<｜begin▁of▁sentence｜>### Instruction:
Imagine you're maintaining a network mapping of a wind farm's internal communication system, which you've represented as a weighted graph in a 'wind_network.gml' file. This graph includes nodes that symbolize the wind turbines and control units, while the edges represent the communication links assigned with weights based on signal strength or latency. After diligently running a Floyd-Warshall analysis on the graph, you've obtained the precursor and distance matrices that hold vital information on the connectivity and shortest paths within this network.

Now, you're tasked with optimizing the routing protocol for a crucial signal from the control unit labeled 'Source' to a wind turbine designated as 'Target'. How could you harness the 'reconstruct_path' function utilizing the existing Floyd-Warshall output matrices to deduce the most efficient communication route between these two nodes?
### Response:
import networkx as nx

# Compute the Floyd-Warshall predecessor and distance
pred, dist = nx.floyd_warshall_predecessor_and_distance(G)

# Reconstruct the shortest path from source to target
path = nx.reconstruct_path(source, target, pred)

print(path)
<|EOT|>
