code: def test_conversion_adaptive_model_classification():
      farm_model = Converter.convert_from_transformers(
          "deepset/bert-base-german-cased-hatespeech-GermEval18Coarse",
          device="cpu",
      )
      transformer_model = farm_model.convert_to_transformers()[0]
      transformer_model2 = AutoModelForSequenceClassification.from_pretrained(
          "deepset/bert-base-german-cased-hatespeech-GermEval18Coarse"
      )
      # compare weights
      for p1, p2 in zip(
          transformer_model.parameters(),
          transformer_model2.parameters(),
      ):
          assert p1.data.ne(p2.data).sum() == 0
  
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 3 for calling a global function with example usage: test_conversion_adaptive_model_classification() without return types