class_docstr: A learning rate decay policy, where the effective learning rate
  follows a polynomial decay, to be zero by the max_iteration.
  Calculation: base_lr (1 - iter/max_iteration) ^ (power)
  
  
  :param power: coeffient of decay, refer to calculation formula
  :param max_iteration: max iteration when lr becomes zero
  
  >>> poly = Poly(0.5, 2)
  creating: createPoly
code: def __init__(self, power, max_iteration):
      from bigdl.optim.optimizer import Poly as BPoly
  
      self.scheduler = BPoly(power, max_iteration)
  
imports: from abc import ABC, abstractmethod
label_desc: Use image node_id 1 to create a new Poly object from inherited base classes: Scheduler with example: obj = Poly(power, max_iteration)