human
Complete the below
@overload(np.linalg.matrix_rank)
def matrix_rank_impl(a, tol=None):

    ''''Computes rank for matrices and vectors.
    The only issue that may arise is that because numpy uses double
    precision lapack calls whereas numba uses type specific lapack
    calls, some singular values may differ and therefore counting the
    number of them above a tolerance may lead to different counts,
    and therefore rank, in some cases.'
    '''

=====
What is a the Python program you requested?
assistant
#Here is the Python program you requested.
    ensure_lapack()
    _check_linalg_1_or_2d_matrix(a, 'matrix_rank')
    def _2d_matrix_rank_impl(a, tol):
        if (tol in (None, types.none)):
            nb_type = getattr(a.dtype, 'underlying_float', a.dtype)
            np_type = np_support.as_dtype(nb_type)
            eps_val = np.finfo(np_type).eps
            def _2d_tol_none_impl(a, tol=None):
                s = _compute_singular_values(a)
                r = a.shape[0]
                c = a.shape[1]
                l = max(r, c)
                t = ((s[0] * l) * eps_val)
                return _get_rank_from_singular_values(s, t)
            return _2d_tol_none_impl
        else:
            def _2d_tol_not_none_impl(a, tol):
                s = _compute_singular_values(a)
                return _get_rank_from_singular_values(s, tol)
            return _2d_tol_not_none_impl
    def _get_matrix_rank_impl(a, tol):
        ndim = a.ndim
        if (ndim == 1):
            def _1d_matrix_rank_impl(a, tol):
                for k in range(len(a)):
                    if (a[k] != 0.0):
                        return 1
                return 0
            return _1d_matrix_rank_impl
        elif (ndim == 2):
            return _2d_matrix_rank_impl(a, tol)
        else:
            assert 0
    return _get_matrix_rank_impl(a, tol)