code: def create_and_check_gpt2_model_attention_mask_past(
      self,
      config,
      input_ids,
      input_mask,
      head_mask,
      token_type_ids,
      *args,
  ):
      model = GPT2Model(config=config)
      model.to(torch_device)
      model.eval()
  
      # create attention mask
      attn_mask = torch.ones(
          input_ids.shape, dtype=torch.long, device=torch_device
      )
      half_seq_length = self.seq_length // 2
      attn_mask[:, half_seq_length:] = 0
  
      # first forward pass
      output, past = model(
          input_ids, attention_mask=attn_mask
      ).to_tuple()
  
      # create hypothetical next token and extent to next_input_ids
      next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size)
  
      # change a random masked slice from input_ids
      random_seq_idx_to_change = (
          ids_tensor((1,), half_seq_length).item() + 1
      )
      random_other_next_tokens = ids_tensor(
          (self.batch_size, 1), config.vocab_size
      ).squeeze(-1)
      input_ids[:, -random_seq_idx_to_change] = random_other_next_tokens
  
      # append to next input_ids and attn_mask
      next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
      attn_mask = torch.cat(
          [
              attn_mask,
              torch.ones(
                  (attn_mask.shape[0], 1),
                  dtype=torch.long,
                  device=torch_device,
              ),
          ],
          dim=1,
      )
  
      # get two different outputs
      output_from_no_past = model(
          next_input_ids, attention_mask=attn_mask
      )["last_hidden_state"]
      output_from_past = model(
          next_tokens, past_key_values=past, attention_mask=attn_mask
      )["last_hidden_state"]
  
      # select random slice
      random_slice_idx = ids_tensor(
          (1,), output_from_past.shape[-1]
      ).item()
      output_from_no_past_slice = output_from_no_past[
          :, -1, random_slice_idx
      ].detach()
      output_from_past_slice = output_from_past[
          :, 0, random_slice_idx
      ].detach()
  
      # test that outputs are equal for slice
      self.parent.assertTrue(
          torch.allclose(
              output_from_past_slice,
              output_from_no_past_slice,
              atol=1e-3,
          )
      )
  
imports: import datetime
  import gc
  import math
  import unittest
  from transformers import GPT2Config, is_torch_available
  from transformers.testing_utils import backend_empty_cache, require_torch, slow, torch_device
  from ...generation.test_utils import GenerationTesterMixin
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
  from ...test_pipeline_mixin import PipelineTesterMixin
label_desc: Use image node_id 9 for calling the GPT2ModelTester obj's underlying member method code with example usage: obj.create_and_check_gpt2_model_attention_mask_past(config, input_ids, input_mask, head_mask, token_type_ids) without return types