<｜begin▁of▁sentence｜>### Instruction:
Imagine a simplified model of a celestial system, where we've classified objects into two distinct categories: first, we have cosmic entities labeled as 'A', 'B', and 'C', perhaps representing different types of stars. In the second category, we have numerical markers 1, 2, and 3, which could signify unique spatial zones or celestial events within a galaxy.

In this universe, we've observed connections akin to gravitational pulls or interactions like star-zone affiliations, denoted by pairs: a star 'A' is linked to both zones 1 and 2, star 'B' linked with zones 1 and 3, and star 'C' is also connected with zones 1 and 2.

To better understand the integrative structure of our cosmic system, we desire to calculate the mean bipartite clustering coefficient—a measure reflective of how tightly-knit clusters of stars and zones are within this cosmic web. This analogy serves to paint the network model we've constructed using our observations.

How would we proceed to compute the average bipartite clustering coefficient for this star-zone network graph using the tools at our disposal in 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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