<｜begin▁of▁sentence｜>### Instruction:
Given a list of multigraphs, can you use mug2vec from graspologic to generate a feature vector for each graph that encapsulates its structure and relationships among the nodes, and then print these embeddings?
### Response:
import networkx as nx
from graspologic.embed import mug2vec
# Create list of multigraphs, for example, we will create three densly connected multigraphs
G1 = nx.MultiGraph()
G1.add_edges_from([(1, 2), (2, 3), (1, 3)])
G2 = nx.MultiGraph()
G2.add_edges_from([(2, 3), (3, 4), (2, 4)])
G3 = nx.MultiGraph()
G3.add_edges_from([(1, 4), (1, 3), (4, 3)])
graphs = [G1, G2, G3]
# Instantiate and fit mug2vec
m2v = mug2vec(pass_to_ranks='simple-all')
embeddings = m2v.fit_transform(graphs)
print('Embeddings:\n', embeddings)
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