code: def train(epoch):
      model.train()
  
      loss_all = 0
      for data in train_loader:
          data = data.to(device)
          optimizer.zero_grad()
          output = model(data)
          loss = F.nll_loss(output, data.y)
          loss.backward()
          loss_all += data.num_graphs * loss.item()
          optimizer.step()
      return loss_all / len(train_dataset)
  
file_path: pytorch_geometric/examples/proteins_topk_pool.py
imports: import os.path
  import torch
  import torch.nn.functional
  from torch_geometric.datasets import TUDataset
  from torch_geometric.loader import DataLoader
  from torch_geometric.nn import GraphConv, TopKPooling
  from torch_geometric.nn import global_max_pool
  from torch_geometric.nn import global_mean_pool
label_desc: Use image node_id 1 for calling a global function with example usage: train(epoch) and returns: unknown