<｜begin▁of▁sentence｜>### Instruction:
Given a karate club graph with each node assigned a unique feature equal to its node ID, can you create node embeddings using the TADW model from the IJCAI '15 paper which incorporates the node attribute matrix along with a factorization matrix to reproduce a power of the adjacency matrix and generate the node representations?
### Response:
import networkx as nx
import numpy as np
from karateclub import TADW
from scipy.sparse import coo_matrix

# Create a simple graph
G = nx.karate_club_graph()

# Create a simple feature matrix where each node has a feature equal to its node ID
features = np.identity(G.number_of_nodes())
feature_matrix = coo_matrix(features)

# Initialize TADW model with example parameters
model = TADW(dimensions=32, reduction_dimensions=64, svd_iterations=20, seed=42, alpha=0.01, iterations=10, lambd=10.0)

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

# Get the embeddings
embeddings = model.get_embedding()

print(embeddings)
<|EOT|>
