<｜begin▁of▁sentence｜>### Instruction:
As a Pollution Control Engineer, imagine that you are examining the connectivity of a network of air quality monitoring stations. Each station is linked to others, forming an undirected graph with connections as follows: Station 0 to Station 1, Station 0 to Station 2, Station 0 to Station 3, Station 1 to Station 2, and Station 1 to Station 3. To evaluate the redundancy and the resilience of the communication network between these stations, you need to calculate how effectively information flows within the network. Specifically, you're interested in the average local efficiency, which is a measure of how well information is exchanged in the smaller sub-networks if a single station were to go offline.

Local efficiency is determined by taking the harm of the shortest paths in a station's neighborhood, excluding itself. You'll need to calculate the efficiency of these sub-networks for each station and then determine the average of these efficiencies across the whole network. This measure will help identify weak points in the monitoring network and guide improvements to ensure that environmental data is relayed effectively, even if one of the stations temporarily fails.

Can you provide this average local efficiency value using your expertise with the NetworkX library, without considering the edge weights in your computation?
### Response:
import networkx as nx

# Create an undirected graph
G = nx.Graph()

# Add edges
G.add_edges_from([(0, 1), (0, 2), (0, 3), (1, 2), (1, 3)])

# Calculate the average local efficiency
efficiency = nx.local_efficiency(G)
print('Average local efficiency:', efficiency)
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