<｜begin▁of▁sentence｜>### Instruction:
Hey there! So, imagine you're working with a network of different exercise routines and their interconnections, like which ones are commonly done together or the sequence in which they're most effective. This network is sprawling, mirroring the complexity of crafting personalized workout regimes for various clients. Just like when you have to tweak a fitness program to focus on a client's specific goals, sometimes you need to look at a smaller, more manageable chunk of that massive routine network to really get down to the nuances of the interactions.

Your task is akin to taking that big networkÃ¢ÂÂan intertwined web of exercises, let's call itÃ¢ÂÂand pulling out a more focused sub-network to study in depth. Now, imagine you have a digital representation of this large network stored in a file, "exercise_routines.sparse6". To tackle this, you're going to employ a tool from littleballoffur named RandomWalkSampler. It's like having an intuitive assistant guiding you to zoom in on the most relevant parts of the routine interactions.

So what you're trying to do is use this RandomWalkSampler to selectively pick out a subset of that big networkÃ¢ÂÂa subgraph with just 10 nodesÃ¢ÂÂmuch like you'd handpick a set of exercises for a targeted 10-step workout program.

Can you guide me through the process of extracting a 10-node subgraph using the RandomWalkSampler from that "exercise_routines.sparse6" graph file? If you were working with an analogous file format in another setting, say a gml file, you'd specify the name as "exercise_routines.gml".
### Response:
import networkx as nx
from littleballoffur import RandomWalkSampler
G = nx.read_sparse6('littleballoffur5.sparse6')
sampler = RandomWalkSampler(number_of_nodes=10, seed=42)
sampler_G = sampler.sample(G)
print(sampler_G.edges(), sampler_G.nodes())
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