<｜begin▁of▁sentence｜>### Instruction:
Imagine you're examining the financial stability and interconnectedness of four different entities within a lending network. This network can be modeled as a path graph, representing the sequenced financial relationships between these parties. To gauge the overall risk and balance of the network, you're interested in determining the spectral bipartivity of this graph—essentially assessing how well the network can be split into two independent groups without overlapping financial obligations. You've previously utilized NetworkX to map out such relationships. How would you harness the 'spectral_bipartivity' function available within the NetworkX toolkit to compute the measure of spectral bipartivity for this particular path graph with four nodes, ensuring a more informed risk assessment on your lending decisions?
### Response:
import networkx as nx
from networkx.algorithms import bipartite

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

# Calculate the spectral bipartivity
sb = bipartite.spectral_bipartivity(G)

print('Spectral Bipartivity:', sb)
<|EOT|>
