<｜begin▁of▁sentence｜>### Instruction:
Given a bipartite graph with two node sets {'A', 'B', 'C'} and {1, 2, 3} and edges [('A', 1), ('B', 1), ('C', 1), ('A', 2), ('B', 3), ('C', 2)], can you compute the average bipartite clustering coefficient using 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(['A', 'B', 'C'], bipartite=0)
B.add_nodes_from([1, 2, 3], bipartite=1)
# Add edges only between nodes of opposite node sets
B.add_edges_from([('A', 1), ('B', 1), ('C', 1), ('A', 2), ('B', 3), ('C', 2)])

# Compute the average clustering
clustering = bipartite.average_clustering(B)
print('The average bipartite clustering:', clustering)
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