code: def test_normal(self):
      np.random.seed(3975)
      x_n = stats.norm.rvs(size=500)
  
      # R function call:
      # require(nortest)
      # lillie.test(x_n)
  
      d_ks_norm, p_norm = lilliefors(
          x_n, dist="norm", pvalmethod="approx"
      )
      # shift normal distribution > 0 to exactly mirror R `KScorrect` test
      # R `KScorrect` requires all values tested for exponential
      # distribution to be > 0
      # R function call:
      # require(KScorrect)
      # LcKS(x_n+abs(min(x_n))+0.001, 'pexp')
  
      d_ks_exp, p_exp = lilliefors(
          x_n + np.abs(x_n.min()) + 0.001,
          dist="exp",
          pvalmethod="approx",
      )
      # assert normal
      assert_almost_equal(d_ks_norm, 0.025957, decimal=3)
      assert_almost_equal(p_norm, 0.64175, decimal=3)
      # assert exp
      assert_almost_equal(d_ks_exp, 0.3436007, decimal=3)
      assert_almost_equal(p_exp, 0.001, decimal=3)
  
file_path: statsmodels/statsmodels/stats/tests/test_lilliefors.py
imports: import numpy
  import pandas
  import pytest
  from numpy.testing import assert_almost_equal
  from scipy import stats
  from statsmodels.stats._lilliefors import lilliefors, get_lilliefors_table, kstest_fit, ksstat
label_desc: Use image node_id 1 for calling the TestLilliefors obj's underlying member method code with example usage: obj.test_normal() without return types