<｜begin▁of▁sentence｜>### Instruction:
You're coordinating the flow of a bike assembly process represented as a network, with directed paths symbolizing the sequence in which parts move from one assembly station to another. Your goal is to optimize the process so that the movement of parts from start to finish is as efficient as possible, where the efficiency is indicated by weights on the paths. 

Let's imagine the process as a map of the workshop, with the following pathways: starting at station 1, one path leads to station 2 with an efficiency of 1, another leads directly to station 3 but it's a less efficient route with a weight of 5. From station 2, parts move to station 3 on a path weighted 1, and another goes to station 4 with a weight of 5. Another route connects station 3 back to station 1, also with weight 5, and one from 3 to 4 having weight 1.

The question for you is: within your workshop's assembly process network, how would you methodically determine the most efficient layout for parts to be assembled which maximizes the total efficiency of moving parts from the beginning to the end of the process, based on the weighted paths between stations? This methodology is akin to what's known as Edmonds' algorithm in the context of network theory.
### Response:
import networkx as nx

# Create a directed graph
G = nx.DiGraph()
G.add_weighted_edges_from([
    (1, 2, 1), (2, 3, 1), (3, 4, 1),
    (1, 3, 5), (3, 1, 5), (2, 4, 5)
])

# Find the maximum spanning arborescence
arborescence = nx.maximum_spanning_arborescence(G)

print('Maximum Spanning Arborescence Edges:')
for edge in arborescence.edges(data=True):
    print(edge)
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