code: def test_lm_uneven_forward(self):
      config = BartConfig(
          vocab_size=10,
          d_model=24,
          encoder_layers=2,
          decoder_layers=2,
          encoder_attention_heads=2,
          decoder_attention_heads=2,
          encoder_ffn_dim=32,
          decoder_ffn_dim=32,
          max_position_embeddings=48,
      )
      lm_model = TFBartForConditionalGeneration(config)
      context = tf.fill((7, 2), 4)
      summary = tf.fill((7, 7), 6)
      outputs = lm_model(
          input_ids=context, decoder_input_ids=summary, use_cache=False
      )
      expected_shape = (*summary.shape, config.vocab_size)
      self.assertEqual(outputs.logits.shape, expected_shape)
  
imports: from __future__ import annotations
  import copy
  import tempfile
  import unittest
  import numpy
  from transformers import BartConfig, BartTokenizer, is_tf_available
  from transformers.testing_utils import require_tf, slow
  from transformers.utils import cached_property
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
  from ...test_pipeline_mixin import PipelineTesterMixin
  from ...utils.test_modeling_tf_core import TFCoreModelTesterMixin
label_desc: Use image node_id 3 for calling the TFBartHeadTests obj's underlying member method code with example usage: obj.test_lm_uneven_forward() without return types