code: def test_maxrsweep_replacement():
      train = h2o.import_file(
          pyunit_utils.locate(
              "bigdata/laptop/model_selection/maxrglm200Cols50KRows.csv"
          )
      )
      train2 = h2o.import_file(
          pyunit_utils.locate(
              "bigdata/laptop/model_selection/maxrglm200Cols50KRows.csv"
          )
      )
      train2 = train2.drop(200, axis=1)
      train = train.cbind(train2)
      response = "response"
      predictors = train.names
      predictors.remove(response)
  
      try:
          maxrsweep3_model = H2OModelSelectionEstimator(
              mode="maxrsweep",
              max_predictor_number=300,
              intercept=True,
              build_glm_model=False,
          )
          maxrsweep3_model.train(
              x=predictors, y=response, training_frame=train
          )
          assert (
              False
          ), "Should have throw exception of bad max_predictor_number!"
      except Exception as ex:
          print(ex)
          temp = str(ex)
          assert (
              "max_predictor_number: Your dataset contains duplicated predictors.  After removal, reduce your"
              " max_predictor_number to" in temp
          )
          print("coefficient test passed!")
  
imports: import sys
  import h2o
  from tests import pyunit_utils
  from h2o.estimators.model_selection import H2OModelSelectionEstimator
label_desc: Use image node_id 1 for calling a global function with example usage: test_maxrsweep_replacement() without return types