code: def test_aggregation_drift_with_model(drifted_data_and_model):
      # Arrange
      train, test, model = drifted_data_and_model
      check_l1 = FeatureDrift(
          categorical_drift_method="PSI",
          numerical_drift_method="EMD",
          max_num_categories=10,
          min_category_size_ratio=0,
          aggregation_method="weighted",
      )
      check_l2 = FeatureDrift(
          categorical_drift_method="PSI",
          numerical_drift_method="EMD",
          max_num_categories=10,
          min_category_size_ratio=0,
          aggregation_method="l3_weighted",
      )
      check_l3 = FeatureDrift(
          categorical_drift_method="PSI",
          numerical_drift_method="EMD",
          max_num_categories=10,
          min_category_size_ratio=0,
          aggregation_method="l5_weighted",
      )
      # Act
      aggregated_result_l1 = check_l1.run(
          train, test, model
      ).reduce_output()
      aggregated_result_l2 = check_l2.run(
          train, test, model
      ).reduce_output()
      aggregated_result_l3 = check_l3.run(
          train, test, model
      ).reduce_output()
      # Assert
      assert_that(
          aggregated_result_l2.keys(),
          has_item("L3 Weighted Drift Score"),
      )
      assert_that(
          aggregated_result_l3.keys(),
          has_item("L5 Weighted Drift Score"),
      )
      assert_that(
          aggregated_result_l3["L5 Weighted Drift Score"],
          greater_than(aggregated_result_l2["L3 Weighted Drift Score"]),
      )
      assert_that(
          aggregated_result_l1.keys(), has_item("Weighted Drift Score")
      )
      assert_that(
          aggregated_result_l2["L3 Weighted Drift Score"],
          greater_than(aggregated_result_l1["Weighted Drift Score"]),
      )
  
imports: import numpy
  import pandas
  from hamcrest import assert_that, calling, close_to, equal_to, greater_than, has_entries, has_item, has_length, is_not, raises
  from deepchecks.core.errors import NotEnoughSamplesError
  from deepchecks.tabular.checks import FeatureDrift
  from tests.base.utils import equal_condition_result
label_desc: Use image node_id 10 for calling a global function with example usage: test_aggregation_drift_with_model(drifted_data_and_model) without return types