code: def test_hdquantiles_sd(self):
      # Standard deviation is a jackknife estimator, so we can check if
      # the efficient version (hdquantiles_sd) matches a rudimentary,
      # but clear version here.
  
      hd_std_errs = ms.hdquantiles_sd(self.data)
  
      # jacknnife standard error, Introduction to the Bootstrap Eq. 11.5
      n = len(self.data)
      jdata = np.broadcast_to(self.data, (n, n))
      jselector = np.logical_not(
          np.eye(n)
      )  # leave out one sample each row
      jdata = jdata[jselector].reshape(n, n - 1)
      jdist = ms.hdquantiles(jdata, axis=1)
      jdist_mean = np.mean(jdist, axis=0)
      jstd = (
          (n - 1) / n * np.sum((jdist - jdist_mean) ** 2, axis=0)
      ) ** 0.5
  
      assert_almost_equal(hd_std_errs, jstd)
      # Test actual values for good measure
      assert_almost_equal(
          hd_std_errs, [0.0379258, 0.0380656, 0.0380013]
      )
  
      two_data_points = ms.hdquantiles_sd([1, 2])
      assert_almost_equal(two_data_points, [0.5, 0.5, 0.5])
  
file_path: scipy/scipy/stats/tests/test_mstats_extras.py
imports: import numpy
  import numpy.ma
  import scipy.stats.mstats
  from numpy.testing import assert_equal, assert_almost_equal, assert_, assert_allclose
label_desc: Use image node_id 2 for calling the TestQuantiles obj's underlying member method code with example usage: obj.test_hdquantiles_sd() without return types