code: def test_maxrsweep_replacement():
      train = h2o.import_file(
          pyunit_utils.locate(
              "smalldata/glm_test/gaussian_20cols_10000Rows.csv"
          )
      )
      response = "C21"
      predictors = train.names
      predictors.remove(response)
      npred = 5
      x = [
          "C11",
          "C12",
          "C13",
          "C14",
          "C15",
          "C16",
          "C17",
          "C18",
          "C19",
          "C20",
      ]
      maxrsweep_model = H2OModelSelectionEstimator(
          mode="maxrsweep",
          max_predictor_number=npred,
          intercept=True,
          build_glm_model=False,
          standardize=True,
      )
      maxrsweep_model.train(x=x, y=response, training_frame=train)
  
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