code: def _global_step(hparams):
    
    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)
docs: Adjust global step if a multi-step optimizer is used.