<｜begin▁of▁sentence｜>### Instruction:
As a makeup artist preparing for a high-profile event, imagine you're tasked with ensuring the performers arrive at their designated spots efficiently. You have a lineup of six performers, each needing to move from one station to the next. Performer 0 starts at the makeup chair, then passes to Performer 1, who moves on to Performer 2, and so forth, until Performer 5 reaches the final touch-up station.

To streamline their journey, you want to utilize the most efficient paths between stations. Think of each transition between performers as a directed pathway, where the weight represents the time it takes to move between stations. Now, you need to use the `get_shortest_paths` function, akin to your makeup artist toolkit, to calculate the shortest paths from the initial station (Performer 0) to all subsequent stations. This way, you ensure each performer moves swiftly and flawlessly through the makeup process, enhancing their appearance for the grand event.
### Response:
from igraph import Graph

# Create a graph
n = 6  # Number of vertices
g = Graph()
g.add_vertices(n)
g.add_edges([(0,1), (1,2), (2,3), (3,4), (4,5)])

# Set weights for the edges
g.es['weight'] = [1, 2, 1, 1, 1]

# Calculate the shortest paths from vertex 0 to all other vertices
path = g.get_shortest_paths(v=0, to=None, weights='weight', mode='OUT', output='vpath')

# Output the paths
for p in path:
    print('Path from 0 to {target}: {path}'.format(target=p[-1], path=p))
<|EOT|>
