<｜begin▁of▁sentence｜>### Instruction:
In the scene of our upcoming tech-thriller series, our protagonist, a savvy network analyst, is faced with a high-stakes challenge. The plot pivots around a digital network represented by a cryptic blueprint, with interconnected nodes and lines laden with numerical values. It turns out these numbers are not just arbitrary figures but indicate the strength of each connection within this web—a pivotal clue to unlocking the network's secrets.

Imagine the blueprint showcases connections as a series of notations: (1 connects to 2 with a strength of 3.0), (1 to 3 with a strength of 7.5), (2 to 3 at 1.2), and (2 connects to 4 with a 4.1 strength). 

Our protagonist, under the glow of a solitary desk lamp, whispers to themselves, “I need to decipher the harmonic resonances of this grid – the so-called eigenvalues of its normalized Laplacian – using NetworkX. These values could reveal the network's vulnerabilities, the backbone of my plan.”

Queue to a tension-filled montage of frenzied coding and algorithmic dance, the scene demands that we crack this mystery with finesse. In our script, we'll have them working against the clock, translating these connections into a form that NetworkX can understand. With each tick of the clock, the stakes get higher, and the vibrations of this network come into sharper focus as they inch closer to discovering its hidden frequencies.
### Response:
import networkx as nx

# Create a graph
G = nx.Graph()
G.add_weighted_edges_from([(1, 2, 3.0), (1, 3, 7.5), (2, 3, 1.2), (2, 4, 4.1)])

# Compute the normalized Laplacian eigenvalues
eigenvalues = nx.normalized_laplacian_spectrum(G)

print('The normalized Laplacian eigenvalues are:', eigenvalues)
<|EOT|>
