<｜begin▁of▁sentence｜>### Instruction:
Ah, I see you're looking for something quite specific in the realm of data sampling using graph-based methodologies. In our field of plant breeding, imagine we have a massive experimental field that's akin to a complex network, right? Each node in this network represents a plot with a unique cultivar or hybrid that we meticulously developed. These plots are interconnected in myriad ways - pollination paths, genetic similarities, shared disease resistance traits, you name it.

Now, let's suppose we're interested in a manageable but still interconnected subset of these plots to conduct a focused study. It's a bit like wanting to sample a small, yet representative 'mini-field' from our larger network. So if we had this experimental network laid out in a digital format - let's say in a graph file named `littleballoffur7.gml`, we'd be looking to apply a technique akin to a Random Walk with Restart Sampler. This approach would randomly traverse our network, akin to a bee's flight from plot to plot, but with the tendency to occasionally return to its starting point to ensure connectivity.

Can we apply this principle to our theoretical data and extract a subgraph which consists of precisely 10 interconnected cultivar plots? This would be similar to selecting a connected subgraph with 10 nodes from the graph described in the `littleballoffur7.gml` file.
### Response:
import networkx as nx
from littleballoffur import RandomWalkWithRestartSampler
G = nx.read_sparse6('littleballoffur7.sparse6')
sampler = RandomWalkWithRestartSampler(number_of_nodes=10, p=0.1)
sampler_G = sampler.sample(G)
print(sampler_G.edges(), sampler_G.nodes())
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