code: def create_and_check_attention_mask_determinism(
      self,
      config,
      input_ids,
      token_type_ids,
      input_mask,
      sequence_labels,
      token_labels,
      choice_labels,
  ):
      model = LongformerModel(config=config)
      model.to(torch_device)
      model.eval()
  
      attention_mask = torch.ones(
          input_ids.shape, dtype=torch.long, device=torch_device
      )
      output_with_mask = model(
          input_ids, attention_mask=attention_mask
      )["last_hidden_state"]
      output_without_mask = model(input_ids)["last_hidden_state"]
      self.parent.assertTrue(
          torch.allclose(
              output_with_mask[0, 0, :5],
              output_without_mask[0, 0, :5],
              atol=1e-4,
          )
      )
  
imports: import unittest
  from transformers import LongformerConfig, is_torch_available
  from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
  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 5 for calling the LongformerModelTester obj's underlying member method code with example usage: obj.create_and_check_attention_mask_determinism(config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels) without return types