code: def test_explanation_grid_pareto_front():
      train = h2o.upload_file(
          pyunit_utils.locate("smalldata/logreg/prostate.csv")
      )
      y = "CAPSULE"
      train[y] = train[y].asfactor()
      gbm_params1 = {"learn_rate": [0.01, 0.1], "max_depth": [3, 5, 9]}
  
      # Train and validate a cartesian grid of GBMs
      grid = H2OGridSearch(
          model=H2OGradientBoostingEstimator,
          grid_id="gbm_grid1",
          hyper_params=gbm_params1,
      )
      grid.train(y=y, training_frame=train, seed=1)
  
      assert isinstance(
          grid.pareto_front(train).figure(), matplotlib.pyplot.Figure
      )
      matplotlib.pyplot.close()
  
      assert isinstance(
          grid.pareto_front(train, "mse", "rmse").figure(),
          matplotlib.pyplot.Figure,
      )
      matplotlib.pyplot.close()
  
imports: import os
  import sys
  import pandas
  from h2o.grid import H2OGridSearch
  import matplotlib
  import h2o
  import pandas
  import matplotlib.pyplot
  from tests import pyunit_utils, test_plot_result_saving
  from h2o.automl import H2OAutoML
  from h2o.estimators import *
  from h2o.explanation._explain import H2OExplanation
label_desc: Use image node_id 15 for calling a global function with example usage: test_explanation_grid_pareto_front() without return types