code: def test_inference_no_head(self):
      model = DebertaV2Model.from_pretrained(
          "microsoft/deberta-v2-xlarge"
      )
  
      input_ids = torch.tensor(
          [[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]
      )
      attention_mask = torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])
      with torch.no_grad():
          output = model(input_ids, attention_mask=attention_mask)[0]
      # compare the actual values for a slice.
      expected_slice = torch.tensor(
          [
              [
                  [0.2356, 0.1948, 0.0369],
                  [-0.1063, 0.3586, -0.5152],
                  [-0.6399, -0.0259, -0.2525],
              ]
          ]
      )
      self.assertTrue(
          torch.allclose(
              output[:, 1:4, 1:4], expected_slice, atol=1e-4
          ),
          f"{output[:, 1:4, 1:4]}",
      )
  
imports: import unittest
  from transformers import DebertaV2Config, 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
  from ...test_pipeline_mixin import PipelineTesterMixin
label_desc: Use image node_id 2 for calling the DebertaV2ModelIntegrationTest obj's underlying member method code with example usage: obj.test_inference_no_head() without return types