<｜begin▁of▁sentence｜>### Instruction:
Picture this: we're on the set of a fascinating new ensemble piece, the scene brimming with artistry and intricate connections. In our cinematic universe, we have a talented trio of lead characters, each represented by the numbers 1, 2, and 3. They are entwined in a narrative dance with a dynamic supporting cast depicted by the letters 'a', 'b', 'c', and 'd'. The plot weaves them together through a series of interactions akin to scenes: pairings of lead and supporting characters like (1, 'a'), (1, 'b'), (2, 'b'), (2, 'c'), (3, 'c'), and (3, 'd').

As the visionary helming this project, we're intent on capturing the essence of closeness amongst our characters, drawing parallels to the real-life technique of measuring the closeness centrality within a network of interpersonal relationships. To translate this concept into our cinematic narrative, imagine we're consulting a special script - let's call it NetworkX - to quantify the level of closeness each lead character exhibits within the complex web of our story.

The challenge, presented to you as the film director, is to frame a scene where this closeness centrality measure is explored. We're focusing solely on our lead characters, akin to favoring close-ups of their faces to capture their connection to the broader cast. How would you visually articulate this measurement of centrality? Keep in mind, this must stay true to the closeness centrality definition, yet be organically woven into the fabric of the film, ensuring it reflects the proximity of leads 1, 2, and 3 to their supporting counterparts. How do you craft this pivotal moment?
### Response:
import networkx as nx
from networkx.algorithms import bipartite

# Create a sample bipartite graph
G = nx.Graph()
G.add_edges_from([(1, 'a'), (1, 'b'), (2, 'b'), (2, 'c'), (3, 'c'), (3, 'd')])

# Define one set of nodes (top nodes)
top_nodes = {1, 2, 3}

# Compute the closeness centrality for the nodes
# in the bipartite network
bipartite_closeness = bipartite.closeness_centrality(G, nodes=top_nodes)
print(bipartite_closeness)
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