code: def grid_wineGBM():
      wine = h2o.import_file(
          path=pyunit_utils.locate("smalldata/gbm_test/wine.data")
      )
      # wine.summary()
      x_cols = list(range(2, 14)) + [0]
  
      from h2o.estimators.gbm import H2OGradientBoostingEstimator
  
      wine_grid = H2OGradientBoostingEstimator(
          ntrees=[5, 10, 15],
          learn_rate=[0.1, 0.2],
          distribution="gaussian",
          max_depth=[2, 3, 4],
      )
      wine_grid.train(x=x_cols, y=1, training_frame=wine)
  
      # wine_grid = h2o.gbm(y=wine[1],
      #                     x=wine[x_cols],
      #                     distribution='gaussian',
      #                     ntrees=[5,10,15],
      #                     max_depth=[2,3,4],
      #                     learn_rate=[0.1,0.2])
      wine_grid.show()
  
imports: from builtins import range
  import sys
  import h2o
  from tests import pyunit_utils
label_desc: Use image node_id 1 for calling a global function with example usage: grid_wineGBM() without return types