def load_annotations(
        self,
        deeplake_dataset,
        imgs=None,
        labels=None,
        masks=None,
        masks_type_kind=None,
        bboxes=None,
        iscrowds=None,
        class_names=None,
        bbox_format=None,
    ):
        """Load annotation from COCO style annotation file.

        Args:
            deeplake_dataset (dp.Dataset): Deeplake dataset object.
            imgs (dp.Tensor): image deeplake tensor.
            labels (List[numpy]): List of labels for every every detection for each image in numpy format.
            masks (List[numpy]): List of masks for every every detection for each image in numpy format.
            bboxes (List[numpy]): List of bboxes for every every detection for each image in numpy.
            iscrowds (List[numpy]): List of iscrowds for every every detection for each image in numpy format.
            class_names (List[str]): List of class names for every every detection for each image.
            bbox_format (Dict[Dict[str, str]]): Dictionary contatining bbox format information.

        Returns:
            list[dict]: Annotation info from COCO api.
        """

        self.coco = DeeplakeCOCO(
            deeplake_dataset,
            imgs=imgs,
            labels=labels,
            bboxes=bboxes,
            masks=masks,
            masks_type_kind=masks_type_kind,
            iscrowds=iscrowds,
            class_names=class_names,
            bbox_format=bbox_format,
        )
        # The order of returned `cat_ids` will not
        # change with the order of the CLASSES
        self.cat_ids = self.coco.get_cat_ids(cat_names=self.CLASSES)

        self.cat2label = {cat_id: i for i, cat_id in enumerate(self.cat_ids)}
        self.img_ids = self.coco.get_img_ids()
        data_infos = []
        total_ann_ids = []
        for i in self.img_ids:
            info = self.coco.load_imgs([i])[0]
            data_infos.append(info)
            ann_ids = self.coco.get_ann_ids(img_ids=[i])
            total_ann_ids.extend(ann_ids)
        assert len(set(total_ann_ids)) == len(total_ann_ids)
        return data_infos
Load annotation from COCO style annotation file.

        Args:
            deeplake_dataset (dp.Dataset): Deeplake dataset object.
            imgs (dp.Tensor): image deeplake tensor.
            labels (List[numpy]): List of labels for every every detection for each image in numpy format.
            masks (List[numpy]): List of masks for every every detection for each image in numpy format.
            bboxes (List[numpy]): List of bboxes for every every detection for each image in numpy.
            iscrowds (List[numpy]): List of iscrowds for every every detection for each image in numpy format.
            class_names (List[str]): List of class names for every every detection for each image.
            bbox_format (Dict[Dict[str, str]]): Dictionary contatining bbox format information.

        Returns:
            list[dict]: Annotation info from COCO api.
        
python/deeplake/integrations/mmdet/mmdet_utils_.py