<｜begin▁of▁sentence｜>### Instruction:
Given a directed graph with nodes 'a', 'b', 'c', and 'd', where 'a' has a demand of -5, 'd' has a demand of 5, and we have edges ('a', 'b'), ('a', 'c'), ('b', 'd'), and ('c', 'd') with respective weights 3, 6, 1, and 2 and capacities of 4, 10, 9, and 5, how do we find the minimum cost flow satisfying all demands using the capacity scaling algorithm?
### Response:
import networkx as nx

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

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

# Add edges with capacity and weight
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 cost of the minimum cost flow and the flow dictionary
print('Flow cost:', flowCost)
print('Flow dict:', flowDict)
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