code: def learning_rate_schedule(hparams):
      """Learning rate schedule based on hparams."""
      mlperf_log.transformer_print(key=mlperf_log.OPT_LR, deferred=True)
      mlperf_log.transformer_print(
          key=mlperf_log.OPT_LR_WARMUP_STEPS,
          value=hparams.learning_rate_warmup_steps,
      )
      step_num = _global_step(hparams)
      schedule_string = hparams.learning_rate_schedule
      names = schedule_string.split("*")
      names = [name.strip() for name in names if name.strip()]
      ret = tf.constant(1.0)
      for name in names:
          ret *= learning_rate_factor(name, step_num, hparams)
      return ret
  
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 2 for calling a global function with example usage: learning_rate_schedule(hparams) and returns: ret