<｜begin▁of▁sentence｜>### Instruction:
Given a list of two random graphs, one with 100 nodes and another with 200 nodes, where each pair of nodes is connected with probability 0.1, can you use the SF model to calculate the k lowest eigenvalues of the normalized Laplacian for these graphs and obtain their embeddings?
### Response:
import networkx as nx
from karateclub import SF
import numpy as np

# Step 1: Create a list of NetworkX graphs.
graph1 = nx.gnp_random_graph(100, 0.1, seed=42)
graph2 = nx.gnp_random_graph(200, 0.1, seed=42)
graphs = [graph1, graph2]

# Step 2: Initialize SF model with default parameters.
model = SF()

# Step 3: Fit the model on the list of graphs.
model.fit(graphs)

# Step 4: Get the embeddings of the graphs.
embeddings = model.get_embedding()

print('Embeddings shape:', embeddings.shape)
print('Embeddings:', embeddings)
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