code: def test_tweet_emotion_properties(tweet_emotion_train_test_textdata):
      # Arrange
      _, test = tweet_emotion_train_test_textdata
      check = TextPropertyOutliers()
      # Act
      result = check.run(test)
  
      # 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(
          len(result.value["Text Length"]["indices"]), equal_to(0)
      )
      assert_that(
          result.value["Text Length"]["lower_limit"], equal_to(6)
      )
      assert_that(
          result.value["Text Length"]["upper_limit"], equal_to(160)
      )
  
      # Categorical property:
      assert_that(
          len(result.value["Language"]["indices"]), equal_to(55)
      )
      assert_that(
          result.value["Language"]["lower_limit"],
          close_to(0.007, 0.001),
      )
      assert_that(
          result.value["Language"]["upper_limit"], equal_to(None)
      )
  
      # Assert display
      assert_that(len(result.display), equal_to(7))
      assert_that(
          result.display[5],
          equal_to("<h5><b>Properties Not Shown:</h5></b>"),
      )
  
      # Check the table of properties not shown:
      expected_series = pd.Series(
          (
              "Text Length, Subjectivity, Fluency",
              "Average Word Length, % Special Characters",
          ),
          index=(
              "No outliers found.",
              "Outliers found but not shown in graphs (n_show_top=5).",
          ),
      )
      result_series = result.display[6].data["Properties"]
  
      assert_that(
          (expected_series != result_series).sum().sum(), equal_to(0)
      )
  
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 1 for calling a global function with example usage: test_tweet_emotion_properties(tweet_emotion_train_test_textdata) without return types