<｜begin▁of▁sentence｜>### Instruction:
As a Nutrition Consultant, imagine you're tasked with organizing a workshop on meal planning for a community health initiative. You've collected data on participants' food preferences and dietary restrictions, represented as a bipartite graph. Each edge in the graph connects a participant with a specific food item they prefer. Additionally, each participant is categorized into one of two groups: those who prefer vegetarian options (False) and those who prefer non-vegetarian options (True).

Now, you're interested in projecting this bipartite graph onto the participants who prefer vegetarian options, while also preserving information about how many times each food preference occurs among them. To accomplish this, you plan to use the bipartite projection method provided by the NetworkX library. Once projected, you aim to display the resulting list of connections between participants who prefer vegetarian options and the food items they favor. This will help you tailor your workshop materials to address the specific dietary needs and preferences within this subgroup.
### Response:
import igraph as ig

# Create a bipartite graph
edges = [(0, 1), (0, 2), (1, 2)]
types = [False, False, True, True]  # False for the first type, True for the second
g = ig.Graph.Bipartite(types, edges)

# Project the bipartite graph
which = 0  # Choose which projection we are interested in: 0 for the first type, 1 for the second
g_proj = g.bipartite_projection(types=types, multiplicity=True, which=which)

# Print the edge list of the projected graph
print(g_proj.get_edgelist())
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