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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 algebraic_distance(A, alpha=0.5, R=5, k=20, epsilon=2.0, p=2):
 """Algebraic Distance Strength Measure. Parameters A : csr_matrix Sparse NxN matrix alpha : scalar Weight for Jacobi R : integer Number of random vectors k : integer Number of relaxation passes epsilon : scalar Drop tolerance p : scalar or inf p-norm of the measure Returns ------- C : csr_matrix Sparse matrix of strength values References .. [SaSaSc] Ilya Safro, Peter Sanders, and Christian Schulz, "Advanced Coarsening Schemes for Graph Partitioning" Notes ----- No unit testing yet. Does not handle BSR matrices yet. See [SaSaSc]_ for more details. """

    if not sparse.isspmatrix_csr(A):
        A = sparse.csr_matrix(A)

    if alpha < 0:
        raise ValueError('expected alpha>0')

    if R <= 0 or not isinstance(R, int):
        raise ValueError('expected integer R>0')

    if k <= 0 or not isinstance(k, int):
        raise ValueError('expected integer k>0')

    if epsilon < 1:
        raise ValueError('expected epsilon>1.0')

    if p < 1:
        raise ValueError('expected p>1 or equal to numpy.inf')

    def distance(x):
        (rows, cols) = A.nonzero()
        if p != np.inf:
            avg = np.sum(np.abs(x[rows] - x[cols])**p, axis=1) / R
            return (avg)**(1.0 / p)
        else:
            return np.abs(x[rows] - x[cols]).max(axis=1)

    return distance_measure_common(A, distance, alpha, R, k, epsilon)