code: def deep_learning_metrics_test():
      # connect to existing cluster
  
      df = h2o.import_file(
          path=pyunit_utils.locate("smalldata/logreg/prostate.csv")
      )
  
      df.drop("ID")  # remove ID
      df["CAPSULE"] = df[
          "CAPSULE"
      ].asfactor()  # make CAPSULE categorical
      vol = df["VOL"]
      vol[vol == 0] = float("nan")  # 0 VOL means 'missing'
  
      r = vol.runif()  # random train/test split
      train = df[r < 0.8]
      test = df[r >= 0.8]
  
      # See that the data is ready
      train.describe()
      train.head()
      train.tail()
      test.describe()
      test.head()
      test.tail()
  
      # Run DeepLearning
      print("Train a Deeplearning model: ")
  
      dl = H2ODeepLearningEstimator(
          epochs=100, hidden=[10, 10, 10], loss="CrossEntropy"
      )
      dl.train(
          x=list(range(2, train.ncol)),
          y="CAPSULE",
          training_frame=train,
      )
      print("Binomial Model Metrics: ")
      print()
      dl.show()
      p = dl.model_performance(test)
      p.show()
  
imports: from builtins import range
  import sys, os
  import h2o
  from tests import pyunit_utils
  from h2o.estimators.deeplearning import H2ODeepLearningEstimator
label_desc: Use image node_id 1 for calling a global function with example usage: deep_learning_metrics_test() without return types