code: def test_lm_uneven_forward(self):
      config = MvpConfig(
          vocab_size=self.vocab_size,
          d_model=14,
          encoder_layers=2,
          decoder_layers=2,
          encoder_attention_heads=2,
          decoder_attention_heads=2,
          encoder_ffn_dim=8,
          decoder_ffn_dim=8,
          max_position_embeddings=48,
      )
      lm_model = MvpForConditionalGeneration(config).to(torch_device)
      context = torch.tensor(
          [[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]],
          device=torch_device,
          dtype=torch.long,
      )
      summary = torch.tensor(
          [[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]],
          device=torch_device,
          dtype=torch.long,
      )
      outputs = lm_model(
          input_ids=context, decoder_input_ids=summary, labels=summary
      )
      expected_shape = (*summary.shape, config.vocab_size)
      self.assertEqual(outputs["logits"].shape, expected_shape)
  
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, 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, floats_tensor, ids_tensor
label_desc: Use image node_id 5 for calling the MvpHeadTests obj's underlying member method code with example usage: obj.test_lm_uneven_forward() without return types