<｜begin▁of▁sentence｜>### Instruction:
As a Green Building Consultant, imagine you're working on a project where you need to efficiently organize the different specialists into two groups to minimize travel and communication path overlaps on the construction site, in alignment with our sustainability goals. The current collaboration network can be visualized as a graph, with connections between the architect 'A', the builder 'B', the carpenter 'C', and the designer 'D'. These pairs indicate the current communication lines: [('A', 'B'), ('A', 'C'), ('B', 'D'), ('C', 'D')].

To streamline this, we're contemplating employing a two-coloring strategy on this network to identify a sustainable way to split the specialists into two distinct groups, ensuring that pathways of communication cross less frequently. Through this strategy, we aim to reduce the travel time on-site and enhance communication efficiency. Could we apply the principles of NetworkX to enrich this network diagram with an attribute that labels each node (specialist) with their respective bipartite set, identifying the two distinct groups for our eco-friendly initiative?
### Response:
import networkx as nx
from networkx.algorithms import bipartite

# Create a bipartite graph
B = nx.Graph()
B.add_edges_from([('A', 'B'), ('A', 'C'), ('B', 'D'), ('C', 'D')])

# Try to color the graph
try:
    color = bipartite.color(B)
    print('Node colors:', color)
except nx.NetworkXError as e:
    print('Error:', e)

# Use the coloring to set node attributes
nx.set_node_attributes(B, color, 'bipartite')

# Print bipartite attribute of a node
print('Node A bipartite set:', B.nodes['A']['bipartite'])
<|EOT|>
