code: def deeplearning_demo():
      # Training data
      train_data = h2o.import_file(
          path=tests.locate("smalldata/gbm_test/ecology_model.csv")
      )
      train_data = train_data.drop("Site")
      train_data["Angaus"] = train_data["Angaus"].asfactor()
      print(train_data.describe())
      train_data.head()
  
      # Testing data
      test_data = h2o.import_file(
          path=tests.locate("smalldata/gbm_test/ecology_eval.csv")
      )
      test_data["Angaus"] = test_data["Angaus"].asfactor()
      print(test_data.describe())
      test_data.head()
  
      # Run DeepLearning
  
      dl = H2ODeepLearningEstimator(
          loss="CrossEntropy", epochs=1000, hidden=[20, 20, 20]
      )
      dl.train(
          x=list(range(1, train_data.ncol)),
          y="Angaus",
          training_frame=train_data,
          validation_frame=test_data,
      )
      dl.show()
  
imports: from builtins import range
  import sys, os
  import h2o
  from tests import pyunit_utils
  import h2o, tests
  from h2o.estimators.deeplearning import H2ODeepLearningEstimator
label_desc: Use image node_id 1 for calling a global function with example usage: deeplearning_demo() without return types