code: def test_hashingvectorizer_alternate_sign():
      # if alternate_sign = True
      # we should have some negative and positive values
      res = HashingVectorizer(alternate_sign=True).fit_transform(
          DOCS_GPU
      )
      res_f_array = res.todense().get().flatten()
      assert np.sum(res_f_array > 0, axis=0) > 0
      assert np.sum(res_f_array < 0, axis=0) > 0
  
      # if alternate_sign = False
      # we should have no negative values and some positive values
      res = HashingVectorizer(alternate_sign=False).fit_transform(
          DOCS_GPU
      )
      res_f_array = res.todense().get().flatten()
      assert np.sum(res_f_array > 0, axis=0) > 0
      assert np.sum(res_f_array < 0, axis=0) == 0
  
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 32 for calling a global function with example usage: test_hashingvectorizer_alternate_sign() without return types