<｜begin▁of▁sentence｜>### Instruction:
As a nutritionist, think of an undirected graph as a network of different foods and their interactions. When two foods interact, it means they are connected or share some characteristics. It's like constructing a map of various ingredients in a recipe book. For example, in a recipe, you might find that carrots (0), onions (1), and celery (2) go together. Using the NetworkX library in Python, you can generate and visualize an adjacency matrix to display these food interactions, similar to how you might create a meal plan based on dietary practices and nutrition goals.
### Response:
from networkx.linalg.graphmatrix import adjacency_matrix
import networkx as nx

# Create a new undirected graph
G = nx.Graph()

# Add some edges to the graph
G.add_edges_from([(0, 1), (1, 2), (2, 0)])

# Generate the adjacency matrix
A = adjacency_matrix(G)

# Since __repr__ is a special method, it gets called behind the scenes when we print an object
# Printing the adjacency matrix object will invoke __repr__ and display the matrix
print(A)
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