<｜begin▁of▁sentence｜>### Instruction:
As a network analyst, you have been entrusted with the task of creating a network representation of a wedding event's seating arrangements. The venue is laid out in a way that 3 tables are arranged in a line, and each table has unique floral decorations denoted by binary features. Blush events are represented as 0, while dusty blue events are displayed as 1. The seating arrangement links are [(Table 1, Table 2), (Table 2, Table 3)].

How would you utilize the multi-scale attributed node embedding (AE) model to generate embeddings for the tables with their distinct floral decoration matrix?
### Response:
import networkx as nx
import numpy as np
from karateclub import AE
from scipy.sparse import coo_matrix

# Create a graph with 3 nodes and 2 edges
G = nx.Graph()
G.add_edges_from([(0, 1), (1, 2)])

# Create a binary feature matrix for each node
features = np.eye(3)
row = np.array([0, 1, 2])
col = np.array([0, 1, 2])
data = np.array([1, 1, 1])
X = coo_matrix((data, (row, col)), shape=(3, 3))

# Initialize the AE model with specified parameters
model = AE(walk_number=10, walk_length=80, dimensions=16, workers=2, window_size=5, epochs=3, learning_rate=0.01, down_sampling=0.0001, min_count=1, seed=42)

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

# Retrieve the embeddings
embeddings = model.get_embedding()

print('Node Embeddings:\n', embeddings)
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