code: def grid_multinomial_auc():
      train = h2o.import_file(
          path=pyunit_utils.locate("smalldata/iris/iris.csv")
      )
      # Run GBM Grid Search
      ntrees_opts = [1, 5]
      hyper_parameters = OrderedDict()
      hyper_parameters["ntrees"] = ntrees_opts
      print(
          "GBM grid with the following hyper_parameters:",
          hyper_parameters,
      )
  
      gbm = H2OGradientBoostingEstimator(auc_type="WEIGHTED_OVO")
  
      gs = H2OGridSearch(gbm, hyper_params=hyper_parameters)
      gs.train(x=list(range(4)), y=4, training_frame=train)
      assert gs is not None
      # Should not fail
      auc = gs.auc(train=True)
      auc_value = auc[list(auc.keys())[0]]
      print(auc_value)
      assert auc_value != "NaN", (
          "AUC expected not to be NaN but is:" + auc_value
      )
  
imports: import sys, os
  import h2o
  from tests import pyunit_utils
  from collections import OrderedDict
  from h2o.grid.grid_search import H2OGridSearch
  from h2o.estimators.gbm import H2OGradientBoostingEstimator
label_desc: Use image node_id 1 for calling a global function with example usage: grid_multinomial_auc() without return types