<｜begin▁of▁sentence｜>### Instruction:
As a Leisure Travel Agent, imagine you're assisting a group of friends in planning their dream vacation itinerary. Each friend has specific preferences for activities and destinations. You're using a specialized tool called 'TripPlanner' API, which helps visualize their preferences in a graph format.

Here's the scenario: You have a group of friends, each denoted by a node in a bipartite graph. The connections between them represent their shared interests and travel plans. For instance, the friends with IDs 0, 1, and 2 are keen on similar activities, as are friends 2, 3, and 4. You need to visualize this graph layout effectively to minimize overlaps and ensure clarity.

Using the 'layout_bipartite' function in the 'TripPlanner' API, you aim to generate a visually appealing layout. This layout should arrange the nodes in two distinct groups, with minimum horizontal and vertical gaps between them, allowing for a specific number of iterations to optimize the arrangement and reduce overlaps between the edges.

Your goal is to create an intuitive visual representation of your friends' vacation preferences, ensuring that the semantics of their connections remain clear and easy to interpret.
### Response:
from igraph import Graph

# Define edges for a bipartite graph
edges = [(0, 1), (0, 2), (2, 3), (4, 1)]

# Create a graph from the edge list
bipartite_graph = Graph(edges=edges)

# Make sure to set up the 'type' attribute for bipartite layout
types = [True, False, True, False, True]
for vertex, vertex_type in enumerate(types):
    bipartite_graph.vs[vertex]['type'] = vertex_type

# Calculate the bipartite layout
layout = bipartite_graph.layout_bipartite(types='type', hgap=2, vgap=2, maxiter=100)

# Plot the graph using the bipartite layout
ig.plot(bipartite_graph, layout=layout, bbox=(300, 300), margin=20)
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