<｜begin▁of▁sentence｜>### Instruction:
Imagine you're managing the staffing network of a burgeoning tech company, where each node represents a department and each edge symbolizes potential candidate placement between two departments. In the current arrangement, we have the following potential pairings: Technology and Sales (1, 2), Technology and Marketing (1, 3), Sales and Marketing (2, 3), Marketing and Design (3, 4), Marketing and Customer Service (3, 5). You're strategizing the optimal placement of candidates to maximize departmental efficiency and are considering a subset of these pairings: Technology and Sales (1, 2), Marketing and Design (3, 4). Could you assess whether this subset represents the most effective candidate assignment without overlooking any potential department pairings, leveraging the insights provided by a tool like NetworkX?
### Response:
import networkx as nx

# Create a graph
G = nx.Graph()
G.add_edges_from([(1, 2), (1, 3), (2, 3), (3, 4), (3, 5)])

# Define a matching as a set of edges
matching = {(1, 2), (3, 4)}

# Check if the matching is maximal
is_maximal = nx.is_maximal_matching(G, matching)
print('Is the matching maximal?', is_maximal)
<|EOT|>
