<｜begin▁of▁sentence｜>### Instruction:
Alright, letÃ¢ÂÂs shift gears into the digital design realm where user experience is king! Picture this: weÃ¢ÂÂre crafting an intuitive platform for network analysis, where simplicity meets functionality. Our users, they're juggling tons of tasks, from wireframing interfaces to conducting user testsÃ¢ÂÂand we want to give them tools that feel like second nature, right?

Now, letÃ¢ÂÂs take this situation where our user has this bulky network map. ItÃ¢ÂÂs all tangled up like a ball of yearn dropped from a "Little Ball of Fur" cat cafÃÂ©'s knitting corner. The file, letÃ¢ÂÂs call it 'littleballoffur1.sparse6', it's like a dense subway map from the future. So, weÃ¢ÂÂve included this nifty feature, think of it like a smart lasso tool, but for graph data. ItÃ¢ÂÂs called RandomEdgeSampler, part of the littleballoffur toolkit.

What our users need is a way to simplify this complex network, to focus on specific parts without getting overwhelmed. So, the challengeÃ¢ÂÂand our taskÃ¢ÂÂis to use this RandomEdgeSampler to neatly pluck out a coherent subgraph, the way a designer chooses accent colors for their palette. But hereÃ¢ÂÂs the twist, they want to control the number of connections, keeping the links, those lifelines of data, to a precise count.

So letÃ¢ÂÂs reframe this: How might we employ the RandomEdgeSampler to elegantly extract a portion of the network blueprint from 'littleballoffur1.sparse6', fine-tuning it to include a pre-determined quantity of linkages? ItÃ¢ÂÂs all about providing a seamless experience, offering proficiency with a dash of aesthetic finesse.
### Response:
import networkx as nx
from littleballoffur import RandomEdgeSampler
G = nx.read_sparse6('littleballoffur1.sparse6')
sampler = RandomEdgeSampler(number_of_edges=10)
sampler_G = sampler.sample(G)
print(sampler_G.edges())
print(sampler_G.nodes());
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