code: def test_countvectorizer_stop_words_ngrams():
      stop_words_doc = Series(["and me too andy andy too"])
      expected_vocabulary = ["andy andy"]
  
      v = CountVectorizer(ngram_range=(2, 2), stop_words="english")
      v.fit(stop_words_doc)
  
      assert (
          expected_vocabulary
          == v.get_feature_names().to_arrow().to_pylist()
      )
  
imports: from cuml.internals.safe_imports import cpu_only_import
  from cuml.internals.safe_imports import cpu_only_import_from
  from cuml.internals.safe_imports import gpu_only_import_from
  from sklearn.feature_extraction.text import TfidfVectorizer
  from sklearn.feature_extraction.text import HashingVectorizer
  from sklearn.feature_extraction.text import CountVectorizer
  import pytest
  from cuml.feature_extraction.text import CountVectorizer
  from cuml.feature_extraction.text import TfidfVectorizer
  from cuml.feature_extraction.text import HashingVectorizer
  from cuml.internals.safe_imports import gpu_only_import
label_desc: Use image node_id 6 for calling a global function with example usage: test_countvectorizer_stop_words_ngrams() without return types