<｜begin▁of▁sentence｜>### Instruction:
As a seasoned Meeting Planner tasked with the logistical intricacies of a digital network conference, you're now faced with an engaging challenge that parallels the complexities of event coordination. Picture a scenario wherein your conference's workflow diagram is represented by a directed graph model. The current graph showcases a sequence of processes with the following connections: [(1, 2), (1, 3), (2, 3), (3, 4), (3, 5)].

Your objective is to conceptualize the reversal of this workflow to understand the feedback mechanisms between different stages of the conference planning. By utilizing the NetworkX toolkit—a powerful Python library for network analysis—you will invert the direction of each edge in the directed graph. This step symbolically mirrors the revising of event plans where you trace responsibilities back to their origins.

Could you elucidate how to gracefully transpose the directionality within our graphical abstraction using NetworkX? And in doing so, provide a comprehensive list of the edges within this newly reversed graph. This inversion of the graph is much like reimagining the agenda to ensure that, in a dynamic setting, we can always adapt backward flows to reflect forward-thinking strategies.
### Response:
import networkx as nx

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

# Reverse the graph
G_reversed = nx.reverse(G, copy=True)

# Print the edges of the reversed graph
print(G_reversed.edges())
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