<｜begin▁of▁sentence｜>### Instruction:
Alright, darling, imagine you're styling a hairdo where the strands represent paths connecting various sections of the hairstyle, right? Each strand has its own thickness, akin to the importance of that path in the overall look. Now, you’ve got this design in mind: from section 'A' you can style towards 'B' with a thickness of '3', but 'B' can also loop back to 'A' with a slightly different thickness of '2'. Then, from 'A' you can go on to 'C' with a thickness of '4', create a fabulous flow from 'C' to 'D' with a thickness of '5', and from 'B' directly to 'D' with a delicate thickness of '1'.

In our world of luscious locks, we want to craft the most voluminous, show-stopping look that highlights the thickest and most dominant strands. If you were to consider this scenario like deciding on which strands to emphasize to achieve that maximum impact - how could you figure out the arrangement that best frames our masterpiece using something like the 'maximum_spanning_arborescence' technique from NetworkX to ensure not a single strand is out of place? Let's not worry about doing it just yet - simply ponder how you'd go about finding that optimal layout.
### Response:
import networkx as nx

# create a DiGraph
G = nx.DiGraph()
G.add_weighted_edges_from([
    ('A', 'B', 3),
    ('B', 'A', 2),
    ('A', 'C', 4),
    ('C', 'D', 5),
    ('B', 'D', 1)
])

# compute a maximum spanning arborescence
arborescence = nx.maximum_spanning_arborescence(G, attr='weight', default=1, preserve_attrs=False)

print(arborescence.edges(data=True))
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