<｜begin▁of▁sentence｜>### Instruction:
As a broadcast journalist, let's dive into a practical scenario involving network analysis. Imagine you're covering a story on urban planning, and you're investigating how cities optimize transportation networks for efficiency.

Suppose you're focusing on a hypothetical city with 100 neighborhoods, each representing a node in a network. Using a model called the Erdos-Renyi graph, which simulates random connections between these neighborhoods, you can represent how roads or pathways might be laid out.

Now, let's say you want to explore a subnetwork within this city. To do this, you're employing a technique called the Metropolis-Hastings Random Walk Sampler. Think of it like this: you're sending out a team of explorers who randomly move between neighborhoods, but with a specific strategy to ensure they cover the network effectively.

Your goal is to sample a subnetwork containing 50 neighborhoods and calculate the average shortest distance between these sampled locations. This distance could resemble the average travel time or distance between different areas in the city, giving insights into its overall accessibility and connectivity.

So, in essence, you're using advanced network analysis tools to understand how efficiently people can navigate this city, shedding light on its urban planning strategies and potential areas for improvement.
### Response:
import networkx as nx
from littleballoffur import MetropolisHastingsRandomWalkSampler

G = nx.gnp_random_graph(n=100, p=0.4)

sampler = MetropolisHastingsRandomWalkSampler(50, seed=123, alpha=0.8)

sampler_G = sampler.sample(G)

result = nx.average_shortest_path_length(sampler_G)

print(result)
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