<｜begin▁of▁sentence｜>### Instruction:
In the midst of organizing a major music festival, imagine we've got a network where the stages are represented by nodes labeled 'A', 'B', 'C', 'D', and the various bands set to play are marked as 1, 2, 3, 4. The relationships between stages and bands (who plays where) form a sort of connection pattern, much like an audio mixer might patch in different instruments to various tracks.

These stages and bands create a bipartite graph, with edges defining which band is booked for which stage: 'A' hosts bands 1 and 2, 'B' has secured band 1, 'C' will feature bands 2 and 3, while 'D' has bands 3 and 4 lined up.

Could you calculate the corresponding bipartite degree centrality for the stages to determine which stage has the strongest influence based on the number of bands it hosts? Imagine you're balancing the audio levels to ensure each stage features prominently in the festival's 'mix.' Provide the centrality values specifically for the stage node set.
### 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), ('A', 2), ('B', 1), ('C', 2), ('C', 3), ('D', 3), ('D', 4)])

# Define top nodes (one set of the bipartite graph)
top_nodes = {'A', 'B', 'C', 'D'}

# Compute degree centrality
centrality = bipartite.degree_centrality(B, top_nodes)

print(centrality)
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