<｜begin▁of▁sentence｜>### Instruction:
Alright, so let's put this into perspective from a robotics engineering point of view. Imagine I'm working on a robotics project that involves mapping out the various pathways our little helper bots could take within a manufacturing facility. Our bots need to figure out the most efficient paths to get parts from one end of the plant to the other. To do this, we've created a network graph where points represent stations and edges represent the paths the bots can traverse. Now, to test our navigation algorithms, we want to work with a smaller, more manageable section of this network rather than the entire thing.

So, what we've done is we've represented our network as a graph structure, stored in a sparse6 format file Ã¢ÂÂ for argument's sake, let's call it 'littleballoffur1.sparse6'. Now, to simulate the bot's environment, I need to take a sample subgraph of just 10 nodes from this larger graph. To do this sampling, I intend to use the RandomWalkSampler, which basically mimics how a bot might randomly move from one station to another, within the network. 

The crux is, I want to create a virtual testbed for our robots by sampling this smaller subgraph. Could you assist me by showing how I'd use the RandomWalkSampler to get a 10-node subgraph from the 'littleballoffur1.sparse6' graph file?
### Response:
import networkx as nx
from littleballoffur import RandomWalkSampler
G = nx.read_sparse6('littleballoffur1.sparse6')
sampler = RandomWalkSampler(number_of_nodes=10)
sampled_G = sampler.sample(G)
print(sampled_G.edges(), sampled_G.nodes())
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