code: def test_with_drift_segmentation(
      segmentation_coco_visiondata_train,
      segmentation_coco_visiondata_test,
  ):
      # Arrange
      check = PredictionDrift(numerical_drift_method="KS")
  
      # Act
      result = check.run(
          segmentation_coco_visiondata_train,
          segmentation_coco_visiondata_test,
      )
      # Assert
      assert_that(
          result.value,
          has_entries(
              {
                  "Samples Per Class": has_entries(
                      {
                          "Drift score": equal_to(0),
                          "Method": equal_to("Cramer's V"),
                      }
                  ),
                  "Number of Classes Per Image": has_entries(
                      {
                          "Drift score": close_to(0.2, 0.01),
                          "Method": equal_to("Kolmogorov-Smirnov"),
                      }
                  ),
                  "Segment Area (in pixels)": has_entries(
                      {
                          "Drift score": close_to(0.09, 0.01),
                          "Method": equal_to("Kolmogorov-Smirnov"),
                      }
                  ),
              }
          ),
      )
  
file_path: deepchecks/tests/vision/checks/model_evaluation/prediction_drift_test.py
imports: from hamcrest import assert_that, calling, close_to, equal_to, greater_than, has_entries, has_length, raises
  from deepchecks.core.errors import NotEnoughSamplesError
  from deepchecks.vision.checks import PredictionDrift
  from tests.base.utils import equal_condition_result
label_desc: Use image node_id 5 for calling a global function with example usage: test_with_drift_segmentation(segmentation_coco_visiondata_train, segmentation_coco_visiondata_test) without return types