<｜begin▁of▁sentence｜>### Instruction:
Imagine you're an Exercise Physiologist developing a new fitness program to improve the health of your clients. As part of your methodology, you've crafted an intricate network representing various exercises where nodes symbolize specific activities, and the edges represent the transition between these activities. This network is akin to a well-balanced workout routine, with 10 key exercises and transitions between them that are as likely as a coin flip - embodying a 0.5 chance for any two exercises to be paired in the routine.

To assess the flexibility and adaptability of your program while maintaining its core structure, you decide to experiment with slight variations of the exercise transitions. You'll employ a technique akin to 'edge swapping,' which in this context could represent substituting one exercise pairing with another. You aim to do this five times, essentially rehearsing five different but structurally similar workout routines.

However, to ensure the results from this experiment can be consistently replicated (important for scientific validation and future referencing), you're considering setting a 'seed value', much like giving a unique ID to each iteration of your fitness program trial.

By using this approach with the aforementioned 'seed value', how would you guide a fellow researcher in understanding how to create these variations in the exercise program while keeping the fundamental network of exercises connected, just as you've meticulously designed it?
### Response:
import networkx as nx
import random

# Create a graph
G = nx.fast_gnp_random_graph(10, 0.5, seed=42)

# Apply the random_reference function to create a randomized graph
randomized_G = nx.random_reference(G, niter=5, connectivity=True, seed=random.seed(42))

# Print random graph
for edge in randomized_G.edges():
    print(edge)
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