code: def test_gce(self):
      # load model
  
      n_samples = 10
      top_k_features = 4
      gce_explainer = GroupedCEExplainer(
          model=self.clf.predict,
          data=self.x_train,
          feature_names=self.feature_names,
          n_samples=n_samples,
          top_k_features=top_k_features,
          random_seed=22,
      )
      explanation = gce_explainer.explain_instance(
          instance=self.x_test[[0], :]
      )
      # validate explanation structure
  
      self.assertIn("selected_features", explanation)
      self.assertEquals(
          len(explanation["selected_features"]), top_k_features
      )
      self.assertTrue(
          set(explanation["selected_features"]).issubset(
              set(explanation.keys())
          )
      )
      feat_1 = explanation["selected_features"][0]
      feat_2 = explanation["selected_features"][1]
      self.assertIn(feat_2, explanation[feat_1])
      self.assertIn("gce_values", explanation[feat_1][feat_2])
      self.assertIn("x_grid", explanation[feat_1][feat_2])
      self.assertIn("y_grid", explanation[feat_1][feat_2])
  
file_path: AIX360/tests/gce/test_gce.py
imports: import unittest
  from aix360.datasets import DiabetesDataset
  from sklearn.ensemble import RandomForestRegressor
  from aix360.algorithms.gce.gce import GroupedCEExplainer
label_desc: Use image node_id 3 for calling the TestGroupedCEExplainer obj's underlying member method code with example usage: obj.test_gce() without return types