code: def h_stats_same_data_but_missing_or_additional_columns():
      x = ["DPROS", "DCAPS", "GLEASON"]
      target = "CAPSULE"
      params = {
          "ntrees": 10,
          "learn_rate": 0.1,
          "max_depth": 2,
          "min_rows": 1,
          "seed": 1234,
      }
  
      train_frame = h2o.import_file(
          path=pyunit_utils.locate(
              "smalldata/logreg/prostate_train.csv"
          )
      )
      train_frame[target] = train_frame[target].asfactor()
  
      train_frame_clean = h2o.import_file(
          path=pyunit_utils.locate(
              "smalldata/logreg/prostate_train.csv"
          ),
          skipped_columns=[1, 2, 5, 6],
      )
      train_frame_clean[target] = train_frame_clean[target].asfactor()
  
      train_frame_missing = h2o.import_file(
          path=pyunit_utils.locate(
              "smalldata/logreg/prostate_train.csv"
          ),
          skipped_columns=[1, 2, 3, 5, 6],
      )  # skip DPROS
      train_frame_missing[target] = train_frame_missing[
          target
      ].asfactor()
  
      gbm_h2o = H2OGradientBoostingEstimator(**params)
      xgb_h2o = H2OXGBoostEstimator(**params)
  
      test_model(
          gbm_h2o,
          x,
          target,
          train_frame,
          train_frame_clean,
          train_frame_missing,
      )
      test_model(
          xgb_h2o,
          x,
          target,
          train_frame,
          train_frame_clean,
          train_frame_missing,
      )
  
imports: import sys, os
  import h2o
  from tests import pyunit_utils, assert_equals
  from h2o.estimators import H2OGradientBoostingEstimator, H2OXGBoostEstimator
label_desc: Use image node_id 2 for calling a global function with example usage: h_stats_same_data_but_missing_or_additional_columns() without return types