code: def test_lm_forward(self):
      config, input_ids, batch_size = self._get_config_and_data()
      lm_labels = ids_tensor(
          [batch_size, input_ids.shape[1]], self.vocab_size
      ).to(torch_device)
      lm_model = MvpForConditionalGeneration(config)
      lm_model.to(torch_device)
      outputs = lm_model(input_ids=input_ids, labels=lm_labels)
      expected_shape = (
          batch_size,
          input_ids.shape[1],
          config.vocab_size,
      )
      self.assertEqual(outputs["logits"].shape, expected_shape)
      self.assertIsInstance(outputs["loss"].item(), float)
  
imports: import copy
  import tempfile
  import unittest
  import timeout_decorator
  from transformers import MvpConfig, is_torch_available
  from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, require_torch_fp16, slow, torch_device
  from transformers.utils import cached_property
  from ...generation.test_utils import GenerationTesterMixin
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
  from ...test_pipeline_mixin import PipelineTesterMixin
label_desc: Use image node_id 4 for calling the MvpHeadTests obj's underlying member method code with example usage: obj.test_lm_forward() without return types