code: def test_all_features_pps_upper_bound_condition_that_should_not_pass():
      # Arrange
      df, _ = util_generate_dataframe_and_expected()
      dataset = Dataset(df, label="label")
      condition_value = 0.4
      check = FeatureLabelCorrelation(
          n_samples=None, random_state=42
      ).add_condition_feature_pps_less_than(condition_value)
  
      # Act
      condition_result, *_ = check.conditions_decision(
          check.run(dataset)
      )
  
      # Assert
      assert_that(
          condition_result,
          equal_condition_result(
              is_pass=False,
              name=f"Features' Predictive Power Score is less than {condition_value}",
              details="Found 3 out of 5 features with PPS above threshold: "
              "{'x2': '0.84', 'x4': '0.53', 'x5': '0.42'}",
          ),
      )
  
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 16 for calling a global function with example usage: test_all_features_pps_upper_bound_condition_that_should_not_pass() without return types