<｜begin▁of▁sentence｜>### Instruction:
As a Probation Officer, you're tasked with supervising and monitoring individuals as they navigate their rehabilitation and reintegration into society. Think of your role as providing support and resources to help individuals successfully comply with their court-ordered conditions or treatment plans. Just as you assess risks and needs to tailor interventions, let's consider a scenario where you're utilizing a tool called RandomEdgeSamplerWithInduction in a different context.

Imagine you're working with a group of individuals transitioning back into the community after completing a rehabilitation program. You've been using network analysis to map out potential pathways to successful reintegration. Now, you want to sample a subset of connections from this network to gain insights into specific interactions. 

Let's say you've constructed a virtual representation of their social connections using the networkx library, specifically an Erdos-Renyi graph with 500 nodes and a probability of connection set at 0.2. Now, to deepen your understanding, you aim to sample 50 edges from this network. Here's where RandomEdgeSamplerWithInduction comes into play Ã¢ÂÂ it's a tool that allows you to efficiently select random edges from a given graph while considering the broader context of the network.

Your task is to utilize RandomEdgeSamplerWithInduction to select and print out these 50 sampled edges. This will provide valuable insights into the dynamics of their social interactions and relationships, aiding your efforts in providing targeted support and interventions to promote successful community reintegration.
### Response:
import networkx as nx
from littleballoffur import RandomEdgeSamplerWithInduction

G = nx.erdos_renyi_graph(n=500, p=0.2)

sampler = RandomEdgeSamplerWithInduction(number_of_edges=50, seed=10)

sampler_G = sampler.sample(G)

print(sampler_G.edges())
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