code: def test_drift_classification_per_class(
      mnist_train_brightness_bias, mnist_visiondata_test
  ):
      # Arrange
      check = PropertyLabelCorrelationChange(per_class=True)
  
      # Act
      result = check.run(
          mnist_train_brightness_bias, mnist_visiondata_test
      )
  
      # Assert
      assert_that(
          result.value,
          has_entries(
              {
                  "Brightness": has_entries(
                      {
                          "train": has_entries(
                              {"1": close_to(0.807, 0.01)}
                          ),
                          "test": has_entries(
                              {"1": close_to(0.0, 0.01)}
                          ),
                          "train-test difference": has_entries(
                              {"1": close_to(0.807, 0.01)}
                          ),
                      }
                  ),
              }
          ),
      )
  
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 8 for calling a global function with example usage: test_drift_classification_per_class(mnist_train_brightness_bias, mnist_visiondata_test) without return types