<｜begin▁of▁sentence｜>### Instruction:
As a lighting designer for a large entertainment production, you need to create a cohesive and visually stunning lighting arrangement for a new show. You have a large space with 100 different areas that need to be highlighted, and you want to use a specific probability to determine the connections between different areas for your lighting design. Additionally, each area has unique characteristics that you need to take into account when designing the lighting plan. To help with this, you've decided to use a specialized Autoencoder (AE) model with specific parameters for analyzing the features of each area and generating optimal lighting arrangements. After fitting the model, you're looking to retrieve the lighting arrangements for each area to enhance the overall visual experience for the audience.
### Response:
import networkx as nx
import numpy as np
from karateclub import AE
from scipy.sparse import coo_matrix

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

# Create a feature matrix where each node has a single attribute set to 1
X = coo_matrix((np.ones(G.number_of_nodes()), (range(G.number_of_nodes()), range(G.number_of_nodes()))), shape=(G.number_of_nodes(), G.number_of_nodes()))

# Initialize AE model with custom parameters
ae_model = AE(walk_number=10, walk_length=80, dimensions=64, workers=2, window_size=5, epochs=3, learning_rate=0.01, down_sampling=0.001, min_count=2, seed=42)

# Fit the model
ae_model.fit(G, X)

# Retrieve node embeddings
embeddings = ae_model.get_embedding()

print(embeddings)
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