I am trying to figure out why this minimal example is not working as expected:

```
# two variables
x = torch.Tensor([1])
y = torch.Tensor([1])
x.requires_grad = True
y.requires_grad = True
# loss (think: training loss)
loss = x * y
loss.backward()
print("loss", loss)
print("x", x, x.grad)
print("y", y, y.grad)
# update x using gradient (which depends on the value of y)
new_x = x - 1e-1 * x.grad
print("new_x", new_x)
# different loss using updated value (think: validation loss)
new_loss = new_x ** 2
print("new_loss", new_loss)
y.grad.data.zero_() # clear grads from first pass
x.grad.data.zero_() # clear grads from first pass
new_loss.backward() # compute gradient for new loss
# Why is grad of y zero?
print("x", x, x.grad)
print("y", y, y.grad)
```

Output:

```
loss tensor([1.], grad_fn=<MulBackward0>)
x tensor([1.], requires_grad=True) tensor([1.])
y tensor([1.], requires_grad=True) tensor([1.])
new_x tensor([0.9000], grad_fn=<SubBackward0>)
new_loss tensor([0.8100], grad_fn=<PowBackward0>)
x tensor([1.], requires_grad=True) tensor([1.8000])
y tensor([1.], requires_grad=True) tensor([0.])
```

Changing the value of y **does** change the value of new_loss, so shouldn’t it’s gradient be non-zero? What am I missing?