code: def get_label_quality_scores(
      labels,
      pred_probs,
      *,
      method: MultilabelScorer = MultilabelScorer(),
      base_scorer_kwargs: Optional[dict] = None,
      **aggregator_kwargs,
  ) -> np.ndarray:
      """Computes a quality score for each label in a multi-label classification problem
      based on out-of-sample predicted probabilities.
  
      Parameters
      ----------
      labels:
          A 2D array of shape (N, K) with binary labels.
  
      pred_probs:
          A 2D array of shape (N, K) with predicted probabilities.
  
      method:
          A scoring+aggregation method for computing the label quality scores of examples in a multi-label classification setting.
  
      base_scorer_kwargs:
          Keyword arguments to pass to the class-label scorer.
  
      aggregator_kwargs:
          Additional keyword arguments to pass to the aggregator.
  
      Returns
      -------
      scores:
          A 1D array of shape (N,) with the quality scores for each datapoint.
  
      Examples
      --------
      >>> import cleanlab.internal.multilabel_scorer as ml_scorer
      >>> import numpy as np
      >>> labels = np.array([[0, 1, 0], [1, 0, 1]])
      >>> pred_probs = np.array([[0.1, 0.9, 0.1], [0.4, 0.1, 0.9]])
      >>> scores = ml_scorer.get_label_quality_scores(labels, pred_probs, method=ml_scorer.MultilabelScorer())
      >>> scores
      array([0.9, 0.5])
  
      See also
      --------
      MultilabelScorer:
          See the documentation for the MultilabelScorer class for more examples of scoring methods and aggregation methods.
      """
      return method(
          labels,
          pred_probs,
          base_scorer_kwargs=base_scorer_kwargs,
          **aggregator_kwargs,
      )
  
imports: from enum import Enum
  from typing import Callable, Dict, Optional, Union
  import numpy
  from sklearn.model_selection import cross_val_predict
  from cleanlab.internal.multilabel_utils import _is_multilabel, stack_complement
  from cleanlab.internal.label_quality_utils import _subtract_confident_thresholds
  from cleanlab.internal.numerics import softmax
  from cleanlab.rank import get_self_confidence_for_each_label, get_normalized_margin_for_each_label, get_confidence_weighted_entropy_for_each_label
label_desc: Use image node_id 3 for calling a global function with example usage: get_label_quality_scores(labels, pred_probs) and returns: method