<｜begin▁of▁sentence｜>### Instruction:
Given a NetworkX graph 'G' with 3 nodes and 2 edges [(0, 1), (1, 2)], and a feature matrix 'X' represented as a Scipy COO array with non-zero elements at (0, 0), (1, 1), and (2, 2), how would you use the MUSAE model to obtain a 16-dimensional embedding for each node when the model is initialized with specific parameters including 10 random walks of length 80, using 5 matrix power orders, over 3 epochs, with a custom learning rate of 0.01 and a random seed of 42?
### Response:
import networkx as nx
import numpy as np
from karateclub import MUSAE
from scipy.sparse import coo_matrix

# Creating a graph with 3 nodes and 2 edges
G = nx.Graph()
G.add_edge(0, 1)
G.add_edge(1, 2)

# Defining a feature matrix as a COO matrix
feature_matrix_data = np.array([1, 1, 1])
feature_matrix_row = np.array([0, 1, 2])
feature_matrix_col = np.array([0, 1, 2])
X = coo_matrix((feature_matrix_data, (feature_matrix_row, feature_matrix_col)), shape=(3, 3))

# Initializing MUSAE model with parameters
model = MUSAE(walk_number=10, walk_length=80, dimensions=16, workers=4, window_size=5, epochs=3, learning_rate=0.01, down_sampling=0.0001, min_count=1, seed=42)

# Fitting the model
model.fit(G, X)

# Obtaining the embeddings
embeddings = model.get_embedding()
print(embeddings)
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