Hi, each layer should be calculated from the last layer as

next_layer = last_layer + Relu( Linear( last_layer ) ) + noise

Each layer has the same input dim as output dim.

This is what I did

```
class MyNN(nn.Module)
def __init__(self, space_dim):
super(MyNN, self).__init__()
self.layers = nn.ModuleList()
for i in range(self.path_length-1):
linear_layer = nn.Linear(space_dim, space_dim)
self.layers.append(nn.Sequential(linear_layer, nn.LeakyReLU(0.2, inplace=True)))
def forward(self, z):
for i in range(self.path_length-1):
z += self.layers[i](z)
noise = (self.noisefactor * torch.normal(0,1,z.size())).detach()
z += noise
return z
```

I am getting `one of the variables needed for gradient computation has been modified by an inplace operation: [torch.FloatTensor [1000, 1]], which is output 0 of AddBackward0,`

. In the stacktrace this happens when executing a linear layer. I guess that is because I am not properly saving the inbetween values of `z`

. Should I just throw them into some list? Or preallocate a tensor where I save them to?