class_docstr: Polynomial learning rate schedule.
  
  This schedule takes as input the total number of epochs :math:`T` and a power :math:`\beta`,
  and produces the learning schedule:
  
  .. math::
      \alpha(t) = \alpha_\circ \times\left(1-\frac{t}{T}\right)^\beta
  
  where :math:`\alpha_\circ` is the initial learning rate.
class_docstr_tok: ["Polynomial","learning","rate","schedule",".","This","schedule","takes","as","input","the","total","number","of","epochs",":","math",":","`","T","`","and","a","power",":","math",":","`","\\beta","`",",","and","produces","the","learning","schedule",":","..","math",":",":","\\alpha","(","t",")","=","\\alpha_\\circ","\\times\\left","(","1-\\frac","{","t","}","{","T","}","\\right",")","^\\beta","where",":","math",":","`","\\alpha_\\circ","`","is","the","initial","learning","rate","."]
docstr: Returns the current learning rate given the epoch and initial learning rate.
  
  Arguments:
      learning_rate (float): Initial learning rate
      epoch (int): Current epoch, used to calculate the adjusted learning rate.
  
  Returns:
      (float): The adjusted learning rate.
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code: def get_learning_rate(self, learning_rate, epoch):
      """
      Returns the current learning rate given the epoch and initial learning rate.
  
      Arguments:
          learning_rate (float): Initial learning rate
          epoch (int): Current epoch, used to calculate the adjusted learning rate.
  
      Returns:
          (float): The adjusted learning rate.
      """
      return float(
          learning_rate
          * (1.0 - epoch // self.total_epochs) ** self.power
      )
  
imports: from __future__ import division
  from neon import NervanaObject
  from neon.util.persist import load_class
  import logging
  import numpy
label_desc: Use image node_id 2 for calling the PolySchedule obj's underlying member method code with example usage: obj.get_learning_rate(learning_rate, epoch) and returns: float