code: def train(epoch):
      model.train()
  
      total_loss = 0
      for data in train_loader:
          data = data.to(device)
          optimizer.zero_grad()
          out, attn_loss, _ = model(data)
          loss = ((out - data.y).pow(2) + 100 * attn_loss).mean()
          loss.backward()
          total_loss += loss.item() * data.num_graphs
          optimizer.step()
  
      return total_loss / len(train_loader.dataset)
  
file_path: pytorch_geometric/examples/triangles_sag_pool.py
imports: import copy
  import os.path
  import torch
  import torch.nn.functional
  from torch.nn import Linear
  from torch.nn import ReLU
  from torch.nn import Sequential
  import torch_geometric.transforms
  from torch_geometric.datasets import TUDataset
  from torch_geometric.loader import DataLoader
  from torch_geometric.nn import GCNConv, GINConv, SAGPooling, global_max_pool
  from torch_geometric.utils import scatter
label_desc: Use image node_id 1 for calling a global function with example usage: train(epoch) and returns: unknown