code: def test_conversion_inferencer_qa():
      # input
      question = "Why is model conversion important?"
      text = "The option to convert models between FARM and transformers gives freedom to the user and let people easily switch between frameworks."
  
      # Load from model hub
      model = "deepset/bert-base-cased-squad2"
      nlp = Inferencer.load(
          model, task_type="question_answering", num_processes=0
      )
  
      assert nlp.processor.tokenizer.do_lower_case == False
      assert nlp.processor.tokenizer.is_fast == True
  
      QA_input = [{"questions": [question], "text": text}]
      result_farm = nlp.inference_from_dicts(dicts=QA_input)
      answer_farm = result_farm[0]["predictions"][0]["answers"][0][
          "answer"
      ]
      assert answer_farm == "gives freedom to the user"
  
      # save it
      farm_model_dir = Path("testsave/bert-conversion-test")
      nlp.save(farm_model_dir)
  
      # free RAM
      del nlp
  
      # load from disk in FARM format
      model = AdaptiveModel.load(farm_model_dir, device="cpu")
      tokenizer = Tokenizer.load(farm_model_dir)
  
      # convert to transformers
      transformer_model = Converter.convert_to_transformers(model)[0]
  
      # free RAM
      del model
  
      # save it (Note: transformers uses strings rather than Path objects)
      model_dir = "testsave/bert-conversion-test-hf"
      os.makedirs(model_dir, exist_ok=True)
      transformer_model.save_pretrained(model_dir)
      tokenizer.save_pretrained(model_dir)
      del transformer_model
      del tokenizer
  
      # run predictions (using transformers)
      nlp = pipeline(
          "question-answering", model=model_dir, tokenizer=model_dir
      )
      result_transformers = nlp({"question": question, "context": text})
      answer_transformers = result_transformers["answer"]
      assert answer_farm == answer_transformers
      del nlp
  
file_path: FARM/test/test_conversion.py
imports: from transformers import BertForSequenceClassification, BertForTokenClassification
  from farm.modeling.adaptive_model import AdaptiveModel
  from farm.conversion.transformers import Converter
  from farm.modeling.language_model import LanguageModel
  from farm.modeling.prediction_head import TextClassificationHead, TokenClassificationHead, QuestionAnsweringHead
  from farm.modeling.tokenization import Tokenizer
  from farm.infer import Inferencer
  from transformers.pipelines import pipeline
  from transformers import AutoModelForQuestionAnswering, AutoModelWithLMHead, AutoModelForSequenceClassification, AutoModelForTokenClassification
  import os
  from pathlib import Path
  import pytest
label_desc: Use image node_id 5 for calling a global function with example usage: test_conversion_inferencer_qa() without return types