<｜begin▁of▁sentence｜>### Instruction:
Imagine you are overseeing a program focused on the rehabilitation of individuals through community-based projects. In this scenario, the program involves a collaboration between participants (labeled 'A', 'B', 'C', 'D') and various community service activities (labeled 1, 2, 3). Each connection between a participant and an activity represents their involvement or contribution to that particular community service. 

The relationships in the program are as follows: Participant 'A' is involved in activities 1 and 2, 'B' in activities 1 and 2, 'C' is involved in activity 3, and 'D' in activity 3 as well. This framework forms a bipartite network of participants and activities.

To effectively monitor and assess the impact each individual has within this network, and to potentially identify how well-connected or isolated they are within this rehabilitative ecosystem, it would be vital to determine the redundancy coefficient for each participant and activity. 

Using the data provided, could you delve into this network and compute the redundancy coefficient for each node (both participants and activities)? This would enable us to better understand the support structure within our program and enhance our strategies for reintegration and community involvement.
### Response:
import networkx as nx
from networkx.algorithms import bipartite
# Create a bipartite graph
B = nx.Graph()
B.add_edges_from([('A', 1), ('B', 1), ('A', 2), ('B', 2), ('C', 3), ('D', 3)])
# Compute the node redundancy
redundancy = bipartite.node_redundancy(B)
print(redundancy)
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