code: def test_lm_generate_xlm_mlm_en_2048(self):
      model = XLMWithLMHeadModel.from_pretrained("xlm-mlm-en-2048")
      model.to(torch_device)
      input_ids = torch.tensor(
          [[14, 447]], dtype=torch.long, device=torch_device
      )  # the president
      expected_output_ids = [
          14,
          447,
          14,
          447,
          14,
          447,
          14,
          447,
          14,
          447,
          14,
          447,
          14,
          447,
          14,
          447,
          14,
          447,
          14,
          447,
      ]  # the president the president the president the president the president the president the president the president the president the president
      # TODO(PVP): this and other input_ids I tried for generation give pretty bad results. Not sure why. Model might just not be made for auto-regressive inference
      output_ids = model.generate(input_ids, do_sample=False)
      self.assertListEqual(
          output_ids[0].cpu().numpy().tolist(), expected_output_ids
      )
  
imports: import unittest
  from transformers import XLMConfig, is_torch_available
  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 XLMModelLanguageGenerationTest obj's underlying member method code with example usage: obj.test_lm_generate_xlm_mlm_en_2048() without return types