<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
 def affinity_distance(A, alpha=0.5, R=5, k=20, epsilon=4.0):
 """Affinity 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 Returns ------- C : csr_matrix Sparse matrix of strength values References .. [LiBr] Oren E. Livne and Achi Brandt, "Lean Algebraic Multigrid (LAMG):
 Fast Graph Laplacian Linear Solver" Notes ----- No unit testing yet. Does not handle BSR matrices yet. See [LiBr]_ 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')

    def distance(x):
        (rows, cols) = A.nonzero()
        return 1 - np.sum(x[rows] * x[cols], axis=1)**2 / \
            (np.sum(x[rows]**2, axis=1) * np.sum(x[cols]**2, axis=1))

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