/SPb1koWquzdat31++IcBxw8iRfxGzN5s1AdjaQk8N7A+bk8J9vltu4+eWX/Ddw5Ejb7YsX84t4/HGZF+4cUKqLIAIEP3Ryk8NQSmNNJk6mrvLp1GzBrLBfwimAD4CIDPIEwACHE1/FIAndVm5HO8h0Z5aaFmw2/YH92/Nx3rDB+GPXbtHOr7zVjwGp7q/FcuIt3c2t4dCE+3a0gv33ISayDgyAvLIF0KT0QHSyHnUowRDzJNniRYkggLHKgMe3LMTWg1d5f7EQw2ilwYWZZfYRtsJCfrtHLe3btgH/+Y/j9mefBR56SPZ6Oxs0pNQOGlJK+Bt/Ozc7G4Zp/8AtrMqSJMDEJoq7YjfWY/CkmySv1zIkNL7Qpl7H2brbzM4Hhuatfgw5qYsk39vdvfxJm1mNH8I3QKVWY0DL9QHzHvr016vxxu4p2PvvQIdIT1hYKyIi2nD8+Jloa+u876vz8vg6H7OZj+w4q3WSPJX9s8+Aa65x3L5yJTBrlgIrDg1oSClBdBCEomB/4KpQmGH4SEdVfTKOZr6PPhOGy+62coWnBduuvILE3rapVeIDQ3e//wGgSfTo3vZ4InrKalOQFFMuKtbkEKY2Y5D5BtQ3xILRKHJJj2A5fp7WPe+83OHb0H2F/QgK65llYlinxkQLoj/+GLjuOsftzz8P3H+/AivunJDwIQiZyI3UBHI2l5Shokmx5TihVrtdk6fT0IUuJLHuMHdfE2dpQcB9kbIwMHRgy0So433XZWXPwfpbcGnsKsWjRHGRdcpdTARBTNqkPTn+k8e3PEmixwliIyiMEt0oHI7btAmYMMHxwJdf5t2WCa8g4UMQMpAz0NPVeceSn0N0QorPxZCS/kGeTAB35c4s9Wtp7RVU+dcO5KQukiwoVAwAtWvRo7RA4cITFbfVUTK95ez11jXFoJWNQFJMpWWbsUrfqep5HngA6NKFL6nZvl3aOZmZ7S7NAnqJwU3Lce+/D9x4o+MBa9fy87QIRSDhQxAe4omZnqTztKegb5sARijglSii5KCkf5An5orOCrY9/VoKacHdPjJ2rG2MRUxknSSB4Uw4sCz/es9WrwUHZcWKktivvaI+Ef8rmIrndzwAABjYdS/S4kpRUquz1PNotUBLC9DYGIAF+4mkpPbucKmi57nn+MyTfZ3O0KG8ICosFE/LCqmxS0+8AzC3Oh7wxhvA5MkerZ9wDwkfgvAAuTO2XJ7HOP5RdCei5NJrxFAUrc10WijsKh1lj1QRtav2OQzJvd8h0uPNvDKp9/YEjgPUqja3URqWZVDXHIu4yFqRfQAYBv+Yc5GT/ISyC/QBz301A4dKetiIG4Hvjwx2OL662p+rCwwVFXy3VVKS+2MF4SImegB+2+rV/PXEzp3MvYHXT94O2Gubd94Bbr5Z1voJ95CPD0F4gNwZW+7O83QqulyU9A/iTRgznHoACSaMQ+64X/R63swrU2oqvO39gGhNk8tjOA4AA/wR/wbySh6E2Wz7J5Tl1PimdA40yR1j+Om3h4fg4/1j8f2RwVS7YwXHAeUShDXH2db0OMNeRN2JtWA5Bq/jdtsd773HX5REj08h4UMQHiC3RkbOzC1fDSp1NVRUyrBQgR8/3gJNWJPo5HUpIsqbeiNXAs5bgw5XdT4MA+yqWAAAGJ62EozKVvWpGDMu1S1Hc/khr9bAcco4Kzu7hivHZUI6sbGu9wsePoKIuhuvgAODtbjL9sBNm/hv1sSJvlkoYUNICp///ve/yM7ORmRkJAYOHIgffvgh0EsiQgSprdj2x3kzc8sXg0rFXI3Tc49KFj1CbU6iE9fgioYktyJK7tdSwJmA87XPTnjSWS4cqgFwQA/1WtkRKbGuKqnn2G/j0O6wLNDeoSXuuExIp66OFzZiDsxmMzBjBv99uA8vgAODV3CPzTF3Jn8EcxsHXH+9n1ZMACEofN5//33Mnj0bTzzxBH7++WdccMEFGDVqFEpLSwO9NCIEkDtjy5vUjK8GlQqFwoMn3eTR/CxXoykA/kHb0haFi8aOcXkdJeaV2Qu4PNOjkl6DN5jrTBJSdKfwtzkXDON52IZhPBdvZs72T3l5fSIe/HgZct9ci+Jq258fY5UeuW++2mk6tPyB2HyuggJg/KnnwIHBC3jAZt9ofAoGHF4rH+sw3oLwPSFX3Lxy5Urk5ubi9tv53Okrr7yCzz//HOvWrcO8efMCvDqio+PKTM9Vy7Y7Ez7x7iDphcb+RIoXkCHhlNvRFHK/lmLXEe6zf3s+UO65MzM/xd39cabaVITFSph5AUCT3B27imdiePoqScfLaafPM83A8eZLsGRtTwzuvge6uBKY6lPw5S+jLNGc7b+PEu3Q6iyoVHBah6YEoiaES5di+MMPY7jdsVdiK7bjSpttUr1+COUIqYhPS0sL9u3bh8suu8yyTaVS4bLLLsP3338vek5zczNqampsPgjCFXJrZJydBzj+YfbXoFI5KOkFpFS9kYDUyJqZtf3TZ6zJRHldskNaSECouTmk/S9iUhy/f2JEJ+uRcL7rqJc1nogelgOKawwwjLoXr39+LjRRLdh36kK8XXAbtv18lY2wYTk1vj8yuNMWMftS9FhjNIIfEsowwMMP2+y7HF+CAecgegDpXj+EcoRUxKesrAxmsxlpaWk229PS0vDXX3+JnrNkyRIsXLjQH8sjQghrMz2L6eCEoW6jE2LnNVSakF0x2++DSuWipBcQIP9rKYbrKBIABthVsRCX5M7DAetBphOG4sePt2BAy3iwnHgKb1fpXAyfNQHmVrNHlgD8sackRZQ84aBmLsp/bURlJVBWJi0KRfiGx7EQN01a4LD9hpSd2FSe49LDZ2hwBXQ7BSElfOTwyCOPYPbs2ZbPa2pqkJWVFcAVER0FuTO2xM4zt45T5MHvD5T0AhLwdl6Z/ciLPZXvi4hJfuhqzv28mLS/n7OxGKbaVBxKeAnDZ423rNWTFN0/zTdBzyxTzBnazKqws+VZ6Prl4Is3U1FW5v01CZ6oKE/MGTkswqOYj6cdd+3aBVx6KW7cDGwaz3/frcWP2HgLwn+ElPBJSUmBWq1GSUmJzfaSkhKkp6eLnqPRaKDRBGDiH0FY4c9Bpd6iVG2OUoiOvKjix4CUWo8BkRmR6zVhKAbbnXfR2DHY9coCXKBZjaTYCst2+0idudWMHpoNALwXPcKDM6/1WZx/5SQ8/zxv++IPoqOBQYOAnTv9cz9/k5ICrFkDHDwIPOHWd5LDM5iHh7HUcdd33wGD240fx43jO9VnzLAdVio23oLwHwzHKeEWETwMHDgQAwYMwAsvvAAAYFkWZ5xxBu677z5Jxc1Sx9oTRGdHEBzW0ZHCqiy/pugsIy/sOswEAeaqTkjusFix111Rn4Rfm2bg0mnzba6xf3s++pTnyH591lQ2JOJA1FJ0y7kKO3Y4lJH4lGXLgPh44O67XR8XFtaKiIg2HD9+JtraOs77asEo2WwG0tKcmRdyWInZmIVVjrv27AEGOvdEMpv54mejka/pGTqUIj2+QOrzu+P8ZEpk9uzZmDx5Mvr3748BAwZg1apVqK+vt3R5EQShDErW5sjBm5EX3gyZFZstlhBViWHRC/DDxz1tzlfCg4nlgMr6ZNSM/AnVf0Rg61bg2We9vqwktFrg0UeBnBy+RkqnA0wmZcwVg4mM0/XqajXw6qu8N0/7a+TwPB7A/XjR4Tzznh+hHtjf7fXVaquOLyLghJzwmThxIkwmEx5//HEUFxejT58+2LZtm0PBM0EQ3hPIFJ3btvrTztf2bfVyh8zKEVqezBQTqwHiu8wYfFH2DFZcHwF/2pGNGAFMmAD068d/rlIBc+fyH6FEVpZtgbGQnpr5AItHCqfjHrzicI75x59RUNcXxn8BfSNFcDoaIdXOLnDffffh+PHjaG5uxt69ezHQRQiSIIiOidRoSn1ZoeX/rswX+floHLpX5mLfFzscZqTJmS3miXFlVWMCaptibLaV1Brw319fxcMvXuVX0QPw9Tz33AOMHg3k5bVvV7o7LdCsXGknWlgW4z6fihOFagfRMzzxV1wzmoP+qr7IyQEmTeKjYdnZ4u7NRHASchEfgiA6B1KjKT3qZmLPxigMmjBOQpQISI6tQHLVZQ6pL6lCq+r3LcDpCJM6XI2vK1fjFq1IITjHgAGHt3+8BfmHh+HnkxeirCzVxmzwz7KBqKoObCihtJSP8lxxBfDllwFdik9ISTn9H7MZuOMO4K23HI45HwfxB84HKgF85niNwkI+PbZpk2PBMtX3BB8hV9zsLVTcTBAdA3OrGSVrs5221Qvw/j0MfojYBLa1GYMxSdL1hTSTUCAttVCZ44C9ER9i0IRxliGVY/ttxgtT7kNGYrt4MlanYemOh7Djn5Eh48PTEYubN7zdhhs/v1W0Re4c/Im/cY6k6wi+PEePtgubzZvFO7pWr6aOLl8g9flNwscOEj4EIR+5nVJysdTrcHAjfnhvoeLs19Gv6jKnx9nDcUBFfTISbuctMnih5dqMULhX6tSjOKubGmZzEQAgTN2KoecUICOpEGX1yfj55IUoNYnbbHRUOpLwUaMNG3ATJmCTw77u+AeH0V3WdfPy+EJmQfTaP2GFOi6x6BDhHVKf3yGWrSUIIlDs2bgZJWuz0ac8B4MxCX3Kc1CyNht7Nvqu+EEYeVFRn+LyOKH+BoBHw2IZBkiOLcc3ryy2+BfBzanCvT569SOYzUVQqThERzdAm1CNAyW9sHnfOGz7+aqQEz0dhTC04mOMQRvCHUXP4cPYsJ6TLXoAPqVlPZndHmGb2GBTwj+Q8CEIwmuEyEu61rZ+Jl17CgNbrsfu9x3fVSvFoAnj8Ffsc5KOba4qtYgXqeIHAHprVsPcasagCePwY+FoafeqLEZsbC2SksoRHV2PqqoElJWloqEhxv3JhOKEowWf4Wq0IgJj8Intzn//5RXJWWd5PTtLr+drek45LyWzGWxK+B8SPgRBeIXLTimGj5oMaLkRu9/b6LM1xHowONTVsFhnJMdW4MDOAuzZuBkXZYhUt4oQro1CZGQTysqSUVaWCrM5uFM/oUoEmrENo9ACDa7GF5bt5rAI4PhxXoV06WLZPngwkCqj5Iph2lvjpU5cp8nsgYGED0EQXuGuzRsAwtRmDDLf4LO0l7u2cZZlUFiVhV4jeMOWQRPGIS33GPYlfI26plhJ96grKzwt8FzDcgxK6tKgybwCLS2pcJsbI3yCBk3YgRFoRiRGob0drR7R+PyVk1C3NgNnnGFzzubNwFln8SaNnmA/e0tq1IgmswcGEj4EQXiFZHdiDqcN/pQvbLCuv7EXP9bzw6wLrdXhavS7aiR+rJfmyGeuM7kVeADAgMOiLxbhvvvVqKnx+KXYXos0k8dEoQHfYCiaEIURaDcgqkQCDChELOpxz+JMh/oaoRjZVYrKGUlJtsXKQ4fy3VvOvn/W0SHC/5DwIQjCK6KTpb1tVangYPCnJM5SWMaaTJczuy6dNh/ldclOxzAI0aKwWGn5j7Xf5OLDH67yaO3WxMTwIymWLZOXcumsRKMe32MQGhCDofjWst2EFKTDiCRUwggDAMf6GlfFyAKuRKj9bC+1mm9ZFzuPJrMHHhI+BEF4hSXNJNEYQ4n5Vc4QUlj7k/OwG+uxPzkP6blHXc7fUoercSjxVXA47fljhXW0iInWSFrD9j+u8OIVAA0NvEjMyQE++wx48EGvLhfyxKAOP6Ef6hGLQdhr2V4IA1JRCh1MKIFjB92WLe3/d1eMDLgXRfZdWsLoiwy7UrLMzM7byl5ZWYmioiIUFRWBtf9l8yNUbUcQhFcIaSZ9y/WSjpcaIfJmPZ7ODxs0YRz2bPzQYeq6sSYTR7SPo9slg9Da0oqS7elIjS2BinF8CrIcA2OVHnv/9X5EztNPA42NQHU1Pyg0Jgaor/f6siFFHGrwLYagNw7YbD+OM9AP+1AO1xYH774LLF/OR128LTK27tKyHkY6bhwwZgw5N1dUVKCpqclmmyqAs09I+BAE4TWDJozD7vc3YkDLjQhTi9fwCMZ+vSb4t7BBqqmi/bR5RIXjjKED0ZVhUFdXh4aGJqzPn4tnRs8ByzE24ofl+PzF41sWguVcP9U0GqC52fl+jgMqK4HHH5f3ekMdLarwPS7GufjLZvthnIUB+AGVSJJ0HZOJTzdVVQHHjimzNjEB1ZknsxcVFTlsS0tLgzrAyo+ED0EQXiEIC5hb8U3loxievPC0k3L7MdYpowwfOjnbs2fjZpxhmtE+n6scDjO4rFGHq6Hr1QNAD7Asi4aGBjQ1NYHjOBw/nop3dt2I8vJ4PDX2cRgSrMZPVOnx+JaF2HrQfW3PoEHArl1KvcLOQyIq8AMGoBuO2Gz/E+fgYnyPaiR4fM05cxRa3Gmsu7Q684wuMcGTnp4e0CiPNSR8CIKQjYOwSAHK65IB8I7HAsaaTJxMXeWy1kZJzK1mfPPKYgxLegLQ2u5Ljy9Eest47NloW/As/LE2m81oaGhAc3MzOI5D6ukK402nPRi3HrwK238fZTNMdO+/A91GegT69iXh4wlJKMfPuBBn4oTN9gPoiSH4FjX232CFEYqRk5KAigrxWh9hTpfQpdVZZ3QFa4THHhI+BEGI4i5FZJmTpbV9EiRGVwAMh7zyhdAkd+fPnTDUZ5Ee+3U2VJqQXTEbOcni1aoqFQeWZU631o9BiYmfwyUIHqEWIdVFSxXLqfH9kcEer1WlAtLSAJ2On3pOOCcFJuxHH2TA9mH6M/piGHahDnF+WUdmJp8SA/h2d4axFT/2XVrOZnS5muDe0Qn2CI89NKTUDhpSShDtkRzrQt+iqvYUkWUyulbc10ao50nPPerzIaX26xT+oknxwNkdtRGZ/QagsbERjY1N+PtvoKEhFSkpfGTG+u/2jz8C99zj/ZoZBujXD/jpJ++vFWwoMaRUhxL8ht5Ig60y3IsBGIGdaIDzkR8REcDDDwPr1vFCwxsefRQYOdI2RSUWycnK4kXPuHF8eis723mHmNgE945MsAkems4uExI+RGfHEsmxG0Eh1On8ELEJkfFJ6FOe4/Za+5PzPO6w8nadHCfd+G9H03Kk9LsMP/0EvPBCqk0URqcDrruON/dNSQF69wauvJLvtCLE8Ub4pMOIP3AeElFls/1bXIIr8CUaEe32GvHxfNGyWs3X17z4IvDhhx4tw8L69cBNNzlud1W7k5/P2xC4Q5jg3lEJNsEjIPX5TakugiAsWOZuaUXmblmliI61LJE0icFTzx6pHViu1umJ23FprRZb307F+vUi+0qBNWvaP9fp+Nbkt96Sfn3CPQYU4i+cgzjU2WzPw3BchS/QhCjJ16qp4X1z1qzhIzBGo3zh42ychKsurVCf0RWsgsdTSPgQBGHhwM6C9kJlEVQqDhkJJ3G41gQpJRaeePZ40oHlbp3uEDx35i4dJdl40WQC3n4buO02YNs2qtHxliycwCF0hwYtNtu/xOW4Fp+gGZGyrltW1l5LI3cWlnWhsieE6owuMcGj1+vBdNCZKh1LphEE4VOkRmjUsakeDQV1h5C2Stfaipn0+EIMaBnvMNzUG/dnQehI8dyxhuP4j48/Fk+BENI4E8fQBjVO4Ewb0fM5rkIEmjEKX8oWPQIcx9fiDB7semaWM1au5NNZGzbw6Sv7uV7OCLUZXYLLsjV6vR4Gg6HDih6AhA9BEFZIjdDEpmR4PBTUGZa0FcTTa2LDTTWJ8t8yG6v0yH3zVUmeO2LU1LTPYSKk0xVHwIHBMXSBGu3jCrbgWoSjBaPxOVoRodj9Tp0CnnnG+cwsMZKTgblzgdmz+VqdSZP4f7Oz+cJmd4TKjK5QFTwCJHwIgrBgmbslIZIjdyioPQd2Fricei6k14Thpps3A9fdNRQny52v075lgz0drfnk2BwMfHqvbNFDeE43HAIHBkfQzWb7hxiHcLRgLLagDeE+ufcTT/D/is3MEoiLA66/Hvj6a+CVV/gxFvZdWUIruhTxM24c8P77vIiypiPM6Ap1wSNAwocgCAvC3C2pkRw5Q0HtkZq2aig3WjxSTp5SY8Zbp9fJ2a5TrE+1tNaAAqxFuX6WR+ktQj498Dc4MDiEHjbbN+BGhKEV4/GhzwSPNTNn8kXpK1eK76+r4wVNRQUwa5b4z4+wzX4QqRibN/MRo7Ky9m2pqfz9g1H0cBzXaQSPABU3EwRhAz+wc5PowE4x92U5Q0GtiU7WA+Xuj9Mk6jHjrvaH0J7CQch981WH8RHFtQb8E/MEwmOT0FJTioh4Hc4YOhDd1GpU75O9TEIi3VoO4XBbN4ftb+MWTMEbYOFf4XnyJF+nM3u2+H7B/mD6dL6A3RnOBpFa48y8sKwMuOGG4Ir4cBwHo0h7WUcuWpYKCR+CUBipLdnBfF/7gZ2+cl82t5phNptRUZeEhOgKiHXFCmaIVbqhOHUK0Ov5d6ZhYa347sQQXLXmM1yY9TMi2RaU1Opw24MDcdEA8XX27cu3pZtM4u/sCfkMaS7AB8YbHba/hjuQi7XgAphgyM93bioI8D8LrkSPNc5a0c1mvqDaWcSIYdqjT4Gs8XEmeAwGQwBWExhI+BCEgng6FDOY7+ttJMcdwpr7JZwCYvlt9uaD1um1EydLoNcD4eGtiI5uQHg43xFUakrHNlN7zc7oSuf3VKn44tWHHhIfPcBxfEFrWRlf88Gyzq9F8AxvysP6ylsctq/BXbgHLwdU8PgCZ63oBQXuxZW7iJEvYVkWxcXFDts7k+ARCK2fSIIIIJ62ZHf0+3qDszXbY6zJxLd4FWdcMghJSa3Qaquh1VYhPLwFZWWpKCtznKeVkiJ+LZYF9u0DWlqAu+7i6y6s0emAZcv4qNBXX5HoccdlTV+hyJjhIHpOhGUhQt2MaVjjF9HjyjtPaB9XSmi4akUPVvNCs9mMoqIiB9FjMBg6pegBKOJDEIog1fHY3DpG0bRXoO7rDe5cl1kOqKpPxh/J/0X2NYOR2criu++qUFbWiqQk4OhR8eGhDMOLl759Hffl5fGixn4kxV13tRer9u8P9OnDpyIoDeacK5u2Yl3lnQ7bD6m7YaT+K0RozOCO++c99cKFwHnn8fUzgPjwUKGoWZiuLgbD8ALKXeHyypXO01TBZl7Y1taGUhGXzc4qdqwh4UMQCiDV8Xj/zgJF00eBuq8UnNUcuV0zAyTFlgMMh6++rsebbzajogKi0R0B4SE3Z45jBCAvj09t2YuZ0lLg1VfbP3/tNSAhAaiq8ux1dhauafwEa6ocp7QeCOuJUSnbAIZBGNPql7VYDwYF+KJh++GhmZnAjTfynVquUlBCilOKSaGzaCLQbl5YWCgunIUBpb42LyTB4x4SPgShAJ60ZIfCfd3hquaoraVZ0pyvE38dw6r3z4XJ5FzwCOh0vOixHxDJsnykR2oEh0SPI9c1foT/Vt3nsH1feF9ck/yp57bICmDfGj5uHB+psx4eajIBEye6/95nZvI+PqtWub+vqzSVYF44frx4/RjgW/PClpYWlFn30J+GBI8jJHwIQgGktmR7MrsqmO/rCsvUdK3tEyc9vhDpLePx9KcLMORa99fZXtDNpeiJjeUjOWlpfHpLrNbjl19oppZcbmh4H6uqHXvAv48YhOuTNgVE8AjMng1cd52tiLAeHmo2827LrkRPfDywcSMwciQvmKQIH3dpqnHjnEefrCNUStLc3Izycsc/AiR4nEPFzQShAJ44HofCfZ0hZfzEbZesxcnyDAfjQQGWY2CsNmDHbwNd3quujn/nX1PjvMBV5A0w4YZJDetRZMxwED27Ii6FQV+I65M/DKjoAdq7o5zhrsMK4H9ubrkF2LJF2Rlb48YBx47xKdb16/l/jx5VXvQ0NTWhqKjIQfR05qJlqUiO+Dz//POSL/rAAw/IWgxBdFQEx+P0lvFgWYZ/yJ/GuiVbaR+cQN1XwL6Ox2w28+3pTlCpOJyRfApLt83BnFErwHIMVIzVmk+LoQ+OSBsgWlXFt6cvW+aY5gJc12QQtkyufwNLauY7bP9KMxKTk94KwIpcI6SdzGbbFNfQodI7p0ym9knuSqaprKNPSlNfX4/q6mqH7SR2pMNwnLTsd5cuXaRdkGHw77//erWoQFJTUwOtVovq6mrEx8cHejlEB0OobbF2PC6syhJ1PO7o9xW7Z0VdEpJinbTOWDHno6Uwc2o8fNlSpMeXtK+50oDHtyz0eJaWVsu3oNtHflgWGD2a0l2umFr/PzxV84TD9s8ir8JdiWs9ulZYWCsiItpw/PiZaGvzbSVFXh7fpSWWVsrNbZ/T5Q6h6PjoUT76Y389+0LqQFFXV4eamhqH7SR42pH6/JYsfIKdY8eO4amnnsLOnTtRXFwMg8GAW265BfPnz0dEhPSJvyR8CG9R0kHZk2v50zHaUsdjl9JiWde+KgJ3rF+LbT9fBRVjxsCue5EWV4qSWh32/jtQ9iytu+/mH3j2PP888FbwBSwCzr11L+HR2sUO2z+KHIvpif+VdU1/CB9BqKxcybex2z/BhIhNcjIvjKQ+4Z57jq8X0+n4z0tL2yNIgXRarqysRGNjo8N2EjyOSH1+h0xx819//QWWZbFmzRp069YNBw8eRG5uLurr67F8+fJAL4/oRCjleOypG7OvnZYFXHsHtT9oxOolWI6BsUqPL38ZdfpzNb4/MliRda1ZA3TrZpvyyssD3n5bkcuHDA/Ursa8uqUO29+LmojZCU4meQYZK1a4HigqpwRp1qz2/2dm8qmvQDgsC5SVlaGlpQVCbEKYn0WCx3tkR3xOnTqFTz75BCdOnEBLS4vNvpXOxuD6mWXLluHll1/2KPVGER8iGHAeUeHrdn6I2OTT1Jkr9m/PR59ykYIaO1gOtms/Xb+T++arHqeypJKWBnz6KS/AKM1ly4O1K/BgnePf5reib8E87bOK3MPXEZ/kZN57KSlJvKbLnoULgRde8LzIXRBOng4VFas38jRaVFpaira2NoftJHjc49OIz44dO3Dttdeia9eu+Ouvv9CzZ08cO3YMHMfhwgsvlL1opamurkZSUpLLY5qbm9Hc3Gz5XCyHShD+xFs3Zl+nvKR6AtU2aaGNai/CLKnR49GPFmLb774RPQBQUsK3sPfrR63sAo/ULMH99S86bH8t+g48pn0qACuSz3338UJkwwZpx3fvzhsKZmZKH0IKyBsqunmzeL3R6tXSxFNRUZHodhI8yiOrnf2RRx7BnDlzcODAAURGRuLDDz/EyZMnMWzYMEyYMEHpNcri8OHDeOGFF3D33Xe7PG7JkiXQarWWj6ysLD+tkCDEObCzAIaEUw6iR0BwYz6w07Gfd8/GzShZm40+5TkYjEnoU56DkrXZis7rkuoJdCBhDXZHbUR+63+xO2ojzKP3YMTkqxxmZCmN8O6+s7eyP16zEEXGDAfR80rM3TDoCzuc6AF4d22z2bPxEBERwCuv8ELGkxSY9VBRd2zezHeE2bfQFxby2ze7+PUrKioSFT0GgwFpqWnYvz0fu9dvwP7t+TC3SrCXdoG51azo9YLlXp4iK9UVFxeH/fv346yzzkJiYiK+/fZbnH/++fj1118xZswYHDt2TLEFzps3D88+6zoM++eff+Kcc86xfF5YWIhhw4Zh+PDh+N///ufyXLGIT1ZWFqW6iICxe/0GDMYk98dhPQZPusnyuZLpMVche3OrGSVrs5GuLbRpRbfcj2NQUqvHD+l7UFGpRkqKrcEgy/LRmLIyPmXxxBP8u3Gl2izWrOEjPvv28QXPnY1F1Y/ijobXHbY/H3Mfnol/xKf39ibVlZPD12S5Iy+P/3nMznY/HuLo0fafW7GIjBTWrwduusn5fsEs0dl1xdYCuI/wiHVNmmpTcEj7Egbf6HmAQex6RVXOawa9wZ/3ssanqa6YmBhLXY9er8eRI0dw/vnnA4CoZbY3PPjgg5gyZYrLY7p27Wr5f1FREXJycjB48GC8aj2IxwkajQYajcbbZRIdBH92PslFjhuzksNK3YXsS0wlOBT/ONJxt1MfniXbF2LT3vb76HS8305ODi+A+vVrv/bcubwDs71/iqcwDD9xnWWB7dt5UaXTdZ5017PVD+HWhncdti+PfRAr4xwdmIMFtRp49FGgvFya8DEa5Y2HsB9rUVJiW9DsDHfRJXdmidaRo+HDpaW0nLmfp8aVIcV8A/Kfm4vhsxwL1J3hzk19z0blagb9eS+5yIr4jB07FldffTVyc3MxZ84cbNmyBVOmTMHmzZuRmJiIr7/+2hdrdUthYSFycnLQr18/vPPOO1DL6EGk4ubQJVDvQjzFElGJL7QxJBRgWQbGmkyk5x61iBipBcf7k/Ncdn4JIXuxFmG9vgjPPsth8OBmNDY2wvjDdvRuXQpdbLsPj7HagEc/cvThER5GS5eKF6WKTU/XagERnzaX2J8THQ00NHh2jY7Gc1UzMbFxo8P2JXEP44VY/5rJyon4uJqaLkZeXnu3lZhIt/bdcRm5NHseNRJjwwZgkvsALdatK8KoUY7b7Wt42iOq4uluYa3fh23E4Inj3d7X3fXE/p7IxZ/3EsOnEZ+VK1eirq4OALBw4ULU1dXh/fffR/fu3QPW0VVYWIjhw4fjzDPPxPLly2GyqmRLT08PyJqI4KEjvAsRkOPGrMSwUrOZf4jYPwT0+iIAHDSaZqxd24iePdvAMMB5o25Ei3kCdv+0Fy01pQiL0+GBZQNRXOL4B00oFl2+HBg2zNHrJyeH3y6kwIT02IsvAu+8w0dxpGAvlEJZ9LxUeS/GNm1x2P5U3KN4OdZxinqw4onosR8bITacVBA37iKXSg0VdRcR4n9/HF3EnRUtH9hZ0G5hIYKwtu5V98Lcep1bAeHuekLN4P6dBV7bYfjzXt4gS/hYp5ZiYmLwyiuvKLYguXz11Vc4fPgwDh8+jMzMTJt9IeLRSMhEyTSQvxg0YRz2bNzkEKEy1mQ6uDGbW81orS0B4txft7n8kNN99iF7a8ETE9MIlaoNlZXAyZOpllSVSq1G9kDeh2ffPqC4xPG6Ahxn23Vlj30KTPDgoV9fW9ZW5uLqpi8ctj8W/yRei5kagBX5jxUrHIWI2HgIZ5FLodhYaFNXYqioMOfLPnIkCB6G4aDTAX37SvPhkfomJjXOJElAKPGmSCr+vJc3yOrq6tq1q+g02KqqKhtR5E+mTJkCjuNEP4jOjTddUoFk0IRxSMs9hv3JediN9difnIf03KM2okfo4hoW575YgeOAYUkLnHZ4CfON9Poi6PWF0GiakJRUibi4WqhUbSgrSz39IX59qeV9Uo5jWT71Rb++7bxVcSuKjBkOomde/NMw6AtDXvQAwG+/Afn5QEsL/++GDfy/ZquGIWeRS6B928yZ7ed4O1RUiBwB7SnhdtEDAAzmzGGQmSlteKjUrklAmoCQej1P7hsM9/IGWRGfY8eOwWx2bE1rbm5GYWGh14siCCXpKO9CxHDlxuwsfefMuZZhAI6F0+hWbGwR9HoOkZHNiI7mIzwAUFZm23/ubPCn1IGgUo4jD5523i+fiKEt3zpsn6NdhvXREopLQohFi/gPtdpW7FinsDwtNga8Hyo6bhzw9ttFDnVqOh3w2GMGj0wQe40YCtO6FKTGuX+H4E5AmFvNMJvNqKhPQkJ0hcu6m14TJIyed0OvEUNRtDbTbX2iEvfyBo+EzyeffGL5//bt26HVai2fm81m7NixA9nZ2YotjiCUQE6XVLDjKn3nyqtELMdeVFQEjuNwzjlN6NatEdXVZnCco+BhGJwO2Ytfu29ffr+z1nR351vT2T14wHH4qHwcBrb+4LBrhvY5bIy+IQCLCh7s33dbp7Cs3ElcInWCuzuELi37OrUuXQyynJvV4Woc0r6EFDP/PRYf/QIYq7NcCgihmaNfwikght9m/6bIWc2gXOTUJwYCj4TP2LFjAfAzQyZPnmyzLzw8HNnZ2VixYoViiyMIJego70I8wV0RoTsayo0WwdPU1ITGxkaYzWZMngw8/rijw6Dwx3LOHOdDSFUq563pUs63Rmr0KOTgOHxRfjX6tP7qsGt6wov4KOq6ACwq+LF2Wn7d0cJIFKkmiM4Qa0tXqYBrrvHeaXnwjROQ/9xcDNMtc9jHcgDAuBQQzqLB9ojVDHqLq/rE40krEB2fhN3rNwTUTsQj4cOebq3o0qULfvzxR6R02r9OREeio7wL8QRv03JcZBgaGxstggcAUlNT8Z//AJGRjq3lOh0vWtzNR8rJ4VvW5Z4v4C56FHJwHL4quxznt/3psOuuhFfwWdQ1AVhUx0JIYQHixcYCQpv6UJnvc/w1WmL4rKXY/f4A9Ki6Fylx7V3Kxuosl2LFXTSYZYHKhiQcy/wAfSYM98nfvUETxsHcOgb7rTzTGtRlyC6fBQMrbeiyL5E9pDRUIR+f0EXMx6ewyvUfkWBFqm+PPSzHoLQuHSUDtwKn/yimisyQsHZXFlrLAcdtzqI39uf37s0Xpjq7njBSr6Kiff+uXXz0CAhh8cNxKDANxVnmow677kh8DdsirwzAouTj6yGlUli/HtBo+NQXIB559HT4KNAuePw9Ld1T01WlPL2UxF9Dl33q4wPwg0p37NiB0tJSSyRIYN26dXIvS3RS/OGoLPYupNeEoR0q0iPgLn0n/LG3yeefdlU+GDEHqYy44BEQay0Xi+IIbsyuzs/L471W7M0JAecGhcK1xaJHAt46PQcShmOxx3QxssyO6cpbE9/EjsjLArCq0ECv5wuVvW1TF7AWPAzD+E3wCLhqcBAj2Jo5gtFORFbEZ+HChXjyySfRv39/6PV6yw+CwEcffaTYAv0NRXz8T0dxVA42LO+iOIik7zhUNSQiKabSsr2kNg0HNfNw7hWeFcbm5fGRFzE3ZwB45hkgIUE8EpSXxwsYT7F2em5rAx7x7Ygpv6HizPil9EKkso7V2zclvYtdmuH+X5SCBDLiI+a07Mq52R3BNC3dkzeGwRbx8ed6pD6/ZQkfvV6PpUuX4tZbb/VqkcEICR//4q8QaKgimr6rNOCJTxZg15FhGNLjW+jiSlFYaUD+7zl4dqlacp0NwKesRo923VquUtk6KwvRmmHDgMsv93zshADDAHFxQF2ddOfmYEXFmXGwpBcSOMcvxoSk9/GdZkgAVqU8gRY+clJY9gST4AE8f2MoZ+SNL5E7dFkOPk11tbS0YPDgwbIXRxBAcIZAOxpC+u7nr/NRXfI33vogFd8dugARmmZERTci/8/haG6OBOB6ZIQzpPjp2IsSk4mPEN11l3zRA/ARppoa+ecHA2quDf8Un40oNDnsuy7pQ+zVDArAqjoW998PvPSSYwu7PYmJ3t0n2AQPIG/UTrA1cwSjnYgs5+Y777wT69evV3otRCejozoqBxOtra0oMZUgrfc5qIwfhZ9OnoPIqEbU1MSjrCzVInoA25ERUpHjpyPEkN9+2/NzQ4VwrgWnjJk4WXymg+i5NvljGPSFJHok8uab7kUPwBfGjx/Pj6vwhKKiIlHRYzBIc1r2FZY3hhB/YwhOMCN1/OIMmjAOP0RsQnFNhs12Y02m36PovUYMRVFVJi+6RGBZBoVVWeg1wn92IrIiPk1NTXj11Vfx9ddfo3fv3ggPD7fZH6hBpUTHItiK8DoSTU1NqKioAMdxlrZ0k4lFZWUSWNb1OzlPxIxcxwqOC44BoXImvHtDBNeMY8XiY3uuSv4M+yMkuDcSNngS9eM43stnzBj3tTzBGOGxxtuBn542c/iqwSTYIlCATOHz22+/oU+fPgCAgwcP2uyzL3QmCGcEYwg02Kmvr0d1dbWN4GFZFsnJySguVkmqhfFEzHRUP52YGODRR4GRI4FnnwU+/NC399NwTThafJbovlEpW3EgvLdvF9ABmTUL+N//gNpaZa9rP47CHvsuLYFgETwCSrwxlNoRJtQRWYSWwh47ngxd9gfk42MHFTf7j2ArwgtmqqurUV9fD47j0NDQgMbGRnAch5SUFDAMI6kIGeBFzGefSa/xAdq7uoCOJX5UKuDss4Hjx30XfYriGnGkuJvovstSvsQf4ef75sZBiifFzXl5fPRx6lTla7neeQe4+WbbbcEe4bHHX91Q/mww8bVtic99fADg8OHDOHLkCC699FJERUU5KGiCcEUwhkCDjfLycjQ3NzsIHnsPHqlDPa+7zjPRAzh3Yw52WBb409EIWRGi2XocLukhum94yk78E362b24cQmzZwg8W9YWYNrUbHSsieMytZuz/Kh81h/IBAPHdh6PP5cN9+obMH6N2/N1g4qknka+QJXzKy8txww03IC8vDwzD4NChQ+jatSumTp2KxMREmtdFSCbYQqDBQnFxMViWdSt4BKTW7Zxxhrz1CAMYX3sNWLNG3jVCgRi2DodKxEXNpam7cDhMPPpDOLJuneeiJy5OWmosNVW5CM+ejZvRvfIu9IstB4Rfv6pFKH89GYcSX3XaUu5tZMMfbwy9rSPyFH8Y1UpBVlfXrFmzEB4ejhMnTiA6OtqyfeLEidi2bZtiiyM6B4MmjENa7jHsT87DbqzH/uQ8pOce7ZSiR+gwaWtrQ319PcrLy9HQ0ICUlBSXTstS63a8Ha/nrTfpiBF8/Y01Oh1wyy3eXdfXxLE1KDJmiIqeS1ILYNAXkujxgPBwz9NbWVnSarX0+iLExyvTpcWnga5HUoxjMWJSTDkGtlyPPRs3O5xTsjYbfcpzMBiT0Kc8ByVrsx2Ok4Kvu7P82WCi5NfFW2RFfL788kts374dmZmZNtu7d++O48ePK7IwonMRLCHQQCG8O2VZFg0NDWhqanIZ4eGPtZ115aoImWH4/X29aCqSmk5zxZ49jvU2TU3Ap596d11foWWr8GeJeJ3OoNTdOBF2pp9XFBq0tnp2vIox48n7ChBVasSYi/X4dM9QsJxtpECv53+H0tJsf87l1vDwaaAHwGhtR78ICCNTzjDNsKSC5PjuuMOXo3b81WDii6+LN8gSPvX19TaRHoGKigpoNBqvF0UQnQU5ggcQn52l1fJ/iMVmWHEcMHasd2uV4+ljj1iRcTCaFCayFfi9pJfovotS96IwLFN0H6E81/XfjNW3zUBWMp+SGXIfcPKmTMx4azU++mmcRfAI4mTOHL6OzduiZT4NVOjyGIYBDAmnsH9nAXqNGOqzehlfvTEMxToiKchKdQ0dOhRvvfWW5XO+q4TF0qVLkeOJHz5BdFKElBbLsqirq0NFRQUaGxvdprSA9i4r++iLICCcNTOsWcN3fuXlyVuzt2myjkCyuQxFxgxR0dNP9yMM+kISPX7kuv6bsWnmeGQk2dahZCQWYtPM8bjj8v9Ztul0fBH+jTekofTAP9i9fgP2b88XNfiTgifpnYZyY4c0ZBXqiMDAwWDQuo7IG0ESjF8XWRGfpUuXYuTIkfjpp5/Q0tKChx56CL///jsqKirw3XffKb1GgggZrCM89fX1lo4td2JHgGX5SI9YOkuI9mg0wN13ixchC+Mkli4Vn6ruio7q6SOFVHMpfi0VzwP21e1DiTrdzysiVIwZq29z7lzMcgwWXfcELpk4Cqk6Na66yoAfN29GyVpl/GikpoGEYzuqIauvG0yC8esiK+LTs2dP/PPPPxgyZAjGjBmD+vp6jBs3Dr/88gvOOkvcxIsgOjPWEZ7a2lpUVFSgqalJUoTHGnd1NhzH7//gA+f7AX5mV2srsG8fsH07/68780OVqn3Sun3NA8PwH7fdxoujjkK62YgiY4ao6Omt2w+DvpBET4AYek4BspJdRAoYDnptES7MPIJrruFFz4CW8UjX2kaH0uMLMaBlvMdFtPyohQyXIp/j+IGhvUYMlVwHE4yGrL5sMAnGr4tsHx+tVov58+cruRaCCDmECI/ZbEZDQ4PHER57pNbZVFY63yfM7PrPf4CqqvbtwlR1V5EgZ54+Oh1fW5GTA1x8MXDPPdLWGSgyzIX4sXSA6L6eaQdQoUry84oIe/QJ0iMFvqgj4dNAzyO95XqAcxT7giA6kboahnC17HqZYGnx7sh1RJ4iW/g0NTXht99+Q2lpKVi7t4rXXnut1wsjiI6M0oJHQMk6G2vRA0hPgwmePkJHWUoKnwYTjBErKuStJzoaaGz0bRots+0kfjCJDwc9L+0gqlRejvgmFKNFHe7+IPCRAl/50fBpoA/RvfIuJMfa5r0q6m19fOT47vh6VIQndKZZXbJGVmzbtg233XYbykTefjIMA7OUUbpBCo2sINxhNvOzgIxGQK8Hhg5tH4joK8EjIIymcNW2npDgOuLjCqHt/dNPPXd4FtizB7jvPnnn+orstqPYbRoiuu+ctD9Qo9L6eUWhiScjK5yxfHkREhM5JCWaMahkEHRxxVAxrkfa7N34AQZjkttr5xfPxPDZz3m8Jk+cmwUxY10vU1iV5VAv469REVIEjdiai6qUFWBSvy7eIPX5LUv4dO/eHVdccQUef/xxpKWlebXQYIOED+GKzZuBGTOAU1ZvLjMzgWeeKUJOTrvgaWpqAgDFBI81zmZnCaH4m28G1q93X7PjijVrgH792j+39gyyj/DYr23xYsdoUqA4q+0wCkzDRPf1SPsLdao4P68otPFG+Aht6YsWcRg1in8TfTjvCwzBXQAH0UiBIA6kzrViOeCHiA99Hk1xJTYEEdWl8AYkRleIegQpNadQiqDpjLO6ZAmf+Pj4kC1kJuFDOGPzZmD8eFuxodcXgWEAtboNTz7ZiL59fSd4rBHz8UlLA0aNAt5+2/t00eLF/LWc3UusHkgQZMHQ8dW99R/sKhN/EHZL+wcNqhjRfUQ70dGeD3eVI3wEwSMgiG7Bh0dKpMDdwGMBlgWMNVkBG3ws9lpc4c0AUimC5qKxY/ivm1a8iLyjDYr26ZDS8ePHIz8/PySFD0GIYTbzkR7hoS78sVar2xAT0wiNpglvvAFcdlmq7BSRJ4jV2fTuDYwZo4zwOHGC/9eZmCktta0HctVm70/Obf0DO8ouF913VtohNKocjVcJcRoagLvuAt57zzcmk+2ChwPAP3XT0oCrrjJYUseANOdioY5E33K9y3uqVFB09pQnOHMvdoXcFm+pxd77v9Kinx9ndQULsoTPiy++iAkTJqCgoAC9evVCeLhtEdoDDzygyOIIIlgoKODTW/aCJyKCj/CYTKkwmXghYp0i8iUqle299u1Tbnr6q68CXboAK1c6FzMcByxa1C7AAjm5vWfrQXxZNkp0X9f0w2hiovy8Is9ITweKiwO9ClsYBti40TPRo2LMGJi9B4P03+NUeSa++3swLjl7N/QJRhir9Cj4awjS0u1/UPgnc3GxAS++2F4v50laxNxqRmR8En769T+4KHOr23X620vHlRBxhdwWb6nF3v8cym8fvOqCYPMe8hZZwmfDhg348ssvERkZifz8fDBWSUqGYUj4ECHH0aNF0OsFwdOAiIhmAEBZme1fDSXGOshF6Xs/+6z7Iunqan5iu9yp795yQct+bC2/WnRfdvq/aGE6xgid9HTeYiDQETNrOM6zIvn/9PwCT419HAarNvQ2sxph6vZml6IqPRZtXYiv/roa9fX8NqPRgKwsYNMmYNzpUhJPup1sjpVoqu1vLx13QsQeb1u8lRYqweg95A2yhM/8+fOxcOFCzJs3Dyp/xPUJIkAIXVpJSW2Ij3cueAQCOdZByXt78tDbsIEXSf6kX8tP+LR8jMN2Fgy6pP+LVibCvwvykv37A70C7/hPzy+wdvJd4NNW7ahVth2+6Voj/nvjXdhZNhs4fylKTWqHzkhPBlo6O1ZwMbfHWlD40z/HEyGiRIu3VNfp+O7DUXTsjaDy2PEHslRLS0sLJk6cSKKHCFkEp+W2tjbU1NTgzDMrkZ7ejPLyVFHRwzCOU6H9jTBSQuwPvi8RUiH+uPeAlr0oMmY4iJ4maHBG+jFk6k91ONET7CQmuv6+qhgznhr7GMRGS9ifpzrt8D0ydSX6H0+E3jgLCc35AMsLJEtKyMmYCnA4bURodnms2KBea0Hx48dbULI2G33KczAYk9CnPAcla7M9dneWiicRE2NNptedVLzrdKbD/C0BlmVQWJWFPpcP9/msrmBElnKZPHky3n//faXXQhABRxA8ra2tqKmpQWVlJZqbm6HTpSI3lxc8YuMagPap0IHC1UgJuURLrAWuqGi/ty8Y3PwdiowZ+Ljc9mFQxWiRlX4cXfX/oo2RZnhHSEMQ8/PmuT7ugZHPw5BQ7FHtCgBoo2oxPH2VjejwZKClu2PtfwcEQQFA0dEW9phbzdi/Pd9mSKp7IQKU1yVhX8LXioyK8GT46KAJ4/BDxCYU12TYHKeEAAtWZLWzP/DAA3jrrbdwwQUXoHfv3g7FzStXrlRsgf6G2tk7J0JKq7W1FQ0NDWhpaQHg2JburI1cGNcQDIitUavlZ3NJbU8WjAzHjOELnd0htCCL3dsbLm3+Bu9V3OSw3aRKQV/dz2CZ0HonGkwwTHvXXl4esORpM85O2ou0uFKU1Oqw99+BGHX+dvxvcq7XQptl+Rt+UzIDw9NXuT1+N9YDgCTTwl3G+6DteT16jeDTNb5s33blmwPwgsudJ5GSeGIaGCyjM7zBpz4+OS7+wjMMg507d3p6yaCBhE/nQqrgsUaqmV8gsV9jZaX7d+4CwkNs6VKgrQ145BHXx6eltTs9KyV8RjTtwDuVtzlsL1QZMEC3FxwTZF/wEEOlAm65BRD6VA7nfYHuNY9Dr22vVSmqSkdURCMSoqoViTCyLFDTqEVCTLXbY/cn5wGAJNNCay8cqUaH1udIFQRSfHMA+Ny92J5QEDRS8anwCWVI+HQO5Aiejoow5kKqGNFqgUcf5c975BH3DtDPPguMHKmMgeEVTV/ijcrbHbYfUXfBpanfkODxE9biNwun3ZPtH+gcPE5veYt1RAaAS9NCsejN7vUbPBptIXWUg8VAUUIkCUCnESL+RurzOyT/ijQ3N6NPnz5gGAb7O3q7BKEo1jU81dXVqKqqQktLC1JTU0NS9ACee+xoNLzYefhhaWMvEhK8NzC8qvFzFBkzHETPX2Fnw5B+CkN135Lo8SPC93HFcjN61D4O0WJjH4seV8XJ6nC1R3UsAlKLjIelrUL+cw9JrgXypDZJmII+eNJN6DNKfN4X4Vskt7OPGzcOb7zxBuLj4zFunOuQ3ObNvqmMl8pDDz0Eg8GAX3/9NaDrIIIH6whPfX09WltbAYRmhMceT/19SkuBJUukH793Ly985KS3xjRuwctV9zps3x9+Aa5K/tz/LWqEBY4DusTuRXp8YMzrxIqT7VNC/PT0TQ5RGbFjgdPdTmszkR5/ymV6muMYDElZCWfdZYLzsbl1DNThasnt6qFmBNhRkSx8tFqtxahQqw3eScZbt27Fl19+iQ8//BBbt7p38GxubkZzc7Pl8xpfeLMTAaMzCx4BOf4+ngwZXbeON5/zhOsbNuGF6hkO238M748xyR+T4AkwKsaMgV334vqLPPzG+ohdtc9hSO79or42UkZaCAhRonS3oy04qGB2ud96lINU35xQMwLsqEgWPq+//joAgOM4LFy4EKmpqYiKCi4b+JKSEuTm5uLjjz9GtMQ+3CVLlmDhwoU+Xhnhbzq64FGygFrw9zGZfOcMLPX9wo0N72Fl9YMO2wsiLsHE5A8UXhUhh//0/AKLxz2K9PiSQC/FQnhcmsuUkJA+ksKgCeOQv3KmpO4xdwgRHEskyUmNj/B711Bp8vqehPd4/KeU4zh069YNp05Jt9/2BxzHYcqUKZg2bRr69+8v+bxHHnkE1dXVlo+TJ0/6cJWErxFqeFpaWlBVVYWqqiq0trZ2qBqevDy+GPnuu4H58/l/R4/mt8vB2t8nUNxa/xaKjBkOomenJgcGfWGnFz06HfDyy8DixcDUqYFZg4ox4/+uXYT/Tc5FWpyt6HEmmFmWQW1jrOR7fFt4K3afuh6m2mSP1qZ0pCThfEfXbzkI61KHq3Es+TkwEP9aCYaKZ1Y8CHOr80gS4R88Fj4qlQrdu3dHebmEuJ4CzJs3DwzDuPz466+/8MILL6C2thaPuOu9tUOj0SA+Pt7mg+h4WAueyspKVFdXdzjBA7R3RtnXy5hM/HZPxQ/L8sNLW1r4Sdv+/lLcUb8ORcYMPFtj+3u5TTMKBn0hbkl6x78LClLmzgUuuggYNQoYMMD391MxZlx81m6M7fMxLj5rN24cvB4/PdYf9136MhhG3KTTWbHxTw3SVLWpNhUXz3wdgx/ahKQ7SrA/OQ/fcu+grDbVrcOw4MGjFFKcjdvMaqfF/WLrik5IEf3aCQiT4Q/sLPB2+YSXyJrV9cwzz2Du3Ll4+eWX0bNnT6XXZMODDz6IKVOmuDyma9eu2LlzJ77//ntoNLZDCfv374+bb74Zb775pg9XSQQKIaXV0tKC+vp6tLW1Aeg4KS1rXHVGCbOHli/np6FLSXuJeeokJCi2XJfcVbcGC2qfdNj+SeQ1mJb4in8W0UGIjua/pwJ9+/KWAtXu7WxkITZMlOPsp2w54qzY+NJpY1C0dq3TgmHh5/lQwksYfDpdZZ2a2rMxCgNaxoNlGVFjv5Opq5AO3oNHqRbw9lof5/f91jQbl+qWg2XFDQftZ2lRgXPHQZaPT2JiIhoaGtDW1oaIiAiHWp+KigrFFiiVEydO2BQmFxUVYdSoUdi0aRMGDhyIzExpY3vJx6djEEqCR2DfPj6t5Q7BJdkV3nrqxMcD//d/fHeXJw/g++pexP/VOraEbYoahwcSXpC3mE6A9fc0L0/51KQQsbEeJmpdi+JsqKcYdU2x+LF+Li6dNt8iPizmfRznIH44DthVOhfDZy11ek1XDsOAo+mfmJeOHNw5G3vifCzHHJFQFqnPb1kRn1WrVsldl88444wzbD6PjeXzzmeddZZk0UMEP6EoeASktp27O85bTx0AMJv5adlffQWsXw+4+5WfWfscHqpb7rD93ahJmJuwDPHxQL/uvLgjHBG+p8L3zluErqy0uFKY6pIBjsHwQaW4qYe4J48nTXTRmjoM0yzADx/3tDz8nbWVm2pTcUj7XwyfNcGyTcxJ2Fln1smPt0ie1C4Hdx1hnnSMtbfKB9ek887k3CwVWcJn8uTJSq+DIFwiCJ7m5mZLtBEIDcEjILXt3N1xnhoWilFfz0eMli4FJk4E3nyTH3thz9zapZhVt9ph+xvRk3H8oaeRmQk8WAUUFgLvvefdmkIZ4XvqzfdOEDtXnLcd1/fbjJRY30TeVQwv0Kx9bABbkVBXVghznQlhsamISUyFudUMdbjaEkHpI4ijcqDijST82jQDl06bbxMJsUxf10rz0pGLu44wqR1jUtJn9ukxXyP29S5aq0y0rCMje2TFkSNH8Prrr+PIkSNYvXo1dDodtm7dijPOOAPnn3++0uv0G5TqCi6KiorAcRxaWlpCVvAICKMlnLWdC4NDhblYzti+ne8G8xaG4VNeGo3jw/jRmkW4t/5lh3PWxORiYfwCl+cS7dh/T+V+78TqdvyBWNrG2ZiHf5pvwqW65RCLOAFAeV0yDiW+ankgd9TUkSfpMV+vw93ssFATPz4dWbFr1y706tULe/fuxebNm1FXVwcA+PXXX/HEE0/IWzFBWFFUVITCwkI0NzejqqoKNTU1YFm2w3VpeYJ127lYVw3AT4F3V9gsx7BQDI7j63ushcvC6sdRZMxwED3/jbkXBn0hFsYvcHou4QjH8Z1cwvdUzvdOqNtJ1/q/aNa+UFd42DqOeTiFYbplYJyIHgBIiinHgJbrLaMgOmqx8KAJ45CWewz7k/OwG+uxPzkP6blH/SoyLNEyJ87T4ISIXedsrZeV6po3bx4WLVqE2bNnIy4uzrJ9xIgRePHFFxVbHNH5ECI8zc3NaGxsRFtbG9RqdciKHXtycvj0kn03lk7Hi54c92+AfWJYuKR6HiY3vO2w/bnYmVgWF2CToA7OW28BvXrx31vhe+dKMNrX7zw1Vrxuxx9Y++u4Tk25vxbDAJxVCk2uG3Iw1LR4YqjoCw7sLGhPb4lg7zzd2ZAlfA4cOID169c7bNfpdCjzdDAQQUBc8Gg0GiQmJgZ6aYoh1Y05J4dvb5br3CxEjh56yNF/RcyPxRUrqh7ETY2OxTnPxs7F6riZ0i/UCYiOBhoa5J27aFG7TcF11/FdXmIEKqVlD8sBxuosm0Jddw9bKQheN/t3FsgqFu7MNS3Wgq/Z9Acg4b1isEXL/IUs4ZOQkACj0YguXbrYbP/ll1+QkZGhyMKIzoGY4ImKigopwQOIe+rodLxAEYviqFTuW9Zd4SxyFB0NtLbyhoaueL7qfoxvdBw2vDju//Df2OnyFxbCLFsGLFwoL71XXc13vF10EWDXoGrBuhU90DBwLNStLyvkdyhAQ7nR42JhS02LjzrAghkHwScxQN5ZZ4fJqvG58cYb8fDDD6O4uBgMw4BlWXz33XeYM2cObrvtNqXXSIQgQg1PU1MTqqqqUFtbC41Gg9TUVIsVQaigtBuzVHJygM8+46MHkyYBMTF8t5Yr0fNK5d0oMmY4iJ4FcY/DoC8k0eOCqirv/Hd++on/V6zOR8WYA5rSsie/YqGNiNizcTN61M1U7PrCA3nQhHH4IWITimts31AbazJtinM7c02Ls7oqjnM9asQXjtgdBVkRn6effhrTp09HVlYWzGYzzjvvPJjNZkyaNAmPPvqo0mskQgghwtPU1ITGxkaYzWbExcUhMjIy0EvzCUq7MXtyXyFVduIE78XjinUVd+DK5u0O2+fHL8LrMbcrt7AQJiWFj9ItW8anrjx1XhZ+RsRqtC4+a3fA01sA/3NlrMnEpdPaW88skZZY15Eolm0v0nfmGySWvpLipePPmpZgqCGyXouzuiohrW1vThmo1vpgQpbwiYiIwNq1a/HYY4/h4MGDqKurQ9++fdG9e3el10eECGKCJz4+3mHESKjhzpeF44CSEv44b1Jb1oil1ZzxbsXNyGnOd9j+UPwzeCfmVmUW1AlITAR69+b/L9RovfYasGGD9Mn1lu5bzoyZk/bih/xSlNTqkBRdgWU3POSTdXtC+wNzteWB6erBK3burtI56BWzDsmxjlXLrh7I7oqF/dUBFmw1RO4En5jAFEaNhGraTwqyhI/AGWecgaysLAAA44n1J9FpEBM8Wq0WERERgV6aX1DKjVkqUkdVbCwfj0tavnfYPku7Eu9HT1RmMZ2IykpgzJj2mi2VCsjN5Setv/IKsG6d+2skJwOH875Aj9pHMSWzBFNu4bcr1Zlnzy7jdAzT/1fy8WIPTKkFzeX1KTiS9AqGzxoHc+sS5L2yGBdoViPJymTRWJOJ48krER2fhN3rN3gUTZHbAeYJwVhDJFXI5ZkehSb1PJfO050J2cLntddew3PPPYdDhw4BALp3746ZM2fizjvvVGxxRMdFTPAkJCQgPDw80EvzK0q5MUvB7agKjsOn5degX+svDrvu0z6PzdHXe7+IIECrDYxxYmlpu9u1ULCuUgEDB4oLnzBVC6Zc8iayk4/jWPmZiC1Pw5CMe8DEOR7r7n2lkEbiAEk1QOV1Sbh4+koUrdviomsKKK9PxT+xzyEmJUP0gSn1watm2tr/H65Gzv2Pw9w63yZ91aAyIbt8Fgxmz6MpDRWllp97sa8VxwFmVo3zhg2WtF57/OUi7SlSBV/iOSM7Zdu6M2QJn8cffxwrV67E/fffj4svvhgA8P3332PWrFk4ceIEnnzScSoz0TkQEzyJiYkIC/MquNhhceepIzj39u3r/b2cptU4DtvLrkSvtoMOu6YlvIRPosZ4f/MgorqaH/b66qv8576KmIjBcbz4jI0FKip4Qdu7t6M3z/yrFmHa8DVQq1ibcwHn5pXOsK6dkVr4/FvzDORERrjtmjqS9AoucSE6pD54E6IrMcAuKmI7oX0zBpknyoqmmFvN6FFzn6hgFGAYIExtxv5du2UJgGD1xQnW+WDBjqyn0csvv4y1a9fipptusmy79tpr0bt3b9x///0kfDohJHjEceWpA/CfS3FjloJDuozjsMs0HN3Nhx2OnZqwFlujrvL+pkFKZSVw1138fDBPi4y9pbQUuOee9s91OuDKK4G33+a/3/OvWoR7cxzHfcitFvDkZ4fjgIr6ZEtxsrPholLrQBqqytBmViNM7bpbSqUSn+8FeB9NObCzAH3iTO5eOr9emTU+weoiHYzzwToCsv7ctra2on///g7b+/XrZ5mlRHQOhLb0xsZGVFRUoK6uDlqtFqmpqZ1e9AgInjpio2O0WuXuI6TLGI7F7tLBKCrOdBA9UxJfh0Ff2CFEj1YrXwx88AHfxl9bq+ya5GAy8aLn1lsBfVoLpg3n3Qn9XRbJcQAHBocSX7UREHJHLOzZuBmD2m6AWiWtRVyIihzYWWCz/cDOAhgSTjmNVlmiKV/li+73RGzIrfGRel4gfHGktvwT7cgSPrfeeiteftnxHcurr76Km2++2etFEcGPmOBJTExEamoq1Orgf3fBsrxh3Pbt/L8s6/4cbxHr7qmpUc7Lp+8FLH4u64/C4ixkm4/b7Ls58W0Y9IX4MvIK72/kJyZO9D5N5Y/vqzuE17B9O/Ds1NehVrF+Fz0AUFaX6vRBKKSdBk+6CX1GDXdbp2Ltm+Ppa7EXKlKFS5fCGyxzvKyRKjZMtamyfWt6jRiKoqpMPooiQqB9cYJhPlhHwqvi5i+//BKDBg0CAOzduxcnTpzAbbfdhtmzZ1uOW7lypferJIIG65RWQ0MDWJZFcnIyVEqa0PgAe1+bjz6S7qKsxL196uVjNiPtggugrqx02HVj0gZ8o7lUxkUDz3uOkzI6BNaztEpqddj770AAwKReq3Bp/Gq/r0coUtZOPoVBkcp0U3oznsJeqEivE6pwqBMCrOpctOJRI+H37pD2vxgsM+XTEVJKgZ4P1pGQJXwOHjyICy+8EABw5MgRAEBKSgpSUlJw8GB7ASW1uIcOguBpbGxEY2MjWJZFSkpKwL7HUudeAdJ8bQQXZeuOHKXYt89HXj5tbUg//3yo6uocdt151kZ80SCvgyVYkOp/E0yIzdKqqE9ETEQtNOH+LwOwLlJWSvQA8mpZnBXauivQFVAx4nVCNqKEE/cT2lU6F8NnTfB4zdZ4Ww9FBA8Mx/mz5yH4qampgVarRXV1NeLFijI6ERzHwWg0BpXgATybeyXV1wZo77D69FPlXJTz8oCnnpL2EF+8GBg1SsJFW1uh79YNjEg9XdlHH6FlwAAbYVhRAaxY4fnaCT7d9tln/KgPd1jP0rJ++No758pFznUKq7J88lDevz0ffcqlv0MQBJizVJvFI4fjJP3u7U/Oc4huCOaC1qLEVJuKQwkvYfDE8ZLX6o5gcm4mbJH6/JYlfEwmE1JTxaegHThwAL169fL0kkEDCZ/gFTyAcyEjLMs6YtPaCvznP/wMJU9Ys0YZF2VPRJeU+7JNLcg8q4voPtMnn6DVycksywsqkUxYh8fTafOe8uCDvPh57TW+QNnZ9HUVY8YP8wciXWv06SwtV+KHj6hkoDj7DTRXlfr0oWxuNaNkbbbTKI39OqUIsD0bN6NHZa6NqaEzdmM9Bk+6yWE7iZLOjdTnt6xUV69evfDaa6/h6quvttm+fPlyPPbYY2hsbJRzWSLABLPgATyrldm1C3j6ac9FD6CMi7JbM0Er3Hr5NDXBcNZZoru2PvkFLph6gdt7TJjQ7msTCkRH8yJEruiRKpgSEtpdmKdMAa66SlxADuy6N6CztKzHSQy6aqTP7yel5iW/fAE0yd0luwUPmjAO+77QIqnqMrf3d1bQTHUuhBRkCZ/Zs2fj+uuvx+23346VK1eioqICt912Gw4cOID17qYhEkGHteBpaGhAY2MjOI5zGtULFFLnXr32Gv+Ql/tQVMJF2d1a7RH18mlshKFbN9Hjr0jZjt8jegKvAEu7Oq9L8mRuV0fCGwPwBx/k29ylCMGqKl7EqlSAWmXG3MntM7T2/jsQLMc/zLsZHL2SfAFzus6FAwO1ldgIRJ2Ju5qXnPs9X0ufy4eTIR/hc2QJn4ceegiXX345br31VvTu3RsVFRUYOHAgfvvtN6Snpyu9RsJHdBTBIyA1ErNhgzzRo6SLstS1arXAo4/aChemoQF6JwN/R6R8jb/Cz+U/cdMR5mmqrSPhjSlhQgLQrRsQEQG0tLg+dsUKPsX14A1f4PKkxzEl02iZoVVUpceynXOw45+RqGoxyF+Qh/DfZw67ap9DeFxaQOcvSZmcLhUhTVXVNB567aqg7Z4KdTpDulB2O3u3bt3Qs2dPfPjhhwCAiRMnkujpILAsi+LiYrAsa0lpBbPgEZAaiZHTDSRk8pRyUU5Kknbc4sXAaUcIMPX10PfoIXrcsJQ8HAp33OesI8yTVFtnY8kS57U6YvTTfYGbz+SLlq1J1xZjxXVzsNH4CgaN/w+Kt+mhiy122ZmkJFz9cQy+e6Zf7uUKJdJLNlPPTz9GzKwKKrSbI1L3lO8JtunzvkLWn/jvvvsOvXv3xqFDh/Dbb7/h5Zdfxv3334+JEyeiMhQrKEMElmVRVFSEoqIi1NfXo6KiAg0NDUhJSQl60QO0z71yVm7EMOLuyFLQ6XzTyu6OAwcAprYWhowMUdEzJPUbGPSFoqLHmh07bI0YPU21dSY8ET0qxoynxj4O+04tfh8vcIbFLkRmhgHHkl8EGDg1uVOavvHrYG6V5poczAgdXelaW18gFWMGywH5xTPJkM8POPs+pMcXnvZPcjSP7KjIEj4jRozAxIkTsWfPHpx77rm488478csvv+DEiRMduqMrVLEWPHV1dR1O8AgIc68A54Mcb3Js9BAlIYEfZLl4Md9N9emnyoqeCveNKYhnq/HEggzozznHYd/Fqd/BoC/Ev2HiRc32fPAB/3pGj+ZTXEoUaCtFENTFy0YoWnY6ToHhkB5fhAM7C5yODnAWdROb2+ZJhE4bXYNdLz8lus/casb+7fnYvX4D9m/PD0qBZG41Y98XO9Cj8k6ICksVAI5B98gPQzLdEkxYO3GLzUsDJ/gnBd/PkRxkpbq+/PJLDBs2zGbbWWedhe+++w6LFy9WZGGE95jNZpSUlIBlWTQ0NKCpqalDpLRcIcy9EvPxmTOHr3X56CPn09ABIDER+OIL7wpk3eEqLZfAVuKPkp6i+0r27MEXf2Th+Hx59zWZeHE4erS8832BTgfMmgU884y8Ljs5CF1f3iK1aFkw9LOveWkuP4Sz1a/CkFBoOZZlGTCM46gHodPME7+eYUlPYvf7PTFw3HWWugzhnn2Ee/ohXeFpXYiQUumXcAqIdX7dQE0972wE6/R5X+GR8LnqqquwYcMGi+h55plnMG3aNCQkJAAAKisrsWHDBjz22GOKL5SQTigKHgGW5dNZ99/PP0QTEtoLkoXaHGfT0IWHyf/9n29FD9CelrMWZ0nmchws7S16/Fev/YDzr+QjBSlepKiE1/vZZ/KvoRTx8XwEbupU/nvz66984bk/WLGCv+cPP/Bdfp6SkmICwKCoUlrRsnV7tX3Ni7l1vkUItdaWYFjcLKfX8TQ6plZxuLhtAipeT0af2NNzH5IdRX96fCHSRcY9KIGndSEWs0Kt9PCWv6eedzaCdfq8r/BI+Gzfvh3Nzc2Wz59++mnccMMNFuHT1taGv//+W9EFEtJpa2tDaWlpSAoewLVjs3VBsruokJSUlicjMcQQ0nJz5wIpZhN+K+0jetyFup9QrNZj6lHg1Hb+Xr1782t1FbXyJQwDCD8y3tQJ1dTwLePduvFfc4MCjU86HdDczF9b7GsjdOb168d/D6Sm/IT5Wt0Mh2GqT8XuQxejrj4OeeU5KKrSIz1evGhZSnu1tRDavV6a8hNem1QhlBRjO+zK/jyVigPLMg7jHrzFmYhxJrQsKRWt+GgJZwRi6nlnQuq8tFD5PngkfOxNnmnaRXDQ2toKk8kElmVRX1+P5ubmkBI8gPPWbGcztnJy+LSXHPHiyUgMV4w8txhFRnE35Qt0v8Ck1lk+t45K6HTAlVfybdS+diZ2hlBLJfzrDUK7/en3R7KZOpWvY9q1y3VEz7ozT0on4H96foHF4x5FenyJZVtRlR6Pffwk1n11J+57/UVsmqnMcEqpDxjr16PUcUqnK1yJGGdCy9PhpiwLGGuyyLdHIZylJN3NSws1/6TgHqlNuKS1tRVFRUUoLi5GbW0tKioq0NTU1OGKlt3hzrEZ4B+uQkeTgErFv/MfNao9AuAOQWDZRzoEgZWX5/4aqqIiGDIykC4yQqKX7lcY9IU2oscek4kXPbfe2h558ReJibYi0tuUoNBuv28fcEreMG8LAwbw30Mhomf/tRHrzHPXCThx8HtYO/ku6OJKbLanxxdj7eS7cF3/zfipZBzeOeZYtGysyXQ6e8oZvUYMRVFVptPOL9bDSI8cGsqNihQ/H9hZAEOC+ER0oF1oHdhZYHNvjzgtLKmw2Xv2bNyMkrXZ6FOeg8GYhD7lOShZm409GzdbnLjFuhKtBX6ofB88ivgwDOMwuiAYRhl0NlpaWlBWVgaz2YyGhoaAR3i8TQu5Q6pjs8fTze2QIrCWLRM3CwQA9alTSBs4UPTa56cdQKVKmrmPUNy6fTuwZQvwv//Jq1PxlJiY9qLvvDxloj0CDz8s/2EuZiwpNaInpBztI0QpKSYwYPHQyGVw1snCsgyenzITujvHIELjuVGfs3fXrkY9MIzvw3vN5Yf4B6CXXi1y6kI8iXgBwK6KhbIcoAlbpKYkO8v0eY9TXVOmTIFGowEANDU1Ydq0aYiJiQEAm/ofQnmCTfAAyqWFXCG1TsPbFm4p3jelpbwIyc1t36Y+fhxpgweLHr9t/e+448EEj9ciiLnffgP69/eP8AkLA9TqdgGoJHJMJQU4ju8KExM1UoSudc0Xy/JFy42NURjY5VfotcVOz1OpOGQmnsT+fD415IlRn7uCX7EHTHl9ClLjTJKuLweWZVDRkIRhSQvgYMYoo/hZTl1Ie0rllMs3RywHFFdn4tJpMtsbOyi+cE32JCWppBN3MOOR8Jk8ebLN57fccovDMbfddpt3KyIcsBc8TU1NABDwdJazupvSUl74PPssMFKBeYlSHZu9nbElVTitWcMX7F525r9IGyqe8zb++Se4+HgYt3u/Jqku0N5SXc2LPyD4zA9XruRFmSCmPY0y9uxpwhtvMDhyJArV1dFITVUhtlyayPA0PSP13bX9A6aOK0QqHP+mukJqETSfruDAHya9Jsca+4fyecMGo2idZ3UhthEvTvR7xqf7GJxIXQ1DiD1wXeEr12RPW9U7w6BXj4TP66+/7qt1ECIIRcvBJngAaSMRHnmEn5B+mfthyy4R6jScdTkpNWNLqnDq1nYYN98yTHSf8e+/wcW2G5N4K8ZSUvwrQsrKgN9/99/9pGJdxA5IjzKaTCYwDIOoqChER0dDp+OftAaDAfu3n6d4J4unBb/WD5j92/M9SgMBAMupwDAsOLu0mb0XkLEmE/+Y70RO8hNOr+Wq+Fn0obwuE/8034R0ZrlHhd/OIl6WtVZnhVxqxR2edsd5QmdrVZcCFTcHIULRstFotBQtNzc3IzU1NShEDyAtLcSywLx50gqCXSHFsVmJGVuCwHLG2a1/ociYgW9MjqLHeOgQigoLbUSP9TU9rW9hGCAtjT/fX6Z/AHDkCF9XFGwIxn6LFvE/C+6Kz00mE8rKyhAVFYWkpCTExsZCpVLBYDDAcLqv3m2hMcugsCoLvUZI72SRU/Ar4G49YqhVLHZVLHQsvK7ORF75QuzGesu4B02y+OBbe+wfgK5GGVyqW45vSud4XPg9aMI4pOUew/7kPHzLvYNdtc/hO+6dTjmawteuyVKFe6i0qktB9pBSQnmElFZbWxsaGxvR1NQEjUYTNGLHGk/qaZxND/cEJbx53GHtvWPNea2/4+uyK0TPKTp8GIiK4lMv+xxTL86Ka6UgiDlv28A9Yd06/91LDs6msgsRjrVrTejZk4EmIgJlfx5ERa0JEfE6DBx9nUP6xl2hsZRWdfv0T11ZISBBt4i9u3a1HldokrsjbcIxh7oM+zSRnJocKRGs7pr3kHLHEezftdujupDOkFKRgq9dkztbq7oUQk74fP7553jyySfx22+/ITIyEsOGDcPHH38c6GW5xFrwCEXLwSp4BDxJ4SjRcQV4583jyT3uvpuv4+nd8iu2lV8lelyX9CN4/tVI9ItyX+DtTLSlpfGt9lu2OD7QrYetetsGbk+gvIGEIbIajbLpO4ZhkZxcDo5jcOpUNP76Kh/DYp/AOfFGIBxAo/NaCW86WXa/txHdq+9Fn7jT7wLKgbK6FCDO/Zo1CeKhRXdpIDGik/WSRIS7ByDHARX1yTYPQMkP5V277dyq+XZ5JYt0QxVfp6KUEPihRkgJnw8//BC5ubl4+umnMWLECLS1teHgwYOBXpZThBoea8ETGRkZ1IJHQGwkgyuUGpoptZPHG+658Gc8YbxGdF92+r9oVWksNUVSjRWdibZdu4C33nK8T00Nf/6tt4rv94b4eOeRE18hpPoefZT/Ouzbx7e4e9PtFRbWhoSESnAcg8bGaDQ2RmPUeVsxQX83POlaktPJkv/cQximWwbGTuQkxXr/gy6sZ99X+ehy6gYkRFeIFwF7+E5deADqW653Og8sMaYcP3y8xfI1kvNQ9lWRbqjiD9fkztSqLgWGCxH75ba2NmRnZ2PhwoWYOnWq7OvU1NRAq9Wiuroa8dZvuxVEiPAIlJeXQ6PRIDbWxbS+IMQTr5c1a3wvWLwl/McfkTp2rMP2VoThrPTDaGPCLQ+LpUv5B/jo0c7Fn1B0/emn4lEplnV9PsCfZ2/M6A3COIqFC4GffvKuTZ5hgJtvBt55x/2xaWmOKUlBNAKeRaAiIxsRG1sHjlOhsTEKTU1R/DtZxozv5w1GRpJ4jY0gFNJzj3oVfdj9/iZc3DYBgHxvol3G+6Dteb2kYZ4DWsYDHETfqXtqoGhuNaPq9TQkxZSLrt3+a7R/ez76lLvPI+9PzkOfUcPb12tXryJ3vZ0Bc6sZJWuz3aaivP25Fe6ldLt8MCH1+R0yxc0///wzCgsLoVKp0LdvX+j1evznP/9xG/Fpbm5GTU2NzYevaGlpQVFRkY3oAYDw8PAOJ3oA/iH27LOuU03WRbrBSsT338OQkeEgelojotDrnOM4U38cbQxvYWztDuyJsaIYUgvElYTj+HuqVEDXrvKvo1Lx0aiZM/kUnn1RuE7HpwwXL+ZF76efOtZhOXNgdkZ8fDVSUkyIiWlAQ0MMKiqS0NAQjfr6GBiNBnTTHkFWsrzCYqmYW83oUXUvGMY7d+Vh+hdtnHOdMWjCOPwQoYxrNMCnrpJjxUUP4Pg18qQA3NdFuqGKP12ThZTo4Ek3WVrXOyMhk+r6999/AQALFizAypUrkZ2djRUrVmD48OH4559/kOTEDGXJkiVYuHChz9fHsqyN4NFqtYiJiUFlZaVPxZavGTmSb1mfN89xn5IdV74goqAAKTfe6LDdnJiI91f/iqUr1Ci3EiaJicDs2e0PcG+NFZVK/8lBSLfJheP4sRq9enlXe2V9rrNJ6vykdIDjVGhoiEFjYxQ4jkFlZRKamiIB8D9r+gTft+0e2FmAPl6YDNqnmKS0KytpKif1tdeVFVpqdJrNuUhnFritD9m/Pd+nRbqhDKWi/EvQC5958+bh2WefdXnMn3/+Cfb0W+P58+fj+uuvB8D7DmVmZmLjxo24++67Rc995JFHMHv2bMvnNTU1yMrKUmj17ahOPwUSEhIQHR2t+PUDyWWX8e/6fdlxpSSavDwki5hvmtPSUPLjj8j7Ri1at1NVxQs8IeLjibGiveFe795ARYX3r0UugjhJTAQqKz0/X3iAW3fsyU1lCufaC0FrwcPX8ESC4xiUlqYBUMNsFTjIzASuvM73bbueiCaWtRV/YnU1UqemK9UBJbWe5Jy6WUhhTgu8ZKC8Lpn/b2z7yfYPZfKL8Y7O4pocDAS98HnwwQcxZcoUl8d07doVRiP/y3TeeedZtms0GnTt2hUnTpxweq5Go7GM4PA1gn9IKOKPjis5WAuO845+hYFPTXE4pu3MM1H67beASiVpXtfTTwNDhkg3VqyqcqzlUbJ2JyICaGmRdqy12aNKBfznP8D69fLu682MNNZsxomf9qKlphQR8Tqc0X8gUlL4P/C2gifKEuExGvUQesU/+IBPkRmNgF4PDB0KgPV9265U4VDfHIXqxiQYEgot29yll3atewFD7rjfp+kH963N/DqTYm2jWonRFQDDIa98ITTJ3UUfyv4o0g11qMXfPwS98JFq2tevXz9oNBr8/fffGDJkCAC+a+rYsWM488wzfb1MAv7puPIEoc38whNbsa7yTof9rd26wZSfb/NEklJ3U1kJXHUV8H//59yjR7jkqFF8lMheGLkTPZ60nIeHSxM+YqnHYcPkCx8Bdyk7+2hXXNUXOKf+cQy2ajcv/lyPKM0c9OgxEuXlthEeo7H9DYNaDWzYAEyYIHIjte/bdgXhoNeecipkOA5obI1Cyh3/Yv+u3ag++CGG6V90e+1hcbNQtHaFT7ufXLc2t/+MOPPs6aH+H9IniBfZkl8M0VEIwsoLecTHx2PatGl44okn8OWXX+Lvv//GPffcAwCYIPpXkghl8vKAgvs/wf5fMxxEz4Hwnnj37VMw7drl8DZcat1NZWV7R5JYga5OByxZAmzbJs8zR6fjC8elmBfW10u/ppCmY1m+pby0lE93eYOrlF9eHh/tuvtuYP584OMXvsClqrugi7NNd+jiijEiYg6mXvEdKiqS0NgYhaKiDBvRAwDvvedE9JxG6WJge9ThavxtznVZ2MwwQEpsBf447W2j7Xm95OunxxdiQMt4lwXP3uLsa1Ren+qyaNtdcbg/i3Q7AoKX0e71G7B/ez4VdQcRQR/x8YRly5YhLCwMt956KxobGzFw4EDs3LkTid7+ZSc6FJoPP8LND9yHm+227wvvi2uSPwWjYqBbAQwb7piK87Tgd/lyvmNJLM0nJXokxoMPAhMn8mv79VfvIzJaLfDMM3w0TqUSN1yUg7sZafYeRyrGjKfGPg7Rrh+GA8sxuDxpMSKfuxuzH7R9OGZlAatWAeMk6BZf10p4OvrBXSTEGqk1P94i9jWqlzgk1VWNDhXp8pCXUXATUsInPDwcy5cvx/LlywO9FCIARH3wARJnzXLYvidiIMYlfWh5K2tdmyIIFOuiY1d1O9bY17jYp/nkdm0lJSmbiqqubh+f4cxwUQyt1rXRIcc579gTq5Ua2HUvDC46r1QMh/T4InTXFuDYseEoKLCt4VF7oAF8VSthbjWjtbZEkjuzUMvi6SgKf3U/yR2S6q5Gp7MX6fpy4CihDCElfIjOSfT69UgQcVL8JmIobkx+z+l5u3YBjz3m2Il25ZV8q7ZUnAkcue3i1ue5K6CWSlmZuBixR2jZT0kBnnA+yBsAL4yGiQ+pF412dTMclrTWhnIj1Gpg+PB2w7W974sbrvnTkE14Fz/MzSgJsVoWOaMo/N39pGSNTmct0pUy28zX0TzCPSFT40N0PqLfeAOGjAwH0fOVZiQM+kKXogfgIyliU77ffps355M6HNSZwJEzmV2lsp3GrlLxQsRZ15hUUlKkF27rdPx93R1bXe3cnNFaDKakmJCSYkJpbZqktQoRhT0bN6NkbTb6lOdgMCY5GP652+8MObUXziaUOxatO69lESaS76p9zu39AP93P1GNjvcc2FkAQ4JvTTQJ7yHhQ3Q4Yv73P17wzJ9vs/2zyKtg0BdicpL74VbO2uyFB9n27cDnn7su/HXnSi1MZheOlQLL8l1geXn853l5wMqV4sfqdHztjitxJayxd2/eIFAKZWXemzOmpLQLHpZVo64uFl/9OhJFVXpJLsDOhIZQ/Jv/3EMu9zsTP3LEkitHYvuvu7sianW4GkPuuB/ldclOI28cB5TVJaPXCP93P/m6ODzUIS+jjgEJH6LDEPvSSzBkZEBrl4P5KHIsDPpC3JW41u01hAeVq3ZyoXbn4EG+ZV2s00WqK7WnYxkEli8Hduzg63GcRV5mz+bNI52JK6ElvkcP3q9H6lwuXrRIP9Yek8mErCwTEhLCUFcXh4qKRDQ1RaGw6Azc9/qLbiMKANyOPhiSstLlfrHRCO7ElDPx4+5dvMCu2ueQnntUEXHgxTQMrxEiU/uT87Ab67E/OU+x1xXqSI3SkZdRYCHhQwQ9satXw5CRgfjFi222nxg5EQZ9IaYn/lfytXQ6YNIkaceWlTkXLtat4a5gWX4a+v33891aTz3F/+sKQXg984zzehyG4SNBLOt8jYIQKiiwTZ+5QujScpemE4t2mUwmmEwmqNVqxMfH4a67EtDcHIni4va29I9+GofxqzahsNJ5REFKuiBMbfYonSBEbRgZc6SkvjsPj0uTlAZyNy+LYXiH5ECmQ2imkzw8mW1GBA4qbiaClrgVKxAnkuepv+UWVD/7LHZsBzDf8Tx7pk7lB3Jat5lL6ZRKSuK9blpa+GnmAD9mQqortVjbuE7HR2mk4GqUhH1HmbVz9q5d/OuT4wxdV8efP2wYcN11/IBRe+yjXSYT7/IbFhaGqKgoaDQaMAyDW24xIDoamDEDOGUVZPmpZBz2po9Bebx4149SaQDr63zzymLkJMubI6W0IzGlQ0IX1waRyphoEt5DwocIOuKWLEHci45Ot3W3346aRYssn0tNxwwYYNtqLmXURHw839VkL1rmzhV3p7Z3Jxbmetlf32Tyvj3dGvsaG5YFPvtM/vUaGvjX6KqVXZjB1rOnCSaTo+CxHs0ybhwwZgxEWtPVAIaLXl+q0HCHdZH0sCQ3LWqnERMbSjsS02iH0Ia8jIIfEj5E0BD/5JOIFQkx1N19N2oef9xhu9RZWfbFx0LRsbNRExwn/tA3mfhzli61NSw8cQL46CPHWVyu5n0phSD+lDIlFHAmeu6+GxgzxgSG4QWPJiICJQf2w1RrQkL6eaIhfKE1XSqeGP6JwbKAsSYLvSYMtWovlnaumNhQ+l08jXYIfTq7l1Gww3Cc0n+KOzY1NTXQarWorq5GfHy8z+9XWVmJkpISv9wrWIl/7DHErlvnsL32vvtQ+8gjLs8VDPkA8VlZrupwnKWimpudP/iFaJBGo5zIcEZ8PFBb61rUffopn5qSakroDampJiQmAi+8EIaYmCic3L0DZ9c9gfT49ihJUZUy7rQWEzgOHosflgN+iPgQgyaMw/7t+ehT7qYQ6/Q5xuospOeKz6ES1mT/Lr6wKkvWu3hnr08QUtRBpQz+9HkiAo/U5zcJHztI+PgP7UMPIebddx221zz4IOpmz5Z8HTEBk5bGp2OkFB9bp6hYFjg94i3g3H038Oqr/P+dibphwxwnvyuNMC3dbA5DQ0M0Vq+OQELtVgzBXbDvrFLywS0mNKSQV74QOffzEcLd6zdgMNxXs3McsPe0WHKFkg9SJYUU4YjY11cpYR5ISMw5h4SPTEj4+J6EmTMRvXGjw/aahx9G3QMPOGy3FydihcVSjpHC9u38MM1AYh/NcSXq9u3jBZIvsBY89fXRaGmJAMBg+bI0DK/ORrpWvPNKSNW4ip5IxfqPvCZBB/2xyUiPLxJPEXFAcXUm0nKPWe4rNeJjLZb8CT3EfIMlouZDYR4IQlXMKYXU5zfV+BB+I+HeexG9ZYvD9upHH0W9kzCLs3TU3Lm20RyVSrzo2FPkjpnwBCFdVl0tXmMEtHdMWXdriYk6ufPAXOEoeDQAYGlJj6jKhyFRXoeUp9iPPtiz8XmXtTYnUlfDYCUc3NfT8EWnl04LjNrtrKMdfEmojo2gGWDKQT4+hM9JzM2FISPDQfRUP/kkigoLXYoeMQM/ochYcDe2hmX5KMj27fy/nrZ0yxkz4QnCdR99lBd0UvyBBFE3alT7hHUBJYWa4LRsNoehpiYelZWJaGnRwGg0wGg0gGH4KenpLgaNWuOLdmxPnYXdj2FgcDJ1dYd6ABKuCcWxEa7cw935UBGOUMSH8BlJt96KyJ07HbZXPf00GiZPdnmuq4GaHMcLiOXL+WiIIASkRodc4arjSwmEVnBhPa6iOVKQMsQ0Ph5YsoS/ruBDVFUFPP00H3USIjxtbWHguBhUV0eAYdojPEC7YFu1CmgtPAQku1+br9qxPe2YofbizkUo+iQd2FmAPi5q3ZSMsnYGSPgQipM8cSI0337rsL1q2TI0SLRNdjdQ097AT4gOifnmCC3o1uLHVU2Q4ISsZHv41Km8n5C9sPE2ReeuNR/gJ9APHOh4bq9eJvz5J/DHH2EAotG/vwb9+gE//GDAjBm2x2Zm8qJnzDVmlKxdaxGfYnAcUFRt246tdC2Lpykiai/uPISiT1IoirlAQsKHUAaOQ/L110Ozd6/DrsrnnkPjDTd4dDlPhmR6Gh2SEhkSamvefx9YscKjpdsgFCrffbfnxdZSC7adCTX76JKA4LQcERGOiy+OxrBhEQBgMR7MzHRmOgjs3+76nafwmv8x51pEhVCQaTmvHCha6/+CTKqn6RyEok9SKIq5QELCh/AOjkPK1Vcj4tdfHXZVvvgiGq+7TtZlPRmS6Ul0qKamfainNWKRIZUKmDgRePtteZEfqYNMxfA0beeuCBpoFzzh4eGIjo5GRISt4LHGmemg1HeUTCs/b4MKMgl/E4pjI0JRzAUSKm4m5MFxSL3sMhgyMx1ET8Urr6CosFC26AE8G5IpNTpUWgpYTbywQYgWLV9uWxC9axdvaOiKu+8Gnn2WX681UgeZ2iOnqBtwXgQtDA8NDw9HQkICEhISEBERAYPBICp6XCH1HeX5Me+ipamFCjKJgOBpEXyw475InxdzVKQvDfLxsYN8fNzAcdANHYqwo0cddlW89hqarrxSsVtJdWWW6mVz7bXAJ5+4P27NGtd1QwJaLd+dJQgbuV5CwnmlpUB5ObBuHR+ZEsPa48fdta0jPDExMQgPDwcgHuGRirnVjMp1eqTEmdweu6v2OQyLm+X2uP3JeZSCInxCqPkkkemla8jHh1AWloXu4osRdsqxvqP8zTfRLHXkuAdIrV2RMrMrNRXIz5d2X3d1QwIaDZ9eEpBTqOzpjC37om4xfCF4BNThahysvxnD41a5P7juCBDn/jAqyCR8RajVdVGRvjKQ8CFcYzYj7cILoRbJJ5W/+y6aPZk+KQMptStSOpuuu46P5EhBSt0QwO93JUDcRYDcRZRcIZbeay9ajkB0dLSigseahPPHAOWr3B8Ye5ak61FBJkFIJ9TEXCAg4UOIYzYjvVcvqESmdZa9/z5ahgzx21KkRFLcRYdaWqTdi2GA3r0BEfshUewFiCB2du0CvviC98uxXotQnCwlouQK6+JvX0Z4xGgvtDwlmm4TCi0vvuNeFK1b0akKMkMttUIQoQgJH8KWtjakn302VE1NDrvKPvwQLYMGBWBR0nAVHdq3T9o1OA747TfPusoE3KWtrDvH4uPld4rpdPzrEgSPRqNBVFSUTwSPswd5e9eM+HTxk6mrkBEZEXLdNa4IlrZ9giBcQ8KH4Glpgb5rVzAiIYiyjz9Gy0UXBWBRnuMsOtS3Ly82nBUNW1NWBlx+ubS6IZblx2OcOMFPUncVwbHuHLvvPmmvR4z77jOhvNz3KS13D3IpbsidxTWZ2vYJouNAwqez09wMQ9euortMn32G1r59/bwg36BSATfdJK3OJyXFfd0Qx/Ft7k7GjLmkpMQ2BSYVnc6E6dOBSy5RLsLjLKIj9UEupdAy1AsyQ3UoJkGEKiR8OitNTTCcJV58atq6Fa29e/t5Qb5n6lTgvff4+VRiWKeRAOd1Q8JkdWfXkYJWy99LSrpLmKXV1KSBTheN+Hj+19bbCI+ziM6xpJXIrpgt+UEupdAylAsyaY4SQXQsSPh0MpjGRui7dRPdV/rll2g7/3w/r8h/qFS8746Yc7Mzl2X7uqGkJOCJJ7xfS3V1e0TJWXpMEDwtLRo0NESjrS0MFRW84DG3mrF/e77sIlqXER3zDVAlOD+XHuS20BwlguhYkPDpJDD19dD36CG6r3TnTrSdfbafVxQYcnL4CI7UuVaAbd3Qvn3KDC5NSHAeUWoXPJGor4+C2cz/mhqNBnTp4n0RrfvUDAAnjtnWBPOD3Gw2o7W11S/3itJloKnqTPfHJWSgSaRpgCAIaYSHh0Ot9j5dTMInxGHq6qB3ImpKd+1Cm5PoT7Aj1yUZ4AXHkCHApk3AyZNAVhYwfjxwumTGJVLHY7hDGG8hRJT27QOeecaE+npxwcMw/DojSjdjQKt3RbTuUzPSXkMw+u9wHIfi4mJUySmikonGkILD8WuhYsyiI1Y4DjBzamhiU3BUxPGcIAjpJCQkID09HYyzeUYSIOETojA1NdCfe67ovpKCApidFDR3BFwN8HRndujs/Lffdj4A1Bqpbe6uEGaMCZSXm5CdDdx9dyQef5wXPBzHCx6gPQ23YrkZ2WXeF9FKjdSwHBzuAwS3/44genQ6HaKjo7364+gJ9VXJiOZOArANlgnytIHJQkyC1i9rIYhQhOM4NDQ0oPT0H269Xv4bLxI+IQZTVQW9kzqdkt27YT7TfUg+mHHmdmwy8cJFq7UtOrafaO7qfPvp7GK4G4/hDoZpryMSfHgiIyMRHR2NK65QIzwcmDfPAOvJIJmZwKpVQNeYAhjavC+ijU7WA+US1gp0KP8ds9lsET3Jycl+vXdkeiTqKiIQ0XYSEep2t8wWcwRawrKQnJTo1/UQRCgSFRUFACgtLYVOp5Od9iLhEyIwFRXQ9+oluq9k716YMzP9vCLXyElVuXI7FrbZd1pZC5phw9yfv3gx8OefvEDp3992yjkgrc397ruB2lpg21Yzzk7ai7S4UpTU6nC0biAenKNGz54mlJUxFsEj/PIaDAbcfDNw441AQQFgNAJ6PTB0KKBWA7vXK1NE2+687NpR+XjySmSXz+ow/jtCTU90dHRA7h+blAiOTUBDbR3YthaowiIQlRiLCLGwGUEQshB+v1tbW0n4dFZUZWVIv+AC0X3FP/4I1kdjC7zBVarKVbRFyvwseziOFyTLlwOxse7Pr6rip6MDwGuvOU5gB6QNTz2c9wXmdHsc6fHtIqS0Lg2/tTyEyMirHQSPNWo1IDYCTWqkxl3tja3zsvOIzuAJ42Buva7D+e/4K70lem8Vg2ithMmsBEHIQonfb4bj5E4LCk2kjrVXisrKSpSUlHh8L1VpKdKdmAsW79sHNj1dieUpjrNUk/Cz7CrVtH07MH++/HuPHAns2CHv3GXLHNflLGp1OO8LDMFdAGxrcViO/+RbvIpuOVd57MNjbjWjZG2220hNeu5RSa3tQneYdUSnsCorIBEdJWZcNTU14ejRo+jSpQsiIyN9tFKCIAKJq99zqc9vivh0MFRGI9L79xfdV7x/P9jUVD+vSDruUlVCZGbYMPG0l7eFxd9/L//cZcsc12U/HoM1m/Hv97vRu3EumCjOocNHxXBgOQZnVT+JtNTbPV6D1EiN1IhMsDgq04yr0GXBggX4+OOPsX///kAvhSAsSGxc7Rj8888/GDNmDFJSUhAfH48hQ4YgLy8v0MtSBHVhIQwZGaKip/jAARQVFga16AHcp6o4jh/n8Msv4vuFwmK5kc6GBnnnAfy6na0L4KM8YZ8PxJDmG5EQXeV0jSqGL0A+sLNA1joGTRiHHyI2obgmw2a7sSYTP0R4Pg9KcFQePOkm9Bk13O8jFQQjxXStbdF2enwhBrSMx56Nm/26nkAxZcoUMAwDhmEQHh6OtLQ0XH755Vi3bh1YlvXoWm+88QYSEhIUWdfw4cMt64qMjMR5552Hl156SfL5c+bMwQ4Pw6zZ2dlYtWqVhyslCOmElPAZPXo02trasHPnTuzbtw8XXHABRo8ejeLi4kAvTTbqkydhyMhA2oABDvuMBw/ygicpKQAr8xypHjjOjhMKiwHPxA/D8GMmvMXZuoTUli5OuqGfN+Z/gyaMQ1ruMexPzsNurMf+5Dyk5x7tcNER3kjxAdinBIHTE985nG7PN/t/bWYgPx/YsIH/1+yHJVx55ZUwGo04duwYtm7dipycHMyYMcPydy1Q5Obmwmg04o8//sANN9yA6dOnY8OGDZLOjY2N9XuHHUG4I2SET1lZGQ4dOoR58+ahd+/e6N69O5555hk0NDTg4MGDTs9rbm5GTU2NzUcwoD56lBc8gwY57DP+8QeKCgvBJXasFlmpqSpXxwmFxfbBLe1pixR7QSR8ftNN0u7t6bpYsxnn1D8KsYe3K7w1/wt0pEYJvnllMQwJhU6/bkJ7vtzomFw2bways/mftUmT+H+zs/ntvkSj0SA9PR0ZGRm48MIL8X//93/YsmULtm7dijfeeMNy3MqVK9GrVy/ExMQgKysL9957L+rq6gAA+fn5uP3221FdXW2J1CxYsAAA8Pbbb6N///6Ii4tDeno6Jk2aZPFEcUV0dDTS09PRtWtXLFiwAN27d8cnn3wCADhx4gTGjBmD2NhYxMfH44YbbkBJSYnl3AULFqBPnz6Wz6dMmYKxY8di+fLl0Ov1SE5OxvTp0y0decOHD8fx48cxa9Ysy/oB4Pjx47jmmmuQmJiImJgYnH/++fjiiy+8+XITnZiQET7Jyck4++yz8dZbb6G+vh5tbW1Ys2YNdDod+lkXYtixZMkSaLVay0dWVpYfV+2I+vBhXvAMGeKwz/jXX7zgEZ7yHQx3qSqGcTT3EyMnB/jsM37S+uLF/L9ffcXX4dgLIp2OF0pTp7a7JcvBenipgMlkwj/ffQldbIlk0cOyDAqrstBrRPCZ//mTPRs3Y3iStKFn/hyNsXkz7+J9ys4uqbCQ3+5r8WPPiBEjcMEFF2Cz1Y1VKhWef/55/P7773jzzTexc+dOPPTQQwCAwYMHY9WqVYiPj4fRaITRaMScOXMA8O2/Tz31FH799Vd8/PHHOHbsGKZMmeLxmqKiotDS0gKWZTFmzBhUVFRg165d+Oqrr/Dvv/9i4sSJLs/Py8vDkSNHkJeXhzfffBNvvPGGRdht3rwZmZmZePLJJy3rB4Dp06ejubkZ33zzDQ4cOIBnn30WsbGxHq+dIIAQKm5mGAZff/01xo4di7i4OKhUKuh0Omzbtg2JLiIjjzzyCGbPnm35vKamJiDiJ+yff6Bz0s5k/OcfcDExfl6R8rjzwAEch4S6upa9nrUfKGrvD+RuKKgr5s5tv47JZALDMIiOjkZlUyMQIe0awgysYDT/8yfts8KkHe+v0RhmMzBjhuvi+5kzgTFjeMsBf3HOOefgt99+s3w+c+ZMy/+zs7OxaNEiTJs2DS+99BIiIiKg1WrBMAzS7To777jjDsv/u3btiueffx4XXXQR6urqJIkIs9mMDRs24LfffsNdd92FHTt24MCBAzh69Kjlb+Zbb72F888/Hz/++CMuuugi0eskJibixRdfhFqtxjnnnIOrr74aO3bsQG5uLpKSkqBWqy1RKYETJ07g+uuvR6/TXmVdO7DzPBF4gj7iM2/ePEvI09nHX3/9BY7jMH36dOh0OhQUFOCHH37A2LFjcc0111jeNYih0WgQHx9v8+FP1L//jnPOPVdU9BgPHeIjPCEgegScpaqEyIy7kRHuEATRqFGO5oPCvT3xt9Nq21vZTSYTysrKEB0djaSkJMTExCAm5SzJ16psSJJVgBxqHNhZAEPCKbd1WiwLv0bHCgocIz3WcBw/263Av5k3cBxn413y9ddfY+TIkcjIyEBcXBxuvfVWlJeXo8FN9f6+fftwzTXX4IwzzkBcXByGDRsGgBcVrnjppZcQGxuLqKgo5ObmYtasWbjnnnvw559/Iisry+aN4nnnnYeEhAT8+eefTq93/vnn2xjP6fV6tym3Bx54AIsWLcIll1yCJ554wkYIEoSnBH3E58EHH3Qbju3atSt27tyJzz77DJWVlRbx8tJLL+Grr77Cm2++iXnz5vlhtR5w9CjQtSvEZFbR4cPAaWvuUMRdZMbX946NBe65x/2xMTHAtm1AVRXvtBwdHW2Z/5SYmIioqCikpabBtC4FqXHuK7ePZX6AQVeNVOBVdGwkp678HB1z8f5I1nFK8eeff6JLly4AgGPHjmH06NG45557sHjxYiQlJeHbb7/F1KlT0dLS4tS1ur6+HqNGjcKoUaPw7rvvIjU1FSdOnMCoUaPQ0tIieo7AzTffjPnz5yMqKgp6vR4qL39Rw+2mATMM47Zz7c4778SoUaPw+eef48svv8SSJUuwYsUK3H///V6theicBL3wSU1NRaqENm3h3Y79L6VKpfK4HdSnHD4MdO8uuqvo338BjcbPCwoMYqkqX2FvNNinD5CYCFRWujqLQ1RUGfbsYXDhhe2CJyUlBRER7bktdbgah7QvIcV8AwDx+iWWA4zVWegzYbiSL6vDItWBelfFQuTc77/omNSZh17MRvSYnTt34sCBA5g1axYAPmrDsixWrFhh+Vv3wQcf2JwTEREBs10b2l9//YXy8nI888wzlgjNTz/9JGkNWq0W3bp1c9h+7rnn4uTJkzh58qTlmn/88Qeqqqpw3nnnefZC3awfALKysjBt2jRMmzYNjzzyCNauXUvCh5BF0Ke6pHLxxRcjMTERkydPxq+//op//vkHc+fOxdGjR3H11VcHenm84GEYB9HDqdX469dfUVRY2GlEjz/JywNGj+bnZ82fz/87ZgzQs6ezMzikpJiQnFyBhoZo1NcnIyYmBmlpaTAYDDaiR2DwjROwq3Su6NVYDgAYnExd1SE7r3xBrxFDUVSVyZsuisByQFFVJi6d5oVNtwyGDuUHwroqvs/K4o/zBc3NzSguLkZhYSF+/vlnPP300xgzZgxGjx6N2267DQDQrVs3tLa24oUXXsC///6Lt99+G6+88orNdbKzs1FXV4cdO3agrKwMDQ0NOOOMMxAREWE575NPPsFTTz3l1Xovu+wy9OrVCzfffDN+/vln/PDDD7jtttswbNgw9HdisiqF7OxsfPPNNygsLETZaQ+JmTNnYvv27Th69Ch+/vln5OXl4dxzz/Vq/UTnJWSET0pKCrZt24a6ujqMGDEC/fv3x7fffostW7bgAiezrPyKfZRn0iSgrQ1VJhMg8jAlvEcYj2FfPmAyOdZpMAxrJXhiUFGRhIaGGGRnp8NgMCAszHVwdPispfg+bCPKam2jk8bqLKrrsUNwoAYDB/HDf87gROpqvwtFtRpYvZr/vzNbhFWrfFfYvG3bNuj1emRnZ+PKK69EXl4enn/+eWzZssVSE3PBBRdg5cqVePbZZ9GzZ0+8++67WLJkic11Bg8ejGnTpmHixIlITU3F0qVLkZqaijfeeAMbN27Eeeedh2eeeQbLly/3ar0Mw2DLli1ITEzEpZdeissuuwxdu3bF+++/79V1n3zySRw7dgxnnXWWJdpvNpsxffp0nHvuubjyyivRo0cPj4wUCcIamtVlh89mdQl/OadMAf73P8tfT7mzugjXsCwf6XFWM8kwwrfEjKSkCnCcCo2NUWhsjALHMSgpSUdGhgpHj3r2oFNi5lRnQelZYUrN6tq8me/usi50zsriRc840q8EEVBoVldHgvSlW5wN/ZSDu/EYDNMueBoaYiyCx2jUWzpo5Ly7F4wFCfcEy6wwe8aN49OhBQV8IbNez6e3/NnCThCE7yDhQwQFeXl827i1WNHpeP8cOS3uzsZLhIW1ISGh0iJ4xo6NwtdfMzhypH1SemYmvbv3F8EqFNVqYPjwQK+CIAhfQMKHCDhCLY59UMxk4rfL8fexHy8RFtaKhIQqsKwKDQ2xaGyMBMcxGD3agGefpXf3BEEQnQUSPkRAYVk+0uPMLRfg9w8b5pj2cpUaE8ZjVFW1ID6+2kHwFBcbkJnZLnI607t7qkMiCKIzQ8KHCCjuanEAfv9rrwG5ue3b3KXGWlubcf/9NVi1ylbwGI0Gv3ToBCtCQXEfoaC4HCham4kTqaup84wgiE5ByLSzEx0TZ7U49qxZw4sdwHWb+mOPNWHrVhPq6+sxfHgsHn44CbGxUSgqyoDRyNfxZGYCmzZ1vhqePRs3Y0DLeKRrbecypMcXYkDLeOzZ6OcJnARBEAGAIj5EQLGvxXHF8uXAkCHiqbHIyEbExtaBZdVYuzYWV1wRCbWawS23GHDTTVTD0z4YlHOYJK9ScWBZBlmmmTC3jqG0F0EQIQ0JHyKgCLU47tJdAFBSwkdqrI+NimpATEw9WFaNurpYNDdHoqKC79IS6nbc1fC0NLXg+zdfAuqOALFn4eLJ9yIiMrRMJQ/sLGhPb4mgUnHISDiJ/TsLgrLLiiAIQilI+BABRaXi63Lmik98cODkSf7f6Oh6REc3WARPU1MkAMaSzpI6SDL/uYcwJGUlhsWZgTh+W9vGOcgvm43hs5Z69mL8gNzCZKmDQSUPECUIguigUI0PAZYF9u0Dtm/n//X3TNecHH6GlhQyMuqQkmJCZGQz6upiUVGRiKamKBiN7TU8gLRBkvnPPYRhumVQq2wHIqpVZgzTLUP+cw958jJ8zp6Nm1GyNht9ynMwGJPQpzwHJWuzJdXmRCdLm6wp9TgiNFmwYAH69OkTsPMJwh+Q8OnkiA3xHD26vZDYX0ydyqe8nBEXV4sePUy4/PIWREbGobJSXPBIHSTZ0tSCISkrLedYI3w+JGUlWppa5LwcxfG2MNntYFCWQWFVFnqN8NEETsIpDMO4/FiwYEGgl2jh2LFjYBgG+/fvF90/Z84c7Nixw7+LIggPIeHTicnL41NM9vU1paX8dn+KHyHl1T5Diyc+vgapqSaEh7fh9tvjkJqaiBkzImE0ZqC42GBzDU/a1L9/8yWEqc0uJ3GHqc187U+AsRQmQ7wwGRxOFyabRc8HJAwGZUAT5AOE0Wi0fKxatQrx8fE22+bMmWM5luM4tLW1BXC1romNjUVycnKgl0EQLiHh00lhWWDRItfHLFrk37RXTg7v0pyaCmi1VUhJMSEszIzIyDjMm5eAyy+PREZGBm6+2YBNm4CMDNvzPWpTrzsibVFSj/MhB3YWwJBwykH0CAiFyQd2FogfcJpBE8bhh4hNKK6x/cIZazJDd4I8xwH19YH5kDifLz093fKh1WrBMIzl87/++gtxcXHYunUr+vXrB41Gg2+//RYsy2LJkiXo0qULoqKicMEFF2DTpk2Wa+bn54NhGOzYsQP9+/dHdHQ0Bg8ejL///tvm3s888wzS0tIQFxeHqVOnoqmpyasvt32qa8qUKRg7diyWL18OvV6P5ORkTJ8+Ha2trZZjmpubMWfOHGRkZCAmJgYDBw5Efn6+V+sgCFdQcXMnZd8+oLra9THV1fxxF13knzUBwIUXVuONN1rwzz9hqK2NQ2qqBhdeyCAz0za64/UgydizlD3OhyhZmBysg0F9RkMDEBsbmHvX1QExMYpcat68eVi+fDm6du2KxMRELFmyBO+88w5eeeUVdO/eHd988w1uueUWpKamYtiwYZbz5s+fjxUrViA1NRXTpk3DHXfcge+++w4A8MEHH2DBggX473//iyFDhuDtt9/G888/j65duyqyZoG8vDzo9Xrk5eXh8OHDmDhxIvr06YPc046k9913H/744w+89957MBgM+Oijj3DllVfiwIED6N69u6JrIQiAhE+n5aefpB/nD+FTXV2NlpYWhIWFQauNxyWXRIBhGBgMBqfneDNq4uLJ96Jt4xyoVeLpLo4DzKwaF0++V94NFCQ6WQ+USzxOAsE6GJRwzpNPPonLL78cAB8hefrpp/H111/j4osvBgB07doV3377LdasWWMjfBYvXmz5fN68ebj66qvR1NSEyMhIrFq1ClOnTsXUqVMBAIsWLcLXX3/tddTHnsTERLz44otQq9U455xzcPXVV2PHjh3Izc3FiRMn8Prrr+PEiROW3/U5c+Zg27ZteP311/H0008ruhaCAEj4EAGmsrISbW1tCAsLQ3x8PCIi3AseJYiIjEB+2WwM0y0Dx9nWFQkZim/LZmO4TD8fJedh9RoxFEVrM5EeX8jX9NjBsgyMNZnoNYEKkx2IjuYjL4G6t0L079/f8v/Dhw+joaHBIoQEWlpa0LdvX5ttvXv3tvxff7rVsbS0FGeccQb+/PNPTJs2zeb4iy++GHkKF/edf/75UFuFYvV6PQ4cOAAAOHDgAMxmM3r06GFzTnNzM9UKET6DhE8npX9/fv6VlON8gb3g0Wg0AOBzwWPN8FlLkf8c370Vpm4vDDazal70yPTxUXoellCYnN4yHizL2Igf68LkkE1XeQPDKJZuCiQxVq+h7rSQ+/zzz5FhV+gm/B4JhIeHW/7PnFb3rJ/9KqzXIKxDWENdXR3UajX27dtnI44AvlCaIHwBCZ9OSr9+gFbrus5Hq+WPU5KKigqYzeaACh5rhs9aipamRdhl59wsN9IjtJ1DaxuZSY8vRHrLeOzZKK+IeNCEcdizcRPOMM2AwcqB2ViTiZOpq0KzMJkQ5bzzzoNGo8GJEyds0lqecu6552Lv3r247bbbLNv27NmjxBIl07dvX5jNZpSWlmKoOw8KglAIEj6dFJUKePRR147Jjz7KH6cEZWVl4DjudA2PFhERvLAIlOCxJiIyAsPunun1dXw9D6vTFSYTosTFxWHOnDmYNWsWWJbFkCFDUF1dje+++w7x8fGYPHmypOvMmDEDU6ZMQf/+/XHJJZfg3Xffxe+//y6puNm+OwzgU1qe0qNHD9x888247bbbsGLFCvTt2xcmkwk7duxA7969cfXVV3t8TYJwBwmfTkxODj/wc9kyWy8fnY4XRDk53t/DZDIBAMLCwhATExNUgkdp/DEPiwqTCQB46qmnkJqaiiVLluDff/9FQkICLrzwQvzf//2f5GtMnDgRR44cwUMPPYSmpiZcf/31uOeee7B9+3a35954440O204K82Q85PXXX8eiRYvw4IMPorCwECkpKRg0aBBGjx4t63oE4Q6G4ySaTXQSampqoNVqUV1djfj4eJ/fr7KyEiUlJX65lzNYFvjlF6CsjJ+W3rev95GeziR4BHav34DBmOT+OKzH4Ek3+WFFnYumpiYcPXoUXbp0QWRkZKCXQxCED3D1ey71+U0RnyBGECSlpUBVFZCQwEdjlBAm1qhUytXyCIInPDwc0dHRnULwCCjddk4QBEEoDwmfICUvzzEFJaBkKkoprAVPTEyMpZOjMwgeAWo7JwiCCH5oZEUQkpcHPPSQuOgB+O0PPeT/QaJimEwmmEwmhIeHIyEhAQkJCQgPD4fBYOhUogegeVgEQRAdARI+QQbL8pEed5VXHAcsX+7fWVrWkOARp1POwyIIguhAUKoryBBqeqRQUsIfr7TXjiuElFZERASio6M7ZUrLHdR2ThAEEbyQ8Akyysp8e7xcSPB4hpJt50qOvyAIgujskPAJMlJSfHu8p5DgCSxKj78gCILo7JDwCTL69uW7tkwm93U+aWn88b5ATPAwDGMZdEj4Hl+NvyAIgujMUHFzkKFSuR4jIcAwwJw5yvr5AO1FyxqNBgkJCdBqtYiMjITBYCDR40cs4y8gPv4CHE6PvzCLnk8QBEGIQ8InCMnJAZYu5SM/YqSl8fuV9PGxFzzx8fGIjo6GwWBAWlqacjciJHFgZwEMCaccRI+AMP7iwM7/b+/Ow6Ks1waOfwcQmJFdQUBBcEFQScEtbVFLxS71wuPWSV+FcqmkxUJTs7Qy1GPLySXTk76gvlaWeszjvqIcRU0MV0RUEEJwCRUVZZl53j9oRkcQQWEG4f5c11w2z/Ob57n5NQz3/NY40wb2hNJqtRQWFprsodVW34RUURTGjBmDi4sLKpWKxMREunXrxrhx48p8nY+PD998841JYqzt/vzzT9zc3EhLSzN3KCZTUFCAj48Phw4dqvJ7SVeXCWi1EBcHWVng4QHPPQeWDxmb2r07dO1a9Ss367u0bGxs0Gg0WFlZYW1tTf2qHjwkypT3Z1allqvNtFotf/zxB4WFhSa7Z506dWjUqBGWD/tFv0d2djZRUVFs2LCBzMxM3NzcaNu2LePGjePFF1+stNg2b95MTEwMsbGxNGnShPr167NmzRrD+L0nWVpaGr6+vobnzs7OBAYG8vnnnz9Ru79HRUURGhqKj4+P4dg777zD3r17OX78OAEBASQmJj70Ot26dWP37t1Gx15//XUWLlxYrtfGxsaWWUar1fLFF18QExPD+fPnUavVNG/enNGjRzNq1CgAwsPDWbp0KVC8hZGLiwtPPfUUr7zyCuHh4Vj89cfM2tqa8ePHM3HiRHbs2PHQ+B6HJD5VbM0aePdd+OOevSsbNYI5c2DAQ4ZnVOZWEvcrLeFxdHSkbt26VXNDUUJZs7Vk+4vKo9PpKCwsxMLCAiurqv/IKyoqorCwEJ1OV+7EJy0tjWeeeQYnJye++OILAgMDKSwsZMuWLURERHDq1KlKi+/s2bN4eHjQpUsXwzEXF5dKu351sH37dlq1asWVK1eIioqib9++nD59+olovc7Ly2PJkiWlbhb72muvceDAAY4ePVru640ePZrPPvvM8Fyj0Tyw7Pr16/H09CQ4ONhw7KeffiI4OBg/P78S5T/99FMWLVrE/Pnzad++Pbm5uRw6dIirV68alevduzfR0dFotVouXrzI5s2beffdd1m1ahXr1q0z/F4OGzaMyMhITpw4QatWrcr9M1aUdHVVoTVrYNAg46QHIDOz+PiaNaaPSd+lZWtri7OzMw4ODtSvXx9PT09Jekxo/y9ruPi9D23/7E4XhtL2z+5c/N6H/b8UvykCX3iOC9calVgBWk+nU5F5zYvAF56cb7HmZmVlZbJHRY0dOxaVSsXBgwcZOHAgfn5+tGrVivfff5/9+/cbyqWnpxMaGoqdnR0ODg4MGTKEixcvGs5/8skntG3bluXLl+Pj44OjoyN///vfuXHjBlD87fvtt98mPT0dlUplaFG4v6vr0qVL9OvXD7Vaja+vLytWrCgR87Vr1xg1ahSurq44ODjwwgsvcOTIkXLHAsVJ6ezZs2nWrBk2NjZ4e3sTFRVlOJ+RkcGQIUNwcnLCxcWF0NDQcnX/1KtXD3d3d1q3bs2HH35Ibm4uBw4cMJxfvnw57du3x97eHnd3d4YOHcqlexZQi42NRaVSsWPHDtq3b49Go6FLly4kJycb3efzzz/Hzc0Ne3t7Ro0axaRJk2jbtq1RmcWLFxMQEICtrS3+/v4sWLCgzNg3btyIjY0NTz/9tNHxuXPnEhERQZMmTR76899Lo9Hg7u5ueJS1eWeTJk2YPHky06ZN49q1awwZMoRdu3Y9sAdg3bp1jB07lsGDB+Pr60ubNm0YOXIk48ePNypnY2ODu7s7DRs2JDg4mA8//JBff/2VTZs2ERMTYyjn7OzMM888w08//VShn7GinpjEJyoqii5duqDRaHByciq1THp6On369EGj0eDm5saECRMoKioybaB/0WqLW3pKm5mlPzZuXHE5U7g/4bG3t8fV1RVPT88yvwGIyqefreXuaJwRuztk0rFgEPt/WSPbX9QiOTk5bN68mYiIiFK/fOg/73Q6HaGhoeTk5LB79262bdvGuXPnePnll43Knz17lrVr17J+/XrWr1/P7t27mTVrFgBz5szhs88+o1GjRmRlZfHbb7+VGlN4eDgZGRns2rWLVatWsWDBAqPEAGDw4MFcunSJTZs2kZCQQHBwMC+++CI5OTnligVg8uTJzJo1i48//piTJ0/yww8/GFplCgsLCQkJwd7enri4OPbu3YudnR29e/emoKCgXHV7+/Ztli1bBmDYMFl/7enTp3PkyBHWrl1LWloa4eHhJV4/ZcoUvvrqKw4dOoSVlRWvvfaa4dyKFSuIioriH//4BwkJCXh7e/Pdd98ZvX7FihVMnTqVqKgokpKSmDFjBh9//LGh66c0cXFxtKvEpv4VK1ZQv359WrduzeTJk8nLy3tg2ZYtW7JlyxZSUlI4cuQIPXr0YNGiRQ9sEXR3d2fnzp2GHoSKeOGFF2jTpg1r7msB6NixI3FxVTt28Ynp6iooKGDw4MF07tyZJUuWlDiv1Wrp06cP7u7u7Nu3j6ysLEaMGEGdOnWYMWOGyeONiyvZ0nMvRYGMDFi2DOzsisf+VPau63C3S8vW1haNRoOlpSUuLi7Y2tpW7o1EuRhmazmWPltLp1P9NVsrlKcHD2D/L6vwvvwunk5330xZuY3IcP1GprLXEGfOnEFRFPz9/csst2PHDo4dO0ZqaipeXl4ALFu2jFatWvHbb7/RoUMHoDhBiomJwd7eHoDhw4ezY8cOoqKicHR0xN7eHktLS9zd3Uu9z+nTp9m0aRMHDx40XHPJkiUEBAQYyvz3v//l4MGDXLp0CRsbGwC+/PJL1q5dy6pVqxgzZsxDY7lx4wZz5sxh/vz5hIWFAdC0aVOeffZZAFauXIlOp2Px4sWoVMW/LNHR0Tg5OREbG0uvXr0eWFddunTBwsKCvLw8FEWhXbt2RuOk7k1gmjRpwty5c+nQoQM3b97Ezs7OcC4qKoquXbsCMGnSJPr06cOdO3ewtbVl3rx5jBw5kldffRWAqVOnsnXrVm7evGl4/bRp0/jqq68Y8Ne4Bl9fX06ePMmiRYsMP/P9zp8/X2nrpA0dOpTGjRvj6enJ0aNHmThxIsnJySWSDb3k5GTGjRtHhw4daNOmDdu3bycxMZGoqCicnZ1LlP/6668ZNGgQ7u7utGrVii5duhAaGspLL71Urvj8/f1LdNt5enpy/vz5iv+wFfDEJD6ffvopgFGz2L22bt3KyZMn2b59Ow0aNKBt27ZMnz6diRMn8sknnxhl+/fKz88nPz/f8Dw3N7dS4s0q55jT6dOLZ2nl5lburuuS8FRPx3bG3V2MsBT62VqJO+NoG9JNtr+oBZSHLdj1l6SkJLy8vAxJDxR/Q3dyciIpKcmQpPj4+BgSDQAPD48SrTUPu4+VlZVRq4O/v79RS/uRI0e4efMm9erVM3rt7du3OXv2rOF5WbEkJSWRn5//wIHbR44c4cyZM0avB7hz547RPUqzcuVK/P39OX78OB988AExMTFGg7cTEhL45JNPOHLkCFevXkX316aH6enptGzZ0lDuqaeeMoodirsBvb29SU5OZuzYsUb37dixIzt37gTg1q1bnD17lpEjRzJ69GhDmaKiIhwdHR8Y++3btyvtc1qfgAIEBgbi4eHBiy++yNmzZ2natGmJ8qdPnyYqKorg4GD27NnDzz//zI8//sjly5dLTXxatmzJ8ePHSUhIYO/evezZs4d+/foRHh7O4sWLHxqfoiiGpFZPrVaX2SpVGZ6YxOdh4uPjCQwMNBq8FhISwptvvsmJEycIesBKfzNnzjQkVZXpUZa8uXy5eNf1x5mqXlrCU69ePcO3stLIlgim8yiztSpz+wtR/TRv3hyVSlVpA5jvn52lUqkMf9gry82bN/Hw8Ch11s+9CVJZsajV6ofeo127dqWOL3J1dS3ztV5eXjRv3pzmzZtTVFTE3/72N44fP46NjQ23bt0iJCSEkJAQVqxYgaurK+np6YSEhJToQrs3fv0f6PLWpb7l5/vvv6dTp05G58oa9F6/fv0Sg4Mriz6OM2fOlJr49OvXr8SxV155pcxrWlhY0KFDBzp06MC4ceP4v//7P4YPH86UKVOMZtiVJikpqUSZnJych/7/fVxPzBifh8nOzi4xYl//PDs7+4Gvmzx5MtevXzc8MjIyKiWe554rnr2lKn1saqn0X/weZdf1y5cvc+XKFWxtbXFxccHe3p4GDRrg6elZZtLzsEG2onKVdxaWzNaqPVxcXAgJCeHbb7/l1q1bJc5fu3YNgICAADIyMow+o06ePMm1a9eMWikel7+/P0VFRSQkJBiOJScnG+IACA4OJjs7GysrK5o1a2b0KO9SGM2bN0etVj9w6nJwcDApKSm4ubmVuEdZLSb3GzRoEFZWVoZBxadOneLPP/9k1qxZPPfcc/j7+1eoRUyvRYsWJcZI3ftc//l77ty5EvGXlRAEBQVx8uTJCsdTHvop8OVZjPZhU9kfRP9eLO29fK+dO3dy7NgxBg4caHT8+PHjD2yoqCxmTXwmTZqESqUq81GZ0zhLY2Njg4ODg9GjMlhaFk9Zh4onP/pd18vj3oRHP2jZ3d0dT0/PB3bv6ZVnkK2oXDJby3yKiopM9qiob7/9Fq1WS8eOHVm9ejUpKSkkJSUxd+5cOnfuDECPHj0IDAxk2LBhHD58mIMHDzJixAi6du1K+/btK62eWrRoQe/evXn99dc5cOAACQkJjBo1yqiFpkePHnTu3Jn+/fuzdetW0tLS2LdvH1OmTCn3AnS2trZMnDiRDz74gGXLlnH27Fn2799vGMM5bNgw6tevT2hoKHFxcaSmphIbG8s777zDH2UNoLyPSqXinXfeYdasWeTl5eHt7Y21tTXz5s3j3LlzrFu3junTp1eskoC3336bJUuWsHTpUlJSUvj88885evSoUdfNp59+ysyZM5k7dy6nT5/m2LFjREdH8/XXXz/wuiEhIZw4caJEq8+ZM2dITEwkOzub27dvk5iYSGJioqGVKjMzE39/fw4ePAgUDyyfPn06CQkJpKWlsW7dOkaMGMHzzz9v1IX3OAYNGsQ///lPDhw4wPnz54mNjSUiIgI/Pz+jMWv5+flkZ2eTmZnJ4cOHmTFjBqGhofTt25cRI0YYXTMuLq7M8VuVwayJT2RkJElJSWU+yjt1z93d3WhaJ2B4/qBBfFVtwABYtQoaNqz4ax+263pZCU95FiKTLRHMQ2ZrmZ6FhQV16tRBp9NRUFBQ5Q+dTkedOnUMC7OVR5MmTTh8+DDdu3cnMjKS1q1b07NnT3bs2GGYKaRSqfj1119xdnbm+eefp0ePHjRp0oSVK1dWep1FR0fj6elJ165dGTBgAGPGjMHtnqXkVSoVGzdu5Pnnn+fVV1/Fz8+Pv//975w/f75Ca+V8/PHHREZGMnXqVAICAnj55ZcNrS8ajYY9e/bg7e3NgAEDCAgIYOTIkdy5c6fCX1DDwsIoLCxk/vz5uLq6EhMTwy+//ELLli2ZNWsWX375ZYWuB8WJ2eTJkxk/fjzBwcGkpqYSHh5uND5n1KhRLF68mOjoaAIDA+natSsxMTFltvgEBgYSHBzMzz//bHR81KhRBAUFsWjRIk6fPk1QUBBBQUFcuHABKJ6plpycbBgfY21tzfbt2+nVqxf+/v5ERkYycOBA/vOf/1T4Z32QkJAQ/vOf/9CvXz/8/PwICwvD39+frVu3Gi3rsHnzZjw8PPDx8aF3797s2rWLuXPn8uuvvxp1+8XHx3P9+nUGDRpUaTGWRqWUd2RdNRETE8O4ceOMml0BNm3aRN++fcnKyjL8gv7rX/9iwoQJRjMPHiY3NxdHR0euX79eaa0/967cfPEivPfe3XNOTldp0OAiubnG91q0qPTFCy9fvoxKpTKM4bGwsMDNza3Ca4ckboml7Z8PH0iUWG+XjC+pAvpd1++drZV5zUtmaz2GO3fukJqaiq+vb4nBoVqtttLHuZTFwsKiQqs2i5qhZ8+euLu7s3z58se6zoYNG5gwYQLHjx+vUAL9pHv55Zdp06YNH3744QPLlPV7Xt6/30/M4Ob09HRycnJIT09Hq9Ua+iqbNWuGnZ0dvXr1omXLlgwfPpzZs2eTnZ3NRx99RERERLmTnqpiaQnduhX/t1YLX31VvIhhaSmnSnV3W4p76RMetVr9WAmPnmyJYD7aQi22Di6cK5hFyo3LWNm5Urd+Q5mtVYUsLS0lERGVKi8vj4ULFxISEoKlpSU//vgj27dvZ9u2bY997T59+pCSkkJmZqbRLL6arKCggMDAQN67t2Wgijwxic/UqVONFn3SD37atWsX3bp1w9LSkvXr1/Pmm2/SuXNn6tatS1hYmNFS3dWBfuzPoEElx/7on9+763ppCU+DBg0e+0NctkQwD31LT1unP0AF2MOFa41It54j3VtCPEH03X1RUVHcuXOHFi1asHr1anr06FEp13/YprE1jbW1NR999JFJ7vXEdXVVtaro6iqNfg+vmzfvdnU1aFCc9HTvXnqXlru7e6U1e2oLtVz83gd3h8ziMT330elUZOU2wn10qvxBriT6weT3j6vSj+05aL1KurkeQ1lN4EKImqFWdXXVNAMGQGgobN1avIKzfuXmP/+8zJUrKjQaDWq1GgsLCzw8PEos8vS49INs3QsGodOpjJKfewfZStdL5ajIis2SaAohRNWpPaOmqiFLS3j6aXj+efD2vkxOzhU0Gg0uLi7UrVuXhg0b4unpWelJj97Tgwdw0HoV2bnG086ychtJ60MlO7YzDk+nP0okPXr6FZuP7azaPWqEEKK2kxYfM7t9+zZFRUVoNBo0Gg0qlapKWngeRLZEMA0ZTC6EENWDJD5mol97AcDZ2dnkCc+9ZEuEqieDyYUQonqQri4Tu3DhglHSA8W70VZll5YwP1mxWQghqgdp8TERRVHIum/LdnO18AjTk8HkVUe/ye7tG1fQNGmKopOJqkKIB5MWHxO5N8GRFp7aSQaTV757N9kNYjzWuosUXj3NzZyq2d36SRYeHk7//v0Nz7t161br1ooRAqTFx6Q8PT3NHYIwMxlMXnkM6yI5Grfw1LEoxFY5y82cpti5OJspukeTkZHBtGnT2Lx5M1euXMHDw4P+/fszdepU6tWrV65rpKWl4evry++//07btm0fWG7NmjXl2tdPiJpGEh8hTEwGkz++stZF0j+1LspA0TmhetAaAmXRaeFyHNzOArUHuD4HFlWbnJ47d47OnTvj5+fHjz/+iK+vLydOnGDChAls2rSJ/fv34+LiUmn3e9xrabVaVCpVrdpLStQM8o4VQjxxHrYukgqwtizg9o2bFb94xhpY5wM7usO+ocX/rvMpPl6FIiIisLa2ZuvWrXTt2hVvb29eeukltm/fTmZmJlOmTAGKu83Xrl1r9FonJydiYmIADDt/BwUFoVKp6KbfKPA+93d15efnM378eBo2bEjdunXp1KkTsbGxhvMxMTE4OTmxbt06WrZsiY2NDenp6cTGxtKxY0fq1q2Lk5MTzzzzDOfPn6+sahGi0kniYwLaQi2JW2LZ98OPJG6JRVuoNXdIQjzRyrveka6ooGIXzlgDcYMg74/7bphZfLyKkp+cnBy2bNnC2LFjUavVRufc3d0ZNmwYK1eupDw7DB08eBCA7du3k5WVxZo15Yv5rbfeIj4+np9++omjR48yePBgevfuTUpKiqFMXl4e//jHP1i8eDEnTpzAxcWF/v3707VrV44ePUp8fDxjxoyR8YuiWpOuripmtCklwJ9w4ftGpLvOkcGsQjyi8q6LZGFlXf6L6rSQ8C5QWnKhACpIGAcNQyu92yslJQVFUQgICCj1fEBAAFevXuXy5csPvZarqysA9erVw93dvVz3T09PJzo6mvT0dMNYxPHjx7N582aio6OZMWMGAIWFhSxYsIA2bdoAxQnb9evX6du3L02bNjXEKkR1Ji0+VUg/+NLd0fjbo7tDJh0LBrH/l6ptOheipnrYukgKUKC1Rm1vV/6LXo4r2dJz/1XzMorLVRFz7Rl97NgxtFotfn5+2NnZGR67d+/m7NmzhnLW1tY89dRThucuLi6Eh4cTEhJCv379mDNnTollO4SobiTxqSKGwZeUviklCn9tSindXkJUlH5dJFSUSH70qUOBlVfFBjbfLucf7PKWq4BmzZqhUqlISkoq9XxSUhLOzs64urqiUqlKJEiFhYWPdf+bN29iaWlJQkICiYmJhkdSUhJz5swxlFOr1SW6saKjo4mPj6dLly6sXLkSPz8/9u/f/1jxCFGVJPGpIrIppRBV60HrIhXq6nBL9QhT2dXl3C6kvOUqoF69evTs2ZMFCxZw+/Zto3PZ2dmsWLGCl19+GZVKhaurq1GrSkpKCnl5eYbn1tbF3Xtabfm/VAUFBaHVarl06RLNmjUzepSnuywoKIjJkyezb98+WrduzQ8//FDuewthapL4VBHZlFKIqvf04AE0GJ1GYr1d/M6XFFg0oI6z36Ot3+P6HGgacXdC/P1UoPEqLlcF5s+fT35+PiEhIezZs4eMjAw2b95Mz549adiwIVFRUQC88MILzJ8/n99//51Dhw7xxhtvGK3H4+bmhlqtZvPmzVy8eJHr168/9N5+fn4MGzaMESNGsGbNGlJTUzl48CAzZ85kw4YND3xdamoqkydPJj4+nvPnz7N161ZSUlJknI+o1iTxqSLl3WxSNqUU4vHo10UK6tsXa7Xto63bA8UDltvpu3Xuv8Zfz9t9U2Xr+TRv3pxDhw7RpEkThgwZQtOmTRkzZgzdu3cnPj7esO7OV199hZeXF8899xxDhw5l/PjxaDQaw3WsrKyYO3cuixYtwtPTk9DQ0HLdPzo6mhEjRhAZGUmLFi3o378/v/32G97e3g98jUaj4dSpUwwcOBA/Pz/GjBlDREQEr7/++uNVhhBVSKWYazRdNZWbm4ujoyPXr1/HwcHhka+jLdRy8Xsf3B0yjfZl0tPpVGTlNsJ9dCqWsmqvEI/tzp07pKam4uvri62t7aNfKGNN8eyuewc6a7yKkx4vmYkphDmV9Xte3r/fMp29isimlEI8obwGFE9ZN/HKzUII05Curiokm1IK8YSysIQG3cDnleJ/JekRosaQFp8qJptSCiGEENWHJD4mIJtSCiGEENWDdHUJIWoUma8hRM1VGb/fkvgIIWoE/Vo29y7mJ4SoWfS/3/euXVVR0tUlhKgRLC0tcXJy4tKlS0DxGjOyS7gQNYOiKOTl5XHp0iWcnJywtHz0cbKS+Aghagz99gr65EcIUbM4OTmVaxuVskjiI4SoMVQqFR4eHri5uT32xp1CiOqlTp06j9XSoyeJjxCixrG0tKyUD0ghRM0jg5uFEEIIUWtI4iOEEEKIWkMSHyGEEELUGjLG5z76xZFyc3PNHIkQQgghykv/d/thixxK4nOfGzduAODl5WXmSIQQQghRUTdu3MDR0fGB51WKrO9uRKfTceHCBezt7U26+Flubi5eXl5kZGTg4OBgsvtWR1IXd0ldGJP6uEvq4i6pC2O1tT4UReHGjRt4enpiYfHgkTzS4nMfCwsLGjVqZLb7Ozg41Ko3almkLu6SujAm9XGX1MVdUhfGamN9lNXSoyeDm4UQQghRa0jiI4QQQohaQxKfasLGxoZp06ZhY2Nj7lDMTuriLqkLY1Ifd0ld3CV1YUzqo2wyuFkIIYQQtYa0+AghhBCi1pDERwghhBC1hiQ+QgghhKg1JPERQgghRK0hiU81EBUVRZcuXdBoNDg5OZVaJj09nT59+qDRaHBzc2PChAkUFRWZNlAzOH36NKGhodSvXx8HBweeffZZdu3aZe6wzGrDhg106tQJtVqNs7Mz/fv3N3dIZpWfn0/btm1RqVQkJiaaOxyTS0tLY+TIkfj6+qJWq2natCnTpk2joKDA3KGZzLfffouPjw+2trZ06tSJgwcPmjskk5s5cyYdOnTA3t4eNzc3+vfvT3JysrnDqpYk8akGCgoKGDx4MG+++Wap57VaLX369KGgoIB9+/axdOlSYmJimDp1qokjNb2+fftSVFTEzp07SUhIoE2bNvTt25fs7Gxzh2YWq1evZvjw4bz66qscOXKEvXv3MnToUHOHZVYffPABnp6e5g7DbE6dOoVOp2PRokWcOHGCf/7znyxcuJAPP/zQ3KGZxMqVK3n//feZNm0ahw8fpk2bNoSEhHDp0iVzh2ZSu3fvJiIigv3797Nt2zYKCwvp1asXt27dMndo1Y8iqo3o6GjF0dGxxPGNGzcqFhYWSnZ2tuHYd999pzg4OCj5+fkmjNC0Ll++rADKnj17DMdyc3MVQNm2bZsZIzOPwsJCpWHDhsrixYvNHUq1sXHjRsXf3185ceKEAii///67uUOqFmbPnq34+vqaOwyT6NixoxIREWF4rtVqFU9PT2XmzJlmjMr8Ll26pADK7t27zR1KtSMtPk+A+Ph4AgMDadCggeFYSEgIubm5nDhxwoyRVa169erRokULli1bxq1btygqKmLRokW4ubnRrl07c4dncocPHyYzMxMLCwuCgoLw8PDgpZde4vjx4+YOzSwuXrzI6NGjWb58ORqNxtzhVCvXr1/HxcXF3GFUuYKCAhISEujRo4fhmIWFBT169CA+Pt6MkZnf9evXAWrF+6CiJPF5AmRnZxslPYDheU3u8lGpVGzfvp3ff/8de3t7bG1t+frrr9m8eTPOzs7mDs/kzp07B8Ann3zCRx99xPr163F2dqZbt27k5OSYOTrTUhSF8PBw3njjDdq3b2/ucKqVM2fOMG/ePF5//XVzh1Llrly5glarLfXzsSZ/Nj6MTqdj3LhxPPPMM7Ru3drc4VQ7kvhUkUmTJqFSqcp8nDp1ytxhmkV560ZRFCIiInBzcyMuLo6DBw/Sv39/+vXrR1ZWlrl/jEpT3vrQ6XQATJkyhYEDB9KuXTuio6NRqVT88ssvZv4pKkd562LevHncuHGDyZMnmzvkKvMonyGZmZn07t2bwYMHM3r0aDNFLswtIiKC48eP89NPP5k7lGrJytwB1FSRkZGEh4eXWaZJkyblupa7u3uJWQoXL140nHvSlLdudu7cyfr167l69SoODg4ALFiwgG3btrF06VImTZpkgmirXnnrQ5/stWzZ0nDcxsaGJk2akJ6eXpUhmkxF3hvx8fEl9iJq3749w4YNY+nSpVUYpWlU9DPkwoULdO/enS5duvCvf/2riqOrHurXr4+lpaXh81Dv4sWLT+RnY2V46623WL9+PXv27KFRo0bmDqdaksSniri6uuLq6lop1+rcuTNRUVFcunQJNzc3ALZt24aDg4PRH8EnRXnrJi8vDyjus7+XhYWFofWjJihvfbRr1w4bGxuSk5N59tlnASgsLCQtLY3GjRtXdZgmUd66mDt3Lp9//rnh+YULFwgJCWHlypV06tSpKkM0mYp8hmRmZtK9e3dDK+D9vzM1lbW1Ne3atWPHjh2GZR10Oh07duzgrbfeMm9wJqYoCm+//Tb//ve/iY2NxdfX19whVVuS+FQD6enp5OTkkJ6ejlarNaxF0qxZM+zs7OjVqxctW7Zk+PDhzJ49m+zsbD766CMiIiJq9O67nTt3xtnZmbCwMKZOnYpareb7778nNTWVPn36mDs8k3NwcOCNN95g2rRpeHl50bhxY7744gsABg8ebOboTMvb29vouZ2dHQBNmzatdd9yMzMz6datG40bN+bLL7/k8uXLhnO1odXj/fffJywsjPbt29OxY0e++eYbbt26xauvvmru0EwqIiKCH374gV9//RV7e3vDGCdHR0fUarWZo6tmzDyrTCiKEhYWpgAlHrt27TKUSUtLU1566SVFrVYr9evXVyIjI5XCwkLzBW0iv/32m9KrVy/FxcVFsbe3V55++mll48aN5g7LbAoKCpTIyEjFzc1Nsbe3V3r06KEcP37c3GGZXWpqaq2dzh4dHV3q50dt+nifN2+e4u3trVhbWysdO3ZU9u/fb+6QTO5B74Ho6Ghzh1btqBRFUUydbAkhhBBCmEPt6AgWQgghhEASHyGEEELUIpL4CCGEEKLWkMRHCCGEELWGJD5CCCGEqDUk8RFCCCFErSGJjxBCCCFqDUl8hBBCCFFrSOIjhKiRwsPDDfs3AXTr1o1x48aZPI7Y2FhUKhXXrl0z+b2FECVJ4iOEMKnw8HBUKhUqlQpra2uaNWvGZ599RlFRUZXed82aNUyfPr1cZSVZEaLmkk1KhRAm17t3b6Kjo8nPz2fjxo1ERERQp04dJk+ebFSuoKAAa2vrSrmni4tLpVxHCPFkkxYfIYTJ2djY4O7uTuPGjXnzzTfp0aMH69atM3RPRUVF4enpSYsWLQDIyMhgyJAhODk54eLiQmhoKGlpaYbrabVa3n//fZycnKhXrx4ffPAB929DeH9XV35+PhMnTsTLywsbGxuaNWvGkiVLSEtLo3v37gA4OzujUqkIDw8HQKfTMXPmTHx9fVGr1bRp04ZVq1YZ3Wfjxo34+fmhVqvp3r27UZxCCPOTxEcIYXZqtZqCggIAduzYQXJyMtu2bWP9+vUUFhYSEhKCvb09cXFx7N27Fzs7O3r37m14zVdffUVMTAz/+7//y3//+19ycnL497//XeY9R4wYwY8//sjcuXNJSkpi0aJF2NnZ4eXlxerVqwFITk4mKyuLOXPmADBz5kyWLVvGwoULOXHiBO+99x7/8z//w+7du4HiBG3AgAH069ePxMRERo0axaRJk6qq2oQQj8LMu8MLIWqZsLAwJTQ0VFEURdHpdMq2bdsUGxsbZfz48UpYWJjSoEEDJT8/31B++fLlSosWLRSdTmc4lp+fr6jVamXLli2KoiiKh4eHMnv2bMP5wsJCpVGjRob7KIqidO3aVXn33XcVRVGU5ORkBVC2bdtWaoy7du1SAOXq1auGY3fu3FE0Go2yb98+o7IjR45UXnnlFUVRFGXy5MlKy5Ytjc5PnDixxLWEEOYjY3yEECa3fv167OzsKCwsRKfTMXToUD755BMiIiIIDAw0Gtdz5MgRzpw5g729vdE17ty5w9mzZ7l+/TpZWVl06tTJcM7Kyor27duX6O7SS0xMxNLSkq5du5Y75jNnzpCXl0fPnj2NjhcUFBAUFARAUlKSURwAnTt3Lvc9hBBVTxIfIYTJde/ene+++w5ra2s8PT2xsrr7UVS3bl2jsjdv3qRdu3asWLGixHVcXV0f6f5qtbrCr7l58yYAGzZsoGHDhkbnbGxsHikOIYTpSeIjhDC5unXr0qxZs3KVDQ4OZuXKlbi5ueHg4FBqGQ8PDw4cOMDzzz8PQFFREQkJCQQHB5daPjAwEJ1Ox+7du+nRo0eJ8/oWJ61WazjWsmVLbGxsSE9Pf2BLUUBAAOvWrTM6tn///of/kEIIk5HBzUKIam3YsGHUr1+f0NBQ4uLiSE1NJTY2lnfeeYc//vgDgHfffZdZs2axdu1aTp06xdixY8tcg8fHx4ewsDBee+011q5da7jmzz//DEDjxo1RqVSsX7+ey5cvc/PmTezt7Rk/fjzvvfceS5cu5ezZsxw+fJh58+axdOlSAN544w1SUlKYMGECycnJ/PDDD8TExFR1FQkhKkASHyFEtabRaNizZw/e3t4MGDCAgIAARo4cyZ07dwwtQJGRkQwfPpywsDA6d+6Mvb09f/vb38q87nfffcegQYMYO3Ys/v7+jB49mlu3bgHQsGFDPv30UyZNmkSDBg146623AJg+fToff/wxM2fOJCAggN69e7NhwwZ8fX0B8Pb2ZvXq1axdu5Y2bdqwcOFCZsyYUYW1I4SoKJXyoNF/QgghhBA1jLT4CCGEEKLWkMRHCCGEELWGJD5CCCGEqDUk8RFCCCFErSGJjxBCCCFqDUl8hBBCCFFrSOIjhBBCiFpDEh8hhBBC1BqS+AghhBCi1pDERwghhBC1hiQ+QgghhKg1/h/aHGab87GcVgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate the standard deviation of the residuals\n",
    "std_residuals = np.std(residuals)\n",
    "\n",
    "# Calculate the confidence range (1.5 * SD)\n",
    "confidence_range = 1.5 * std_residuals\n",
    "\n",
    "# Identify points outside the confidence range\n",
    "lower_bound = trend_line - confidence_range\n",
    "upper_bound = trend_line + confidence_range\n",
    "\n",
    "# Find indices of points outside the confidence range\n",
    "outliers = (test_sol_array < lower_bound) | (test_sol_array > upper_bound)\n",
    "outlier_indices = np.where(outliers.flatten())[0]\n",
    "\n",
    "# Trace back to the SMILES strings\n",
    "outlier_smiles = [test_smi[i] for i in outlier_indices]\n",
    "\n",
    "# Print or store the outliers\n",
    "print(f\"Outlier SMILES strings and their corresponding actual and predicted values:\")\n",
    "for idx in outlier_indices:\n",
    "    print(f\"SMILES: {test_smi[idx]}, Actual: {test_sol[idx][0]}, Predicted: {res[idx][0]}\")\n",
    "\n",
    "# Optional: Save the outlier details to a CSV file\n",
    "outliers_df = pd.DataFrame({\n",
    "    'SMILES': [test_smi[i] for i in outlier_indices],\n",
    "    'Actual': [test_sol[i][0] for i in outlier_indices],\n",
    "    'Predicted': [res[i][0] for i in outlier_indices],\n",
    "    'Residual': [test_sol[i][0] - trend_line[i][0] for i in outlier_indices]\n",
    "})\n",
    "\n",
    "outliers_df.to_csv('outliers.csv', index=False)\n",
    "print(\"Outliers have been saved to 'outliers.csv'.\")\n",
    "\n",
    "# Plot the results\n",
    "plt.clf()\n",
    "plt.scatter(res, test_sol, label='Data Points', color='blue')\n",
    "\n",
    "# Plot the trend line\n",
    "plt.plot(res, trend_line, color='red', label='Trend Line')\n",
    "\n",
    "# Plot the confidence range\n",
    "plt.fill_between(res.flatten(), \n",
    "                 (trend_line.flatten() - confidence_range).flatten(), \n",
    "                 (trend_line.flatten() + confidence_range).flatten(), \n",
    "                 color='gray', alpha=0.2, label='Confidence Range (1.5 * SD)')\n",
    "\n",
    "# Highlight outliers\n",
    "plt.scatter(np.array(res)[outlier_indices], np.array(test_sol)[outlier_indices], \n",
    "            color='orange', label='Outliers')\n",
    "\n",
    "# Label the axes\n",
    "plt.xlabel('Predicted')\n",
    "plt.ylabel('Experimental')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "74f0721f-55cb-4e04-98f8-66256c8b9005",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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