code: def _test_lm_generate_xglm_helper(
      self,
      gradient_checkpointing=False,
      verify_outputs=True,
  ):
      model = XGLMForCausalLM.from_pretrained("facebook/xglm-564M")
      if gradient_checkpointing:
          model.gradient_checkpointing_enable()
      else:
          model.gradient_checkpointing_disable()
      model.to(torch_device)
      input_ids = torch.tensor(
          [[2, 268, 9865]], dtype=torch.long, device=torch_device
      )  # The dog
      # </s> The dog is a very friendly dog. He is very affectionate and loves to play with other
      expected_output_ids = [2, 268, 9865, 67, 11, 1988, 57252, 9865, 5, 984, 67, 1988, 213838, 1658, 53, 70446, 33, 6657, 278, 1581]  # fmt: skip
      output_ids = model.generate(
          input_ids, do_sample=False, num_beams=1
      )
      if verify_outputs:
          self.assertListEqual(
              output_ids[0].tolist(), expected_output_ids
          )
  
imports: import datetime
  import gc
  import math
  import unittest
  from transformers import XGLMConfig, is_torch_available
  from transformers.testing_utils import require_torch, require_torch_accelerator, require_torch_fp16, 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 XGLMModelLanguageGenerationTest obj's underlying member method code with example usage: obj._test_lm_generate_xglm_helper(gradient_checkpointing, verify_outputs) without return types