How to use the braindecode.datautil.trial_segment.create_signal_target_from_raw_mne function in braindecode

test_cnt = mne_apply(lambda a: a * 1e6, test_cnt)
    test_cnt = mne_apply(
        lambda a: bandpass_cnt(a, low_cut_hz, high_cut_hz, test_cnt.info['sfreq'],
                               filt_order=3,
                               axis=1), test_cnt)
    test_cnt = mne_apply(
        lambda a: exponential_running_standardize(a.T, factor_new=factor_new,
                                                  init_block_size=init_block_size,
                                                  eps=1e-4).T,
        test_cnt)

    marker_def = OrderedDict([('Left Hand', [1]), ('Right Hand', [2],),
                              ('Foot', [3]), ('Tongue', [4])])

    train_set = create_signal_target_from_raw_mne(train_cnt, marker_def, ival)
    test_set = create_signal_target_from_raw_mne(test_cnt, marker_def, ival)

    train_set, valid_set = split_into_two_sets(
        train_set, first_set_fraction=1-valid_set_fraction)

    set_random_seeds(seed=20190706, cuda=cuda)

    n_classes = 4
    n_chans = int(train_set.X.shape[1])
    if model == 'shallow':
        model = ShallowFBCSPNet(n_chans, n_classes, input_time_length=input_time_length,
                            final_conv_length=30).create_network()
    elif model == 'deep':
        model = Deep4Net(n_chans, n_classes, input_time_length=input_time_length,
                            final_conv_length=2).create_network()

init_block_size=init_block_size,
            eps=1e-4,
        ).T,
        test_cnt,
    )

    marker_def = OrderedDict(
        [
            ("Left Hand", [1]),
            ("Right Hand", [2]),
            ("Foot", [3]),
            ("Tongue", [4]),
        ]
    )

    train_set = create_signal_target_from_raw_mne(train_cnt, marker_def, ival)
    test_set = create_signal_target_from_raw_mne(test_cnt, marker_def, ival)

    train_set, valid_set = split_into_two_sets(
        train_set, first_set_fraction=1 - valid_set_fraction
    )

    set_random_seeds(seed=20190706, cuda=cuda)

    n_classes = 4
    n_chans = int(train_set.X.shape[1])
    input_time_length = train_set.X.shape[2]
    if model == "shallow":
        model = ShallowFBCSPNet(
            n_chans,
            n_classes,
            input_time_length=input_time_length,

assert len(test_cnt.ch_names) == 22
    test_cnt = mne_apply(lambda a: a * 1e6, test_cnt)
    test_cnt = mne_apply(
        lambda a: bandpass_cnt(a, low_cut_hz, high_cut_hz, test_cnt.info['sfreq'],
                               filt_order=3,
                               axis=1), test_cnt)
    test_cnt = mne_apply(
        lambda a: exponential_running_standardize(a.T, factor_new=factor_new,
                                                  init_block_size=init_block_size,
                                                  eps=1e-4).T,
        test_cnt)

    marker_def = OrderedDict([('Left Hand', [1]), ('Right Hand', [2],),
                              ('Foot', [3]), ('Tongue', [4])])

    train_set = create_signal_target_from_raw_mne(train_cnt, marker_def, ival)
    test_set = create_signal_target_from_raw_mne(test_cnt, marker_def, ival)

    train_set, valid_set = split_into_two_sets(
        train_set, first_set_fraction=1-valid_set_fraction)

    set_random_seeds(seed=20190706, cuda=cuda)

    n_classes = 4
    n_chans = int(train_set.X.shape[1])
    if model == 'shallow':
        model = ShallowFBCSPNet(n_chans, n_classes, input_time_length=input_time_length,
                            final_conv_length=30).create_network()
    elif model == 'deep':
        model = Deep4Net(n_chans, n_classes, input_time_length=input_time_length,
                            final_conv_length=2).create_network()

eps=1e-4,
        ).T,
        test_cnt,
    )

    marker_def = OrderedDict(
        [
            ("Left Hand", [1]),
            ("Right Hand", [2]),
            ("Foot", [3]),
            ("Tongue", [4]),
        ]
    )

    train_set = create_signal_target_from_raw_mne(train_cnt, marker_def, ival)
    test_set = create_signal_target_from_raw_mne(test_cnt, marker_def, ival)

    train_set, valid_set = split_into_two_sets(
        train_set, first_set_fraction=1 - valid_set_fraction
    )

    set_random_seeds(seed=20190706, cuda=cuda)

    n_classes = 4
    n_chans = int(train_set.X.shape[1])
    input_time_length = train_set.X.shape[2]
    if model == "shallow":
        model = ShallowFBCSPNet(
            n_chans,
            n_classes,
            input_time_length=input_time_length,
            final_conv_length="auto",