code: def test_mitie_featurizer(
      create: Callable[[Dict[Text, Any]], MitieFeaturizer],
      mitie_model: MitieModel,
      mitie_tokenizer: MitieTokenizer,
  ):
      featurizer = create({"alias": "mitie_featurizer"})
  
      sentence = "Hey how are you today"
      message = Message(data={TEXT: sentence})
      mitie_tokenizer.process([message])
      tokens = message.get(TOKENS_NAMES[TEXT])
  
      seq_vec, sen_vec = featurizer.features_for_tokens(
          tokens, mitie_model.word_feature_extractor
      )
  
      expected = np.array(
          [
              0.00000000e00,
              -5.12735510e00,
              4.39929873e-01,
              -5.60760403e00,
              -8.26445103e00,
          ]
      )
      expected_cls = np.array(
          [0.0, -4.4551446, 0.26073121, -1.46632245, -1.84205751]
      )
  
      assert 6 == len(seq_vec) + len(sen_vec)
      assert np.allclose(seq_vec[0][:5], expected, atol=1e-5)
      assert np.allclose(sen_vec[-1][:5], expected_cls, atol=1e-5)
  
file_path: rasa/tests/nlu/featurizers/test_mitie_featurizer.py
imports: import pytest
  import numpy
  from typing import Text, Dict, Any, Callable
  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
  from rasa.shared.nlu.constants import TEXT, INTENT, RESPONSE
  from rasa.shared.nlu.training_data.training_data import TrainingData
  from rasa.shared.nlu.training_data.message import Message
  from rasa.nlu.tokenizers.mitie_tokenizer import MitieTokenizer
  from rasa.nlu.featurizers.dense_featurizer.mitie_featurizer import MitieFeaturizer
  from rasa.nlu.utils.mitie_utils import MitieModel, MitieNLP
label_desc: Use image node_id 4 for calling a global function with example usage: test_mitie_featurizer(create, mitie_model, mitie_tokenizer) without return types