<｜begin▁of▁sentence｜>### Instruction:
Given a list of 10 Erdős-Rényi graphs with 20 nodes and a probability of edge creation of 0.05, how can you use the GL2Vec model to obtain their embeddings?
### Response:
import networkx as nx
from karateclub import GL2Vec

# List of graphs
graphs = [nx.gnp_random_graph(20, 0.05) for _ in range(10)]

# Initialize GL2Vec with predefined hyperparameters
model = GL2Vec(wl_iterations=2, dimensions=128, workers=4, down_sampling=0.0001,
               epochs=10, learning_rate=0.025, min_count=5, seed=42)

# Fit the model
model.fit(graphs)

# Get the embeddings
embeddings = model.get_embedding()

print(embeddings)
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