class_docstr: Piecewise learning rate decay
  Args:
      step_each_epoch(int): steps each epoch
      learning_rate (float): The initial learning rate. It is a python float number.
      step_size (int): the interval to update.
      gamma (float, optional): The Ratio that the learning rate will be reduced. ``new_lr = origin_lr * gamma`` .
          It should be less than 1.0. Default: 0.1.
      last_epoch (int, optional):  The index of last epoch. Can be set to restart training. Default: -1, means initial learning rate.
class_docstr_tok: ["Piecewise","learning","rate","decay","Args",":","step_each_epoch","(","int",")",":","steps","each","epoch","learning_rate","(","float",")",":","The","initial","learning","rate",".","It","is","a","python","float","number",".","step_size","(","int",")",":","the","interval","to","update",".","gamma","(","float",",","optional",")",":","The","Ratio","that","the","learning","rate","will","be","reduced",".","``","new_lr","=","origin_lr","*","gamma","``",".","It","should","be","less","than","1.0",".","Default",":","0.1.","last_epoch","(","int",",","optional",")",":","The","index","of","last","epoch",".","Can","be","set","to","restart","training",".","Default",":","-1",",","means","initial","learning","rate","."]
code: def __init__(
      self,
      learning_rate,
      step_size,
      step_each_epoch,
      gamma,
      warmup_epoch=0,
      last_epoch=-1,
      **kwargs,
  ):
      super(Step, self).__init__()
      self.step_size = step_each_epoch * step_size
      self.learning_rate = learning_rate
      self.gamma = gamma
      self.last_epoch = last_epoch
      self.warmup_epoch = round(warmup_epoch * step_each_epoch)
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from __future__ import unicode_literals
  from paddle.optimizer import lr
  from .lr_scheduler import CyclicalCosineDecay, OneCycleDecay, TwoStepCosineDecay
label_desc: Use image node_id 1 to create a new Step object from inherited base classes: object with example: obj = Step(learning_rate, step_size, step_each_epoch, gamma, warmup_epoch, last_epoch)