code: def test_construct_language_modeling_prompt(self):
      adapter_spec = AdapterSpec(
          method=ADAPT_LANGUAGE_MODELING,
          input_prefix="",
          model="openai/davinci",
          output_prefix="",
          max_tokens=0,
      )
      adapter = AdapterFactory.get_adapter(
          adapter_spec, self.tokenizer_service
      )
  
      # The tokens translate to: '�Excuse me�'
      conditioning_tokens: List[TokenizationToken] = [
          TokenizationToken(110),
          TokenizationToken(40127),
      ]
      pred_tokens: List[TokenizationToken] = [
          TokenizationToken(1904),
          TokenizationToken(502),
          TokenizationToken(447),
      ]
      (
          prompt,
          num_conditioning_tokens,
      ) = adapter.construct_language_modeling_prompt(
          conditioning_tokens=conditioning_tokens,
          pred_tokens=pred_tokens,
          max_req_len=5,
          text="",
      )
  
      # Ensure the prompt is correct
      assert prompt == "Excuse me"
  
      # Ensure the number of conditioning tokens is correct
      assert num_conditioning_tokens == 1
  
file_path: H2O/h2o_hf/helm/src/helm/benchmark/adaptation/adapters/test_language_modeling_adapter.py
imports: from typing import List
  from helm.common.tokenization_request import TokenizationToken
  from helm.benchmark.adaptation.adapter_spec import AdapterSpec
  from .adapter_factory import AdapterFactory, ADAPT_LANGUAGE_MODELING
  from .test_adapter import TestAdapter
label_desc: Use image node_id 1 for calling the TestLanguageModelingAdapter obj's underlying member method code with example usage: obj.test_construct_language_modeling_prompt() without return types