code: def grid_parallel():
      train = h2o.import_file(
          path=pyunit_utils.locate("smalldata/iris/iris_wheader.csv")
      )
      # Run GBM Grid Search
      ntrees_opts = [1, 3, 5]
      hyper_parameters = OrderedDict()
      hyper_parameters["ntrees"] = ntrees_opts
      print(
          "GBM grid with the following hyper_parameters:",
          hyper_parameters,
      )
  
      gs = H2OGridSearch(
          H2OGradientBoostingEstimator,
          hyper_params=hyper_parameters,
          parallelism=2,
      )
      gs.train(x=list(range(4)), y=4, training_frame=train)
      assert gs is not None
      assert len(gs.model_ids) == len(ntrees_opts)
  
imports: import sys, os
  import tempfile
  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_parallel() without return types