<｜begin▁of▁sentence｜>### Instruction:
Given a random graph G with 100 nodes and 500 edges, and a binary feature matrix representing node features, how can you use the AE model (from the Arxiv '19 paper 'MUSAE: Multi-Scale Attributed Node Embedding') to obtain node embeddings with attributed random walks, where the node feature matrix is provided as a sparse COO matrix?
### Response:
import networkx as nx
import numpy as np
from karateclub import AE
from scipy.sparse import coo_matrix

# Create a graph
G = nx.gnm_random_graph(100, 500)

# Create a binary node feature matrix as a COO matrix
data = np.ones(100)
row = np.arange(100)
col = np.arange(100)
node_features = coo_matrix((data, (row, col)), shape=(100, 100))

# Initialize AE model with custom parameters
model = AE(walk_number=10, walk_length=80, dimensions=64, workers=2, window_size=5, epochs=3, learning_rate=0.1, down_sampling=0.0001, min_count=1, seed=42)

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

# Get the node embeddings
embeddings = model.get_embedding()
print(embeddings)
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