code: def legacy_learning_rate_schedule(hparams):
    
    step_num = _global_step(hparams)
    warmup_steps = tf.to_float(hparams.learning_rate_warmup_steps)
    if hparams.learning_rate_decay_scheme == "noam":
      ret = 5000.0 * hparams.hidden_size**-0.5 * tf.minimum(
          (step_num + 1) * warmup_steps**-1.5, (step_num + 1)**-0.5)
    else:
      warmup_steps = hparams.learning_rate_warmup_steps
      warmup = _learning_rate_warmup(warmup_steps, hparams=hparams)
      decay = _learning_rate_decay(hparams, warmup_steps)
      ret = tf.where(step_num < warmup_steps, warmup, decay)
    optimizer_correction = 0.002 if "adam" in hparams.optimizer else 1.0
    tf.logging.info("Base learning rate: %f", hparams.learning_rate)
    return ret * optimizer_correction * hparams.learning_rate
docs: Backwards-compatible learning-rate schedule.