<｜begin▁of▁sentence｜>### Instruction:
In the context of optimizing the logistics of hydrocarbon distribution for our network of pipelines, we have a scenario where we must evaluate the flow of resources from a starting point 'a' (the supply node representing a deficit of 5 units of petroleum products) to a terminal point 'd' (the demand node requiring 5 units of petroleum products). The distribution network includes two intermediary transfer stations 'b' and 'c'. The connection from 'a' to 'b' has a transportation cost of 3 monetary units per product unit and a maximum capacity of 4 units, while the link from 'a' to 'c' comes with a higher transportation cost of 6 monetary units per product unit but can handle a larger volume, up to 10 units. Furthermore, the pipeline from 'b' to 'd' incurs a cost of 1 monetary unit per product unit and can transport up to 9 units, and the 'c' to 'd' connection has a cost of 2 monetary units per product unit with a capacity limit of 5 units.

Our objective is to determine the most cost-effective flow of petroleum products through this pipeline network that meets the specified supply and demand constraints. We aim to leverage the network simplex algorithm capabilities of NetworkX to model and solve this problem efficiently. How can this be practically implemented to ensure the minimum transportation cost while adhering to the given pipeline capacities and meeting the end destination demand?
### 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)

# Find a minimum cost flow satisfying all demands
flowCost, flowDict = nx.network_simplex(G)

print('Flow cost:', flowCost)
print('Flow dict:', flowDict)
<|EOT|>
