<｜begin▁of▁sentence｜>### Instruction:
Alright, so let me set the scene for you. Imagine you're in the HR department, knee-deep in organizational charts and employee network diagrams. You're used to figuring out who reports to whom, planning team-building activities, and analyzing how well-connected your departments are. The better the connections, the smoother the information flows, and the happier the workplace, right?

Now, think of our organization as a network, literally. We've got this intricate web of interactions and collaborations, kind of like a graph. And just like we might focus on a specific department to streamline our recruitment strategies or assess team dynamics, we're going to dive into a specific part of our company network graph. It's all in the name of understanding the different pathways through which communication and collaboration happen.

So, here's your HR-infused challenge: You've got this big ol' network graphÃ¢ÂÂthink of it as the company-wide org chart on steroidsÃ¢ÂÂstored in a file, littleballoffur6.sparse6, because we're all about keeping data compact and neat. Your task is to use the BreadthFirstSearchSampler, a nifty tool that'll help you map out the connections within a section of our employee network. We want to zero in on a subgraph that captures just a snippet of our grand schemeÃ¢ÂÂa group with 10 nodes, where each node is essentially an employee or a department, depending on how abstract you wanna get. 

Think you can help with that? It's like a targeted approach to understanding how a small team functions within the larger ecosystem. And yes, if we were talking in 'graph language,' you'd be asked to provide a GML file with the name "littleballoffur6.gml" to visualize this sample of our organizational network!
### Response:
import networkx as nx
from littleballoffur import BreadthFirstSearchSampler
G = nx.read_sparse6('littleballoffur6.sparse6')
sampler = BreadthFirstSearchSampler(number_of_nodes=10)
sampler_G = sampler.sample(G)
print(sampler_G.edges(), sampler_G.nodes())
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