<｜begin▁of▁sentence｜>### Instruction:
As a musician, you're preparing for a concert where you want to create the perfect ambiance for your audience. You've planned to set up a network of speakers throughout the venue to ensure optimal sound distribution. However, each speaker has a limited capacity in terms of the sound it can produce.

Imagine you have a network of speakers represented as an undirected graph, with connections between them indicating how sound can travel. Each connection has an associated capacity, representing the maximum volume of sound it can transmit effectively. For example, if one speaker is connected to another with a capacity of 10, it means that the first speaker can transmit up to 10 units of sound to the second speaker.

Your goal is to find the most efficient way to transmit sound from a specific starting speaker to a target speaker. To do this, you're interested in determining the minimum amount of sound that needs to be blocked or reduced (the "minimum cut") to prevent sound from reaching the target speaker from the starting speaker. In this case, let's say you're interested in finding the minimum cut between Speaker 0 and Speaker 3.

To accomplish this task, you plan to use the mincut function available in your sound distribution network management software, which is analogous to the networkx library in Python. With this function, you can analyze the network's structure and determine the optimal arrangement of sound transmission paths to achieve your desired sound distribution.
### Response:
import igraph as ig

g = ig.Graph(edges=[(0, 1), (1, 2), (2, 3), (3, 0)], directed=False)
g.es['capacity'] = [10, 10, 1, 1]
min_cut = g.mincut(source=0, target=3, capacity='capacity')
print(min_cut.value)
print(min_cut.partition)
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