<｜begin▁of▁sentence｜>### Instruction:
As a Process Engineer, you're constantly refining the production line to enhance efficiency and minimize downtime. Lately, you've noticed that certain sequences of operations are looping back on themselves without adding value, which could indicate redundancies in your process flow.

Imagine you've mapped out the process flow as a directed graph where each node represents a different operation, and edges indicate the direction of process flow. The current operation sequence is defined by the following transitions: from Operation 1 to Operation 2, Operation 2 to Operation 3, Operation 3 to Operation 1, Operation 3 to Operation 4, and finally from Operation 4 back to Operation 2.

In this scenario, could you investigate the presence of any chordless cycles within this graph using NetworkX's 'chordless_cycles' feature? Chordless cycles here would represent sequences of operations that loop back on themselves without intersecting with other operations in the flow, potentially highlighting areas where the process could be streamlined.
### Response:
import networkx as nx

# Create a directed graph
G = nx.DiGraph()

# Add edges
G.add_edges_from([(1, 2), (2, 3), (3, 1), (3, 4), (4, 2)])

# Find all chordless cycles in the graph
chordless_cycles = list(nx.chordless_cycles(G))

print('Chordless cycles:', chordless_cycles)
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