<｜begin▁of▁sentence｜>### Instruction:
Imagine we're crafting a documentary about the dynamics within a local karate club, where the relationships and interactions between members create a complex web of alliances and rivalries. In order to visually represent this intricate social network, we've constructed a graphical model of the club's interpersonal connections. 

For a dramatic twist in our storyline, we want to delve deeper into the underlying structure of this community by examining the spectral properties of this network. Specifically, we aim to analyze the vibrational frequencies of the relationships within the club—akin to identifying the resonant frequencies of a grand, architectural structure.

Our method involves utilizing a mathematical construct known as the Bethe Hessian matrix, a matrix that allows us to peer into the stability and dynamics of the network. We plan to add a creative element by setting a regularizer parameter 'r' that is equal to the square root of the total number of ties that bind this club together—each relationship acting as a bridge in our story.

As the script for our documentary takes shape, could you, as part of our research team, calculate the eigenvalues of the Bethe Hessian matrix for our karate club network using this artistic regularizer? This computation will help us understand the harmonics of the club's social network and provoke a thought-provoking narrative for our audience.
### Response:
import networkx as nx
import numpy as np
from networkx.utils import bethe_hessian_spectrum

# Create a graph
G = nx.karate_club_graph()

# Set the regularizer parameter
r = np.sqrt(G.number_of_edges())

# Compute the Bethe Hessian spectrum
spectrum = bethe_hessian_spectrum(G, r)

# Print the eigenvalues
print(spectrum)
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