code: def _global_step(hparams):
      """Adjust global step if a multi-step optimizer is used."""
      step = tf.cast(tf.train.get_or_create_global_step(), tf.float32)
      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.cast(multiplier, tf.float32)
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from six.moves import range
  from tensor2tensor.layers import common_attention
  from tensor2tensor.layers import common_layers
  from tensor2tensor.layers import transformer_glow_layers_ops
  from tensor2tensor.models.transformer import transformer_decoder_layer
  from tensor2tensor.models.transformer import transformer_encoder
  from tensor2tensor.models.transformer import transformer_prepare_encoder
  from tensor2tensor.utils import learning_rate
  from tensor2tensor.utils import mlperf_log
  import tensorflow.compat.v1
  from tensorflow.compat.v1 import estimator
label_desc: Use image node_id 18 for calling a global function with example usage: _global_step(hparams) and returns: unknown, step