<｜begin▁of▁sentence｜>### Instruction:
Ah, what a fascinating task we have on our hands! As a museum curator, I'm perpetually intrigued by the intricate connections that bind our artifacts, each a node in the grand tapestry of history. Rather like the branching tree of a family lineage, would you say?

Today, we find ourselves facing a digital reflection of such historical complexities: a graph, reminiscent of a web of relations amongst civilizations or a network of trade routes from ancient times. It resides in an antiquated, digital archive, intriguingly titled 'littleballoffur14.sparse6'. Quite the modern artifact, isn't it?

Our mission, should we accept it, is akin to curating a small but meaningful exhibition. From the vast network depicted in this graph, we endeavor to extract a smaller, yet representative sample. A microcosm of the whole, comprised of merely 10 nodes, using what is known in the digital realm as the DepthFirstSearchSampler. Just like handpicking select pieces for a new exhibit, our task is to dive into the depths of this connective web, and bring forth a subgraphÃ¢ÂÂa curated selection, if you willÃ¢ÂÂthat encapsulates the essence of the original.

Therefore, my fellow curator of digital antiquities, could you be so kind as to employ the DepthFirstSearchSampler from our latest computational tools to procure such a subset from our 'littleballoffur14.sparse6' collection? If it were an ancient scroll or artifact register, weÃ¢ÂÂd be renaming it, perhaps 'Exhibit10Nodes.gml', for ease of reference and display.
### Response:
import networkx as nx
from littleballoffur import DepthFirstSearchSampler
G = nx.read_sparse6('littleballoffur14.sparse6')
sampler = DepthFirstSearchSampler(number_of_nodes=10)
sampler_G = sampler.sample(G)
print(sampler_G.edges(), sampler_G.nodes());
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