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

def most_uncertain_by_mask(self, mask, y):
        """ 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
        """
        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]]