code: def test_countvectorizer_separate_fit_transform():
      res = CountVectorizer().fit(DOCS_GPU).transform(DOCS_GPU)
      ref = SkCountVect().fit(DOCS).transform(DOCS)
      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 15 for calling a global function with example usage: test_countvectorizer_separate_fit_transform() without return types