code: def classifier_margin(
      classifier: BaseEstimator, X: modALinput, **predict_proba_kwargs
  ) -> np.ndarray:
      """
      Classification margin uncertainty of the classifier for the provided samples. This uncertainty measure takes the
      first and second most likely predictions and takes the difference of their probabilities, which is the margin.
  
      Args:
          classifier: The classifier for which the prediction margin is to be measured.
          X: The samples for which the prediction margin of classification is to be measured.
          **predict_proba_kwargs: Keyword arguments to be passed for the :meth:`predict_proba` of the classifier.
  
      Returns:
          Margin uncertainty, which is the difference of the probabilities of first and second most likely predictions.
      """
      try:
          classwise_uncertainty = classifier.predict_proba(
              X, **predict_proba_kwargs
          )
      except NotFittedError:
          return np.zeros(shape=(X.shape[0],))
  
      if classwise_uncertainty.shape[1] == 1:
          return np.zeros(shape=(classwise_uncertainty.shape[0],))
  
      part = np.partition(-classwise_uncertainty, 1, axis=1)
      margin = -part[:, 0] + part[:, 1]
  
      return margin
  
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 5 for calling a global function with example usage: classifier_margin(classifier, X) and returns: margin, np, np