\mu_x^)(y_{i} - \mu_y^)}{\sum^{N}_{i = 1}w_i~-~1}
where w denotes "fweights" or "aweights" based on whichever is
   provided, or w = fweights \times aweights if both are provided, and
   \mu_x^* = \frac{\sum^{N}{i = 1}w_ix }{\sum^{N}_{i = 1}w_i} is
   the weighted mean of the variable.
Parameters:
      input (Tensor) -- A 2D matrix containing multiple
      variables and observations, or a Scalar or 1D vector
      representing a single variable.
Keyword Arguments:
      * correction (int, optional) -- difference between the
        sample size and sample degrees of freedom. Defaults to
        Bessel's correction, "correction = 1" which returns the
        unbiased estimate, even if both "fweights" and "aweights" are
        specified. "correction = 0" will return the simple average.
        Defaults to "1".
  * **fweights** (*tensor**, **optional*) -- A Scalar or 1D tensor
    of observation vector frequencies representing the number of
