<｜begin▁of▁sentence｜>### Instruction:
As a game developer crafting a new puzzle game where players traverse a network of interconnected rooms labeled 'A', 'B', and 'C', you're keen on using the structure of the underlying graph to influence gameplay mechanics. The rooms are connected by doors, where the door between 'A' and 'B' takes 2 energy units to open, 'B' to 'C' costs 3 energy units, and 'C' to 'A' costs 4 energy units. You’re considering using the properties of the graph at a deeper mathematical level to enhance the gameplay experience. Specifically, you want to tap into graph theory to extract the eigenvalues of the adjacency matrix representing the weighted connections between the rooms. This matrix could potentially inform the balance and difficulty of your in-game puzzles. Could you illustrate how this information might be extracted using NetworkX, the well-known Python library for the analysis of complex networks? Please keep in mind that the adjacency matrix in this case also needs to reflect the varying energy costs associated with each door.
### Response:
import networkx as nx
import numpy as np

# Create a graph
G = nx.Graph()
G.add_edge('A', 'B', weight=2)
G.add_edge('B', 'C', weight=3)
G.add_edge('C', 'A', weight=4)

# Compute eigenvalues of the adjacency matrix
eigenvalues = np.round(nx.adjacency_spectrum(G, weight='weight'), 2)

print('The eigenvalues of the adjacency matrix are:', eigenvalues)
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