code: def create_language_model_featurizer(
      default_model_storage: ModelStorage,
      resource_language_model_featurizer,
      default_execution_context: ExecutionContext,
  ) -> Callable[[Dict[Text, Any]], LanguageModelFeaturizer]:
      def inner(config: Dict[Text, Any]) -> LanguageModelFeaturizer:
          return LanguageModelFeaturizer.create(
              config={
                  **LanguageModelFeaturizer.get_default_config(),
                  **config,
              },
              model_storage=default_model_storage,
              resource=resource_language_model_featurizer,
              execution_context=default_execution_context,
          )
  
      return inner
  
imports: import os
  from typing import Text, List, Dict, Tuple, Any, Callable
  import numpy
  import pytest
  import logging
  from _pytest.monkeypatch import MonkeyPatch
  from _pytest.logging import LogCaptureFixture
  from rasa.engine.graph import ExecutionContext
  from rasa.engine.storage.storage import ModelStorage
  from rasa.engine.storage.resource import Resource
  from rasa.nlu.constants import TOKENS_NAMES, NUMBER_OF_SUB_TOKENS
  from rasa.nlu.tokenizers.whitespace_tokenizer import WhitespaceTokenizer
  from rasa.shared.nlu.training_data.training_data import TrainingData
  from rasa.shared.nlu.training_data.message import Message
  from rasa.nlu.featurizers.dense_featurizer.lm_featurizer import LanguageModelFeaturizer
  from rasa.shared.nlu.constants import TEXT, INTENT
  from rasa.nlu.tokenizers.tokenizer import Token
label_desc: Use image node_id 2 for calling a global function with example usage: create_language_model_featurizer(default_model_storage, resource_language_model_featurizer, default_execution_context) and returns: inner, LanguageModelFeaturizer