<｜begin▁of▁sentence｜>### Instruction:
Imagine you're leading a fitness class designed to bolster teamwork and strategic thinking, employing a network of exercises where 'Alpha', 'Beta', and 'Gamma' are different workout stations. The relationships between these stations have varying levels of difficulty influencing transition times, akin to weights in a network: moving from Alpha to Beta burns 0.5 energy units, Alpha to Gamma expends 1.5 units, and Beta to Gamma takes 1.0 unit. To optimize the workout regimen for maximal efficiency, you're looking to apply the concept of a 'normalized energy flow matrix,' analogous to the normalized Laplacian matrix in network science. How could you, inspired by NetworkX's 'normalized_laplacian_matrix' function, compute this matrix to ensure the energy exerted through station transitions remains balanced and beneficial to the overall fitness experience?
### Response:
import networkx as nx
from networkx.linalg.graphmatrix import normalized_laplacian_matrix

# Create a graph
G = nx.Graph()

# Add nodes to the graph
G.add_nodes_from([
    ('a', {'label': 'Alpha'}),
    ('b', {'label': 'Beta'}),
    ('c', {'label': 'Gamma'})
])

# Add weighted edges to the graph
G.add_weighted_edges_from([
    ('a', 'b', 0.5),
    ('a', 'c', 1.5),
    ('b', 'c', 1.0)
])

# Get the normalized Laplacian matrix of the graph
n_laplacian = normalized_laplacian_matrix(G, weight='weight')

# Print the normalized Laplacian matrix
print(n_laplacian)
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