<｜begin▁of▁sentence｜>### Instruction:
As a Quality Assurance Analyst, we have been tasked with evaluating and testing the quality of two software applications. The first application, consisting of 100 modules, and the second application, consisting of 200 modules, have been designed to connect with each other with a probability of 0.1. Our objective is to use the Spectral Embedding algorithm to calculate the k lowest eigenvalues of the normalized Laplacian for these applications and obtain their embeddings. This will help ensure that the applications meet the required quality standards and specifications.
### 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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