code: def learning_rate_schedule(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
docs: Learning rate schedule based on hparams.