code: def train():
      model.train()
  
      total_examples = total_loss = 0
      for batch in tqdm(train_loader):
          optimizer.zero_grad()
          batch = batch.to(device, "edge_index")
          batch_size = batch["paper"].batch_size
          out = model(batch.x_dict, batch.edge_index_dict)["paper"][
              :batch_size
          ]
          loss = F.cross_entropy(out, batch["paper"].y[:batch_size])
          loss.backward()
          optimizer.step()
  
          total_examples += batch_size
          total_loss += float(loss) * batch_size
  
      return total_loss / total_examples
  
file_path: pytorch_geometric/examples/hetero/to_hetero_mag.py
imports: import argparse
  import os.path
  import torch
  import torch.nn.functional
  from torch.nn import ReLU
  from tqdm import tqdm
  import torch_geometric.transforms
  from torch_geometric.datasets import OGB_MAG
  from torch_geometric.loader import HGTLoader, NeighborLoader
  from torch_geometric.nn import Linear, SAGEConv, Sequential, to_hetero
label_desc: Use image node_id 2 for calling a global function with example usage: train() and returns: unknown