<｜begin▁of▁sentence｜>### Instruction:
Imagine you're in the midst of editing a premium photo collection capturing the intricate networks of a city—unity in diversity, where each image is a different intricate pattern of roads, bridges, and intersections. Now, each of these images is akin to a multigraph, with diverse connections and intersections representing relationships between nodes.

To truly capture the essence of each distinctive network, you'd want to apply a kind of filter or enhancement that symbolizes the underlying structure and connection traits within each cityscape. This is where I'm thinking of utilizing the mug2vec feature from the graspologic toolbox. It's like having a specialized lens that can distill the complexity of each photo into a single characteristic signature—a feature vector that reveals the photo's unique structural composition.

Could you envision a way of applying this mug2vec 'lens' to each image in your collection to extract these signature vectors? Then, much like reviewing the enhanced photos, could we display these distilled embeddings, letting us appreciate the fundamental beauty of each city's connectivity blueprint?
### 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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