code: def deeplearning_multi():
      print(
          "Test checks if Deep Learning works fine with a categorical dataset"
      )
      # print(locate("smalldata/logreg/protstate.csv"))
      prostate = h2o.import_file(
          path=pyunit_utils.locate("smalldata/logreg/prostate.csv")
      )
      prostate[1] = prostate[1].asfactor()  # CAPSULE -> CAPSULE
      prostate[2] = prostate[2].asfactor()  # AGE -> Factor
      prostate[3] = prostate[3].asfactor()  # RACE -> Factor
      prostate[4] = prostate[4].asfactor()  # DPROS -> Factor
      prostate[5] = prostate[5].asfactor()  # DCAPS -> Factor
      prostate = prostate.drop("ID")  # remove ID
      prostate.describe()
  
      hh = H2ODeepLearningEstimator(
          loss="CrossEntropy",
          hidden=[10, 10],
          use_all_factor_levels=False,
      )
      hh.train(
          x=list(set(prostate.names) - {"CAPSULE"}),
          y="CAPSULE",
          training_frame=prostate,
      )
      hh.show()
  
imports: 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: deeplearning_multi() without return types