<｜begin▁of▁sentence｜>### Instruction:
As a radiation therapist, you're tasked with administering radiation treatments to cancer patients using various techniques and equipment like linear accelerators or intensity-modulated radiation therapy (IMRT). You collaborate closely with radiation oncologists and medical physicists to ensure the effectiveness of the treatment while monitoring patients' conditions and responses. Now, imagine you're working in a cutting-edge cancer center where advanced techniques are constantly explored to improve patient outcomes. In this scenario, you're tasked with utilizing a novel approach called the GLEE model, as described in the Journal of Complex Networks, to generate node embeddings from patient data. This involves applying geometric Laplacian Eigenmap Embedding to the network representation of patient data, perhaps obtained from different diagnostic modalities or treatment responses, to better understand underlying patterns and potentially improve treatment strategies.
### Response:
import networkx as nx
from karateclub import GLEE
import numpy as np

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

# Initialize GLEE model with default parameters
glee_model = GLEE()

# Fit the model
glee_model.fit(G)

# Get the embedding
embedding = glee_model.get_embedding()

print(embedding)
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