None. Default: None.
Raises:
      RuntimeError -- if the "A" matrix or any matrix in a batched
      "A" is not Hermitian     (resp. symmetric) positive-definite. If
      "A" is a batch of matrices,     the error message will include
      the batch index of the first matrix that fails     to meet this
      condition.
Examples:
  >>> A = torch.randn(2, 2, dtype=torch.complex128)
  >>> A = A @ A.T.conj() + torch.eye(2) # creates a Hermitian positive-definite matrix
  >>> A
  tensor([[2.5266+0.0000j, 1.9586-2.0626j],
          [1.9586+2.0626j, 9.4160+0.0000j]], dtype=torch.complex128)
  >>> L = torch.linalg.cholesky(A)
  >>> L
  tensor([[1.5895+0.0000j, 0.0000+0.0000j],
          [1.2322+1.2976j, 2.4928+0.0000j]], dtype=torch.complex128)
  >>> torch.dist(L @ L.T.conj(), A)
  tensor(4.4692e-16, dtype=torch.float64)

  >>> A = torch.randn(3, 2, 2, dtype=torch.float64)
