code: def test_perform_inference(self):
      from sahi.models.yolonas import YoloNasDetectionModel
  
      # init model
      download_yolonas_s_model()
  
      yolonas_detection_model = YoloNasDetectionModel(
          model_name=YOLONAS_TEST_MODEL_NAME,
          model_path=YoloNasTestConstants.YOLONAS_S_MODEL_PATH,
          class_names_yaml_path=CLASS_NAMES_YAML_PATH,
          confidence_threshold=CONFIDENCE_THRESHOLD,
          device=MODEL_DEVICE,
          category_remapping=None,
          load_at_init=True,
          image_size=IMAGE_SIZE,
      )
  
      # prepare image
      image = read_image(TEST_IMAGE_PATH)
  
      # perform inference
      yolonas_detection_model.perform_inference(image)
      original_predictions = (
          yolonas_detection_model.original_predictions
      )
  
      pred = original_predictions[0].prediction
  
      # find box of first car detection with conf greater than 0.5
      for box, score, label in zip(
          pred.bboxes_xyxy, pred.confidence, pred.labels
      ):
          if int(label) == 2:  # if category car
              if score > 0.5:
                  break
  
      # compare
      desired_bbox = [447, 309, 495, 341]
      predicted_bbox = list(map(int, box[:4].tolist()))
      margin = 2
      for ind, point in enumerate(predicted_bbox):
          assert (
              point < desired_bbox[ind] + margin
              and point > desired_bbox[ind] - margin
          )
      self.assertEqual(len(yolonas_detection_model.category_names), 80)
      self.assertGreaterEqual(score, CONFIDENCE_THRESHOLD)
  
file_path: sahi/tests/test_yolonasmodel.py
imports: import unittest
  from sahi.utils.cv import read_image
  from sahi.utils.yolonas import YoloNasTestConstants, download_yolonas_s_model
label_desc: Use image node_id 3 for calling the TestYoloNasDetectionModel obj's underlying member method code with example usage: obj.test_perform_inference() without return types