code: def airlines_gbm():
      # Now, train the GBM model:
      from h2o.estimators.gbm import H2OGradientBoostingEstimator
  
      # Load the data and prepare for modeling
      airlines_hex = h2o.import_file(
          "http://h2o-public-test-data.s3.amazonaws.com/smalldata/airlines/allyears2k_headers.zip"
      )
  
      # Generate random numbers and create training, validation, testing splits
      r = airlines_hex.runif()  # Random UNIForm numbers, one per row
      air_train_hex = airlines_hex[r < 0.6]
      air_valid_hex = airlines_hex[(r >= 0.6) & (r < 0.9)]
      air_test_hex = airlines_hex[r >= 0.9]
  
      myX = [
          "Year",
          "Month",
          "DayofMonth",
          "DayOfWeek",
          "CRSDepTime",
          "UniqueCarrier",
          "Origin",
          "Dest",
      ]
  
      air_model = H2OGradientBoostingEstimator(
          model_id="gbm_pojo_test",
          distribution="bernoulli",
          ntrees=100,
          max_depth=4,
          learn_rate=0.1,
      )
      air_model.train(
          x=myX, y="IsDepDelayed", training_frame=air_train_hex
      )
  
      air_model.download_pojo("/Users/ludirehak/jython_h2o")
  
label_desc: Use image node_id 1 for calling a global function with example usage: airlines_gbm() without return types