<｜begin▁of▁sentence｜>### Instruction:
As a professional organizer, you're well-versed in arranging elements into coherent and functional systems. Imagine you're tasked with designing a network structure for a new collaborative workspace. This space is divided into two zones: Zone A for team leaders and Zone B for project coordinators. Each individual in Zone A (team leaders) must establish certain collaborations with different coordinators in Zone B, as defined by their collaboration capacity. 

The team leaders in Zone A have the following collaboration capacities: [3, 3, 2, 2, 2, 1], whereas the project coordinators in Zone B have these capacities: [3, 3, 3, 2, 1]. You are to generate an effective partnership network between Zones A and B using the Havel-Hakimi algorithm, which simulates who should collaborate with whom and how often, based on their capacities.

Your goal is to lay out the connections in a way that reflects the collaboration potentials of each zone's members while maintaining the space's balance and productivity. As part of the process, you need to confirm that the resulting partnership network is, in fact, a bipartite graph, ensuring that collaborations only exist between members of Zone A and Zone B, not within the same zone.

Incorporate this structure and confirm its viability in the context of our efficient, organized workspace setting, keeping in mind that if this were documented in a standard network file, it might resemble a .gml file named "workspace_collaboration_network.gml".
### Response:
import networkx as nx
from networkx.algorithms import bipartite

# Degree sequences for node sets A and B
aseq = [3, 3, 2, 2, 2, 1]
bseq = [3, 3, 3, 2, 1]

# Create a bipartite graph using the Havel-Hakimi algorithm
B = bipartite.havel_hakimi_graph(aseq, bseq)

# Check if the graph is bipartite
is_bipartite = bipartite.is_bipartite(B)

# Print the result
print('The graph is bipartite:', is_bipartite)
<|EOT|>
