text: import os.path as osp
  
  import argparse
  import torch
  from torch_geometric.datasets import DBP15K
  
  from dgmc.models import DGMC, RelCNN
  
  from timeit import default_timer as timer
  
  parser = argparse.ArgumentParser()
  parser.add_argument('--category', type=str, required=True)
  parser.add_argument('--dim', type=int, default=256)
  parser.add_argument('--rnd_dim', type=int, default=32)
  parser.add_argument('--num_layers', type=int, default=3)
  parser.add_argument('--num_steps', type=int, default=10)
  parser.add_argument('--k', type=int, default=10)
  args = parser.parse_args()
  
  
  class SumEmbedding(object):
      def __call__(self, data):
          data.x1, data.x2 = data.x1.sum(dim=1), data.x2.sum(dim=1)
          return data
  
  
  device = 'cuda' if torch.cuda.is_available() else 'cpu'
  path = osp.join('..', 'data', 'DBP15K')
  data = DBP15K(path, args.category, transform=SumEmbedding())[0].to(device)
  
  psi_1 = RelCNN(data.x1.size(-1), args.dim, args.num_layers, batch_norm=False,
                 cat=True, lin=True, dropout=0.5)
  psi_2 = RelCNN(args.rnd_dim, args.rnd_dim, args.num_layers, batch_norm=False,
                 cat=True, lin=True, dropout=0.0)
  model = DGMC(psi_1, psi_2, num_steps=None, k=args.k).to(device)
  optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
  
  
  def train():
      model.train()
      optimizer.zero_grad()
  
      _, S_L = model(data.x1, data.edge_index1, None, None, data.x2,
                     data.edge_index2, None, None, data.train_y)
  
      loss = model.loss(S_L, data.train_y)
      loss.backward()
      optimizer.step()
      return loss
  
  
  @torch.no_grad()
  def test():
      model.eval()
  
      _, S_L = model(data.x1, data.edge_index1, None, None, data.x2,
                     data.edge_index2, None, None)
  
      hits1 = model.acc(S_L, data.test_y)
      hits10 = model.hits_at_k(10, S_L, data.test_y)
  
      return hits1, hits10
  
  print("num steps = " + str(args.num_steps))
  
  print('Optimize initial feature matching...')
  model.num_steps = 0
  maxhits = 0
  best_epoch = 0
  for epoch in range(1, 201):
      if epoch == 101:
          print('Refine correspondence matrix...')
          model.num_steps = args.num_steps
          model.detach = True
  
      start = timer()
      loss = train()
      end = timer()
  
      time = end-start
      
      if epoch % 10 == 0 or epoch > 100:
          hits1, hits10 = test()
          if(hits1 > maxhits):
            maxhits = hits1 
            best_epoch = epoch 
            
          print((f'{epoch:03d}: Loss: {loss:.4f}, Hits@1: {hits1:.4f}, '
                 f'Hits@10: {hits10:.4f}, Time: {time:.2f}'))
  
  print(best_epoch)
  