code: def test_reduce_output_with_nones(
      drifted_data_with_nulls, drifted_data_and_model
  ):
      # Arrange
  
      _, _, model = drifted_data_and_model
      train, test = drifted_data_with_nulls
      check = FeatureDrift(
          min_samples=999,
          ignore_na=False,
          aggregation_method="l3_weighted",
      )
      fi = pd.Series(
          np.ones(len(train.features)), index=train.features
      ) / len(train.features)
  
      # Act
      aggregated_result = check.run(
          train, test, model, feature_importance=fi
      ).reduce_output()
  
      # Assert
      assert_that(
          aggregated_result.keys(), has_item("L3 Weighted Drift Score")
      )
      assert_that(
          aggregated_result["L3 Weighted Drift Score"],
          close_to(0.152, 0.01),
      )
  
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 8 for calling a global function with example usage: test_reduce_output_with_nones(drifted_data_with_nulls, drifted_data_and_model) without return types