<｜begin▁of▁sentence｜>### Instruction:
In an intriguing twist to our ongoing analysis of the intricate web of city connections, we've come across a set of data that documents the transportation lines between major urban hubs. Specifically, the conduit includes routes: ('A', 'B') with a 3-unit toll, ('A', 'C') with a 2-unit toll, ('B', 'D') with a 1-unit toll, ('C', 'D') with a 4-unit toll, and finally, ('C', 'E') with a 5-unit toll. It's fascinating to delve into the utility of these connections by leveraging the 'effective_size' metric from NetworkX—a tool that could shed light on the relative importance of each city within this network. Could you provide insights into the territory's network efficiency by calculating this metric for each urban center?
### Response:
import networkx as nx
from networkx.algorithms.structuralholes import effective_size

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

# Calculate effective size of nodes
eff_size = effective_size(G, weight='weight')
print(eff_size)
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