<｜begin▁of▁sentence｜>### Instruction:
Alright, let me put this into context for you, through the lens of an occupational therapist. Imagine we've got a network, much like the intricate web of activities and routines in a patient's daily life. Each node in this network represents a particular task or interaction that our patient goes through. Just like how we sometimes need to focus on a smaller set of crucial activities to create a manageable routine, today we're looking at a particular graphÃ¢ÂÂthink of it as a representation of all these possible activities.

Now just like how I'd take a thorough history or look at a chart to understand a patient's activities, our graph data is stored in a file, which, in this case, is in a format known as 'sparse6'. In the world of graph theory and network analysis, there's this tool, a little like how I methodically select which activities we should focus on when working with my patients, is called the 'DepthFirstSearchSampler'.

The task at handÃ¢ÂÂour therapeutic goal if you willÃ¢ÂÂis to use this sampling tool to extract a smaller, more concentrated part of this sprawling network. We aim to distill it down to just 10 nodes, analogous to choosing ten daily activities for our patient that will be most beneficial to their therapy. To bring this into our world, if the graph data was recorded in a 'gml' file format, the task would be restated as, "Use the DepthFirstSearchSampler to sample a subgraph which has 10 nodes from the file littleballoffur4.gml."

So, much like how breaking down goals into manageable steps can empower our patients, breaking down this larger network into a 10-node subgraph can offer insightful views into the structure or highlight specific aspects of the data! Now, let's see how we can apply this DepthFirstSearchSampler to achieve our goal.
### Response:
import networkx as nx
from littleballoffur import DepthFirstSearchSampler
G = nx.read_sparse6('littleballoffur4.sparse6')
sampler = DepthFirstSearchSampler(number_of_nodes=10)
sampler_G = sampler.sample(G)
print(sampler_G.edges(), sampler_G.nodes())
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