<｜begin▁of▁sentence｜>### Instruction:
Imagine you're working with a child who has created two different puzzle structures using linkage sticks, which we'll call Structure A and Structure B. Structure A consists of three connection points forming a triangle (with links from point 0 to 1, 1 to 2, and 2 back to 0), while Structure B is a square with a diagonal (links from point 0 to 1, 1 to 2, 2 to 3, and finally 3 back to 0).

Your task is to guide the child through modifying Structure A into Structure B using a series of steps that involve adding or removing linkage sticks or reattaching them to different connection points. However, there is an important challenge to consider: the child can only stretch their arm so far, so any change made must be within a reach of 5 units, which we will refer to as the "maximum allowed distance".

Could you come up with a strategy that respects this limit and uses minimal actions to transition from Structure A's simple triangle to Structure B's square formation? This process of planning the transformation carefully will help the child understand the concept of step-by-step changes while staying within certain physical constraints.
### Response:
import networkx as nx
from networkx.algorithms.similarity import optimize_edit_paths

# Define two graphs G1 and G2
G1 = nx.Graph()
G1.add_edges_from([(0, 1), (1, 2), (2, 0)])

G2 = nx.Graph()
G2.add_edges_from([(0, 1), (1, 2), (2, 3), (3, 0)])

# Example use of optimize_edit_paths
paths = list(optimize_edit_paths(G1, G2,
                                  node_subst_cost=lambda n1, n2: 1 if n1 != n2 else 0,
                                  node_del_cost=lambda n: 1,
                                  node_ins_cost=lambda n: 1,
                                  edge_subst_cost=lambda e1, e2: 1 if e1 != e2 else 0,
                                  edge_del_cost=lambda e: 1,
                                  edge_ins_cost=lambda e: 1,
                                  upper_bound=5))

# Output one of the paths and its cost
for node_edit_path, edge_edit_path, cost in paths:
    print('Node edit path:', node_edit_path)
    print('Edge edit path:', edge_edit_path)
    print('Cost:', cost)
    break
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