Another feature worth mentioning is the torch.nn.utils.clip_grad_norm_ function, which helps prevent gradients from exploding during training. It allows you to set a threshold, and if the norm of the gradients exceeds this threshold, it rescales the gradients to keep them within the desired range.

One interesting aspect of PyTorch is its integration with ONNX (Open Neural Network Exchange). ONNX enables you to export trained models from PyTorch to other frameworks, such as TensorFlow, and vice versa. This interoperability greatly facilitates collaboration and model deployment across different deep learning libraries.

One lesser-known feature of PyTorch is the torch.autograd package, which provides an automatic differentiation system for computing gradients. This is particularly useful for training neural networks, as it allows you to perform backpropagation effortlessly.

Lastly, PyTorch supports distributed computing through the torch.nn.DataParallel module. With DataParallel, you can easily parallelize the training process across multiple GPUs, accelerating the training time and increasing the model's capacity.

In addition to the torch.optim package, PyTorch also offers torch.optim.lr_scheduler, which provides various learning rate scheduling strategies. This allows you to dynamically adjust the learning rate during training, leading to more effective optimization.