code: def test_log_longer_sequence(
      sequence_length: int,
      model_name: Text,
      model_weights: Text,
      should_overflow: bool,
      caplog: LogCaptureFixture,
      create_language_model_featurizer: Callable[
          [Dict[Text, Any]], LanguageModelFeaturizer
      ],
      whitespace_tokenizer: WhitespaceTokenizer,
  ):
      config = {
          "model_name": model_name,
          "model_weights": model_weights,
      }
  
      featurizer = create_language_model_featurizer(config)
  
      text = " ".join(["hi"] * sequence_length)
      message = Message.build(text=text)
      td = TrainingData([message])
      whitespace_tokenizer.process_training_data(td)
      caplog.set_level(logging.DEBUG)
      featurizer.process([message])
      if should_overflow:
          assert "hi hi hi" in caplog.text
      assert len(message.features) >= 2
  
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 10 for calling a global function with example usage: test_log_longer_sequence(sequence_length, model_name, model_weights, should_overflow, caplog, create_language_model_featurizer, whitespace_tokenizer) without return types