<｜begin▁of▁sentence｜>### Instruction:
Imagine you're an Occupational Therapist who uses a specialized software program to model the various treatment pathways for your clients. Think of each treatment step as a node in a network, and the possible transitions from one step to another as directed edges. You have designed a treatment network with steps connected as follows: [(0, 1), (1, 2), (2, 3), (3, 0), (2, 4), (4, 2)], creating a system where each step is accessible from every other, ensuring a holistic and interconnected approach to patient care.

Initially, your software confirms that the treatment network is comprehensive, ensuring no patient is without options to progress in their therapy – it's a strongly connected graph, meaning there's a path between every pair of steps.

Now, consider the impact of removing a particular treatment transition, like the step between the second and third stages of therapy, represented by the removal of the edge (2, 3). The software needs to reassess the network: is the treatment pathway still robust enough that a client can move through each necessary stage of their therapy without interruption, or has the removal of this option created gaps in the treatment flow?

In essence, you're examining the removal of a specific treatment option and evaluating if the therapy program remains strongly connected, allowing full accessibility between all the intervention stages, or if the network's integrity is compromised, requiring adjustments to ensure every client can continue to receive comprehensive and continuous care.
### Response:
import networkx as nx

# Create a directed graph
G = nx.DiGraph()
# Add edges
G.add_edges_from([(0, 1), (1, 2), (2, 3), (3, 0), (2, 4), (4, 2)])
# Check if the graph is strongly connected
c = nx.is_strongly_connected(G)
print('Graph is strongly connected:', c)
# Now remove an edge and check the connectivity again
G.remove_edge(2, 3)
c = nx.is_strongly_connected(G)
print('After removing an edge, graph is strongly connected:', c)
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