code: def pubdev_2223():
      covtype = h2o.import_file(
          pyunit_utils.locate("smalldata/covtype/covtype.20k.data")
      )
      covtype[54] = covtype[54].asfactor()
      dlmodel = H2ODeepLearningEstimator(
          hidden=[17, 191],
          epochs=1,
          balance_classes=False,
          reproducible=True,
          seed=1234,
          export_weights_and_biases=True,
      )
      dlmodel.train(x=list(range(54)), y=54, training_frame=covtype)
  
      print(
          "Normalization/Standardization multipliers for numeric predictors: {0}\n".format(
              dlmodel.normmul()
          )
      )
      print(
          "Normalization/Standardization offsets for numeric predictors: {0}\n".format(
              dlmodel.normsub()
          )
      )
      print(
          "Normalization/Standardization multipliers for numeric response: {0}\n".format(
              dlmodel.respmul()
          )
      )
      print(
          "Normalization/Standardization offsets for numeric response: {0}\n".format(
              dlmodel.respsub()
          )
      )
      print(
          "Categorical offsets for one-hot encoding: {0}\n".format(
              dlmodel.catoffsets()
          )
      )
  
imports: import sys
  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: pubdev_2223() without return types