<｜begin▁of▁sentence｜>### Instruction:
Certainly! Here's a practical scenario tailored to the profession of an Energy Engineer:

"Imagine you're tasked with optimizing the energy flow within a new smart grid system. The grid is structured as a directional network with power substations represented by nodes and power lines by edges, and each edge has an associated energy transit cost. The current configuration of substations and their power lines is described by the following data: substation 0 links to substation 1 with a transit cost of 2 units, substation 1 to substation 2 with a cost of 3 units, substation 2 to substation 3 with a cost of 4 units, and substation 3 to substation 4 with a cost of 5 units. You're required to determine the most energy-efficient route to deliver power from substation 0 to substation 4. Could you use the Bellman-Ford algorithm to analyze the system's efficiency and identify this optimal energy transfer path?"
### Response:
import networkx as nx

# Create a weighted graph
G = nx.DiGraph()
G.add_weighted_edges_from([(0, 1, 2), (1, 2, 3), (2, 3, 4), (3, 4, 5)])

# Use Bellman-Ford algorithm to find the shortest path
path = nx.bellman_ford_path(G, source=0, target=4, weight='weight')
print('Shortest path from node 0 to 4:', path)
<|EOT|>
