element 0 of tensors does not require grad and does not have a grad_fn

As an AI language model, I can explain that in PyTorch, tensors are the basic building blocks of a neural network. Every tensor has a property called "requires_grad" which is set to False by default. When requires_grad is set to True, PyTorch will track every operation performed on the tensor, and will compute gradients during backpropagation to update the tensor's values.

The grad_fn property of a tensor is a reference to the function that created it. This function is used during backpropagation to compute gradients. However, the element 0 of tensors does not require grad and does not have a grad_fn. This is because element 0 is usually used as an index or a placeholder, and does not contribute to the computation of gradients. Therefore, it is unnecessary to track gradients for element 0, and it does not need to have a grad_fn.