code: def setup_class(cls):
      cls.res = coint_johansen(dta, 1, 2)
      cls.nobs_r = 173 - 1 - 2
  
      cls.res1_m = np.array(
          [
              241.985452556075,
              166.4781461662553,
              110.3298006342814,
              70.79801574443575,
              44.90887371527634,
              27.22385073668511,
              11.74205493173769,
              3.295435325623445,
              169.0618,
              133.7852,
              102.4674,
              75.1027,
              51.6492,
              32.0645,
              16.1619,
              2.7055,
              175.1584,
              139.278,
              107.3429,
              79.34220000000001,
              55.2459,
              35.0116,
              18.3985,
              3.8415,
              187.1891,
              150.0778,
              116.9829,
              87.7748,
              62.5202,
              41.0815,
              23.1485,
              6.6349,
          ]
      )
      cls.res2_m = np.array(
          [
              75.50730638981975,
              56.14834553197396,
              39.5317848898456,
              25.8891420291594,
              17.68502297859124,
              15.48179580494741,
              8.446619606114249,
              3.295435325623445,
              52.5858,
              46.5583,
              40.5244,
              34.4202,
              28.2398,
              21.8731,
              15.0006,
              2.7055,
              55.7302,
              49.5875,
              43.4183,
              37.1646,
              30.8151,
              24.2522,
              17.1481,
              3.8415,
              62.1741,
              55.8171,
              49.4095,
              42.8612,
              36.193,
              29.2631,
              21.7465,
              6.6349,
          ]
      )
  
      evec = np.array(
          [
              0.01102517075074406,
              -0.2185481584930077,
              0.04565819524210763,
              -0.06556394587400775,
              0.04711496306104131,
              -0.1500111976629196,
              0.03775327003706507,
              0.03479475877437702,
              0.007517888890275335,
              -0.2014629352546497,
              0.01526001455616041,
              0.0707900418057458,
              -0.002388919695513273,
              0.04486516694838273,
              -0.02936314422571188,
              0.009900554050392113,
              0.02846074144367176,
              0.02021385478834498,
              -0.04276914888645468,
              0.1738024290422287,
              0.07821155002012749,
              -0.1066523077111768,
              -0.3011042488399306,
              0.04965189679477353,
              0.07141291326159237,
              -0.01406702689857725,
              -0.07842109866080313,
              -0.04773566072362181,
              -0.04768640728128824,
              -0.04428737926285261,
              0.4143225656833862,
              0.04512787132114879,
              -0.06817130121837202,
              0.2246249779872569,
              -0.009356548567565763,
              0.006685350535849125,
              -0.02040894506833539,
              0.008131690308487425,
              -0.2503209797396666,
              0.01560186979508953,
              0.03327070126502506,
              -0.263036624535624,
              -0.04669882107497259,
              0.0146457545413255,
              0.01408691619062709,
              0.1004753600191269,
              -0.02239205763487946,
              -0.02169291468272568,
              0.08782313160608619,
              -0.07696508791577318,
              0.008925177304198475,
              -0.06230900392092828,
              -0.01548907461158638,
              0.04574831652028973,
              -0.2972228156126774,
              0.003469819004961912,
              -0.001868995544352928,
              0.05993345996347871,
              0.01213394328069316,
              0.02096614212178651,
              -0.08624395993789938,
              0.02108183181049973,
              -0.08470307289295617,
              -5.135072530480897e-005,
          ]
      )
      cls.evec_m = evec.reshape(cls.res.evec.shape, order="F")
  
      cls.eig_m = np.array(
          [
              0.3586376068088151,
              0.2812806889719111,
              0.2074818815675726,
              0.141259991767926,
              0.09880133062878599,
              0.08704563854307619,
              0.048471840356709,
              0.01919823444066367,
          ]
      )
  
imports: import os
  import warnings
  import numpy
  from numpy.testing import assert_almost_equal, assert_equal
  import pandas
  import pytest
  from statsmodels.tools.sm_exceptions import HypothesisTestWarning
  from statsmodels.tsa.vector_ar.vecm import coint_johansen
label_desc: Use image node_id 1 for calling the TestCointJoh12 obj's underlying member method code with example usage: obj.setup_class(cls) without return types