code: def most_by_mask(self, mask, y, mult):
          
          idxs = np.where(mask)[0]
          cnt = min(4, len(idxs))
          return idxs[np.argsort(mult * self.probs[idxs,y])[:cnt]]
docs: Extracts the first 4 most correct/incorrect 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
                  mult (int): sets the ordering; -1 descending, 1 ascending
  
              Returns:
                  idxs (ndarray): An array of indexes of length 4