<｜begin▁of▁sentence｜>### Instruction:
Imagine you're gently and carefully arranging a nursery for a new delightful little bundle of joy, wanting to make sure everything is perfectly in place for ease and accessibility. Now, envision that there's a family of four cherished toys, each connected in a line sequence: toy 0 is linked to toy 1, toy 1 to toy 2, and toy 2 to toy 3, forming a tidy little toy train ready for storytime.

In a similar nurturing and thoughtful manner, could we ponder upon utilizing the nurturing touch of NetworkX's reverse_cuthill_mckee_ordering function, just like how we would organize a nurturing space? This technique could potentially be applied to reorganize the toy family into a pattern that corresponds to a sparse matrix—streamlining our nursery, so the toys (our graph nodes) offer the little one the most serene and harmonious environment. Each toy (or node) would have its special spot, ensuring the optimal storytelling path through the nursery. Would this strategy provide the ideal matrix setup for our graph of joy?
### Response:
import networkx as nx
from networkx.utils import reverse_cuthill_mckee_ordering

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

# Get the RCM ordering
rcm = list(reverse_cuthill_mckee_ordering(G))

# You can now reorder your adjacency matrix or do other operations with this new ordering.
# For example:
# A = nx.adjacency_matrix(G, nodelist=rcm)

print('Reverse Cuthill-McKee ordering:', rcm)
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