code: def test_hashingvectorizer():
      corpus = [
          "This is the first document.",
          "This document is the second document.",
          "And this is the third one.",
          "Is this the first document?",
      ]
  
      res = HashingVectorizer().fit_transform(Series(corpus))
      ref = SkHashVect().fit_transform(corpus)
      assert_almost_equal_hash_matrices(
          res.todense().get(), 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 26 for calling a global function with example usage: test_hashingvectorizer() without return types