<｜begin▁of▁sentence｜>### Instruction:
Given a graph with weighted directed 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)], can you find a Traveling Salesman Problem (TSP) tour that starts and ends at node 'D', using the threshold_accepting_tsp function from the NetworkX library? What is the total cost of this TSP tour if the initial cycle is given by ['D', 'B', 'A', 'C', 'D'] and the threshold value is set to 10?
### Response:
from networkx.algorithms import approximation as approx
import networkx as nx

# Create a directed graph
G = nx.DiGraph()

# Add weighted edges
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 the threshold_accepting_tsp function to find a TSP tour
init_cycle = ['D', 'B', 'A', 'C', 'D']  # Initial cycle
threshold = 10  # Threshold value

# Find the TSP path
path = approx.threshold_accepting_tsp(G, init_cycle, threshold=threshold, source='D')

print('TSP path:', path)

# Calculate the total cost of the path
cost = sum(G[u][v]['weight'] for u, v in zip(path, path[1:]))
print('Total cost:', cost)
<|EOT|>
