<｜begin▁of▁sentence｜>### Instruction:
As a property manager overseeing multiple assets, imagine we have a residential complex consisting of four interconnected buildings—designated as Buildings 0, 1, 2, and 3. These buildings are connected by pathways that allow residents to travel directly from one building to another. Specifically, there are pathways from Building 0 to Building 1, from Building 1 to Building 2, from Building 2 to Building 3, and from Building 3 back to Building 0, forming a closed loop of travel.

Now, to enhance the visual appeal and aid in the maintenance scheduling of these pathways, we're considering implementing a color-coded system. Each pathway will be assigned a distinct color to ensure efficient management and use by the residents. To achieve this, we're looking at employing an algorithmic approach that selects colors effectively.

We're interested in utilizing a method akin to the 'largest_first' strategy from our software's scheduling tool, which works similarly to NetworkX's greedy_color function. This method is expected to assign colors in such a manner that it starts with the pathway requiring the most attention, thereby minimizing the total number of colors used.

Could you assist by rephrasing the instructions on how to apply this 'largest_first' color-coding strategy to our pathway system using the software's scheduling tool? Remember, this is not about programming but about translating the algorithm's logic into operational guidance for our property management context.
### Response:
import networkx as nx

# Create a cycle graph with 4 nodes
g = nx.cycle_graph(4)

# Apply the greedy_color function with the 'largest_first' strategy
coloring = nx.coloring.greedy_color(g, strategy='largest_first')

print('Node colorings:', coloring)
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