<｜begin▁of▁sentence｜>### Instruction:
Certainly! Imagine you're a social worker who often utilizes sociograms to visualize and analyze the social connections within a community or a support group that you're working with. You've been mapping these connections meticulously, and now have a digital representation of the social network. This rich, intricate web of relationships is stored in a format called Sparse6, a type of graph format, and the specific file you've been updating is titled 'littleballoffur8.sparse6'.

Now, you're interested in exploring smaller segments of this network to better understand and support more targeted interventions. To do this, you'd like to extract a representative sub-sample of the network Ã¢ÂÂ a smaller graph that maintains the essence of the original but is more manageable in size. This is a technique you've heard can provide insights into the larger network's dynamics without being overwhelmed by its complexity.

You've learned about a tool from network science called RandomNodeEdgeSampler, which you think could help with this task. You're hoping to employ this tool to extract a subgraph, but the challenge is to make sure this smaller segment contains an exact number of connections - or edges - that you specify.

So essentially, as a social worker asking in the context of your field, how would you use the RandomNodeEdgeSampler to create a subgraph with a predetermined number of edges from your social network graph stored in the 'littleballoffur8.sparse6' file? To maintain consistency with your profession's terminology, when we're discussing this process, we would refer to this file as 'littleballoffur8.gml' if it were in GML format, although in this case, that's not the specific file type we're using.
### Response:
import networkx as nx
from littleballoffur import RandomNodeEdgeSampler
G = nx.read_sparse6('littleballoffur8.sparse6')
sampler = RandomNodeEdgeSampler(number_of_edges=10, seed=42)
sampler_G = sampler.sample(G)
print(sampler_G.edges())
print(sampler_G.nodes());
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