def METHOD_NAME(n=None, n_jobs=1, rootpath='', output_path='',
                 annot_beats=False):
    audio_files = msaf.io.get_dataset_files(rootpath)
    if n is None:
        n = len(audio_files)
    data = Parallel(n_jobs=n_jobs)(delayed(import_data)(
        file_struct, rootpath, output_path, annot_beats)
        for file_struct in audio_files[:n])
    X, Y, B, T, F, L = [], [], [], [], [], []
    for d in data:
        if d is None:
            continue
        X.append(d['features'])
        Y.append(d['segments'])
        B.append(d['beats'])
        T.append(d['segment_times'])
        F.append(d['filename'])
        L.append(d['segment_labels'])
    return X, Y, B, T, F, L