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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_aggregation(C, ratio=0.03, distance='unit', maxiter=10):
 """Aggregate nodes using Lloyd Clustering. Parameters C : csr_matrix strength of connection matrix ratio : scalar Fraction of the nodes which will be seeds. distance : ['unit','abs','inv',None] Distance assigned to each edge of the graph G used in Lloyd clustering For each nonzero value C[i,j]: ======= =========================== 'unit' G[i,j] = 1 'abs' G[i,j] = abs(C[i,j]) 'inv' G[i,j] = 1.0/abs(C[i,j]) 'same' G[i,j] = C[i,j] 'sub' G[i,j] = C[i,j] - min(C) ======= =========================== maxiter : int Maximum number of iterations to perform Returns ------- AggOp : csr_matrix aggregation operator which determines the sparsity pattern of the tentative prolongator seeds : array array of Cpts, i.e., Cpts[i] = root node of aggregate i See Also -------- amg_core.standard_aggregation Examples -------- matrix([[ 2., -1., 0., 0.], [-1., 2., -1., 0.], [ 0., -1., 2., -1.], [ 0., 0., -1., 2.]]) matrix([[1], [1], [1], [1]], dtype=int8) """

    if ratio <= 0 or ratio > 1:
        raise ValueError('ratio must be > 0.0 and <= 1.0')

    if not (isspmatrix_csr(C) or isspmatrix_csc(C)):
        raise TypeError('expected csr_matrix or csc_matrix')

    if distance == 'unit':
        data = np.ones_like(C.data).astype(float)
    elif distance == 'abs':
        data = abs(C.data)
    elif distance == 'inv':
        data = 1.0/abs(C.data)
    elif distance is 'same':
        data = C.data
    elif distance is 'min':
        data = C.data - C.data.min()
    else:
        raise ValueError('unrecognized value distance=%s' % distance)

    if C.dtype == complex:
        data = np.real(data)

    assert(data.min() >= 0)

    G = C.__class__((data, C.indices, C.indptr), shape=C.shape)

    num_seeds = int(min(max(ratio * G.shape[0], 1), G.shape[0]))

    distances, clusters, seeds = lloyd_cluster(G, num_seeds, maxiter=maxiter)

    row = (clusters >= 0).nonzero()[0]
    col = clusters[row]
    data = np.ones(len(row), dtype='int8')
    AggOp = coo_matrix((data, (row, col)),
                       shape=(G.shape[0], num_seeds)).tocsr()
    return AggOp, seeds