<｜begin▁of▁sentence｜>### Instruction:
As a Renewable Energy Engineer working on optimizing renewable energy systems, you're tasked with enhancing the efficiency of a network of solar panels in a solar farm. To accomplish this, you're considering utilizing a cutting-edge approach called the FeatherGraph model. This model allows you to extract meaningful representations, or embeddings, from complex network data, such as graphs.

In your scenario, you have data from two different sections of the solar farm, each consisting of 100 solar panels. These sections exhibit varying levels of interconnectedness, akin to Erdős-Rényi graphs, where the probability of connection between any two panels is 0.05.

Your goal is to apply the FeatherGraph model to these two sections of the solar farm to obtain embeddings that capture the structural and connectivity information of the panels. You plan to use mean pooling and the default parameters of the FeatherGraph model to transform the graph data into a fixed-size representation, facilitating further analysis and optimization of the solar farm's performance.
### Response:
import networkx as nx
from karateclub import FeatherGraph
import numpy as np

# Create an example graph
G1 = nx.erdos_renyi_graph(n=100, p=0.05)
G2 = nx.erdos_renyi_graph(n=100, p=0.05)
graphs = [G1, G2]

# Define the FeatherGraph model
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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