code: def xgb_smoke():
      df = h2o.import_file(
          path=pyunit_utils.locate("smalldata/logreg/prostate.csv")
      )
      train = df.drop("ID")
      vol = train["VOL"]
      vol[vol == 0] = None
      gle = train["GLEASON"]
      gle[gle == 0] = None
      train["CAPSULE"] = train["CAPSULE"].asfactor()
      xgb = H2OXGBoostEstimator(ntrees=10, learn_rate=0.1)
      xgb.train(
          x=list(range(1, train.ncol)),
          y="CAPSULE",
          training_frame=train,
      )
      xgb.predict(train)
  
imports: import sys
  import os
  from tests import pyunit_utils
  import h2o
  from h2o.estimators import H2OXGBoostEstimator
label_desc: Use image node_id 1 for calling a global function with example usage: xgb_smoke() without return types