code: def test_spacy_featurizer_can_be_case_sensitive(
      spacy_case_sensitive_nlp_component,
  ):
      ftr = create_spacy_featurizer({})
      spacy_nlp = spacy_case_sensitive_nlp_component.provide().model
      td = loading.load_data("data/examples/rasa/demo-rasa.json")
      example_is_case_insentive = []
      for e in td.intent_examples:
          doc = spacy_case_sensitive_nlp_component._doc_for_text(
              spacy_nlp, e.get(TEXT)
          )
          doc_capitalized = (
              spacy_case_sensitive_nlp_component._doc_for_text(
                  spacy_nlp, e.get(TEXT).capitalize()
              )
          )
  
          vecs = ftr._features_for_doc(doc)
          vecs_capitalized = ftr._features_for_doc(doc_capitalized)
  
          example_is_case_insentive.append(
              np.allclose(vecs, vecs_capitalized, atol=1e-5)
          )
      assert not all(example_is_case_insentive)
  
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 8 for calling a global function with example usage: test_spacy_featurizer_can_be_case_sensitive(spacy_case_sensitive_nlp_component) without return types