code: def test_property_outliers_check_mnist(mnist_visiondata_train):
      # Arrange
      properties = [
          {
              "name": "test",
              "method": lambda labels: labels,
              "output_type": "categorical",
          }
      ]
      check = LabelPropertyOutliers(label_properties=properties)
      # Act
      result = check.run(mnist_visiondata_train)
  
      # Assert
      assert_that(
          result, is_correct_label_property_outliers_result(properties)
      )
      assert_that(
          result.value,
          has_entries(
              {
                  "test": has_entries(
                      {
                          "outliers_identifiers": has_length(0),
                          "lower_limit": is_(0),
                          "upper_limit": is_(9),
                      }
                  )
              }
          ),
      )
  
file_path: deepchecks/tests/vision/checks/data_integrity/label_property_outliers_test.py
imports: from hamcrest import all_of, any_of, assert_that, calling, close_to, contains_exactly, equal_to, has_entries, has_key, has_length, has_properties, instance_of, is_, raises
  from hamcrest.core.matcher import Matcher
  from deepchecks.core import CheckResult
  from deepchecks.core.errors import DeepchecksProcessError
  from deepchecks.vision.checks import LabelPropertyOutliers
  from deepchecks.vision.utils.label_prediction_properties import DEFAULT_OBJECT_DETECTION_LABEL_PROPERTIES
label_desc: Use image node_id 6 for calling a global function with example usage: test_property_outliers_check_mnist(mnist_visiondata_train) without return types