<｜begin▁of▁sentence｜>### Instruction:
In the fast-paced world of network theory, a story has emerged that might pique the curiosity of our tech-savvy audience. Imagine this: we've uncovered a diagram, a digital footprint of a network, with connections that span from node 1 to node 5. The edges in question form pathways between these points—(1, 2), (1, 3), (2, 3), (3, 4), and (3, 5)—crafting a web of interactions that could hold the key to understanding a complex system.

The plot thickens as a subset of these connections—(1, 2) and (3, 4)—are under the spotlight, speculated to be an intricate piece of the network's puzzle. Through a cutting-edge algorithm available in the NetworkX toolkit, traditionally used by researchers to deep dive into graph theories and their applications, the real question emerges: Could this pair of connections be the linchpin—a maximal matching—signifying a point of no further extension without breaking its existing architecture?

Our readers are left on the edge of their seats, awaiting confirmation—does this subset of pathways constitute a maximal matching within the network? Only the sophisticated algorithm within NetworkX can reveal the truth behind this technological enigma, and we are here to break that story wide open. Stay tuned as we delve into the digital labyrinth and unlock the mysteries of maximal matchings.
### 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
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)
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