<｜begin▁of▁sentence｜>### Instruction:
Assuming you have a list of 3 undirected, unweighted graphs with 5 nodes each, constructed as Erdős-Rényi random graphs with a probability of 0.5 for forming each edge, how would you use the mug2vec algorithm to derive a feature vector for each graph that captures the graph structure while considering the relationships amongst all graphs?
### Response:
```python
import networkx as nx
from graspologic.embed import mug2vec
import numpy as np

# Generate a list of synthetic graphs to embed
n_graphs = 3
n_vertices = 5
graphs = [nx.gnp_random_graph(n=n_vertices, p=0.5, seed=i) for i in range(n_graphs)]

# Create mug2vec instance with desired parameters
mv2 = mug2vec(pass_to_ranks='simple-nonzero', omnibus_components=None, omnibus_n_elbows=2,
               cmds_components=None, cmds_n_elbows=2, svd_seed=42)

# Fit the model and obtain the embeddings
embeddings = mv2.fit_transform(graphs)

# embeddings is now a 2D numpy array with shape (n_graphs, n_components)
# where each row corresponds to the embedding of a graph
print(embeddings)
```
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