class_docstr: Adafactor with Multiple LR schedule.
docstr: Construct a new Adafactor optimizer.
  
  See class comment.
  
  Args:
    variable_search: list of regex strings to use alt learning rate.
    alt_lr_schedules: list of learning_rate_schedules
    **kwargs: Adafactor keyword args
  
  Raises:
    ValueError: if absolute_update_scale and relative_update_scale_fn are both
      present or both absent.
docstr_tok: ["Construct","a","new","Adafactor","optimizer",".","See","class","comment",".","Args",":","variable_search",":","list","of","regex","strings","to","use","alt","learning","rate",".","alt_lr_schedules",":","list","of","learning_rate_schedules","*","*","kwargs",":","Adafactor","keyword","args","Raises",":","ValueError",":","if","absolute_update_scale","and","relative_update_scale_fn","are","both","present","or","both","absent","."]
code: def __init__(
      self, variable_search=None, alt_lr_schedules=None, **kwargs
  ):
      """Construct a new Adafactor optimizer.
  
      See class comment.
  
      Args:
        variable_search: list of regex strings to use alt learning rate.
        alt_lr_schedules: list of learning_rate_schedules
        **kwargs: Adafactor keyword args
  
      Raises:
        ValueError: if absolute_update_scale and relative_update_scale_fn are both
          present or both absent.
      """
      super(AdafactorWithMultiLRSchedule, 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 AdafactorWithMultiLRSchedule object from inherited base classes: AdafactorOptimizer with example: obj = AdafactorWithMultiLRSchedule(variable_search, alt_lr_schedules)