<｜begin▁of▁sentence｜>### Instruction:
As a Virtual Reality Designer creating an immersive training simulation, you need to identify the minimum cut between two key points in a virtual network, represented by a graph. Using networkx, you can apply the st_mincut function to determine the most efficient path for information flow while considering the varying capacities of different connections. In this scenario, imagine designing a virtual training environment for emergency response personnel, where the graph represents the network of communication and resource distribution. By applying the st_mincut function, you can optimize the virtual infrastructure to ensure efficient information flow and resource allocation in the event of a disaster.
### Response:
import igraph as ig

# Create a graph with given edges and edge capacities
edges = [(0, 1), (0, 2), (1, 2), (1, 3), (2, 3)]
edge_caps = [10, 5, 1, 4, 2]
g = ig.Graph(edges=edges)
g.es['capacity'] = edge_caps

# Calculate the minimum cut between source vertex 0 and target vertex 3
cut_value, partition1, partition2, cut_edges = g.st_mincut(0, 3, capacity='capacity')

# Output the results
print('Cut value:', cut_value)
print('Partition 1:', partition1)
print('Partition 2:', partition2)
print('Cut edges:', cut_edges)
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