code: def test_none_aggregation_drift_with_model(drifted_data_and_model):
      # Arrange
      train, test, model = drifted_data_and_model
      check = FeatureDrift(
          categorical_drift_method="PSI",
          numerical_drift_method="EMD",
          aggregation_method=None,
      )
  
      # Act
      aggregated_result = check.run(train, test, model).reduce_output()
      # Assert
      assert_that(aggregated_result.keys(), has_length(4))
      assert_that(
          aggregated_result.keys(), has_item("numeric_with_drift")
      )
      assert_that(
          aggregated_result["numeric_with_drift"], close_to(0.343, 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 11 for calling a global function with example usage: test_none_aggregation_drift_with_model(drifted_data_and_model) without return types