code: def classifier_entropy(
      classifier: BaseEstimator, X: modALinput, **predict_proba_kwargs
  ) -> np.ndarray:
      """
      Entropy of predictions of the for the provided samples.
  
      Args:
          classifier: The classifier for which the prediction entropy is to be measured.
          X: The samples for which the prediction entropy is to be measured.
          **predict_proba_kwargs: Keyword arguments to be passed for the :meth:`predict_proba` of the classifier.
  
      Returns:
          Entropy of the class probabilities.
      """
      try:
          classwise_uncertainty = classifier.predict_proba(
              X, **predict_proba_kwargs
          )
      except NotFittedError:
          return np.zeros(shape=(X.shape[0],))
  
      return np.transpose(entropy(np.transpose(classwise_uncertainty)))
  
file_path: modAL/modAL/uncertainty.py
imports: import numpy
  from scipy.stats import entropy
  from sklearn.base import BaseEstimator
  from sklearn.exceptions import NotFittedError
  from modAL.utils.data import modALinput
  from modAL.utils.selection import multi_argmax, multi_argmin, shuffled_argmax, shuffled_argmin
label_desc: Use image node_id 6 for calling a global function with example usage: classifier_entropy(classifier, X) and returns: np, np