<｜begin▁of▁sentence｜>### Instruction:
Imagine you're working as a Pharmaceutical Sales Representative, and you've got a network where the nodes represent healthcare professionals, and the edges represent the frequency of your visits, with the 'weight' capturing the importance or priority of each connection. You have the following visitation data for a given period: [(1, 2, with a priority of 1), (1, 3, with a priority of 3), (2, 3, with a priority of 2), (3, 4, with a priority of 4), (4, 5, with a priority of 1)].

In order to optimize your schedule for the next quarter, you're considering applying a method that sorts these healthcare professionals in a sequence that could potentially maximize your sales efficiency. Could you reorganize these connections in a spectral order using a tool like NetworkX to find the most effective path for your upcoming visits? This method might reveal a path that balances out your high-priority engagements with the necessary follow-ups.
### Response:
import networkx as nx
from networkx.linalg.algebraicconnectivity import spectral_ordering

# Create a graph
G = nx.Graph()
G.add_edges_from([(1, 2, {'weight': 1}), (1, 3, {'weight': 3}), (2, 3, {'weight': 2}), (3, 4, {'weight': 4}), (4, 5, {'weight': 1})])

# Compute the spectral ordering of the graph
order = spectral_ordering(G, weight='weight', normalized=True, tol=1e-8, method='tracemin_pcg')

print('Spectral ordering of the nodes:', order)
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