def __init__(self, cfg, model, tta_mapper=None, batch_size=3):
        """
        Args:
            cfg (CfgNode):
            model (GeneralizedRCNN): a GeneralizedRCNN to apply TTA on.
            tta_mapper (callable): takes a dataset dict and returns a list of
                augmented versions of the dataset dict. Defaults to
                `DatasetMapperTTA(cfg)`.
            batch_size (int): batch the augmented images into this batch size for inference.
        """
        super().__init__()
        if isinstance(model, DistributedDataParallel):
            model = model.module
        assert isinstance(
            model, GeneralizedRCNN
        ), "TTA is only supported on GeneralizedRCNN. Got a model of type {}".format(type(model))
        self.cfg = cfg.clone()
        assert not self.cfg.MODEL.KEYPOINT_ON, "TTA for keypoint is not supported yet"
        assert (
            not self.cfg.MODEL.LOAD_PROPOSALS
        ), "TTA for pre-computed proposals is not supported yet"

        self.model = model

        if tta_mapper is None:
            tta_mapper = DatasetMapperTTA(cfg)
        self.tta_mapper = tta_mapper
        self.batch_size = batch_size

        Args:
            cfg (CfgNode):
            model (GeneralizedRCNN): a GeneralizedRCNN to apply TTA on.
            tta_mapper (callable): takes a dataset dict and returns a list of
                augmented versions of the dataset dict. Defaults to
                `DatasetMapperTTA(cfg)`.
            batch_size (int): batch the augmented images into this batch size for inference.
        
EVA-01/det/detectron2/modeling/test_time_augmentation.py