code: def test_inference_masked_lm_long(self):
      model = LongformerForMaskedLM.from_pretrained(
          "allenai/longformer-base-4096"
      )
      model.to(torch_device)
  
      # 'Hello world! ' repeated 1000 times
      input_ids = torch.tensor(
          [[0] + [20920, 232, 328, 1437] * 1000 + [2]],
          dtype=torch.long,
          device=torch_device,
      )  # long input
      input_ids = input_ids.to(torch_device)
  
      loss, prediction_scores = model(
          input_ids, labels=input_ids
      ).to_tuple()
  
      expected_loss = torch.tensor(0.0074, device=torch_device)
      expected_prediction_scores_sum = torch.tensor(
          -6.1048e08, device=torch_device
      )
      expected_prediction_scores_mean = torch.tensor(
          -3.0348, device=torch_device
      )
  
      self.assertTrue(torch.allclose(loss, expected_loss, atol=1e-4))
      self.assertTrue(
          torch.allclose(
              prediction_scores.sum(),
              expected_prediction_scores_sum,
              atol=1e-4,
          )
      )
      self.assertTrue(
          torch.allclose(
              prediction_scores.mean(),
              expected_prediction_scores_mean,
              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
label_desc: Use image node_id 11 for calling the LongformerModelIntegrationTest obj's underlying member method code with example usage: obj.test_inference_masked_lm_long() without return types