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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 classical_strength_of_connection(A, theta=0.0, norm='abs'):
 """Classical Strength Measure. Return a strength of connection matrix using the classical AMG measure An off-diagonal entry A[i,j] is a strong connection iff:: A[i,j] >= theta * max(|A[i,k]|), where k != i (norm='abs') -A[i,j] >= theta * max(-A[i,k]), where k != i (norm='min') Parameters A : csr_matrix or bsr_matrix Square, sparse matrix in CSR or BSR format theta : float Threshold parameter in [0,1]. norm: 'string' 'abs' : to use the absolute value, 'min' : to use the negative value (see above) Returns ------- S : csr_matrix Matrix graph defining strong connections. S[i,j]=1 if vertex i is strongly influenced by vertex j. See Also -------- symmetric_strength_of_connection : symmetric measure used in SA evolution_strength_of_connection : relaxation based strength measure Notes ----- - A symmetric A does not necessarily yield a symmetric strength matrix S - Calls C++ function classical_strength_of_connection - The version as implemented is designed form M-matrices. Trottenberg et al. use max A[i,k] over all negative entries, which is the same. A positive edge weight never indicates a strong connection. - See [2000BrHeMc]_ and [2001bTrOoSc]_ References .. [2000BrHeMc] Briggs, W. L., Henson, V. E., McCormick, S. F., "A multigrid tutorial", Second edition. Society for Industrial and Applied Mathematics (SIAM), Philadelphia, PA, 2000. xii+193 pp. .. [2001bTrOoSc] Trottenberg, U., Oosterlee, C. W., Schuller, A., "Multigrid", Academic Press, Inc., San Diego, CA, 2001. xvi+631 pp. Examples -------- """

    if sparse.isspmatrix_bsr(A):
        blocksize = A.blocksize[0]
    else:
        blocksize = 1

    if not sparse.isspmatrix_csr(A):
        warn("Implicit conversion of A to csr", sparse.SparseEfficiencyWarning)
        A = sparse.csr_matrix(A)

    if (theta < 0 or theta > 1):
        raise ValueError('expected theta in [0,1]')

    Sp = np.empty_like(A.indptr)
    Sj = np.empty_like(A.indices)
    Sx = np.empty_like(A.data)

    if norm == 'abs':
        amg_core.classical_strength_of_connection_abs(
            A.shape[0], theta, A.indptr, A.indices, A.data, Sp, Sj, Sx)
    elif norm == 'min':
        amg_core.classical_strength_of_connection_min(
            A.shape[0], theta, A.indptr, A.indices, A.data, Sp, Sj, Sx)
    else:
        raise ValueError('Unknown norm')

    S = sparse.csr_matrix((Sx, Sj, Sp), shape=A.shape)

    if blocksize > 1:
        S = amalgamate(S, blocksize)

    # Strength represents "distance", so take the magnitude
    S.data = np.abs(S.data)

    # Scale S by the largest magnitude entry in each row
    S = scale_rows_by_largest_entry(S)

    return S