parameters: (t, fan_in=0, fan_out=0)
docstring: Initialize the values of the input tensor following a Gaussian
      distribution with specific std.
  
      Args:
          fan_in(int): for the weight Tensor of a convolution layer,
              fan_in = nb_channel * kh * kw; for dense layer,
              fan_in = input_feature_length
          fan_out(int): for the convolution layer weight Tensor,
              fan_out = nb_filter * kh * kw; for the weight Tensor of a dense
              layer, fan_out = output_feature_length
  
      Ref Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun: Delving Deep into
      Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
docstring_summary: Initialize the values of the input tensor following a Gaussian
      distribution with specific std.
docstring_tokens: Initialize the values of the input tensor following a Gaussian distribution with specific std .
function: def gaussian(t, fan_in=0, fan_out=0):
      '''Initialize the values of the input tensor following a Gaussian
      distribution with specific std.
  
      Args:
          fan_in(int): for the weight Tensor of a convolution layer,
              fan_in = nb_channel * kh * kw; for dense layer,
              fan_in = input_feature_length
          fan_out(int): for the convolution layer weight Tensor,
              fan_out = nb_filter * kh * kw; for the weight Tensor of a dense
              layer, fan_out = output_feature_length
  
      Ref Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun: Delving Deep into
      Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
      '''
      assert fan_in > 0 or fan_out > 0, \
          'fan_in and fan_out cannot be 0 at the same time'
      avg = 2
      if fan_in * fan_out == 0:
          avg = 1
      std = math.sqrt(2.0 * avg / (fan_in + fan_out))
      t.gaussian(0, std)
function_tokens: def gaussian ( t , fan_in = 0 , fan_out = 0 ) : assert fan_in > 0 or fan_out > 0 , 'fan_in and fan_out cannot be 0 at the same time' avg = 2 if fan_in * fan_out == 0 : avg = 1 std = math . sqrt ( 2.0 * avg / ( fan_in + fan_out ) ) t . gaussian ( 0 , std )