code: def test_output_embeds_base_model(self):
      model = AutoModel.from_pretrained("amazon/bort")
      model.to(torch_device)
  
      input_ids = torch.tensor(
          [
              [
                  0,
                  18077,
                  4082,
                  7804,
                  8606,
                  6195,
                  2457,
                  3321,
                  11,
                  10489,
                  16,
                  269,
                  2579,
                  328,
                  2,
              ]
          ],
          device=torch_device,
          dtype=torch.long,
      )  # Schloß Nymphenburg in Munich is really nice!
      output = model(input_ids)["last_hidden_state"]
      expected_shape = torch.Size((1, 15, 1024))
      self.assertEqual(output.shape, expected_shape)
      # compare the actual values for a slice.
      expected_slice = torch.tensor(
          [
              [
                  [-0.0349, 0.0436, -1.8654],
                  [-0.6964, 0.0835, -1.7393],
                  [-0.9819, 0.2956, -0.2868],
              ]
          ],
          device=torch_device,
          dtype=torch.float,
      )
      self.assertTrue(
          torch.allclose(output[:, :3, :3], expected_slice, atol=1e-4)
      )
  
imports: import unittest
  from transformers import is_torch_available
  from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
label_desc: Use image node_id 1 for calling the BortIntegrationTest obj's underlying member method code with example usage: obj.test_output_embeds_base_model() without return types