<｜begin▁of▁sentence｜>### Instruction:
Imagine you're studying a small ecosystem, where various species form symbiotic relationships. You have documented these interactions, noting that certain pairs coexist with varying degrees of benefit to one another, quantified by a set of weights. Specifically, you've observed the following pairs and the respective intensity of their interactions: (Species 1, Species 2, Benefit 6), (Species 1, Species 3, Benefit 2), (Species 2, Species 3, Benefit 1), (Species 2, Species 4, Benefit 7), (Species 3, Species 5, Benefit 9), and (Species 4, Species 5, Benefit 3). 

These interactions form a complex web that can be thought of as an undirected weighted graph, where nodes represent species and edges represent the benefit of their interaction. Your task now is to employ this graph to elucidate a scenario where the ecological benefits are maximized across the network, leaving no species pairing without consideration. As a biologist, discerning the scenario with the greatest cumulative benefit would provide insights into which species pairings are most crucial for the vitality of this ecosystem. Can you reframe this scenario using the principles of NetworkX to discover the optimal set of interactions that ensures the maximum combined benefit in the ecosystem?
### Response:
import networkx as nx 

# Create a weighted undirected graph 
G = nx.Graph()
edges = [(1, 2, 6), (1, 3, 2), (2, 3, 1), (2, 4, 7), (3, 5, 9), (4, 5, 3)]
G.add_weighted_edges_from(edges)

# Use the max_weight_matching API 
matching = nx.max_weight_matching(G, maxcardinality=False)

# Output the matching 
print('Maximum weight matching:', sorted(matching))
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