human
Solve the following python programming problem given the following code:
@ verbose 
def iterative_mixed_norm_solver(M, G, alpha, n_mxne_iter, maxit = 3000, tol = 1e-08, verbose = None, active_set_size = 50, debias = True, n_orient = 1, solver = 'auto') : 
    "Solves L0.5/L2 mixed-norm inverse problem with active set strategy

    Algorithm is detailed in:

    Strohmeier D., Haueisen J., and Gramfort A.:
    Improved MEG/EEG source localization with reweighted mixed-norms,
    4th International Workshop on Pattern Recognition in Neuroimaging,
    Tuebingen, 2014

    Parameters
    ----------
    M : array, shape (n_sensors, n_times)
        The data.
    G : array, shape (n_sensors, n_dipoles)
        The gain matrix a.k.a. lead field.
    alpha : float
        The regularization parameter. It should be between 0 and 100.
        A value of 100 will lead to an empty active set (no active source).
    n_mxne_iter : int
        The number of MxNE iterations. If > 1, iterative reweighting
        is applied.
    maxit : int
        The number of iterations.
    tol : float
        Tolerance on dual gap for convergence checking.
    verbose : bool, str, int, or None
        If not None, override default verbose level (see mne.verbose).
    active_set_size : int
        Size of active set increase at each iteration.
    debias : bool
        Debias source estimates.
    n_orient : int
        The number of orientation (1 : fixed or 3 : free or loose).
    solver : 'prox' | 'cd' | 'bcd' | 'auto'
        The algorithm to use for the optimization.

    Returns
    -------
    X : array, shape (n_active, n_times)
        The source estimates.
    active_set : array
        The mask of active sources.
    E : list
        The value of the objective function over the iterations.
    " 
    def g(w) : 
        return np.sqrt (np.sqrt (groups_norm2 (w.copy (), n_orient))) 
def gprime(w) : 
        return (2.0 * np.repeat (g (w), n_orient).ravel ()) 
E = list () 
    active_set = np.ones (G.shape [1], dtype = np.bool) 
    weights = np.ones (G.shape [1]) 
    X = np.zeros ((G.shape [1],...

    
    

    Fix the buggy line: (X, _active_set, _) = mixed_norm_solver (M, G_tmp, alpha, debias = False, n_orient = weights, maxit = maxit, tol = tol, active_set_size = None, solver = solver, verbose = verbose)
assistant
#Here is Python program to fix the buggy line: (X, _active_set, _) = mixed_norm_solver (M, G_tmp, alpha, debias = False, n_orient = weights, maxit = maxit, tol = tol, active_set_size = None, solver = solver, verbose = verbose)
.

(X, _active_set, _) = mixed_norm_solver (M, G_tmp, alpha, debias = False, n_orient = n_orient, maxit = maxit, tol = tol, active_set_size = None, solver = solver, verbose = verbose)