能不能优化一下这个方法：def find_fitting_most_capacited_pathgraph1 graph2 origin destination minimum_capacity # this is a dijkstra like algorithm that computes the most capacited path from the origin to the de

Here are a few possible optimizations for the given method:

1. Use a binary heap instead of a list for the priority queue: The current implementation uses a list for the priority queue, which takes O(n) time to extract the minimum element. Using a binary heap instead can reduce this to O(log n) time.

2. Use a set or a boolean array for visited nodes: The current implementation uses a list to keep track of visited nodes, which takes O(n) time to check if a node has been visited. Using a set or a boolean array instead can reduce this to O(1) time.

3. Avoid unnecessary comparisons: In the loop that checks the neighbors of the current node, the condition "not visited[neighbor]" is checked twice. Moving this condition to the beginning of the loop can avoid unnecessary comparisons.

4. Use tuple unpacking for readability: The tuples in the priority queue and the heap operations can be unpacked for better readability and less typing.

Here's the optimized code incorporating these changes:

import heapq

def find_fitting_most_capacited_path(graph1, graph2, origin, destination, minimum_capacity): # this is a dijkstra like algorithm that computes the most capacited path from the origin to the destination # the best capacity is according to graph1 but only edges with capacities >= minimum_capacity in graph2 are taken into account priority_q = [(-10.**10, origin, None)]

parent_list = [None] * len(graph1)
visited = [False] * len(graph1)
best_capacity = [None] * len(graph1)

while priority_q:
    capa_of_current_node, current_node, parent_node = heapq.heappop(priority_q)

    if visited[current_node]:
        continue

    visited[current_node] = True
    parent_list[current_node] = parent_node
    best_capacity[current_node] = -capa_of_current_node

    if current_node == destination:
        break

    for neighbor, (capacity, weight) in graph1[current_node].items():
        if visited[neighbor] or graph2[current_node][neighbor] < minimum_capacity:
            continue

        new_capacity = min(capa_of_current_node, capacity)
        heapq.heappush(priority_q, (-new_capacity, neighbor, current_node))

if parent_list[destination] is None:
    return None, None

path = [destination]
current_node = destination
while current_node != origin:
    current_node = parent_list[current_node]
    path.append(current_node)
path.reverse()

return path, best_capacity[destination]