code: def test_inference_no_head(self):
      model = MobileBertModel.from_pretrained(
          "google/mobilebert-uncased"
      ).to(torch_device)
      input_ids = _long_tensor(
          [[101, 7110, 1005, 1056, 2023, 11333, 17413, 1029, 102]]
      )
      with torch.no_grad():
          output = model(input_ids)[0]
      expected_shape = torch.Size((1, 9, 512))
      self.assertEqual(output.shape, expected_shape)
      expected_slice = torch.tensor(
          [
              [
                  [-2.4736526e07, 8.2691656e04, 1.6521838e05],
                  [-5.7541704e-01, 3.9056022e00, 4.4011507e00],
                  [2.6047359e00, 1.5677652e00, -1.7324188e-01],
              ]
          ],
          device=torch_device,
      )
  
      # MobileBERT results range from 10e0 to 10e8. Even a 0.0000001% difference with a value of 10e8 results in a
      # ~1 difference, it's therefore not a good idea to measure using addition.
      # Here, we instead divide the expected result with the result in order to obtain ~1. We then check that the
      # result is held between bounds: 1 - TOLERANCE < expected_result / result < 1 + TOLERANCE
      lower_bound = torch.all(
          (expected_slice / output[..., :3, :3]) >= 1 - TOLERANCE
      )
      upper_bound = torch.all(
          (expected_slice / output[..., :3, :3]) <= 1 + TOLERANCE
      )
  
      self.assertTrue(lower_bound and upper_bound)
  
imports: import unittest
  from transformers import MobileBertConfig, is_torch_available
  from transformers.models.auto import get_values
  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
  from ...test_pipeline_mixin import PipelineTesterMixin
label_desc: Use image node_id 1 for calling the MobileBertModelIntegrationTests obj's underlying member method code with example usage: obj.test_inference_no_head() without return types