<｜begin▁of▁sentence｜>### Instruction:
As an Inventory Planner tasked with optimizing our distribution network, you've stumbled upon an interesting challenge. Suppose your network is a system of connected warehouses represented by a graph. You have identified that there are certain delivery routes forming loops within this network. Specifically, there is one loop connecting warehouses 0, 1, 2, and 3, and another connecting warehouses 0, 3, 4, and 5.

Given that each path between warehouses incurs a delivery cost – essentially, the 'weight' of an edge in your network graph – you're looking to determine the set of delivery loops that will minimize the total cost of operating these circular routes. This set of cost-effective loops is known as the minimum weight cycle basis of the graph.

Could you reconfigure this inquiry into a practical task? Visualize the above-mentioned warehouse network graph and use NetworkX to find the most economical combination of circular routes (minimum weight cycle basis). This will ensure that the flow of goods is smooth and cheap, keeping our inventory system lean and efficient. Remember, achieving minimal carrying costs while maintaining optimal stock availability is our ultimate goal.
### Response:
import networkx as nx

# Create a graph
G = nx.Graph()

# Add cycles
G.add_cycle([0, 1, 2, 3])
G.add_cycle([0, 3, 4, 5])

# Find the minimum weight cycle basis
min_cycle_basis = nx.minimum_cycle_basis(G)

print(min_cycle_basis)
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