<｜begin▁of▁sentence｜>### Instruction:
Imagine you're a Food Safety Inspector monitoring various food processing facilities, represented by nodes {A, B, C, D}, and associated food safety practices {1, 2, 3, 4}. You have the relationships between these entities already established: {('A', 1), ('A', 2), ('B', 1), ('C', 3), ('D', 4), ('B', 2), ('C', 2), ('D', 3)} - which facilities adhere to which practices. As part of your intricate analysis to ensure compliance with food safety regulations and standards, how could you utilize NetworkX to determine the Robins and Alexander bipartite clustering coefficient? This is a useful technique to highlight how the practices are interrelated across facilities.
### Response:
import networkx as nx
from networkx.algorithms import bipartite

# Create a bipartite graph
G = nx.Graph()
G.add_nodes_from(['A', 'B', 'C', 'D'], bipartite=0)
G.add_nodes_from([1, 2, 3, 4], bipartite=1)
G.add_edges_from([('A', 1), ('A', 2), ('B', 1), ('C', 3), ('D', 4), ('B', 2), ('C', 2), ('D', 3)])

# Compute the Robins and Alexander bipartite clustering coefficient
clustering_coefficient = bipartite.robins_alexander_clustering(G)
print(f'Robins and Alexander bipartite clustering: {clustering_coefficient:.3f}')
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