<｜begin▁of▁sentence｜>### Instruction:
Picture this: You're in the studio, manning the decks and crafting the next chart-topper. Now, think of the Desargues graph like an intricate melody, a symphony of nodes and edges harmoniously intertwined yet complex and impossible to flatten into a simple plane.

We need to compose this masterwork by hitting the right notes: a graph with 20 vertices—these are our beats—and 30 connections, like our rhythm, pulsing through the structure. It's critical that this graph isn't flat; it's got to be as non-planar as the most avant-garde jazz riff, with its cubic architecture ensuring each beat vibes with three others.

To remix this into a visual anthem, imagine using NetworkX as your digital audio workstation, synthesizing this Desargues graph until it's tangible, visible—something we can all groove to. Can you sling those beats and paths into a sight that's distance-transitive, where each note's reach is as uniform as the next?

Now, we're not riffing freeform here. This composition has a score, a meticulous framework we follow. There's no GML file echo here; the data is in the concept, the intricate interplay of edges and vertices that you need to transpose into a NetworkX visualization chart. Get this tune bumping, and let's see this Desargues graph light up like a soundboard!
### Response:
import networkx as nx

# Returns the Desargues Graph
G = nx.desargues_graph()

# Draw the graph using matplotlib (optional, requires matplotlib)
import matplotlib.pyplot as plt
nx.draw(G, with_labels=True)
plt.show()
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