<｜begin▁of▁sentence｜>### Instruction:
Alright, let's picture a scenario where I'm working with the layout of a new community park. Now, as a landscaper, IÃ¢ÂÂve got a vast array of plants, paths, and garden features to place, but I'm looking to create a small, focused area that connects seamlessly with the rest. You see, just like planning which shrubs and flowers should go where to make paths intuitively wanderable, dealing with graph data is pretty similar.

Imagine the garden layout is represented in a digital form, a graph, where each node is a planting spot or a garden feature and edges show how they're connectedÃ¢ÂÂlike a blueprint of our park's ecosystem. However, I've got this graph saved as a 'littleballoffur5.sparse6' file, and my goal here is to extract a more manageable section of it to work with, specifically a subgraph that feels natural and connected. To translate that into my digital plan, I'm looking at using a toolÃ¢ÂÂthe RandomWalkWithRestartSamplerÃ¢ÂÂthat's like taking a stroll in the park and noting down the first 10 spots (nodes) I come across that form a coherent journey.

So what I need is to use this RandomWalkWithRestartSampler tool to pick out a sub-design from my master plan, a connected subgraph that includes 10 nodes, which should give me a focused area to detail out. Now, if we were to shift this conversation into the more tech-savvy graph terminology, I'd be asking: Can you employ the RandomWalkWithRestartSampler to extract a connected subgraph consisting of 10 nodes from the graph data contained in my file 'littleballoffur5.sparse6'? And since we're working with digital files here, if it were a gml file instead, we would certainly rename it appropriately for consistency's sake.
### Response:
import networkx as nx
from littleballoffur import RandomWalkWithRestartSampler
G = nx.read_sparse6('littleballoffur5.sparse6')
sampler = RandomWalkWithRestartSampler(number_of_nodes=10, seed=42, p=0.1)
sampler_G = sampler.sample(G)
print(sampler_G.edges(), sampler_G.nodes())
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