<｜begin▁of▁sentence｜>### Instruction:
Imagine, as a hydroponic farmer, you meticulously manage the flow of nutrient-rich solutions to your plants, ensuring that every drop takes the most efficient route through your complex network of water channels. Now, treat this network of channels as a graph, "G", where intersections and endpoints are like nodes, and the channels themselves act as the edges connecting these nodes. Suppose you've already employed a sophisticated system akin to the Floyd-Warshall algorithm to analyze and determine the shortest paths and their corresponding predecessors throughout this network, to ensure that your nutrient solutions reach their intended plants in the most efficient way possible.

Could you, leveraging the insights gained from this 'system', specifically use the 'reconstruct_path' function to meticulously trace back and delineate the optimal route for the nutrient solution, from its source reservoir to a specific target plant, thereby ensuring the most efficient delivery of nutrients? Just imagine this process as if you were ensuring the prosperity of one particular plant out of the myriad in your hydroponic farm.
### Response:
import networkx as nx

# Compute the shortest paths and predecessors
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('Shortest path from', source, 'to', target, ':', path)
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