Let’s say we have a logistic regression model that takes `num_features`

and outputs `num_classes`

. Traditionally, `num_classes`

is equal to one.

Now if I want to weight my loss function, I would make something like this:

`F.binary_cross_entropy(probas, target, weight=torch.tensor([2]))`

Where my weights were calculated by:

```
# n samples / n_classes * bincount
((183473+47987) / (2 * np.array([183473, 47987])))
[0.63074419 2.41213088] # [0, 1]
```

My question is, the weight in the loss function, as I can only give it one value, which class is it for, 0 or 1?

`target`

, in our data set, contains either zeros or ones, like a traditional binary regression problem.