<｜begin▁of▁sentence｜>### Instruction:
Let's imagine we're mapping out a habitat restoration network using NetworkX, where we have to track two types of nodes representing different groups within the ecosystem. The first group (nodes 0, 1, 2) can be thought of as various plant species, while the second group (nodes 3, 4) represents different animal species. 

In this complete bipartite ecological network, every plant species is linked with each animal species, and we're looking to provide each species with the resources it needs. To properly manage our resources, we need to assess the connections each species has. Could you suggest how we might efficiently evaluate the number of interactions – or 'degrees' – that each plant and animal species node has within our bipartite network model using NetworkX? This will allow us to ensure every species receives the attention and care it needs to thrive in the rehabilitation habitat we're cultivating.
### Response:
import networkx as nx
from networkx.algorithms import bipartite

# Create a complete bipartite graph B with two sets of nodes
# The first set contains nodes 0, 1, 2 and the second set contains nodes 3, 4
B = nx.complete_bipartite_graph(3, 2)

# Select one of the bipartite sets as a list
Y = {3, 4}

# Get the degrees of the nodes in the bipartite sets
# bipartite.degrees returns a tuple of two dictionaries keyed by the nodes
# degX will represent degrees of the first set and degY of the second
degX, degY = bipartite.degrees(B, Y)

# Convert the dictionaries to standard dict for printing
print('Degrees of the first bipartite set:', dict(degX))
print('Degrees of the second bipartite set:', dict(degY))
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