How do you implement a custom Dataset in PyTorch?

A custom Dataset defines how data samples are loaded and indexed using __getitem__ and __len__.

PyTorch Dataset is an abstraction that allows lazy loading of data. You define how each sample is retrieved and optionally transformed. It decouples data logic from training loops and works seamlessly with DataLoader.