code: def test_tweet_emotion_metadata_fully_annotated(
      tweet_emotion_train_test_textdata,
  ):
      # Arrange
      _, test = tweet_emotion_train_test_textdata
      check = (
          UnderAnnotatedMetaDataSegments().add_condition_segments_relative_performance_greater_than()
      )
  
      # Act
      result = check.run(test)
  
      # Assert
      assert_that(
          result.value["message"],
          equal_to(
              "Under annotated metadata segments check is "
              "skipped since your data annotation ratio is > 95.0%. "
              "Try increasing the annotation_ratio_threshold parameter."
          ),
      )
  
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 4 for calling a global function with example usage: test_tweet_emotion_metadata_fully_annotated(tweet_emotion_train_test_textdata) without return types