code: def test_multiclass_proba(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",
      )
  
      # Act
      result = check.run(train, test, model)
  
      # Assert
      assert_that(
          result.value,
          has_entries(
              {
                  "Drift score": has_entries(
                      {
                          0: close_to(0.06, 0.01),
                          1: close_to(0.06, 0.01),
                          2: close_to(0.03, 0.01),
                      }
                  ),
                  "Method": equal_to("Earth Mover's Distance"),
              }
          ),
      )
  
      assert_that(result.display, has_length(5))
  
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 15 for calling a global function with example usage: test_multiclass_proba(iris_split_dataset_and_model_rf) without return types