code: def bigcat_gbm():
      covtype = h2o.import_file(
          path=pyunit_utils.locate("smalldata/covtype/covtype.20k.data")
      )
      covtype[54] = covtype[54].asfactor()
      covtypeTest = h2o.import_file(
          path=pyunit_utils.locate("smalldata/covtype/covtype.20k.data")
      )
      covtypeTest[54] = covtype[54].asfactor()
  
      regular = H2OGradientBoostingEstimator(ntrees=10, seed=1234)
      regular.train(x=list(range(54)), y=54, training_frame=covtype)
  
      # do prediction on original dataset, no warnings
      check_warnings(regular, 0, covtypeTest)
      # drop response, no warnings
      covtypeTest = covtypeTest.drop(54)
      check_warnings(regular, 0, covtypeTest)
  
      covtypeTest = covtypeTest.drop(1)
      covtypeTest = covtypeTest.drop(1)
      check_warnings(regular, 2, covtypeTest)
  
      covtypeTest = h2o.import_file(
          path=pyunit_utils.locate("smalldata/covtype/covtype.20k.data")
      )
      covtypeTest[54] = covtype[54].asfactor()
      covtypeTest = covtypeTest.drop(3)
      covtypeTest = covtypeTest.drop(5)
      covtypeTest = covtypeTest.drop(7)
      check_warnings(regular, 3, covtypeTest)
  
imports: import sys, os
  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: bigcat_gbm() without return types