Assume I have 3 neural networks (linear layer for example):

```
nn1 = nn.Linear(1, 1) # takes t, generates x
nn2 = nn.Linear(1+1, 1) # takes t and x, generates y
nn3 = nn.Linear(1+1+1, 1) # takes t, x, and y, generates z
```

And I have some unknown expression h that takes all outputs (x, y, and z) and yield some value.

```
t = th.rand(10, 1)
x = nn1(t)
y = nn2(th.cat([t, x], dim=1))
z = nn3(th.cat([t, x, y], dim=1))
h = 3*x*y+z*y + 4 # Assume h is not known
```

Can I compute a function/expression/graph/etc for dh/dy (for example) that assumes the variables x, y, and z are independent, but that I can use to do back-propagation acknowledging their dependence?