code: def test_lm_generate_gptneox(self):
      tokenizer = AutoTokenizer.from_pretrained(
          "EleutherAI/pythia-410m-deduped"
      )
      for checkpointing in [True, False]:
          model = GPTNeoXForCausalLM.from_pretrained(
              "EleutherAI/pythia-410m-deduped"
          )
  
          if checkpointing:
              model.gradient_checkpointing_enable()
          else:
              model.gradient_checkpointing_disable()
          model.to(torch_device)
  
          inputs = tokenizer(
              "My favorite food is", return_tensors="pt"
          ).to(torch_device)
          # The hub repo. is updated on 2023-04-04, resulting in poor outputs.
          # See: https://github.com/huggingface/transformers/pull/24193
          expected_output = "My favorite food is a good old-fashioned, old-fashioned, old-fashioned.\n\nI'm not sure"
  
          output_ids = model.generate(
              **inputs, do_sample=False, max_new_tokens=20
          )
          output_str = tokenizer.batch_decode(output_ids)[0]
  
          self.assertEqual(output_str, expected_output)
  
imports: import unittest
  from parameterized import parameterized
  from transformers import AutoTokenizer, GPTNeoXConfig, is_torch_available, set_seed
  from transformers.testing_utils import require_torch, slow, torch_device
  from ...generation.test_utils import GenerationTesterMixin
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
  from ...test_pipeline_mixin import PipelineTesterMixin
label_desc: Use image node_id 1 for calling the GPTNeoXLanguageGenerationTest obj's underlying member method code with example usage: obj.test_lm_generate_gptneox() without return types