code: def test_train_test_condition_pps_positive_difference_pass():
      # Arrange
      (
          df,
          df2,
          expected,
      ) = util_generate_second_similar_dataframe_and_expected()
      condition_value = 0.4
      check = FeatureLabelCorrelationChange(
          n_samples=None, random_state=42
      ).add_condition_feature_pps_difference_less_than(
          threshold=condition_value, include_negative_diff=False
      )
  
      # Act
      result = FeatureLabelCorrelationChange(
          n_samples=None, random_state=42
      ).run(
          train_dataset=Dataset(df, label="label"),
          test_dataset=Dataset(df2, label="label"),
      )
      condition_result, *_ = check.conditions_decision(result)
  
      # Assert
      assert_that(
          condition_result,
          equal_condition_result(
              is_pass=True,
              details="Passed for 5 relevant columns",
              name=f"Train-Test features' Predictive Power Score difference is less than {condition_value}",
          ),
      )
  
imports: import numpy
  import pandas
  from hamcrest import assert_that, calling, close_to, equal_to, greater_than, has_entries, has_length, raises
  from deepchecks.core.errors import DatasetValidationError, DeepchecksNotSupportedError, DeepchecksValueError
  from deepchecks.tabular.checks.data_integrity import FeatureLabelCorrelation
  from deepchecks.tabular.checks.train_test_validation import FeatureLabelCorrelationChange
  from deepchecks.tabular.dataset import Dataset
  from tests.base.utils import equal_condition_result
label_desc: Use image node_id 18 for calling a global function with example usage: test_train_test_condition_pps_positive_difference_pass() without return types