def METHOD_NAME(
    self,
    image_paths: Dict[str, List[str]],
    is_training: bool,
):
    mm_data = dict(img_prefix=None, bbox_fields=[])
    for per_col_name, per_col_content in image_paths.items():
        if is_rois_input(per_col_content):
            rois = np.array(per_col_content)
            # https://github.com/open-mmlab/mmdetection/blob/ecac3a77becc63f23d9f6980b2a36f86acd00a8a/mmdet/datasets/transforms/loading.py#L155
            mm_data["instances"] = []
            for roi in rois:
                mm_data["instances"].append(
                    {
                        "bbox": roi[:4],
                        "bbox_label": roi[4],
                        "ignore_flag": 0,
                    }
                )
        else:
            with PIL.Image.open(per_col_content[0]) as img:
                # mm_data["img_info"] = dict(filename=per_col_content[0], height=img.height, width=img.width)
                mm_data["img_path"] = per_col_content[0]
    if self.requires_column_info:
        pass  # TODO
    mm_data["img_id"] = 0  # TODO: use a non trivial image id for TTA (test time augmentation)
    return mm_data