another PyTorch newbie here trying to understand their computational graph and autograd.

I’m learning the following model on potential energy and corresponding force.

```
model = nn.Sequential(
nn.Linear(1, 32),
nn.Linear(32, 32), nn.Tanh(),
nn.Linear(32, 32), nn.Tanh(),
nn.Linear(32, 1)
)
optimizer = torch.optim.Adam(model.parameters())
loss = nn.MSELoss()
```

```
# generate data
r = torch.linspace(0.95, 3, 50, requires_grad=True).view(-1, 1)
E = 1 / r
F = -grad(E.sum(), r)[0]
inputs = r
for epoch in range(10**3):
E_pred = model.forward(inputs)
F_pred = -grad(E_pred.sum(), r, create_graph=True, retain_graph=True)[0]
optimizer.zero_grad()
error = loss(E_pred, E.data) + loss(F_pred, F.data)
error.backward()
optimizer.step()
```

However, if I change the `inputs = r`

to `inputs = 1*r`

, the training loop breaks and gives the following error

```
RuntimeError: Trying to backward through the graph a second time (or directly access saved tensors after they have already been freed). Saved intermediate values of the graph are freed when you call .backward() or autograd.grad(). Specify retain_graph=True if you need to backward through the graph a second time or if you need to access saved tensors after calling backward.
```

Could you please explain why this happens?