<｜begin▁of▁sentence｜>### Instruction:
Given a random graph with 100 nodes and a 10% chance of edge creation between any two nodes, along with 256-dimensional random features for each node, how can the ASNE model be applied to this graph to produce node embeddings using the parameters defined in the code example?
### Response:
import networkx as nx
import numpy as np
from karateclub import ASNE
from scipy.sparse import coo_matrix

# Create a synthetic graph
G = nx.fast_gnp_random_graph(100, 0.1)

# Generate random node features
features = np.random.rand(100, 256)
row = np.arange(100)
col = np.arange(100)
feature_matrix = coo_matrix((features.flatten(), (row, col)))

# Initialize ASNE with specified parameters
model = ASNE(dimensions=128, workers=4, epochs=100, down_sampling=0.0001, learning_rate=0.05, min_count=1, seed=42)

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

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