<｜begin▁of▁sentence｜>### Instruction:
Imagine you are overseeing a network of automated robots tasked with the efficient transportation of materials between several workstations, labeled 'A', 'B', 'C', and 'D'. The transport paths between these stations have varying distances, which are critical for optimizing the robots' travel. The distances are as follows: between stations 'A' and 'B' it's 3 units, 'A' and 'C' is 1 unit, 'B' and 'C' are 2 units apart, 'C' and 'D' have a distance of 4 units, and finally, 'D' and 'A' are 5 units apart.

Your goal is to configure the robotic network's route assignments such that the overall travel distance is minimized, specifically focusing on pairing stations with the smallest combined path distance. This setup requires you to find the minimum weight matching in the network. Can you determine this optimal path configuration using the weight data provided?
### Response:
import networkx as nx

# Create a weighted undirected graph
G = nx.Graph()
G.add_edge('A', 'B', weight=3)
G.add_edge('A', 'C', weight=1)
G.add_edge('B', 'C', weight=2)
G.add_edge('C', 'D', weight=4)
G.add_edge('D', 'A', weight=5)

# Compute the minimum weight matching
matching = nx.algorithms.matching.min_weight_matching(G, maxcardinality=True)

# Print the matching
print(matching)
<|EOT|>
