code: def test_vectorized(self, axis):
      tm = self.tm
  
      x = tm.data
      x2 = np.column_stack((x, 2 * x))
      if axis == 0:
          tm2d = TrimmedMean(x2, self.k / 19, axis=0)
      else:
          tm2d = TrimmedMean(x2.T, self.k / 19, axis=1)
      t1 = [tm.mean_trimmed, 2 * tm.mean_trimmed]
      assert_allclose(tm2d.mean_trimmed, t1, rtol=1e-13)
  
      t1 = [tm.var_winsorized, 4 * tm.var_winsorized]
      assert_allclose(tm2d.var_winsorized, t1, rtol=1e-13)
  
      t1 = [tm.std_mean_trimmed, 2 * tm.std_mean_trimmed]
      assert_allclose(tm2d.std_mean_trimmed, t1, rtol=1e-13)
  
      t1 = [tm.mean_winsorized, 2 * tm.mean_winsorized]
      assert_allclose(tm2d.mean_winsorized, t1, rtol=1e-13)
  
      t1 = [tm.std_mean_winsorized, 2 * tm.std_mean_winsorized]
      assert_allclose(tm2d.std_mean_winsorized, t1, rtol=1e-13)
  
      s2, pv2, df2 = tm2d.ttest_mean()
      s, pv, df = tm.ttest_mean()
      assert_allclose(s2, [s, s], rtol=1e-13)
      assert_allclose(pv2, [pv, pv], rtol=1e-13)
      assert_allclose(df2, df, rtol=1e-13)
  
      s2, pv2, df2 = tm2d.ttest_mean(transform="winsorized")
      s, pv, df = tm.ttest_mean(transform="winsorized")
      assert_allclose(s2, [s, s], rtol=1e-13)
      assert_allclose(pv2, [pv, pv], rtol=1e-13)
      assert_allclose(df2, df, rtol=1e-13)
  
imports: import numpy
  from numpy.testing import assert_equal, assert_allclose, assert_raises
  import pytest
  from statsmodels.stats.robust_compare import TrimmedMean, trim_mean, trimboth
  import statsmodels.stats.oneway
  from statsmodels.tools.testing import Holder
  from scipy.stats import trim1
label_desc: Use image node_id 6 for calling the TestTrimmedR1 obj's underlying member method code with example usage: obj.test_vectorized(axis) without return types