code: def test_word_analyzer(ngram_range):
      v = CountVectorizer(ngram_range=ngram_range).fit(DOCS_GPU)
      ref = SkCountVect(ngram_range=ngram_range).fit(DOCS)
      assert (
          ref.get_feature_names()
          == 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 2 for calling a global function with example usage: test_word_analyzer(ngram_range) without return types