code: def test_inference_no_head(self):
      model = OPTModel.from_pretrained("facebook/opt-350m").to(
          torch_device
      )
      input_ids = _long_tensor(
          [[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]
      )
  
      with torch.no_grad():
          output = model(input_ids=input_ids).last_hidden_state
  
      expected_shape = torch.Size((1, 11, 512))
      self.assertEqual(output.shape, expected_shape)
      # expected value works for CPU, as well as GPU (with TF32 disabled)
      expected_slice = torch.tensor(
          [
              [-0.28726277, -1.9241608, -0.3058734],
              [-1.2737825, -0.13332152, -0.18766522],
              [0.41159445, 0.1191957, -1.3107123],
          ],
          device=torch_device,
      )
      assert_tensors_close(output[0, :3, :3], expected_slice, atol=5e-5)
  
imports: import copy
  import tempfile
  import unittest
  import timeout_decorator
  from transformers import OPTConfig, is_torch_available
  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, ids_tensor
label_desc: Use image node_id 1 for calling the OPTModelIntegrationTests obj's underlying member method code with example usage: obj.test_inference_no_head() without return types