code: def _learning_rate_warmup(warmup_steps, warmup_schedule="exp", hparams=None):
    
    if not warmup_steps:
      return tf.constant(1.)
  
    tf.logging.info("Applying %s learning rate warmup for %d steps",
                    warmup_schedule, warmup_steps)
  
    warmup_steps = tf.to_float(warmup_steps)
    global_step = _global_step(hparams)
  
    if warmup_schedule == "exp":
      return tf.exp(tf.log(0.01) / warmup_steps)**(warmup_steps - global_step)
    else:
      assert warmup_schedule == "linear"
      start = tf.constant(0.35)
      return ((tf.constant(1.) - start) / warmup_steps) * global_step + start
docs: Learning rate warmup multiplier.