<｜begin▁of▁sentence｜>### Instruction:
Imagine we're constructing a diagram that represents the relationships and interactions among different staff members and departments within a veterinary clinic. Let's say the nodes ['A', 'B', 'C', 'D'] signify four key units in the clinic – 'A' might be the reception, 'B' the treatment area, 'C' could be the surgical suite, and 'D' the recovery room. The edges [('A', 'B'), ('A', 'C'), ('B', 'C'), ('C', 'D')] would then illustrate how these units interact, showing for instance that reception directs patients to both the treatment area and the surgical suite, while the treatment area also works in conjunction with the surgical suite, which in turn is connected to the recovery room.

In this scenario, it would be insightful to gauge the closeness and efficiency of these interactions by calculating the density of our clinic's network. Doing so would tell us how interconnected each unit is within the entire clinic's workflow. How could we go about measuring the network density of our clinic's interaction chart using NetworkX?
### Response:
import networkx as nx

# Create a graph
G = nx.Graph()

# Add some nodes and edges
G.add_nodes_from(['A', 'B', 'C', 'D'])
G.add_edges_from([('A', 'B'), ('A', 'C'), ('B', 'C'), ('C', 'D')])

# Compute the density of the graph
density = nx.density(G)
print('Density of the graph:', density)
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