code: def test_countvectorizer_empty_vocabulary():
      v = CountVectorizer(max_df=1.0, stop_words="english")
      # fitting only on stopwords will result in an empty vocabulary
      with pytest.raises(ValueError):
          v.fit(
              Series(
                  ["to be or not to be", "and me too", "and so do you"]
              )
          )
  
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 5 for calling a global function with example usage: test_countvectorizer_empty_vocabulary() without return types