<｜begin▁of▁sentence｜>### Instruction:
Given a directed graph where nodes 'a' and 'd' have a supply and demand of -5 and 5 units respectively, and edges ('a', 'b'), ('a', 'c'), ('b', 'd'), and ('c', 'd') with given capacities and costs, can you compute the minimum cost flow using the capacity_scaling function from NetworkX?
### Response:
import networkx as nx

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

# Add nodes with supply (negative demand) and demand (positive demand)
G.add_node('a', demand=-5)
G.add_node('d', demand=5)

# Add edges with capacity and cost
G.add_edge('a', 'b', weight=3, capacity=4)
G.add_edge('a', 'c', weight=6, capacity=10)
G.add_edge('b', 'd', weight=1, capacity=9)
G.add_edge('c', 'd', weight=2, capacity=5)

# Compute the minimum cost flow
flowCost, flowDict = nx.capacity_scaling(G)

print('Flow cost:', flowCost)
print('Flow dict:')
for u in flowDict:
    for v in flowDict[u]:
        print(f'({u}, {v}) : {flowDict[u][v]}')
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