<｜begin▁of▁sentence｜>### Instruction:
Alright, let me run this through a scenario more in line with your day-to-day activities. Imagine we're looking at a patient's network of tooth issues, where we might create a graph representing connections between problematic areas. Now, let's say instead of teeth, we're using a well-known structure called the Chvátal Graph, a bit like a template for dental issues but in the realm of mathematics.

In a similar way to how we examine and chart a patient's mouth, could we apply those organizational skills to map out this Chvátal Graph, identifying the connections, which are like the edges between teeth, and count how many "teeth", or nodes, are present, as well as tallying the total number of "issues", or edges. It's a methodical approach, just as you would meticulously document in a dental chart. How might one go about visualizing and enumerating these aspects of the Chvátal Graph using NetworkX, treating it as if it were a patient's oral chart?
### Response:
import networkx as nx

# Create Chvátal Graph
G = nx.chvatal_graph()

# Print the edges of the graph
print('The Chvátal Graph edges:', G.edges())

# Print the number of nodes
print('The Chvátal Graph has', G.number_of_nodes(), 'nodes')

# Print the number of edges
print('The Chvátal Graph has', G.number_of_edges(), 'edges')
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