code: def most_uncertain_by_mask(self, mask, y):
          
          idxs = np.where(mask)[0]
          # the most uncertain samples will have abs(probs-1/num_classes) close to 0;
          return idxs[np.argsort(np.abs(self.probs[idxs,y]-(1/self.num_classes)))[:4]]
docs: Extracts the first 4 most uncertain indexes from the ordered list of probabilities
  
              Arguments:
                  mask (numpy.ndarray): the mask of probabilities specific to the selected class; a boolean array with shape (num_of_samples,) which contains True where class==selected_class, and False everywhere else
                  y (int): the selected class
  
              Returns:
                  idxs (ndarray): An array of indexes of length 4