code: def test_multiclass_proba_reduce_aggregations(
      iris_split_dataset_and_model_rf,
  ):
      # Arrange
      train, test, model = iris_split_dataset_and_model_rf
      train = remove_label(train)
      test = remove_label(test)
      check = PredictionDrift(
          categorical_drift_method="PSI",
          numerical_drift_method="EMD",
          max_num_categories=10,
          min_category_size_ratio=0,
          drift_mode="proba",
          aggregation_method="weighted",
      ).add_condition_drift_score_less_than(
          max_allowed_drift_score=0.05
      )
  
      # Act
      result = check.run(train, test, model)
  
      # Assert
      assert_that(
          result.reduce_output(),
          has_entries({"Weighted Drift Score": close_to(0.05, 0.01)}),
      )
  
      check.aggregation_method = "mean"
      assert_that(
          result.reduce_output(),
          has_entries({"Mean Drift Score": close_to(0.05, 0.01)}),
      )
  
      check.aggregation_method = "max"
      assert_that(
          result.reduce_output(),
          has_entries({"Max Drift Score": close_to(0.06, 0.01)}),
      )
  
      check.aggregation_method = "none"
      assert_that(
          result.reduce_output(),
          has_entries(
              {
                  "Drift Score class 0": close_to(0.06, 0.01),
                  "Drift Score class 1": close_to(0.06, 0.01),
                  "Drift Score class 2": close_to(0.03, 0.01),
              }
          ),
      )
  
      # Test condition
      condition_result, *_ = check.conditions_decision(result)
  
      assert_that(
          condition_result,
          equal_condition_result(
              is_pass=False,
              name="Prediction drift score < 0.05",
              details="Found 2 classes with model predicted probability Earth Mover's "
              "Distance drift score above threshold: 0.05.",
          ),
      )
  
imports: import pandas
  from hamcrest import assert_that, calling, close_to, equal_to, greater_than, has_entries, has_length, raises
  from deepchecks.core.errors import DeepchecksValueError, NotEnoughSamplesError
  from deepchecks.tabular import Dataset
  from deepchecks.tabular.checks import PredictionDrift
  from tests.base.utils import equal_condition_result
label_desc: Use image node_id 17 for calling a global function with example usage: test_multiclass_proba_reduce_aggregations(iris_split_dataset_and_model_rf) without return types