code: def pubdev_5167():
      training_data = h2o.import_file(
          pyunit_utils.locate("bigdata/laptop/airlines_all.05p.csv")
      )
  
      if "IsDepDelayed" in training_data.names:
          training_data["IsDepDelayed"] = training_data[
              "IsDepDelayed"
          ].asfactor()
      else:
          raise AttributeError(
              "label {0} not found".format("IsDepDelayed")
          )
  
      estimator = h2o.estimators.deeplearning.H2ODeepLearningEstimator(
          hidden=[50, 50, 50, 50, 50],
          activation="rectifier",
          adaptive_rate=True,
          balance_classes=True,
          epochs=50,
          shuffle_training_data=True,
          score_each_iteration=True,
          stopping_metric="auc",
          stopping_rounds=5,
          stopping_tolerance=0.01,
          use_all_factor_levels=False,
          variable_importances=False,
          export_weights_and_biases=True,
          seed=200,
      )
      estimator.train(
          x=training_data.names[:-1],
          y=training_data.names[-1],
          training_frame=training_data,
      )
  
label_desc: Use image node_id 1 for calling a global function with example usage: pubdev_5167() without return types