code: def create_and_check_decoder_model_past_large_inputs(
      self, config, inputs_dict
  ):
      model = (
          PLBartModel(config=config)
          .get_decoder()
          .to(torch_device)
          .eval()
      )
      input_ids = inputs_dict["input_ids"]
      attention_mask = inputs_dict["attention_mask"]
      head_mask = inputs_dict["head_mask"]
  
      # first forward pass
      outputs = model(
          input_ids,
          attention_mask=attention_mask,
          head_mask=head_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_attn_mask = ids_tensor((self.batch_size, 3), 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_attn_mask], dim=-1
      )
  
      output_from_no_past = model(
          next_input_ids, attention_mask=next_attention_mask
      )["last_hidden_state"]
      output_with_past_key_values = model(
          next_tokens,
          attention_mask=next_attention_mask,
          past_key_values=past_key_values,
      )
      output_from_past = output_with_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
  from transformers import PLBartConfig, is_torch_available
  from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, require_torch_fp16, slow, torch_device
  from transformers.utils import cached_property
  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 5 for calling the PLBartModelTester obj's underlying member method code with example usage: obj.create_and_check_decoder_model_past_large_inputs(config, inputs_dict) without return types