code: def bigcat_gbm():
      # Log.info("Importing bigcat_5000x2.csv data...\n")
      bigcat = h2o.import_file(
          path=pyunit_utils.locate(
              "smalldata/gbm_test/bigcat_5000x2.csv"
          )
      )
      bigcat["y"] = bigcat["y"].asfactor()
      # Log.info("Summary of bigcat_5000x2.csv from H2O:\n")
      # bigcat.summary()
  
      # Train H2O GBM Model:
  
      model = H2OGradientBoostingEstimator(
          distribution="bernoulli", ntrees=1, max_depth=1, nbins=100
      )
      model.train(x="X", y="y", training_frame=bigcat)
      model.show()
      performance = model.model_performance(bigcat)
      performance.show()
  
      # Check AUC and overall prediction error
      # test_accuracy = performance.accuracy()
      test_auc = performance.auc()
  
imports: import sys, os
  import h2o
  from tests import pyunit_utils
  from h2o.estimators.gbm import H2OGradientBoostingEstimator
label_desc: Use image node_id 1 for calling a global function with example usage: bigcat_gbm() without return types