code: def test_segmentation(model):
      test_img_file = os.path.join("test_imgs", "runners.jpg")
      img = mx.image.imread(test_img_file)
      img, _ = mx.image.center_crop(
          img, size=(model.input_shape[1], model.input_shape[2])
      )
      img = img.expand_dims(0).astype("float32")
  
      onnx_session = rt.InferenceSession(model.onnx_model, None)
      input_name = onnx_session.get_inputs()[0].name
  
      gluon_result = model.predict(img)
      onnx_result = onnx_session.run([], {input_name: img.asnumpy()})
  
      assert len(gluon_result) == len(onnx_result)
      for i in range(len(gluon_result)):
          # deeplab_v3b_plus_wideresnet_citys has a different output format
          if model.model_name == "deeplab_v3b_plus_wideresnet_citys":
              assert_almost_equal(
                  gluon_result[i].asnumpy(),
                  onnx_result[i][0],
                  decimal=3,
              )
          else:
              assert_almost_equal(
                  gluon_result[i].asnumpy(), onnx_result[i], decimal=3
              )
  
file_path: gluon-cv/tests/onnx/test_inference.py
label_desc: Use image node_id 4 for calling a global function with example usage: test_segmentation(model) without return types