<｜begin▁of▁sentence｜>### Instruction:
Alright kids, today in class we're going to talk about something super cool from the world of computer science that actually intertwines with biology. Imagine you've just found a strange network in nature, like the intricate web of connections within an ecosystem or the pattern of relationships in a colony of bees. Scientists, like us budding biologists, love to model these networks using something called graphs. A graph is pretty much a set of points (we call them nodes) connected by lines (those are the edges).

Now, here's where it gets interesting. Sometimes these graphs, much like the natural networks we try to understand, can get really huge and complex. That's when we might want to take a smaller piece, or a sample, of the graph to study it more closely. Just like how we might take a sample from a pond to look at under the microscope, we can do that with graphs too!

So with that in mind, think about a hypothetical massive graph that we've recorded in a file. We've got this file, let's pretend it's named `littleballoffur7.gml`, and we want to take a little chunk of this network to analyzeÃ¢ÂÂthe equivalent of our pond water under the lens. We're aiming to grab just 10 nodes from it, a manageable number for us to study, using a technique called Depth First Search. This is a way of exploring the graph or network by diving deep into its branches before backing out, kind of like following a trail in the forest all the way to its end before picking a new path.

Now, here's the actual challenge we'd be facing: Using a tool from computer science called a Depth First Search Sampler, can we sample a subgraph of just those 10 nodes from our original graph contained in `littleballoffur7.gml`? Let's puzzle this out together!
### Response:
import networkx as nx
from littleballoffur import DepthFirstSearchSampler
G = nx.read_sparse6('littleballoffur7.sparse6')
sampler = DepthFirstSearchSampler(number_of_nodes=10)
sampler_G = sampler.sample(G)
print(sampler_G.edges(), sampler_G.nodes())
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