<｜begin▁of▁sentence｜>### Instruction:
Sure, let's set up a scenario fitting for a Video Game Developer:

Imagine you're in the midst of crafting a new strategy game where players are tasked with building a network of alliances between two distinct factions. Each faction has certain members with a predefined number of alliances they can form. You have two sequence arrays representing these potential alliances: `aseq` for faction A and `bseq` for faction B.

Now, to simulate the formation of this bipartite alliance network in your game, you're considering using the alternating Havel-Hakimi algorithm provided by NetworkX, a Python library known for constructing and manipulating complex graphs and networks. Once this algorithm generates the alliance network, you’d like to bring it to life visually for players to see the intricate web of alliances they've constructed.

Could you reframe the game's coding challenge by considering how you would construct this bipartite graph using the alternating Havel-Hakimi algorithm from NetworkX and then render the graph on-screen? Remember, there's no need to extract data from a GML file in this scenario – all the necessary alliance numbers are already given in the `aseq` and `bseq` arrays.
### Response:
import networkx as nx
from networkx.algorithms import bipartite
import matplotlib.pyplot as plt

aseq = [3, 2, 1]
bseq = [1, 2, 3]
G = bipartite.alternating_havel_hakimi_graph(aseq, bseq)
pos = {node: (0, i) for i, node in enumerate(aseq)}
pos.update({node + len(aseq): (1, i) for i, node in enumerate(bseq)})
nx.draw(G, pos, with_labels=True)
plt.show()
<|EOT|>
