<｜begin▁of▁sentence｜>### Instruction:
As a real estate developer, imagine you're in the process of studying the structural blueprints and planning models of various housing complexes during different development phases, including initial planning (rest), the bustling construction stage (stress), and the final touches before opening (task performance). You have accumulated detailed schematics for each phase and want to find a unified framework that encapsulates the essential structural details across these different phases. How could you leverage the Multiple Adjacency Spectral Embedding (MASE) algorithm available in the Graspologic toolkit to identify a shared foundational plan that comprehensively represents the underlying structure of these housing projects? This approach would allow you to analyze and compare the core characteristics of the construction designs at each stage.
### Response:
import numpy as np
import networkx as nx
from graspologic.embed import MultipleASE
from graspologic.simulations import sbm

# Simulate a stochastic block model graph
n_communities = [50, 50]
p_matrix = [[0.5, 0.1], [0.1, 0.5]]
graphs = [sbm(n_communities, p_matrix) for _ in range(3)]

# Convert to networkx graphs
gnx = [nx.Graph(graph) for graph in graphs]

# Instantiate MultipleASE model
mase = MultipleASE()

# Embed the networkx graphs with MultipleASE
embedded_graphs = mase.fit_transform(gnx)

print('Latent left positions from the embedded graphs:')
print(mase.latent_left_)
<|EOT|>
