code: def test_weighted_aggregation_drift_no_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="mean",
          max_num_categories=10,
          min_category_size_ratio=0,
      )
      # Act
      aggregated_result = check.run(train, test).reduce_output()
      # Assert
      assert_that(aggregated_result.keys(), has_length(1))
      assert_that(
          aggregated_result.keys(), has_item("Mean Drift Score")
      )
      assert_that(
          aggregated_result["Mean Drift Score"], close_to(0.1475, 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 12 for calling a global function with example usage: test_weighted_aggregation_drift_no_model(drifted_data_and_model) without return types