code: def test_spacy_featurizer_using_empty_model():
      import spacy
  
      sentence = "This test is using an empty spaCy model"
  
      model = spacy.blank("en")
      doc = model(sentence)
  
      ftr = create_spacy_featurizer({})
  
      message = Message(data={TEXT: sentence})
      message.set(SPACY_DOCS[TEXT], doc)
  
      ftr._set_spacy_features(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 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 10 for calling a global function with example usage: test_spacy_featurizer_using_empty_model() without return types