<｜begin▁of▁sentence｜>### Instruction:
Given a bipartite graph with two sets of nodes U={1,2,3,4} and V={'a','b','c'}, and edges connecting nodes from different sets {(1,'a'),(1,'b'),(2,'b'),(2,'c'),(3,'c'),(4,'a')}, can you compute the bipartite clustering coefficient using the robins_alexander_clustering function from NetworkX?
### Response:
import networkx as nx
from networkx.algorithms import bipartite

# Create a bipartite graph
B = nx.Graph()
# Add nodes with the node attribute "bipartite"
B.add_nodes_from([1, 2, 3, 4], bipartite=0)
B.add_nodes_from(['a', 'b', 'c'], bipartite=1)
# Add edges only between nodes of opposite partition sets
B.add_edges_from([(1, 'a'), (1, 'b'), (2, 'b'), (2, 'c'), (3, 'c'), (4, 'a')])

# Compute the bipartite clustering coefficient
clustering = bipartite.robins_alexander_clustering(B)
print(f'Bipartite clustering coefficient: {clustering:.3f}')
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