<｜begin▁of▁sentence｜>### Instruction:
Given a list of two randomly generated graphs, each with 100 nodes and 200 edges, can you use mug2vec in graspologic to fit the model with these graphs and obtain a feature vector for each graph using the transform method of the model? You should print the embeddings result.
### Response:
import networkx as nx
from graspologic.embed import mug2vec

# Generate random graphs with the same number of nodes
G1 = nx.gnm_random_graph(100, 200)
G2 = nx.gnm_random_graph(100, 200)

# Multigraph-2-Vectors model instantiation
model = mug2vec(pass_to_ranks='simple-nonzero',
                omnibus_components=5,
                omnibus_n_elbows=2,
                cmds_components=5,
                cmds_n_elbows=2,
                svd_seed=42)

# List of graphs
graphs = [G1, G2]

# Fit the model with graphs
model.fit(graphs)

# Transform the list of graphs to embeddings
embeddings = model.fit_transform(graphs)

# Output the result
print(embeddings)
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