code: def test_spacy_featurizer(sentence, spacy_nlp):
      ftr = create_spacy_featurizer({})
  
      doc = spacy_nlp(sentence)
      vecs = ftr._features_for_doc(doc)
      expected = [t.vector for t in doc]
  
      assert np.allclose(vecs, expected, atol=1e-5)
  
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 3 for calling a global function with example usage: test_spacy_featurizer(sentence, spacy_nlp) without return types