code: def test_multilabel_just_dance(
      just_dance_train_test_textdata,
      just_dance_train_test_textdata_probas,
  ):
      # Arrange
      _, data = just_dance_train_test_textdata
      _, probabilities = just_dance_train_test_textdata_probas
      assert_that(data.is_multi_label_classification(), equal_to(True))
  
      data = data.copy(rows_to_use=range(1000))
      probabilities = probabilities[:1000, :]
      check = PropertySegmentsPerformance()
  
      # Act
      result = check.run(data, probabilities=probabilities)
  
      # Assert
      assert_that(result.value["avg_score"], close_to(0.615, 0.001))
      assert_that(len(result.value["weak_segments_list"]), equal_to(5))
      assert_that(
          result.value["weak_segments_list"].iloc[0, 0],
          close_to(0.41, 0.01),
      )
  
file_path: deepchecks/tests/nlp/checks/model_evaluation/weak_segment_performance_test.py
imports: import numpy
  import pandas
  import pytest
  from hamcrest import assert_that, calling, close_to, equal_to, has_items, is_in, matches_regexp, raises
  from deepchecks.core.errors import DeepchecksNotSupportedError, NotEnoughSamplesError
  from deepchecks.nlp.checks import MetadataSegmentsPerformance, PropertySegmentsPerformance
  from tests.base.utils import equal_condition_result
label_desc: Use image node_id 7 for calling a global function with example usage: test_multilabel_just_dance(just_dance_train_test_textdata, just_dance_train_test_textdata_probas) without return types