code: def process_training_text(
      texts: List[Text],
      model_name: Text,
      model_weights: Text,
      create_language_model_featurizer: Callable[
          [Dict[Text, Any]], LanguageModelFeaturizer
      ],
      whitespace_tokenizer: WhitespaceTokenizer,
  ) -> List[Message]:
      """Creates a featurizer and process training data"""
      config = create_pretrained_transformers_config(
          model_name, model_weights
      )
      lm_featurizer = create_language_model_featurizer(config)
  
      messages = [Message.build(text=text) for text in texts]
      td = TrainingData(messages)
  
      whitespace_tokenizer.process_training_data(td)
      lm_featurizer.process_training_data(td)
      return messages
  
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 5 for calling a global function with example usage: process_training_text(texts, model_name, model_weights, create_language_model_featurizer, whitespace_tokenizer) and returns: messages