<｜begin▁of▁sentence｜>### Instruction:
Imagine you're a musician, and you've got two sheet music scores, let's call them Score A and Score B. Think of these scores as similar pieces of music but with their notes potentially arranged in different order. Each score represents a unique melody with its own sequence of notes but they might share some common tunes. Now, your challenge is to match the notes from Score A with those from Score B in a way that the maximum number of identical sequences or harmonies are aligned.

This task is akin to a composer trying to fine-tune two similar melodies to create a perfect harmony by ensuring that as many corresponding notes as possible are played together. In your toolkit, there's a nifty 'instrument' known as the graph_match function from the library graspologic that you could use. It works like a charm for finding the best way to line up the notes (nodes) from both scores (graphs) so that the number of chords (edges) that resonate together is at its peak.

What you're aiming to discover through this process is the highest number of note pairs that when played together, sound just right – this is your edge agreement score. So, how can you apply your musicianship, along with the graph_match 'instrument', to orchestrate these two scores and find out this harmonious edge agreement score they can achieve?
### Response:
import numpy as np
from graspologic.match import graph_match

# Defining two adjacency matrices A and B representing two different graphs.
A = np.array([[0, 1, 0, 0],
              [1, 0, 1, 1],
              [0, 1, 0, 1],
              [0, 1, 1, 0]])
B = np.array([[0, 1, 0, 0],
              [1, 0, 1, 0],
              [0, 1, 0, 1],
              [0, 0, 1, 0]])

# Attempt to match graph A to graph B maximizing edge agreement
result = graph_match(A, B, maximize=True)

# Print the results
print('Matched indices in A:' , result.indices_A)
print('Matched indices in B:' , result.indices_B)
print('Objective function value:' , result.score)
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