<｜begin▁of▁sentence｜>### Instruction:
Imagine we've received two separate emergency calls from different locations, each with a distinct set of requirements. Location A needs three distinct resources - one that's in high demand, another slightly less so, and the last with a minimal request. Location B is facing a similar scenario, requiring resources with the exact same level of demand as Location A. 

In order to efficiently coordinate the dispatch of our medical units and ensure that each location receives the appropriate level of assistance, we need to figure out a method that's akin to an alternating Havel-Hakimi approach. This method would help us in determining how we can connect the resources (think of them as 'nodes' with varying 'degrees' of connections representing the demand level) across the two locations in a structured manner. 

Can you lay out a plan that will enable us to visualize this resource allocation between Locations A and B as a bipartite graph? We would greatly appreciate if this plan includes a visual representation using a tool like NetworkX, allowing us to clearly see the distribution of assistance between the two locations and ensure that all needs are met.
### Response:
import networkx as nx

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

# Use the alternating_havel_hakimi_graph function
G = nx.bipartite.alternating_havel_hakimi_graph(aseq, bseq)

# Draw the graph
nx.draw(G, with_labels=True)
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