<｜begin▁of▁sentence｜>### Instruction:
Imagine we're illustrating a concept of connections in a playful learning environment, akin to a simple game that could enlighten young minds about the idea of relationships and how they evolve over time in a network. Picture a basic game board consisting of four lovely distant, vibrantly colored park areas labeled 0, 1, 2, and 3. These parks are connected by walking paths so that little ones can easily meander from one to another, forming friendships along the way. In our initial set-up, each park is directly linked to the next: park 0 to park 1, park 1 to park 2, and then 2 to 3, just like neighbors in a friendly community.

Now, let's stretch our imagination a bit further. Consider what might happen if friendships could extend beyond immediate neighbors, expanding like a web, allowing our vibrant little park-goers from park 0 to visit friends not just in park 1, but also in parks 2 and 3 without having to stop for playdates at the intermediate parks. This magical twist reflects 'higher powers' of our game board paths, making our network of parks interconnected in new and exciting ways.

Could you elucidate the transformed sets of these walking paths – these special connections between the parks – at the 2nd and 3rd power levels using our metaphorical representation? Essentially, we're curious about the direct routes available during this 'enhanced' playtime, and how each park becomes more accessible to others as the power of our 'friendship' network grows. This evolving network exhibits how relationships and connections can deepen, much like the bonds we encourage among children as they grow and their circles expand.
### Response:
import networkx as nx

# Create a path graph with 4 nodes
G = nx.path_graph(4)

# Compute the 2nd power of the graph
G2 = nx.power(G, 2)

# Print out the edges of the 2nd power graph
print(list(G2.edges()))

# Compute the 3rd power of the graph
G3 = nx.power(G, 3)

# Print out the edges of the 3rd power graph
print(list(G3.edges()))
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