code: def setup_class(cls):
      x = np.random.standard_normal(1000)
      e1, e2, e3, e4, e5, e6, e7 = np.percentile(
          x, (12.5, 25.0, 37.5, 50.0, 62.5, 75.0, 87.5)
      )
      c05, c50, c95 = np.percentile(x, (5.0, 50.0, 95.0))
      f025, f25, f75, f975 = np.percentile(x, (2.5, 25.0, 75.0, 97.5))
      mean = np.mean
      kr1 = mean(((x - mean(x)) / np.std(x)) ** 4.0) - 3.0
      kr2 = ((e7 - e5) + (e3 - e1)) / (e6 - e2) - 1.2330951154852172
      kr3 = (mean(x[x > c95]) - mean(x[x < c05])) / (
          mean(x[x > c50]) - mean(x[x < c50])
      ) - 2.5852271228708048
      kr4 = (f975 - f025) / (f75 - f25) - 2.9058469516701639
      cls.kurtosis_x = x
      cls.expected_kurtosis = np.array([kr1, kr2, kr3, kr4])
      cls.kurtosis_constants = np.array(
          [
              3.0,
              1.2330951154852172,
              2.5852271228708048,
              2.9058469516701639,
          ]
      )
  
imports: import numpy
  import pandas
  from numpy.testing import assert_almost_equal, assert_raises, assert_equal, assert_allclose
  from statsmodels.stats._adnorm import normal_ad
  from statsmodels.stats.stattools import omni_normtest, jarque_bera, durbin_watson, _medcouple_1d, medcouple, robust_kurtosis, robust_skewness
label_desc: Use image node_id 1 for calling the TestStattools obj's underlying member method code with example usage: obj.setup_class(cls) without return types