<｜begin▁of▁sentence｜>### Instruction:
Imagine you're guiding a therapeutic art session, where participants use painting to map out the flow of their personal growth and milestones, much like a journey from a starting point to various meaningful destinations. Each brushstroke represents a decision or a significant event, forming a unique pathway on the canvas.

Now, think of a canvas that currently displays a series of connected pathways: starting from the bottom, we see lines reaching out to two separate points, and from each of those points, lines extend to a shared destination at the top. These pathways are akin to brushstrokes, with the connections between the points resembling a directional flow of energy or growth. This painted scene is much like a graph with pathways flowing from a base or root (at the bottom of the canvas) towards culmination points (at the top). To truly reflect the individual journey, each pathway from the root to its final point needs to be unique and distinct.

As an Art Therapist, you want to create a new representation of this painted journey. Using a method akin to NetworkX's 'dag_to_branching' conversion, you aim to re-envision the original pathways so that each root-to-culmination stroke on the initial canvas corresponds to a singular, unbranching trail on a new canvas. This new painting will have a tree-like structure where each branch captures the essence of a complete, unshared personal journey from the original artwork. 

In this therapeutic setting, the original pathways are like the edge set [(1, 2), (1, 3), (2, 4), (3, 4)] of a directed graph, depicting an uninterrupted flow from one milestone to the next. Your task is to transpose this into a branching representation, while preserving the narrative that each complete pathway from root to culmination in the first canvas is uniquely mirrored in the new artistic tree formation. How might you approach this transformation in your art therapy session?
### Response:
import networkx as nx

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

# Apply the dag_to_branching function to create a branching representation of G
B = nx.dag_to_branching(G)

# Print the edges of the branching
print(list(B.edges()))
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