<｜begin▁of▁sentence｜>### Instruction:
Imagine you're a scholar versed in the ancient art of the abacus, and you're tasked with translating a scroll depicting a grand network—from a land where every route between two villages varies in difficulty depending on which way you're coming from. You need to advise your students on how to find a fair middle path, where each journey's difficulty is the average of the two possible directions.

In this analogy, the scroll is akin to an asymmetric adjacency matrix of a directed graph. This matrix contains numerical values indicating varying difficulties of travel between point 'i' and point 'j', and vice versa.

Your intellectual challenge here is to determine how you can take the knowledge inscribed on the scroll, consisting of this asymmetrical matrix, and create a new symmetrical matrix. This new matrix should represent a simplified map, an undirected graph where the difficulty of the pathways between each pair of villages has been balanced to an average—equal no matter the direction of travel.

To frame this in terms specific to our profession, how would you teach your pupils to transform the complex directives given by the asymmetric scroll into a simplified symmetrical abacus matrix, where each bead's position on the rods signifies an equal and averaged difficulty of the paths between each village?
### Response:
import numpy as np
from graspologic.utils import symmetrize

# Example adjacency matrix representing a directed graph
a = np.array([
    [0, 1, 2],
    [3, 0, 0],
    [4, 5, 0]
])

# Apply the symmetrize function using the 'avg' method
g = symmetrize(a, method='avg')

print(g)
<|EOT|>
