<｜begin▁of▁sentence｜>### Instruction:
Imagine you're choreographing a complex dance number that involves pairs of dancers where one set represents the leads, labeled A, B, C, and D, and the other set represents the follows, numbered 1, 2, 3, and 4. The pairing of dancers is crucial for a harmonious performance, and you've already decided on some potential pairs: A is a good match with 1 and 2, B pairs nicely with 1, C complements both 2 and 3, while D perfectly syncs with 3 and 4.

For the performance to be visually stunning and the interactions between the dancers to flow seamlessly, you're interested in understanding the level of clustering – like a measure of how interconnected the dance partners are within this network of dancers.

Using a mathematical approach for choreography, imagine you had a way to calculate a special kind of dance clustering coefficient, much like the Robins and Alexander clustering coefficient in network analysis. With this intricate calculation, you'd have a quantifiable way to gauge the tightness of your dance troupe's formation, ensuring everyone is in tandem, and the performance is cohesive.

How would you, with your choreographic intuition, compute this clustering coefficient for your assembled dance ensemble, keeping in mind the connections you've established among your leads and follows?
### Response:
from networkx.algorithms import bipartite
import networkx as nx

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

# Compute the bipartite Robins and Alexander clustering
clustering = bipartite.robins_alexander_clustering(B)

print(f'The bipartite Robins and Alexander clustering coefficient is: {clustering:.3f}')
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