<｜begin▁of▁sentence｜>### Instruction:
Given a directed graph with nodes 'a' and 'd' having demands of -5 and 5 respectively (where negative demand indicates the node wants to send flow), and with edges ('a', 'b'), ('a', 'c'), ('b', 'd'), ('c', 'd') having certain capacities and weights, can you compute the minimum cost flow using the capacity scaling algorithm with NetworkX that satisfies all the demands?
### Response:
import networkx as nx

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

# Add nodes with their demands
G.add_node('a', demand=-5)
G.add_node('d', demand=5)

# Add edges with weights and capacities
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)

# Run the capacity scaling algorithm
flowCost, flowDict = nx.capacity_scaling(G)

# Print the results
print('Flow Cost:', flowCost)
print('Flow Dict:')
for u in flowDict:
    for v, flow in flowDict[u].items():
        print(f'{u} -> {v}: {flow}')
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