def METHOD_NAME(
    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)