code: def test_tweet_emotion_metadata_interesting_segment(
      tweet_emotion_train_test_textdata,
  ):
      # Arrange
      _, test = tweet_emotion_train_test_textdata
      test = test.copy()
      idx_to_change = test.metadata[
          (test.metadata["user_age"] > 30)
          & (test.metadata["user_region"] == "Europe")
      ].index
      label = test._label.copy().astype(object)
      label[idx_to_change] = None
      test._label = label
  
      # Act
      result = UnderAnnotatedMetaDataSegments(
          multiple_segments_per_column=True
      ).run(test)
  
      # Assert
      assert_that(result.value["avg_score"], close_to(0.844, 0.001))
      assert_that(len(result.value["weak_segments_list"]), equal_to(3))
      assert_that(
          result.value["weak_segments_list"].iloc[0, 0],
          close_to(0, 0.01),
      )
      assert_that(
          result.value["weak_segments_list"].iloc[0, 1],
          equal_to("user_region"),
      )
  
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 3 for calling a global function with example usage: test_tweet_emotion_metadata_interesting_segment(tweet_emotion_train_test_textdata) without return types