code: def metric_json_check():
      iris_hex = h2o.import_file(
          path=pyunit_utils.locate("smalldata/iris/iris.csv")
      )
  
      try:
          hh = H2ODeepLearningEstimator(
              hidden=[],
              loss="CrossEntropy",
              export_weights_and_biases=True,
              distribution="quasibinomial",
          )
          hh.train(x=list(range(4)), y=4, training_frame=iris_hex)
          assert (
              False
          ), "Deeplearning should have thrown an error since Quasibinomial is not supported at this time."
      except:
          print("Quasibinomial is not supported for deeplearning.")
  
      try:
          model = H2ORandomForestEstimator(
              ntrees=50, max_depth=100, distribution="quasibinomial"
          )
          model.train(y=4, x=list(range(4)), training_frame=iris)
          assert (
              False
          ), "DRF should have thrown an error since Quasibinomial is not supported at this time."
      except:
          print("Quasibinomial is not supported for deeplearning.")
  
imports: import sys
  import h2o
  from tests import pyunit_utils
  from h2o.estimators.deeplearning import H2ODeepLearningEstimator
  from h2o.estimators.random_forest import H2ORandomForestEstimator
label_desc: Use image node_id 1 for calling a global function with example usage: metric_json_check() without return types