<｜begin▁of▁sentence｜>### Instruction:
Imagine you're architecting the information flow within a digital system that tracks dependencies between various components. You've represented the dependencies as a directed graph within a GML file named "dependencies.gml." The relationships between the components are mapped out as directed edges: [(0, 1), (1, 2), (2, 0), (2, 3), (3, 2)], where each tuple (a, b) indicates a one-way dependency from component 'a' to component 'b'.

In optimizing the system for performance, you've identified that detecting all the simple cycles (those that do not repeat vertices) within this dependency graph is crucial since they might reveal possible redundancies or infinite loops in the dependency structure.

Utilizing NetworkX's 'recursive_simple_cycles' function, how would you determine these simple cycles directly from the "dependencies.gml" graph data to better refine the digital information architecture?
### Response:
import networkx as nx

# Create a directed graph
G = nx.DiGraph()
# Add edges
G.add_edge(0, 1)
G.add_edge(1, 2)
G.add_edge(2, 0)
G.add_edge(2, 3)
G.add_edge(3, 2)

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

print('Simple cycles found:', cycles)
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