<｜begin▁of▁sentence｜>### Instruction:
Imagine that as a Cloud Architect, you're tasked with optimizing the network flow within a distributed cloud infrastructure. Your objective is to divide the data centers into two groups such that the communication cost between the two groups is maximized, potentially reducing internal network congestion.

To approach this, you plan to model the data centers and their connections as a network graph, where each node represents a data center and each edge carries a weight that represents the communication cost. You have the following connection data for a subsection of your network: 

Data center connections:
- Data centers 1 and 2 have a communication cost of 3
- Data centers 2 and 3 have a communication cost of 2
- Data centers 3 and 4 have a communication cost of 1
- Data centers 4 and 1 have a communication cost of 1
- Data centers 1 and 3 have a communication cost of 4

With this setup, you plan to apply the 'one_exchange' algorithm using NetworkX to your graph to find the optimal partition, which will result in a maximum communication cut value. What will be the communication cut value observed, and how will the data centers be partitioned into two groups accordingly?
### Response:
import networkx as nx
from networkx.algorithms import approximation as approx

# Create a graph
G = nx.Graph()
G.add_weighted_edges_from([(1, 2, 3), (2, 3, 2), (3, 4, 1), (4, 1, 1), (1, 3, 4)])

# Use the one_exchange algorithm to find a maximum cut
seed = 42 # For reproducibility
cut_value, partition = approx.one_exchange(G, seed=seed)

print('Cut value:', cut_value)
print('Partition:', partition)
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