<｜begin▁of▁sentence｜>### Instruction:
Imagine you're overseeing the health and well-being of a small ecosystem comprising various species that traverse a network of interconnected trails symbolized by our graph. These trails, which are effectively used by our animal friends, are represented by connections between sites: [(0, 1), (1, 2), (2, 3), (0, 3), (0, 2), (1, 3)].

Just as we track the flow of wildlife to understand the stress on different paths and to maintain the balance of the ecosystem, we need to assess the frequency at which each of these trails is utilized. Specifically, we're looking to determine the 'trail load centrality' for each segment, a measure analogous to edge load centrality in network analysis, indicating the number of shortest paths that traverse each trail connection.

By examining this, much like how we monitor foot traffic to prevent overuse and preserve our natural habitats, we can pinpoint which trails in our network require extra attention or may need reinforcement to handle the traffic and ensure the migratory patterns of our animals remain uninterrupted and safe. Can you help us reorganize our data to spotlight the trail load centrality for the well-being of our ecosystem?
### Response:
import networkx as nx

# Create a graph
G = nx.Graph()
G.add_edges_from([(0, 1), (1, 2), (2, 3), (0, 3), (0, 2), (1, 3)])

# Compute edge load centrality
edge_load = nx.edge_load_centrality(G)

print("Edge Load Centrality:", edge_load)
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