<｜begin▁of▁sentence｜>### Instruction:
A network scientist is analyzing a simple undirected triangular network consisting of 3 nodes, where each node is connected to every other node. The scientist needs to modify the network's adjacency matrix so the diagonal reflects the average degree of the nodes (in terms of unweighted edges or total edge weights), which will be used in subsequent analyses that require regularized versions of adjacency matrices. How can they accomplish this using Graspologic's augment_diagonal function?
### Response:
import numpy as np
import networkx as nx
from graspologic.utils import augment_diagonal

# Step 1: Create a graph
G = nx.Graph()
G.add_edge(1, 2)
G.add_edge(2, 3)
G.add_edge(3, 1)

# Step 2: Convert it to an adjacency matrix
adj_matrix = nx.to_numpy_array(G)

# Step 3: Augment the diagonal
augmented_matrix = augment_diagonal(adj_matrix, weight=1)

print('Original Adjacency Matrix:\n', adj_matrix)
print('Augmented Adjacency Matrix:\n', augmented_matrix)
<|EOT|>
