number generator for sampling
  * **out** (*Tensor**, **optional*) -- the output tensor.

Example:
  >>> torch.normal(mean=torch.arange(1., 11.), std=torch.arange(1, 0, -0.1))
  tensor([  1.0425,   3.5672,   2.7969,   4.2925,   4.7229,   6.2134,
            8.0505,   8.1408,   9.0563,  10.0566])

torch.normal(mean=0.0, std, *, out=None) -> Tensor
Similar to the function above, but the means are shared among all
   drawn elements.
Parameters:
      * mean (float, optional) -- the mean for all
        distributions
  * **std** (*Tensor*) -- the tensor of per-element standard
    deviations

Keyword Arguments:
      out (Tensor, optional) -- the output tensor.
Example:
  >>> torch.normal(mean=0.5, std=torch.arange(1., 6.))
  tensor([-1.2793, -1.0732, -2.0687,  5.1177, -1.2303])

torch.normal(mean, std=1.0, *, out=None) -> Tensor
Similar to the function above, but the standard deviations are