<｜begin▁of▁sentence｜>### Instruction:
As a cartographer working on a mapping project for a network infrastructure, I need to represent the spectral features of a set of random graphs generated using the GNP model with 100 nodes and a probability of 0.05 for edge creation. To achieve this, I want to use the SF model to create fixed-size embeddings that capture the essential network properties for visualization and analysis.

The data consists of several random graphs generated using the GNP model, each with 100 nodes and a probability of 0.05 for edge creation. The goal is to use the SF model to extract spectral features from these graphs in order to create a comprehensive mapping of the network topology.

API: NetworkX can be used to create and analyze the graphs, and the Spectral Features (SF) model can be implemented for obtaining fixed-size embeddings representing the network's properties.
### Response:
import networkx as nx
from karateclub import SF

# Create a list of graphs
G1 = nx.gnp_random_graph(100, 0.05)
G2 = nx.gnp_random_graph(100, 0.05)
graphs = [G1, G2]

# Initialize SF model
model = SF(dimensions=128, seed=42)

# Fit model
model.fit(graphs)

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