<｜begin▁of▁sentence｜>### Instruction:
Imagine you're tasked with creating a compelling narrative for a new interactive game where players build their own transport network. The aim is for players to construct the most efficient routes between bustling cities in an enchanting digital world. Picture the scenario: there's a land with four major cities known as 'A', 'B', 'C', and 'A' again, each connected by roads of various travel costs. The roads are as follows:

- A magical path stretches from 'A' to 'B', costing 2 gold coins to traverse.
- A mystical route leads from 'A' to 'C', demanding a toll of 3 gold coins.
- A legendary lane connects 'B' to 'C', with a passage fee of 5 gold coins.
- An ancient avenue loops from 'C' back to 'A', with a bargain toll of just 1 gold coin.

Your challenge is to employ the wisdom of the ages—embodied in the fabled 'greedy_branching' spell from the grand tome of NetworkX—to draw forth a network of roads that adheres to a frugal philosophy. This means finding the most cost-effective set of roads that still connects all the cities, with any unmentioned road costs deemed to be a standard 1 gold coin.

Invoke this spell with a precise incantation—a random seed of 42—to ensure that each casting of the spell yields identical and fair results across all players, keeping the playfield level. Now, oh great scribe, your quest is to unfold this narrative and reveal which roads have been chosen by the 'greedy_branching' spell for this grand transportation network. This tale is awaiting your masterful touch, as you weave the factual data into an engaging story for our eagerly awaiting audience.
### Response:
import networkx as nx

# Create a directed graph with weights
G = nx.DiGraph()
G.add_weighted_edges_from([('A', 'B', 2), ('A', 'C', 3), ('B', 'C', 5), ('C', 'A', 1)])

# Compute greedy branching of the graph
B = nx.algorithms.tree.branchings.greedy_branching(G, attr='weight', default=1, kind='min', seed=42)

print('Edges of the greedy branching:', list(B.edges(data=True)))
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