def METHOD_NAME(self,
            bgef_path: str = None,
            gem_path: str = None,
            mask_path: str = None,
            image_path: str = None,
            model_path: str = None,
            mask_save: bool = True,
            model_type: str = 'deep-learning',
            depp_cro_size: int = 20000,
            overlap: int = 100,
            gen_mask_on_gpu: str = '-1',
            tissue_seg_model_path: str=None,
            tissue_seg_method: str=None,
            post_processing_workers: int=10
    ):
    """
    Generate CGEF resutl via following combinations:
        * BGEF and mask
        * BGEF and ssDNA image
    :param bgef_path: the path to BGEF file.
    :param gem_path: the path to GEM file.
    :param mask_path: the path to mask file.
    :param image_path: the path to ssDNA image file.
    :param model_path: the path to model file.
    :param mask_save: whether to save mask file after correction, generated from ssDNA image.
    :param model_type: the type of model to generate mask, whcih only could be set to deep learning model and deep cell model.
    :param depp_cro_size: deep crop size.
    :param overlap: overlap size.
    :param gen_mask_on_gpu: specify gpu id to predict when generate mask, if `'-1'`, use cpu for prediction.
    :param tissue_seg_model_path: the path of deep-learning model of tissue segmentation, if set it to None, it would use OpenCV to process.
    :param tissue_seg_method: the method of tissue segmentation, 0 is deep-learning and 1 is OpenCV.