class_docstr: The optimizer class handles the gradient update stage of training a neural network.
  Given the current parameters :math:`w`, update parameters
  :math:`\Delta w`, and current state :math:`s`, the optimizer specifies an
  algorithm for performing the update.
  
  This base class contains to helper functions for scaling the gradients.
  specifices the abstract method optimize, which subclasses should implement. The optimize
  method is called at every minibatch to update the layer parameters.
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docstr: Update the parameters for a provided list of layers.
  
  Args:
      layer_list (list): List of layers to optimize
      epoch (integer): Epoch count of training
docstr_tok: ["Update","the","parameters","for","a","provided","list","of","layers",".","Args",":","layer_list","(","list",")",":","List","of","layers","to","optimize","epoch","(","integer",")",":","Epoch","count","of","training"]
code: def optimize(self, layer_list, epoch):
      """
      Update the parameters for a provided list of layers.
  
      Args:
          layer_list (list): List of layers to optimize
          epoch (integer): Epoch count of training
      """
      raise NotImplementedError()
  
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 Optimizer obj's underlying member method code with example usage: obj.optimize(layer_list, epoch) without return types