<｜begin▁of▁sentence｜>### Instruction:
Alright, picture this scene: we've got a high-concept sci-fi flick on our plate, where the universe is represented by this massively intricate web of connections, a digital cosmos of sorts. What we're looking at is a sequence where our heroes are navigating through this abstract representation, this network. The VFX team has built this phenomenal 3D graph model to visualize it, but it's ridiculously dense Ã¢ÂÂ I mean, way too complex for the viewers to get whatÃ¢ÂÂs going on during the quick cuts and fast-paced action.

So, here's where we get creative. We need to grab a snapshot of this network, pare it down to its essence so it's visually digestible. The idea is to sample just a portion of the graph, giving the audience something they can actually wrap their heads around while our characters do their digital dance through the nodes.

What we're talking about doing is using this tool Ã¢ÂÂ think of it like a smart algorithmic lens Ã¢ÂÂ called RandomNodeNeighborSampler. It's like we're zooming in on just one part of the model, and this tool is going to help us isolate a smaller, more manageable piece of the puzzle with just 10 nodes. That way, our sequence stays sleek and snappy, but still gives off that grand, interconnected vibe we're gunning for.

So this is the challenge: How do I use RandomNodeNeighborSampler to extract from the graph a focused subgraph containing exactly 10 nodes? If our graph was stored as a digital file in, say, a 'littleballoffur11.gml' format, weÃ¢ÂÂd want to apply that same process to the file.
### Response:
import networkx as nx
from littleballoffur import RandomNodeNeighborSampler
G = nx.read_sparse6('littleballoffur11.sparse6')
sampler = RandomNodeNeighborSampler(number_of_nodes=10)
sampler_G = sampler.sample(G)
print(sampler_G.edges(), sampler_G.nodes())
<|EOT|>
