code: def create_and_check_decoder_model_past_large_inputs(
      self,
      config,
      input_ids,
      token_type_ids,
      input_mask,
      sequence_labels,
      token_labels,
      choice_labels,
      encoder_hidden_states,
      encoder_attention_mask,
  ):
      config.is_decoder = True
      config.add_cross_attention = True
      model = PhiForCausalLM(config=config)
      model.to(torch_device)
      model.eval()
  
      # first forward pass
      outputs = model(
          input_ids,
          attention_mask=input_mask,
          encoder_hidden_states=encoder_hidden_states,
          encoder_attention_mask=encoder_attention_mask,
          use_cache=True,
      )
      past_key_values = outputs.past_key_values
  
      # create hypothetical multiple next token and extent to next_input_ids
      next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size)
      next_mask = ids_tensor((self.batch_size, 3), vocab_size=2)
  
      # append to next input_ids and
      next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
      next_attention_mask = torch.cat([input_mask, next_mask], dim=-1)
  
      output_from_no_past = model(
          next_input_ids,
          attention_mask=next_attention_mask,
          encoder_hidden_states=encoder_hidden_states,
          encoder_attention_mask=encoder_attention_mask,
          output_hidden_states=True,
      )["hidden_states"][0]
      output_from_past = model(
          next_tokens,
          attention_mask=next_attention_mask,
          encoder_hidden_states=encoder_hidden_states,
          encoder_attention_mask=encoder_attention_mask,
          past_key_values=past_key_values,
          output_hidden_states=True,
      )["hidden_states"][0]
  
      # select random slice
      random_slice_idx = ids_tensor(
          (1,), output_from_past.shape[-1]
      ).item()
      output_from_no_past_slice = output_from_no_past[
          :, -3:, random_slice_idx
      ].detach()
      output_from_past_slice = output_from_past[
          :, :, random_slice_idx
      ].detach()
  
      self.parent.assertTrue(
          output_from_past_slice.shape[1] == next_tokens.shape[1]
      )
  
      # 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 unittest
  import pytest
  from transformers import PhiConfig, is_torch_available
  from transformers.testing_utils import require_bitsandbytes, require_flash_attn, require_torch, require_torch_gpu, slow, torch_device
  from ...generation.test_utils import GenerationTesterMixin
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
  from ...test_pipeline_mixin import PipelineTesterMixin
label_desc: Use image node_id 7 for calling the PhiModelTester obj's underlying member method code with example usage: obj.create_and_check_decoder_model_past_large_inputs(config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, encoder_hidden_states, encoder_attention_mask) without return types