<｜begin▁of▁sentence｜>### Instruction:
Imagine you're managing the operations of a restaurant, which we'll liken to overseeing the flow within a network of five different departments, arranged in a perfect loop, ensuring seamless service. We're considering shaking things up and reorganizing the links between these departments to potentially optimize our operations. To visualize this, let's think of it as a cycle graph with 5 nodes, where each node represents a department.

Now, we want to try something similar to a "menu shuffle" but for our department connections, known in graph terms as double-edge swaps. We're aiming to perform up to 10 of these swaps. However, it's crucial that after each swap, all departments remain accessible from every other department—maintaining the "connectedness" of our restaurant operations.

To keep our experiment consistent and our results replicable, we'll use what's like a recipe or a fixed ingredient list, called a 'seed', for these swaps.

What I'd like you to do is execute these 10 strategic double-edge swaps on our departmental graph while using this seed for reproducibility. After doing so, please report back to me how many successful reconnections or "swaps" you were able to perform, along with the updated layout of our department links, essentially the new edges of our graph.

This task is crucial for our strategic operational change, akin to testing out a new kitchen workflow without interrupting the restaurant's service. Your knack for maintaining the connectedness while testing the new arrangements will be fundamental in executing a flawless transition.
### Response:
import networkx as nx

# Create a connected graph G
G = nx.cycle_graph(5)

# Perform 10 double-edge swaps on the graph G
nswap = 10
seed = 42 # For reproducible results
actual_swaps = nx.connected_double_edge_swap(G, nswap, seed=seed)

print('Number of successful swaps:', actual_swaps)
print('Updated edges:', G.edges())
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