class_docstr: Linear learning rate decay
  Args:
      lr (float): The initial learning rate. It is a python float number.
      epochs(int): The decay step size. It determines the decay cycle.
      end_lr(float, optional): The minimum final learning rate. Default: 0.0001.
      power(float, optional): Power of polynomial. Default: 1.0.
      last_epoch (int, optional):  The index of last epoch. Can be set to restart training. Default: -1, means initial learning rate.
class_docstr_tok: ["Linear","learning","rate","decay","Args",":","lr","(","float",")",":","The","initial","learning","rate",".","It","is","a","python","float","number",".","epochs","(","int",")",":","The","decay","step","size",".","It","determines","the","decay","cycle",".","end_lr","(","float",",","optional",")",":","The","minimum","final","learning","rate",".","Default",":","0.0001.","power","(","float",",","optional",")",":","Power","of","polynomial",".","Default",":","1.0.","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,
      epochs,
      step_each_epoch,
      end_lr=0.0,
      power=1.0,
      warmup_epoch=0,
      last_epoch=-1,
      **kwargs,
  ):
      super(Linear, self).__init__()
      self.learning_rate = learning_rate
      self.epochs = epochs * step_each_epoch
      self.end_lr = end_lr
      self.power = power
      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 Linear object from inherited base classes: object with example: obj = Linear(learning_rate, epochs, step_each_epoch, end_lr, power, warmup_epoch, last_epoch)