<｜begin▁of▁sentence｜>### Instruction:
As a forester who is responsible for managing and protecting a large forest area, you have been using advanced technology to monitor the health and connectivity of the trees within the forest. Now, you have collected data from a network of 100 key locations within the forest, and these locations are connected by 500 different paths. You want to apply the Role2Vec model to obtain a node embedding that incorporates the structural information of these connections while respecting the Weisfeiler-Lehman graph representation. Can you explain how this can be achieved using the NetworkX library in Python?
### Response:
import networkx as nx
from karateclub import Role2Vec

# Create a graph
G = nx.gnm_random_graph(100, 500)

# Initialize Role2Vec model with default parameters
model = Role2Vec()

# Fit the model
model.fit(G)

# Get the embedding
embedding = model.get_embedding()

# Output the embedding
print(embedding)
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