<｜begin▁of▁sentence｜>### Instruction:
Given two random graphs generated with the GNP random graph model (each graph having 100 nodes and a probability of 0.1 for edge creation between any two nodes), how can you use the 'SF' model to obtain a structured embedding of these graphs based on the k lowest eigenvalues of their normalized Laplacian, padding with zeros if there are fewer eigenvalues than k?
### Response:
import networkx as nx
from karateclub import SF

# Create two graphs
G1 = nx.gnp_random_graph(100, 0.1)
G2 = nx.gnp_random_graph(100, 0.1)
graphs = [G1, G2]

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

# Fit the model to the graphs
model.fit(graphs)

# Get the embedding of the graphs
embedding = model.get_embedding()

print(embedding)
<|EOT|>
