code: def train():
      model.train()
  
      total_loss = 0
      for data in train_loader:
          data = data.to(device)
          optimizer.zero_grad()
          loss = loss_op(model(data.x, data.edge_index), data.y)
          loss.backward()
          optimizer.step()
          total_loss += loss.item() * data.num_nodes
      return total_loss / train_data.num_nodes
  
file_path: pytorch_geometric/examples/cluster_gcn_ppi.py
imports: import os.path
  import time
  import torch
  import torch.nn.functional
  from sklearn.metrics import f1_score
  from torch_geometric.data import Batch
  from torch_geometric.datasets import PPI
  from torch_geometric.loader import ClusterData, ClusterLoader, DataLoader
  from torch_geometric.nn import BatchNorm, SAGEConv
label_desc: Use image node_id 1 for calling a global function with example usage: train() and returns: unknown