<｜begin▁of▁sentence｜>### Instruction:
As an antique dealer, imagine you have acquired two sets of antique furniture, each with 100 unique items. However, you want to understand the overall style and characteristics of each set. You've heard about a tool called the FeatherGraph model, which can help you analyze and compare the stylistic features using parameters such as order, evaluation points, and pooling method. How could you utilize the FeatherGraph model with parameters (order=5, eval_points=25, pooling='mean', seed=42) to create a comprehensive understanding of the characteristics and styles represented by the two sets of antique furniture?
### Response:
import networkx as nx
from karateclub import FeatherGraph

# Generate a couple of graphs
G1 = nx.gnp_random_graph(n=100, p=0.1)
G2 = nx.gnp_random_graph(n=100, p=0.1)
graphs = [G1, G2]

# Initialize FeatherGraph
model = FeatherGraph(order=5, eval_points=25, theta_max=2.5, seed=42, pooling='mean')

# Fit the model
model.fit(graphs)

# Get the embeddings
embeddings = model.get_embedding()

print(embeddings)
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