code: def multilabel_health_summary(
      labels=None,
      pred_probs=None,
      *,
      class_names=None,
      num_examples=None,
      confident_joint=None,
      verbose=True,
  ) -> Dict:
      """Prints a health summary of your multi-label dataset.
  
      This summary includes useful statistics like:
  
      * The classes with the most and least label issues.
      * Overall label quality scores, summarizing how accurate the labels appear across the entire dataset.
  
      **Parameters**: For information about the arguments to this method, see the documentation of
      `~cleanlab.multilabel_classification.dataset.common_multilabel_issues`.
  
      Returns
      -------
      summary : dict
          A dictionary containing keys (see the corresponding functions' documentation to understand the values):
              - ``"overall_label_health_score"``, corresponding to output of `~cleanlab.multilabel_classification.dataset.overall_multilabel_health_score`
              - ``"classes_by_multilabel_quality"``, corresponding to output of `~cleanlab.multilabel_classification.dataset.rank_classes_by_multilabel_quality`
              - ``"common_multilabel_issues"``, corresponding to output of `~cleanlab.multilabel_classification.dataset.common_multilabel_issues`
      """
      from cleanlab.internal.util import smart_display_dataframe
  
      if num_examples is None:
          num_examples = _get_num_examples_multilabel(labels=labels)
  
      if verbose:
          longest_line = (
              f"|   for your dataset with {num_examples:,} examples "
          )
          print(
              "-" * (len(longest_line) - 1)
              + "\n"
              + f"|  Generating a Cleanlab Dataset Health Summary{' ' * (len(longest_line) - 49)}|\n"
              + longest_line
              + f"|  Note, Cleanlab is not a medical doctor... yet.{' ' * (len(longest_line) - 51)}|\n"
              + "-" * (len(longest_line) - 1)
              + "\n",
          )
  
      df_class_label_quality = rank_classes_by_multilabel_quality(
          labels=labels,
          pred_probs=pred_probs,
          class_names=class_names,
          confident_joint=confident_joint,
      )
      if verbose:
          print(
              "Overall Class Quality and Noise across your dataset (below)"
          )
          print("-" * 60, "\n", flush=True)
          smart_display_dataframe(df_class_label_quality)
  
      df_common_issues = common_multilabel_issues(
          labels=labels,
          pred_probs=pred_probs,
          class_names=class_names,
          confident_joint=confident_joint,
      )
      if verbose:
          print(
              "\nCommon multilabel issues are" + "\n" + "-" * 83 + "\n",
              flush=True,
          )
          smart_display_dataframe(df_common_issues)
          print()
  
      health_score = overall_multilabel_health_score(
          labels=labels,
          pred_probs=pred_probs,
          confident_joint=confident_joint,
      )
      if verbose:
          print("\nGenerated with <3 from Cleanlab.\n")
      return {
          "overall_multilabel_health_score": health_score,
          "classes_by_multilabel_quality": df_class_label_quality,
          "common_multilabel_issues": df_common_issues,
      }
  
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 5 for calling a global function with example usage: multilabel_health_summary(labels, pred_probs) and returns: dict