<｜begin▁of▁sentence｜>### Instruction:
Imagine you're crafting a podcast series that maps out the intricate network of interviews between a group of experts. You've been tracking the connections, or "interview pathways," with each connection having a "conversation cost" representing the time taken to travel from one expert to another. Your data sheet currently lists the following connections: Interview from Expert 1 to Expert 2 has a conversation cost of 1, from Expert 2 to Expert 3 costs 2, a direct discourse between Expert 1 and 3 sits at a cost of 4, and finally between Expert 3 and Expert 4, it costs 3.

Now, for the next episode, you want to create a master plan, a "complete interview roadmap," where you can determine the shortest "conversation path" between any two experts without having to re-calculate each time. To pull this off, you'd take the initial set of connections and establish a network where any pair of experts that currently don't have a direct interview line between them should have a new one, with the "conversation cost" equating to the minimum time it would take jumping through intermediates in the original schedule.

How could you transform your original list of interview pathways into this fully connected roadmap, with the new pathways reflecting the least time-consuming route found in your initial data?
### Response:
import networkx as nx

# Create a graph
G = nx.Graph()
G.add_edges_from([(1, 2, {'weight': 1}), (2, 3, {'weight': 2}), (1, 3, {'weight': 4}), (3, 4, {'weight': 3})])

# Compute the metric closure
M = nx.metric_closure(G)

# Print the edges with weights in the metric closure
print(sorted(M.edges(data=True)))
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