class_docstr: Initializes parameters with a gaussian distribution with the provided mean
  and standard deviation. Defaults to (loc=0, scale=1)
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docstr: Class constructor.
  
  Args:
      loc   (float, optional): Mean parameter (mu). Defaults to 0.
      scale (float, optional): Standard deviation parameter (sigma). Defaults to 1.
      name (string, optional): Name to assign an instance of this class.
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code: def __init__(self, loc=0.0, scale=1.0, name="gaussianInit"):
      """
      Class constructor.
  
      Args:
          loc   (float, optional): Mean parameter (mu). Defaults to 0.
          scale (float, optional): Standard deviation parameter (sigma). Defaults to 1.
          name (string, optional): Name to assign an instance of this class.
      """
      super(Gaussian, self).__init__(name=name)
      self.loc, self.scale = (loc, scale)
  
imports: import numpy
  from neon import NervanaObject
  from neon.backends.backend import Tensor
label_desc: Use image node_id 1 to create a new Gaussian object from inherited base classes: Initializer with example: obj = Gaussian(loc, scale, name)