<｜begin▁of▁sentence｜>### Instruction:
Imagine you're working at an exclusive boutique that specializes in abstract artistry and decorative puzzles. A discerning client is interested in a unique piece that embodies both mathematical elegance and aesthetic appeal. To their delight, you present them with an intricately designed model of a truncated tetrahedron – a fascinating 3D shape that combines hexagons and triangles in harmonious symmetry.

As the boutique's sales associate, you understand the profound beauty of this shape is matched by the complexity of its structure. The client is curious to understand not just the physical form but the underlying connections that make up this geometric gem. In response, you mention that the boutique can provide a digital map of the connections between the vertices and edges of the shape in the form of a JSON file, which is essentially the graph's digital blueprint.

You explain that the truncated_tetrahedron_graph contains details for each of the 12 vertices and 18 edges. Each vertex would have a unique 'id' and a 'label' denoting its position or characteristic within the shape. Likewise, each edge would be neatly represented with a 'source' and 'target' indicating which vertices it connects, along with an added 'label' for identification or categorization.

The client is thrilled at the possibility of exploring this digital file, known as the 'truncated_tetrahedron_graph.json'. As they make their purchase, you assure them that this supplemental information will offer a deeper understanding of their newest decorative acquisition, providing insight into the abstract connection points that underpin the beauty of their selected artwork.
### Response:
import networkx as nx

# Use the truncated_tetrahedron_graph API to generate the graph
truncated_tetrahedron = nx.truncated_tetrahedron_graph()

# Iterate over the nodes and edges to create a JSON-compatible
# representation of the graph
g_nodes = [{'id': node, 'label': f'Node {node}'} for node in truncated_tetrahedron.nodes()]
g_edges = [{'source': u, 'target': v, 'label': f'Edge {u}-{v}'} for u, v in truncated_tetrahedron.edges()]

# Create the JSON object
graph_json = {
    'graph_nodes': g_nodes,
    'graph_edges': g_edges
}

# Print the JSON representation of the graph
print(graph_json)
<|EOT|>
