code: def test_find_label_issues(self, data):
      true_labels, noisy_labels, pred_probs = data
      noisy_labels_list = onehot2int(noisy_labels)
      is_issue = filter.find_label_issues(
          labels=onehot2int(noisy_labels_list),
          pred_probs=np.array(pred_probs),
      )
      threshold = 0.5
      predicted_labels = (pred_probs >= threshold).astype(int)
  
      # Check if predicted labels are the same as noisy labels for each example
      labels_match = np.all(predicted_labels == noisy_labels, axis=1)
  
      # For any example flagged as having an issue, there should be at least one label mismatch
      assert not np.any(
          is_issue & labels_match
      ), "Examples with issues must have at least one label mismatch."
  
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 1 for calling the TestMultiLabel obj's underlying member method code with example usage: obj.test_find_label_issues(data) without return types