<｜begin▁of▁sentence｜>### Instruction:
Imagine you've stumbled upon the financial transaction ledger of a company you're auditing. As you comb through the entries, you notice that the record as it stands, resembles an asymmetrical adjacency matrix, reflecting a network of transactions where the flow of money between parties isn't consistent in both directions. To get a clearer picture of the average flow of funds between each pair of entities and ensure equitable representation, you need a way to adjust this ledger so that each entry is balanced by its counterpart.

In the language of your investigation, this would involve taking the given imbalanced matrix of transactions and applying an averaging method to create a symmetric ledger, where each pair of accounts shows an averaged value of transactions between them, nullifying any discrepancies. To achieve this, you'd be looking at using the 'graspologic.utils.symmetrize' function – a tool from your forensic analytical arsenal. How would you employ this function to this scenario to reflect a fair and symmetrical overview of the company's financial interactions?
### Response:
import numpy as np
from graspologic.utils import symmetrize

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

# Symmetrize the adjacency matrix by averaging the corresponding weights
symmetric_adjacency_matrix = symmetrize(asymmetric_adjacency_matrix, method='avg')

# Print the symmetrized adjacency matrix
print(symmetric_adjacency_matrix)
<|EOT|>
