<｜begin▁of▁sentence｜>### Instruction:
Imagine you're in a bike workshop and your task is to fine-tune each bike (node) by adjusting the tension in the wheel spokes (edges). You keep a record of the connection each wheel (node) has with its spokes (edges) in a chart (adjacency matrix). You're looking to adjust each wheel based on how commonly it's used (average degree). 

In bike mechanic jargon, let's say you've got a tool from the graspologic toolkit named 'augment_diagonal'. How would you use this tool to tweak the tension (add to the diagonal) of each wheel based on the mean usage (average degree) from your record chart (matrix)? Also, what would your chart look like after each wheel's tension has been adjusted accordingly (resulting matrix)? Remember, this is all about giving those wheels a uniform feel when they roll out (ensuring the semantics remain unchanged).
### Response:
import numpy as np
from graspologic.utils import augment_diagonal

# Example adjacency matrix representing an unweighted graph
a = np.array([
   [0, 1, 1],
   [1, 0, 0],
   [1, 0, 0]])

# Use augment_diagonal to add the average degree to the diagonal
augmented_matrix = augment_diagonal(a, weight=1)
print(augmented_matrix)
<|EOT|>
