code: def test_common_multilabel_issues(
      class_names, pred_probs_multilabel, labels_multilabel
  ):
      df = common_multilabel_issues(
          labels=labels_multilabel,
          pred_probs=pred_probs_multilabel,
          class_names=class_names,
      )
      expected_issue_probabilities = [
          0.14285714285714285,
          0.14285714285714285,
          0.0,
          0.0,
          0.0,
          0.0,
          0.0,
          0.0,
          0.0,
          0.0,
      ]
      assert len(df) == 10
      assert np.isclose(
          np.array(expected_issue_probabilities),
          df["Issue Probability"],
      ).all()
      if class_names:
          expected_res = [
              "Apple",
              "Dog",
              "Apple",
              "Cat",
              "Cat",
              "Dog",
              "Peach",
              "Peach",
              "Bird",
              "Bird",
          ]
          assert list(df["Class Name"]) == expected_res
      else:
          assert "Class Name" not in df.columns
  
imports: import itertools
  import typing
  import numpy
  import pytest
  import sklearn
  from hypothesis import given, settings
  from hypothesis import strategies
  from hypothesis.strategies import composite
  from hypothesis.extra.numpy import arrays
  from sklearn.linear_model import LogisticRegression
  from sklearn.multiclass import OneVsRestClassifier
  from cleanlab import multilabel_classification
  from cleanlab.internal import multilabel_scorer
  from cleanlab.internal.multilabel_utils import get_onehot_num_classes, onehot2int, stack_complement
  from cleanlab.multilabel_classification import filter
  from cleanlab.multilabel_classification.dataset import common_multilabel_issues, multilabel_health_summary, overall_multilabel_health_score, rank_classes_by_multilabel_quality
  from cleanlab.multilabel_classification.rank import get_label_quality_scores_per_class
label_desc: Use image node_id 13 for calling a global function with example usage: test_common_multilabel_issues(class_names, pred_probs_multilabel, labels_multilabel) without return types