<｜begin▁of▁sentence｜>### Instruction:
As a 3D animator, you might be asked to enhance simulated visual effects for a film, game, or multimedia project using the Karate Club Graph, where each character or element in the project has a unique identifier. Could you employ the TADW model, as described in the IJCAI '15 paper, to incorporate the characteristics of each character or element alongside a factorization matrix? This combined approach aims to replicate a power of the relationships and connections between the characters or elements and generate their visual 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)
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