<｜begin▁of▁sentence｜>### Instruction:
Imagine you're the coach of a tight-knit fitness group that comprises two core squads of three members each. These squads are your star teams, and they're like cliques because everyone within a squad knows each other very well. Now, think of these squads as being joined together by a trio of new members who are friends with each other and serve as a bridge connecting the two squads.

Your goal, coach, is to develop a specialized training regimen that includes the largest possible subset of your original group, but with a particular catch. You want this regimen to be inclusive enough that it only requires members with a certain minimum level of fitness or connection strength to be part of this training group. Essentially, you're searching for the beefiest sub-group possible under this condition.

In network terms, you're looking for a threshold subgraph of the original barbell-shaped group that is nearly as substantial in size as the original, and you want to identify which of your fitness group members would qualify for this elite training regimen.
### Response:
import networkx as nx
from networkx.algorithms.threshold import find_threshold_graph

# Create a barbell graph
G = nx.barbell_graph(3, 3)

# Find a threshold subgraph close to the largest in G
T = find_threshold_graph(G)

# Print the nodes of the threshold graph
print(T.nodes())

# Output may vary: NodeView((7, 8, 5, 6))
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