code: def create_and_check_decoder_model_past_large_inputs(
      self,
      config,
      input_ids,
      decoder_input_ids,
      attention_mask,
      decoder_attention_mask,
      lm_labels,
  ):
      model = (
          Pop2PianoForConditionalGeneration(config=config)
          .get_decoder()
          .to(torch_device)
          .eval()
      )
      # first forward pass
      outputs = model(
          input_ids, attention_mask=attention_mask, use_cache=True
      )
  
      output, past_key_values = outputs.to_tuple()
  
      # 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(
          [attention_mask, next_mask], dim=-1
      )
  
      output_from_no_past = model(
          next_input_ids, attention_mask=next_attention_mask
      )["last_hidden_state"]
      output_from_past = model(
          next_tokens,
          attention_mask=next_attention_mask,
          past_key_values=past_key_values,
      )["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[
          :, -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 copy
  import tempfile
  import unittest
  import numpy
  from datasets import load_dataset
  from transformers import Pop2PianoConfig
  from transformers.feature_extraction_utils import BatchFeature
  from transformers.testing_utils import require_essentia, require_librosa, require_onnx, require_scipy, require_torch, slow, torch_device
  from transformers.utils import is_essentia_available, is_librosa_available, is_scipy_available, is_torch_available
  from ...generation.test_utils import GenerationTesterMixin
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, ids_tensor
  from ...test_pipeline_mixin import PipelineTesterMixin
label_desc: Use image node_id 10 for calling the Pop2PianoModelTester obj's underlying member method code with example usage: obj.create_and_check_decoder_model_past_large_inputs(config, input_ids, decoder_input_ids, attention_mask, decoder_attention_mask, lm_labels) without return types