code: def train(train_loader):
      model.train()
      loss_all = 0
  
      for data in train_loader:
          data = data.to(device)
          optimizer.zero_grad()
          out, tot_loss = model(data.x, data.edge_index, data.batch)
          loss = F.nll_loss(out, data.y.view(-1)) + tot_loss
          loss.backward()
          loss_all += data.y.size(0) * float(loss)
          optimizer.step()
      return loss_all / len(train_dataset)
  
file_path: pytorch_geometric/examples/proteins_dmon_pool.py
imports: import os.path
  import time
  from math import ceil
  import torch
  import torch.nn.functional
  from torch.nn import Linear
  from torch_geometric.datasets import TUDataset
  from torch_geometric.loader import DataLoader
  from torch_geometric.nn import DenseGraphConv, DMoNPooling, GCNConv
  from torch_geometric.utils import to_dense_adj, to_dense_batch
label_desc: Use image node_id 1 for calling a global function with example usage: train(train_loader) and returns: unknown