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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 lloyd_cluster(G, seeds, maxiter=10):
 """Perform Lloyd clustering on graph with weighted edges. Parameters G : csr_matrix, csc_matrix A sparse NxN matrix where each nonzero entry G[i,j] is the distance between nodes i and j. seeds : int array If seeds is an integer, then its value determines the number of clusters. Otherwise, seeds is an array of unique integers between 0 and N-1 that will be used as the initial seeds for clustering. maxiter : int The maximum number of iterations to perform. Returns ------- distances : array final distances clusters : int array id of each cluster of points seeds : int array index of each seed Notes ----- If G has complex values, abs(G) is used instead. """

    G = asgraph(G)
    N = G.shape[0]

    if G.dtype.kind == 'c':
        # complex dtype
        G = np.abs(G)

    # interpret seeds argument
    if np.isscalar(seeds):
        seeds = np.random.permutation(N)[:seeds]
        seeds = seeds.astype('intc')
    else:
        seeds = np.array(seeds, dtype='intc')

    if len(seeds) < 1:
        raise ValueError('at least one seed is required')

    if seeds.min() < 0:
        raise ValueError('invalid seed index (%d)' % seeds.min())
    if seeds.max() >= N:
        raise ValueError('invalid seed index (%d)' % seeds.max())

    clusters = np.empty(N, dtype='intc')
    distances = np.empty(N, dtype=G.dtype)

    for i in range(maxiter):
        last_seeds = seeds.copy()

        amg_core.lloyd_cluster(N, G.indptr, G.indices, G.data,
                               len(seeds), distances, clusters, seeds)

        if (seeds == last_seeds).all():
            break

    return (distances, clusters, seeds)