code: def test_spacy_featurizer_sequence(spacy_nlp):
      sentence = "hey how are you today"
      doc = spacy_nlp(sentence)
      token_vectors = [t.vector for t in doc]
  
      ftr = create_spacy_featurizer({})
  
      greet = {
          TEXT: sentence,
          "intent": "greet",
          "text_features": [0.5],
      }
  
      message = Message(data=greet)
      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
  
      vecs = seq_vecs[0][:5]
  
      assert np.allclose(token_vectors[0][:5], vecs, atol=1e-4)
      assert sen_vecs is not 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 6 for calling a global function with example usage: test_spacy_featurizer_sequence(spacy_nlp) without return types