code: def pubdev_8309():
      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 = ["ArrTime", "DepTime", "CRSArrTime", "CRSDepTime"]
  
      # initialize the estimator and train the model
      airlines_gbm = H2OGradientBoostingEstimator(
          ignored_columns=col_list, seed=1234
      )
  
      airlines_gbm.train(
          y=response, training_frame=train, validation_frame=valid
      )
  
      original_model_filename = tempfile.mkdtemp()
      # Download the MOJO
      original_model_filename = airlines_gbm.download_mojo(
          original_model_filename
      )
      # Load the model from the temporary file
      mojo_model = h2o.import_mojo(original_model_filename)
      assert isinstance(mojo_model, H2OGenericEstimator)
      assert (
          mojo_model.params["ignored_columns"]
          == airlines_gbm.params["ignored_columns"]
      )
      mojo_model.params["ignored_columns"]["actual"] == col_list
  
imports: from builtins import range
  import sys, os
  from h2o.estimators import H2OGenericEstimator
  import h2o
  from tests import pyunit_utils
  from h2o.estimators.gbm import H2OGradientBoostingEstimator
  import tempfile
label_desc: Use image node_id 1 for calling a global function with example usage: pubdev_8309() without return types