<｜begin▁of▁sentence｜>### Instruction:
As a network analyst, you have been tasked with enhancing the network embedding of a karate club graph by using the NEU model, which was proposed in the IJCAI '17 paper 'Fast Network Embedding Enhancement via High Order Proximity Approximation'. The specific parameters to be used are L1=0.5, L2=0.25, T=1, and seed=42. Your goal is to consider higher order proximities in order to better understand the relationships and connections within the karate club network.
### Response:
import networkx as nx
from karateclub import NEU

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

# Initialize the NEU model with specified parameters
neu_model = NEU(L1=0.5, L2=0.25, T=1, seed=42)

# Fit the model
neu_model.fit(G)

# Get the resulting embedding
embedding = neu_model.get_embedding()

print(embedding)
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