code: def product_learning_rate(
      step, total_train_steps, factors=gin.REQUIRED, offset=0
  ):
      """Learning rate is the product of one or more factors.
  
      Takes a list of factors which are either numbers or learning-rate functions
      each taking step and total_train_step arguments.
  
      If `offset` is nonzero, then subtract offset from the step and from
      total_train_steps before computing the learning rate.
  
      Args:
        step: a tf.Scalar
        total_train_steps: a number
        factors: a list of numbers and/or functions
        offset: an optional float
  
      Returns:
        a tf.Scalar, the learning rate for the step.
      """
      ret = 1.0
      for f in factors:
          ret *= (
              f(step - offset, total_train_steps - offset)
              if callable(f)
              else f
          )
      return ret
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import functools
  import re
  import gin
  from mesh_tensorflow import layers
  from mesh_tensorflow import ops_with_redefined_builtins
  import tensorflow.compat.v1
label_desc: Use image node_id 6 for calling a global function with example usage: product_learning_rate(step, total_train_steps, factors, offset) and returns: ret