<｜begin▁of▁sentence｜>### Instruction:
As an event planner organizing a large conference with 200 attendees, you've decided to create a networking activity to foster connections among the participants. You've employed a networking tool called "EventNet" that simulates social interactions within the conference attendees. Using the EventNet API, you've set up a virtual environment modeled as an Erdos-Renyi network with parameters `n=200` and `p=0.15`, where each attendee represents a node and the probability of any two attendees connecting is 0.15.

Now, you're tasked with arranging a focused networking session where 50 attendees will participate. To ensure the effectiveness of this session, you plan to utilize the "FrontierSampler" feature of EventNet to randomly sample a sub-network consisting of 50 attendees. Once you have this sub-network, you aim to compute the average shortest path length within this sampled group to gauge the connectivity and accessibility of the network.

By leveraging the capabilities of EventNet's FrontierSampler API, you'll be able to execute this task seamlessly, enhancing the networking experience for the conference attendees.
### Response:
import networkx as nx
from littleballoffur import FrontierSampler

G = nx.gnp_random_graph(n=200, p=0.15)

sampler = FrontierSampler(10, 50, 42)

sampler_G = sampler.sample(G)

result = nx.average_shortest_path_length(sampler_G)

print(result)
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