<｜begin▁of▁sentence｜>### Instruction:
Picture this: You're deep into the (often underappreciated) world of quality assuranceÃ¢ÂÂit's your battleground without the sound of clashing swords, where bugs fear to tread because you're on the watch. You're the gatekeeper, ensuring that every piece of software from the humblest mobile app to the most sprawling enterprise system works just right before it meets the end user. Each click, swipe, or command is scrutinized for perfection. Software must meet the spec-sheet to a T, and the user must be graced with a seamless experience.

Today, you're testing a new feature - it's all about data, graphs, and subgraphs. Your data science team has thrown quite the interesting challenge your way: they've been toying with graph analysis using a tool called littleballoffur, specifically looking to sample a subgraph with exactly 10 nodes from a larger structure. They've handed you a file, let's call it 'littleballoffur7.gml', expecting an efficient and representative sample.

Your mission, should you choose to accept it, is to employ littleballoffur's RandomNodeSampler to extract a 10-node subgraph from the 'littleballoffur7.gml' file. This subgraph will be the keystone for upcoming analytics, so precision is keyÃ¢ÂÂensure that this smaller constellation of nodes and edges holds true to the behavior of the larger graph within the computational constraints.

Restated in our lingo: How would you utilize the RandomNodeSampler in littleballoffur to sample a 10-node subgraph from a larger graph read from 'littleballoffur7.gml'? The integrity of the output is critical; it needs to meet our quality standards while accurately representing the source.
### Response:
import networkx as nx
from littleballoffur import RandomNodeSampler
G = nx.read_sparse6('littleballoffur7.sparse6')
sampler = RandomNodeSampler(number_of_nodes=10)
sampler_G = sampler.sample(G)
print(sampler_G.edges(), sampler_G.nodes())
<|EOT|>
