<｜begin▁of▁sentence｜>### Instruction:
In the context of optimizing a nuclear facility's operational team assignments, consider a system where we have a set of experienced nuclear engineers {'A', 'B', 'C'} and a set of specialized tasks {1, 2, 3} that need to be performed with utmost precision and safety. The compatibility between the engineers and the tasks is represented by the following pairings: Engineer 'A' is qualified for Task 1 and Task 3, Engineer 'B' for Task 2, and Engineer 'C' for Task 3.

To ensure the most efficient allocation of resources where each task is handled by one engineer and vice versa, we propose utilizing a computational approach. We aim to employ the Eppstein's algorithm for maximum cardinality matching in a bipartite graph—essentially seeking the ideal assignment strategy. How would we apply this technique using the eppstein_matching function from the NetworkX software library to determine the best possible pairing between engineers and tasks to maximize the operational efficiency in our nuclear plant?
### Response:
import networkx as nx
from networkx.algorithms import bipartite

# Create a bipartite graph
B = nx.Graph()
# Add nodes with the node attribute "bipartite"
B.add_nodes_from(['A', 'B', 'C'], bipartite=0)
B.add_nodes_from([1, 2, 3], bipartite=1)
# Add edges only between nodes of opposite node sets
B.add_edges_from([('A', 1), ('B', 2), ('C', 3), ('A', 3)])

top_nodes = {n for n, d in B.nodes(data=True) if d['bipartite']==0}
matching = bipartite.eppstein_matching(B, top_nodes=top_nodes)

print("Matching:", matching)
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