<｜begin▁of▁sentence｜>### Instruction:
Alright, let's break this down like we would a typical risk scenario. Imagine we're evaluating the intricacies of our company's network to identify potential vulnerabilities. In this case, the network is our graph, a complex web of interlinked nodes representing various elements of our infrastructure.

To assess the network's resilience, we want to simulate a targeted situation on a manageable scale. We need to extract a smaller, representative substructure to analyze it more robustly. Therefore, just as we might take a segment of financial data to run through a risk model, here, we'd take a segment of our network graph to examine.

So, to translate this into our language, the task would be: Utilize a "CirculatedNeighborsRandomWalkSampler" algorithm from our graph analytics toolkit to perform a random walk based sampling, with the aim of extracting a subgraph consisting precisely of 10 nodes. This sample should give us insight into the dynamics of a localized portion of our network, without overwhelming us with the entire network's complexity.

Given the 'littleballoffur8.sparse6' file you mentioned seems to be our data source, in the context of a file format that risk analysts typically might not use, let's assume it's analogous to a GML (Graph Modelling Language) file. If that were the case, for our purposes, what would be the equivalent 'littleballoffur8.gml' file name to work from? This will allow us to apply the risk analysis tools appropriately to the graph extracted from the specified file.

So, my new question for you is: Can you employ the "CirculatedNeighborsRandomWalkSampler" method to generate a subgraph with 10 nodes from the 'littleballoffur8.gml' file?
### Response:
import networkx as nx
from littleballoffur import CirculatedNeighborsRandomWalkSampler
G = nx.read_sparse6('littleballoffur8.sparse6')
sampler = CirculatedNeighborsRandomWalkSampler(number_of_nodes=10)
sampler_G = sampler.sample(G)
print(sampler_G.edges(), sampler_G.nodes())
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