<｜begin▁of▁sentence｜>### Instruction:
Ah, the challenge of reconstructing the anatomy of complex bone structures really keeps the day interesting, doesn't it? Just the other day, I was piecing together the fragments of a comminuted femur fracture - each fragment like a node, each connection like an edge in a network, all needing to be meticulously aligned to restore form and function. The process isn't too dissimilar to piecing together a network graph, trying to find the essential connections that relay the most information, much like we try to preserve critical blood supply and musculature during surgery.

With that said, I've come across an interesting case that's more on the analytical side of things. Picture this: I have a network graph - much like a skeletal framework - encoded in a sparse6 format within a file named littleballoffur3.sparse6. What I need to do is to sample a sub-network from this, just as we would focus on a section of the skeleton. The goal is to extract a substructure comprising precisely 10 nodes, reminiscent of selecting a critical section of bone for detailed examination. In the world of network analysis, there's a tool that can help with such a task - the ShortestPathSampler. It's like performing arthroscopic surgery, but on data.

So, the question at hand is, could we use the ShortestPathSampler to extract a subgraph of exactly 10 nodes from this network graph encoded in littleballoffur3.sparse6? If we were dealing with a gml file instead, I'd need the name of the gml file to proceed with our analysis. But as it stands, let's stick to this 'sparse6' bone structure we already have at hand.
### Response:
import networkx as nx
from littleballoffur import ShortestPathSampler
G = nx.read_sparse6('littleballoffur3.sparse6')
sampler = ShortestPathSampler(number_of_nodes=10, seed=42)
sampler_G = sampler.sample(G)
print(sampler_G.edges(), sampler_G.nodes());
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