class_docstr: Initialization class that has gain property
  
  Parameters
  ----------
  gain : float
      Scales variance of the distribution by this factor. Value ``2``
      is a suitable choice for layers that have Relu non-linearity.
      Defaults to ``1``.
  
  seed : None or int
      Random seed. Integer value will make results reproducible.
      Defaults to ``None``.
class_docstr_tok: ["Initialization","class","that","has","gain","property","Parameters","--","--","--","--","--","gain",":","float","Scales","variance","of","the","distribution","by","this","factor",".","Value","``","2","``","is","a","suitable","choice","for","layers","that","have","Relu","non-linearity",".","Defaults","to","``","1","``",".","seed",":","None","or","int","Random","seed",".","Integer","value","will","make","results","reproducible",".","Defaults","to","``","None","``","."]
code: def __init__(self, gain=1.0, seed=None):
      self.gain = gain
      self.seed = seed
      super(InitializerWithGain, self).__init__()
  
imports: import abc
  import six
  import numpy
  import tensorflow
  from neupy.core.docs import SharedDocsABCMeta
label_desc: Use image node_id 1 to create a new InitializerWithGain object from inherited base classes: Initializer with example: obj = InitializerWithGain(gain, seed)