<｜begin▁of▁sentence｜>### Instruction:
Certainly, let's imagine we're dealing with two interconnected networks of care, one for facility A and another for facility B. Each network has been mapped out, detailing which caregivers work closely with which patients, similar to a friendship network.

Now, facility A is looking to transfer one of their patients (the Patient of Interest or POI) to facility B, but they want to ensure that the patient continues to receive personalized care. To assist with this, they have a chart (like a square grid) showing the connections between caregivers and patients in facility A (Graph A), and a similar chart for facility B (Graph B). 

They also know a few pairings of caregivers between the two facilities who provide similar levels of care and attention to their patients (these are the seed matches). With this information, facility A wants to "nominate" potential caregivers in facility B who could match the dedication and services the POI is accustomed to.

Using a method similar to creating a ranked list of substitutes when a caregiver is absent, can we apply Vertex Nomination via Seeded Graph Matching (VNviaSGM) to sort the potential caregiver-partner matches for the POI in facility B, based on what we know from the charts and seed matches?
### Response:
import numpy as np
from graspologic.nominate import VNviaSGM

# Create adjacency matrices for two graphs
A = np.array([[0, 1, 1], [1, 0, 0], [1, 0, 0]])
B = np.array([[0, 1, 0], [1, 0, 1], [0, 1, 0]])

# Define the vertex of interest and known seeds
voi = 1
seeds_A = [0]
seeds_B = [2]

# Initialize the Vertex Nomination via Seeded Graph Matching
vnsgm = VNviaSGM(order_voi_subgraph=2, order_seeds_subgraph=2, n_init=100, max_nominations=None)

# Fit the model and predict the nomination list
vnsgm.fit(A, B, voi, seeds=[seeds_A, seeds_B])
nomination_list = vnsgm.fit_predict(A, B, voi, seeds=[seeds_A, seeds_B])

print("Nomination List: ", nomination_list)
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