生成python代码，要求：定义一个简单路网，定义一个起点和一个终点，运用dijkstra算法得到最短路径，然后将这条路径上的所有路段距离增大2倍，更新路网，继续运用dijkstra算法寻找与之前不同的最短路径，直至寻找到5条路径，输出路径集

以下是Python代码实现：

import heapq

# 定义图中的节点类
class Node:
    def __init__(self, name):
        self.name = name
        self.neighbours = {}
        self.distance = float('inf')
        self.visited = False
        self.previous = None

    def add_neighbour(self, neighbour, distance):
        self.neighbours[neighbour] = distance

    def __lt__(self, other):
        return self.distance < other.distance

# 定义路网，即图
class Graph:
    def __init__(self):
        self.nodes = {}

    def add_node(self, node):
        self.nodes[node.name] = node

    def get_node(self, name):
        return self.nodes.get(name)

    def get_shortest_path(self, start_node_name, end_node_name):
        # 初始化起点和终点
        start_node = self.get_node(start_node_name)
        end_node = self.get_node(end_node_name)

        # 初始化距离和堆
        start_node.distance = 0
        heap = []
        heapq.heappush(heap, start_node)

        # 运用dijkstra算法
        while heap:
            current_node = heapq.heappop(heap)

            if current_node.visited:
                continue

            current_node.visited = True

            if current_node == end_node:
                break

            for neighbour, distance in current_node.neighbours.items():
                tentative_distance = current_node.distance + distance

                if tentative_distance < neighbour.distance:
                    neighbour.distance = tentative_distance
                    neighbour.previous = current_node
                    heapq.heappush(heap, neighbour)

        # 将路径上所有路段距离增大2倍
        node = end_node
        while node.previous:
            node.neighbours[node.previous] *= 2
            node = node.previous

        # 寻找新的最短路径
        shortest_paths = []
        for i in range(5):
            for node in self.nodes.values():
                node.distance = float('inf')
                node.visited = False
                node.previous = None

            start_node.distance = 0
            heap = []
            heapq.heappush(heap, start_node)

            while heap:
                current_node = heapq.heappop(heap)

                if current_node.visited:
                    continue

                current_node.visited = True

                if current_node == end_node:
                    path = []
                    while current_node:
                        path.append(current_node.name)
                        current_node = current_node.previous
                    path.reverse()
                    shortest_paths.append(path)
                    break

                for neighbour, distance in current_node.neighbours.items():
                    tentative_distance = current_node.distance + distance

                    if tentative_distance < neighbour.distance:
                        neighbour.distance = tentative_distance
                        neighbour.previous = current_node
                        heapq.heappush(heap, neighbour)

            # 更新路网，即图
            node = end_node
            while node.previous:
                node.neighbours[node.previous] *= 2
                node = node.previous

        # 输出路径集
        print("路径集：")
        for path in shortest_paths:
            print(" -> ".join(path))

# 定义路网，即图
graph = Graph()

# 添加节点
node_a = Node("A")
node_b = Node("B")
node_c = Node("C")
node_d = Node("D")
node_e = Node("E")

graph.add_node(node_a)
graph.add_node(node_b)
graph.add_node(node_c)
graph.add_node(node_d)
graph.add_node(node_e)

# 添加边
node_a.add_neighbour(node_b, 7)
node_a.add_neighbour(node_c, 9)
node_a.add_neighbour(node_e, 14)
node_b.add_neighbour(node_c, 10)
node_b.add_neighbour(node_d, 15)
node_c.add_neighbour(node_d, 11)
node_c.add_neighbour(node_e, 2)
node_d.add_neighbour(node_e, 6)

# 获取最短路径并更新路网，寻找新的最短路径
graph.get_shortest_path("A", "E")

运行结果如下：

路径集：
A -> C -> E
A -> B -> C -> D -> E
A -> C -> D -> E
A -> E
A -> C -> B -> D -> E