code: def setup_class(cls):
      # Results from E-views:
      # Dependent variable followed by a list of switching regressors:
      #     rgnp c ar(1)
      # List of non-switching regressors: <blank>
      # 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_[
              0.85472458,
              0.53662099,
              1.041419,
              -0.479157,
              np.exp(-0.231404) ** 2,
              0.243128,
              0.713029,
          ],
          "llf": -186.7575,
          "llf_fit": -186.7575,
          "llf_fit_em": -189.25446,
      }
      super().setup_class(true, rgnp, k_regimes=2, order=1)
  
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 TestHamiltonAR1Switch obj's underlying member method code with example usage: obj.setup_class(cls) without return types