<｜begin▁of▁sentence｜>### Instruction:
Given a list of two NetworkX graphs with 10 and 15 nodes, and edge probabilities of 0.5 and 0.3 respectively, how can you use the SF model to obtain a low-dimensional representation of their structure by calculating the k lowest eigenvalues of the normalized Laplacian, where k is set to 128?
### Response:
import networkx as nx
from karateclub import SF

# Creating two small graphs
G1 = nx.gnp_random_graph(10, 0.5, seed=42)
G2 = nx.gnp_random_graph(15, 0.3, seed=42)
graphs = [G1, G2]

# Initializing SF model with the desired number of dimensions
model = SF(dimensions=128, seed=42)

# Fit the model
model.fit(graphs)

# Get the embedding
embedding = model.get_embedding()
print(embedding)
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