code: def show_decision_tree():
      prostate_train = h2o.import_file(
          path=pyunit_utils.locate("smalldata/prostate/prostate.csv")
      )
      prostate_train["CAPSULE"] = prostate_train["CAPSULE"].asfactor()
      prostate_train = prostate_train.drop("ID")
  
      decision_tree = H2ORandomForestEstimator(
          ntrees=1,
          max_depth=3,
          sample_rate=1.0,
          mtries=len(prostate_train.columns) - 1,
      )
      decision_tree.train(y="CAPSULE", training_frame=prostate_train)
      decision_tree.show()
  
      tm = decision_tree._model_json["output"]["training_metrics"]
      assert tm._metric_json["max_criteria_and_metric_scores"] is None
      assert tm.confusion_matrix() is None
      assert tm.gains_lift() is None
  
imports: from builtins import range
  import sys
  import h2o
  from tests import pyunit_utils
  from h2o.estimators.random_forest import H2ORandomForestEstimator
label_desc: Use image node_id 1 for calling a global function with example usage: show_decision_tree() without return types