code: def train():
      model.train()
  
      total_loss = 0
      for data in train_loader:
          data = data.to(device)
          optimizer.zero_grad()
          out = model(
              data.x, data.edge_index, data.batch, data.batch_size
          )
          loss = F.cross_entropy(out, data.y)
          loss.backward()
          optimizer.step()
          total_loss += float(loss) * data.num_graphs
      return total_loss / len(train_loader.dataset)
  
file_path: pytorch_geometric/examples/compile/gin.py
imports: import os.path
  import time
  import torch
  import torch.nn.functional
  import torch_geometric
  from torch_geometric.datasets import TUDataset
  from torch_geometric.loader import DataLoader
  from torch_geometric.nn import MLP, GINConv, global_add_pool
label_desc: Use image node_id 1 for calling a global function with example usage: train() and returns: unknown