<｜begin▁of▁sentence｜>### Instruction:
Imagine you're part of the team at our bustling karate-themed restaurant, "The Dojo Diner," where every staff member is not just serving food but also part of a great network, much like in a karate club. We have a menu item called "The Centrality Special" that's all about the heart of connections—just like the betweenness centrality that highlights the importance of each connection in a network.

Now, in the kitchen of our Dojo Diner, we have a recipe book (let's call it the 'gml file' for fun) that details the interactions of the original karate club, a classic example of a social network. For tonight's special, we need to cook up something that represents the flow of energy (or communication) through these connections. What we're looking at is each pair of our team members (edges in the network lingo) and figuring out who has the most influence on the night's service—like who's passing the most orders or who's the key connector between the chef and the front house.

So, before service begins, could we look into our recipe book and compute how influential each pairing is in our team network? I mean, calculate the betweenness centrality for each edge, but make sure it's normalized—just like how we balance flavors—so we see the big picture of our team's dynamics. This will help us deliver the best possible experience for our guests tonight, with everything flowing smoothly just like a well-executed kata. Can you do that?
### Response:
import networkx as nx

# Generate a graph
G = nx.karate_club_graph()

# Calculate edge betweenness centrality
betweenness = nx.edge_betweenness_centrality(G, normalized=True)

# Print the edge betweenness centrality
for edge, centrality in betweenness.items():
    print(f'Edge {edge}: Betweenness Centrality = {centrality}')
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