Furthermore, PyTorch's autograd is Pythonic, making it easy for developers to understand and debug their computational graphs. The dynamic nature of PyTorch also allows for easier debugging with standard Python debugging tools.

In addition, PyTorch's autograd also supports higher-order gradients, which enables advanced techniques like meta-learning, where a model can learn how to learn. This is not possible or straightforward in many other deep learning frameworks.

PyTorch's autograd package provides dynamic computational graphs, allowing for more flexibility and efficient memory usage compared to static graphs used by other frameworks like TensorFlow. With dynamic graphs, developers can easily change the network architecture on-the-fly during training, making it easier to experiment with different network structures.