<｜begin▁of▁sentence｜>### Instruction:
Imagine you, as a real estate developer, are working on a new housing complex layout using a specialized software that models your community plan as a network of weighted pathways connecting homes, amenities, and services. Each pathway's weight signifies the cost to travel it, which might differ depending on the direction, reflecting a situation like uphill vs. downhill travel or one-way streets.

However, as part of a cost analysis, you're looking to streamline your network by ensuring that the cost between any two points is consistent, regardless of the travel direction. Essentially, if it costs more to travel from A to B than from B to A, you'd like to average these costs, so the expense is equal both ways.

Could you suggest a method to adjust your network model's interconnection matrix, which is originally asymmetrical in terms of pathway costs, such that every bidirectional travel cost becomes symmetrical? This would mean averaging any differing weights between two points in such a way that the resulting matrix reflects a network where each pathway's cost is the same, no matter the direction of travel.

In technical network modeling terms, this involves taking an asymmetric adjacency matrix that represents your current network and transforming it into a symmetric matrix by averaging the edge weights. How might you employ an API from the NetworkX library to achieve a symmetrical adjacency matrix fitting this new, balanced cost distribution?
### Response:
import numpy as np
from graspologic.utils import symmetrize

# Given an asymmetric adjacency matrix
a = np.array([
   [0, 1, 2],
   [3, 0, 4],
   [5, 6, 0]])

# Symmetrize the adjacency matrix, averaging conflicting entries
symmetric_a = symmetrize(a, method="avg")

print(symmetric_a)
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