code: def create_and_check_codegen_model_past(
      self,
      config,
      input_ids,
      input_mask,
      head_mask,
      token_type_ids,
      *args,
  ):
      model = CodeGenModel(config=config)
      model.to(torch_device)
      model.eval()
  
      # first forward pass
      outputs = model(
          input_ids, token_type_ids=token_type_ids, use_cache=True
      )
      outputs_use_cache_conf = model(
          input_ids, token_type_ids=token_type_ids
      )
      outputs_no_past = model(
          input_ids, token_type_ids=token_type_ids, use_cache=False
      )
  
      self.parent.assertTrue(
          len(outputs) == len(outputs_use_cache_conf)
      )
      self.parent.assertTrue(len(outputs) == len(outputs_no_past) + 1)
  
      output, past = outputs.to_tuple()
  
      # create hypothetical next token and extent to next_input_ids
      next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size)
      next_token_types = ids_tensor(
          [self.batch_size, 1], self.type_vocab_size
      )
  
      # append to next input_ids and token_type_ids
      next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
      next_token_type_ids = torch.cat(
          [token_type_ids, next_token_types], dim=-1
      )
  
      output_from_no_past = model(
          next_input_ids, token_type_ids=next_token_type_ids
      )["last_hidden_state"]
      output_from_past = model(
          next_tokens,
          token_type_ids=next_token_types,
          past_key_values=past,
      )["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 unittest
  from transformers import CodeGenConfig, is_torch_available
  from transformers.testing_utils import require_torch, slow, torch_device
  from ...generation.test_generation_utils import GenerationTesterMixin
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
label_desc: Use image node_id 7 for calling the CodeGenModelTester obj's underlying member method code with example usage: obj.create_and_check_codegen_model_past(config, input_ids, input_mask, head_mask, token_type_ids) without return types