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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 _k_prototypes_iter(Xnum, Xcat, centroids, cl_attr_sum, cl_memb_sum, cl_attr_freq, membship, num_dissim, cat_dissim, gamma, random_state):
 """Single iteration of the k-prototypes algorithm"""

    moves = 0
    for ipoint in range(Xnum.shape[0]):
        clust = np.argmin(
            num_dissim(centroids[0], Xnum[ipoint]) +
            gamma * cat_dissim(centroids[1], Xcat[ipoint], X=Xcat, membship=membship)
        )
        if membship[clust, ipoint]:
            # Point is already in its right place.
            continue

        # Move point, and update old/new cluster frequencies and centroids.
        moves += 1
        old_clust = np.argwhere(membship[:, ipoint])[0][0]

        # Note that membship gets updated by kmodes.move_point_cat.
        # move_point_num only updates things specific to the k-means part.
        cl_attr_sum, cl_memb_sum = move_point_num(
            Xnum[ipoint], clust, old_clust, cl_attr_sum, cl_memb_sum
        )
        cl_attr_freq, membship, centroids[1] = kmodes.move_point_cat(
            Xcat[ipoint], ipoint, clust, old_clust,
            cl_attr_freq, membship, centroids[1]
        )

        # Update old and new centroids for numerical attributes using
        # the means and sums of all values
        for iattr in range(len(Xnum[ipoint])):
            for curc in (clust, old_clust):
                if cl_memb_sum[curc]:
                    centroids[0][curc, iattr] = cl_attr_sum[curc, iattr] / cl_memb_sum[curc]
                else:
                    centroids[0][curc, iattr] = 0.

        # In case of an empty cluster, reinitialize with a random point
        # from largest cluster.
        if not cl_memb_sum[old_clust]:
            from_clust = membship.sum(axis=1).argmax()
            choices = [ii for ii, ch in enumerate(membship[from_clust, :]) if ch]
            rindx = random_state.choice(choices)

            cl_attr_sum, cl_memb_sum = move_point_num(
                Xnum[rindx], old_clust, from_clust, cl_attr_sum, cl_memb_sum
            )
            cl_attr_freq, membship, centroids[1] = kmodes.move_point_cat(
                Xcat[rindx], rindx, old_clust, from_clust,
                cl_attr_freq, membship, centroids[1]
            )

    return centroids, moves