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{1}{fan_{in}}}`,
  where :math:`fan_{in}` is the number of input units.
  
  Reference: LeCun 98, Efficient Backprop
  http://yann.lecun.com/exdb/publis/pdf/lecun-98b.pdf
  
  Args:
      scale (float): A constant that determines the scale
          of the standard deviation.
      dtype: Data type specifier.
      rng (xp.random.RandomState): Pseudo-random number generator.
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code: def __call__(self, array):
      if self.dtype is not None:
          assert array.dtype == self.dtype, "{} != {}".format(
              array.dtype, self.dtype
          )
      fan_in, fan_out = initializer.get_fans(array.shape)
      s = self.scale * numpy.sqrt(1.0 / fan_in)
      Normal(s, rng=self.rng)(array)
  
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 2 for calling the LeCunNormal obj's underlying member method code with example usage: obj.__call__(array) without return types