<｜begin▁of▁sentence｜>### Instruction:
Imagine you're conducting an energy audit of a building, and you've mapped out a network of various systems (like heating, cooling, and electrical) as nodes on a grid. These nodes are connected by their operational relationships, forming an unweighted grid that outlines how these systems interact. You've jotted down this information in an adjacency matrix, where each cell indicates the presence of a connection between two systems.

Now, consider that you'd like to tweak this matrix a bit to represent a more nuanced piece of information. Specifically, instead of merely knowing if a connection exists, you want to understand how 'busy' or 'in-demand' each system is. To gauge this, you plan to adjust each system node's value within the matrix to reflect the average operational interactions it has with other systems. But there's more – you wish to fine-tune this value by applying a scalar—let's call it an 'efficiency factor'.

Could you reframe your approach on how to transform this raw adjacency matrix so that the diagonal values represent the average interactions per system, modified by this 'efficiency factor'? This would provide you with an at-a-glance representation of each system's average connections, helping to prioritize where efficiency improvements are most needed.
### Response:
import numpy as np
from graspologic.utils import augment_diagonal

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

# Use augment_diagonal to add average degrees to the diagonal
augmented_matrix = augment_diagonal(a, weight=1)
print(augmented_matrix)
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