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.
class_docstr_tok: ["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","."]
docstr: Element-wise clip a gradient or parameter tensor to between
  ``-abs_bound`` and ``+abs_bound``.
  
  Arguments:
      v (tensor): Tensor of gradients or parameters for a single layer
      abs_bound (float, optional): Value to element-wise clip gradients
                                   or parameters. Defaults to None.
  
  Returns:
      v (tensor): Tensor of clipped gradients or parameters.
docstr_tok: ["Element-wise","clip","a","gradient","or","parameter","tensor","to","between","``","-abs_bound","``","and","``","+abs_bound","``",".","Arguments",":","v","(","tensor",")",":","Tensor","of","gradients","or","parameters","for","a","single","layer","abs_bound","(","float",",","optional",")",":","Value","to","element-wise","clip","gradients","or","parameters",".","Defaults","to","None",".","Returns",":","v","(","tensor",")",":","Tensor","of","clipped","gradients","or","parameters","."]
code: def clip_value(self, v, abs_bound=None):
      """
      Element-wise clip a gradient or parameter tensor to between
      ``-abs_bound`` and ``+abs_bound``.
  
      Arguments:
          v (tensor): Tensor of gradients or parameters for a single layer
          abs_bound (float, optional): Value to element-wise clip gradients
                                       or parameters. Defaults to None.
  
      Returns:
          v (tensor): Tensor of clipped gradients or parameters.
  
      """
      if abs_bound:
          return self.be.clip(v, -abs(abs_bound), abs(abs_bound))
      else:
          return v
  
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 4 for calling the Optimizer obj's underlying member method code with example usage: obj.clip_value(v, abs_bound) and returns: self, v