torch.cov
torch.cov(input, *, correction=1, fweights=None, aweights=None) -> Tensor
Estimates the covariance matrix of the variables given by the
   "input" matrix, where rows are the variables and columns are the
   observations.
A covariance matrix is a square matrix giving the covariance of
   each pair of variables. The diagonal contains the variance of each
   variable (covariance of a variable with itself). By definition, if
   "input" represents a single variable (Scalar or 1D) then its
   variance is returned.
The unbiased sample covariance of the variables x and y is given
   by:
  \text{cov}_w(x,y) = \frac{\sum^{N}_{i = 1}(x_{i} -
  \bar{x})(y_{i} - \bar{y})}{N~-~1}

where \bar{x} and \bar{y} are the simple means of the x and y
   respectively.
If "fweights" and/or "aweights" are provided, the unbiased weighted
   covariance is calculated, which is given by:
  \text{cov}_w(x,y) = \frac{\sum^{N}_{i = 1}w_i(x_{i} -
