<｜begin▁of▁sentence｜>### Instruction:
Suppose you have a directed graph representing a network flow from source 'x' to sink 'y', with edges [('x', 'a', 3.0), ('x', 'b', 1.0), ('a', 'c', 3.0), ('b', 'c', 5.0), ('b', 'd', 4.0), ('d', 'e', 2.0), ('c', 'y', 2.0), ('e', 'y', 3.0)], where each tuple represents an edge with its respective capacity. How would you use the Edmonds-Karp algorithm to find the maximum flow in this network using NetworkX?
### Response:
from networkx.algorithms.flow import edmonds_karp
G = nx.DiGraph()
G.add_edge('x', 'a', capacity=3.0)
G.add_edge('x', 'b', capacity=1.0)
G.add_edge('a', 'c', capacity=3.0)
G.add_edge('b', 'c', capacity=5.0)
G.add_edge('b', 'd', capacity=4.0)
G.add_edge('d', 'e', capacity=2.0)
G.add_edge('c', 'y', capacity=2.0)
G.add_edge('e', 'y', capacity=3.0)
R = edmonds_karp(G, 'x', 'y')
flow_value = nx.maximum_flow_value(G, 'x', 'y')
print('Flow value:', flow_value)
print('Flow value matches R:', flow_value == R.graph['flow_value'])
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