code: def test_postprocess(self, model):
      # return_datasamples
      img_path = "tests/data/color.jpg"
  
      mock_load = Mock(return_value=None)
      with patch("mmengine.infer.infer._load_checkpoint", mock_load):
          inferencer = DetInferencer(model)
  
      # In the case of not loading the pretrained weight, the category
      # defaults to COCO 80, so it needs to be replaced.
      if model == "panoptic_fpn_r50_fpn_1x_coco":
          inferencer.visualizer.dataset_meta = {
              "classes": get_classes("coco_panoptic"),
              "palette": "random",
          }
  
      res = inferencer(img_path, return_datasamples=True)
      self.assertTrue(is_list_of(res["predictions"], DetDataSample))
  
      with tempfile.TemporaryDirectory() as tmp_dir:
          res = inferencer(
              img_path, out_dir=tmp_dir, no_save_pred=False
          )
          dumped_res = mmengine.load(
              osp.join(tmp_dir, "preds", "color.json")
          )
          self.assertEqual(res["predictions"][0], dumped_res)
  
imports: import os.path
  import tempfile
  from unittest import TestCase, mock
  from unittest.mock import Mock, patch
  import mmcv
  import mmengine
  import numpy
  import torch
  from mmengine.structures import InstanceData
  from mmengine.utils import is_list_of
  from parameterized import parameterized
  from mmdet.apis import DetInferencer
  from mmdet.evaluation.functional import get_classes
  from mmdet.structures import DetDataSample
label_desc: Use image node_id 5 for calling the TestDetInferencer obj's underlying member method code with example usage: obj.test_postprocess(model) without return types