<｜begin▁of▁sentence｜>### Instruction:
As an ergonomist, your expertise lies in designing and evaluating products, equipment, and workspaces to ensure optimal comfort, safety, and efficiency for users. Imagine you're tasked with enhancing the layout and functionality of a bustling office space. Within this context, you've been asked to utilize a specialized tool called LoopErasedRandomWalkSampler to analyze the flow and connectivity within the office network.

Suppose the office comprises 200 interconnected workstations, akin to nodes in a network. These workstations are linked following an Erdos-Renyi model with parameters n=200 and p=0.25, simulating a network where each pair of workstations has a 25% probability of being connected. Now, your task involves employing the LoopErasedRandomWalkSampler to extract a subset of 50 interconnected workstations from this network, mimicking a sample of office sections or departments.

Once you've obtained this sampled subgraph, your objective is to determine its diameter, which essentially measures the maximum distance between any pair of workstations within this subset. Understanding the diameter is crucial for optimizing communication, workflow, and accessibility within various departments or sections of the office space.
### Response:
import networkx as nx
from littleballoffur import LoopErasedRandomWalkSampler

G = nx.gnp_random_graph(n=200, p=0.25)
sampler = LoopErasedRandomWalkSampler(50)
sampler_G = sampler.sample(G)
result = nx.diameter(sampler_G)
print(result)
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