code: def test_pubdev_6686():
      train = h2o.import_file(
          path="http://h2o-public-test-data.s3.amazonaws.com/smalldata/jira/pubdev_6686.csv"
      )
  
      # Start a "long" running model that needs at least one column to be adapted (categorical_encoding="enum_limited")
      rf1 = H2ORandomForestEstimator(
          nfolds=3,
          ntrees=100,
          max_depth=10,
          categorical_encoding="enum_limited",
      )
      rf1.start(
          y="model_pred",
          x=train.names.remove("y"),
          training_frame=train,
      )
  
      # Start a model that finishes quickly and calls clean-up
      rf2 = H2ORandomForestEstimator(ntrees=1, max_depth=2)
      rf2.start(
          y="model_pred",
          x=train.names.remove("y"),
          training_frame=train,
      )
  
      # Both models should finish without errors
      rf2.join()
      rf1.join()
  
imports: 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: test_pubdev_6686() without return types