<｜begin▁of▁sentence｜>### Instruction:
Imagine we are analyzing the distribution network of fresh water reservoirs across a region, with reservoir sites represented as nodes: 1, 2, 3, 4, and 5. The connection pathways between these reservoirs have varying capacities, denoted by weights, reflecting the volume of water they can transport. Specifically, the connection from reservoir 1 to 2 can transport 6 units of water, from 1 to 3 can transport 2 units, from 2 to 3 can handle 1 unit, the pathway between 2 and 4 can carry 7 units, between 3 and 5 can handle 9 units, and finally, the connection from 4 to 5 can support 3 units.

To optimize the water distribution and to ensure that the water resources are managed efficiently, we aim to determine the set of connections that can maximize the total water transfer capacity across the network. We wish to utilize the max_weight_matching function provided by NetworkX to compute this optimal configuration. Could you suggest how we might reframe our goal to align with the function's capabilities, while preserving the intent to efficiently manage our water resources?
### Response:
import networkx as nx

# Create a weighted graph
G = nx.Graph()
edges = [(1, 2, {'weight': 6}), (1, 3, {'weight': 2}), (2, 3, {'weight': 1}), (2, 4, {'weight': 7}), (3, 5, {'weight': 9}), (4, 5, {'weight': 3})]
G.add_edges_from(edges)

# Find the maximum weight matching
matching = nx.max_weight_matching(G, maxcardinality=False, weight='weight')

# Print the matching
print(sorted(matching))
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