<｜begin▁of▁sentence｜>### Instruction:
Imagine you're coaching a highly strategic eSports team where the players represent points in a complex battle arena, and the paths they can take between positions are your directed edges. Currently, the game map includes routes forming a directed graph with interactions as follows: players can move from point 0 to 1, from 1 to 2, from 2 back to 0 to complete a loop, from 2 to 3, from 3 to 4, and from 4 can strategize to hit back to point 2.

To enhance the team's tactical play, you're looking to review all potential loops where a player can move from one point to another through a series of moves without retracing their steps, so that you can train your players to anticipate and break the opponents' loops. How would you employ NetworkX to identify all these non-repetitive traversal paths—which we refer to as simple cycles or elementary circuits—in the game's movement graph?
### Response:
import networkx as nx

# Create a directed graph
G = nx.DiGraph()
G.add_edges_from([(0, 1), (1, 2), (2, 0), (2, 3), (3, 4), (4, 2)])

# Find simple cycles in the graph
simple_cycles_list = list(nx.simple_cycles(G))

# Print the list of simple cycles
print('Simple cycles in the graph:', simple_cycles_list)
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