<｜begin▁of▁sentence｜>### Instruction:
Given a graph created using the Newman-Watts-Strogatz model with 100 nodes, each connected to 3 nearest neighbors and a rewiring probability of 0.5, along with a feature matrix represented as a sparse COO matrix with dimensions 100x32 (where features are binary indicators generated randomly with a threshold of 0.15), can the FSCNMF model be used to compute node embeddings that fuse the graph structure and node features? Embeddings should capture both the adjacency relations and feature similarity, using the provided algorithm parameters.
### Response:
import networkx as nx
from karateclub import FSCNMF
from scipy.sparse import coo_matrix
import numpy as np

# Create a graph
G = nx.newman_watts_strogatz_graph(100, 3, 0.5)

# Create a feature matrix
features = coo_matrix((np.random.rand(100, 32) > 0.85).astype(int))

# Initialize FSCNMF
model = FSCNMF(dimensions=16, lower_control=1e-15, iterations=200, alpha_1=500.0, alpha_2=0.5, alpha_3=0.5, beta_1=500.0, beta_2=0.5, beta_3=0.5)

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

# Get the embedding
embedding = model.get_embedding()
print(embedding)
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