<｜begin▁of▁sentence｜>### Instruction:
Given a bipartite graph with node sets {A, B, C, D} and {1, 2, 3, 4} and edge set {('A', 1), ('A', 2), ('B', 1), ('C', 3), ('D', 4), ('B', 2), ('C', 2), ('D', 3)}, how can we compute the Robins and Alexander bipartite clustering coefficient using NetworkX?
### 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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