code: def test_inference_no_head_absolute_embedding(self):
      model = DistilBertModel.from_pretrained("distilbert-base-uncased")
      input_ids = torch.tensor(
          [[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 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]
      expected_shape = torch.Size((1, 11, 768))
      self.assertEqual(output.shape, expected_shape)
      expected_slice = torch.tensor(
          [
              [
                  [-0.1639, 0.3299, 0.1648],
                  [-0.1746, 0.3289, 0.1710],
                  [-0.1884, 0.3357, 0.1810],
              ]
          ]
      )
  
      self.assertTrue(
          torch.allclose(output[:, 1:4, 1:4], expected_slice, atol=1e-4)
      )
  
imports: import os
  import tempfile
  import unittest
  from transformers import DistilBertConfig, is_torch_available
  from transformers.testing_utils import require_torch, require_torch_gpu, 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 1 for calling the DistilBertModelIntergrationTest obj's underlying member method code with example usage: obj.test_inference_no_head_absolute_embedding() without return types