code: def test_convert_original_predictions_without_mask_output(self):
      from sahi.models.torchvision import TorchVisionDetectionModel
  
      torchvision_detection_model = TorchVisionDetectionModel(
          config_path=TorchVisionTestConstants.FASTERRCNN_CONFIG_PATH,
          confidence_threshold=CONFIDENCE_THRESHOLD,
          device=MODEL_DEVICE,
          category_remapping=None,
          load_at_init=True,
          image_size=IMAGE_SIZE,
      )
  
      # prepare image
      image_path = "tests/data/small-vehicles1.jpeg"
      image = read_image(image_path)
  
      # perform inference
      torchvision_detection_model.perform_inference(image)
  
      # convert predictions to ObjectPrediction list
      torchvision_detection_model.convert_original_predictions()
      object_prediction_list = (
          torchvision_detection_model.object_prediction_list
      )
  
      # compare
      self.assertEqual(len(object_prediction_list), 7)
      self.assertEqual(object_prediction_list[0].category.id, 3)
      self.assertEqual(object_prediction_list[0].category.name, "car")
      np.testing.assert_almost_equal(
          object_prediction_list[0].bbox.to_xywh(),
          [315.79, 309.33, 64.28, 56.94],
          decimal=1,
      )
  
file_path: sahi/tests/test_torchvision.py
imports: import unittest
  import numpy
  from sahi.utils.cv import read_image
  from sahi.utils.torchvision import TorchVisionTestConstants
label_desc: Use image node_id 5 for calling the TestTorchVisionDetectionModel obj's underlying member method code with example usage: obj.test_convert_original_predictions_without_mask_output() without return types