code: def _adjust_learning_rate(self, epoch):
      old_lr = self.backend.get_value(self.model.optimizer.lr)
      new_lr = self.initial_lr * self.multiplier(epoch)
      self.backend.set_value(self.model.optimizer.lr, new_lr)
  
      if (
          hasattr(self.model.optimizer, "momentum")
          and self.momentum_correction
      ):
          # See the paper cited above for more information about momentum correction.
          self.restore_momentum = self.backend.get_value(
              self.model.optimizer.momentum
          )
          self.backend.set_value(
              self.model.optimizer.momentum,
              self.restore_momentum * new_lr / old_lr,
          )
  
imports: from packaging import version
  import warnings
  import horovod.tensorflow
  import tensorflow
label_desc: Use image node_id 3 for calling the LearningRateScheduleCallbackImpl obj's underlying member method code with example usage: obj._adjust_learning_rate(epoch) without return types