class_docstr: Initializes parameters with a zero-mean Gaussian distribution. The standard deviation
  is automatically set as :math:`\sigma=\sqrt{2 / n_{in}}`, where :math:`n_{in}` is
  the input dimension of the tensor.
  
  
  Based on the initializer described in: http://arxiv.org/pdf/1502.01852.pdf.
class_docstr_tok: ["Initializes","parameters","with","a","zero-mean","Gaussian","distribution",".","The","standard","deviation","is","automatically","set","as",":","math",":","`","\\sigma=\\sqrt","{","2","\/","n_","{","in","}","}","`",",","where",":","math",":","`","n_","{","in","}","`","is","the","input","dimension","of","the","tensor",".","Based","on","the","initializer","described","in",":","http",":","\/\/arxiv.org\/pdf\/1502.01852.pdf","."]
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
      """
      fan_in = param.shape[0 if self.local else 1]
      scale = np.sqrt(2.0 / fan_in)
      param[:] = self.be.rng.normal(0, 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 Kaiming obj's underlying member method code with example usage: obj.fill(param) without return types