<｜begin▁of▁sentence｜>### Instruction:
Given a graph with an edge set {('x', 'a', 3), ('x', 'b', 1), ('a', 'c', 3), ('b', 'c', 5), ('b', 'd', 4), ('d', 'e', 2), ('c', 'y', 2), ('e', 'y', 3)}, can you compute the minimum edge cut and the corresponding partition of the graph using the Stoer-Wagner algorithm provided by NetworkX?
### Response:
import networkx as nx

# Create a graph
G = nx.Graph()
G.add_edge("x", "a", weight=3)
G.add_edge("x", "b", weight=1)
G.add_edge("a", "c", weight=3)
G.add_edge("b", "c", weight=5)
G.add_edge("b", "d", weight=4)
G.add_edge("d", "e", weight=2)
G.add_edge("c", "y", weight=2)
G.add_edge("e", "y", weight=3)

# Use the Stoer-Wagner algorithm to find the minimum edge cut
# and the corresponding partition of the graph
# Defaults to using the ‘weight’ attribute on the edges
# and the BinaryHeap implementation for the heap
try:
    cut_value, partition = nx.stoer_wagner(G)
    print('The minimum cut value is', cut_value)
    print('The partition that defines the minimum cut is', partition)
except nx.NetworkXError as e:
    print(e)
except nx.NetworkXNotImplemented as e:
    print(e)
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