<｜begin▁of▁sentence｜>### Instruction:
As a purchasing manager responsible for procurement activities and vendor relationships, you're tasked with optimizing supply chain efficiency through data-driven decisions. Imagine you have a set of diverse suppliers, each represented as a unique network graph. Among these, two key graphs stand out: a complete graph capturing intricate interconnections between suppliers, and a cycle graph representing a more cyclical relationship pattern.

Now, to enhance your procurement strategy, you're exploring cutting-edge techniques like the FeatherGraph model introduced in the CIKM '20 paper 'Characteristic Functions on Graphs: Birds of a Feather, from Statistical Descriptors to Parametric Models'. This model offers a novel approach to generating embeddings for graphs, potentially revealing hidden patterns and similarities crucial for supplier selection and negotiation.

Your task is to leverage the fit method provided by the FeatherGraph API to process these supplier graphs effectively. Once fitted, you can then utilize the get_embedding method to extract meaningful embeddings for each graph. These embeddings will serve as compact representations of the complex relationships within your supplier network, empowering you to make informed decisions and optimize procurement processes.
### Response:
import networkx as nx
from karateclub import FeatherGraph
import numpy as np

# Creating a list of NetworkX graphs
g1 = nx.complete_graph(5)
g2 = nx.cycle_graph(5)
graphs = [g1, g2]

# Instantiate 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
embedding = model.get_embedding()

print(embedding)
<|EOT|>
