<｜begin▁of▁sentence｜>### Instruction:
Imagine you're tasked with devising a new strategy for an intricate network of corridors connecting various points within a sprawling, state-of-the-art eye care facility. These corridors are analogous to pathways of visual information in the human eye, and it's crucial to ensure a seamless flow to maintain the wellness of operational dynamics, akin to optimal eye health. In the facility, we have essential junctions represented by rooms numbered 1 through 5, with corridors connecting them in the following manner: from Room 1 to Room 2, Room 2 to Room 3, Room 3 to Room 4, Room 4 to Room 5, Room 5 back to Room 1, and an additional corridor linking Room 2 straight to Room 5.

Given this scenario, how might we utilize a theoretical tool such as NetworkX to construct a directional version of this corridor system, effectively enabling us to evaluate the robustness of inter-room connections by measuring the potential flow of foot traffic? This would help us identify how many alternate routes we have to bypass any section that may become temporarily inaccessible due to maintenance or other unforeseen events.

Furthermore, once we've modeled this state-of-the-art connectivity system, how could we decipher the correspondence between the original consultation spaces and their counterparts in this enhanced, flow-oriented model? This understanding will assist in ensuring that when flow assessments are performed, we can precisely pinpoint potential disruptions to patient movement and address them efficiently, just as we would diagnose and treat obstructions in ocular pathways to maintain clear vision.
### Response:
import networkx as nx

# Create an undirected graph G
G = nx.Graph()
G.add_edges_from([(1, 2), (2, 3), (3, 4), (4, 5), (5, 1), (2, 5)])

# Use build_auxiliary_node_connectivity to create the auxiliary digraph D
D = nx.build_auxiliary_node_connectivity(G)

# Get the mapping of D
mapping = D.graph['mapping']

print('Auxiliary digraph nodes:', D.nodes())
print('Mapping:', mapping)
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