class_docstr: Enum for the different methods to compute label quality scores.
class_docstr_tok: ["Enum","for","the","different","methods","to","compute","label","quality","scores","."]
docstr: Returns adjusted predicted probabilities by subtracting the class confident thresholds and renormalizing.
  
  This is used to adjust the predicted probabilities for the SELF_CONFIDENCE and NORMALIZED_MARGIN methods.
docstr_tok: ["Returns","adjusted","predicted","probabilities","by","subtracting","the","class","confident","thresholds","and","renormalizing",".","This","is","used","to","adjust","the","predicted","probabilities","for","the","SELF_CONFIDENCE","and","NORMALIZED_MARGIN","methods","."]
code: def _adjust_pred_probs(
      self, labels: np.ndarray, pred_probs: np.ndarray, **kwargs
  ) -> np.ndarray:
      """Returns adjusted predicted probabilities by subtracting the class confident thresholds and renormalizing.
  
      This is used to adjust the predicted probabilities for the SELF_CONFIDENCE and NORMALIZED_MARGIN methods.
      """
      if kwargs.get("adjust_pred_probs", False) is True:
          if self == ClassLabelScorer.CONFIDENCE_WEIGHTED_ENTROPY:
              raise ValueError(
                  f"adjust_pred_probs is not currently supported for {self}."
              )
          pred_probs = _subtract_confident_thresholds(
              labels, pred_probs
          )
      return pred_probs
  
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 2 for calling the ClassLabelScorer obj's underlying member method code with example usage: obj._adjust_pred_probs(labels, pred_probs) and returns: pred_probs