code: def test_character_ngrams(analyzer, ngram_range):
      data = ["ab c", "" "edf gh"]
  
      res = CountVectorizer(analyzer=analyzer, ngram_range=ngram_range)
      res.fit(Series(data))
  
      ref = SkCountVect(analyzer=analyzer, ngram_range=ngram_range).fit(
          data
      )
  
      assert (
          ref.get_feature_names()
      ) == res.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 19 for calling a global function with example usage: test_character_ngrams(analyzer, ngram_range) without return types