shared among all drawn elements.
Parameters:
      * mean (Tensor) -- the tensor of per-element means
  * **std** (*float**, **optional*) -- the standard deviation for
    all distributions

Keyword Arguments:
      out (Tensor, optional) -- the output tensor
Example:
  >>> torch.normal(mean=torch.arange(1., 6.))
  tensor([ 1.1552,  2.6148,  2.6535,  5.8318,  4.2361])

torch.normal(mean, std, size, *, out=None) -> Tensor
Similar to the function above, but the means and standard
   deviations are shared among all drawn elements. The resulting
   tensor has size given by "size".
Parameters:
      * mean (float) -- the mean for all distributions
  * **std** (*float*) -- the standard deviation for all
    distributions

  * **size** (*int**...*) -- a sequence of integers defining the
    shape of the output tensor.

Keyword Arguments:
      out (Tensor, optional) -- the output tensor.
Example: