code: def train(epoch):
      model.train()
      loss_all = 0
  
      for data in train_loader:
          data = data.to(device)
          optimizer.zero_grad()
          output, _, _ = model(data.x, data.adj, data.mask)
          loss = F.nll_loss(output, data.y.view(-1))
          loss.backward()
          loss_all += data.y.size(0) * float(loss)
          optimizer.step()
      return loss_all / len(train_dataset)
  
file_path: pytorch_geometric/examples/proteins_diff_pool.py
imports: import os.path
  import time
  from math import ceil
  import torch
  import torch.nn.functional
  import torch_geometric.transforms
  from torch_geometric.datasets import TUDataset
  from torch_geometric.loader import DenseDataLoader
  from torch_geometric.nn import DenseSAGEConv, dense_diff_pool
label_desc: Use image node_id 1 for calling a global function with example usage: train(epoch) and returns: unknown