How to use the kmodes.util.get_max_value_key function in kmodes

if init_tries == MAX_INIT_TRIES:
            # Could not get rid of empty clusters. Randomly
            # initialize instead.
            init = 'random'
        elif init_tries == RAISE_INIT_TRIES:
            raise ValueError(
                "Clustering algorithm could not initialize. "
                "Consider assigning the initial clusters manually."
            )

    # Perform an initial centroid update.
    for ik in range(n_clusters):
        for iattr in range(nnumattrs):
            centroids[0][ik, iattr] = cl_attr_sum[ik, iattr] / cl_memb_sum[ik]
        for iattr in range(ncatattrs):
            centroids[1][ik, iattr] = get_max_value_key(cl_attr_freq[ik][iattr])

    # _____ ITERATION _____
    if verbose:
        print("Starting iterations...")
    itr = 0
    labels = None
    converged = False

    _, cost = _labels_cost(Xnum, Xcat, centroids,
                           num_dissim, cat_dissim, gamma, membship)

    epoch_costs = [cost]
    while itr <= max_iter and not converged:
        itr += 1
        centroids, moves = _k_prototypes_iter(Xnum, Xcat, centroids,
                                              cl_attr_sum, cl_memb_sum, cl_attr_freq,

current_attribute_value_freq = to_attr_counts[curattr]
        current_centroid_value = centroids[to_clust][iattr]
        current_centroid_freq = to_attr_counts[current_centroid_value]
        if current_centroid_freq < current_attribute_value_freq:
            # We have incremented this value to the new mode. Update the centroid.
            centroids[to_clust][iattr] = curattr

        # Decrement the attribute count for the old "from" cluster
        from_attr_counts[curattr] -= 1

        old_centroid_value = centroids[from_clust][iattr]
        if old_centroid_value == curattr:
            # We have just removed a count from the old centroid value. We need to
            # recalculate the centroid as it may no longer be the maximum
            centroids[from_clust][iattr] = get_max_value_key(from_attr_counts)

    return cl_attr_freq, membship, centroids

for _ in range(n_clusters)]
    for ipoint, curpoint in enumerate(X):
        # Initial assignment to clusters
        clust = np.argmin(dissim(centroids, curpoint, X=X, membship=membship))
        membship[clust, ipoint] = 1
        # Count attribute values per cluster.
        for iattr, curattr in enumerate(curpoint):
            cl_attr_freq[clust][iattr][curattr] += 1
    # Perform an initial centroid update.
    for ik in range(n_clusters):
        for iattr in range(n_attrs):
            if sum(membship[ik]) == 0:
                # Empty centroid, choose randomly
                centroids[ik, iattr] = random_state.choice(X[:, iattr])
            else:
                centroids[ik, iattr] = get_max_value_key(cl_attr_freq[ik][iattr])

    # _____ ITERATION _____
    if verbose:
        print("Starting iterations...")
    itr = 0
    labels = None
    converged = False

    _, cost = _labels_cost(X, centroids, dissim, membship)

    epoch_costs = [cost]
    while itr <= max_iter and not converged:
        itr += 1
        centroids, moves = _k_modes_iter(
            X,
            centroids,