<｜begin▁of▁sentence｜>### Instruction:
Imagine you are in an optometry clinic, and you've come across an intriguing challenge that closely resembles the way you would approach diagnosing a patient's vision issues, where each part of the vision system is interconnected in a complex network. In this scenario, you have two distinct visual pathways. The first pathway, let's call it 'Pathway G', is akin to a simple journey of light entering the eye and passing through four straightforward structures (which we'll label 0-3), much like light travelling along a straight path through different media.

The second pathway, 'Pathway H', is a more complex scenario where you have three components (labeled 0-2) that are all interconnected, similar to how the various aspects of the eye's anatomy, such as the cornea, lens, and retina, work in unison to create a complete vision network.

Now, in order to assess a comprehensive visual function where 'Pathway H' becomes the central point of reference (rooted at component 0), you're tasked with creating a combined visual network from 'Pathway G' and 'Pathway H'. This new network will give insight into how light information is integrated through various pathways, leading to a detailed understanding much like how an optometrist assesses the complexity of visual processing in the human eye.

Transpose this process into the language of graph theory, and what you have is the creation of a rooted product of 'Pathway G' and 'Pathway H', with 'Pathway H' being rooted at component 0. How can this rooted product be structured to represent the combined pathways effectively?
### Response:
import networkx as nx

# Create graphs G and H
G = nx.path_graph(4)
H = nx.complete_graph(3)

# Choose a root in H
root = 0

# Generate the rooted product of G and H at the specified root
R = nx.algorithms.product.rooted_product(G, H, root)

# Print information about the rooted product graph
print('Nodes of rooted product graph:', R.nodes())
print('Edges of rooted product graph:', R.edges())
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