class_docstr: Initializes array with a normal distribution.
  
  Each element of the array is initialized by the value drawn
  independently from Gaussian distribution whose mean is 0,
  and standard deviation is ``scale``.
  
  Args:
      scale (float): Standard deviation of Gaussian distribution.
      dtype: Data type specifier.
      rng (xp.random.RandomState): Pseudo-random number generator.
class_docstr_tok: ["Initializes","array","with","a","normal","distribution",".","Each","element","of","the","array","is","initialized","by","the","value","drawn","independently","from","Gaussian","distribution","whose","mean","is","0",",","and","standard","deviation","is","``","scale","``",".","Args",":","scale","(","float",")",":","Standard","deviation","of","Gaussian","distribution",".","dtype",":","Data","type","specifier",".","rng","(","xp.random.RandomState",")",":","Pseudo-random","number","generator","."]
code: def __init__(self, scale=0.05, dtype=None, **kwargs):
      self.scale = scale
      rng = None
      if kwargs:
          (rng,) = argument.parse_kwargs(kwargs, ("rng", rng))
      self.rng = rng
      super(Normal, 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 Normal object from inherited base classes: initializer with example: obj = Normal(scale, dtype)