<｜begin▁of▁sentence｜>### Instruction:
Imagine you are an insurance underwriter and you are assessing a network of clients to determine potential risks and the extent of coverage necessary. Each client is connected to others based on specific criteria, creating a direct line of influence or liability. The dataset reflecting these connections includes key relationships: client 1 influences client 2, client 2 influences clients 3 and 5, client 3 influences client 4, and client 4 influences client 3. To effectively evaluate this scenario, you could benefit from understanding the network's "moral graph," which connects any two clients that share a common influence and ignores the directionality of that influence.

Could you restructure this dataset using NetworkX to generate the moral graph, representing a scenario where indirect risks are made explicit through their mutual associations? Once this is accomplished, could you provide a summary of the new connections present in the moral graph? This will give you a clearer picture of the network's interdependencies and help you make more informed decisions regarding risk assessment and policy development.
### Response:
import networkx as nx

# Create a directed graph
G = nx.DiGraph()
G.add_edges_from([(1, 2), (2, 3), (2, 5), (3, 4), (4, 3)])

# Moralize the graph
G_moral = nx.moral_graph(G)

# Output the edges of the moralized graph
print(G_moral.edges())
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