code: def setup_class(cls):
      # Setup the model and estimate it.
      pid = np.repeat([0, 1], 5)
      x0 = np.repeat([1], 10)
      x1 = [1, 5, 7, 3, 5, 1, 2, 6, 9, 8]
      x2 = [6, 2, 1, 0, 1, 4, 3, 8, 2, 1]
      y = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
      df = pd.DataFrame(
          {"y": y, "pid": pid, "x0": x0, "x1": x1, "x2": x2}
      )
      endog = df["y"].values
      exog = df[["x0", "x1", "x2"]].values
      groups = df["pid"].values
      cls.res = MixedLM(endog, exog, groups=groups).fit()
  
imports: from statsmodels.compat.platform import PLATFORM_OSX
  import os
  import csv
  import warnings
  import numpy
  import pandas
  from scipy import sparse
  import pytest
  from statsmodels.regression.mixed_linear_model import MixedLM, MixedLMParams, _smw_solver, _smw_logdet
  from numpy.testing import assert_almost_equal, assert_equal, assert_allclose, assert_
  from statsmodels.base import _penalties
  import statsmodels.tools.numdiff
  from .results import lme_r_results
label_desc: Use image node_id 1 for calling the TestMixedLMSummary obj's underlying member method code with example usage: obj.setup_class(cls) without return types