<｜begin▁of▁sentence｜>### Instruction:
Hey there! So, I've been working with network data on various freelance projects from the cozy corner of my home office, and quite often, I come across situations where I need to simplify my network data to focus on specific patterns or to speed up computations. Networks can be pretty large, you know, and sometimes less is more when it comes to pinpointing the insights I'm after.

Just this morning, I was sipping on my cup of Joe and staring at the computer screen, pondering over this massive graph data packed into a file called littleballoffur12.gml. It's a pretty neat dataset, but I wanted to trim it down a bitÃ¢ÂÂkeep it lean and meanÃ¢ÂÂby focusing on a subgraph with just a certain number of edges to see if any interesting structures pop out.

So here's the dealÃ¢ÂÂI need to use this fancy tool called RandomEdgeSampler from the graph-toolkit to get my hands on a sampled subgraph that retains, let's say, exactly 100 edges from the original. It's a bit like panning for gold; you shake off the excess to find those shiny nuggets of info. How would I go about doing that? Can you walk me through the steps of applying RandomEdgeSampler on my littleballoffur12.gml graph to sieve out that subgraph with just the right number of edges for my analysis?

Cheers!
### Response:
import networkx as nx
from littleballoffur import RandomEdgeSampler
G = nx.read_sparse6('littleballoffur12.sparse6')
sampler = RandomEdgeSampler(number_of_edges=30, seed=42)
sampled_G = sampler.sample(G)
print(sampled_G.edges())
print(sampled_G.nodes());
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