code: def _learning_rate_warmup(
      warmup_steps, warmup_schedule="exp", hparams=None
  ):
      """Learning rate warmup multiplier."""
      if not warmup_steps:
          return tf.constant(1.0)
  
      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(1.0) - start) / warmup_steps
          ) * global_step + start
  
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 8 for calling a global function with example usage: _learning_rate_warmup(warmup_steps, warmup_schedule, hparams) and returns: tf, unknown, unknown