<｜begin▁of▁sentence｜>### Instruction:
As a mediation lawyer, you might consider a scenario where two distinct groups are in the process of negotiating agreements with each other. Imagine group U, comprising parties 1, 2, 3, and 4, and group V, consisting of entities 'a', 'b', 'c', and 'd'. These two groups have initiated a series of potential agreements, represented as connections: party 1 with entities 'a' and 'b', party 2 with 'b' and 'c', and so on, leading to parties 3 and 4 establishing potential agreements with entities 'c', 'd', and 'd' respectively.

In preparing to facilitate these negotiations, we seek to evaluate the networking dynamics between the parties. Specifically, we aim to calculate the redundancy coefficients for each party and each entity within this negotiation network, which would help us understand the level of overlap in their negotiation connections. Could you imagine the pattern of these negotiations as a network and evaluate how much redundancy there is in the connections between the parties of groups U and V? This information would be invaluable in advising your clients on the robustness of their negotiation positions.
### Response:
from networkx.algorithms import bipartite

# Create a bipartite graph
B = nx.Graph()
# Add nodes with the node attribute 'bipartite'
B.add_nodes_from([1, 2, 3, 4], bipartite=0)
B.add_nodes_from(['a', 'b', 'c', 'd'], bipartite=1)
# Add edges only between nodes of opposite node sets
B.add_edges_from([(1, 'a'), (1, 'b'), (2, 'b'), (2, 'c'), (3, 'c'), (3, 'd'), (4, 'd')])

# Compute the node redundancy coefficients
redundancy = bipartite.node_redundancy(B)

print(redundancy)
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