<｜begin▁of▁sentence｜>### Instruction:
In the grand theater of our choreographed performance, imagine that the stage is a network of spotlights, with pathways of light connecting them. The spotlights are positioned at points 0, 1, 2, and 3. Now, the beams of light that link these spotlights have their own lengths, creating a pattern on the stage as such: a beam from spot 0 to spot 1 has a length of 1, from spot 1 to spot 2 has the same length, from 2 to 3, a beam stretches with the same short length, but there is also a longer beam, taking a length of 2, which directly connects spot 0 to spot 3, offering a different path.

As a dancer moving from spotlight 0 to spotlight 3, you'd want to know all the possible paths that you can take across the stage, tracing the beams from light to light, ensuring that each step is a new one—no repeating your movements to create a beautiful, varied performance.

With this imagery, can we exploit the ethereal map of our illuminated stage—let's refer to it as a graph with its edge set [(0, 1, 1), (1, 2, 1), (2, 3, 1), (0, 3, 2)]—to seek out and elegantly display each unique dance route from spotlight 0 to spotlight 3? We'd want to lay out all the simple paths available to us, arranged like a crescendo, from the shortest dance to the one with the most flourish, the longest.
### Response:
import networkx as nx

# Create a graph
G = nx.Graph()
G.add_weighted_edges_from([(0, 1, 1), (1, 2, 1), (2, 3, 1), (0, 3, 2)])

# Find the shortest simple paths
paths = list(nx.shortest_simple_paths(G, 0, 3, weight='weight'))

# Print out the paths
for path in paths:
    print(path)
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