code: def test_multilabel_find_label_issues(data_multilabel):
      labels, pred_probs = data_multilabel
      issues = filter.find_label_issues(
          labels=labels,
          pred_probs=pred_probs,
          return_indices_ranked_by="self_confidence",
      )
      issues_lm = filter.find_label_issues(
          labels,
          pred_probs,
          low_memory=True,
          return_indices_ranked_by="self_confidence",
      )
      intersection = len(list(set(issues).intersection(set(issues_lm))))
      union = len(set(issues)) + len(set(issues_lm)) - intersection
      assert float(intersection) / union > 0.95
      # Check with return_indices_ranked_by=None
      issues_mask = filter.find_label_issues(
          labels=labels, pred_probs=pred_probs
      )
      issues_lm_mask = filter.find_label_issues(
          labels, pred_probs, low_memory=True
      )
      issues_from_mask = np.where(issues_mask)[0]
      issues_lm_from_mask = np.where(issues_lm_mask)[0]
      intersection = len(
          list(
              set(issues_from_mask).intersection(
                  set(issues_lm_from_mask)
              )
          )
      )
      union = (
          len(set(issues_from_mask))
          + len(set(issues_lm_from_mask))
          - intersection
      )
      assert float(intersection) / union > 0.95
      # Check with low_memory=True, unused parameters rank_by_kwargs and n_jobs
      rank_by_kwargs = {"adjust_pred_probs": None}
      issues_lm2 = filter.find_label_issues(
          labels,
          pred_probs,
          low_memory=True,
          return_indices_ranked_by="self_confidence",
          rank_by_kwargs=rank_by_kwargs,
          n_jobs=1,
      )
      np.testing.assert_array_equal(issues_lm2, issues_lm)
  
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 14 for calling a global function with example usage: test_multilabel_find_label_issues(data_multilabel) without return types