class_docstr: Kaiming He parameter initialization method based on the
  normal distribution.
  
  Parameters
  ----------
  {InitializerWithGain.Parameters}
  
  Methods
  -------
  {Initializer.Methods}
  
  References
  ----------
  [1] Kaiming He, Xiangyu Zhan, Shaoqing Ren, Jian Sun.
      Delving Deep into Rectifiers: Surpassing Human-Level
      Performance on ImageNet Classification, 2015.
class_docstr_tok: ["Kaiming","He","parameter","initialization","method","based","on","the","normal","distribution",".","Parameters","--","--","--","--","--","{","InitializerWithGain.Parameters","}","Methods","--","--","--","-","{","Initializer.Methods","}","References","--","--","--","--","--","[","1","]","Kaiming","He",",","Xiangyu","Zhan",",","Shaoqing","Ren",",","Jian","Sun",".","Delving","Deep","into","Rectifiers",":","Surpassing","Human-Level","Performance","on","ImageNet","Classification",",","2015","."]
code: def sample(self, shape, return_array=False):
      fan_in, _ = identify_fans(shape)
      variance = 1.0 / fan_in
      std = np.sqrt(self.gain * variance)
  
      normal = Normal(0, std, seed=self.seed)
      return normal.sample(shape, return_array)
  
imports: import abc
  import six
  import numpy
  import tensorflow
  from neupy.core.docs import SharedDocsABCMeta
label_desc: Use image node_id 1 for calling the HeNormal obj's underlying member method code with example usage: obj.sample(shape, return_array) and returns: normal