def __init__(self, cfg, model, transform_data, tta_mapper=None, batch_size=1):
        """
        Args:
            cfg (CfgNode):
            model (GeneralizedRCNN): a GeneralizedRCNN to apply TTA on.
            transform_data (DensePoseTransformData): contains symmetry label
                transforms used for horizontal flip
            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.
        """
        self._transform_data = transform_data.to(model.device)
        super().__init__(cfg=cfg, model=model, tta_mapper=tta_mapper, batch_size=batch_size)

        Args:
            cfg (CfgNode):
            model (GeneralizedRCNN): a GeneralizedRCNN to apply TTA on.
            transform_data (DensePoseTransformData): contains symmetry label
                transforms used for horizontal flip
            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-02/det/projects/DensePose/densepose/modeling/test_time_augmentation.py