<｜begin▁of▁sentence｜>### Instruction:
As a digital marketing specialist, you may encounter situations where you need to analyze the connections between customer behaviors and product preferences. Imagine you have a list of customers and products they have shown interest in. For instance, customers labeled as "True" and products labeled as "False" have certain connections as follows: Customer 0 is interested in Product 2, and Customer 1 is interested in Product 3. In this scenario, you might want to utilize network analysis to determine the sizes of the customer and product groups that have the most connections with each other. One way to approach this is by using the bipartite_projection_size method in the igraph API. This method can help you calculate the sizes of the top (customer) and bottom (product) projections, giving you valuable insights for targeted marketing strategies.
### Response:
import igraph as ig

# Assume you have a bipartite graph
bipartite_graph = ig.Graph.Bipartite([True, True, False, False], [(0, 2), (1, 3)])

# Calculate the sizes of the projections
top_projection_size, bottom_projection_size = bipartite_graph.bipartite_projection_size()

# Print the sizes
print('Top projection size:', top_projection_size)
print('Bottom projection size:', bottom_projection_size)
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