times each observation should be repeated. Its numel must
        equal the number of columns of "input". Must have integral
        dtype. Ignored if "None". Defaults to `None.
  * **aweights** (*tensor**, **optional*) -- A Scalar or 1D array
    of observation vector weights. These relative weights are
    typically large for observations considered âimportantâ and
    smaller for observations considered less âimportantâ. Its
    numel must equal the number of columns of "input". Must have
    floating point dtype. Ignored if "None". *Defaults to
    ``None`*.

Returns:
      (Tensor) The covariance matrix of the variables.
See also: "torch.corrcoef()" normalized covariance matrix.
Example::
      >>> x = torch.tensor([[0, 2], [1, 1], [2, 0]]).T
      >>> x
      tensor([[0, 1, 2],
              [2, 1, 0]])
      >>> torch.cov(x)
      tensor([[ 1., -1.],
              [-1.,  1.]])
      >>> torch.cov(x, correction=0)