code: def test_call(self, model):
      # single img
      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_path = inferencer(img_path, return_vis=True)
      # ndarray
      img = mmcv.imread(img_path)
      res_ndarray = inferencer(img, return_vis=True)
      self.assert_predictions_equal(
          res_path["predictions"], res_ndarray["predictions"]
      )
      self.assertIn("visualization", res_path)
      self.assertIn("visualization", res_ndarray)
  
      # multiple images
      img_paths = ["tests/data/color.jpg", "tests/data/gray.jpg"]
      res_path = inferencer(img_paths, return_vis=True)
      # list of ndarray
      imgs = [mmcv.imread(p) for p in img_paths]
      res_ndarray = inferencer(imgs, return_vis=True)
      self.assert_predictions_equal(
          res_path["predictions"], res_ndarray["predictions"]
      )
      self.assertIn("visualization", res_path)
      self.assertIn("visualization", res_ndarray)
  
      # img dir, test different batch sizes
      img_dir = "tests/data/VOCdevkit/VOC2007/JPEGImages/"
      res_bs1 = inferencer(img_dir, batch_size=1, return_vis=True)
      res_bs3 = inferencer(img_dir, batch_size=3, return_vis=True)
      self.assert_predictions_equal(
          res_bs1["predictions"], res_bs3["predictions"]
      )
  
      # There is a jitter operation when the mask is drawn,
      # so it cannot be asserted.
      if model == "rtmdet-t":
          for res_bs1_vis, res_bs3_vis in zip(
              res_bs1["visualization"], res_bs3["visualization"]
          ):
              self.assertTrue(np.allclose(res_bs1_vis, res_bs3_vis))
  
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 3 for calling the TestDetInferencer obj's underlying member method code with example usage: obj.test_call(model) without return types