code: 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)
  
file_path: tensor2tensor/tensor2tensor/utils/learning_rate.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import numpy
  from tensor2tensor.utils import mlperf_log
  import tensorflow.compat.v1
label_desc: Use image node_id 4 for calling a global function with example usage: _global_step(hparams) and returns: unknown, step