<｜begin▁of▁sentence｜>### Instruction:
Imagine we are examining a theoretical model of a particle system that forms a closed loop, representing it with a quadrilateral configuration where each particle (node) has a direct interaction only with its immediate neighbors. Conceptually, we need to assign distinct energy levels (colors) to each particle in such a way that adjacent particles have non-overlapping energy states, thus adhering to the Pauli exclusion principle in a simplified analogy.

In this model, we wish to employ the 'saturation largest first' heuristic, akin to determining the sequence of filling these energy states by prioritizing particles with the maximum number of distinct adjacent states already occupied (saturation), and then breaking ties by the degree of particles (largest first). How can we apply this strategy using the computational tool, NetworkX, to achieve an optimal energy state distribution (coloring) among our particles?
### Response:
import networkx as nx
from networkx.algorithms.coloring import greedy_color

# Create a graph
G = nx.cycle_graph(4)

# Use the saturation largest first strategy to color the graph
colors = greedy_color(G, strategy=nx.coloring.strategy_saturation_largest_first)

print(colors)
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