How to use the textdistance.hamming.normalized_similarity function in textdistance

def localitySensitiveHashing(self, colName, blockSize=6, method = "levenshtein", threshold = 0.81):
        """
        colName: the column to be clustered
        blockSize: size of blocking
        method: methods to calculate the similarity
        threshold: controls how similar two items should be thought to be the same
        """
        self._tf._assert_cols_in_df(columns_provided=[colName], columns_df=self._tf._df.columns)
        rdd = self._tf._df.select(["id", colName]).rdd.map(list)
        methodDict = {
            # Edit Based
            "hamming": textdistance.hamming.normalized_similarity,
            "mlipns": textdistance.mlipns.normalized_similarity,
            "levenshtein": textdistance.levenshtein.normalized_similarity,
            # Token based
            "jaccard": textdistance.jaccard.normalized_similarity,
            "overlap": textdistance.overlap.normalized_similarity,
            "cosine": textdistance.cosine.normalized_similarity,
            # Sequence based
            "lcsseq": textdistance.lcsseq.normalized_similarity,
            "lcsstr": textdistance.lcsstr.normalized_similarity,
            # Phonetic based
            "mra": textdistance.mra.normalized_similarity,
        }
        try:
            sim = methodDict[method]
        except:
            print("Waring: %s is not a valid method\n, changes into levenshtein by default.")

def other_distance(s1, s2):
    return [textdistance.hamming.normalized_similarity(s1, s2),
            textdistance.mlipns.normalized_similarity(s1, s2),
            textdistance.damerau_levenshtein.normalized_similarity(s1, s2),
            textdistance.strcmp95.normalized_similarity(s1, s2),
            textdistance.needleman_wunsch.normalized_similarity(s1, s2),
            textdistance.gotoh.normalized_similarity(s1, s2),
            textdistance.smith_waterman.normalized_similarity(s1, s2),
            textdistance.ratcliff_obershelp.normalized_similarity(s1, s2)]