code: def binomial_plot_test():
      benign = h2o.import_file(
          pyunit_utils.locate("smalldata/logreg/benign.csv")
      )
      response = 3
      predictors = [0, 1, 2, 4, 5, 6, 7, 8, 9, 10]
      model = glm(family="binomial")
      model.train(x=predictors, y=response, training_frame=benign)
  
      # test saving:
      with TemporaryDirectory() as tmpdir:
          path1 = "{}/plot1.png".format(tmpdir)
          path2 = "{}/plot2.png".format(tmpdir)
          test_plot_result_saving(
              model.plot(
                  timestep="AUTO", metric="objective", server=True
              ),
              path2,
              model.plot(
                  timestep="AUTO",
                  metric="objective",
                  server=True,
                  save_plot_path=path1,
              ),
              path1,
          )
  
          test_plot_result_saving(
              model.permutation_importance_plot(benign),
              path2,
              model.permutation_importance_plot(
                  benign, save_plot_path=path1
              ),
              path1,
          )
  
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 1 for calling a global function with example usage: binomial_plot_test() without return types