<｜begin▁of▁sentence｜>### Instruction:
Imagine you're working with a group of vacationers interested in exploring a picturesque island that happens to be shaped exactly like a perfectly symmetrical four-leaf clover. Each leaf of this clover represents a lovely point of interest or site, and each site is conveniently connected by scenic routes that form a cycle, allowing for a delightful stroll between any two adjacent sites. Additionally, to the delight of our more adventurous travelers, there's also a special hiking trail directly connecting the northwest site to the southeast site, providing a shortcut through the island.

To ensure that our visitors can enjoy all the island has to offer without unnecessary backtracking or extra expenses, you've decided to plan the most efficient route that covers all the sites, making use of this unique trail connection, which has a travel cost of 2. 

The challenge is to utilize your expertise with travel routes to determine a map that provides the minimum total distance covering all the sites, allowing travelers to visit each site at least once without the extra legwork. You're considering using the NetworkX tool to leverage Kruskal's algorithm, a well-known method for finding an optimal path, to plot out this minimum spanning tree on the island's map.

Could you design such a travel itinerary, keeping the total distance as short as possible and incorporating all the sites using Kruskal's method?
### Response:
import networkx as nx

# Create a cycle graph
G = nx.cycle_graph(4)
# Add an edge with a specific weight
G.add_edge(0, 3, weight=2)

# Compute the minimum spanning tree using Kruskal's algorithm
T = nx.minimum_spanning_tree(G, algorithm='kruskal')

# Print sorted list of edges in the minimum spanning tree
print(sorted(T.edges(data=True)))
<|EOT|>
