<｜begin▁of▁sentence｜>### Instruction:
Absolutely! Picture this: as a recruitment specialist, every day I'm diving into the vast talent pool, scanning through countless resumes and online profiles to hunt down the perfect candidates who not only match the job descriptions but also fit into the unique culture of the company. It's all about finding that ideal blend of skills and personalityÃ¢ÂÂa bit like piecing together a complex puzzle.

Now, just like sifting through potential hires, I've got this task where I've got to work with a complex network of data, kind of like a social graph mapping out connections between professionals. This graph is stored in an interesting file format, a sparse6 file named littleballoffur13.sparse6, to be exact.

My aim is to get a manageable chunk of this network, kind of how I would shortlist candidates for a position. This involves using a tool not too dissimilar to my analytical strategiesÃ¢ÂÂRandomEdgeSamplerWithInductionÃ¢ÂÂfrom the graph manipulation library littleballoffur. What I need to do is extract a sample, a subgraph, with a precise number of connections (edges) from this larger web of nodes. And, I've got to ensure consistency and replicability in my sampling approach by setting the random seed to 42.

Now, if this were a real-life scenario and our graph data was stored in a GML file, I'd specify that format for clarity, but as it stands, the sparse6 file is what we're dealing with. So, can you walk me through the process of using RandomEdgeSamplerWithInduction, with a set number of edges, ensuring the randomness is controlled by a seed set at 42, grabbing that subgraph from our so-called "virtual networking event"?
### Response:
import networkx as nx
from littleballoffur import RandomEdgeSamplerWithInduction
G = nx.read_sparse6('littleballoffur13.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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