class_docstr: An  Adam optimizer that includes "correct" L2 weight decay.
  
  Adam optimizer that is able to processes multiple learning rate schedules
  for different variables within the optimizer class itself. This function
  takes in a list of variables to search and a list of corresponding
  alt lr schedules.
  
  The original variables are processed with the original learning rate
  controlled from outside the loop.
  
  Learning rate schedule should use the product learning rate.
docstr: Adam LR with multi LR schedule.
  
  Args:
    variable_search: list of regex strings to use alt learning rate.
    alt_lr_schedules: list of learning_rate_schedules
    **kwargs: Adam keyword args
docstr_tok: ["Adam","LR","with","multi","LR","schedule",".","Args",":","variable_search",":","list","of","regex","strings","to","use","alt","learning","rate",".","alt_lr_schedules",":","list","of","learning_rate_schedules","*","*","kwargs",":","Adam","keyword","args"]
code: def __init__(
      self, variable_search=None, alt_lr_schedules=None, **kwargs
  ):
      """Adam LR with multi LR schedule.
  
      Args:
        variable_search: list of regex strings to use alt learning rate.
        alt_lr_schedules: list of learning_rate_schedules
        **kwargs: Adam keyword args
      """
      super(AdamWithMultiLRSchedule, self).__init__(**kwargs)
      self.variable_search = variable_search
      self.alt_lr_schedules = alt_lr_schedules
  
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 1 to create a new AdamWithMultiLRSchedule object from inherited base classes: AdamWeightDecayOptimizer with example: obj = AdamWithMultiLRSchedule(variable_search, alt_lr_schedules)