code: def test_inference_no_head_long(self):
      model = LongformerModel.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
  
      attention_mask = torch.ones(
          input_ids.shape, dtype=torch.long, device=input_ids.device
      )
      global_attention_mask = torch.zeros(
          input_ids.shape, dtype=torch.long, device=input_ids.device
      )
      global_attention_mask[
          :, [1, 4, 21]
      ] = 1  # Set global attention on a few random positions
  
      output = model(
          input_ids,
          attention_mask=attention_mask,
          global_attention_mask=global_attention_mask,
      )[0]
  
      expected_output_sum = torch.tensor(
          74585.8594, device=torch_device
      )
      expected_output_mean = torch.tensor(0.0243, device=torch_device)
      self.assertTrue(
          torch.allclose(output.sum(), expected_output_sum, atol=1e-4)
      )
      self.assertTrue(
          torch.allclose(output.mean(), expected_output_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 10 for calling the LongformerModelIntegrationTest obj's underlying member method code with example usage: obj.test_inference_no_head_long() without return types