torch.std_mean
torch.std_mean(input, dim=None, *, correction=1, keepdim=False, out=None)
Calculates the standard deviation and mean over the dimensions
   specified by "dim". "dim" can be a single dimension, list of
   dimensions, or "None" to reduce over all dimensions.
The standard deviation (\sigma) is calculated as
  \sigma = \sqrt{\frac{1}{N - \delta
  N}\sum_{i=0}^{N-1}(x_i-\bar{x})^2}

where x is the sample set of elements, \bar{x} is the sample mean,
   N is the number of samples and \delta N is the "correction".
If "keepdim" is "True", the output tensor is of the same size as
   "input" except in the dimension(s) "dim" where it is of size 1.
   Otherwise, "dim" is squeezed (see "torch.squeeze()"), resulting in
   the output tensor having 1 (or "len(dim)") fewer dimension(s).
Parameters:
      * input (Tensor) -- the input tensor.
  * **dim** (*int** or **tuple of ints**, **optional*) -- the
