code: def test_workaround_for_distribution():
      try:
          h2o.rapids(
              '(setproperty "{}" "{}")'.format(
                  "sys.ai.h2o.automl.algo_parameters.all.enabled",
                  "true",
              )
          )
          ds = import_dataset("regression")
          aml = H2OAutoML(
              project_name="py_test",
              algo_parameters=dict(
                  distribution="poisson",
                  family="poisson",
              ),
              exclude_algos=["StackedEnsemble"],
              max_runtime_secs=60,
              seed=1,
          )
          aml.train(y=ds.target, training_frame=ds.train)
          model_names = [
              aml.leaderboard[i, 0]
              for i in range(0, (aml.leaderboard.nrows))
          ]
          for mn in model_names:
              m = h2o.get_model(mn)
              dist = (
                  m.params["distribution"]
                  if "distribution" in m.params
                  else m.params["family"]
                  if "family" in m.params
                  else None
              )
              print("{}: distribution = {}".format(mn, dist))
      except:
          h2o.rapids(
              '(setproperty "{}" "{}")'.format(
                  "sys.ai.h2o.automl.algo_parameters.all.enabled",
                  "false",
              )
          )
  
imports: import sys, os
  import h2o
  from h2o.automl import H2OAutoML
  from tests import pyunit_utils
  from _automl_utils import import_dataset
label_desc: Use image node_id 2 for calling a global function with example usage: test_workaround_for_distribution() without return types