code: def ignored_columns():
      airlines = h2o.upload_file(
          pyunit_utils.locate(
              "smalldata/airlines/allyears2k_headers.zip"
          )
      )
  
      # convert columns to factors
      airlines["Year"] = airlines["Year"].asfactor()
      airlines["Month"] = airlines["Month"].asfactor()
      airlines["DayOfWeek"] = airlines["DayOfWeek"].asfactor()
      airlines["Cancelled"] = airlines["Cancelled"].asfactor()
      airlines["FlightNum"] = airlines["FlightNum"].asfactor()
  
      # set the predictor names and the response column name
      predictors = airlines.columns[:9]
      response = "IsDepDelayed"
  
      # split into train and validation sets
      train, valid = airlines.split_frame(ratios=[0.8], seed=1234)
  
      # create a list of column names to ignore
      col_list = ["DepTime", "CRSDepTime", "ArrTime", "CRSArrTime"]
  
      # initialize the estimator and train the model
      airlines_gbm = H2OGradientBoostingEstimator(
          ignored_columns=col_list, seed=1234
      )
  
      # throws H2OValueError: Properties x and ignored_columns cannot be specified simultaneously
      message = "Properties x and ignored_columns cannot be specified simultaneously"
      try:
          airlines_gbm.train(
              x=predictors,
              y=response,
              training_frame=train,
              validation_frame=valid,
          )
          assert False, "It should throw H2OValueError: " + message
      except Exception as e:
          assert message in e.args, (
              "It should throw H2OValueError: " + message
          )
  
      airlines_gbm.train(
          y=response, training_frame=train, validation_frame=valid
      )
  
      # get the ignored columns from the model JSON
      ignored_cols_from_model = [
          param_dict["actual_value"]
          for param_dict in airlines_gbm._model_json["parameters"]
          if param_dict["name"] == "ignored_columns"
      ][0]
      assert set(col_list) <= set(ignored_cols_from_model)
  
      # get the list of columns used by the model
      used_columns = airlines_gbm._model_json["output"]["names"]
      assert set(col_list).isdisjoint(set(used_columns))
  
imports: import sys
  import h2o
  from tests import pyunit_utils
  from h2o.estimators.gbm import H2OGradientBoostingEstimator
label_desc: Use image node_id 1 for calling a global function with example usage: ignored_columns() without return types