code: def vi_toy_test():
      toy_data = h2o.import_file(
          path=pyunit_utils.locate("smalldata/gbm_test/toy_data_RF.csv")
      )
      # toy_data.summary()
  
      toy_data[6] = toy_data[6].asfactor()
      toy_data.show()
      rf = H2ORandomForestEstimator(
          ntrees=500, max_depth=20, nbins=100, seed=0
      )
      rf.train(x=[0, 1, 2, 3, 4, 5], y=6, training_frame=toy_data)
  
      ranking = [
          rf._model_json["output"]["variable_importances"].cell_values[
              v
          ][0]
          for v in range(toy_data.ncol - 1)
      ]
      print(ranking)
      assert tuple(ranking) == tuple(
          ["V3", "V2", "V6", "V5", "V1", "V4"]
      ), "expected specific variable importance ranking"
  
imports: from builtins import range
  import sys
  import h2o
  from tests import pyunit_utils
  from h2o.estimators.random_forest import H2ORandomForestEstimator
label_desc: Use image node_id 1 for calling a global function with example usage: vi_toy_test() without return types