code: def test_drift_classification(
      mnist_train_brightness_bias, mnist_visiondata_test
  ):
      # Arrange
      check = PropertyLabelCorrelationChange(per_class=False)
  
      # Act
      result = check.run(
          mnist_train_brightness_bias, mnist_visiondata_test
      )
  
      # Assert
      assert_that(
          result.value,
          has_entries(
              {
                  "train": has_entries(
                      {"Brightness": close_to(0.248, 0.005)}
                  ),
                  "test": has_entries(
                      {"Brightness": close_to(0.02, 0.001)}
                  ),
                  "train-test difference": has_entries(
                      {"Brightness": close_to(0.228, 0.001)}
                  ),
              }
          ),
      )
  
imports: import numpy
  import torch
  from hamcrest import assert_that, close_to, equal_to, greater_than, has_entries, has_length
  from deepchecks.vision.checks import PropertyLabelCorrelationChange
  from deepchecks.vision.utils.test_utils import replace_collate_fn_visiondata
  from tests.base.utils import equal_condition_result
label_desc: Use image node_id 3 for calling a global function with example usage: test_drift_classification(mnist_train_brightness_bias, mnist_visiondata_test) without return types