I have defined a custom loss function which depends on the First and second derivatives of the output of the NN with respect to the input.

```
def PDE(x,y):
first_derivative = torch.autograd.grad(y, x,
grad_outputs=y.data.new(y.shape).fill_(1),
create_graph=True, retain_graph=True)[0]
print("dydx: \n", first_derivative)
# We now have dy/dx
second_derivative = torch.autograd.grad(first_derivative, x,
grad_outputs=first_derivative.data.new(first_derivative.shape).fill_(1),
create_graph=True, retain_graph=True)[0]
print("d2ydx2: \n", second_derivative)
# This computes d/dx(dy/dx) = d2y/dx2
eq = 4*first_derivative[:,1] - second_derivative[:,0]
loss = torch.mean(eq**2)
return loss
```

When I run ` loss.backward()`

I get an error saying

RuntimeError: leaf variable has been moved into the graph interior

I can’t understand why this is happening.

Here’s my complete code:

```
# Define the NN model to solve the problem
class Model(nn.Module):
def __init__(self):
super(Model, self).__init__()
self.lin1 = nn.Linear(2,10)
self.lin2 = nn.Linear(10,1)
def forward(self, x):
x = torch.sigmoid(self.lin1(x))
x = self.lin2(x)
return x
model = Model()
y = model(X_train)
loss = PDE(X_train, y)
loss.backward()
```