<｜begin▁of▁sentence｜>### Instruction:
Certainly! Let's rephrase your question within a scenario befitting a Loan Officer's perspective:

Imagine you're evaluating the financial network of a small community investment group which comprises three members, each with equal stake and influence — essentially forming a tripartite funding cycle. To assess the resilience and distribution of financial responsibility within the group, you wish to analyze the impact of the temporary absence of each member on the group's overall funding efficiency. Employing a popular tool in financial network analysis, you wish to calculate the 'closeness vitality' for each member within this circular funding model using NetworkX. This metric will aid in determining how critical each individual is to the speed and flow of funds within the group's lending circle. How would you phrase this request for analytical computation to a data specialist fluent in NetworkX, ensuring that the task captures the need to compute the closeness vitality for each node in the cycle?
### Response:
import networkx as nx

# Create a graph
G = nx.cycle_graph(3)

# Compute the closeness vitality of the graph
vitality = nx.closeness_vitality(G)

print(vitality)
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