code: def setup_class(cls):
      data = ds.df
      data_unordered = ds.df_unordered
  
      # standard fit
      mod = OrderedModel(
          data["apply"].values.codes,
          np.asarray(data[["pared", "public", "gpa"]], float),
          distr="logit",
      )
      res = mod.fit(method="bfgs", disp=False)
      # standard fit with pandas input
      modp = OrderedModel(
          data["apply"], data[["pared", "public", "gpa"]], distr="logit"
      )
      resp = modp.fit(method="bfgs", disp=False)
      # fit with formula
      modf = OrderedModel.from_formula(
          "apply ~ pared + public + gpa - 1",
          data={
              "apply": data["apply"].values.codes,
              "pared": data["pared"],
              "public": data["public"],
              "gpa": data["gpa"],
          },
          distr="logit",
      )
      resf = modf.fit(method="bfgs", disp=False)
      # fit on data with ordered=False
      modu = OrderedModel(
          data_unordered["apply"].values.codes,
          np.asarray(data_unordered[["pared", "public", "gpa"]], float),
          distr="logit",
      )
      resu = modu.fit(method="bfgs", disp=False)
  
      from .results.results_ordinal_model import res_ord_logit as res2
  
      cls.res2 = res2
      cls.res1 = res
      cls.resp = resp
      cls.resf = resf
      cls.resu = resu
  
imports: import warnings
  import numpy
  from numpy.testing import assert_allclose, assert_equal
  import pandas
  import pytest
  import scipy.stats
  from statsmodels.discrete.discrete_model import Logit
  from statsmodels.miscmodels.ordinal_model import OrderedModel
  from statsmodels.tools.sm_exceptions import HessianInversionWarning
  from statsmodels.tools.tools import add_constant
  from .results.results_ordinal_model import data_store
label_desc: Use image node_id 1 for calling the TestLogitModel obj's underlying member method code with example usage: obj.setup_class(cls) without return types