<｜begin▁of▁sentence｜>### Instruction:
Imagine that our software development team is working on an application designed to optimize network flow in a transportation logistics platform. During the quality assurance phase, we've come across a feature that constructs a 'residual network' from a given 'directed graph' representing transportation routes with various capacities. Specifically, the application processes a set of routes: [('A', 'B', 3), ('A', 'C', 2), ('B', 'C', 5), ('B', 'D', 4), ('C', 'D', 7), ('C', 'E', 1), ('D', 'E', 6)], where each tuple denotes a direct route from one point to another with an associated maximum shipment capacity.

Our objective as QA Analysts is to simulate this feature accurately by using NetworkX, ensuring that it constructs the residual network correctly according to the defined parameters, and to verify that the initial flow values on the residual network are set to their expected starting values. We will then need to prepare a comprehensive report detailing whether the current implementation meets the specified functional requirements with respect to the network flow simulation. Can you describe how the feature behaves under these conditions and ensure that it aligns with our quality standards?
### Response:
import networkx as nx

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

# Add edges with capacities
G.add_edge('A', 'B', capacity=3)
G.add_edge('A', 'C', capacity=2)
G.add_edge('B', 'C', capacity=5)
G.add_edge('B', 'D', capacity=4)
G.add_edge('C', 'D', capacity=7)
G.add_edge('C', 'E', capacity=1)
G.add_edge('D', 'E', capacity=6)

# Build residual network
R = nx.algorithms.flow.build_residual_network(G, 'capacity')

# Display flow capacities on the residual network
for u, v, data in R.edges(data=True):
    print(f'({u}, {v}) - Capacity: {data['capacity']} - Flow: {data['flow']}
')
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