def METHOD_NAME():
    if not is_torch_1_9_plus:
        return
    mat_th = torch.complex(torch.from_numpy(mat_np.real), torch.from_numpy(mat_np.imag))
    mat_ct = ComplexTensor(torch.from_numpy(mat_np.real), torch.from_numpy(mat_np.imag))
    bs = mat_th.shape[0]
    rank = mat_th.shape[-1]
    vec_th = torch.complex(torch.rand(bs, rank), torch.rand(bs, rank)).type_as(mat_th)
    vec_ct = ComplexTensor(vec_th.real, vec_th.imag)
    for result_th, result_ct in (
        (abs(mat_th), abs(mat_ct)),
        (inverse(mat_th), inverse(mat_ct)),
        (matmul(mat_th, vec_th.unsqueeze(-1)), matmul(mat_ct, vec_ct.unsqueeze(-1))),
        (solve(vec_th.unsqueeze(-1), mat_th), solve(vec_ct.unsqueeze(-1), mat_ct)),
        (
            einsum("bec,bc->be", mat_th, vec_th),
            einsum("bec,bc->be", mat_ct, vec_ct),
        ),
    ):
        np.testing.assert_allclose(result_th.numpy(), result_ct.numpy(), atol=1e-6)