code: def test_aggregator_get_mapping_frame():
      winequality_df = h2o.import_file(
          pyunit_utils.locate(
              "smalldata/wine/winequality-redwhite-no-BOM.csv"
          )
      )
  
      params = {
          "target_num_exemplars": 650,
          "rel_tol_num_exemplars": 0.25,
          "save_mapping_frame": True,
      }
      agg = H2OAggregatorEstimator(
          ignored_columns=["quality", "type"], **params
      )
      agg.train(training_frame=winequality_df)
  
      mapping_frame = agg.mapping_frame
  
      assert mapping_frame.names == ["exemplar_assignment"]
      assert mapping_frame.nrows == winequality_df.nrows
  
imports: import h2o
  from tests import pyunit_utils
  from h2o.frame import H2OFrame
  from h2o.estimators.aggregator import H2OAggregatorEstimator
  from tests import pyunit_utils
label_desc: Use image node_id 1 for calling a global function with example usage: test_aggregator_get_mapping_frame() without return types