<｜begin▁of▁sentence｜>### Instruction:
Given two Erdős-Rényi graphs each with 100 nodes and the probability of edge creation p=0.05, can you use the NetLSD model to calculate the heat kernel trace of the normalized Laplacian matrix over a vector of time scales and obtain their embeddings?
### Response:
import networkx as nx
from karateclub import NetLSD
import numpy as np

# Create two graphs G1 and G2
G1 = nx.erdos_renyi_graph(n=100, p=0.05)
G2 = nx.erdos_renyi_graph(n=100, p=0.05)

# Initialize NetLSD with custom parameters
model = NetLSD(scale_min=-2.0, scale_max=2.0, scale_steps=250, scale_approximations=200, seed=42)

# Fit the model on the list of graphs
model.fit([G1, G2])

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

print(embedding)
<|EOT|>
