code: def test_inference_head(self):
      model = PegasusXForConditionalGeneration.from_pretrained(
          "google/pegasus-x-base"
      ).to(torch_device)
  
      # change to intended input
      input_ids = _long_tensor(
          [[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]
      )
      decoder_input_ids = _long_tensor(
          [[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]
      )
      inputs_dict = prepare_pegasus_x_inputs_dict(
          model.config, input_ids, decoder_input_ids
      )
      with torch.no_grad():
          output = model(**inputs_dict)[0]
      expected_shape = torch.Size((1, 11, model.config.vocab_size))
      self.assertEqual(output.shape, expected_shape)
      # change to expected output here
      expected_slice = torch.tensor(
          [
              [0.0, 9.5705185, 1.5897303],
              [0.0, 9.833374, 1.5828674],
              [0.0, 10.429961, 1.5643371],
          ],
          device=torch_device,
      )
      self.assertTrue(
          torch.allclose(
              output[:, :3, :3], expected_slice, atol=TOLERANCE
          )
      )
  
imports: import copy
  import math
  import tempfile
  import unittest
  from transformers import is_torch_available
  from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
  from transformers.utils import cached_property
  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 3 for calling the PegasusXModelIntegrationTests obj's underlying member method code with example usage: obj.test_inference_head() without return types