<｜begin▁of▁sentence｜>### Instruction:
Certainly! Let's rephrase your query in a manner akin to an Insurance Agent's situation:

Imagine ourselves examining the complex network of clients and policies within our insurance database. We have a directed relations chart where each node represents a client or policy, and directed edges signify the referral or policy relationships between them. Specifically, we have data that demonstrates these connections: client 0 referred client 1, who subsequently referred client 2; client 2 brought us back to client 0 creating a potential referral loop, there's also a direct referral from client 0 to client 2, and finally, clients 2 and 3 have a mutual referral agreement.

In an effort to understand the full spectrum of our client referral dynamics and ensure there's no overlap or redundancy in our marketing efforts, we're looking to identify each unique referral loop within this directed graph. Could you elaborate on how we might employ NetworkX to uncover every fundamental referral cycle in this scenario? The key is to pinpoint the simple, non-repeating paths that return to their starting point, reflecting each elementary circuit that exists within our client network.
### Response:
import networkx as nx

# Create a directed graph
G = nx.DiGraph()
G.add_edges_from([(0, 1), (1, 2), (2, 0), (0, 2), (2, 3), (3, 2)])

# Find simple cycles
cycles = nx.simple_cycles(G)

# Since recursive_simple_cycles is resource-intensive, we recommend using simple_cycles
# However, if you need to use the recursive version:
# cycles = nx.recursive_simple_cycles(G)

print(list(cycles))
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