<｜begin▁of▁sentence｜>### Instruction:
Certainly! Let's imagine we're working together on a project at a youth summer camp, and we've created a fun game for our campers that involves connecting four checkpoints in a loop. Each path between the checkpoints represents an activity with varying levels of difficulty, represented by "weights" or "points" that need to be collected to progress in the game. To make the game fair and ensure that every team has an equal chance, we've designed the checkpoints (nodes) so that there's a slightly harder activity (edge) between checkpoint 0 and checkpoint 3 with a weight of 2 points, while all the other activities between checkpoints have a default difficulty of 1 point.

Now, we want to plan a mini-tournament where each team has to complete activities with the lowest cumulative difficulty score, so we want to figure out the optimal path or sequence of activities. To accomplish this, we're going to use a strategy similar to Kruskal's algorithm, a way to ensure that each team gets the fairest play, by finding the minimum "path" score that connects all the checkpoints without any loops. 

Could you help us determine which activities (edges) should make it to the final round that requires the least amount of points while still visiting all the checkpoints (nodes)? This will form the least challenging path for our campers that we're calling the "Minimum Activity Tree."
### Response:
import networkx as nx
from networkx.algorithms import tree

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

# Find minimum spanning edges by Kruskal's algorithm
mst = tree.minimum_spanning_edges(G, algorithm='kruskal', data=False)
edgelist = list(mst)

# Print the edges in the minimum spanning tree
print(sorted(sorted(e) for e in edgelist))
<|EOT|>
