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.
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code: def __call__(self):
      learning_rate = lr.PolynomialDecay(
          learning_rate=self.learning_rate,
          decay_steps=self.epochs,
          end_lr=self.end_lr,
          power=self.power,
          last_epoch=self.last_epoch,
      )
      if self.warmup_epoch > 0:
          learning_rate = lr.LinearWarmup(
              learning_rate=learning_rate,
              warmup_steps=self.warmup_epoch,
              start_lr=0.0,
              end_lr=self.learning_rate,
              last_epoch=self.last_epoch,
          )
      return learning_rate
  
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 2 for calling the Linear obj's underlying member method code with example usage: obj.__call__() and returns: learning_rate