给我一段用dfs生成最短路径的代码

DFS（深度优先搜索）通常用于查找图中的连通分量、是否存在环等问题，而Dijkstra算法或A*算法更适合找到最短路径。不过，如果你想了解如何利用DFS探索所有可能的路径并找到其中最短的一个（假设每个边的权重都是正数），可以使用启发式搜索结合记忆化技术。下面是一个简单的Python示例，展示如何用DFS找出两个节点之间的最短路径，但注意这并非标准的最短路径求解方法：

from collections import defaultdict

def shortest_path_with_dfs(graph, start, end):
    distances = {node: float('inf') for node in graph}
    distances[start] = 0
    path = {}

    def dfs(node, current_path, curr_distance):
        if node == end:
            # 如果到达目标节点，更新最短距离
            if curr_distance < distances[node]:
                distances[node] = curr_distance
                path[end] = current_path + [end]
        else:
            for neighbor, weight in graph[node].items():
                new_distance = curr_distance + weight
                if new_distance < distances[neighbor]:
                    distances[neighbor] = new_distance
                    path[neighbor] = current_path + [neighbor]
                    dfs(neighbor, path[neighbor], new_distance)

    dfs(start, [], 0)
    
    return distances[end], path[end] if end in path else None

# 使用示例，这里假设graph是一个邻接权重字典
graph = {
    'A': {'B': 1, 'C': 4},
    'B': {'A': 1, 'C': 2, 'D': 5},
    'C': {'A': 4, 'B': 2, 'D': 1},
    'D': {'B': 5, 'C': 1}
}

shortest, path = shortest_path_with_dfs(graph, 'A', 'D')
print(f"Shortest distance from A to D is: {shortest}")
print(f"Path: {path}")