<｜begin▁of▁sentence｜>### Instruction:
If we have a weighted directed graph with edges [('A', 'B', 3), ('A', 'C', 17), ('A', 'D', 14), ('B', 'A', 3), ('B', 'C', 12), ('B', 'D', 16), ('C', 'A', 13), ('C', 'B', 12), ('C', 'D', 4), ('D', 'A', 14), ('D', 'B', 15), ('D', 'C', 2)], how can we use the threshold_accepting_tsp algorithm from NetworkX to find a traveling salesman path that starts and ends at node 'D' and what is the cost of this path?
### Response:
from networkx.algorithms import approximation as approx
import networkx as nx

# Create a weighted directed graph
G = nx.DiGraph()
G.add_weighted_edges_from([
    ('A', 'B', 3), ('A', 'C', 17), ('A', 'D', 14),
    ('B', 'A', 3), ('B', 'C', 12), ('B', 'D', 16),
    ('C', 'A', 13), ('C', 'B', 12), ('C', 'D', 4),
    ('D', 'A', 14), ('D', 'B', 15), ('D', 'C', 2)
])

# Use threshold_accepting_tsp to find a TSP route starting from 'D'
cycle = approx.threshold_accepting_tsp(G, 'greedy', source='D')

# Calculate the cost of the TSP route
cost = sum(G[n][nbr]['weight'] for n, nbr in nx.utils.pairwise(cycle))

# Print the TSP cycle and its cost
print('TSP Cycle:', cycle)
print('Total Cost:', cost)
<|EOT|>
