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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def ng_dissim(a, b, X=None, membship=None):
 """Ng et al.'s dissimilarity measure, as presented in Michael K. Ng, Mark Junjie Li, Joshua Zhexue Huang, and Zengyou He, "On the Impact of Dissimilarity Measure in k-Modes Clustering Algorithm", IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 29, No. 3, January, 2007 This function can potentially speed up training convergence. Note that membship must be a rectangular array such that the len(membship) = len(a) and len(membship[i]) = X.shape[1] In case of missing membship, this function reverts back to matching dissimilarity (e.g., when predicting). """

    # Without membership, revert to matching dissimilarity
    if membship is None:
        return matching_dissim(a, b)

    def calc_cjr(b, X, memj, idr):
        """Num objects w/ category value x_{i,r} for rth attr in jth cluster"""
        xcids = np.where(memj == 1)
        return float((np.take(X, xcids, axis=0)[0][:, idr] == b[idr]).sum(0))

    def calc_dissim(b, X, memj, idr):
        # Size of jth cluster
        cj = float(np.sum(memj))
        return (1.0 - (calc_cjr(b, X, memj, idr) / cj)) if cj != 0.0 else 0.0

    if len(membship) != a.shape[0] and len(membship[0]) != X.shape[1]:
        raise ValueError("'membship' must be a rectangular array where "
                         "the number of rows in 'membship' equals the "
                         "number of rows in 'a' and the number of "
                         "columns in 'membship' equals the number of rows in 'X'.")

    return np.array([np.array([calc_dissim(b, X, membship[idj], idr)
                               if b[idr] == t else 1.0
                               for idr, t in enumerate(val_a)]).sum(0)
                     for idj, val_a in enumerate(a)])