code: def test_gpt():
      MODEL_LIST = [
          transformers.GPT2Model,
          transformers.GPT2LMHeadModel,
          transformers.GPT2DoubleHeadsModel,
          transformers.GPT2ForTokenClassification,
          # transformers.GPT2ForSequenceClassification, # not supported yet
      ]
      config = transformers.GPT2Config(
          n_position=64, n_layer=4, n_head=8
      )
  
      def data_gen():
          input_ids = torch.zeros(
              (BATCH_SIZE, SEQ_LENGHT), dtype=torch.int64
          )
          token_type_ids = torch.zeros(
              (BATCH_SIZE, SEQ_LENGHT), dtype=torch.int64
          )
          attention_mask = torch.zeros(
              (BATCH_SIZE, SEQ_LENGHT), dtype=torch.int64
          )
          kwargs = dict(
              input_ids=input_ids,
              token_type_ids=token_type_ids,
              attention_mask=attention_mask,
          )
          return kwargs
  
      for model_cls in MODEL_LIST:
          model = model_cls(config=config)
          split_model_and_compare_output(model, data_gen)
  
imports: import pytest
  import torch
  import transformers
  from hf_utils import split_model_and_compare_output
label_desc: Use image node_id 1 for calling a global function with example usage: test_gpt() and returns: kwargs