class_docstr: PyTorch implementation containing all the modelling needed for your NLP task. Combines a language
  model and a prediction head. Allows for gradient flow back to the language model component.
class_docstr_tok: ["PyTorch","implementation","containing","all","the","modelling","needed","for","your","NLP","task",".","Combines","a","language","model","and","a","prediction","head",".","Allows","for","gradient","flow","back","to","the","language","model","component","."]
docstr: Convert an adaptive model to huggingface's transformers format. Returns a list containing one model for each
  prediction head.
  
  :return: List of huggingface transformers models.
code: def convert_to_transformers(self):
      """
      Convert an adaptive model to huggingface's transformers format. Returns a list containing one model for each
      prediction head.
  
      :return: List of huggingface transformers models.
      """
      return conv.Converter.convert_to_transformers(self)
  
imports: import copy
  import json
  import logging
  import os
  from pathlib import Path
  import multiprocessing
  import numpy
  import torch
  from torch import nn
  from transformers import AutoConfig
  from transformers.convert_graph_to_onnx import convert, quantize
  from farm.data_handler.processor import Processor
  from farm.modeling.language_model import LanguageModel
  from farm.modeling.prediction_head import PredictionHead, pick_single_fn
  from farm.utils import MLFlowLogger, stack
  import farm.conversion.transformers
label_desc: Use image node_id 14 for calling the AdaptiveModel obj's underlying member method code with example usage: obj.convert_to_transformers() and returns: conv