code: def test_aggregator_effective_parameters():
      frame = h2o.create_frame(
          rows=10000,
          cols=10,
          categorical_fraction=0.6,
          integer_fraction=0,
          binary_fraction=0,
          real_range=100,
          integer_range=100,
          missing_fraction=0,
          factors=100,
          seed=1234,
      )
  
      agg1 = H2OAggregatorEstimator(
          target_num_exemplars=1000,
          rel_tol_num_exemplars=0.5,
          categorical_encoding="eigen",
      )
      agg1.train(training_frame=frame)
  
      agg2 = H2OAggregatorEstimator(
          target_num_exemplars=1000, rel_tol_num_exemplars=0.5
      )
      agg2.train(training_frame=frame)
  
      assert agg2.parms["categorical_encoding"]["input_value"] == "AUTO"
      assert (
          agg2.parms["categorical_encoding"]["actual_value"]
          == agg1.parms["categorical_encoding"]["actual_value"]
      )
  
imports: import sys
  import h2o
  from tests import pyunit_utils
  from h2o.estimators.aggregator import H2OAggregatorEstimator
label_desc: Use image node_id 1 for calling a global function with example usage: test_aggregator_effective_parameters() without return types