def METHOD_NAME(grad, l1_ratio=0.5):
    """Compute divergence + id of image gradient + id.
    Parameters
    ----------
    grad : ndarray, shape (4, nx, ny, nz, ...)
        where `img_shape` is the shape of the brain bounding box, and
        n_axes = len(img_shape).
    l1_ratio : float in the interval [0, 1]; optional (default .5)
        Constant that mixes L1 and spatial prior terms in the penalization.
    Returns
    -------
    res : ndarray, shape (nx, ny, nz, ...)
        The computed divergence + id operator.
    Raises
    ------
    RuntimeError
    """
    if not (0.0 <= l1_ratio <= 1.0):
        raise RuntimeError(
            f"l1_ratio must be in the interval [0, 1]; got {l1_ratio}"
        )
    res = np.zeros(grad.shape[1:])
    # the divergence part
    for d in range(grad.shape[0] - 1):
        this_grad = np.rollaxis(grad[d], d)
        this_res = np.rollaxis(res, d)
        this_res[:-1] += this_grad[:-1]
        this_res[1:-1] -= this_grad[:-2]
        if len(this_grad) > 1:
            this_res[-1] -= this_grad[-2]
    res *= 1.0 - l1_ratio
    # the identity part
    res -= l1_ratio * grad[-1]
    return res