<｜begin▁of▁sentence｜>### Instruction:
As a radiologist specializing in diagnosing and treating diseases using medical imaging techniques like X-rays, MRI, or CT scans, imagine you're tasked with analyzing a large network of interconnected medical facilities to optimize patient care pathways. Suppose you're using a sophisticated tool like PageRankBasedSampler, akin to a specialized imaging software, to analyze this network.

In this scenario, you have a network representing medical facilities across a region, generated using an Erdos-Renyi model with parameters \( n = 500 \) (representing the number of facilities) and \( p = 0.2 \) (denoting the probability of connection between any pair of facilities). Your task is to leverage PageRankBasedSampler, a tool similar to a powerful imaging algorithm, to extract a subgraph containing 50 medical facilities. Then, utilizing network analysis techniques akin to examining scans, you'll determine the shortest paths between all pairs of medical facilities within this sampled subgraph.

By doing so, you aim to enhance the efficiency of patient referrals and treatment planning, much like how radiologists strive to accurately diagnose and treat diseases by analyzing medical images.
### Response:
import networkx as nx
from littleballoffur import PageRankBasedSampler

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

sampler = PageRankBasedSampler(50)

sampler_G = sampler.sample(G)

result = nx.shortest_path(sampler_G)

print(result)
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