code: def test_countvectorizer_max_features():
      expected_vocabulary = {"burger", "beer", "salad", "pizza"}
      expected_stop_words = {
          "celeri",
          "tomato",
          "copyright",
          "coke",
          "sparkling",
          "water",
          "the",
      }
  
      # test bounded number of extracted features
      vec = CountVectorizer(max_df=0.6, max_features=4)
      vec.fit(DOCS_GPU)
      assert (
          set(vec.get_feature_names().to_arrow().to_pylist())
          == expected_vocabulary
      )
      assert (
          set(vec.stop_words_.to_arrow().to_pylist())
          == expected_stop_words
      )
  
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 7 for calling a global function with example usage: test_countvectorizer_max_features() without return types