text: import torch
  import numpy as np
  import torch.nn.functional as F
  from deeprobust.graph.defense import GCN
  from deeprobust.graph.utils import *
  from deeprobust.graph.data import Dataset
  from deeprobust.graph.data import PtbDataset
  import argparse
  
  parser = argparse.ArgumentParser()
  parser.add_argument('--seed', type=int, default=15, help='Random seed.')
  parser.add_argument('--dataset', type=str, default='citeseer', choices=['cora', 'cora_ml', 'citeseer', 'polblogs', 'pubmed'], help='dataset')
  parser.add_argument('--ptb_rate', type=float, default=0.05,  help='pertubation rate')
  
  args = parser.parse_args()
  args.cuda = torch.cuda.is_available()
  print('cuda: %s' % args.cuda)
  device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
  
  # make sure you use the same data splits as you generated attacks
  np.random.seed(args.seed)
  torch.manual_seed(args.seed)
  if args.cuda:
      torch.cuda.manual_seed(args.seed)
  
  # load original dataset (to get clean features and labels)
  data = Dataset(root='/tmp/', name=args.dataset)
  adj, features, labels = data.adj, data.features, data.labels
  idx_train, idx_val, idx_test = data.idx_train, data.idx_val, data.idx_test
  
  # load pre-attacked graph
  perturbed_data = PtbDataset(root='/tmp/', name=args.dataset)
  perturbed_adj = perturbed_data.adj
  
  # Setup GCN Model
  model = GCN(nfeat=features.shape[1], nhid=16, nclass=labels.max()+1, device=device)
  model = model.to(device)
  model.fit(features, perturbed_adj, labels, idx_train, train_iters=200, verbose=True)
  
  # # using validation to pick model
  # model.fit(features, perturbed_adj, labels, idx_train, idx_val, train_iters=200, verbose=True)
  model.eval()
  
  # You can use the inner function of model to test
  model.test(idx_test)
  
  