<｜begin▁of▁sentence｜>### Instruction:
Given a list of two graphs, a cycle graph with 5 nodes and a star graph with 4 nodes, how can you use the 'WaveletCharacteristic' model from the CIKM ‘21 paper 'Graph Embedding via Diffusion-Wavelets-Based Node Feature Distribution Characterization' to get their graph level embeddings?
### Response:
import networkx as nx
from karateclub import WaveletCharacteristic
import numpy as np

# Create sample graphs
G1 = nx.cycle_graph(5)
G2 = nx.star_graph(4)
graphs = [G1, G2]

# Instantiate the model
model = WaveletCharacteristic(order=5, eval_points=5, theta_max=2.5, tau=1.0, pooling='mean')

# Fit the model
model.fit(graphs)

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