<｜begin▁of▁sentence｜>### Instruction:
Imagine you're crafting an exquisite plant-based menu, and you've designed a culinary wheel that groups four key ingredient types for your dishes—grains, greens, fruits, and nuts. You've envisioned a perfect balance, connecting each group with flavorsome pairings. To add a dash of innovation, you've decided that grains and nuts should be paired with a double-intensity flavor boost, represented by a weight of 2 in your recipe network.

In order to create the ultimate menu that emphasizes the most robust and satisfying flavor pairings, you'd like to use a digital tool, much like the NetworkX library in the realm of network analysis, that can help you identify the strongest possible connections between these ingredient groups. You're interested in understanding which pairings would make it into this 'Maximum Flavor Spanning Tree', a concept similar to a maximum spanning tree in a graph.

How would you, with your culinary expertise, spin this tool to find and list the pairings (or edges) that contribute to the most potent combination of flavors for your guests, assuming that your recipe network is akin to the cycle graph with the specially intensified grains-nuts connection?
### Response:
import networkx as nx

# Create a graph
G = nx.cycle_graph(4)
G.add_edge(0, 3, weight=2)

# Compute the maximum spanning tree of the graph
T = nx.maximum_spanning_tree(G)

# Print the edges of the maximum spanning tree
print(sorted(T.edges(data=True)))
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