code: def test_hashingvectorizer_n_features():
      n_features = 10
      res = (
          HashingVectorizer(n_features=n_features)
          .fit_transform(DOCS_GPU)
          .todense()
          .get()
      )
      ref = (
          SkHashVect(n_features=n_features)
          .fit_transform(DOCS)
          .toarray()
      )
      assert res.shape == ref.shape
  
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 30 for calling a global function with example usage: test_hashingvectorizer_n_features() without return types