<｜begin▁of▁sentence｜>### Instruction:
Imagine that we represent a patient's journey through different treatment stages within a network where each stage is connected to the next. In this hypothetical scenario, we have a sequence of interconnected treatments denoted by the stages 0, 1, 2, 3, and 4, and each connection has an associated 'weight' or 'cost' of transition, symbolizing the effort or resources involved in moving from one stage to the next.

The connections between these stages are as follows: from stage 0 to stage 1 the cost is 1, from stage 1 to stage 2 the cost is also 1, from stage 2 to stage 3 it's 1, and finally from stage 3 to stage 4, the cost is 1.

In our medical network, how could we determine the most efficient path of treatment from the initial stage (stage 0) to the recovery stage (stage 4)? Specifically, could you use our data to map out the optimal sequence of transitions a patient should take from the first to the last stage of their treatment plan?
### Response:
import networkx as nx

# Create a weighted graph
G = nx.Graph()
G.add_weighted_edges_from([(0, 1, 1), (1, 2, 1), (2, 3, 1), (3, 4, 1)])

# Compute shortest paths between all nodes
paths = dict(nx.all_pairs_dijkstra_path(G))

# Output the shortest path from node 0 to 4
print(paths[0][4])
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