code: def gbm_feature_interaction_converged_with_less_trees():
      train = h2o.upload_file(
          pyunit_utils.locate("smalldata/covtype/covtype.20k.data")
      )
      train["C55"] = train["C55"].asfactor()
  
      model = H2OGradientBoostingEstimator(
          ntrees=200, min_split_improvement=1e-1
      )
      model.train(x=list(range(55)), y="C55", training_frame=train)
      print(model)
  
      feature_interactions = model.feature_interaction()
      print(feature_interactions[0])
  
imports: import h2o
  from h2o.estimators.gbm import H2OGradientBoostingEstimator
  from tests import pyunit_utils
  import sys
label_desc: Use image node_id 1 for calling a global function with example usage: gbm_feature_interaction_converged_with_less_trees() without return types