<｜begin▁of▁sentence｜>### Instruction:
Imagine we're developing a new initiative to improve collaboration between different healthcare professionals 'A', 'B', 'C', 'D' and services '1', '2', '3', '4'. To visualize and enhance our network of interactions, we've created a model where healthcare workers are connected to the services they provide or collaborate with. We have a network that includes connections such as: professional 'A' is linked to service '1' and '2', 'B' to service '2', 'C' to '3', and 'D' to '4'.

To assess the strength of interdisciplinary cooperation within this network, we'd like to use a tool to compute the bipartite clustering coefficient for each individual and service in our model. This measure will help us identify how closely-knit our healthcare clusters are, which is key in designing programs that promote integrated service delivery. Could we employ NetworkX to calculate this, and if so, could you give us an insight into the clustering patterns present in our current healthcare professional-service network? This information will be pivotal for our next health education and service optimization campaign.
### 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), ('B', 2), ('C', 3), ('D', 4), ('A', 2)])

# Compute the bipartite clustering coefficients
clustering = bipartite.latapy_clustering(B)
print(clustering)
```
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