def extend_feature_columns(feature_columns):
    """ Use to define additional feature columns, such as bucketized_column and crossed_column
    Default behaviour is to return the original feature_column list as is

    Args:
        feature_columns: [tf.feature_column] - list of base feature_columns to be extended
    Returns:
        [tf.feature_column]: extended feature_column list
    """

    # examples - given:
    # 'x' and 'y' are two numeric features:
    # 'alpha' and 'beta' are two categorical features

    # feature_columns['alpha_X_beta'] = tf.feature_column.crossed_column(
    #     [feature_columns['alpha'], feature_columns['beta']], int(1e4))
    #
    # num_buckets = parameters.HYPER_PARAMS.num_buckets
    # buckets = np.linspace(-2, 2, num_buckets).tolist()
    #
    # feature_columns['x_bucketized'] = tf.feature_column.bucketized_column(
    #     feature_columns['x'], buckets)
    #
    # feature_columns['y_bucketized'] = tf.feature_column.bucketized_column(
    #     feature_columns['y'], buckets)
    #
    # feature_columns['x_bucketized_X_y_bucketized'] = tf.feature_column.crossed_column(
    #     [feature_columns['x_bucketized'], feature_columns['y_bucketized']], int(1e4))

    return feature_columns