code: def _test_lm_generate_gpt2_helper(
      self,
      gradient_checkpointing=False,
      reorder_and_upcast_attn=False,
      scale_attn_by_inverse_layer_idx=False,
      verify_outputs=True,
  ):
      model = GPT2LMHeadModel.from_pretrained(
          "gpt2",
          reorder_and_upcast_attn=reorder_and_upcast_attn,
          scale_attn_by_inverse_layer_idx=scale_attn_by_inverse_layer_idx,
      )
      if gradient_checkpointing:
          model.gradient_checkpointing_enable()
      else:
          model.gradient_checkpointing_disable()
      model.to(torch_device)
  
      # The dog
      input_ids = torch.tensor(
          [[464, 3290]], dtype=torch.long, device=torch_device
      )
  
      # The dog was found in a field near the intersection of West and West Streets.\n\nThe dog
      expected_output_ids = [464, 3290, 373, 1043, 287, 257, 2214, 1474, 262, 16246, 286, 2688, 290, 2688, 27262, 13, 198, 198, 464, 3290,]  # fmt: skip
      output_ids = model.generate(input_ids, do_sample=False)
      if verify_outputs:
          self.assertListEqual(
              output_ids[0].tolist(), expected_output_ids
          )
  
imports: import datetime
  import gc
  import math
  import unittest
  from transformers import GPT2Config, is_torch_available
  from transformers.testing_utils import backend_empty_cache, require_torch, slow, torch_device
  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 2 for calling the GPT2ModelLanguageGenerationTest obj's underlying member method code with example usage: obj._test_lm_generate_gpt2_helper(gradient_checkpointing, reorder_and_upcast_attn, scale_attn_by_inverse_layer_idx, verify_outputs) without return types