code: def _convert_to_transformers_lm(adaptive_model, prediction_head):
      # remove pooling layer
      adaptive_model.language_model.model.pooler = None
      # init model
      transformers_model = AutoModelWithLMHead.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,
      )
      # Adding decoder bias (required for conversion to transformers)
      prediction_head.decoder.bias = prediction_head.bias
  
      ph_state_dict = prediction_head.state_dict()
      ph_state_dict["transform.dense.weight"] = ph_state_dict.pop(
          "dense.weight"
      )
      ph_state_dict["transform.dense.bias"] = ph_state_dict.pop(
          "dense.bias"
      )
      ph_state_dict["transform.LayerNorm.weight"] = ph_state_dict.pop(
          "LayerNorm.weight"
      )
      ph_state_dict["transform.LayerNorm.bias"] = ph_state_dict.pop(
          "LayerNorm.bias"
      )
      transformers_model.cls.predictions.load_state_dict(ph_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 5 for calling the Converter obj's underlying member method code with example usage: obj._convert_to_transformers_lm(adaptive_model, prediction_head) and returns: transformers_model