<｜begin▁of▁sentence｜>### Instruction:
Imagine you're crafting the ultimate itinerary for a group of adventurers on a quest to explore the most captivating cities. Their journey starts at City 1 and can whirl through Cities 2, 3, and 4 before looping back, each path paved with its own unique experiences (positive or negative) represented by varying levels of satisfaction (+5, -3, +4, +2 respectively). Your mission, as their dedicated travel agent, is to chart a journey that maximizes their overall satisfaction, ensuring they experience the most enriching path possible, despite some routes offering challenges (-3 from City 2 to 3). Using Edmonds' algorithm, can you devise the most exhilarating, maximum satisfaction-giving itinerary for this audacious band? Remember, their travels can form a loop, but each city visit should contribute to the utmost joy across their entire journey, considering the given route satisfaction scores.
### Response:
import networkx as nx

# Create a directed graph
G = nx.DiGraph()

# Add weighted edges
G.add_weighted_edges_from([(1, 2, 5), (2, 3, -3), (3, 1, 4), (2, 4, 2)])

# Find the maximum spanning arborescence
arborescence = nx.algorithms.tree.branchings.Edmonds(G).find_optimum(attr='weight', default=0, kind='max', style='arborescence', preserve_attrs=True)

# Print the edges of the maximum spanning arborescence
print(sorted(arborescence.edges(data=True)))
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