<｜begin▁of▁sentence｜>### Instruction:
Given a list of random graphs generated with the gnm_random_graph model in NetworkX, each having 10 nodes and 15 edges, how can you use the LDP model from the ICLR Representation Learning on Graphs and Manifolds Workshop ‘19 paper “A Simple Yet Effective Baseline for Non-Attributed Graph Classification” to get the embeddings of these graphs?
### Response:
import networkx as nx
from karateclub import LDP

# List of graphs
graphs = [nx.gnm_random_graph(10, 15), nx.gnm_random_graph(10, 15)]

# Initialize the LDP model
model = LDP()

# Fit the model to the list of graphs
model.fit(graphs)

# Get the embedding of the graphs
embedding = model.get_embedding()
print(embedding)
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