<｜begin▁of▁sentence｜>### Instruction:
Given a bipartite graph with top nodes ['A', 'B', 'C', 'D'] and bottom nodes [1, 2, 3, 4], and edges [('A', 1), ('A', 2), ('B', 1), ('B', 3), ('C', 2), ('D', 3), ('D', 4)], how to compute the bipartite clustering coefficient for nodes using NetworkX with the 'dot' pairwise clustering method?
### Response:
import networkx as nx
from networkx.algorithms import bipartite

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

# Compute bipartite clustering
clustering_coeffs = bipartite.clustering(B, mode='dot')
print(clustering_coeffs)
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