code: def test_output_embeds_base_model(self):
      model = CamembertModel.from_pretrained("camembert-base")
      model.to(torch_device)
  
      input_ids = torch.tensor(
          [[5, 121, 11, 660, 16, 730, 25543, 110, 83, 6]],
          device=torch_device,
          dtype=torch.long,
      )  # J'aime le camembert !
      output = model(input_ids)["last_hidden_state"]
      expected_shape = torch.Size((1, 10, 768))
      self.assertEqual(output.shape, expected_shape)
      # compare the actual values for a slice.
      expected_slice = torch.tensor(
          [
              [
                  [-0.0254, 0.0235, 0.1027],
                  [0.0606, -0.1811, -0.0418],
                  [-0.1561, -0.1127, 0.2687],
              ]
          ],
          device=torch_device,
          dtype=torch.float,
      )
      # camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
      # camembert.eval()
      # expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()
  
      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 CamembertModelIntegrationTest obj's underlying member method code with example usage: obj.test_output_embeds_base_model() without return types