code: def test_inference_no_head_relative_embedding_key(self):
      model = BertModel.from_pretrained(
          "zhiheng-huang/bert-base-uncased-embedding-relative-key"
      )
      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(
          [
              [
                  [0.0756, 0.3142, -0.5128],
                  [0.3761, 0.3462, -0.5477],
                  [0.2052, 0.3760, -0.1240],
              ]
          ]
      )
  
      self.assertTrue(
          torch.allclose(output[:, 1:4, 1:4], expected_slice, atol=1e-4)
      )
  
imports: import os
  import tempfile
  import unittest
  from transformers import BertConfig, is_torch_available
  from transformers.models.auto import get_values
  from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
  from ...generation.test_generation_utils import GenerationTesterMixin
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
label_desc: Use image node_id 2 for calling the BertModelIntegrationTest obj's underlying member method code with example usage: obj.test_inference_no_head_relative_embedding_key() without return types