code: def test_tweet_emotion_condition_property_with_nans(
      tweet_emotion_train_test_textdata,
  ):
      # Arrange
      _, test = tweet_emotion_train_test_textdata
      test = test.copy()
      test._properties["Subjectivity"] = (
          test._properties["Subjectivity"] * np.nan
      )
      check = (
          TextPropertyOutliers().add_condition_outlier_ratio_less_or_equal()
      )
      # Act
      result = check.run(test)
      conditions_decisions = check.conditions_decision(result)
  
      # Assert
      assert_that(
          len(result.value["Sentiment"]["indices"]), equal_to(65)
      )
      assert_that(
          result.value["Sentiment"]["lower_limit"],
          close_to(-0.90, 0.01),
      )
      assert_that(
          result.value["Sentiment"]["upper_limit"], close_to(0.92, 0.01)
      )
  
      assert_that(
          conditions_decisions[0],
          equal_condition_result(
              is_pass=False,
              name="Outlier ratio in all properties is less or equal than 5%",
              details="Found 1 properties with outlier ratios above threshold.</br>"
              "Property with highest ratio is Toxicity with outlier ratio of 16.43%",
          ),  # type: ignore
      )
  
file_path: deepchecks/tests/nlp/checks/data_integrity/text_property_outliers_test.py
imports: import numpy
  import pandas
  from hamcrest import assert_that, close_to, equal_to
  from deepchecks.nlp.checks import TextPropertyOutliers
  from deepchecks.nlp.text_data import TextData
  from tests.base.utils import equal_condition_result
label_desc: Use image node_id 3 for calling a global function with example usage: test_tweet_emotion_condition_property_with_nans(tweet_emotion_train_test_textdata) without return types