code: def test_count_vectorizer():
      corpus = [
          "This is the first document.",
          "This document is the second document.",
          "And this is the third one.",
          "Is this the first document?",
      ]
  
      res = CountVectorizer().fit_transform(Series(corpus))
      ref = SkCountVect().fit_transform(corpus)
      cp.testing.assert_array_equal(res.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 1 for calling a global function with example usage: test_count_vectorizer() without return types