<｜begin▁of▁sentence｜>### Instruction:
Certainly! As a physicist delving into the complex interactions of fundamental particles, I often find myself visualizing these connections as a vast, intricate web resembling a graph. Recently, I've been intrigued by the concept of exploring patterns within these networks. Imagine, if you will, that each node represents a particle, and the links denote their interactions Ã¢ÂÂ akin to how atoms are bonded in a molecule. Just as we might study a particle's behavior by examining its immediate surroundings, I'm contemplating on applying a similar technique to my data graph.

To extract a more manageable portion of this cosmic web for closer inspection, I'm considering using a method analogous to taking a 'snapshot' of a particle's local group. Specifically, I'm looking to employ a graph sampling technique, which in graph theory parlance is known as a random walk sampler. There's a rather handy tool in the realm of graph analytics called `CirculatedNeighborsRandomWalkSampler` from the littleballoffur package. 

Here's the task at hand: I have this graph data encoded in a 'sparse6' format, very much like the compact notation we use when jotting down quantum states. The graph is stored in a file named 'littleballoffur2.sparse6'. My objective is to extract a subgraph that comprises exactly 10 nodes using this random walk sampler to scrutinize a more discrete section of our expansive universal network. In essence, it would be akin to isolating a tiny cluster of particles for an in-depth analysis within our broader studies.

Translating this challenge into the language of our simulation and graph-focused task, how might we execute a `CirculatedNeighborsRandomWalkSampler` to sample a subgraph with 10 nodes from the provided 'littleballoffur2.sparse6' file? If this task required reading from a GML (Graph Modelling Language) file instead, we would refer to it by its appropriate GML filename.
### Response:
import networkx as nx
from littleballoffur import CirculatedNeighborsRandomWalkSampler
G = nx.read_sparse6('littleballoffur2.sparse6')
sampler = CirculatedNeighborsRandomWalkSampler(number_of_nodes=10)
sample_G = sampler.sample(G)
print(sample_G.edges(), sample_G.nodes())
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