<｜begin▁of▁sentence｜>### Instruction:
In the realm of game design, let's say we are crafting a complex storyline for a new adventure game, where the narrative branches out as players make decisions. Within this game, we have a system representing the unfolding of events, akin to a directed acyclic graph. Now, to enhance our game's depth, we have certain events linked as follows: a player starting at event 1 can move to event 2 or 3, from event 2 they can proceed to event 4, and event 3 also leads to event 4.

As part of refining our narrative structure, we're interested in outlining all the possible paths a player could traverse, essentially computing the transitive closure of these events. This would ensure that our game avoids any unforeseen narrative dead-ends or missing links that could impede the player's journey.

Could we use NetworkX, as part of our game development toolkit, to determine this transitive closure in our storyline graph? Specifically, we'll need to identify any indirect connections between these events that aren't immediately apparent, turning our current set of event pathways [(1, 2), (1, 3), (2, 4), (3, 4)] into a comprehensive map of all reachable states in our game's narrative. What would be the resultant set of pathways once we've completed this computation?
### Response:
import networkx as nx

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

# Compute the transitive closure of the DAG
TC = nx.transitive_closure_dag(G)

# Print the edges of the transitive closure
print(TC.edges())
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