<｜begin▁of▁sentence｜>### Instruction:
As a physical therapist, part of my role involves assessing, treating, and rehabilitating individuals with various physical impairments or injuries to help them regain mobility, function, and independence. Let's imagine a scenario where I'm working with a patient who has recently undergone surgery to repair a knee injury sustained during a sports activity. 

To aid in their rehabilitation, I utilize techniques and tools to create personalized exercise routines and treatment plans. In this case, I might use a specialized tool called the RandomWalkSampler to assist in sampling a subset of exercises from a larger pool. 

Imagine our patient, let's call them Alex, needs a tailored exercise routine to improve knee stability and strength. We have a vast library of exercises, represented as nodes in a network, with connections indicating how they complement each other in a routine. Our network, akin to an erdos_renyi_graph, consists of various exercises designed to target different muscle groups and movement patterns.

Alex's routine needs to be concise yet effective, so I aim to select a subset of 15 exercises that cover a broad range of motions and muscle groups. The RandomWalkSampler tool allows me to efficiently navigate this exercise network, randomly selecting exercises while ensuring diversity and relevance to Alex's needs.

Once we've established the routine, another crucial aspect of rehabilitation is monitoring progress and adjusting the plan as necessary. Here's where identifying simple cycles, or elementary circuits, in the exercise network becomes valuable. These cycles represent combinations of exercises that form a complete sequence, potentially indicating repetitive or overlapping movements in Alex's routine. By identifying and analyzing these cycles, I can ensure the routine is well-balanced and avoids overuse or redundancy, ultimately supporting Alex's recovery journey.

In summary, leveraging tools like the RandomWalkSampler and analyzing simple cycles in our exercise network enables me to create effective and tailored rehabilitation plans for patients like Alex, promoting their recovery and overall well-being.
### Response:
import networkx as nx
from littleballoffur import RandomWalkSampler

G = nx.gnp_random_graph(n=830, p=0.2)

sampler = RandomWalkSampler(15)

sampler_G = sampler.sample(G)

result = nx.simple_cycles(sampler_G)

print(result)
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