<｜begin▁of▁sentence｜>### Instruction:
As an automotive engineer, you're tasked with optimizing various components of a vehicle to ensure top-notch performance and efficiency. Imagine you're tasked with enhancing the engine control system of a cutting-edge electric vehicle (EV) model. To accomplish this, you're leveraging advanced computational techniques, specifically a method called Diff2Vec, to generate node embeddings that capture crucial neighborhood-based information within the vehicle's intricate network of electronic components.

Your team has collected extensive data on the vehicle's electrical system, mapping out the connections between components such as batteries, motor controllers, and sensors. Drawing inspiration from the Diff2Vec model outlined in the CompleNet ‘18 paper, you aim to harness this data to create embeddings that encapsulate the relationships and interactions between these components.

To dive deeper, you're adjusting parameters such as the number of diffusions, cover size, and embedding dimensions to tailor the model to the specifics of the EV's electrical network. This fine-tuning process is crucial to ensure that the resulting embeddings effectively capture the nuances of the system, empowering engineers to make informed decisions when optimizing the vehicle's performance and reliability.

By integrating cutting-edge computational techniques like Diff2Vec into the development process, you're paving the way for innovations that drive the automotive industry forward, ultimately leading to more efficient, safer, and technologically advanced vehicles.
### Response:
import networkx as nx
from karateclub import Diff2Vec

# Create a sample graph (e.g., using the famous Zachary's Karate Club graph)
G = nx.karate_club_graph()

# Initialize the Diff2Vec model with custom parameters
model = Diff2Vec(diffusion_number=10, diffusion_cover=80, dimensions=128, workers=4,
                 window_size=5, epochs=1, learning_rate=0.05, min_count=1, seed=42)

# Fit the model on the graph
model.fit(G)

# Retrieve the embedding for the nodes
embedding = model.get_embedding()

print(embedding)
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