device with the CPU.
See also:
 "torch.linalg.cholesky_ex()" for a version of this operation that
 skips the (slow) error checking by default and instead returns
 the debug information. This makes it a faster way to check if a
 matrix is positive-definite.

 "torch.linalg.eigh()" for a different decomposition of a
 Hermitian matrix. The eigenvalue decomposition gives more
 information about the matrix but it slower to compute than the
 Cholesky decomposition.

Parameters:
      A (Tensor) -- tensor of shape (, n, n)* where *** is
      zero or more batch dimensions consisting of symmetric or
      Hermitian positive-definite matrices.
Keyword Arguments:
      * upper (bool, optional) -- whether to return an upper
        triangular matrix. The tensor returned with upper=True is the
        conjugate transpose of the tensor returned with upper=False.
  * **out** (*Tensor**, **optional*) -- output tensor. Ignored if
