code: def test_image_property_drift_check_not_enough_samples_in_one_property(
      coco_visiondata_train, coco_visiondata_test
  ):
      # Arrange
      properties = [
          {
              "name": "ok_values",
              "method": lambda x: [1] * len(x),
              "output_type": "numerical",
          },
          {
              "name": "with_non_values",
              "method": lambda x: list(
                  np.random.choice([1, None], size=len(x))
              ),
              "output_type": "numerical",
          },
      ]
  
      # Run
      result = ImagePropertyDrift(
          min_samples=60, image_properties=properties
      ).run(coco_visiondata_train, coco_visiondata_test)
  
      # Assert
      assert_that(
          result.value["with_non_values"]["Drift score"], equal_to(None)
      )
      assert_that(
          result.display[2],
          equal_to(
              "<span>The following image properties do not have enough samples to calculate drift score: ['with_non_values']</span>"
          ),
      )
  
file_path: deepchecks/tests/vision/checks/train_test_validation/image_property_drift_test.py
imports: import numpy
  from hamcrest import all_of, assert_that, calling, close_to, equal_to, greater_than, has_entries, has_key, has_length, has_properties, instance_of, raises
  from deepchecks.core import CheckResult
  from deepchecks.core.errors import NotEnoughSamplesError
  from deepchecks.vision.checks import ImagePropertyDrift
  from deepchecks.vision.utils.image_properties import default_image_properties
  from tests.base.utils import equal_condition_result
label_desc: Use image node_id 3 for calling a global function with example usage: test_image_property_drift_check_not_enough_samples_in_one_property(coco_visiondata_train, coco_visiondata_test) without return types