In my torch model, the last layer is a `torch.nn.Sigmoid()`

and the loss is the `torch.nn.BCELoss`

.

In the training step, the following error has occurred:

```
RuntimeError: torch.nn.functional.binary_cross_entropy and torch.nn.BCELoss are unsafe to autocast.
Many models use a sigmoid layer right before the binary cross entropy layer.
In this case, combine the two layers using torch.nn.functional.binary_cross_entropy_with_logits
or torch.nn.BCEWithLogitsLoss. binary_cross_entropy_with_logits and BCEWithLogits are
safe to autocast.
```

However, when trying to reproduce this error while computing the loss and backpropagation, everything goes correctly:

```
import torch
from torch import nn
# last layer
sigmoid = nn.Sigmoid()
# loss
bce_loss = nn.BCELoss()
# the true classes
true_cls = torch.tensor([
[0.],
[1.]])
# model prediction classes
pred_cls = sigmoid(
torch.tensor([
[0.4949],
[0.4824]],requires_grad=True)
)
pred_cls
# tensor([[0.6213],
# [0.6183]], grad_fn=<SigmoidBackward>)
out = bce_loss(pred_cls, true_cls)
out
# tensor(0.7258, grad_fn=<BinaryCrossEntropyBackward>)
out.backward()
```

What am i missing?

I appreciate any help you can provide.