code: def xgboost_prostate_small():
      assert H2OXGBoostEstimator.available()
  
      prostate_frame = h2o.import_file(
          pyunit_utils.locate("smalldata/prostate/prostate.csv")
      )
      x = ["RACE"]
      y = "CAPSULE"
      prostate_frame[y] = prostate_frame[y].asfactor()
  
      prostate_frame.split_frame(
          ratios=[0.75],
          destination_frames=[
              "prostate_training",
              "prostate_validation",
          ],
          seed=1,
      )
  
      training_frame = h2o.get_frame("prostate_training")
      test_frame = h2o.get_frame("prostate_validation")
  
      # Model with maximum of 2 trees
      model_2_trees = H2OXGBoostEstimator(
          training_frame=training_frame,
          learn_rate=0.7,
          booster="gbtree",
          seed=1,
          ntrees=2,
          distribution="bernoulli",
      )
      model_2_trees.train(x=x, y=y, training_frame=training_frame)
      prediction_2_trees = model_2_trees.predict(test_frame)
  
      assert prediction_2_trees.nrows == test_frame.nrows
  
      # Model with 10 trees
      model_10_trees = H2OXGBoostEstimator(
          training_frame=training_frame,
          learn_rate=0.7,
          booster="gbtree",
          seed=1,
          ntrees=10,
          distribution="bernoulli",
      )
      model_10_trees.train(x=x, y=y, training_frame=training_frame)
      prediction_10_trees = model_10_trees.predict(test_frame)
  
      assert prediction_10_trees.nrows == test_frame.nrows
  
      ## Performance of a model with 10 dec. trees should be better than model with 2 trees allowed only
      assert model_2_trees.logloss(train=True) > model_10_trees.logloss(
          train=True
      )
  
label_desc: Use image node_id 1 for calling a global function with example usage: xgboost_prostate_small() without return types