class_docstr: Unify different panoptic annotation/prediction formats
code: def instance_masks(self):
      for sid in self._seg_ids:
          sinfo = self._sinfo.get(sid)
          if sinfo is None or not sinfo["isthing"]:
              continue
          mask = (self._seg == sid).numpy().astype(np.bool)
          if mask.sum() > 0:
              yield mask, sinfo
  
imports: import colorsys
  import logging
  import math
  from enum import Enum, unique
  import numpy
  import matplotlib
  import matplotlib.colors
  import matplotlib.figure
  import torch
  from matplotlib.backends.backend_agg import FigureCanvasAgg
  from PIL import Image
  from ..data.registry.catalog import MetadataCatalog
  from ..data.structures import BitMasks, Boxes, BoxMode, Keypoints, PolygonMasks
label_desc: Use image node_id 4 for calling the _PanopticPrediction obj's underlying member method code with example usage: obj.instance_masks() without return types