code: def get_normalized_entropy(
      pred_probs: np.ndarray, min_allowed_prob: Optional[float] = None
  ) -> np.ndarray:
      """Return the normalized entropy of pred_probs.
  
      Normalized entropy is between 0 and 1. Higher values of entropy indicate higher uncertainty in the model's prediction of the correct label.
  
      Read more about normalized entropy `on Wikipedia <https://en.wikipedia.org/wiki/Entropy_(information_theory)>`_.
  
      Normalized entropy is used in active learning for uncertainty sampling: https://towardsdatascience.com/uncertainty-sampling-cheatsheet-ec57bc067c0b
  
      Unlike label-quality scores, entropy only depends on the model's predictions, not the given label.
  
      Parameters
      ----------
      pred_probs : np.ndarray (shape (N, K))
        Each row of this matrix corresponds to an example x and contains the model-predicted
        probabilities that x belongs to each possible class: P(label=k|x)
  
      min_allowed_prob : float, default: None, deprecated
        Minimum allowed probability value. If not `None` (default),
        entries of `pred_probs` below this value will be clipped to this value.
  
        .. deprecated:: 2.5.0
           This keyword is deprecated and should be left to the default.
           The entropy is well-behaved even if `pred_probs` contains zeros,
           clipping is unnecessary and (slightly) changes the results.
  
      Returns
      -------
      entropy : np.ndarray (shape (N, ))
        Each element is the normalized entropy of the corresponding row of ``pred_probs``.
  
      Raises
      ------
      ValueError
          An error is raised if any of the probabilities is not in the interval [0, 1].
      """
      if np.any(pred_probs < 0) or np.any(pred_probs > 1):
          raise ValueError(
              "All probabilities are required to be in the interval [0, 1]."
          )
      num_classes = pred_probs.shape[1]
  
      if min_allowed_prob is not None:
          warnings.warn(
              "Using `min_allowed_prob` is not necessary anymore and will be removed.",
              DeprecationWarning,
          )
          pred_probs = np.clip(
              pred_probs, a_min=min_allowed_prob, a_max=None
          )
  
      # Note that dividing by log(num_classes) changes the base of the log which rescales entropy to 0-1 range
      return -np.sum(xlogy(pred_probs, pred_probs), axis=1) / np.log(
          num_classes
      )
  
imports: import warnings
  import numpy
  from typing import Optional
  from scipy.special import xlogy
  from cleanlab.count import get_confident_thresholds
label_desc: Use image node_id 2 for calling a global function with example usage: get_normalized_entropy(pred_probs, min_allowed_prob) and returns: unknown