code: def test_spacy_featurizer_train(spacy_nlp):
      featurizer = create_spacy_featurizer({})
  
      sentence = "Hey how are you today"
      message = Message(data={TEXT: sentence})
      message.set(RESPONSE, sentence)
      message.set(INTENT, "intent")
      message.set(SPACY_DOCS[TEXT], spacy_nlp(sentence))
      message.set(SPACY_DOCS[RESPONSE], spacy_nlp(sentence))
  
      featurizer.process_training_data(TrainingData([message]))
  
      seq_vecs, sen_vecs = message.get_dense_features(TEXT, [])
      if seq_vecs:
          seq_vecs = seq_vecs.features
      if sen_vecs:
          sen_vecs = sen_vecs.features
  
      assert 5 == len(seq_vecs)
      assert 1 == len(sen_vecs)
  
      seq_vecs, sen_vecs = message.get_dense_features(RESPONSE, [])
      if seq_vecs:
          seq_vecs = seq_vecs.features
      if sen_vecs:
          sen_vecs = sen_vecs.features
  
      assert 5 == len(seq_vecs)
      assert 1 == len(sen_vecs)
  
      seq_vecs, sen_vecs = message.get_dense_features(INTENT, [])
      if seq_vecs:
          seq_vecs = seq_vecs.features
      if sen_vecs:
          sen_vecs = sen_vecs.features
  
      assert seq_vecs is None
      assert sen_vecs is None
  
imports: from typing import Any, Dict, Text
  import numpy
  import pytest
  from rasa.nlu.utils.spacy_utils import SpacyModel, SpacyNLP
  from rasa.shared.nlu.training_data import loading
  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.spacy_featurizer import SpacyFeaturizer
  from rasa.nlu.constants import SPACY_DOCS
  from rasa.shared.nlu.constants import TEXT, INTENT, RESPONSE
label_desc: Use image node_id 9 for calling a global function with example usage: test_spacy_featurizer_train(spacy_nlp) without return types