<｜begin▁of▁sentence｜>### Instruction:
Given a graph G with nodes {0, 1, 2} and edges {(0, 1), (1, 2)}, and a feature matrix represented as a COO matrix where each node has a single feature of value 1, can you use the MUSAE model to generate node embeddings using the specified hyperparameters (e.g., walk_number, walk_length, dimensions, etc.)?
### Response:
import networkx as nx
import numpy as np
from karateclub import MUSAE
from scipy.sparse import coo_matrix

# Construct a graph
G = nx.Graph()
G.add_nodes_from([0, 1, 2])
G.add_edges_from([(0, 1), (1, 2)])

# Create a feature matrix as a COO matrix
data = np.array([1, 1, 1])
row = np.array([0, 1, 2])
col = np.array([0, 1, 2])
X = coo_matrix((data, (row, col)), shape=(3,3))

# Initialize and fit the MUSAE model
model = MUSAE(walk_number=10, walk_length=80, dimensions=16, workers=2, window_size=5, epochs=3, learning_rate=0.01, down_sampling=0.001, min_count=1, seed=42)
model.fit(G, X)

# Retrieve the embedding
embedding = model.get_embedding()
print(embedding)
<|EOT|>
