Is it a possibility to calculate the Multiclass crossentropy loss by successively using the `nn.BCELoss()`

implementation

This is what I have tried.

```
# Implementation of the Multiclass Cross Entropy classification
def SegLossFn(predictions,targets):
_, c, _, _ = predictions.size()
loss=0
m=nn.Sigmoid()
loss_fn=nn.BCELoss()
#BCE-> MCE by adding for each of the classes BCE
for i in range(c):
loss+=loss_fn(m(predictions[0][i]),Variable(targets[i][0]).cuda())
return loss
```