code: def test_countvectorizer_custom_vocabulary():
      vocab = {"pizza": 0, "beer": 1}
      vocab_gpu = Series(vocab.keys())
  
      ref = SkCountVect(vocabulary=vocab).fit_transform(DOCS)
      X = CountVectorizer(vocabulary=vocab_gpu).fit_transform(DOCS_GPU)
      cp.testing.assert_array_equal(X.todense(), ref.toarray())
  
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 3 for calling a global function with example usage: test_countvectorizer_custom_vocabulary() without return types