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

def most_by_mask(self, mask, y, mult):
        """ 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
        """
        idxs = np.where(mask)[0]
        cnt = min(4, len(idxs))
        return idxs[np.argsort(mult * self.probs[idxs,y])[:cnt]]