03_second_order.py

```python import torch

x = torch.tensor(2.).requires_grad_() y = torch.tensor(3.).requires_grad_()

z = x * x * y

grad_x = torch.autograd.grad(outputs=z, inputs=x) # this mean dz/dx

grad_y = torch.autograd.grad(outputs=z, inputs=y)

print(grad_x[0], grad_y[0])

the problem is after the first forward pass, the graph is released, so you need to explicitly retain graph.

this is also the same for backward as well.

grad_x = torch.autograd.grad(outputs=z, inputs=x, retain_graph=True) # this mean dz/dx grad_y = torch.autograd.grad(outputs=z, inputs=y) print(grad_x[0], grad_y[0])```