code: def detr_resnet50_panoptic(
      pretrained=False,
      num_classes=250,
      threshold=0.85,
      return_postprocessor=False,
  ):
      """
       DETR R50 with 6 encoder and 6 decoder layers.
       Achieves 43.4 PQ on COCO val5k.
  
      threshold is the minimum confidence required for keeping segments in the prediction
      """
      model = _make_detr(
          "resnet50", dilation=False, num_classes=num_classes, mask=True
      )
      is_thing_map = {i: i <= 90 for i in range(250)}
      if pretrained:
          checkpoint = torch.hub.load_state_dict_from_url(
              url="https://dl.fbaipublicfiles.com/detr/detr-r50-panoptic-00ce5173.pth",
              map_location="cpu",
              check_hash=True,
          )
          model.load_state_dict(checkpoint["model"])
      if return_postprocessor:
          return model, PostProcessPanoptic(
              is_thing_map, threshold=threshold
          )
      return model
  
file_path: detr/hubconf.py
imports: import torch
  from models.backbone import Backbone, Joiner
  from models.detr import DETR, PostProcess
  from models.position_encoding import PositionEmbeddingSine
  from models.segmentation import DETRsegm, PostProcessPanoptic
  from models.transformer import Transformer
label_desc: Use image node_id 6 for calling a global function with example usage: detr_resnet50_panoptic(pretrained, num_classes, threshold, return_postprocessor) and returns: model, model,  PostProcessPanoptic