<｜begin▁of▁sentence｜>### Instruction:
Imagine you're tasked with overseeing the design of a new manufacturing system. This system is represented by a schematic where certain mechanical components, labeled 'A', 'B', 'C', and 'D', must be precisely paired with specific operation units, numbered 1, 2, 3, and 4. In the design blueprint, we've already established connections between components and units as follows: 'A' pairs with units 1 and 2, 'B' with units 1 and 3, 'C' with units 2 and 4, and 'D' with units 3 and 4.

To ensure the efficiency and reliability of our mechanical system, we would like to perform an analysis to determine the redundancy of each component and operation unit within this network. The aim is to understand how the failure or removal of a single node might affect the overall system, considering that some pairs have multiple shared connections.

Could you utilize the NetworkX software to calculate the node redundancy coefficients for each component ('A', 'B', 'C', 'D') and each operation unit (1, 2, 3, 4) within our schematic? This metric will help us understand the resilience of our system's design and guide us in potential improvements.
### 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', 1), ('C', 2), ('D', 3), ('A', 2), ('B', 3), ('C', 4), ('D', 4)])

# Compute the redundancy coefficient of each node in the graph
redundancy = bipartite.node_redundancy(B)
print(redundancy)
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