This is what I have so far:

input_size_prot = 1024

input_size_comp = 196

hidden_size_prot = 32

hidden_size_all = 20

output_size = 1

batch_size = 80

class pcNet(nn.Module):

```
def __init__(self, input_size_prot, input_size_comp, hidden_size_prot, hidden_size_all, output_size):
super(pcNet, self).__init__()
self.fc_prot = nn.Linear(input_size_prot, hidden_size_prot)
self.fc_all = nn.Linear(hidden_size_prot+input_size_comp, hidden_size_all)
self.fc2 = nn.Linear(hidden_size_all, output_size)
def forward(self, x):
out = F.leaky_relu(self.fc_prot(x[0]))
out = torch.cat((out, x[1]), 0) #here lies the problem
out = F.leaky_relu(self.fc_all(out))
out = F.relu(self.fc2(out))
return out
```

So x is a list of two tensors. The first has 1024 elements and gets compressed to 32 nodes, which I now try to combine with the second tensor in x. Currently I get the following error:

RuntimeError: Sizes of tensors must match except in dimension 0. Got 32 and 196 in dimension 1