<｜begin▁of▁sentence｜>### Instruction:
Imagine you're assessing the financial network connectivity of a small community bank that has four branches. Each branch is akin to a node, and only adjacent branches have direct transaction paths. Picture these branches arranged in a straight line where each branch is connected sequentially to its neighbor, thus forming a path graph with four nodes.

As part of your risk assessment and to enhance our understanding of the interbranch connectivity, you are interested in exploring how transactions might flow if not just direct, but also indirect paths are considered. To do this, you've decided to utilize NetworkX to model the potential transaction paths.

Your goal is to compute the square and the cube of the initial path graph of branches, representing one and two intermediaries in a transaction path respectively. Each power increment is expected to unveil additional indirect paths between branches, and you'd like to list all possible transaction paths in these squared and cubed graphs.

You aim to analyze and document the edges in each powered graph, which will help in evaluating the robustness of the network and potential transaction opportunities or risks associated with increased connectivity. Could you reframe the problem by maintaining correct semantics, while outlining the edges present in each graph power according to the scenario described?
### Response:
import networkx as nx

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

# Compute the square of the graph
G_squared = nx.power(G, 2)

# Print the edges of the squared graph
print('Edges in G^2:', list(G_squared.edges()))

# Check if taking another power increases the number of edges
G_cubed = nx.power(G, 3)
print('Edges in G^3:', list(G_cubed.edges()))
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