code: def test_classification_with_alternative_properties(
      mnist_visiondata_train,
  ):
      alt_props = [
          {
              "name": "med",
              "method": med_prop,
              "output_type": "numerical",
          },
          {
              "name": "mean",
              "method": mean_prop,
              "output_type": "numerical",
          },
      ]
      result = PropertyLabelCorrelation(image_properties=alt_props).run(
          mnist_visiondata_train
      )
      assert_that(result.value.keys(), contains_exactly("mean", "med"))
      assert_that(result.value["med"], close_to(0.0, 0.005))
  
file_path: deepchecks/tests/vision/checks/data_integrity/property_label_correlation_test.py
imports: import numpy
  from hamcrest import assert_that, close_to, contains_exactly, has_entries
  from deepchecks.vision.checks import PropertyLabelCorrelation
  from tests.base.utils import equal_condition_result
label_desc: Use image node_id 5 for calling a global function with example usage: test_classification_with_alternative_properties(mnist_visiondata_train) without return types