Reworked validation data selection

[arminshzd]

Since the input data for the SRVs are time series, randomly splitting the data for training and validation is not an option. The dataset creation workflow now supports three common cases for automatic train/validation data splitting as well as allowing the user to feed the validation and train data manually.
If val_frac is set to zero during model definition, all data fed to the fit function is treated as training data, and no validation will be performed. The user can manually provide the validation data to the fit function using the val_data keyword.
If val_frac is non-zero, one of the three cases can happen:

1. If the input data is a single torch.tensor (a single continuous trajectory), the last val_frac of the data is separated and used for validation.

2. If the input data is a list of torch.tensors:

   • If all the trajectories are the same length, ceil(val_frac*len(data)) of the trajectories are used for validation. E.g., if a list of 5 trajectories of the same length is given as input and val_frac=0.2, the last trajectory in the list will be used for validation. If val_frac*len(data) is not an integer, it will be rounded up. E.g., a list of 5 trajectories with val_frac=0.25 will use 2 of the five trajectories (without splitting any of the trajectories) for validation (this means val_frac will be automatically adjusted to 0.4 by the code).

   • If all the trajectories are not the same length, the shortest trajectory is used for validation.
     The user can override the above default behavior through val_frac=0.0 and the val_data keyword.

I suggest a full review of the new lines before merging.