code: def _process(self, sample_dict, net_out_dict):
      mask_cls = net_out_dict["logits"]
      mask_pred = net_out_dict["masks"]
      mask_cls = mask_cls[0]
      mask_pred = mask_pred[0]
      sem_prob, sem_pred = self._semantic_inference(mask_cls, mask_pred)
      _, pan_pred = self._panoptic_inference(mask_cls, mask_pred)
      sem_pred = sem_pred.unsqueeze_([0, 1])
      pan_pred = pan_pred.unsqueeze_([0, 1])
      sem_prob = sem_prob.unsqueeze_(0)
      pp_out = build_info_dict(
          _type_="pp_out",
          pan_pred=pan_pred,
          sem_prob=sem_prob,
          sem_pred=sem_pred,
      )
      return pp_out
  
file_path: PaddleSeg/contrib/PanopticSeg/paddlepanseg/postprocessors/maskformer_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 MaskFormerPostprocessor obj's underlying member method code with example usage: obj._process(sample_dict, net_out_dict) and returns: pp_out