code: def iris_gbm():
      train = h2o.import_file(
          path=pyunit_utils.locate("smalldata/iris/iris_wheader.csv")
      )
      train.describe()
  
      # Run GBM
  
      my_gbm = H2OGradientBoostingEstimator(
          ntrees=50, learn_rate=0.1, distribution="multinomial"
      )
      my_gbm.train(
          x=list(range(1, 4)),
          y="class",
          training_frame=train,
          validation_frame=train,
      )
      my_gbm.show()
  
      my_gbm_metrics = my_gbm.model_performance(train)
      my_gbm_metrics.show()
  
      print(my_gbm_metrics)
  
imports: import sys
  from builtins import range
  import h2o
  from tests import pyunit_utils, test_plot_result_saving
  from h2o.estimators.gbm import H2OGradientBoostingEstimator
label_desc: Use image node_id 1 for calling a global function with example usage: iris_gbm() without return types