code: def fiftycatRF():
      # Training set has only 45 categories cat1 through cat45
      train = h2o.import_file(
          path=pyunit_utils.locate(
              "smalldata/gbm_test/50_cattest_train.csv"
          )
      )
      train["y"] = train["y"].asfactor()
  
      # Train H2O DRF Grid:
      hyper_parameters = {"ntrees": [10, 50], "max_depth": [20, 10]}
      model = H2OGridSearch(
          H2ORandomForestEstimator, hyper_params=hyper_parameters
      )
      print(model)
      model.train(x=["x1", "x2"], y="y", training_frame=train)
      model.show()
      model.summary()
  
      for m in model:
          assert isinstance(m, H2ORandomForestEstimator)
  
imports: import sys
  import h2o
  from tests import pyunit_utils
  from h2o.grid.grid_search import H2OGridSearch
  from h2o.estimators.random_forest import H2ORandomForestEstimator
label_desc: Use image node_id 1 for calling a global function with example usage: fiftycatRF() without return types