<｜begin▁of▁sentence｜>### Instruction:
Imagine we're crafting the soundscape for a new animated feature set in a world where two distinct tribes, the As and the Bs, maintain a tenuous connection. Each member of tribe A has a predetermined number of connections to the Bs, and vice versa. Their connections are vital for the storyline. The connections are as follows: tribe A has members with 3, 2, and 1 ties respectively to tribe B; tribe B members have 1, 1, 2, and 2 ties back to the As.

To accurately depict the intertwined relationships between these tribes on screen, our writers need a visual representation, like a chart for reference. That's where I thought of creating a unique bipartite social map using NetworkX's creative tool, the reverse_havel_hakimi_graph function.

Could we visualize this social network, ensuring that each tribe is distinctly represented, so our voice actors have a clearer picture of how each character is interlinked? Ensuring the bipartite sets of each tribe member is made crystal clear will surely enhance the depth of our acting and storytelling. If we could generate this portrayal in an interactive graph, it would greatly benefit our collective understanding of the storyline and relationship dynamics.
### Response:
import networkx as nx
from networkx.algorithms import bipartite

# Given degree sequences
aseq = [3, 2, 1]
bseq = [1, 1, 2, 2]

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

# Print adjacency list of B to show edges
for edge in B.edges():
    print(edge)

# Print node data to show bipartite sets
for n, d in B.nodes(data=True):
    print(f'Node {n} has bipartite set {d['bipartite']}')
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