<｜begin▁of▁sentence｜>### Instruction:
Imagine you're currently developing a software application that involves analyzing networks, and you're working with a bipartite graph data structure. The graph you're dealing with has two distinct sets of nodes: the top nodes are labeled as 'A', 'B', 'C', 'D', and the bottom nodes are numbered 1, 2, 3, 4. The connections between these nodes are specified by the following set of edges: [('A', 1), ('A', 2), ('B', 1), ('B', 3), ('C', 2), ('D', 3), ('D', 4)].

Your current task in the software development process is to calculate the bipartite clustering coefficient for the nodes within this graph. Specifically, you've been asked to use the 'dot' pairwise clustering method provided by the NetworkX library. As a Computer Programmer, could you write the piece of code that efficiently computes this particular clustering coefficient, according to the specified method within the confines of the NetworkX API? Keep in mind that your code should maintain the existing structure and semantics of the graph as is standard in network analysis tasks.
### 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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