code: def test_sequence_classification_forward(self):
      config, input_ids, batch_size = self._get_config_and_data()
      labels = _long_tensor([2] * batch_size).to(torch_device)
      config.num_labels = 3
      model = MvpForSequenceClassification(config)
      model.to(torch_device)
      outputs = model(
          input_ids=input_ids,
          decoder_input_ids=input_ids,
          labels=labels,
      )
      expected_shape = torch.Size((batch_size, config.num_labels))
      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 2 for calling the MvpHeadTests obj's underlying member method code with example usage: obj.test_sequence_classification_forward() without return types