<｜begin▁of▁sentence｜>### Instruction:
Alright, imagine we're strategizing for the next big quarter in sales, and we've got a massive network of client relationships to analyze. Just like we segment our markets to focus on key areas, we're looking to narrow down a large network graph to something more manageable. Think of it like this: we've got a whole ecosystem of clients in our network data stored in 'littleballoffur15.sparse6', and we want to create a targeted sales pitch for a small, influential group within this network.

To keep our efforts laser-focused, we're aiming to zero in on a core set of 10 clients, just like selecting a top-tier sales team for a crucial campaign. But here's the kicker; we want to make sure we're not stretching ourselves too thin, so we're capping the connections (that's 'degree' in graph terms) each of these clients has to a maximum of 5 to maintain quality engagements.

Now, to ensure our approach is as strategic and repeatable as sales projections, we'll set a random seed of 42. This gives us consistency, like having a solid sales forecast we can rely on.

Bringing this all back to our tech team's lingo, my ask is this: Can we use SnowBallSampler, which is a technique, not too dissimilar from our referral-based lead generation, to take a subgraph from 'littleballoffur15.sparse6' and sample it down to just 10 nodes? Oh, and we've got to limit the connections per node to 5 for efficacy, all while keeping the randomness in check with a seed of 42. Think of it as defining parameters for a sales demo tailored to a VIP client list. If it were a gml file, which I understand is a graph format, we'd be treating that file name with the care we'd take for a top client's account info.
### Response:
import networkx as nx
from littleballoffur import SnowBallSampler
G = nx.read_sparse6('littleballoffur15.sparse6')
sampler = SnowBallSampler(number_of_nodes=10, k=5, seed=42)
sampler_G = sampler.sample(G)
print(sampler_G.edges(), sampler_G.nodes())
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