def METHOD_NAME(
    img_idx: int, image_path: str, batch: Dict, class_name_map: Dict = None
) -> List[Dict[str, Any]]:
    """Get ground truth bounding box annotation data in the form required for `wandb.Image` overlay system."""
    indices = batch["batch_idx"] == img_idx
    bboxes = batch["bboxes"][indices]
    cls_labels = batch["cls"][indices].squeeze(1).tolist()
    class_name_map_reverse = {v: k for k, v in class_name_map.items()}
    if len(bboxes) == 0:
        wandb.termwarn(
            f"Image: {image_path} has no bounding boxes labels", repeat=False
        )
        return None
    cls_labels = batch["cls"][indices].squeeze(1).tolist()
    if class_name_map:
        cls_labels = [str(class_name_map[label]) for label in cls_labels]
    original_image_shape = batch["ori_shape"][img_idx]
    resized_image_shape = batch["resized_shape"][img_idx]
    ratio_pad = batch["ratio_pad"][img_idx]
    data = []
    for box, label in zip(bboxes, cls_labels):
        box = scale_bounding_box_to_original_image_shape(
            box, resized_image_shape, original_image_shape, ratio_pad
        )
        data.append(
            {
                "position": {
                    "middle": [int(box[0]), int(box[1])],
                    "width": int(box[2]),
                    "height": int(box[3]),
                },
                "domain": "pixel",
                "class_id": class_name_map_reverse[label],
                "box_caption": label,
            }
        )
    return data