<｜begin▁of▁sentence｜>### Instruction:
Imagine you're crafting a visual aid chart designed to simulate the complexity and interconnectedness of the human visual system for a lecture to optometry students. To illustrate this, you plan on using a model akin to the intricately structured Dorogovtsev-Goltsev-Mendes graph. Your task is to generate this graph with 'n' representing the generation of the visual network using the NetworkX toolkit.

Your goals are twofold:

1. First, you will need to elucidate the expansive nature of this visual network by ascertaining the total quantity of nodes, akin to the multitude of neural connections in the human eye.

2. Secondly, you will examine the architecture of the graph to evaluate whether its layout is planar, mirroring the flat, two-dimensional surface on which a patient's eye chart is presented. 

This exercise will not only provide a dynamic teaching tool but will also challenge the students to consider the complex networks involved in visual processing. Remember to document your process as if compiling a clinical report, maintaining precision and clarity akin to the high standards of an optometrist's practice.
### Response:
import networkx as nx

# Usage of the dorogovtsev_goltsev_mendes_graph function to generate a graph
n = 3  # Set the generation number
G = nx.dorogovtsev_goltsev_mendes_graph(n)

# Print out some properties of the graph
print('Total number of nodes:', G.number_of_nodes())
print('Total number of edges:', G.number_of_edges())

# Check if the graph is planar
is_planar, _ = nx.algorithms.planarity.check_planarity(G)
print('Is the graph planar?', is_planar)
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