PyTorch also provides torch.utils.data.Dataset and torch.utils.data.DataLoader modules to efficiently load and manage large datasets for training deep learning models. These modules handle data loading, shuffling, and batching, making it easier to work with large datasets and write efficient training loops.

Additionally, PyTorch has torch.jit, a just-in-time (JIT) compiler that allows you to dynamically compile and optimize PyTorch models. This feature can significantly accelerate inference in production environments.

One lesser-known feature of PyTorch is torch.autograd.Function, which allows you to define custom differentiation functions, giving you more control over the autograd process.

Another useful but not well-known feature is torch.nn.Sequential, which enables you to create a neural network model by simply passing in the layers as arguments. This can greatly simplify your code and make it more readable.

Lastly, PyTorch supports distributed training, which enables you to scale your deep learning models across multiple GPUs or even multiple machines. This can speed up training time and allow for tackling larger and more complex deep learning tasks.