<｜begin▁of▁sentence｜>### Instruction:
Imagine you're a researcher working with a complex dataset that represents a series of processes in a biological pathway. This dataset is structured as a directed acyclic graph (DAG) with interactions defined by various connections, akin to chemical reactions where certain compounds are precursors for others. The interactions in your study are represented by the following edges: [(1, 2), (1, 3), (2, 3), (3, 4), (2, 4)].

In the course of streamlining your analysis, you are interested in simplifying this network by removing any intermediary steps that are not essential to the overall pathway progression. This process is known as the transitive reduction of the graph, which trims the graph to its minimal form without altering the reachability of the nodes.

Your task is to utilize the NetworkX toolkit to execute the transitive reduction on your current DAG, and thereafter, diligently ensure that the resulting reduced graph maintains all the node and edge attributes from the original. Could you reformat the graph accordingly and maintain the integrity of your dataset in this condensed representation? Remember, your aim is to preserve the foundational cause-and-effect relationships within your biological model while discarding redundant connections.
### Response:
import networkx as nx

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

# Perform transitive reduction on the DAG
TR = nx.transitive_reduction(DG)

# Output the edges of the transitive reduction
print(list(TR.edges()))

# Transfer node/edge data
TR.add_nodes_from(DG.nodes(data=True))
TR.add_edges_from((u, v, DG.edges[u, v]) for u, v in TR.edges())
print(list(TR.edges(data=True)))
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