def METHOD_NAME(workload: spec.Workload,
                  current_param_container: spec.ParameterContainer,
                  current_params_types: spec.ParameterTypeTree,
                  model_state: spec.ModelAuxiliaryState,
                  hyperparameters: spec.Hyperparameters,
                  batch: Dict[str, spec.Tensor],
                  loss_type: spec.LossType,
                  optimizer_state: spec.OptimizerState,
                  eval_results: List[Tuple[int, float]],
                  global_step: int,
                  rng: spec.RandomState) -> spec.UpdateReturn:
  """Return (updated_optimizer_state, updated_params, updated_model_state)."""
  del current_params_types
  del loss_type
  del eval_results
  current_model = current_param_container
  current_model.train()
  optimizer_state['optimizer'].zero_grad()
  logits_batch, new_model_state = workload.model_fn(
      params=current_model,
      augmented_and_preprocessed_input_batch=batch,
      model_state=model_state,
      mode=spec.ForwardPassMode.TRAIN,
      rng=rng,
      update_batch_norm=True)
  label_smoothing = (
      hyperparameters.label_smoothing if hasattr(hyperparameters,
                                                 'label_smoothing') else 0.0)
  loss_dict = workload.loss_fn(
      label_batch=batch['targets'],
      logits_batch=logits_batch,
      mask_batch=batch.get('weights'),
      label_smoothing=label_smoothing)
  summed_loss = loss_dict['summed']
  n_valid_examples = loss_dict['n_valid_examples']
  if USE_PYTORCH_DDP:
    # Use dist_nn.all_reduce to ensure correct loss and gradient scaling.
    summed_loss = dist_nn.all_reduce(summed_loss)
    n_valid_examples = dist_nn.all_reduce(n_valid_examples)
  loss = summed_loss / n_valid_examples
  loss.backward()
  if hasattr(hyperparameters, 'grad_clip'):
    grad_clip = hyperparameters.grad_clip
    torch.nn.utils.clip_grad_norm_(
        current_model.parameters(), max_norm=grad_clip)
  optimizer_state['optimizer'].step()
  optimizer_state['scheduler'].step()
  # Log training metrics - loss, grad_norm, batch_size.
  if global_step <= 10 or global_step % 500 == 0:
    with torch.no_grad():
      parameters = [p for p in current_model.parameters() if p.grad is not None]
      grad_norm = torch.norm(
          torch.stack([torch.norm(p.grad.detach(), 2) for p in parameters]), 2)
    if workload.metrics_logger is not None:
      workload.metrics_logger.append_scalar_metrics(
          {
              'loss': loss.item(),
              'grad_norm': grad_norm.item(),
          }, global_step)
    logging.info('%d) loss = %0.3f, grad_norm = %0.3f',
                 global_step,
                 loss.item(),
                 grad_norm.item())
  return (optimizer_state, current_param_container, new_model_state)