code: def overall_multilabel_health_score(
      labels=None,
      pred_probs=None,
      *,
      confident_joint=None,
  ) -> float:
      """Returns a single score between 0 and 1 measuring the overall quality of all labels in a multi-label classification dataset.
      Intuitively, the score is the average correctness of the given labels across all examples in the
      dataset. So a score of 1 suggests your data is perfectly labeled and a score of 0.5 suggests
      half of the examples in the dataset may be incorrectly labeled. Thus, a higher
      score implies a higher quality dataset.
  
      **Parameters**: For information about the arguments to this method, see the documentation of
      `~cleanlab.multilabel_classification.dataset.common_multilabel_issues`.
  
      Returns
      -------
      health_score : float
          A overall score between 0 and 1, where 1 implies all labels in the dataset are estimated to be correct.
          A score of 0.5 implies that half of the dataset's labels are estimated to have issues.
      """
      num_examples = _get_num_examples_multilabel(labels=labels)
      issues = find_label_issues(
          labels=labels,
          pred_probs=pred_probs,
          confident_joint=confident_joint,
      )
      return 1.0 - sum(issues) / num_examples
  
file_path: cleanlab/cleanlab/multilabel_classification/dataset.py
imports: import pandas
  import numpy
  from typing import Optional, cast, Dict, Any
  from cleanlab.multilabel_classification.filter import find_multilabel_issues_per_class, find_label_issues
  from cleanlab.internal.multilabel_utils import get_onehot_num_classes
  from collections import defaultdict
label_desc: Use image node_id 4 for calling a global function with example usage: overall_multilabel_health_score(labels, pred_probs) and returns: unknown