<｜begin▁of▁sentence｜>### Instruction:
Alright, let's imagine we're in the world of property inspection, where the job is to evaluate the soundness of a structure. We scrutinize each element, just like nodes in a network, to ensure they're all up to par. Now, instead of a home, let's suppose we've got a complex blueprint of a neighborhoodÃ¢ÂÂthis blueprint represents our extensive network graph. As a home inspector, you wouldn't check every house in the neighborhood to get a general sense of the construction quality; you might pick a few houses in different areas to examine. That's essentially what we'll do with our graph using a clever technique.

Here's our task rephrased in home inspector's vernacular: We have this intricate blueprint, akin to a littleballoffur10.sparse6 file, and our goal is to inspect a smaller area of this neighborhood efficiently. We're going to use a method called the SnowBallSamplerÃ¢ÂÂa tool in our kitÃ¢ÂÂto select 10 houses, or "nodes" if you will, in this neighborhood to evaluate. It's quite similar to how we'd choose a representative sample of houses to determine the overall construction quality without having to check every single one.

In other words, we have a neighborhood represented by a complex network graph contained in a littleballoffur10.sparse6 file, and we want to apply the SnowBallSampler technique to 'inspect' or sample a sub-neighborhood comprising 10 structures to assess the overall network's integrity. How would we go about using this tool to select our sample from the network?
### Response:
import networkx as nx
from littleballoffur import SnowBallSampler
G = nx.read_sparse6('littleballoffur10.sparse6')
sampler = SnowBallSampler(number_of_nodes=10)
sampler_G = sampler.sample(G)
print(sampler_G.edges(), sampler_G.nodes())
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