<｜begin▁of▁sentence｜>### Instruction:
In the midst of a complex criminal investigation, we've uncovered a network of suspects and possible connections that could be critical in mapping out the relationships within this group. Our intelligence suggests we're looking at two distinct categories of individuals involved - let's call them "Type A" suspects, where we have 3 individuals, and "Type B" suspects, numbering 4.

Our goal is to construct a theoretical model that includes every potential interaction between these two groups, a model that assumes every individual from "Type A" has had contact with all individuals from "Type B." It's a hypothetical but comprehensive approach, akin to creating a 'complete bipartite graph' in our analytical software.

Leveraging the tool NetworkX, I'd like to ask for assistance to generate this model. This is crucial for visualizing the full scope of connections, ensuring no possible interaction is overlooked. Additionally, if the resulting model could be saved in a GML file format, that would significantly streamline our ability to share and review the data with other departments. Can you advise on how one could approach this using NetworkX to create and then visualize such a graph?
### Response:
import networkx as nx

# Create a complete bipartite graph K_{3,4}
B = nx.bipartite.complete_bipartite_graph(3, 4)

# Draw the graph
import matplotlib.pyplot as plt
nx.draw(B, with_labels=True)
plt.show()
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