Improve CUDA performance of `PathData` class

[IngoScholtes]

A major performance bottleneck in the current implementation is the use of a dictionary to store path tensors in the class PathData. This creates a huge performance overhead when using CUDA vs. CPU., as each individual tensor in the dictionary must be copied to the GPU separately.

A better solution could possibly be implemented based on the new nested tensor class available in pyTorch 2.1. This would allow to store all paths with varying lengths in a single nested tensor, i.e. we would only need a single copy operation when moving the data to the GPU.

[IngoScholtes]

Some good and bad news on this issue:

• For a test implementation in which I replaced the dictionary in PathData by a nested_tensor the performance of the PathData creation on the GPU increases by a factor of five.
• The unfold operation, which we need to create edge_indices of k-th order models is not yet implemented for nested_tensors (neither for the CPU nor for GPU backend)

[M-Lampert]

This is outdated. For updates on this see #132