class_docstr: Warm up learning rate in exponential mode
  Args:
      steps (int): warm up steps.
      epochs (int|None): use epochs as warm up steps, the priority
          of `epochs` is higher than `steps`. Default: None.
      power (int): Exponential coefficient. Default: 2.
class_docstr_tok: ["Warm","up","learning","rate","in","exponential","mode","Args",":","steps","(","int",")",":","warm","up","steps",".","epochs","(","int|None",")",":","use","epochs","as","warm","up","steps",",","the","priority","of","`","epochs","`","is","higher","than","`","steps","`",".","Default",":","None",".","power","(","int",")",":","Exponential","coefficient",".","Default",":","2","."]
code: def __init__(self, steps=1000, epochs=None, power=2):
      super(ExpWarmup, self).__init__()
      self.steps = steps
      self.epochs = epochs
      self.power = power
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import sys
  import math
  import paddle
  import paddle.nn
  import paddle.optimizer
  import paddle.regularizer
  from ppdet.core.workspace import register, serializable
  import copy
  from .adamw import AdamWDL, build_adamwdl
  from ppdet.utils.logger import setup_logger
label_desc: Use image node_id 1 to create a new ExpWarmup object from inherited base classes: object with example: obj = ExpWarmup(steps, epochs, power)