hello,

I have created a model in order to do **binary** classification, However the model I created returns a vector and based on this vector I returns either torch.FloatTensor([0]) or torch.FloatTensor([1])

so forward function is as follows:

```
def forward(self, x):
x = self.model(x) #output is [n, 1]
x = self.function_check(x) #output is a new vector [m, 1]
if x.sum() == 10:
return torch.FloatTensor([0]).requires_grad_()
else:
return torch.FloatTensor([1]).requires_grad_()
```

so I am afraid that using this technique, my graph of output is not connected to my model and therefore, the gradients will not backpropagated through the model. am I right ?

Edit: I displayed the graph using torchviz, and I was right my graph is disconnected when I am returning new labels, so my question now is how can I attach my output to the graph