code: def test_train_test_condition_pps_diff_pass_per_class(
      mnist_visiondata_train,
  ):
      # Arrange
      train, test = mnist_visiondata_train, mnist_visiondata_train
      condition_value = 0.3
      check = PropertyLabelCorrelationChange(
          per_class=True, random_state=42
      ).add_condition_property_pps_difference_less_than(condition_value)
  
      # Act
      result = check.run(train_dataset=train, test_dataset=test)
      condition_result, *_ = check.conditions_decision(result)
  
      # Assert
      assert_that(
          condition_result,
          equal_condition_result(
              details="0 PPS found for all properties",
              is_pass=True,
              name=f"Train-Test properties' Predictive Power Score difference is less than {condition_value}",
          ),
      )
  
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 19 for calling a global function with example usage: test_train_test_condition_pps_diff_pass_per_class(mnist_visiondata_train) without return types