<｜begin▁of▁sentence｜>### Instruction:
Imagine you're overseeing a political campaign where the organization structure forms a hierarchy, akin to a directed acyclic graph. The nodes ("A," "B," "C," and "D") represent individuals within your campaign team, and the edges (for example, from 'A' to 'B' and from 'A' to 'C') illustrate who directly reports to whom. In preparation for a critical strategy meeting, you need to establish common supervisory responsibility—essentially, a chain of command—for coordination between different team members. To ensure effective communication and collaborative strategy, could you task your analytics department to use NetworkX and identify the lowest common supervisory node for every pair of individuals within our campaign's structure, as per the hierarchical relationships defined by our current reporting graph?
### Response:
import networkx as nx

# Create a directed graph
G = nx.DiGraph()

# Add edges
G.add_edge('A', 'B')
G.add_edge('A', 'C')
G.add_edge('B', 'D')
G.add_edge('C', 'D')

# Compute the lowest common ancestor for all pairs
lca_dict = dict(nx.all_pairs_lowest_common_ancestor(G))

print(lca_dict)
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