code: def test_20news_vectorized():
      try:
          datasets.fetch_20newsgroups(
              subset="all", download_if_missing=False
          )
      except IOError:
          raise SkipTest("Download 20 newsgroups to run this test")
  
      # test subset = train
      bunch = datasets.fetch_20newsgroups_vectorized(subset="train")
      assert_true(sp.isspmatrix_csr(bunch.data))
      assert_equal(bunch.data.shape, (11314, 130107))
      assert_equal(bunch.target.shape[0], 11314)
      assert_equal(bunch.data.dtype, np.float64)
  
      # test subset = test
      bunch = datasets.fetch_20newsgroups_vectorized(subset="test")
      assert_true(sp.isspmatrix_csr(bunch.data))
      assert_equal(bunch.data.shape, (7532, 130107))
      assert_equal(bunch.target.shape[0], 7532)
      assert_equal(bunch.data.dtype, np.float64)
  
      # test subset = all
      bunch = datasets.fetch_20newsgroups_vectorized(subset="all")
      assert_true(sp.isspmatrix_csr(bunch.data))
      assert_equal(bunch.data.shape, (11314 + 7532, 130107))
      assert_equal(bunch.target.shape[0], 11314 + 7532)
      assert_equal(bunch.data.dtype, np.float64)
  
file_path: catboost/contrib/python/scikit-learn/py2/sklearn/datasets/tests/test_20news.py
imports: import numpy
  import scipy.sparse
  from sklearn.utils.testing import assert_equal
  from sklearn.utils.testing import assert_true
  from sklearn.utils.testing import SkipTest
  from sklearn import datasets
label_desc: Use image node_id 3 for calling a global function with example usage: test_20news_vectorized() without return types