<｜begin▁of▁sentence｜>### Instruction:
As a health inspector, I often analyze complex systems in terms of their individual components and how they interact within the larger structure to ensure they meet certain standards. Let’s metaphorically apply this approach to verify the integrity of a network which is much like inspecting the layout of a public facility.

Imagine we have a network that's intricately structured like a dodecahedron, akin to a facility with a regular and highly coordinated layout. This network, much like a well-designed building, adheres to certain regularity conditions. Each point of connection, or 'room', in this network is identically connected, much like how each room in a facility might have the same number of doors (which we can term as 'degree'). The network's 'blueprint' or intersection array, which is a set of numerical parameters [b_0, b_1, ..., b_{d-1}; c_1, c_2, ..., c_d], specifies the immediate connection details just as a floor plan specifies the number of exits and entrances to each room.

Now, within this symmetry, each parameter is constrained to balance the equation a_i + b_i + c_i = k, where k represents the uniform degree akin to the consistent number of exits (or entrances) each room maintains. In the scenario where we are 'inspecting' this network to verify its parameters—akin to ensuring a facility meets our safety regulations—how can we utilize NetworkX, a tool much like our inspection toolkit, to determine these global parameters and ensure they are up to our standard for regularity?

Imagine this task as verifying that all exits and entrances are accounted for and comply with our codes, but in a network analysis context. Do note that, to proceed with the inspection, one should have the 'blueprint' or data file (akin to a .gml file in network analysis) readily available.
### Response:
import networkx as nx

# Create a distance-regular graph, for example, a dodecahedral graph
G = nx.dodecahedral_graph()

# Calculate the intersection array of the graph
b, c = nx.intersection_array(G)

# Compute the global parameters based on the intersection array
parameters = list(nx.global_parameters(b, c))
print(parameters)
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