code: def onnx_runtime_example():
      """
      This example shows conversion of a transformers model from the Model Hub to
      ONNX format & inference using ONNXRuntime.
      """
  
      model_name_or_path = "deepset/roberta-base-squad2"
      onnx_model_export_path = Path("./roberta-onnx")
  
      AdaptiveModel.convert_to_onnx(
          model_name_or_path,
          onnx_model_export_path,
          task_type="question_answering",
      )
  
      # for ONNX models, the Inferencer uses ONNXRuntime under-the-hood
      inferencer = Inferencer.load(
          model_name_or_path=onnx_model_export_path
      )
  
      qa_input = [
          {
              "questions": [
                  "Who counted the game among the best ever made?"
              ],
              "text": "Twilight Princess was released to universal critical acclaim and commercial success. "
              "It received perfect scores from major publications such as 1UP.com, Computer and Video Games, "
              "Electronic Gaming Monthly, Game Informer, GamesRadar, and GameSpy. On the review aggregators "
              "GameRankings and Metacritic, Twilight Princess has average scores of 95% and 95 for the Wii "
              "version and scores of 95% and 96 for the GameCube version. GameTrailers in their review called "
              "it one of the greatest games ever created.",
          }
      ]
  
      results = inferencer.inference_from_dicts(qa_input)
      print(results)
      inferencer.close_multiprocessing_pool()
  
label_desc: Use image node_id 1 for calling a global function with example usage: onnx_runtime_example() without return types