<｜begin▁of▁sentence｜>### Instruction:
Imagine you're a food scientist coordinating a tour of inspection visits to three food processing facilities, each labeled {0, 1, 2}. Your goal is to optimize your travel route to ensure maximum efficiency and minimum travel time. You have mapped out the travel times between the facilities: traveling directly from Facility 0 to 1, and from Facility 1 to 2, and then from Facility 2 back to 0, each journey takes 2 hours. However, if you journey from Facility 0 to 2, from Facility 2 to 1, or from Facility 1 back to 0, each of these routes only takes 1 hour.

Given this setup and adhering to the principles of the triangle inequality to maintain a logical and time-efficient route, you're seeking a computational method to approximate the most efficient sequence for visiting each facility starting and ending your journey at Facility 0. The method on your radar for solving this optimization puzzle is the 'asadpour_atsp' function provided by NetworkX, which is a tool designed to tackle precisely such problems. 

Your task is now to feed the travel time data into the 'asadpour_atsp' function to obtain an approximate itinerary for your inspection tour. What sequence of facilities should you expect to visit, beginning and concluding at Facility 0, to make the most of your time?
### Response:
import networkx as nx
import networkx.algorithms.approximation as approx

G = nx.complete_graph(3, create_using=nx.DiGraph)
nx.set_edge_attributes(G, {(0,1): 2, (1,2): 2, (2,0): 2, (0,2): 1, (2,1): 1, (1,0): 1}, 'weight')
tour = approx.asadpour_atsp(G, source=0)
print(tour)
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