<｜begin▁of▁sentence｜>### Instruction:
Imagine you're a tour guide, and you've been tasked with two groups of tourists, each with their own interests and tour paths, represented by our "itinerary matrices" A and B. Your goal is to align the interests of group B with the stops of group A in such a way that the overall satisfaction (or in our lingo, the sum of squared differences in tour stops' attractiveness) is maximized.

We're not just sightseeing here; think of it as solving a puzzle where we're arranging the itinerary for group B so that their path best mirrors the experience of group A, square by square, attraction by attraction. In this sophisticated challenge, we're using graspologic's `graph_match` function, which isn't guiding tourists but rather shuffling these stops around like a masterful game of tour guide chess.

Once we've performed this high-level matching, we'll reveal the outcome like a grand finale of a tour—showing the matched stops (indices) from itinerary A, the rearranged matched stops from itinerary B, and the "objective function value," which in this context is a numerical score of how well we've aligned the two tours.

Now, in the grand narrative of our little excursion, how would you suggest we proceed to realign group B's tour to the pattern of group A, enhancing their travel experience while minimizing differences between each stop's offerings?
### Response:
import numpy as np
from graspologic.match import graph_match

# Define two adjacency matrices
A = np.array(
    [[0, 1, 0],
    [1, 0, 1],
    [0, 1, 0]]
)

B = np.array(
    [[0, 1, 1],
    [1, 0, 0],
    [1, 0, 0]]
)

# Perform the graph matching
result = graph_match(A, B)

# 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)
<|EOT|>
