def create_structure_for_group(self, group_prefix: str, file_key: str):
        """Create dataset structure for a specific annotation group"""
        tensor_prefix = f"{group_prefix}/" if group_prefix else ""

        self.dataset.add_column(
            f"{tensor_prefix}{self.key_to_column_mapping['categories']}",
            dp.types.ClassLabel(dp.types.Array("uint32", 1)),
        )
        self.dataset.add_column(
            f"{tensor_prefix}{self.key_to_column_mapping['super_categories']}",
            dp.types.ClassLabel(dp.types.Array("uint32", 1)),
        )
        self.dataset.add_column(
            f"{tensor_prefix}{self.key_to_column_mapping['boxes']}",
            dp.types.BoundingBox(dp.types.Float32(), "LTWH", "pixel"),
        )
        self.dataset.add_column(
            f"{tensor_prefix}{self.key_to_column_mapping['masks']}",
            dp.types.BinaryMask(sample_compression="lz4"),
        )

        cat_path = f"{tensor_prefix}{self.key_to_column_mapping['categories']}"
        supercat_path = (
            f"{tensor_prefix}{self.key_to_column_mapping['super_categories']}"
        )

        self.dataset[cat_path].metadata["class_names"] = self.cat_names[file_key]
        self.dataset[supercat_path].metadata["class_names"] = self.super_cat_names[
            file_key
        ]

        sample_ann = self.coco_instances[file_key].loadAnns(
            self.coco_instances[file_key].getAnnIds(self.img_ids[0])
        )[0]

        if "area" in sample_ann:
            self.dataset.add_column(
                f"{tensor_prefix}{self.key_to_column_mapping['areas']}",
                dp.types.Array("uint32", 1),
            )

        if "iscrowd" in sample_ann:
            self.dataset.add_column(
                f"{tensor_prefix}{self.key_to_column_mapping['iscrowds']}",
                dp.types.Array("bool", 1),
            )

        if "keypoints" in sample_ann:
            self.keypoints_group[group_prefix] = True
            self.dataset.add_column(
                f"{tensor_prefix}{self.key_to_column_mapping['keypoints']}",
                dp.types.Array("int32", 2),
            )

            if "keypoints" in self.category_info[file_key][0]:
                self.dataset[
                    f"{tensor_prefix}{self.key_to_column_mapping['keypoints']}"
                ].metadata["keypoints"] = [
                    category["keypoints"] for category in self.category_info[file_key]
                ][
                    0
                ]
Create dataset structure for a specific annotation group
python/deeplake/ingestion/coco/from_coco.py