<｜begin▁of▁sentence｜>### Instruction:
As a Health Inspector tasked with ensuring compliance with health and safety regulations in public facilities and businesses, imagine you're examining two different restaurants. Each restaurant has a unique layout, and you need to assess their cleanliness and adherence to sanitation standards.

One restaurant, let's call it "Chef's Delight," has a spacious dining area with 100 tables. The kitchen is bustling with activity, and there are 100 chefs preparing dishes. However, the layout of the restaurant is quite intricate, with various pathways connecting different sections.

The other restaurant, "Culinary Haven," also boasts a similar setup with 100 tables and 100 chefs in the kitchen. However, the arrangement of tables and kitchen stations differs from Chef's Delight.

To thoroughly evaluate the cleanliness and organization of these restaurants, you decide to employ a cutting-edge technology called the "FeatherGraph Analyzer." This tool allows you to analyze the spatial arrangement and connectivity within each restaurant to generate comprehensive embeddings representing their layouts.

You've heard that the FeatherGraph Analyzer uses a sophisticated model called "FeatherGraph" from the KarateClub API. You need to configure this tool with specific parameters: order=5, eval_points=25, theta_max=2.5, and pooling='mean'. These settings will help capture the intricate details of the restaurant layouts and produce embeddings that reflect their unique characteristics.

With the embeddings generated by the FeatherGraph Analyzer, you'll be able to gain insights into the structural organization of Chef's Delight and Culinary Haven, allowing you to make informed assessments regarding their compliance with health and safety regulations.
### Response:
import networkx as nx
from karateclub import FeatherGraph
import numpy as np

# Create a list with two graphs
G1 = nx.gnp_random_graph(100, 0.1, seed=42)
G2 = nx.gnp_random_graph(100, 0.1, seed=24)
graphs = [G1, G2]

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

# Fit the model
model.fit(graphs)

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