code: def _process(self, sample_dict, net_out_dict):
      r = net_out_dict["sem_out"].squeeze(0)
      c = net_out_dict["center"].squeeze(0)
      o = net_out_dict["offset"].squeeze(0)
  
      if r.ndim != 3:
          raise ValueError
  
      if c.ndim != 3:
          raise ValueError
  
      if o.ndim != 3:
          raise ValueError
  
      pp_out = build_info_dict(_type_="pp_out")
  
      # For semantic segmentation evaluation.
      sem_prob = F.softmax(r, axis=0)  # [C, H, W]
      pp_out["sem_prob"] = sem_prob.unsqueeze(0)
      pp_out["sem_pred"] = paddle.argmax(
          sem_prob, axis=0, keepdim=True
      ).unsqueeze(0)
  
      # Post-processing to get panoptic segmentation.
      panoptic_image = self._get_panoptic_segmentation(
          pp_out["sem_pred"], c, o
      )
      pp_out["pan_pred"] = panoptic_image
      return pp_out
  
file_path: PaddleSeg/contrib/PanopticSeg/paddlepanseg/postprocessors/panoptic_deeplab_pp.py
imports: from collections import Counter
  import paddle
  import paddle.nn.functional
  from paddlepanseg.cvlibs import build_info_dict, manager
  from paddlepanseg.utils import encode_pan_id
  from paddlepanseg.postprocessors.base_pp import Postprocessor
label_desc: Use image node_id 2 for calling the PanopticDeepLabPostprocessor obj's underlying member method code with example usage: obj._process(sample_dict, net_out_dict) and returns: pp_out