<｜begin▁of▁sentence｜>### Instruction:
Alright, let's imagine we're working on a public health education initiative where we've been tracking interactions in a community to understand how information about wellness and disease prevention spreads. We've got a big network graph that represents all the different connections between individuals in our program, and it's stored in a file named 'community_network.sparse6'.

Now, to make sense of this robust dataset and create more targeted educational materials, we want to sample a smaller, manageable subgraph to analyze a snapshot of our community dynamics closely. The method we've decided to use for this is what's called "Random Edge Sampling with Induction," which will allow us to take a random selection of the interactions (that's the edges in graph terminology) and then, very importantly, include all the connections (edges) of the selected individuals (nodes) to get the full picture of their interaction patterns.

In essence, for our health education project, what I'll be doing is taking the 'community_network.sparse6' file, using the RandomEdgeSamplerWithInduction tool from littleballoffur to extract a subgraph. The goal here is to ensure that the subgraph represents an accurate miniature model of our community interactions that can help inform the design of our health programs.

So, can you walk me through how to use this technique in littleballoffur to sample a subgraph with an induced set of interactions from our 'community_network.sparse6' dataset?
### Response:
import networkx as nx
from littleballoffur import RandomEdgeSamplerWithInduction
G = nx.read_sparse6('littleballoffur7.sparse6')
sampler = RandomEdgeSamplerWithInduction(number_of_edges=50, seed=42)
sampler_G = sampler.sample(G)
print(sampler_G.edges())
print(sampler_G.nodes());
<|EOT|>
