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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 symmetric_strength_of_connection(A, theta=0):
 """Symmetric Strength Measure. Compute strength of connection matrix using the standard symmetric measure An off-diagonal connection A[i,j] is strong iff:: abs(A[i,j]) >= theta * sqrt( abs(A[i,i]) * abs(A[j,j]) ) Parameters A : csr_matrix Matrix graph defined in sparse format. Entry A[i,j] describes the strength of edge [i,j] theta : float Threshold parameter (positive). 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 ----- - For vector problems, standard strength measures may produce undesirable aggregates. A "block approach" from Vanek et al. is used to replace vertex comparisons with block-type comparisons. A connection between nodes i and j in the block case is strong if:: ||AB[i,j]|| >= theta * sqrt( ||AB[i,i]||*||AB[j,j]|| ) where AB[k,l] is the matrix block (degrees of freedom) associated with nodes k and l and ||.|| is a matrix norm, such a Frobenius. - See [1996bVaMaBr]_ for more details. References .. [1996bVaMaBr] Vanek, P. and Mandel, J. and Brezina, M., "Algebraic Multigrid by Smoothed Aggregation for Second and Fourth Order Elliptic Problems", Computing, vol. 56, no. 3, pp. 179--196, 1996. http://citeseer.ist.psu.edu/vanek96algebraic.html Examples -------- """

    if theta < 0:
        raise ValueError('expected a positive theta')

    if sparse.isspmatrix_csr(A):
        # if theta == 0:
        #     return A

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

        fn = amg_core.symmetric_strength_of_connection
        fn(A.shape[0], theta, A.indptr, A.indices, A.data, Sp, Sj, Sx)

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

    elif sparse.isspmatrix_bsr(A):
        M, N = A.shape
        R, C = A.blocksize

        if R != C:
            raise ValueError('matrix must have square blocks')

        if theta == 0:
            data = np.ones(len(A.indices), dtype=A.dtype)
            S = sparse.csr_matrix((data, A.indices.copy(), A.indptr.copy()),
                                  shape=(int(M / R), int(N / C)))
        else:
            # the strength of connection matrix is based on the
            # Frobenius norms of the blocks
            data = (np.conjugate(A.data) * A.data).reshape(-1, R * C)
            data = data.sum(axis=1)
            A = sparse.csr_matrix((data, A.indices, A.indptr),
                                  shape=(int(M / R), int(N / C)))
            return symmetric_strength_of_connection(A, theta)
    else:
        raise TypeError('expected csr_matrix or bsr_matrix')

    # 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