<｜begin▁of▁sentence｜>### Instruction:
Imagine you're a tour operator tasked with designing a travel route through a series of destinations. Your current challenge is to plan the most efficient path for a tour that starts at Point A (destination 0) and concludes at Point D (destination 3). The available routes between these destinations have varying travel times, and you have the following options: a direct route from Point A to Point B (destination 1) takes 4 hours, another from Point B to Point C (destination 2) takes 3 hours, a route directly from Point A to Point C takes 5 hours, and finally, a short hop from Point C to Point D takes just 1 hour.

As an experienced tour operator, you want to provide your guests with the quickest itinerary possible, and for this purpose, you're considering employing a strategic approach similar to the Bellman-Ford algorithm. Could you determine the shortest travel time for your guests to get from Point A to Point D using this meticulous routing strategy?
### Response:
import networkx as nx

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

# Compute the shortest path length using Bellman-Ford algorithm
length = nx.bellman_ford_path_length(G, source=0, target=3, weight='weight')
print(f'The shortest path length from node 0 to 3 is {length}')
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