code: def setup_class(cls):
      # Results from E-views:
      # Dependent variable followed by a list of switching regressors:
      #     rgnp c
      # List of non-switching regressors:
      #     ar(1) ar(2) ar(3) ar(4)
      # Do not check "Regime specific error variances"
      # Switching type: Markov
      # Number of Regimes: 2
      # Probability regressors:
      #     c
      # Method SWITCHREG
      # Sample 1951q1 1984q4
      true = {
          "params": np.r_[
              0.754673,
              0.095915,
              -0.358811,
              1.163516,
              np.exp(-0.262658) ** 2,
              0.013486,
              -0.057521,
              -0.246983,
              -0.212923,
          ],
          "llf": -181.26339,
          "llf_fit": -181.26339,
          "llf_fit_em": -183.85444,
          "bse_oim": np.r_[
              0.0965189,
              0.0377362,
              0.2645396,
              0.0745187,
              np.nan,
              0.1199942,
              0.137663,
              0.1069103,
              0.1105311,
          ],
      }
      super().setup_class(
          true, rgnp, k_regimes=2, order=4, switching_ar=False
      )
  
imports: import warnings
  import os
  import numpy
  from numpy.testing import assert_equal, assert_allclose
  import pandas
  import pytest
  from statsmodels.tools import add_constant
  from statsmodels.tsa.regime_switching import markov_autoregression
label_desc: Use image node_id 1 for calling the TestHamiltonAR4 obj's underlying member method code with example usage: obj.setup_class(cls) without return types