Hi guys,

I am trying to edit forward function of some pretrained model (f.e. resnet50 or senet154) from pretrainedmodels library by adding additional input parameter.

What do u think about solution like this:

```
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.net = pretrainedmodels.__dict__["model_name"](pretrained=True)
self.fc1 = nn.Linear(x_dim+y_dim, output_size_like_in_last_linear)
def forward(self, x, y):
x1 = self.net(x)
x2 = y.
x = torch.cat((x1, x2), dim=1)
x = F.relu(self.fc1(x))
return x
```

Do you think it will work proper? Do you have any suggestions?

What I want to do is to consider parameter y in the last layer of the pretrained model.

Anyone?