<｜begin▁of▁sentence｜>### Instruction:
As a furniture maker who designs and crafts unique furniture pieces, imagine you have two sets of custom-designed chairs, each set comprising 50 distinct chairs. Now, if you seek ways to determine the unique characteristics and style of each chair set using a specialized tool called Chair2Vec, how would you efficiently generate embeddings for these chair collections to capture their distinct design features?
### Response:
import networkx as nx
from karateclub import Graph2Vec

# Create a list of graphs
g1 = nx.fast_gnp_random_graph(50, 0.1)
g2 = nx.fast_gnp_random_graph(50, 0.1)
graphs = [g1, g2]

# Initialize Graph2Vec model with default parameters
model = Graph2Vec()

# Fit the model on the list of graphs
model.fit(graphs)

# Retrieve the embeddings
embeddings = model.get_embedding()
print(embeddings)
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