code: def test_display_mode():
      train = h2o.upload_file(
          pyunit_utils.locate("smalldata/titanic/titanic_expanded.csv")
      )
      y = "fare"
  
      gbm = H2OGradientBoostingEstimator(
          seed=1234, model_id="my_awesome_model"
      )
      gbm.train(y=y, training_frame=train)
  
      ice_plot_result = gbm.ice_plot(
          train, "body", output_graphing_data=True
      )
      assert isinstance(
          ice_plot_result.figure(), matplotlib.pyplot.Figure
      )
      assert isinstance(ice_plot_result, H2OTwoDimTable)
  
      assert isinstance(
          gbm.ice_plot(train, "title").figure(),
          matplotlib.pyplot.Figure,
      )
      assert isinstance(
          gbm.ice_plot(train, "title", show_pdp=True).figure(),
          matplotlib.pyplot.Figure,
      )
      assert isinstance(
          gbm.ice_plot(train, "title", show_pdp=False).figure(),
          matplotlib.pyplot.Figure,
      )
  
      assert isinstance(
          gbm.ice_plot(train, "age").figure(), matplotlib.pyplot.Figure
      )
      assert isinstance(
          gbm.ice_plot(train, "age", show_pdp=True).figure(),
          matplotlib.pyplot.Figure,
      )
      assert isinstance(
          gbm.ice_plot(train, "age", show_pdp=False).figure(),
          matplotlib.pyplot.Figure,
      )
      matplotlib.pyplot.close("all")
  
imports: import sys
  import os
  import matplotlib
  import h2o
  import math
  import matplotlib.pyplot
  from tests import pyunit_utils
  from h2o.estimators import *
  from h2o.explanation._explain import *
  from h2o.explanation._explain import _handle_orig_values, _factor_mapper
  from h2o.two_dim_table import H2OTwoDimTable
label_desc: Use image node_id 5 for calling a global function with example usage: test_display_mode() without return types