code: def test_grid_search(self):
      tuned_parameters = [
          {"kernel": ["rbf"], "gamma": [1e-3, 1e-4], "C": [1, 10, 100]},
          {"kernel": ["linear"], "C": [1, 10, 100]},
      ]
  
      df = pdml.ModelFrame(datasets.load_digits())
      cv = df.model_selection.GridSearchCV(
          df.svm.SVC(C=1), tuned_parameters, cv=5
      )
  
      with tm.RNGContext(1):
          df.fit(cv)
  
      result = df.model_selection.describe(cv)
      expected = pd.DataFrame(cv.cv_results_)
      self.assertIsInstance(result, pdml.ModelFrame)
      tm.assert_frame_equal(result, expected)
  
imports: import sklearn.datasets
  import sklearn.svm
  import sklearn.naive_bayes
  import numpy
  import pandas
  import pandas_ml
  import pandas_ml.util.testing
label_desc: Use image node_id 1 for calling the TestHyperParameterOptimizer obj's underlying member method code with example usage: obj.test_grid_search() without return types