code: def setUp(self):
      self.configspace = CS.ConfigurationSpace(42)
  
      self.add_hyperparameters()
  
      x_train_confs = [
          self.configspace.sample_configuration()
          for i in range(self.n_train)
      ]
      self.x_train = np.array([c.get_array() for c in x_train_confs])
  
      x_test_confs = [
          self.configspace.sample_configuration()
          for i in range(self.n_test)
      ]
      self.x_test = np.array([c.get_array() for c in x_test_confs])
  
      self.sm_x_train = self.sm_transform_data(self.x_train)
      self.sm_x_test = self.sm_transform_data(self.x_test)
  
      self.sm_kde = sm.nonparametric.KDEMultivariate(
          data=self.sm_x_train, var_type=self.var_types, bw="cv_ml"
      )
  
      self.sm_1d_kdes = [
          sm.nonparametric.KDEMultivariate(
              data=self.sm_x_train[:, i],
              var_type=self.var_types[i],
              bw="cv_ml",
          )
          for i in range(len(self.var_types))
      ]
  
      self.hp_kde_full = MultivariateKDE(
          self.configspace, fully_dimensional=True, fix_boundary=False
      )
      self.hp_kde_factor = MultivariateKDE(
          self.configspace, fully_dimensional=False, fix_boundary=False
      )
      self.hp_kde_full.fit(self.x_train, bw_estimator="mlcv")
      self.hp_kde_factor.fit(self.x_train, bw_estimator="mlcv")
  
imports: import os
  import unittest
  import numpy
  import statsmodels.api
  import ConfigSpace
  from hpbandster.optimizers.kde.mvkde import MultivariateKDE
  from pdb import set_trace
label_desc: Use image node_id 1 for calling the BaseNdTest obj's underlying member method code with example usage: obj.setUp() without return types