chunk_content: ) method based on your configuration:
  
  
  	Copied
    for epoch in range(num_epochs):
        with TorchTracemalloc() as tracemalloc:
            model.train()
            total_loss = 0
            for step, batch in enumerate(tqdm(train_dataloader)):
                outputs = model(**batch)
                loss = outputs.loss
                total_loss += loss.detach().float()
  +             accelerator.backward(loss)
                optimizer.step()
              