code: def test_qa_format_and_results(
      adaptive_model_qa, streaming, multiprocessing_chunksize
  ):
      qa_inputs_dicts = [
          {
              "questions": ["In what country is Normandy"],
              "text": "The Normans are an ethnic group that arose in Normandy, a northern region "
              "of France, from contact between Viking settlers and indigenous Franks and Gallo-Romans",
          },
          {
              "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.",
          },
      ]
      ground_truths = ["France", "GameTrailers"]
  
      results = adaptive_model_qa.inference_from_dicts(
          dicts=qa_inputs_dicts,
          multiprocessing_chunksize=multiprocessing_chunksize,
          streaming=streaming,
      )
      # sample results
      # [
      #     {
      #         "task": "qa",
      #         "predictions": [
      #             {
      #                 "question": "In what country is Normandy",
      #                 "question_id": "None",
      #                 "ground_truth": None,
      #                 "answers": [
      #                     {
      #                         "score": 1.1272038221359253,
      #                         "probability": -1,
      #                         "answer": "France",
      #                         "offset_answer_start": 54,
      #                         "offset_answer_end": 60,
      #                         "context": "The Normans gave their name to Normandy, a region in France.",
      #                         "offset_context_start": 0,
      #                         "offset_context_end": 60,
      #                         "document_id": None,
      #                     }
      #                 ]
      #             }
      #         ],
      #     }
      # ]
      predictions = list(results)[0]["predictions"]
  
      for prediction, ground_truth, qa_input_dict in zip(
          predictions, ground_truths, qa_inputs_dicts
      ):
          assert prediction["question"] == qa_input_dict["questions"][0]
          answer = prediction["answers"][0]
          assert answer["answer"] in answer["context"]
          assert answer["answer"] == ground_truth
          assert {
              "answer",
              "score",
              "probability",
              "offset_answer_start",
              "offset_answer_end",
              "context",
              "offset_context_start",
              "offset_context_end",
              "document_id",
          } == answer.keys()
  
imports: import pytest
  import numpy
  from farm.infer import Inferencer
  from transformers import BertTokenizerFast
label_desc: Use image node_id 1 for calling a global function with example usage: test_qa_format_and_results(adaptive_model_qa, streaming, multiprocessing_chunksize) without return types