code: def train(epoch):
      model.train()
  
      pbar = tqdm(total=int(len(train_loader.dataset)))
      pbar.set_description(f"Epoch {epoch:02d}")
  
      total_loss = total_correct = total_examples = 0
      for batch in train_loader:
          optimizer.zero_grad()
          y = batch.y[: batch.batch_size]
          y_hat = model(batch.x, batch.edge_index.to(device))[
              : batch.batch_size
          ]
          loss = F.cross_entropy(y_hat, y)
          loss.backward()
          optimizer.step()
  
          total_loss += float(loss) * batch.batch_size
          total_correct += int((y_hat.argmax(dim=-1) == y).sum())
          total_examples += batch.batch_size
          pbar.update(batch.batch_size)
      pbar.close()
  
      return total_loss / total_examples, total_correct / total_examples
  
file_path: pytorch_geometric/examples/reddit.py
imports: import copy
  import os.path
  import time
  import torch
  import torch.nn.functional
  from tqdm import tqdm
  from torch_geometric.datasets import Reddit
  from torch_geometric.loader import NeighborLoader
  from torch_geometric.nn import SAGEConv
label_desc: Use image node_id 1 for calling a global function with example usage: train(epoch) and returns: unknown,  unknown