def _eval_inverse(self, **kwargs):
        """Return the matrix inverse using Cholesky or LDL (default)
        decomposition as selected with the ``method`` keyword: 'CH' or 'LDL',
        respectively.

        Examples
        ========

        >>> from sympy import SparseMatrix, Matrix
        >>> A = SparseMatrix([
        ... [ 2, -1,  0],
        ... [-1,  2, -1],
        ... [ 0,  0,  2]])
        >>> A.inv('CH')
        Matrix([
        [2/3, 1/3, 1/6],
        [1/3, 2/3, 1/3],
        [  0,   0, 1/2]])
        >>> A.inv(method='LDL') # use of 'method=' is optional
        Matrix([
        [2/3, 1/3, 1/6],
        [1/3, 2/3, 1/3],
        [  0,   0, 1/2]])
        >>> A * _
        Matrix([
        [1, 0, 0],
        [0, 1, 0],
        [0, 0, 1]])

        """
        sym = self.is_symmetric()
        M = self.as_mutable()
        I = M.eye(M.rows)
        if not sym:
            t = M.T
            r1 = M[0, :]
            M = t*M
            I = t*I
        method = kwargs.get('method', 'LDL')
        if "LDL" in method:
            solve = M._LDL_solve
        elif method == "CH":
            solve = M._cholesky_solve
        else:
            raise NotImplementedError(
                'Method may be "CH" or "LDL", not %s.' % method)
        rv = M.hstack(*[solve(I[:, i]) for i in range(I.cols)])
        if not sym:
            scale = (r1*rv[:, 0])[0, 0]
            rv /= scale
        return self._new(rv)
dataset/ETHPy150Open sympy/sympy/sympy/matrices/sparse.py/SparseMatrix._eval_inverse/swapped operands of `in`