Adjust global step if a multi-step optimizer is used.

def _global_step(hparams):
  """Adjust global step if a multi-step optimizer is used."""
  step = tf.to_float(tf.train.get_or_create_global_step())
  multiplier = hparams.optimizer_multistep_accumulate_steps
  if not multiplier:
    return step

  tf.logging.info("Dividing global step by %d for multi-step optimizer."
                  % multiplier)
  return step / tf.to_float(multiplier)