class_docstr: Base class for activation or cost functions and their derivatives. Child classes
  can either implement the below ``__call__`` and ``bprop`` methods, or alternatively
  define ``self.func`` and ``self.funcgrad``. The latter is typically used for code
  compactness when the operations can be fit into a lambda function.
class_docstr_tok: ["Base","class","for","activation","or","cost","functions","and","their","derivatives",".","Child","classes","can","either","implement","the","below","``","__call__","``","and","``","bprop","``","methods",",","or","alternatively","define","``","self.func","``","and","``","self.funcgrad","``",".","The","latter","is","typically","used","for","code","compactness","when","the","operations","can","be","fit","into","a","lambda","function","."]
docstr: Class constructor.
docstr_tok: ["Class","constructor","."]
code: def __init__(self, name=None):
      """
      Class constructor.
      """
      super(Transform, self).__init__(name)
      self.is_mklop = False
  
imports: from neon import NervanaObject
label_desc: Use image node_id 1 to create a new Transform object from inherited base classes: NervanaObject with example: obj = Transform(name)