code: def setup_class(cls):
      params = [2, 0, 2]
      nobs = 100
      np.random.seed(1234)
      rvs = stats.pareto.rvs(*params, **dict(size=nobs))
  
      mod_par = MyPareto(rvs)
      mod_par.fixed_params = None
      mod_par.fixed_paramsmask = None
      mod_par.df_model = 0
      mod_par.k_extra = k_extra = 3
      mod_par.df_resid = (
          mod_par.endog.shape[0] - mod_par.df_model - k_extra
      )
      mod_par.data.xnames = ["shape", "loc", "scale"]
  
      cls.mod = mod_par
      cls.res1 = mod_par.fit(disp=None)
      cls.k_extra = k_extra
  
      # Note: possible problem with parameters close to min data boundary
      # see issue #968
      cls.skip_bsejac = True
  
imports: import numpy
  from scipy import stats
  from statsmodels.base.model import GenericLikelihoodModel
  from numpy.testing import assert_array_less, assert_almost_equal, assert_allclose
label_desc: Use image node_id 1 for calling the TestMyPareto1 obj's underlying member method code with example usage: obj.setup_class(cls) without return types