<｜begin▁of▁sentence｜>### Instruction:
Imagine we're working together to create a community network map that reflects the strength of connections and support between different members within a neighborhood. This map is represented by an undirected graph with weights on the edges indicating the level of support. These weights are captured in what we call a weighted adjacency matrix. 

Now think of each individual within this community as a node on our network map. To really strengthen our understanding of the individual support levels, we'd like each person to be aware of the average support they give and receive within the community, symbolized by the average sum of connections or edge weights linked to them.

As a proactive step in our initiative, we need to refine our network map by adjusting the individual's self-connection value—that is, the value on the diagonal of our adjacency matrix—so that it equals this average sum of edge weights for them.

We can achieve this delicate adjustment by using the `augment_diagonal` function from the graspologic library. This tool will help us update our network map respectfully and accurately, ensuring that each member's contribution to community support is recognized equally in our model.

In essence, we want to print out this renewed adjacency matrix that holds our adjusted values, offering us a clearer and more empowering picture of our community’s interconnected structure.
### Response:
import numpy as np
from graspologic.utils import augment_diagonal

# Define a simple weighted adjacency matrix for an undirected graph
A = np.array([[0, 1.5, 0],
               [1.5, 0, 2.0],
               [0, 2.0, 0]])

# Augment the diagonal of the adjacency matrix
A_augmented = augment_diagonal(A)

# Print the augmented adjacency matrix
print('Augmented Adjacency Matrix:')
print(A_augmented)
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