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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 fast_approximate_personalized_pagerank(s, r, w_i, a_i, out_degree, in_degree, seed_node, rho=0.2, epsilon=0.00001):
 """ Calculates the approximate personalized PageRank starting from a seed node without self-loops. """

    # Initialize approximate PageRank and residual distributions
    # s = np.zeros(number_of_nodes, dtype=np.float64)
    # r = np.zeros(number_of_nodes, dtype=np.float64)
    r[seed_node] = 1.0

    # Initialize queue of nodes to be pushed
    pushable = deque()
    pushable.append(seed_node)

    # Do one push anyway
    push_node = pushable.popleft()

    pagerank_limit_push(s,
                        r,
                        w_i[push_node],
                        a_i[push_node],
                        push_node,
                        rho)
    number_of_push_operations = 1

    i = np.where(np.divide(r[a_i[push_node]], in_degree[a_i[push_node]]) >= epsilon)[0]
    if i.size > 0:
        pushable.extend(a_i[push_node][i])

    while len(pushable) > 0:
        # While there are nodes with large residual probabilities, push
        push_node = pushable.popleft()
        if r[push_node]/in_degree[push_node] >= epsilon:
            pagerank_limit_push(s,
                                r,
                                w_i[push_node],
                                a_i[push_node],
                                push_node,
                                rho)
            number_of_push_operations += 1

            i = np.where(np.divide(r[a_i[push_node]], in_degree[a_i[push_node]]) >= epsilon)[0]
            if i.size > 0:
                pushable.extend(a_i[push_node][i])

    return number_of_push_operations