class_docstr: Initializes array with scaled Gaussian distribution.
  
  Each element of the array is initialized by the value drawn
  independently from Gaussian distribution whose mean is 0,
  and standard deviation is
  :math:`scale \times \sqrt{\frac{2}{fan}}`.
  If ``fan_option == 'fan_in'``, :math:`fan` is the
  number of input units.
  If ``fan_option == 'fan_out'``, :math:`fan` is the
  number of output units.
  
  Reference:  He et al., https://arxiv.org/abs/1502.01852
  
  Args:
      scale (float): A constant that determines the scale
          of the standard deviation.
      dtype: Data type specifier.
      fan_option ({'fan_in', 'fan_out'}): Decides how to compute the
          standard deviation. The default value is ``'fan_in'``.
      rng (xp.random.RandomState): Pseudo-random number generator.
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code: def __init__(
      self, scale=1.0, dtype=None, fan_option="fan_in", **kwargs
  ):
      self.scale = scale
      self.fan_option = fan_option
      rng = None
      if kwargs:
          (rng,) = argument.parse_kwargs(kwargs, ("rng", rng))
      self.rng = rng
      super(HeNormal, self).__init__(dtype)
  
imports: import numpy
  from chainer import backend
  from chainer.backends import cuda
  from chainer import initializer
  from chainer.utils import argument
label_desc: Use image node_id 1 to create a new HeNormal object from inherited base classes: initializer with example: obj = HeNormal(scale, dtype, fan_option)