class_docstr: Initializes parameters with a gaussian distribution with the provided mean
  and standard deviation. Defaults to (loc=0, scale=1)
class_docstr_tok: ["Initializes","parameters","with","a","gaussian","distribution","with","the","provided","mean","and","standard","deviation",".","Defaults","to","(","loc=0",",","scale=1",")"]
docstr: Fill the provided tensor with random values drawn from a gaussian
  distribution.
  
  Args:
      params (tensor): Tensor to fill
docstr_tok: ["Fill","the","provided","tensor","with","random","values","drawn","from","a","gaussian","distribution",".","Args",":","params","(","tensor",")",":","Tensor","to","fill"]
code: def fill(self, param):
      """
      Fill the provided tensor with random values drawn from a gaussian
      distribution.
  
      Args:
          params (tensor): Tensor to fill
      """
      param[:] = self.be.rng.normal(self.loc, self.scale, param.shape)
  
imports: import numpy
  from neon import NervanaObject
  from neon.backends.backend import Tensor
label_desc: Use image node_id 2 for calling the Gaussian obj's underlying member method code with example usage: obj.fill(param) without return types