<｜begin▁of▁sentence｜>### Instruction:
Imagine we're developing a strategic plan to plant trees along a stretch of road that's separated into five distinct segments. We wish to ensure we're maximizing the environmental impact without overcrowding the segments, which can be challenging due to limited resources and space. 

Thus, envision these road segments as a linear network where each segment can potentially receive a tree. To model our scenario, think of it as a path graph with five nodes, akin to the ecological corridors that we use in conservation efforts. Using this model, we want to identify a selection pattern of where to plant the trees so that no two adjacent segments have a tree, allowing for optimal growth and resource distribution. 

We're committed to a fair and balanced approach, so we've agreed to use a nature-inspired method, akin to how random seed dispersal might occur in the wild. To keep our strategy consistent and reproducible, we'll use the "seed value" of 42, ensuring that we can replicate our pattern in future expansion of the project. 

So, within this framework, could you help us discover a random pattern of tree planting that meets our criteria, using the data from our path graph model?
### Response:
import networkx as nx

# Create a path graph with 5 nodes
G = nx.path_graph(5)

# Find a random maximal independent set in the graph
# The seed ensures the randomness is reproducible
indep_set = nx.maximal_independent_set(G, seed=42)

print('Maximal independent set:', indep_set)
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