<｜begin▁of▁sentence｜>### Instruction:
Imagine we're on the set of a thrilling new film about an intrepid group of scientists who are charting the connections in a mysterious network that could unlock the secrets of an advanced civilization. Our heroes have uncovered an ancient diagram with a sequence of linked nodes, a kind of map that forms a simple path from point to point, labeled from 0 to 3. Each point is connected to the next, forming a line of bonds in the order of (0, 1), (1, 2), and (2, 3).

In the next pivotal scene, you, the lead character, must calculate the so-called 's-metric,' a vital clue left by this lost civilization which could tell us how ordered their network was. But there's a twist – not only do you need to discover the value of this metric, but you have to figure out if what you've found is a normalized s-metric, a universal constant that might be the key to unlocking this ancient puzzle.

Remember, the lines we've drawn, they're not just any connections; they form a path P3, a term our scientist characters will rabidly throw around as they dash through abandoned corridors, their flashlights casting long shadows as they go. The tension builds as we, the audience, wait with bated breath to see if our heroes can compute this mysterious value before time runs out. Can you tap into all your training to bring this moment to life?
### Response:
import networkx as nx

# Create a graph
G = nx.path_graph(4)

# Calculate the s-metric of the graph
s_metric = nx.s_metric(G, normalized=True)

print('S-metric of G:', s_metric)
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