How to use the braindecode.datasets.bcic_iv_2a.BCICompetition4Set2A function in braindecode

max_epochs = 800
    max_increase_epochs = 80
    batch_size = 60
    high_cut_hz = 38
    factor_new = 1e-3
    init_block_size = 1000
    valid_set_fraction = 0.2

    train_filename = 'A{:02d}T.gdf'.format(subject_id)
    test_filename = 'A{:02d}E.gdf'.format(subject_id)
    train_filepath = os.path.join(data_folder, train_filename)
    test_filepath = os.path.join(data_folder, test_filename)
    train_label_filepath = train_filepath.replace('.gdf', '.mat')
    test_label_filepath = test_filepath.replace('.gdf', '.mat')

    train_loader = BCICompetition4Set2A(
        train_filepath, labels_filename=train_label_filepath)
    test_loader = BCICompetition4Set2A(
        test_filepath, labels_filename=test_label_filepath)
    train_cnt = train_loader.load()
    test_cnt = test_loader.load()

    # Preprocessing

    train_cnt = train_cnt.drop_channels(['EOG-left',
                                         'EOG-central', 'EOG-right'])
    assert len(train_cnt.ch_names) == 22
    # lets convert to millvolt for numerical stability of next operations
    train_cnt = mne_apply(lambda a: a * 1e6, train_cnt)
    train_cnt = mne_apply(
        lambda a: bandpass_cnt(a, low_cut_hz, high_cut_hz, train_cnt.info['sfreq'],
                               filt_order=3,

high_cut_hz = 38
    factor_new = 1e-3
    init_block_size = 1000
    valid_set_fraction = 0.2

    train_filename = "A{:02d}T.gdf".format(subject_id)
    test_filename = "A{:02d}E.gdf".format(subject_id)
    train_filepath = os.path.join(data_folder, train_filename)
    test_filepath = os.path.join(data_folder, test_filename)
    train_label_filepath = train_filepath.replace(".gdf", ".mat")
    test_label_filepath = test_filepath.replace(".gdf", ".mat")

    train_loader = BCICompetition4Set2A(
        train_filepath, labels_filename=train_label_filepath
    )
    test_loader = BCICompetition4Set2A(
        test_filepath, labels_filename=test_label_filepath
    )
    train_cnt = train_loader.load()
    test_cnt = test_loader.load()

    # Preprocessing

    train_cnt = train_cnt.drop_channels(
        ["EOG-left", "EOG-central", "EOG-right"]
    )
    assert len(train_cnt.ch_names) == 22
    # lets convert to millvolt for numerical stability of next operations
    train_cnt = mne_apply(lambda a: a * 1e6, train_cnt)
    train_cnt = mne_apply(
        lambda a: bandpass_cnt(
            a,

batch_size = 60
    high_cut_hz = 38
    factor_new = 1e-3
    init_block_size = 1000
    valid_set_fraction = 0.2

    train_filename = 'A{:02d}T.gdf'.format(subject_id)
    test_filename = 'A{:02d}E.gdf'.format(subject_id)
    train_filepath = os.path.join(data_folder, train_filename)
    test_filepath = os.path.join(data_folder, test_filename)
    train_label_filepath = train_filepath.replace('.gdf', '.mat')
    test_label_filepath = test_filepath.replace('.gdf', '.mat')

    train_loader = BCICompetition4Set2A(
        train_filepath, labels_filename=train_label_filepath)
    test_loader = BCICompetition4Set2A(
        test_filepath, labels_filename=test_label_filepath)
    train_cnt = train_loader.load()
    test_cnt = test_loader.load()

    # Preprocessing

    train_cnt = train_cnt.drop_channels(['EOG-left',
                                         'EOG-central', 'EOG-right'])
    assert len(train_cnt.ch_names) == 22
    # lets convert to millvolt for numerical stability of next operations
    train_cnt = mne_apply(lambda a: a * 1e6, train_cnt)
    train_cnt = mne_apply(
        lambda a: bandpass_cnt(a, low_cut_hz, high_cut_hz, train_cnt.info['sfreq'],
                               filt_order=3,
                               axis=1), train_cnt)
    train_cnt = mne_apply(

max_epochs = 1600
    max_increase_epochs = 160
    batch_size = 60
    high_cut_hz = 38
    factor_new = 1e-3
    init_block_size = 1000
    valid_set_fraction = 0.2

    train_filename = "A{:02d}T.gdf".format(subject_id)
    test_filename = "A{:02d}E.gdf".format(subject_id)
    train_filepath = os.path.join(data_folder, train_filename)
    test_filepath = os.path.join(data_folder, test_filename)
    train_label_filepath = train_filepath.replace(".gdf", ".mat")
    test_label_filepath = test_filepath.replace(".gdf", ".mat")

    train_loader = BCICompetition4Set2A(
        train_filepath, labels_filename=train_label_filepath
    )
    test_loader = BCICompetition4Set2A(
        test_filepath, labels_filename=test_label_filepath
    )
    train_cnt = train_loader.load()
    test_cnt = test_loader.load()

    # Preprocessing

    train_cnt = train_cnt.drop_channels(
        ["EOG-left", "EOG-central", "EOG-right"]
    )
    assert len(train_cnt.ch_names) == 22
    # lets convert to millvolt for numerical stability of next operations
    train_cnt = mne_apply(lambda a: a * 1e6, train_cnt)