code: def test_inference_no_head_absolute_embedding(self):
      model = ElectraModel.from_pretrained(
          "google/electra-small-discriminator"
      )
      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]])
      output = model(input_ids, attention_mask=attention_mask)[0]
      expected_shape = torch.Size((1, 11, 256))
      self.assertEqual(output.shape, expected_shape)
      expected_slice = torch.tensor(
          [
              [
                  [0.4471, 0.6821, -0.3265],
                  [0.4627, 0.5255, -0.3668],
                  [0.4532, 0.3313, -0.4344],
              ]
          ]
      )
  
      self.assertTrue(
          torch.allclose(output[:, 1:4, 1:4], expected_slice, atol=1e-4)
      )
  
imports: import unittest
  from transformers import ElectraConfig, is_torch_available
  from transformers.models.auto import get_values
  from transformers.testing_utils import require_torch, slow, torch_device
  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 1 for calling the ElectraModelIntegrationTest obj's underlying member method code with example usage: obj.test_inference_no_head_absolute_embedding() without return types