<｜begin▁of▁sentence｜>### Instruction:
Given a Karate Club graph where each node has a feature vector containing two values - one representing affiliation mod 2 and another mod 3, how do you extract the node embeddings using the FeatherNode model from the CIKM '20 paper, with a reduced SVD dimension of 32, 10 SVD iterations, a maximal evaluation point of 1.5, 20 evaluation points for the characteristic function, an adjacency matrix order of 3, and a random seed of 24?
### Response:
import networkx as nx
from karateclub import FeatherNode
import numpy as np

# Creating a graph with node features
G = nx.karate_club_graph()
for i, node in enumerate(G.nodes(data=True)):
    G.nodes[node[0]]['feature'] = np.array([i%2, i%3])

# Prepare the node features matrix
node_features = np.array([G.nodes[node]['feature'] for node in G.nodes()])

# Initialize FeatherNode model with custom parameters
model = FeatherNode(reduction_dimensions=32, svd_iterations=10, theta_max=1.5, eval_points=20, order=3, seed=24)

# Fit the model
model.fit(G, node_features)

# Get the embeddings
embeddings = model.get_embedding()
print('Node embeddings:\n', embeddings)
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