<｜begin▁of▁sentence｜>### Instruction:
Hey there, friend! You know, in the midst of advocating for a greener planet, I often think about how everything in nature is connected, kind of like nodes in a big web, or let's say, a graph! ItÃ¢ÂÂs fascinating stuff, especially when you see patterns emerge in how animals interact within ecosystems or even how waste management systems tie into larger environmental networks. 

So here's what I'm pondering: let's imagine we've got this complex network data of, I dunno, maybe recycling centers and their connections in a city, and it's been stored in this pretty neat format called 'littleballoffur2.sparse6', right? Now, to really get an insight into this network without overwhelming ourselves with data overload, we need to pick a smaller, more manageable section of this network to analyze Ã¢ÂÂ a subgraph, with, let's say, 10 vital nodes in the system. But you know, we can't just take any 10 nodes; we must be smart and look at nodes that aren't too connected. I'm thinking of capping their connections, or degrees, at 5 to keep things tidy and representative.

And since we want our sample to be reproducible, just like when we monitor the same patch of forest to track changes over time, we should use a random seed. Stick with 42 Ã¢ÂÂ it's the answer to life, the universe, and everything, after all!

To boil that down, could you walk me through using a tool like SnowBallSampler to fetch a subgraph from our 'littleballoffur2.sparse6' data file, ensuring the subgraph has exactly 10 nodes and each with a degree no more than 5, and make sure you start the whole sampling randomness with a seed of 42?

P.S.: Just for the sake of clarity, if we had been working with a .gml file, it would be named 'environmental_network.gml'.
### Response:
import networkx as nx
from littleballoffur import SnowBallSampler
G = nx.read_sparse6('littleballoffur2.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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