<｜begin▁of▁sentence｜>### Instruction:
As a diplomatic representative fostering relations with foreign entities, I have been tasked with leveraging the MUSAE model to generate personalized node embeddings for a network with specific parameters. My objective is to utilize the 10 random walks, each with a length of 80, and a 64-dimensional embedding to create the node embeddings. The network consists of three nodes, each with distinct feature vectors: node 0 with features [1, 0], node 1 with features [0, 1], and node 2 with features [1, 1]. This effort aims to enhance our understanding of the network structure and its underlying features, ultimately facilitating informed decision-making in our diplomatic engagements.
### Response:
import networkx as nx
import numpy as np
from karateclub import MUSAE
from scipy.sparse import coo_matrix

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

# Creating node features
features = np.array([[1, 0], [0, 1], [1, 1]])
feature_matrix = coo_matrix(features)

# Instantiating MUSAE model with custom parameters
model = MUSAE(walk_number=10, walk_length=80, dimensions=64, workers=4, window_size=5, epochs=3, learning_rate=0.01, down_sampling=0.0001, min_count=1, seed=42)
model.fit(G, feature_matrix)

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