<｜begin▁of▁sentence｜>### Instruction:
Given a list of two Erdős-Rényi graphs each with 100 nodes and a probability of 0.05 for edge creation, how can the GL2Vec model be used to generate graph embeddings following the methodology described in the ICONIP ‘19 paper 'GL2vec: Graph Embedding Enriched by Line Graphs with Edge Features'? Provide the embeddings obtained for these graphs.
### Response:
import networkx as nx
from karateclub import GL2Vec

# Generate sample graphs
G1 = nx.erdos_renyi_graph(100, 0.05)
G2 = nx.erdos_renyi_graph(100, 0.05)
graphs = [G1, G2]

# Initialize GL2Vec model with custom parameters
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 embedding for the graphs
embeddings = model.get_embedding()

print(embeddings)
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