code: def test_auto_padding(self):
      ids = [[7, 6, 9] * 65]
      config, _ = self.model_tester.prepare_config_and_inputs()
      input_ids = torch.tensor(
          ids, device=torch_device, dtype=torch.long
      )
      attention_mask = input_ids.new_ones(input_ids.shape)
      decoder_input_ids = torch.tensor(
          [[33, 5, 8] * 3], device=torch_device, dtype=torch.long
      )
  
      config.block_size = 8
      model = (
          BigBirdPegasusForConditionalGeneration(config)
          .eval()
          .to(torch_device)
      )
      output1 = model(
          input_ids=input_ids,
          attention_mask=attention_mask,
          decoder_input_ids=decoder_input_ids,
      )["logits"]
  
      ids = [[7, 6, 9] * 65 + [0] * 5]
      input_ids = torch.tensor(
          ids, device=torch_device, dtype=torch.long
      )
      attention_mask = torch.tensor(
          [[1] * 3 * 65 + [0] * 5],
          device=torch_device,
          dtype=torch.long,
      )
      output2 = model(
          input_ids=input_ids,
          attention_mask=attention_mask,
          decoder_input_ids=decoder_input_ids,
      )["logits"]
  
      self.assertTrue(torch.allclose(output1, output2, atol=1e-5))
  
imports: import copy
  import tempfile
  import unittest
  from transformers import BigBirdPegasusConfig, is_torch_available
  from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
  from ...generation.test_generation_utils import GenerationTesterMixin
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, ids_tensor
label_desc: Use image node_id 15 for calling the BigBirdPegasusModelTest obj's underlying member method code with example usage: obj.test_auto_padding() without return types