Thingsvision

- added support for `torch>=2.0.0` and `torchvision=0.15.*` (fixes issue 165)
- implemented CKA in both NumPy and PyTorch (PyTorch runs 2x faster than NumPy)
- added CUDA support for CKA in PyTorch (100x faster than CKA in NumPy)
- added new function called `get_cka` where the user can set the `backend` (`torch` or `numpy`); function returns the CKA object
- added an unbiased CKA variant (from [here](https://github.com/google-research/google-research/blob/master/representation_similarity/Demo.ipynb)) in addition to vanilla CKA

- improved the efficiency of batch-wise feature extraction (with custom data pipeline)
- added pretrained MAEs to the `thingsvision` model collection

- efficient batch-wise feature extraction (using a `with` statement for PyTorch models) for custom data pipeline

- added a public batch-wise extraction method to the feature extractor (for custom dataset and data loader classes)
- updated README and docs with explanations about how and when to use the mini-batch extraction method
- small bug fixes and refactorings

- added method to extractor class for aligning representations using [gLocal](https://proceedings.neurips.cc/paper_files/paper/2023/hash/9febda1c8344cc5f2d51713964864e93-Abstract-Conference.html)
- small bug fixes and refactoring

- new DreamSim models
- support for CLS token extraction for supervised ViTs