code: def binomial_pd_multi_plot():
      train = h2o.upload_file(
          pyunit_utils.locate("smalldata/logreg/prostate.csv")
      )
      y = "CAPSULE"
      train[y] = train[y].asfactor()
      # get at most one column from each type
      cols_to_test = []
      for col, typ in train.types.items():
          for ctt in cols_to_test:
              if typ == train.types[ctt] or col == y:
                  break
          else:
              cols_to_test.append(col)
  
      aml = H2OAutoML(seed=1234, max_models=5)
      aml.train(y=y, training_frame=train)
  
      with TemporaryDirectory() as tmpdir:
          path1 = "{}/plot1.png".format(tmpdir)
          path2 = "{}/plot2.png".format(tmpdir)
          for col in cols_to_test:
              test_plot_result_saving(
                  aml.pd_multi_plot(train, col),
                  path2,
                  aml.pd_multi_plot(train, col, save_plot_path=path1),
                  path1,
              )
          matplotlib.pyplot.close()
  
imports: import sys
  import matplotlib
  from h2o.automl import H2OAutoML
  from h2o.estimators import H2OGradientBoostingEstimator
  from h2o.exceptions import H2OError
  from h2o.transforms import H2OPCA
  import h2o
  from tests import pyunit_utils, test_plot_result_saving, TemporaryDirectory
  from h2o.estimators.glm import H2OGeneralizedLinearEstimator, H2OGeneralizedLinearEstimator
  from h2o.plot import decorate_plot_result, RAISE_ON_FIGURE_ACCESS
label_desc: Use image node_id 3 for calling a global function with example usage: binomial_pd_multi_plot() without return types