<｜begin▁of▁sentence｜>### Instruction:
Imagine we're working with a company's data network, and we want to optimize the flow of information. The network is mapped out as a digital blueprint in 'network_layout.gml'. In this blueprint, each point (or node) represents a routing station, and the connections (or edges) between these stations have a 'capacity' value that tells us the maximum amount of data that can move between them.

Our task is to figure out the best way to prevent data overload by finding a bottleneck point in the network. Specifically, we need to identify the critical points where, if we had to, we could "cut" the network to separate the main server (let's call it 'Point X') from the backup server ('Point Y') without reducing the overall data flow capacity.

Could you provide insight on how we determine this 'minimum cut' in the 'network_layout.gml' so that the data flow between 'Point X' and 'Point Y' is at its maximum?
### Response:
import networkx as nx

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

# Add weighted edges to the graph
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)

# Compute the value of the minimum cut and the node partition
# from source 'x' to sink 'y'
cut_value, partition = nx.minimum_cut(G, 'x', 'y')
reachable, non_reachable = partition

# Output the result
print('Cut value:', cut_value)
print('Reachable:', reachable)
print('Non-reachable:', non_reachable)
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