<｜begin▁of▁sentence｜>### Instruction:
Given a directed graph with weighted 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 use the threshold_accepting_tsp function to compute an approximate solution for the traveling salesman problem, starting and ending at node 'D'?
### Response:
from networkx.algorithms import approximation as approx
import networkx as nx

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 the threshold_accepting_tsp function
# Note: init_cycle can be a specific cycle like ['D', 'B', 'A', 'C', 'D'] or 'greedy' for a greedy solution
# The example uses a 'greedy' initial cycle
# The source is the node where the cycle starts and ends
# Parameters like max_iterations, N_inner, alpha, and seed can be adjusted depending on specific needs
cycle = approx.threshold_accepting_tsp(G, init_cycle='greedy', source='D', threshold=1, max_iterations=10, N_inner=100, alpha=0.1, seed=None)

# Calculate the cost of the cycle
# nx.utils.pairwise creates an iterator that returns pairs of elements (used here to get edges in the cycle)
cost = sum(G[n][nbr]['weight'] for n, nbr in nx.utils.pairwise(cycle))

# Output the cycle and it's cost
print('Cycle:', cycle)
print('Cost:', cost)
<|EOT|>
