<｜begin▁of▁sentence｜>### Instruction:
Imagine you're crafting a new orthopedic brace with a complex yet precise series of supports that resemble a network of interconnected nodes, akin to the structure of the Chvátal graph. This graph is a robust framework comprising 12 pivotal points (nodes) and 24 connecting links (edges), renowned for its multiple distinct pathways akin to Hamiltonian cycles which ensure uninterrupted support.

To emulate this blueprint within our design software, we could employ NetworkX, a potent tool for graph construction and analysis, which contains a function named 'chvatal_graph.' This function, when executed, can generate a virtual model of the Chvátal graph. It also allows us to specify the type of graph structure we want to create with an optional parameter 'create_using.'

In this scenario, as we're keen on visualizing our brace's internal support network, could you reformulate the necessary command to activate this 'chvatal_graph' function in NetworkX? It would be instrumental in reviewing the digital prototype as it enables us to visualize all the connecting points (nodes) and their respective linkages (edges). By doing this, we'd be one step closer to ensuring that the support provided by the brace mirrors the strength and versatility of the Chvátal graph.
### Response:
import networkx as nx

# Generate the Chvátal graph
G = nx.chvatal_graph()

# Now G is the Chvátal graph
print("Nodes:", G.nodes())
print("Edges:", G.edges())
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