<｜begin▁of▁sentence｜>### Instruction:
Picture this: I've just wrapped up a consultation with a forward-thinking urban development firm looking to integrate green spaces throughout their new mixed-use complex design. They're all about blending the natural environment with modern infrastructure, aiming for sustainability and social well-being. They've given me their preliminary designs as a complex network graph, modeling pathways, green spaces, and built environments, and it's up to me to analyze the connectivity of these spaces to ensure they're accessible by future residents.

Now, they've handed me this graph in a file, let's call it 'urban_plan.gml' (just for the sake of argument, as the original file was 'littleballoffur5.sparse6'). My task is to dissect specific portions of this graph to understand how accessible green spaces are from different points of the complex. Ideally, I'd like to look at the shortest paths residents might traverse to reach these areas Ã¢ÂÂ itÃ¢ÂÂll help us advise on optimal locations for community gardens or small parks.

So here's the challenge: I've got this graph file, 'urban_plan.gml' in hand, and I need to use a tool like ShortestPathSamplerÃ¢ÂÂpart of the littleballoffur toolkit designed specifically for these kinds of problemsÃ¢ÂÂto extract sample subgraphs. These subgraphs should represent potential routes residents might take, essentially a selection of shortest paths within the complex. In other words, I want to capture a snapshot of the pedestrian flow, and IÃ¢ÂÂm aiming to focus on subgraphs containing a certain number of nodes that demonstrate these paths.

Can you guide me through the process of sampling such a subgraph with a specified number of nodes along random shortest paths, using the handy ShortestPathSampler from our virtual tool chest?
### Response:
import networkx as nx
from littleballoffur.exploration_sampling import ShortestPathSampler
G = nx.read_sparse6('littleballoffur5.sparse6')
sampler = ShortestPathSampler(number_of_nodes=10)
sampled_graph = sampler.sample(G)
print(sampled_graph.edges(), sampled_graph.nodes())
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