Hey! I have already to define my own loss function. It does work. But I am not sure whether it is correct because I don’t define the backward(). I am not sure whether I need to define backward(), and I don’t know how to define backward().

```
class _Loss(nn.Module):
def __init__(self, size_average=True):
super(_Loss, self).__init__()
self.size_average = size_average
class MyLoss(_Loss):
def forward(self, input, target):
loss = 0
weight = np.zeros((BATCH_SIZE,BATCH_SIZE))
for a in range(BATCH_SIZE):
for b in range(BATCH_SIZE):
weight[a][b] = Censor(target.data[a][0])
for i in range(BATCH_SIZE):
for j in range(BATCH_SIZE):
a_ij= (input[i]-input[j]-target[i]+target[j])*weight[i,j]
loss += F.relu(a_ij)
return loss
```