<｜begin▁of▁sentence｜>### Instruction:
As a UX Designer tasked with enhancing the analytical features of our network analysis platform, imagine we're currently developing a new visualization tool that will allow users to better understand the characteristics of various undirected graphs. Your role involves creating a user-friendly interface for a feature that utilizes 'mug2vec', an API for generating graph representations.

Your current challenge is to design an interface that enables users to input a series of undirected graphs, specifically three that have been structured using the Erdős-Rényi model with a consistent number of nodes (n=100) but varying connection probabilities (p-values of 0.2, 0.3, and 0.4, respectively). 

The user should be guided to apply a 'simple-all' strategy for rank transformation, which is integral to the preprocessing step before feeding the data into the mug2vec model. Additionally, within the settings, they must have the option to specify the number of elbows (set to 2 in this case) for both the omnibus matrix and the classical Multidimensional Scaling (cMDS) embedding processes.

The interface must present the outcome of these computations clearly, ultimately displaying the feature vectors that mug2vec produces for each graph. Remember, the focus is on delivering a seamless and intuitive experience, making the complex process of graph analysis more accessible to the user without altering the semantic content of the task at hand.
### Response:
import networkx as nx
from graspologic.embed import mug2vec
from graspologic.simulations import er_np

# Generate a list of graphs using the ER model
g1 = nx.from_numpy_matrix(er_np(n=100, p=0.2))
g2 = nx.from_numpy_matrix(er_np(n=100, p=0.3))
g3 = nx.from_numpy_matrix(er_np(n=100, p=0.4))
graphs = [g1, g2, g3]

# Initialize the mug2vec object
m2v = mug2vec(pass_to_ranks='simple-all', omnibus_n_elbows=2, cmds_n_elbows=2)

# fit the model
graph_vectors = m2v.fit_transform(graphs)

# Print the graph feature vectors
for i, vec in enumerate(graph_vectors):
    print(f'Graph {i+1} vector:', vec)
<|EOT|>
