<｜begin▁of▁sentence｜>### Instruction:
Ah, it's like when you sit with someone in your cozy counseling office, peering through the complexities of their life stories and experiences, trying to identify just the right pieces that need to come together to help them understand themselves better, isn't it? Just like focusing on certain patterns can provide deeper insights into someone's psyche, in the world of network analysis, sometimes we need to simplify the vast connections of a graph to study the underlying structure more clearly.

You've got this intricate network, like the interconnected stories of people's lives, and you want to look at a smaller section of that network to really home in on the dynamics at play. The goal here is not to see everything but to sample a part of the network with enough detail to understand the bigger picture.

So, bringing the essence of your work into the realm of graph theory, imagine you have a graph described in a file, akin to the life narrative of an individual written down for you to study. The file is called "littleballoffur15.sparse6", a unique name that, if we were speaking in terms of our counseling sessions, could represent someone's coded journal or a case file.

As you would carefully select which bits of the narrative to focus on during a therapy session, you need to use a tool, the RandomEdgeSamplerWithInduction from littleballoffur, to extract a sample from this graph. But just like you don't want to lose the essence of the individual's experiences, you want to keep the structure intact by using edge set induction during the sampling process. Specifically, you want this sample to have an exact number of edges, offering a concise yet representative look into the complexity of the network.

So, shall I help you figure out how to use RandomEdgeSamplerWithInduction to extract a subgraph containing a particular number of edges, and maintain the relational ties through edge set induction? If this were to be translated into the language of a file more commonly used in our world, like the personal records we keep, the question would then be: how do you apply this sampling method to a network data stored in a "littleballoffur15.gml" file, aiming to preserve the original relationships as much as possible within the sample?
### Response:
import networkx as nx
from littleballoffur import RandomEdgeSamplerWithInduction
G = nx.read_sparse6('littleballoffur15.sparse6')
sampler = RandomEdgeSamplerWithInduction(number_of_edges=50, seed=42)
sampler_G = sampler.sample(G)
print(sampler_G.edges(), sampler_G.nodes())
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