code: def create_pretrained_transformers_config(
      model_name: Text, model_weights: Text
  ) -> Dict[Text, Text]:
      """Creates a config for LanguageModelFeaturizer.
  
      If CI, skips model/model_weight combinations that are too large (bert with
      LaBSE).
  
      Args:
          model_name: model name
          model_weights: model weights name
      """
      if skip_on_CI_with_bert(model_name, model_weights):
          pytest.skip(
              "Reason: this model is too large, loading it results in"
              "crashing of GH action workers."
          )
      config = {"model_name": model_name}
      if model_weights:
          config["model_weights"] = model_weights
      return config
  
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 4 for calling a global function with example usage: create_pretrained_transformers_config(model_name, model_weights) and returns: config