code: def __init__(
      self,
      backend,
      initial_lr,
      multiplier,
      start_epoch=0,
      end_epoch=None,
      staircase=True,
      momentum_correction=True,
      steps_per_epoch=None,
      *args,
  ):
      super(LearningRateScheduleCallbackImpl, self).__init__(*args)
      self.backend = backend
      self.start_epoch = start_epoch
      self.end_epoch = end_epoch
      self.staircase = staircase
      self.momentum_correction = momentum_correction
      self.initial_lr = initial_lr
      self.restore_momentum = None
      self.steps_per_epoch = steps_per_epoch
      self.current_epoch = None
  
      # set multiplier, which is a fn(epoch) and is the amount by which self.initial_lr is
      # multiplied by on each batch / epoch begin (depending on whether you set staircase or not)
      if not callable(multiplier):
          # If multiplier is a constant, it corresponds to exponential decay
          self.multiplier = lambda epoch: multiplier ** (
              epoch - start_epoch
          )
      else:
          self.multiplier = multiplier
  
      if self.initial_lr is None:
          raise ValueError("Parameter `initial_lr` is required")
  
imports: from packaging import version
  import warnings
  import horovod.tensorflow
  import tensorflow
label_desc: Use image node_id 1 to create a new LearningRateScheduleCallbackImpl object from inherited base classes: object with example: obj = LearningRateScheduleCallbackImpl(backend, initial_lr, multiplier, start_epoch, end_epoch, staircase, momentum_correction, steps_per_epoch)