code: def get_lilliefors_table(dist="norm"):
      """
      Generates tables for significance levels of Lilliefors test statistics
  
      Tables for available normal and exponential distribution testing,
      as specified in Lilliefors references above
  
      Parameters
      ----------
      dist : str
          distribution being tested in set {'norm', 'exp'}.
  
      Returns
      -------
      lf : TableDist object.
          table of critical values
      """
      # function just to keep things together
      # for this test alpha is sf probability, i.e. right tail probability
  
      alpha = 1 - np.array(PERCENTILES) / 100.0
      alpha = alpha[::-1]
      dist = "normal" if dist == "norm" else dist
      if dist not in critical_values:
          raise ValueError(
              "Invalid dist parameter. Must be 'norm' or 'exp'"
          )
      cv_data = critical_values[dist]
      acv_data = asymp_critical_values[dist]
  
      size = np.array(sorted(cv_data), dtype=float)
      crit_lf = np.array([cv_data[key] for key in sorted(cv_data)])
      crit_lf = crit_lf[:, ::-1]
  
      asym_params = np.array(
          [acv_data[key] for key in sorted(acv_data)]
      )
      asymp_fn = _make_asymptotic_function(asym_params[::-1])
  
      lf = TableDist(alpha, size, crit_lf, asymptotic=asymp_fn)
      return lf
  
file_path: statsmodels/statsmodels/stats/_lilliefors.py
imports: from functools import partial
  import numpy
  from scipy import stats
  from statsmodels.tools.validation import string_like
  from ._lilliefors_critical_values import critical_values, asymp_critical_values, PERCENTILES
  from .tabledist import TableDist
label_desc: Use image node_id 3 for calling a global function with example usage: get_lilliefors_table(dist) and returns: lf