<｜begin▁of▁sentence｜>### Instruction:
Imagine you're in a laboratory setting where, as a phlebotomist, you're familiar with the precision required in labeling and analyzing different blood samples for research purposes. In this case, the samples are not vials of blood but rather two distinct sets of data representing the intricate web of relationships between patients, depicted as Erdos-Renyi graphs. Each dataset reflects a network with an identical count of patients (nodes), yet the likelihood of direct connections (edges) between them differs due to varying underlying conditions or treatments.

Your task, akin to preparing samples for a shared diagnosis protocol, involves applying a specialized technique named Multiple Adjacency Spectral Embedding (MASE) from a toolkit called graspologic. This technique will enable you to process the patient networks so that they can be visually represented within a unified three-dimensional space. By doing so, you aim to uncover a shared underlying structure (a common latent position matrix) across both datasets, which is much like seeking a common set of biomarkers in the blood that might link disparate groups of patients.

Would you be able to reorganize these intricate networks through the MASE method from the graspologic suite, ensuring that both patient networks are transposed into a space where you can examine their shared characteristics, akin to how you'd ensure all blood samples adhere to a uniform guideline for analysis?
### Response:
import networkx as nx
from graspologic.embed import MultipleASE
import numpy as np

# List of graphs (undirected, with the same number of nodes)
graph_1 = nx.erdos_renyi_graph(n=100, p=0.2)
graph_2 = nx.erdos_renyi_graph(n=100, p=0.3)
graphs_list = [graph_1, graph_2]

# Initialize the Multiple ASE model
mase = MultipleASE(n_components=3, n_elbows=2, algorithm='randomized')

# Fit the model to the graphs
mase.fit(graphs_list)

# Get the embedding of nodes
embedding = mase.latent_left_
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