<｜begin▁of▁sentence｜>### Instruction:
Hey there, hope you've been holding up well in the bustling world of guiding our younger generations. You know, just yesterday, I found myself surrounded by a group of eager teens, all bustling with energy, looking to map out their connections and friendships on a massive chart. It was quite the task Ã¢ÂÂ we wanted to really narrow down the bonds and figure out the strongest ties within the group. 

So, think of it like this Ã¢ÂÂ we've got this big network of friends, right? And we've sketched it out, got all the connections on paper, a real spiderweb of support and camaraderie! Now, what if we want to hone in and get a closer look at just a portion of this network Ã¢ÂÂ say, a snapshot with a certain number of friendships highlighted, but we don't want to lose the context, all those links connecting them?

Here's where I need your savvy tech assistance. Imagine we've got this digital version of our friendship chart Ã¢ÂÂ let's call it teenspeak.gml Ã¢ÂÂ because why not give it a fun name that speaks their language? I'm looking to use this digital tool, the RandomEdgeSamplerWithInduction, and what I want to achieve is to pull out a bite-sized chunk of our network, focusing on just a certain number of friendships (edges) to sample. But the key part, and this is crucial, we gotta keep the direct friendships (edges) between these teens (nodes) in the sample through an induction step Ã¢ÂÂ can't be losing the very fabric that holds them together. 

Can you help me with that? Using our teenspeak.gml file, how do we go about using this fancy tool to get a representative but rich subgraph of these kids' friendships?
### Response:
import networkx as nx
from littleballoffur import RandomEdgeSamplerWithInduction
G = nx.read_sparse6('littleballoffur8.sparse6')
sampler = RandomEdgeSamplerWithInduction(number_of_edges=50, seed=42)
sampler_G = sampler.sample(G)
print(sampler_G.edges())
print(sampler_G.nodes())
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