docstr: Tests the number of sub tokens when calling the function
  process (messages)
docstr_tok: ["Tests","the","number","of","sub","tokens","when","calling","the","function","process","(","messages",")"]
code: def test_lm_featurizer_num_sub_tokens_process_messages(
      self,
      model_name: Text,
      model_weights: Text,
      texts: List[Text],
      expected_number_of_sub_tokens: List[List[float]],
      create_language_model_featurizer: Callable[
          [Dict[Text, Any]], LanguageModelFeaturizer
      ],
      whitespace_tokenizer: WhitespaceTokenizer,
  ):
      """Tests the number of sub tokens when calling the function
      process (messages)"""
      messages = process_messages(
          texts,
          model_name,
          model_weights,
          create_language_model_featurizer,
          whitespace_tokenizer,
      )
      self.check_subtokens(
          texts,
          messages,
          expected_number_of_sub_tokens,
          whitespace_tokenizer,
      )
  
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 3 for calling the TestSubTokensTrainAndProcess obj's underlying member method code with example usage: obj.test_lm_featurizer_num_sub_tokens_process_messages(model_name, model_weights, texts, expected_number_of_sub_tokens, create_language_model_featurizer, whitespace_tokenizer) without return types