code: def test_tweet_emotion_properties(tweet_emotion_train_test_textdata):
      # Arrange
      _, test = tweet_emotion_train_test_textdata
      test = test.copy()
      test._label = np.asarray(
          list(test._label[: round(len(test._label) / 2)])
          + [None] * round(len(test._label) / 2),
          dtype=object,
      )
  
      check = UnderAnnotatedPropertySegments().add_condition_segments_annotation_ratio_greater_than(
          0.5
      )
      # Act
      result = check.run(test)
      condition_result = check.conditions_decision(result)
  
      # Assert
      assert_that(
          condition_result,
          has_items(
              equal_condition_result(
                  is_pass=False,
                  details=r"Most under annotated segment has annotation ratio of 40.74%.",
                  name=r"In all segments annotation ratio should be greater than 50%.",
              )
          ),
      )
  
      assert_that(result.value["avg_score"], close_to(0.5, 0.001))
      assert_that(
          len(result.value["weak_segments_list"]), close_to(6, 1)
      )
      assert_that(
          result.value["weak_segments_list"].iloc[0, 0],
          close_to(0.407, 0.01),
      )
  
file_path: deepchecks/tests/nlp/checks/data_integrity/under_annotated_segments_test.py
imports: import numpy
  from hamcrest import assert_that, calling, close_to, equal_to, has_items, raises
  from deepchecks.core.errors import NotEnoughSamplesError
  from deepchecks.nlp.checks import UnderAnnotatedMetaDataSegments, UnderAnnotatedPropertySegments
  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