<｜begin▁of▁sentence｜>### Instruction:
In the complex anatomy of our healthcare systems, think of a directed graph as a representation of the referral pathways within a medical facility or among different healthcare providers. Each node could symbolize a specialist, a department, or a service, and the directed edges could indicate the referral direction from one provider to another. Now, consider the specific set of connections: [(0, 1), (0, 2), (2, 3), (3, 4)], which, in our scenario, would represent a sequence of referrals starting from a primary care physician.

In assessing the efficiency and structure of this pathway, one query that arises is whether this referral chain forms an arborescence, a tree-like structure with a single, unambiguous route from the primary provider to any other. Such a structure is vital for ensuring that patients are always referred correctly without the potential confusion or systemic errors that could result from more cyclical referral patterns.

Utilizing NetworkX, a tool akin to the diagnostic software in our medical toolkit, could you examine the recorded referral sequence to ascertain whether the pattern indeed constitutes an arborescence? This knowledge is key to optimizing patient flow and resource allocation within our healthcare matrix.
### Response:
import networkx as nx

# Create a directed graph
G = nx.DiGraph([(0, 1), (0, 2), (2, 3), (3, 4)])

# Check if the graph is an arborescence
is_arbo = nx.is_arborescence(G)
print('Is the graph an arborescence?', is_arbo)
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