code: def test_get_prediction_torchvision(self):
      from sahi.models.torchvision import TorchVisionDetectionModel
      from sahi.predict import get_prediction
  
      # init model
      torchvision_detection_model = TorchVisionDetectionModel(
          config_path=TorchVisionTestConstants.FASTERRCNN_CONFIG_PATH,
          confidence_threshold=CONFIDENCE_THRESHOLD,
          device=MODEL_DEVICE,
          category_remapping=None,
          load_at_init=False,
          image_size=IMAGE_SIZE,
      )
      torchvision_detection_model.load_model()
  
      # prepare image
      image_path = "tests/data/small-vehicles1.jpeg"
      image = read_image(image_path)
  
      # get full sized prediction
      prediction_result = get_prediction(
          image=image,
          detection_model=torchvision_detection_model,
          shift_amount=[0, 0],
          full_shape=None,
          postprocess=None,
      )
      object_prediction_list = prediction_result.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 7 for calling the TestTorchVisionDetectionModel obj's underlying member method code with example usage: obj.test_get_prediction_torchvision() without return types