when I run a regular model, and pass only one layer to the optimiser, it behaves as expected and only one layer is trained. but when training a model that contains `nn.ModuleList`

, even parameters not passed to the optimiser are trained.

```
class testModel(nn.Module):
def __init__(self):
super(testModel, self).__init__()
self.layers = nn.ModuleList([nn.Linear(3,3)]*2)
def forward(self, x):
x = self.layers[0](x)
x = F.relu(x)
x = self.layers[1](x)
return x
t = testModel()
x = torch.ones((1,3)) * -1
target = torch.ones((1,3)) * 200
w1_b4_train= t.layers[0].weight.data.numpy().copy()
w2_b4_train= t.layers[1].weight.data.numpy().copy()
opt = optim.Adam(t.layers[1].parameters(), lr = 0.01)
loss = nn.MSELoss()
for i in range(10):
res = t(x)
opt.zero_grad()
l = loss(res, target)
l.backward()
opt.step()
w1_after_train = t.layers[0].weight.data.numpy().copy()
w2_after_train = t.layers[1].weight.data.numpy().copy()
print(np.array_equal(w1_b4_train,w1_after_train))
print(np.array_equal(w2_b4_train,w2_after_train))
```

this code returns:

```
False
False
```

why is that? is there a way to fix this?