code: def test_hashingvectorizer_norm(norm):
      if norm not in ["l1", "l2", None]:
          with pytest.raises(ValueError):
              res = HashingVectorizer(norm=norm).fit_transform(DOCS_GPU)
      else:
          res = HashingVectorizer(norm=norm).fit_transform(DOCS_GPU)
          ref = SkHashVect(norm=norm).fit_transform(DOCS)
          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 31 for calling a global function with example usage: test_hashingvectorizer_norm(norm) without return types