code: def test_countvectorizer_max_features_counts():
      JUNK_FOOD_DOCS_GPU = Series(JUNK_FOOD_DOCS)
  
      cv_1 = CountVectorizer(max_features=1)
      cv_3 = CountVectorizer(max_features=3)
      cv_None = CountVectorizer(max_features=None)
  
      counts_1 = cv_1.fit_transform(JUNK_FOOD_DOCS_GPU).sum(axis=0)
      counts_3 = cv_3.fit_transform(JUNK_FOOD_DOCS_GPU).sum(axis=0)
      counts_None = cv_None.fit_transform(JUNK_FOOD_DOCS_GPU).sum(
          axis=0
      )
  
      features_1 = cv_1.get_feature_names()
      features_3 = cv_3.get_feature_names()
      features_None = cv_None.get_feature_names()
  
      # The most common feature is "the", with frequency 7.
      assert 7 == counts_1.max()
      assert 7 == counts_3.max()
      assert 7 == counts_None.max()
  
      # The most common feature should be the same
      def as_index(x):
          return x.astype(cp.int32).item()
  
      assert "the" == features_1[as_index(cp.argmax(counts_1))]
      assert "the" == features_3[as_index(cp.argmax(counts_3))]
      assert "the" == features_None[as_index(cp.argmax(counts_None))]
  
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 8 for calling a global function with example usage: test_countvectorizer_max_features_counts() and returns: x