code: def test_tweet_emotion_properties(
      tweet_emotion_train_test_textdata,
      tweet_emotion_train_test_probabilities,
  ):
      # Arrange
      _, test = tweet_emotion_train_test_textdata
      check = PropertyLabelCorrelation().add_condition_property_pps_less_than(
          0.1
      )
      # Act
      result = check.run(
          test, probabilities=tweet_emotion_train_test_probabilities[1]
      )
      condition_result = check.conditions_decision(result)
  
      # Assert
      assert_that(
          condition_result,
          has_items(
              equal_condition_result(
                  is_pass=False,
                  details="Found 1 out of 10 properties with PPS above threshold: {'Sentiment': '0.11'}",
                  name="Properties' Predictive Power Score is less than 0.1",
              )
          ),
      )
  
      assert_that(result.value["Sentiment"], close_to(0.11, 0.01))
      assert_that(result.value["Text Length"], close_to(0.02, 0.01))
  
imports: from hamcrest import assert_that, close_to, equal_to, has_items
  from deepchecks.nlp.checks import PropertyLabelCorrelation
  from deepchecks.nlp.datasets.classification import tweet_emotion
  from tests.base.utils import equal_condition_result
label_desc: Use image node_id 1 for calling a global function with example usage: test_tweet_emotion_properties(tweet_emotion_train_test_textdata, tweet_emotion_train_test_probabilities) without return types