code: def test_countvectorizer_max_df():
      test_data = Series(["abc", "dea", "eat"])
      vect = CountVectorizer(analyzer="char", max_df=1.0)
      vect.fit(test_data)
      assert "a" in vect.vocabulary_.to_arrow().to_pylist()
      assert len(vect.vocabulary_.to_arrow().to_pylist()) == 6
      assert len(vect.stop_words_) == 0
  
      vect.max_df = 0.5  # 0.5 * 3 documents -> max_doc_count == 1.5
      vect.fit(test_data)
      assert (
          "a" not in vect.vocabulary_.to_arrow().to_pylist()
      )  # {ae} ignored
      assert (
          len(vect.vocabulary_.to_arrow().to_pylist()) == 4
      )  # {bcdt} remain
      assert "a" in vect.stop_words_.to_arrow().to_pylist()
      assert len(vect.stop_words_) == 2
  
      vect.max_df = 1
      vect.fit(test_data)
      assert (
          "a" not in vect.vocabulary_.to_arrow().to_pylist()
      )  # {ae} ignored
      assert (
          len(vect.vocabulary_.to_arrow().to_pylist()) == 4
      )  # {bcdt} remain
      assert "a" in vect.stop_words_.to_arrow().to_pylist()
      assert len(vect.stop_words_) == 2
  
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 9 for calling a global function with example usage: test_countvectorizer_max_df() without return types