code: def test_lm_generate_gpt_neo(self):
      for checkpointing in [True, False]:
          model = self.model
          if checkpointing:
              model.gradient_checkpointing_enable()
          else:
              model.gradient_checkpointing_disable()
          input_ids = torch.tensor(
              [[464, 3290]], dtype=torch.long, device=torch_device
          )  # The dog
          # The dog-eared copy of the book, which is a collection of essays by the late author,
          expected_output_ids = [464, 3290, 12, 3380, 4866, 286, 262, 1492, 11, 543, 318, 257, 4947, 286, 27126, 416, 262, 2739, 1772, 11]  # fmt: skip
          output_ids = model.generate(input_ids, do_sample=False)
          self.assertListEqual(
              output_ids[0].tolist(), expected_output_ids
          )
  
imports: import unittest
  from transformers import GPTNeoConfig, is_torch_available
  from transformers.testing_utils import require_torch, 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, random_attention_mask
  from ...test_pipeline_mixin import PipelineTesterMixin
label_desc: Use image node_id 3 for calling the GPTNeoModelLanguageGenerationTest obj's underlying member method code with example usage: obj.test_lm_generate_gpt_neo() without return types