code: def _convert_to_transformers_qa(adaptive_model, prediction_head):
      # TODO add more infos to config
  
      # remove pooling layer
      adaptive_model.language_model.model.pooler = None
      # init model
      transformers_model = AutoModelForQuestionAnswering.from_config(
          adaptive_model.language_model.model.config
      )
      # transfer weights for language model + prediction head
      setattr(
          transformers_model,
          transformers_model.base_model_prefix,
          adaptive_model.language_model.model,
      )
      transformers_model.qa_outputs.load_state_dict(
          prediction_head.feed_forward.feed_forward[0].state_dict()
      )
  
      return transformers_model
  
file_path: FARM/farm/conversion/transformers.py
imports: import logging
  from transformers import AutoModelForSequenceClassification, AutoModelForQuestionAnswering, AutoModelWithLMHead, AutoModelForTokenClassification
  from farm.modeling import adaptive_model
  from farm.modeling.language_model import LanguageModel
  from farm.modeling.prediction_head import BertLMHead, QuestionAnsweringHead, RegressionHead, TextClassificationHead, TokenClassificationHead
label_desc: Use image node_id 4 for calling the Converter obj's underlying member method code with example usage: obj._convert_to_transformers_qa(adaptive_model, prediction_head) and returns: transformers_model